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NEW EVIDENCE ON AID EFFECTIVENESS:
ASSESSING THE LINKS BETWEEN ECONOMIC GROWTH,
VOLATILITY AND AID
by
Bineswaree Bolaky
B.A., University ofCambridge, 1995
M.A., University of Cambridge, 1999
M.A., University of Maryland at College Park, 2002
PROJECT SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OF
MASTER OF ARTS
In the
Department
of
Economics
© Bineswaree Bolaky 2008
SIMON FRASER UNIVERSITY
Summer 2008
All rights reserved. This work may not be
reproduced in whole or in part, by photocopy
or other means, without permission of the author.
APPROVAL
Name:
Degree:
Title of Project:
Examining Committee:
Chair:
Bineswaree Bolaky
Master of Arts
New Evidence on Aid Effectiveness: Assessing theLinks between Economic Growth, Volatility and Aid
Gregory DowProfessor, Department of Economics
Peter KennedySenior SupervisorProfessor, Department of Economics
Don DeVoretzSupervisorProfessor, Department of Economics
Terry HeapsInternal ExaminerAssociate Professor, Department of Economics
Date Defended/Approved: August 6,2008
ii
I
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Revised: Fall 2007
ABSTRACT
Using an initial panel dataset of more than 100 countries over the period 1960 to
2005 and using a two-equation system that analyzes jointly the determinants of both
economic growth and volatility in growth, we find evidence that first, aid raises economic
growth and lowers volatility in growth and second, that volatility in aid lowers economic
growth and raises volatility in growth. Within a framework that conceives of economic
development as the promotion of stable economic growth, this paper establishes a case
for increasing stable aid flows to developing countries in order to promote economic
development. The high volatile nature of aid disbursed that characterizes the current aid
architecture is an issue that needs to be addressed by the international donor community
in order to make aid more effective.
Keywords: Aid; Aid Effectiveness; Economic Growth; Volatility.
Subject Terms: Aid; Economic Assistance; Economic Development; Economic Growth.
iii
DEDICATION
To my parents & my husband, Omkar Pertaub
iv
ACKNOWLEDGMENTS
I thank the faculty of the Department of Economics for all the support and
opportunities it has offered to me as an international student. Special thanks to Professor
Peter Kennedy, Professor Don DeVoretz, Professor Stephen Easton and Professor
Richard Lipsey for their wonderful teachings, inspiration and support. I thank Professor
Terry Heaps whose courses in mathematical economics taught me the skills I needed to
successfully pass the much dreaded comprehensive examinations in my doctoral
program.
Last but not least, my gratitude goes to my father who believed in me, to my
mother for her care and my siblings for their support and guidance.
v
TABLE OF CONTENTS
Approval ii
Abstract iii
Dedication iv
Acknowledgments v
Table of Contents vi
List of Figures vii
List of Tables viii
1. Introduction 1
2. Review of the Literature on the Determinants of Volatility 6
2.1. Volatility in aid 6
2.2. Volatility in growth 7
3. Why Should Stable Aid Matter for Stable Growth? 11
4. A Preliminary Look at Aid 15
4.1. Aid: Definition, composition, and trends 15
4.2. A note on aid effectiveness 33
5. Economic Growth, Volatility and Aid 37
5.1. Controls in growth equation 38
5.2. Controls in growth volatility equation 42
5.3. Preliminary data analysis .44
5.3.1. Growth and volatility in growth .44
5.3.2. Aid and volatility in aid .45
5.4. Regression results 55
5.4.1. Economic growth equation 55
5.4.2. Volatility in growth equation 60
5.4.3. Systems equation 62
6. Caveats and Conclusions 65
Reference List 67
Appendices 76
Appendix A Figures and Tables
vi
LIST OF FIGURES
Figure I
Figure 2
Figure 3
Figure 4
Hypothesized relationships: growth, volatility and aid
Trend in ODA committed and disbursed 1965-2006 (constant U.S.
dollar)
Ratio ofODA committed to disbursed 1966-2006 (constant U.S. dollar)
Components of aid effectiveness
vii
LIST OF TABLES
Table 1.1
Table 1.2
Table 1.3
Table 1.4
Table 2
Table 3
Table 4
Table 5
Table 6.1
Table 6.2
Table 6.3
Table 6.4
Table 6.5
Table 6.6
Table 6.7
Table 7.1
Table 7.2
Net disbursements (current million U.S. dollar) and distribution by region
Grants disbursements (current million U.S. dollar) and distribution by
regIon
Distribution of net disbursements (current million U.S. dollar) by income
group
Distribution of grants disbursements (current million U.S. dollar) by
income group
Distribution of net disbursements (constant 2005 million U.S. dollar)
by type as a percentage of aDA
Distribution of official bilateral gross disbursements (current million U.S.
dollar) by sector
Proportion of tied bilateral commitments by donor
Net aid disbursed in current prices as a percentage of donors GNP by
donor
Fluctuations in sign of growth rates
Measures of mean and volatility in growth
Economic growth, aid and volatility
Economic growth and volatility by types of aid
Fluctuations in sign of growth rates
Measures of mean and volatility in growth and aid
Economic growth, aid and volatility
Shea partial R squared first -stage regressions of growth equation
Shea partial R squared first -stage regressions of growth volatility
equation
viii
1 Introduction
"The reconstruction ofAfghanistan requires a sustained and substantial commitment
ofaid - but donors have failed to meet their aid pledges to Afghanistan. Too much aid
from rich countries is wasted, ineffective or uncoordinated Spending on tackling
poverty is a fraction ofwhat is spent on military operations. While the US military is
currently spending $100m a day in Afghanistan, aid spent by all donors since 2001 is
on average less than a tenth ofthat - just $7m a day". Matt Waldman, Oxfam, 2008.
The literature on aid effectiveness has been dominated for the past 5 decades by
macro-economic studies investigating the impact of aid on economic growth. Within
these studies focus was usually laid on the conditions that need to be present in recipient
countries in order to make that impact significant (e.g. quality of political institutions
(Boone, 1995; Svensson, 1999); governance and type of policies (Burnside and Dollar,
2000; Collier and Dollar, 2004); vulnerability to natural disasters (Guillaumont et aI,
2001); presence of shocks (Collier and Dehn, 2001); recovery from conflict (Collier and
Hoeffler, 2004) and even location outside the tropics (Dalgaard et aI, 2004)). From that
literature conclusions were made about the conditionalities that should be attached to aid
and the country selectivity criteria that need to be applied when allocating aid in order to
make aid work. Over the past 5 years or so, however, increased attention has been paid
to the institutional characteristics of the aid delivery apparatus that can derail the
effectiveness of aid (such as principal-agent problems associated with misalignment of
incentives (Martens et aI, 2001; Zinnes and Bolaky, 2002)). Increasing attention has also
been paid to failures on the part of donors, rather than recipients alone, on issues relating
to lack of coordination and harmonization, lack of accountability and poor quality of aid
given. The unpredictability and volatile nature of the aid given by donors has come
under increased scrutiny in more recent years. Birdsall (2004), in an essay reflecting on
donors' so-called "sins" or shortcomings highlight among others 2 donors' characteristics
that can weaken aid's intended positive impact. First, donors are impatient when it
comes to achieving results and building new institutions. Donors very often are reticent
to wait in order to give recipient countries with weak institutions the time they need to
build their institutions and augment their national capacities in order to make the most out
of the aid they receive. Donors can choose to "turn the taps off' after only a short period
of time and return when the situation has improved rather than sustain the aid they give.
Second donors tend to be unreliable according to Birdsall. Aid tends to be volatile and
unpredictable, a problem that can be compounded by donors' impatience.
Bulir and Hamann (2006), using a sample of 76 developing countries for the
period 1975-2003, find that the volatility of aid was a multiple of the volatility of
domestic fiscal revenue and that such relative volatility had actually worsened in the early
2000's as compared to the late 1990s. In addition there was also evidence that aid had
been disbursed in a mildly pro-cyclical fashion, echoing previous findings by Pallage and
Robe (2001). Shortfalls in de-trended aid were associated (albeit not significantly) with
shortfalls in domestic revenue. An earlier study based on data for the period 1975-1997
(2003) had also found that shocks to domestic revenue were positively correlated to
shocks to foreign aid. This will imply that when needed (namely to supplement falling
domestic revenues), aid actually fails to come through in larger levels. When aid is
volatile and pro-cyclical, its impact on development is unlikely to be large as it fails in
this case to assist developing countries to smooth out their consumption in the presence
of shocks or reduce the costs of macro-economic instability to their welfare levels.
Besides volatile' and unpredictable aid make it difficult for developing countries to
strategically use aid for long-tenn budgeting of public expenditures and development
planning. The authors find that aid committed is a poor predictor ofaid disbursed. This
will suggest that promises for aid by donors do not always materialize in the levels
pledged to developing countries. Worse the actual commitment-to-disbursement ratio
rose to its highest levels in 20 years in the early 2000s. Furthennore, the problem of
commitments exceeding actual disbursements was negatively correlated with GDP per
capita. Where needed the most (namely in the poorest countries), actual aid disbursed
fell far short of the actual amount committed (according to the authors, countries at the
In the aid literature, the words "volatile' and "unpredictable" are used interchangeably. However for thepurpose of this paper, we distinguish between "volatile" aid (meaning unstable or erratic) and"unpredictable" aid (meaning difficult to forecast or anticipate). Clearly the stability of aid affects thepredictability of aid. When aid committed is not disbursed according to schedule and the percentage ofcommitments that is disbursed vary from year to year, it adds to the instability and unpredictability ofaid.
2
lower-end of the income scale received only about half of their aid commitments,
compared to about a full 100% in countries at the upper-income scale). In addition there
was evidence that aid had not acted as a buffer to large negative GDP shocks in
developing countries. Again, when needed the most (such as whenever a country is hit
by a natural disaster, conflict or terms of trade shock), aid does not arrive either on time
or in amounts large enough in order to insure the country from losses in welfare. Bulir
and Hamann's study highlights shortcomings on the part of donors that need to be
addressed, namely a need for donors to honor their aid commitments in a timely manner
and keep aid disbursements stable and predictable.
The issue of aid volatility or unpredictability has been highlighted in several
international donor initiatives in the past 5 years or so. Indicator 7 of The Paris
Declaration on Aid Effectiveness (2005) is based on making aid more predictable by
having donor countries halving the proportion of the aid that is not disbursed within a
given year for which it was scheduled. The Department for International Development
(DFID), the UK overseas aid agency, has through the UK government announced
commitments to make aid more predictable. Plans include engaging in 10 year
arrangements with countries, rolling multi-year Poverty Reduction Budget Support
(PRBS) in countries where the Poverty Reduction Strategies (PRS) are well-functioning,
informing countries way in advance of its aid disbursement schedules, and changing aid
disbursements only if certain fundamental conditions are breached. In 2003, the UK
government launched a proposal for an International Finance Facility, a financing
mechanism designed to frontload aid in order to assist developing countries in accessing
the large and predictable aid they need to achieve the United Nations Millennium
Development Goals. In 2005 the G8 finance ministers reaffirmed their view to "deliver
aid in a more predictable way" (HM Treasury, 2005).
However to date, surprisingly little empirical work has been done to assess the
impact of aid volatility on economic development. In the words of Bulir and Hamann
(2006), "the issue of the large economic costs associated with macro-economic volatility
in low income countries and in particular the role played by an erratic stream of aid
3
disbursements is only now starting to be addressed in a systematic manner. Significant
work remains to be done in order to understand the real extent of the problem and its key
underlying causes". Lensink and Morissey (2000) find evidence that aid raises economic
growth after controlling for aid instability. However their measures of aid instability
were meant to capture aid uncertainty2 rather than instability in aid per se. When they
use a measure of overall aid instability, they find that the latter was not significant in the
growth regression whereas the other 2 measures that captured aid uncertainty were
negative and significant. This led them to conclude that it is aid uncertainty, that is
deviations from expected aid flows, that matters rather than instability in aid per se.
This paper aims to contribute to the aid effectiveness literature by assessing the
impact of aid instability, defined as aid volatility, on economic development where
development is conceived as stable economic growth. The effectiveness of aid should be
judged not only in terms of raising economic growth but also in terms of lowering
volatility in growth. Growth volatility is costly for developing countries (Pallage and
Robe, 2001) and hinders development that requires sustained increases in income
(Betancourt, 1996). Following Mobarak (2005), we estimate a two-equation system that
jointly determines mean economic growth rate and growth volatility after incorporating
mean aid and aid volatility as explanatory variables. In order to control for quality of aid,
we consider only aid disbursed per capita3, as opposed to aid committed which is the
norm in the aid effectiveness literature. This is to take into account the fact that only a
fraction of aid pledged actually reaches intended recipients. We use aid that is in the
form of grants only as opposed to loans. Volatility in aid is calculated as the standard
deviation of aid. We start with an initial sample of 127 developing countries for the
period 1960 to 2005 and divide the sample into 9 five-year intervals. Our results indicate
that aid significantly raises economic growth; that aid volatility deters economic growth
but raises growth volatility. There is also partial evidence to suggest that aid can lower
growth volatility. These results are robust to several measures of growth volatility used.
2 Calculated as the standard deviation of residuals from forecasting equations on aid.
3 We prefer the measure of aid per capita over aid as a fraction of GDP. The latter can rise either becauseaid rises or GDP falls. Roodman (2007) argues that higher aid/GDP at time t-l may appear to cause highgrowth at time t simply because faster growing countries will have a smaller GDP at time t-1. Moreover,volatility in aid/GDP will capture both volatility in growth as well as volatility in aid.
4
However an important finding of this paper is that the positive impact of aid on economic
growth is more than wiped out by the larger negative impact of aid volatility on economic
growth (by a factor of 3-4). These findings indicate that recent calls made in the
international community to make aid stable and predictable merit serious attention.
The remaining of this paper is organized as follows. Section 2 summarizes the
empirical literature on the determinants of aid volatility and growth volatility. Section 3
motivates the hypotheses of this paper notably that aid raises economic growth and
lowers growth volatility and that aid volatility produces the opposite effects. Section 4
analyzes the data on aid in terms of its trends and composition and comments on aid
effectiveness factors. Section 5 analyzes the measures of aid and growth volatility in the
sample data. It then presents the estimation results of the two-equation system, using
several estimation methods (Ordinary Least Squares, Generalized Least Squares and
Random Effects), for alternative measures of growth volatility and for cases where aid,
aid volatility and growth volatility are treated as endogenous variables. Single equation
estimates and the system equation estimates are presented. Finally Section 6 concludes.
5
2 Review of the Literature on the Determinants of Volatility
This Section reviews briefly the literature on the 2 volatility aspects that are analyzed
in this paper, namely volatility in aid and volatility in economic growth.
2.1 Volatility in aid
The empirical literature on the determinants of aid volatility is up to this date
rather sparse. As Bulir and Hamann commented in 2006, significant work remains to be
done in the area of understanding the underlying causes of aid volatility. The same
authors comment that one source of aid volatility relates to the way the aid apparatus
functions in donor countries. In any given donor country, aid is committed by
development agencies while the actual amounts of aid are approved by legislative
authorities such as Parliaments and disbursed by Ministries of Finance. Variations in the
commitment to disbursement ratios induced by institutional failures in donor countries
can cause the actual aid disbursed to be highly variable. Conditionality can also cause aid
to be volatile. The actual amounts of aid received by a recipient country may vary
according to that country's ability to meet donors' conditionality requirements.
Fielding and Mavrotas (2005) employ 2 disaggregated aid measures namely
sector-specific (project) aid and non-sector allocable (programme) aid to determine the
factors that drive cross-country variation in aid volatility in a sample of 66 countries for
the period 1973-2002. The authors hypothesize that instability in aid flows can be driven
by the size of aid flows as well as recipient's characteristics that will include per capita
income, institutional quality and policy regime. By the law of large numbers, countries
that receive large aid inflows should experience proportionately smaller variations in total
flows. The effect of a shock to a particular type of aid dissipates in the aggregate when
averaging across all types of aid. Poorer countries are more likely to receive a stable
flow of aid flows based on their needs while aid for richer and middle-income countries
is likely to vary from year to year based on exceptional circumstances such as balance of
payments shocks or geo-political considerations. Countries with good quality-institutions
6
may be rewarded by donors with stable aid flows4. Countries that receive aid conditional
on good policies may experience disruptions in their aid flows more frequently according
to changes in policy indicators. Their results showed that aid volatility is indeed
negatively related to aid volumes and to country size. Institutional quality such as quality
of political institutions and economic policy affect stability of sector aid flows but not
program aid flows. Open economies that tend to be smaller and richer showed greater
variability in sector aid flows but not program aid flows.
A more recent study by Clarke, Fry and Mihajilo (2007) analyzed conditional
volatility in aid in a sample of 44 small island states from the regions of Asia-Pacific,
Africa and the Americas over the period 1973 to 2004. They consider several measures
of aid: first bilateral aid disaggregated into sector aid and program aid and second
multilateral aid. Their analysis revealed 4 major findings. Past aid flows are highly
correlated with present aid flows. The Americas and the Asia-Pacific regions receive aid
that is far more volatile than that of the African region. Volatility in bilateral aid is
persistent and has an element of predictability, that is, past shocks to bilateral aid and past
volatility in aid explain future aid volatility. However they find that multi-lateral aid is
far more volatile than bilateral aid with the volatility of the former being far less
predictable than the volatility of the latter.
2.2 Volatility in growth
While there is an abundant literature on the growth aspects of economIC
development, the literature on the volatility of growth and thus stability of development
has on the other hand been less prolific. Mobarak (2005) was the first to estimate a two
way equation system that analyzed jointly the determinants of mean economic growth
and volatility in growth within a framework that depicts economic development as
requiring stable periods of economic growth. As his article points out, volatility in
4 Levin and Dollar (2005) find that Difficult Partnership Countries (DPCs) receive substantially morevolatile aid than Lower Income Countries (LICs) and ascribe it to the fact that DPCs have weakinstitutional capacities. Donors may lack the patience to give to DPCs the time needed to achieve thedesired results from aid and may consequently tum the taps off on the DPCs.
7
growth is an important aspect of development for 2 reasons: (i) first growth tends to be
more volatile and unstable in poorer countries and in the developing world (Pritchett,
2000), with disproportionate welfare consequences for the poorest within these countries
as their consumption is likely to be more sensitive to current income and (ii) second,
volatility in growth itself deters mean economic growth. Though his work focuses on the
democracy-growth volatility relationship, it palliates to the literature that has analyzed the
key determinants of volatile economic growth. Based on Rostow (1956) and Reynolds
(1985) theoretical contributions to long-term development analysis, the author identifies
the conditions for successful "take-off' of an economy towards self-sustained positive
steady growth as key determinants of volatility. These are transformation of the economy
away from agriculture towards industry and services, development of education, health
and infrastructure, openness to international markets, stable political institutions, absence
of conflict and well-developed financial institutions among others. The author also
includes measures of external shock and policy variability as determinants of volatility as
well as measures of the extent of and potential for sectoral diversification. The three
stage least squares results reveal that volatility in growth is negatively explained by
democracy, initial real GDP per capita and measures of actual and potential for
diversification such as indicators of sectoral diversification, export diversification, share
of services in GDP and size of population.
An earlier paper by Fiaschi and Lavezzi (2003) confirmed the argument of
Acemoglu and Zilibotti (1997) and Pritchett (2000) that development as proxied by
higher real GDP per capita and by structural change (namely declines in share of
agriculture in GDP) is accompanied by lower growth volatility. The argument is that as
development proceeds, it is accompanied by a decline in the weight of sectors that have
volatile output such as agriculture and primary production and an increase in the weight
of sectors with less volatile output such as manufacturing and services. They also found
strong evidence that size of the economy matters in reducing growth volatility. Larger
economies tend to have a higher number of sectors and exhibit more diversification so
that by the law of large numbers, any idiosyncratic shock to a particular sector that
renders output volatile has its impact ironed out in the aggregate.
8
There has also been research on other specific channels for growth volatility,
namely openness to trade, financial and currency crises and monetary shocks. Bejan
(2006) finds evidence that trade openness increases growth volatility in developing
countries though the effect seems to have weakened in recent decades. Theoretically,
trade liberalization fosters specialisation and increases therefore the vulnerability of an
economy to sector-specific shocks in a narrow set of sectors. On the other hand greater
openness to world markets allows a country to diversify its markets and cushion itself
against country-specific shocks since the whole world is less prone to shocks than any
given specific set of countries. In addition openness to trade can reduce the "ex-post
output costs associated with recovery from a crisis and smooths adjustment in the
aftermath of external shocks" (Cavallo, 2007). Cavallo (2007) on the other hand finds
evidence that the net effect of trade openness is stabilizing. While trade openness can
expose countries to greater volatility through terms of trade shocks, it has a stabilizing
effect on output through the financial channel, specifically in countries that are more
exposed to capital flows. This is in line with the argument that countries that are more
open to trade are credit-worthier and less likely to be credit-constrained and less likely to
be subject to costly financial crises associated with sudden stops in capital flows.
