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Second Among Equals: The Middle Class Kingdoms of India and China**
By Surjit S. Bhalla*
May 21, 2007 [Under Revision at Present]
*Chairman, Oxus Investments, New Delhi, India and Senior India Analyst, The
Observatory Group, a New York based policy advisory group
** This book was written in 2007 for Peterson Institute for International Economics.
e-mail address: [email protected]
i
Contents
Abstract vi
Acknowledgements vii
Preface ix
Part I
1. Introduction and overview 1
2. Paths to prosperity: why are some countries poor? 17
3. The world till 1950 57
4. Planning as Panacea 78
Part II
5. Middle Class: Development‘s Secret Weapon 94
6. Currency Undervaluation: A Growth Policy par excellence 127
7. There are no growth miracles 168
8. Institutions as Luxury Goods 211
Part III
9. India-China 1950-1980: Three Aberrant Decades 238
10. India China 1980-2006: The End of the Debate 245
11. The Search for Meaning of Inequality: Kuznets curve and beyond 280
Part IV
12. History as we should know it 305
13. History as we will know it 317
14. Conclusions 325
ii
Glossary
References: 333 Appendix I: The SAE data set-variable definitions, sources, etc. 357 Appendix II: SAE real exchange rate: Application to the US 362 Appendix III: China and India: Some basic data for 2006 369 References 251
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To all the huddled masses, yearning to be middle class.
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Acknowledgements
Though the last piece to be written, ―acknowledgements‖, actually contains information
about the most essential ingredient of a book: why did the person write it? My first India
China comparison was written as a journalist piece about 20 years ago, and in that
match-up, India definitely came out second, and definitely not equal to China. But that
was a piece purely on economic policies, and very recent economic growth.
In 1990, while working on the World Development Report at the World Bank, I half-
jokingly offered an obsessive policy wonk‘s three joint sufficient conditions for
successful economic growth: low tariffs, girl‘s education and a free press. Go back in
history, and forward, and it is difficult to find a society which fails this forecast. While
couched carefully, there was more than a suggestion that these were not just sufficiency
conditions, but also necessary. But this was before China was acknowledged as the
most remarkable economic success story, ever.
Sept 18, 1992, just two days after Black Wednesday, and in pressurized conditions, I
concluded that the British devaluation could have been predicted on the basis of good
old fashioned ―productivity adjusted purchasing power parity economics‖. Wow. This
model worked ex-ante in the Mexican and Thai devaluations, 1994 and 1997. While it
might seem that I am following the dictum of my professor Ray Fair too faithfully (he said
that as a forecaster one should forecast often and always remind people when one is
right), the point about the story is that exchange rates, both theoretically and as a trader,
have been a major preoccupation, and hobby, of mine for the last twenty years (my first
ever trade was not the stock market but dollar yen and dollar mark). The gains (and
losses!) from currency trading (I would like to think) helped fine-tune my obsession with
exchange rates, and their role in generating, or inhibiting, economic growth.
In 1986, working in India on leave from the World Bank, I lived catch-up economics on
very low productivity adjusted wages. My productivity was the same as in Washington,
yet my wage was a tenth. How could this be? Purchasing power parity differences I was
told. But even adjusting for PPP, my wage was only a fifth. Transaction costs,
imperfections and …The fact remained that I, like others, had moved voluntarily to a
lower paying job. The world wasn‘t yet flat then, but the writing was on the horizon. Yet
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people still moved voluntarily and sometimes even eagerly. Why? Because the seeds of
flattening were beginning to take hold!
So there you have it, an analysis of the psychology of motivations behind this book, if not
the economics. Development of ideas is an interactive process and if individuals find
similarities in the discussions they have had with me, a sense of déjà vu, rest assured it
is not all a coincidence and the ideas are not fictional. This book could have been the
globalization and productivity project that Rajiv Lall started with Bo Cutter and myself. I
thank both of them for helping in the idea generation that has allowed me to publish this
book. If successful, perhaps we can finally finish Globalization: No place to hide?
Then there are the usual suspects who have been ever generous with their time, and
their intellect, to contribute to the rejection, and sometimes acceptance, of back-of-the-
envelope findings, and ideas. Shankar Acharya, Montek Ahluwalia, Suman Bery,
Ravinder Kaur, Homi Kharas, Farrukh Iqbal, Robert Lawrence, Rakesh Mohan, and
Arvind Virmani. Thanks also to seminar participants at various places where the findings
of this book have been presented. From an ICRIER seminar entitled Chinese
Mercantilism: Currency Wars and How the East was Lost in 1999 to an NBER-NCAER
seminar on There are no growth miracles in Jan 2007 – a long eight year quest for
understanding the dynamics of growth in China, and India.
Other individuals helped at different stages. Abhijit Banerjee, Nadeem ul-Haque, Sunil
Jain, Simon Johnson, Vijay Joshi, Rajeev Malik, Ijaz Nabi, Lant Pritchett, Raghu Rajan,
Sanjeev Sanyal, Ruchir Sharma: thank you for listening, and advising, even while
(partially?) disagreeing. My fellow limousine liberal election junkies – you know who you
are - thanks for listening to sometimes unwanted earfuls. And for your comments and
suggestions.
The team at Oxus Investments was ever willing to work on this data and time intensive
book; no religious holiday was sacrosanct; lately, every Sunday was a working day.
Thanks to all, and especially Tirthantomoy Das, for giving a special meaning to
―research assistance‖.
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Finally, thanks to Fred Bergsten for being the model policy wonk which all us juniors
aspire to. And for being so patient with the normal and abnormal delays. The book was
to have been completed a year ago. The seminars at the Peterson Institute were
essential in the generation and ―cleaning‖ up of ideas. And thanks to Adam Posen and
John Williamson for providing encouragement and support, even while being critical.
The book has taken so long that it is no longer a family joke as to when it would finish. At
least I have now learnt that the only realistic deadline is that there isn‘t one. But humor is
only part of the survival and life enjoyment kit that the family provides. Simran now
contributes to ideas, titles and quotes; her description of from the heart only poverty
alleviation as Bononomics is a gem which I stole and now gratefully acknowledge. (If you
like some of the song lyrics quoted, then yes, she contributed – I am not that up with
contemporary music, though I used to be once!).
Sahil helped in asking some plaintive 15 year old questions like ―Why if China produces
everything, does India not trade much with it‖. This book, I hope, answers partially, albeit
indirectly, that question. And my wife Ravinder helped in all stages of ideas, and
particularly helped by constantly reminding, and educating, me on that other Marxist
point of view. There is one important similarity about China-India that is not much
discussed here: son-preference. For that, the reader has to wait for Ravinder Kaur‘s
book; the family has now moved onto this new angst – when will that be finished?
New Delhi, May 21, 2007
7
“I can see clearly now, the rain is gone,
Gone are the dark clouds that had me blind”
Johnny Nash, 1972
Chapter 1: Introduction and Overview
The first line of a book is always important and informative; and equally difficult. How can
one equal ―It was a bright cold day in April, and the clocks were striking thirteen‖ (1984);
or , "It is a truth universally acknowledged, that a single man in possession of a good
fortune must be in want of a wife." (Pride and Prejudice); or ―it was the best of times, it
was the worst of times‖ (Tale of Two Cities). So I won‘t try; better still, I will borrow the
last line of my previous book on a broadly similar subject: ―Growth is sufficient, period‖.
(Imagine there’s no country).
Not really, and of course, there are countless objections to such seemingly facile
generalizations. Growth for whom, growth of what, growth when? But let us keep it
simple and straightforward: no matter what the objective (improvement in welfare of the
poor, equality of opportunity for the masses, increase in incomes, and lives, of the
poorest) economic growth, especially fast and sustained economic growth, just makes
policy and life that much easier. For everybody.
This book is about China and India, their past influence on the world, and their future
prospects. As will become obvious - despite their respective contributions in giving us
the concept of zero, or the printing press, or discovery of America in 1421, (some 50
years before the official belief that Christopher Columbus did it), or the practice of yoga
and/or the standing on one‘s head – the importance of India and China, past, present
and future, is really about the importance of SIZE. Size matters. Even when the two
countries were very poor, absolutely and relative to any other group in the world, the
gravity of their importance derived from the simple fact of size: 40 to 50 percent of the
world‘s population. If now one adds economic growth to this size, it becomes a gorilla of
a story. It is the story of the 1980s and early nineties i.e. the large and rapid decline in
world poverty.
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In addition, the story of the last decade has been the story of the unprecedented pace of
growth in the size of the middle class. Sometimes this period is also known as the period
of Goldilocks economy and/or the surge in productivity growth worldwide, with not so
much awareness that it is the middle class that is the major driving force behind these
―unusual‖ events. The next twenty years will come to be known as the period when the
world changed into a truly multi-polar world – again, because of ―movements‖ in these
two population giants.
India and China - for centuries they have been ―equal‖ in an extraordinary number of
ways. Both have been large economies for hundreds of years; both were relatively rich
until the Industrial Revolution; both were absolutely poor until they became independent,
again at approximately the same time in the late 1940s. Both plummeted further due to
their separate, but equal, obsessions with planning and control during their dark aberrant
decades between independence and the late 1970s. Both started economic reforms at
almost exactly the same time, though China proceeded faster and quicker. China began
serious economic reforms in 1978, India waited till 1991. That is a thirteen year lag.
Indian economic reforms were unquestionably later than China by these 13 years,
though there are some who believe that reforms started in India as early as 1980. There
is little question that the lag was larger than two years. Today, India is China with a five
to ten year lag. The price India has paid for gradualism in policy is that for a thousand
years, its per capita income was within 10 to 25 percent of China. This historical identity
has radically changed – today, China‘s per capita income is more than twice India‘s. This
glaring divergence is forgotten by most, especially when India-China comparisons are
made. The Indian economy will have a much lower impact on the world than China – to
be expected, since India is only half as ―rich‖. Infrastructure is way behind that of China;
also to be expected since China ―needed‖ infrastructure ten years earlier. But the
comparison ―error‖ is understandable. After all, the world, and the two civilizations
themselves, had always viewed each other as ―equal‖. A mere thirty years is not even a
ripple in history.
But in a very short period of time, history should begin to restore the old balance. A
necessary condition for the China-India gap to be closed is for Indian growth rates to
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exceed that of China. One of the findings of this book is that this is about to happen
shortly, almost certainly by 2010.
Golden Ages of Prosperity
Lag or no lags, the near joint occurrence of reforms and growth in these two large
population giants has meant a transformation of the world, a transformation without
precedent, and one that may be unique for all time. This revolution has involved close to
2.5 billion individuals in just these two countries, or close to 40 percent of the world‘s
population. In the 1960s, Asia was given up as a lost cause, especially India and China.
In the early 1980s, India and China accounted for close to 80 percent of the worlds‘
poor; today, the absolute numbers of poor (below the one $ a day poverty line) in these
two countries likely number less than 150 million. Never in history has an economic
revolution involved so many at one time – not the Industrial Revolution (at best 250
million or 15 percent of the world‘s population) and not the European and/or the East
Asian miracles in the second half of the twentieth century. These transformation
countries, along with all of Europe, accounted for less than an eighth of the world‘s
population, or a third of the size of China-India.
There have been other episodes of poor countries transforming themselves. In the 19th
century, Japan steadily started to emerge from its own self-imposed darkness and by
1980 had become an economic superpower, a power with the rank to sit at the highest
table of nations. The reconstruction of Europe in the 1950s and 1960s is another very
successful development story. Then there was the gang of four Asian nations (Hong
Kong, Singapore, Taiwan, and South Korea) who choose to depart from the so
conventional model of (mis)-development commonly followed by the members of the
international ―intellectual‖ liberal elite – a model that involved dependency for aid, that
advocated import substitution as a panacea for the desire to develop, and that failed
miserably. The final buy by date had most likely expired within years of its advocacy by
the Russian communists. However, intellectual and policy leadership in most developing
countries (remember the non-aligned movement?) was too mesmerized by the Russian
quickie development model to question its lack of economic foundation. So while the rest
of the developing world floundered, the gang of four became a gang of nine in the 1970s
and 1980s; the six additional countries being Botswana and Mauritius in sub-Saharan
Africa, and the three (additional) East Asian tigers, Indonesia, Malaysia, and Thailand.
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And then came China and India
And ushered in the golden age of prosperity. From basket cases to models of
development. From pin-ups of dehumanizing poverty to emerging global powers. And all
in a matter of just two decades. In the 1970s the world had set up institutions to attack
world poverty, which really was poverty in China and India. Today, we need institutions
that will address global imbalances caused by some of the erstwhile poorest.
Never before has the world witnessed such a surge, and never again will it. It is in this
context that the China-India prism is the most useful. Few of us realize the magnitude of
this transformation, a transformation not just for China and India, but the world. Forget
the Industrial revolution. No don‘t forget – remember. That happening did transform the
world, and our lives. Historians now write about the discovery of electricity, the steam
engine, about machines doing the work of men. Historians fifty years from now will write
about machines doing the work of women, and about how the poor of the world became
the middle. They will write about the revolutions of instant communication, and of near
instant transportation. But they will write most about the emergence of India and China
from long-term darkness to quite sudden light and prosperity.
Some facts about the developing India China story. Such a large mass of people having
a per capita growth rate significantly above the world average? Never happened before.
UK and US in the 19th century, Japan and Asean8 in the 20th century and every success
story in between; get out the calculators and you won‘t get a different story.
The two continental size economies have achieved higher rates of growth for a longer
period than most thought possible. Their combined 5.3 per cent per annum growth for
the last twenty-five years nearly matches the performance of Japan for its peak growth
period (1960-85) and about 1 % per annum below that achieved by Korea and Taiwan
(1970-95). The big difference being, of course, that India-China did it on a population
base about 20 times the size of Japan. The only common factor among the countries is
that they are all Asian. But there were inevitably, and obviously, common practices
(practice?) among these countries that made this exceptional growth possible.
While history is replete with instances of individual countries growing fast, and of regions
or select groups of countries growing fast, the world has not witnessed the kind of mass
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population progress as in India and China since the early 1980s. This has implications
for commodity prices, oil prices, behavior of multinationals, and power equations. By
2025, only twenty years hence, the India-China share of world output will exceed its
population share of around 40 percent – a phenomenon last observed more than 500
years ago. In just 45 years (1980 to 2025), the two economies will recover the ground
they lost over the last 450 years. That is the history we are witnessing.
What determines growth – geography, institutions, policy ?
What explains this growth – how come it has hit us all of a sudden? Few years ago there
was no buzz; some say that now all there is is buzz! Actually much more than that and
many years in the making. This book attempts to delve deep into the fundamental
determinants of economic growth, an issue that has pre-occupied the minds of
economists and policy makers for close to a 1000 years. Investigation of world growth
(made possible by the pioneering work of Maddison) allows one to investigate the
determinants of growth across space, time, and technology. The role of geography,
institutions, and policy are all examined. Is there a magic potion of growth? Of course
not. But the results do suggest some conclusions. That geography has been vastly
overrated as an explanator of differences in levels of income. Ditto the case with
―institutions‖, the most recent advertisement of the economics profession. We are told
that countries are rich and poor depending on whether they have institutions that protect
property rights, institutions that promote democracy. But it could be the case that
institutions are a luxury service – richness and good institutions go together because a
rich country demands good institutions. This is the first major conclusion – institutions
most likely are not the explanatory factor for why countries are rich.
In the beginning, there was the ―Washington Consensus‖ (sometimes WC), a shorthand
for policy as an important determinant of economic outcomes. Most, if not all,
economists subscribed to the notion that there were good policies and bad policies –
either way, the outcome was a function of a policy decision. There were no long-term
determinants because policy could affect a series of short-term directions. Monetary
policy, budget deficits, trade openness were all considered choices affecting
development. No more, for the ascendancy of the institution crowd has meant the
decline of the policy wonks. The results of this book resurrect an old favorite, often
discarded but never forgotten: the policy of export led growth. One of the strongest
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conclusions in this book emerging from the analysis of 181 countries for a period over
500 years, and especially the last 200 years, is that real exchange rates matter; and that
the attainment of a weak currency, weaker than the ―neutral‖ rate prescribed in the
Washington Consensus as ―unified and competitive exchange rates‖, is the strongest
asset for an economy wanting to help itself. A ―weak‖ currency also means an
undervalued exchange rate, and when practiced to the limit and taken to the other side,
it turns into the ugly swan of mercantilism. Thus is the second major conclusion.
Another major finding is that the middle class matters – and how. This hypothesis can be
traced to Aristotle, and John Stuart Mill was an early champion of its virtues. The middle
class got a bad press when the Communist Manifesto confused it with whatever was felt
to be bad and transitory – feudalism, imperialism, fascism, and Communism? It was left
to Barrington Moore to revive and justifiably credit the middle class with several liberal
and progressive forces in history. No bourgeoisie, no democracy is how he eloquently
and presciently put it. This book examines the Aristotle-Mill-Moore hypothesis and finds
it to be extremely valid.
Why does the middle class matter? First, size, for without achieving at least a 10 to 15
percent presence it is unlikely to be effective. But after attaining this size, the interests
of the middle class begin to dominate political and economic discourse i.e. the
implementation of policies and the development of institutions that yield better economic,
and social, and cultural, performance. This is the third major conclusion of this book.
Obviously, the higher the income or richness of a country, the larger the middle class,
ceteris paribus. By emphasizing its size, one does not necessarily fall into a tautological
trap. Growth will get you the middle class, but how does one generate a sustained
increase in income levels? Not geography, and not institutions. The surest way is most
likely via an undervalued exchange rate. This generates strong growth, and enhances
the middle class and the inter-action between the two makes for a self-sustaining
process. For proof, just ask China, or India, or Viet-Nam, or Japan, or Korea, or
Botswana. And for a description how, read this book!
This book is in four parts. The first part (Chapters 2 and 3) review the experience of
countries for the last 500 years, and examines the various hypotheses or explanations
13
that have been offered. The initial richness of India and China are discussed, as is their
contributions to innovations and development. Also discussed are the reasons for their
individual and collective decline. Chapter 2 is the path traveled by most economists, and
all policymakers. How does a country achieve faster growth, and how does it increase
its growth potential? There are several contenders: geography is meant to explain why
Africa remains relatively (and absolutely) poor. Evidence from 1500 to the present is
used to document the effect of various indicators of geography – latitude, temperature
rainfall, tropics – on per capita income. The effect is found to be of varied intensity and in
the end, minimal. Chapter 2 finds that policy is ultimately what can change a country‘s
destiny. And it is not really ten policies, nor the augmented Washington Consensus
involving twenty, that are necessary for success. Apparently, just one policy has
sufficed for countries to turn ugly poor ducklings miraculously into beautiful, white
swans. This policy worked over two hundred years ago, and apparently works with the
same certitude today. Produce cheaply and conquer world markets. Some consider this
to be synonymous with a market economy; others see it, when in extreme form, as
synonymous with mercantilism.
Chapter 3 discusses world economic developments till 1950 and concludes that the
divergence in world economies was confined to Asia and Africa, but not to Latin
America. And examination of various different sources indicates that while parts of India
and China were as rich as the richest parts of the Western world, the average for these
two economies, was, well, average. Which means that as far back as 1700, average
incomes, and development, in India and China were only about half the level of incomes
in Great Britain – and the Industrial Revolution was yet to start in earnest.
Chapter 4 examines the inherited world of India and China at the time of their
independence from colonialism (India) and military monarchy rule (China) in 1947/1948.
Despite choosing very different political structures, both economies had more than a
fleeting love affair with state controlled capitalism or with the suppression of economic
freedom. It has been argued that such (economic) despotism was necessary to pull the
two economies out of the dire poverty they found themselves in. This explanation is
found wanting on several counts; final confirmation that this was a wrong ideology
waltzing as beauty in distress is provided by the experience of these economies when
they rather belatedly discovered the virtues of economic freedom in 1978 (China) and
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1991-1993 (India). That there was vengeance in the discovery, and profitable
development, is revealed by the fact that we speak of China and India in tones different
than the begging and doomed developing world envisaged by Nobel prize winner,
Gunnar Myrdal, in his treatise entitled Asian Drama.
Part II turns the discussion to what really does, and perhaps does not, matter. The one
seemingly secret weapon of successful development is the middle class, a subject
examined in some detail in Chapter 5. This book (sometimes referred to as SAE
(Second Among Equals…)) offers a new definition of the middle class. The definition is
simple, straightforward, and unlike other attempts, absolute. Once an individual‘s income
is more than PPP $ 3650 per person per year in 2006 international prices, then that
person crosses the line from not middle class to the beginning of the middle class; from
being poor to being non-poor, or middle class. This definition is extracted from
information about the poverty line in the developed western world i.e. the average
poverty line in rich countries, a line that differentiates between the absolute poor and the
non-poor and being non-poor begins with being the lowest of the middle class category.
Since PPP (purchasing power parity) estimates, by definition, reflect comparable levels
of living, the same definition of middle class (MC) should, and does, equally apply to
residents of the poor developing world.
This definition of the middle class helps explain several past and present phenomenon.
Major institutional changes (child labor laws, universal male suffrage, etc.) occurred for
the first time when the country in question had an emerging middle class. This was over
two centuries ago in Europe. The major impetus to economic reforms in India was in
1991 – a year when the size of its‘ middle class was close to 10 percent.
Chapter 6 is concerned with the measurement of the real exchange rate (RER), its ―fair‖
value and of deviations from this fair value. Chapter 7 examines the ―cheap and cheaper
currency equals higher growth hypothesis‖ in some detail. Development patterns from
mid 19th century to the late 20th century (and the beginnings of the globally imbalanced
21st century) are examined and they reveal a common thread. It may have been a weak
currency in the 19th century that wove the rich future of the West through the Industrial
Revolution and into a seemingly insurmountable lead at the end of the Second World
War. In addition, Chapter 7 discusses all the known (and some less discussed) growth
15
miracles. The conclusion, there aren‘t any, since there is a known factor - an
undervalued currency, which is an important feature of most miracles.
Chapter 8 examines the institutional hypothesis of development in considerable
econometric detail. Also examined is the success (or failure) of institutions in delivering
growth to the poor in micro India. One back of the envelope calculation yields the
opposite to expected result: as institutions worsened in India, growth and development
accelerated. But China hints at the opposite conclusion – openness and markets and
individual initiative (but not property rights strictly defined) caused high Chinese growth.
So was it institutions – or was it the operation of one of the most undervalued currencies
in the world that made possible the Chinese miracle?
Part III of the book is an attempt to separate out and identify the different causes helping
explain both the (bad) miracle of no growth between 1950 and 1980 in China and India
(Chapter 9) and the miracle of very high growth post 1980 in China and India (Chapter
10). This chapter also examines the possibility that as recently as 2003/4, there was a
structural break in the Indian growth process.
Chapter 11 begins part IV with a detailed discussion of the twin side of growth – change
in inequality. The findings reported in this chapter maybe controversial but are robust.
The most famous of inequality laws - the Kuznets curve hypothesizing an inverted U
curve relationship between inequality and growth - may be more parts fiction and less
parts fact. Further, inequality in the world has now been declining for over 25 years, is
presently at a level last seen a century ago, and by 2025 is likely to be as equal as the
halcyon days of the early 19th century.
Chapter 12 recapitulates the past (History as we should know it) and speculates on the
next 20 years (History as we will know it). The recent past has been witness to the
Greatest of Transformations: the eradication of absolute poverty for over a billion people.
More than a third of the world‘s population was absolutely poor in 1980; less than a tenth
of the worlds‘ population is absolutely poor today. Equally important for world growth
was the emergence of the middle class in the developing world. From the low teens to
now almost half of its (developing world) population – that is the other transformation.
China and India continue to play their important joint role in the world economy, with one
16
important difference. Perhaps as early as 2010, the growth rate of the Indian economy
will begin to exceed that of China. The story of India and China is one of equality since
time immemorial and until 1980. Today, in 2007, China is much ahead. Such an
imbalance has never before occurred, and the long journey towards correction of this
imbalance is likely to begin soon. Chapter 13 concludes with some observations and a
Top Ten list of findings.
17
“We shall not cease from exploration, But at the end of all our exploring We shall come back to where we started And know the place for the first time”
T S Eliot, The Four Quartets
Chapter 2: Growth, and its determinants There is growth everywhere. Only a handful of countries, and these include war ravaged
countries like Zimbabwe and Lebanon, have registered negative GDP growth in 2006.
World growth has averaged more than 4.5 percent per annum for four successive years
and it is very likely that the final estimate for world growth in 2006 will top 5 percent1.
(Chart 2.1)
This is not unprecedented, but rare. The glory days of world growth were in the 1960s
when world growth averaged 5.1 percent per annum. Post-war reconstruction and catch-
up with the technology frontier of the US meant that Japan and Europe grew, and grew
at an average rate of 5.5 % per annum. Starting with the oil price shock of 1973, world
growth nose-dived and around 1982, such growth was only 1 percent per annum. Almost
4.5 billion people in the world, barely registering any growth. Not co-incidentally, 1982
was also the year of global recession led by the United States. The Dow Jones stock
index was less than 800. There was doom and gloom all around, and Business Week
confidently pronounced in an August 1982 cover story that it was ―The Death of
Equities‖. That was then.
Little recognized in 1982, by Business Week and most, if not all, academics and policy
makers and politicians, was the emerging mega change in China and India. China
registered GDP growth of 8.8 percent in 1982, India 5.2 percent. That was almost two
billion people finally beginning to join the world economy. The start of the present era of
globalization, if you will. The start of Goldilocks economy, only not recognized even
today as such. Many today think that the world economy is where it is because of central
bankers in the West and their inflation targeting, and inflation fighting, abilities. Credit is
reluctantly given to developments in the Third World;
1 All levels of income, and GDP growth rates are, unless otherwise stated, in constant 1996 PPP
dollars. All growth rates are annualized (log) changes. See Appendix I for details of construction.
18
Chart 2.1: World Growth in Income, PPP
02
46
8
perc
ent(
%)
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2006Year
Annual 5 year MA
World: Growth in income
Source: SAE dataset (2006), see Appendix I for details
19
just like the fact that Cinderella is really a Chinese fairy tale written in the ninth century,
not a French tale written in the 17th century.
The entry of China and India into the world economy (more China in the 1980s, with
India joining in the early 1990s, and really joining in around the turn of the 21st century)
unleashed forces beyond most people‘s control, and beyond nearly everybody‘s
imagination. Today, there is a near identical role reversal. World growth is really about
growth in the developing countries, the poor world; it is growing much faster than the
developed rich world, and its share in global output is no longer small. Look no further
than the East young woman, look no further.
World Economic Growth, 1500 to 2025
The concern in this chapter is what happened in the world in the past, to examine how
the world got to today. Table 2.1 documents the evolution of world growth from 1500 to
the present, and with projections for the future, till 2025. The table describes the world
according to four broad regions.
The West is Europe (including Russia and Eastern Europe), North America, Australia
and New Zealand. Asia includes Japan, East Asia, South Asia, and the countries
classified by the World Bank as Middle East and North Africa (e.g. Turkey, Israel, Saudi
Arabia, etc.). Africa is really sub-Saharan Africa and Latin America is central and south
America and the Caribbean islands. The developed world is West excluding countries
belonging to the former Soviet Union and Eastern Europe but including Japan; the
developing world excludes the developed world, the former Soviet Union and Eastern
Europe.
Until 1820, world growth averaged only 0.04 percent per annum i.e. virtually no change
in either absolute or relative living standards. The advent of the Industrial Revolution
began to change the picture of stagnation and real per capita incomes in the West. It
grew at almost ten times the previous rate of 0.12 percent per annum, and did so for the
next 130 years. If it had not been for the interruption of the two world wars in the 20th
century, this pace would have been a lot faster. There was almost zero growth for the
20
Table 2.1: World Regions, Annualized growth(in %)
1500-1700 1700-1820 1820-1950 1950-1980 1980-2006 2006-2025
West 0.12 0.13 1.23 2.9 1.9 2.1
Asia 0.01 0.01 0.16 3.3 4.0 5.3
Latin America 0.07 0.12 0.95 2.8 0.7 1.8
Africa 0 0.01 0.73 1.5 -0.1 1.7
Developing Economies 0.01 0.01 0.34 2.7 3.2 4.9
Developed Economies 0.1 0.14 1.17 3.1 1.9 2.2
China 0 0 -0.24 2.7 7.1 6.2
India 0 -0.03 0.11 1.7 4.2 8.3
World 0.04 0.06 0.89 2.5 1.9 3.4 Source: Penn world Table 6.1; Maddison (2006), World Bank WDI (2006); International Monetary Fund WEO
(2006 sep). Appendix I for details on construction of real incomes.
Note: West includes all of Europe, Eastern Europe, Northern America, Australia, New Zealand, former
Soviet Union. Asia includes Japan, as does the region ―developed economies‖. Developing economies is the entire world excluding the West and Japan.
21
developing economies for the long period 1500 to 1820. The industrial revolution did
help, but increased the growth rate to only 0.34 percent per annum, 1820 to 1950.
After a long stagnation period of two decades, Latin America (sometimes referred to as
LA) and sub-Saharan Africa (sometimes referred to as Africa) are growing today, with
several countries exceeding a 5 percent growth rate. The number of countries topping 5
percent, from being close to zero for 1980-2000, was 34 in Latin America in the last six
years; in Africa, it was even higher at 40 countries. Economists, and policy workers, are
searching for clues for this resurgence, especially for the poorest countries. Explanations
abound, but most involve China; some even involve India.
Rear-window economics
Since 2003, there has been a structural change in the Indian economy2, unnoticed by
most professional economists, but not unnoticed by the financial markets. It all started
with weather; after a series of bad years, and an average growth rate of only 5.1 percent
per annum between 1999 and 2002, Indian GDP growth catapulted to above 8 percent
in 2003/04. This ostensibly weather-related growth has stayed that way for four years
running, and the final estimates for 2006/7 should come in around 9.5 percent GDP
growth, and 8 percent per capita growth.
Thus, it is very likely that the reluctant shrugging of the shoulders vis-à-vis India with
regard to world growth and world impact will change shortly. It is rear-window economics
that is refusing to acknowledge the increasingly large role that India is beginning to play
on the world economic stage. Rear window economics means looking exclusively at the
past to project the future, it is mean-reversion economics. This kind of reasoning,
analysis, economics, serves well most of the time, perhaps even 90 percent of the time.
But when there is structural change, the old mean is left far behind as the system
searches and arrives at a new equilibrium level.
Rear window economics most likely is what was practiced in 1996 by Alan Greenspan,
chairman of the US FED, who opined that the stock markets were exhibiting ―irrational
exuberance‖. The day before this pronouncement (Dec. 4, 1996), the Dow had closed at
2 See Bhalla et. al. (2006), in which this hypothesis is documented in some detail; also see
Chapter 10.
22
6423. On Dec. 19, eleven trading days later, the Dow closed at 6474, having never
traded below 6207 in the intervening period. Greenspan was ―right‖ for less than two
weeks. To be sure, the Dow did briefly dip below 6423 on April 11, 1997, but that was to
be the last hurrah for rear-window economics. The Dow has not seen that irrational
exuberant level since, not after the bust of 2000, not after 9/11.
In 20063, replace irrational exuberance by the ―Indian economy‖ and most likely we have
a near identical situation. Over the last four years, GDP growth in India has accelerated
from a long run (20 years plus) average of 5.9 percent per annum to an average of 8.3
percent per annum. Notwithstanding this sustained performance, the broad consensus
seems to be that the potential GDP growth rate in India, subject to business as usual, is
no more than 7 percent, or a level marginally above the pre-reform 1980s growth rate
and a level almost 2.5 percentage points below the growth rate for 2006.
Like all structural changes, the established old guard refuses to acknowledge the arrival
of a new force. Many a policy maker and most market analysts, are crying ―irrational
exuberance‖. This is as much true of the economic establishment within India, as of the
―establishment‖ outside e.g. most major investment banks, most international
organizations as well as the reputed magazine, the Economist. That this is likely to be a
case of déjà vu, or Irrational Exuberance Mach II, is examined in some detail in Chapter
10. What all this means is that it is very unlikely that China-India will not figure as a duo
in the future. But that is getting ahead, way ahead of the story.
Casual growth empirics
Along with world growth, there are records being broken in world temperature as well.
The last ten years all figure in the Top 12 of the highest world temperatures recorded
since 1870. There is thus a strong correlation between climate change and world growth;
should we then conclude that higher temperatures are conducive to higher growth? Most
certainly not, especially since all the historical evidence available suggests that higher
temperatures (lower latitudes) are associated with lower economic growth, and lower
levels, of per capita income. But, nevertheless, welcome to the world of growth empirics,
3 The Indian fiscal year runs from April to March; henceforth, 2006 for India will mean the same
as the fiscal year April 1 2006 to March 31, 2007.
23
a world where casual empiricism has played a larger than life role in generating, and
verifying, hypothesis about the determinants of growth.
For example: East Asian success led to several ―valid‖ explanations for the happening.
Some contended (e.g. Wade (1987)) that this happened because unlike other countries,
the visible hand of the state was managing things rather well in the interventionist Asian
economies. State intervention was not always bad, and sometimes very good i.e. Japan,
Korea grew at above average levels in at least some part due to good government
intervention. This reasoning, of course, is no different than the hypothesis that climate
change has caused world growth to accelerate. What was also not answered by the
―state intervention is good‖ believers was the counter-factual hypothesis that perhaps
these intervened economies would have grown even faster if the state had not
meddled4.
Yet another explanation for East Asian success is that they grew because of
authoritarian governments. Equally, one could argue that it was Confucian philosophy
that was largely responsible (this with the added advantage that it helps explain the rise
of China). Authoritarianism and fast East Asian growth have also been linked positively;
analogously, one should note that authoritarianism and lack of growth in Africa are
heavily, perhaps causally, related.
Not unlike success, failure also has many fathers. The slow development in Africa has
been the subject of many an investigation; the African dummy in cross-country
regressions is de riguer in many empirical studies (including, sometimes, this book). The
result of all these investigations: Africa grew slowly because of bad geography, or
because of colonialism, or because of bad institutions, or ethnic conflict, or…The real
challenge to growth economics is to help explain Africa, East Asia, China before and
4 Before you say ―aw shucks you cannot grow faster than a rate equal to the second fastest
growing country‖ note the example of India, a country which has always grown slower than its potential. Many a woman over the last 60 years has been found six feet under because she bet that India would grow at potential. The most common reason ascribed for this failure: the heavy hand of state intervention in all walks of life, from hiring, to firing, to setting up a business, to closing a business, to…In simple terms, fast growth per se is not necessarily excess of potential growth – just look at China, whose growth has constantly outstripped forecasts of ―potential‖ growth.
24
after 1978, India before and after 1991, Japan before and after the mid-1980s, and so
on. Something attempted in this book.
Growth in different eras
What compounds the difficulty is that what matters for growth is different in different
eras. In the 16th century, it is likely that geography played a critical role. Land-locked vs.
access to the sea was an important distinction, as was the presence of a Mediterranean
agriculture-friendly climate. In the 19th century, the determinants moved towards the
ability to capitalize on technological change i.e. the capacity to partake, and profit from,
the Industrial Revolution. Post World War II, it was reconstruction and American aid that
most likely provided the primary impetus, and since 1980 (the approximate start of the
―new‖ era of globalization) catch-up with the technological frontier is likely the major
reason why developing countries are growing at 5 % plus rates today.
Identification is another problem for growth analysis. Economic outcomes are often
confounded by multiple causes; and per capita income as an outcome has more
explanations than there are economists, and sociologists, and political scientists in the
world. Not to mention politicians and sundry policy makers. Is it religion that explains
backwardness, or is it the weather? Is it colonialism that determines your relative welfare
today, or is it your culture? Or is it democracy that helps you progress, economically,
socially and politically? Or is democracy the ultimate luxury good, desired by those who
become rich, not unlike their desire for a Toyota rather than a Ford?
A large menu which has kept the economists busy with creating theories to explain the
facts. Growth in so many countries demanding an explanation; so many factors that can
make a difference. End concern of all is the same – can one identify a brief set of
causes, levers that can be pulled, to enhance growth? But it is this identification that is
the most problematical – each outcome has multiple causes and therefore multiple
explanations. How does one identify the cause, as separate from identifying a correlate?
One can best do that through a natural experiment.
Natural experiments
Everybody loves natural experiments. Nature provides one such pattern through twins;
even more powerful is the pattern provided by identical twins. When data on identical
25
twins are available, and if such twins had different levels of schooling, were of different
gender etc., then differences in earnings are powerful indicators of differences in
education or gender (since differences in genetic ability are controlled for).
Analogously, natural experiments in growth patterns are provided by countries separated
at birth. For example, North and South Korea. Or East and West Germany. Or countries
much like each other e.g. India and the neighboring countries in the sub-continent.
Pakistan and Bangladesh were part of one culture for thousands of years along with
neighbor Sri Lanka5. Since partition in 1947, the countries have gone their separate and
unequal ways. Sri Lanka and Malaysia – both island economies, both rubber plantation
economies, both with a significant fraction of indentured labor from South India. In 1950,
Malaysia had a per capita income of PPP $ 2100 and Sri Lanka PPP$ 1250, in 2006,
Malaysia was 3 times as rich as Sri Lanka. A divergence in just 56 years, to equal any
great divergence over the last 200 years.
Natural experiments: Korea and China and Germany
There were no differences in culture, or religion, or history, or climate. Yet today, per
capita income in South Korea is more than 15 times as large as that in North Korea. It is
as if one were comparing China in 2006 with seemingly pre-historic China of 1950 (per
capita income in China today is 8.5 times the level of 1950). This ―divergence‖ within
Korea, and China, over the last 50 years, is greater than most divergences observed
over 500 years and if ever a confirmation was needed as to how capitalism works, and
communism and/or socialism does not, this would certainly be it. If ever a confirmation
were needed that openness to the international world is beneficial, the Korea and China
example would be it.
There are a few other natural experiments. The relative decline of Cuba, and the
separation of Germany into two after World War II. The latter is another Korea – same
reasons for relative success, the same explanations for relative failure. Two equal
countries, both before 1945 and post 1989. Yet in 1989, per capita income in West
Germany was almost twice that in peoples ruled by communism. In 40 years, a relative
income change of a 100 percent. In a different era, and for different countries, it is called
5 With interesting differences. In the epic Ramayana, the Sri Lankan king fighting the Indian god
king Ram is a villain; he is, obviously, viewed differently in Sri Lanka.
26
a great divergence. Nor was it the case that the East Germans were in better health, or
better educated. Plain, simple natural experiment result: one region far ahead than the
other purely because of the economic policies that were followed. Some call it a
difference in institutional structure; others call it a policy favoring open markets and
individual enterprise.
These natural experiment successes are just a few occurrences among many
divergences, many societies, many cultures. In addition, we should remember that
South Korea was helped enormously by American aid, with total aid constituting more
than three quarters of total investments in the 1960s and 1970s. Finally, the population
of these two economies is just 70 million, a small proportion of a 6 billion plus world
population. East and West Germany together account for the same number of people –
about 82 million. Maybe, these natural experiments are sui generis?
If yes, then these natural explanations cannot offer much advice to developing countries
today. For example, how can we improve conditions in sub-Saharan Africa with
knowledge that capitalism works, or that openness to markets is one time-tested path to
development? Many such economies in Africa are open, are capitalistic, and some of
them are even democratic. So why are they so poor, and what policies can we
recommend?
The prism of India and China
The India China experience, historical and futuristic, maybe the most enlightening. In
many ways, it is the natural experiment. Two equal societies for thousands of years, but
made poor by time and fate; two equal cultures, weighing nearly the same in terms of
size of population. Both formerly rich and then poor from 18th century onwards; both
attempt independent paths at almost the same time in the late 1940s, but what different
paths. Totalitarian communism versus democratic socialism. A big difference, but a big
similarity in suppressing economic freedom. So from 1950 to 1980 both suppress
economic freedom and grow slowly, and in relative terms (relative to the rest of the
world) grow just as slowly as in the colonial era. Divergence continues and reaches a
nadir in 1980. But then, both wake up and smell the coffee of growth, and rediscover
their roots as capitalist entrepreneurial people, but the adrenalin inducing effect for
China is a lot faster.
27
Between themselves, the two countries offer enough heterogeneity to test a thousand
hypotheses. Similar initial conditions and very different outcomes today. What made this
divergence possible?
One possibility: colonialism. ―True‖ colonialism in India occurred with the advent of
British rule circa 1850; prior to the British, the colonizers of India had settled and merged
with the locals Thus, in the context of the conventional wisdom (and some debate)
about the importance of political and economic institutions in both facilitating and
generating growth, the India China natural experiment can help identify the importance
of institutions. One alleged benefit of colonialism is that colonizers transfer institutions to
the colonized; and these institutions, if of the ―good‖ variety, enhance economic growth.
Thus, given that one benefit of colonialism was ―better‖ institutions for India, a higher
relative to China growth for India post 1980 would lend support to the institutionalist
position. But it hasn‘t. Score a minus one for institutions for the post 1980 period.
Between 1950 and 1978, China had seemingly the worst institutional structure with no
economic freedom, no political freedom, no markets and no property rights. Almost
anything that can be wrong, institution-wise, was obtained in China 1950-1978. In
contrast, India was the pin-up model for those believing in political freedom and liberal
values (but no economic freedom please). Yet China grew at a rate of 0.5 percent per
annum faster than India in these three long decades. Score minus two for institutions.
Size does matter
It is alleged that, despite the alleged benefits of globalization, poor country growth since
1980 was lower in developing countries than in rich countries if China and India are
excluded. Or that world inequality worsened, but only if India and China are excluded.
But excluding these two countries from the poor countries is excluding half of the
developing country population; excluding these two countries is excluding 40 percent of
the world. You just cannot do that.
For most questions, a simple analysis based on a large set of countries does yield the
right answers. If 50 countries are rich, and 150 countries poor, and if the rich countries
all share something in common (X), and the 150 countries also are similar but have
28
something different in common (Y), then the search for the Holy Grail of explanations is
near over – the rich are rich because of X, and the poor are poor because of Y. What
gives weight to these arguments, and rightly so, is the evidence of the number of
countries – more countries analyzed, more convincing the evidence, more applicable the
analysis.
In conventional terms, India and China are just two countries. But their size is anything
but conventional. A useful pointer is the fact that if the countries of the world were
stacked up according to population size, then it would take 174 countries to reach the
size of India and 181 countries to reach the size of China.6 So analysis of just two
countries can be a useful summary of the experience of over 350 countries i.e. the entire
world twice over. The within country analysis of either India or China or both may be
more useful than a cross country analysis of 200 countries. Further, and most
importantly, when inferences are made from an analysis of populations, then to exclude
China and India on the grounds that one is just excluding two countries is a
catastrophic mistake.
Cross-country studies can rightly be questioned on the grounds that different countries
have different initial conditions, different cultures, different policies, and different
nuances of development. How can common lessons be derived from such heterogeneity
of experience? Advantage, India-China. The same within country culture, the same set
of initial conditions, the same homogeneity over time, albeit considerable heterogeneity
at a point in time. So while India and China are many countries, many cultures, many
populations, their study may not be nearly so complex as studying the fortunes of a New
Zealander versus the misfortunes of an Ethiopian.
Thus, this book is about very old questions; what is relatively new is that this book
analyzes events, and hypotheses, using as prism the economies of India and China.
This prism operates at several levels, and is likely to be useful. For almost a 1000 year
history, from a 1000 AD to 1980, India and China have been remarkably similar,
especially till 1950, with some important differences emerging between 1950 and 1980.
6 The smallest country in 2005 was Palau in East Asia, population 20,303. The cross-over country
for India‘s billion plus population is Peru (population 27.9 million) and the cross-over country for China‘s population of 1310 million is Tanzania (36.8 million). It takes 125 countries to reach the 300 million population size of the USA.
29
The same per capita income, whether the time of measurement was the year 1000 or
1980. The same geographical (obviously) and the same political union, for most of this
time-period.7 The divergence started to appear in 1980, and today, China is more than
twice as ―rich‖ as India. So lots of convergence, and divergence, to explain.
The Importance of India and China, 1500-2006
The last thousand years of economic progress in India and China are summarized by
the simple illustration in Chart 2.2. This figure reports the share of world population
contained in India and China since 1000 AD and their share in world income. When the
two shares are equal, it means that the average income in India and China is equal to
world average income.
The figure conceals several mysteries, whose solving is one of the major objectives of
this book. In 1500, India-China had almost 50 percent of the world‘s population, and
almost the same share in income. During this time, the two countries were about
average and equal to one another. Not much mystery here, though as we shall soon
see, even the average statistic for the Middle Ages hides more than it reveals. For
example, some 250 years before the onset of the Industrial revolution, India and China
were 80 percent of the world average, but were only two-thirds of the average of the
soon to be industrialized power, United Kingdom. Thus, divergence vis-à-vis the UK was
already present long ago, a divergence that got manifestly exaggerated over the
subsequent 400 years.
Starting around 1600, the India China share of world population started to increase, and
peaked at 54 percent in the early nineteenth century. The lag in the demographic
transition among the Asian and African economies is well known, and well explained, so
this increase in the India-China population share is not a mystery. By 1980, the share of
population had declined to 38 percent, but the share of world income reached an
historical low of 8 percent. What cries out for an explanation is this decline. The
explanations for this Grand Canyon like decline are, well, controversial. Hence, the
development of the ―Why countries are poor‖ or ―why isn‘t the whole world developed‖
7 The issue of the same political union is more of a debatable issue in India than in China. While
understandable that this debate should occur, a divergence (now) made famous by Amartya Sen‘s Argumentative Indian, the truer story is that, like China, India has also been a loose political union for most of the last thousand years, and before.
30
Chart 2.2
Share in income
Share in population
10
20
30
40
50
60
Sh
are
of in
com
e/p
op
ula
tion
(%
)
1000 1500 1700 1820 1890 1913 1929 1950 1960 1980 2000 2006 2025Year
Note: Dotted portion of the graph represent the period 2005 to 2025
India China: Share of World Income/Population, 1000-2025
Source: Maddison (2006); Penn World Table version 6.1; World Development Indicators, World Bank(2006); World Economic Outlook, IMF (2006), US Census Bureau (2006) Notes: Data for 2007 onwards are obtained from forecasts based on per capita growth rates between 2000 and 2006.
See Appendix I for details
31
industry. But since 1980, the two countries have seen a turnaround unlike any other
experience in history (and possibly a phenomenon not to be witnessed in the future as
well). In 2006, India-China had increased their share of world output to 25 percent. And
history is literally just around the corner. In another 15 years, if the relative growth rates
of India and China are as today, as is likely, then the global share of these two
economies will be over 40 percent. Hence, India and China would have collectively
recovered in 40 years what they lost in more than 400 years.
Now we have two big facts to explain. First, why did relative incomes decline, and
decrease by so much. Second, what is behind the super recovery? But this means we
need to take a detour into the determinants of long run growth. A long, involved detour.
Economists have offered a myriad of explanations for successful growth. The
explanations are also co-incident with the ideological fashions of the day, which makes
one wonder whether the ideology came first or research findings. For example, in the
1950s it became hugely fashionable to espouse the planning led economic model of the
Soviet Union. Without any regard, indeed contempt, for the notion of economic freedom,
leaders in even non-authoritarian economies, embraced the idea that that government is
best which decides which color shoe you should wear, as long as it is black. Sure
enough, economists started writing about the benefits of planning, the ―equivalence‖
between planning and prices etc. Bureaucrats and experts at international organizations
like the World Bank, IMF and the United Nations chimed in with support. Soon after, the
vogue shifted to the advantages of import substitution, and how high tariffs, a protected
economy, and planning was a sure fire way to become rich. The experts chimed in
again. And so on.
32
The Mystery of growth and the sleuthsayers One of the, change that to the, oldest investigation in economics: the determinants of
growth. Why is one country poor and the other rich? Not the first time this question has
been asked, and this is not the last attempt at an answer either. But search we must,
because answers, especially correct answers, can affect lives and fortunes of nations.
Most policy makers and development economists empathize with this search for the
subject for them is ever recurring. How can my country grow faster? Why has Asia
grown, and not sub-Saharan Africa? Why does Latin America not perform? And why is
the Western world so much richer than the developing world?
From Smith to Solow, the concern has been with this very issue: the wealth and poverty
of nations. Over the last forty years, and especially over the last twenty, economists
have offered a variety of formal explanations of why some countries are rich and others
poor. Factor accumulation, education, trade, openness, geography, institutions,
demography, democracy, colonial heritage, political freedom, economic freedom, foreign
aid. Each has been ―proven‖ to be an important determinant of growth, and the opposite
has also been proven.
Questions remain, but not because of a scarcity of attempts to answer. In the modern
era, the earliest answer was provided by the Master himself, Adam Smith. With an
appropriate title, The Wealth of Nations, published in 1776. Yet another Nobel laureate
Sir Arthur Lewis laid down the basics in his Theory of Economic Growth. These basics
are still as rich, and as relevant, in 2006 as they were in 1955. Another Nobelian Robert
Solow (along with Trevor Swan) provided the mathematical underpinnings of growth
theory in the mid 1960s, a framework that is both bread and butter for any discussion of
growth today. (Each additional author just brings in varieties of jam).
Gershenkron voiced concerns about the relative pace of growth in some European
countries, and Gunnar Myrdal about the lack of growth in poor countries. Myrdal offered
an Asian Drama treatise, a document that had ―An Inquiry into the Poverty of Nations‖ as
its sub-title. The two concerns, growth and poverty, are really the same concern; so
whether it is the great debate on poverty or the great debate on growth, the investigation
is the same.
33
The field has now become crowded and titles more creative. The attempt in modern-
modern literature (post the late 1970s) goes under various headings. ―Why isn‘t the
whole world developed‖ is how Easterlin phrased it in 1981. In a 1971 American
Economic Review article, John Hause countered with ―If you are so smart, why aren‘t
you rich‖. Elhanan Helpman(2005) describes his concerns as the analysis of the
―Mystery of Growth‖, William Easterly(2001) is quixotic in his ―Quest for Growth‖, and
Benjamin Friedman(2005) is concerned with the ―Moral Consequences of Growth‖. And
one might add, the ―Second Among Equals…‖
What have we learnt from all these treatises other than the fact that there might indeed
be nothing new about the oldest investigation? But we shall not cease from exploration.
But some useful pointers about research on the economics of growth. The economics of
institutions, the rage today and discussed at length in Chapter 8, was fully anticipated by
Arthur Lewis in his Theory of Growth. Part III of his book is called ―Economic
Institutions‖; in addition, the notion and importance of economic freedom, articulated and
emphasized by Friedrich von Hayek and Milton Friedman, is given prominent mention by
Arthur Lewis. But the profession was to ignore economic freedom till the very late 1980s.
The Great Divergence – some explanations
Divergence-Big Time is how Lant Pritchett brilliantly and evocatively termed the
divergence in inter-country incomes over the last 200 years. But we need to establish
the basic facts first. And these have to do with the levels of current real income, and the
determinants of the changes (growth) in initial 200 years ago incomes. And there is
some debate about the relative initial levels of development, or income, in the hundred
years or so before the Industrial Revolution. Pomeranz disputes the conventional
wisdom that China was significantly poorer than the Western nations around 1500 or so.
He cites evidence, both of the qualitative (inventions by China, like gunpowder) and
quantitative (levels of income in parts of China being comparable to the best levels of
living in Europe) kind.
China‘s contributions include inventions like the printing press, the compass, gunpowder
etc. And the civil service examination system to select the best and brightest mandarins.
Again, in a parallel that will become increasingly common, India was like China in its
emphasis upon the desirability of a ―civil service‖. Presumably, the reasons
34
bureaucracies were/are such a prominent feature of India and China is because both
countries were/are large population countries. In either country, a small district is the
size of an independent European country. The Indian state of Uttar Pradesh has a
population just slightly below Pakistan and this makes it the seventh largest country in
the world today. And Pakistan was a part of India two thousand years ago when the
Indian philosopher/state strategist Kautilya wrote, in 300 BC, the Arthashastra (Science
of the Polity). In it he outlined a code of conduct for government servants, and the
principles involved in running an efficient and disciplined civil service.
Income data tells a different story for average incomes. It is undoubtedly the case that
parts of China (and India) were very rich prior to the Industrial Revolution. That
institutions of governance were well developed (the bureaucracy?) and that several
industrial inventions originated in China. But China is a large country and the data
provided by Maddison(2003) and other historians (examined in the next chapter) is for
average, not best, levels of living in different countries. And these average levels of living
were not that different from world average but significantly lower than the average level
of living in England.
Regardless of precise estimates, the consensus belief is that around 1500 A.D., the
world was near equal. This was a period some 250 to 300 years before the Industrial
Revolution. There were still rich and poor countries, but the range was narrow. Mean
income was around PPP$ 660 a year and the standard deviation was only $ 180.8 In
2006, the mean income was $8630, and the standard deviation now larger than the
mean ($9104). In 1500, India and China were near identical ($ 627 and $ 653,
respectively); in 2006, China was almost exactly double (PPP$ 3500 for India vs. PPP $
6900 for China).
So what happened? Why this divergence in incomes? And if everything in the world is a
bell-shaped curve, and if everything reverts back to the mean (two of the most famous,
and accurate, theorems in economics, and life) why this seemingly permanent
8 All income data have been reduced to a common base, 1996, and a common currency, PPP.
Further, most discussion will be in per day incomes, in order to facilitate comparison with the `dollar per person a day‘ notion of absolute poverty. As discussed in Chapter 5, the middle class definition is also derived from the notion of an absolute poverty line – but this time, the poverty line for developed, not developing, economies.
35
divergence? Precisely because this is so a priori unexpected, is why the economists,
and policy advocates, and politicians, and everyone else spends so much energy and
time to find answers.
Proximate determinants of growth The debate, and heat, about the ―true‖ determinants of growth becomes crowded very
quickly, but in the main there is consensus on what Arthur Lewis calls the ―principles of
growth‖. While each generation of economists brings in their own twists and turns, these
principles have been the same for hundreds of years, and in the case of India and
China, perhaps for at least a couple of thousand years. There are four such principles:
factor accumulation, human capital, institutions and policy. While there are four
principles, the proximate causes of growth, again according to Lewis, are three:
―effort to economize (efficiency); increase of knowledge and its application; and
increasing the amount of capital or other resources per head‖. (1955, p.??).
To be sure, there are some additional factors. One, in particular, deserves prominent
mention and that is ―catch-up‖. Work by Barro and Sala-I-Martin now allows us to put a
value to this catch-all term. Catch-up means the ability of poorer countries to grow faster
than rich countries and do so because of two factors: first, that income growth for poorer
countries can be enhanced by simple technology transfer rather than be based on the
research and production of new technology, an inherently slow process; and second,
that poorer countries have cheaper labor, and labor whose wage is lower in international
productivity adjusted terms. All this means that poor countries can grow at about 0.5 to
1.5 percent per annum faster rate than their rich country peers, ceteris paribus. In other
words, in identifying patterns of growth, it is important to recognize that the poor
countries have a natural (comparative?) advantage to grow faster.
Factor accumulation
The oldest determinant: apply more capital and grow faster. Russia did it under Stalin
and according to some, so did East Asia. The simple point is that higher inputs can
mean higher output. But several developing countries, especially India and China, along
with Russia and others e.g. ECLA‘s Latin America, showed in the pre-open era that
investment without openness, or investment without competition, was a sure recipe for
some growth today and subsequently disastrous growth tomorrow. In the mid 1990s, it
was fashionable to argue that East Asian success (see Young(1995), Krugman(1994))
36
was mostly about factor accumulation. But recent research (see also Ch. 6 and Ch. 7)
indicates that the conclusion that East Asian countries grew primarily due to high
investment rates is incorrect; there was more capital input in these economies, and
because of openness, there was productivity growth. Not at all just factor accumulation.
Another important factor input is education. Private returns to education have universally
been found to be large, and endogenous growth theory emphasized that there should be
―externalities‖ as well. Various production function models do find that education has a
return on an economy wide basis but a significant externality effect of education has
been difficult to find.9
These factor controversies notwithstanding, a key question in growth empirics is to
establish the determinants of human and capital factor accumulation. What causes
investment to increase, and therefore countries to become richer? Some popular
explanations of why countries are poor are discussed below under various headings.
Whenever applicable, a cross-country regression result is reported for the partial impact
of the ―new‖ variable under discussion; partial because the reported effect is in the
presence of three ―base case‖ variables: initial per capita income Y0 , initial level of
undervaluation (UV0 ) and the average change in UV, dUV, over the period under
consideration, i.e. from t0 to end of the period tt. The construction of UV and dUV is
discussed in Appendix I and Chapter 6. Essentially, UV is the level of currency
undervaluation at any point in time i.e. the difference between the ―equilibrium‖ real
exchange rate (RER*) and the measured RER; dUV is just the average per year change
in UV. The sample size is restricted to developing countries which are not oil exporting
and did not have a population below 1 million in 2006; the period of estimation is 1980-
2006 with initial values pertaining to 1980.
The next few paragraphs discuss the different determinants in the context of an added
variable to the above basic model. The base case result, for developing economies10
(see Table 2.6, for this and other ―added variable‖ results) shows a rather large
9 For some early rejections of an externality effect of education – a prime ingredient in
endogenous growth models – see Bhalla(1994), Pritchett(1997). 10
Developing economies throughout are all countries in the world excluding the industrialized countries and countries belonging to Eastern Europe and the former Soviet Union.
37
explanatory power for the regressions, adjusted R-squared above 0.5, and with very
significant coefficients for both UV and dUV (table 2.6, row 1).
In 1980, the beginning of the period, China was overvalued by (log) 169 percent and
India by (log) 131 percent. China experienced an average decline in RER of 8.8 percent
per annum; India an average decline of 6.2 percent per annum. The average growth
rates for the two countries, 7.1 and 4.2 percent per annum, respectively. This regression
indicates that (2.6*.64 = 1.7) or more than half of the difference in the growth rates is
explained by China‘s more rapid pace of real currency depreciation.
The poor countries were always poor
This explanation is founded on the belief that because of unknown geographical and or
cultural and/or other factors, most countries that are poor today were poor several
hundred years ago as well. This is not true, and there is Maddison and other data to
prove it. Equally invalid is the assertion that the poor countries of today were actually the
rich countries of yesterday. This allows for a ―reversal of fortune‖ interpretation, but it is
factually incorrect. What is, true, however, is that large parts of Asia and Africa (but not
Latin America) have shown divergence, a divergence that demands an explanation.
The poor are poor because of colonialism
Another explanation, at least for India, is that ―colonialism did it‖ where the ―it‖ is
enduring poverty. That colonialism did it comes in different flavors. There is the
traditional (Indian) heart beating kind of explanation. To this day, even contemporary
Indian failure is blamed on the British, and contemporary successes (especially to the
use of English) are attributed to Indian ingenuity.
This explanation does not apply with as much force to China, one of the few Asian,
African or Latin American countries to have never been colonized. Nor does this
explanation help much in explaining divergence in incomes that occurred prior to the
Industrial Revolution, and prior to England‘s domination of India. In 1820, per capita
incomes in India and China have been estimated by Maddison to be only a third of the
level in UK, compared to being 80 percent of the UK level in 1500.
38
Thus, between 1500 and 1820, and between 1820 and 1950, India and China had very
divergent experiences. One country was colonized by the British; the other remained
independent. Colonization meant different institutions. Yet, at least for these two
countries, different institutions did not mean a different growth path, or a different level of
income for almost 200 years after the introduction of colonial rule. For this small sample
of two countries, institutional background and development has meant little difference in
development.
Variables representing colonialism - colonized by the British, colonized by France, and
colonized by others11 - are jointly significant with magnitudes in the following order: UK
colonies have the least negative effect, only –0.9 percent per annum and barely
significant at the 10 percent level; being a French or other (e.g. Dutch, Spanish etc)
colony means approximately a –1.4 percent per annum lower growth (table 2.6, row 2).
The poor are poor because of geography; But geography is not Destiny
The despair over lack of growth in Africa has led scholars to articulate a ―geography is
destiny‖ hypothesis. This hypothesis has a lot of history in the non-economics literature;
the addition of economists to this sociological belief is a recent innovation. This thesis
contends that countries close to the equator have a natural disadvantage, while
countries further away are destined to be richer (better climate, better soil, higher
productivity, higher growth etc). The climate in the tropics leads to diseases, is not
conducive to work and effort and therefore leads to lower development. In other words,
the inherited deck is stacked against these economies and this is an additional important
reason for the slow historical growth observed for sub-Saharan Africa.
The geography theory seems the least valid. There are several proxies for geography –
absolute value of latitude, number of tropical days, number of days in frost, minimum
temperature, minimum monthly rain, maximum temperature etc. Geography variables
tend to be the most ―moody‖, empirically. Sometimes significant, but never often. As far
as the most commonly used geography variable, latitude, is concerned, too many
counter-examples abound.
11
In recent years, colonialism has become an important explanatory variable for growth regressions, and particularly as an instrument variable for ―institutions‖. See Bhalla(1994), for the first use of colonialism variables in growth regressions, and for the first use of colonialism data as an instrument variable for political institutions (political and civil liberties).
39
Indirect effect of geography
One of the proxies (instruments) for institutions in the early 19th century is the mortality
rate of the settlers – higher the mortality rate, lower the chances of white settlers, and
therefore, a lower probability of western institutions being developed. But there are
important exceptions. Singapore, at the equator, along with Hong Kong, a colony with a
higher latitude, are the two countries with the lowest 19th century settler mortality rate, a
rate even lower than that which prevailed in the settler origin economy of UK (as well as
the US). It is also noteworthy that Singapore had a per capita income (in US, not PPP,
dollars) equal to that of the US in 1995. But both these countries are small, and may
encounter different obstacles to growth. But then there is the example of Thailand and
Indonesia, both close to or at the equator, and both very reasonable success stories.
Table 2.2 reports, for selected years, two sets of ―geography‖ regressions. The
dependent variable for each regression is (log) per capita income. There are three
independent variables – absolute value of the latitude of a country, the mean annual
temperature, and either the mean monthly rainfall or the minimum monthly rainfall.
These basic regression results suggest that the explanatory power of just geography to
explain the variance in levels of living in 2006 was as high as 63 percent; in 1500, when
divergence was much less, the explanatory power was only 25 percent. This would
seem to confirm the hypotheses of those that contend that geography matters, though
there are not too many plausible stories to explain why, after the industrial revolution and
globalization, geography should matter more, much more. Geography variables can lead
one to several wrong inferences; that geography explains differential levels of living is
one such error. Documentation of the alternative hypotheses, that geography does not
matter much, if at all, is provided in later chapters.
The growth experiences of China and India also negate, in their own partial way, the role
of geography in development.
India – the (non) role of geography
The Indian experience is overwhelmingly against the geography thesis. Kerala, at the
southernmost tip of India and very close to the equator (same latitude as several African
nations) has been the leader of living standards in India e.g. infant mortality rate of less
40
than 20 in 2003. This is comparable to the developed world, let alone to other parts of
the developing world. Even at the time of independence in 1947, Kerala was far ahead
of the rest of the states in India, in terms of achievement in education, female education,
and infant mortality. In this regard, Kerala‘s success is very similar to the country across
the narrow Polk straits – Sri Lanka (latitude the same as Kerala, 7.6o N).
The other three southern states of India, Andhra Pradesh, Karnataka, and especially
Tamil Nadu, have also performed better than their northern neighbors. These four close
to the equator southern states have grown at a pace of 3 per cent per annum faster than
their northern neighbors and done so for the last 45 years. Not much support for the
geography is destiny thesis.
Table 2.2: Growth and geography
R square in regression
Year Minimum rainfall Average rainfall
1500 0.252 0.221
1700 0.339 0.327
1820 0.416 0.413
1950 0.503 0.405
1980 0.601 0.474
2006 0.634 0.466 Source: Geography variables from Parker (2002)
Note: 1. Rain min is the minimum rain in a month. Rain is the average
monthly rain computed on the basis of minimum and maximum rainfall in the month and minimum and maximum number of days of rainfall.
2. Each regression has, in addition to the rainfall variable, the absolute value of latitude, and mean temperature.
Major oil exporting economies, and countries with population less than 1 million in 2006 (see Appendix I ) are
excluded from the analysis.
China – the (non) role of geography
There is some support for the ―raw‖ geography hypotheses for China. The high income
states of Tianjin, Liaoning and Jiangsu are also the high latitude states. But Zhejiang,
Guandong and Fujian are low latitude states and just as rich. Table 2.3 also reports on
an inter-state regression explaining per capita incomes in 1978 and 1999. Unlike the
41
global regression reported above, latitude has zero explanatory power in explaining
inter-state variation in incomes in either India or China in 1999. Chart 2.3 presents a
scatter pattern to reinforce the conclusion that latitude has had precious little to do with
the levels of income in countries today.
The base model regression also negates any significant role for geography in explaining
growth rates in the period 1980 to 2006; neither of the three geography variables are
significant, either individually, or jointly (Table 2.6, row 3).
The poor are poor because of “bad” institutions
The objective is to explain the large differences observed in per capita income among
different countries today. The first assumption is that in some distant past all countries
had close to the same per capita income. If true (and it is broadly true) then one source
of difference today is in the nature of political and economic institutions in the past. If, for
example, in 1500 AD England had the same per capita income as other countries, and
today it is considerably richer than most others, then the reasoning of the institutionalists
is that, subject to several qualifications, England is relatively richer today because it had
better institutions yesterday.
Table 2.3: Latitude and per capita income, China-India 1978 & 1999
1978 1999
Mean
latitude
R-
Square Coefficient t-statistic R- Square Coefficient t-statistic
India 23.0 0.00 0.04 0.22 0.03 -0.18 -0.87
China 32.8 0.17 0.92 2.95 0.03 0.45 1.13
Both 29.0 0.86 0.40 2.18 0.08 0.08 0.37 Source: SAE dataset (2006); Parker (2002)
Note: Regression for each year has log per capita income as the dependent variable and log
value of the latitude as an independent variable; row 3 reports the results for a pooled data India and China taken together with a China dummy.
42
Chart 2.3 Does geography (latitude) have any effect on per capita income, 1999
Source: India: State GDP database compiled by Economic and Political Research Wing (2003); China state GDP data:
Notes:
43
Institutions mean different things to different economists. For Hayek, institutions are
those that emphasize political and economic freedom for the individual. For him,
property rights are critical, but so is the role of the state. A state could guarantee
property rights (as China has recently done) but if political freedom is not there, then the
state does not have institutions conducive to efficient growth. Hayek famously equated
socialism and totalitarianism contending that both hampered, differently but equally,
incentives for individuals and both deposited too much power with the agents of the
state.
