sutirtha bagchi† & jan svejnar‡ † villanova university ‡ columbia university november...

SUTIRTHA BAGCHI† & JAN SVEJNAR‡

† Villanova University ‡ Columbia University

NOVEMBER 2015

Does Wealth Inequality Matter for Growth? The Effect of Billionaire Wealth, Income Distribution, and

Poverty

Motivation for the paper

“. . . the absence of data on the distribution of wealth for a sufficient number of countries forces researchers to use proxies in empirical studies. The most common approach is to use data on income inequality as a proxy for wealth inequality.” Aghion, Caroli, and Garcia-Penalosa (1999)

Bénabou (1996) echoes this point and notes that the lack of almost any data on the distribution of wealth is a general problem, given that in most theories it is this distribution rather than that of income which is the determinant of outcomes.

Ravallion (2012) emphasizes that “wealth inequality is arguably more relevant though this has been rarely used due to data limitations.”

Research questions

1. How does wealth inequality affect economic growth?

2. Does the relationship between growth and inequality depend on the nature (source) of this inequality?

e.g. Does inequality based on political connections differ from one that is based on success as an entrepreneur?

3. What is the relative growth effect of wealth inequality, income inequality, and poverty?

Theoretical literature provides arguments for why inequality is good for growth

1) Marginal propensity to save of the rich is higher than that of the poor

2) Investment indivisibilities: Low inequality Low levels of innovation

Low productivity growth Low growth in real GDP per capita

3) Trade-off between equity and efficiency

… but it also provides arguments for why inequality is bad for growth

1) Credit market imperfections: You cannot borrow against your human capital

2) Greater demand for redistribution leading to a choice of economically inefficient policies; and

3) Greater social unrest, possibly also leading to a higher degree of macroeconomic volatility

Existing empirical evidence: Mixed

Cross - country & cross-sectional regressions suggest that income inequality is bad for growth: Alesina & Rodrik (QJE, 1994) Persson & Tabellini (AER, 1994)

Results do not always hold up under robustness checks; do not answer the question of what happens when inequality in a given country changes

Distinctly different results when examined in a panel set-up Forbes (AER, 2000)

Data source for the paper

Forbes magazine’s list of billionaires: Published list of billionaires from around the world

since 1987 Estimate wealth based on the holdings of individuals

in public companies or estimated holdings in private companies using standard price multiples

We use the Forbes ’ billionaire data set to create two variables:

Proxy measure of wealth inequality = Sum of wealth of all billionaires in a country/ Country GDP

E.g. Country 1 has 3 billionaires with wealths equal to $5 billion, $2 billion and $1 billion, and country’s GDP = $500 billion.

Measure of wealth inequality = (5 + 2 + 1)/ 500 = 1.6%

Correlations between wealth distribution data from UNU–WIDER & Forbes’ list of billionaires

Raw correlation coefficient and Spearman rank correlation coefficient for the share of wealth going to the top decile and our measure of wealth inequality for a sample of 18 countries are 0.54 (p-value = 0.0199) and 0.58 (p-value = 0.0122).

Cross-country correlation between the Gini coefficients of wealth available for 22 countries for the year 2000 from the Davies et al. (2008) data set and our measure of wealth inequality for 2002: 0.50 (p = 0.0188).

These are relatively high positive correlations

We split wealth inequality into two components

Wealth Inequality (or Billionaire wealth/GDP)

Classify billionaires as politically connected or not (A billionaire can be in only one of the two categories)

Previous example: Suppose billionaire 2 gets classified as politically connected

Politically connected billionaire wealth / GDP = $2/$500 = 0.4%

Politically unconnected billionaire wealth / GDP = $6/$500 = 1.2%

“Politically connected” “Politically unconnected”billionaire wealth /GDP billionaire wealth /GDP

How do we classify someone as “politically connected”? Extensive search on Factiva & Lexis-Nexis “Criteria”:

Have political connections played a material role in the success of the billionaire?

Would they have been billionaires absent political connections?

Careful to distinguish between explicit government support from a generally pro-business regulatory environment

Classic examples: Oligarchs from Russia or the cronies of Suharto (Indonesia)

Ranking of countries in terms of politically connected matches priors

Countries that rank highest in terms of politically connected wealth inequality

1. Malaysia 2. Colombia 3. Indonesia 4. Thailand5. Mexico

Other countries which just follow these include – Chile, South Korea, Philippines, Argentina, and, India. Italy has the 11th highest level of politically connected wealth inequality in our sample – the highest of any European country.