On the issue of crises per se, it is widely recognized that large drops in capital
flows (e.g. a sudden stop in a financial crisis) can have devastating consequences on
output (Calvo, 2001). Guidotti et al (2003) demonstrate that sudden stops have been a
fairly common occurrence since at least the late 1970s. Their empirical study finds that
countries that were more open and maintained floating exchange rate regimes were better
able at recovering from an output contraction in the aftermath of a sudden stop. In the
same line of research, Mukerji (2004) finds evidence that capital account liberalization
can augment growth volatility in countries with low levels of financial development.
Various authors have also documented some extent of output contraction in the aftermath
of a currency crisis (Barro, 2001; Bordo et aI, 2001). Gupta et al (2003) finds evidence
that such output contraction was more frequent in countries that traded less with the rest
of the world, had relatively open capital accounts and received large capital flows before
the crisis, thereby pointing to the significance of trade openness and capital liberalization
9
on the impact of currency crisis on output volatility. The contraction was more
pronounced however whenever oil prices rose during the crisis. Hakura (2007), whilst
arguing that output volatility and large drops in output have declined over the last 3
decades over all countries, find evidence that exchange rate flexibility and terms of trade
volatility were key determinants of output volatility.
We shall rely on these empirical results to identify the determinants of aid
volatility and growth volatility later on for empirical estimation purposes.
10
3 Why Should Stable Aid Matter for Stable Growth?
This section motivates the 4 main hypotheses of this paper namely that aid raises
economic growth and lowers growth volatility; while aid volatility lowers economic
growth and raises growth volatility.
Aid is expected to raise economic growth through various channels. The so
called first generation studies on aid effectiveness relied on the Harrod-Domar one sector
growth model to analyze the impact of aid on growth. In this model, economic growth is
single-handedly driven by capital stock accumulation or investment, with the latter being
financed by total savings, consisting of domestic savings and foreign resources in the
form of private capital flows and aid. Aid contributes towards relieving a domestic
saving constraint to finance investment. In the second generation studies, the direct
impact of aid on investment was tested empirically, using either the Harrod-Domar or
Solow growth model as analytical frameworks and any positive aid-investment
relationship was taken to imply a positive aid-growth relationship. However aid can have
positive effects on growth other than through its impact on investment. Aid is allocated
across different sectors and for different purposes, (see Table 3 page 14). Aid can
stimulate economic growth through the building of infrastructure but it can also do so by
stimulating productivity growth by for instance, supporting productive sectors such as
agriculture and manufacturing through technical assistance; allowing the introduction of
new ideas and technologies; accelerating human capital accumulation through the
building of schools and technical training of educators; and reducing diseases through the
building of hospitals, training of health professionals, dispensation of vaccines and drugs.
Successful aid initiatives include the Green Revolution, the combat against river
blindness and the introduction of oral rehydration therapy (Radelet, 2006). In addition
aid going towards debt relief releases resources in developing countries for other growth
enhancing initiatives in multiple sectors.
11
Figure 1 Hypothesized relationship - growth, volatility and aid
II Volatility in Growth
+
+ I Economic Growth I-..~
~ I Volatility in Gmwth I
_________I-----------..h.l..._E_c_o_no_m_I_·c-;-G_r_o_w_th 1I Volatility in Aid . + .. _
I Aid
Aid can lower growth volatility if it is used to stabilize the economy after shocks.
As Pallage, Robe and Berube (2005) state, developing countries are subject to macro
economic shocks that are mostly exogenous (Kose, 2002) and which result in
consumption volatility that reduce economic welfare (Pallage and aI, 2003). Given that
their access to private international financial markets becomes restricted precisely when
their economies are doing badly (Kaminsky, Reinhart and Vegh, 2004), these developing
countries rely on aid as a non-negligible source of external capital for smoothing out
consumption fluctuations driven by output fluctuations. In 2005, according to the Global
Development Finance Report (2007), net financial flows to all developing countries
amounted to US$480.7 billion; and aid (net official development assistance from all
donors) amounted to US$120A billion or 25% of the totals. In the aftermath ofa natural
disaster (such as the Tsunami event in 2005 or earthquakes) or wars for instance (e.g.
Democratic Republic of Congo), aid is often disbursed to help countries to recover and
stabilize their economies. Pallage, Robe and Berube showed in a dynamic model of aid
flows between 2 endowment economies (consisting of a rich donor and a poor recipient)
that altering the timing of aid disbursements in order to smooth aggregate consumption in
these 2 economies can actually lower the welfare costs associated with macroeconomic
5 In our sample, total net ODA (loans and grants) disbursed in current prices (source OEeD) was 40.2% oftotal net financial flows in current prices (source: World Bank).
12
fluctuations in the poor country by 75%. Guillaumont and Chauvet (2002) find evidence
that aid effectiveness increases when aid is given to countries with larger structural
economic vulnerabilities. Their index of economic vulnerabilities is a weighted average
of instability in agricultural production (as a proxy for climatic shocks); instability in
export earnings; trend in terms of trade and smallness of population (to indicate exposure
to shocks). They argue that aid has a larger impact in countries affected by external and
climatic shocks as it allows such countries on one hand to lower their probabilities of
collapsing after a shock and sustain their growth; and on the other to sustain reforms and
avoid policy reform reversals in the face of such shocks.
Aid is an important external source for financing investment and productivity
growth in developing countries, especially in countries with limited access to private
capital markets. Volatility in aid can thus be an added source of growth volatility.
Volatility in aid can impact on growth volatility given that aid is a source of finance in
developing countries for the sources of growth such as investment, human capital
accumulation and productivity growth. Variability in aid can thus lead to variability in
investment and consumption with significant welfare losses (Arellano, Bulir et aI, 2005)
as well as variability in other sources of growth. Besides adding to growth volatility, aid
volatility can lower economic growth through its impact on uncertainty. When aid is
volatile, it becomes harder for governments especially in highly aid dependent countries
to plan their investment decisions.
Guillaumont and Chauvet (2008) however have recently argued for the opposite.
Their previous research cited above demonstrated that aid can cushion the negative
effects of external shocks on growth. If aid levels are increased in the aftermath of
economic shocks to mitigate the adverse impacts of these shocks, then inevitably aid will
have a volatile profile but such aid volatility has a stabilizing rather than a destabilizing
13
effect on economic growth6. This lead them to argue along the lines of Lensink and
Morissey (2000) that it is aid uncertainty rather than aid volatility per se that hurts growth
and that the counter or pro-cyclicality of aid matters. If aid is highly pro-cyclical as Bulir
and Hamann (2006) have found, then the stabilizing impact of aid volatility on growth is
likely to be low. Guillaumont and Chauvet however counter-argue that aid is not as pro
cyclical as originally thought, albeit using a different measure of pro-cyclicality that
compares aid to exports of goods and services. The stabilizing effect of aid with respect
to export shocks they argue depends on the relative levels of aid, its counter cyclicality
and its relative volatility with respect to exports. Nevertheless their panel growth
estimations revealed that aid significantly lowers income volatility while aid volatility
raises it; and they find that the stabilizing effect of aid on growth when aid is counter
cyclical depends on aid volatility not exceeding a threshold level. All in all their research
indicates that aid volatility matters for the stabilizing or destabilizing effect of aid on
growth and that aid volatility needs to be controlled for in a growth equation.
If aid raises economic growth and lowers growth volatility while aid volatility
lowers economic growth and raises growth volatility, then we will expect that any
negative causal relationship running from volatility in growth to economic growth to
reinforce these impacts. There is a strand of literature that argues that growth volatility
deters economic growth (Ramey and Ramey, 1995; Aizenman and Marion, 1999;
Mobarak, 2005). If aid volatility is higher in more aid-dependent countries as Bulir and
Hamann found in their earlier 2003 study, then the implications of aid and its volatility on
growth and growth volatility become graver for the economic development of poor aid
dependent countries.
6 It can be argued that not all aid volatility is "bad". When aid is volatile because it is dispensed in theaftermath of a shock to help countries stabilize and recover, aid volatility is actually "good". Howeverin this case we should observe that aid volatility is associated with aid being counter-cyclical and withaid being given to help countries recover from negative shocks. However as Bulir and Hamann haveshown, there is no significant evidence that aid is counter-cyclical and that aid is being used to cushioncountries from shocks.
14
4 A Preliminary Look at Aid
4.1 Aid: Definition, composition, and trends
Official development assistance (ODA) or aid is defined by the Organization for
Economic Development and Cooperation (OECD) as "flows to countries on the
Development Assistance Committee (DAC) list and to multilateral institutions for flows to
aDA recipients, which are provided by official agencies, including state and local
governments, or by their executive agencies and where each transaction is administered
with the promotion of economic development and welfare of developing countries as its
main objective; and each transaction is concessional in character and conveys a grant
element ofat least 25 per cent (calculated at a rate ofdiscount of10 per cent)"7. aDA
can be characterized in terms of several dimensions, such as: donor source (bilateral vis
multilateral), recipients characteristics (region and income group), financial terms and
degree of concession (loans vis grants), purpose of aid (sectors and sub-sectors), type of
aid (investment project, sector programme, technical cooperation or a combination of
these), tying status (tied vis untied), channel of disbursement (public sector, NGO and
civil society, public-private partnership, multilateral organizations or other) and more
recently policy objectives (e.g. earmarked for environment, gender or promotion of good
governance).
For the purpose of this paper, using OECD's definition we consider only aid granted
by DAC donor countries to DAC8 recipient countries and we distinguish between aid
committed and aid disbursed. While donor countries may pledge certain amounts in
7 See http://www.oecd.org/dataoecd/21/21/34086975.pdf.Military aid is excluded as well as loans for lessthan one year. Flows refer to transfer of resources either in cash or in the form of commodities orservices. Repayments made on the principal of aDA loans count as negative flows and are deducted toarrive at net aDA. Loans count as aDA if concessional in nature that is if provided at below marketinterest rates.
8 DAC or Development Assistance Committee is the aECD's committee that deals with development cooperation matters. It currently consists of 30 members (Australia, Austria, Belgium, Canada, CzechRepublic, Denmark, European Community, Finland, France, Germany, Greece, Hungary, Iceland,Ireland, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland,Portugal, Spain, Sweden, Switzerland, Turkey, U.K and USA). There are on DAC list of recipients 50Least Developed countries, 18 other low income countries, 49 lower middle income countries andterritories, and 36 upper middle income countries and territories.
15
development assistance for a specific period of time, in practice the actual amounts
disbursed may differ. Discrepancies between aid disbursed and aid committed in any
particular time period can be significant.
Figure 2
~ 120000
~S 100000,2:.'J1
"" SOOOO;:,01,¢¢ 60000N-....~ 40000-:;Qv 20000
0
Trend in aDA committed and disbursed 1965-2006
(constant U.S. dollars)
......... QD.:\' (cQllllll1hllentS)
_ODA{netdisbursements)
Ye:u
Average growth rates
1971- 1976- 1981- 1986- 1991- 1996- 2001-1975 1980 1985 1990 1995 2000 2005
Aid Committed 3.917 4.041 2.560 6.061 -3.367 -0.441 9.849Net Aid Disbursed 3.716 4.885 2.560 2.694 -2.298 1.493 10.034
Source: OECD. DAC countries only.
16
Figure 3 Ratio of ODA committed to disbursed 1966-2006
(constant U.S. dollars)
100 Ratio ofNet Disbljj'sejnefits to C01'ttfitifittefits
;~:~~60
50
40
30
20
10
o
Source: OECD. DAC countries only. Data on vertical axis are ratios multiplied by 100.
A cursory look at the data on aDA in constant 2005 US $ for the period 1965 to
2006 in Figure 1 shows that net aDA committed has always exceeded net aDA
disbursed in any particular year9• Figure 2 depicts the ratio of net disbursements to
commitments to be below one over the whole period. In evaluating the impact of aid on
development outcomes, it is important therefore to empirically use measures of actual
amounts of net aid disbursed rather than aid committed for more accurate results. aDA
both in terms of commitments and disbursements grew steadily in real terms from the
1960s till the 1990s.
The 1990s was marked by a severe cutback in aDA a result of aid fatigue;
however there was a reversal of this downward trend as from 2000 as the international
community rallied around the United Nations Millennium Development Goals and a
series of high-profile financing conferences. Total net aDA disbursed for the period
2001 to 2005 averaged US $ 85 billion at 2005 prices compared to an average US $ 63
9 Data on total net ODA committed is available for 179 countries from the OEeD database; however dataon total net aDA disbursed is available for only 50 countries. Using aid disbursed rather than aidcommitted significantly reduces our sample size. In the initial sample of 127 developing countries forthe 9 five-year average periods, there were 860 available observations for aid committed (grants) and387 observations for aid disbursed (grants).
17
billion in the preceding 5 year period, representing a 10 per cent increase which is the
highest increase on record. From 1961 to 2005, there has been a marked increase in the
share of aDA from multilateral sources (29% over 2001-2005 compared to 11% over
1961 to 1965). An increasing percentage of net aDA disbursed has been in the form of
grants (71 % on average over 2001-2005 compared to a low of 48% on average over
1971-1975). The proportion of tied aid relative to untied aid!O (Table 4) has fallen from
an average of51% in 1979-1980 to 9.9 % in 2001-2005, albeit with substantial variations
across DAC donor countries. Countries like Luxembourg and Norway tied less than 1%
of their commitments in the 2001-2005 period compared to more than 45% for countries
such as Canada, Greece and Italy.
In terms of regional distribution (Table 1), about 65% of net aDA disbursed flew
to Africa and Asia in the 2001-2005 periods. The share of Asia has been declining from
about 45% on average in 1961-1965 to 29% in 2001-2005, while that of Africa for the
same comparative periods rose from 28% to 33%. Aid allocation by income group has
however remained rather stable with about 40% of net aDA disbursed to low income
countries including Least Developing Countries (LDCs) and 34% to middle-income
countries.
Table 2 and Figure A.3 in the Appendix depict the distribution of net aDA
disbursed in constant 2005 US dollars by type. an the basis of a comparison of the
graphs for 1965, 1985 and 2002, we distinguish 4 changes over time: (1) a rise in the
share of multilateral aDA relative to bilateral (2) the increasing share of grants in
bilateral aDA (3) a rising share of bilateral grants aDA disbursed in the form of
technical cooperation programs and (4) a significant rise in debt forgiveness. Table 3 and
Figure AA in the Appendix show the distribution of gross bilateral disbursements in
current prices by sector!!. By this measure, we also note a substantial increase over time
in the share allotted to debt-related actions (5% in the late 1960s to 17% as from 2000).
10 Data on tying status is available only for bilateral official commitments.
11 Ideally all analysis should have been based on total net aDA disbursed in constant prices as a measure ofaid. However data by this measure by sector was not available from the OEeD database. Grossdisbursements at current prices on a bilateral basis are used instead.
18
There has been a diversion of aid away from economic productive sectors (mainly
industry, mining and construction) and towards social infrastructure and services
(education, health, population control and reproductive health, water and sanitation,
strengthening government and civil society) and towards the use of multi-sectoral/cross
cutting approaches over the past 30 years.
In sum, in the 1960s, aid as measured by net aDA disbursed came mainly from
bilateral tied sources and was earmarked mostly for project and program assistance and
technical cooperation in economic sectors in predominantly Asian low- developing
countries. In the 2000s, on the other hand, more of that aid is coming from either
multilateral or bilateral untied sources as grants and is funding technical cooperation
programs and debt relief mostly in social sectors and in predominantly African low
developing countries. Efforts over the last decade aimed at improving aid effectiveness
by DAC donors have focused on a). untying aid in an effort to reduce distortions in the
use of aid and mitigate aid-giving for donors' political and commercial own interests; b).
increasing the grant element and debt-relief component of aid in order to relieve
developing countries from a debt burden that diverts resources from growth-enhancing
sectors and compromises fiscal and external balance sustainability ; c). channeling more
aid through multilateral sources that are less prone to allocating aid on political and
strategic criteria divorced from development needs; and d). ensuring that aid flows where
it is needed the most and where its returns for long-term development are high, that is
improving social infrastructure and services in low-income countries (especially in Africa
where access to international private finance is limited) in order to boost human
development.
19
Tab
le1.
1N
etdi
sbur
sem
ents
(cur
rent
mil
lion
U.S
.dol
lar)
and
dist
ribu
tion
by
regi
on
1961
-19
66-
1971
-19
76-
1981
-19
86-
1991
-19
96-
2001
-
1965
1970
1975
1980
1985
1990
1995
2000
2005
Dev
elop
ing
Cou
ntri
esto
tal
(am
ount
scu
rren
tUS
doll
ar)
5422
.740
5481
.018
7183
.754
1251
5.25
218
565.
722
3148
3.57
841
246.
658
3611
6.86
252
470.
424
Eur
ope,
Tot
al7.
947
4.11
82.
555
3.09
62.
542
1.62
23.
500
4.34
94.
824
Afr
ica,
Tot
al27
.871
22.7
3324
.691
35.4
5437
.164
37.9
0435
.712
31.2
0633
.122
Am
eric
a,T
otal
14.5
0814
.073
9.72
48.
295
11.1
2411
.211
11.0
4912
.026
9.34
3
Asi
a,T
otal
44.9
3652
.202
49.6
0338
.277
33.0
3831
.849
31.3
7529
.729
31.9
68
Oce
ania
,Tot
al1.
090
3.60
95.
994
5.95
54.
913
3.95
23.
578
3.74
01.
564
Dev
elop
ing
Cou
ntri
esun
spec
ifie
d3.
649
3.26
57.
432
8.92
311
.221
13.4
6214
.786
18.9
5219
.180
Sour
ce:
DE
CD
.D
AC
coun
trie
son
ly.
20
Tab
le1.
2G
ran
tsdi
sbur
sem
ents
(cu
rren
tm
illi
onU
.S.d
olla
r)an
ddi
stri
buti
onb
yre
gion
1961
-19
66-
1971
-19
76-
1981
-19
86-
1991
-19
96-
2001
-
1965
1970
1975
1980
1985
1990
1995
2000
2005
Dev
elop
ing
Cou
ntri
esto
tal
(am
ount
scu
rren
tUS
doll
ar)
3892
.270
3166
.214
4567
.412
8976
.580
1394
7.04
225
018.
568
3525
0.72
433
459.
774
5298
2.62
0
Eur
ope,
Tot
al6.
613
2.03
71.
638
2.15
62.
216
1.39
04.
258
5.32
35.
117
Afr
ica,
Tot
al31
.011
29.5
5930
.151
34.9
2837
.315
39.0
8337
.119
33.8
4234
.192
Am
eric
a,T
otal
11.0
1510
.426
9.03
59.
192
10.0
5211
.284
12.3
6113
.210
10.6
37
Asi
a,T
otal
45.5
5944
.649
37.7
1532
.580
29.5
7326
.438
25.0
0923
.103
29.3
30
Oce
ania
,Tot
al1.
438
6.15
78.
529
8.02
96.
292
4.81
94.
018
4.01
91.
603
Dev
elop
ing
Cou
ntri
esun
spec
ifie
d4.
364
7.17
112
.932
13.1
1514
.552
16.9
8617
.236
20.5
0419
.120
Sour
ce:
OE
CD
.DA
Cco
untr
ies
only
.
21
Tab
le1.
3D
istr
ibut
ion
ofn
etdi
sbur
sem
ents
(cu
rren
tm
illio
nU
.S.d
olla
r)b
yin
com
egr
oup
1961
-19
66-
1971
-19
76-
1981
-19
86-
1991
-19
96-
2001
-
1965
1970
1975
1980
1985
1990
1995
2000
2005
Rec
ipie
nt
LD
Cs,
Tot
al11
.347
13.0
0419
.249
24.7
0527
.968
30.6
3128
.009
25.1
4829
.321
Oth
erL
ICs,
Tot
al31
.878
37.0
2023
.696
16.2
4214
.446
13.7
7515
.507
16.0
5915
.580
LM
ICs,
Tot
al27
.852
24.6
0029
.114
31.4
4728
.912
27.4
8231
.586
31.3
1229
.696
UM
ICs,
Tot
al8.
645
8.39
45.
695
4.78
95.
678
4.72
94.
727
3.54
13.
245
MA
DC
Ts,
Tot
al8.
258
7.81
06.
846
7.27
66.
971
5.53
93.
998
2.25
60.
172
Una
lloc
ated
by
inco
me
12.0
219.
172
15.4
0115
.541
16.0
2517
.844
16.1
7421
.684
22.0
21
Sour
ce:
DE
CD
.D
AC
coun
trie
son
ly.
22
Tab
le1.