Arthur Lewis also emphasizes the role of institutions: ―Institutions promote or restrict
growth according to the protection they accord to effort, according to the opportunities
they provide for specialization, and according to the freedom of manoeuvre they permit‖.
Lewis (1955, p. 57). As does North in his pioneering work on the role (and rule) of
institutions:‖ Institutions affect the performance of the economy by their effect on the
costs of exchange and production. Together with the technology employed, they
determine the transaction and transformation (production) costs that make up total
costs.‖ (1991, p.5-6)
Institutions cause growth – the evidence
Recently, evidence has accumulated that institutions may indeed be the dominant factor
explaining the divergence in incomes. The empirical verification has been led by the
pioneering, and ingenious, papers of the team of Acemoglu, Johnson and Robinson
(hereafter AJR). Their seminal 2002 American Economic Review paper, Colonial
Origins of Economic Development, examined several of the inter-related issues
pertaining to institutions and levels of development. For institutions, they had several
proxies but in the main, two widely accepted measures were used: an index of
expropriation risk (ICRG data) to measure the magnitude of property rights, and an index
of executive constraints from the Polity IV data (measure of political institutions). Since it
is well recognized that institutions can affect growth, and vice-versa, an identifying
variable is needed to isolate the effect of institutions on income. This variable has to be
one that causally affects the development of institutions, but has no independent effect
on the level of income (or growth in incomes).
44
AJR offer a variable, the settler mortality rate in colonized countries in the 19th century,
as an instrument for early institutions. Briefly, their reasoning is as follows. Europeans
settled in some countries and not in others. How was the choice made? On the basis of
probability of dying. If an environment was not hospitable to one‘s health, the settler
chose an alternative abode or country. In areas where the Europeans did settle, they
tended to persist with the institutions of their ―mother‖ country. Why did they not do that
in the areas they colonized but ones in which they did not settle? Because the
colonialists, given that they were not settling, had no incentive to change the feudal,
extractive, nature of these colonies. Instead, the incentive was the opposite i.e. to
plunder and extract rents from taxation, just as had been done by the ―locals‖ over the
previous centuries.
Engerman-Sokoloff(2002) have an analogous explanation for settlement in Latin
America. There was a lot of settlement in Latin America but because of possibilities of
extractive rent from plantations, and availability of cheap and sometimes slave labor, the
white settlers were content with extracting rent and keeping with ―local‖ institutions. In
contrast, Engerman and Sokoloff offer the example of North America and Canada. Also
settled by white foreigners, but very different institutions, and very different and higher
rates of growth. Why the difference between North and South America? Because of a
large land to labor ratio, a largeness that provided wealth to all and led to more
egalitarian and democratic development in North America.
The institutionalists cite a considerable amount of evidence to support their theory. Two
of the institution variables- risk of expropriation and business regulation, are significant
when added to the basic model - political and civil liberties is not. These are OLS
regressions and not done in an econometrically correct manner. When done so, Chapter
8, the evidence suggests that institution variables, including the above two economic
freedom variables, are not significant. In addition, the next chapter shows that because
the comparison is with North America, Latin America appears backward and less
developed. But if comparison is with the colonizers – UK, Spain, Portugal – then there
was no divergence in Latin America, at least until post 1980. Regarding the divergence
observed in Africa and Asia, Chapter 8 offers evidence to suggest that institutions played
only a minor, if any, role in explaining the lack of growth in these two continents.
45
The poor are poor because of lack of political freedom A parallel institution argument is that political liberties and democracy play a strong role
generating higher growth. The reasoning is that democracy allows for more sensible
decisions because of greater checks and balances. However, the evidence is decidedly
mixed, indeed often in the negative. The opposite argument is often fashionable. It has
been argued that authoritarianism helps growth, as abundantly shown by the strong
growth example of East Asia. But for every East Asian dictator that produced high
growth, there are ten African and Latin American dictators who did not. Political freedom
(average of political and civil liberties ) is not statistically significant when added to the
base model.
The poor are poor because of lack of economic freedom
This argument is on considerably firmer footing. Scully pioneered the incorporation of
economic freedom or economic liberty variables in explaining growth. Bhalla(1994)
provided an explanation for higher East Asian growth by incorporating both political and
economic freedom roles into the analysis. These countries grew faster because of the
much larger economic freedom in their countries, a magnitude whose positive effect on
growth far outweighed the negative effects of authoritarianism. The results showed that
political freedom mattered, especially after controlling for economic freedom.
For both political and economic freedom, the key is to uncover their separate roles; while
political freedom has been much talked about, discussed, and estimated, economic
freedom has had a shaky relationship with the economics profession. Both political and
economic freedom; are age-old concepts, but the latter was rediscovered only in the late
1980s. Hayek had talked endlessly about its importance in the 1940s, but the developing
world ignored him as they ignored Milton Friedman in the 1950s, 1960s and 1970s. It
was only in 1988 when Scully wrote The Institutional Framework and Economic
Development that the economic profession began to take notice. Dasgupta(1990) soon
followed, as did the World Bank in its World Development Report 1991. The ―liberty
information institutions‖ started to compile data on economic freedom. Freedom House,
publisher of the pioneering political liberties series, available since 1973, started an
economic freedom index as late as the mid 1990s.
46
Results for two economic freedom variables are presented below; first, the most
commonly used expropriation risk variable12, and the second is the business regulations
index from the Economic Freedom in the World indices published by The Fraser
Institute. Both variables are significant and more so than the political freedom variables
reported earlier (Table 2.6, row 5-6).
The poor are poor because of culture/religion
A variant on the institutions thesis is the assertion that cultural and/or religious
background can be influential in affecting development. While instinctively appealing e.g.
east Asian countries all have been successful, and all subscribe to Confucianism; or the
West is rich because of the Protestant ethic, the cultural hypothesis has rarely, if at all,
been empirically successful as an explanation. Religion variables have had mixed
results; the following regression shows that countries not belonging to the three major
religions (Catholicism, Protestantism and Muslim) have a higher growth rate, and that
being Catholic is the most disadvantageous (Table 2.6, row 7)13.
The poor are poor because of bad luck In a much cited paper Good policy or good luck? , Easterly et. al. hold out little hope for
reversing divergence. If geography is given, and institutions are a long run (and
questionable) affair, then only policy can change misfortunes. On the basis of a wide-
ranging cross-country decadal and cross-country analysis, the authors rather
convincingly argue that
―Growth rates are highly unstable over time, while country characteristics are highly persistent. The correlation across decades of countries‘ growth rates of income per capita is around 0.1 to 0.3, while most country characteristics display cross-decade correlations of 0.6 to 0.9. Correlations of growth across periods as long as two decades – period lengths comparable to those used in the cross-section empirical literature – are similarly low‖. (p. 460).
But Easterly et.al. may have spoken a bit too soon, most likely because their period of
analysis ended in 1988, only eight years after the ―advent‖ of the new age of
globalization. That this globalization has had a major structural effect has been argued
12
This is the Inter Country Risk Guide variable used by AJR and others; it pertains to the level of expropriation risk in the 1980s; see Appendix I for details. 13
These religion variables, like several others, become insignificant when more variables are introduced into the analysis
47
by others, and in several chapters of this book. Table 2.4 shows that luck is diminishing
in importance, and that the cross-decades correlation increases, especially in the 1990s
and 2000s. Also, the across periods (1960-1980 and 1980-2006) correlation in per
capita growth rates is a high 0.56. Further, Chapters 6 and 7 suggest a reason for the
Table 2.4: Running out of Good Luck? Growth persistence, 1960-2006
Period Coefficient R-square
Correlation
Coefficient
Vs earlier decade
1960s 0.07 0.03 0.05
1970s 0.32* 0.08 0.28
1980s 0.43* 0.19 0.43
1990s 0.36* 0.17 0.38
2000s 0.24 0.06 0.25
Vs 2 decades earlier
1970s vs1950s 0.08 0.03 0.05
1980s vs1960s 0.38* 0.09 0.30
1990s vs1970s 0.40* 0.19 0.43
2000-06 vs 1980s 0.23 0.05 0.22
1980-06 vs 1960-80 0.56* 0.28 0.52 Source: SAE dataset, see Appendix I for details. Notes:
1. The model of persistence is as follows ẏt = α + β1*y0 + β1* ẏt-1 where ẏ is the growth of per capita income in period t and t-1, and y0 is the log of initial per capita income.
2. Asterisks indicate significant at 5% level of confidence interval
48
lack of role of good luck – good policy, particularly in the form of openness to world
markets, and especially in the form of a competitive undervalued exchange rate.
The poor are poor because of lack of education
It is almost a tautology - that is how true the observation is. Education brings about
higher incomes for individuals, and for societies. It helps make investment more
productive. But, and as was found out by India and China between 1950 and 1980, and
several other developing economies (and the former Soviet Union and its empire of East
European economies) the worth of education is preciously little unless the economy is
open to the outside world. It is openness which allows the import of ―technology‖ which a
higher level of education can capitalize on; it is openness which allows for catch-up with
the frontier. It is little known, but as shown by Morrisson-Murtin, educational
development in China was at a significantly high level in the late 19th century, and
among the top educational attainments in the developing world at the time of the
Communist revolution in 1948. The literacy rate in China in 1870 was 21 percent
compared to 3.7 percent for Africa, 13.1 percent for Asia, and 15.1 percent for Latin
America. It was close to 70 percent for Europe.14
Yet China was far behind the frontier between 1880 and 1950, and far behind its peers,
especially given its education advantage, between 1950 and 1980. However, contrary to
expectations, initial educational attainment (in 1980) is not a significant explanatory
variable for economic growth subsequent to 1980 (Table 2.6, row 8).
The poor will become rich after the demographic transition
Demography : this is a recent add-on to the determinants literature. Population growth
rates are a drag, but not the growth rate of the worker population (increased supply).
The extra availability of workers, and a decrease in the dependency ratio, coupled with
the life-cycle hypothesis of consumption, leads to a prolonged period of higher savings
and higher growth i.e. the demographic dividend. The demography thesis has intuitive
appeal, but is subject to several ifs and buts. This shows up in the empirical analysis,
with demographic variables sometimes being significant, other times not, depending on
the specification, estimation methods, and the selection of countries.
14
Data from Morrisson-Murtin(2005).
49
The intuitive argument behind the demography thesis is straightforward. In a period of
high population growth, the future supply of workforce increases sharply. In the transition
period when the birth rates decline, the dependency burden decreases for the existing
workforce (which is high because of the lack of fertility decline earlier). This birth decline
leads to a higher fraction of the population being employed, a higher level of savings,
investment and therefore growth.
There are two key assumptions behind the demography thesis. First and foremost is that
the pace of job growth increases to accommodate the higher workforce. This need not
always be the case. Second, countries differ in the labor contribution of women. The
labor force participation rate (LFPR) of women can differ sharply across societies. In
India, the urban LFPR of women is as low as 20 percent today, albeit sharply above the
15 % level of just a few years ago. It is this increase in LFPR which is likely to be the
biggest positive shock for developing countries who are presently behind the LFPR
curve – subject, of course, to macroeconomic policies conducive to economic growth
and employment.
Initial worker to dependent ratio (population 15-64 years to total population) is significant,
but it has a very small effect. Its coefficient is 0.16 i.e. for each 1 percentage point
increase in the ratio, an additional 0.16 annual percent growth is obtained (Table 2.6,
row 9). In 1980, China had 3.7 percentage points higher worker ratio than India, so it
gained an extra 0.6 percentage points extra growth over the 1980-2006 period.
The poor are poor because of lack of openness
One of the oldest economic theorems in the world is that openness to trade is conducive
to growth. No matter how much further back into history one goes, one keeps arriving at
the same result. Countries became richer the more they looked outward. Policy of
openness was recommended by Kautilya (p. 236-37, emphasis added):
―The following incentives shall be provided [to encourage the imports of foreign goods needed in the country]:
(i) [local] merchants who bring in foreign goods by caravans or by water routes shall enjoy exemption from taxes, so that they can make a profit;
(ii) foreign merchants shall not be sued in money disputes unless they are legal persons in the country; their local partners can, however, be sued.‖
50
Schleiffer-Long document it for ―princes and merchants‖ in the 15th century; Acemoglu-
Johnson-Robinson document this phenomenon for 17th century onwards in their reversal
of fortune analysis. Several economists have shouted themselves hoarse on the
matter15. Thus it is no surprise that openness is the second broad policy category in
Williamson‘s list.
Notwithstanding this historical support, openness has had its fair share of critics. The old
order, especially of the planning variety, has consistently objected to the opening of the
economy. These scholars/politicians feel that decreasing import tariffs would be akin to
re-inviting colonialism. Foreign firms, with modern methods and lower costs, would
swamp all competition and not allow domestic industry and expertise to develop.
As evidence of the failure of openness to generate extra growth, the experience of
developing countries in the colonial era is cited. At that time, free trade was everywhere,
yet the developing countries diverged from the income levels of their colonial masters.
Further, the argument goes, there is evidence that high tariffs actually helped the
developed countries to grow faster.16
A popular measure of openness was offered by Sachs-Warner (1995); without the
variables representing undervaluation, the Sachs-Warner (SW) variable is statistically
significant and with a magnitude of 1.8 i.e. more open economies, according to the SW
index, grow at about 1.8 percent per annum faster. However, when introduced into the
regression alongside the currency undervaluation variables, the coefficient drops to only
1 percent extra growth, and becomes insignificant at the 5 percent level (but significant
at the 7 % level of confidence) (Table 2.6, row 11).
The poor are poor because of bad policy
Since the ascendancy of institutional explanations of growth, the loser has not only been
the geography, culture, and religion explanations, but also the traditional ―policy‖
explanations. But what are good policies? How does one distinguish them, ex-ante, from
15 A major cause of lack of growth during the post colonial era, 1950 to 1980, was most likely the lack of
openness to international markets, foreign ideas, and foreign trade (Chapter 9 for India and China).
16
See Rourke. But also see the next chapter where evidence is presented to suggest that the case that tariffs helped growth in the 19
th century is fragile, and very weak.
51
bad policies? And in either case, how does one distinguish policy from institutions?
These major questions are explored in detail later; for the moment, it is important to
analyze the contribution to growth of the ―policy set‖. Most agree that bad policy
outcomes, e.g. high inflation, is a major handicap for higher growth. Another favorite of
the policy-wallahs is the fiscal deficit of the government. Higher fiscal deficits lead to
lower growth, to lower revenues, to yet higher fiscal deficits etc. And openness to trade
is a perennially recommended policy.
Washington Consensus policies
Just fifteen years ago, John Williamson enshrined policies as part of a ―Washington
Consensus‖ (WC) set. The last five years, ironically the period of the fastest growth in
the developing world, has also been the period when academic conclusions have
diverged significantly from those of the practitioners. While ―policy does not matter‖ is not
a majority conclusion, it does have significant support among the academics. And if the
phrasing were to be changed to ―policy matters only when all the appropriate conditions
are in place, and when the government is well intentioned, etc.‖ – well, that will have a
near universal support.
As the WC list (left side of Table 2.5) makes clear, the policy menu is large. In the main,
however, there are two major policies – openness and fiscal responsibility. Of the ten
policies, numbers 1, 2, 3 , 8 and 9 all belong directly or indirectly to the fiscal club. Tax
reform (#3) enhances tax revenue; reorientation of public expenditures (#2) also helps
fiscal discipline (#1). Privatization, deregulation and secure property rights17 (#‘s 8, 9,
and 10) all help growth via their effects on incentives to the private sector. These,
however, belong to the institution list discussed above. However, in part, they belong to
policy. Given that a lot of government activity in developing countries is inefficient (until
recently, bread in India was made by the public sector; inefficient and loss making
publicly owned hotels still dominate the landscape in India), deregulation and
privatization is really about increasing government tax revenue and therefore fiscal
discipline.
17
It is unclear whether the WC list is in order of priority, but it is interesting that ―secure property rights‖ comes last, especially given the fact that for the institutionwallahs it not only comes first, but almost to the exclusion of everything else.
52
Table 2.5: Policies: Washington consensus (Old and New)
Original Washington Consensus “Augmented” Washington Consensus the previous 10
items, plus:
1 Fiscal discipline 11 Corporate governance
2 Reorientation of public expenditures 12 Anti-corruption
3 Tax reform 13 Flexible labor markets
4 Financial liberalization 14 WTO agreements
5 Unified and competitive exchange rates 15 Financial codes and standards
6 Trade liberalization 16 “Prudent” capital-account opening
7 Openness to DFI 17 Non-intermediate exchange rate regimes
8 Privatization 18 Independent central banks/inflation targeting
9 Deregulation 19 Social safety nets
10 Secure Property Rights 20 Targeted poverty reduction
Source: Rodrik (2006) available at http://ksghome.harvard.edu/~drodrik/papers.html ; Original Washington consensus is from
Williamson (1995).
Notes:
53
Fiscal deficits are barely significant at the 10 percent level and have a magnitude of 0.12
i.e. for each 1 percentage point reduction in fiscal deficits, there is an extra 0.12 percent
GDP growth (Table 2.6, row 10).
Trade Policy Another line of reasoning, popularized by Rodrik and his co-authors18, asserts that while
openness could facilitate trade, and trade could facilitate growth, the causality could just
as easily, if not more likely, be in reverse. In other words, as countries grow, demands
for varied products increase, and more trade (imports and exports to finance the imports)
occurs. So econometric exercises that purport to show an acceleration of growth with
openness are actually showing the reverse.
This problem can be econometrically ―solved‖; techniques are available to identify the
direction of causation. The key word is econometric which means not the reality but an
estimate of reality. Estimates, as we know, are subject to error and just the mere
presence of this error, regardless of its size, allows the both sides to claim victory. The
protagonists say they have identified the problem away; the opposition says that the
instruments are weak. The debate goes on.
Trade policy can either be assessed in terms of outcomes i.e. share of trade in GDP and
changes therein, or in terms of the instruments that affect trade. The latter is captured by
tariff policy, and generally, strong statistical support for this indicator is lacking; in many
instances, tariffs have been reduced, import protection has declined, and yet growth has
failed to accelerate. Many others, growth has persisted with high tariffs. However, no
study for the post-war era has actually found that high tariffs can lead to faster growth.
Trade policy, as measured by trade shares, has had more than limited success as an
explanatory variable for growth. (See Frankel, Roemer). If initial trade shares19 are
introduced into the base model, then the observed coefficient is .012 i.e. each 10
percent increase in the share of trade increases the growth rate by 0.1 percent;
significant, but a small magnitude (Table 2.6, row 12).
18
See several papers, Rodrik and Roghebon etc 19
To avoid simultaneity, the independent variable is the trade share in 1980 while the dependent variable is growth from 1980 to 2006.
54
The poor are poor because of high inequality This is possibly the most popular assertion. There is a considerable literature on the
subject, some of which is discussed in Chapter 11. For thirty nine developing countries
(for which income inequality data are available for years close to the beginning of period
year 1980) there seems to be resounding support for the assertion that high inequality
leads to lower growth – if undervaluation variables are not in the model, then initial
inequality is strongly significant and has an estimated magnitude of -.11 i.e. each 5
percentage points in the inequality measure Gini, lowers subsequent growth by 0.5
percent per annum. However, once the two undervaluation measures are introduced, the
coefficient falls to -.025 and the t-statistic to 1.07, or significant at only the 30 percent
level of confidence (Table 2.6, row 14).
Middle Class and growth
Does the initial size of the middle class matter in affecting growth? Yes, and a whole
chapter is devoted to examining the role of the middle class. The definition used here is
that of an absolute level of income, above $ 10 per capita per day in 2006 prices. The
result: even after controlling for undervaluation, initial middle class (size in 1980) has an
important statistical effect for the subsequent 26 years; each 10 percentage points of
extra size of the middle class in 1980 resulted in a 0.5 percent per annum extra growth
between 1980 and 2006. It is one of the few variables to be significant in the presence of
variables representing undervaluation (Table 2.6, row 13).
Some brief conclusions: While theoretical elaborations have occurred, and empirical results obtained, the basic
―law‖ of growth has not changed: the economic productivity of individuals is
overwhelmingly affected by the incentives they face. Investments in education will take
place if there are opportunities for return. As World Bank pointed out in the World
Development Report for 1991, The Challenges of Development, education is particularly
productive when combined with openness to international trade. The openness is what
allows education to yield a higher return.
Capital investments will be undertaken if there are fewer constraints on production and
enterprise. If individuals are productive, they enjoy a higher level of income; collectively,
the economy or society grows faster. If all economies start from the same point, then
55
those societies where incentives are in place, or where private enterprise is rewarded,
those societies will grow faster. The experience of India and China, is consistent with
these time-old truths.
The most significant result emerging from this sensitivity analysis is that currency
undervaluation retains its significance no matter what additional variables are entered
into the regression, either individually or jointly. Nor is this significance due to the
presence of outliers; indeed, if outliers are excluded, the role is even more significant.
This result is in the background, and often at the foreground, in the rest of the chapters.
56
Table: 2.6: What determines growth?
Initial Additional variables
Constant
(log) per
capita
income Uvi dUV 1 2 3 R2 #of OBS
1. Base 2.47 -0.4 -.018 - .64 0.59 79
(1.92) (-0.95) (-0.3.53) (-9.00)
2. Base + Variables added
Colonization 3.52 -.41 -.02 -.58 -.90 -1.36 -1.53 0.65 79
(2.65) (-0.95) (-3.46) (-8.84) (-1.68) (-2.48) (-2.87)
3. Geography 3.13 - 0.65 -.02 -.63 .015 -.01 -.02 0.59 79
(-1.90) (-1.37) (-4.01) (-9.66) (0.81) (-0.19) (-1.41)
4. Political and civil liberties 2.40 -.73 -.02 -.64 .178
(-1.76) (-1.50) (-3.72) (-9.06) (1.19) 0.6 78
5. Expropriation risk (ICRG) -1.43 -.351 -.01 -.58 .50 0.67 65
(-1.03) (-0.75) (-2.44) (-8.34) (2.83)
6. Business Regulations -.27 -.90 -.02 -.67 .76 0.71 56
(EFW) (-0.37) (-1.94) (-4.25) (-9.77) (3.46)
7. Religion 2.81 -.26 -.016 -.58 -1.33 -.83 -.95 0.66 79
(-2.61) (-0.73) (-3.60) (-7.91) (-3.22) (-1.71) (-2.42)
8. Education 2.30 -.70 -.02 -.63 .17 0.61 79
(Barro-Lee) (-2.00) (-1.48) (-3.93) (-9.73) (1.53)
9. Fraction workers/total -5.32 -.84 -.02 -.53 .157134 0.66 78
Population (-1.99) (-2.22) (-3.53) (-6.55) (3.17)
10. Fiscal Deficit (% GDP) 3.28 -.62 -.02 -.61 .12 0.59 72
(2.34) (-1.34) (-3.46) (-7.88) (1.71)
11. Sachs Warner Openness .81 -.17 -.01 -.65 1.00 0.64 73
(-0.72) (-0.37) (-2.64) (-7.70) (1.85)
12. Fraction trade/GDP 1.12 -.23 -.01 -.71 .012 0.63 68
(-1.10) (-0.56) (-3.06) (-9.14) (2.34)
13. Initial Middle class (% pop) 4.60 -2.11 -.02 -.64 .05 0.64 79
(-3.76) (-3.37) (-4.77) (-8.58) (2.96)
14. Initial income Gini (1980) 2.79 -0.048 -.012 -.64 -.025 .67 39
(1.68) (-0.09) (-1.97) (-5.33) (-1.07)
Source: SAE dataset, see Appendix I for details Notes:
1. The base regression is Y = α + β1 * YO + β2 * UVi + β3 * dUVi , where y is growth in per capita income, and YO is (log)
initial per capita income; UVi is initial undervaluation, and dUVi is average rate of change.
2. Colony: 1-British, 2-French, 3-Other colonizer;
3. Geography: 1-Latitude, 2-Temperature, 3-Rainfall;
4. Religion: 1-Catholic, 2-Protestant, 3-Muslim.
57
Chapter 3: The World till 1950
Four major global events define the 500 years prior to 1950. Event one – a non-event.
For the first two hundred years, the world was poor, generally equal and stagnant. Event
2: The big event, the Industrial Revolution, with origins in the late 18th century. This had
a relationship with the next happening. For various reasons, Europe was at the forefront
of this revolution, and its major beneficiary. Event 3, as if to bring spice into life, came
colonialism – the practice of militarily superior Western nations of conquering, ruling and
robbing countries of their taxes and wealth. The colonialism practice preceded the
industrial revolution, and indeed, was over for Latin America by 1825 or so. For the next
big event, one needs to fast forward for a 100 years, and then there were three inter-
related sub-events: two world wars and the first (and to date last) worldwide Economic
Depression.
Thus, what one observes in 1950 is a world divided into two halves: the haves and have-
nots, North and South, developed and developing and now emerging. World inequality
had zoomed to inconceivable heights in a space of just 150 years. No matter how or
where one looked, there was divergence. How did this happen? How from being near
equal we all became so unequal? The above four events define, in separate and
unequal ways, the contributions towards this great divergence.
There are many parts to this story and economic historians have done brilliantly in
piecing together hard to come by data, and evidence, to rigorously substantiate the
nature, and causes, of this divergence. Looking at nations poor and rich in 1950, and the
vast unbelievable distance between them, two questions come to mind: first, maybe the
poor nations like China and India were always poorer and the divergence today is mostly
a function of divergence yesterday i.e. the grand events affected all nations more or less
equally; second, the divergence observed today is understated because there wasn‘t
really that much divergence yesterday.
The first possibility, on reexamination of data, seems to be the Latin American reality;
the second possibility has been offered by historians, especially for China, and
especially by Pomeranz(2000). He contends that China‘s relative income prior to the
Industrial Revolution has been vastly understated.
58
Pomeranz (p. 17): ―Core regions in China and Japan circa 1750 seem to resemble the
most advanced parts of Western Europe, combining sophisticated agriculture,
commerce, and nonmechanized industry in similar, arguably even more realized, ways.
Thus we must look outside these cores to explain their subsequent divergence‖. On
page 49, Pomeranz is specific that he means average levels of living. ―It seems likely
that average incomes in Japan, China, and parts of Southeast Asia were comparable to
(or higher than) those in Western Europe even in the late eighteenth century‖. (p.49)
Is Maddison right?
The conclusion about stagnation in incomes in India and China, and most of the
developing world, is based on the pioneering work of Maddison. Table 3.1 presents his
estimates and those of Allen(2005), Broadberry-Gupta(2006) and Jeffrey
Williamson(2003). There are wage series available for common laborers, unskilled
workers, farm workers, and skilled workers (paid in silver wages). All data are indexed
with England as 100 in each of the selected years. The Maddison per capita income
data places India and China at around 80 percent of the UK level in 1500, and only
about 50 percent of the UK level in 1700. Thus, according to Maddison, divergence had
set in well before colonialism in India, and China was not as rich as believed by
Pomeranz.
For the controversial year 1700 (the year for which there is dispute since in 1500 all
estimates have China, India and the UK at broadly the same level of income) all
estimates converge to around 40 to 50 percent i.e. average incomes in India and China
were only half the UK level. The common laborers wage is at 41 percent (of UK) for India
and 34 percent for China. Wages fro unskilled and skilled workers, whether provided by
Allen or Broadberry-Gupta, are between 40 and 50 percent; the farm wage in China is at
57 percent in 1700. The different sources converge for 1820 as well. 20
20
This may not be entirely co-incidental for it is likely that Maddison looked at different sources of wages and incomes to arrive at his own estimates. The fact remains, however, that the contention that average China was just as rich as average England in 1700 is based on very weak evidence.
59
Table 3.1: Was Maddison Right?
1500 1600 1700 1820 1913
India
Maddison (in %) 80.9 58.6 46.2 32.8 14.4
Common labourers (Allen1) 77.6 40.6 26.3
Unskilled, (Allen2) 53.2 72.5 50.0
Unskilled, (B-G) 82.5 95.0 40.0 29.1
Farm wage (Allen3) 113.0
China
Maddison 84.3 62.1 48.2 35.3 11.0
Common labourers (Allen) 58.2 34.3 21.1
Farm wage (Allen3) 57.2
United States
Maddison 57.8 41.4 43.5 76.1 111.6
United Kingdom
Maddison, actual index 100 100 100 100 100
Common labourers 10.8 6.7 10.6 9.5 15.5
Unskilled, (Allen2) 1.0 1.0 1.0
Unskilled, (B-G) 6.3 4.0 8.0 8.6
Farm wage* (Allen3) 1.7 1.0 1.5 Source: Maddison (2006); Broadberry and Gupta (2005); Allen (2005).
Note:
1. Maddison represents the relative per capita income (in 1996 PPP $) of India and China with
respect to United Kingdom.
2. Allen1 reports the relative wheat-wage of common labour in kgs per day with respect to United Kingdoms
wage.
3. Allen2 actually reports European wage relative to Indian. From these numbers the wages with respect to
United Kingdom are derived.
4. Allen3 reports relative real wage with respect to United Kingdom. CPI deflator is used to compute real wage.
5. B-G represents Broadberry and Gupta and report wage both in terms of silver and grain. B-G1 reports the
silver wage (gms/day) with respect to United Kingdom. B-G2 reports the grain wage (kg/day) with respect to
United Kingdom.
6. * indicates that the farm wage corresponding to United Kingdom is multiplied by 100.
60
Measuring divergence
Most accounts of ―why some countries are rich and others poor‖ 21 conclude that the
great divergence involves most of the countries known today as the developing
economies. The assumption is that the great divergence is the story about the relative
decline of the countries of Asia, Africa and Latin America. This divergence is measured
with respect to the US, and as shown in Table 3.2, the conclusion seems correct. For
example, the developing world (the present poor countries) had per capita incomes that
were equal to, or slightly above, the level of income in the US in 1700. In 1980, some
280 years later, the three diverged continents (Africa, Asia and Latin America) show a
sharp decline in incomes relative to the US – from equality, the ratios are down to
around 10 percent for Asia and Africa. For Latin America, the ratio is down to a third.
This is divergence, big time.
This is divergence that Engerman-Sokoloff talk about to develop their hypotheses about
colonialism and ―bad‖ extractive institutions; this is the divergence that AJR analyze and
conclude in favor of good colonial institutions (in the large land surplus colonies of US,
Canada, Australia and New Zealand) and the bad colonial institutions in the commodities
rich and/or labor surplus economies of Asia, Africa and Latin America.
Surprisingly, the divergence for the leading industrial revolution country, the UK, is not
noted by most. UK incomes, relative to the US, also decline, and decline by a rather
large amount. In 1700, UK incomes were 229 percent higher than the US; in 1980, they
were only two-thirds! It is the case that almost every country in the world shows a
relative decline with respect to the US for the period 1700-1980. To be meaningful,
divergence has to be with respect to some central tendency, not with respect to the
fastest growing country.
Sometimes the number one student is just exceptional, and far ahead of No. 2. Often
times, most times, there is little to choose between 1 and 2. When there is an exception,
like the US, it is inappropriate to measure divergence with respect to No. 1; it is
appropriate to measure divergence with respect to the former No 1, i.e. the United
Kingdom.
21
For example, see Pritchett(1997), Engermann-Sokoloff, and AJR.
61
Table 3.2: Yearly per capita income with respect to USA, (1996 PPP $)
1500 1700 1820 1950 1980 2006 2025
West 189.4 181.5 89.1 58.2 69.9 66.6 69.1
Asia 139 107.4 45.6 7.4 10.1 16.5 31.4
Latin America 131.7 114.8 55.7 25.2 29.5 20.8 20.2
Africa 100 76.5 32.4 11 8.7 4.9 4.6
Developing countries 135.8 104.3 44.6 9.1 10.4 13.9 24.5
Developed countries 187 172.2 86 51.8 66.1 62.8 65.9
China 145.5 110.5 46.4 4.5 5 18.6 41.9
India 139.8 106.2 43 6.6 5.4 9.5 31.6
Russia 252.0 210.0 95.8 39.6 41.4 32.2 43.4
United Kingdom 172.4 229 131.3 70.3 67.1 67.9 69.4
World 148 121.6 54.9 23.1 24.7 23.5 31.4
United States 100 100 100 100 100 100 100
United States(actual) 449 591 1409 10702 21334 36715 52746 Source: Maddison (2006); Penn World Table version 6.1; World Development Indicators, World Bank(2006); World Economic Outlook, IMF (2006). Notes:
1. West includes all of Europe, Eastern Europe, former Soviet Union. Asia includes Japan, as does the region ―developed economies‖. Developing economies is the entire world excluding the West and Japan.
2. All figures are relative to incomes except for USA (actual) which reports the actual figures for USA. See Appendix I for details.
62
Table 3.3 reports the same ratios but this time against the ―lead‖ country: United
Kingdom. The picture is somewhat different. Asian incomes which were half of UK
incomes in 1700, are only one-seventh in 1980. African incomes which were a third of
UK in 1700, are only one-eighth in 1980. Crystal clear divergence .
But, most surprisingly, no divergence is revealed by the Maddison data for Latin
America. In 1700, average relative incomes in LA were half of those in UK; in 1980,
these relative incomes were 44 percent. Thus, as late as 1980, relative to UK incomes in
Latin America were near identical to the relative income level of 1700. Individual country
data also supports the hypothesis of no long run divergence for the 280 year period e.g.
the ratios for Argentina, Brazil, Chile and Mexico were 0.70, 0.40, 0.70 and 0.54 in
1600; in 1980, these ratios were 0.74, 0.45, 0.38 and 0.53.22
Most, if not all, of the empirical studies on the great divergence were written after the mid
to late 1990s. Latin America witnessed several crises in the 1980s and 1990s – debt,
hyper-inflation, currency etc. For the two decade period, 1980-2000, the average rate of
growth in Latin America was only 0.7 percent per annum; UK grew at 2.2 percent per
annum. In 1980, LA relative income was 44 percent; in 2000, it was 32 percent. In the
entire 280 year period 1700 to 1980, relative incomes in Latin America declined by only
6 percentage points; in just 20 years, 1980 to 2000, they declined by 13 percentage
points, or by twice as much.
Divergence by 1820
Even by 1820, the presently classified developing economies (excluding Latin America)
had fallen behind the developed world. In 1500, Asian income was 80 percent of UK; in
1700, it was almost down to half the 1500 level. For sub-Saharan Africa the decline was
similar. Thus, the search for explanation for the great divergence has to consist of two
parts: first, what happened to Asia and Africa between 1500 and 1700? Second, why did
Latin America not share in the divergence between 1820 and the time of independence
of most developing countries, circa 1960. This ―evidence‖ contrasts with the assertion of
22
Somewhat surprisingly, Chile rather than Argentina turns out to be the country with a large relative decline. The other three major Latin American countries show near constancy for the long 380 year period. If the comparison is with 1700, then there isn‘t that much of a decline for Chile either – relative to UK per capita income in 1700 and 1980 was 0.56 and 0.38. In 2006, 0.38 had improved to 0.48. The forecast for 2025 for Chile is 0.61 which would mean near constancy with 1600 and an improvement since 1700.
63
Table 3.3:Yearly per capita income with respect to UK, (1996 PPP $)
1500 1700 1820 1950 1980 2006 2025
West 109.9 79.2 67.9 82.7 104.2 98.1 99.6
Asia 80.7 46.9 34.7 10.5 15 24.2 45.2
Latin America 76.4 50.1 42.4 35.8 44 30.6 29
Africa 58 33.4 24.7 15.6 12.9 7.2 6.7
Developing countries 78.8 45.6 33.9 12.9 15.5 20.4 35.3
Developed countries 108.5 75.2 65.5 73.7 98.5 92.4 94.9
China 84.4 48.2 35.3 6.4 7.5 27.4 60.4
India 81.1 46.4 32.7 9.4 8.1 13.9 45.6
Russia 146.2 91.7 73.0 56.3 61.7 47.4 62.5
United States 58 43.7 76.1 142.2 149 147.2 144.1
World 85.8 53.1 41.8 32.8 36.7 34.6 45.3
United Kingdom 100 100 100 100 100 100 100
United Kingdom (actual) 774 1354 1851 7526 14315 24940 36613 Source: Maddison (2006); Penn World Table version 6.1; World Development Indicators, World Bank(2006); World Economic Outlook, IMF (2006). Notes:
1. West includes all of Europe, Eastern Europe, former Soviet Union. Asia includes Japan, as does the region ―developed economies‖. Developing economies is the entire world excluding the West and Japan.
2. All figures are relative to incomes except for UK (actual), which reports the actual figures for UK.
See Appendix I for details.
64
the ―institutionalists‖ who contend that the divergence appeared after the advent of
colonialism in these economies.
Reversal of fortune – the institutional argument
Presently, the most accepted explanation, with caveats, for the great divergence is that
the developing countries were rich at first but became poorer because of ―bad‖
institutions. The explanation goes broadly as follows. Before the advent of colonialism,
say 1700 or so, the various countries were near equal in terms of per capita income.
Some were richer than others, but the magnitude was not that great. Then came
colonialism in the Americas, and around mid 19th century in Asia, and late 19th century in
Africa. The divergence we observe today has not much to do with either initial conditions
(since per capita incomes were near equal) or with colonialism (since several formerly
colonial countries like the US, Canada, Australia, New Zealand etc. are rich today).
Rather, the divergence has to do with the presence/absence of Western institutions i.e.
the institutions of the colonial masters.
Since most of the non-Western world was colonized at one time or another, one should
find little divergence. The argument therefore hinges on whether the colonized
economies adopted Western institutions or not. This adoption, it is conjectured, is a
function of European settlements, and such settlements were a function of three factors:
geography, the initial level of development and natural endowments. AJR emphasize
the first two aspects, and Engerman-Sokoloff, in their analysis of the divergence
between South and North America, emphasize the second. Mortality risk faced by
colonial settlers was a primary determinant of whether colonial settlement occurred.
Other determinants included the population density of the local population: higher this
density, lower the probability of settling. So the colonialists settled in much greater
proportion in North and South America (and Australia and New Zealand) than in Asia or
Africa.
Settlers brought with them the institutions of the ―home country‖ and the countries
became relatively rich. ―Europeans were more likely to develop institutions of private
property when they settled in large numbers, for the natural reason that they themselves
were affected by these institutions. Moreover, when a large number of Europeans
settled, the lower strata of the settlers demanded rights and protection similar to, or even
65
better than, those in the home country. This made the development of effective property
rights for a broad cross section of the society more likely‖. (AJR, 2002, p. 15)
In countries where the colonial powers did not settle in large numbers (e.g. India), the
powers were content to subscribe to local ―institutions‖ which were extractive and rent-
seeking in nature. ―The distinguishing feature of these institutions was a high
concentration of political power in the hands of a few who used their power to extract
resources from the rest of the population‖ (AJR, 2002, p. 14). This old style feudal order
then delayed, or slowed, the adoption of new ways of doing business, of methods of
successfully adopting new technology (the Industrial Revolution) etc. So the countries
with low settlement and/or a large initial presence of extractive industries, diverged.
Engerman-Sokoloff substitute initial inequality and factor endowments (a large land/man
ratio) to explain why South America diverged while North America moved ahead –
despite both being settler economies.
As just documented, the argument about extractive industries is not needed since Latin
America did not diverge. Maddison‘s estimates of per capita income circa 1500-1820 are
supported by Broadberry-Gupta, among others; these estimates provide compelling
evidence that instead of a reversal of fortune, there indeed was a deepening of
misfortune for the developing world post 1700. The Industrial Revolution is dated around
the mid eighteenth century. Just prior to that time, say 1700, per capita incomes in the
developed world were already 50 percent higher than the developing world. If one adds
the two set of facts, first that Latin America did not diverge, and second that developing
countries were considerably poorer before the advent of colonialism, then the institution
argument for the great divergence loses much of its bite. There is relatively little to
explain, except the fact that the US (and Canada) grew faster than all other countries. If
one adds Australia and New Zealand, one is left with the not so politically correct result
that countries which grew the fastest were those that killed off the local institutions, and
local populations. Chapter 8 examines the institutions hypothesis in a non-heuristic and
empirically rigorous manner, using the same definitions, variables, and econometric
techniques as those used by the institutionalists. The result is the same as the above
heuristic conclusion: divergence has little to do with the presence or absence of ―settler‖
institutions.
66
Thus, the growth experience over the last 200 years is not so much a difference
between colonies and non-colonies, as between East and West, North and South,
developed and developing. Latin America turns out to be the surprise plus performer,
with growth rates equal to the West (and UK) and therefore no divergence, at least until
as late as 1980. There is divergence since then, but this divergence cannot be attributed
to settlement patterns of over 150 to 200 years ago.
Alternative explanations for the great divergence
There are alternative explanations for divergence, most importantly by Nobel laureate
Arthur Lewis(1978)23, and more recently by Williamson(2005, 2006). Chapter 2 of
Lewis‘s The Evolution of the International Economic Order is entitled ―The Division of the
World”. Why countries are poor or rich today is explained by Arthur Lewis as follows.
The world became divided (into rich and poor, into agricultural and industrial, into
developed and developing) primarily because of differences in labor productivity in
agriculture, the factoral terms of trade in Lewis‘s terminology. If farm productivity was
high, then the released labor had to be absorbed elsewhere, and equal to its opportunity
cost.
―In a closed economy, the size of the industrial sector is a function of agricultural
productivity. Agriculture has to be capable of producing the surplus food and raw
materials consumed in the industrial sector, and it is the affluent state of the farmers that
enables them to be a market for industrial products‖ (1978, p.9-10). The size of the
domestic market is critical for Lewis, and he does state that ―it is hard to begin
industrialization by exporting manufactures‖ (p.10). Low productivity in agriculture
means a small industrial market, and in turn, a not so good industrial climate. Hence,
industrialization got delayed in some countries i.e. the ones that are poor today, the
periphery in Williamson‘s terms. The commodity boom brought about by the demand for
primary goods in the late 19th century meant a further disincentive to not industrialize.
―So if trade was the engine of growth of the tropics, and industry the engine of growth of
the industrial countries, we can say that the tropical engine was beating as fast as the
industrial engine‖. (p.12).
23
Somewhat surprisingly, the new institution literature of the last forty years has almost zero reference to Arthur Lewis, despite his detailed discussion of the role of institutions in economic development in his classic Theory of Economic Growth, published in 1955.
67
Williamson has a parallel explanation to Lewis, and in its essence, similar24. He divides
the world into the (industrial) core and the (primary producing) periphery. The commodity
boom of the late 19th century is also critical for him, for this boom allowed the terms of
trade to improve, and exports to boom, and industrialization to be delayed. ―The secular
rise in its terms of trade had powerful de-industrialization effects in the periphery which
suppressed growth‖ (2005, abstract).
For both Lewis and Williamson, there is a ―Dutch disease‖ aspect to the de-
industrialization, or delayed industrialization story. Since the boom in discussion was for
primary goods (demanded by rapidly growing industrial countries) let us term this the
―Tulip disease‖. As Clark(2006) documents, capital costs were similar across the world in
the mid 19th century. For industrialization, wage differences mattered, and the
commodity boom allowed the wages in the periphery to be much higher, and for much
longer, than would have been dictated by ―deep‖ fundamentals. This was the Tulip
disease as the periphery priced itself out of competition for the production, and export, of
manufactured goods. Another term for the Tulip disease is exchange rate overvaluation,
a theme that occurs frequently in this book.
Core-Periphery
The fact that Latin America was not part of the divergence story until as late as 1980
casts some doubt on both the AJR and the Engerman-Sokoloff explanations for why
countries are rich. Extractive rent gathering institutions were as much, if not more so, in
Latin America than in Asia. Yet it is Asia that diverged. If Latin America is excluded, then
the institutional explanation for divergence is reduced to a Marxist explanation. The
colonial empires exploited their colonies simply because the colonial masters were, well,
masters.
Nevertheless, the colonial episode provides one with yet another natural experiment. As
Africa began to be colonized, and colonial penetration into Asia increased, the colonial
powers began to withdraw from Latin America. In 1804, Haiti became independent; in
1810, several other countries were granted independence e.g. Mexico, Chile, Colombia.
24
Perhaps not coincidentally. Williamson(2006) dedicates the book to Arthur Lewis, Moses Abramovitz and Bertil Ohlin.
68
By 1825, the job was complete as Bolivia was one of the last mainland25 colony to
become independent. Around 1890, all of Latin America was independent, almost all of
Africa and Asia colonized. The exceptions: in Africa, Ethiopia was never colonized; in
Asia, China, Japan and Thailand escaped the axe. So the natural experiment is one of
formerly colonized and the newly colonized countries growing (or not) between 1820 and
1950, or 1980.
The 19th century was a period when all colonial countries, called the ‗periphery‘ gained
substantially in their terms of trade. The Industrial Revolution meant that manufactured
goods prices exported by the `core‘ declined, while prices of primary goods/commodities
used in manufacture increased. There was technological growth in the former, and
―supply response‖ in the latter. Developing countries (periphery) showed a surplus in
their balance of payments, the (core) developed countries a deficit. The core countries,
helped by productivity growth in manufacturers, grew at almost twice the rate of the
periphery.
The excess growth of the two regions are similar, and reversed, in the two very different
time-periods. (Chart 3.1) What processes were at work in the 19th century, and are the
processes the same today? There is one explanation of the growth process which is
consistent with both what happened in the 19th century between the core and the
periphery, and what is happening today with the role reversal. And that is the role of
undervaluation of the currency in generating ―excess‖ (to your neighbors) growth.
Nineteenth century – the core leads
First, the 19th century. Until the middle of that century, most countries were on a silver
standard, and near the end, moved on to the gold standard. This meant that the
exchange rates adjusted to bring about ―purchasing power parity‖. However, the initial or
ex-ante exchange rates with respect to silver or gold were set by the monetary
authorities of the individual countries. For the ―core‖ Western countries, these rates were
set by the ―local‖ government; the same was the case for the formerly colonial countries
25
The Caribbean islands did not gain independence till the middle of the 20th century – Jamaica
in 1962, Barbados in 1966.
69
Chart 3.1: Growth in per capita income
0.10.0
0.10.0
1.2
0.4
3.2
2.5
1.9
2.2
1.9
4.7
2.0
2.6
01
23
45
Gro
wth
in in
com
e, p
c pd
199
6 pr
ices
(%
)
1600 1820 1950 1980 2000 2006 2025
C P C P C P C P C P C P C P
Source: Maddison (2006); Penn World Table version 6.1; World Development Indicators, World Bank(2006);
World Economic Outlook, IMF (2006). Notes:
1. The core is defined as the Western world (including East Europe and Russia). Japan is included in the core
post 1950. Non-core countries are periphery.
2. The numbers related to average per capita growth for the years between the year in question and the prior
year reported. For example, 2.5 for the periphery is the average per capita growth rate in the periphery
economies for the period 1950 to 1980.
70
in Latin America. But for colonized countries in Asia and Africa, the exchange rate was
set by the colonial government. This is the natural experiment. A natural experiment
which allows the testing of the hypothesis of whether the exchange rate was more
overvalued in colonies, than in the colonial master countries.
Table 3.4 documents the data on per capita income and the real exchange rate for
countries for which data are available. The real exchange rate is defined in the
conventional manner i.e. the ratio of PPP exchange rate to the US dollar exchange rate.
The former, obtained from Penn Tables, was extrapolated backwards from the 1950
level, using the consumer price index for the individual countries.26
Four stylized facts emerge out of even a cursory perusal of Table 3.4. First, that for each
country, there is near constancy in the real exchange rates between the late 18th or early
19th century and the start of the post War period. India and China have virtually identical
RERs for 1870 and 1950, as do Australia and France. Second, that there is not much
difference in the RER in the 19th century between the rich and poor countries. Third, that
adjusting for the B-S effects27, one obtains the result that the poorer countries in 1870 to
1890 had significantly more overvalued exchange rates. India and China were both
overvalued by more than 200 percent, while UK emerges as the country with the least
overvaluation (54 percent). Fourth, that perhaps not coincidentally, poorer countries
grew slower and diverged.
These data can be used to test whether undervaluation of the exchange rate had any
effect on output growth in years prior to 1913, and between 1913 and 1938. Note that
since the countries were on a silver and/or gold standard, the real exchange rate,
conventionally defined, would not change over time.28 But the real exchange rate
adjusted for relative productivity or Balassa-Samuelson effects would change. Thus, one
can test whether real currency devaluation has an effect on GDP growth. However, as
26
These data have been assembled from various sources, see Appendix I for details. 27
The same formula (relationship between RER and per capita income) is used to derive undervaluation as is done for data in the post 1950 period. See Appendix I for details. 28
Unless one country was on the silver standard and the other on the gold standard during the late 19
th century; the price of silver halved between 1875 and 1900, the price of gold remained
the same. This meant that countries like India which moved from a silver standard to a gold standard should show a sharp devaluation; this does not happen (see Table Table 3.5). Indeed, by RER staying constant and a decline in relative per capita income, the real exchange rate goes towards more overvaluation, not less.
71
Table 3.4: Real exchange rate, per capita income and undervaluation, 1870 & 1950, selected countries.
RER Income, pcpd 1996 prices Undervaluation
1870 1950 1870 1950 1870 1950
Argentine 0.7 1.19 4.6 17.6 114 53
Brazil (1890) 0.28 0.97 1.9 4.5 141 150
Chile 3.2 9.1 .
Mexico 0.68 0.36 2.3 8.2 -2
China 0.45 0.55 1.6 1.3 219 195
South Korea (1913) 0.27 2.1 2.9
Australia 0.66 0.58 11.1 25.1 76 -45
France (1880) 0.73 0.71 5.3 14.9 140 14
Germany 0.72 5.8 12.2 32
Italy (1890) 0.56 0.48 4.7 11.1 126 -1
Japan (1890) 0.25 0.34 2.3 6.1 93 17
Netherlands 0.55 0.45 8.8 19 79 -51
New Zealand 0.6 9.7 26.5 -44
United Kingdom 0.47 0.55 9.5 20.6 55 -36
United States 1 1 7.5 29.3 151 -10
India 0.55 0.56 1.7 1.9 233 176 Source: SAE data set, see Appendix I for details Note:
1. Undervaluation data corresponding to 19870 are either 1870 or 1890 data. 2. A negative sign for undervaluation means the exchange rate is undervalued.
72
Bergin et. al. (2004) document, the B-S relationship (i.e. a positive relationship between
the RER and per capita income) fluctuates, and they obtain mixed results for the
relationship in the 19th century. However, for the ―Taylor/PWT‖ regressions, Bergin et. al
find a significant RER effect is found, and except for India, China, and a few Asian
countries, post 1913, the data used here is Taylor‘s. The elasticity between RER and per
capita income for years prior to 1913 is 0.35 and for years 1913-1949 is 0.58 –
statistically significant and with levels corresponding to the post-war years and the late
1990s.
The base model, mentioned in the previous chapter, and discussed in detail in Chapter
6, is estimated below for three time-periods: 1870 to 1913, 1913 to 1938, and pooled
1870 to 1938 (with a dummy variable representing the two periods). The hypothesis
being addressed is whether the exchange rate practices in the different economies had
an effect on growth. Table 3.5 presents data for selected countries for the two end years
1870 and 1938. A cursory reading of the data suggests that change in UV and growth
are causally related. This is formally tested below.
For twenty countries, and the time-period 1870 to 1913, the following results are
obtained (Table 3.6). The initial value of undervaluation (i.e. estimated undervaluation in
the first year between 1870 and 1890 for which data are available) and the average
annual change in undervaluation between 1870 and 1913 are both highly significant.
The Western countries, and Latin America, had an average initial undervaluation rate of
128 and 153 percent respectively. The two regions grew at 1.32 and 1.66 percent per
annum. In contrast, India and China had an average initial undervaluation level of 226
percent (i.e. were overvalued with respect to Latin America and the West by
approximately 80 percent) and also grew at a much lower level of growth of only 0.32
percent per annum. The rate of change of the real exchange rate (dUV) was also the
lowest for India and China: only -0.35 percent per annum compared to a higher rate of –
0.48 for Latin America and an even higher rate of decline of –0.74 percent per annum for
the West. According to this model, India and China grew at approximately 1 percentage
point less than the independent West and Latin America.
73
Table 3.5 : Income growth and change in undervaluation
Income, pcpd (1996
PPP $) UV, initial (%)
Change in
UV (%)
Growth in
pc income
(%)
1870 1938 1870 1938 1870-1938 1870-1938
United States 7.5 18.8 154.4 77.3 -1.1 1.4
United Kingdom 9.48 18.6 68.7 64.6 0.1 6.0
Netherlands 8.75 16.7 92.9 46.8 11.2 7.1
Norway 4.55 14.3 167 90.8 1.7 14.4
Canada 5.79 15.5 150.3 78.8 0.9 10.2
Australia 11.1 20.0 90.2 40.3 0.7 6.0
India 1.66 2.1 236.7 185.9 1.0 2.6
China 1.58 1.7 222.8 206.4 0.3 14.3 Source: SAE dataset, see Appendix I for details.
Notes:
74
The level of initial undervaluation and the change in undervaluation accounts for about
0.8 percentage point less growth for India and China i.e. almost the entire lower growth
in these two countries is explained by exchange rate policy.
There has been a lot of discussion, and debate, about the empirically positive effects of
tariffs on economic growth in the 19th and early 20th century. (See Jeffrey
Williamson(2003), and Rourke(2000)). Irwin(2002) documents how this relationship
could be spurious. If initial tariff rates are inserted into the equation, alongside
undervaluation, the coefficient on tariffs is significant and positive. And with a value of
0.02, it means that each 10 % higher tariff made possible a 0.2 percent higher growth
rate. However, for the real beggar thy neighbor tariff period 1913 to 1938, the coefficient
on initial tariffs is negative, significant at the 0.1 percent level, and rather large, -.10. This
coefficient suggests that for each 10 percent higher (than average) tariff rates, a country
grew at a 1 percent lower rate, ceteris paribus. In the pooled 1870 to 1938 regression,
tariffs have a negative, but insignificant effect, on per capita growth.
Macarthur exchange rate for Japan in 1950 – how derived?
Many claim that the RER is not worth much analysis since it is endogenous. That this
may, or may not be so, is examined in detail in Chapter 6. For the moment, it is worth
examining the role RER, and its fair value, might have played in the setting of the
Japanese exchange rate after hyper inflation.
After World War II, General Macarthur set the Japanese exchange rate to the US dollar
at 360 yen for one US dollar. Before the war, in 1938, only 3.56 yen were needed to
purchase the dollar. Did the US just conveniently use a round number of a 100 to obtain
the new exchange rate? Unlikely. Table 3.6 documents the evolution of the real
exchange rate for Japan 1890 to 1960 along with its level of undervaluation, and the
level of per capita income. These data are suggestive of at least two stylized facts. First,
to a rather surprising degree given that Japan is known as the pre-eminent
undervaluer‖29, Japan appears as a country that has consistently played ball, at least the
ball authorized in play by Balassa-Samuelson. The real exchange rate shows a steady
29
Particularly among US academics and international investment banks, and particularly in contrast to China. See Chapter 7 for a discussion of this anomaly and the political economy of ―analysis‖ of real exchange rates.
75
Table: 3.6: Effect of undervaluation on growth, 1870-1938
Constant
Initial
income UV dUV
Period
dummy R-Square # of Obs
1870-1913 2.19 -0.3 -0.006 -0.47 0.66 20
(3.5) (1.1) (4.2) (4.6)
1913-1938 2.84 -0.55 -0.008 0.036 0.32 24
(3.1) (1.8) (2.1) (0.27)
Base + Period dummy
1870 -1938 2.3 -0.22 -0.006 -0.13 -0.53 0.34 44
(4.1) (1.0) (3.6) (1.8) (2.13) Source: SAE dataset, see Appendix I for details. Note: Value of period dummy is 0 for period 1870-1913, and 1 for 1913 to 1938.
76
increase with income; in 1890, Japanese per capita income was PPP$ 3.2 per capita per
day, and the real exchange rate was 0.25. In 1913, per capita income was 4.4 and the
RER had increased, in the spirit of B-S, to 0.37. Then the wars intervened and in 1948,
the real exchange rate had risen to 0.49. What should the US administrators do about
the nominal (and real) exchange rate? They choose the real exchange rate before the
fluctuations caused by the wars and economic depression. They choose the exchange
rate prevailing in 1913, the last ―clean‖ observation. In that year, the real exchange rate
was 0.37. Imposing this RER on the PPP exchange rate in 1949 of 132.3 gave an
exchange rate of 360.30 And that exchange rate was chosen and fixed!
In 1950, at an exchange rate of 360, and the real exchange rate at 0.37, the Japanese
currency was overvalued by 24 percent, in contrast to an estimated undervaluation of
the US dollar of –10 percent, of the British pound of –36 percent, and of the Deutsche
mark at 32 percent. The Netherlands guilder, in keeping with its historical tradition of
consistently undervaluing its currency, a result also found by Balassa(1964), was
undervalued at –51 percent, the fourth most undervalued currency in that year.31 It
appears that undervaluation went hand in hand with victory in the War.
30
The data for price levels and exchange rates for the pre-1950 period for Japan (and several other countries including the US) were kindly provided by Alan Taylor. 31
The most undervalued currency in 1950 was Mauritius (-61 percent), followed by Switzerland(-60 percent) and Denmark9-52 percent). Australia and UK followed Netherlands along with South Africa, Uruguay, Norway, US, Canada and Mexico.
77
Table 3.7: How the 360 yen/$ rate was selected: Japan, 1950
Exchange rate
PPP US $ RER UV (%)
Income,
pc pd
(1996 PPP
$)
1890 0.30 1.19 0.25 93.2 3.21
1913 0.75 2.03 0.37 101.2 4.4
1938 1.03 3.56 0.29 24.4 7.78
1950 121.7 361.1 0.34 16.7 6.1
1960 156.8 360 0.44 -11.6 12.5 Source: SAE data set, see Appendix I for details Note:
1. See text for the definition of the variables.
2. The PPP exchange rate for years prior to 1950 are obtained by linking
Japan and US inflation to the PPP exchange rate obtained from Penn
World Table 6.1 for 1950.
78
Chapter 4: Planning as Panacea
China and India have not only been similar for a thousand years, they have been near
identical. Actually, one can think of them as twins, giant twins. The story of India-China is
the all too familiar (at least for Indian movie audiences) story of twins separated at birth.
In the Indian movies, the twins belong to an upwardly mobile poor family, but a family
which stresses education and ―good‖ values. Invariably, the twins are boys reflecting a
dominant strain of son preference. It is in the genes. An accident separates the twins.
The twins go to school, and one twin is invariably good in studies, the other invariably
bad. But the ―bad‖ one is smart (he‘s got the genes). The bad student lands in bad
company, and becomes a renegade. But he doesn‘t forget his genetic values. He wants,
most of all, to help the poor. The good student becomes an establishment cop, and he
also has a deep desire to do good, even if it means he has to arrest his long-lost ―bad‖
brother. But that is getting slightly ahead of the story.
There is a pretty girl. Not only pretty but rich. She falls in love with the two brothers, but
really fancies the bad one. The good brother is nerdy to boot – correlation or causation?
Now we have the complication – the good brother has to arrest the bad brother. So what
happens? Is he able to do it? Who does the girl eventually go with? And does the bad
become good at the end? And where, or who, in the movie is the United States? Or is
that in a sequel? I have given away too much already, now you go see the movie.
Between India and China, who is good and who is bad depends on who is beholding,
and for which time-period. In the late fifties, there was a grand reception for Prime
Minister Chou Enlai (now Zhou Enlai) in India; sounds of Hindi-Chini Bhai-Bhai (Indians
and Chinese are brothers) littered the air. A few years later, in 1962, India and China
were involved in a serious war32. Bhai-Bhai became Bye-Bye.
India and China at birth (independence)
India gained independence at midnight Aug. 15, 1947. On October 1, 1949, Communist
rule was established in China, with Chiang Kai Sheik fleeing Mao‘s China to
Kuomintang, Taiwan. Less than 4 months later, on Jan. 26, 1950 India adopted a
constitution and became a Republic. The twins were born.
32
See Neville Maxwell India’s China War, a riveting account of the events that led up to Nehru‘s major foreign policy disaster.
79
The political system the two leaders, Jawaharlal Nehru and Mao Tse Tung, chose for
themselves was one of democracy and authoritarianism, respectively. Many have
applauded the democratic course taken by India, much before its due time. It is one of
the major unanswered puzzles in Barrington Moore‘s seminal treatise on dictatorship
and democracy. But part of the colonial heritage of the British was a proclivity for
democracy. It is not a coincidence that countries of East and South Asia ruled by the
British had more democracy and its accoutrements like press freedom than their
neighbors.
The actions of the two societies, both in the wrong forks they have taken, and in the right
paths, have been uncannily similar. The two therefore constitute a natural study. The two
largest countries, in terms of population. The two poorest countries. Both with nearly the
same per capita income and very likely, the same distribution of income.33 Both proud,
and old civilizations.
Somewhat twin like, India and China followed very similar economic freedom paths for
the first thirty years after independence. Not just similar, contemptuous. A democracy
and a communist state, both choosing economic totalitarianism. Thus brilliantly proving
Hayek‘s 1945 prophecy that there wasn‘t a dime‘s worth of difference between
totalitarianism and ―democratic socialism‖. And as in confirmation, both countries came
out swinging in favor of five year plans of development, following the example of the
Soviet Union. For China, this was a natural path – economic totalitarianism usually
followed political totalitarianism. However, the case of democratic India swearing
allegiance to economic totalitarianism was more than a surprise.
But in the environment was the Cold War, and the ideological imperative. The
―intellectual‖ non-aligned leaders, clearly preferred the example of Soviet Union rather
than that of Japan, Europe, or the US. Maybe some of this was to be expected - even
parts of the liberal democratic West were leaning towards some state intervention, but
this was along ―liberal‖ Keynesian lines, rather than the draconian Soviet methods.
33
Bourguignon-Morrisson(2002) estimate the income inequality Gini to be slightly higher for India than China in 1950 – 42.7 vs. 39.5.
80
However, in this Soviet environment in the Third World, there were countries pursuing a
very different economic path. Korea and Taiwan grew spectacularly, post their
respective ―independence‖, along with the city states of Hong Kong and Singapore.34
Using a different economic model, a large country Brazil had also grown at over 4.5
percent per capita, and Chile began to reveal a different Latin American path to
prosperity in the mid 1970s. But there was ill-disguised contempt for the Asian success,
especially in India and China, notwithstanding the obvious reality that poverty had been
removed by non-Soviet style economic policies in these countries; and that poverty had
been removed without the glitterati ―in the name of the poor commitment‖ that Indian
political leaders have always been, and are, so fond of. The refrain in India was (and
presumably also in China) that Korea and Taiwan had both done it with extraordinary
help from the Americans, and Singapore and Hong Kong were just small economies with
few lessons for policy makers. So they were exceptions to the natural order of large
countries, and therefore nothing could be learnt from the different, and successful,
economic policies of small economies.
The ―natural‖ order was for dictation from above, with strict bureaucratic control over
economic activities. But it was always not so. Chow quotes a Chinese historian, Sima
Qian, writing about the prevailing economic philosophy at the time of the Han dynasty,
circa 200 AD:
―There must be farmers to produce food, men to extract the wealth of mountains and marshes, artisans to produce these things, and merchants to circulate them. There is no need to wait for government orders: each man will pay his part, doing his best to get what he desires. So cheap goods will go where they will fetch more, while expensive goods will make men search for cheap ones. When all work willingly at their trade, just as water flows ceaselessly downhill day and night, things will appear unsought and people will produce them without being asked. For clearly this accords with the Way and is in keeping with nature.‖ Chow (2002 p.13) And anticipating Deng Xiao Ping, and writing around 200 BC, the Indian political
strategist, Kautilya, described what was needed for a society to progress: ―Just as
elephants are needed to catch elephants, so does one need wealth to capture more
wealth.‖ (p. 253).
34
The respective 1960-80 average per capita growth rates for the four countries: Korea 6 % p.a., Taiwan 7 % p.a., Singapore 8.1 % p.a. and Hong Kong 6.8 % p.a.
81
The differences Along with the similarities, the differences were also pronounced. Where shall one
begin? India, a society of immigrants and invaders, a society of multiple languages and
diverse climates, a society of multiple gods and goddesses. China, a more homogenous
society35, and one without invaders or colonizers. And one which became godless in
1949. In terms of political freedom, the two societies could not be more different. India a
poor society trying to play by rich country rules of the game, and China, an equally poor
society, believing in the traditional feudal method of growth with direction exclusively
from the top. India arguing its way to progress, and when it does arrive, to be in chest-
beating self denial; China, a society where action is more important than argument.
India, where to appear to be concerned about the poor is what intellectuals aspire for,
along with a glass of Scotch whiskey; in China, the drinking of whiskey is a non-
ideological act. Direction (economic and political) comes from the top in China, and if
that means a million killed, or millions becoming rich, so be it. In India, endless and often
pointless debates about economic direction keep it from swinging to extremes. In China,
there is no such thing as populism or individualism. In India, often to be an Indian, is to
be a populist.
The Economic Environment at independence: Planning is equal to development
At the time of India‘s independence and China‘s entry into their own new order, the world
was emerging out of the double whammy of the Depression and World War II. But in the
first post-war decade, a lot changed. In the West, government planned reconstruction
achieved wonders – the European economies enjoyed unprecedented economic
growth. Economic depression had become a distant memory, and it seemed that some
planning was extremely productive. Some European countries, and particularly those
with a strong left tradition like England, extended enormously the role of the government.
For the state to intervene was for the state to do good.
With their erstwhile colonial masters choosing a strong(er) role for the state, planning
became the norm in the 1950s, and bigger planning meant a better future. The original
purpose of increased government intervention - avoidance of severe economic macro-
35
According to the Alesina et. al.(2004) index of ethnic fragmentation, India‘s heterogeneity is almost 3 times that of China.
82
economic contraction – was lost in the assumed superiority of government in handling all
economic activities. Western economists also purported to show the duality/equality
between a market, individual system and a government/central planning system. This
theoretical equivalence ignored all of the political dangers pointed out by some,
especially Hayek.
Friedman‘s important study Capitalism and Freedom in the early 1960s was to be the
last warning before truly mega-planning of most of the developing, and several of the
developed economies. (The US escaped the worst excesses). So isolated were the
forces of liberalism – Hayek, Friedman and Bauer – that what they taught, and
advocated, did not make even the reading lists of important American universities. So
universal was the hold of democratic socialism and/or planning that the phrase
―economic freedom‖ and/or ―economic liberties‖ is not to be found in most (or any)
economic literature published between 1962 and 1988. The lack of any discussion of
economic freedom by economists, and the considerable political science and economic
literature on political freedom (see Bhalla(1992, 1994) for a sampling) only emphasizes
the stranglehold of prevailing ―liberal‖ economic opinion. Discussion of political freedom
was politically correct – discussion of economic freedom was politically incorrect.