Median rank on TI’s Corruption Perceptions Index: 32 /41 (1995) & 94/174 (2012)

Countries that rank lowest in terms of politically connected wealth inequality

1. Hong Kong2. Netherlands3. Singapore 4. Sweden 5. Switzerland and 6. United Kingdom

Median rank on TI’s Corruption Perceptions Index: 9 /41 (1995) & 8/174 (2012)

What we include in our data set

20-year period from 1988 – 2007 divided into 4 periods of 5 years duration each

All countries in the world subject to availability of data on covariates. When a country does not have billionaires, we set billionaire wealth = 0 (more on this later)

~ 60 countries (and 160 country-period combinations) appear in the final estimation

Growthi,t = β0 + β1Wealth inequalityi,(t−1) + β2Income inequalityi,(t−1) + β3Headcount povertyi,(t−1) + β4Incomei,

(t−1)+ β5Schoolingi,(t−1) + β6PPPIi,(t−1) + β7Dummyi,(t−1) + αi + ηt + νi,t

One may be concerned about reverse causality

Relationship runs not from inequality to growth but from growth to inequality (Kuznets’ hypothesis, 1955) The early stages of development exacerbate

inequality while later stages of development improve equality.

Empirically this lacks support. (See e.g. Fields, 2001)Empirical strategy - use lags of the

explanatory variables, which are pre-determined as regressors

Also we use IV & GMM estimation approaches

Number of countries & billionaires on the list

Year Countries Number of billionaires/ billionaire families

1987 23 201

1992 31 340

1996 38 543

2002 42 568

Impact of wealth inequality, income inequality, and poverty on economic growth (Benchmark)

Including the controls: GDP/ capita; Schooling & Price level of investment S.e. in parentheses * p < 0.10, ** p < 0.05, *** p<0.01

(1) (2) (3) (4) (5) (6) Dependent variable: Growth rate in real GDP per capita Wealth -0.132* -0.547 -50.07*** Inequality (0.0771) (0.351) (13.27) Politically unconnected

-0.0464 -0.154 -48.98

wealth inequality (0.0714) (0.301) (36.52) Politically connected -0.331*** -1.625*** -51.01** wealth inequality (0.0965) (0.536) (22.79) Income Inequality 0.000564 0.000763* 0.000498 0.000530 0.000753 0.000498 (0.000422) (0.000455) (0.000417) (0.000426

)(0.000456

)(0.000418)

Headcount Poverty 0.000301 0.000252 0.000353 0.000298 0.000243 0.000352(0.000296) (0.000307) (0.000286) (0.000298

)(0.000310

)(0.000297)

N 160 149 160 160 149 160R2 0.59 0.59 0.61 0.60 0.60 0.61

Comparing our results with Forbes (2000) (1/2)

S.e.in parentheses * p < .10, ** p <.05, *** p <.01

(1) (2) (3) (4)

Panel A: Assuming income and wealth inequality to have the same effect during the entire sample period

Income Inequality 0.000751 0.000991 0.00102 0.000947

(0.000886) (0.000830) (0.000858) (0.000840)

Wealth Inequality -0.154***

(GDP used for normalization) (0.0484)


(Physical capital used for normalization) (0.179)


(Population used for normalization) (2.061)

Number of observations 162 162 152 162

R20.39 0.45 0.44 0.42

F 5.343 8.717 8.740 7.138

Comparing our results with Forbes (2000) (2/2)

S.e.in parentheses * p < .10, ** p <.05, *** p <.01

(1) (2) (3) (4)

Panel B: Introducing dummy variable for first half of the sample period & corresponding interactions

Income Inequality 0.000419 0.000757 0.000698 0.000630(0.000894) (0.000858) (0.000896) (0.000847)

Wealth Inequality -0.131**

(GDP used for normalization) (0.0493)Wealth Inequality -0.525**

(Physical capital used for normalization) (0.201)Wealth Inequality -7.771***

(Population used for normalization) (2.690)Income Inequality X First half of sample period 0.000750** 0.000492 0.000614* 0.000742**

(0.000327) (0.000333) (0.000327) (0.000317)Wealth Inequality X First half of sample period(GDP used for normalization) 0.0691Wealth Inequality X First half of sample period (0.0797)(Physical capital used for normalization) -0.0110Wealth Inequality X First half of sample period (0.324) -6.665(Population used for normalization) (5.169)

Number of observations 162 162 152 162R2 0.41 0.46 0.46 0.46F 4.720 9.321 9.280 6.751

Robustness checks

RC1: Robustness to Forbes magazine’s choice of countries for the billionaires in the data set

RC2: Use of alternative econometric approaches:i. Random effects instead of a fixed effects specificationii. Instrumental variablesiii. Dynamic panel methods of estimation (Arellano & Bond

difference-GMM and Blundell & Bond system-GMM)

RC3: Robustness to inclusion of additional control variables:

i. Adding a measure of institutional qualityii. Controlling for the exchange rate

RC4: Using $1.25 per day per person as the poverty line

Impact of wealth inequality, income inequality, and poverty on economic growth (Using RE)


(1) (2) (3) (4) (5) (6) Dependent variable: Growth rate in real GDP per capita Wealth -0.162* -0.652 -59.05*** Inequality (0.0962) (0.431) (14.67) Politically unconnected

-0.0145 0.0261 -17.52

wealth inequality (0.0688) (0.284) (48.52)Politically connected -0.458*** -2.332*** -90.14***

wealth inequality (0.0600) (0.409) (20.85)Income Inequality -0.000143 -

0.0000126-0.000145 -0.000171 -

0.0000258

-0.000151

(0.000441)