4D
istr
ibut
ion
ofg
rant
sdi
sbur
sem
ents
(cur
rent
mil
lion
U.S
.dol
lar)
byin
com
egr
oup
1961
-19
66-
1971
-19
76-
1981
-19
86-
1991
-19
96-
2001
-
1965
1970
1975
1980
1985
1990
1995
2000
2005
Rec
ipie
nt
LD
Cs,
Tot
al13
.696
18.3
1123
.016
27.6
2828
.137
28.4
5028
.346
25.1
5627
.660
Oth
erL
ICs,
Tot
al27
.186
31.6
7119
.266
13.9
1911
.685
11.6
9711
.749
11.5
0513
.154
LM
ICs,
Tot
al28
.337
21.0
7125
.119
26.7
8424
.441
23.2
0927
.522
27.7
8230
.309
UM
ICs,
Tot
al6.
396
5.45
14.
439
5.02
05.
499
4.89
15.
706
5.15
04.
053
MA
DC
Ts,
Tot
al10
.301
7.06
85.
857
8.61
29.
638
7.55
85.
607
3.39
10.
090
Una
lloc
ated
byin
com
e14
.084
16.4
2922
.303
18.0
3820
.601
24.1
9521
.069
27.0
1624
.752
Sour
ce:
OE
CD
.DA
Cco
untr
ies
only
.
Not
e:L
DC
s:le
ast
deve
lopi
ngco
untr
ies.
LIC
s:L
ow-i
ncom
eco
untr
ies.
LM
ICs:
low
er-m
iddl
ein
com
eco
untr
ies.
UM
ICs:
uppe
r-m
iddl
ein
com
e
coun
trie
s.M
AD
CT
s:M
ore
adva
nced
deve
lopi
ngco
untr
ies
and
terr
itor
ies.
23
Tab
le2
Dis
trib
utio
nof
net
disb
urse
men
ts(c
onst
ant
2005
mil
lion
U.S
.dol
lar)
by
type
asa
per
cen
tage
ofO
DA
(ave
rage
sfr
om19
61to
2005
)
1961
-19
66-
1971
-19
76-
1981
-19
86-
1991
-19
96-
2001
-
1965
1970
1975
1980
1985
1990
1995
2000
2005
LO
DA
(am
oun
tsco
nst
ant
US
$200
5)37
540.
018
3876
8.40
039
635.
422
4744
4.52
059
170.
444
6735
7.29
470
836.
040
6370
5.31
285
164.
028
LA
.Bil
ater
alO
DA
89.1
9484
.407
77.7
1368
.832
67.9
6870
.038
69.6
0667
.896
71.0
19
LA
.I.
Gra
nts
65.0
9450
.890
48.4
6149
.686
50.4
7756
.380
61.0
6364
.189
71.9
39
LA
.1.1
.Pro
ject
and
Pro
gram
me
Aid
9.72
312
.406
15.8
5317
.931
20.0
7920
.813
15.6
6715
.340
16.1
25
LA
1.2
.T
echn
ical
Co-
oper
atio
n15
.746
21.4
0023
.031
20.2
9920
.238
21.0
8022
.850
24.8
6923
.485
LA
1.3
.aD
AG
rant
sin
AF
.P
acka
ges
......
...0.
218
0.39
60.
580
0.70
60.
621
0.28
4
LA
.1.4
.Dev
elop
men
talF
ood
Aid
6.30
95.
559
5.19
83.
975
3.17
03.
674
2.94
91.
894
1.39
5
LA
1.5
.H
uman
itar
ian
Aid
...0.
113
1.21
31.
162
1.26
61.
783
3.98
64.
453
5.70
9
LA
1.6
.D
ebtF
orgi
vene
ss,t
otal
...0.
066
0.93
92.
401
0.69
92.
260
6.58
25.
405
12.5
72
LA
.1.7
.O
ther
Act
ion
On
Deb
t...
......
......
...0.
078
0.37
60.
661
LA
1.8
.S
uppo
rtto
NG
O's
......
0.00
10.
005
0.33
51.
517
1.42
82.
112
2.12
6
LA
1.9
.S
uppo
rtto
Int'l
Pri
vate
Org
....
......
0.17
20.
241
0.23
60.
247
0.43
60.
582
LA
1.10
.C
ontr
ibut
ions
toP
PP
s...
......
......
......
...0.
388
LA
1.II
.Pro
mot
ion
ofD
evel
opm
ent
Aw
aren
ess
......
......
...0.
085
0.09
00.
175
0.25
5
LA
I.1
2.
Adm
inis
trat
ive
Cos
ts...
...0.
001
0.98
53.
125
3.60
04.
285
5.41
74.
787
LA
1.13
.Ref
ugee
sin
Don
orC
ount
ry.
......
......
......
1.66
71.
743
2.29
6
24
1961
-19
66-
1971
-19
76-
1981
-19
86-
1991
-19
96-
2001
-
1965
1970
1975
1980
1985
1990
1995
2000
2005
LA
.1.1
4.O
ther
Gra
nts
16.5
085.
380
2.89
83.
558
1.30
31.
330
1.44
71.
369
1.34
0
LA
.1.
***M
emo:
Pos
t-C
onfl
ictP
eace
-
buil
ding
Op.
......
......
......
1.02
60.
315
0.63
0
LA
.2.
Non
-Gra
ntB
ilat
eral
OD
A23
.871
33.2
6729
.252
19.1
4617
.490
13.6
588.
543
3.70
7-0
.920
LB
.M
ulti
late
ral
OD
A10
.806
16.4
0626
.640
35.0
6232
.032
29.9
6230
.394
32.1
0428
.981
LB.
***M
emo:
Cap
ital
Sub
scri
ptio
n.on
anE
ncas
hmen
tBas
is...
0.74
11.
257
9.26
79.
961
7.58
25.
827
8.73
38.
355
LB.*
**M
emo:
Mul
tilat
eral
Inte
rest
Rec
eive
d...
-0.0
05-0
.018
-0.0
28-0
.041
-0.0
27-0
.014
-0.0
10-0
.004
L**
*Mem
o(bi
1+m
u1):
RIP
eIn
itia
tive
......
......
.....
0.00
20.
434
2.73
1
L**
*Mem
o(bi
l+m
u1):
IDA
Deb
t
Red
ucti
onF
acil
ity
......
......
......
...0.
069
0.11
1
Sou
rce:
OE
CD
.DA
Cco
untr
ies
only
.
Not
e:ab
ove
figu
res
are
perc
enta
ges
ofO
DA
,ave
rage
dov
erth
ere
leva
ntti
me
peri
od.
Wit
hin
sub-
cate
gori
es,f
igur
esm
ayno
tad
dup
to10
0%fo
rth
e
who
leca
tego
rydu
eto
erro
rsan
dom
issi
ons
inor
igin
alda
ta.
25
Tab
le3
Dis
trib
utio
nof
offi
cial
bila
tera
lgro
ssdi
sbur
sem
ents
(cur
rent
mil
lion
U.S
.dol
lar)
by
sect
or
(Ave
rage
sfr
om19
61to
2005
) 1967
-19
71-
1976
-19
81-
1986
-19
91-
1996
-20
01-
1970
1975
1980
1985
1990
1995
2000
2005
I.So
cial
Infr
astr
uctu
rean
dSe
rvic
es8.
555
24.2
6620
.924
2504
7224
.532
25.7
9430
.689
33.8
13
1.1.
Edu
cati
on...
15.9
6911
.299
11.5
4010
.354
9.98
810
.252
8.22
4
1.2.
Hea
lth
...2.
500
4.63
75.
340
3.84
13.
371
3.98
24.
263
1.3.
Pop
ulat
ion
Pol
./Pro
gr.
&R
epro
duct
ive
Hea
lth
......
'"0.
230
1.06
61.
097
1.74
63.
144
104.
Wat
erS
uppl
y&
San
itat
ion
0.00
71.
926
1.38
42.
781
3.64
64.
772
5.95
53.
929
1.5.
Gov
ernm
ent&
Civ
ilS
ocie
ty..
1.85
31.
106
1.94
82.
527
2.94
43.
960
9.32
3
1.6.
Oth
erS
ocia
lIn
fras
truc
ture
&S
ervi
ces
8.55
32.
017
2049
73.
771
3.09
73.
622
4.79
44.
930
II.E
cono
mic
Infr
astr
uctu
rean
dSe
rvic
es20
0434
9.59
915
.557
18.0
8619
.291
20.5
2019
.976
13.1
05
11.1
.Tra
nspo
rt&
Sto
rage
8.76
63.
386
6.08
86.
955
7.56
580
473
9.60
95.
525
11.2
.Com
mun
icat
ions
3.51
820
466
1.65
52.
582
2.95
32.
130
1.12
80.
570
11.3
.Ene
rgy
8.15
03.
569
7.15
57.
877
6.91
48.
031
6.20
14.
864
11.4
.Ban
king
&F
inan
cial
Ser
vice
s...
...'"
1.31
10.
582
0.73
10.
727
1.03
6
11.5
.Bus
ines
s&
Oth
erS
ervi
ces
...0.
298
1.64
60.
371
1.27
71.
155
2.31
01.
109
III.
Pro
du
ctio
nSe
ctor
s25
.946
15.2
5822
.912
22.6
7716
.806
12.0
319.
730
6.62
4
III.
I.A
gric
ultu
re,
For
estr
y,F
ishi
ng7.
022
5.64
310
0407
11.8
7010
.344
7.62
47.
131
4.32
9
III.
2.In
dust
ry,
Min
ing,
Con
stru
ctio
n18
.924
5.60
25.
608
5.54
25.
302
3.12
12.
021
1.64
1
III.
3.a.
Tra
deP
olic
ies
&R
egul
atio
ns...
...'"
0042
50.
350
0.35
90.
197
0.59
3
26
1967
-19
71-
1976
-19
81-
1986
-19
91-
1996
-20
01-
1970
1975
1980
1985
1990
1995
2000
2005
IV.M
ulti
sect
or/C
ross
cutt
ing
1.13
61.
525
2.16
03.
461
2.61
33.
964
7.24
97.
185
V.T
otal
Sec
tor
All
ocab
le(I
+II
+II
I+IV
)55
.786
50.6
4861
.551
69.6
9763
.241
62.3
0867
.689
60.7
26
VI.
Com
mod
ity
Aid
/G
ener
alP
rogr
amm
e.
Ass
ista
nce.
28.5
2419
.233
13.6
8114
.341
18.1
3212
.257
6.41
84.
679
VII
.A
ctio
nR
elat
ing
To
Deb
t4.
866
4.39
44.
332
1.93
66.
744
9.89
27.
844
17.0
32
VII
I.H
um
anit
aria
nA
id...
1.11
21.
101
1.64
51.
830
4.70
05.
389
6.59
5
VII
I.I.
Em
erge
ncy
Res
pons
e...
......
......
2.33
14.
162
4.61
8
VII
I.2.
Rec
onst
ruct
ion
Rel
ief&
Reh
abil
itat
ion
......
......
......
...0.
787
VII
I.3.
Dis
aste
rP
reve
ntio
n&
Pre
pare
dnes
s...
......
......
......
0.00
8
IX.A
dmin
istr
ativ
eC
osts
OfD
onor
s...
......
3.62
13.
841
3.80
45.
899
5.41
7
X.S
up
po
rtT
oN
GO
'S...
'"...
3.26
71.
736
1.22
71.
750
3.09
1
XI.
Ref
ugee
sIn
Don
orC
ount
ries
......
......
...0.
864
1.79
82.
299
XII
.Una
lloc
ated
lUns
peci
fied
10.8
2324
.613
19.3
359.
625
4.47
55.
811
5.01
02.
460
TO
TA
L
(Am
ount
scu
rren
tm
illi
onU
Sdo
llar
s)36
04.8
775
1022
3.90
819
003.
038
2425
0.57
841
743.
802
5178
1.61
4467
1.39
6509
2.11
(V+
VI+
VII
+V
III+
IX+
X+
XI+
XII
)
Sou
rce:
OE
CD
.DA
Cco
untr
ies
only
.
Not
e:ab
ove
figu
res
are
perc
enta
ges
ofto
tal
bila
tera
lgro
ssdi
sbur
sem
ents
,av
erag
edov
erth
ere
leva
ntti
me
peri
od.W
ithi
nsu
b-ca
tego
ries
,fig
ures
may
nota
ddu
pto
100%
for
the
who
leca
tego
rydu
eto
erro
rsan
dom
issi
ons
inor
igin
alda
ta.
27
Table 4 Proportion of tied bilateral commitments by donor
1979- 1981- 1986- 1991- 1996- 2001-
1980 1985 1990 1995 2000 2005
DAC Countries, Total 51.517 46.281 39.303 33.077 15.406 9.930
Australia 34.408 41.485 63.909 66.072 20.388 33.565
Austria 98.941 96.347 86.760 50.172 42.964 ...
Belgium 73.679 66.990 58.837 83.701 21.033 6.013
Canada 86.241 74.910 50.531 42.750 69.220 46.109
Denmark 37.126 37.988 34.257 38.716 26.867 13.984
Finland 34.158 20.417 69.967 39.012 16.505 12.273
France 58.904 57.715 48.984 44.290 16.296 5.298
Germany 20.222 28.744 49.455 51.372 20.403 9.796
Greece ... ... ... ... 86.604 54.129
Ireland ... ... 33.612 ... ... ...
Italy 46.631 57.686 87.859 58.862 57.416 50.069
Japan 49.215 24.703 15.017 12.032 6.912 7.674
Luxembourg '" ... ... 16.627 4.733 0.925
Netherlands 8.640 17.881 10.779 6.488 7.838 7.701
New Zealand 51.628 47.862 43.853 ... ... 15.357
Norway 29.164 28.288 34.560 18.652 6.752 0.514
Portugal ... ... ... 29.178 11.367 27.937
Spain ... ... ... 100.000 80.389 32.116
Sweden 18.871 19.163 26.972 14.060 8.543 4.531
Switzerland 50.121 34.005 30.948 11.773 9.992 3.507
United Kingdom 79.509 74.979 78.326 54.234 15.840 6.103
United States 70.703 54.146 37.630 43.710 71.598 ...
Source: OECD. DAC countries only.
The focus of development assistance has indeed changed several times over the
years, in line with changes in development thinking, international political conditions and
the motives of donors for giving aid. The Marshall plan after the Second World War is
considered as having laid the development for today's aid machinery (Hjertholm and
28
White, 199912) and largely consisted of program aid for reconstruction purposes in war
wrecked Europe. The 1950s saw the US as the dominant institution in foreign aid,
focusing on fostering a role for the state while supporting anti-communist countries
through the disbursement of food aid and projects. Bilateral programs were established
in the 1960s as the US pushed for burden sharing by other countries in staving off
communism through aid. In this wake, the Development Assistance Committee (DAC)
was created to monitor aid performance. During this period, focus was on supporting the
state in productive sectors, with the bilateral agencies giving technical assistance and
budget support and the mu1ti1atera1s funding projects. The 1970s saw an expansion of the
multilaterals such as the World Bank, the IMF and Arab-funded agencies with a
continued focus on supporting state activities in the productive sectors and meeting basic
needs to reduce poverty. The 1970s saw an emphasis on import support aid and a fall in
food aid as some countries needed assistance in the face of commodity and oil price
shocks. For a brief period of time, there was a special focus on poverty but by the late
1970s, with mounting balance of payments problems and a looming debt crisis, the focus
of aid shifted towards alleviating the costs of adjustment in the form of program aid and
debt relief. The 1980s saw a focus on market-based adjustment, and a rolling back of the
state. Emphasis was on macroeconomic reform in the form of financial program aid and
debt relief (this decade saw the emergence of structural adjustment loans at the World
Bank). The 1990s saw a renewed focus of aid on poverty alleviation as the social costs of
macro-economic adjustment became evident and also an engagement on governance,
namely ensuring that aid is allocated on the basis of good governance rather than political
and ideological ties. Towards the end of the decade aid shifted towards sector-wide
support. Over the last 5 years, attention has again shifted to reducing poverty and
achieving the Millennium Development Goals while promoting good governance, gender
equality and more recently environmental sustainability.
Hjertho1m and White (1999) have come up with the concept of the aid diamond in
order to evaluate the quality of aid given by donors. The aid diamond has 4 axes that
12 See Chapter 3 in Tarp (1999) for a good overview of the historical background of foreign aid and alsoHjertholm and White (1999) from which this paragraph draws on.
29
measure how donors are performing on certain dimensions relative to the targets set by
the OECD DAC countries or compared to the average DAC effort. These dimensions
are: (i) aid volume as a percentage of donors GNP with a target set by DAC at 0.7%
which is also the UN target (ii) the grant element with a target set at 84% by DAC (iii)
the share of net aid disbursement going to the least developed countries (LLDCs), with
the target set at 0.15% of donors GNP and (iv) the percentage of bilateral aid
commitments which is untied with a target set at 100% by Hjertholm and White. Hence
the larger the aid diamond for a given donor, the higher is the quality of aid disbursed by
that donor and the larger the expected impact on development. For aid to be effective,
according to the aid diamond, it needs to be large enough to help countries meet their
domestic resource constraints; it needs to be mostly in the form of grants to alleviate debt
burden of developing countries; it should flow to the neediest countries where the
potential for economic growth is highest and it should be untied to ensure a cost
effective use of aid. Current data however reveals that these targets are far from being
met.
As Table 5 reveals, almost all DAC donor countries have never historically
fulfilled the target set in 1970 by the United Nations13 for aid to be 0.7% of a DAC
donor's GNp 14• Exceptions are Denmark, the Netherlands, Norway and Sweden. Nor
have the targets for tied aid and grant aid been satisfied historically. This is to say that
when discussing aid effectiveness, attention needs to be paid both to limitations on the
recipients' side relating to their propensity to effectively using aid as well as to
limitations on the donors' side relating to them doling out the "right" quality of aid. This
paper argues that stability of aid disbursed is another dimension that needs to be
taken into account to improve the "quality of aid" dispensed by donors.
13 This target was reiterated by the United Nations at the 1992 Earth summit, the 1995 World Summit forSocial Development, the 2002World Summit on Sustainable Development and the 2002 InternationalConference on Financing for Development in Monterrey.
14 See Clemens and Moss (2005) for a critical assessment of the viability of this target and its origins.
30
Tab
le5
Net
aid
disb
urse
din
curr
ent
pric
esas
ape
rcen
tage
ofd
onor
sG
NP
by
dono
r
1961
-19
66-
1971
-19
76-
1981
-19
86-
1991
-19
96-
2001
-
1965
1970
1975
1980
1985
1990
1995
2000
2005
Aus
tral
ia0.
478
0.59
40.
570
0.47
80.
478
0.39
80.
356
0.26
80.
252
Aus
tria
0.06
20.
116
0.14
00.
208
0.31
80.
192
0.16
80.
232
0.31
0
Bel
gium
0.58
60.
450
0.53
20.
520
0.57
80.
454
0.37
80.
332
0.46
8
Can
ada
0.15
20.
330
0.45
80.
478
0.45
60.
466
0.43
40.
298
0.27
0
Den
mar
k0.
114
0.27
60.
480
0.64
60.
776
0.90
61.
000
1.01
40.
898
Fin
land
0.03
40.
070
0.14
80.
182
0.32
60.
570
0.50
20.
320
0.37
0
Fra
nce
1.05
40.
652
0.46
20.
408
0.57
20.
590
0.61
00.
396
0.39
2
Ger
man
y0.
430
0.37
40.
352
0.39
00.
470
0.40
80.
350
0.27
80.
292
Gre
ece
'"...
......
...'"
...0.
158
0.18
4
Irel
and
......
0.09
00.
148
0.21
80.
200
0.21
80.
304
0.38
6
Ital
y0.
132
0.16
80.
118
0.11
60.
220
0.37
40.
274
0.15
80.
192
Japa
n0.
190
0.26
80.
230
0.24
60.
302
0.30
80.
290
0.24
60.
226
Lux
embo
urg
......
...
0.11
00.
130
0.18
60.
340
0.60
20.
806
Net
herl
ands
0.37
00.
510
0.62
60.
860
0.99
60.
966
0.82
60.
810
0.79
6
New
Zea
land
0.19
20.
198
0.30
60.
362
0.27
00.
256
0.24
60.
252
0.24
0
Nor
way
0.15
20.
254
0.48
80.
854
1.00
41.
122
1.04
20.
840
0.88
4
Por
tuga
l...
......
0.02
00.
040
0.17
80.
304
0.24
40.
316
Spa
in...
......
0.08
00.
096
0.11
60.
262
0.23
00.
260
Sw
eden
0.13
80.
312
0.58
00.
858
0.87
00.
892
0.93
00.
770
0.82
4
Sw
itze
rlan
d0.
068
0.12
20.
158
0.20
60.
282
0.31
00.
368
0.33
80.
380
Uni
ted
Kin
gdom
0.51
80.
422
0.40
00.
430
0.36
20.
298
0.30
80.
272
0.36
0
31
1961
-19
66-
1971
-19
76-
1981
-19
86-
1991
-19
96-
2001
-
1965
1970
1975
1980
1985
1990
1995
2000
2005
Uni
ted
Stat
es0.