Economics and public policy departments at liberal Eastern universities (and I can vouch
for Princeton in the early seventies) did not even mention Hayek let alone study his
prescient analysis of economic freedom and growth; and Friedman was mentioned only
for his views on monetarism and for his ―battle‖ with fiscalism.
Freedom – China
In the case of China, totalitarianism was a necessary consequence of adopting the
Communist form of government. In an authoritarian state, as in traditional monarchies,
all direction flows from the top – the king and his ministers. So it was no surprise that in
addition to political, all other freedoms – cultural and economic – were suppressed in
China. However, as events were soon to prove in Italy and different states of India,
communism was not incompatible with democracy i.e. it wasn‘t really necessary that
communism be accompanied by dictatorship.
83
Totalitarianism and Mao
China, under the dictatorship of its ―peasant‖ elite, Mao Tse-Tung, was to move from one
wretched experiment in human affairs to another. The goal of Chinese economic policy
1950-1980 was clear: industrialize, and do so rapidly. The means did not matter, nor the
logic. First, one needed a plan, so five year plans became the norm. Revolutionary
fervor substituted for market efficiency, and China‘s polity, and economy lurched from
one disaster to another. Growth was desirable, at any cost. This is how Chow describes
the transformation:
―In 1958 Mao launched the Great Leap Forward movement with the purpose of increasing China‘s output dramatically and developing its economy rapidly. Mao did not understand economics and was extremely skillful in mobilizing the masses. He thought economic objectives could be achieved in the same way as political objectives and revolutions, simply by rallying mass support. In 1958, he organized the farmers into ―communes‖; in the previous few years they had been organized successively from family farms, to cooperatives, and more advanced forms of cooperatives. Within less than a year during 1958, almost all farms in China were converted to communes where people worked as a team and ate together in mess halls. Mao also assigned unreasonable output targets for the communes. Industrial output was also to be rapidly increased. People were asked to build furnaces in their backyards to produce iron. To satisfy output targets, finished products were put into the furnace to produce iron and steel. The end result was an economic disaster. Food production was greatly reduced, and bad weather was to blame. From 1958 to 1962 it was estimated that over 25 million people died of famine, the most severe in Chinese history.‖ (2002, p.)
Four years later, perhaps as a cover-up for the famine and other economic disasters,
Mao launched the Cultural revolution, a program designed to reinvent communism. It
was to be another 16 years before yet another revolutionary change was imposed from
above – the change of mice, and men.
Post-independence India – Born to be socialist
The surprise was India. It became a controlled economic state. The memory, and
vestiges, of those aberrant decades still resonate in Indian policy today. There wasn‘t
much that was not controlled in India in the pre-reform era. 36 So economic freedom was
36
The control-freakness manifested in the economic domain extended to cultural areas as well. In the late 1960s, India banned Louis Malle‘s brilliant nine hour documentary called Phantom India. A few months earlier, India had obliged Russia by changing the title of the film From Russia, With Love, to From 007 with Love. To this day, arbitrary acts of cultural control still exist in India. And
84
not part of the post-independence Indian psyche, though it had been very much part of
India (Bharat‘s) psyche for a few thousand years before.
What happened? Why when the ideology was of freedom, did India not extend it to the
economic realm? A possible answer: ideology, the ideology of the elite. The Indian elite
followed British manners, British beliefs, and the British language. But they also believed
that had it not been for British colonialism, India would be rich with spices, tea, and
technology. Why this perversity? Russia. Because Russia had ostensibly defied Western
imperialism and grown fast. And Russia had economic totalitarianism as its weapon of
success. The ideologues, led by the liberal Indian Prime Minister, Mr. Jawahar Lal
Nehru, were impressed by Russia, and saw no contradiction between the simultaneous
practice of heightened political freedom and submerged economic freedom. Support for
economic liberties was intensely frowned upon by this elite, and considered heartless
and unpatriotic. Only the state could efficiently ―force‖ economic development. In this
important regard, there really was very little difference between the temples of
destruction in Russia, China, or India.
It is difficult to over-estimate the influence of the get rich quick (substitute industrialize for
rich) model of the Soviet Union on the Indian leaders. As early as 1948, the Congress
party of India, the ruling and founding political party, adopted The Industrial Policy
Resolution, a policy document that was to become a formal part of Indian Planning. In
1950, the Constitution of India came into being, and it contained an important section
―Directive Principles‖. These principles did not have the force of law: for example, the
state could not be sued if the promise of universal primary education was not met (one
of the directive principles). But the State was directed to adopt policies which would
enhance the direction of these Principles. And the setting up of the Planning
Commission was an explicit following of the directive principles.
It was believed that government involvement in the production system was necessary to
get the economy to move towards a higher growth path. In India, it was argued that
whether it was the operation of hotels, or the making of bread, and later the making of
years after the economic reform began in 1991, the government still does not blink twice before banning futures trading in wheat on the grounds that such trading leads to higher prices, and therefore higher inflation, in wheat!
85
computers, government ownership and production was vital. It all began as an innocent
claim by the original planners – the country needed investment, and the private sector
was just not available, let alone willing, to do the job.
This view, and policy, has continued for a very long time, and even today, some political
leaders swear by it. This view was present at the time of discussions of the Indian
constitution, and the constitution itself. Indeed, the much reviled bank nationalization of
Indira Gandhi in 1969 was recommended in the Industrial Disputes Act of 1947, an Act
which contained a list of industries which could be declared public utilities, in the
interests of the state or development, and therefore subject to being nationalized. First
on the list was ―transport, other than railways‖. Second on the list was ―banking‖, third
was cement, fourth was coal, fifth was cotton textiles, sixth was foodstuffs, seventh was
iron and steel. The fact that even foodstuffs and textiles were ―recommended‖ to be
nationalized makes a mockery of the belief that the public sector was ―forced‖ to step in
because the private sector was unwilling.
The lack of any thought towards the concept of economic freedom has been systemic in
modern India, and credit, or blame, lies squarely with the leadership, specifically the
Congress party and the Nehru-Gandhi political dynasty. So pervasive has this Nehruvian
leadership been that Indian intellectuals were to recognize economic freedom only in the
late 1990s, and only after a non Nehru-Gandhi Congress leader had changed course in
the early 1990s (see Chapter 10). One of the world‘s leading philosophers and
champions of liberty, Nobel prize winner Amartya Sen, was to recognize economic
freedom somewhat belatedly in his 1997 book, Freedom as Development.
The sequence of events/thinking leading to India being a socialist state, in word and
deed, was most likely as follows. First, as cited in Austin (1999), was the ideology of the
supreme leader in the 1930s:
―the content of the [Congress] party‘s socialism became clear in its 1931 Karachi Resolution. Among other things, it said that ‗key industries and services, mineral resources, railways, waterways [and] shipping‘ were to be government controlled, and the government was to safeguard the interests of ‗industrial workers‘ and women and children…..The Congress Socialist Party- formed in 1934 - of which Nehru was a supportive non-member supported a policy of ―elimination of princes and landlords and
86
all other classes of exploiters without compensation and ‗redistribution of land to peasants‘. (emphasis added).
Second, was the thinking in the late 1950s:
―Socialism to some people means two things: Distribution which means cutting off the pockets of the people who have too much money and nationalization. Both these are desirable objectives‖ (Jawaharlal Nehru, Hindustan Standard, Delhi, May 17, 1958; emphasis added).
That Nehru was a Fabian socialist is well known, but the general impression remains
that he did not let these sentiments affect economic policy. But they did, starting with the
Constitution of India. It has his personal stamp, and it does not provide for much
economic freedom. Economic intervention is writ large in a document ostensibly about
fundamental rights37. Both in ideology, and deed, Nehru was more than an arm-chair
socialist.
One defense of the deep socialist experiment in India is that it was not Nehru‘s fault in
choosing this path, because the path was the ―environment‖ of the times. But even this
defense is only partially invalid. There were important dissenters to the socialist view
among those advising the Indian government: e.g. Mr. B R Shenoy, Milton Friedman. Mr.
Shenoy was involved with all the official organs of government and was a strong, and
sole, dissenter to the path and economic freedom breaking Second Five Year Plan38.
He seems to have waged a lonely battle in the 1950s and 1960s, but was prominently
joined by Bhagwati-Desai in the late 1960s.
Mr. Shenoy consistently opposed the extreme socialist and/or communist leanings of Mr.
Nehru and his daughter, Mrs. Indira Gandhi. He realized early that economic freedom
was not on the list of freedoms of India‘s populist leaders. He objected to the fact that
37 That institutions like democracy and a written constitution cannot be exported like Coca-Cola is
revealed by the malleable Indian constitution. In its 230 year history, US has had 27 amendments; India has had 104 to date, and the clock is still ticking fast. When in doubt, or when you want to be the populist champion of the world, amend has been the Indian motto. 38
Two years later, China launched the Great Leap Forward. As documented in Bhalla(2007), the two countries have followed each other at several important moments in history. Note that China formulated the one-child policy in 1976, close on the heels of the ill-fated population control sterilization drive initiated by Mrs. Gandhi.
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for Indian politicians, economic freedom was equated with discrimination in favor of the
rich, and hence immoral. For Indian socialist leaders, including prominently Nehru, until
the poor became rich, one could not, should not, even conceive of freedom for the rich.
For ―in the name of the poor‖ Indian leaders, and intellectuals, the state was required to
play a heavy role in order to ensure wealth for all and growth for the poor39. Shenoy
objected at several points to state monopoly capitalism; for example, in the late sixties,
he noted the lack of progress for the Indian workers, the targeted beneficiaries of Indian
socialism:
―In India… the productivity of industrial workers in 1964-65 was 2.3 times the productivity in 1951-52, wages, met by the rise in cost of living, rose only by about 20 per cent. The bulk of the benefit of the higher productivity of workers was retained by the Management. This was but a manifestation in the industrial sector of the overall phenomenon of the perverse income transfers through inflation and monopolies‖. B. R Shenoy, Economic Policy Resolution of AICC at Bangalore and Indian Economic and Social Progress. Pg. 4
Planning as a panacea
Given the direction from the political leaders, and the Constitution, Indian planners
proceeded quickly to assume draconian controls over economic activity within an
essentially free political system. 40 The main architect of Indian planning and controls
was Mr. P C Mahalanobis, a brilliant statistician who had the full support, and
confidence, of the PM, Nehru. Some scholars e.g. Panagariya(2006) maintain that
Nehru was essentially a liberal and a reformer, and if India went astray under his watch,
then it was due to his advisers, in whom perhaps Nehru placed a bit too much
confidence. As usual, there is a problem of identification or self-selection. It could just as
easily, and plausibly, be that Nehru chose his advisers on the basis of his own ideology.
It is more than conventional wisdom that Nehru, as a leader, made sure that alternative
leaders did not arise to give him competition. The above quotes about Nehru pre-1947
leave no doubt as to where his own ideology, and predilections, lay.
39
The fact that the ostensible benefits to the poor of this intervention never reached the poor was a point for future historians to ponder. 40
There is a not apocryphal story about a senior policy maker, when asked about the reasoning behind some highly unreasonable financial market controls, stated ―If you ask for logic, we will not help you!‖
88
Mahalanobis had the controls, and Nehru‘s ideology. There is an interesting comment
about planning and Mahalanobis by Milton Friedman, one of the several advisers41 to
India on economic policy (so it is not true that alternative voices were not present about
the direction of Indian, and developing world‘s, economic policy). In a memo on his
advisory visit to India, Friedman wrote about Indian planning:
―Mahalanobis began as a mathematician and is a very able one. Able mathematicians are usually recognized for their ability at a relatively early age. Realizing their own ability as they do and working in a field of absolutes, tends, in my opinion, to make them dangerous when they apply themselves to economic planning. They produce specific and detailed plans in which they have confidence, without perhaps realizing that economic planning is not the absolute science that mathematics is. …Mahalanobis was unquestionably extremely influential in drafting the Indian five-year plan…. The scheme of the Five Year Plan attributed to Mahalanobis faces two problems; one, that India needs heavy industry for economic development; and two, that development of heavy industry uses up large amounts of capital while providing only small employment. Based on these facts, Mahalanobis proposed to concentrate on heavy industry development on the one hand and to subsidize the hand production cottage industries on the other. The latter course would discriminate against the smaller manufacturers. In my opinion, the plan wastes both capital and labour and the Indians get only the worst of both efforts‖. (Milton Friedman, 1955, Mahalanobis Plan).
Post-Independence growth: higher across the world
One consistent apology made for India forsaking economic freedom under the
leadership of the liberal Nehru is that India could not have done any better, and/or did
not know any different. Thus, India‘s mixed-up strategy of development was
unavoidable. In addition, it is argued that India did not pay any price for having a socialist
control economy, because if one looks at India‘s GDP growth, one would note that it
accelerated markedly in post-independence India. GDP growth rate in the immediate 20
years following independence was higher than at any previous time in Indian history.
This is true. Hence, some conclude, that the mixed-up economy idea could not have
been bad for India, and indeed, even ex-post, one would recommend it.
It is hard to believe that private initiative and industry was either unwilling or unavailable
to make the daily bread, so the state had to do it. While India grew in the post World War
41
Some foreign advisers were not at all critical, indeed encouraged India to go forward on a close economy no economic freedom path e.g. John Galbraith.
89
II era, so did most countries on planet Earth. And most countries grew at an accelerated
pace. And, incidentally, India recorded one of the lowest rates of post war acceleration
as shown in Table 4.1. The table shows the regional, and selected country growth rates
for the period 1890 to 1950, and 1950-1980. India did just as well as its neighbors,
though Burma emerges as the star. East Asian economies, almost all do systematically
better. Per capita income in India grew at 2.1 percent per annum in India between 1950
and 1970, compared to a near zero growth rate over the previous sixty years. In Burma,
the acceleration was much faster, from minus 0.7 percent to plus 2.4 percent. For China,
the growth rate was even higher than India, 2.7 percent. Pakistan also showed a marked
acceleration, as did Nepal.
So it is a bit of an exaggeration to claim that Indian growth , post 1950, had a positive
contribution from its socialist policies. As is obvious from Table 4.1, there was a marked
acceleration no matter what the economic system. And India‘s acceleration, as well as
the level of GDP growth, was lower than most of its neighbors. So maybe Nehru‘s close
door economic policies did have an impact - a negative impact.
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Table 4.1 : Growth in per capita income
Country 1890 to 1950 1950 to 1970
South Asia
India 0.1 2.1
Sri Lanka 0.3 1.1
Pakistan 0.2 1.7
Burma -0.7 2.4
Nepal 0.1 1.2
East Asia
China -0.3 2.7
Hong Kong 1.1 5.5
Indonesia 0.4 1.7
Korea 0.2 4.7
Malaysia 1.1 1.6
Taiwan 0.5 5.3
Philippines 0.3 2.9
Singapore 1.1 4.7
Thailand 0.1 2.8
Vietnam 0.1 0.6
Latin America
Brazil 1.2 3.9
Jamaica 1.4 5.4
Cost Rica 1.4 2.6
Ghana 1.0 2.0
Tanzania 0.8 2.4 Source: Maddison (2006); Penn World Table version 6.1; World Development Indicators, World Bank(2006); World Economic Outlook, IMF (2006). Note: All growth rates are endpoint logarithmic growth computed for each of the period mentioned.
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India and democracy – an outlier?
No one gave India a chance to succeed as a democracy; that it did may be more than
just luck, or prescient foresight on the part of its founding fathers. It has been argued that
it was because of the liberalism and humane nature of India‘s leaders, especially its first
Prime Minister Jawaharlal Nehru, that India embarked upon its possibly premature
experiment with democracy. In the period from the end of colonialism in 1947 to Nehru‘s
death in 1964, there were precious few other poor societies that dabbled in experiments.
Or so it was believed.
There is a different interpretation for India adopting democracy and an alternative
hypothesis as to why it has stayed that way. This explanation, the one that provides a
solution to Barrington Moore‘s puzzle, is that there is no puzzle. India adopted a
democracy because that is, broadly, what ex-British colonies were expected
to do, and did. Table 4.2 shows the averages for British, and non-British colonies, for
three different indicators of political freedom in 1970: two indicators are from the Polity IV
data set, average index of executive constraints42 and democracy, and the average
index of political and civil liberties in 1972, the first year for which these data are
available. No matter what the index, British colonies obtain a higher value.43 Part of the
heritage of British colonialism was this political institution fallout44. Regression analysis
also confirms this tendency, with being a colony of UK being always a significant, and
positive, contributor to political freedom and democracy. These models also include the
level of per capita income, and mean years of education in 1970. The results suggest
that Indian democracy is not such a great surprise, and especially, that the Indian
democracy experiment, believed by many to be sui generis, was just not so.
These data suggest that, at the time of independence, there was a strong tendency in
South Asia towards democracy. This is also supported by the fact that the four major
South Asian economies (India, Pakistan, Bangladesh, Sri Lanka) all adopted democracy
as their first form of government. They did not stay that way, most notably Pakistan. So
42
Executive constraints as defined in the Polity IV data refers to ―the extent of institutionalized constraints on the decision making powers of chief executives, whether individuals or collectives‖. 43
These tabulations exclude those countries which were colonies before and are now part of the developed world e.g. USA, Canada, etc. 44
See Bhalla(1997) in which econometric estimates are presented to show that the British colonies had a higher probability of democracy and political rights. [This should be discussed in Chapter 8].
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some other factors may have been important in sustaining democracy in India. One such
factor is likely to be the extreme nature of heterogeneity in the Indian politic.
Table 4.2: Colonization and political freedom
Political liberty
(1973)
Executive
constraint
(1960)
Democracy
(1960)
British colonies 3.2 3.5 2.8
Non British colonies 2.3 1.8 0.2 Source: Polity IV dataset; SAE dataset, see Appendix I for details Note:
1. Political liberty index is from Freedom in the World.
2. Executive constraint and “Democracy” indices are from Polity IV data.
3. For all indices, higher value means greater political freedom.
India may have succeeded as a democracy because it was the only political system
compatible with a heterogeneous population. Most analysts have focused on India‘s
poverty in not expecting India to be democratic, not fully appreciating that only
democracy can keep everyone the least unhappy. A democratic process gives, at least
in theory, every group, and each individual to participate in the decision making. A small
chance, one might say, but an infinitely higher chance than if the system was non-
democratic – a monarchy or communist, and all flavors in between. It is important to
appreciate the existence of these small probabilities; the fact that they exist is the glue
for solidifying expectations, and for perpetuating democracy. To deny the channeling of
this heterogeneity via a democratic process is to invite civil war – something another
heterogeneous country had gone thru some hundred and fifty years ago.
The logic of Indian democracy can therefore be summarized as follows. Inheritance of
British institutions meant a strong, positive, initial proclivity towards democracy. The vote
empowerment of different social, cultural and religious groups meant that each group,
especially the small groups, had a strong stake in democracy. A correlate of this
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empowerment was the desire among all groups for a united India, for only in a united
India would each non-majority group have a stake. So democracy was the preferred
choice among all sections of society.
If this reasoning is correct, then there should be some possibility that more
homogeneous societies will have less of a chance to stay together, and/or be
democratic. There are at least two examples in recent history that this is so – the division
of homogeneous Pakistan into two separate states in 1971, and the division of
Czechoslovakia into the Czech Republic and Slovakia.
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“Working men of all countries, unite”
Last line in Engels and Marx, The Communist Manifesto, 1888
“The middle classes of the world have always been united”
with apologies to Engels and Marx
Chapter 5 – Development’s Secret Weapon
The ―theme‖ of this book, at least as reflected in the title!, is that the biggest
transformation in the world, unprecedented, and a possible explanator of several
unusual world phenomena over the last 20 years, is the increase in the size of the
world‘s middle class. It is this creation and expansion, at a rapid pace in the last five
years, that maybe largely responsible for the high savings rates in Asia, the commodity
price boom, the sustained rise of the oil price, the world goldilocks economy, record
setting worldwide growth, low inflation and record and rising levels for a broad class of
assets. And there is more to come. This phenomena of rapid world middle class
expansion should last another decade or so i.e. China‘s growth of middle class will be
replaced by other large economies in Asia, and to a lesser extent, by the populations of
Latin America and sub-Saharan Africa.
Who is this wunderkind middle class?
A much talked about subject, at cocktail parties and academic discourses. Everyone not
only has an opinion on it, but a strong opinion. By its very name, middle class is often
treated as the middle of the population. Indeed, popular academic discourses on the
middle class define it to be the second, third and fourth quintiles of the population ranked
by per capita income, with the two tails (the bottom and top quintiles) bringing up the
edges. This middle of the distribution, like the poor, is always with us and always
approximately the same percentage of the population. However, this middle is unlikely
to be the middle class as either historically defined or understood.
According to the ―middle of the distribution‖ formula, the middle class is always 40 to 50
percent of the population. But historically, from Aristotle to Barrington Moore, the middle
class is often a very small fraction of the population; for long periods, often less than a
tenth if not less than a twentieth of total population.
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Some have defined the middle to be the middle tendency in terms of values, or opinions
e.g. a middle of the road opinion or someone between the left and right, a liberal or a
conservative. Middle class is also assumed to be the same as the middle tendency. For
example, in the US, the die hard Republicans are the rich elite; the dyed in the wool
Democrats represent the poor. So the middle class is one which is neither rich nor poor,
neither Republican nor Democrat.
This might be the modern definition of the middle class, but historically, a particular kind
of elite has been defined as the middle class. For Aristotle, the middle class was an
owner of property. As quoted in Anesi(2003),
―Private property is abolished in the Republic, but in a polity Aristotle views it as necessity. ‗Property must also belong to [the citizens], for the citizens must have a supply of property…land should belong to those who bare arms and to those who share in the constitution‘. Aristotle claims that the middle class will not be able to rule unless they are essentially given a push up, and that push is property ownership-private property enables the middle class to rule. …‘Since it is admitted that moderation and the mean are always best it is clear that the ownership of all gifts of fortune a middle condition will be the best‘. (emphasis added).
It is difficult to obtain an explicit Marxian definition of the middle class. At various points
in the Communist Manifesto, this is how it is defined (or left ambiguous). ―The
bourgeoisie has stripped of its halo every occupation hitherto honored and looked up to
with reverent awe. It has converted the physician, the lawyer, the priest, the poet, the
man of science, into its paid wage labourers‖. So one knows who the middle class is not.
It is also not ―the lower middle class, the small manufacturer, the shopkeeper, the
artisan, the peasant, all these fight against the bourgeoisie, to save from extinction their
existence as fractions of the middle class.‖ But there are clues. ―The theory of the
Communists may be summed up in the single sentence: Abolition of private property‖.
And finally, something explicit: ―By freedom is meant, under the present bourgeois
conditions of production, free trade, free selling and buying. You must, therefore,
confess that by "individual" you mean no other person than the bourgeois, than the
middle-class owner of property. This person must, indeed, be swept out of the way, and
made impossible‖. (emphasis added). So, according to Engels and Marx, the
bourgeoisie middle class was really the property owning elite i.e. the same as Aristotle.
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Aristotle was brought up as a technocrat aristocrat; his father was a professional
(physician), so it is likely that Aristotle was reflecting, via the middle class, his own family
background. As was John Stuart Mills, more than two thousand years later. Mills had a
similar definition of the middle class. He also had a near identical upbringing; brought up
as an aristocrat with his father a major philosopher (John Mills) and his godfather
Jeremy Bentham. Again, his visualization of the middle class was the elite (like himself
and like Aristotle).
―Mills defines the middle class also by amount rather than source of income. The middle class consists of persons with moderate income or property. …. Mill places so much emphasis upon class and upon changes in class structure because he assumes that ideology follows from class position. He assumes that the middle class participates in public discussions, attends to good leaders, and comes to understand its situation and its interests‖. (Sullivan, 1981,)
Barrington Moore (p.418) agrees with Marx: ―We may simply register strong agreement
with the Marxist thesis that a vigorous and independent class of town dwellers has been
an indispensable element in the growth of parliamentary democracy. No bourgeois, no
democracy‖.
In his book on the middle class in Great Britain, Lawrence James concludes the same:
―As entrepreneurs and manufacturers, the middle class created modern, urban Britain, and on the whole central government was happy to let them mould its environment. In what was some of the most influential and far-reaching legislation ever passed, Westminster delegated extensive powers to elected councils. These were dominated by a middle class with a compelling faith in its own capacity to make the world a cleaner, healthier, more secure and better-educated place. It started with the urban infrastructure, laying drains, purifying water and paving and lighting streets. Then it turned its attentions to civic amenities such as baths, libraries, parks, museums and the supply of gas and electricity. These often massive programmes of regeneration and modernization enhanced the middle class‘s image of Britain as a progressive and civilized nation‖. (p.232)
The real middle class stands up
The consensus therefore appears to be that the middle class is neither aristocracy
(landed or otherwise) nor feudal. The middle class represents the polity that has
benefited the most from economic growth; hence, as part of their class interests, the
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middle class is very desirous of economic reforms, and is often in a position to ensure its
perpetuation. As the above quotes reveal, the middle class is a class that believes in
education, and merit. Indeed, belief in merit maybe the middle class‘s signature view.
In the early stages of development, the rich are the landed elite; in early stages, some of
the landed elite venture into industry, and some become industrial entrepreneurs. As
well understood by all, this landed industrial elite is interested in its own gains, and
therefore believes in high protection, low economic freedom etc. They are the ―rent-
seekers‖, and it is logical for them to want to stay that way. They are the anti-reformers.
By definition, the middle class are not the poor, and not the rich. The middle class is a
sense of values, an indicator of aspirations, a belief in ―law and order‖. In contrast to the
landed industrial elite, the middle class comprises of individuals who made money the
old fashioned way – by earning it. Thus, it is logical is for the middle class to believe in
the opposite of what the traditional elite believes. Its own self-interest demands an
increase in its own welfare, but its gains can come only from a more open economy,
from less controls on its own enterprise, from more economic freedom. Thus, the middle
class and the traditional elites demand opposite ―rules‖ of behavior, opposite institutions.
In this battle, the middle class has to win out. Due to sheer size of its numbers, if not the
logic of its position. This is why ―good‖ institutions, and development, are inevitable.
Institutional development is the development of the middle class. In Aristotle‘s Greece, in
Mills‘s England and today in India and China; and the rest of the poorer developing world
tomorrow.
Especially institutional development pertaining to economic freedom. There are
numerous instances in history (Korea in the 1970s, Chile in the 1970s and 1980s, China
in the 1990s and today) when the middle class has shied away from demanding what it
believes in the political sphere. For it, merit based economic growth, which enhances its
own relative value, is at a lexicographic premium to everything else. And merit can only
be enhanced by increases in both the quantity, and especially the quality, of education.
But extra education is no good in a feudal, closed economy. Therefore, the middle class
is at the forefront in demands for opening up the economy. So economic freedom, in all
its manifestations, is the first demand of the middle class. After such demands are near
fully met, does the middle class turn its considerable clout and attention to demanding
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improvements in the political institutions landscape. The rise of the middle class gives
rise to institutions; hence, institutional development most likely follows economic growth.
Middle Class – other more recent definitions
Though not as old, or popular, as the research on absolute poverty, research on the
middle class, and its effects, has been increasing. (See Bhalla-Kharas(1991), Birdsall et.
al.(2000), Easterly(2004)). Bhalla-Kharas, in their work on Malaysia came across a
research shortcoming: ―Though considerable research has been undertaken on
pinpointing the poverty line, relatively little attention has been concerned on defining the
demarcation line for the rich, and for the middle class.‖ (1991, p.26). They then
proceeded to provide such a definition, and indeed provided explicit levels for the
beginning of the lower middle class, upper middle class etc. Both Birdsall et. al and
Easterly offer a relative definition i.e. the share of the middle three quintiles (Easterly) or
the share of the population between 75 and 125 % of the median (Birdsall).
Bhalla-Kharas offered an absolute definition, where the middle class is where the poor
end i.e. the middle class starts at $ 2 per capita per day, or at the beginning of the non-
poor in the developing world. This book extends that definition: the middle class is where
the poor end in the developed rich world. Since PPP estimates, by definition, reflect
comparable levels of living, the same definition of middle class (MC) should, and does,
apply to all residents of the world. The middle class is defined for all peoples, in a
common PPP currency, no matter where they reside. By extension, this definition of the
middle class extends across time, in the future as well as in the past.
This definition of middle class is simple, straightforward, and unlike other attempts,
absolute. In 1996 prices, the population weighted poverty line in developed countries
was PPP $ 2988 a year45. In 2006 international prices, this translates into a level of PPP
45
Poverty lines have conventionally been defined in 1993 PPP per capita per day terms. In the US, this
poverty line was equal to $ 10.4 per capita per day; in Japan, 6.4 dollars, and in Germany 6.7 dollars a day.
The population weighted average of the poverty lines in OECD economies: PPP$ 7.7 per capita per day. In
1996 prices, this becomes $ 8.185 per day. Note that the World Bank has set the poverty line for
developing economies at $ 1.08 , so the middle class definition is approximately 7 times the developing
country poverty level.
99
$ 3658 per year46 or an easy to remember $ 10 per person a day. Note that these are
the same levels in US dollar prices since one US dollar, by definition, is equal to one
PPP dollar at any point of time. The definition says that once an individual‘s income is
more than PPP $ 3658 a year, then that person has just crossed from being poor to
being non-poor – or from poor to being the beginning of the middle class.
Once a middle class definition is obtained, the beginning of the rich class should be a
straightforward matter. It is, but the definition is also arbitrary. There are no accepted
definitions of the rich though a reasonable starting point (and one used here) is that the
rich have a starting level of income that is ten times the starting level of the middle
class. In 2006 prices, this is conveniently at $ 100 per capita per day. According to this
line, approximately 10 % of the German population, 20 percent of the Japanese
population and 35 percent of the US population was rich in 2006.
In per capita per day 2006 PPP terms, the definitions are as follows: the poor in
developing countries are those with consumption below $ 1.41 per day, or roughly with
incomes below $ 1.6 a day. The middle class are those with incomes above $ 10 and
below $ 100 a day, and the rich are those with incomes above $ 100 a day. What about
those with incomes between $ 1.6 and $ 10 a day? In class terms, they are undefined
but could be described as the not-poor in the developing world and poor in the
developed world.
46
In per capita per day terms, the middle class line is $8.185 per day in 1996 prices or $ 10 (actually 10.02) per person per day in 2006 prices.
100
Middle Class: A non-linear emergence
A lot follows from the development of the middle class. As Charts 5.1 (a thru c) make
clear, the emergence of the middle class is non-linear and it is this non-linear property
that allows the middle class to surprise all by its sudden influence. Distributions have
tails and in the early and middle income stages of development, the cross-over point for
the middle class is in the distant horizon, in the far right tail. In the beginning, there are
only a few people there; in 1980, only 3 % of the Indian population was middle class, in
China less than 0.5 percent. Literally, the middle class were the rich, the super rich in
1980 India and China.
The non-linearity in middle class growth can be noted from the ―area‖ to the right of the
distribution as demarcated by the vertical line. Notice the tail for the India and China
distributions in 1980; there is practically nobody above log(8.2) or 2.1, the middle class
line drawn vertically. By 2000, it is practically half the population for China and 23
percent for India. The non-linearity takes over for India in the last six years, as the size of
the middle class expands to 38 percent (Charts 5.3 a and b). This is also the period of a
structural break in India‘s growth which, as documented in Chapter 10, occurs around
200347.
Table 5.1 shows some representative middle class ranges, in US dollars, for households
in developed countries (household size assumed equal to 4) and developing economies
(household size assumed equal to 5). The definition of middle class is in PPP terms, and
equal to $10 PPP dollars a day per person, or annually $ PPP 14600 for a family of 4,
and $ PPP 18250 for a family of 5. Since US dollar exchange rates are different from
PPP exchange rates, the beginning of middle class levels in US dollars varies between
countries.
47
In 2002, the size of the middle class in India was 25 percent. China‘s structural growth break, after 1978, occurs in the early 1990s when the middle class was also of a similar magnitude. In the 1980s, first and second half, per capita growth in China averaged 5 percent, with higher growth in the immediate years after the reforms. In the first six years of the 1990s, 1990 to 1995, average per capita growth was an average of 8.7 percent, a level that was sustained over the next 11 years. To be sure, this sustained growth has been aided by a highly undervalued exchange rate (Chapter 7), but middle class expansion has played its part.
101
Chart 5.1a: Income distribution, World
Source: Maddison (2006); Penn World Table v5; WIDER inequality database, 2006; Bourguignon and Morrison
(2001).
Notes:
1. Percentile distributions are based on a modified Kakwani methods; see Appendix I and Bhalla (2002).
2. Log(2.1) PPP per capita per day, 1996 prices, is the beginning level of the middle class.
102
Chart 5.1b: Income distribution, China
Chart 5.1c: Income distribution, India
Source: Maddison (2006); Penn World Table v5; WIDER inequality database, 2006; Bourguignon and Morrison
(2001).
Notes:
1. Percentile distributions are based on a modified Kakwani methods; see Appendix I and Bhalla (2002).
2. Log(2.1) PPP per capita per day, 1996 prices, is the beginning level of the middle class.
103
For US, the middle class level starts at an annual family income close to 15,000 dollars a
day (the beginning of the rich level is 10 times, or $ 150,000). In India, the level starts at
$ 3526, and for China at $ 4570.
Middle Class as an attention-getter
Both China and India started reforming and accelerating in the eighties. At that time,
absolute poverty in India was around 50 percent of the population and in China
somewhat higher at 65 percent. Rapid growth since then has allowed for the reduction in
poverty in both countries to less than 10 percent48 and for the emergence of the middle
class. With poverty reduction, there is a diversion of expenditure to discretionary
spending – spending on durables, income elastic goods, housing etc.
This poverty decline is a necessary first stage in the move towards the middle class.
Poverty started declining in both countries in the early eighties but recognition of the
economies as investor outlets came much later. This is because the first stage of
poverty reduction, a movement of people to above a dollar a day, does not cause any
waves. Neither does the next stage as people move from consumption of cereals to the
consumption of fruits and vegetables and milk. Even this stage can last a decade or so.
Through these stages, a vast majority of the population moves to a consumption level of
first above $ 1.5 a day, (approximately the absolute poverty line in 2006 PPP prices),
then to above $ 3 a day (the absolute poverty line in middle income countries) , then
greater than $ 5 a day, and so on. It is only when a significant fraction crosses into the
international discretionary income stage – the luxury good stage of watches, and
scooters, and motorcycles, and refrigerators, and cars etc – that the situation begins to
get converted from a genuine human interest and welfare concern to a story about the
middle class. This stage is close to PPP $10 a day. One needs to walk before one can
run, and crawl before walking. Each stage is critical, and important, and necessary. But it
is the running for which there are Olympic medals. And it is the middle class that is in the
running business (with its Nike shoes).
48
See Chapter 10 and Bhalla(2002) for a discussion about how vastly exaggerated the World Bank poverty numbers are for the developing world, and especially for India.
104
Table 5.1: Middle class household thresholds, 2006 US $ (000)
Minimum Maximum
United States 14.7 147
United Kingdom 17.9 179
Germany 17.2 172
Japan 16.4 164
Brazil 9.9 99
Chile 10.2 102
Mexico 11.9 119
Egypt 4.5 45
India 3.5 35
Russian Federation 5.9 59
China 4.6 46 Source: SAE dataset, see Appendix I for details.
Note:
1. Numbers show the threshold & between which individuals are
considered to be a part of middle class population.
2. For developing countries, household size assumed to be 5; for
developed countries, 4.
105
In China the transformation from a near zero level of middle class to around 15 to 20
percent was rapid – only 14 years after the initiation of change, the middle class
level in China in 1992 was 22 percent. India was to reach that level 8 years later in 2000.
Markets do not notice economies when they are growing at miracle rates; but markets
do notice, and the media, and foreign investors, when there is international purchasing
power; and when the middle class proportion is large, everybody is interested.
Thus, one insight that the absolute middle class definition provides is as to when
countries become important internationally. during the initial phases of development, till
around 2003, there was little reason to notice India, especially not as far as world growth
(or world fortunes) were concerned. Ditto for China in the pre-1990s period. What these
two economies were undergoing in these preliminary stages was a structural
transformation from a poor developing economy to a poor emerging market.
Average GDP growth in India was above 5.5 % per annum for more than 20 years
before it started attracting attention in the late 1990s – attention for FDI, FII etc. For
years, analysts have lamented the low level of FDI in India, especially compared to
China. Part of the explanation for the difference in attention is that the main attraction for
FDI is purchasing power, and China‘s per capita income level in 1995, was 50 % higher
than India; so obviously it attracted a lot more attention. The middle class might also
help partly explain the differential pattern of stock prices in different economies at
different points in the growth and business cycles, and future prospects of the same.
On attention getting – the rich
There is yet another related phenomena at work – the rapid expansion of rich people
(above PPP $ 100 a day). There are about 20 million such individuals in the world today,
with a fourth of them in the developing economies. In 2015, there are likely to be 30
million, with developing economies contributing a third. Recently, the news came out
about the rapid addition of Asian billionaires in the Forbes list. Predictably, it caused
heartburn in India as there was concern about increasing inequality within India. There
is, of course, no necessary relationship between the size of the rich, or the middle class,
and per capita income. Chapter 11 shows there isn‘t even a relationship between per
capita income and inequality. Today, Asia has a quarter of the worlds‘ rich, and India
and China have only 5 percent. This fraction is likely to double in the next four years,
106
and by 2025, China and India will not only have 40 percent of the world‘s population, but
40 percent of the world‘s rich as well.
Middle class and growth – the theory
There are strong implications about the evolution of the size of the middle class and
economic growth. The first most important implication is in terms of the efficiency of
growth; given the belief in meritocracy, as well as ways of getting ahead the old
fashioned way, the middle class should help engineer growth by being at the forefront of
innovation, and adoption of advances in technology, regardless of whether this
technology was foreign owned or domestic. Middle class should be associated with
increased openness, increased foreign trade etc. Obviously, extra growth will result in a
higher share of the middle class as income distribution moves to the right. So middle
class growth and income growth are highly correlated. A perfect correlation is not there
because middle class is defined as the proportion of people above a certain pre-defined
income level.
The testable and interesting hypothesis is about the initial size of the middle class and
subsequent economic growth. The expectation is that the first causes the second, at
least till the size of the middle class crosses about 60 percent or so. The reasoning is as
follows. Assume either just steady growth or an exogenous shock (economic reforms?)
that gets the distribution to probe deeper into the middle class zone. Assume this share
is now around 10 percent, which would be China in 1986 or India in 1993. Why 10
percent – just a minimum size which has had effects on various social and economic
reforms in different countries and different times. This size means the middle class has
begun to have a significant stake in the economy and its policies.
Growth is now likely to be faster because reforms are more likely to be undertaken. As
growth occurs, a larger fraction is saved, because if anything defines the middle class, it
is its higher propensity to save and invest. This higher savings leads to higher
107
investment, and the middle class tries to ensure49 that the growth is efficient. The
virtuous cycle continues, savings increase further, as does investment and GDP growth.
Another part of this cycle is the effect of the middle class on per capita GDP growth i.e.
on fertility and population growth. The middle class has always been at the forefront of
those who have substituted Becker‘s education quality for child quantity. The middle
class has fewer kids, and those it has, it emphasizes education quality. Fertility levels
start dropping and the virtuous beat goes on.
Savings rates and investment rates and growth rates – all are affected, econometrically,
when the initial size of the middle class is in the equation. Per capita growth is higher by
half a percentage point a year for each 10 percentage point increase in the size of the
initial middle class. This is after controlling for initial income, undervaluation, and its rate
of change (see Chapter 7). Obviously, there are many other determinants of investment
and growth. The next two chapters are devoted to one such determinant – exchange
rate policy, particularly the undervaluation of the exchange rate. But middle class is an
important determinant, and not just in terms of economic growth.
Rediscovery of India It was only about 15 years ago when China started to be discovered and as alluded to
above, this start was dictated by a permanent and rising presence of the middle class. In
India‘s case, the story of being noticed is sometimes ascribed to the Y2K craze in 1999
and the disproportionate presence of Indian programmers in the US. Some other times,
the discovery is related to a string of beauty queen victories, personified most beautifully
by the stunning Aishwarya Rai. But can this cause discovery? Or was the string of
beauty queens a not so subtle plot by the cosmetics industry to capture the Indian
market. But why didn‘t they think of this before? Because there wasn‘t a middle class to
capture before!
So the major attraction of India today (relative to its earlier high growth past of the last 20
odd years) is the emergence of a discretionary income spending middle class. At about
38 percent of the population, this class is about 380 million people. Like its counterparts,
49
Sometimes, as in Russia, it does not succeed. At the time of the Russian revolution in 1917, the share of the middle class was a ―high‖ 24 percent. As table 5.1 shows, communist Russia is the exception, not the rule.
108
this class likes to spend on traditional durables, and housing. The latest data show that
total bank lending for housing is expanding at a rate of 40 percent per year, and
presently (2005-06) is running at a pace of Rs. 72,000 crores, or about $ 18 billion. This
figure was less than $ 3 billion in 2001-02.
Middle Class and its Effects
There are several phenomenon whose explanation is easier via the prism of the middle
class. Some events, particularly in the last two decades, seem strange and new. For
example, the worldwide productivity boom and the famed Goldilocks economy. Neither
too hot, nor too cold, is how Goldilocks wanted her porridge, and the world has been just
that for the last two decades. Related is the phenomena of low inflation in the developed
world. In 1973, oil prices quadrupled and the world experienced stagflation. In the last
four years, oil prices quadrupled (from $20 a West Texas intermediate barrel in Nov.
2001 to $80 in July 2006) and worldwide inflation was lower by half a percentage point.50
What is going on? There are several claimants to this success, most importantly the
central bankers of the world. The number of inflation targeting countries has increased,
and any rise in inflation rates is fought with the tenacity of a raging bull.51 Independence
of central banks has also increased and this institutional change is thought by some to
be an important contributory factor.
There is an alternate explanation which proceeds as follows. There has been a sharp
increase in globalization (however defined) since about the time the inflationary effects
of the first oil price boom were forced out of the system, most notably by the inflation
fighting efforts of Paul Volcker. Monetary policy mattered a lot more then, because the
world was not as globalized. Coincidentally, both China and India decided to enter the
global world at about the same time, perhaps reinforced by each other‘s positioning. Part
of their strategy (especially China‘s) was to leap frog on the basis of an undervalued
exchange rate (discussed in considerable detail in the next two chapters). This was a
double blow to inflation – not only were cheap workers entering the world labor force, but
50
Median developed country inflation (CPI) declined from 2.8 % per annum in 2001 to 2.5 % in 2006; worldwide, median inflation declined from 7.5 % to 7 percent. 51
In the last four years, investment rates are higher by 10 percentage points in India, GDP growth is higher by 3 percentage points, and inflation higher by 1 percentage point. Yet, the Indian central bank is arguing that both growth and inflation are out of control. See Chapter 10 for a detailed discussion.
109
the workers were entering at a rate far cheaper than they ―should‖ have. At the margin,
therefore, a large supply of workers entered the system at more than the normal gap of
wages with respect to productivity.
Low global inflation
Workers in already open and globalized countries e.g. East Asia, lost out to the Chinese
workers and the only way out was for the currencies of these countries to depreciate in
real terms. Though a controversial hypothesis52, it does fit the facts rather well. This ―old‖
traditional East Asian supply was now available, after the late 1990s crisis, at
significantly lower costs. Since then, Chinese productivity growth has exceeded most
expectations, and the Chinese real exchange rate has depreciated by a large amount.
Supply of goods from China has been obtained at ever cheaper rates. Thus, there has
been considerable global supply response leading to lower inflation.
Charts 5.2(a) and (b) document the trend in world inflation since 1960. The computation
is on the basis of the GDP deflator but an identical trend is observed with the consumer
price index. For each year, inflation data for 150 plus countries are slotted into a hundred
percentiles, and the average of the last three percentiles in each decile is reported. For
example, the lowest trend line is for countries belonging to the 8th, 9th and 10th percentile,
the next line is for countries belonging to the 18th 19th and 20th percentile. Note that any
individual country can be different percentiles in each year.
The picture of world inflation tells a very different story than the popular one of Western
central banks becoming more independent or some selected banks targeting inflation
etc. Globally, including in countries that are far from having a good institution like a good
central bank, inflation has declined. The volatility of inflation rates has also declined. In
the last six years, inflation volatility has declined by 25 % over the previous decade.53
52
See Bhalla(1998), Bergsten(1997) who independently argued that the East Asian crisis may have been caused by the Chinese devaluation of 1990-1994. See Fernald-Edison-Loungani(1998) for an opposing view. 53
If inflation rates above 30 percent per annum are excluded, the mean inflation is down almost 1 percent (from 6.9 to 6) and the standard deviation is down by a quarter (from 8.6 to 6.3). The comparison is for 2000 to 2006 compared to 1990 to 1999.
110
Chart 5.2a
8
1010
2030
4050
Ann
ual I
nfla
tion
(% p
er a
nnum
)
1965 1975 1985 1995 2006Year
Inflation: The Great Decline & Convergence, 1965-2006
Chart 5.2b
10
90
100
200
300
400
Annu
al In
flatio
n (%
per
ann
um)
1965 1975 1985 1995 2006Year
Inflation: The Great Decline & Convergence, 1965-2006
Source: World Bank, World Development Indicators, 2006.
Note: The numbers next to the chart represent the average of the deciles, e.g. chart labeled 1 represents average, un-
weighted, inflation (as measured by the GDP deflator) of countries belonging to the 8th, 9th, and 10th percentile in that
particular year; chart labeled 8 represents countries at the 78th, 79th and 80th percentile, etc.
111
Middle Class – size matters
It is hypothesized that it is the size of the middle class that differentiates the high
growth India-China economy of 1985 or even 1990 (ten years of high growth) with the
high growth India-China economy of 2000, and later. In 1980, less than 2 % of the India-
China population was middle class; today, 2006, it is more than 40 percent. This means
that the middle class is a different phenomenon in different times. In 1980, they were the
elite, and in China where this fraction was only 0.5 % of the population in 1980, the
super-elite. In 2006, to be middle class in China is to be an average citizen.
In 1980, only 37 percent of the world‘s middle class population originated in the
developing world – that is 461 million individuals. In 1995, helped along primarily by
China, the numbers were 57 percent and 1,237 million, respectively. In 2005, helped
along by India, the numbers were 71 percent and 2,151 million. The increase from 1980
to 1995, fifteen years, was 776 million; in just the next 10 years, the increase was a fifth
more at 914 million.
112
Chart 5.3(a): Share of Middle class, developing world
0.9 1.0 1.0 1.5
4.2
13.8
45.5
50.0
010
2030
4050
Perc
ent (
%)
1500 1700 1820 1890 1950 1980 2006 2025
Chart 5.3(b): Share of Middle class, India
1.63.5
8.7
22.5
37.9
89.5 90.1
020
4060
8010
0
% o
f pop
ulat
ion
1960 1980 1990 2000 2006 2020 2025
Share of Middle class, India
Source: Penn world Table 6.1; Maddison (2006), World Bank WDI (2006); International Monetary Fund
WEO (2006 sep). Appendix I for details on construction of real incomes and middle class.
113
Chart 5.3c: Share of Middle class, China
0.1
10.6
48.9
69.5
83.0
77.9
020
4060
80
% o
f pop
ulat
ion
1980 1990 2000 2006 2020 2025
Share of Middle class, China
Source: Penn world Table 6.1; Maddison (2006), World Bank WDI (2006); International Monetary Fund WEO (2006
sep). Appendix I for details on construction of real incomes and middle class.
114
Foreign Direct Investment (FDI) – related to middle class size
FDI patterns are related not only to the growth prospects within a country, and the
profitability of investment, and the tax benefits, but also the size of the domestic market
and the size of the domestic skilled labor force (to produce for the outside world). Prior to
1990, total foreign investment (sum of portfolio flows (FII) and direct investment) into
China had never averaged more than 1.5 percent of GDP. Not the Chinese Diaspora,
not the tax benefits for foreign investors, nothing could attract much investment into
China. Starting 1992, when the middle class size was stable above 20 percent, did total
foreign investment exceed 2 percent of GDP. And once it did, there was a rapid increase
and the share soon started averaging 4 to 5 percent of GDP.
Economists have theorized on various determinants of this foreign investment size, and
contrasted with the low level of such investment in India. This research may have erred
twice over. First, it has ignored the differential development of income and middle class
growth in the two countries.54 Second, it has erred in not adding up the two investment
flows – portfolio and direct. The two countries have had very different attitudes to the two
sources of investment. The Chinese have always preferred direct investment and been
willing to give up ownership tomorrow for greater efficient growth today. In contrast, the
Indians, perhaps because they have had a protected and until recently a monopolistic,
reactionary and foreign-fearing industrial sector55, have welcomed portfolio flows and
rejected, or influenced the pliant politicians and bureaucrats to reject, foreign direct
investment. Today, somewhat surprisingly, the joint size of all investment flows, as a
percentage of GDP, is similar for the two countries (Chart 5.4).
54
The analysis may have also erred in thinking that the bad state of infrastructure in India is a deterrent. The error here is that it is not sufficiently recognized that the state of inadequate infrastructure in an economy is endogenous, and endogenous to the growth of the middle class. 55
This presence was not there in China because the industrial class was extinguished by the Communist revolution, 1949-1978.
115
Chart 5.4: Foreign Investments in China & India (% GDP)
Source: Lane (2006)
Note: Foreign Investment is composed of foreign direct investment (FDI) and foreign portfolio investment (FII).
116
Middle Class and Infrastructure
If there is one constant, and universal, refrain about the India China comparison it is that
India just cannot do a China (in terms of growth rates) because it just does not have the
infrastructure. How universal this argument is can be gauged by the fact that it is not just
the foreigners who make this comment; most Indians say so too. And it is a very
confident, and laid back, assertion. The evidence is for all to see. Have you been to
Shanghai recently? Have you been to Mumbai? Case closed.
Not so fast. Let us first recognize some stylized facts. India in 2006 is obviously being
compared to China in 2006. But China has more than twice India‘s per capita income, so
one would expect the infrastructure to be somewhat behind China. Mid 1990s is where
―convergence‖ is between China then and India today, in terms of per capita income and
size of the middle class. India is therefore China with about a 10 year lag. If so, the
question is whether in 2010 or 2015 will visitors come to India and say that infrastructure
is way out of line? Possible, but unlikely.
The most important part of infrastructure development is that it is related to the size of
the middle class. They are the demanders of better roads, better airports, better schools,
better hospitals, less bureaucratic delays etc. The middle class has purchasing power
and exercises it. The middle class has clout and uses it. In the mid-1990s, Delhi, capital
of India, was unlivable. The pollution count was beyond control, a blue sky lived only in
one‘s imagination, respiratory diseases were on the rise. The cause for this extra
amount of pollution was more cars, and in Delhi, more two-wheeler taxis running on 2-
stroke pollution causing engines. Scooter manufacturers thought their profits would be
hurt if pollution emission standards were made mandatory. A familiar story.
The Indian Supreme Court got into the act, and to date, that has been the finest hour of
their ―social activism‖. Many believe, including possibly the judges, that the social
conscience of a few has made Delhi today a vibrant, cleaner, city. There is an alternative
explanation. The Supreme Court judges also belong to the middle class and share its
values56. So it could have very well been a self-serving judgment – which
it was. But as often with middle class values and goals, what they
56
There is yet another explanation which does not have that much to do with the middle class. The Supreme Court judges could, and did, avoid many of the travesties of injustice visited upon
117
demand selfishly is often consistent with the social good. Just ask all the
NGOs of the world.
Information on several different infrastructure variables for India and China are
presented for two different years, 1995 and 2004. Also reported is the ―gap‖ between the
two countries. For various indicators, the gap in 2004 is huge – often, the Indian level is
only about 20 to 30 percent of the Chinese level. Hence, the conclusion that
infrastructure in India is way behind that of China. But if the comparison is made
between India of 2004 and China of 1995, the India/China ratios rise considerably but
still remain in the 50 to 60 percent range. (Table 5.2)
There is an explanation for why the relationship is weak, but not non-existent. As Chart
5.1 documented, the growth of the middle class is a highly non-linear affair. It is this non-
linearity that causes the bottlenecks. The non-linearity means that the expansion of the
middle class, and hence the demand for infrastructure, cannot be accurately anticipated.
The middle class expansion, and concurrent demands for improved infrastructure,
happen before the infrastructure can physically be made available. Even the best of
intentions, and even better planning, cannot help. Hence, no one has built infrastructure
before its time.
the citizenry by mindless government controls and government inefficiency. The lack of public services, the dirt, the slums could all be avoided by the judges, and their children. They, like senior bureaucrats and all the politicians, were able to live in areas where power cuts were not allowed. They were cool while the rest were sweltering. But pollution no one can avoid. It is an all time public bad which neither money, nor power, nor prestige, can miss. So the Supreme Court cleaned up the act because that was the only way for their children, and themselves, to not die prematurely.
118
Table 5.2: Infrastructure, India is China with a 5-10 year lag. China India Gap
Variables Units 1995 2004 1995 2004
India 2004/China
1995
India 2004/China
2004
Per capita income In PPP$ 2818 6252 1978 3234 114.8 51.7
Energy
Total Installed Capacity Gwh 1010 1910 418 633 62.7 33.1
Total Production Kwh 770.1 1378.5 364.5 435.3 56.5 31.6
GDP per unit of
energy use
constant 2000 PPP $
per kg of oil
equivalent 3.1 4.5 4.2 5.3 170.1 115.7
Ports
Cargo handled at ports MT 802 2928 215 423 52.7 14.4
Roads
Roads, paved As % of total roads . 79.49 55.4 62.6 78.8
Highway Length In Kms 115.7 187.07 34.5 58.1 50.2 31.1
Railways
Total Rail Lines Total route-km 54616 61015 62660 63221 115.8 103.6
Airports
Freight transported million tons per km 2200 7200 654 689 31.3 9.6
Total no. of
passengers carried in millions 47.6 120.0 14.3 23.8 50 19.8
Telephony
Telephone Mainlines per 1000 people 33.8 241.1 12.8 40.7 120.5 16.9
Fixed and Mobile Line Subscribers per 1000 people 36.8 499.4 12.9 84.5 229.7 16.9
Water and Sanitation
Improved sanitation
facilities
% of population
with access . 44 30 68.2
Improved water
source
% of
population with
access . 77 86 111.7 Source: World Bank (2006) Notes:
119
Historical Importance of Middle Class
There is more than just number additions to the middle class story. The most important
contribution of the middle class seems to be to the growth process. It may be that the
size of the middle class is an ―institution‖ par excellence. The evolution of the world
middle class is as shown in Chart 5.5a. In 1850 only 3.0 percent of the world‘s
population was middle class. In that year, the middle class had a per capita income
above 40 cents or 8 cents a day. For a family of five, this translated into earnings above
$730 or £146 a year.57 The middle class then starts to grow exponentially and by 1913
its size had quadrupled to 13 percent. At the end of World War II, the size had doubled
to 24 percent and today (2006) more than half of the worlds population is middle class.
The increasing role of Asia as a world force is shown by the figures in Chart 5.5b. Back
in 1700, the share of West and Asia in the middle class was equal, but the total size was
miniscule. In 1950, the West contained 71 percent of the world‘s middle class, Asia only
6 percent. By 1980, Asia forms 20 percent of the worlds middle class, which rapidly
increases to 60 percent in 2006; the share of the West declines to only 20 percent. This
is purchasing power, and hence the story of foreign investments. Given the
undervaluation practiced by Asia, this also means a large part of the productivity growth
is not being reflected in higher wages, and prices.
57
The British £/$ exchange rate in 1850 was 20 pennies to a dollar, compared to 50 pennies to a dollar in April 2007. The US price level in 1850 was 5.2 compared to 129 in 2006. So 10 2006 dollars are equal to (10*5.2/129) 0.4 dollars in 1850. But the £/$ 1850 exchange rate was 20 pennies, yielding the equivalence of 5 £s today to 0.08 £s per day in 1850. If a one-earner family was the norm then, and an average size of family considered to be 5, then a person earning (0.08*5*365) £ 146 a year would be the beginning of the middle class. James(2006) in his study of the British middle class arrives at an earning level of £ 150 a year to be the beginning of the middle class. This level was also the beginning of the taxable bracket in England, a finding found for China and India more than 150 years later.
120
Chart 5.5a: Evolution of the World’s Middle Class, 1820-2025
1.8
7.7
13.2
23.1 23.5
28.8
32.234.2
42.6
50.2
64.0 63.40
2040
60
% o
f pop
ulat
ion
1820 1890 1913 1938 1950 1960 1980 1990 2000 2006 2020 2025
Share of Middle class, World
Source: Penn world Table 6.1; Maddison (2006), World Bank WDI (2006); International Monetary Fund
WEO (2006 sep). Appendix I for details on construction of real incomes and middle class.
Chart 5.5b: Evolution of the Middle Class, West and Asia, 1820-2025
40
46
62
24
71
6
50
20 20
60
11
71
020
4060
80
% o
f wor
ld m
iddl
e cl
ass
1700 1850 1950 1980 2006 2025
Share of Middle class in West and Asia
West Asia
Source: Penn world Table 6.1; Maddison (2006), World Bank WDI (2006); International Monetary Fund
WEO (2006 sep). Appendix I for details on construction of real incomes and middle class.
Notes: West includes all of Europe, Eastern Europe, Northern America, Australia, New Zealand, former
Soviet Union. Asia includes Japan, as does the region ―developed economies‖. Developing economies is the entire world excluding the West and Japan.
121
Chart 5.6 plots the pattern of world growth with developments in the size of the world
middle class. It was hypothesized above that by definition, as the size of the initial
middle class grows, it should result in higher savings, higher investment and higher GDP
growth. The figures shown in the chart are broadly consistent with this hypothesis. World
middle class growth in the period of the Industrial revolution is explosive (a tenfold
increase in the size of the middle class, from 1.8 percent of the population in 1820 to
13.2 percent in 1913), and the 90 odd years between 1820 and 1913 are also the years
when world growth expands from an insignificant low level to nearly 1 percent per
annum. Between 1913 and 1950 there are world wars and depression so factors other
than the middle class intervene. The next growth spurt is 1950 to 1980; expansion of
middle class from 23.5 to 32.2 percent. Between 1980 and 2000, middle class expansion
is very slow; a gradual increase till 42.6 percent of the population. Post 2000, the growth
in the middle class is non-linear, expanding quite rapidly to 50.2 percent in 2006 i.e. an 8
percentage point expansion in just six years, or a rate of expansion almost 4 times faster
than the 1950-1980 expansion and the fastest 8 year expansion to date. This expansion
is fuelling the high world growth today.
Chart 5.6: Middle class and income growth, 1500-2025
01
23
4
% g
row
th p
er a
nnum
0.0
20.0
40.0
60.0
% o
f pop
ulat
ion
1500 1700 1820 1913 1950 1980 1990 2000 2006 2015 2025Year
Middle class Income growth, p.c. per year
World: Middle Class and Income
Source: U.S Census Bureau (2006); Penn world Table 6.1; Maddison (2006), World Bank WDI (2006); International
Monetary Fund WEO (2006 sep). Appendix I for details on construction of real incomes.
122
Middle Class and Social Innovations
The absolute definition of the middle class is useful in explaining several past
phenomenon – and present. Major institutional changes (child labor laws, universal male
suffrage, health insurance etc.) occurred for the first time when the country in question
had a middle class in the 10-20 percent range. This was over two centuries ago in
Europe. The major impetus to economic reforms in India was in 1991 – a year when the
size of its‘ middle class was 9.4 percent.
Table 5.3 documents the per capita income level in various countries around the time of
major social change. Some suggestive stylized facts. Two revolutions, French and
Chinese, occurred when the size of the middle class was very small. Two other events,
the American Civil War, and the Russian revolution, occurred when the share of the
middle class was nearer a quarter of the population. So far, no predictive properties. But
non-revolutionary major change, occurs around the time when the middle class is small
and growing. In the 5 to 20 percent range, there is predictive power.
Another predictive power is in terms of the fraction of population eligible to pay income
tax. There is no necessary relationship between the two and one would have thought
that individuals other than the ―middle class‖ are taxed in poor countries. Table 5.4
indicates otherwise. For both India and China, the tax schedules have been set by the
government to tax only people with high discretionary income i.e. the middle class. In
1986, China had only 9 percent of its population as middle class, and 10 % was its
eligible population to pay tax; for India, the numbers are 7 % and 9 percent. In 2003, with
much reform, and growth, and changes in the distribution (especially in China), the
relationship is still firm; 58 percent middle class and 54 percent eligible to pay tax in
China; in India, 28 and 35 percent, respectively.
123
Table 5.3: The Historical Role of the Middle Class
Social/Political/Economic change Country Year
Middle class
(%)
Puritan Revolution* UK 1640-1650 4.3
Central Bank Establishment Sweden 1688 2.7
USA Independence* USA 1776 4.5
Child Labour Regulation (First Attempt) Austria 1787 5.0
French Revolution France 1790 2.6
Enclosure Movement* UK 1800 7.7
Central Bank: Note Issue Monopoly UK 1844 14.7
Enclosure Consolidation Act UK 1801 7.7
Universal Male Suffrage France 1848 4.3
Civil War USA 1861-1865 24.0
Industrial Accident Insurance Germany 1871 14.3
Health Insurance Germany 1883 22.1
Pension Germany 1889 22.1
Unemployment Insurance France 1905 33.0
Universal Suffrage New Zealand 1907 65.7
Russian Revolution* Russia 1917 24.8
Communist Revolution* China 1948 0 Source: Penn world Table 6.1; Maddison (2006), World Bank WDI (2006); International Monetary Fund
WEO (2006 sep).
Notes:
1. The Middle class size is the fraction of population with per capita income above PPP $10 a day, 2006 prices.
See Appendix I for details.
2. Except for items marked with asterisk, the source for the change and year is Chang. For middle class
estimates, see SAE database, and Appendix I.
Table 5.4: Potential Tax-Payers equal to middle class
China India
Year Middle class
(%) Tax payers (%) Middle class (%) Tax payers (%)
1986 8.7 10 6.9 9
1990 13.6 2 10.5 19
1993 24.2 15 12.1 26
1997 38.0 52 17.0 23
2000 46.3 47 22.5 25
2003 58.3 54 28.3 35
2006 69.5 37.9 Source: Penn world Table 6.1; Maddison (2006), World Bank WDI (2006); International Monetary Fund
WEO (2006 sep). Appendix I for details on construction of real incomes and middle class.; WIDER
inequality database, 2006; Bourguignon and Morrison (2001); Bhalla (2004); Tax payers data in china ??
Notes:
124
In conclusion: Middle class and world events
Since the analysis is in common currency terms (PPP) it does not matter whether the
increase in the middle class came out of Europe in the 50s and 60s, or out of Japan and
east Asia in the 60s and 70s or out of India China in the 1990s and 2000s. The interest
is in whether one could have forecast any of the major developments in the past from a
middle class perspective. If yes, then one can have confidence in the predictions of the
future; and whether 1995 onwards (when the non-linear increase in China‘s middle class
first began) was the turning point for a new world economy. One side-product of this
middle class analysis is the identification of the major turning points for major economies
in the past e.g. is the reason India is now ―hot‖ because it has a middle class which has
crossed an important international threshold58? Also, is it the case that China became
important from a world perspective in the mid-nineties because it had then, among its
citizens, an important purchasing power for the products of the Western world?
The answers are yes, yes and yes. Table 5.5 divides world growth into three periods:
1950 to 1980, 1980 to 1995 and 1995 to 2006. In addition to growth, data are supplied
for the rate of growth of various commodities, and the size and the rate of growth of the
middle class. Despite the oil shocks in the 1970s, the thirty year period 1950-1980 was a
golden era – world growth at an average of 4.4 percent per annum. The next fifteen
years was witness to a large reduction in absolute poverty in the developing world,
especially India and China. But while GDP growth in these countries was high, and
poverty reduction even higher, the rate of expansion of middle class did slow down –
from 8.7 percent earlier to only two-thirds of that level. Recall that growth does not
automatically mean a commensurate increase in the middle class since the population
has to traverse the distance between $ 1, when it is not poor, to $ 8 (1996 PPP prices),
when it begins to be middle class. In this journey, growth maybe rapid in individual poor
countries, but it is growth off a low base, and hence world growth is not as affected.
58
For domestic events, a lower threshold of 5 to 10 percent is often significant.
125
Table 5.5: Growth in income, middle class and commodity prices (% pa)
1950-80 1980-95 1995-06
Growth (% per year)
Iron 2.4 -5.6 6.7
Copper 1.1 -2.0 4.7
Aluminum 0.8 -3.3 0.9
Nickel 2.0 -2.2 3.2
Zinc -0.8 -1.4 3.7
Gold 5.5 -7.2 1.9
Silver 7.0 -13.4 4.5
Oil -9.4 9.0
GDP 4.37 2.9 4.0
Middle class growth (% per
year) 8.7 5.8 12.2
Middle class (average) 27.9 35.1 44.1 Source: Maddison (2006); Penn World Table version 6.1; World Development Indicators, World Bank (2006); World Economic Outlook, IMF (2006); WIDER inequality database, 2006; Bourguignon and Morrison (2001) Notes:
126
The period 1995 to 2006 is when the growth in the middle class is poised to take off, and
does. World middle class growth increases at the fastest pace in human history – at 12.2
percent per annum for a decade. In 1995, Greenspan, and many others, saw what they
were witnessing and decided that it should be ―regression towards the mean‖. Rear
window economics or irrational exuberance, or both. The fact remains that the
underlying forces (again, mostly middle class) ensured that the 1995-2006 decade would
be a period of rising commodity prices, and rising world growth, and declining world
inflation. The last few years‘ world growth has approached 5 percent and there are many
observers in the world, most actually, who attribute the surge in world growth, and world
stock prices, to the easy liquidity that the FED and other central banks created over the
last few years. It is observed that world growth, world savings rates, and world asset
prices have risen at a fast pace. It is also observed that there was (but only in short-term
rates) easy liquidity. Casual empiricism would suggest a strong relationship. But casual
empiricism should also suggest an increase in inflation because of the ―easy liquidity‖.