(0.000513) (0.000435)

(0.000438)

(0.000509)

(0.000437)

Headcount Poverty 0.000386* 0.000364 0.000417*

*

0.000434** 0.000406* 0.000425**

(0.000214)

(0.000223) (0.000205)

(0.000209)

(0.000218)

(0.000205)

N 160 149 160 160 149 160

Impact of wealth inequality, income inequality, and poverty on GDP per capita (Using Arellano-Bond difference-GMM estimator)


(1) (2) (3) (4) (5) (6)

Dependent variable: Log of GDP per capita

Wealth -0.498* -2.059 -167.3**

Inequality (0.292) (1.263) (66.25)

Politically unconnected

-0.112 -0.437 -126.2

wealth inequality (0.220) (0.969) (174.7)

Politically connected -1.514** -6.960* -202.2


Income Inequality 0.00144 0.00171 0.00146 0.00160 0.00184 0.00154

(0.00173) (0.00182) (0.00180) (0.00186) (0.00194) (0.00176)

Headcount Poverty 0.00330* 0.00300* 0.00340* 0.00348** 0.00323* 0.00329*

(0.00174) (0.00167) (0.00174) (0.00176) (0.00173) (0.00173)

Lagged log GDP per 0.660** 0.659** 0.682** 0.715*** 0.716*** 0.663**

capita (0.275) (0.277) (0.276) (0.265) (0.271) (0.280)

N 89 88 89 89 88 89

Impact of wealth inequality, income inequality, and poverty on GDP per capita (Using Blundell-Bond system-GMM estimator)


(1) (2) (3) (4) (5) (6)


Wealth -0.593* -2.135 -210.9***

Inequality (0.355) (1.439) (57.86)


-0.277 -0.564 -183.6


Politically connected -1.405* -6.699* -236.7


Income Inequality -0.00207 -0.000981 -0.00199 -0.00215 -0.00110 -0.00200

(0.00404) (0.00334) (0.00399) (0.00395) (0.00327) (0.00387)

Headcount Poverty 0.00726*** 0.00633*** 0.00720*** 0.00733*** 0.00647*** 0.00726***

(0.00271) (0.00240) (0.00267) (0.00277) (0.00238) (0.00272)

Lagged log GDP per 0.865*** 0.903*** 0.868*** 0.867*** 0.907*** 0.868***

capita (0.105) (0.109) (0.103) (0.107) (0.111) (0.109)

N 161 149 161 161 149 161

Impact of wealth inequality, income inequality, and poverty on GDP per capita (Using Blundell-Bond system-GMM estimator) (Taking wealth inequality as pre-determined)


(1) (2) (3) (4) (5) (6)


Wealth -0.833* -3.154* -258.2***

Inequality (0.470) (1.898) (90.84)


-0.389 -0.731 -94.39


Politically connected -2.092*** -10.04*** -403.3**


Income Inequality -0.000634 -0.000545 -0.000931 -0.000348 0.000497 -0.00107

(0.00255) (0.00267) (0.00256) (0.00302) (0.00296) (0.00306)

Headcount Poverty 0.00310* 0.00333* 0.00334** 0.00320* 0.00297 0.00343*

(0.00174) (0.00189) (0.00166) (0.00187) (0.00192) (0.00184)

Lagged log GDP per 0.991*** 1.031*** 1.000*** 0.987*** 1.003*** 1.000***

capita (0.0345) (0.0429) (0.0323) (0.0232) (0.0310) (0.0291)

N 161 149 161 161 149 161

Why is politically connected wealth inequality detrimental?

Example 1: Birla family of India: “The nationalists who later became free India’s power elite rewarded the Birla family with lucrative contracts. After independence, the Birlas continued their lavish contributions to the ruling Congress Party. So accomplished are they in manipulating the bureaucracy, and so vast their network of intelligence, that they frequently obtain preemptive licenses, enabling them to lock up exclusive rights for businesses as yet unborn.” (Forbes, 1987)

Why is politically connected wealth inequality detrimental?

Example 2: Tobacco billionaires in Indonesia:Indonesia is the only country in Asia to have not signed the WHO Framework Convention on Tobacco Control, a treaty that as of September 2013 had been signed by 177 parties.This is in spite of the fact that in Indonesia, Muslims constitute 86 percent of the population and “smoking is either completely prohibited in Islam or abhorrent to such a degree as to be prohibited.” (WHO Regional Office for the Eastern Mediterranean). Indonesia’s average tobacco tax of 37 percent is the lowest in Southeast Asia and well below the global average of 70 per cent of the sales price (South China Morning Post, 2008).

Conclusions

1. High levels of wealth inequality appear to have negative consequences for economic growth; income inequality and headcount poverty do not

2. Wealth inequality arising on account of political connections reduces economic growth v. wealth inequality arising otherwise

3. Growth-related policy debate should focus on distribution of wealth

sutirtha bagchi† & jan svejnar‡ † villanova university ‡ columbia university november...

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