578
0.40
60.
268
0.24
20.
236
0.20
00.
158
0.10
20.
158
Sou
rce:
DE
CD
.T
he
abov
ear
ese
lect
edD
AC
do
no
rco
untr
ies.
32
4.2 A Note on aid effectiveness
The impact of aid on development or the extent of aid effectiveness will depend
on several factors that can be grouped into 3 main institutional interfaces of the aid
assistance process (see Figure 3), namely factors within the control of donors before aid
is disbursed to recipients (that we refer to in Figure 3 as aid provision and aid allocation),
those within the control of recipient governments after aid is disbursed by donors (aid
management) and factors that lie within the control of other institutional actors or
forces during aid implementation. These other institutional actors or forces may be
responsible for some of the transaction costs involved in the aid assistance process.
Figure 3 illustrates.
On the donors' side, for instance, the quality of aid provided matters as
Hjertholm and White (1999) pointed out. The aid volume matters as well as the aid given
as a percentage of donors' GNP (Is aid large enough to make an impact? Is it a sufficient
proportion of total recipients' needs?). The proportion of aid that is tied can also affect
development impact (are recipients free to use the aid in the most efficient way or are
they constricted to satisfy the commercial interests of donors?). The tying of aid can be
compared to an import leakage that reduces the domestic multiplier effect of aid in the
recipient country15• Is the aid channeled through NGOs or the government bureaucracy?
In recipient countries with poor public administration beset by regulations and corruption,
the channel of delivery matters and can make the difference as to the amounts of aid that
eventually reaches the ultimate recipients. As this paper argues, the percentage of
commitments that materializes in actual disbursements and the stability of the aid
disbursement flows also matter in ensuring continuity in financial resources to achieve
long-term development results. When most of the aid pledged does not materialize and
when aid is highly volatile, it hinders the planning process of governments, puts pressure
on their fiscal and external balances and creates boom-bust financial cycles that hinder
15 If out of every $1 disbursed to a recipient country by a donor, say 50% is tied, this implies that 50 centsof every dollar of aid disbursed is re-exported to the donor country and 50cents is injected in therecipient domestic economy. In Afghanistan, it is argued that abut 40% of the aid goes back to donorscountry thereby blunting the impact of aid on the local economy (source: Waldman, 2008).
33
sustained growth. The motives for aid allocation by donors determine the impact of aid
on development including on economic growth. If aid is allocated by donors mostly
based on strategic and political reasons (rather than based on the recipients' ability to
manage aid effectively for development or rather than based on recipients' development
needs), then evidence for aid effectiveness will be empirically weak. On the recipient
governments' side, aid will be effective if recipient governments do not misuse or
mismanage aid for purposes other than development; if recipient governments maintain
an environment favorable to growth such as macro-economic stability, political stability
and good governance and if aid is well coordinated and aligned behind the
implementation of a national development plan rather than used on an ad-hoc basis.
The efficiencies of the aid implementation machineries both in the donor and in
the recipient countries affect the effectiveness of aid. The aid delivery process is a
complex one involving many actors with different objectives and incentive sets along
many stages. Martens, Murrell et al (2001) and Zinnes and Bolaky (2002), among others,
have analyzed the reasons behind aid failure from the New Institutional Economics
perspective as related to transaction costs that arise from a misalignment of incentives
among aid institutional actors. Aid assistance from its inception to implementation to
delivery is done within a chain of organization involving principal and agents. Within
the recipient country there may be conflict of interests or misalignment of incentives
between the donor implementer (a principal) and the recipient implementer (an agent);
between recipient implementers and sub- contractors; and between implementers and
ultimate recipients. Within the donor country, similar misalignment of incentives may
happen between the government and the aid agency in charge of administering the aid
package. This misalignment of incentives could give rise to volatility in aid disbursed or
discrepancies between what is committed and what is actually disbursed. Such
misalignment of incentives can prevent donors or recipients from realizing the
development objectives set by the aid they want to dispense or receive. Aid may fail in
its goals due to adverse selection problems (donors wrongly selecting recipients) but also
due to moral hazard problems (inability for donors to observe and monitor proper
implementation by recipients, collusion between aid agency and sub-contractors on the
34
ground reflecting transaction costs). For instance, in cases where a large part of aid is
channeled outside of the government apparatus (for example in Afghanistan where two
thirds of aid bypasses the government), a lack of competitive bidding in allotment of
contracts to private contractors by the donor aid agencies, due to potential collusion
among both parties, can lead to mismanagement of aid through over-invoicing of
contracts16.
Whether aid promotes stable economIC growth and eventually welfare and
development or not is likely to depend on many factors as Figure 4 illustrates. Volatility
in aid disbursed, as one characteristic of aid provision by donors, is a potential factor as
this paper attempts to demonstrate.
16 According to ACBAR, an alliance of international aid agencies in Afghanistan, half of the fundsallocated by USAID, the biggest donor, goes to 5 big US-based contractors.
35
Figure 4 Components of aid effectiveness
Donors Side1. Effectiveness in Aid Provision. This is dependent on the quality or characteristics of aid
disbursed by donors that lie within the control of the donors. These will refer to some of thedimensions described in this section, namely: aid volumes, aid as a proportion of donors'GNP, grants vis loans, proportion of aid untied, proportion given through NGOs, governmentsor public-private alliances, ratio of aid disbursed over aid committed, stability of aid flows,proportion of conditional aid, proportion of aid earmarked for specific sectors or areas.
2. Effectiveness in Aid Allocation. This refers to the factors that donors take into account inallocating aid flows. Whether aid is allocated on the basis of recipients' country developmentneeds such as poverty reduction and human development (for instance "poverty-efficiency"advocated by Dollar and Collier (2001)*); or whether aid is allocated selectively on the basisof good performance by recipients based on good policy-making and good governance(advocated by Burnside and Dollar (2000)); or aid is allocated based on ideological, politicaland strategic donors' interests. For instance aid can be given on ideologically-basedconditionality, that is subject to recipients pursuing certain specific reforms and policymeasures advocated by the donors. Aid can also be allocated based on military, political orstrategic gains expected by the donors.
Recipient Governments' side3. Effectiveness in Aid Management. This refers to factors within the recipients' control that
affect aid's impact on development in the recipients' countries. These factors directly relateto how effectively aid is managed by the recipients. Such factors include the quality of theinstitutional environment within the aid administrative machinery (for example controllingcorruption within the recipients' aid bureaucracy, availability of aid monitoring and evaluationmechanisms, availability of long-term budgeting and planning frameworks, minimizing redtape, setting aid coordination mechanisms), macro-economic stability and good governanceamong others.
Other Institutional Actors and Forces4. Minimizing transaction costs. During the aid implementation process that starts in the donor
country and ends with the ultimate recipient, aid is transacted through various stages andacross various actors and institutional interfaces including bureaucracies. The ultimateobjective of aid set by the donor (which ideally should be to raise welfare for the ultimaterecipients of aid notably the poor) may get derailed as actors throughout the process maypursue their own interests at the expense of the ultimate objective. At each stage, transactioncosts may arise due to principal-agent problems and opportunistic behavior by the actorsinvolved.
Source: Author.
*Aid allocation can be based on different principles. The "poverty-efficiency" principle advocates for more
aid to be given to countries with higher levels of poverty. Llavador and Roemer (2001) advocate instead for
an "equal opportunity aid" principle where more aid is given to counties with higher levels of poverty as
long as these levels of poverty are due to unjust or unfair factors that are not within the control of
governments.
36
5. Economic Growth, Volatility and Aid
The premise of this paper is that both the level and volatility of aid disbursed are
significant determinants of development where development is conceived as stable
economic growth, leading to sustained increases in real income. Following Mobarak
(2005), we estimate a two-equation system with both economic growth rate and volatility
in growth as dependent variables 17• So far in the aid effectiveness literature, most studies
have used single equation models with growth as a dependent variable to assess the
impact of aid on development. This paper highlights the role of volatility in both the aid
delivery process and in economic development and assesses the contribution of stable aid
flows in promoting stable economic growth.
The following equation system was estimated:
Eq (1)
GROWTH RATEit = Pga + Pgl AIDit + P g2 VOLAIDit + P g3 VOLGROWTHit + pgx Xit + egit.
. Eq(2)
VOLGROWTHt = PVa + PVl AIDit + PV2 VOLAIDit + PYx X'it + e \.
i= country t=year ,. eit and eVil are mean-zero scalars reflecting the random or non-measurable
component ofgrowth and volatility in growth respectively. All the {J's are scalars exceptfor {Jgx, and {Jvx'
Equation (1) estimates a panel growth equation that controls for the effect of a
measure of aid disbursed per capita (AIDit) and volatility of aid disbursed per capita
(VOLAIDit) on economic growth after including a vector of exogenous control variables
(Xit) borrowed from the growth literature. Equation (2) estimates a panel volatility in
growth equation that includes aid and volatility in aid disbursed per capita and a range of
other explanatory variables as determinants of volatile economic growth. The list of
variables and data sources is listed in Table A.I in the Appendix. We estimate the system
17 As Mobarak (2005) notes "average growth and its variability are two moments of the same underlyingincome process and are likely to be jointly determined. Furthermore since volatility deters growth andsince mean growth and its variability have common underlying determinants, it is essential that oneestimate a two-equation system ".
37
of equations for 9 five -year averages intervals as from 1960 to 2005 for a maximum
potential sample of 127 countries that had a developing status as at 196018•
5.1 Controls in growth equation
Barro (1991) in his seminal work presented empirical evidence that growth rate
for a cross-section of 98 countries for the period 1960-1985 was significantly positively
correlated with initial human capital and physical investment to GDP but inversely
related to government consumption. After controlling for human capital effects, there
was also evidence of conditional convergence in the sample of countries. These results
were in broad accordance with the standard predictions of neo-classical endogenous
growth models and with the view that large governments deter private investment
through higher distortionary tax rates. Since Barro's work, there has been a major
extension to the list of variables found to be significantly correlated with economic
growth. These variables can be grouped into 3 main classes: (i) Economic policy
variables. Fischer (1993) found evidence that inflation, large budget deficits and distorted
foreign exchange markets hamper economic growth through adverse effects on
investment and productivity growth. High inflation was not found to be consistent with
sustained growth. There is evidence that trade promotes growth (Frankel and Romer,
1996), especially when properly instrumented in the presence of institutional variables
(Dollar and Kraay, 2002) or in the presence of good institutions (Bolaky and Freund,
2004). Growth in government's size is detrimental to growth and even more so in non
democratic socialist systems than in democratic market systems (Guseh, 2002). (ii)
Institutional Variables. Economic institutional variables such as quality of governance
(property rights, law and order, corruption, quality of bureaucracy), legal origins,
financial depth or regulatory quality have also been found to be significant determinants
of growth (North, 1989; King and Levine, 1993; Mauro, 1995; Knack and Keefer, 1995;
Levine, 1998; Acemoglu, Johnson and Robinson, 2001; Rodrik, Subramanian and Trebbi,
2002; Djankov, McLiesh and Ramalho 2006). Political variables such as political
18 We also try to estimate the equations using 10 year averages data. However to maximize the number ofobservations, we opted for 5 year averages instead. Data averages are taken to net out business cycleeffects as is the usual approach in the literature.
38
stability, avoidance of wars and conflicts and civil liberties matter for growth as these
political features impinge on uncertainty and property rights that affect private
investment (Barro, 1991; Olson, 1991; Levine and Renelt, 1992; Sala-i-Martin, 1997;
Mauro, 1994; Alesina et aI, 1996). Institutions affect growth mainly by affecting the
incentives provided to economic agents to save, invest, produce, and reallocate resources
namely by affecting information and transaction costs, access to finance and the returns
on economic activity. (iii) Environmental, geographic and ethnic variables. Ethnic
diversity within a country, as measured by an ethnic fractionalization index, adversely
affects growth by inhibiting consensus on supply of growth-promoting public goods and
favoring instead growth-retarding policies associated with rent-seeking behavior
(Easterly and Levine, 1997). It also affects policy and growth when a country's
institutions are poor while poor institutions have a greater adverse effect on policy and
growth when ethnic diversity is high (Easterly, 2001). Others in the literature argue that
geographical factors undermine economic growth. Sachs et al (1998) argue that location
and climate have large effects on income growth through its effects on transport costs,
disease burden, agricultural productivity and choice of economic policies. Environmental
vulnerability such as exposure to natural disasters, on the other hand, can be compared to
adverse external shocks on the macro-economy, destroying the stock of capital and
reducing output. (World Bank Development Report 2001; Raddatz, 2005; Noy, 2007;
Cuaresma et aI, 2008).
Cross-country growth empirics have been subject to criticisms on account of the
lack of robustness in their results. Levine and Renelt (1991) address this issue and
attempt to use extreme bounds analysis to identify robust determinants of long-term
growth. One conclusion of their paper is that measures of economic policy are related to
long-run growth. Other robustly significant determinants of long-term growth include
investment share of GDP, initial real GDP per capita in 1960 and initial secondary school
enrolment rate. Almost all other variables were found not to be robust. These
pessimistic findings were contradicted by Sala-i-Martin (1997), after using a different
methodology that looks at confidence intervals for the estimated coefficients of the
growth determinants. At a 95% confidence interval, Sala-i-Martin find the following
39
group of variables to be robustly related to growth: (1) regional dummy variables (2)
political variables measuring political stability, civil rights and liberties, absence of wars
and rule of law (3) religious variables (4) market distortions and market performance (5)
types of investment (6) primary sector production (7) Sachs and Warner's openness
dummy (8) type of economic organization and (9) Spanish colonial dummy. In an
updated study, Sala-i-Martin, Dopelhoffer and Miller (2004) used a Bayesian Averaging
Classical Estimates Approach (BACE) to identify robust variables out of a potential list
of 32 variables. Their findings reveal that initial real GDP per capita is the most robustly
correlated to growth.
In what follows, we select the determinants for economic growth based on both
neo-classical growth theory and empirical findings across the 3 classes of variables
mentioned above (economic policy, institutional, environmental/geographic and ethnic)
whilst recognizing the need to look for evidence of aid effectiveness outside of growth
empirics as robustness checks.
Based on the neo-classical growth models, we include share of gross domestic
fixed capital formation in GDP as a measure of physical capital stock accumulation and
embodied total factor productivity growth. Human capital is captured by including total
years of schooling (both male and female). We use this rather than initial primary or
secondary-school enrolment rates as the latter do not account for school drop-outs.
Convergence towards the long-run steady state growth path is controlled for by adding
initial real GDP per capita. Following Sala-i-Martin's findings discussed above, we
include a set of regional dummy variables, a dummy variable indicating political crisis 19
and the democracy index (both constructed from the POLITY IV dataset). To capture the
role of economic policy variables, we include a measure of the inflation rate (GDP
deflator); trade share of GDP (exports plus imports to GDP) and share of government
final consumption expenditure in GDP. The latter is also used as a proxy for capturing
market distortions induced by government's actions. M2 as a fraction of GDP is included
19 The dummy variable takes a value of I whenever special codes are assigned to the democracy indexDEMOC from POLITY IV. These special codes reflect a period of regime interruption, interregnum ortransition. See Table A.I.
40
to account for financial development, which is likely to be low where property and
contract rights are not properly enforced (Knack, 2006). Ethnic-linguistic
fractionalization is added to control for potential adverse effects of ethnic diversity on
choices of public policies. We introduce a new measure to capture the effect of
environmental vulnerability on growth, namely the Environmental Vulnerability Index
from the South Pacific Applied Geoscience Commission (SOPAC) and the United
Nations Environment Program (UNEP)2o.
Empirical evidence suggests a negative relationship between volatility in growth
and growth (Ramey and Ramey, 1995; Aizenman and Marion, 1999; Mobarak, 2005)
although evidence of a positive relationship has also been found (Kormendi and Meguire,
1985; Grier and Tullock, 1989). Growth volatility, by inducing uncertainty over future
profits, can lower growth by discouraging private investment by disappointment-averse
entrepreneurs (Aizenman and Marion, 1999). Aysan (2006) develops a model that shows
that growth volatility aggravates capital market imperfections and increases the cost of
borrowing that inhibits investment in more productive technologies by low-income
entrepreneurs and retards total factor productivity growth, thereby establishing a link
between volatility and growth through productivity. Loayza and Hnatkovska (2004) find
evidence of a negative volatility-growth relationship that is exacerbated in countries that
are poor, institutionally underdeveloped, undergoing intermediate stages of financial
development, or countries that are unable to conduct countercyclical fiscal policies. A
positive volatility-growth relationship can also be theoretically motivated on account that
first, higher volatility induces precautionary savings that in tum leads to higher
investment and higher growth (Mirman 1971) and second, that countries may have a
choice between high expected returns high variance technologies and low-expected
20 The original EVI index is transformed into a scale from 0 to 1. This enters as a fixed factor. Values arefor 2005. The index is made up of 50 indicators reflecting: cold, dry, hot, wet periods; high winds; seatemperatures; volcanoes; earthquakes; tsunamis; slides; land area; country dispersion; isolation; relief;lowlands; borders; ecosystem imbalance; environmental openness; migration; endemics; introductions;endangered species; extinctions; vegetation cover; loss of cover; habitat fragmentation; degradation;terrestrial and marine reserves; intensive farming; fertilisers; pesticides; biotechnology; productivityoverfishing; fishing effort; renewable water; sulphur dioxide emissions; waste production; wastetreatment; industry; spills; mining; sanitation; vehicles; population; population growth; tourists; coastalsettlements; environmental agreements and conflicts.
41
returns low vanance technologies such that countries with higher growth rates also
exhibit high volatility owing to investment in riskier technologies (Black 1987). More
recently, Kose, Prasad and Terrones (2005) presented evidence that the growth-volatility
relationship could be dependent on the extent of trade and financial integration of
countries into the global system. We thus control for volatility in growth. Following
Mobarak (2005), we use 2 alternative measures for volatility: the standard deviation of
growth (measured as growth in GDP per capita at constant 2005 US dollars) and the
inter-quartile range of the growth rate. Finally, we also control for external debt service
burden on growth. It is widely recognized that a severe external debt burden act as an
impediment to stable economic growth. On one hand, resources get diverted away from
growth-enhancing investment towards payment of debt and on the other the foreign debt
payments acts as a tax on future output and discourages private investment and
productivity (debt overhang hypothesis; Sachs (1986); IMF (1989); Deshpande (1997);
Karagol (2002».
To sum up, we estimate an economIC growth equation with the following
controls: regional dummy variables, initial real GDP per capita, gross capital formation as
a share of GDP, total years of schooling, inflation rate, share of trade in GDP, share of
government final consumption expenditure in GDP, M2 as a share of GDP, POLITY IV
democracy index, a dummy variable for political crisis, ethno-linguistic fractionalization
index, an environmental vulnerability index, debt service as a share of exports, a measure
of volatility in economic growth, aid per capita disbursed at constant prices and volatility
in aid per capita disbursed at constant prices. The latter is measured as the standard
deviation in aid disbursed per capita.
5.2 Controls in growth volatility equation
Based on the literature review in Section 2, we introduce 3 sets of determinants
for growth volatility. In the first set, we include measures of actual and potential
economic diversification. We construct a Herfindahl index of sectoral diversification
(based on agriculture, manufacturing, services and other as a share of GDP). Following
42
Mobarak (2005) we also include share of services in GDP to capture diversification in
services-dependent economies. We add the logarithm of population as a measure of size
of the economy to reflect the inherent larger potential for diversification in larger
economies. In the second set, we include measures that focus on sources of growth
volatility from trade patterns and different trade structures. We include share of fuel
exports in merchandise exports and fuel imports in merchandise imports to differentiate
between fuel-exporting and fuel-importing countries. Both the share of agricultural and
manufacturing exports in merchandise exports are added to account for export
diversification or alternatively for visible export-dependency on primary or manufactured
products. Instead of using a terms of trade shock variable, we use the ratio of
merchandise exports to imports. Introducing terms of trade on its own, in addition to the
aid disbursed measures, considerably reduces the sample size21• By using the rate of
change in the ratio of merchandise exports to imports22, we attempt to capture both
movements in the terms of trade (relative price index of exports to imports) as well as
movements in the relative quantity index of exports to imports. In the third set of
determinants, we control for external shocks, namely oil price shock (measured by
growth in oil prices); exchange rate shocks23 (measured by rate of change in nominal
exchange rate of the US dollar per unit of local currency), domestic agricultural shocks
(measured by volatility in a food production index), and financial shock24 (measured by
growth in net nominal financial flows).
21 The sample size drops to 32 observations if the terms of trade data from the IMF International financialStatistics are used and to 65 if the net barter terms of trade are used.
22 Changes in the ratio of exports to imports is a composite ofchange in export price relative to importprice weighted by export quantity relative to import quantity and change in exports quantity relative toimport quantity weighted by the terms of trade.