But that hasn‘t happened in this decade.
What is happening is a world structural change, and change that can last for several
more years, perhaps a decade or two. World growth will inevitably slow down, and it will
when the share of the world middle class, and/or the rate of increase in this share,
begins to slow down. The data and charts presented earlier suggests that haste in
predicting the demise of the world economy, as several commentators looking
exclusively at the US economy have suggested, will likely be a wasted prediction.
Whether growth and asset prices have much further to go is a different question; but
what the simple prism of the middle class does reveal is that the common widespread
belief that what we are witnessing in the world today is all because of ―easy liquidity‖
maybe too facile a conclusion. It is likely that this interpretation of world events will go
the way of ―irrational exuberance‖.
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Chapter 6 – Currency Undervaluation: A growth policy par excellence
In Chapter 2, the importance of identifying the proximate causes of growth was
emphasized. The previous chapter dealt with one important such determinant – the
middle class. Chapter 8 discusses the present undisputed king of explanations for
growth, development, and why countries are rich: institutions. This chapter (and next)
deals with an old, discarded determinant of growth, but one, however, that really does
rule – policy.
Policy was believed to be, until a decade ago, one of the most important determinants of
growth. Both in increasing growth, and in making it decelerate, one looked for the causal
policy. Whether import substitution, or export promotion, or high and low fiscal deficits,
we all believed that policy mattered. The debate was over what form the policy should
take; not on whether it would be effective. Times have changed. The policy wonks, and
their advocacy, have come under severe scrutiny, and stinging criticism. For the two
decades 1980-2000, large parts of the developing world did not make much progress.
Per capita incomes in sub-Saharan Africa were stagnant, as was the case for most of
Latin America. The much touted Washington Consensus policies did not make any
difference to growth outcomes; several argued that the ―consensus‖ was the cause for
growth failure. Especially for Latin America.
Here was a continent that faithfully followed the recommended policies – decreased
tariffs, so a more open economy was possible; privatized, so a substantial reduction for
the role of the government was achieved; decreased fiscal deficits, so responsible and
restrained government spending was a reality. But Latin America grew at only 0.7 per
annum, 1980-2000, compared to 3.2 percent per annum, 1960-80. Certainly proof
positive of the false mantra of the Washington Consensus?
The Washington Consensus critics (hereafter WCC) mostly point to growth failures, but
not to growth successes, especially in Asia, a continent with more than twice the
population of sub-Saharan Africa and Latin America combined. If it wasn‘t policies, and it
wasn‘t institutions (as documented in Chapter 8), and it wasn‘t geography, then what
was it that allowed close to half the world population to severely reduce poverty, and
grow at ―miraculous‖ rates? Also ignored by WCC members is the simple reality that in
several Latin economies, inflation was brought down from double-digit to single digit
128
rates, and from hyper-inflation to common place inflation. Was this policy or a changed
institution? Granted it was policy that caused the hyper inflation and low growth. But it
was also policy that brought inflation down with, in most instances, no change in the
institutions.
The criticism of ineffectiveness of policies goes far beyond the lack of growth in Latin
America. Detailed econometric analysis by scholars has attempted to establish that if
institutions and geography are adequately controlled for, then the effect of policy on
growth is muted and minimal. The conventional wisdom today is revealed by the
following.
In a review of various analyses, and ―horse-race‖ tests of their own, Rodrik-
Subramaniam-Trebbi, reach a slightly nuanced (yes, policy can be effective, but..)
conclusion. In their own words,
―We estimate the respective contributions of institutions, geography, and trade in determining income levels around the world, using recently developed instruments for institutions and trade. Our results indicate that the quality of institutions "trumps" everything else. Once institutions are controlled for, measures of geography have at best weak direct effects on incomes, although they have a strong indirect effect by influencing the quality of institutions. Similarly, once institutions are controlled for, trade is almost always insignificant, and often enters the income equation with the "wrong" (i.e., negative) sign, although trade too has a positive effect on institutional quality. (2002, abstract)
Zagha et. al. (2006) also reach a similar conclusion; again slightly nuanced, and again
admitting to the theoretical role of policy, the authors conclude:
―Recent empirical research has found that, when a measure of institutional quality is included in cross-country regressions, the explanatory power of other variables, including all measures of policies, becomes negligible (Acemoglu, Johnson, and Robinson 2001; Rodrik, Subramaniam, and Trebbi 2002; Easterly and Levine 2003; IMF 2003e).This reasoning suggests that good institutions matter more for growth than do good policies. From a syndrome viewpoint, it is easy to see that this is not an assertion that ―policies don‘t matter‖—of course they do. Rather the question is whether good policies can be sustained and implemented in the absence of adequate public sector organizations and institutions‖. (p.49-50)
129
In 2003, Bosworth-Collins, in a study that is a benchmark in the field of growth analysis,
The Empirics of Growth: An Update, somewhat pessimistically conclude on the role of
policies:
―A striking aspect of these regressions is the relatively minor evidence of a direct role for conventional government policies. Instead, the most important determinants of growth appear to be factors that cannot be changed substantially in the short run. (p.159)
Easterly(2005) is not nuanced. He conducts his own analysis, and surveys others, and
concludes:
―The new growth literature, using both endogenous growth and neoclassical models, has generated strong claims for the effect of national policies on economic growth. Empirical work on policies and growth has tendered to confirm these claims. This paper casts doubt on this claim for strong effects of national policies, pointing out that such effects are inconsistent with several stylized facts and seem to depend on extreme observations in growth regressions. (p. 2) In 2005, as part of its flagship World Economic Outlook report, the IMF examined in
detail the literature and controversy surrounding the ―institutions hypothesis‖. Their
conclusion:
―Our main finding is that policy variables do not appear as significant determinants of the level of income when institutional quality is taken into account. Some positive results for policies, however, are found in models explaining growth and volatility‖ (IMF, WEO 2003, emphasis in original).
Thus while not universal, a large set of academic research has recently concluded that
economic policies, at best, have considerably less effect on growth than previously
believed. This is the ―best case‖ conclusion; worst case is that, in the final analysis,
policies haven‘t really mattered.
But even when policy matters, its effect is small
Fiscal Deficits
A favorite policy recommendation for both developed and developing economies has
been to ―reduce the fiscal deficit‖. This reduction, and fiscal rectitude per se, is assumed
to be synonymous with the Consensus. The benefits are supposed to be manifold:
greater efficiency in production, less losses in government undertakings, and less
130
―crowding out‖ of private investment. Government deficits mattered, and their reduction
was necessary for macro-economic stability and sustained growth. Indeed, institutions
like the creation of the European union, and the Maastricht treaty, have partly been
based on the notion that government deficits matter a lot. Related to deficits is the notion
that interest rates matter. High fiscal deficits mean a higher than ―normal‖ real interest
rate for private investors – the crowding out thesis59.
But no matter what the empirical specification, or what the individual country case study,
the empirical effect of fiscal deficits is found to be less than 0.1 i.e. for each 1
percentage point reduction in the fiscal deficit, growth is found to be 0.1 percentage point
higher. Period. This means that as a country moves from a fiscal deficit of 4 % of GDP to
zero, it will only add about 0.4 % to annual GDP growth. A revolution in economic policy
and only 0.4 % of extra GDP growth? This ‗extra growth‘ may seem ―reasonable‖ for
developed countries whose potential GDP growth is around 3 percent. Even then it is a
small effect for a policy revolution. But the total fiscal revolution effect is insignificantly
small for developing countries, whose average GDP growth in the last few years has
been above 5 percent per annum.
Currency Undervaluation
The minimal role of fiscal deficits gives credibility to the (large?) majority of economists
who have argued that policy does not matter. And their pessimism is aided by the results
on that other important WC policy measure - a ―unified and competitive exchange rate‖.
This policy, like the fiscal deficits, has made the rounds of testing. And the new results
are not very encouraging for the policy wonks.
While stated as a ―competitive‖ rate, the policy advocacy has really been one of avoiding
currency overvaluation. That overvaluation is harmful for economic growth is well
accepted and non-controversial. There is considerable amount of evidence to support
this conventional wisdom, both theoretically and empirically. Regarding the ill effects of
overvaluation, several examples are available (e.g. years prior to the British devaluation
in 1992 or the Mexican devaluation of 1994, Thai and the East Asian currency crises in
59
China does fit the conventional model. In the case of India, the causation most likely was the other way around, with high managed real interest rates leading to larger interest payments, and larger fiscal deficits. See Bhalla(2000), ―Apne pair pe apne kulhadi‖, or axing one‘s own feet, or scoring an own goal.
131
1997, the generally and consistently overvalued nature of Latin economies, e.g.
Argentina in the late 1990s) to substantiate the view that currency overvaluation is a
mega-bad.
Strangely, though, the literature is less vocal about the flip side of ―overvaluation hurts
growth‖ conclusion – that undervaluation helps growth. When this topic is brought up,
most economists take recourse in one (or both) of two defense positions; either the real
exchange rate is hard to define, let alone measure, so how can one measure
overvaluation; and/or that one should not worry about the impact of the real exchange
rate on growth because the real exchange rate is endogenous. There is not only
symmetry but identity in the overvaluation undervaluation relationship. If one currency is
overvalued (which is easy to define and accept) then the other pair currency, by
definition, is undervalued. So if the argument is made that overvaluation hurts growth,
then undervaluation helps growth. The question of measurement or endogeneity applies
with equal force to both sides of valuation.
In empirical work, researchers do assume equivalence, though discussions of the results
are often oriented around overvaluation. Three recent studies (Acemoglu et. al. (2002),
IMF(2003) and Easterly(2005)), use the Easterly extended Dollar(1997) measure of
currency undervaluation60 and report very mixed results. Both Easterly and the IMF find,
in growth regressions, that the coefficient of undervaluation is around 0.01 i.e. each 20
percent increase in undervaluation leads to only a 0.2 % increase in annual growth61.
Even this low magnitude coefficient, however, becomes half and insignificant when
institutions are controlled for (IMF, WEO, 2005); and excluding outliers, Easterly finds
that the coefficient becomes insignificant and perverse in sign i.e. currency
undervaluation subtracts from growth. If institutions are included in the regression,
Acemoglu et. al. 2002 find that the undervaluation coefficient is not significant at all, and
the sign is also sometimes perverse.
There are very few large cross-section studies that do not use the Dollar-Easterly
exchange rate undervaluation series; given a common series, it is not surprising that the
60
See below for some details on the Easterly-Dollar measure of currency undervaluation. 61
Twenty or thirty percent is the approximate ―normal‖ band for the real exchange rate to vary. See Williamson(2007).
132
same result is reached. So the result that exchange rate undervaluation is insignificant
(after outliers, mostly of the deep overvaluation kind are excluded) is not really a broad-
based conclusion. As will soon be documented, the Dollar-Easterly series is a deeply
flawed measure of under or overvaluation of a currency.
Nevertheless, these influential findings have formed the basis for the pessimism about
the only policy that seemed to have a chance. The two sets of findings – those
pertaining to explicit policy initiatives like reduction in fiscal deficits and changes in
nominal exchange rates – have seemingly sealed the fate of those who believed in the
efficacy of policies. In addition, it is argued that even if exchange rate undervaluation is a
significant variable, it does not mean much, because it is the real exchange rate that
matters for growth, and the real exchange rate is endogenous.
Thus, the few remaining die-hard policywallahs have now been provided with an
internally consistent explanation for both medium term and long term performance –
institutions. It seems, therefore, that policy wonks have lost the game, set and match.
Washington Consensus is a much failed prescription for growth is the new conventional
wisdom.
But policy is alive and well and ruling. The inefficacy of policy conclusion is premature.
There is considerable evidence to suggest that economists have read the econometric
leaves wrong. The ―institutions are trumps‖ argument is examined in detail in Chapter 8.
The results indicate that the role of institutions is vastly exaggerated62, and the role of
policy vastly under-estimated. The policy that matters is not overvaluation, but Balassa‘s
favorite recommendation: export-led growth which really was a code-word for currency
undervaluation. However, given that we are, in the main, employing the same broad
definition of exchange rate undervaluation as others, an explanation needs to be
provided as to ―how come‖ we reach such different answers. 63
62
The institution advocates might well retort, like Mark Twain, that reports of the small role of institutions in generating growth are vastly exaggerated! 63
One commentator stated that ―the results on currency undervaluation affecting growth either mean a home run has been struck, or that I was dead wrong‖. I would like to think that I have struck a six-filled century, and am not wrong!
133
Given this imperative, the next few pages (and Appendix II) go into more detail than
usual into the discussion, and development, of the exchange rate undervaluation
hypothesis. That currency undervaluation does not work conclusion may have more to
do with the flawed messenger rather than an inappropriate message. Part of the reason
for this revisionist conclusion is that, from essentially the same data, estimating the
relationship between RER as a non-linear (and non log linear) one with convergence at
high and low incomes, leads to much more significant results of the impact of currency
misalignment on economic growth. Further, a larger part of the ―undervaluation is
important‖ result is due to the fact that the exchange rate effect is specified via two, not
one, variables: the initial level of undervaluation, and the change in undervaluation from
this initial level.64 The messenger has been changed, and doubled. In terms of
terminology introduced earlier, both the initial level of undervaluation, UV, and the
average change in undervaluation, dUV, are possible determinants of economic growth.
But why should currency undervaluation matter for growth?65
In the first instance, a non-misaligned exchange rate allows production to be dictated by
the laws of comparative advantage. But a fair exchange rate does not give a country an
extra edge in growth. That can only come about via currency undervaluation, because
such undervaluation makes the costs of (productivity adjusted) labor to be lower than the
costs faced by one‘s competitors. This lower cost of labor (given a near similar cost of
internationally mobile capital) increases profitability and therefore the rate of investment.
The extra investment leads to extra growth. This extra growth is likely, in the first
instance, perceived to be transitory by many economic actors, and this perception can
lead to an increase in a higher savings rate. The virtuous cycle continues: higher
investment, higher growth, higher savings.
This simple model also emphasizes the role of both an initial level of undervaluation, and
the change in this level. A fair valuation means a neutral level of profits. The initial level
sets extra profitability at a particular level; a further depreciation in the real exchange
rate can aid in providing extra profits and extra growth. In a symmetric fashion, a
change towards appreciation would mean higher labor costs, lower profitability, lower
64
See Bhalla(2002) when the change in undervaluation variable was first introduced in a study of growth acceleration. 65
A detailed formulation is provided later in the chapter.
134
investments, and lower growth. So both the initial level of undervaluation matters, as well
as its rate of change.
Measurement of Currency Undervaluation
The assertion that a currency is undervalued or overvalued requires knowledge of the
fair or equilibrium exchange rate. This is a much discussed issue. One of the earliest
attempts at defining a fair currency value was in terms of purchasing power parity (PPP).
The phrase itself implies that when there is ―parity‖, prices are in equilibrium. Origins of
PPP go back to Cassel(1923) who offered it in terms of the ―law of one price‖ i.e. prices
of traded goods, adjusted for tariffs and transportation costs, should be the same in all
countries. The reasoning is that if this is not the case, then arbitrage would lead to
convergence in prices.
According to the law of one price, goods in country or region A should be priced the
same as goods in country B. The transformation of country A‘s price into country B‘s
price is of course via the exchange rate. A weaker version is the law of relative prices. If
there are any differences in prices in any beginning period (called the base year or base
period) then what one can expect is that over time, the changes in relative prices will be
equalized across countries.
An example can help illustrate. Assume a computer costs $1000 in the US. Given that
there are hardly any transportation costs or transaction costs (and zero tariffs), the
computer should cost 8000 yuan in China. Why? Because the yuan/dollar exchange rate
is currently at 8 to the dollar. If the US price were to increase by 10 percent to $ 1100,
then unless additional supply was not forthcoming from China, or Vietnam, or India, or
any other country, importers would bring in additional supply from outside and the price
in the US would have to fall back to $ 100066.
66
Only if there was full employment in the entire developing world would it be impossible to supply the additional computers at the same ―fixed‖ price.
135
This arbitrage argument, however, cannot apply to the cost of haircut, the cost of a
school-teacher, the cost of local transportation, or the cost of a maid. An additional maid
cannot be flown from Kerala without passing through stringent immigration controls.67
The Real Exchange Rate (RER) – A definition
In the above example, substitute a composite good for the computer, and international
financial markets instead of a capricious exploitative computer sales company. In this
new world, domestic price changes take place via inflation in the general price index
(e.g. consumer price index) and arbitrage takes place through changes in the nominal
exchange rate. This nominal exchange rate, adjusted for inflation differences between
countries, gives, at any point in time, an estimate of the real exchange rate (RER).
Let the domestic price level of a country be Pd and the exchange rate (expressed in
units of domestic currency with respect to the US dollar) be Xus . The ―price level‖ could
be the price of a particular good, or for a set of goods. It could be defined according to
various indices e.g. consumer price index, wholesale price index, or the GDP deflator.
The exchange rate is expressed as units of domestic currency for each US dollar e.g.
Xus in 2006 for India was 45 and for China was 8. Let the US price level be Pus. Then,
RER = (Domestic price level/ Exchange rate with US dollar) /US price level
Or RER = ([Pd / Xus ]/ Pus)
This is the measured RER at any point in time and it says nothing about misalignment.
For that, one needs a method to define fairness. The IMF uses the convention of a ―base
year‖ to define ―equilibrium‖. In that year, there is zero misalignment, by definition. The
terms undervaluation, and misalignment are synonymous; as are the terms equilibrium
and fair. Deviations from the latter underpin the former.
67
Though one hears that the market never ceases to work – witness the number of illegal immigrants, maids and taxi-drivers in the US. It is well said that besides haircuts, there is nothing non-tradeable anymore.
136
There are extensions, and modifications, to the basic definition but its essential content
is straightforward. There can be discussion about which price measure should be used
to signify inflation – among the choices are the consumer price index, the wholesale
price index, and the GDP deflator. There are choices about which basket of currencies
should be used. Should it be just the bilateral exchange rate (typically with the US$), or
should a multi-currency basket be used? While such modifications make one‘s measure
―purer‖, the extra benefit gained from all the extra sophistication is small. The most
pertinent variable is the choice of the base year in defining overvaluation.
But the base year definition has the severe disadvantage that all countries are forced to
be in equilibrium in a particular year.68 Another disadvantage with this conventional base
year definition of RER (hereafter RERimf ) is that it does not adjust at all for differences in
productivity growth between countries.69 A considerable part of research on
misalignment has concentrated on objective measurement. The most original, the most
popular, and most likely the most accurate method of measuring overvaluation is due to
independent papers by Balassa and Samuelson in 1964, hereafter the Balassa-
Samuelson or the B-S effect.
RER: Adjusting for Balassa-Samuelson
The seminal papers of Balassa(1964) and Samuelson(1964) arose out of a need to go
―behind the scenes‖ of RER to measure equilibrium. The concern then (in the early
1960s) was the presumption that the US dollar was massively overvalued, as revealed
by raw, unadjusted measures of the RER. Both authors pointed out that in rich or richer
countries, productivity growth in the manufacturing/industrial sector was higher than in
the services sector. If so, then with labor market equilibrating wages across sectors, it
would be observed that the prices of services would be higher in richer countries. But
68
If interest is in (log) changes in undervaluation, then this disadvantage is eliminated. 69
But it is theoretically straightforward to do so. However, an estimate of productivity differences is required and Bhalla(1992, 1994,1997,1998) estimates productivity differences to be proportional to differences in per capita GDP growth. For example, if there are no differences in inflation rates, and if per capita GDP growth in China is 7.5 percent and in US 2.5 percent, then the nominal exchange rate of China should appreciate by half the distance or 2.5 percent per year. This model was first used at Goldman Sachs (Bhalla(1992) to (ex-post) explain the overvaluation of the British pound. The same model was used to correctly predict, this time ex-ante, the overvaluations of the Mexican peso Bhalla(1994) and the Thai baht (Bhalla(1997). Perhaps because the model works both in theory and in practice is why it forms the basis of the Goldman Sachs currency prediction models. (Goldman Sachs(1999)).
137
goods arbitrage would ensure that prices of goods were approximately equal across
countries. Thus, the overall price level (price of a ―composite‖ item composed of both
manufactures and services) would be higher in richer economies (like the US) and ―pure‖
purchasing power parity would not be observed i.e. differences in unadjusted RER as an
indication of overvaluation would be highly misleading.
In 1960, the US was considerably richer than Europe and Japan. The US dollar was
observed to be over-valued by about 23 percent. (Table 6.1) The average composite
good. relative to the US, cost about two-thirds as much in Japan, Denmark, and the
Netherlands. Did this imply that that the exchange rate was undervalued for these three
countries? Not so, according to both Balassa and Samuelson. Why? Because the US
was more productive, richer, and therefore had a higher overall price level.
The Balassa model of estimating undervaluation is extremely straightforward. Estimate
the simple equation: RER = a + b*Y, where Y is per capita income, and obtain the
predicted ―equilibrium‖ value RER*, and the percentage deviation between RER* and
RER is the misalignment. This adjustment yields a very different picture of under or over
valuation. Several countries are on the ―line‖ including Japan, which the unadjusted RER
indicated was about 35 percent undervalued, but the adjusted RER suggests was fairly
valued.70 The two real outliers are Sweden and Netherlands. Sweden is overvalued by 9
percent in 1960 and Netherlands is undervalued by 17 percent. Adjusted for B-S income
effects, the US dollar is observed to be 0.8 percent undervalued.71
The coefficient on income for this simple equation was 0.025 and the implied elasticity of
RER with respect to income was 0.38 (In a log-log formulation, Balassa‘s data yields an
elasticity of 0.33). Thus, for each 10 percent of increase in relative incomes (relative to
the US), the RER would appreciate by 3.8 percent. This result is most remarkable since
70
Balassa(1964), Table 1 and Figure 1, p. 588 and p. 590. 71
In Fig. 1 of Balassa‘s paper (pg. 590) the US appears to be 1 % overvalued after adjustment for relative income effects. In those days, hand held computers were not as precise!
138
Table 6.1: The original Balassa effect, 1960
Income, pcpd
in 1999 PPP $
Per capita
income in
US$,
Balassa RER RER, adj UV, unadj UV, adj
Belgium 21.3 1273 80.4 81.3 -21.8 1.1
Canada 28.4 1550 92.8 88.2 -7.5 -5.1
Germany 24.2 1200 77.9 79.5 -25 2
Denmark 30.1 1269 77.4 81.2 -25.6 4.8
France 21.4 1152 77.4 78.3 -25.6 1.2
United Kingdom 26.5 1212 82.4 79.8 -19.4 -3.2
Italy 18.9 704 70.1 67 -35.5 -4.5
Japan 12.5 507 62.6 62.1 -46.8 -0.8
Netherlands 25.3 1166 66.6 78.6 -40.6 16.6
Norway 22.6 1186 80.4 79.1 -21.8 -1.6
Sweden 27.9 1307 90 82.1 -10.5 -9.2
United States 33.6 2051 100 100.8 22.6 -0.8 Source: Balassa (1964) and own computations
Note:
1. RER is defined as the ratio of CPI country price levels transformed into US dollars. For
example, the ratio 77.9 for Germany means that the German price level, in US dollar terms,
was 77.9% of the US price level.
2. Undervaluation, UV unadjusted is 100 times the log of the ratio (RER/100).
3. RER, adjusted is the prediction Balassa obtains from his regression RER = a+ b*y,
where y is the individual country per capita income.
4. Column 4, UV adjusted is 100*log(RERadj/100) and represents the Balassa-estimates
of undervaluation.
5. A negative sign for UV represents an undervaluation.
139
approximately the same elasticity is obtained in 2006 for all 173 countries as Balassa
obtained for just 12 Western countries in 1960!
What Balassa showed empirically (Samuelson‘s separate and independent paper
contains the theory) was that a very simple adjustment for relative incomes would
change one‘s entire perception about whether a currency was undervalued or not. The
B-S effect for the US dollar turns out to be a large 24 percent in 1960 i.e. the US dollar
appeared to be 23 percent overvalued, but in reality was 1 percent undervalued.
How common is the B-S effect?
Since the Balassa Samuelson articles, there has been a lot of growth in the world, and
several formerly poor countries have become middle income. In addition, several
developed economies have ―converged‖ to the US standard of living. So there are
several ―natural experiments‖ to test for the applicability of the B-S effect. The results
suggest (see Bergin et. al. (2005) for a detailed analysis) that the B-S effect has held to
a remarkable degree since 1960. So remarkably, that it is one of the most accepted, and
therefore stylized, facts about post War macro-economics.
Chart 6.1 reports the pattern of RER for various regions. The charts are presented for
two periods, from 1960 to 1980 (Charts 6.1a) and from 1980 to 2006 (Charts 6.1b). The
reason for the break up is China. It is likely that the pattern of RER changed after
China‘s aggressive devaluations in the 1980s. Countries, especially its Asian
competitors, could not afford to let ―nature‖ operate and the RER to appreciate a la the
B-S effect. If true, then one should observe the tendency for RER to increase to
diminish. This is exactly what happens; not only does the RER stop increasing with
growth, it actually declines, led by China‘s ―reversal of history‖ policy.
Charts 6.2a and 6.2b report the pattern of undervaluation for the period 1980-2006 and
Chart 6.2(c) reports the same for China and India. The undervaluation charts have two
horizontal lines at plus and minus 30 percent undervaluation. This could be viewed as a
―target zone‖ for the currency valuation i.e. various circumstances, including
measurement error, could induce an economy to fluctuate between these levels. Beyond
+- 30 percent, however, is likely to be something structural. On average, the OECD and
Latin American economies have not strayed beyond this zone.
140
Undervaluation for developed countries is very often (80 percent of the time) in the range
–30 to + 30 percent, with a historical mean of only 3.5 percent. Developing countries all
started the post-war period with extremely high (mean of 92 percent) levels of
overvaluation, a fact whose historical origins were traced to colonialism in Chapter 2.
Japan for many years, and Germany, have both been on the extreme side of
overvaluation, but never been undervalued by more than 17 percent in the post 1980
period. At its peak, Japan was less undervalued than Germany (- 5 % vs. –17 % in
1985). Starting with the East Asian crisis, East Asia is near the extreme undervaluation
line of –30 percent. India was also there in mid-2006 though the recent sharp
appreciation suggests that the Indian rupee is today (May 2007) only 20 percent
undervalued. China, by contrast, has been undervalued by at least 40 percent in each of
the last four years. And it is a large economy with whom several developing country, and
Asian economies, compete.
141
Chart 6.1a: Trend in RER, various regions, 1960-1980
Source: SAE dataset, see Appendix I for details.
Chart 6.1b: Trend in RER, various regions, 1980-2006
Source: SAE dataset, see Appendix I for details.
142
Chart 6.2a: Trend in Currency Undervaluation, various regions, 1960-80
Source: SAE dataset, see Appendix I for details.
Chart 6.2b: Trend in Currency Undervaluation, various regions, 1980-2006
Source: SAE dataset, see Appendix I for details.
143
Chart 6.2c: Currency Undervaluation in China and India, 1980-2006
China
India
-50.0
0.0
50.0
100
.01
50
.0
UV
(in
%)
1980 1985 1990 1995 2000 2006Year
Source: SAE dataset, see Appendix I for details
144
Non Balassa-Samuelson models of Real Exchange Rate
Since Balassa‘s initial estimation of overvaluation, various attempts have been made to
improve on his formulation; introduction of logs on both sides of equation (i.e. log RER
as a function of Log per capita income), use of PPP rather than US dollar incomes etc.
Kravis-Lipsey (1988) extended Balassa‘s analysis and regressed RER (price levels in
PPP terms) against a variety of indicators meant to capture the degree of non-tradables,
terms of trade, services, etc. in an economy. Dollar(1997) does the same, but with the
difference that non-linearity is brought in on the right hand side (per capita income and
per capita income squared). Rogoff(1999) and Frankel(2006) employ a log-log
formulation, with no additional variables on the right hand side72 i.e. identical to Balassa
formulation but with the simple transformation of logs.
The center of most discussions of what a ―fair‖ exchange rate should be is the current
account balance. (Until recently, it used to be the trade imbalance). This property of
―equilibrium‖ was used by John Williamson(1994) in the development of the
Fundamental Equilibrium Exchange Rates (FEER).73 The FEER is a medium term
construct, and does not rely on the assumption that the medium term current account
balance is zero, but does assume a target level, or zone, for the current account to be in
―equilibrium‖. While intuitively appealing, the FEER concept suffers from computational
drawbacks (import and export elasticities are needed for most goods). In addition, by
being definitional medium-term, the FEER is assumed fixed for several years e.g. a
decade. For countries with high per capita growth rates, the ―fair‖ value of the real
exchange rate is likely to change by 20 to 30 percent over a decade i.e. a much larger
variation than expected by the FEER.
Another problem with measures like the FEER is that such measures assume that an
imbalance in the current account is necessarily symptomatic of an imbalance in the real
exchange rate. While for most developed economies, and for large imbalances this
assumption is most likely true, for most developing economies, and for most small
72
The B-S effect is ultimately about how relative incomes capture differences in the structure of the economy; consequently, it is doubtful how much terms of trade or other variables like continental dummies can add to the explanation of the RER, once relative income levels are in the equation. Addition of such variables could result in a severe case of double and/or mis-counting, as appears to have been the case with Dollar‘s analysis. 73
This concept has led to modifications, other attempts e.g. Cline.
145
imbalances (say plus or minus 3 percent of GDP), negative imbalances may not be
indicative of overvaluation.
In the context of using current account balance as a reference point for exchange rate
equilibrium, it is important to recall what Samuelson had to say on the issue:
―In 1948, I shocked at least one of my teachers by saying that the theory of comparative advantage does not guarantee a country against balance of payments difficulties, nor does it even keep a country from being undersold in terms of every good‖. (1964, p. 145)
Estimating the relationship between RER and per capita income
Theory suggests a non-linear relationship between RER and per capita income. Study
of European economies (after World War II) and other OECD economies indicates a
strong tendency for this ratio to slowly increase. Overall, an S-shaped pattern is
indicated i.e. RER stays at a low level for long periods of development (till approximately
(log) 1 a day or PPP $ 1000 per capita per year) and then rises towards its convergence
level of around (log) 5 or PPP $90 a day or 33000 a year. (Chart 6.3a) This convergence
level, for the real exchange rate, is close to 1.10. The chart displays a scatter plot for
173 countries in 2006. An empirical counterpart to this pattern is the ―exponential
regression with one asymptote‖ i.e. an S shaped curve!
The estimated non-linear relationship for the S-shaped curve is as follows:
RER = 1.103*(1 - .971^Y), Nobs = 1910, R2 = 0.86
where Y is per capita per day income in 1996 PPP dollars and ^ signifies ―to the power
of‖74. The time-period of estimation is annual data for the period 1996 to 2006. The
starting year was chosen as 1996 because the base year for estimates of PPP (Penn
Tables) is 1996.
74
Nominal PPP 1996 income for years after 2000 are obtained from IMF (give details) and World Bank (World Development Indicators) and linked to the Penn data. Conversion into real values is done via the US GDP deflator. See Appendix I for details.
146
The predicted value of RER at a given value of Y yields the equilibrium or fair value
RER*. The log difference (RER – RER*) multiplied by 100 yields the estimate of
undervaluation, (UV‘it) for a particular country i and time-period t. A negative value
signifies undervaluation. This initial estimate of UV, UV‘, is not against the US dollar, but
against the PPP dollar. An estimate of UV of the US dollar is also obtained, for each
year, against the PPP dollar (UVusat). The difference between the two (UVit - UVusat)
yields the estimate of undervaluation UV for each country and time-period t.
For the US, the SAE estimate is not UVusat but is rather the negative of the weighted
average of its trading partners. The FED‘s Broad index is a weighted basket of the dollar
against 37 countries. The observed trade share for these 37 countries (World Bank
World Development Indicators data) is used to estimate UV for the US. Details are
provided in Appendix II.
Annual changes in the value of UV are defined as dUV. Since income levels in year prior
to 1996 are available (indeed, given Maddison‘s data, are available going back to the
sixteenth century, and before), the equilibrium exchange rate at any time can be derived,
given an estimate of per capita income. In order to obtain an estimate of undervaluation,
UV, one needs information on exchange rate with the US, and the US and individual
country price indices. For years prior to 1950, these data are difficult to obtain.
The relationship of RER is with real per capita income and almost a necessary feature of
such a relationship is that it ―converge‖ to a value at high levels. The estimate is 1.10 i.e.
as incomes go beyond a certain level (around $ 90 per capita per day, or the same as
that obtained for the US in 1990, and will likely obtain in Japan in 2020), the fair real
exchange rate will not change much. This is in contrast to the conventionally estimated
log-log model where the real exchange rate increases to infinity as incomes in the upper
range rise. An unreasonable prediction. Note also that this PPP based measure of
RER is independent of savings rates, balance of payments, investment rates etc. But not
independent of the level of per capita income – it increases non-linearly, and with
varying elasticity, as incomes rise (See Table 6.3a for how this elasticity is different than
the conventionally measured constant log-log elasticity).
147
Table 6.3a: Elasticity of RER with respect to income
1960s 1980s 2006
Non-linear model
OECD 0.59 0.44 0.25
East Asia 0.4 0.61 0.81
Eastern Europe 0.18 0.74 1.11
Latin America 0.5 0.58 0.68
MENA 0.39 0.52 0.56
South East Asia 0.22 0.4 0.82
Sub Saharan Africa 0.44 0.35 0.44
All countries
SAE 0.46 0.49 0.64
Log-Log model 0.17 0.26 0.38 Source: SAE dataset, see Appendix for details. Notes:
1. RER is defined as the ratio of PPP exchange rate to US dollar exchange rate for each country at a particular time t.
2. The SAE model is RER = b1*(1-b2^y), where y is the real per capita per day income.
3. The elasticity of RER is the average percentage change in RER expected with each 1 percent extra relative to US growth.
148
Charts 6.3a and 6.3b plot the real exchange rate, and the predicted or ―fair‖ exchange
rate for all countries (Chart 6.3a) and selected countries (Chart 6.3b). The latter plot
clearly highlights the extreme outliers. (Chart 6.3b). The Asian economies are
distinguished by their extreme undervaluation status. The African economies are
distinguished by their over-valuation status. Asian economies have grown very fast in
the last two decades; African economies have grown very slow, if at all. This is,
heuristically, the first indication that undervaluation matters for growth, a hypothesis
substantiated with very different methods below.
Box 6.1 documents the estimates obtained from the various RER and real income
models. The choice of countries, time-periods etc are kept the same to make the results
comparable.75 The first model reported is the Dollar model; and this yields the same
higher overvaluation for Africa and Latin America (about 6.6 percent) and it has an R-
squared of 0.66. The second model is the conventional log-log model estimated by
Begin et. al., Frankel, Rogoff and others; it has an R-squared of 0.49. The third is the
SAE model reported above. The non-linear SAE model has the highest explanatory
power of 86 percent.
Box 6.1: RER determination – Three Different Models
Models (Time period 1996-2006, annual observations, Sudan excluded)
Dollar : RER = .17 + .014*Y -.00004* Y2 + .066*dAfr + .066*dLat R-square: 0.66; Obs: 1910
(15.5) (20.6) (-4.7) (5.6) (6.52)
Log-Log: rer = -1.91 + 0.38*y R-square: 0.49; Obs: 1910
(-67.57) (40.97)
SAE : RER = 1.10 *(1-.971Y) R-square: 0.86; Obs: 1910
(43.4) (758.37)
Definitions :
Y: Per capita income; y: (log) Per capita income; Y2 : Square of per capita income; RER: Real exchange
rate; rer : (log) Real exchange rate; dAfr: African dummy; dLat: Latin American dummy
75
For example, Dollar estimates the model with consumption PPP rather than income PPP.
149
Chart 6.3a: Real Exchange Rates, 2006
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Chart 6.3b: Real Exchange Rates, 2006, selected countries; predicted and actual
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as a function of real per capita income, measured in per capita per day 1996 prices, for the years 1996-2006. The estimated equation is n exp2a RER = pcpd 1996 prices; see Appendix I and text for details.
2. The fitted line reports on the following equation: RER = b1*(1-b2^y) where ^ represents to the power. All
countries, except extreme outlier Sudan are included in the regression. The regression result is RER=1.10*(1-
0.97^y) where y is real per capita per day incomes in PPP prices.
150
Charts 6.4(a) and (b) compare the undervaluation estimates obtained by SAE for 1976-
85 versus the original Dollar estimates for 1976-85; and Chart 6.4(b) compares the SAE
estimates for 2003 with the Easterly estimates for the same year. So regardless of the
time-period chosen, there is a wide dispersion between the two sets of estimates. The
results presented in the next chapter show that the SAE undervaluation estimate has a
much larger explanatory power in growth regressions; indeed, it is the non-linear and
more accurate specification that allows the policy of undervaluation to be significant in
growth regressions, compared to insignificance in the Easterly regressions.
Evidence that the Dollar-Easterly estimates of undervaluation may be deeply flawed are
indicated by the observations that lie significantly outside the +- 30 percent range
(between the two estimates). For example, during 1976-85, India was one of the most
protected economies in the world; SAE estimates the Indian currency to be overvalued
by 125 percent; Dollar estimates suggest the rupee during that time-period to be
undervalued by 8 percent. Analogously, Sri Lanka is the most undervalued currency (-75
percent) according to Dollar; SAE estimates it to be overvalued by 80 percent. Similar
problems obtain with Easterly‘s update of the Dollar series to 2003. Bangladesh, Nepal,
Pakistan, Ethiopia, Costa Rica etc are all considered overvalued by SAE; the Easterly
measure has their currencies undervalued.
151
Chart 6.4a: Undervaluation – SAE and Dollar
Source: Dollar (1997) and own computations. Note:
1. The straight 45-degree line represents the axis at which the Second Among Equal (SAE) estimates of average UV for 1976-85 is equal to that reported by Dollar.
Countries in black are those for whom the two undervaluation levels are within +(-) 30% of each other. Countries in
gray are those with estimates even wider than a +(-)30 % range.
152
Chart 6.4b: Undervaluation - SAE and Easterly
Source: Easterly(2003 ) and own computations. Note:
1. The straight 45-degree line represents the axis at which the Seconds Among the Equal (SAE) estimates of average UV for 1976-85 is equal to that reported in Easterly.
2. Countries in black are those whose two undervaluation levels are within +(-) 30% of each other. Countries in black are those with estimates even wider than a +(-)30 % range.
153
How well does the model fit?
Chart 6.5 plots the SAE estimates of undervaluation for selected countries in 2006. The
US dollar appears to be mildly undervalued (estimate is -3 percent). While this goes
against the grain of conventional wisdom (which is related to the large US current
account deficit), it is supported by Samuelson‘s contention that the undervaluation
derived through a PPP exchange rate need not mean an equilibrium in
the balance of payments. It also is consistent with the FEER calculations that define
equilibrium around a ―target‖ level of disequilibrium e.g. a US dollar exchange rate that
will imply a balance of payments deficit of not 6 percent but 3 percent. It is also
consistent with the FED Broad index which yields an estimate of –8 percent.
Indeed, as detailed in Appendix II, the SAE estimate of the real exchange rate for the US
mimics very well the FED‘s Broad real exchange rate index for 37 countries. It only does
so with a close correspondence of the levels for the years 1973 to 2006, but also
captures all the turning points. The correlation between the two series is around 93 to 96
percent, depending on the time-period chosen between 1973 and 2006. Finally, as
documented in Appendix II, the SAE UV series has greater explanatory power than the
FED Broad index in explaining growth rates in the ratio of US exports to imports (as in
the Baily-Lawrence (2006) model.) All of this reinforces the confidence in the
undervaluation estimates obtained for the different countries, and different years
(including, under certain assumptions, the estimates of undervaluation for the historical
period 1870 to 1938, as presented in Chapter 3).
Another potential surprise is the estimate of the undervaluation for Japan – it appears to
be overvalued by 18 percent against the US dollar, and Germany appears to be 26
percent overvalued.76 Some clue about the effects of currency valuation and growth are
obtained by observing some of the extreme values. China, Russia, India, Vietnam are
the high undervaluers and high growth economy; African economies (e.g. Ghana,
Kenya) overvalue their currencies enormously and find it difficult to grow.
76
These estimates are for an exchange rate level of 116 yen and 1.25 euro to the dollar. At present, the euro has appreciated by a further 8 percent making the German currency presently outside the ―fair‖ zone of +-30 percent and equal to the danger overvaluation levels of 1994-1996.
154
Chart 6.5: Undervaluation (%) in 2006
119
109
75
57
47
28
26
18
16
15
10
-3
-4
-14
-17
-19
-30
-31
-49
-54
-59
-50 0 50 100 150Percent (%)
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jpn
sau
mex
bra
usa
mar
kor
chl
bwa
ind
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rus
arg
chn
Note: 1. A negative sign signifies (log) undervaluation.
Currency undervaluation is defined as the gap between actual RER (ratio of PPP and US $ exchange rates) and
predicted RER.
155
Table 6.3(b) compares the undervaluation for selected countries obtained by different
authors. The Big Mac undervaluation for Germany is the estimate for the Euro area.
There are some differences (e.g. Cline obtains that the yen was 62 percent undervalued
in 2004 while SAE reports an overvaluation level of 31 percent for the same year). By
and large, however, and regardless of the source of computation, the SAE results are
comparable. Note also (and this becomes relevant for the section on ―the political
economy of undervaluation‖ in the next chapter) that none of the estimates for China are
less than (log) –43 percent, or a fair value for the yuan to be a maximum of 5.4 yuan to
the dollar.
Table 6.3(b): Different estimates of undervaluation, 2004
Easterly Cline SAE Big Mac
USA -11 -6 0
Germany -14 -21 29
Japan 51 -62 31 -22
Russia -6 -76 -69
Brazil 13 -23 -53
Chile -44 -9 -35 -29
Singapore -25 -92 -23 -41
South Korea -10 -19 -23 -6
China -43 -50 -83
India -75 -32 -25 Source: Cline (2005), Economist (2004); SAE dataset, see Appendix for
details, Easterly(2003).
Note: Easterly estimates correspond to year 2003.
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Modeling RER, Investment, and Growth
The Balassa-Samuelson formulation is explicit about how the RER is expected to evolve
with income growth, but is silent on whether currency misalignment (undervaluation) is
helpful to the growth process. Samuelson does imply the same as does Balassa in his
writings (Balassa 1978), and recommendations, about export led growth. The following
model attempts to relate currency undervaluation to higher investment, and thence to
higher GDP and total factor productivity growth.77 The investment rate is the driver, as
equipment investment was for de Long-Summers(1993).
The model is very simple. In a globalized world, capital costs are near equal for most
investors.78 So where should a global investor invest? Different countries have different
attributes – different climates, languages, culture, and different tax regimes. The most
crucial difference, however, is likely to be differences in the cost of (productivity
adjusted) labor. An undervalued exchange rate means a lower productivity adjusted cost
of labor, and therefore, a lower cost of production.
The model makes two simplifying assumptions. First, that the cost of labor is
proportional to average per capita income in US dollars. Second, that the However, the
productivity of labor is proportional to per capita income measured not in current US
dollars but in current PPP dollars. This is exactly what PPP conversions are meant to do
– translate income (or productivity) across borders into a common purchasing power
unit.
This means that
Investment ∞ [Costs/Productivity]
Investment = k*(GDP per capita in US dollars)/( GDP per capita in PPP dollars)
where k is the proportionality constant and negative i.e. higher costs lead to lower
investment.
77
This model was first offered in Bhalla(2002).
78
As Clark(2006) documents, this was also the case in the 19th century.
157
Investment = k*(GDP per capita in local currency/XUS)/(GDP per capita in local
currency/Xppp)
where XUS, Xppp are the exchange rates with respect to the US and PPP dollars,
respectively.
This reduces to
Investment = k*( Xppp/XUS )
or Investment = k*(RER)
Thus, investment is proportional to RER as defined by Balassa and others. But
RER per se may not be that informative because of the presence of B-S effects. In 2006,
the RER for India and China was 0.19 and 0.25, respectively.79 This 27 percent lower
RER for India did not necessarily make it a more profitable destination than China; the
RER was lower in India because China was more than twice as rich as India and its
labor twice as productive. So even if labor costs more in China (relative to India) the
productivity of this labor is also higher. The attractiveness of investment is dependent on
how much lower the costs are relative to productivity and this relationship is yielded by
the undervaluation of the exchange rate or the difference between actual RER and the
RER purged of the B-S effects, RER* i.e.
I is not equal to f(RER), but I = f(UV),
Where UV = log(RER/RER*)
The ratio of the fair exchange rate to the actual US$ exchange rate80 is what one needs
to know in order to correctly evaluate the respective profitability of investments in the two
79
In 2006 in India, Xppp was Rs. 8.7 for one current PPP dollar; the US$ exchange rate was Rs. 45.4 yielding an RER (ratio of Xppp / XUS$ ) of 0.19. Analogously, for China the values were 8 yuan/dollar, 2 yuan to the PPP dollar, yielding a RER of 2/8 or 0.25. 80
The ratio RER/RER* is equal to the ratio X/X* where X is the current exchange rate and X* is the ―fair‖ exchange rate.
158
destinations. And this ratio is nothing more than the ratio (RER/RER*) which is equal to
currency undervaluation.
Thus, when the exchange rate is more than competitively priced81, as China has done in
2006 and for most, if not all, of the prior decade, then that gives it a large
cost/productivity advantage, an advantage that pulls in extra investment and generates
extra growth.82 The net result is that investments, and economic growth, are negatively
proportional to not the RER, nor RER*, but to currency undervaluation, UV.
The equations of interest therefore become:
UV = RER – RER* (where both are in log terms)
And I = f(UV)
The fair value of a currency constantly changes from year to year, depending on inflation
and productivity movements in the selected country and the US.83 The level of
undervaluation implies a certain equilibrium level of investments, and therefore growth.
The attractiveness of undervaluation is balanced off against other investment
considerations – real interest rates, bureaucratic delays, hospitality of investment
environment, corruption etc. If now the real exchange rate of China were to depreciate,
investments would be that much more profitable because the costs relative to
productivity would decline; hence, investments are negatively related to both the level of
undervaluation and to the change in undervaluation (dUV).
The relevant equation for determining investments (and economic growth Gt )is therefore
It (or Gt ) = a + b*UVt0 + c*(dUVt) + d*Zt + et,
81
As will soon be documented, there is a thin policy line dividing a competitive and a mercantilist exchange rate. 82
This effect of UV on investment helps to partly explain the ―unexpected‖ attraction of China for FDI investors relative to India. 83
The same obviously holds for other countries. The estimate of fair value is based on a model which relates each currency, including the US, to the PPP dollar. Since exchange rates are not quoted against PPP but are quoted mostly against the US dollar, a ―net‖ estimate of the fair exchange rate against the US dollar is more meaningful. Appendix II discusses in detail the SAE estimates for the US dollar itself.
159
where UV t0 is the initial level of undervaluation, dUVt is the average change in
undervaluation in time period t, Z is a vector of other determinants (e.g. real interest
rates, tax rates, corruption costs etc.) and e is the error term.
Isn’t the real exchange rate endogenous?
Thus, it would appear that higher economic growth can be achieved by undervaluing the
exchange rate. And undervaluation can be achieved by a policy of exchange rate
depreciation. It is in this sense that various scholars have tested the impact of
undervaluation on growth. But several researchers contend that this is a wrong
specification because the real exchange rate is endogenous. According to the critics, a
devaluation will lead to excess demand, over-heating, and eventually to a higher level of
inflation. This higher inflation would negate any real devaluation that was achieved by a
nominal change. Regardless of the method of measurement of equilibrium, the real
exchange rate is endogenous, and very little can be gained by changing the nominal
exchange rate. In other words, the real exchange rate is almost always in equilibrium,
and certainly for any reasonable length of time.
Mckinnon-Scnabl(2006) summarize the real exchange rate is endogenous argument
rather forcefully:
―In a world where many countries peg their nominal dollar exchange rates, changes in these nominal pegs, (as in the case of China) could be considered a legitimate right-hand side or ―exogenous‖ variable. But then relative monetary policies…must be altered to sustain any such nominal changes – either easy money and inflation in the U.S. associated with the nominal depreciation, or tight money and deflation in the foreign country whose currency appreciates in nominal terms. With the passage of time, the macroeconomic upshot could then be little or no change in real exchange rates‖. (p.6)
While intuitively appealing, the ―real exchange rate is endogenous‖ (RERIE) argument is
dependent on several assumptions, and is deeply flawed, at least empirically. The
endogeneity argument assumes that all countries are at full employment at all times. The
real problem with the endogeneity argument, however, is that it just fails to hold true,
empirically. According to RERIE, over a sufficiently long period of time, say twenty years
or so, the change in the real exchange rate should be approximately zero. That is,
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domestic inflation should approximately add up to the sum of the nominal depreciations,
plus US inflation84. Whether the time-period is a short 3 years or 10 years or 20 years,
there are too many instances of (equivalent) inflation not following devaluation, and
(equivalent) deflation not following overvaluation, that holding onto the RERIE argument
is a matter of ideology, and raw belief, and not reality.
The next few pages detail various estimates of real exchange rate change for different
regions of the world, different countries, and different sample-periods. Two estimates of
real exchange rate change are presented – RERimf and RERsae. The first, RERimf, is
the conventional estimate of real exchange change. It is the difference between the
exchange rate change (vis-à-vis the US dollar) and change in net inflation, again vis-à-
vis the US. This measure does not incorporate differences in productivity growth or the
B-S effect. The second real exchange rate change measure, RERsae, is the real
exchange rate change defined above via the non-linear equation, and it, by definition,
includes the B-S effect.
Table 6.4 reports average values for the two estimates of RER change for different
regions of the world, and for two time-periods, 1965 to 1980, and 1980 to 2006.
(Regional changes are weighted by individual country GDP in PPP dollars). Neither
measure of RER change is particularly endogenous. The null hypothesis is that the
change should be approximately zero. The changes are particularly pronounced for the
period when China begins to aggressively devalue. In the first period, RERimf for East
Asia appreciates by 69 percent; in the second, there is a depreciation of 69 percent.
Only for the OECD countries is the RER change close to zero, though in the first period,
even these countries show an appreciation of 27 percent. Note the difference in
exchange rate change for the Asean7 countries in the two periods; in the pre-China
period, the currencies of these countries appreciate by a largish 47 percent; after
China‘s aggressive entry into global competitiveness, the Asean7 countries show a real
depreciation. After incorporating the B-S effect, these economies show a 91 percent
depreciation 1980-06, compared to a 21 percent depreciation, 1965-1980.
84
Throughout, inflation in all countries is measured by the GDP deflator. This is only a matter of convenience since the GDP deflator is available for a larger number of countries, and is likely to be a more reliable indicator of inflation for many developing countries (than the conventional CPI measure of inflation). In any case, none of the results are affected by the use of the GDP proce deflator than the CPI.
161
Table 6.5 presents the calculations of RERimf for specific countries. The first non-China
period conforms to the expected pattern, with countries (except the heavily overvalued
currencies like China and India) showing an increase in RER with income growth. For
the 1980-06 period, a lot more real depreciations occur. Chile shows outstanding and
exceptional growth (relative to other latin American countries); it also shows a real
depreciation of 27 percent.
Chart 6.5 graphically represents the cumulative changes in inflation and exchange rates
for selected countries. Except for a few developed economies, the pattern is of a real
exchange rate divergence, and not convergence as predicted, and postulated, by
adherents to the real exchange rate is endogenous hypothesis.
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Table 6.4: Is the real exchange rate endogenous?
Cumulative change in
RERimf RERppp
1965-80
OECD 26.5 17.7
East Asia 68.5 -30.2
Eastern Europe 0.8 -134.7
Latin America 23.1 10.3
MENA 42.7 4.0
South Asia -33.3 -20.1
Sub Saharan Africa 81.1 49.2
ASEAN 7 43.8 -21.3
ASEAN 8 66.1 22.2
World 30.8 11.9
1980-06
OECD 1.0 7.2
East Asia -65.8 -163.1
Eastern Europe 57.5 -38.3
Latin America -23.3 -20.0
MENA -18.5 -30.2
South Asia -59.0 -152.7
Sub Saharan Africa -7.6 -37.1
ASEAN 7 -22.0 -91.2
ASEAN 8 0.7 -42.8
World -9.6 -37.8 Source: SAE dataset, see Appendix 1 for details Source: Penn World Table 6.1; World Development Indicators, 2006 (World Bank); World Economic Outlook, 2006 (IMF). Note: RERIMF measures the cumulative real exchange rate change for the years indicated, while RERPPP
measures the cumulative real exchange rate change adjusted for the Balassa-Samuelson effect. See text for
details.
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Table 6.5: Is the real exchange rate endogenous?
Cumulative change in
Exchange rate Net inflation
Real
depreciation
(RERIMF)
1960-79
Brazil -265 310 45
China 46 -80 -34
Germany 64 -15 49
India -53 46 -7
Singapore 34 -23 11
South Korea -60 167 107
1980-06
Chile -264 237 -27
China -164 57 -107
India -172 109 -63
Mexico -619 639 20
Philippines -195 176 -19
Singapore 31 -29 2
South Korea -67 74 6
Thailand -69 27 -42
United Kingdom -15 41 25
Source: SAE dataset, See Appendix I for details
Note:
1. A plus sign for exchange rate change signifies depreciation. 2. Net inflation is the excess of domestic inflation over US inflation. 3. Real depreciation is just the difference between exchange rate change and net inflation. 4. The real exchange rate is endogenous hypothesis would suggest that the value in the final
column is close to zero.
164
Chart 6.5: Is the real exchange rate really endogenous?
165
Several formal tests of the endogeneity of RER have been conducted in the literature.
These tests come with different names. There is the ―unit root‖ test which tests whether
the RER is different than a ―random walk‖; there is the ―half-life‖ calculation which
estimates, if there is a unit root, the number of years before 50 percent of the shock
(change) in RERimf is recovered. Rose-Taylor, Rogoff, Rogoff-Rose and
Edwards(1999) comprehensively summarize the findings. The stylized facts are four;
first, that in hyper-inflation economies, there is almost complete pass thru i.e. inflation
follows devaluation rather quickly. In such instances, however, the cause and the effect
are not easily separated. Second, the RER does have a unit root (i.e. there is a
systematic tendency for the RER to revert back to the pre-change level) for developed
economies. Third, that 50 percent of the reversion back to the mean (the half-life) takes
place, for the economies or time-periods with a unit root, over an average of 4 years.
Fourth, that for non hyper-inflation developing economies, the RER pattern generally
does not have a unit root. In English, that in such instances nominal changes in
exchange rates (via policy) become permanent changes in the real exchange rate. None
of these models test for RER change after incorporation of the B-S effect; they are
strictly oriented around RERimf. The theory of endogeneity, however, emphasizes real
exchange rate change after the incorporation of productivity differences and/or B-S
effects. Given a pronounced B-S effect, an upward tendency in the RER should be
observed with growth, especially galloping growth. This was/is the reality, and it is all the
more remarkable that a giant continental economy like China has not followed this
pattern.
RERIE: Some spectacular errors in prediction
Most recently, over the last decade or so, there have been several spectacular
devaluations, and revaluations, and yet the RER is endogenous argument has failed to
hold, every time. In Sept. of 1993, the British pound fetched 3 deutsche marks; within
days of Black Wednesday, it fetched 2.2, a large 30 percent plus devaluation. The half-
life of RER ―convergence‖ is estimated by several authors (see Rogoff(1999) for a
summary) to be around 4 years i.e. 15 percent of the 30 percent devaluation should be
made up by excess inflation in 4 years. Actual excess British over German inflation for
the four years 1994 to 1997: 4.5 percentage points. To date (1994 to 2006), excess
inflation has been 23 percent.
166
Next stop on the major devaluation episode is Mexico, Dec. 1994, when in a matter of
days, the peso depreciated against the dollar by more than (log) 30 percent. In 2006, the
peso averaged 11.15 vs. a pre-devaluation level of 3.3. For the first five years, there was
a real depreciation but by the sixth year (2000), excess inflation had caught up and the
Mexican peso was back to its ore-devaluation level, in real terms. The Mexican example
fits the RERIE argument almost perfectly.
The examples from the East Asian currency crisis are even more spectacular in their
rejection of the RERIE argument. The Thai baht was 25 in June of 1997; in 2006, it
averaged 40.6. The nominal exchange rate has depreciated at a (log) rate of 2.9 percent
per annum; excess inflation in Thailand has been only 0.57 percent per annum. The Thai
baht has depreciated, in real terms, by about 25 percent.
In recent years, the ―real exchange rate is endogenous‖ argument is most often brought
up in the case of China.85 It is informative to see if this endogeneity holds in the case of
China itself. During the 1980 to 2006 period, the yuan depreciated by a cumulative (log)
167 percent. US inflation during this period was a cumulative (log) 90 percent. If the real
exchange rate was endogenous, then over this long period the devaluation should have
resulted in higher inflation, and this higher inflation should have been (log) 257 percent.
Instead, domestic inflation was only (log) 145 percent leading to a real devaluation of
(log) 112 percent or a real exchange rate that is a third of the level of 198086.
But this is not the real devaluation that matters for growth. There are two components to
real exchange rate change. The first is the conventional change noted above i.e. the
difference between the predicted inflation on the basis of the devaluation and the actual
inflation. The second component often ignored by analysts and this ―standing still‖
component can often underlie exchange rate policy in the developing countries. Assume
for a moment that a country has the same inflation as the US, and it has a fixed
exchange rate. Then, by definition, its real exchange rate is unchanging and one could
conclude that the country was not adding to its competitiveness by (unfairly) depreciating
its currency. But B-S tells us that this effect can be large, and at China‘s stage of
85
The next chapter discusses the Chinese real exchange rate policy in somewhat greater detail. 86
The calculation is as follows. The exponential of log(112/100) is 3.06 i.e. if the real exchange rate was 1 in 1980, it became 1/3.06 or .33 in 2006. This large change is significantly different than zero, the expected value if the real exchange rate was truly endogenous.
167
development today the effect is as large as 1.5 percent per annum (Table 6.6 reports the
average effects for each region) This means that by maintaining a constant US dollar
exchange rate, China has been able to devalue, in real terms, its currency by
approximately 4 percent per annum during the last decade. Or that since the 1997 East
Asian crisis, the Chinese currency has real(ly) depreciated by more than 50 percent.
Why the real exchange rate is endogenous argument is likely to fail, especially for
developing countries
Theoretically, there are several reasons why inflation will not follow a depreciation,
especially in developing countries. Invariably, such countries have a large amount of
labor slack. First, it is unlimited supplies of labor; then there is underemployment; then
there is catch-up. With globalization, and even with full employment, a devaluation may
not necessarily lead to correspondingly higher inflation. Catch-up means a skilled wage
level in developing economies which is often a fifth to half that of the corresponding
worker in the developed world. So workers may not demand a commensurate wage
increase with devaluation. This is most likely what happened in East Asia following the
currency crisis. While devaluations were of the order of 40 percent, inflation was barely
in double digits the first year and two years later, inflation rates were below the levels
before the devaluation.87
If the real exchange rate is not endogenous, then nominal exchange rate changes are
part of a policy. Several had always believed that that was the case, but the lengthy
detour above was necessary to document, for the theorists, that the real world often
does not approximate the theories one chooses to believe. Just how important exchange
rate policy can be (again, always thought to be self-evident by scholars like Balassa) is
documented in the next chapter. So important that it is safe to conclude that growth
miracles are exchange rate policy made.
87
This is excepting Indonesia, but even in this extreme outlier case (devaluation of log 123.7 percent in 1997), inflation rates only hit a high of 56.2 percent in subsequent years. In 2006, the inflation rate in Indonesia was at 12.5 percent, almost close to the pre-crisis inflation level of 9 percent.
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Chapter 7: There are no growth miracles A miracle is defined in the dictionary as something inexplicable, something that can only
be explained by recourse to the super-natural, the unknown, to God. There are miracles
in each research discipline. In economics, the favorite miracle is one pertaining to high
and sustained economic growth. The earliest known high growth was in the period of the
Industrial Revolution. The countries involved – the US, UK, and continental Europe, in
that order. But economists do not term this a miracle because the cause has been
identified – new technology which radically increased labor productivity and therefore
brought about higher growth.
Growth miracles began to be identified in the post-war period. The first was Europe and
the second, Japan. But Europe‘s fast growth in the 1950s and 1960s was also easily
explained by post-war reconstruction and catch-up. The countries had similar per capita
incomes before the first World War and so it was logical to assume that post
―reconstruction‖, per capita incomes would be back to parity. So Europe, despite faster
growth, was not a miracle. But Japan seems to be.
Defining a growth miracle
Simplified, growth is the sum total of contributions of capital, labor and the third factor,
total factor productivity growth (TFPG), also referred to as the residual in growth
regressions. Labor force typically grows at about 2 percent per annum, and the share of
labor is usually around 55 to 75 percent.88 This means that around 1 percent of GDP
growth can be explained by labor force growth. Capital stock typically grows at around 5
percent, with 10 percent being a definite outlier. So a typical contribution of capital to
overall growth is around 2 to 3 percent. Thus, 4 percent GDP growth is around the
median of growth expectations. There is also growth in total factor productivity and a
sustained 2 percent growth in TFPG is considered good, and achievable.89This yields a
6 percent GDP growth as well within expectations.
For capital accumulation to occur at a much faster pace than 5 percent, investment rates
have to rise to the 30 to 35 percent plus zone90. Again, doable, and this can easily add
88
An all country Cobb Douglas regression (see appendix I) yields the share of labor to be 56 percent and the share of capital to be 44 percent. 89
For the post 1980s period, the median TFP growth is around 1 percent per annum. 90
A typical investment to GDP share is around 20 percent.
169
another 2 percentage points to the growth process. So one arrives at a stylized figure of
8 percent GDP growth as those stories that are eligible to be considered as miracles.
Statistically, in the post war period, and excluding oil economies and very small
population countries, there are 914 episodes of growth above 8 percent (12.8 percent in
a sample of 7104 annual growth episodes). For growth above 9 percent, there are 653
episodes. So instead of 8 percent, a stylized 9 percent figure might be the beginning of
entry into miracle status. This roughly translates into a per capita growth above 7
percent. So growth around 8 or 9 percent, and per capita growth around 7 percent per
annum is what could constitute eligibility into the miracle club.
Growth Miracles
For the two decades 1950-1970, Japanese per capita income grew at an average rate of
8.2 percent per annum. Excluding the oil-rich countries like Saudi Arabia and Libya, the
second highest growth performance was recorded by Spain, at 5.8 percent per annum.
Thus Japan had an excess growth rate of 2.4 percent per annum over the second
fastest growing country, a miracle. Over the next half century, there were to be several
other such miracles e.g. Brazil in the 1960s and 1970s, Mauritius between 1985 and
1995, Korea in the 1960s and 1970s, Singapore in the 70s and 80s, and China in the
1980s, 1990s, and 2000s (till 2006).
There are several other countries that could constitute a growth miracle. In the mid-
1990s, the World Bank(1993) came out with the first comprehensive miracle study. The
East Asian Miracle explored the reasons for the miracle growth experienced by eight
Asian countries during the 25 year period 1965 to 1990. It documented the contributions
to growth of various known determinants, and concluded that the major factor behind the
miracle growth was openness i.e. these countries grew at a faster rate because they had
lower tariffs, and higher trade shares. The book cursorily mentions undervaluation as a
possible determinant, but (a) does not go into details and (b) does not consider
undervaluation as an important cause for higher growth.
But it does consider export-push as a major policy, but surprisingly, does not consider
exchange rate undervaluation as a major contributor to export growth. It is worth quoting
its‘ major conclusion in some detail:
170
―The export-push strategy appears to hold great promise for other developing economies. Fortunately, many powerful instruments of export promotion are not only within the institutional capacity of many developing economies but remain viable in today‘s economic environment. Creating a free trade environment for exporters, providing finance and support services for small and medium size exporters, improving trade-related aspects of the civil service, aggressively courting export-oriented direct foreign investment, and focusing infrastructure on areas that encourage exports are all attainable goals that are unlikely to provoke opposition from trading partners. Indeed, some or all of these have been part of the export push in Indonesia, Malaysia, and Thailand. These three economies, the most recent participants in the ―economic miracle,‖ may show the way for the next generation of developing economies to follow export-push strategies.‖ World Bank, East Asian Miracle, pg. 25
Openness was the mantra in the 1990s, but several people criticized the miracle study
on grounds of reverse causation i.e. growth caused higher trade and openness per se
may have had little to do with the miracle of growth. As discussed in the next chapter, a
new explanator of miracles has now become available after the World Bank study – the
presence/absence of western style institutions, particularly institutions dealing with
democracy and property rights. In his edited book, In Search of Prosperity, Dani Rodrik
discusses several growth miracles, among them some well known exceptions or
miracles outside of East Asia, and China e.g. Botswana, Mauritius. According to the
dozen or so detailed case studies, the conclusion is simple: growth miracles occur
because of ―institutions‖. Rodrik concludes
―Institutions that provide dependable property rights, manage conflict, maintain law and order, and align economic incentives with social costs and benefits are the foundation of long-term growth. This is the clearest message that comes across from the individual cases‖. (p.10).
In a recently released volume, Balcerowicz-Fisher(2006) conclude that what matters for
a successful growth process in Europe and the transitional economies of Eastern
Europe is ―reliance on market forces within an open economy in a stable macro-
economic environment‖ (p. 4). So far it looks that policy and openness matters but then
the authors add ―with assured property rights‖. While the exchange rate is mentioned as
a possible determinant of growth (especially in the chapter on Chile) the overall
conclusion remains – openness, stable macro but especially good institutions!
171
In yet another study, Hausmann et. al. (2004) discuss sustained growth accelerations
for some 60 odd countries. They define such exceptions as countries which fulfill three
criteria: first, growth is rapid (above 3.5 percent per annum); second, growth accelerates
by at least 3 % per annum, and third, that the post growth output exceeds the pre-
episode peak. This is not a miracle study, but extreme accelerations from a reasonably
low pre-acceleration base can constitute a miracle. The conclusion from the study:
―sustained and unsustained growth accelerations tend to be triggered by different
conditions. Financial liberalization and positive external shocks are associated with
growth accelerations that eventually fizzle out. Fundamental economic reform and
positive political regime change increase the likelihood of sustained accelerations‖.
However, ―most growth accelerations are not preceded or accompanied by major
changes in economic policies, institutional arrangements, political circumstances, or
external conditions‖ [p.15, emphasis added]. So the authors are quite candid about their
failure to find anything positive: ―the determinants of growth episodes-whether of the
sustained or unsustained kind – are very poorly captured by our explanatory variables‖.