23 We opt for a nominal exchange rate series over the series of real effective exchange rate to maximizesample size. The literature recognizes that nominal exchange rate shocks can have real effects on aneconomy (Benczur and Konya, 2006).
24 Net financial flows in real terms were not available from the World Bank Development Indicator. It isacknowledged in the macro-economics literature that nominal shocks have can have real effects(Andersen, 2004).
43
5.3 Preliminary data analysis.
5.3.1 Growth and volatility in growth
As previously mentioned, we consider two measures of growth volatility: the standard
deviation of the economic growth rate and the inter-quartile range of the growth rate25.
Following Mobarak (2005), we also weigh each measure by 2 indicators: an indicator for
whether the growth rate changes sign within the 5-year interval and the frequency with
which the growth rate changes sign. By doing so, we give greater weight to observations
where growth was unstable within the 5-year interval, thereby capturing the higher
negative welfare costs associated with growth volatility when growth is highly unstable.
From Figure A.5.7 in the Appendix, we can notice the high correlation between the 2
alternative measures, standard deviation and inter-quartile range and the high correlation
between each measure and its weighted value26. Weighing the measures of growth
volatility reduces the dispersion in the data and should lead to more precise estimates on
the coefficients in the growth volatility equation but not on the coefficients on growth
volatility in the growth equation estimation. The standard deviation of the standard
deviation and inter-quartile range of growth rate in the sample are 3.52 and 3.80
respectively compared to 1.89 and 2.19 for their weighted values.
To maximize the sample size, the starting sample of 127 developing countries was
divided into 9 periods of five year intervals (from 1961-1965 until 2001-2005) instead of
decade intervals and values were averaged for each five year interval. While decade
intervals will have yielded less biased estimates of mean and volatility measures for
growth and aid, we are constrained due to limited data availability on aid disbursements
to average over five years instead27• Using five year intervals rather than 10 year
25 This is the difference between the largest and smallest values. It is likely to be very sensitive to extremevalues as only two values are used.
26 The simple correlation coefficient in the whole sample between standard deviation and inter-quartilerange of growth rate is 0.79 and between their weighted values, it is 0.87.
27 As Rajaram and Subramanian note in their aid paper (2005, Page 7): "In order to have enoughobservations to estimate panel regressions, however, we will have to bow to fashion and examine five yeargrowth horizons".
44
intervals yields a lower percentage of countries for which growth changed sign in any
given period28 (Table 6.1 vis Table 6.5) and thus lower values on the weighted volatility
measures. Using the inter-quartile range rather than the standard deviation measure in
general yields higher values for growth volatility for the period until 1990 (Table 6.6).
There are regional differences in mean growth and volatility in growth (Table 6.6). Sub
Saharan Africa exhibits lower mean growth but higher volatile growth relative to Latin
America and the Caribbean, and East Asia. There are also time differences (Table 6.6).
Growth became less volatile in the periods after 1990 (as compared to the prior 2
decades) and markedly lower in the 2001-2005 periods. Mean growth was lowest in the
1981-1985 and 1991-1995 periods associated with world-wide recessions. We therefore
include both regional and time dummy variables in our estimations.
The simple correlation coefficient between growth and growth volatility varies
across time intervals and is negative in the five-year interval sample as a whole,
irrespective of the growth volatility measure used. The correlation coefficients were
larger and significant at a 5% level when the weighted measures are used (above 0.17)
and smaller when the standard deviation or inter quartile range were used (less than 0.09
and significant only when inter-quartile range is used). Our estimation results could be
sensitive depending on which growth volatility measure is used. We choose therefore to
report the regression results across all 4 growth volatility measures29•
5.3.2 Aid and volatility in aid
The data for aid was taken from the OECD DAC database. We consider aid
disbursed per capita (grants only). Mean aid per capita in the sample peaked in the
period from 1976 to 1985 at around US $78 and declined thereafter to reach the range of
US $ 50-60 from 1996 to 2005 (Table 6.6). Sub-Saharan Africa in the sample received
on average more than twice the aid per capita of Latin America and the Caribbean and
28 The percentage of countries for which growth changed sign in any given period ranged from 59% to 76%in the 5 year interval data compared to above 78% in the 10 year interval data.
29 When the data is averaged over 10 years, the correlation coefficient between growth and its volatility isnegative and significant at a 5% level only when the weighted growth volatility measures are used withvalues above 0.18.
45
more than East Asia. However a notable feature in the sample is the high positive
correlation between aid and volatility in aid, the latter measured as standard deviation in
aid30 (See Figure A.5.5. in the Appendix). Whether the data is averaged over 10 years
(Table 6.3) or five years (Table 6.7), we denote a strong positive correlation between aid
disbursed per capita and volatility in aid disbursed per capita. The correlations are
significant at a 5% level and are larger in the 10 year average data (0.85) than in the 5
year average data (0.65).
When we average the data over 10 years, and look at the 10 countries that
received the highest and lowest mean aid per capita (Table 6.4 for 1961-1970 and 1991
2000), we notice that the top 10 countries that receive the highest level of aid also exhibit
higher mean growth rate and higher volatile growth, as compared to the bottom 10
countries. The top 10 Countries that receive the most volatile aid exhibit higher mean
growth rate and in general more volatile growth as compared to the bottom 10 countries.
From Table 6.4, for both decades 1961-1970 and 1991-2000, we observe that there were
8 countries that were among both the top 10 countries with the highest aid per capita and
the top 10 countries with the most volatile aid (Cape Verde, Republic of Congo, Djibouti,
Kiribati, Liberia, Sao Tome and Principe, Solomon Islands and Vanuatu). There were 7
countries that were both among the top 10 countries with the lowest aid and the least
volatile aid namely Bhutan, Ethiopia, Haiti, Mali, Mozambique, Nepal and Sudan. While
no inference can be made about causality yet, there seems to be in the sample a positive
correlation between levels of aid per capita, its volatility and mean economic growth rate.
Figures A.l and A.2 in the appendix depict the top 10 countries with the highest and
lowest mean economic growth, volatile growth, aid levels and volatile aid for the decades
1961-1970 and 1991-2000 using 10 year average data. This preliminary analysis
indicates that it is important to disentangle the separate effects of aid and its
volatility on growth and growth volatility when establishing causal relationships
between aid and growth, given the high correlation between aid and aid volatility
and yet their potentially opposite effects on growth. From Figure A.5.1 in the
30 In small samples, the standard deviation may underestimate variation as opposed to inter-quartile range.We choose to use the under-estimated measure of aid variability in order not to bias the results in favorof the hypotheses chosen.
46
Appendix, there is evidence of a weak positive relationship between economic growth
and aid. The positive relationship is stronger in non-African countries. From Figure
A.5.2, there is evidence of a negative relationship however between economic growth
and aid volatility. Evidence of a negative relationship between aid and growth volatility
is apparent only in African countries (Figure A.5.3) while evidence of a positive
relationship between aid volatility and growth volatility is apparent from Figure A.5.4
except for African countries.
47
Tab
le6.
1
Ana
lysi
sus
ing
10ye
arav
erag
ed
ata
Flu
ctua
tion
sin
sign
ofgr
owth
rate
s
Neg
ativ
eG
row
thC
hang
ein
Sig
no
fgro
wth
*P
osit
ive
Gro
wth
Tot
al
1961
-197
0
Num
ber
ofc
ount
ries
066
1985
Did
itch
ange
sign
?Y
IN...
0.77
6..
.
Ave
rage
chan
gein
sign
s...
0.30
2..
.
1971
-198
0
Num
ber
ofc
ount
ries
083
1295
Did
itch
ange
sign
?Y
IN..
.0.
874
...
Ave
rage
chan
gein
sign
s...
0.32
6...
1981
-199
0
Num
ber
ofc
ount
ries
210
019
121
Did
itch
ange
sign
?Y
IN...
0.78
7...
Ave
rage
chan
gein
sign
s..
.0.
31...
1991
-200
0
Num
ber
ofc
ount
ries
210
915
126
Did
itch
ange
sign
?Y
IN...
0.86
5...
Ave
rage
chan
gein
sign
s...
0.30
0..
.
*Fro
mon
eye
arto
the
next
wit
hin
the
10ye
arpe
riod
.F
orea
chpe
riod
,on
lyco
untr
ies
wit
hfu
llda
tapo
ints
are
cons
ider
ed.
48
Tab
le6.
2M
easu
res
ofm
ean
and
vola
tili
tyin
grow
th
Vol
atil
ity
inG
row
th*
Sh
ift
inM
ean
Gro
wth
Rat
e*
Std
Dev
iati
onIn
ter-
qua
rtil
e19
71-1
980
to19
81-1
990
1981
-199
0to
1991
-200
0
All
4.75
5.14
2.05
to0.
770.
77to
1.48
Sub
-Sah
aran
Afr
ica
3.86
4.26
1.37
to(0
.26)
(0.2
6)to
0.56
Lat
inA
mer
ica
and
the
Car
ibbe
an3.
844.
302.
25to
0.33
0.33
to1.
86
Eas
tAsi
a3.
684.
024.
66to
3.34
3.34
to3.
72
*Ave
rage
over
all
coun
trie
sov
eral
l10
year
peri
ods
Tab
le6.
3E
con
omic
grow
th,a
idan
dvo
lati
lity
1961
-19
71-
1981
-19
91-
All
1970
1980
1990
2000
Aid
and
Vol
atil
ity
inA
id0.
847
0.86
00.
907
0.81
00.
761
Gro
wth
and
Vol
atil
ity
inG
row
th0.
005
0.21
6-0
.134
-0.2
480.
123
Not
e:V
alue
sar
esi
mpl
eco
rrel
atio
nco
effi
cien
tsbe
twee
nai
dan
dvo
lati
lity
inai
d,gr
owth
and
vola
tili
tyin
grow
th(s
tand
ard
devi
atio
no
fgro
wth
).
49
Tab
le6.
4E
cono
mic
grow
than
dvo
lati
lity
by
type
sof
aid
1961
-197
0
Mea
nG
row
th*
Vol
atil
ity
inG
row
th*
Std
Dev
iati
onIn
ter-
quar
tile
Hig
hA
id2.
245
4.77
56.
386
Low
Aid
0.31
64.
391
4.63
9
Sta
ble
Aid
0.78
74.
646
5.18
8
Vol
atil
eA
id1.
775
4.52
05.
836
Hig
hN
olat
ile
Aid
3...
......
Low
/Sta
ble
Aid
4...
......
*Top
10co
untr
ies
for
whi
chda
taar
eav
aila
ble.
**C
a1cu
late
dov
er10
year
aver
age
data
1.A
idw
ashi
ghan
dvo
lati
lefo
rC
ape
Ver
de,
Con
goR
ep,
Dji
bout
i,K
irib
ati,
Lib
eria
,Sa
oT
ome
and
Pri
ncip
e,S
olom
onIs
land
san
dV
anua
tu.
2.A
idw
aslo
wan
dst
able
for
Bhu
tan,
Eth
iopi
a,H
aiti
,Mal
i,M
ozam
biqu
e,N
epal
,an
dSu
dan.
3.G
row
thfi
gure
sw
ere
not
avai
labl
efo
rC
ape
Ver
de,
Dji
bout
i,K
irib
ati,
Sao
Tom
ean
dP
rinc
ipe,
Sol
omon
Isla
nds
and
Van
uatu
.
4.G
row
thfi
gure
sw
ere
nota
vail
able
for
Bhu
tan,
Eth
iopi
a,M
ali,
Moz
ambi
que. 50
1991
-200
0
Mea
nG
row
th*
Vol
atil
ity
inG
row
th*
Std
Dev
iati
onIn
ter-
quar
tile
Hig
hA
id2.
492
5.69
15.
354
Low
Aid
0.39
54.
676
4.86
1
Sta
ble
Aid
1.65
84.
205
4.54
8
Vol
atil
eA
id1.
708
5.75
65.
837
Hig
h/V
o1at
i1e
Aid
l0.
554
7.21
27.
793
Low
/Sta
ble
Aid
21.
724
4.11
84.
281
*Top
10co
untr
ies
for
whi
chda
taar
eav
aila
ble
**C
alcu
late
dov
er10
year
aver
age
data
1.A
idw
ash
igh
and
vola
tile
for
Cap
eV
erde
,C
ongo
Rep
,D
jibo
uti,
Kir
ibat
i,L
iber
ia,
Sao
Tom
ean
dP
rinc
ipe,
Sol
omon
Isla
nds
and
Van
uatu
.
2.A
idw
aslo
wan
dst
able
for
Bhu
tan,
Eth
iopi
a,H
aiti
,M
ali,
Moz
ambi
que,
Nep
al,a
ndS
udan
.
51
Tab
le6.
5
Ana
lysi
su
sin
g5
year
aver
age
data
Flu
ctua
tion
sin
sign
ofgr
owth
rate
s
Neg
ativ
eG
row
thC
hang
ein
Sign
ofgr
owth
Pos
itiv
eG
row
thT
otal
1961
-196
5
Num
ber
ofc
ount
ries
050
2575
Did
itch
ange
sign
?...
0.66
7...
Ave
rage
chan
gein
sign
s...
0.31
1...
1966
-197
0
Num
ber
ofc
ount
ries
057
2986
Did
itch
ange
sign
?...
0.66
3...
Ave
rage
chan
gein
sign
s...
0.35
3..
.
1971
-197
5
Num
ber
ofc
ount
ries
064
2488
Did
itch
ange
sign
?..
.0.
727
...
Ave
rage
chan
gein
sign
s...
0.37
4...
1976
-198
0
Num
ber
ofc
ount
ries
168
2290
Did
itch
ange
sign
?...
0.74
7...
Ave
rage
chan
gein
sign
s...
0.30
3...
1981
-198
5
Num
ber
ofc
ount
ries
983
1799
52
Neg
ativ
eG
row
thC
hang
ein
Sign
ofg
row
thP
osit
ive
Gro
wth
Tot
al
Did
itch
ange
sign
?...
0.76
1...
Ave
rage
chan
gein
sign
s...
0.33
9...
1986
-199
0
Num
ber
ofc
ount
ries
680
3211
8
Did
itch
ange
sign
?...
0.67
8...
Ave
rage
chan
gein
sign
s...
0.30
5...
1991
-199
5
Num
ber
ofc
ount
ries
586
3112
2
Did
itch
ange
sign
?..
.0.
705
...
Ave
rage
chan
gein
sign
s...
0.29
6...
1996
-200
0
Num
ber
ofc
ount
ries
281
4012
3
Did
itch
ange
sign
?...
0.65
9...
Ave
rage
chan
gein
sign
s...
0.30
0..
.
2001
-200
5
Num
ber
ofc
ount
ries
270
4611
8
Did
itch
ange
sign
?..
.0.
593
...
Ave
rage
chan
gein
sign
s...
0.21
6...
53
Tab
le6.
6M
easu
res
ofM
ean
and
Vol
atil
ity
inG
row
than
dA
id
Vol
atil
ity
inG
row
thM
ean
Gro
wth
Rat
eM
ean
Aid
per
Cap
ita
Mea
nV
olat
ility
inA
id
Std
Inte
r-
Dev
iati
onqu
arti
le
All
4.08
24.
205
1.72
264
.960
16.4
06
Sub
-Sah
aran
Afr
ica
4.59
44.
703
0.90
746
.776
13.2
37
Lat
inA
mer
ica
and
the
Car
ibbe
an3.
434
3.59
71.
644
20.6
528.
480
Eas
tAsi
a3.
247
3.46
53.
981
32.9
408.
398
Vol
atil
ity
inG
row
thM
ean
Gro
wth
Rat
eM
ean
Aid
per
Cap
ita
Mea
nV
olat
ilit
yin
Aid
Std
Dev
iati
onIn
ter-
quar
tile
1961
-196
54.
371
4.32
82.
679
43.4
3416
.132
1966
-197
03.
990
4.52
92.
726
58.1
0712
.218
1971
-197
55.
157
5.47
32.
271
65.7
2319
.319
1976
-198
05.
281
5.52
91.
982
78.3
8822
.053
1981
-198
54.
295
4.82
80.
116
78.5
2813
.222
1986
-199
04.
108
4.26
31.
336
77.2
0915
.155
1991
-199
54.
064
3.99
30.
945
69.1
4914
.583
1996
-200
03.
483
3.32
42.
026
51.7
097.
923
2001
-200
52.
648
2.44
12.
146
59.8
2827
.002
54
Table 6.7 Economic growth, aid and volatility
Note: Values are Simple correlatiOn coefficients between ald and volatility m ald, growth and volatility in
growth (standard deviation ofgrowth).
Aid and Volatility in Aid Growth and Volatility in Growth
All 0.653 -0.016
1961-1965 0.900 0.088
1966-1970 0.840 0.099
1971-1975 0.878 0.046
1976-1980 0.952 -0.354
1981-1985 0.822 -0.196
1986-1990 0.721 -0.248
1991-1995 0.511 -0.261
1996-2000 0.891 0.565
2001-2005 0.821 -0.151..
5.4 Regression results
5.4.1 Economic growth equation
Single OLS estimates: Tables A.2.l - A.2A report estimation results for the economic
growth equation, using four different measures of growth volatility (standard deviation
and inter-quartile range of economic growth rate and their weighted counterparts), using
three different types of estimation methods (pooled ordinary least squares OLS with
panel-level heteroskedasticity, pooled generalized least squares GLS with panel-level
heteroskedasticity and a random effects panel model RE31) and using both weighted and
unweighted equations. We weigh the equation by initial real GDP per capita under the
assumption that the variances of all the measured variables for a given country are
inversely related to the real GDP per capita of that country. We assume that less
developed countries, as compared to more developed countries, have less reliable data
collection techniques that yield larger measurement errors in the data collected.
31 A random effects model is used over fixed effects model
55
From Table A.2.l, using an unweighted regression, we first estimate by OLS the
growth equation without the measures of aid across countries. Columns 1 and 2 show the
results using measures of standard deviation and inter-quartile range of growth as growth
volatility measures. Almost all the explanatory variables enter with the expected signs
and are significant at a 5% level. There is evidence that growth volatility deters growth;
however the result is significant only when standard deviation is used as a measure of
growth volatility. Political crisis, inflation rate, government consumption and debt
service burden are significantly detrimental to growth. There is evidence of convergence
in the sample, which is a robust finding in the literature. Democracy, gross capital
formation as a share of GDP and years of schooling are significantly positively related to
economic growth. There is no evidence that trade or M2 as a share of GDP are correlated
with growth. Ethnic fractionalization and environmental vulnerability enter with the
negative expected signs but are not significant. When aid and volatility in aid are added
as determinants of economic growth the sample size drops from 402 to 118 observations.
The only variables that remain significant at a 10% leveez in the smaller sample size are
initial GDP per capita and gross capital formation as a share of GDP. 3 variables change
in sign notably growth volatility, inflation rate and environmental vulnerability, with the
first two not being significant. At a 10% level of significance, aid disbursed per capita
significantly raises economic growth while volatility in aid significantly deters economic
growth as hypothesized in Section 3. With the addition of the aid variables, the proxy for
education (years of schooling) as well as debt service burden become insignificant.
Given that the education sector has traditionally been the leading socio-economic sectoral
recipient of aid (see Table 3) and that debt relief has grown to be a significant sectoral
use of aid over the fast 20 years (see Table 3), there is tentative evidence to suggest that
part of the positive impact of aid on economic growth could be taking place through the
channel of human capital accumulation and the granting of debt relief. These results on
the significance of the aid variables remain unchanged at a 10% level when the weighted
measures of growth volatility are used and estimation is done either through OLS, GLS
or a random effects modee3. The results also hold when the regression equation is
32 We will use from now onwards the 10% level of significance to report the regression results.33 For the random effect model, the volatility in aid variable is significant for p values above 12%.
56
weighted using initial real GDP per capita (Table A.2.2). In this case both aid and
volatility in aid are significant even at a 5% level of significance with the expected signs.
The literature on aid has recognized the contribution of aid to economic growth
through the investment channel. One postulate is that aid stimulates growth by providing
the finance for investment in addition to domestic savings and other foreign capital flows
(Papanek 1973, Hansen and Tarp 1999). As a robustness check, we add another measure
of foreign flows to the economic growth equation, namely foreign direct investment as a
share of GDp34. Both aid disbursed per capita and volatility in aid remain significant, the
first raising economic growth and the second lowering it (Tables A.2.3 and A.2A).
Foreign direct investment enters the equation significantly only in the unweighted
regression. There is strong evidence from these single equation estimates to suggest
that an increase in aid by one constant US dollar raises real economic growth by an
estimated 0.04 to 0.05 percentage points and that a one unit increase in the standard
deviation of aid reduces real economic growth by a magnitude in the range of 0.05 to
0.09 points. As an example, if say aid to a given country within the 5-year interval
averages US $ 32 and its standard deviation increases from say 10 to 11, so that within
the 5 year period, aid no longer ranges from $22 to $ 42 but from $ 21 to $ 43, then this
increased volatility more than wipes out the positive effect of a dollar increase in aid on
economic growth.
Two-Stage Least Squares (2SLS) estimates: Table A.2.8 reports results for the weighted
regression where aid, volatility in aid and volatility in growth are instrumented to control
for potential endogeneity either between the aid and volatility variables and the
disturbance term (due to omitted variables) or between the aid and volatility variables and
the dependent variable. We choose a list of instruments for aid based on previous
findings in the aid allocation literature. As argued previously, aid is allocated based both
on "recipients' needs" as well as "donors' interests". Key factors behind donors'
34 Remittances can also be an important source of financial flows for some developing countries; howeverit is recognized that remittances finance mainly private consumption rather than productive investment.Its effect on growth through financial development or investment can be captured by the proxyexplanatory variable used in the growth equation to measure financial depth or investment. Due tolimited data availability, we do not explicitly include it in our estimations.
57
interests are political and strategic motives for giving aid (such as former colonial ties,
trade and investment relations, military exports). Donors such as the World Bank can
also give aid conditional on what they perceive as good policies (e.g. open trade and low
inflation), good governance and democratic reforms being pursued by countries.
Recipients' needs will include recipients' poverty levels and quality of life indicators
such as life expectancy and infant mortality rate. Boone (1995) finds evidence that aid is
allocated based on country size (proxied by size of population) and that aid allocation has
a permanent component. The literature on the determinants of aid volatility on the other
hand is rather sparse. Fielding and Mavrotas (2005) as mentioned in Section 2
hypothesize that aid is likely to be more volatile in richer countries, countries with
smaller aid volumes, countries with better policy regimes and countries with poor
institutional quality. They find evidence that countries' size, level of aid dependency,
quality of political institutions and degree of openness are factors affecting either sector
aid volatility or programme aid volatility. We estimate the growth equation again using
two-stage least squares based on the assumption that only aid, volatility in aid and
volatility in growth are endogenous. We select a set of instruments that are based on
previous literature and that satisfy the Hansen J statistics test for validity of instruments at
a 5% levees. The instruments are given below.
These are:
1) For aid: first, a set of factors reflecting donors motives for giving aid such as a).
colonial dummy variables to account for colonial ties; b). distance from the
equator as a deep determinant of institutional quality that influences quality of
govemance36 in the recipient country and that donors take into account for
dispensing aid and c). an interactive term between initial aid disbursed and the
democracy index to account for aid allocation that is made conditional by donors
35 From now onwards we use a 5% level of significance to report the Hansen J statistics.
36 Distance from equator has been used in the growth literature as a deep determinant of institutional quality(Rodrik et aI, 2002). Rodrik et al (2002) found that distance from the equator affects economic growththrough its impact on trade and institutions but does not have a significant impact on growth on its own.We use this variable as an instrument for institutional quality but do not include it in the growthequation.
58
on quality of good governance or democratic governance. Second, a set of factors
that reflect recipients' needs such as: d). quality of life indicators namely infant
mortality rate and life expectancy. Third, a factor to account for any persistent
trend in aid allocation such as: e). initial aid disbursed at the start of the 5 year
period to account for permanent trends in aid allocation. The exogenous controls
in the growth equation such as democracy index, trade openness, inflation rate
and initial GDP per capita complement that list of instruments: the first as an
indicator of quality of governance, the second and third as indicators of good
policies and the fourth as an indicator of recipients' level of development needs.
2) For volatility in aid: we include a few of the controls used in the growth volatility
equation, namely population to account for size of country following Fielding and
Mavrotas (2005). Variables such as a fractionalization index of sectoral
diversification, share of services in GDP, shock to crude oil price, volatility in
food production and shock to the ratio of merchandise exports to imports are
included to reflect potential structural and external vulnerabilities of the economy
to shocks that can destabilize economic growth and create an exceptional demand
for aid, especially emergency or humanitarian aid. As Guillaumont and Chauvet
(2008) argue, when aid is given to stabilize shocks, it is also likely to have a
volatile profile. We use these variables as well to instrument for volatility in
growth.
Table A.2.8 reports the pooled two-stage least squares results for a weighted
regression that includes foreign direct investment as a share of GDP in the growth
equation3? Both aid and volatility in aid remain significant after instrumentation at a
10% leve138, albeit at larger magnitudes. Aid disbursed per capita significantly raises
economic growth (an increase in aid per capita by one constant dollar raises economic
growth by a magnitude of about 0.05 percentage points, which is comparable to the
37 The standard errors are assumed to be panel heteroskedastic and clustered by country.
38 Except for the equation with inter-quartile range as the measure of growth volatility where the p-value onthe volatility in aid variable is 11.6%. In the unweighted regression, aid is significant at a 5% level andvolatility in aid is significant at a 10% level, whether the growth volatility measure is standard deviation,inter-quartile range or their weighted counterparts. In all 4 cases, the Hansen J test statistic has p valuesabove 0.40. The null that the instruments are valid cannot be rejected even at a p-value of 0.40.
59
impact of an increase in domestic investment by one per cent of GDP on growth) while
volatility in aid significantly lowers economic growth (an increase in the standard
deviation of aid by one unit lowers economic growth by about 0.11 to 0.13 percentage
points)39. The Shea partial R-squared of the first-stage regressions of the instrumented
variables are given below. The instruments explain aid better than volatility in aid or
growth volatility and consequently we observe stronger results in terms of significance
for aid rather than the volatility variables.
Table 7.1 Shea Partial R squared
First -Stage Regressions of Growth Equation
Weighted Regressions*
Measure of Growth Volatility Aid Equation Volatility in Volatility in
Aid Equation Growth Equation
Standard Deviation 0.4637 0.0877 0.1273
Inter-quartile Range 0.4566 0.1060 0.0857
Weighted Std Dev 0.4559 0.0872 0.1657
Weighted Inter-quartile 0.4405 0.1007 0.1552
5.4.2 Volatility in growth equation
Single OLS estimates: Table A.2.5 reports estimation results for an unweighted growth
volatility equation for the different measures of growth volatility and using OLS, GLS
and a random effects model4o. Based on columns 3-8, we find evidence at a 10% level
that aid significantly lowers growth volatility and that aid volatility significantly raises
growth volatility, irrespective of the measure of growth volatility used. The significance
is maintained in most cases at a 5% level as well. From columns 3-8, factors that
significantly raise growth volatility include: trade openness, government consumption,
39 When a random effects two-stage least squares model was used instead of the pooled two-stage leastsquares, aid remains positively significantly related to growth and volatility in aid remains significantlynegatively related to growth at the 10% level.
40 The conventional Hausman test to check for the efficiency of the coefficients of the random effectsmodel does not produce valid results due to a non-positive definite variance covariance matrix. Insteadthe F version of the test was computed for the weighted growth and growth volatility regressions (seeTables A.2.4; A.2.6 and A.2.7).
60
lack of sectoral diversification, large populations, dependency on fuel imports and oil
price shocks. Factors that significantly lower growth volatility include democracy and
ethnic fractionalization. We find partial evidence that dependency on services and a debt
service burden lowers growth volatility, the latter indicating that countries may be
borrowing in order to smooth shocks to growth. When the regression is weighted (Table
A.2.6), the significance of the aid variables holds in almost all cases41 with aid lowering
growth volatility and aid volatility raising it. However the sample size is small with only
89 observations due to the inclusion of a large number of explanatory variables in the
equation.
In Table A.2.7, we repeat the estimation of the weighted regression equation on a
bigger sample of 141 observations with a narrower set of variables. The variables on fuel
exports and fuel imports are dropped, under the assumption that shocks to oil prices
should capture the influence of shock in fuel prices on growth volatility. The variables
on share of agriculture and manufacturing exports in merchandise exports are also
dropped, under the assumption that the variables on sectoral diversification of GDP and
share of services in GDP will control for the effect of economic diversification on growth
volatility. In this larger sample, we do not find significant evidence from single equation
estimates that aid lowers growth volatilitl2 (the coefficient on aid is always negative but
not significant). We find only partial evidence that aid volatility significantly raises
growth volatility43 (the coefficient on aid volatility is always positive but not always
significant). We retain this narrower set of variables in further estimations in order to
keep the sample size above 100.
Two-Stage Least Squares (2SLS) estimates: When both aid and volatility in aid are
instrumented using the same set of instruments as those for the economic growth
41 Aid is not significant when weighted inter-quartile is used. Volatility in aid is marginally significantwith a p value of 0.119 when weighted inter-quartile is used in OLS and not significant when weightedinter-quartile is used in a random effects model.
42Aid significantly lowers growth volatility only when weighted inter-quartile is used as the measure ofgrowth volatility and estimation is done by OLS and in a random effects model.
43 Aid volatility is not significant when weighted standard deviation is used and when weighted interquartile is used in OLS and a random effects model.
61
equation, there is now strong evidence at the 10% level for the weighted regression in the
larger sample that aid volatility raises growth volatility (Table A.2.9). There is partial
evidence that aid significantly lowers growth volatility; however this result is sensitive to
the growth volatility measure used. We find that an increase in aid per capita by one
constant dollar lowers the standard deviation or inter-quartile range of growth by 0.02
0.04 units; that an increase in the standard deviation or inter-quartile range of aid by one
unit raises the standard deviation or inter-quartile range of growth by 0.10 to 0.21 units
and this latter result is comparable to the impact of an agricultural shock (measured by
volatility in food production) on growth volatility.
Table 7.2 Shea Partial R squared
First -Stage Regressions of Growth Volatility Equation
Weighted Regressions*
Measure of Growth Volatility Aid Equation Volatility in
Aid Equation
Standard Deviation 0.5873 0.1527
Inter-quartile Range 0.5873 0.1527
Weighted Std Dev 0.5873 0.1527
Weighted Inter-quartile 0.5873 0.1527
5.4.3 Systems Equations
Table A.2.l0 reports results when the two-equation system is simultaneously
estimated using three-least stage least squares (3SLS) for a weighted regression44. Given
that both growth and growth volatility are "two moments of the same underlying income
process" (Mobarak, 2005) and are likely to be jointly determined (any shocks that affect
economic growth will also affect growth variability), we give greater weight to the 3SLS
results than to the single equation estimates. Also given that data reliability is likely to
vary across countries, we also give greater credence to the results from the weighted
regression than those from the un-weighted regressions. Greater weight is given to
44 The larger sample is used and foreign direct investment as a share of GDP is added to the growthequation.
62
results from the weighted growth volatility measures that reflect the higher welfare costs
of growth volatility. The same list of instruments from the previous two-stage least
squares estimations are used for aid and aid volatility. From the growth equation, the
3SLS estimation gives strong evidence that aid raises economic growth (an increase in
aid by one constant dollar raises growth by an estimated 0.04-0.05 percentage points)
while aid volatility lowers economic growth (a one unit increase in the standard deviation
of aid lowers growth by an estimated 0.11 to 0.22 percentage points). The results for aid
are significant at a 1% level while the results from aid volatility are significant at a 5%
level. From the growth volatility equation, 3SLS gives strong evidence at a 2% level that
volatility in aid raises volatility in growth (a one unit increase in the standard deviation of
aid raises the standard deviation of growth volatility by 0.22 to 0.34 units) but there is
only partial evidence that aid lowers volatility in growth (this result is significant at a
10% level only when weighted inter-quartile is used). The latter weaker result coincides
with Bulir and Hamman's findings that aid is not being used by donors to cushion against
large negative GDP shocks, thereby indicating that this stabilizing use of aid should be
given greater consideration by donors. It will also indicate that aid volatility cannot be
explained in terms of aid being used in a counter-cyclical manner to buffer against
shocks. The results go against the argument made by Guillaumont and Chauvet (2008)
recently that not all aid volatility is bad because aid volatility may actually be the result
of donors using aid to cushion against shocks such that aid volatility may actually be
having a stabilizing effect on development. The joint test of significance on the aid
variables shows that aid and aid volatility matter in explaining jointly economic growth
and its variability.
The growth equation confirms that both Sub-Saharan Africa and Latin America
and the Caribbean have lower economic growth than East Asia. Gross capital formation
as a share of GDP is a significant driver of economic growth. There is no evidence
however that growth volatility deters growth after controlling for aid volatility. Debt
service burden significantly retards growth. This implies that aid that goes towards
providing debt relief indirectly promotes growth besides having a positive impact on
growth through other channels. From the growth volatility equation, there is firm
63
evidence that initial real GDP per capita and democracy are negatively related to growth
variability and that political crisis fosters growth volatility. From the first-stage
regressions (which are not reported) there is evidence at a 10% level that initial aid, oil
price shocks and distance from equator are positively related to aid and that life
expectancy, trade openness and population are negatively related to aid. These results
confirm Boone's findings (2003) that there is a permanent component to aid allocation
and that higher aid flows to smaller countries. There is also evidence that countries with
a lower level of human development (a lower life expectancy) get higher aid levels.
However if distance from the equator is a proxy for poverty level (as it is often used for
in the growth literature), there is no evidence that aid is flowing to countries that are
necessarily poorer. There is no evidence that higher aid levels flow to countries that have
"good" policies, as defined by the World Bank (lower inflation rates or greater trade
openness). There is evidence that aid may be given to cushion the impact of oil price
shocks but not other types of shocks. Aid volatility in our sample is significantly
positively correlated at a 10% level only with environmental vulnerability and
significantly negatively related only with political crisis and nominal exchange rate
shocks. These results do not provide evidence that aid volatility is caused by exogenous
shocks. We do not find evidence that aid is being given to cushion exogenous shocks
that in tum account for aid volatility. This is again refutes the arguments of Guillaumont
and Chauvet (2008).
64
6. Caveats and Conclusions
This paper departs from previous studies in the aid effectiveness literature as it is
the first paper (to the best of our knowledge) that assesses aid effectiveness in terms of
simultaneously raising economic growth and lowering growth volatility. We find
evidence that aid that is volatile and hence unpredictable harms the economIC
development of developing countries. Volatile aid more than wipes out the positive
benefits of aid on economic growth and raises growth volatility thereby hindering
developing countries from embarking on a sustained growth path. We find that an
increase in aid per capita by one constant dollar raises economic growth by an estimated
0.04 to 0.05 points which is comparable to the impact of an increase in domestic
investment by one percentage of GDP on growth. An increase in aid volatility by one
unit lowers economic growth however by about 0.11 to 0.22 points and raises growth
volatility by about 0.22 to 0.34 units, which is comparable to the impact of an agricultural
shock on growth volatility. While there is partial evidence that aid itself may be lowering
growth volatility, the results are not strong enough to allow us to conclude that aid is
contributing to cushion countries from negative macro-economic shocks. We also do not
find evidence that aid volatility can be attributed to the stabilizing effect of aid on
exogenous shocks and is therefore not necessarily "bad", thereby contradicting recent
findings by Guillaumont and Chauvet (2008). Our results seem to confirm Bulir and
Hamman (2006) analysis that aid volatility is an institutional donor feature that needs to
be addressed by the international donor community if aid is to achieve its stated goal of
promoting economic development as stated by the OECD DAC. This paper supports the
calls of the UK government and the United Nations for making aid less volatile and more
predictable. Going back to Mark Waldman' statement cited at the beginning of this paper,
in order for aid not to be wasted and ineffective, there is a need for donors to honor their
aid pledges, minimize deviations from their aid disbursement schedules and provide high
and sustained aid levels to developing countries in order to allow such countries to
sustain its growth.
65
Given skepticism about cross-country growth empmcs, there is a need to
complement the findings of this paper by further work on aid effectiveness at a sector
level (e.g. impact of aid and aid volatility on education and health outcomes). There is
also a need to test for the sensitivity of the results of this paper by using decade average
data rather than five-year average data in the future in order to utilize less biased
estimates of volatility. Further work could also involve estimation with fixed effects and
a more parsimonious set of explanatory variables, a comprehensive exploration of the
sources of aid volatility (namely separating "good" sources of aid volatility from "bad"
sources of aid volatility), the incorporation of remittances as an explanatory variable, as
well as an investigation of any asymmetric impact of aid and aid volatility on growth and
growth volatility. There is a potential identification problem for small aid dependent
countries where aid is a major source of finance for economic growth. In these cases aid
volatility will be spuriously positively correlated with growth volatility. Further work
will need to control for countries by aid -dependency levels. There are many factors that
affect aid effectiveness and this paper provides preliminary evidence that aid volatility is
a major determinant of the effectiveness of aid in promoting stable growth.
66
References
Acemoglu, D. & Johnson, S. & Robinson, 1. The Colonial Origins of Comparative
Development: An Empirical Investigation. American Economic Review. 91(5).
Acemoglu, D. & Zilibotti, F. (1997). Setting Standards: Information Accumulation in
Development. CEPR Discussion Papers. No.1641.
Aizenman,1. & Marion, N. (1999). Volatility and Investment; Interpreting Evidence from
Developing Countries. Economica. 66:157-179.
Alesina, A. & OzIer, S. & Roubini, N. & Swagel, P. (1996). Political Instability and
Economic Growth. NBER Working PaperNo. W4173.
Andersen, T.M (2004). Real and Nominal Propagation of Nominal Shocks. Economic
Journal, Vol No. 114.
Arellano, C. & Bulir, A. & Lane, T. & Lipschitz, L. (2005). The Dynamic Implications of
Foreign Aid and Its Variability. International Monetary Fund. Working Paper
WP/05/119.
Aysan, A.F. (2006). The Effects of Volatility on Growth and Financial Development
through Capital Market Imperfections. MPRA Paper 5486, University Library of
Munich, Germany.
Barro, R.J. (1991). Economic Growth in a Cross-Section of Countries. Quarterly Journal
of Economics. Vol. 106 (2).
Barro, R.I. (2001). Economic Growth in East Asia Before and After the Financial Crisis.
NBER Working Papers 8330, National Bureau of Economic Research, Inc.
67
Bejan, M. (2006). Trade Openness and Output Volatility. MPRA Paper 2759, University
Library ofMunich, Germany.
Benczur , P & Konya, 1. (2006). Nominal Growth of a Small Open Economy.
Department of Economics , Central European University.
Betancourt, R. (1996). Growth Capabilities and Development: Implications for Transition
Processes in Cuba. Economic Development and Cultural Change: 315-331.
Birdsall, N. (2004). Seven Deadly Sins; Reflections on Donor Failings. Center for Global
Government. Working Paper, No.50.
Bolaky, B. & Freund, C. Trade, Regulations and Growth. The World Bank. Policy
Research Working Paper No. 3255. Also in : Freund, C. & Bolaky, B. Trade, Regulations
and Income, Forthcoming Journal of Development Economics.
Boone, P. (1995). Politics and the Effectiveness of Foreign Aid. Center for Economic
Performance Discussion paper. No. 272.
Bordo, M. & Eichengreen, B. & Klingebiel, D. & Martinez-Peria, M. (2001). Is the Crisis
Problem Growing More Severe? Economic Policy._ 32:51-75.
Bulir, A. & Hamann, J. (2006). Volatility of Development Aid: From the Frying Pan into
the Fire? International Monetary Fund. Working Paper WP/06/05.
Bulir, A. & Hamann, J. (2003). Aid Volatility: An Empirical Assessment. International
Monetary Fund StaffPapers. Vo1.50 (1).
Burnside, C . & Dollar, D. (2000). Aid, Policies and Growth. American Economic
Review. Vol. 90 (4).
68
Calvo, G. (2001). Capital Markets and the Exchange Rate: with special reference to the
dollarization debate in Latin America. Journal of Money, Credit and Banking, May 2001.
Also in G. Calvo Emerging Capital Markets in Turmoil: Bad Luck or Bad Policy? MIT
Press.
Cavallo, C. (2007). Output Volatility and Openness to Trade: A Reassessment. RES
Working Papers 1033, Inter-American Development Bank, Research Department.
Chauvet, L. & Guillaumont, P. (2008). Aid, Volatility and Growth Again. When Aid
Volatility Matters and When It Does Not. CERDI Working Paper. No. 20077.
Chauvet,L. & Guillaumont, P. (2002). Aid and Growth Revisited: Policy, Economic
Vulnerability and Political Instability. CERDI Working Paper. No. 200327.
Clarke, M. & Fry, T. & Mihajilo, S. (2007). Aid Allocation Volatility to Small Island
States. United Nations University WIDER Research paper No. 2007/18.
Clemens, M & Moss, T. (2005). Ghost of 0.7%: Origins and Relevance of the
International Aid Target. Working Papers 68, Center for Global Development.
Collier, P & Dehn, J. (2001). Aid, shocks, and growth. The World Bank. Policy Research
Working Paper Series No. 2688.
Collier, P. & Dollar, D. (2004). Development Effectiveness: What have we learnt?
Economic Journal. Vol. 114 (496).
Collier, P. & Dollar, D. (2001). Can the world cut poverty in half? Row policy reform
and effective aid can meet international development goals. World Development 29 (11).