Taken at face value, the above sets of conclusions suggest that little is known, about
growth, or growth accelerations, or growth miracles. Increased openness is a possibility
but there is the problem of identification. Fiscal policy is a possibility but its effect is too
small to affect growth let alone a significant acceleration. Various authors have used
some variant of real exchange rate undervaluation; it is occasionally significant but never
very important in terms of explanatory power. The presence of Western institutions
seems to be the only variable successfully explaining success.
Revisiting growth miracles
This is not very helpful for developing countries embarking on a growth drive. We do not
know, as yet, how to transplant western institutions in a hurry. Thus one is left with
precious little information about the determinants of exceptional Chinese growth in the
1980s, 1990s, and 2000s; nor with much information about the acceleration of growth in
India in recent years, nor the acceleration of Indian growth in the 1980s.
This chapter attempts to fill the explanatory gap. Growth miracles are revisited with the
introduction of the SAE measure of undervaluation defined in the previous chapter. That
is the only ―new‖ variable among the numerous traditional variables used in growth
172
models. The results are surprising, especially given the literature. Undervaluation, and
change in undervaluation, explains growth, and growth accelerations, to a surprisingly
large degree. Even if undervaluation is entered in the traditional fashion (average for a
period of time rather than as two variables, the initial level of undervaluation and the
average change in the undervaluation) it is still quite significant, and even so when
outliers are excluded. And even in instrument variable regressions with institutions as
the major determinant of growth.
This is one of the two major conclusions about the growth process (the other being the
importance of the middle class). Export-led growth or ―openness‖ or exchange rate
undervaluation – the name does not matter, the policy does. This conclusion is contrary
to most, if not all, of the recent growth evaluations though consistent with the arguments
of Balassa and some pre-1990s literature.91 The test is to empirically document this
conclusion, and conduct tests for robustness.
How important is currency undervaluation to economic growth ?
Regardless of the theoretical expectation, the real question about undervaluation is
whether it empirically matters. Several econometric methods, and several time-periods,
are used to establish the significance, and robustness of currency undervaluation.
Growth models are estimated for all countries, and just developing countries; for short
periods of data (5 year panel data) and for long periods (twenty years, forty years etc).
Different statistical methods are used – ordinary least squares, OLS with country
dummies, instrument variable estimation etc. Across a broad spectrum of econometric
techniques, time periods, country selections etc, the results are overwhelmingly the
same – currency undervaluation matters, and matters a lot for growth.
There are three levels of analysis of growth models; these levels can be thought of as
levels of difficulty in passing tests of significance. Ordinary least squares regressions
allow even weak relationships to appear robust. This estimation technique is eschewed
by most, but undertaken by all; it is a reference point. Analysts quickly move on from
OLS estimation to that involving instrument variables. This is the next level of difficulty.
Several variables significant in OLS regressions fail at the instrument variable (IV)
91
See Bhattacharya-Linn(1988) for one of the early summaries emphasizing the importance of export led (currency undervaluation) growth.
173
regression threshold. IV regressions are necessary to control for simultaneity and
economists have been quite creative in identifying identification variables e.g. variables
that affect institutions but not the level of income.
The third level of ―tests‖ of growth regression variables is estimation by generalized
method of moments (GMM). This method uses lagged values of both the levels as well
as first differences of time-dependent variables. The lagged values serve as instruments
and so the quest for identification variables is not necessary. Since lagged values are
used, it statistically becomes very difficult for variables to pass significance. In this
regard, a variable passing the GMM test is likely to be a robust determinant of growth.
The literature has also been varied in terms of the ―control‖ variables used. Ever since
the Levine-Renelt (1992) paper on the (non)-robustness of variables in cross-country
regressions, economists have been careful to supplement their analysis with checks on
robustness. These checks involve problems with exclusion of data e.g. do the results
hold for the entire sample of data rather than just the specific sample (e.g. data for ex-
colonies used by AJR). In addition, the models are tested for robustness against a wide
range of geography related variables. Geography is about the most exogenous variable
one can find, and the literature has canvassed dozens of such variables. Finally,
researchers have been rightly concerned about the possibility that the results, even after
controlling for geography, may not explain the poor economic performance of sub-
Saharan African (SSA) countries. Hence, continental dummies, especially for SSA, have
a prominent place in the literature.
The basic model being estimated is the same as that done in the literature. Per capita
growth is the dependent variable, and initial per capita income is a necessary right hand
side variable in all regressions; this is needed to capture the effects of convergence. To
this basic model, variables can be added e.g. variables representing demography, trade,
policies, etc. With regards to exchange rate undervaluation, Easterly and others add the
average value of overvaluation. As mentioned earlier, undervaluation in this study is
entered as two variables, a combination that essentially yields average undervaluation.
This specification says that undervaluation should enter as a level variable (initial
undervaluation), and as the average change in undervaluation. Thus the equation
estimated is
174
Average per capita growth = a + b0*y0 + b1*UV + b2*dUV + b3*Z + e
where y0 is initial (log) per capita income, UV is initial undervaluation, and dUV is the
average per year change in undervaluation for the period under consideration, and Z
incorporates other determinants of growth e.g. initial size of the middle class, etc.
One possible objection to the use of the equation
Growth = f(UV, dUV, Z)
is that there might be simultaneous equation bias between UV, dUV and economic
growth. Consider the following: a country has a fixed exchange rate (let us call it China)
and manages to keep its inflation rate the same as the US. It also manages to grow at a
much faster rate than the US. Thus, because of B-S effects (incorporated in the
estimation of RER*), it would show an exchange rate that is continuously under fair
value and therefore a negative relationship is built in between dUV and higher economic
growth. However, now consider the case of another country called Japan, or all Western
European countries, or even South Korea. These countries also have the same inflation
as the US. However, because of B-S effects, and/or because of the phenomenon of
convergence, the expectation and reality is that there should be an increase in the RER
towards the US level i.e. the natural process of development should lead to a positive
relationship being observed between dUV and higher economic growth. Higher growth
means a higher equilibrium RER and therefore a positive or less negative change in
undervaluation. With higher growth, and the exchange rate not managed, a currency
should become less undervalued. Thus, the expected relationship for most countries is a
positive relationship between dUV and growth. If a negative relationship is observed,
then this is a very robust result indicating that the policy of currency undervaluation is an
important determinant of higher economic growth.
175
Table 7.1(a) and 7.1(b) report the results for a variety of regressions relating growth92 to
the level and change in undervaluation. The sample size is restricted to non-oil countries
and those with population above 1 million in 2006. There are 122 countries overall, and
79 countries for the developing countries sample. The results are extremely robust.
Convergence, in line with other estimates, is approximately at the rate of 3.4 percent per
year (developing country sample, 1980 to 2006) or 1.6 percent per year (all country
sample). Undervaluation of currency retains its significance throughout most models,
specifications, and coverage (only developing countries, only developed economies, or
all countries). Thus policy, exchange rate policy, matters a lot for growth. The coefficient
for the initial value of undervaluation93 is -.022 for the developing country sample i.e.
each 10 percent of undervaluation per se adds approximately 0.2 percent of extra GDP
growth a year. The change in undervaluation is the most significant variable and has a
magnitude around –0.5 in the cross section data, and around –0.2 in the panel
regressions94.
Table 7.2 estimates the same model as in Table 7.1 but with the addition of two
prominent control variables – a Sachs-Warner openness variable95 and a dummy
variable for sub-Saharan Africa. These models are estimated for the most recent 1980 to
2006 period for developing countries. Most of the exchange rate variables offered in the
literature (e.g. an estimate of undervaluation obtained from the conventional log-log
model rather than the SAE non-linear model, the Dollar-Easterly undervaluation
estimate) as well as variables representing the nature of the currency regime (e.g.
Chinn, Rogoff) are incorporated separately into the growth regression. The result: the
non-linear SAE model performs the best in terms of explanatory power, and the
coefficients of the level of undervaluation retains its significance (and magnitude) even
92
The dependent variable is average per capita GDP growth in 1996 PPP prices. Similar results are obtained if growth is measured in local currency units. In the latter case, initial income is still measured in comparable PPP units. 93
For example, for the 1980 to 2006 average growth regression, the initial value is for 1980. 94
The above countries do not exclude the hyperinflation economies. If these countries are excluded from the analysis, on the grounds that they are outliers, then the significance of the undervaluation variables increases. 95
Sachs-Warner have cut-off points for black market premium and tariff rates to help decide whether an economy was open or not; since most of the countries pass their filter, a short-cut assumption was made that most economies were open in the forecast time-period 1995-2000. the revised draft will include a reconstructed openness variable for the period 1995-1999.
176
Table 7.1a: Explaining growth: Developing Economies only, 1980-2006
Initial
(log) pcpd
income UV
% middle
class dUV
R-
Square
OLS cross section
1965-1990 -1.46 -0.02 0.02 -0.32 0.29
-1.77 -2.05 1.05 -3.57
1960-1980 -0.10 -0.01 0.00 -0.63 0.64
-0.16 -1.12 0.29 -8.46
1980-2006 -2.25 -0.02 0.05 -0.62 0.66
-3.98 -5.35 3.42 -8.65
1960-2006 -0.50 -0.01 0.00 -0.68 0.52
-0.86 -2.72 0.19 -8.44
Panel, non overlapping 5 years
GMM, Arellano Bond (time
dummies) -2.66 -0.04 0.13 -0.21
-1.20 -2.06 2.45 -4.33
OLS, fixed effects (country
dummies) -4.86 -0.02 0.06 -0.22 0.38
-6.46 -5.02 3.51 -8.33
OLS -1.03 -0.01 0.04 -0.24 0.26
-2.29 -2.54 3.22 -9.42
Source: SAE dataset, see Appendix for details Note:
1. Above regression reports results for non oil exporting countries with population greater than 1 million. In all models, the dependent variable is average per capita growth for the period under consideration.
177
Table 7.1b: Explaining growth: All Economies, 1980-2006
Initial
(log) pcpd
income UV
% middle
class dUV R-Square
OLS cross section
1965-1990 0.11 0.00 0.01 -0.22 0.26
0.15 -0.14 0.50 -3.07
1960-1980 0.34 0.00 0.00 -0.46 0.29
0.37 -0.49 0.05 -3.29
1980-2006 -1.29 -0.02 0.03 -0.47 0.43
-3.09 -4.97 2.67 -6.23
1960-2006 -1.26 -0.01 0.03 -0.45 0.30
-2.87 -3.51 2.52 -4.98
Panel, non overlapping 5
years
GMM, Arellano Bond
(time dummies) -1.49 -0.03 0.03 -0.20
-0.74 -2.33 0.58 -5.11
OLS, fixed effects
(country dummies) -2.71 -0.01 -0.01 -0.18 0.37
-5.88 -3.92 -0.79 -6.20
OLS -0.27 -0.01 0.02 -0.20 0.24
-0.94 -2.06 2.33 -5.04
Source: SAE dataset, see Appendix for details Note:
1. Above regression reports results for non oil exporting countries with population greater than 1 million. In all models, the dependent variable is average per capita growth for the period under consideration.
.
178
Table 7. 2: Alternative growth models: Developing Economies only
UV
Initial
Level Change
Africa
dummy
Sachs-
Warner
openness R Square # Obs
Undervaluation
SAE -.014 -.416 -.519 .735 0.79 74
(-3.12) (-4.88) (-1.82) (1.77) 0.79 74
Easterly -.015 .081 -.258 1.493 0.72 60
(-3.56) (.88) (-.66) (2.80) 0.72 60
Log-Log model -.015 -.308 -.808 1.011 0.70 74
(-2.52) (-2.43) (-2.41) (2.021) 0.70 74
IMF method, base 2000
= 0 .0129 .253 -1.31 .726 0.70 72
(1.65) (1.34) (-3.20) (1.45) 0.70 72
Currency Regime
Chinn -.002 -1.08 -1.18 1.381 0.63 67
(-.02) (-.65) (-3.22) (2.60) 0.63 67
Rogoff -.187 -1.057 .633 0.67 66
(-0.87) (-2.78) (1.08) 0.67 66
Rogoff fine -.025 -1.044 .661 0.66 66
(-0.49) (-2.72) ( 1.14) 0.66 66
Source: SAE dataset, see Appendix for details Notes:
1. All models estimated for the time period 1980-2006. Each model has, in addition to the selected RER variable, a dummy for Sub-Saharan Africa, Sachs-Warner openness variable (log) initial per capita income, and initial size of the middle class.
179
with the Easterly undervaluation variable. Neither the Chinn nor the Rogoff currency
regime variables are significant. The coefficient for the Africa dummy is around 0.5 to
1.0, but on several occasions it is not significant. The otherwise hugely significant
Sachs-Warner variable96 also loses its significance in the SAE model.
Table 7.3 summarizes the results for the various growth and undervaluation models
estimated. The basic model consists of period per capita growth rate as the dependent
variable, and initial UV and average annual change (dUV) as the independent variables.
The instrument variable regression results are for the average value of the coefficient in
regressions which contain an institution variable (e.g. World Bank governance variable)
on the right hand side. Whether the estimation is for a historical period (1870 to 1913) or
a recent period (1980 to 2006) currency undervaluation plays an important role in
generating extra growth. The coefficients are also broadly of the same magnitude – for
initial UV, about -.01 to -.03, and for change in UV, about –0.2 to –0.5.
The coefficients from the 1980-2006 regression, Table 7.1a, can be used to generate
estimates of the advantage a particular country can gain from systematic undervaluation.
In 2000, currency undervaluation in China was (log) –20 percent. Over the next six years
(2001 through 2006) undervaluation in China increased at the rate of –6.6 percent per
year. If the coefficients of UV and dUV are taken as -.02 and -.35 respectively, then the
extra growth China has obtained from its exchange rate policy is [(-0.02*-20) + (-.35*-
6.6)] a high 2.7 percent per year, a very high fraction of its TFP growth. This explains
part of the miracle of Chinese growth. Not Japan, not Korea, not Taiwan, no one
compares with the high growth rate recorded by China in recent years. One reason
might be that no other country has either been allowed to, or dared to, undervalue its
currency by such a large margin.
96
See Rodriguez-Rodrik for a convincing critique of the Sachs-Warner variable.
180
Table 7.3: Contribution of undervaluation to economic growth
UVinitial dUV
Cross section data
1870-1913
Base Model -0.01 -0.59
Base + import tariff -0.45
1870-1938
Base Model -0.05 -0.20
1960-80 -0.005* -0.63
1980-06 -0.02 -0.62
1960-06 -0.01 -0.58
1965-90 -0.02 -0.60
Panel data (non overlapping 5 years)
GMM (Arellano Bond)
1960-06 -0.04 -0.21
1980-06 -0.03 -0.22
Cross-Section OLS with country
dummies
1980-06 -0.03 -0.15
Cross-Section only, OLS
1980-06 -0.03 -0.21
Instrumental variable regression -0.02 -0.50
(with institutions, see Chapter 8) Source: Maddison (2006); Penn World Table version 6.1; World Development Indicators, World Bank(2006); World Economic Outlook, IMF (2006), SAE dataset. See Appendix for details. Notes:
1. All models have initial per capita income; asterisk indicates not significant.
2. The Base model is defined as Per capita growth = α + β1*Y0 + β2 *UVi + β3*dUVi
181
Recall that the definition of a miracle was something which defies a rational explanation.
Currency undervaluation has been a very consistent policy of the high growth miracles,
from China to Chile to Vietnam. The robustness of the effect of undervaluation on growth
suggests that there are no growth miracles, only an explicit policy of exaggerating one‘s
competitiveness by cheapening one‘s currency, and lowering one‘s cost of production,
often at the expense of other economies and their economic growth. One country‘s
miracle success is often another country‘s miraculous failure.
The many effects of exchange rate undervaluation
The previous chapter documented in some detail the relative undervaluation of the
Chinese yuan in 2006. At 60 percent undervaluation, there weren‘t too many countries to
battle China for the numero uno position. Even if there were some (like Mauritius with an
undervaluation level of 117 percent), the size of the economy was very small (less than
0.25 percent of Chinese GDP, in PPP terms). Size matters, and in case of
undervaluation, it matters more. If undervaluation leads to higher savings, higher
investment, higher growth and higher current account surpluses (all of these fall out of a
theoretical model relating profits to investment and are supported by ample empirical
evidence) then a size country undervaluing by such a large amount is most likely a
recipe for global imbalances.
Table 7.4 documents the effects of currency undervaluation on savings rates, investment
rates and TFP growth. In Chapter 6 a simple model of investment was offered; this table
confirms the predictions of the model. The effect on savings is likely via the following
channel. A real devaluation enhances investment opportunities, increases investment,
and therefore increases economic growth. This extra growth is first viewed as transitory
and a large fraction of it is therefore saved. As the process continues, the country ends
up with a higher income level, and a higher savings rate. The explanatory power of the
model on savings rates is a high 52 percent; that is, for 78 countries, the average
savings rate for the twenty six year period, 1980-2006, is very well explained by currency
devaluation.
182
Table 7.4: Impact of undervaluation on TFPG, savings and investment rates, 1980-2006 : Developing economies only
Constant
(log) per
capita
income UV dUV R-square # of Obs
TFPG -0.53 0.07 0.00 -0.46 0.52 55
(-0.47) (0.18) (-0.90) (-5.73)
Savings rate (%GDP) 7.04 4.85 -0.04 -1.76 0.52 78
(1.48) (2.53) (-2.19) (-5.08)
Investment rate (% GDP) 18.67 1.37 -0.02 -0.90 0.18 79
(5.82 (1.12) (-1.68) (-3.08) Source: SAE dataset, see Appendix I for details
Notes: The equation of estimation is Dependent Variable = α + β0* (log)Y + β1*UV + β2 *dUV
183
Growth miracles – are there any?
After the East Asian miracle study, the East Asian crisis happened and miracle studies
took a breather. In recent years, possibly because of high worldwide growth and the
unprecedented high growth in China, there has been a renewed interest in growth
miracles. Table 7.5 looks at the data from two such studies, Hausmann et. al. and
Balcerowicz-Fisher. The former is explicitly concerned with growth accelerations; the
latter has case studies detailing ―good‖ growth experiences. Hausmann et al provide a
useful methodological framework for evaluating growth accelerations; they identify a
growth break year and look at growth in the seven years prior, and seven years post,
this break year. The table also contains results according to an SAE break year; this
year is identified as one which occurs after a convincing break below the 50th percentile
level in terms of currency undervaluation.
Each set of data is subjected to the same regression test: the dependent variable is the
change in per capita growth rates (the growth acceleration or miracle) and the
independent variable is the acceleration (or change in the rate of change) in currency
undervaluation i.e. is the rate of change of currency depreciation more or less than the
rate of change in the earlier pre-acceleration period.
Note that the acceleration in growth, and the acceleration in dUV, is very different in the
three different samples. Yet the results are robust; the acceleration in dUV is always
significant in explaining the acceleration in growth. The mean coefficient of dUV is
approximately -.2, not much different than the coefficient obtained from conventional
growth models.
Table 7.6 contains yet another attempt to assess the importance of currency
undervaluation on growth. Countries have been chosen according to a simple criteria –
the number of years taken to double per capita income; Japan and China both did it in 8
years, Singapore in 7, Ireland in 11 and India in 15. India and Israel are the outliers; if
they are excluded from the regression, then one obtains the following result: dependent
variable is the number of years taken to double per capita income and the independent
variable is the change in dUV.
Doubling time = 12.4 + 0.55*dUV, Nobs = 23, Rsq = 0.22 (16.5) (3.6)
184
Table 7.5: Explaining growth accelerations – role of changes in exchange rate undervaluation
Hausmann et.al. SAE Balcerowicz-Fisher
Summary Statistics
No. of countries 43 37 13
Avg. acceleration 4.5 6 8.9
Avg. acceleration in change in UV -0.81 -5.7 -12.7
Coefficient in regression
Acceleration in dUV -0.13 -0.21 -0.28
(2.73) (3.61) (3.79) Source: Hausmann et. al. (2005); Balcerowicz and Fischer (2006). Notes:
1. Each study has its own method of identifying the ―break‖ year in growth acceleration. The SAE break year is identified when for seven successive years the undervaluation of the currency is below the 50
th percentile.
2. The model estimated is dt = α + β0*d(dUV)t+ β1* y0 where dt is the acceleration on growth in the seven years after the break year compared to seven years prior to the break year. D(dUV) is the first difference in the rate of change of undervaluation in the two periods. Acc Y = α + βa*Acc(dUV)t+ β2* y0, where y0 is (log) initial years. The acceleration is the change between the break year +7 and break year – 7, and per capita income in the break year is takes as ―initial‖ per capita income.
185
Table 7.6: Doubling of per capita income: How Explained ?
Min. years taken to double per
capita income
Actual Predicted
Avg.Change
in dUV
Israel 11 7 -9.5
Singapore 7 8 -7.6
China 8 9 -6.7
Taiwan 9 9 -5.6
India 15 10 -4.9
Jamaica 10 10 -4.9
Guinea-Bissau 12 10 -4.3
Greece 10 10 -4
Jordan 12 10 -3.7
Germany 10 10 -3.7
Botswana 7 11 -3
Thailand 9 11 -2.7
Vietnam 12 11 -2.6
Malaysia 14 11 -2.4
South Korea 9 11 -2.3
Brazil 14 11 -2.2
Italy 13 11 -2.1
Austria 14 11 -2.1
Japan 8 11 -1.8
Mauritius 14 11 -1.8
Spain 10 11 -1.6
Ireland 11 12 -1.1
Portugal 10 12 -1
Indonesia 13 12 -1
Hong Kong 11 13 0.5 Source: SAE dataset, see Appendix I for details. Notes: The average change in dUV is for the time period
in which per capita income doubles.
186
Chart 7.1 plots the relationship. Again, dUV is a robust predictor of growth miracles.
Whether it is the onset of a financial crisis, high growth, a growth acceleration, or a
growth miracle - currency undervaluation plays an important role. It is not just
overvaluation that hurts growth; symmetrically, undervaluations help growth.
Labor competitiveness
If lower cost of labor is a determinant of faster growth (―convergence‖), then, it follows,
that an even lower cost of labor will lead to even faster growth. This is the so-called
Asian model of development – make the domestic cost of capital equal to (or even lower
than) the international cost of capital, and leverage the cost of labor by keeping the
exchange rate undervalued. Table 7.7 presents some implications of lower labor costs
and resulting competitiveness. The first column shows the ratio of labor to productivity
costs, normalized with respect to China. (UN, Trade and Development Report, 2002,
Table 5.4). If it is assumed that this ratio within each country stays broadly the same
between 1990 and 2006, then the estimate of undervaluation can be used to assess the
relative advantage or disadvantage that a country has in productivity adjusted labor
costs relative to China.
All the countries have lost out significantly to China, some more so than others. US is
almost twice as uncompetitive today relative to just 8 years ago (compare 2.26 vs. 1.28).
Ditto the case for Mexico; Korea shows a 50 percent increase, India a 33 percent
increase (1.98 compared to 1.48 in 1998). Japan has more than doubled its relative
costs, Sweden close to triple. Chile, by aggressively managing its currency, manages to
let its costs increase by ―only‖ 50 percent. This is partial evidence of the global
imbalance being ―made in China‖.
187
Chart 7.1: Years takes to double per capita income
isr
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kor
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idn
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10
12
14
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Yea
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aken
to d
ou
ble
gro
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-10.0 -8.0 -6.0 -4.0 -2.0 0.0dUV
Actual Predicted
Note: Doubling time (in years) = 12.4 + 0.55*dUV (average per year change), Nobs = 23, Rsq = 0.22
188
Table 7.7: Competitiveness” in manufacturing: Getting better for China, 1990-2006
Competitiveness ratio (with respect to China)
Unit labour
cost, 1998 1990 1996 1998 2002 2006
Bolivia 0.6 1.23 1.93 1.14 2.29 2.84
Chile 0.8 0.44 0.64 0.70 0.61 1.07
Egypt 1.5 1.94 1.88 1.80 2.26 2.39
Indonesia 0.9 0.30 0.41 0.42 0.41 0.59
India 1.4 1.76 1.64 1.48 1.67 1.98
Japan 1.2 1.51 2.75 1.72 3.18 3.73
Kenya 2 11.42 13.61 5.82 21.67 34.64
South Korea 0.8 0.41 0.60 0.59 0.63 0.93
Mexico 0.7 0.50 0.75 0.78 1.37 1.66
Malaysia 1.1 0.39 0.56 0.64 0.62 0.79
Taiwan 2.3 1.65 2.13 2.07 2.17 2.38
Philippines 0.7 0.57 1.14 0.79 0.98 1.26
Singapore 1.3 0.71 1.20 1.18 1.30 1.81
Sweden 1.8 2.71 3.89 2.54 3.94 6.13
Turkey 0.9 0.72 0.86 0.88 1.02 1.64
United States 1.3 0.68 1.00 1.28 2.03 2.26
China 1 1.00 1.00 1.00 1.00 1.00 Source: Unit labour cost data from UN Trade and Development Report, 2002, Table 5.4, pg. 158
Note:
1. The competitive disadvantage (in %) is the unit labour cost in 1998 adjusted for currency undervaluation.
2. Column 1 is taken from ?? and represents the ratio of labour cost to labour productivity. This ratio is then
indexed to the Chinese ratio, taken to be 1in 1998.
3. ULC of 0-6 for Bolivia in 1998 means that the productivity adjusted cost of Bolivia worker was 60 % of
that of Chinese worker in manufacturing.
4. For each of the years 1990 to 2006, the ULC 1998 level has been adjusted by the relative rates of undervaluation for the
different countries.
189
Towards an index of mercantilism India and China in 2006 are similar with respect to their policy on reserve accumulation
and currency depreciation. They are dissimilar with respect to the size of their current
account – India has a current account deficit (CAD) of about 2 percent of GDP while
China has a current account surplus (CAS) of about 10 percent. The CAS of both Japan
and Germany peaked at 4.3 percent in 1986. In 2006, Japan has a CAS of 3.7 while
Germany is revisiting its previous peak (estimated to be 4.2 in 2006).
Differences in savings behavior can dictate the direction of CADs or CASs. There is
some reason to believe that changes in undervaluation of currency can lead to increased
savings rates in developing countries. The argument is as follows: increased
undervaluation leads to increased growth, presumed increase in transitory income,
higher savings, higher investment, higher growth etc.
Reserve accumulation and mercantilism are closely associated. But there is a difference.
Countries that have a current account deficit (imports exceeding exports) and an
undervalued exchange rate are not as mercantilist as countries that have both a deeply
undervalued exchange rate and large current account surpluses. A tentative index of
mercantilism could be an ―average‖ of the ranks of these two. Such an average is
provided by a Borda rank which proceeds as follows. For each year, countries are
ranked according to the degree of undervaluation – the most undervalued gets a rank of
1. Analogously, countries are ranked according to the size of their current account
surplus (as percent of GDP). The country with the highest CAS obtains a rank of 1. The
two ranks are then averaged for each year and countries ranked according to this
average rank. This rank (Borda rank) is the proposed index of mercantilism and
examples of such ranks, for selected countries, are reported in Table 7.8(a) and (b).
For the past ten years (1995-2004, data only available till 2004), several East Asian
economies are in the top ten, with Singapore being the most mercantilist economy.
China is at number 5, though if the data for the last two years is taken, its rank will most
likely move up. India is 23rd and the three major OECD economies are very close to
each other and beyond a rank of 46.
190
Table 7.8a : Mercantilism and its Component Ranks, 1980-84 and 2003-06
Ranks, 1981-84 Ranks, 2003-06
UV CAB
Mercantil-
ism UV CAB
Mercantil-
ism
Brazil 23 55 35 38 32 23
Chile 53 81 71 24 37 17
China 80 11 40 11 14 5
Germany 25 14 11 69 20 36
Hong Kong 7 15 3 20 5 5
India 77 28 51 27 46 23
Japan 22 11 7 71 18 36
Malaysia 44 82 64 16 2 3
Mexico 15 26 15 59 48 52
Singapore 17 61 34 30
South Korea 40 42 37 33 26 16
Taiwan 45 4 17 17 10 6
Thailand 51 62 57 10 33 10
United States 23 27 19 47 86 68 Source: SAE dataset, see Appendix I for details. Note:
1. UV is undervaluation, and CAB is the current account surplus as % of GDP. 2. Rank of undervaluation and current account surplus is the rank for the time-period
selected among all countries excluding oil exporting countries and countries with population less than 1 million.
3. Mercantilism rank is the joint Borda rank or the average rank for currency undervaluation UV and current account surplus as a percent of GDP, CAB.
191
Table 7.8b: Mercantilism, 1995-2004
Mercantilism
rank Avg. CAS/GDP Avg. UV
Singapore 1 14.8 -14.1
Ukraine 2 2.6 -82.1
Taiwan 3 4.5 -17.2
Mauritius 4 0.1 -99.7
China 5 2.1 -19.5
Botswana 6 7.6 -3.4
Indonesia 7 1.7 -33.8
Thailand 8 3.3 -39
Canada 9 0.8 -14.1
Malaysia 10 6 -35.7
South Korea 11 2.1 -18.5
Hong Kong 17 3.2 -11.7
Chile 19 -1.9 -27.3
India 23 -0.4 -7.2
Netherlands 25 4.6 18.0
United States 47 -3.3 -5.8
Germany 48 0.2 21.9
Japan 50 2.6 38.3
Mexico 52 -2.0 8.5 Source: SAE dataset, see Appendix I.
192
Japan 1980-84, China 2002-2006
It is valid to compare the Japanese economy (and its effects on the world economy) for
the five year period preceding the 1985 Plaza agreement (1980-84) which found the yen
to be heavily undervalued, with that of China in the last five years (2002 to 2006), Table
7.8 (c). The first row documents per capita income growth: 2.2 percent per annum for
Japan and a level almost five times higher for China, 10.2 percent. Judging by income
growth alone, it would seem that the Plaza agreement should have been in reverse!
Japanese exports grew at 9.8 percent per annum (relative to median growth rate of only
1.5 %) while Chinese exports have been growing at over 25 percent, some 10
percentage points above the median.
Reserve accumulation of both Japan and China seems excessive, but China more so. At
an average level of 650 billion 2002 to 2006,97 China reserves were 24 times the mean
while Japan reserves were only about 8 times the mean (in the corresponding periods of
reference). The table also reports a mercantilism index, defined below as the rank of
ranks (Borda rank) of two indicators – undervaluation and ratio of current account
surplus to GDP. Japan was ranked 11 in 1984, behind Hong Kong at 1, Netherlands at
398, Germany at 8, and Brazil at 10. In 2006, only Uzbekistan is ahead of China and
Hong Kong (part of China since 1997) at number 4. India ranks 24th, and Japan 26th.
Finally, the comparison of currency undervaluation per se suggests that the yen
was not significantly undervalued in 1984. Indeed, it was overvalued by 4 percent
though the yen did into undervaluation (-4.6 percent) in the next Plaza year of
1985. In striking contrast, the Chinese yuan has been continuously undervalued
since 1998 and outside the +- 30 percent band for the last six years, not co-
incidentally the time when the US current account balance started to sharply
deteriorate.
This comparison of Japan 1980-84 and China 2002-2006 emphasizes the fallacy of
projecting deflation and/or a sharp slowdown in Chinese growth post a correction from
97
In March 2007, China reserves exceeded 1.2 trillion and were thus almost twice the average over the preceding five years. 98
Even in 1960, Balassa had found Netherlands to have the most undervalued currency in Europe. See Table 6.1.
193
its present deeply undervalued levels. In the main, there are two reasons for the fallacy.
First, that Japanese growth rate was already sharply lower at the time of appreciation of
its currency. Second, real deflation and slow growth in Japan started in the early 1990s
when the yen became overvalued by more than 30 percent and therefore beyond the
range of ―tolerance‖. For China to feel the same overvaluation pressure on growth, the
yuan would have to appreciate to a level of 3.5 over the next decade, an unlikely event.
Even if the real appreciation is a gradual 5 percent over the next 10 years99, as now
appears likely, the Chinese yuan would still be around 25 to 30 percent undervalued in
2015. So the rate of appreciation has to be significantly faster to make any dent in the
deeply undervalued nature of the Chinese currency.
During 1981 to 1984, both Germany and Japan are among the top 11 mercantilist
countries. Despite having ―peak‖ current account surpluses today, both countries have a
low, and identical, mercantilism rank for the period 2003-2006: rank 36. Hong Kong
manages to have a single digit rank in both periods. South Korea does not appear to
have played unfairly in either period; Taiwan today has a mercantilism rank of 6. India‘s
mercantilism is ranked 51 1981-84, and 23rd, 2003 to 2006. China ranked 40th in 1981-
84, and 5th today.
For those who argue that it is China‘s high savings rate that causes its current account
surplus to be so large, the following statistic is revealing. In the mid 1980s, China‘s
savings rate was in the mid 30s, as it was for most of the years till 2003. Its
undervaluation level in the late 1990s: less than –30 percent but rising. It has to be a
large coincidence that China‘s savings rate should rise to the present level of around 50
percent at precisely the same time, when through very active intervention, China
prevented its exchange rate from appreciating.
99
It is worth emphasizing that the Goldman Sachs BRICS report assumes, in its rather bullish forecasts, a real appreciation of the Chinese yuan at the rate of 3.5 percent per year. One of the flaws in the report is that it did not recognize the dependence of Chinese high growth on a depreciating real currency. In the four years since the report was written, the Chinese yuan has depreciated, in real terms, by 5 percent a year.
194
Table 7.8c: Testing for mercantilism, Japan and China
Japan, 1980-84 China, 2002-06
Growth in
Per capita income 2.2 10.2
GDP 2.9 10.8
Exports 9.8 25.4
Imports 4.5 23.3
Average UVSAE 4.0 -49.7
Change in UV, dUVSAE -5.0 -6.4
Average UVIMF -45.4 2.5
Change in UV, dUVIMF -4.4 1.2
Level of Reserves
In billion dollars* 25.4 438.0
Current account deficit (% GDP) 0.9 4.6
Share in world
Exports 8.1 11.3
Output 7.5 9.9
Mercantilism index in end year
Rank 11 2 Source: Maddison (2006); Penn World Table version 6.1; World Development
Indicators, World Bank(2006); World Economic Outlook, IMF (2006).
Notes:
1. reserve data are obtained from Lane (2006).
2. The average value of UVIMF and change in UVIMF are .36 and -11.2
respectively.
195
One counter to the argument that China should revalue (most forcefully argued by
McKinnon) is that such a revaluation would lead to a post-Plaza Japan disaster.
Because of a G-7 Plaza agreement in 1985, Japan was forced to revalue its currency by
more than a 100 percent in the short space of three years. This revaluation caused the
growth to decelerate and price deflation to occur. As McKinnon-Schnabl(2006) state: ―In
the new millennium, history is repeating itself, but now China bashing is superseding
Japan bashing. But, after the disastrous Japanese precedent of an ever higher yen, we
shall show that China‘s exchange rate policy itself remains unchanged‖. (p.277).
East Asian crisis foretold?
Currency undervaluation can help illustrate the origins of the East Asian financial and
currency crisis. Examination of the data for the crisis economies does not indicate any
macro variable to be out of line in the 1990s, except for the current account imbalance in
Thailand (close to 6 % of GDP at the time of the crisis in June 1997). Several economies
had fiscal surpluses! So why did the East Asian currencies crumble?
It is one story if a small country depreciates its currency (obviously in real terms); it is
another when the country depreciating it has more than a fifth of the world‘s population.
Table 7.9 documents the real depreciation that occurred in select countries, 1990 to
1996, and the last seven years, 2000 to 2006. Data are presented according to both
measures of undervaluation, UVIMF and UVSAE . UVIMF is the conventional IMF measure
of currency appreciation or depreciation (adjustment of currency by relative own and US
inflation rates with a negative sign signifying a real depreciation). The East Asian
economies had lost ground to China by an average of about 50 percent in the seven
years 1990 to 1996.
What is most striking is the large difference in results (and interpretation) if the bilateral
real exchange rate change is used rather than the PPP real exchange rate. The Chinese
yuan depreciates by 24 % in RER terms between 1990 and 2006 i.e. the large
devaluation was negated by higher relative inflation (with respect to the US). But in
terms of PPP, the Chinese yuan depreciates by a rather large 73 percent.
196
Table 7.9a: Change in RER, two different measures
UVimf UVsae
1990-06 2000-06 1990-06 2000-06
Argentina 14.2 -57.9 -20.9 -57.4
Brazil 19.3 18.9 14.5 14.8
Chile 18.7 20.7 -3.2 11.3
Hong Kong 27.8 -29.1 23 -30.4
India -29.4 9.7 -47.1 -16.8
Indonesia 9.8 33.5 -9.8 16.8
Japan 18.3 -29.1 19.8 -26.9
Malaysia 14.5 7.2 -2.8 -1.5
Mexico -2.7 4.1 0.8 4.7
Singapore 26.3 -1.3 12.8 2.5
South Korea 16.5 12.3 -1 4.2
Taiwan 1.4 -23.8 -14.5 -26.5
Thailand 14.8 2.7 -12.3 -11.9
China -2.2 6.2 -40.1 -39.8
Table 7.9b: Change in competitiveness with respect to China
UVimf UVsae
1990-06 2000-06 1990-06 2000-06
Argentina 16.4 -64.1 19.2 -17.6
Brazil 21.4 12.7 54.6 54.6
Chile 20.9 14.5 36.9 51.1
Hong Kong 30 -35.3 63.1 9.4
India -27.2 3.5 -7 23
Indonesia 12 27.3 30.3 56.6
Japan 20.5 -35.3 59.9 12.9
Malaysia 16.6 1.1 37.3 38.3
Mexico -0.5 -2.1 40.9 44.5
Singapore 28.5 -7.5 52.9 42.3
South Korea 18.7 6.1 39.1 44
Taiwan 3.6 -30 25.6 13.4
Thailand 17 -3.5 27.8 27.9
China 0 0 0 0
Source: World Economic Outlook, IMF (2006); SAE dataset, see Appendix for details Notes:
1. Negative sign means more undervaluation. 2. UVIMF is obtained as the (log) difference between the actual and predicted value of RERMF from
inflation and exchange rate data. This is the conventional IMF method of defining undervaluation. UVSAE is undervaluation based on a non-linear PPP based method of estimating misalignment.
197
The East Asian crisis period, the two years 1996-1998, appears to have been a time of
―payback‖, with East Asian economies gaining back some of their now competitive
disadvantage. The currencies of Malaysia, Singapore, Korea and Thailand all
depreciated (in real terms) by near identical amounts in the one year following the crisis
– between 30 and 36 percent (this is the difference in the two UVIMF values). India,
Japan and Taiwan depreciated the least; 6, 10 and 17 percent respectively. Even in
Indonesia, the real depreciation did not stray much away from the magic norm of 30 to
35 percent.
But the depreciation that really matters, currency values adjusted for productivity
differences (UVSAE ), indicates that the east Asian currencies are largely back to their
1996 levels in terms of lack of competitiveness with China. The real exchange rate in
these economies has appreciated by about the same amount in 2000-2006 as in 1990-
1996. Nor is this appreciation small – upwards of 40 to 50 percent.
Thus, the East Asian real (with respect to China) currency depreciation was short-lived.
By 2006, most economies had appreciated quite significantly with respect to China.
Malaysia, Singapore and South Korea have appreciated by 60 to 70 percent. Indonesia,
Taiwan, and Thailand have all appreciated by substantially lower margins (20 to 40
percent). India has appreciated the least relative to China, only 3.3 percent. But its level
of relative disadvantage stays the same – about 20 percent less undervalued in 2005
(or 20 percent more undervalued in 1990).
198
Global Imbalances: Origins and Rhetoric or A Cost Benefit analysis of reserve accumulation
What started off as a growth strategy has now evolved into the mother of all imbalances
– a huge a current account surplus in China exceeding 10 percent of GDP. Normally, a 5
to 7 percent deficit is a sign that some macro parameters are seriously out of line; so
should a 10 % surplus.
This surplus number is unlike anything the world has ever experienced, and certainly not
from a large, large, economy, the largest. Mercantilism is not a new policy; what is new
is the reality that a very large country, China, is practicing it. When Singapore, or
Taiwan, or Korea, or even Japan practiced currency undervaluation, it was something
that the world could absorb without significant disruption or imbalances. But when a
country with more than a fifth of the world‘s population practices a beggar thy neighbor
policy, it can cause neighbors to have substantially lower growth than would have been
otherwise. This is indeed what is likely to have happened in the mid 1990s and what
ultimately led to the East Asian financial crisis. Many countries are significantly worse off
with respect to China in 2006 relative to their competitive position in 1998; other
countries would be forced to depreciate their currencies in real terms in order to
compete.100
A side effect of currency undervaluation is accumulation of foreign exchange reserves.
Many contend that the developing countries, and especially China, is foolish to
accumulate such a large amount of foreign exchange reserves101. By keeping its
exchange rate undervalued, the argument goes, China is actually losing money. Why?
Because China (and other countries like it) has a higher GDP growth rate, and a much
higher return on capital. By accumulating dollars, and investing in US treasuries, the
countries doubly lose; first, on a lower return on capital, and second, because of the
devaluation of the dollar.
The argument is logical, but faces several reality checks. First, by keeping the exchange
rate fixed, countries can avoid the depreciation effect. Second, and more significantly,
100
The previous chapter documented how it empirically was quite feasible to undervalue in real terms i.e. the argument that the real exchange rate is endogenous is hugely inaccurate. 101
But not Dooley et. al. who offer very convincing arguments for the self-interest of countries like China to keep undervaluing their currencies and to keep accumulating FX reserves.
199
and where the real bang for the buck is, is in the higher economic growth rate that the
currency undervaluation and accumulation of FX reserves allows a country to buy.
Table 7.10 presents a cost/benefit analysis for such reserve accumulation for India and
China. If there is extra GDP growth from undervaluation, as has been documented
above, then that extra growth is part of the calculation. As documented earlier, and as
the revealed preference of several Asian economies (and others like Chile) show,
currency undervaluation is a very potent policy for delivering extra growth, and more
potent than other options. A 5 percentage point decline in the fiscal deficit is likely to
yield an extra 0.5 percent of GDP growth; this is achieved by a country by just
undervaluing by 13 percent, or by keeping the nominal exchange rate constant for three
years, or by accumulation of about $15 to $ 50 billion of reserves.
Explicit calculations yield the same answer. At the margin, Indian dollar reserves are
increasing by about $ 18 billion a year, in China by at least $ 100 billion. If parked in the
US, these reserves return 3 percent less; add to that another 3 percent for the loss when
the currency appreciates several years hence. (Alternatively, think that the developing
countries have a 6 percent higher return domestically). The total loss for India, in 2006,
was close to $ 1.1 billion, for China close to $ 6 billion. These are the calculations the
critics of FX reserve accumulation have in mind. (Table 7.5)
By keeping the exchange rate undervalued (or competitive, or export-led growth, or …)
India and China were able to grow at a faster rate in 2006. The costs are lower interest
returns for the reserves and a lower return through dollar depreciation; the benefits are
more employment, more industrial profits, and a higher GDP growth rate. Due to this
extra GDP growth in 2006, India gained 19.5 and China gained 74.2 billion dollars,
respectively. This gives a large benefit to cost ratio of above 12. In 2002, the minimum
benefit/cost ratio is 14 i.e. each dollar invested in accumulation of reserves and the
maintenance of an undervalued exchange rate means a return of at least 12 dollars!
This large bang for the policy buck of undervaluation helps explain why countries like
China have followed a mercantilist trade policy for decades; and why countries like India
have learnt this historic lesson and are now actively preventing the exchange rate from
appreciation. There are precious few investments with such a return anywhere in the
world, and at any time in history. These numbers suggest that the fact that the
200
Table 7.10: Foreign reserve accumulation - how costly?
2002 2006
India China India China
GDP (in $ billions), 506 1450 880 2560
Reserves (in $ billions), end year 120 700 180 1000
Approximately annual increase in reserves (in
billions) 12 70 18 100
Level of undervaluation (%) in 2006 -25.4 -37.6 -25 -54
Change in undervaluation (average 2001 -
2006)) -4.3 -10 -4.9 -5.2
Loss from undervaluation (via accumulation
of reserves), $ billion
Loss due to lower interest rate in US (@ 3 %
per annum) 0.36 2.1 0.54 3
Loss due to appreciation of domestic currency
(@ 3 % per annum) 0.4 2.1 0.5 3.0
Total loss (at 6 % per annum) 0.7 4.2 1.1 6.0
Gains from undervaluation (via higher GDP
growth)
% Gain in GDP from undervaluation
(coefficient -0.02) 0.51 0.75 0.5 1.08
% Gain in GDP from change in
undervaluation (coefficient -0.35) 1.5 3.5 1.7 1.8
Total gain in GDP from currency
undervaluation per year ($ billion) 10.2 61.7 19.5 74.2
Benefit/cost ratio 14.1 14.7 18.0 12.4 Source: IMF WEO (2006); Penn World Tables version 6.1, and own computations (Appendix I).
Notes:
1. Average contribution of undervaluation to growth from several growth models, see Table 6.5.
201
developing countries are willing to hold ostensibly ―worthless‖ US dollars is because the
accumulation of such reserves is massively worthwhile.
This simple calculation supports the argument of Dooley et. al. (2003,2006). Contrary to
most other academics, and investment banks, Dooley, Folkerts-Landau and Garber
have been quite forthright in arguing that the undervaluation strategy was at the center
of Asian, especially China‘s, development strategy. Though they refrain from providing
estimates as to the relative or absolute amounts of undervaluation, they state ―a sensible
development policy might involve creating a distortion in the real exchange rate in order
to bias domestic investment toward export industries‖ (2006, p.18, quoting their study,
2003).
Several people have been shocked, especially the dollar bears who predicted,
somewhat incorrectly, that a large US current account deficit could not be sustained, and
therefore the US dollar would depreciate by a large amount. Again, there have been
several elements of rear window economics in this forecast. The dollar bears were
targeting the yen and the euro, not realizing that what was appropriate for a US current
account balance in 1985 is different than what is appropriate today. Also, not
appreciating that surpluses and deficits are really twins separated by policy.
Table 7.11 provides some calculations of what a new ―dollar order‖ might, and should,
look like. The table contains the share of some major countries in the US trade
imbalance for 2006. Of the close to a trillion dollar deficit, China accounted for 25.6
percent. The magnitude is put in perspective by noting that the past ―culprits‖ Germany
and Japan (euro and the yen) accounted for only 5.5 and 10 percent of the deficit,
respectively.
The estimate of the depreciation or appreciation needed is based on a simple sharing
formula, a formula that states that the change required is proportional to the deviation of
each country‘s undervaluation from the ―fair‖ value of zero. Consequently, economies
with overvalued currencies should devalue, and those with undervalued currencies,
should revalue. If the share in US trade is taken as a weight, then the exchange rate
change expected from China, for an overall 3 percent depreciation of the US dollar, is 44
percent.
202
Table 7.11: Adjustment of the US dollar, 2006
Share of
Trade/GDP
Trade/US
trade
US balance
of Trade
Currency
Undervalu-
ation (2006)
Currency
change
required
Australia 1.4 1.1 -0.9 7.4 -1.0
Belgium 3.4 1.5 -0.6 24.1 -5.0
Brazil 1.1 1.5 2.3 9.8 -1.0
Canada 4.1 18.9 11.4 7.0 -2.0
China 11.2 10.6 25.7 -59.4 44.0
France 6.1 2.2 1.6 28.4 -11.0
Germany 11.3 4.5 5.5 25.6 -19.0
Hong Kong 3.5 0.9 -0.6 -40.5 9.0
India 2.3 1.1 1.4 -30.4 5.0
Ireland 1.6 1.3 2.6 31.5 -3.0
Israel 0.6 1.0 1.2 -2.3 0.0
Italy 5.0 1.5 2.3 24.4 -8.0
Japan 6.8 7.0 10.1 17.7 -8.0
Malaysia 1.2 1.6 2.8 -38.8 3.0
Mexico 2.4 11.5 9.0 15.5 -2.0
Netherlands 4.2 2.1 -1.1 35.8 -10.0
Saudi Arabia 1.1 1.3 2.6 16.0 -1.0
Singapore 2.6 1.7 -0.5 -16.3 3.0
South Korea 3.3 2.9 1.5 -14.0 3.0
Switzerland 1.9 1.0 0.3 46.7 -6.0
Taiwan 2.6 2.2 1.8 -45.1 8.0
Thailand 1.3 1.0 1.6 -55.5 5.0
United Kingdom 6.6 3.7 1.3 28.2 -12.0
United States 16.9 . . -2.6 2.3
Venezuela 0.4 1.5 3.0 46.9 -1.0
Source: SAE dataset, see Appendix I for details.
Notes:
1. Currencies are adjusted according to whether they were over valued or
undervalued against the US dollar in 2006. If over valued e.g. Japan, then a
depreciation is warranted.
2. The currency change formula is –(UV/range in UV)*trade share, where UV
is the value of undervaluation in 2006; range of UV is the difference between
the maximum and minimum.
203
The yen does not change that much because its share in the US imbalance, or world
trade, is not as high as before. Also, the yen should depreciate since it is presently
overvalued. The Indian rupee should appreciate by around 5 percent. These calculations
are based on 2006 data, when the Indian rupee averaged 45.12. In May 2007, it was
trading at 40.5, an 11 percent appreciation. Or the rupee has already overshot the
expected appreciation for the entire next few years. What the simple calculation outlined
above shows is that an appreciation of the Asian currencies in general, and China in
particular, is absolutely essential for a ―fairness‖ move towards redressing global
imbalances caused by the exchange rate policies of the Asian governments.
Why isn’t every country practicing undervaluation?
If currency undervaluation is a silver bullet, then why isn‘t every country practicing it?
The second part of this chapter examines the ―political economy of undervaluation‖ and
possible constraints to the adoption of the globalization version of a beggar-thy-neighbor
policy. In the 1930s, high tariffs meant you could temporarily move ahead of your
neighbors; today, a cheaper real exchange rate means a country can ―steal‖
investments, and growth, from its neighbors. An undervalued and declining real
exchange rate means that costs are declining relative to productivity; this enhances
profitability, and attracts FDI. The FDI that comes into the undervalued economy is not
available to go elsewhere – plus it won‘t go elsewhere because the profitability
elsewhere is by definition less. Just as with tariffs in a semi-closed economy, this is an
open economy version of the beggar-thy-neighbor game.
So why isn‘t every economy practicing it? It is beginning to, and several had started
years earlier. In the previous chapter, the evolution of the RER across various regions
was documented, and it was observed that the evolution of RER changed course after
the string of mega Chinese devaluations ended in 1993. Rather than the RER increasing
with growth, for several countries (including China and India) the RER continued to
decline. Others (especially in east Asia) followed price setting China. Those that did not
– mostly the Latin economies – were left a considerable distance behind in the growth
sweepstakes. This, the relative overvaluation of the currencies in Latin America, is one
very strong reason for their dismal growth failure post 1980.
204
Lately, even Latin American countries have joined the real devaluation bandwagon, and
rightly so. Some, like Brazil, are fighting to prevent the currency from further
appreciations; others, like Chile, are successful in keeping the currency undervalued at
the 20 percent level. Yet others are today enjoying the fruits of undervaluation e.g.
Argentina.
The political economy of expert opinion
Unlike the nominal exchange rate, the real exchange rate is a virtual variable. It is useful
to recall that the Balassa Samuelson papers were an answer to an anything goes
environment among economists and other experts102. There is no precise measure of an
undervaluation; there most likely isn‘t even a definition that most economists can agree
on. Like obscenity, just because you cannot see undervaluation, it does not mean it does
not exist.
Recent history is also revealing. In the early 1980s, there wasn‘t a single newspaper
article, nor a single academic article, nor a single bureaucrat or academic either in Japan
or the US, who did not agree that the US dollar was heavily overvalued and that the yen
and deutsche mark were heavily undervalued. And there was nobody who claimed that
while the yen and deutsche mark were undervalued that they were ―only‖ undervalued
by a mere 5 to 15 percent. As just discussed, a plus minus 20 percent range is
considered normal by most experts; a contention that the yuan is 15 percent
undervalued essentially means that the yuan is fairly valued.
The conventional near universal wisdom today is that the yuan is only 5 to 15 percent
undervalued (see Table 7.12). There is no investment bank that claims it is more than
this103. Most academics claim this level also to be small, obtained by a variety of
methods. Some academics claim it to be larger, but find fault with the methodology e.g.
Bosworth(2004). Only a few stray academics Frankel(2006), industry lobbyists and the
economists at the Peterson Institute for International Affairs claim that the yuan is
severely undervalued.
102
Samuelson is particularly caustic against his ―opponents‖; his 1964 article is a must read for anybody interested in the contemporary debate on Chinese undervaluation. 103
Dooley et. al. believe that currency undervaluation is an explicit development strategy of China; but they refrain from making explicit their calculations.
205
Table 7.12: Expert Opinion on the fair value of the yuan, 1995-2006
Author UV by Comments
(%)
1995
Bhalla -16.0
1998
Bhalla -5.0
Bu-Tyres -5 to -12 "Undervaluation is between 5 and 12% in 1998"
Anderson, UBS -28.0
1999
Xianpou -6.0
2000
Xianpou -10.0 "The estimation results show that the exchange rate of RMB is close to the
equilibrium level in 1999"
2003
Williamson -40.0
Goldstein - Lardy -15 to -25 "Our preliminary estimated suggest that the undervaluation if the RNB is on the
order of 15 to 25%"
Liang, Goldman Sachs -15.0 "We consider three scenarios based on the assumption that the renminbi is
undervalued on a real effective basis by 15%"
Jen, Morgan Stanley 0
"The EUR, not the RMB, is the problem .. arguments for revaluation leased on
perceptions that the RMB is overvalued are largely flawed in my view. Further, it
is unclear why China should 'import' deflation to 'save the rest of the world'"
Walter, Deutsche Bank -20.0
"The nominal US Dollar/Chinese Yuan reflects an undervaluation of the CNY of
some 20% … Little maybe achieved for China and the global economy by a
stronger CHY"
Roach, Morgan Stanley 0
As quoted in Yang et. Al (2004) "China does not compete on the basis of an
undervalued currency, but mainly in terms if labor costs, technology, quality
control, infrastructure, the improved human capital of its work force, and a
passion for and commitment to reform"
206
Table 7.12 Contd.:
Author UV by Comments
(%)
2004
Chang-Chun-Fuji -6.0
"First, the RMB has been persistently undervalued by this [PPP] criterion since
the mid 1980s, even in 1997-98, when China was lauded for its refusal to devalue
its currency despite the threat to its competitive position. Second, and perhaps
most importantly, in 2003 the RMB was more than one standard error - but less
than two standard errors - away from predicted value, which in the present
context is interpreted as the "equilibrium" value. in other words, by the standard
statistical criterion that applied economists commonly appeal to, the RMB is not
undervalued (as of 2003) in a statistically significant sense"
Eichengreen -5.0
"Relative to its average between the middle of 1996 and the middle of 2002, the
RMB was undervalued on a real effective basis (Weighted relative to the relative
labor costs of its principal trading partners) by only about 5 percent"
Bosworth -40.0
"The RMB is undervalued on a PPP basis, but the PPP standard provides very
weak guidance as to the appropriate exchange rate for low income countries…
consideration of macro economic balance suggests little or no undervaluation.
There is no evidence of a growing surplus in the most recently available trade
data. In fact, the most recent information suggests a declining current account
balance ". PPP undervaluation found to be 40%
2005
O’Neill, Goldman Sachs -10.5 "Yuan is undervalued by about 10.5 percent"
Hacker 0 "China would be foolish to consider revaluing. Should China revalue the Yuan by
25 percent, it would lead to 20 percent deflation".
2006
Bergsten -45
McKinnon - Schnabl "This common presumption of renminbi undervaluation is wrong, and its
appreciation need not reduce China's trade surplus".
Big Mac -59.0
Anderson (UBS) -10 to -15
"We looked at all the evidence and concluded that the renminbi was probably
15% to 20% undervalued in the first half of 2005 - which means 10% to 15%
undervalued today (April 2006)"
Frankel -35.0
"The Yuan was undervalued by ~35% in 2000, and is by at least as much as that
today. Typically across countries, such gaps are corrected halfway, on average,
over the subsequent decade".
2007
Goldstein - Lardy -30 to –40
Bergsten, Peterson Inst. -40
"An increase of 40% in the RMB and other Asian currencies against the dollar
would reduce the US global current account deficit by about $150 billion per
year.
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One of the leading experts on exchange rate and development economics John
Williamson (creator of the term Washington Consensus, creator of the FEER concept of
exchange rates etc.) was provoked enough by this surprising stand among the academic
fraternity to state, somewhat boldly and somewhat in despair:
―Even in what seem to many of us to be absolutely unambiguous cases, like the renminbi peg, one finds economists with Nobel prizes and others paid inordinate sums by investment banks and at least part of the IMF staff are prepared to assert that they do not know whether the renminbi is undervalued or not. If one then tries to take the analysis back a step, to search for exchange rates that will achieve an agreed set of current account targets, one finds that it is similarly impossible to secure agreement on what current account targets should be. Unless and until this sort of intellectual laissez-fair leads to the world ending up in a new depression, it seems hopeless to imagine that it can be changed.‖ John Williamson (2007, p.150)
All the data available – export growth, GDP growth, current account surpluses,
mercantilism indices, historical tendencies – indicate only one possibility: the Chinese
yuan is much more undervalued today than the yen was at the time of the Plaza
agreement. This statement says nothing about the absolute degree of undervaluation; it
may even be the case, as Steve Hanke and Ronald McKinnon consistently argue, that
the yuan is absolutely fairly valued. The statement only makes a comment about relative
undervaluation i.e. if you think that the yen was undervalued in 1984, then you must
think that the yuan is much more undervalued in 2006. Given this reality, why the
differential treatment towards China by the investment banks?
Possibly because of dollar signs. In the 1980s, the Japanese economy was relatively
closed to imports, to FDI and to mergers and acquisitions. There wasn‘t much business
income American firms could have, other than via exports of goods to Japan. The
situation in China is different. In order to profit from the China experience, the feeling
among firms is that US investment banks cannot profit if they are critical of the currency
regime. This belief about China has brought major American firms (e.g. Microsoft,
Google) to believe that investing in China is different, and considerably more profitable,
than investing in Japan in the early 1980s.
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Lawrence(2007, p.17) reaches a similar conclusion:
―Japanese firms were rivals for leading U.S. firms in key industries such as electronics and automobiles, and the closed Japanese market was seen as giving them an unfair advantage. Moreover, the barriers to the Japanese market were often opaque and not covered by GATT rules. In addition, the fact that Japan developed with a market that was closed to foreign investment meant that few U.S. firms had a strong interest in maintaining the U.S.-Japan trading relationship and opposing protection against Japan. By contrast, today China accounts for a large share of the U.S. trade deficit. While labor-intensive sectors in the United States have experienced job losses, in many cases the goods the United States buys from China are no longer produced locally. Thus relatively fewer U.S. firms feel threatened‖.
Apparently, this reverential approach to China is also being manifested itself at
international organizations like the World Bank, whose President recently agreed to vet
with the Chinese authorities, all World Bank reports (and not just China specific reports
as is customary) containing a reference to China.
It is also revealing to track the excuses China has made with regard to its exchange rate
policy (thereby implicitly admitting, investment banks please note) that the currency is
more than marginally undervalued. In 1997, at the onset of the East Asian currency
crisis, a crisis whose origins most likely were in the large Chinese devaluations over the
period 1989 to 1994, China claimed that as a gesture of solidarity with the US and the
global economy, it would not devalue in retaliation. Next the argument was that since
China is a poor country with large unemployment104, it needed an undervalued currency
to grow fast. Next it was that despite huge trade surpluses, and an oversized
involvement in world trade and world finance, that the financial system was very weak
and could not take a currency adjustment. Next is the contention that yes, we need to
increase consumption among the poor but no, we cannot appreciate the currency
because this will hurt the farmers.
104
By implication, the rest of the developing and developed world must have a lesser unemployment problem.
209
On exchange rate policy
Given the loading of academic and investment bank experts on the side that the Chinese
yuan is marginally undervalued, at most; and on the other side the unprecedented
growth in China in whatever is even tangentially related to exchange rate undervaluation
(exports, reserves, investment, easy availability of capital, lower growth in real wages
then that warranted by productivity growth, a reversal of the Balassa-Samuelson stylized
fact etc), what are policy makers to do? If jobs are lost in US, or France, or Pakistan, or
India, to the Chinese juggernaut, one is told to not worry, because these are simple
forces of nature.
It might be relevant to go back in history to what Samuelson said on a similar occasion.
In 1960, there was also talk of imbalances in the US, though they were necessarily baby
like in comparison. Nevertheless, the laws of economics have not changed.
What Samuelson wrote in 1964 for the world economy (and the US) is eerily applicable
to the world economy (and China) today. Because of the parallel, two sets of a lengthy
quote are presented; the first is Samuelson (1964, the sister paper of the Balassa
Samuelson effect), per se; the second is Almost Samuelson, where I have taken the
liberty to invoke symmetry and change overvaluation to undervaluation, jobs lost to jobs
gained, etc. The original is presented as is; Almost Samuelson is presented in italics.
One difference, though, between the US of 1960 and China of 2006 – by the Balassa
calculation, the US was fairly valued in 1960; by the same Balassa calculation, the yuan
is massively undervalued in 2006.
Samuelson(1994, p.153); Almost Samuelson, 2007 (in italics)
My own diagnosis of the dollar/yuan problem. 1: The dollar has been somewhat overvalued in this last decade. This does not imply that we should depreciate. It does imply that economists everywhere would prefer, if they could rerun history, that the 1949 depreciations abroad had been somewhat less sharp. 1: The yuan has been somewhat undervalued in this last decade. This does not imply that the yuan should appreciate. It does imply that economists everywhere would prefer, if they could rerun history, that the 1990-93 yuan depreciations had been somewhat less sharp.
210
2: The overvaluation has hampered a high employment policy at home; it has unduly limited America's freedom to spend abroad in an efficient manner; 2: The yuan undervaluation has promoted a high employment policy at home; it has unduly limited Chinese freedom to spend at home in an efficient manner . 3:The productivity improvements abroad since 1949 … have not yet been matched by commensurate rises in foreign money wages relative to ours 3: The productivity improvements in China since 1990 … have not yet been matched by commensurate rises in domestic wages relative to foreign wages 4: Our overvaluation has had one effect that some will deem a virtue: it has kept pressure on our price levels. This anti-inflation benefit has been dearly bought in terms of unemployment, excess capacity, slow growth, and low domestic profits. 4: Chinese undervaluation has had one effect that some will deem a virtue: it has kept pressure on domestic price levels. This pro-inflation cost has not been much since as benefit the Chinese economy has high employment, near full capacity, fast growth and high domestic profits. 5: Our overvaluation has helped to redistribute our disproportionate share of world gold, thus providing the miracle nations of Europe and Japan with needed secular increases in liquidity. 5: The Chinese undervaluation has helped to accumulate a disproportionate share of world reserves. 6: Our overvaluation has put some upward pressure on foreign price and cost levels. By voluntary currency appreciation, the surplus countries could choose to offset this. 6: Chinese undervaluation has put some downward pressure on foreign price and cost levels. 7: Overvaluation pushes American capital abroad, and in turn is intensified by foreign investment. These are secondary reactions to the technological miracles of growth abroad. 7: Undervaluation of the yuan pushes American capital into China, as well as investment from all over the world; this is in reaction to the miracle of high growth in China, and low growth everywhere else.
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Chapter 8 – Institutions as Luxury Goods Today, and for the last few years, there has been a new mantra in town – institutions. A
new conventional wisdom about that same old question (and our major concern in this
book): why are some countries rich while others are poor? The old answer was a
combination of inheritance and bad policy, especially bad macro-economic policy. The
inheritance was in terms of geography, education, and the backwardness induced by
colonialism. Poor countries were poor primarily because they had low factors of
production – physical, and especially human capital. And human capital was low
because of low incomes (a self-fulfilling cycle) but also because the poor countries had
been ―mis-governed‖ by colonialism.
That was then. Today, we are told that long run growth is primarily about the
development of institutions. If a country has good institutions, it is likely to grow faster.
Why? Because ―good‖ institutions help facilitate business, establish fair rules of the
game, enhance competition. And this was so in the past, as far back as before the
industrial revolution. There is some circularity i.e. countries that are rich today are also
likely to have had good institutions in the past, however defined. So it could be that
higher incomes lead to better institutions rather than the other way around. A voluminous
amount of research in recent years has addressed this very question. And in a very
sophisticated econometric fashion, helped to break the circularity by isolating the
independent causative role of institutions in generating higher growth. The ―good
institutions lead to higher growth‖ literature is not new. What is new is the breakthrough
in identifying causality as being primarily from good institutions to a higher growth rate in
incomes, and therefore a (considerably) higher level of income measured a hundred or
so years later105.
The sweeping success of this literature106 is reminiscent of the fervor associated with the
East Asian growth story in the 1990s which culminated in two books: World Bank, The
East Asian Miracle published in 1993 and a book with similar conclusions, Emerging
Asia published in 1996 by the Asian Development Bank. Then came the Asian crisis
105 This literature has exploded in the last decade, and especially in the last five years. Rare is a
conference without some paper detailing the virtues of institutions or governance. 106
In India political slogans often start with ―Galli Galli mein shor hai‖ or ―that in every street corner there is a shout‖. Institutions are so popular, it is as if people are shouting ―even my research shows that….‖.
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and some controversial literature stating that most of the East Asian growth was factor
accumulation rather than productivity growth (see Young(1995), Krugman(1994)).
Others attributed the East Asian crisis to too much capital account convertibility. As
discussed in the previous chapter, the most likely cause was China‘s extreme
devaluations in the late 1980s and early 1990s, devaluations that enhanced its own
growth and set back the competitiveness of its neighbors107.
The institution literature attempts to explain very long-term phenomena and traces its
origins to the recognition of private property as a major arbiter of human and market
relations. Its goals are ambitious – to explain why the West is rich and the south is poor
(and also to indirectly explain growth miracles). To explain why Latin America floundered
while the United States of America flourished. The volume of empirical research on
institutions is testimony to its reach and conviction. And presidents of the World Bank
and IMF have committed themselves to the incorporation of ―institution‖ considerations in
their policy making.