Collier, P. & Roeffler, A. (2004). Aid, policy and growth in post-conflict societies.
European Economic Review. Vol. 48(5).
69
Cuaresma, J.C & Hlouskova, J. & Obersteiner, M. (2005). Natural Disasters as Creative
Destruction? Evidence from Developing Countries. Economic Inquiry. Vol. 46. No.2.
Deshpande, A. (1997). The Debt Overhang and the Disincentive to Invest. Journal of
Development Economics. Vol. 52: 169-187.
Dollar, D. & Kraay, A. (2002). Institutions, Trade and Growth. Paper prepared for the
Carnegie-Rochester Conference Series on Public Policy.
Dollar, D. & Levin, V. (2004). Increasing selectivity of foreign aid, 1984-2002. The
World Bank. Policy Research Working Paper Series No. 3299.
Djankov, S. & McLiesh, C. & Ramalho, R.M. (2006). Regulation and Growth. The
World Bank.
Easterly, W. & Levine, R. (1997). Africa's Growth Tragedy: Policies and Ethnic
Divisions. Quarterly Journal ofEconomics. 112(4).
Easterly, W. (2001). Can Institutions Resolve Ethnic Conflict? Economic Development
and Cultural Change. Vol. 49 (4).
Fiaschi, D. & Lavezzi, A.M. (2003). Explaining Growth Volatility. Mimeo. University of
Pisa.
Fielding, D. & Mavrota, G. (2005). The Volatility of Aid. United Nations University
WIDER Discussion paper No. 2005/06.
Fischer, S. (1993). The Role of Macroeconomic Factors in Growth. Journal of Monetary
Economics. (32): 485-512.
70
Frankel, J. & Romer, D. Trade and Growth: An Empirical Investigation. NBER Working
Paper No. W5476. National Bureau of Economic Research, Inc.
Grier, K.B. & Tullock, G. (1989). An Empirical Analysis of Cross-National Economic
Growth. Journal of Monetary Economics. Vol 24: 259-276.
Guidotti, P. & Sturzenegger, F. & Villar, A. (2003). On the Consequences of Sudden
Stops. Economia. Vol.4 (2)
Guillaumont, P. & Chauvet,L. (2001). Aid and Performance: A Reassessment. The
Journal of Development Studies. Vol. 37 (6).
Gupta, P. & Mishra, D. & Sahay, R. (2003). Output Response to Currency Crises.
International Monetary Fund Working Paper. WP/03/230.
Guseh. 1. Government Size and Economic Growth in Developing Countries: A Political
Economy Framework. Journal of Macroeconomics. Vol. 19(1).
Hakura, D. (2007). Output Volatility and Large Output Drops in Emerging Market and
Developing Countries. International Monetary Fund Working Paper. WP/071114.
Hjertholm, P. & White, H. (2000). Survey of Foreign Aid: History, Trends and
Allocation. Discussion Papers. University of Copenhagen, Institute of Economics.
IMF. (1989). World Economic Outlook, Supplementary Note 1, Washington, USA.
Kaminsky, G. & Reinhart, C. & Vegh, C. (2004). When it Rains, it Pours: Procyc1ical
Capital Flows and Macroeconomic Policies. NBER Working Papers 10780, National
Bureau of Economic Research, Inc.
71
Karagol, E. (2002). A Critical Review of External Debt and Economic Growth
Relationships: A Lesson for Indebtedness Countries. Available at: http://
eab.ege.edu.tr/pdf/4/C4-S l-2-%20M7.pdf.
King, R. & Levine, R. Finance and Growth: Schumpeter Might be Right. Quarterly
Journal of Economics. 108 (3).
Knack, S. & Keefer, P. (1995). Institutions and Economic Performance: Cross-country
Tests Using Alternative Institutional Measures. Economics and Politics. Vol. 7 No.3.
Kormendi,R. & Meguire, P. (1985). Macroeconomic Determinants of Growth: Cross
country evidence. Journal of Monetary Economics. Vol. 16(2).
Kose, E. & Prasad, S. & Terrones, M. (2002). Growth and Volatility in an Era of
Globalization. International Monetary Fund Staff Papers. Vol. 52. Special Issue.
Lensink, R. & Morissey, O. (2000). Aid Instability as a Measure of Uncertainty and the
Positive Impact of Aid on Growth. Journal of Development Studies. Vol.36 (3).
Levine, R. (1998). The Legal Environment, Banks, and Long-Run Economic Growth.
Journal of Money, Credit and Banking. Vol. 30 (3).
Levine, R. & Renelt, D. A Sensitivity Analysis of Cross-country Growth Regressions.
The American Economic Review. Vol. 82 No.4.
Loayza, N. & Hnatkovska, V.(2004). Volatility and Growth. The World Bank. Policy
Research Working Paper No. 3184.
Martens, B. & Mummert, U. & Murrell, P. & Seabright, P. (2002). The Institutional
Economics of Foreign Aid. Cambridge University Press.
72
Mauro, P. (1995). Corruption and Growth. Quarterly Journal of Economics. 110: 681
712.
Mauro, P. (1994). Corruption, Country Risk and Growth, paper presented at the XIII
Latin American Meeting of the Econometric Society, Venezuela, August 1994.
Mirrnan, L. (1971). Uncertainty and Optimal Consumption Decisions. Econometrica.
39(1).
Mobarak, AM. (2005). Democracy, Volatility and Development. The Review of
Economics and Statistics. Vo. 87 (2).
Mukerji, P. (2004). Ready for Capital Account Convertibility? PhD. Dissertation.
Department of Economics. University of Maryland at College Park.
North, D. (1989). Institutions and Economic Growth: An Historical Introduction. World
Development. Vol. 17(9).
Noy, 1. (2007). The Macroeconomic Consequences of Disasters. Working Papers
200707, University of Hawaii at Manoa, Department of Economics.
Olson, M. (1991). Autocray, Democracy and Prosperity. In Strategy and choice, edited
by Richard J. Zeckhauser. MIT Press, pp. 131-57.
Pallage, S. & Robe, M.A (2001). Foreign Aid and the Business Cycle. Review of
International Economics. 9 (4).
Pallage, S. & Robe, M.A (2003). On the Welfare Cost of Economic Fluctuations in
Developing Countries. International Economic Review. 44 (2).
73
Pallage, S. & Robe, M.A. & Berube, C. (2005). On the Potential of Foreign Aid as
Insurance. International Monetary Fund Staff Papers. 53 (3).
Pritchett, L. (2000). Understanding Patterns of Economic Growth: Searching for Hills
among Plateaus, Mountains and Plains. World Bank Economic Review. 14(2).
Ramey, G. & Ramey, V. (1995). Cross-country Evidence on the Link Between Volatility
and Growth. American Economic Review. 85(5).
Raddatz, C. (2007). Are External Shocks Responsible for the Instability of Output in
Low-income Countries? Journal ofDevelopment Economics. Vol. 84 (1).
Rajan, R. & Subramanian, A. (2005). Aid and Growth: What Does the Cross-Country
Evidence Really Show? International Monetary Fund Working Paper. WP/05/127.
Reynolds, L. (1983). The Spread of Economic Growth to the Third World: 1850-1980.
Journal of Economic Literature. 21: 941-980.
Rodrik, D. & Subramanian, A. & Trebbi, F. (2002). Institutions Rule: The Primacy of
Institutions over Geography and Integration in Economic Development. NBER Working
Papers 9305, National Bureau of Economic Research, Inc.
Rostow, W. (1990). The Stages of Economic Growth: A Non-Communist Manifesto.
Third Edition. Cambridge University Press.
Sachs, J. D. & Gallup, J.L & Mellinger, A. (1998). Geography and Economic
Development. In Pleskovic, Boris and Joseph E. Stiglitz (eds.), Annual World Bank
Conference on Development Economics. The World Bank.
Sachs, J. (1986). Managing the LDC Debt Crisis. Brookings Papers on Economic
Activity, Economic Studies Program, The Brookings Institution, Vol. 17.
74
Sala-i-Martin, X. (1997). I Just Ran Two Million Regressions. The American Economic
Review. Vol. 87. No.2.
Sala-i-Martin, X. & Doppelhofer, G. & Miller, R.I. (2004). Determinants of Long-Term
Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach. American
Economic Review. Vol. 94(4).
Svensson, 1. (1999). Aid, Growth and Democracy. Economics and Politics, Vol. 11 (3).
Tarp, F. (2000). Foreign Aid and Development: Lessons learnt and directions for the
future. Edited by Finn Tarp. (Routledge).
Waldman M. (2008). Falling Short: Aid Effectiveness III Afghanistan. ACBAR
Advocacy Series.
Woolridge, 1. (2002). Econometric Analysis of Cross-section and Panel Data. First
Edition. MIT Press.Pages 290-291.
World Bank. (2001). World Development Report 2000/2001: Attacking Poverty. Oxford
University Press.
World Bank. (2007). Global Development Finance Report: The Globalization of
Corporate Finance in Developing Countries.
Zinnes, C. & Bolaky, A. (2002). Harnessing the Power of Incentives: An Institutional
Framework for Aid Effectiveness. Center on Institutional Reform and the Informal
Sector. Working Paper and Research Brief.
75
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93
Tab
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.IS
umm
ary
Des
crip
tive
Sta
tist
ics
Sam
ple
of5
year
inte
rval
sfr
om19
60to
2005
Sour
ce:
Wor
ldB
ank
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men
tIn
dica
tors
unle
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herw
ise
spec
ifie
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iabl
es(N
oo
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nS
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alue
Max
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ueR
emar
ks
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iati
on
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cono
mic
grow
thra
te0.
516
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2-6
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DP
per
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taat
cons
tant
2000
US$
2In
ter-
quar
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rang
eo
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wth
rate
4.15
03.
624
0.24
117
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Ref
ers
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mic
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e
(unw
eigh
ted)
3S
tand
ard
devi
atio
no
fgro
wth
rate
3.85
22.
570
0.30
511
.361
Ref
ers
toE
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mic
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wth
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e(u
nwei
ghte
d)
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ted
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r-qu
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lera
nge
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th1.
852
2.80
10
17.4
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eeen
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te#1
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ran
expl
anat
ion
ofw
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ting
rate
proc
edur
eus
ed.
5W
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ted
stan
dard
devi
atio
no
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wth
1.69
21.
993
011
.361
See
end
note
#10
for
anex
plan
atio
no
fwei
ghti
ng
rate
proc
edur
eus
ed.
6In
itia
lre
alG
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ta26
9.69
397
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139.
468
641.
934
Val
ues
atst
art o
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GD
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cons
tant
2000
US
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7D
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029
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tire
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arpe
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eob
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atio
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urce
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proj
ect,
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hich
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her
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cale
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led
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21S
hare
ofa
gric
ultu
rale
xpor
tsin
55.3
7729
.258
1.19
296
.475
mer
chan
dise
expo
rts
22S
hare
ofm
anuf
actu
ring
expo
rts
in22
.378
21.6
550.
954
84.5
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erch
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port
s
23S
hock
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gera
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861
2.10
5-3
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6.29
9G
row
thra
tein
Off
icia
lex
chan
gera
te.
LC
Upe
rU
S$.
24S
hock
into
taln
etfi
nanc
ial
flow
s-2
3.48
051
2.96
3-3
963.
034
489.
956
Gro
wth
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ein
Cur
rent
US
$
25F
orei
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rect
inve
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ent(
Net
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ows)
1.53
81.
992
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1210
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fGD
P
96
Var
iabl
es(N
oo
fOb
s=
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Mea
nS
tdM
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alue
Max
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ueR
emar
ks
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iati
on
26S
hock
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tio
of
mer
chan
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617
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-18.
249
102.
161
Gro
wth
Rat
e
toim
port
s
27S
hock
incr
ude
petr
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mpr
ice
12.8
3117
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7558
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Gro
wth
Rat
e
28G
ener
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men
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alco
nsum
ptio
n13
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139
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pend
itur
e(%
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DP
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urce
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hili
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oede
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noli
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stic
Fra
ctio
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LF)
Indi
ces,
1961
and
1985
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ownl
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ary
16,2
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oney
asa
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fGD
P21
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27.
168
41.6
79
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ife
expe
ctan
cyat
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h(t
otal
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s)48
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536
.920
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70V
alue
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star
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iest
year
for
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chda
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ble
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ally
seco
ndye
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lity
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fant
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ve11
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ally
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33D
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nce
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tor
12.3
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962
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atit
ude
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ty
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ww
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om
97
Var
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Mea
nS
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Max
Val
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emar
ks
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iati
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34V
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per
capi
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8.49
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057
0.57
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ndar
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sed
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Dis
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ts-g
rant
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ly
35A
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32.6
5816
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5.08
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21.h
tm
Not
es:
1.T
hest
arti
ngsa
mpl
eco
nsis
ted
of
127
coun
trie
sfo
rth
epe
riod
1960
to20
05fo
ra
pote
ntia
lm
axim
umsa
mpl
eo
f11
43ob
serv
atio
ns.
The
abov
eca
lcul
atio
nsar
eba
sed
ona
sam
ple
of7
0ob
serv
atio
nsfo
rw
hich
data
for
all
the
list
edva
riab
les
are
avai
labl
e.T
hesa
mpl
eis
divi
ded
into
9fi
veye
arin
terv
als:
1961
-196
5;19
66
1970
;19
71-1
975
and
soon
unti
l20
01-2
005.
The
sam
ple
ofc
ount
ries
excl
udes
Wes
tern
Eur
ope
and
Nor
thA
mer
ica,
Japa
n,an
dO
PE
Cco
untr
ies
(all
indo
nor
coun
try
grou
p)an
dco
untr
ies
that
wer
eno
tin
exis
tenc
eat
the
star
toft
hesa
mpl
epe
riod
(e.g
.B
alti
cR
eps,
exU
SS
R,
Rus
sia,
Tim
or-L
este
).
2.T
ime
and
regi
onal
dum
my
vari
able
sar
eno
tinc
lude
din
the
abov
eta
ble.
3.A
llva
lues
are
aver
aged
over
the
5ye
arpe
riod
unle
ssot
herw
ise
indi
cate
d.
4.H
ighe
rva
lues
onth
eH
erfi
ndah
llnd
exin
dica
tele
sser
sect
oral
dive
rsif
icat
ion.
5.T
hedu
mm
yfo
rpo
liti
cal
cris
isis
crea
ted
from
the
spec
ial
code
sas
sign
edto
the
Dem
ocra
cyIn
dex
from
Pol
ity
IVpr
ojec
t,in
case
of
regi
me
inte
rrup
tion,
tran
sitio
nor
inte
rreg
num
s.
6.A
llda
taso
urce
sar
efr
omth
eW
orld
Ban
kD
evel
opm
entI
ndic
ator
data
base
unle
ssot
herw
ise
spec
ifie
d.
7.N
ote
onth
eS
choo
ling
vari
able
used
:T
heto
tal
year
so
fsch
ooli
ngre
fer
toav
erag
esc
hool
ing
year
sin
the
mal
ean
dfe
mal
epo
pula
tion
aged
15an
dab
ove
take
nfr
omth
eB
arro
-Lee
data
base
.D
ata
are
avai
labl
efo
rth
eye
ars
1960
,19
65,
1970
,19
75,1
980,
1985
,199
0,19
95an
d19
99.
8.G
ross
capi
tal
form
atio
n(f
orm
erly
gros
sdo
mes
ticin
vest
men
t)co
nsis
tso
fout
lays
onad
diti
ons
toth
efi
xed
asse
tso
fthe
econ
omy
plus
net
chan
ges
inth
ele
vel
of
inve
ntor
ies.
Fix
edas
sets
incl
ude
land
impr
ovem
ents
(fen
ces,
ditc
hes,
drai
ns,
and
soon
);pl
ant,
mac
hine
ry,
and
equi
pmen
tpu
rcha
ses;
and
the
cons
truc
tion
of
98
road
s,ra
ilw
ays,
and
the
like,
incl
udin
gsc
hool
s,of
fice
s,ho
spit
als,
priv
ate
resi
dent
ial
dwel
ling
s,an
dco
mm
erci
alan
din
dust
rial
buil
ding
s.In
vent
orie
sar
est
ocks
of
good
she
ldby
firm
sto
mee
ttem
pora
ryor
unex
pect
edfl
uctu
atio
nsin
prod
ucti
onor
sale
s,an
d"w
ork
inpr
ogre
ss."
Acc
ordi
ngto
the
1993
SNA
,net
acqu
isiti
ons
of
valu
able
sar
eal
soco
nsid
ered
capi
tal
form
atio
n.
9.A
idre
fers
toO
FF
ICIA
LD
EV
EL
OP
ME
NT
AS
SIS
TA
NC
E(O
DA
)de
fine
das
:"G
rant
sor
Loa
nsto
coun
trie
san
dte
rrito
ries
onP
artI
oft
heD
AC
Lis
tofA
idR
ecip
ient
s(d
evel
opin
gco
untr
ies)
whi
char
e:(a
)un
dert
aken
byth
eof
fici
alse
ctor
;(b
)w
ith
prom
otio
no
fec
onom
icde
velo
pmen
tan
dw
elfa
reas
the
mai
nob
ject
ive;
(c)
atco
nces
sion
alfi
nanc
ial
term
s[i
fa
loan
,ha
ving
aG
rant
Ele
men
t(q
.v.)
of
atle
ast
25pe
rce
nt].
Inad
ditio
nto
fina
ncia
lfl
ows,
Tec
hnic
alC
oop
erat
ion
(q.v
.)is
incl
uded
inai
d.G
rant
s,L
oans
and
cred
itsfo
rm
ilit
ary
purp
oses
are
excl
uded
.T
rans
fer
paym
ents
topr
ivat
ein
divi
dual
s(e
.g.
pens
ions
,re
para
tion
sor
insu
ranc
epa
yout
s)ar
ein
gene
ral
not
coun
ted"
.
10.
Fol
low
ing
Mob
arak
(200
06),
the
mea
sure
ofv
olat
ilit
yin
grow
this
wei
ghte
dby
2in
dica
tors
:a
dum
my
(tak
ing
the
valu
eo
f0or
I)in
dica
ting
whe
ther
ther
ew
asa
chan
geo
fsig
nin
grow
thbe
twee
n2
cons
ecut
ive
avai
labl
eda
tapo
ints
wit
hin
the
time
peri
odon
lyan
dth
eav
erag
enu
mbe
ro
fcha
nges
insi
gn,m
easu
red
byth
epe
rcen
tage
ofy
ears
ther
ew
asa
chan
gein
sign
wit
hin
that
time
peri
od(5
year
).If
only
Igr
owth
data
poin
tis
avai
labl
epe
rti
me
peri
od,
itis
omit
ted
from
the
calc
ulat
ions
.
99
Tab
leA
.2E
stim
atio
nR
esul
ts
OL
SS
ingl
eE
quat
ion
Est
imat
es(T
able
sA
.2.I
.to
A.2
.7)
A.2
.I.
Gro
wth
Equ
atio
n(U
nwei
ghte
dE
stim
ates
)
Dep
ende
ntV
aria
ble:
Gro
wth
inM
easu
reo
fVol
atil
ity
inG
row
thde
note
din
Col
umns
GD
Pco
nst
ant
2000
US$
Poo
led
OL
Sw
ith
Pra
is-W
inst
enpa
nel-
corr
ecte
dst
and
ard
erro
rs*
Poo
led
Ran
do
mG
LS
*E
ffec
ts
Ex
pla
nat
ory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)
Std
Inte
r-S
tdIn
ter-
Wei
ghte
dW
eigh
ted
Wei
ghte
dW
eigh
ted
Dev
iati
onq
uar
tile
Dev
iati
onq
uar
tile
Std
Dev
Inte
r-In
ter-
Inte
r-q
uar
tile
qu
arti
lequ
arti
le
Inte
rcep
t0.
4146
0.00
921.
8909
1.57
111.
8674
1.72
482.
0731
1.72
48(0
.66)
(0.9
92)
(0.2
23)
(0.3
07)
(0.2
28)
(0.2
56)
(0.0
96)
(0.3
64)
Vol
atil
ity
ing
row
th-0
.110
7-0
.027
60.
0230
0.08
650.
0407
0.08
020.
1051
0.08
02(0
.036
)(0
.555
)(0
.709
)(0
.177
)(0
.651
)(0
.288
)(0
.095
)(0
.410
)
Du
mm
yfo
rS
ub
-Sah
aran
Afr
ica
-0.1
264
-0.1
209
-0.8
591
-0.8
269
-0.8
789
-0.8
915
-1.2
193
-0.8
915
(0.7
66)
(0.7
74)
(0.2
47)
(0.2
68)
(0.2
35)
(0.2
30)
(0.0
41)
(0.2
79)
Du
mm
yfo
rL
atin
Am
eric
a-0
.943
1-0
.870
9-1
.835
3-1
.869
5-1
.816
1-1
.847
6-1
.698
8-1
.847
6(0
.008
)(0
.016
)(0
.181
)(0
.177
)(0
.185
)(0
.178
)(0
.184
)(0
.216
)
En
vir
on
men
talv
ulne
rabi
lity
inde
x-0
.616
8-0
.473
83.