The World Bank has launched a very impressive research and data collection program
on governance and the IMF has published, twice in the last three years, papers on the
subject in its flagship document, World Economic Outlook. A summary view of the IMF
position was recently succinctly stated by Mr. Rodrigo de Regato, Managing Director of
the IMF. Writing in a special issue of the Cato Journal on Monetary Institutions and
Economic Development, Mr. de Regato states:
―How much does improving the quality of institutions contribute to economic development? The results of the IMF‘s research on this question are quite striking. For instance, suppose that average institutional quality in Africa could be raised to the level currently prevailing in developing Asia. We estimate that this would be associated with almost a doubling of per capita GDP in Africa over the long term-that is, from about $800 to more than $1400 a year. And, likewise, incomes in Developing Asia would roughly double if their institutions could be strengthened to around the average of all countries‖ (de Rato(2006), p. 216-217).
The IMF calculations talk about doubling Asian and African incomes over the long-term,
a doubling induced exclusively by better institutions. If this long-term is considered to be
107
The simple fact remains that never before, in peace time and without the provocation of hyper-inflation, a large economy had managed to knock competitors out of business by devaluations. The Chinese yuan was 2.32 in 1984; by 1994 it was 8.62, a 272 percent nominal devaluation, and a 128 percent real devaluation.
213
20 years, that is an extra 3.5 percent a year, or equal to twice the growth rate
experienced by the median developing country in the post-War period. If long-term is
considered 40 years, then the extra institutions growth is equal to the past median
growth. This would be an incredible policy, but regardless of the precise calculations
what the IMF is suggesting that policy measures may have found the most golden of all
silver bullets.
Some mis-steps in the institutions rule calculations
The institutions literature rarely mentions the greatest growth miracle of the twentieth
century. The miracle growth in East Asia, China and India extends to over half of the
world‘s population. This half has achieved in 25 short years what the developed world,
less than a tenth of the world‘s population, achieved in a 100 years, and in a period that
included the Industrial Revolution (1810 to 1910). Did East Asia, and China, achieve
spectacular growth because of institutions? Or, as the results in the previous chapter
suggested, it achieved high growth via an export push policy induced by currency
undervaluation, or mercantilist trade made possible by an extreme undervaluation of the
currency?
Nor does the institutions literature mention India108 much, nor the flip side of ―good
institutions equal good growth‖: the side where good institutions were present, but extra
growth was not. Both in the period 1950-1980, and in the period 1980-2006, Indian
growth rates have trailed those observed in China. At the time of independence in 1947,
India had inherited a robust set of institutions, and the probability of decent growth must
have increased when it adopted, and persisted, with the ultimate in political institutions,
democracy. Thus, in the pre-1980 period, India had much better institutions than China
but managed to grow at a significantly slower pace. In the 1980-2006 period, India had
worsening institutions, but grew considerably faster than its own earlier growth path. But
if good institutions mean good growth, what happened?
How are institutions different than policies
The old view, policies are important, while gloomy, was nevertheless optimistic. The
major premise of this old fashioned view was to get policies right (some phrased it as
108
An important exception is Banerjee and his co-authored research, Banerjee-Newman(1993) and Banerjee-Iyer(2004).
214
―get prices right‖) – and to educate the population. It was also the policy favored by
domestic leaders, and by international organizations like the World Bank and the
International Monetary Fund. Of course, which policies are right has been a constant
source of debate, and conjecture. Nevertheless, whether clouded in terms like the
Washington Consensus, Confusion, or Contentious, 109 the center of attention had
always been the making of good policy, and the unmaking of bad policy.
That was until institution economics became the alternative. And institutions are closely
related with another buzzword, governance. This is a newer term, but it has the same
parentage. Its genesis lies in the disappointment of all actors (citizens, academics,
policymakers and politicians) with the way policies have worked. There has been
disappointment with institutional delivery or governance in the new parlance. But bad
governance is the result of bad policies; for example higher corruption results from the
use of trade quotas (versus tariffs); bad delivery of social services most often is the
result of a bad policy (government run schools vs. a student voucher program). So bad
policy leads to bad results, lower growth, and institutions don‘t have much to do with it
i.e. policies rule.
The logic behind the institution theory is similar to that behind policy; indeed, institutions
are ―cumulative policies‖. But there is more to institutions than just a difference with
policy. The emphasis is on an environment where incentives are rewarded, where
market forces are allowed to operate, where enterprise is encouraged, where private
property rights are paramount. The institution theory has been able to explain, somewhat
convincingly, why the West is rich today, and why ―extractive economies‖ are poor. The
West emphasized a legal and economic framework that protected rights, and
encouraged competition. The poor economies remained feudal because the small elite
derived a lot of rents from the existing ―natural order‖, and prevented enterprise (the
middle class ?) from rising and removing its advantages.
The difference between policies and institutions can be illustrated by several examples.
The previous chapter had emphasized the role of currency undervaluation. Is that a
policy, a cumulative policy or an institution? Existence of a central bank is an institution.
Whether a central bank has independence is another aspect of an institution. When the
109
See Rodrik(2006), Birdsall(2006).
215
central bank changes the exchange rate, then that is policy. If it decides to operate a
currency board, that is policy. If the central bank targets inflation, that is policy. In other
words, the institution remains the same, but the policies are different.
Take fiscal policy. All countries have the ―institution‖ of a government, and all countries
have a budget. But it is policy which determines whether a budget is balanced, or in
deficit. It is not difficult, therefore, to differentiate economic policies from institutions.
However, there are some gray areas. Law and order, corruption, bureaucratic red tape,
legal institutions and the prevalence of checks and balances in society; all have their
counterparts in policy. Where is the difference? Another gray area: the efficiency of
delivery of social services. When such delivery fails, as it often does, is that a fault of the
institutional structure or is it better described as bad policy? The checks and balances
provided by a democracy, or globalization, enhance the effectiveness of policy. So
should credit be given to institutions or policy?
There are differences in political institutions as well. Countries may be democratic, but
the institutional structure can be different. A presidential system is different than a prime
minister based institution; proportional voting is different than a first past the post
system.
Corruption: because of bad institution or bad policy?
How and why does corruption occur? Economists have many models, but there are few
with the compelling logic of the following simple explanation. Corruption cannot arise
with open competition. So if a policy of open and transparent bidding is followed, it is
difficult to have corruption. If the government is not handing out licenses, then where is
the corruption? It is bureaucratic discretion that oils the palm. Discretion cannot be
profitable if a sweetheart deal is made impossible. With globalization, there are many
firms producing the same cell phone, many farmers producing the same grain. So where
are the returns to discretion? Not in Big Business, because no matter how Big you are,
there is always somebody small in the global world who will bring you down to size. But
in the steamy corridors of Big Government, there are oily possibilities.
In order to do good for the people, governments tax. There are many who argue that
increased public investment is necessary for development. But if such public investment
216
is made transparent, who loses? The bureaucracy, the politicians, the people manning
the institutions. Kautilya, writing around 300 B.C. had warned about the consequences
of discretion empowered civil servants: ―It is possible to know even the path of birds
flying in the sky but not the ways of government servants who hide their [dishonest]
income‖ (p. 283)
Efficient and inefficient corruption: There is a difference. And if the difference is large, is
that due to policy or institutions? Not very long ago, when import tariffs were sky high in
India (in the late 1980s), the most sought after job in government was to be a Customs
official. If several items are banned, and if ―regular‖ items are being imported with a
customs duty of 180 %, then obviously there is room for informal exchange between the
ordinary citizen and the not so ordinary government official. This is analogous, if not near
identical, to the phenomenon of prohibition and bootlegging. Fortunes are made when
government imposes a ban, and somewhat co-incidentally, the individuals making the
greatest fortune are those linked to the government administrative machinery involved in
the ban. Intended consequences follow.
In the 1980s, an Indian customs official commanded the highest price in the marriage
dowry pecking order. He had discretion, and the exercise of that discretion (one could
either pay a very large tax or split the difference with the customs official) meant a large
present discounted value of his income stream. Which meant that, ceteris paribus, he
commanded a higher price in the arranged marriage system. When economic reforms
reduced the peak customs tariff rate to 40 percent in 1991, the clout of the customs
official declined. Today, custom duties are typically in the low teens. Little reason to
bribe and the dowry value has also declined.
Today, as globalization and markets have taken over, the longest (and only) line for
government paying jobs in India is for peons, clerks and street cops. The best and
brightest no longer go into government service – they either set themselves up as
entrepreneurs (NGOs , Silicon valley types), or go into investment banking.
A policy change, low import tariffs, results in an institutional improvement, better
governance and low corruption. So is lower corruption, as measured by several indices,
an outcome of policy or a derivative of good institutions?
217
Institutions and Development Thesis: Not Well Supported by India-China
The India-China experience is not that supportive of the institutions thesis. The Chinese
growth experience can easily be explained by the introduction of capitalist institutions
like the market i.e. private gain for private effort, the introduction of incentives in
agriculture, etc. When they did not have respect for private property, the economy
floundered. With respect, came robust economic growth. A good correlation.
But not with the Indian growth experience. Indeed, it is likely that institutions in India in
1980 were a lot worse than at the time of independence. There was greater government
involvement in 1980, tax rates were higher, private property was less respected, and the
politicians were a lot more corrupt. Public services had begun to falter, and the role of
the market had been replaced by the role of the babu-neta110 ―license raj‖.
Yet, seemingly, India has come into its own with worse institutions. Per capita income
growth has tripled from a 1.3 % per annum growth 1950-1980 to a 3.8 % per capita per
annum growth, 1980-2002. Since 1980, institutions have worsened and are likely today
to be at their lowest level in Indian history (the joke that it cannot get worse than this is
no longer a joke, it is a reality). Yet, the Indian economy has recorded a structural break
starting 2003. For the last four years, India has recorded GDP growth of over 8.5
percent per annum, and per capita income growth of 7 percent per annum. This is near
double the 3.8 percent per capita growth rate of the last two decades, and more than five
times the growth rate when institutions were reigning supreme. Unlike China, a reverse
correlation.
But the future holds promise. GDP growth is likely to accelerate, and part hope and part
forecast111, ―institutions‖ are likely to improve in India. In other words, a positive
correlation will be obtained between improving institutions and improving growth. But it
would be irresponsible hastiness to conclude that (expected) better institutions had
caused this (expected) higher growth.
110
Babu-neta is bureaucrat-politician. 111
Because of the role of the middle class. See Chapter 5.
218
Institutions in India China – The macro results
For example, in India, according to World Bank data, India is ranked 134 out of 175
countries in terms of ―ease of doing business‖. China‘s rank was 93 and Pakistan was
74th. Inefficient corruption is likely to be considerably more in India than in China or
Pakistan.
These six indices relate to six different aspects of governance. Law and order, voice of
the people, the extent of bureaucratic strangulation, the extent of corruption, political and
civil rights, and the regulatory framework. These World Bank data are the most
comprehensive, and offer data on a maximum number of countries (around 160). The
only drawback with these data is that the earliest time period for which they are available
is 1996.
Table 8.1 documents selected institution indices for India, China and the developing
economies. In addition to the World Bank governance data for the two periods 1996-00
and 2000-06, data are also presented for various components if the Economic Freedom
indices (regulation, Judiciary etc.).
The World Bank data are in standard deviation terms i.e. an index of –0.1 for China,
corruption 1996-2000 means that corruption in China was worse than the mean (the
negative sign) and 0.1 standard deviation worse. For both the time periods, overall
governance in China is a lot worse than India; the overall rank, for developing countries,
in 2005 is57 for India and 82 for China.
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Table 8.1: Institution Indices for India, China, and the developing world
1975-85 2000-06
China India Developing China India Developing
World Bank (1975-85 is 1996-00)
Corruption -0.19 -0.29 -0.39 -0.55 -0.37 -0.55
Bureaucracy 0.15 -0.23 -0.19 0.04 -0.07 -0.23
Political -0.10 -0.80 -0.62 -0.15 -1.04 -0.73
Rule of Law -0.42 0.07 -0.36 -0.39 0.02 -0.43
Regulation -0.10 -0.19 -0.18 -0.37 -0.42 -0.44
Voice of accountability -1.55 0.27 -0.67 -1.54 0.31 -0.62
Composite index -0.37 -0.20 -0.40 -0.49 -0.26 -0.50
Economic Freedom indices for
Regulation 3.1 5.5 5.2 4.6 5.5 5.7
Judiciary independence 4.3 7 4.3
Law 6.8 4.4 4.3 5.1 6 4.8
Labour market regulation 3.7 4.5 5.9 5.4
Bureaucracy 3.6 5.8 5.5
Days to start a new business 5.7 5 5
Corruption 6.1 4.9 5.6
Business regulations 4.5 4.7 4.9
Overall index 4.5 4.9 5.1 5.8 6.4 6.1
Transparency international 5.1 3.7 3.8 3.3 2.9 3.6
Risk of expropriation (AJR, 1980s) 8.11 8.07 6.47 Source: Kaufman et. al., World Bank (2006); Transparency International (2005)SAE dataset, Polity IV (2004), SAE
dataset, see Appendix I for details.
Note:
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Institutions and Outcomes: Some Micro results from India The efficiency of delivery of public services is probably one of the best indicators of the
―goodness‖ of an institution, of its importance in making a difference to outcomes. One
way to look at institutional delivery is to ascribe it to bad policy design. Another is to
collectively call the entire operation as an ―institution‖. Bad delivery is most often, and
most likely, due to bad policy. But if an institutions approach is taken, then the micro
results from India are a comment on how seemingly good institutions can lead to bad
outcomes.
Public policy, particularly policies which involve redistribution, involves a large amount of
expenditure. Especially, but not exclusively, in developing countries, the pursuit of
redistribution is often preceded with the battle cry ― I begin this in the name of the poor‖.
No policy announcement is made without recourse to the laudable goal of redressing
poverty. The Indian Prime Minister Indira Gandhi had a whole political campaign
oriented around the populist theme ―Garibi Hatao‖ or Remove Poverty in 1971. This was
hugely successful. Many years later, the World Bank, a leading institution among
institutions, was to declare that its dream of the institution was a world free of poverty.
Yet in 2004, the World Bank‘s own World Development Report, extensively documented
how institutions had singularly failed in this objective. If so, was this the failure of
institutions, or the particular policy of redistribution? In some parts, both.
In 1985, Rajiv Gandhi, a young Prime Minister of India (and son of Mrs. Indira Gandhi)
announced, after a day long meeting with several young bureaucrats from the Indian
Administrative Service112, that he felt that Indian institutions had failed miserably in
reaching the poor. In particular, he concluded, based on estimates given to him by the
officers in the field, that only about 15 percent of every rupee spent for the poor actually
reached the poor. He did not offer any evidence of the sort that economists would
require. The empirical evidence available today would characterize Mr. Gandhi as a wild
eyed optimist.
Table 8.2 documents some evidence on governance in India. For a poor country, food
distribution is a very important part of government policy to alleviate poverty. The
112
Another institution inherited from the British and patterned after the famed British Civil Service. Yet another example of high institutions in India and low economic performance.
221
institutional mechanism set up by the government is the public distribution system
(PDS). This system requires an elaborate government machinery to first procure grain
and rice from the farmers (cannot rely on individual agents or the market to procure food
since the market is a ―bad‖ institution), then another government machinery to provide
this procured food to government ―fair‖ price shops, from whom the poor people buy food
at a discounted price. The poor people have to be in possession of an identity card to
make them eligible to receive subsidized food. An elaborate and by all accounts a
meaningful institutional structure. Not obvious why this policy should be preferred to a
policy that just gives cash, or food stamps, to the poor. When asked, the Indian
authorities claim that cash to the poor would mean liquor to the poor.113
Perhaps the authorities are right – the elaborate system most likely has a minimum of
―leakage‖. Most likely the rich do not obtain this subsidized food, and the poor cannot
purchase extra liquor from the savings made possible by all the food purchased at a
discounted price. There is a method to test the efficacy of the food distribution program;
as well as test other government delivery programs. The large sample National
Statistical Survey Organization (NSSO) data for 1999/2000 can be used to test the
above propositions. According to these data, the proportion of poor households who
actually accessed rice from the PDS was only 11.3 percent. In other words, of the
eligible poor population, only 11 percent was able to able to buy the subsidized rice. For
wheat, the proportion was even smaller – only 5.7 percent. The third row shows that only
29 percent of the wheat and rice that the government claims it distributed via the public
distribution system was actually distributed. This low fraction means that 70 percent of
the food is unaccounted for – not accruing to either the poor or the rich.
113
It is another story that there isn‘t enough liquor in India to satisfy the demand emanating from the 1 percent to 2 percent of GDP that India spends on its food redistribution policy.
222
Table 8.2: Governance in India
Service delivery Government NSSO
Rajeev Gandhi
index of govt.
efficiency
Public distribution of food
% poor household accessing rice 11.3
% poor household accessing wheat 5.7
Consumption of PDS
Wheat and rice distribution via PDS 101.8 29.2 28.7
Mid day meal program
No of children covered (in millions) 99 9.7 9.8
No. of meals delivered (in millions) 2376 266 11.2
Employment programs
Man days created, (crores) 54.7 32 58.5
Employment, poor (crores) 7.8 14.3
Source: NSSO consumer expenditure survey 1999/00; various government of India document on public programs.
223
The mid-day meal program for school children is yet another government program with
considerable public support. Indeed, the present government has imposed an education
cess in each of the last two years. This additional tax revenue is meant to provide for
education for the poor, as well as to expand school mid-day meal programs. The NSS
data on mid-day meals is revealing. The government claims that 99 million school
children were covered in 1999/2000, and that 2376 million meals were delivered.
According to NSSO, the success ratio, according to either criteria, was less than 12
percent.
Yet another ―in the name of the poor‖ government program is to provide jobs to the poor.
This has recently been institutionalized in the form of an ―Employment Guarantee
Scheme‖, a program with annual expenditures to eventually surpass 1 % of GDP. The
first such employment guarantee scheme was started in the state of Maharashtra in
1973, so states and governments in India have considerable experience and expertise
with this government program or ―institution‖. Again, the NSSO data, the only verification
available, indicates a government efficiency index, hereafter the Rajiv Gandhi index of
efficiency, to be considerably less than desirable – less than 60 percent of the jobs
claimed by the government to have been created seem to have actually been created.
And only 14 percent of these jobs accrued to the poor for whom they were meant.
The survey data, by definition, provides an estimate of the number of jobs, the number of
meals, etc. It is possible that there is undercounting in the surveys. The mean estimate
of NSSO consumption was only 55 percent of the corresponding estimate from national
accounts. In the most recent 2004/5 survey, this ratio had further gone down to 48
percent. For a total consumption estimate, these low ratios are a record of sorts, since
cross-country experience is that income survey estimates tend to be in the 40 to 60
percent range. Consumption is easier to track, and such estimates are often above 70
percent. It is also the case that recall of whether in the previous week one had worked in
an employment guarantee scheme is likely to be more accurate than what the total
consumption was during the previous week, or month, or year. So while there is
measurement error in the NSSO estimates of food purchase, mid-day meals, and work
in a public works program, it is highly unlikely that the under-estimate is as large as a
hundred percent i.e. a capture ratio of only 50 percent. But what the NSS data show is
that in several programs, the capture ratio is considerably less than even the extreme 50
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percent, and that often these ratios are less than the Rajiv Gandhi Index of inefficiency
of 15 percent.
And 15 percent is a frightfully small number. This suggests that the assumption of the
institution literature that developed country institutions can easily be transplanted into
developing countries is extremely questionable.114 This is where, in a broad sweep
sense, the institutionalists have pushed their empirical findings much further than
permitted by a reality check. Admittedly, the reality check is provided for just one
developing country, albeit a large developing country where the institutions thesis may
be the most applicable. Nevertheless, this case study micro evidence is informative and
suggestive of the possibility that perhaps the efficacy of institutions has been overstated.
The meaning and measurement of Institutions The institutions literature has not really looked at the micro effects of institutions on
growth. Its strength, and advocacy, derives from the substantial amount of cross-country
evidence that has accumulated to show that institutions affect growth. This success is
largely based on the macro cross-country empirical evidence provided, in the first
instance by Hall-Jones, and later, in a large series of papers, by Acemoglu-Johnson-
Robinson (AJR). While questions have been raised about the particular estimates of
either the institutions or the instruments used to identify the institutions, the broad
conclusion reached by AJR is supported by reviews, and horse-race115 studies
conducted by other authors (e.g. Easterly-Levine, Rodrik-Subramaniam-Trebbi)
Easterly (2002, p.33) ―Still, we are struck by the way that endowments and polices have no independent effect once we control for institutions, contrary to a number of stories, and that institutional quality seems to be sufficient statistic for accounting for economic development.‖
It is recognized by all scholars that no single variable can adequately capture the
complex phenomenon of institutions. Several variables may be needed, and each
114
See Chang(2005) who strongly makes this point; the IMF and the World Bank, believe the opposite; see de Rato‘s quote above and note the World Bank president, Mr. Wolfowitz‘s signature campaign to reduce corruption in developing countries by advocating Western ―remedies‖. 115
The horse race econometrically evaluates the relative contributions of geography, policy and institutions towards economic growth. The overwhelming consensus opinion – institutions not only matter the most, but maybe the only horse that matters.
225
maybe an imperfect proxy. Nevertheless, even with large measurement error, AJR and
others find a strong empirical role for institutions in explaining differences in levels of
living. This is taken as evidence of the robust importance of institutions, since
measurement error is expected to diminish statistical significance, not enhance it.
Institution Types
There are at least three very broad categories of institutions. The first, and possibly the
most important, are institutions that preserve private property rights. Individuals will
neither lend, nor invest, if they feel that the probability of ones assets (investments)
being expropriated was high. So institutions (or laws) that help preserve property rights
are expected to be conducive to private gain, investment, and growth.
A second set of institutions relate to the provision of political freedom. The argument
here is also straightforward and explained by a counter-factual. Assume one is living in a
society with low political freedom. In such a society the welfare of the people will be a
function of both how benign and how forward looking the dictator is. The probability of
getting both ―reject‖ is very small. Simply put, for every good dictator there are 10 bad
dictators. Chances of economic success have to be, and are, larger in societies who can
throw out the leader. When the dictator is bad, the welfare loss for the country is large;
when the dictator is ―good‖ the country is likely to do just as well as with a non-
dictatorship. Hence, non-democracies, over the long run, end up with lower levels of well
being.
The third and fourth sets of institutions are closely related – the legal116 and bureaucratic
structures, respectively. Countries differ in the type of legal system that they have; the
British system may provide greater impetus to private entrepreneurship than the French
system. If the legal system is litigation friendly, then the costs of economic transactions
(the costs of doing business) rises, and growth declines. The fourth kind of institution is
the dominance of bureaucratic procedures. If a citizen has to jump several bureaucratic
loops (as well as bribe several officials) to start a new business, then such delays are a
detriment to economic activity, and hence investment and output in such economies is
likely to be lower, ceteris paribus.
116
See La Porta et.al. who evaluate the impact of different legal institutions (Anglo-Saxon, German, Scandanavian) on growth.
226
Institutions - Measures
There are several measures, or proxies, for institutions. The oldest political institution
measure is the Gastil index of political freedom published by Freedom House. Separate
indices are available for political and civil liberties from 1973 onwards. In recent years,
the Polity IV dataset, maintained by the University of Maryland, has gained in popularity.
It has several different measures of political freedom e.g. the degree of autocracy, the
magnitude of ―executive constraints‖ etc. For some measures, and some countries, the
data go back for at least a 100 years.
Institutions – Measures of Identification
The estimation technique for testing the efficacy of institutions is very straightforward. If
Y‘ is output growth, and I is an institution, and Id an identification variable, then the
following equation can be estimated:
(8.1) Y‘ = a + bo*I‘ + bz*Z,
Where I‘ is the predicted value form the equation
(8.2) I‘ = a‘ + b‘1*Id + bz‘*Z
The critical assumption(the exclusion criterion) is that Id affects I and I affects Y‘; Id does
not have an independent effect on Y‘.
The search is therefore to come out with variables that affect I but not Y‘. In order to
―identify‖ equation 8.1 relating per capita income to institutions, researchers have used a
variety of variables. Among the first to do so was Bhalla(1997) who used a colonial
heritage variable (colonizer British or non-British) to identify the institution of ―political
liberties‖. It was argued that the British transferred democratic institutions onto their
colonies to a much larger degree than the French or Spanish colonialists. Thus, colonial
status would affect the presence of political freedom (institution) in a society, but such
status should not affect growth directly.
Unlike Bhalla(1997), AJR consider the four developed economies – USA, Canada,
Australia and New Zealand – as colonies per se and consider them to have the same
colonial ―heritage‖ as say India. For AJR, the identification variable is the rate of settler
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mortality. This mortality rate determined whether the white colonialists settled in the
colonies (like they did in USA etc.) or not (e.g. Ghana, India).
When the colonialists settled, they transferred or adapted or implemented the institutions
from their mother countries like England, Spain, Portugal or France. This adaptation was
not automatic but was dependent on local conditions. For example, in Latin America , it
was more attractive to settle and extract wealth than to advance the mother country
institutions. ―The main objective of the Spanish and the Portuguese colonization was to
obtain gold and other valuables from [Latin] America‖. (AJR, p.8).
Colonial heritage in a slightly varied form than that used by Bhalla is the basis of the
―legal origin‖ identification variable used by La Porta et. al. (1998). The authors classify
the countries according to the legal system they possess. There is a strong correlation
with the identity of the colonizer but there are some exceptions e.g. non-colonized
countries like Ethiopia and Thailand, as well as British colonies like Egypt which end up
with the French legal system.
Instruments but not based on colonial heritage
Indices of ethnic fragmentation have been used by some authors to identify institutions
(null hypothesis is that if a society is ethnically fragmented, then it is more liable to have
bad institutions, and therefore lower growth.) AJR also use the population density in
1500 as an identification variable. The hypothesis: more densely populated countries
meant lower white settler probabilities and therefore ―lower‖ institutions. Ditto the case
with the settler population per se, though these data are for the late 19th century.
The above variables (or variants thereof) are now standard in the literature. As argued in
Chapter 5, there is reason to believe that the size of the middle class in the mid 19th
century117 may be an important indicator of institutional development then (and a higher
level of income today). This instrument is added to the list. La Porta et. al. also argue
that perhaps education levels in the 19th century is what led countries to have better
institutions i.e. the more emphasis and attainment of education, the higher the demand
for institutions. This is yet another instrument possibility. However, both these variables
(middle class in 1850 and primary school enrollment in 1870 to 1890) could affect
117
The average reference year for the settler mortality variable is around 1850.
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income directly, and thus violate a necessary condition for the instrument to be valid (if
the instrument directly affects both per capita income and the level of an institution, it is
an invalid instrument for both). While this is an empirical matter, other instruments like
settler mortality, also theoretically suffer from this potential drawback.
And this theoretical drawback is not trivial. But none are as forthright as Chang who
advances two hypotheses: first, that western style institutions may not be an important
factor in the economic development of poor nations today; and second, that western
style institutions, the ones whose existence is purported to have made possible the
richness of the West today, may be a form of a luxury good i.e. rich countries possess
good institutions because they are rich enough to demand it.
―There are many examples in history where the preservation of property rights proved harmful for economic development and where the violation of certain existing property rights (and the creation of new property rights) was actually beneficial for economic development…. The best known example is probably the Enclosure in Britain, which violated existing communal property rights by confiscating the commons but contributed to the development of woolen industry by promoting sheep farming on the land thus confiscated. De Soto (2000) documents how the recognition of squatter rights in violation of the existing property owners was crucial in developing the American West. Land reform in Japan, Korea, and Taiwan after the Second World War violated the existing property rights of the landlords but contributed to the subsequent development of these countries‖. Chang(2001)
And
―This suggests that, contrary to what is assumed in the ‗good governance‘ discourse,
many institutions follow, rather than lead, economic development‖. Chang(2001, p. ??).
Do Institutions matter? Revisiting the evidence
In its survey of studies pertaining to the effect of institutions on growth, and in a section
entitled ―A clear case of institutional improvement‖ the IMF (2003, p. 111-112)
concluded:
―The key finding from the empirical analysis in the previous section is that institutional quality has a significant impact on economic performance. This result holds whether performance is measured by cross-country difference in the level of income per capita, in growth rates, or in the volatility of growth. Specifically, improvements in institutions lead to the higher incomes, stronger growth, and lower volatility. These results are quite
229
robust and are independent of the specific measure of institutional quality adopted: similar results emerge whether one focuses on political, legal, or economic institutions. Moreover, the relationship hold across all the main regions, and are not driven by one or two specific country group. The analysis also indicates the presence of ―catch-up‖ or convergence effects. While countries at all level of development would benefit from stronger institutions, the impact of institutional improvement growth appears to be strongest for countries starting from a lower level of economic development. This result further emphasizes the need for institutional strengthening to be at the fore front of efforts to improve growth and reduce poverty, particularly among the low income countries. A key question then is how to create a ―virtuous circle‖ whereby policies are put in place to strengthen institutions, and stronger institutions help support and sustain better policies‖.
The reason the quote has been reproduced in full is to emphasize the magnitude and
the near universal acceptance of the ―institutional‖ conventional wisdom. The argument
is being made that even policies per se are not important unless they operate within an
institutional improvement setting. And that the number one policy facing developing
countries today, especially the poorest ones, was a policy to improve largely Western
style institutions.
It is worth re-examining the empirical evidence. In their original article, AJR had
subjected their model to a battery of sensitivity and robustness tests; others have used
their data and found similar results. Hence, the near cult status of the result that
institutions ―rule‖. Today, how a variable (determinant of growth) performs either
theoretically or empirically has to be benchmarked by the inclusion of institutions in the
model.
In a rather detailed examination of the AJR settler mortality data, Albouy(2005) offers
new estimates of settler mortality for a few countries, and comes to the conclusion that
the AJR ―institutions are significant‖ is extremely sensitive to AJR‘s settler mortality data,
and to the inclusion of two additional geography variables: average temperature and the
minimum monthly rainfall in a year. The results are fragile with respect to the statistical
significance of institutions (expropriation risk) in an instrument variable regression.
Table 8.3 reproduces the original AJR regression, based on the original data for all
variables. In other words, Albouy estimates of settler mortality are ignored. Panel A, row
230
1 reproduces the now widely accepted result: institutions are very strongly significant,
and remain so even when geographic variables like latitude (row 2) are introduced into
the regression. Row 3 adds the two new variables suggested by Albouy (mean
temperature and minimum monthly rainfall) and the institution variable is no longer
significant. If, however, minimum rain is replaced by average monthly rain118, the
institution variable remains strongly significant and with a magnitude near identical to the
base regression.
Panel B in the table replaces the dependent variable with (log) per capita income in
2006. Apart from the difference in the year (2006 vs. 1995), there is also the difference
of the base PPP year, 1996 vs. 1985. Despite these differences, there is virtually no
difference in the results; if anything, the institution variable has a larger magnitude and is
just as significant. All the rest of the results (Table 8.4, 8.5) are with the 1996 PPP
series on per capita income.
118
This is derived from data on days with minimum rainfall and days with maximum rainfall in the month, along with the magnitudes of minimum and maximum rain in a month.
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Table 8.3: Why are some countries rich ? The AJR model relating institutions to levels of per capita income
Dependent variable Risk Latitude
Mean
Temp
Min.
Rain Rain Constant R2 Obs
Panel A
(log) per capita income, 1995
Risk 0.91 -3.91 0.30 64
(5.95) (-3.73)
Risk + Latitude 0.93 0.00 -3.99 0.27 64
(4.57) (-0.22) (-3.18)
Risk + Latitude + Temperature + Rainmin 1.35 -0.01 0.04 -0.01 -7.26 -0.37 64
(1.67) (-0.45) (0.65) (-0.76) (-1.21)
Risk + Latitude + Temperature + Rain 0.91 0.00 0.02 -0.01 -4.11 0.34 64
(4.19) (-0.25) ( 0.64) (-2.31) (-2.17)
Panel B
(log) per capita income, 2006
Risk 1.02 -4.41 0.31 64
(5.40) (-3.41)
Risk + Latitude 1.03 0.00 -4.43 0.30 64
(4.14) (-0.05) (-2.89)
Risk + Latitude + Temperature + Rainmin 1.71 -0.01 0.09 -0.02 -10.39 -0.78 64
(1.60) (-0.32) (1.09) (-0.85) (-1.32)
Risk + Latitude + Temperature + Rain 1.13 0.00 0.06 -0.01 -6.40 0.21 64
(3.73) (0.32) ( 1.60) (-1.52) (-2.51)
Source: SAE dataset, see Appendix I for details.
Note:
232
While the AJR results stand affirmed with ―new‖ per capita income data, it nevertheless
is disturbing that just the replacement of average monthly rain with minimum monthly
rain changes the entire significance of the results. This lack of robustness possibly hints
at other factors at play. La Porta et. al., were skeptical of the institutions cause growth
thesis and wondered whether the result was not being driven by a third factor, education.
If education affects institutions, and institutions affect growth, then it is really initial
education differences, and not institutions, which affect growth. Further, there is the
possibility of reverse causality i.e. higher growth, and higher levels of living, leads to the
demand for, and creation of, better institutions. Which is Chang‘s argument - western
style institutions are the result, not the cause, of growth.
Bhalla(2007a) assembles all the various proxies for institutions, and all the instruments
that have been used in the literature, and conducts a ―horse race‖. Two popular political
institution indices were selected: one measure from the Polity data (executive
constraints) and one from Freedom House (average of political and civil liberties). Two
economic institution variables were selected: a common economic institution variable
(indeed the most widely used) is the risk of expropriation measure published by ICRG
(Inter Country Risk Guide). These data are available for the mid 1980s. The fourth, and
final, measure of institutions used in the empirical analysis is the World Bank Composite
index of governance (a composite of the six components mentioned above).
Eight measures of instruments were selected for the horse race. There are four relating
to colonial heritage – colonial heritage per se, settler mortality, legal origin and the
fraction of settler population in 1900. One instrument is indirectly related to colonial
heritage, population density in 1500. The three additional instruments are ethnic
fragmentation, educational enrollment in the late 19th century, and the percent of
population middle class in 1850.
There are three horse races – one among instruments and the second among
institutions (which institution yields the most robust results) and between institutions and
policy (currency undervaluation). There are two sets of country classifications – the 64
country set used by AJR, and all countries for which data were available (major oil
exporting countries, and countries with population less than 1 million in 2006 were
excluded). There are two dependent variables, log level of per capita income in 2006,
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and per capita growth rate between 1980 and 2006119. For each country selection, for
each institution and each instrument, 9 different models were estimated: four each with
the two dependent variables. For the level regressions, the first regression is the base
regression i.e. log(Y) = a + b*Institution, estimated as an instrument variable regression
with one selected instrument; the second adds latitude, the third adds temperature and
average rainfall, and the fourth adds latitude, temperature and rainfall.
With growth as the dependent variable, there are five models. The base model is growth
Y‘ = a + bo*Y0 + b*Institution where Y0 is log per capita income in 1980. The second
regression adds the two currency undervaluation variables – initial undervaluation in
1980, and the average rate of change in undervaluation, 1981 to 2006. This is the ―base‖
growth model. The three additional models have latitude, temperature plus rain, and all
three geography variables added. In all, therefore, there are 64 regressions for each
model or (64*9) 576 total models estimated. In addition, the Hansen over-identification
(exclusion criterion) test meant that each instrument had to be paired with another
instrument. These are additional regressions.
Tables 8.4 and 8.4b report the findings. The results are presented in terms of
percentage of models passing each stage, and three stages are reported. Stage I is the
OLS result; for example, in 75.1 percent of the models estimated (AJR sample of 64
countries, and log per capita GDP in 2006 as the dependent variable) the summary
World Bank institutions index was statistically significant in a simple OLS regression (i.e.
without the institution variable being ―instrumented‖). When an instrument is used, the IV
regression yields the institution index to be significant in 62.4 percent of the cases.
Stage 3 tests for the ―exclusion criterion‖ i.e. it tests for whether the selected instrument
does not affect both the institution selected as well as the level (or growth) of per capita
income. Stage 3 is the final stage and is a robust test for the influence of institutions on
growth, after incorporation for the possibility of simultaneity. The success ratio goes
down to 43.7 percent.
119
Acemoglu et. al. 2003 also use growth as a dependent variable when analyzing the impact of the policies like undervaluation.
234
Panel B reports analogous results for models with growth as the dependent variables,
and policy variables included as possible determinants. There is a steep fall
The presence of policy variables (UV for 1980 and dUV, 1981-2006) sharply reduces
the significance of institutions. In models without policy, a third pass the third stage – not
as universally strong or robust as claimed by the institution believers. But with policy
variables, only 13.9 percent of the models yield a significant result for institutions.
Among instruments, the settler mortality variable does not allow institutions to pass even
the first stage if policy variables are included. Neither of the two political institutions
variables, executive constraints or political and civil liberties, are significant when policy
variables are included.
In their 2003 article, Acemoglu et. al. test for the importance of exchange rate
overvaluation using the Dollar-Easterly measure. As pointed out earlier, this is a heavily
unrepresentative index of exchange rate overvaluation. When more appropriate indices
of exchange rate overvaluation are used, policy rules. The other interpretation of the
results is that institutions are like a BMW – they are luxury ―goods‖ more of which is
demanded by richer countries. Once purged of simultaneity, institutions do not seem to
have any effect on growth.
Table 8.5 documents two important results for the policy variable, currency
undervaluation. First, that for a very large set of models, at least one of the two currency
undervaluation variables (initial level of undervaluation or average annual change in
undervaluation 1980 to 2006) is significant. Second, the coefficients do not vary that
much across the instruments or the institutions chosen to be in the model. Broadly
speaking, the coefficient for UV is -.02, and for dUV, -0.5. The coefficients are very
similar to those obtained by a variety of different methods. This adds to the generally
robust nature of results for currency undervaluation affecting growth that was
documented in Chapter 7.
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Table 8.4b: Various level of income and growth income models: By Institutions
Level equation, dependent
variable (log) p.c. income,
without UV
Growth equation, dependent
variable growth in p.c. income,
with UV
Stage1 Stage2 Stage3 Stage1 Stage2 Stage3
World Bank index 68.6 58.6 38.0 41.8 27.6 20.9
Political + Civil liberties 45.5 38.2 28.6 24.5 1.5 1.0
Polity IV index 51.2 38.2 31.2 19.9 0.0 0.0
Risk of expropriation 68.2 50.2 30.6 51.0 31.1 24.2
All 58.4 46.3 32.1 34.3 15.1 11.5
AJR countries only (64)
World Bank index 75.1 62.4 43.7 44.9 32.7 26.5
Political + Civil liberties 49.0 36.7 30.2 30.6 3.1 2.0
Polity IV index 60.4 36.7 31.4 27.6 0.0 0.0
Risk of expropriation 65.7 50.2 33.5 51.0 32.7 27.6
All 62.6 46.5 34.7 38.5 17.1 14.0
All countries (non oil exporting & population>1 million in 2006)
World Bank index 62.0 54.7 32.2 38.8 22.4 15.3
Political + Civil liberties 42.0 39.6 26.9 18.4 0.0 0.0
Polity IV index 42.0 39.6 31.0 12.2 0.0 0.0
Risk of expropriation 70.6 50.2 27.8 51.0 29.6 20.9
All 54.2 46.0 29.5 30.1 13.0 9.1 Source: SAE dataset, see Appendix I for details.
Note:
236
Table 8.4b: Various level of income and growth income models: By Instruments
Level equation, dependent
variable (log) p.c. income,
without UV
Growth equation, dependent
variable growth in p.c.
income, with UV
Stage1 Stage2 Stage3 Stage1 Stage2 Stage3
All
Colony 55.0 32.5 23.6 50.0 43.8 33.9
Ethnic Fragmentation 30.0 30.0 18.3 6.3 6.3 5.2
Legal origin 30.0 7.5 4.6 56.3 37.5 29.2
(Log) settler mortality (around 1850) 47.5 47.5 28.3 0.0 0.0 0.0
(Log) Population density (1500) 37.5 22.5 17.5 31.3 12.5 8.9
Education: Enrollment 1870-1890 92.5 77.5 57.1 43.8 6.3 4.2
Percent Middle class (1850) 90.0 80.0 59.2 34.4 9.4 7.3
Percent settler population (1900) 85.0 75.0 49.6 50.0 0.0 0.0
Total 58.4 46.3 32.1 34.3 15.1 11.5
AJR countries only (64)
Colony 65.0 30.0 23.6 50.0 50.0 46.4
Ethnic Fragmentation 15.0 15.0 9.2 0.0 0.0 0.0
Legal origin 30.0 10.0 5.8 56.3 50.0 37.5
(Log) settler mortality (around 1850) 50.0 50.0 30.0 0.0 0.0 0.0
(Log) Population density (1500) 65.0 40.0 32.5 37.5 0.0 0.0
Education: Enrollment 1870-1890 95.0 75.0 62.5 68.8 12.5 8.3
Percent Middle class (1850) 95.0 80.0 59.2 43.8 18.8 14.6
Percent settler population (1900) 85.0 75.0 56.7 50.0 0.0 0.0
Total 62.6 46.5 34.7 38.5 17.1 14.0
All countries (non oil exporting & population>1 million in 2006)
Colony 45.0 35.0 23.6 50.0 37.5 21.4
Ethnic Fragmentation 45.0 45.0 27.5 12.5 12.5 10.4
Legal origin 30.0 5.0 3.3 56.3 25.0 20.8
(Log) settler mortality (around 1850) 45.0 45.0 26.7 0.0 0.0 0.0
(Log) Population density (1500) 10.0 5.0 2.5 25.0 25.0 17.7
Education: Enrollment 1870-1890 90.0 80.0 51.7 18.8 0.0 0.0
Percent Middle class (1850) 85.0 80.0 59.2 25.0 0.0 0.0
Percent settler population (1900) 85.0 75.0 42.5 50.0 0.0 0.0
Total 54.2 46.0 29.5 30.1 13.0 9.1
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Table 8.5: Significance of Policy in Institution Regressions
Percent Coefficient (median)
Significant UV dUV
All Average
World Bank governance index 89.3 -0.014 -0.514
Political liberties 69.4 -0.012 -0.503
Polity IV (executive constraints) 61.2 -0.009 -0.539
Risk of expropriation (ICRG) 69.4 -0.011 -0.478
Total 72.3 -0.012 -0.508
AJR countries
World Bank governance index 84.7 -0.011 -0.370
Political liberties 81.6 -0.010 -0.466
Polity IV (executive constraints) 74.5 -0.007 -0.457
Risk of expropriation (ICRG) 69.4 -0.008 -0.351
Total 77.6 -0.009 -0.405
All countries
World Bank governance index 93.9 -0.026 -0.545
Political liberties 57.1 -0.022 -0.601
Polity IV (executive constraints) 48 -0.022 -0.593
Risk of expropriation (ICRG) 69.4 -0.014 -0.530
Total 67.1 -0.019 -0.555 Source: SAE datasets, see Appendix I for details
Notes: 1) the column percent significant is the fraction of models where either the coefficient of UV
or the coefficient of dUV was statistically significant at the 5 % level in stage 3 of the regressions.
238
“Will it make it easier on you now You got someone to blame… Have you come here to play Jesus To the lepers in your head” U-2, One, 1992
Chapter 9 – India China 1950-1980: Three Aberrant Decades When China and India look back, and much after the last ones who led them during this
period pass away, they will conclude that in their long, rich history, these were three
aberrant decades. The two societies have been entrepreneurial and innovative for
thousands of years; what made them trust this new system, wildly different from their
past experience, to dictate their life, and mostly their misfortunes for these three
decades? When history is written, we will know; but one fact we know now: the leaders
of China and India both believed that they had inherited a raw deal. India believed that
colonialism had done it in, and China believed that capitalism was responsible for its
poor misfortunes. There was another belief, a belief shared by both leaders, a belief that
dictated their economic policies – a complete disregard for the ―market‖, and a complete
contempt for economic freedom. The less of it the better, was the rallying cry. The
political leaders of both countries believed in playing God, believed that they, and the
state, had all the answers, and all the questions.
Both countries gained independence at the same time. India, in 1947, from British
colonial rule; China, in 1948, from feudalism. For the next thirty years, the two followed
genuinely divergent political paths. Communism vs. democracy. Dictatorship versus
political participation. Both societies eschewed capitalism in their first 30 years of
―independence‖. At about the same time, in the 1950s, both societies announced plans
for a major transformation. China proceeded with forced steel mills in 1958 as part of its
―Great leap Forward‖. This was part of the newly adopted Five Year Plans (Soviet style)
to transform in a hurry, again Russia like, an agrarian society into an industrial one. India
was on the same mission, and twin-like, also believed strongly in five year plans. So
strongly that in 1956 it launched the Second Five Year Plan, a document with several
initiatives, and orders, to transform India in a hurry. The private sector was presumed to
be incapable, so the state had to take the lead. Forced industrialization, and forced
much else.
239
There were more twin like actions to follow in these aberrant decades. The pre-
occupation with planning, with industry, with controls, and with state led suppression of
economic freedom. It is unlikely that the twins were acting without observing and
following the other. In 1966, Mao, as continuation of the Great Leap Backward, launched
the Cultural Revolution with the objective: ―to struggle against and crush those persons
in authority who are taking the capitalist road‖. Indira Gandhi, co-incidentally, came to
power in the same year, and as part of the same objective of crushing the capitalist path,
nationalized all banks in 1969. More of the Indian cultural revolution was to follow; a
political emergency was imposed in 1975, but not before a population control forced
sterilization campaign was launched in 1974. In the same year, China introduced its one
child policy.
Given that the economic system adopted by the two countries was broadly the same, it
is not surprising that economic misfortunes followed a similar path. The stress on import
substitution financed by an overvalued exchange rate had exceeded its use date,
especially in the wake of large increases in the price of oil in the 1970s. One economic
policy mistake had led to another in these three decades; whatever policy could go
wrong, did, and all were found in both India and China.
Three Decades – Experiments in Failures
Both countries had inherited a deeply overvalued exchange rate, and whether it was
false nationalist pride, or ignorance, both countries persisted with this overvaluation. This
overvaluation was around 180 percent. As discussed in Chapter 3, the real exchange
rate in both societies in 1950 was nearly identical to the real exchange rate in the late
19th century. If the currencies were overvalued a 100 years earlier, they were much more
overvalued in 1950, after almost 80 years of faster growth in the US than at home.
This overvaluation led to a distorted pattern of development in both countries. Agriculture
was ignored, industry emphasized; imports were favored, exports were taxed. A familiar
tale in those days; the fact that we have the opposite problem today indicates how much
the development models, and the world, has changed. Industrialization in a hurry meant
a neglect of agriculture. China paid for it in terms of the famine of 1960/61, and India in
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terms of not emphasizing irrigation enough. In 1965/66 and 1966/67, India had the two
severest droughts of the century, back to back.
In terms of patterns, there are three noteworthy factors about China and India in those
early years . First, the share of agriculture in both societies was the same, around 50
percent. Where the two countries differed was in the respective shares of industry, a
difference that persists till today. Even as far back as the 1960s, the share of industry in
China‘s GDP was 35 percent, and in India almost half that, at 19 percent. This second
fact is little recognized in discussions about the differential pattern of growth in the two
countries. China is exceptional in the share of industry today (around 48 percent) and
India is exceptional in the share of services (around 48 percent)120. But the disparity was
there much before economic reforms, or economic policies, began to misshape the
development pattern in the two countries.
Today, the share of agriculture is within a few percentage points (13 vs. 19), but there is
a huge gap in industry, 46 versus 25 percent. Simple pattern fits indicate that both India
and China were far off their respective predictions in 1965, and China more so. For
example, the share of industry in Chinese GDP (based on a simple regression relating
shares to per capita income) in 2004 was 46 percent when it should have been 34
percent; for India, the actual share was 25 percent, and it should have been 30. So the
―industrial gap‖ between India and China is explained one-third by India‘s
industrialization being much lower than predicted, and two-thirds by China‘s being much
more than predicted.
Some of the reasons for the initial gap i.e. the sorry state of India‘s industrialization in the
Nehru era, 1950 to the late 1960s, is explored intensively by Bhagwati-Desai(1971).
Their book, Planning for Industrialization: Industrialization and Trade Policies since
1951, was the first wake-up call to Nehruvian socialism and is a must read for anyone
wanting to understand how, and why, and where, Indian industrial development went
wrong – especially given the rich industrial and entrepreneurial heritage, a point the
authors emphasize. The reason for the still large gap today: the much larger, and for a
longer time, magnitude of exchange rate undervaluation in China.
120
World Bank definition, World Development Indicators, 2005.
241
Along with the share of industry, another historical difference in the two countries is in
their savings rates.121 Even with very similar per capita income levels in the mid 1960s,
China was saving, and investing, around 25 percent of its income; India around 15
percent. Indeed, until 2003, India‘s investment rate was hovering around 25 percent.
This historical statistic about the high Chinese savings rate, even when very poor,
suggests that the explanation that China‘s high current account surplus today is due to
its high savings, and not due to its deeply undervalued exchange rate, is not entirely
correct.
Education Policy: Twin like and behind the curve
One consistent argument, brought out in defense of China more than India, is that
Chinese communism did a lot to spread education among its people. According to data
assembled by Morrisson-Murtin (2006), educational attainment in China in 1980 was
above that of developing countries, but with the income level only half the corresponding
average (Table 9.1). This education base is often credited with the super miracle growth
of China post 1980. Hence, it is argued, the three Mao decades were not all that
aberrant, and perhaps even necessary, in laying the foundation of future growth in
China.
A similar argument is made for India. Here the argument goes as follows. The Indian
growth breakout in the late 1990s was really due to its emergence as a software power;
that one good policy of Nehru, like the good policy of Mao, was in creating the ―high
temples of learning‖, the Indian Institutes of Technology or the IITs. Five IITs were
created in the late fifties early sixties as specialized engineering colleges; only about
2000 people were selected each year out of applications upwards of 100,000 then and
over 300,000 today. By instituting the IITs, Nehru laid the foundation of India‘s software
prowess; indeed, if IITs had not been set up, the argument goes, the software revolution
would have been missed by India.
Both the arguments are quite wrong. The defense of an elite education policy is
erroneous on at least three counts. First, it does not acknowledge the counter-factual –
121
The household savings patterns have been broadly similar, but not the savings ratios of the corporate and/or public sector. In China, the non-household share of savings is higher today, and was much higher in the 1950s.
242
Table 9.1: Education years
Developing economies
Year India China Education
years Per capita
income
1870 0.0 1.0 0.9 1.7
1880 0.1 1.1
1890 0.1 1.1 1.0 1.9
1900 0.2 1.2
1910 0.2 1.3 1.1 2.2
1920 0.3 1.3
1930 0.4 1.4 1.3 2.6
1940 0.6 1.6 1.4 2.7
1950 0.8 1.7 1.6 2.6
1960 1.3 2.3 2.0 3.4
1970 2.4 3.3 2.8 4.6
1980 3.1 4.6 3.6 6
1990 3.5 5.7 4.6 7.3
2000 4.3 6.6 5.6 10.3 Source: Morrison-Murtin (2006)
Notes: Numbers represent the mean years of education attained by the population age>= 15 years.
Table 9.2: How different have China and India been?
India (dummy) China (dummy)
Education-
Income
Elasticity Coef t-stat Coef t-stat
1950 0.67 -0.42 -1.43 0.77 1.98
1960 0.55 -0.33 -2.09 0.51 2.76
1970 0.40 -0.18 -1.41 0.43 2.83
1980 0.31 -0.16 -1.44 0.34 2.90
2004 0.32 -0.21 -3.71 -0.11 -2.83
Note: a positive coefficient for the dummy variable means the country in question had higher education achievement level compared to the average country.
243
surely, the argument cannot be that if IITs had not been set up, Indians would not have
gone into software development, and succeeded. Second, little was known about the
software revolution in 2000 at the time the founding fathers set up the institutes in the
late 1950s. Third, and most importantly, India‘s education policy was geared towards the
elite, and came at the expense of expenditures on primary and secondary education;
expenditures substituted that has made India educationally backward. According to
Table 9.1, India was far behind the education curve in 1950, even after controlling for per
capita income levels, and has stayed behind the curve till today.122
Table 9.2 reports how far behind (or ahead) India and China were with respect to their
peers. In case of China, the argument that Communist dictatorship was good for
education for the masses is proven wrong by the simple observation that China was well
ahead at the time of the Communist revolution. Indeed, China‘s advantage drops from
77 percent ahead of the peers in 1950, to 50 percent ahead in 1960, to only 34 percent
ahead in 1980.
Statistics relating to growth (output, capital, labour force) etc.) and related parameters for
China and India are presented in the next chapter. A brief summary of the three aberrant
decades is as follows. The total disregard for economic freedom meant that both
societies grew at abysmally low rates – China at 2.5 % per capita per annum, 1950-
1979, India a percentage point lower at 1.4 percent. Total factor productivity growth in
China and India reached their lowest levels in the 1970s; China 0 for the entire decade,
and India, a decline of –0.8 percent per annum. The sorry story continued in parallel till
1980, at which time Indian per capita income was about 10 % higher than China. Thirty
years of independence for India, and 30 years of Communist rule for China, did nothing
to change their traditional parity in per capita incomes.
Both India and China were poor in 1950, and remained poor in 1980. China achieved a
much higher growth rate than India, but both grew at rates significantly below the
122
This is according to a simple model relating log years of education to log income for 47 developing countries (excluding oil dependent economies, economies with population less than 1 million in 2006 and countries in sub-Saharan Africa). If African countries are included then India remains a below average country in terms of educational attainment, but for some years the coefficient is not negative, or significant. For 2004, both sets of regressions (with and without Africa) have India as a significant, negative outlier. The coefficient is either 32 percent below the ―predicted‖ value (without Africa) or 13 percent (with Africa).
244
developing countries average, and at rates much lower than what they achieved when
they began to open up in the 1980s, and when they began to allow ―markets‖ to develop.
In 1960, poverty in India and China was around 55 percent, with China somewhat
higher. In the late 1970s, poverty was little different in either society.
The reason for slow growth, and even slower poverty reduction, was the pre-occupation
of both countries with fast induced industrial growth a la Russia. The Russian model
required heavy state intervention, and very large tariff walls, and a very overvalued
exchange rate. This model did not work, even though the necessary ingredients of
savings was present in both countries, and somewhat more in China. In addition, China
had inherited a more advantageous growth base in terms of education, but this
advantage was squandered away by the closed nature of the economy. In the case of
India, its elite bias ensured that educational improvement for the masses would be slow
in coming. Finally, there is one other economic statistic worthy of note about the two
economies – the surprisingly high (and higher than expected) share of industry in China,
and the low, and lower than expected, share of industry in India. Some 50 years after
this inheritance, economists would still be talking about the industry gap(s) in the two
countries.
245
“Indian national income might, then, very well increase at an annual rate of 8-10 percent; and we might have in addition, natural import substitution, too.” B.R Shenoy, Indian Economic Crises, 1968
Chapter 10: India China 1980-2006: The End of the Debate
In 1978, Deng Xiao Ping, a communist leader who had been banished during the
Cultural Revolution, brought about his own revolution of ―catching mice‖. ―The color of
the cat did not matter, as long as it caught mice‖ is a homily with few equals in the
developing world. For all the ideologues, this was Deng‘s answer. India, a slower cat123,
was to start catching mice only 13 years later. Which is why China is a much bigger cat
today.
The China India experience post 1978 is the most natural of natural experiments. Two
giant economies in 1978: population sizes 985 billion, China and 685 million, India. Per
capita incomes: about the same, PPP$ 2.9 and 3.2 per person per day, respectively.
Per capita growth over the preceding twenty years: 2.2 and 1.6 percent per annum.
While the two (then pygmy size) giants accounted for 40 percent of the world‘s
population in 1980, they contributed only 8 percent of the world‘s income. The lowest in
the last 500 years and, by inference, the lowest, ever. In the next 25 to 30 years, the
world changed. Per capita income in 2006 is more than six times higher in China; in
India more than thrice.
This post economic reform experience in both countries has been a great silencer. The
old debates have ended, giving in, predictably, to new debates. With a difference – the
debates are now global. It has all been so sudden. There are few people I know, indeed
none, who predicted that this would be the talk. Not even five years ago when the writing
was clearly on the wall. The argumentative Indians, were doing what they have always
done best – assume, and argue, that it was impossible that India could grow much
above its new ―Hindu‖ rate of GDP growth of 5.5 to 6 percent per annum, up marginally
from the 3.5 to 4 % growth rate of some thirty years earlier. Today, GDP growth has
averaged 8.3 % per annum for the last four years. More startlingly, per capita growth
rate has averaged over 7 % per annum, with population growth at 1.5 percent per year
123
This is still strange news to some Indians, especially those belonging to the Communist party. The catching of mice is protested at every left corner in India with placards stating that economic reforms only hurt the poor.
246
and declining. That is a doubling of per capita incomes every 10 years, compared to the
earlier Hindu rate of doubling only once every 48 years.
What was this new development model that delivered such rapid growth? A natural
economic freedom experiment. Political freedom in both societies stayed the same post
1980 as before.124 Economic freedom changed in both societies, much more so in China
than in India, at least in the initial two decades post 1980. Market forces were allowed to
operate in China, and property rights introduced, though it is really now, in 2007, that
property rights are truly provided for. Nevertheless, the ―rights‖ were an infinite increase
in the early 1980s, albeit from a zero base. Some producers were allowed to be
independent of the state (in China); in India, the license-permit raj began to be wound
down. Like the previous inglorious period (1950 to 1980), the two big ones adopted
similar strategies, this time getting it right: they began to get the international trade
sector right.
The post-1980 experience of China and India is a triumph of policy, pure policy. Most
economists recognize that once the train leaves the station, it really does accelerate,
and disappear from the horizon. Most often in a positive direction, and sometimes, but
not so rarely, in approaching the dark side. In both cases, the search is for the triggers to
growth. Geography and institutionalists need not apply.
This chapter attempts to retell the now very thoroughly researched story about the
economic transformation of China and India. The story about China is very well known,
and without much controversy. The real controversy about China is the exchange rate,
and its repercussions for global imbalances, and its role in global disorder. This was
extensively discussed in Chapters 6 and 7. India was not much discussed in those two
chapters; this chapter will restore some equality because the post 1980 Indian growth
experience is steeped in controversy.
There are some minor China controversies, two actually, and both pertaining to
estimates of Chinese growth. The first controversy arose when Maddison estimated that
Chinese growth prior to 1995 was actually lower by about 2 percent per annum. This
was a reduction from about 9.5 percent per annum to 7.5 percent per annum. The
124
Verification is received from all the political indices available – the political liberties index, constraint on executive, polity index, etc.
247
accepted wisdom today (at least according to the Penn Tables) is that Maddison was
right. The second minor controversy arose in the late 1990s when some contrarian
economists took the stand that economic growth rates in China were vastly exaggerated.
It was argued that looking at electricity consumption data gave a more accurate picture
of considerably slow growth in China. This belief gained some currency on the back of
the obvious point that income levels in developing countries are measured with error.
But normally the error goes in the opposite direction as underground or black incomes
are hard to catch, and therefore total income gets under-reported. To the extent that this
share of black income is declining, reported income growth will be biased upwards. But
this effect is likely to be gradual.
There was one factor going significantly against the hypothesis of slower than reported
growth in China. Exports and imports are unlikely to be measured with much error and
when they are (e.g. Chinese exports being understated for political reasons), one has
the data for imports from China from the importing countries. And these cross checked
export and import data showed that the Chinese economy was booming, at a minimum,
and that the growth rate was likely understated, not overstated. This controversy has
now died – the autopsy report says ―much ado about nothing‖.
China 1978-2006
The natural experiment took a new turn when Deng Xiao Ping led China into the modern
world in 1978. On the surface, one dictator Mao Tse-Tung was replaced by another. But
that was all there was to any connection between the old and the new China (to be).
Economic reforms were brought quickly, and across most sectors of the economy in
China. Perhaps most important, at least initially, were reforms pertaining to agriculture.
From being a mass collective, China was transformed into a billion individual capitalists.
Farmers had a right to keep some of the produce for themselves once a certain
minimum was sold to the state. Foreign investment, hitherto unwelcome, was now
showered with subsidies to help build the new China.
China was not witness to any political institutional change in 1978, or afterwards. It was,
and remains, a Communist party dictatorship. Only the harrowing experience of
devastating unnatural experiments in the form of the Cultural Revolution and the Great
Leap Backward would have predicted the vengeance with which China would change its
248
economic institutions, the rules of the all important game. The growth rate in China
accelerated by 2 percentage points in the first decade (the 1980s) and by an additional
2 % the next . The growth rate post 2000 was to increase still further, but that is getting
ahead of the story. (Chart 10.1)
China: the growth policy
Ever since China changed course in 1978, its‘ model development path has been that of
export led growth. This is the famed Asian model of growth, but in China it seemed to
have been a lot more extreme. The share of industrial output was already high in China
in the 1960s, and had reached 41 percent of GDP by 1970. (In the same year, the
corresponding share in India was only 19 percent). This output was to be further
increased to 48 percent at the time reforms began in 1978. The share of industry has
stayed at this level through out. This large inherited share had to be made efficient,
internationally competitive. And for that to be possible, China has had only one policy –
no, not just catching mice, but devaluing, deep devaluing.
This is what is so different about the China model of development, a model that is having
its repercussions today in global imbalancing. With rather aggressive exchange rate
undervaluation (and supported equally aggressively by the World Bank and the IMF),
China devalued from 1.50 yuan/$ in 1980 to 8.6 yuan/$ in 1994, a 250 percent real
devaluation, one of the largest in recorded history125. In contrast, India‘s real devaluation
for the same period was a relatively muted 100 percent (nominal exchange rate went
from 7.9 to the dollar to 31.4)126 For the shorter period 1990-1994, China‘s real
devaluation was 60 percent, India‘s 35 percent. Yet another of the strange co-incidences
between India and China – both countries devalued significantly in the period 1990-1994
with China having again taken the lead in 1990. However, India‘s depreciation has
always been of a much lesser magnitude than China.
125
In contrast, Indonesia, despite a large 250 percent devaluation in 1997/1998, had managed only a 15 percent real devaluation by 2003. 126
These are real devaluations and therefore are yet another example of how the real exchange rates are not really endogenous, or that a large proportion of nominal devaluations do not end up in correspondingly higher inflation rates.
249
Chart 10.1: Growth in GDP, China (%)
250
What explains the Chinese miracle 1978-2006?
While export-led growth has been the assumed model behind East Asian growth of the
1960s through the 1980s, somewhat surprisingly, and perhaps in keeping with the
political economy of the Chinese currency (discussed at some length in the previous
chapter), very few analysts have dared to call the Chinese model of growth as such. One
of the leading experts on the Chinese economy, Jonathan Anderson of the investment
bank UBS, recently stated in an article entitled The Seven Biggest China Fables, ―China
is not an ‗export-led‘ economy!‖ (May 21, 2007, emphasis and exclamation mark added).
The Chinese model of growth was much discussed in the mid to late 1990s, and a useful
summary of the prevailing views can be found in Naughton(1995), World Bank‘s World
Development Report(1996), Sachs-Woo(1997) and Sachs-Basu(1997). Exchange rate
devaluations (and these were historically the largest of those observed for any country,
and certainly the largest, by a very wide margin, for a large country) do not appear to be
much discussed as a factor behind the growth. If mentioned, these devaluations are
interpreted to be in terms of ―unification‖ of dual exchange rate system. Undervaluation
of the currency does not appear anywhere in these papers. Export-led growth is
mentioned, but mostly as a by-product of the Chinese growth strategy, never as an
explicit goal (as the growth process of other East Asian countries, especially Japan, has
usually been described).
Second, the debate about Chinese growth seems to have revolved around whether
Chinese growth belonged to the ―experimentalist or the convergence school‖. The former
school e.g. Naughton, ―gives great credit to the evolutionary, experimental, and
incremental nature of China‘s reforms. In this view, China has been groping, with
considerable success, towards a unique Chinese economic model… A faster approach
to reforms, according to the experimentalist school, would have led to more social
conflict, instability, and poorer economic policies‖ (Sachs-Woo p.4)
The alternative school of thought is the ―convergence school‖ to which Sachs and Woo
belong. This view held that it was natural for China to converge to East Asian levels of
living and that ―Gradualism, in this view, has not been a strategy so much as a result of
continuing political conflict and other difficulties inherent in setting a policy course in a
country of some 1.2 billion people (Sachs-Woo p.4)
251
A firm conclusion which emerges is that both schools of thought believed China to be
gradualist in its policies; this despite the record setting changes, in record time, of the
most major instrument of policy – currency devaluation. Which raises the obvious
question – if policy change in China was to be described as ―gradual‖, then how would
one describe India‘s economic reforms, glacial?
Thus, the development story of China post 1980 is not controversial. Most analysts
believed exchange rate to be a non-policy; and currency undervaluation to be a non-
issue. Recently, however, some China specialists e.g. Branstetter-Lardy, have
concluded that whatever the original intention or policy, the present Chinese exchange
rate is significantly undervalued. The cue is taken from the excessive build up of
reserves, and the excessive trade imbalance with the US, and the world. Many others,
however, including Collins-Bosworth, believe that the exchange rate explanation for
Chinese growth is either inconsequential, too little, or irrelevant127
Tables 10.1 to 10.3 describe the Chinese growth process in some detail. The present
day China growth story is not in dispute. Though its causes are. If a sustained growth
rate above 10 percent per annum is not miraculous, then no growth is a miracle. But as
discussed in Chapters 6 and 7 is accepted, at least 2.5 percentage points of growth is
due to the extra devaluation that China has achieved; excluding this, China is an
average east Asian fast economy; excluding this growth, there is unlikely to have been
the generation of global imbalances.
There are few other explanations which can place the exceptional growth in China into
perspective. If the deep undervaluation explanation is not accepted, then we are left with
a miracle to explain. Is it possible that China should get its growth policies so right that it
can grow at double the rate of most fast growing countries in the world? Over the last
seven years (growth rates since 2000 and including 2006) capital growth in China has
127
Bosworth-Collins(2006) fall into this category; their paper on India China growth post 1980 does not contain a single mention of exchange rate policy.
252
Table 10.1: Sectoral GDP: China, 1960-2006
Growth (in %) Share in GDP
Agriculture Industry Services Agriculture Industry Services
China
1960s 2 5.9 2.9 39.2 34.8 26.1
1970s 2.9 10.4 5.9 32.3 44.5 23.1
1980s 5.1 10 11.9 29.3 44.6 26.1
1990s 4.2 12.1 8.9 20.3 45.5 34.2
2000s 3.3 9.8 9.4 13.6 45.6 40.7
2003-06 4.29 11.31 8.58 12.85 46.03 41.01 Source: World Bank World Development Indicators (2006).
Table 10.2: Annualized growth (%) and TFPG, China
Growth in
Population GDP GDP, pc TFPG
1950s 1.6 4.9 3.3 1.5
1960s 2 3.5 1.5 1.9
1970s 2 4.6 2.6 0
1980s 1.5 6.4 4.9 1
1990s 1.1 8.2 7.2 2.8
2000s 0.6 10.5 9.9 5.7
2003-06 0.6 10.7 10.1 5.5 Source: Maddison (2006); Penn World Table version 6.1; World Development Indicators, World Bank(2006); World Economic Outlook, IMF (2006). Notes:
1. TFPG is calculated as a residual from a panel data for developing countries for the period 1960-2006. See Appendix I for details. Coefficient of capital and labour were 0.40 and 0.60 respectively (e.g. 1960-69).
2. Each decade ends in the ninth year except 2000s which is from 2000-2006.
253
Table 10.3: GDP and its determinants, China, 1950-2006
Growth (in %)
Capital
Labour
force
Income
per
worker TFPG UVSAE dUVSAE dUVIMF
1950s 5.2 2.1 2.8 1.5 189.5 2.3
1960s 1.4 1.6 1.9 1.9 190.3 -2.1 -4.7
1970s 7.2 2.5 2.1 0.0 175.0 -0.5 -1.5
1980s 9.5 2.2 4.2 1.0 94.1 -9.8 -8.4
1990s 10.7 1.3 6.9 2.8 7.1 -8.6 -2.8
2000s 9.9 0.8 9.8 5.7 -42.2 -7.0 0.9
2003-06 10.8 0.8 9.9 5.5 -52.7 -5.4 2.0 Source: Maddison (2006); Penn World Table version 6.1; World Development Indicators, World Bank(2006); World Economic Outlook, IMF (2006).
254
averaged 10 percent, and with very little volatility. Per capita growth has averaged the
same. Labor force growth has averaged less than 1 percent, actually 0.8 percent per
annum. Total factor productivity growth in the last four years (2003 to 2006) has
averaged 5.3 percent per annum; if Bosworth-Collins(2006) estimates of capital share
(0.40 Vs. our estimate of 0.46) are used, TFPG growth has averaged 6 percent per
annum. This is a near doubling of productivity growth over the previous 1990s decade
average of 2.6 percent per annum. The numbers are placed in perspective by noting that
the median TFPG is less than 1 percent per annum, with the mean even lower. So even
before the recent blow-off in growth (and real exchange rate undervaluation), China was
already growing at an aggressively exceptional pace. In a competitive world, faster
growth in China means slower growth in Latin America. Asia, and rest of the world.
What happened in India, 1980-2006
The change started slowly. First, some political upheavals. Mrs. Indira Gandhi who had
violated the Constitution by assuming dictatorial powers in 1975 was back in power in
1980. This was heady stuff for a still young democracy. In a space of five years, there
was a constitutional amendment making India a socialist state, another amendment
granting the not in name only socialist leader dictatorial powers, the emergency dictator
losing elections in 1977 and three years later, the bringing back of the now no more
dictator via elections.128 Indians were justifiably proud to have the political system they
did. But they had always, at least since independence, had political freedom. What was
lacking was the twin side of freedom. But starting in the early 1980s, unbeknown to
most, if not all, subtly but surely, India‘s economic fortunes were to change forever.
If China had moved towards economic reforms, could India be far behind? We know
from past record that India and China had made it almost a habit, sometimes forced by
circumstance, other times by a parallel ideology, to broadly mimic each other‘s policies.
So it was entirely predictable that India would also open itself internally and externally.
This time, however, the lag was to be 13 years, starting in June 1991, when a new
government was installed, and the Indian Prime Minister, Mr. Narasimha Rao, choose a
economist technocrat Dr. Manmohan Singh to run the most important ministry,
128
The ―socialist state‖ has remained as part of the preamble to the constitution.
255
Finance129. This established a long 13 year gap between India and China‘s change in
economic policy and fortunes.
Post 1980 growth story in some detail
Chart 10.2 documents the Indian growth performance from 1950 to 2006; both annual
changes and five-year average growth are plotted. The most significant economic story
of India in the post-independence era is the story of economic reforms in 1991. That,
year India faced its most serious economic crisis. After a decade of high growth in the
1980s, and a slow ―Hindu‖ rate of growth before, the economy stuttered, foreign
reserves were close to zero and GDP barely grew (0.9 percent) in 1990-91. Elections
were held and the incumbent, crisis inducing government was thrown out. The response
by the government was different in style and structure than any policy initiative of the
post-independence era. Given that the Finance Minister Mr. Singh‘s Oxford Ph.D. thesis
was on the necessity of export growth for successful development, perhaps it was not a
coincidence that foreign trade initiatives defined the policy response. A series of policy
initiatives were introduced, starting in early July 1991. For starters, the rupee was
devalued by 20 percent, peak tariffs were reduced from 300 percent to 110 percent, and
a structural adjustment loan from the International Monetary Fund (IMF) was obtained.
Starting in 1994, the Indian economy felt the full impact of these reforms as growth
accelerated to above 7 percent in each of the next 3 years. Agricultural growth
fluctuations had caused GDP growth to often grow above 7 % (e.g. in 1964, 1967, 1975
etc.); however, this was the first time such growth had occurred without a snap back
from a preceding drought year.
There are five broad stages of Indian growth post independence. In stage I, the five-
year average growth was consistently below 5 percent in the period 1950 to 1980, but
what is noteworthy is that about once each decade average GDP growth did poke above
the 5 percent barrier. In Stage II, starting in the early 1980s, there is a marked
acceleration in GDP growth to above 5.5 percent per annum. Stage III is the growth
129
It is noteworthy that the only time major economic reforms were undertaken in India, in 1991-1993, they were initiated by the largest and oldest Indian political party, Congress, but without a Nehru-dynasty individual as its head. The fact that India had just experienced an economic crisis after 45 years of economic controls was no doubt a contributory cause; it is a moot question, however, whether a Nehru-dynasty individual would have had the courage to go against a 100 year dynastic commitment to socialism and economic controls. Could a clean break from the past have been initiated by a Nehru-Gandhi dynasty person, no questions asked? Doubtful.
256
Chart 10.2: Growth in GDP, India(%)
257
acceleration for the three year period starting in 1994 - an acceleration from 5.9 percent
per annum in the 1980s to 7.6 percent per annum. Stage IV (1997 to 2002) is the great
surprise slowdown – during these years, GDP growth averages only 5.3 percent, making
the average post-reform growth to equal the pre-reform growth of the 1980s. Just as
miraculously (though there is a consistent explanation provided below), average GDP
growth rate accelerated to 8.5 per cent per annum, 2003-2006. This is Stage V. (See
Tables 10.4.4 to 10.6).
Indian growth record post 1980 – steeped in controversy
Unlike China, India‘s growth post 1980 is very controversial. There are two disputes. The
first dispute pertains to the lack of acceleration of GDP growth after economic reforms of
the early 1990s: about growth that did not happen (Tables 10.4-10.6). The 1980s growth
acceleration was not preceded by any known economic reforms. In the 1990s, economic
reforms happened, but no growth acceleration. This first controversy involves
explanation of two ―surprising‖ phenomena; the acceleration in the 1980s and the lack of
acceleration in the 1990s. It is important to resolve what happened; if no resolution or
answer is forthcoming then one would be forced to conclude that policies do not matter.
There is a new controversy130, this time about the dog that did bark. There have been
zero economic reforms the last few years, yet GDP growth has accelerated to an
average of 8.5 percent, with the most recent year, 2006-07, most likely registering 9.5
percent. Many claim, including several economists, senior government officials, the
Economist and the IMF that this is a sure case of over-heating and growth much in
excess of potential GDP growth of 7 percent. Others, e.g. Bhalla et. al.(2006), claim that
there was a structural break in Indian growth rate starting 2003, and that the potential
GDP growth of India, without any additional economic reforms, is close to 8.5 percent.
Conventional wisdom on Indian growth
A virtual research growth industry, surrounding the first controversy, has developed
since 2000. Prominent in this debate are DeLong(2001), Panagariya(2004), Rodrik and
Subramaniam (2004a), Virmani(2004a) and Kohli(2006).
130
Which is why there is never a dull moment in India. Controversies occur even when everything looks normal. Sen, The Argumentative Indian, is spot-on.
258
Table 10.4: Sectoral GDP: India, 1960-2006
Growth (in %) Share in GDP
Agriculture Industry Services Agriculture Industry Services
1960s 4.5 4.7 4.1 43.7 18.5 31
1970s 1.0 3.6 4.5 38.8 20.5 32.3
1980s 4.2 6.6 6.3 31.2 23.5 35.3
1990s 2.9 5.6 7.3 26.7 24.4 39.6
2000s 1.8 6.0 7.3 21.1 24.3 45.9 Source: World Bank World Development Indicators (2006).
Table 10.5: Annualized growth (%) and TFPG, India
Growth in
Population GDP GDP, pc TFPG Agriculture
1950s 1.6 2.9 1.3 0.8
1960s 2.2 5.0 2.9 1.3 4.5
1970s 2.1 2.4 0.3 -0.7 1.0
1980s 2.0 5.9 3.9 2.7 4.2
1990s 1.8 5.7 3.9 2.1 2.9
2000s 1.7 6.8 5.2 2.5 1.8
2003-06 1.7 8.3 6.8 3.4 5.2 Source: Maddison (2006); Penn World Table version 6.1; World Development Indicators, World Bank(2006); World Economic Outlook, IMF (2006). Notes:
1. TFPG is calculated as a residual from a panel data for developing countries for the period 1960-2006. See Appendix I for details. Coefficient of capital and labour were 0.40 and 0.60 respectively (e.g. 1960-69).
2. Each decade ends in the ninth year except 2000s which is from 2000-2006.
259
Table 10.6: GDP and its determinants, India, 1950-2006
Growth (in %)
Capital
Labour
force
Income
per
worker TFPG UVSAE dUVSAE dUVIMF
1950s 4.3 1.1 1.9 0.8 156.6 -3.1
1960s 5.7 2.2 2.9 1.3 138.5 -3.4 -1.5
1970s 4.4 2.2 0.2 -0.7 123.4 1.8 0.4
1980s 4.8 1.9 4.0 2.7 94.1 -7.0 -3.3
1990s 5.7 2.1 3.6 2.1 22.4 -6.9 -3.7
2000s 7.2 2.0 4.8 2.5 -23.7 -3.3 1.0
2003-06 8.6 2.0 6.3 3.4 -26.5 -1.3 3.5 Source: Maddison (2006); Penn World Table version 6.1; World Development Indicators, World
Bank(2006); World Economic Outlook, IMF (2006).
260
First, a mea culpa. Major reforms but no growth acceleration hypothesis was first
advanced by Bhalla (2000), ironically in a study entitled, ―Start of India’s decade”. 131
Until this study, all economic research on India (e.g. Bhalla(1997), Virmani(1997),
Ahluwalia and other papers in the Bajpai-Sachs volume (1999)) had highlighted the
important causative role of 1991 reforms in accelerating India‘s growth. ―Start of India’s
decade‖ highlighted the fact that there was not one, but several ―Hindu growth‖
constants, in addition to the constancy of India‘s growth rate post the 1970s.
Table 10.7 reproduces the table from the 2000 paper, with updates for the period post
2000. Whether it is GDP growth, or money supply growth, or fiscal deficits as a
proportion of GDP; no matter what the indicator, non-overlapping three year averages
suggested that the Indian economy was an unchanging constant. The paper went on to
point out that the consolidated (state plus central) fiscal deficit of the Indian economy
had stayed in a narrow range of 8 to 10 percent for close to two decades. That India‘s
money supply growth had never wandered much from an average growth rate of 17
percent during the entire 20-year period.132 That, surprisingly, industrial production
growth had also not wandered much above 7 percent per annum. Thus, the study
concluded, that despite major economic reforms, the Indian economy had failed to show
any acceleration in the 1990s over the 1980s:
―The Indian economy has been growing at a steady rate of 5.5% to 6.5% for the last twenty years – a fact ignored by most analysts. Excluding the crisis year of 1991-92, the lowest GDP growth rate observed in Indian economy has been 3.0% witnessed in 1982-83. In spite of continuous economic reforms, there has been no acceleration in the growth rate. This presents a key question for both analysts and policy makers. (2000, p.2, emphasis added).
The study also concluded that the primary cause of the high fiscal deficits in India was
the high administered rates on savings, and that high interest payments had caused the
high fiscal deficits. Not the other way around as most commonly assumed.
131
It is possible that other articles highlighted this fact before; the important point is about the nature of research and how ―collective‖ its mind-set is i.e. until Bhalla (2000) (or another paper), no researcher had pointed to the lack of acceleration of growth post the 1991 reforms. 132
The paper also pointed out that the volatility of money supply growth rate in India was the lowest in the world, and that the volatility of ―no 2‖ (Thailand) was three times higher!
261
Table 10.7: Indian Economy – Several Hindu Constants except Economic Growth
Period
GDP Industrial M3 Centre+State Inflation Real Interest Rates
Growth Production Growth FD/GDP WPI CPI Overnight Prime
1980-82 5.6 4.6 16.1 8.7 10.1 10 -1.9 6.3
1983-85 5.6 8 17.5 10.7 6 8.2 1.3 8.3
1986-88 6.4 8.5 17.4 11.8 6.9 8.6 1.3 7.9
1989-91 4.0 5.6 17.6 10.4 10 9.8 6.5 7.3
1992-94 5.6 5.8 17.8 9.2 9.8 8.7 1.3 8.7
1995-97 6.8 9.9 16.3 8.3 5.5 8.5 3 6.6
1998-00 6.3 5.1 17.6 10.2 5.3 6.4 1.6 5.2
2001-03 6.3 5 15 9.8 4.1 4 2 7
2004-06 8.2 8.7 17.6 7.1 5.2 4.8 1.4 6.2 Source: Start of India’s Decade, Developing Trends (Feb’2000), Oxus Investments & RBI Notes: Data for 1980-82 to 1998-00 was published in Bhalla et. al. (2000); 2001 to 2006 have been updated with preliminary data for 2006.
262
―the consolidated (State plus Center) fiscal deficit has stayed constant around 9-10% of GDP for the last twenty years. Abnormally high interest rates (no arbitrage with world rates possible because of a closed capital account) have resulted in higher cost of borrowings….interest payments have increased from 3% of GDP in the early eighties to almost 8% of GDP today; as a proportion of the fiscal deficit, the percentage is 35% in the early eighties, and more than 75 per cent today. High interest rates are a major cause for the high fiscal deficits in India‖. (2000, p. 2).
While there is no consensus on what really happened in the 1980s, there are three
distinct points of view. One view holds that broadly speaking, economic reforms were
irrelevant. De-Long, observing the constancy of GDP growth across the two decades,
1980s and 1990s, contends that there is lack of evidence to suggest that the 1991
reforms were ―needed‖ for Indian GDP growth to accelerate;
―Conventional wisdom traces the growth acceleration to neoliberal economic reforms implemented under the government of Narasimha Rao. Yet the timing of the growth acceleration suggests an earlier start for the current Indian boom under the government of Rajiv Gandhi‖… There‘s lack of hard evidence to support the view that in the absence of the second wave of reforms in the 1990s, it is unlikely that the rapid growth of the second half of the 1980s could be sustained.‖ (2001,p.?)
The second view holds that there was indeed some, albeit vague, policy shift in the early
1980s. Growth accelerated in the early 1980s because of a changed attitude of Mrs.
Indira Gandhi, a socialist and a severe market interventionist. She had nationalized the
banking sector in 1969; raised the marginal income tax rate to 98 percent in 1971 as part
of her ―garibi hatao‖ or ―poverty removal‖ campaign; via a constitutional amendment had
inserted the term ―socialist‖ in the constitution in 1976; had started on a failed forced
sterilization program in 1974; had imposed a national emergency in 1974, and got a
pliant parliament and judiciary to authorize her intervention. All of this and when she
came back to power in 1980, some believe that she had a change of heart, became pro-
business and caused the Indian growth rate to accelerate to 5.5 percent from the
prevailing ―Hindu rate of growth‖ of 3.5 percent per annum.
Rodrik-Subramaniam are the most forceful in concluding that the big-scale and big-item
Indian trade reforms of the early 1990s had little role to play in inducing a growth
263
acceleration; rather, the transition to a higher growth path had been achieved more than
a decade earlier:
―India‘s growth transition began in the early 1980s rather than after the crisis of 1991. The performance of the 1980s appears to have been triggered by a perception on the part of the private sector that the government‘s attitude toward it had changed, a perception that was subsequently (in the mid-to-late 1980s), mildly validated by piecemeal reforms of the industrial licensing system. The attitudinal shift signaled by the Congress governments in the 1980s elicited a large productivity response‖.
Kohli (2006) echoes the Rodrik-Subramaniam conclusion about changed attitudes and in
particular the changed attitudes towards business of Mrs. Gandhi.
―Instead of the statist and the nationalist model of development of the Nehru era, that was then accentuated in a populist direction by Indira Gandhi during the 1970s, Indira Gandhi herself shifted India‘s political economy around 1980 in the direction of a state and business alliance for economic growth. This change was not heralded loudly and has often been missed by scholars, especially because Indira Gandhi remains deeply associated with the politics of ‗garibi hatao‘…. As suits a complex democracy, these changes emerged in fits and starts…the changes were nevertheless profound; they involved a shift from left-leaning state intervention that flirted with socialism, to right-leaning state intervention…. big capital, understood these changes pretty clearly, expressing their satisfaction by investing more and helping India‘s economy grow rapidly.‖ Kohli(2006, p.1255)
Virmani and Panagariya differ from both de Long and the Rodrik-Subramaniam-Kohli
interpretation. Panagariya maintains both that reforms made the 1980s growth possible
and that reforms were needed in the 1990s; the former set of reforms were
unsustainable and led to a crisis in 1991.
―At the same time, reforms played a significant role in spurring growth in the 1980s…The difference between the reforms in the 1980s and those in the 1990s is that the former were limited in scope and without a clear road map whereas the latter were systematic and systemic.‖ (Panagariya, p. ?)
Virmani believes that the 1991 reforms were far-reaching and offers qualitative reasons
for failure of the reforms to produce a noticeable acceleration in GDP growth:
―Four reasons are found to be most compelling: Gaps in the reform process, the failure of public monopolies to provide critical infrastructure services like electricity and rail
264
transport, the deterioration of government supply of public and quasi public goods (quantity and quality), and dissension within the ruling coalition/party/organization that undermine credibility of reform.‖(Virmani, p.75)
Thus, both these authors admit that a structural break in growth rate did occur around
1980; however, both contend that this accelerated growth was unsustainable. So the
growth pre-1990s was an unsustainable aberration; the economy, after all, did encounter
its worst economic crisis after the growth of the 1980s. Their analysis suggests that
growth was above ―normal‖ in the 1980s and ―normal‖ post reforms. So reforms did
have a positive effect on growth.
None of the 1980s growth breakout authors really address the issue of the growth
slowdown in the mid-1990s i.e. why did economic growth acceleration post reforms die
out after a mere three years? Bosworth-Collins-Virmani(2006) in their study on Indian
growth, and Bosworth-Collins(2006) in their comparative study of India-China growth,
also fail to answer this question. The is answer to the third question ―what caused the
growth rate to accelerate by about 3 to 4 percent per annum starting in 2003/4‖ is one of
denial. The potential growth rate has not really accelerated, so there is no question to
answer.
Alternative Explanations of Indian growth, 1980-2006
A detailed perusal of the data on India suggests the following as alternative -
interpretations of Indian growth since 1980. First, there was no significant acceleration in
Indian growth around 1980; second, the reason that the 1991 reforms did not result in a
sustained acceleration in growth was because of a catastrophic mis-management of
monetary policy. Real long-term interest rates rose to double-digit levels in the mid-
1990s and growth collapsed. Third, the revival in ―high‖ growth around 2003 was
preceded by a decline in real interest rates of around 600 basis points in a matter of
three years (1999 to 2002).
(i) Why there was only a small break in Indian GDP growth in the 1980s
Annual growth pattern for India (Chart 10.2) begins to tell a somewhat different story.
This chart shows that 5 percent per annum growth in India prior to the 1980s wasn‘t that
unusual, but clearly there is a breakout in the very early 1980s.
265
Table 10.5 shows decadal (log) growth rates for India since 1950 (and ending in 2006).
GDP growth shows a clear acceleration from an average of 2.8 percent in the 1970s to a
level double that in the 1980s – 5.7 percent per annum. Hence the conclusion about a
trend setting growth acceleration in the early 1980s seems to be valid. This is a first
blush result. A re-examination of Indian growth data, however, suggests that there was a
minimal of acceleration in GDP growth in the 1980s.
This conclusion is based on two considerations. First, the conclusion about a large
acceleration or breakout in GDP growth is based on a comparison of 1980s vs. 1970s.
But for most countries, 1970s is a bad ―benchmark‖ and most countries would anyway
show a marked acceleration in the 1980s. The 1970s were a turbulent period for the
world economy, with food, commodity and oil prices sky-rocketing and bringing in their
wake stagflation. The 1980s were a lot better in terms of lower oil prices and lower world
inflation. India was not immune to these events. This is reflected in the macro data. GDP
growth in the 1950s and 1960s averaged 4 percent; the 1970s average was only 2.8
percent.
The second fact to consider is the role of rainfall and agriculture in the Indian economy.
In the 1980s, the average share of agriculture in Indian GDP was 35 percent. Primarily
due to better rainfall133, 1980s has been the best period for Indian agriculture – a 4.7
percent average agricultural growth compared to its trend rate of 3.0 percent. This is an
extra 2 percent over the normal 1950s and 1960s, and weighted by the agricultural
share, it means that GDP growth in the 1980s was at a minimum higher by 0.7 percent
per annum. Rainfall adjusted GDP growth in India in the 1980s was therefore around 5.0
(5.7 – 0.7) percent per annum. The acceleration over the pre 1970s period is therefore
only1.2 percent per annum. An acceleration yes, but not the very large acceleration
assumed by most.
What caused this acceleration? The real exchange rate in India finally began to
significantly turn downward in the 1980s. Currency overvaluation averaged around 160
133
See Bhalla et. al. (2006) who show that rainfall has a strong effect in causing variability in agricultural growth in India around its mean value of 3 percent per annum agricultural growth. This mean has stayed constant for both the 1950-77 and 1978-2006 periods.
266
percent in the 1950s and 1960s, and ―only‖ 108 percent in the 1980s. The rate of decline
in the 1980s was also at more than twice the earlier rate (-6.8 percent pa versus –3.1 p a
earlier). The two together (using coefficients of –0.02 and -.35 for the two variables; see
Chapter 7)134 added 2.1 percentage points of extra growth. Thus, more than entire real
acceleration of growth in the 1980s is explained by this one policy – not wink-wink
between Mrs. Gandhi and businessmen, not monopoly capitalism, just old fashioned
Washington Consensus move towards a competitive exchange rate. Thus, the much
touted growth acceleration in the 1980s is not really a mystery at all. The acceleration
was minimal, and explained largely by some hesitant, yet correct moves, towards
decreasing exchange rate overvaluation.
(ii) Why there was a lack of acceleration in Indian GDP growth in the 1990s
The puzzle about the lack of growth acceleration after the reforms of the early 1990s
remains. Indeed, it makes the explanation that much more difficult. Depreciation of the
exchange rate was literally the first reform policy, announced just weeks after the new
government was installed in June 1991. The real exchange rate continued to depreciate
at a rate of 7 percent per annum, and the level approached zero overvaluation in the late
1990s. At a minimum, growth should have accelerated by 0.5 percent, perhaps even 1.5
percent given the other industrial and trade (elimination of quotas, lowering of tariffs etc.)
reforms. So instead of the GDP growth rate accelerating to 7 percent per annum, why
did it remain constant at around 5.6 percent?
Thus, growth should have been in the 7 percent plus range in the 1990s. As indeed it
was: GDP growth averaged above 7.4 percent in each of the three years 1994 to 1996.
But this acceleration to potential had some unintended consequences. In the mindset of
the Indian politicians and policy makers was the ―large‖ acceleration over the Hindu rate
of growth at 7.0 percent versus 3.5 percent ―potential‖ growth was, well, inconceivable.
So when the growth acceleration occurred, it made the government panic. The irony is
that the government itself (or elements within it) did not believe that the reforms it had
instituted would increase the potential GDP growth rate to above 7 percent.135 It itself did
134
The ―initial‖ level of undervaluation was 184 percent in 1950 and 145 percent in 1980; so a difference of 40 adds 40*.02 or 0.8 percent extra GDP growth. A difference of 3.7 adds (3.7*.35) 1.3 percent extra yielding a total of 2.1 extra GDP growth in the 1980s over the 1950s and 1960s. 135
Why the Indian government would engineer far reaching reforms in order to panic when growth accelerated by barely 1 to 1.5 percent per annum (something as noted above was
267
not realize that real currency depreciation change itself would deliver growth into the 6.5
to 7 percent range. Why the far reaching industrial and trade reforms if not to increase
investments, increase efficiency, and increase GDP growth to beyond 7 and perhaps to
8 percent?
That was not to be till almost a decade later. Back in the 1990s, the reaction to the panic
was the implementation of a tight monetary policy. Inflation rates declined in India, as
they did in the rest of the world. Median world inflation (as measured by the GDP
deflator) among non high-inflation economies136 declined from 6.7 percent in 1994 to 5.2
percent in 1996. Inflation in India (same measure) declined from 9 to 6.7 percent.
Inflation rates continued to decline in the rest of the world and in India. But the Indian
government failed to incorporate this global phenomenon and failed to adjust monetary
policy. Savings deposit rates were kept at 12.5 percent through the 1990s and real rates
for even good corporations were soon in double digits. This increase in real interest
rates proved to be the ―other‖ side of financial repression (too high rather than too low
real interest rates). Investment rates stayed constrained, as did GDP growth. (Chart
10.3).
(i) Structural break in the Indian economy, 2003/4
There is a similar acceleration in GDP growth, and a similar, nee identical, reaction from
the government. A four year average of 8.5 percent must mean overheating;137 for
example, Bosworth-Collins(p.19) state that ―current rates of capital accumulation are
consistent with a GDP growth rate near 7 percent, but higher rates would require
reductions in the public sector deficit or increases in capital flows from abroad‖. This
study ends in 2004, and therefore fails to capture the strong spurt in savings and
investment that happened over the next two years. By ending in 2004, the study also
fails to capture the structural break in the Indian economy that occurred in 2003.
achieved in the 1980s by minor, but much needed, adjustment in the real exchange rate) deserves analysis. 136
Defined as those with an annual inflation rate less than 30 percent per annum. 137
How growth can be more than 2 % above expectations for 4 consecutive years, and still be transitory, is an issue not addressed by the pessimists. See Bhalla et. al.(2006) for a detailed discussion about the likelihood that there was a structural break in Indian growth around 2003/4.
268
Chart 10.3: Real interest rate and income growth, India
269
The final question remains: what caused the structural break in growth in 2003/4? First,
some facts. Savings rates had hovered around 25 percent the previous decade (1993 to
2002) and investment rates had averaged the same. (Charts 10.4) The standard
deviation was only around 1.6 percent. Since 2002, in just four years138 savings and
investment rates have increased by 11 and 13 percentage points respectively.139 What
caused this sudden and large structural break? In 1999, inflation had reached a low of
3.5 percent and the government took the first major step towards interest rate reforms.
Within a space of four years, government bond yields were at 5 percent, down from
double digit plus levels of the late 1990s. In ―normal‖ economies, such a large decline in
long-term real interest rates would ordinarily be headline news for several years.
Analysts would relate industrial growth, GDP growth, stock prices, to this mega event.
After all, in western economies, a mere 25 basis point change in interest rates is a
momentous occasion. So it is in several developing economies, including China.
Industry has been the biggest beneficiary of this lower interest rate regime. Growth in
industry rose at its fastest pace in 2004-06. While industry grew at 8% for 2004-06,
manufacturing growth was strong at around 9.1%. The increase in GDP growth since
2002 is the sharpest in Indian history: a sharp 2 to 3 percentage point acceleration to
beyond 8.5 percent per annum. This is to date the longest and the fastest period of
expansion in India‘s economic history. However, in an eerie replay of the mid 1990s,
skepticism remains. Most analysts, and economists, and especially the monetary
authorities, doubt the sustainability of this acceleration and feel that the economy is in a
substantial overheating phase. It just cannot be is the refrain; India cannot grow above
its trend of 7 percent, and not grow given the lack of labour reforms, poor state of
infrastructure and poor growth in agriculture.
138
Figures for 2006 are based on models of investment and saving which proved exceedingly accurate in forecasting the jump in these ratios in 2005 (see Bhalla et. al. 2006) 139
The accelerations are ―spikes‖ for less than a third of country-year observations; in other words, the probability is large that the break in savings and investment and growth rates is permanent.
270
Chart 10.4a: Investment rate, India
Chart 10.4b: Savings rate, India
271
This skepticism suggests that India maybe sui generis. No policy maker, and very very
few analysts140, have pointed to the decline in real interest rates as an important cause,
let alone the cause, for India‘s belated entry on the world stage. The stock market has
tripled in the last four years, but more than half of that increase is due to this changed
interest rate regime. Lower real interest rates also add to GDP growth, and a 500 basis
point decline in real rates is enough to add at least 1 percent to GDP growth – and with
no contribution from any other determinants including real exchange rate devaluations.
Higher GDP growth leads to higher savings rates, and expectations of higher growth
lead to an increase in investment rates. This is what explains the jump in investment
rates, savings rates, and GDP growth rates; and this jump has lasted for four years and
can no longer be considered a spike.
The Indian interest rate story helps complete the circle and explain Indian growth
puzzles. Unwarranted tight monetary policy, and much higher than warranted real
interest rates, brought the Indian economy crashing down from a potential GDP growth
rate of 7.5 percent to less than 5 percent. Reversal of this policy brought the economy
back to 7.5 percent; further decline in the level of undervaluation (from 13 percent
overvaluation in 1996 to 30 percent undervaluation in 2006) has added another 1
percentage point to GDP growth; hence, a potential GDP growth in India of at least 8.5
percent per annum.
Industrial Sector in India – Another structural break
One of the big puzzles in the India China comparison is the stark difference in the shares
of industry in the two economies. The figures are too stark to be missed: In 2006,
industry‘s share of GDP in India was only 26 percent; in China it was 22 percentage
points higher at 48 percent i.e. almost twice the size!
What explains this radically differential pattern? One important factor is initial conditions.
In 1965 and 1980, the industrial share in China was 35 and 48.5 percent respectively;
the corresponding figures for India are 19 and 22 percent. So the share of industry in
India has always been behind, way behind, that of China. Since the attitudes towards
economic freedom were much the same in India and China prior to 1980, this large
140
Again mea culpa; I have pointed to interest rates as the cause several times e.g. Bhalla(2004, 2006).
272
difference is hard to explain. One possible explanation is that since industry was part of
the state sector in China, and originally was part of the private sector in India, it did not
come under the ―exploitative‖ filter in China. In India, and especially during the period
1947 to 1991, profits and money making was not something an upper caste Brahmin
was supposed to indulge in. Add to that Fabian socialism, and old-fashioned
Communism, and one had a heady recipe for disaster. Marginal tax rates on individual
income peaked at 97.5 percent under the socialist Mrs. Indira Gandhi; her dynasty rules
India even today.
Industrialists were under constant suspicion, and taxation, in India. Until recently, and
compared to most of its Asian competitors, Indian industry has paid a higher cost of
capital, and paid higher taxes. The one advantage it ostensibly had, cheap labor, was
reduced to zero (if not negative) by both an overvalued exchange rate and restrictions
on employers for hiring and firing. All of these policies have contributed to India‘s pitifully
lower share of industry, compared to an economy at its level of development and size.
The bias against industry also shows up in the results. Table 10.8 reports the highest 10
year moving average growth in industrial value added achieved in the selected
countries. Data are till 2003 and are revealing. The maximum 10 year average achieved
in China was 12.7 percent; the maximum ever achieved in India, at 6.9 percent, a figure
almost half that of China. Out of 81 developing countries141, India‘s rank is 48. The
average 10 year industrial growth in India has never exceeded 7 percent. But there are
several countries that have; Myanmar has done it, Ethiopia has achieved it, Pakistan has
surpassed it,….
What explains the difference between China and India?
The contrast in attitudes to policy making defined the difference between China and
India in the 1980s and 1990s, and this contrast continues till today. In both countries, the
political order does not change, only the economic order. In China, the economic change
is considerably more radical, the transformation considerably more structural. As
documented in the next chapter, income distribution change in China has been large, the
change in India miniscule. Yet there is more debate and heartburn in India about both
141
Oil dependent countries and countries with population less than 1 million in 2006 are excluded; see Appendix I for a list of the excluded countries.
273
Table 10. 8: Industrial growth in the world – India a low outlier
Country
Highest average 10 year
growth in industrial value
added Year in which achieved
OECD
Japan 8.6 1975
Greece 7.1 1977
East Asia
Cambodia 14.2 2004
Lao 13.5 1998
Indonesia 13.3 1977
China 12.7 1994
Singapore 12.6 1976
Thailand 12.2 1995
South Korea 12.0 1980
Vietnam 10.9 2001
Malaysia 10.4 1997
Myanmar 10.0 2000
Papua New Guinea 9.2 1994
Philippines 7.7 1979
Eastern Europe
Bosnia and Herzegovina 14.2 2004
Mongolia 8.0 2004
Latin America
Dominican Republic 12.1 1975
Brazil 10.0 1977
Costa Rica 8.9 1977
Haiti 8.3 1980
Chile 8.1 1997
Guatemala 7.2 1977
Mexico 7.2 1981
274
Table 10. 8 (contd): Industrial growth in the world – India a low outlier
Country
Highest average 10 year
growth in industrial value
added Year in which achieved
MENA
Jordan 16.2 1985
Egypt 10.7 1984
Tunisia 9.0 1975
Turkey 7.0 1992
South Asia
Bhutan 13.5 1990
Bangladesh 10.0 1982
India 6.9 1990
Nepal 9.5 1985
Pakistan 8.1 1988
Sub Saharan Africa
Cameroon 15.2 1985
Swaziland 12.4 1995
Rwanda 11.4 2004
Uganda 11.1 1996
Cote d'Ivoire 11.1 1979
Kenya 10.1 1980
Chad 10.1 1990
Burundi 8.5 1985
Mali 8.1 2002
Togo 7.5 1980 Source: World Bank, World Development Indicators, 2006
275
the change in inequality (even when it is not there) and the acceleration in the GDP
growth rate (deemed not possible and therefore likely to be due to overheating).
Why this contrast? Again, most likely because of the intellectual and political ―leadership‖
in India. Why this leadership has had, and does have, the particular attitudes it displays
is beyond the scope of this study.142 If echoed in the international media, it is most likely
so because ―international‖ attitudes follow, rather than lead, the ideology of the Indian
intelligentsia. Why the domestic ideology is one of a negative, defeatist, and populist
kind may be a subject more for a psychiatrist to examine. What has been attempted here
is a fact based of analysis of Indian (and Chinese) growth, the acceleration in such
growth, and the determinants thereof. (Charts 5 and 6 describe some of the data
presented in this chapter in pictures)
142
But see Bhalla(2007a) in which I try to begin to understand this fascinating phenomenon.
276
Chart 10.5: Real interest rate and income growth, China
277
Chart 10.6a: TFPG, India
Chart 10.6b: TFPG, China
278
Chart 10.6c: Capital growth, India
Chart 10.6d: Capital growth, China
279
Chart 10.6e: Savings rate, China
Chart 10.6f: Investment rate, China
280
"The worst form of inequality is to try to make unequal things equal" - Aristotle
“Cause this is nothing like we'd ever dreamt Tell Sir Thomas More we've got another failed attempt” Shins, So Says I, 2003
Chapter 11 – The Search for Meaning of Inequality: Kuznets curve and beyond
It is fair to state that the interest in the determinants of growth, considerable as it is, is
matched, if not exceeded, by the interest in what to do about ―the inevitable worsening of
inequality‖ that accompanies growth. Part of the appeal, and endurance, of presumed
worsening inequality is the support it obtains from politicians of all hues and in all
developing countries. Given that a major raison d‘etre of taxation is redistribution, the
concern about inequality worsening is not misplaced. And as long as inequality in
incomes is present, which it is and will be, one can safely forecast that this issue will be
with us forever – and deserves to be.
Add the spice of ideology143 and we have the fact that no cocktail party discussion,
especially among NGOs and concerned citizens, is ever complete without a swipe at the
evil forces behind the assumed inequality change. In the West, globalization is an evil
which makes the rich richer and the poor poorer. In the South, there is the same
surprising refrain. Can both sets of concerned citizens be right? No.
Both China and India are prominent in this discussion, especially China. Given the
relative size of the two economies, much of what happens in these two countries affects
wages, profits, growth and inequality everywhere else. It is likely that with each Gini
point of inequality increase within China, world inequality probably decreased by a
proportional amount. It is important to recognize, and emphasize, this important point. As
the poor gain income in India and China, and as the middle class and rich in these
countries grow at a faster pace than their rich country counterparts, world inequality
declines.
This description of the evolution of inequality is in stark contrast to the outpourings of
scholars at international institutions, especially the World Bank and the United Nations.
These scholars assert that growth is not pro-poor because of the evolution of Kuznets
143
It is a subject worthy of another study but the reality is that concerned citizens and those of the leftist persuasion invariably believe, or want to believe, that inequality is worsening.
281
style income distribution. They contend that there is a large growth-equity tradeoff i.e.
more growth brings in its wake more inequality so if the poor are the target for
development, they do not obtain their ―fair‖ share of economic growth.
The basic complaint against capitalism or ―markets‖ is that while it can and does
generate extra growth, it leaves a lot to be desired in terms of equality. The much too
often and erroneous refrain is that under capitalism the rich get richer and the poor get
poorer. But like the invisible hand, this deemed politically correct adversity is nowhere to
be seen. Indeed, the data are consistent with the alternate explanation – growth is good
for poverty reduction, and often good for inequality reduction as well. An old truism, but
somewhat surprisingly, one that needs to be emphasized every second day.
What happened to global inequality, 1820-2006
Much has been written, and discussed, about world inequality during the globalization
period, post 1980. That globalization has led to an increase in world inequality has
emanated from international financial institutions (primarily the World Bank and United
Nations). An alternate view, that inequality had stabilized at a high level in the 1980s,
after increasing for 150 years, was offered by Bourguignon-Morrisson (2001). Their data
extends to only 1992, which is a major reason why their result differs from Bhalla(2002)
and Sala-I-Martin(2002), two reports that contend that inequality had declined post 1980.
Both these reports use data for the 1990s.
Bhalla(2002) discusses in detail the evolution of world inequality and how the decreasing
poverty in India and China, and faster than world average growth rates in these erstwhile
poorest states, had to have led to a decline in world inequality. hence, the different
results from Bourguignon-Morrisson whose time-period ended too early to catch this
important trend.
Inequality and inequality change – some facts Table 11.1 uses the same methodology as employed in Bhalla(2002)144. Data are
presented for selected years from 1500 to 2025; income and population data are
projected till 2025 (see Appendix I), and distributions are assumed equal to the last
144
Essentially, a Kakwani method of estimating percentile distributions from information on quintile distributions, the most common form of inequality data available. The percentile distributions for each country, and each year, are then aggregated to form the world distribution.
282
survey available for each country. Over the long 130 year period 1820 to 1980,
developing world income inequality increased from a Gini level of 50.6145 to 66.6. As
indicated by Bourguignon-Morrisson , world inequality stabilizes at this level (1990
inequality is 66.6) and then begins to decline. Not-coincidentally, since this is the time-
period when average per capita growth in the poorest countries (e.g. India, China)
started exceeding average growth in the rich countries. By 2006, the Gini coefficient is
down to 63.4. By 2025, it is projected that world inequality would have declined to a level
last witnessed in the late 19th century.
Yet another indicator of inequality change is the ratio of incomes of those at the 80th
percentile relative to those at the 20th percentile. This index also peaks in 1980, at 12.3.
In 2006, the index was at 8.5 or about a 33 percent decline in the earnings of the upper
middle class to the earnings of the poor.
This improvement in world inequality has occurred with higher, not lower, growth in per
capita incomes. (Charts 11.1a and 11.1b). The charts indicate that there was a time
when world (or developing world) inequality went up. But this inequality has a trend
increase, while growth has accelerated and dipped. Given first a trend increase, and
now a trend decline (except that developing world inequality is fluctuating around a
mean) in world inequality, it is the case that all relationships are possible – inequality
increase and accelerating growth, inequality increase and decelerating growth, etc.
The picture about regional inequality change over the last sixty years is as follows. In
Latin America, virtually no change; Asia a mild 5 percent increase, and sub-Saharan
Africa a 10 percent increase in the Gini. Sub-Saharan Africa is also the region with the
slowest growth, post War. Eastern Europe, with a major structural change, shows an
increase in Gini from 31.7 in 1950 to 46.5 in 2000. (Table 11.2)
145
Gini coefficient multiplied by 100. If one person had all the income, the Gini would be 100; if each person had the sane income, the Gini would be 0.
283
Table 11.1: Trend in World inequality, 1500-2025
Year Gini Theil
Ratio of
incomes
80/20
Theil across
(share)
1500 47.9 0.57 3.1 0.03
1700 48.9 0.59 3.2 0.05
1820 50.6 0.64 3.3 0.07
1890 60.7 0.87 5.0 0.27
1950 64.4 0.8 9.9 0.53
1980 66.6 0.85 12.3 0.58
1990 66 0.85 9.3 0.57
2006 63.4 0.79 8.5 0.43
2015 60.4 0.7 8.1 0.34
2025 59.1 0.66 9.2 0.29
Source: WIDER World Income Inequality database v 2.0a June 2005; Bourguignon- Morrison (2001) Notes:
1. See Appendix I for details on income distribution data different countries/regions of the world.
2. Country income data for 2007 to 2025 are based on growth of per capita income 2003-2006.
3. Income distribution prior to 1820 is assumed equal to 1820; and income distribution post 2006 is assumed equal to 2006.
4. When data are unavailable, income distribution is assumed equal to the latest year.
284
Table 11.2: trend in regional inequality, 1950-2025
1950 1980 1990 2006 2015 2025
Gini
Asia 47.4 60.3 57.8 55 52.8 52.2
Developed economies 46 37 38.2 40 40.6 41.5
Eastern Europe 31.7 31.1 30.6 46.4 46.6 46.8
Industrialized countries 35 33.8 35.6 47.9 46.8 45.2
Latin America 58.2 56.1 56.5 57.6 57.7 58
Developing economies 54.5 59.2 55.1 56.9 57.1 58.6
Sub Saharan Africa 63.2 67 68.3 69.4 69 69
West 64.9 66.9 65.7 63.2 60.4 59.2
World 64.4 66.6 66 63.4 60.4 59.1
Ratio of income of 80th
/20th
percentile
Asia 3.3 3.7 3.6 5 5.1 5.3
Developed economies 4.8 3.1 3.2 3.1 3.2 3.3
Eastern Europe 2.7 2.7 2.5 4.5 4.5 4.6
Industrialized countries 2.9 2.9 2.7 4.4 4.1 3.8
Latin America 5 5.7 5.8 5.9 5.9 6
Developing economies 3.5 4.1 4.1 5.9 6.5 8.9
Sub Saharan Africa 4.8 6.3 6 7 7.1 7.4
West 10.2 11.5 8.5 8.1 8 9.2
World 9.9 12.3 9.3 8.5 8.1 9.2 Source: WIDER World Income Inequality database v 2.0a June 2005; Bourguignon- Morrison (2001).
285
Chart 11.1a: World: Level of inequality and per capita income growth
Per capita income growth
Gini
50.0
55.0
60.0
65.0
70.0
Gin
i
01
23
4
(Log
) Per
cap
ita in
com
e
1820 1850 1870 1890 1910 1929 1950 1980 2000 2025Year
Chart 11.1b: Developing World: Level of inequality and per capita income growth
pc inc. growth
Gini
45.0
50.0
55.0
60.0
Gin
i
01
23
45
(Log
) Per
cap
ita in
com
e
1820 1850 1870 1890 1910 1929 1950 1980 2000 2025ycat...
286
Individual country inequality change is examined in Table 11.3. The discussion of the
worrisome pattern of inequality change, the one that politicians, NGOs and concerned
citizens get so concerned with, is about what happens at an individual country level. The
politics are within the country, so global changes have little meaning, at least that is what
the stated argument is.
The statistics are revealing. (Log) changes in inequality are reported for three time-
periods: 1960 to 1980, 1980 to 2004, and 1960 to 2004. Several countries show a
decline in inequality in the recent globalization period, 1980 to 2004 e.g. Botswana,
France, Japan, Thailand, Korea. Some high growth countries have a decline in
inequality; many are close to the plus minus 10 percent range.
Chart 11.2 (a and b) presents inequality change for the two time-periods 1960-1980 and
1980 to 2000 for several individual countries. A ―fan‖ deviation of plus or minus 10
percent is shaded; countries below the fan suggest an improvement, countries above the
fan a deterioration. Consistent with all the results reported above, the inequality change
is one of broad constancy at the individual country level. But three exceptions deserve
special analysis – US, UK and China.
Trend setting change
The results presented so far do not justify the perceived conventional wisdom that
globalization has been bad for inequality, and/or that high growth is invariably
accompanied by a large change in inequality. Why this disconnect? One possibility is
that the perceptions in the Anglo-Saxon world dominate ―intellectual‖ and media thinking
on the subject. There has been a large increase in inequality in UK during the last two
decades – the Gini has increased by nine percentage points, from 25.3 in 1980 to 34.2
in 2002. This is a large increase of 37 percent.
In US, the Gini has moved from 39.7 in 1980 to 46.5 in 2001, a seemingly largish
increase of 17 percent. In 1992, the US Gini was 43.2, and in 1993, 45.2. Between 1980
and 1992 the increase was only half, 8.8 percent. The reason to benchmark the US data
to the adjoining years 1992 and 1993 is because starting with 1993, the US Census
Bureau changed the manner in which they collected data on incomes of the rich. Prior to
287
Table 11.3: (mostly) high growth countries, inequality change 1960-2000 Gini
Gini (Log) change
1960 1980 1990 2000 first-last
Australia 31.9 40 41.6 44.7 33.7
Botswana . . . .
Brazil 56.7 60.6 61.3 62 8.9
Chile . . 56.4 60.2 6.5
China . 29.4 32.6 45.1 42.8
France . 31.4 32.8 27 -15.1
Hong Kong 51.2 39.8 43.9 51.7 1.0
India (consumption) 32.7. 33.2 32.8 35.7. 8.8
Indonesia . 40.7 38.9 39.9 -2.0
Japan 41.1 33.5 . . -20.4
Malaysia 57 51.3 . . -10.5
Mauritius . 33.5 . .
New Zealand 30.2 34.8 40.2 . 28.6
Singapore . 39.8 . .
South Korea . . 34.8 37.2 6.7
Sri Lanka 47.1 . 44.2 -6.1
Taiwan 44.6 27.9 31.1 34 -27.1
Thailand 41.8 45.5 44.1 43 2.8
United Kingdom 25.7 25.3 33.5 34.2 28.6
United States 39.6 39.7 42 46.5 16.1
World 64.2 66.6 66.0 65.1 1.4
288
Chart 11.2a
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hkg
phl mdg
zaf
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20
40
60
80
20 30 40 50 60giniy60
giniy60-80, earliest and latest, developing
Chart 11.2b
oan
chn
col
tha
phl
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pan
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chlnic
gtmbrabol
20
40
60
80
20 40 60 80giniy80
giniy80-00, earliest and latest, developing
289
1993, all households with income above a million dollars were coded as having income
of only a million dollars. Post 1992, the reported incomes of all individuals were coded as
actual incomes. This changed definition most likely accounts for most of the 2 Gini points
increase between 1992 and 1993. Since 1993, income distribution in the US has barely
changed.
As noted by all (and noted throughout this book), China has emerged as a major player
on the world scene. Whether the concern is global imbalances, or mercantilism, or
commodity prices, China appears to be at the center of it all. And income distribution in
China has significantly worsened since the beginning of economic reforms in 1978. From
a Gini level of 29.5 in 1980, the 2003 Gini level of income inequality in China was a
―high‖ 44.9 i.e. an increase of more than 50 percent. China has also had high growth and
therefore it is an easy conclusion to defend that the Kuznets curve is alive and well and
that high growth ―inevitably‖ leads to increasing inequality. In absolute, comparative
terms, this level of inequality is not that high. Countries in Latin America and sub-
Saharan Africa have inequalities somewhat higher (around 55 to 65) while in developed
economies, the level of inequality is considerably lower.
But should the change in inequality in China be compared with other developing
economies or with the countries of Eastern Europe? The latter countries have
undergone a structural change – as have the economies of China, Viet-Nam and
Cambodia. Table 11.4 presents summary inequality results for these countries; what is
remarkable is that inequality change in China is near identical to the large inequality
change observed in Eastern Europe – 43 and 39 percent, respectively. Inequality
change for Vietnam, Laos and Cambodia is only for the 1990s, but even in that short-
period, the rise in inequality has not been small. Before one concludes that inequality
change has been large in these societies, one needs to account for the fact that the
measure of inequality before the structural change may not be that meaningful for the
formerly Communist regimes. A safe, conclusion, however, is that measured inequality
change in societies undergoing structural change is likely to be as large as (log) 40
percent.
290
Table 11.4: Change in inequality, former socialist or communist
countries
Around
(Log) change
(%)
1980/90 2000 1980-00
Eastern Europe 31.1 45.8 38.7
China 29.4 45.1 42.8
Vietnam 39.4 43.2 9.2
Cambodia 34.9 45.7 27.0
Laos 28.9 36.1 22.2 Source: SAE dataset, see Appendix I for details.
291
Should India follow the China change in inequality?
There is a widespread expectation, if not the conclusion, that inequality change in India
should follow that observed in China. This expectation is based on the premise, and
indeed, some of the conclusions in this book – the two societies have been, and are,
similar. However, unlike China, India has not undergone a major structural change. True,
there were some economic reforms in the early 1990s, and the reforms have continued;
but it is also true that the pace of the reforms, by the reformers themselves, has been
described as gradual, if not glacial. Given this reality, why the expectation that India
should expect the same kind of inequality change as that which has occurred in
countries undergoing a large structural change?
While no obvious reasons are apparent, the conclusion has been aired by some
scholars. In a detailed analysis of consumption inequality in India, Sen-Himanshu (2004,
p. 36, emphasis added) state,
―Nonetheless, although these results from adjusted NSS data may disturb some, it may be noted that of the distributional data presented so far, the 1990s urban Gini increase was similar to that in China, the percentage increase in nominal urban-rural ratio was two-thirds that of the Chinese, and the rural Gini increase in India was less than half‖
This is not the appropriate place to go into the nature of the adjustments to survey data
made by Sen-Himanshu; the point is simply to document that the expectation or
inference is that inequality change in India is comparable to that observed in China;
which, given the lack of a structural change in India, makes it a very unusual
expectation.
The China-India connection is not just for an understanding of developments in these
two countries alone. The presumption is often that since inequality is worsening in
China, and by inference if not by facts, the same is happening in India, then ipso facto
inequality is worsening everywhere else. Leipziger and Nobel laureate Michael Spence,
of the very respected growth commission, recently opined:
―Inequality often rises in the growth process…In China, the bottom 10 per cent of the income distribution has seen its income rise by 42 per cent in the past 10 years. The middle has grown by 115 per cent and the top 10 per cent by 168 per cent. Large numbers. Everyone has benefited but not equally. Similar patterns can be seen in other rapidly growing countries such as India‖. (Financial Times, May 14, 2007).
292
The specific India China experiences with inequality are outlined in the two charts below.
The two countries have different surveys – the National Sample Survey Organization of
India conducts primarily consumption and wage surveys whereas in China only income
surveys have been undertaken. Charts 11.3(a) and (b) document the trend in the two
countries. The first few reform years in China yield a decline in inequality – from a Gini of
32 in 1978 to a Gini of 25 in 1983. Since then inequality has sharply increased. (Chart
11.3a)
Chart 11.3(b) , for India, has data going back to 1951 and until 1983, real consumption
inequality has been taken from Datt-Ravillon(1998). Since, and including 1983, the real
consumption inequality data estimates are based on unit-level data for the years 1983,
1987/88, 1993/94, 1999/00 and 2004/5. economies. The data tell a very different story
than that generally assumed. Two patterns are apparent. First, that in the low growth
period 1950-1980, consumption inequality in India improved by about 15 to 20 percent.
The second obvious pattern is that since 1983, inequality levels have stayed within a
tight 1 to 3 Gini point range around a mean of 31. Third, that consumption inequality in
India in 2004 is very close to the level observed at the start of the high growth period in
1983 – each year has a real Gini close to 32 (and a nominal Gini close to ?/).
This small or no change in Gini has occurred in an environment when per capita
incomes have increased by about 4 percent per annum. That is fast growth, but nowhere
does the data suggest anything close to the change expected by Sen-Himanshu, the
Growth Commission, and several others.146 Nevertheless, the major debate in the policy
making circles in India is what is happening to inequality. Some things are slow to
change, even in the fast moving and fast changing India of today.
What about the Kuznets effect? In 1955, Simon Kuznets postulated that as part of the development process, the income
distribution in a country would first worsen, and then improve i.e. the relationship
between inequality (y-axis) and per capita income (x-axis) could be represented by ,
an inverted U. The development process was later refined and defined to mean the
146
See Bhalla(2002) for a detailed discussion of the expectation and research pointing to a systematic rise in inequality in India.
293
Chart 11.3a: Real consumption inequality in India, 1951-2004
Annual
5 yr MA
2830
3234
3638
Gin
i
19511955 1960 1965 1970 1975 1980 1985 1990 1995 20002004Year
Source: Datt-Ravillion for data from 1951-1983; since then , own computations NSS unit-level data. Note: Datt-Ravillion do not present data on nominal consumption inequality.
Chart 11.3b: Nominal income inequality in China, 1978-2003
25.0
30.0
35.0
40.0
45.0
Inco
me g
ini
1978 1983 1988 1993 1998 2003Year
294
higher productivity in the urban industrial sector. As labor migrated to move there, it
received higher wages because of the higher productivity; a small fraction growing at a
faster rate meant that inequality would worsen.
This hypothesis set off a flurry of research and we are now past the 50th anniversary,
and counting. There has been significant research, and insignificant findings147
– on the existence of the Kuznets curve, that is. Several tests of the Kuznets curve have
confirmed its validity; several others have rejected it.148 The analysis presented above
would also seem to reject the Kuznets curve, but its hold is quite pervasive. As Lindert
observed ―The Kuznets curve has to some extent tyrannized the literature on inequality
trends‖. In a thorough analysis of inequality trends in the UK and US for the past 300
years, a long enough horizon for the test of any development process, Lindert(1998)
finds the Kuznets curve to be non-existent in these two countries. ―The obsolescence of
the Kuznets curve, in any case, stands out clearly enough in these two countries‘ recent
experience‖ (p.29)
Table 11.5 reports on Lindert‘s data for UK (since 1688) and the US (since 1929). The
numbers are for household inequality and therefore different than the numbers contained
in the earlier tables. The point to note is that over very long periods of time, inequality
has not changed in either the UK or the US.
Given his finding about the inapplicability of the Kuznets curve, Lindert goes on to
speculate about why Kuznets reached his conclusion.
―Kuznets did not feel the same about the rise as he did about the fall of inequality. The inequality tended to decline at some advanced stage of development, he seemed quite confident. He barely asserted – rather, wondered about – the possibility of earlier rise. His confidence in his explanations for it all were similarly mixed: He emphasized the role
147
Mea Culpa. I wrote a paper for the World Bank [Bhalla 1978] showing that if the data were assembled correctly, it would show that there was a worsening of income inequality in Korea during the period 1965 to 1975. How much had inequality worsened – by a miniscule 5 percent. What was ―important‖, however, was to show that growth was not accompanied by an improving income distribution, as some had argued. Whether this was political correctness or a desire to show that the Kuznets curve held is unclear. What is clear, with hindsight, is that Korean income distribution has not only not worsened but improved with growth and the Kuznets curve works very rarely, even in the medium term, say 5 to 15 years. 148
See Bhalla(2002) for an extended discussion, and summary, of the available evidence.
295
of Sectoral shifts as an engine of inequality, and mused more vaguely about the possible importance of the demographic transition.‖ (p. 8)
Why the Kuznets curve should not be expected to hold
Why does the Kuznets curve fail to exist, at least in a systematic fashion? Most likely
because while Kuznets got one force completely right, the higher productivity growth in
the urbanizing, industrial areas of a country, he missed out on an equally, if not more
Table 11.5: Inequality in UK and USA, 1688-2003
Gini Gini, adj
Year UK USA UK
1688 55.6
1759 52.2
1801 59.3
1867 50.6
1911 48.3
1929 49
1938 42.3 48.5
1947 40
1949 41.1 47.1
1956 39
1959 39.8 45.6
1969 38 43.6
1970 39.4
1978 37.5 43.0
1978 37.5 43.0
1979 40.4
1989 43.1 50.0
1992 43.4
1993 45.4
1995 52.0
2000 45.7
2003 46.4 Source: Lindert (2000) Notes: The adjusted Gini for UK links the pre 1978 series (CSO Hybrid estimates) with CSO-ONS estimates.
(Lindert Table 1).
296
powerful , opposing force – the spread of education. In poor countries, labor is a major
asset for at least 90 percent of the population, if not more. And educational attainment is
a major component of this ―labor asset‖. The rich always had education; and had good
quality education. Ditto for the children of the rich. Their educational attainment shows
no change over time. In contrast, the bottom 90 percent increases educational
attainment by leaps and bounds.
In the second instance, the increase is in just attending school, and perhaps not learning
much. But there is an increase, albeit small, in educational attainment and therefore a
decline in education inequality. Initially, the concentration is on building schools, not
necessarily in having teachers in schools, or even textbooks for the students. In the third
instance, there is most likely disillusionment with the way the public sector is providing
education. Unionized teachers do not show up for work, text books get sold in the black
market, etc. In the fourth instance149, the poor take their kids out of government schools
and put them into private schools. In the fourth phase (yet to come for India) there is
significant reform in the education sector, as the poor and the middle class demand
better accountability from the government in the provision of education. In the final
stage, the quality of education begins to improve for the lower half or two-thirds of the
population.
Data from unit-level data for India supports the above conclusion. While many
similarities between India and China have been noted, academic and intellectual
freedom provides for a sharp difference. Several household surveys, by various
organizations, government and private, are constantly collected in India, and
disseminated to the public. Some are available free, others for a nominal fee. There is
debate on the minutiae behind the numbers, and some unnecessary controversy. But
the principle of free flow of opinion is held high. Not so with China, another instance of
inequality between the two. In Chapter 7, the ―conspiracy of silence‖ at best, and
perhaps influenced research and guided opinion at worst, was noted with regard to the
strange controversy on whether the most undervalued currency in the world was actually
undervalued!
149
This sequence is not imaginary and happens in most developing countries, especially India.
297
Table 11.6: Education inequality in India (Gini), persons aged 15-59
1983 1993/94 2004/05
Rural 0.73 0.66 0.57
Urban 0.48 0.42 0.37
All India 0.67 0.60 0.52 Source: NSS unit level data
Note:
Table 11.7: Share of expenditure on education
1983 1993 1999 2004/05
Poor
Rural 0.52 1.07 1.25 1.68
Urban 1.23 1.93 2.27 2.66
All India 0.70 1.32 1.57 2.01
Non-Poor
Rural 0.86 1.61 2.06 3.46
Urban 2.30 4.30 4.62 7.31
All India 1.32 2.58 3.04 4.95
All population
Rural 0.76 1.51 1.93 3.22
Urban 2.06 3.94 4.34 6.60
All India 1.16 2.35 2.82 4.57 Source: NSS unit level data
Note:
298
China does not allow free access to its household surveys. Even the World Bank has
limited access. Consequently, the same tests reported for India below cannot be
undertaken for China, even though this is an India-China study. Table 11.6 uses the
NSS unit level data to compute Gini indices of education for rural, urban and all-India.
Indices are reported for three years. No matter what region is looked at, the results
strikingly affirm what one expected – education (represented as means years of
educational attainment with no allowance for the quality of schooling) has improved quite
significantly since 1983. For all-India, the log change over the last two decades is an
impressively large decline of 25 percent.
Inequality depressing cause: Girls attend school, and work
Besides education, there is a second powerful factor which can, and does, act counter to
the Kuznets productivity increases. As part of the development process, after some
initial lag, girls begin to enter school in equal numbers as the boys, and not much later,
begin to exceed the educational attainment of boys. Increased education leads to
increased labor force participation (LFP), and increased LFP leads to lower fertility. This
further increases the quality of education of the non-rich, and therefore their absolute
and relative earning power. In all of this transition, the rich may have accumulated more
physical capital, but have certainly not accumulated more quality adjusted labor. This
education led countervailing force is likely to be considerably more than Kuznets‘s higher
productivity growth in the urban areas. Therefore, instead of the inverted U-curve, one
should actually expect income distribution to improve with economic growth.
Table 11.7 looks at expenditure on schooling by the poor and non-poor in different parts
of India. Poverty is here defined according to the Indian poverty line, which is equal to
the $1.08 World Bank poverty line for the world.150 Each cell reports on the share of
expenditures devoted by households to the education of their children. Education in
India is to a very large extent provided by the public sector so in theory one should not
be finding much education expenditures among the poor. But one does, and in 2004/5,
the poor spent 2 percent on education, compared to 5 percent for the non-poor. The
reason non-zero entries are observed for the poor is because increasingly, the poor are
attending private schools, while government schools are deserted because the
150
See Bhalla(2002) for a detailed accounting of how it is very likely that the World Bank poverty line was chosen at $ 1.08 because the Indian poverty line was at $ 1.08.
299
unionized non-fireable teachers do not show up for work. Both segments of society show
a sharp increase over 1983, with the poor recording a tripling in real expenditure.
If one looks at individual countries then the result is the same – the Kuznets effect is one
of the more mythical patterns, for reasonably long periods of time. More trees were felled
in the Amazon deciding on the income distribution consequences of growth, rather than
the poverty reducing consequences of paths to prosperity.
Perception of Inequality vs. the reality If the lack of change in inequality is so robust (except for formerly socialist economies),
and if world inequality is actually improving, and if the have-nots are growing faster than
the haves, then why are most commentators and politicians concerned so much with
inequality. It could just be a matter of perception. Inequality changes have to do with
changes in relative income, while perception most likely is on the basis of absolute
incomes. If a poor person‘s income doubles from an annual income of $ 20,000 to $
40,000, and a hedge fund manager‘s income doubles from $ 10 million to $ 20 million,
inequality does not change but we all feel that inequality has gone up. For we, substitute
the elite, because it is very likely that the poor do not care as much about the hedge
fund manager doubling her income as it cares about doubling its own come. But the
other elite, especially the politicians, do care because they think that the poor person
cares. That is the nature of the elite, and the world. However, elite discussion about
inequality should not blind us to the obvious reality that the sine qua non of development
is most likely an increase in incomes of the poor.
300
Poverty
It all happened somewhat suddenly. The World Bank had just launched a major policy
initiative. Instead of being a ―reconstruction agency‖ it would now become a
―development bank‖. Its major goal – removal of absolute poverty; improvement in the
lives of those living below a dollar a day. In 1980, India housed 27 percent of the world‘s
poor; a China a somewhat larger 44 percent. All of Asia was home to 83 percent of the
worlds‘ poor. The Asian drama was a reality, and living hell, for more than half of the
regions population. Worse, there had been little improvement over the preceding thirty
years. Myrdal was proving more right than he could possibly have imagined.
But it all has changed, and especially because of what has happened in India and China.
Asia now contains less than 15 percent of the worlds‘ poor, and India and China together
less than 3 percent. According to the $ a day poverty line, poverty in the world is today
close to 10 percent. The time has come to raise the international poverty line.
But that is a separate story. The numbers cited above are for 2006, and assume that the
survey capture ratio (the ratio of average survey consumption compared to average
national accounts consumption or S/NA) for India in 2006 was the same as that which
was observed in 1987, namely 71.2 percent. In other words, in 1987, the national
expenditure survey was able to account for only 71 percent of the consumption estimate,
with 29 percent not accruing to anyone, rich or poor. Surveys seldom capture all of
consumption, so under-estimation is a common occurrence in both developed and
developing economies. In the same year, 1987, the Chinese survey was able to capture
87 percent of national accounts consumption.
The last China survey for which data are available, namely 2001, had a consumption
survey capture ratio of 78.8 percent; the last India survey, of 2004/5, has a survey
capture ratio of only 48.7 percent i.e. more than 50 percent of consumption does not
accrue to anyone, rich or poor. How low this India number is for 2004/5 is indicated by
the fact that of the 193 consumer surveys conducted in Asia between 1951 and 2004,
this survey capture ratio is the third lowest; the median is 85 percent.
All of this makes a difference for the all important calculation of the head count ratio of
poverty according to the $ a day line. If the S/NA ratio for India had stayed at its
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relatively low 1987 level, and not precipitously declined, the average person in India
would have reported a survey consumption level 40 percent higher than that reported in
2004. even allowing for some inequality worsening, the poor in India would have
reported a consumption level at least 30 to 35 percent higher. Which means that rather
than a level of absolute poverty in India of 22 percent, according to the $ a day line,
actual poverty in India would have been less than 5 percent.
The last comparable year of poverty in India and China is 2000/2001. The World Bank
reports absolute poverty in India at 35 percent in 1999/2000 and 16 percent for China in
2001. This difference arises mostly, if not exclusively, because of the differences in the
S/NA ratio for the two countries.151 That poverty in India and China is close to the same
for any given poverty line is indicated by the calculations below. Indeed, it has to be the
case that for any poverty line, poverty in China has to be higher.
Poverty in India China the same
A not-appreciated fact is that despite the more than 2:1 advantage that China enjoys
over India in terms of per capita income, the poverty levels in the two countries are about
the same (Table 11.8). This surprising conclusion emerges as follows. The ratio of US
dollar incomes in 2004 was 2.4 i.e. Chinese per capita income was 2.4 times that of an
Indian. In PPP terms, this gets reduced to an advantage of ―only‖ 1.81. But poverty is
defined in terms of consumption, not income. The consumption to GDP ratio in China is
much lower than in India - about 41 percent, in India about 58 percent. This means that
the 2.4:1 ratio of per capita income (China: India, US dollars) is virtually eliminated in
terms of consumption in PPP dollars (ratio now 1.28:1, or a (log) 25 percent advantage).
This last remaining China advantage gets eliminated because of the higher inequality in
China. The bottom 20 percent have a lower (log) share of 49 percent in China (log of the
ratio 4.7 to 7.7); the bottom 40 percent have a lower (log) share of 33 percent.
Thus, no matter where the poverty line is, the average consumption level of the bottom
40 to 50 percent of the population is lower in China than in India. It follows, therefore,
151
In addition, the World Bank estimate for India cannot be reproduced from available Indian data, which indicates a poverty level of 26 percent in 1999/2000 for the same poverty line as used by the World Bank. See Bhalla(2002) for an extended discussion.
302
Table 11.8: Poverty in India and China; in China it is higher, if not the same
2004 Ratio
India China China/India
Population 1075 1299
GDP per capita per year
local currency 28684 12309
US dollar 619 1487 2.40
PPP 3438 6212 1.81
Share of consumption in GDP (%) 58 41
Therefore Consumption, PPP per capita per day 5.46 6.98 1.28
Share of the
Bottom 20% 7.7 4.7
Bottom 40% 19.1 13.7
Average Consumption of
Bottom 20% 2.1 1.64 0.78
Bottom 40% 2.6 2.4 0.92
Source: SAE dataset, see Appendix I for details. Note: (1) A consumption China India ratio less than 1 implies that absolute poverty (fraction of poor or the
head count ratio) in China is higher.
303
that for all the poverty lines below the 50th or the 60th Chinese percentile consumption,
the fraction of poor will be greater in China than in India.
What this means is that the rich in China form a disproportionately (and disproportionate
relative to income levels) higher fraction of the population than in India, but
correspondingly, the non-poor non-rich middle class forms a lower fraction in China. And
lower by enough to suggest that in terms of absolute numbers, there are more middle
class people in India (than China).
So why the near universal perception that the fraction of people absolutely poor in India
is not only higher, but much higher than in China? Two reasons are possible. First, and
this is a problem of definition, a non-poor person saving a reasonable fraction of her
income is considered poor because poverty is defined in terms of consumption. Second,
the poor in China are not allowed to migrate with as much freedom as the poor in India.
Visitors see the beggars in the streets of the major cities and form an impression; if
migration of the poor is curtailed, this vision might be distorted.
304
Chart 11.4: Poverty, Headcount Ratio, India and China, 1950-2006
Notes: The line measures poverty ($1.08 poverty line) according to survey data adjusted by the mean S/NA ratio in 1987; the dots represents poverty according to unadjusted survey data. See Appendix I for details.
China
India
ChinaIndia
0.0
20.0
40.0
60.0
80.0
Per
cent
(%)
1950 1960 1970 1980 1990 2000 2006Year
305
Chapter 12 – History as we should know it Truly this is the age of revolution. The world has been turned upside down and
sideways, and it is still changing, and emerging. Yesterday‘s core is tomorrow‘s
periphery. Just ten years ago, it was very different, and just twenty five years ago, it was
ancient. What changed in such a short time period? To be sure, the communications
revolution has something to do with it. But the emergence of China first, and India later,
explains it all.
The last few years have been witness to a resurgence in world growth; and both Latin
America and sub-Saharan Africa have shared in it. World per capita GDP expanded at a
slow pace for the 400 odd years, 1500 to 1880, and then grew at approximately a 1
percent per annum pace for the next eighty. The real boom game between 1950 and
1980 with world growth expanding at more than twice the earlier rate (2.5 % per capita
per year). The next 26 years were not so kind to world growth, with per capita incomes
only expanding at a muted pace of 1.9 percent per annum.
But we most likely have just witnessed history, again due to the China India effect. The
last twenty six years have been good for growth in the developing countries, and have
been very good for poverty reduction – indeed, the best ever. And even better for the
growth of the middle class. Before running, one must walk. The emergence out of
poverty was the walk. Two thirds of the developing world was dirt poor in 1960. Half of
the developing world was dirt poor as recently as 1980. Not much purchasing power
there for the world to take notice. It did not. Over the next 26 years, over a billion people
were moved out of poverty. From about 1.6 billion poor in 1980, poverty in 2006 was
close to 500 million. In no other period in history has the number of poor people
declined, let alone a decline of such historic proportions. (Tables 12.1a and 12.1b).
Calculations of poverty reduction go back to at least 1820, but calculations of the decline
in the number of poor are unfair to history. Because of health improvements, life
expectancy has improved enormously over the last 200 years. This has enhanced
population growth for all levels of income, poor and rich alike. The share of population in
absolute poverty has declined at a rate of approximately 4 percentage points every
twenty years for the 130 year period, 1820 to1950. Between 1950 and 1980, the pace
increased to a rate of 14 percentage points. But the golden age for the poor has been
306
Table 12.1 (a): World economic pattern, 1500-2025
Income pcpd (1996 PPP
prices)
Year
Population
(billions) Level
Annualized
growth (%)
Share of
middle class
Share of
rich (%) Gini
1500 0.44 1.82 1.19 0.00 47.9
1700 0.61 1.97 0.04 1.48 0.00 48.9
1820 1.08 2.12 0.06 1.82 0.01 50.6
1913 1.84 5.00 0.92 13.21 0.25 64
1950 2.53 6.76 0.82 23.54 0.21 64.4
1960 3.01 8.83 2.68 28.80 0.42 64.2
1980 4.40 14.41 2.45 32.23 2.24 66.6
2006 6.46 23.65 1.91 50.22 5.54 63.4
2015 7.16 31.92 3.33 61.43 8.30 60.4
2025 7.89 45.44 3.53 63.35 14.39 59.1 Source: SAE dataset, see Appendix 1 for details
Table 12.1 (b): Developing world economic pattern, 1500-2025
Population
Income pcpd (1996
PPP prices) Headcount ratio
Year
Level
(billions)
Share of
World
(%) Level
Annual
growth
(% per
year) $1.08 $2.16
Share of
middle
class (%)
Share
of rich
(%) Gini
1500 0.3 76.2 1.7 88.8 96.8 0.9 0.0 45.8
1700 0.5 74.5 1.7 0.0 88.7 96.8 1.0 0.0 45.6
1820 0.8 74.7 1.7 0.0 89.4 97.0 1.0 0.0 46.1
1913 1.2 64.7 2.3 0.3 80.9 92.9 2.4 0.0 51.3
1950 1.7 67.2 2.7 0.5 75.2 89.8 4.2 0.1 54.5
1960 2.1 68.8 3.5 2.7 67.0 86.0 6.1 0.1 54.8
1980 3.3 74.3 6.1 2.8 48.1 75.9 13.8 0.3 59.2
2006 5.2 80.3 14.0 3.2 9.7 35.2 45.5 1.2 56.9
2015 5.9 81.8 22.0 5.1 7.9 16.2 60.9 3.2 57.1
2025 6.6 83.3 35.4 4.8 7.4 13.3 65.0 9.0 58.6 Source: SAE dataset, see Appendix 1 for details
307
the period post 1980. During this age, the record is of an astonishingly large 20
percentage point plus decline, and with the head-count ratio of poverty close to 10 % for
the developing countries.
The march forward was also for the all important middle class. In 1980, only 14 percent
of the developing world was middle class and this constituted 32 percent of the world‘s
middle class; in 2006, the percentages had increased to 46 and 73 percent respectively.
In 2025, the formerly poor countries will house more than 85 percent of the all important
purchasing class. China and India‘s contributions to this growth has been above
average. In 2006, more than 40 percent of the worlds‘ middle class was in these two
countries, compared to a 38 percent share in the population.
World inequality is also declining; in 1950, world Gini was 64.4; in 2006, it was nearly the
same, indeed slightly lower (Gini of 63.4). In twenty years time, world inequality will have
declined to 59.1, a level last seen in the late nineteenth century.
Fast poverty reduction and improving inequality is not the news one obtains from a
cursory perusal of major international newspapers, or the outpourings of international
organizations dedicated to the removal of absolute poverty. The chorus: poverty
reduction, especially in the last 20 years, has been a failure. Indeed, according to the
World Bank, the number of poor in the world barely budged between 1.2 billion in 1990
and 1.1 billion in 2001. This deemed lack of poverty reduction has been the mantra (e.g.
Stiglitz, Globalization and It’s Discontents), and the cause has been variously, but
mostly, attributed to ―capitalistic growth‖ models. History shows these conclusions as
false. Poverty reduction has been of such gargantuan proportions that it is time for the
world to think about raising the absolute poverty line. Most of the present poor, and
future poor, are relatively poor. This fact should be recognized, and the absolute poverty
line, currently at $1.08 1993 PPP dollars per capita per day, needs to be raised to about
$ 2 (2006 PPP) dollars a day.
Golden era for the poor
While income might be a good indicator of welfare, there are additional indicators e.g.
life expectancy, literacy etc. And these indicators are not flashing as robust signs of
progress. And they cannot, because of problems in measurement. What these indicators
308
measure is attainment for an entire population, and therefore suffer from a severe
―overhang‖ problem. Literacy, even if measured accurately, pertains to both 8 and 80
year olds. But education policy, and progress, can do precious little for those about 30
years and above. The old illiterates, unfortunately, are always with us. Some adult
education programs can provide relief to the education unfortunates, but most countries
have either not launched, or been successful, at adult literacy programs. The same kind
of reasoning applies to life expectancy data. So while literacy has not increased by that
much in the developing world ( only 18 percentage points increase to 75 percent in
2000, from 57 percent in 1980) mean educational attainment has increased by more
than 50 percent – from 3.6 years in 1980 to 5.6 years in 2000.
The great fertility decline Woman does not live by bread alone, though progress can be measured by how much
less the woman is baking today. Throughout this book, the discussion has been,
sometimes nauseatingly, about growth in per capita income of India, China, the world.
As if nothing else mattered, and the book did start with the statement that growth was
sufficient, period! Everything else does matter, except it is strongly related to income
growth. And the fertility decline is one of the classic indicators of welfare improvement
for the worse off sex.
It is believed that history would have been even better if somehow the poor countries
had been able to control population growth. This belief no longer reflects the recent
transformation. A centuries old phenomena, associated with all countries, is that with
development, fertility rates (number of children ever born per woman) declines, and
labor force participation of women increases, and both fuel each other. This is indeed
what has happened in China, India, and most poor countries. The fertility rate in India in
2004 was 2.7, in Bangladesh about the same, and in China and Iran, less than 2 births
per woman. Population growth rate in India today is close to 1.5 percent per annum; in
China, primarily because of a forced one-child policy, it is only 0.6 percent per annum.
The new story in the world today is not population growth, but the great fertility decline. It
is coming soon to your favorite poor country.152
152 Population projections do not capture this fertility decline that is emerging across the
developing world. The ―benchmark‖ official document produced by the UN, World Population 2300, projects that average world fertility will decline by only 0.2 points in the next ten years (from
309
What made all the growth possible?
What has made all this progress happen. Very simply, economic and political freedom.
Economic freedom has been the major factor behind historical rates of poverty
reduction. But economic freedom is widely considered to be a 4 letter word (according to
those professing political correctness). The basic complaint against capitalism or
―markets‖ is that while it can and does generate extra growth, it leaves a lot to be desired
in terms of equality. The much too often and erroneous refrain is that under capitalism
the rich get richer and the poor get poorer. But like the invisible hand, this deemed
politically correct adversity is nowhere to be seen. Indeed, the data are consistent with
the alternate explanation – growth is good for poverty reduction, and most likely neutral
for inequality change.
The question does arise: was the growth made possible by state interventions or by the
―capitalistic market‖? Phrased differently, what are the lessons for Africa from all of this
history? Is it Communism that did it? Or was it dictatorship? Or was it enlightened state
intervention as argued by some? In all of this heart-burning, let us also not forget about
the ―bad‖ globalization period – so ―bad‖, that it has helped remove close to billion
people out of poverty in the last 20 sweet years. The growth did it, not government
intervention, benign or otherwise. And growth for the poor was helped by a decline in
world inequality, as poor countries grew faster than rich countries.
Openness, or pro-trade, policies are the most effective instruments for enhancing
growth; the last twenty six pro-poor years, when poverty declined by a record margin of
32 percentage points from 1980 to 2006, was not coincidentally when exchange rate
undervaluations were at their peak. These two decades were also witness to a
(population-weighted) acceleration in developing country growth rate to 3.2 percent per
annum, up 0.4 percentage points a year from the level observed for the previous two
decades. How much of the poverty decline of 32 percentage points can be attributed to
trade? All the empirical evidence suggests that this contribution is at least half; hence,
2.65 in 2005 to 2.45).In the previous ten years (1993 to 2003), fertility declined by a considerably larger amount, approximately 0.34 points.
310
trade contributed to at least 600 million being removed from poverty – there are few
other policies, indeed none, that can claim this Nobel prize.
Political Freedom No matter what index of political liberties is taken, the world has been witness to a large
increase since 1980 (and before). All the three indices from the Polity IV political data
collected by the University of Maryland, suggests an approximate 20 percent increase
since 1980. That is close to 1.5 billion people, and all of them in the developing world,
witnessing an increase in political freedom. (Table 12.2)
This means that governments today have a lesser chance of survival if they pursue anti-
growth policies. For some time now, the confusing ―Confucian‖ hypothesis has prevailed
in the world i.e. that East Asian economies like Korea and China grew fast because they
had able dictatorships (an oxymoron). This correlation conveniently ignores the fact that
most African and Latin American countries also had dictatorships and have not grown
particularly fast. Political liberties enhance growth prospects because they limit the
tenure of bad governments.
Aid and Poverty Decline
Given this historical and miraculous improvement for the world‘s poor, the question
remains: why isn‘t this one of the biggest stories of our time? There are several reasons,
some good, some perverse, for this disconnect between rock band political correctness
and economic reality. It could be argued that by constantly downplaying the success in
poverty reduction, the poor of the world would actually gain more resources to redress
their poverty. Extended, this argument means that agencies like the World Bank etc. can
actually lobby the rich governments to give more money for poverty alleviation. Extended
further, the assumption is that aid monies will be ―correctly‖ allocated to the needy in
poor countries. Even if all this is done, the extra money gained due to drawing attention
to the world‘s poor by downplaying poverty achievements has still not reached the poor.
That involves the assumption that developing country governments will actually deliver
money meant for the poor to the poor. Anybody who buys this sequence of probabilities
is ―knowledge-proof‖ about the political reality in the developing world. There maybe
such buyers of snake oil in rich countries, but developing country practitioners know
better.
311
Table 12.2a: Political freedom, 1820-2003 (% of Population)
Executive
constraint Polity IV index
Democracy
index
Political +
Civil liberties
All economies
1820 5.3 1.7 1.7
1890 33.0 32.0 20.5
1929 35.2 34.1 27.1
1950 52.2 48.2 43.7
1960 45.7 48.2 46.7 52.3
1973 41.8 41.4 41.3 47.5
1980 38.9 39.7 38.3 49.1
1990 52.8 49.6 46.2 52.9
2000 62.2 63.7 57.4 58.6
2003 63.3 64.7 59.0 58.9
Developing economies
1820
1890 3.4 4.9 1.6
1929 4.0 6.9 1.6
1950 47.0 41.4 35.1
1960 38.7 39.9 37.6 45.7
1973 33.4 32.9 32.7 37.5
1980 28.8 30.0 28.0 38.8
1990 40.8 40.3 37.8 43.1
2000 53.5 55.6 47.8 52.4
2003 55.2 57.1 50.0 52.9 Source: SAE dataset; see Appendix I for details.
Notes:
312
Table 12.2(b): Political freedom, 1820-2003 (% of countries)
Executive
constraint Polity IV index
Democracy
index
Political + Civil
liberties
All economies
1820 8.7 4.2 4.3
1890 38.5 36.5 23.1
1929 41.3 46.2 34.9
1950 45.2 47.9 35.6
1960 40.0 43.1 36.0 62.6
1973 32.0 31.5 28.8 41.0
1980 33.6 34.3 32.1 42.9
1990 49.2 50.0 43.2 54.4
2000 61.7 63.2 53.2 64.6
2003 63.6 64.9 56.5 65.1
Developing economies
1820 0.0 0.0 0.0
1890 17.2 27.6 10.3
1929 15.2 29.4 9.1
1950 34.0 38.3 19.1
1960 31.5 34.7 24.7 60.3
1973 23.5 23.0 19.4 32.4
1980 23.1 24.3 21.2 34.9
1990 36.5 38.0 32.7 45.4
2000 49.5 53.7 40.2 57.4
2003 52.8 55.7 45.3 57.4 Source: SAE dataset; see Appendix I for details.
Notes:
313
But aid practitioners keep asking for more aid to remove the same poverty. In his book,
The End of Poverty Professor Jeffrey Sachs of Columbia University, makes the case for
more aid, about $ 50 billion per year more, and for a redirection of such aid to make it
more effective. This rationale for more aid is simply spelt out in a sub-section entitled
―The Simplest Calculation‖ (Sachs(2005, p. 290).
Sachs accepts the poverty estimate of the World Bank that there were approximately
1.1 billion living below the $ 1.08 poverty line in 2001, and estimates that PPP $ 125
billion is needed per year to remove absolute poverty. Given that the present flow of aid
is estimated to be around US $ 60 billion, the ―simplest calculation‖ means that extra aid
of about $ 65 billion is needed per year.
That vastly more aid is needed to ―theoretically make poverty history‖ on an on-going
basis is also the conclusion reached by several other organizations. On a base of
current aid flows of $ 70 billion, the Zedillo committee estimated incremental aid needs
to be $ 50 billion a year, or an actual level of aid of $ 130 billion; ditto for the World Bank
in 2001; in 2003, the World Bank ―noted that low-income countries could immediately
absorb some $ 30 billion per year of additional aid, given their absorptive capacity‖ . In
2004, the leaders of UK and France called for ―roughly a doubling of ODA from 0.25
percent of donor GNP to around 0.5 percent of donor GNP‖ (Sachs(2005), p.301
emphasis added).
Table 12.3 (taken from Bhalla(2007)) reports on aid adequacy153 for the world, and for
sub-Saharan Africa. The columns are labeled actual, adequate, and extra. The adequate
column is a theoretical calculation of what is needed in terms of aid transfers to the
developing countries in order to theoretically remove poverty according to the $ a day
poverty line. The third column, extra, is the difference between the actual and adequate
amounts.
The ―extra‖ column suggests that aid has been in excess of that needed for the last
sixteen years for the developing world. There is an argument for redirecting more aid to
153
The ―adequacy‖ is easily defined: the amount of aid (or money) needed to lift everyone out of
poverty, to make poverty ―history‖.
314
Table 12.3: Aid Adequacy, 1975-2003 (in current US dollars, billion)
World Sub Saharan Africa
Year Actual Adequate Extra Actual Adequate Extra
1975 15.5 113.2 -97.7 3.3 10.1 -6.8
1976 15.0 105.2 -90.2 3.1 11.8 -8.7
1977 16.2 104.8 -88.7 3.6 14.3 -10.7
1978 19.1 127.6 -108.5 4.9 17.1 -12.2
1979 23.3 122.0 -98.7 6.3 18.8 -12.5
1980 27.5 115.2 -87.8 7.4 21.2 -13.8
1981 26.9 97.5 -70.6 7.3 22.5 -15.2
1982 25.4 78.6 -53.2 7.5 24.5 -17.1
1983 24.6 70.1 -45.4 7.3 27.8 -20.5
1984 24.8 62.6 -37.8 7.5 28.3 -20.8
1985 27.0 59.7 -32.8 8.5 24.4 -15.8
1986 31.0 46.5 -15.5 10.4 15.8 -5.4
1987 33.7 42.9 -9.3 12.1 11.8 0.3
1988 36.0 46.2 -10.2 13.7 16.7 -3.0
1989 37.6 44.0 -6.4 14.5 14.5 -0.1
1990 50.7 37.5 13.2 17.2 11.1 6.1
1991 56.7 37.6 19.1 16.9 11.9 5.0
1992 54.9 33.4 21.4 18.2 9.8 8.4
1993 49.3 36.4 12.9 16.8 12.1 4.7
1994 54.6 37.0 17.6 18.1 15.6 2.5
1995 54.8 44.0 10.8 17.9 23.2 -5.3
1996 49.6 45.9 3.7 15.1 29.0 -13.9
1997 42.0 43.6 -1.5 13.4 27.5 -14.2
1998 45.3 41.0 4.4 13.3 27.7 -14.3
1999 47.3 25.9 21.4 12.3 12.2 0.0
2000 44.4 25.9 18.5 12.2 14.0 -1.8
2001 46.4 25.1 21.3 13.0 13.4 -0.4
2002 53.0 22.9 30.1 17.4 12.6 4.8
2003 58.8 22.4 36.4 22.3 13.3 9.0
Source: Table 6, Bhalla(2004, p.)
Notes:
1. Aid Adequacy is defined as aid enough to lift every persons expenditure level above the $1.08 per capita per
day poverty line.
2. Actual aid is a composite figure of aid given to developing economies.
Extra aid is the difference between actual and adequate aid.
315
Africa, but even for that continent, aid was in excess of about $ 9 billion in 2003. So what
is going on? How come Sachs obtains a deficit of $ 65 billion and our calculations
suggest a surplus?
The difference has to do with Sachs‘s erroneous assumption that one PPP dollar has the
same purchasing power as one US dollar for all countries154. While that is more or less
the case for the donor countries, it manifestly is not the case for the recipient countries.
For the poor countries one US dollar is approximately equal to 3 or 5 PPP dollars.
Hence, what was presumed to be a deficit in terms of aid, is actually a healthy surplus.
Implications for Mary
The rapid decline in world poverty has been only, and entirely, made possible by the
forces of globalization. These forces have disproportionately benefited the poor, the
middle class, and the rich in the developing world. Why this disproportion? Because of
catch-up – the workers in developing countries were behind the curve, and in the
process of catching up, grew faster.
But globalization has its costs, though in a reversal of the Industrial Revolution days, it is
the core that is losing from globalization. In the first phase of this globalization (let us call
it 1980-2006) it is likely that the working class in the developed world lost out to the
workers in the developed world; in the next phase (2007 to 2025) it is more than likely
that the middle and upper class workers will lose out. An appreciation of this effect can
be gleaned from the following story. 155
An average rich country person (let us call her Mary), was born in 1960 and witnessed
her parents income double by the time she was 20 years old. In contrast, an average
poor person in a poor country (let us call her Sita) saw her parents income increase by
only 50 percent, or half the rate of her rich country counterpart.
154
Recall the discussion about the real exchange rate etc. conducted throughout this book. Sachs is assuming that the PPP exchange rate is equal to the US dollar exchange rate, and therefore the real exchange rate for all countries is equal to 1. This is obviously not the case. In India and China in 2006, the real exchange rate was 0.19 and 0.25 respectively. 155
See Bhalla (2004), Why the rich fear globalization.
316
Mary went to college in 1982, and was able to ―easily‖ enter the best universities. She
graduated in 1986, but her own working period, however, has only witnessed a glacial
increase in family income. After 20 years of hard work, family income is up only 35
percent, and most of the increase is driven by the fact that she is now working along with
her husband. Five years of wage growth in the pre-globalization days is equal to 20
years of wage growth in the era of globalization. To make matters worse, Mary finds that
her children are finding great difficulty in entering the best universities, especially
because of the large number of worthy applicants from Asia.
Put yourself in Mary‘s position – would you also not rant and rave against globalization?
Of course you would; but would you do so if you were Sita? Of course not, because you
have never had it so good. And if you were Ms. Wong, you would rant even less. The
situation will only get worse in the future for middle to upper class Mary‘s. Catch up is
inevitable. What is important for all nations and politicians to recognize is that catch-up
cannot proceed smoothly if there is perceived to be a significant unfairness in the
system. The middle class is opposed to mercantilism, just as it was opposed to
feudalism, or monopoly capitalism. Right now the middle class in the West perceives
that something is deeply wrong when countries unfairly benefit their own citizens‘ jobs at
the expense of others. I agree with them.
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Chapter 13 : History as we will know it
Future growth of India and China – alternative calculations.
There are some reasonable predictions that can be made about the Indian and Chinese
growth prospects for the next 20 years. Since the Chinese growth pattern is well known,
and since it is the Indian process that has been involved in a structural break, the
discussion is centered more around India, though estimates are also reported for China.
One major conclusion emerging from the calculus is that by the end of this decade 2010
(if not earlier) Indian GDP growth rate would be at least equal to that of China. Within a
decade, the gap between the respective population growth rates should be halved –
from near 1 percentage point gap in 2006 (China population growth at 0.7 percent per
annum and India at 1.6) to only 0.5 percent. So, per capita growth rates will also
converge, and by 2015, projections suggest that per capita growth in India will exceed
China by at least 1 percent per annum. There is little reason to believe that India GDP
growth will average less than 8.5 percent in the future, ceteris paribus, and with no
additional reforms.
In terms of growth rates, it is unambiguously the case that China‘s growth rate has been
a few percentage points faster than India for the last two decades (about 4-5 percentage
points higher in per-capita terms, and 2.5-3.5 percentage points faster in aggregate
terms). This excess has been achieved by favorable initial conditions, a higher
investment rate, and a policy of exchange rate under-valuation. Each of these factors
has contributed about a third to overall ―excess‖ growth for China. Going forward, an
expected appreciation of the Chinese exchange rate and less advantageous initial
conditions should mean a somewhat lower than India growth rate for China, ceteris
paribus. Any excess growth rate observed for China will have to be a function of how
easy it is for China to sustain the rather high investment levels of 40 percent and above.
Realistically, and on the basis of experience of other countries, the investment ratio for
China should fall back to the mid-30s level i.e. investment as a share of GDP of around
35 percent. When that happens, the Indian growth rate will be higher than China‘s.
Combining all the three factors (initial conditions, exchange rate change and investment
levels) India‘s growth rate should exceed China‘s by as early as 2010. This is a radically
different forecast than that articulated by other experts e.g. the market-oriented BRIC‘s
report by Goldman Sachs (2003).
318
There are at least three different methods for arriving at the forecast of growth rates. The
first method relies on a sectoral approach i.e. growth in the three different sectors
(agriculture, industry and services) are forecast, based on past patterns, to arrive at an
assessment of the future. The second method is a production function approach with
most importantly the estimate of growth in investment or capital inputs. The third method
relies on the catch-up and policy (mostly currency undervaluation) models discussed
earlier. (Table 13.1 shows the pattern of GDP growth, its determinants, and associated
statistics for the previous forty years).
Method 1: Forecast based on sectoral growth rates Today, China has one of the highest shares of industry in the world. This has been made
possible because of historical reasons (this share was even high during the 1960s) and
due to its strategy of deep undervaluation. This policy has already led to severe global
imbalances and one of the central assumptions in the forecasts is that this
undervaluation will have to at least partially be reduced. This will have an implication for
industrial and GDP growth – both will be reduced. It is also the case that policy makers
in China are targeting a long-run slowdown in GDP growth. So considering all factors
together, it is a reasonable forecast that over the next several years, GDP growth in
China will decline towards the 8 percent level, if not slightly lower.
In India, the trend of GDP growth is in the opposite direction. The first estimate of at least 8.5 percent GDP growth, is arrived at as follows. Services presently account for close to 55 percent of Indian GDP, industry is 25 percent, and agriculture is 20 percent. Industry has never averaged more than 7 percent growth on a sustained basis to date (see Chapter 10), but there are ample reasons to believe that history is being made as this is being written. For the last 4 years, industrial growth in India has averaged 8.7 percent, for the last two, 9.7 percent per annum. For the four year period 1999-2002, industrial growth was only 5 percent. It is true that world and developing country industrial growth has also accelerated, but by nowhere by this magnitude. There are structural reasons behind this change (as discussed earlier) so it is a safe conclusion
319
Table: Growth and Indian excess growth over China, 1960-2025
Level, 2007-2025
Indian excess growth (% per
year)
Coefficie
nt India China 1960-80 1980-06 2007-25
Growth determinants
Catch up (log Initial income) -1.243 2.25 2.93 -0.4 0.0 0.8
Demography (Initial worker population) 0.143 62.32 70.50 0.1 -0.5 -1.2
Education (initial year) 0.140 4.83 6.46 -0.2 -0.2 -0.2
Undervaluation (initial year) -0.016 -30.38 -59.40 1.0 0.6 -0.5
Annual change in undervaluation -0.500 1.00 3.60 -0.6 -1.3 1.0
Fiscal deficit (% GDP) 0.126 -3.59 -2.14 -0.3 -0.5 -0.2
Total excess growth for India (% per year) -0.34 -2.0 0.1
Alternative method via production function
Labour force growth (% per year) 0.560 2 1.2 0.1 0.3 0.4
Capital growth (% per year) 0.440 10.5 8.5 0.3 -1.9 0.9
Share of investment in GDP (%) 0.100 40 35 -1.1 -1.2 0.5
TFP growth (% per year) 3.5 2.5 -0.7 -0.5 1.0
Total excess growth for India -0.3 -2.1 2.3
Source: SAE dataset, see Appendix 1 for details
Notes (1): The share of investment in GDP does not form part of the total excess growth
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that industrial growth in India will average above 10 percent for the foreseeable future.156
If this happens, then the historical relationship between services and industrial growth
(arbitraged through the labor market) suggests that services will grow by at least 11
percent. Even if agriculture grows at 0 percent, the combined GDP growth rate will be
above 8.5 percent. Thus, an 8.5 percent average GDP growth rate for India is a
conservative estimate.
Method 2: Production function approach.
This calculus also overwhelmingly favors India. It has a higher labor force growth rate,
and that will persist for the next two decades; and investment rates, and capital growth,
in India are expected to increase. Very rarely have investment rates jumped by 10
percentage points over three years (as has just happened in India) and then reverted
back in a hurry. This ―episode‖ of increasing investment rates, and then stabilization
around 40 percent plus levels should occur in the near future. (In the year just ended,
the Indian investment rate is likely to be around 36 percent of GDP, up from 23 percent
of GDP just four years earlier).
China is on the other side of the curve – been there, done that. With consumption only
38 percent of GDP, it is unlikely that the share of consumption can politically or
economically go down much further. In addition, the pattern in the East Asian economies
has been for investment rates to revert back to the mid 30s from the 40 plus levels –
over a period of maturing time, obviously. This suggests that the investment rate in India
will exceed that of China in the not too distant future and a 5 percentage point advantage
for India would mean at least an extra 0.5 percent GDP growth. Add an extra 0.5 percent
of labor force growth in India and zero contributions from expected higher TFP growth (it
accompanies an increasing investment rate in an open economy) and the case that
Indian GDP growth will soon exceed China‘s is quite strong.
156
Given comparative data on other country experiences, there is a greater likelihood of this industrial growth not only not reverting back to its traditional mean (rear window economics) but actually accelerating a few percentage points higher.
321
Method 3: Policy Model approach The policy growth model (whose results have been presented throughout this book) for
the 1980-2006 period is ―imposed‖ on the other periods. The important unknown for the
forecast for 2007 onwards is the rate of real exchange rate change in the two countries.
Given the need for a much larger exchange rate appreciation for China than India (and
acknowledging that China has a huge current account and trade surplus and India has
deficits for both) it is a safe prediction that the gap between them will be at least 2.6
percentage points a year i.e. the rate of China‘s appreciation will be, on average, at least
2.6 percent per annum faster than India. (For the 1980 to 2006 period, China
depreciated at an annual 2.6 percent rate faster rate than India.) If this is the case, then
Indian GDP growth will exceed that of China by 0.2 percent per annum.
The Chinese yuan, in mid-2006, was undervalued by 82 percent (or log 59 percent; thus
the fair value for the yuan was 4.4 in contrast to the managed exchange rate of 8
yuan/dollar). Even if China agrees to a revaluation of 5 percent per annum, it would
mean only a ―real‖ revaluation of about 3.5 percent given that the yuan needs to
appreciate by about 1.3 to 1.5 percent each year just to stay at the same level of
undervaluation (this due to B-S considerations). Which means that it will be at least 20
years before the Chinese yuan is even fairly valued. Thus, the fear that the Chinese
growth rate will collapse with a revaluation (e.g. McKinnon) is somewhat unfounded. But
it is likely that with a 10 percent revaluation per year, the Chinese growth rate will
decline by about 1 to 3 percent per year i.e. even at the top end of this range the slowest
future Chinese growth, due to a revaluation, will be about 7 to 8 percent per annum.
No matter what the calculation, if history and economic calculations are any guide, the
higher than China GDP growth in India will soon be a reality. All considerations point to
the Indian per capita GDP growth rate exceeding that of China, possibly as early as
2010.
The changing world landscape can be gleaned from Charts 13.1a and b, which shows
the contribution to world growth (in PPP prices) of the two major regions of the world, the
West and Asia. In 1950, the West (industrialized countries plus Eastern Europe)
accounted for almost 90 percent of total output. By 1960, the West was still accounting
322
for about sixty percent, and Asia about 15 percent. The first cross-over point (when
Asia‘s contribution exceeded that of the West) occurred just prior to 1980. In 2006, Asia
accounts for close to sixty percent of world growth, and the West a few percentage
points below at 30 percent. The importance of China and India is underscored by the
fact that in 2006, China accounts for almost the same share of global output change as
the West (30 percent versus 35 percent). In this same year, India‘s share was 12
percent.
323
Table 13.1a: Contribution to World growth, 1000-1980
Table 13.1b: Contribution to World growth, 1980-2025
Source: Maddison (2006); Penn World Table version 6.1; World Development Indicators, World Bank(2006); World Economic Outlook, IMF (2006). Notes: Contribution to growth for each region in the ratio of each region’s growth to world growth (multiplied by 100).
324
A permanent core-periphery change seems to have occurred starting in 2001. In the last
six years, Asia‘s contribution has averaged 56 percent, and the periphery 33 percent. If
present relative growth rates hold, then in 2025, the regional contribution to world growth
would look as follows: Asia 73 percent, West 18 percent, China 27 percent, and India 35
percent. (Note that China and India are both part of Asia, and Japan is part of West).
India‘s contribution starts to exceed that of China by 2021.
325
Chapter 14: Conclusions
The poor shall inherit the earth, or how fast the world can change. Just 40 years ago,
both India and China were given up as basket cases; 40 years from now they will be two
of the four most important economies in the world. Just a few years ago, many were
talking about deep poverty in India, and how the country had miles to go before it could
begin to make a dent in its poverty, or make a significant contribution to world growth.
Today, it is different. India-China is the flavor of the year, and China-India the flavor of
the decade. One has to go deep into history to find another occasion when India and
China were mentioned together with the same respect; indeed, one has to go back
several thousand years. Not times they are a‘ changing; times they have changed.
If the growth rate for these two economies stays around its recent historical path, and
even if the rest of the world accelerates its growth somewhat, then soon – by 2025 –
history would have been achieved, or repeated. The India-China share in world output
(PPP terms) is projected to reach 40 percent, or equal to their share of population. A
phenomenon last seen more than 500 years ago. Recall that just in 1980, the India-
China world output share was less than 10 percent.
This book has attempted to trace the importance of India and Chine – from 500 years
ago to about 20 years from now. Whether the time period is 1500, or 2006, the
importance of India and China cannot be denied. When poor, the importance was as a
burden to the rest of the world; when growing less poor, the importance was in terms of
future potential; when becoming middle class, the importance is as a growth pole, and
as a magnet for development for other emerging economies. In another twenty years,
the average per capita income of India and China is likely to be around $20,000 1996
PPP dollars; their joint population is likely to be close to 3 billion157. The entire developed
world had the same per capita income just twenty years ago (in 1987). But their
population was only about a fourth – 782 million.
This book started with a quest for the determinants of growth. It noted that a universal
truth acknowledged by all economists is that openness to trade facilitates growth. But
157
Co-incidentally, projections suggest that by 2025, the two countries will have equal populations – both around 1450 million.
326
how could one best harness the advantages of openness? Through currency
undervaluation. But wasn‘t the real exchange rate endogenous? No, most often not.
Don‘t believe all the results presented here, all the investigations into devaluations and
subsequent price changes; just ask China, or Uzbekistan, or Malaysia.
The other important determinant of growth is the middle class. This is middle class not
conventionally defined (i.e. the middle of the distribution) but rather middle class defined
by Aristotle or John Stuart Mill. This middle class starts of as the technocratic elite, but
once its ranks swell, it begins to exercise its influence. This middle class is interested in
merit, so it is natural for it to be for economic reforms; a leveling of the playing field helps
it the most. Through reforms and sensible policies is how the middle class is able to
affect future growth.
In the early 1980s, most of the middle class in the world (?? Percent) resided in the
developed OECD world. In 2006, most of the middle class was in the developing
countries - ?? percent. This has had repercussions, and will continue to have. The first
twenty years of globalization was about catch-up by the blue collar workers in
developing countries to those residing in the West. The next twenty years will be about
the educated, white collar Western workers feeling the heat.
Briefly, the story of this book is as follows. The inherited world of India and China in 1950
was bleak. While the two countries took distinctly different political paths, their obsession
with economic control was near identical. Perhaps it was desperation to get ahead;
perhaps it was the example of Russia; perhaps it was the ideology of the elite, or most
likely, the ideology of ―in the name of the poor peasant‖. Like all such excuses, this one
also failed, and failed miserably. As economic freedom was curtailed, so was economic
growth. The correlation does not involve rocket science, but it took the (arrogant) leaders
of the developing world some time to realize this obvious, historical, reality.
China was the first one to blink and change course. While the two societies have been,
and are, remarkably similar (with democracy the big outlier difference), there is one
difference that recent history illustrates. When China changes course, it does so firmly,
quickly, and vehemently. It is as if yesterday did not exist. India is different; here people
like to debate, and argue, and discuss, and argue. Even today, the debate in India,
327
ironically led by the Communists, is about issues long settled. The debate is still about
how markets are evil, and how the state is good. Black and white, just as in China. With
the difference that in China the black and white pertains to the present, and most
importantly, tomorrow. In China, the debate first was how to compete in the Western
world, and now is on how to manipulate its size advantage.
But China took the lead with the bold declaration that greed was good, that if each
person caught mice, the plague of poverty will be eradicated. China started to provide
economic freedom a ‗plenty to its‘ citizens. The results were quick and startling. Growth
catapulted to above 7 or 8 percent an annum almost instantly. The medium of choice,
apart from removing the straitjacket of the state, was the currency. Rapid devaluations,
the largest in history, allowed China to impress upon the world the advantages of size. It
also allowed the poor to become less poor, and soon to become middle class.
India followed, with an initial thirteen year lag, 1991 versus 1978. But in its tradition of
slowness, aided and abetted by the political class, India moved gradually, ever so
glacially. But move it did and soon it was not necessary to ask the government‘s
permission to brush one‘s own teeth. It is likely that what we call globalization today was
initiated by China in 1978. That decision to catch mice first and discuss ideology
afterwards was most likely the most definitive move towards the eradication of poverty in
the world, towards the development of the middle class, and towards the creation of the
world goldilocks economy.
World supply increased by large magnitudes as 40 percent of the labor force was
suddenly more efficient. This was/is the mother of all supply shocks. And given that the
wage levels in China and India are way below their productivity levels, there is much
more to come. If you are wondering as to how come the world is booming, commodity
prices are rising, inflation is declining, asset prices are rising, and that you have never
seen it so good – wonder no more. No, it is not that central bankers have suddenly
discovered the correct magic potion of money; it is rather the arrival of a historically
unprecedented fast growing middle class.
But not all is good with globalization. There are losers, and in a reversal of history, the
losers today are in the Western world. The arrival of this middle class, at lower wages,
328
means a threat to competitors in the West, or the east. But the competitors in the West
are those with higher wages, relative again to their internationally defined productivity. In
the first instance of evolution, that is in the 1990s and today, the competitor workers
most hurt were the blue-collar workers, or workers in the bottom half or two-thirds of the
population. The anti-globalization protesters one has been witnessing were most likely
representing this losing class. The protests are likely to move upscale, as inevitably, and
again because of the simple matter of size of China and India, it is the Western middle
class that will be affected.
This upheaval is inevitable, but surely, like global warming, there is something that the
world citizens can do, for the benefit of all. And there is. Something that is misaligned
should be corrected. Report after report has identified the problem of carbon emissions;
report after report has suggested the same remedy. Analogously, it is quite obvious that
correction of currency misalignments is a necessary first step towards a less
disharmonious economic and political future. And really, this misalignment has nothing
to do with either Europe or Japan. Thinking that would be thoughts along the lines of the
Indian, not Chinese, intellectual. It would be thinking about yesterday. Today and
tomorrow dictate that the major realignment has to come with China. A misalignment as
large as 60 percent, and a misalignment increasing by 3 to 5 percent per year. Never
has a currency been so misaligned; never, obviously, with such a large mass of
population. Add to it the need by China‘s Asian neighbors, including Japan, to stay
competitive. The problem becomes worse. The problem is no more the American
consumer. It has gone far beyond American shores. Size coupled with size has added
up to several Godzillas.
Job losses, job insecurity, slow or negative growth in incomes for large upscale
populations in the West. This is political dynamite, and ignoring it would be economic
suicide for the burgeoning middle class in the East. China has taken the lead many
times in the past – both in the right, and wrong, directions. Most times, it has acted
swiftly, much to the envy of the lead-footed Indians. The next lead is awaited.
The top ten conclusions, in ascending order of importance.
329
10. India was not that much a democracy before its time. Everybody has been
(positively) perplexed by India‘s tryst with democracy. The common assumption has
been that India was too poor to be democratic at the time of its independence in 1947.
―No bourgeoisie, no democracy‖ is how Barrington Moore had famously put it. There are
two reasons to be not in total awe with India being an early democrat. First, that India
had Britain as it‘s colonial power, and in this regard India was fortunate to have the best
among the worst. Almost invariably, the one practice common to British colonies – going
as far back as 1776 – was that their political leaders were enamored by democracy. Rich
or poor, past or present, there is a long stretch of former British colonies adopting
democracy or a relatively high level of political liberties. Being a British colony, the
―base‖ probability of India being democratic was high. Second, India is an extremely
heterogeneous society and the greater the diversity, ceteris paribus, the higher the
chance of staying with democracy. Only democracy can give the minority groups a
chance.
9. From being equal for a thousand years to being unequal. India is China with a five to
ten year lag. It has been a surprise to note how similar the seemingly different
continental economies have been. But by early and faster adoption of integration with
the world economy, China has taken a large lead. But across several dimensions, the
gap between India and China is reducing. To understand India today, one just has to
look at China in the mid 1990s.
8. No one built infrastructure before its time. A universal accepted fact about the two
countries is that in China the infrastructure is excellent, India it is way behind. The
assumption is that the state of infrastructure in the two economies, today, should be
about the same. Twenty six years ago, or even 1026 years ago, it was reasonable to
have this expectation. No more, because of the much faster growth experienced in
China in the most recent two decades. So while habits die hard, the correct expectation
should be that infrastructure in India should be less than half as good as China. Further,
reasons for the extreme non-linearity in infrastructure demand, and therefore supply,
were explored. This non-linearity arises because of the major non-linearity in the growth
of the middle class, the major demander of infrastructure. It is fair to say that this growth
―creeps up‖ and then suddenly emerges – at which time, the infrastructure is woefully
inadequate and policy makers, and the markets, rush in to make amends. This is broadly
330
what happens in all fast growing economies, what happened in China in the early
nineties, and what is happening in India today.
7. The real exchange rate is not really that endogenous. A gospel among many, and
especially among those who disagree that China has been, and is, pursuing extremely
mercantilist policies, is that the real exchange rate cannot be manipulated. So China,
indeed no one, can pursue mercantilism by voluntary depreciations of the currency and
accumulation of foreign exchange reserves. This assumption was not only shown to be
false, but exposed to be wrong for a large set of countries, and for an even larger set of
currency depreciations. Unlike the prediction of those maintaining the RERIE (real
exchange rate is endogenous) mantra, the Chinese real exchange rate has depreciated,
in real terms, by over a 100 percent since 1980. The yuan exchange rate was frozen at
8.28 yuan to the dollar since 1993, and only started to begin to change in August 2005.
During this fixed rate period, the higher productivity growth in China should have led the
nominal value of the yuan to appreciate by more than 70 percent! So the RERIE
enthusiasts have been proven to be twice wrong with China. And third time wrong if one
observes the large trade and current account surpluses observed in China. Fourth time
wrong if one notices that foreign exchange reserves in China have now exceeded $ 1.2
trillion, a little less than half of China‘s GDP!
6. Latin America not divergent until after 1980: One of the central assumptions in the
literature is that Latin America shared in the Great Divergence with Asia and Africa.
Theories have been built around this ―fact‖, and it is true that Latin American incomes
were higher than the US around 1700, and significantly lower around 1980. But so was
the case with practically every other country in the world. If relative incomes are
measured against the UK, then Latin American incomes stayed broadly the same as UK
for almost five hundred years; divergence only occurred post 1980, especially the two
decades 1980-2000. So theories of divergence should include Latin America with the
West; only Asia (ex-Japan) and Africa diverged. And both these continents were
colonized after Latin America became independent in the early 19th century.
5. Structural break in Indian economy, circa 2003: The remarkable story of China and
India is that these are continent size economies with giant size growth rates. For China,
the structural break to a 9 percent plus growth rate occurred after the last mega currency
331
devaluation in 1990-1993. In India‘s case it happened in 2003, slightly more than a full
decade after the economic reforms of 1991-1993. Those reforms were incomplete, and
particularly so in the financial, fiscal, and monetary sectors of the economy. The gradual
relaxation of controls, along with a movement towards market determined interest rates
starting in 1999, meant that over the period 1999 to 2003 there was a 500 to 600 basis
point decline in real interest rates in India. This led to predictable surge in
competitiveness of Indian industry; the rest is, (and becoming), history. As part of this
structural break, in the last four years, investment and savings rate have both jumped in
India to the mid-thirties plus levels. The investment rate, at around 38 % of GDP, is now
close to Chinese levels.
4. Lost in 450 years, gained in 45: The share of China and India in world output was
equal to their share in world population for hundreds of years. But there was a long term
decline, and in 1980, their joint share in global output declined to less than 10 percent
i.e. given that their population share was 40 percent, this meant that per capita incomes
in these two countries was only a fourth of the world average. Today, the share has
recovered to 28?? Percent, and by 2020, the two shares, population and output, will be
equal. The countries will be average again. This will be the sharpest V shaped recovery
on record. What the two countries lost over 45o years, they will gain back in a mere 40
years. This is revolution.
3. Middle Class as a major force: How major can only be realized by correlating the size
of the middle class with major social and economic and political changes in individual
countries over the last 300 years. The definition of the middle class adopted is an
absolute one; in today‘s prices, approximately US $ 10 per capita per day in developed
countries, and approximately US $ 3 per capita per day in developing countries. This
definition is consistent with the arguments of Aristotle and John Stuart Mill, both of whom
believed the middle class starts of as the educated and professional elite, and that it is
born to rule. And it does.
2. Institutions don’t rule: One of the more startling conclusions. In recent years, and in
less than a decade, world economics and policy makers opinion has converged to the
belief that Western style institutions are the key to long-term growth. There has been a
small minority of detractors, and the results of this book are firmly on the side of this
332
minority. In a ―horse race‖ with several institutions variables, and several instruments for
these institutions (as required by simultaneity economics), institution variables are
significant explanators of growth or levels of development less than 15 percent of the
time. Geography also does not explain growth differences. Then what does?
1. There are no growth miracles: What really matters for growth is whether the exchange
rate is attractive to investors, and in the first instance, whether it is undervalued,
preferably significantly undervalued. Across a wide variety of sensitivity and robustness
tests, and across a wide variety of time-periods (historical 1870-1938), contemporary
(1980-2006) and periods in between; across a wide variety of estimation techniques, and
subject to joint testing with a wide range of variables (variables representing health,
education, openness, continental dummies, geography, initial conditions, fiscal deficits,
etc.) two variables; currency undervaluation in the initial year of any time-period, and the
average change in undervaluation, are jointly the most significant explanators of
economic growth, and differences in the levels of income. Statistically significance and
with a magnitude that can explain close to half (sometimes more) of total factor
productivity growth. Behind most growth miracles, is a currency undervaluation story.
Truly, there are no growth miracles.
333
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Appendix I - Data and Definitions Population: Country population data were obtained from the US Census Bureau:
http://www.census.gov/ and Maddison 2006. Population figures from Census Bureau for
the period 1950-2025 are linked backwards to 1 A.D. using country, region and world
population data from Maddison. When Maddison data for individual countries are not
available, regional population growth rates were used to compute the missing population
data. Linking of the two sets of data allows for the generation of estimated population,
per country, for selected years between 1 AD and 1950 AD and for each year thereafter
till 2025 AD.
Labor Force: Population in labor force aged 15-64 years, source WDI
Capital Stock: Nehru-Devashar series used till 1990; from 1990, investment (deflated by
GDP deflator) and depreciation of 5 percent assumed.
Total factor productivity growth (TFPG): Conventional Cobb-Douglas model estimated
with regional dummies (seven regions) and time dummies for three time periods: 1950 to
1973, 1974 to 1990, and 1991 to 2006. The estimated share of capital is 45.5 percent
and of labor 54.5 percent. TFPG is calculated as GDP growth – 0.455*growth of capital
stock - .545*growth of labor force.
Real Income:
The base source of per capita income data is Penn World Tables version 6.1 (PWT)
available at http://pwt.econ.upenn.edu/php_site/pwt_index.php. PWT contains
incomplete panel data for per capita nominal and real (RGDPCH) income in PPP
international prices for the years 1950-2000.
The gaps in PWT country specific real income data are filled in by using Maddison data,
available at http://www.ggdc.net/maddison/ . The linkage factor used is the ratio of PWT
and Maddison data for 1996. The Maddison data are available for selected years
between 1 AD and 2003. This 1996 base per capita income series (combination of PWT
and Maddison) is then extended to 2006 by using real per capita GDP growth data from
World Economic Outlook (2006) available at
358
http://www.imf.org/Pubs/FT/weo/2006/01/index.htm. Per capita income data from 2007
to 2025 is projected on the basis of the growth in per capita income 2003 to 2006.
Distribution of income (quintile): Primary source of the distribution data is WIDER 2006
database on income distribution. This panel data ranges from 1950 to 2002. Data prior
to 1950 are obtained from Bourguignon and Morrison (2002). B-M report income
distribution quintiles for 33 countries, and several regions, for the time-period 1820 to
1992. When individual country data are not available, regional income distribution data
are used. The pooling of the two sets allows the construction of individual country
quintile data from 1820 to 2003. Income distribution data for years prior to 1820 is
assumed to be the same as 1820; for years post 2003, to be the same as 2003. This
assumption makes possible an income distribution (in quintiles) series from 1 AD to
2025 AD.
Distribution of income (percentiles): Quintile data are converted into percentile data
according to the modified Kakwani procedure outlined in Bhalla (Imagine There’s No
Country, 2003).
Middle Class: Middle class is defined as the proportion of people whose per capita
income is equal to or higher than the weighted average of poverty lines in developed
countries. This weighted average is observed to be $ 8.2 per capita per day in 1996 PPP
prices, or PPP 10.02 in 2006 international prices.
Real Exchange Rate (RER): Real exchange rate is defined as the ratio of the PPP
exchange rate to the US $ exchange rate. Real exchange rate data since 1950 are
obtained from the Penn World Tables and updated using World Bank (World
Development Indicators) and IMF World Economic Outlook data sets. Data prior to 1950
were kindly made available by Taylor (1997).
Equilibrium Real Exchange Rate (RER*): See Appendix II
Undervaluation (UV): Defined as the log ratio 100*(RER/RER*); a negative value
signifies undervaluation.
359
Average change in UV, dUV: per year change in the magnitude of undervaluation;
negative sign means the currency became more undervalued.
Geography: Four geography variables are used. Latitude, mean temperature, minimum
rainfall, average rainfall, are obtained from parker(2002). Proportion of surface land in
tropical areas are obtained from Bosworth and Collins (2005).
Institutions:
Property rights: Average protection against risk of expropriation (ICRG index) is used as
an index of property rights. These data for the 1980s are obtained rom databases
developed by Sachs (2003); Acemoglu et. al. (2001); Albouy (2006); Schleiffer et. al
(2003). These data were previously used by Knack and Keefer (1995) and were
organized in electronic form by the IRIS Center (University of Maryland). The original
compilers of this data are Political Risk Services.
Executive Constraints: Data are obtained for Polity 4 datasets available at
Political Liberties: This database is obtained from the Freedom House datasets available
at http://www.freedomhouse.org/template.cfm?page=1
Colonial Heritage: These data are as used in Bhalla (1997) and expanded from
information available in Wikipedia.
World Bank data on governance: Control of corruption, rule of law, political stability and
absence of violence, voice and accountability, government effectiveness, regulatory
quality are obtained from Kaufmann et. al. (2006) from the World Bank website.
Instruments
Several variables are used as instruments for the estimation of equations relating
institutions to economic growth. Settler‘s mortality in nineteenth century, urbanization in
sixteenth century, population density in sixteenth century, share of European population
in 1900 etc are variables are obtained from Acemoglu et. al. (2001). Data on the middle
class in mid 19th century are obtained from own computations; data on educational
360
attainment in the late 19th century are obtained from Benovot(200?) and Morrisson-
Murtin(2005). The latter kindly made available their data.
Tariffs: Tariff data are collected from many sources. Data for post 1980s are collected
from World Development Indicators, 2006. Data prior to 1950 are collected from
Williamson (200X) available at
http://www.economics.harvard.edu/faculty/jwilliam/papers.html.
The Penn Tables data (version 6.1) are available from 1950 onwards and these data
form the basis of the tests of various hypotheses. For the pre-1950 period, real income
data are available via Maddison and as described in Appendix I, the two data sets are
linked with 1996 as the base. Alan Taylor(1996) tests for the evolution of the real
exchange rate for 23 countries for the period 1884 to 1996158. These data consist of
price (CPI or GDP deflator) and nominal exchange rate for these countries. The Taylor
data were supplemented for India, China, and some other Asian countries. This provides
us with data on nominal exchange rate and price indices for years prior to 1950. In an
analogous fashion to the Western countries, these data were also linked to the 1950
PPP exchange rate. The same formula relating the real exchange rate to real income is
used to obtain the predicted RER for years prior to 1950. Thus, for some countries real
income, real exchange rate, predicted real exchange rate (after accounting for B-S
effects) are available for years prior to 1950.
158
I am thankful to him for making these data available.
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Major oil exporter country
Countries with population less than
1 million
Algeria Aruba
Angola Bahamas
Azerbaijan Bahrain
Bahrain Barbados
Congo Belize
Equatorial Guinea Cape Verde
Gabon Comoros
Iran Cyprus
Iraq Djibouti
Kazakhstan Dominica
Kuwait Equatorial Guinea
Libya Fiji
Nigeria Grenada
Norway Guyana
Oman Iceland
Qatar Kiribati
Russian Federation Luxembourg
Saudi Arabia Macao
Sudan Maldives
Syrian Arab Republic Malta
Trinidad and Tobago Micronesia, Fed.Sts
UAE Netherlands Antilles
Venezuela Qatar
Yemen SaoTomeandPrincipe
Seychelles
Solomon Islands
St. Lucia
St.Kitts and Nevis
St.Vincent and the Grens.
Suriname
Tonga
Vanuatu
Western Samoa
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Appendix 2 – The Real Exchange Rate and the US
The currency undervaluation measure defined in Chapter 7 is a measure that estimates
for each country the deviation of the real exchange rate from its ―fair‖ or predicted value.
The equation modeling the real exchange rate (RER) process is one which does not
imply, but tests for, the expected Balassa-Samuelson effects on the (RER).
The RER is defined as the ratio of the two nominal exchange rates, the PPP nominal
exchange rate, PPPxr (Penn Tables) and the nominal exchange rate against the US $
(US$xr). The ratio RER is therefore given by the expression: RER = PPP xr/US$ xr;
Balassa-Samuelson hypothesize that RER should show a strong relationship with the
level of per capita income Y. Recent research (e.g. Rogoff, Frankel) estimates the
relationship between the two variables (RER and Y) as a log-log relationship and finds
the constant elasticity to be around 0.4.
In Chapter 7, a constant elasticity function rather than being assumed is estimated from
the data and the following simple non-linear expression (called an ―exponential
regression with one asymptote‖) results in a much better bit. The following two
regressions detail the findings (lower case represents logs, and ^ means ―to the power‖;
estimation based on annual observations for the 11 year period 1996 to 2006 for 173
countries; Sudan not included ):
Traditional model159: rer = -1.9 + 0.38y, R2 = 0.49 (1)
Non-linear model: RER = 1.1*[1 – 0.97^Y], R2 = 0.86 (2)
In the log-log model, the RER is unbounded (can go to infinity with an increase in
income) while the non-linear model constrains the upper bound of the real exchange rate
to be 1.1.
159
Sometimes the independent income variable is expressed not in absolute terms but as a ratio to the US income. Note that Y represents real per capita income (in constant, not current, 1996 PPP dollars). See Appendix I for details.
363
Both equations yield, for any point in time, the expected value of the real exchange rate.
Deviations between the actual and expected value of RER yields an estimate of
currency devaluation (when actual is less than expected) and currency over-valuation
(when expected is more than average). Given that data on RER and Y are available till
1950 (and before, see Chapter 2), one can estimate currency misalignment for all the
previous years. This ―forecast‖ is an out of sample fit and for years before 1950 the
assumption is that the relative structure of the economies is the same as in 1950.
Estimation of undervaluation for all countries other than the US
There is a two stage method of estimating from equation 2 above the degree of
undervaluation for each country-year. The first stage involves the estimation X equal to
100*log(RER/RER*) where RER* is the predicted value of the equation. Note that X is
the misalignment of each currency against the PPP dollar. Let X for the US be denoted
by XUS . So the degree of undervaluation against the US dollar for each country is given
by X - XUS . For example, the model predicts that the Chinese yuan is undervalued by
(log) 64 percent in 2006. But the US dollar is itself undervalued against the PPP dollar
by 4.6 percent in the same year. So, in effect, the Chinese yuan is undervalued by 59.4
percent in 2006. In yuan/$ terms, this means that the ―fair‖ exchange rate for the yuan is
4.41 versus the current rate of 8 yuan/dollar.
Estimation of undervaluation for the US
Given that the US is a numeraire country, a different procedure is followed to obtain an
estimate of its undervaluation. By definition, the US undervaluation is the negative of the
weighted average of all other countries. There are 37 major countries that are in the US
BROAD index; these same countries are used to estimate US undervaluation, with trade
shares used as weights.
How accurate are these estimates of misalignment? The text provides various examples
of how this undervaluation measure helps explain several growth ―miracles‖ and how in
growth equations, the level and change in undervaluation are the most important
explanators, by far. The text also shows that once account is taken of currency
undervaluation, the robust ―institutional‖ variables lose a considerable portion (often all)
of their explanatory power in explaining differential growth rates. The undervaluation
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variable also helps explain why India and China did not grow pre 1980s and why they
are growing so strongly since they effected a real exchange rate depreciation.
SAE and US BROAD index – A comparison
The US publishes a trade-weighted real exchange index, the Fed BROAD index of real
exchange rate. This is based on a basket of 37 countries and the series is available
since 1973. The index has a value of 100 in 2000. The above method (hereafter the
Second Among Equals or SAE method) yields an estimate of the real exchange rate of
the US dollar against all the countries of the world. In order to compare the SAE
estimate with the BROAD index, the sample was restricted to the 37 countries, and a
trade weighted estimate obtained of the undervaluation in each year for the aggregate of
37 countries. This weighted average yields how much the 37 country is under or
overvalued with respect to the US dollar. The negative of this average is the
undervaluation/overvaluation of the US dollar with respect to the 37 BROAD countries.
Table A2.1 lists two estimates of currency undervaluation for the US for the period 1973-
2006 – the Fed BROAD index, and the SAE index against the BROAD set of countries.
Chart A2.1 plots the (log) of the two series, and it is observed that the SAE and BROAD
index are strongly correlated both in levels and in turning points. The correlation
between these two is 0.95 (for levels) and 0.82 (log changes). This suggests that the
SAE construction of the US RER, indirect as it might have been, rather tightly follows
both the levels, and changes, of the RER directly measured.
There is one further test of the ―accuracy‖ of the SAE measure of the real exchange rate
(and obviously of deviations from it). In an elegant paper, Baily-Lawrence(2006) attempt
to explain the US current account deficit. Their dependent variable is the (log) ratio of US
exports and imports, and the independent variable is the lagged and twice lagged real
exchange rate. Their equation is reproduced below for the time-period they consider,
1980 onwards, for both the BROAD index as well as the SAE index.
How good is the estimate of undervaluation; pretty good and for the US it is the negative
of the trade weighted value for the US Broad index group of countries; this explains the
US current account better than the Baily-Lawrence model in the new Bergsten volume;
and it is uncanny, but the level of the Broad index (1973 to 2006) and my estimate for
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the US by the above method is a phenomenal 0.93 - and even the absolute values are
near identical.
For reference:
The Balassa regression for 1960 (with our data) for the year 1960 for OECD countries,
would yield the following,
RER = 0.34 + 0.023*YD Nobs. = 23, R2 = 0.71
where YD is the nominal per capita income in US dollars, as per the Balassa formulation.
The Dollar regression for 1976 to 1985 (only developing economies) yields the following
RER = 0.396 + .017*Y - .000055*Y2 + 0.007*dAfr - 0.014*dLat,
Nobs =1178, R2 = 0.30
The Dollar regression for 1976 to 1985 (all countries) yields the following
RER = 0.373 + .016*Y - .000085*Y2 + 0.008*dAfr - 0.04*dLat,
Nobs =1448, R2 = 0.49
In neither regression is the dummy for Africa significant, while the dummy variable for
Latin America is significant in the regression for only developing countries (Eastern
Europe and OECD countries excluded from the analysis).
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Table A 2.1: RER - SAE and Fed Broad Index
Year SAE -
RER(2000=100) Fed Broad RER
(2000=100)
1973 83.6 93.3
1974 79.9 91.3
1975 81.5 90.3
1976 83.5 90.2
1977 81.1 88.6
1978 74.8 83.3
1979 72.4 84.3
1980 71.4 85.6
1981 81.7 92.4
1982 93.8 101.3
1983 102.0 105.7
1984 107.9 112.5
1985 112.0 117.1
1986 95.6 102.5
1987 85.8 94.1
1988 82.3 87.9
1989 85.3 89.5
1990 79.0 87.1
1991 83.1 85.8
1992 79.7 84.0
1993 84.2 85.3
1994 81.5 85.2
1995 76.2 82.8
1996 78.2 84.7
1997 85.3 89.3
1998 90.2 96.9
1999 92.0 96.3
2000 100.0 100.0
2001 106.5 105.7
2002 105.6 105.9
2003 96.3 99.5
2004 91.8 95.1
2005 92.7 93.5
2006 94.6 92.4 Source: Penn World Table 6.1; World Development Indicators, 2006 (World Bank); World Economic Outlook, 2006 (IMF), Baily-Lawrence (2006).
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SAE - RER
Fed Broad RER4
.34
.44
.54
.64
.74
.8
(Log
) R
ER
1973 1980 1985 1990 1995 2000 2006Year
Source: Penn World Table 6.1; World Development Indicators, 2006 (World Bank); World Economic Outlook, 2006 (IMF), Baily-Lawrence (2006).
368
-.4
-.3
-.2
-.1
0
(Log
) R
ER
1980 1985 1990 1995 2000 2006Year
Actual Predicted - Baily-lawrence
Predicted - SAE
Source: Penn World Table 6.1; World Development Indicators, 2006 (World Bank); World Economic Outlook, 2006 (IMF), Baily-Lawrence (2006).
369
Appendix III: Basic Indicators for India and China, 2006
Indicator Unit India China
National Accounts
Population mn 1107.0 1310.0
Population % chg. 1.6 0.6
GDP Growth % 9.2 10.7
Per Capita GDP. (1996 PPP prices) Per year 3478.5 6844.9
Share of agriculture in GDP (2004) % 19.2 13.1
Share of industry in GDP (2004) % 24.7 46.2
Share of services in GDP (2004) % 46.9 40.6
Capital growth % 10.7 10.8
Labour force growth % 2.0 0.8
Growth in female lab force % per annum 2.0 0.8
TFPG 2.2 4.3
Inflation
WPI % 5.4
CPI % 6.8 1.5
Price deflator , 1996 % 159.1286 120.6
Financial Markets
Interest Rate % 11.3
Prime Lending Rate % 12.4 1.8
3-Month Interbank % 7.6
Bank Rate (Year end) % 6.0
10-Year Bond % 7.6 2.7
Credit Growth % chg. 30.3 15.8
Industrial Production % 11.3 20.0
Import Growth % 23.9 19.9
Export Growth % 18.6 27.2
Savings Rate % 32.4
Investment Rate % 33.8
Broad Money % 19.5 18.0
Current Account % of GDP -1.3 9.1
Fiscal Deficit (Cos.) 7.1
Exchange Rate vis-à-vis $ 44.2 7.8
Undervaluation Level 30.4 -59.4
Undervaluation % chg. 19.2 9.6
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Indicator Unit India China
Social Indicators (2004)
Literacy % 73.5 116.6
Life expectancy Year 63.8 71.1
Life expectancy, female Year 64.8 72.8
Life expectancy, male Year 62.9 69.4
Infant Mortality (2004) % 62.0 28.0
Education Attainment (years) % 4.9 6.5
Education Attainment (primary), 2004 % 96.5 105.1
Education Attainment (secondary), 2004 % 48.8 74.3
Education Attainment (tertiary), 2004 % 11.7 24.6
Fertility 2.8 1.9
Poverty and Inequality
Gini 44.9
Poverty (headcount ratio, $ 1.08 a day) % 4.1 0
Poverty (headcount ratio, $ 2.16 a day) % 46.0 24.0
Institutions
World Bank composite (2005) -0.2 -0.6
Corruption -0.31 -0.69
Bureaucracy -0.11 -0.11
Political liberty -0.85 -0.18
Rule of law 0.09 -0.47
Regulation -0.34 -0.28
Voice of accountability 0.35 -1.66
Transparency score (2003) 2.9 3.4
Economic freedom
Executive constraint(2003) 7 3
Political and civil liberty (2004) 6 1
Autocracy (2003) 0 7
Source: SAE dataset, see Appendix I for details.