0795
3.28
423.
1215
3.32
683.
1498
3.32
68(0
-1sc
ale)
(0.5
09)
(0.6
13)
(0.0
62)
(0.0
48)
(0.0
59)
(0.0
46)
(0.0
20)
(0.1
11)
100
Dep
ende
ntV
aria
ble:
Gro
wth
inM
easu
reo
fVol
atil
ity
inG
row
thde
note
din
Col
umns
GD
Pco
nst
ant
2000
US$
Poo
led
OL
Sw
ith
Pra
is-W
inst
enpa
nel-
corr
ecte
dst
and
ard
erro
rs*
Poo
led
Ran
do
mG
LS
*E
ffec
ts
Ex
pla
nat
ory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)
Std
Inte
r-S
tdIn
ter-
Wei
ghte
dW
eigh
ted
Wei
ghte
dW
eigh
ted
Dev
iati
onq
uar
tile
Dev
iati
onq
uar
tile
Std
Dev
Inte
r-In
ter-
Inte
r-q
uar
tile
qu
arti
lequ
arti
le
Init
ial
GD
Pp
erca
pita
-0.0
002
-0.0
002
-0.0
049
-0.0
044
-0.0
050
-0.0
049
-0.0
052
-0.0
049
(0.0
85)
(0.0
52)
(0.0
44)
(0.0
59)
(0.0
38)
(0.0
38)
(0.0
08)
(0.0
83)
(con
stan
t20
00U
S$)
Dem
ocra
cyin
dex
(Pol
ity)
0.08
750.
0917
0.05
690.
0779
0.05
620.
0621
0.00
290.
0621
(0.0
36)
(0.0
30)
(0.4
62)
(0.3
23)
(0.4
56)
(0.4
09)
(0.9
59)
(0.5
25)
Du
mm
yva
riab
lefo
rpo
liti
calc
risi
s-1
.189
6-1
.327
8-0
.353
4-0
.548
3-0
.315
7-0
.328
4-0
.881
5-0
.328
4(P
olit
y)(0
.009
)(0
.004
)(0
.636
)(0
.449
)(0
.662
)(0
.646
)(0
.112
)(0
.646
)
Tra
de
asa
shar
eo
fGD
P-0
.006
1-0
.006
3-0
.012
8-0
.014
3-0
.012
6-0
.013
0-0
.011
8-0
.013
0(0
.225
)(0
.218
)(0
.146
)(0
.102
)(0
.152
)(0
.138
)(0
.061
)(0
.281
)
Infl
atio
nra
te(G
DP
defl
ator
)-0
.001
1-0
.001
10.
0088
0.00
830.
0082
0.00
750.
0060
0.00
75(0
.000
)(0
.000
)(0
.561
)(0
.584
)(0
.589
)(0
.625
)(0
.579
)(0
.584
)
Gro
ssca
pita
lfo
rmat
ion
assh
are
of
0.17
320.
1800
0.09
700.
1013
0.09
680.
0968
0.1
021
0.09
68
GD
P(0
.000
)(0
.000
)(0
.002
)(0
.001
)(0
.002
)(0
.002
)(0
.000
)(0
.007
)
101
Dep
ende
ntV
aria
ble:
Gro
wth
inM
easu
reo
fV
olat
ilit
yin
Gro
wth
deno
ted
inC
olu
mn
s
GD
Pco
nst
ant
2000
US
$P
oole
dO
LS
wit
hP
rais
-Win
sten
pane
l-co
rrec
ted
stan
dar
der
rors
*P
oole
dR
and
om
GL
S*
Eff
ects
Ex
pla
nat
ory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)
Std
Inte
r-S
tdIn
ter-
Wei
ghte
dW
eigh
ted
Wei
ghte
dW
eigh
ted
Dev
iati
onq
uar
tile
Dev
iati
onq
uar
tile
Std
Dev
Inte
r-In
ter-
Inte
r-q
uar
tile
qu
arti
leq
uar
tile
Tot
aly
ears
ofs
choo
ling
0.27
200.
2836
0.19
580.
1905
0.20
620.
2140
0.07
620.
2140
(0.0
03)
(0.0
02)
(0.3
85)
(0.3
88)
(0.3
62)
(0.3
37)
(0.6
65)
(0.4
12)
(mal
ean
dfe
mal
e)
Go
ver
nm
ent
fina
lco
nsum
ptio
n-0
.085
0-0
.088
8-0
.047
8-0
.052
4-0
.049
1-0
.052
7-0
.023
3-0
.052
7
expe
ndit
ure
asa
shar
eo
fGD
P(0
.005
)(0
.003
)(0
.312
)(0
.264
)(0
.303
)(0
.264
)(0
.459
)(0
.404
)
Eth
no-l
ingu
isti
cfr
acti
onal
izat
ion
-0.7
117
-0.6
882
-0.4
700
-0.4
471
-0.4
392
-0.4
151
-0.4
381
-0.4
151
inde
x(0
.190
)(0
.207
)(0
.658
)(0
.675
)(0
.682
)(0
.695
)(0
.629
)(0
.746
)
M2
and
quas
i-m
oney
asa
shar
eo
f-0
.000
1-0
.000
1-0
.000
8-0
.000
9-0
.000
8-0
.000
7-0
.001
0-0
.001
0
GD
P(0
.689
)(0
.638
)(0
.599
)(0
.542
)(0
.621
)(0
.624
)(0
.509
)(0
.483
)
Aid
per
cap
ita
disb
urse
d(g
rant
s)...
...0.
0417
0.04
240.
0420
0.04
350.
0400
0.04
3(0
.016
)(0
.014
)(0
.010
)(0
.013
)(0
.009
)(0
.001
)(c
onst
ant
2005
US
$)
Vol
atil
ity
inai
dp
erca
pita
disb
urse
d...
....
-0.0
550
-0.0
591
-0.0
539
-0.0
543
-0.0
425
-0.0
543
(0.1
00)
(0.0
70)
(0.0
99)
(0.0
94)
(0.0
73)
(0.1
15)
102
Dep
ende
ntV
aria
ble:
Gro
wth
inM
easu
reo
fVol
atil
ity
inG
row
thde
note
din
Col
umns
GD
Pco
nsta
nt20
00U
S$P
oole
dO
LS
wit
hP
rais
-Win
sten
pane
l-co
rrec
ted
stan
dar
der
rors
*P
oole
dR
and
om
GL
S*
Eff
ects
Exp
lana
tory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)
Std
Inte
r-S
tdIn
ter-
Wei
ghte
dW
eigh
ted
Wei
ghte
dW
eigh
ted
Dev
iati
onq
uar
tile
Dev
iati
onq
uar
tile
Std
Dev
Inte
r-In
ter-
Inte
r-q
uar
tile
qu
arti
leq
uar
tile
Tot
ald
ebt
serv
ice
asa
shar
eo
f-0
.020
3-0
.020
4-0
.038
9-0
.037
2-0
.037
5-0
.035
8-0
.052
0-0
.035
8ex
port
so
fgoo
ds,s
ervi
ces
and
(0.0
82)
(0.0
84)
(0.1
70)
(0.1
89)
(0.1
88)
(0.2
12)
(0.0
13)
(0.1
83)
inco
me
Rsq
uar
e0.
412
0.40
40.
458
0.46
50.
459
0.46
2...
.0.
462
No
ofO
bser
vati
ons
402
402
118
118
118
118
118
118
*Ass
umes
cros
s-se
ctio
nalh
eter
oske
dast
icit
yan
dno
cont
empo
rane
ous
corr
elat
ion
acro
sspa
nels
.P
anel
sar
eun
bala
nced
.
Not
es:
1.D
umm
yva
riab
les
for
each
oft
he5
year
inte
rval
(fro
m19
61-
2000
)ar
ein
clud
edin
the
esti
mat
ions
,bu
tres
ults
are
notr
epor
ted
here
for
reas
ons
of
spac
e.T
heom
itte
dca
tego
ryis
the
last
peri
od20
01-2
005.
2.P
-val
ues
are
inpa
rent
hese
s.3.
Whe
nw
eigh
ted
stan
dard
devi
atio
nis
used
and
esti
mat
ion
isdo
neus
ing
GL
San
da
Ran
dom
effe
cts
mod
el,
the
coef
fici
ento
nai
dis
posi
tive
and
sign
ific
antw
ith
pva
lues
ator
belo
w0.
020
and
the
coef
fici
ento
nvo
lati
lity
inai
dis
nega
tive
wit
hp-
valu
eso
f0.0
53in
case
ofG
LS
and
0.12
1in
case
oft
hera
ndom
effe
cts
mod
el.
103
A.2
.2.
Gro
wth
Equ
atio
n(W
eigh
ted
Est
imat
es)
Dep
ende
ntV
aria
ble:
Gro
wth
inM
easu
reof
Vol
atil
ity
inG
row
thde
note
din
Col
umns
GD
Pco
nsta
nt20
00U
S$P
oole
dO
LS
wit
hP
rais
-Win
sten
pane
l-co
rrec
ted
stan
dar
der
rors
*P
oole
dG
LS*
Ran
dom
Eff
ects
Exp
lana
tory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)
Std
Inte
r-S
tdIn
ter-
Wei
ghte
dW
eigh
ted
Wei
ghte
dW
eigh
ted
Dev
iati
onqu
arti
leD
evia
tion
quar
tile
Std
Dev
Inte
r-qu
arti
leIn
ter-
quar
tile
Inte
r-qu
arti
le
Inte
rcep
t19
3.12
1610
7.74
4253
2.09
8758
3.45
9449
8.26
9353
2.72
1143
5.13
3553
2.72
11(0
.563
)(0
.758
)(0
.009
)(0
.004
)(0
.016
)(0
.009
)(0
.005
)(0
.023
)
Vol
atil
ity
ingr
owth
-0.2
205
-0.1
419
-0.0
151
-0.1
109
0.05
16-0
.040
00.
0501
(0.4
53)
-0.0
400
(0.0
13)
(0.0
46)
(0.8
12)
(0.1
13)
(0.5
89)
(0.6
54)
(0.7
18)
Dum
my
for
Sub
-Sah
aran
Afr
ica
1.58
261.
8375
-1.1
916
-1.2
196
-1.2
590
-1.1
905
-1.3
221
-1.1
905
(0.0
36)
(0.0
13)
(0.0
64)
(0.0
55)
(0.0
48)
(0.0
65)
(0.0
26)
(0.1
32)
Dum
my
for
Lat
inA
mer
ica
0.30
960.
4012
-3.7
059
-3.8
016
-3.5
721
-3.7
345
-2.9
656
-3.7
345
(0.5
49)
(0.4
47)
(0.0
05)
(0.0
03)
(0.0
07)
(0.0
05)
(0.0
06)
(0.0
02)
Env
iron
men
talv
ulne
rabi
lity
0.40
010.
4921
2.70
882.
6327
2.65
092.
6889
(0.0
98)
2.24
68(0
.066
)2.
6890
inde
x(0
-1sc
ale)
(0.7
41)
(0.6
84)
(0.0
94)
(0.1
00)
(0.0
99)
(0.1
37)
Init
ial
GD
Pp
erca
pita
-0.0
004
-0.0
004
0.00
310.
0029
0.00
290.
0031
(0.3
11)
0.00
20(0
.345
)0.
0031
(0.0
17)
(0.0
11)
(0.3
11)
(0.3
29)
(0.3
38)
(0.2
75)
(con
stan
t20
00U
S$)
104
Dep
ende
ntV
aria
ble:
Gro
wth
inM
easu
reo
fVol
atil
ity
inG
row
thde
note
din
Col
umns
GD
Pco
nsta
nt20
00U
S$
Poo
led
OL
Sw
ith
Pra
is-W
inst
enpa
nel-
corr
ecte
dst
and
ard
erro
rs*
Poo
led
GL
S*
Ran
do
mE
ffec
ts
Ex
pla
nat
ory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)
Std
Inte
r-S
tdIn
ter-
Wei
ghte
dW
eigh
ted
Wei
ghte
dW
eigh
ted
Dev
iati
onq
uar
tile
Dev
iati
onq
uar
tile
Std
Dev
Inte
r-q
uar
tile
Inte
r-q
uar
tile
Inte
r-q
uar
tile
Dem
ocra
cyin
dex
(Pol
ity)
-0.0
431
-0.0
436
0.04
160.
0178
0.05
020.
0416
(0.5
87)
-0.0
160
0.04
16(0
.525
)(0
.525
)(0
.593
)(0
.815
)(0
.513
)(0
.775
)(0
.646
)
Du
mm
yva
riab
lefo
rpo
liti
cal
-2.5
360
-2.6
823
-0.4
808
-0.2
234
-0.5
927
-0.4
737
-0.7
741
-0.4
737
cris
is(P
olit
y)(0
.01)
(0.0
01)
(0.5
19)
(0.7
63)
(0.4
04)
(0.5
03)
(0.1
24)
(0.4
63)
Tra
de
asa
shar
eo
fGD
P-0
.004
6-0
.005
2-0
.007
7-0
.006
4-0
.007
5-0
.007
6-0
.010
0-0
.007
6(0
.478
)(0
.415
)(0
.351
)(0
.440
)(0
.358
)(0
.356
)(0
.131
)(0
.513
)
Infl
atio
nra
te(G
DP
defl
ator
)-0
.001
4-0
.001
40.
0049
0.00
540.
0032
0.00
53(0
.700
)0.
0094
(0.4
38)
0.00
53(0
.036
)(0
.031
)(0
.721
)(0
.688
)(0
.815
)(0
.702
)
Gro
ssca
pita
lfor
mat
ion
assh
are
0.10
100.
0974
0.07
160.
0681
0.07
470.
0717
(0.0
11)
0.09
07(0
.000
)0.
0717
ofG
DP
(0.0
04)
(0.0
05)
(0.0
11)
(0.0
17)
(0.0
08)
(0.0
37)
To
tal
year
so
fsch
ooli
ng0.
7128
0.71
62-0
.170
0-0
.158
7-0
.146
8-0
.174
9-0
.225
1-0
.174
9(0
.000
)(0
.000
)(0
.484
)(0
.513
)(0
.555
)(0
.480
)(0
.248
)(0
.508
)(m
ale
and
fem
ale)
Go
ver
nm
ent
fina
lco
nsum
ptio
n-0
.146
3-0
.146
9-0
.052
4-0
.049
2-0
.055
4-0
.052
0-0
.001
5-0
.052
0
expe
ndit
ure
asa
shar
eo
fGD
P(0
.002
)(0
.001
)(0
.229
)(0
.252
)(0
.207
)(0
.236
)(0
.320
)(0
.362
)
105
Dep
ende
ntV
aria
ble
:G
row
thin
Mea
sure
ofV
olat
ilit
yin
Gro
wth
deno
ted
inC
olum
nsG
DP
con
stan
t20
00U
S$P
oole
dO
LS
wit
hP
rais
-Win
sten
pane
l-co
rrec
ted
stan
dar
der
rors
*P
oole
dG
LS
*R
and
om
Eff
ects
Exp
lana
tory
var
iab
les
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Std
Inte
r-S
tdIn
ter-
Wei
ghte
dW
eigh
ted
Wei
ghte
dW
eigh
ted
Dev
iati
onqu
arti
leD
evia
tion
qu
arti
leS
tdD
evIn
ter-
qu
arti
leIn
ter-
qu
arti
leIn
ter-
quar
tile
Eth
no-I
ingu
isti
cfr
acti
onal
izat
ion
0.92
700.
7974
-1.2
604
-1.2
565
-1.1
113
-1.2
415
-1.1
026
-1.2
415
inde
x(0
.383
)(0
.449
)(0
.245
)(0
.237
)(0
.312
)(0
.254
)(0
.212
)(0
.298
)
M2
and
quas
i-m
oney
asa
shar
eo
f0.
0001
0.00
01-0
.001
5-0
.001
4-0
.001
5-0
.001
5-0
.001
5-0
.001
5
GD
P(0
.614
)(0
.834
)(0
.320
)(0
.357
)(0
.336
)(0
.313
)(0
.320
)(0
.312
)
Aid
per
capi
tadi
sbur
sed
(gra
nts)
......
0.04
340.
0420
0.04
220.
0430
(0.0
03)
0.03
93(0
.000
)0.
0430
(0.0
03)
(0.0
04)
(0.0
04)
(0.0
12)
(con
stan
t200
5U
S$)
Vol
atil
ity
inai
dp
erca
pita
......
-0.0
791
-0.0
691
-0.0
819
-0.0
796
-0.0
560
-0.0
796
disb
urse
d(0
.012
)(0
.023
)(0
.009
)(0
.010
)(0
.003
)(0
.004
)
Tot
ald
ebt
serv
ice
asa
shar
eo
f-0
.005
0-0
.005
9-0
.043
6-0
.046
4-0
.042
2-0
.045
1-0
.055
6-0
.045
1
expo
rts
ofg
oods
,ser
vice
san
d(0
.748
)(0
.701
)(0
.099
)(0
.079
)(0
.114
)(0
.092
)(0
.014
)(0
.124
)
inco
me
Rsq
uar
e0.
527
0.52
30.
576
0.58
30.
576
0.57
6...
.0.
576
No
ofO
bse
rvat
ion
s40
240
211
811
811
811
811
811
8
*Ass
umes
cros
s-se
ctio
nal
hete
rosk
edas
tici
tyan
dco
ntem
pora
neou
sco
rrel
atio
nac
ross
pane
ls
106
Not
e:1.
Dum
my
vari
able
sfo
rea
cho
fthe
5ye
arin
terv
al(f
rom
1961
-20
00)
are
incl
uded
inth
ees
tim
atio
ns,b
utre
sult
sar
eno
trep
orte
dhe
refo
rre
ason
so
fspa
ce.
The
omit
ted
cate
gory
isth
ela
stpe
riod
2001
-200
5.2.
P-v
alue
sar
ein
pare
nthe
ses.
3.W
eigh
ted
regr
essi
ons
are
used
whe
reth
ew
eigh
tis
init
ial
GD
Ppe
rca
pita
.4.
Whe
nw
eigh
ted
stan
dard
devi
atio
nis
used
and
esti
mat
ion
isdo
neus
ing
GL
San
da
Ran
dom
effe
cts
mod
el,
the
coef
fici
ento
nai
dis
posi
tive
and
sign
ific
antw
ithp
valu
esat
orbe
low
0.01
5an
dth
eco
effi
cien
ton
vola
tili
tyin
aid
isne
gati
vew
ith
p-va
lues
of0
.003
inbo
thca
ses.
107
A.2
.3.
Gro
wth
Equ
atio
n(U
nwei
ghte
dE
stim
ates
)-I
nclu
ding
For
eign
Dir
ectI
nves
tmen
t
Dep
ende
ntV
aria
ble:
Gro
wth
inG
DP
Mea
sure
ofV
olat
ilit
yin
Gro
wth
deno
ted
inC
olum
nsco
nsta
nt20
00U
S$
Poo
led
OL
Sw
ith
Pra
is-W
inst
enpa
nel-
corr
ecte
dP
oole
dR
and
om
Ran
do
mst
and
ard
erro
rs*
GL
S*
Eff
ects
Eff
ects
Ex
pla
nat
ory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Std
Inte
r-W
eigh
ted
Wei
ghte
dW
eigh
ted
Wei
ghte
dW
eigh
ted
Dev
iati
onq
uar
tile
Std
Inte
r-In
ter-
Std
-In
ter-
Dev
iati
onqu
arti
leq
uar
tile
Dev
iati
onqu
arti
le
Inte
rcep
t2.
231
(0.1
53)
1.85
82.
262
2.06
2(0
.179
)1.
861
2.26
22.
062
(0.2
29)
(0.1
49)
(0.1
43)
(0.2
46)
(0.2
84)
Vol
atil
ity
ing
row
th0.
013
(0.8
29)
0.08
80.
015
0.06
900.
089
0.01
50.
069
(0.1
67)
(0.8
75)
(0.3
64)
(0.1
63)
(0.8
93)
(0.4
81)
Dum
my
for
Su
b-S
ahar
anA
fric
a-0
.991
-0.9
71-0
.996
-1.0
26-1
.278
-0.9
97-1
.026
(0.1
88)
(0.1
98)
(0.1
84)
(0.1
72)
(0.0
37)
(0.2
33)
(0.2
16)
Dum
my
for
Lat
inA
mer
ica
-1.1
27-1
.131
-1.1
22-1
.132
-1.0
94-1
.122
-1.1
32(0
.422
)(0
.423
)(0
.423
)(0
.419
)(0
.384
)(0
.473
)(0
.468
)
En
vir
on
men
tal
vuln
erab
ilit
yin
dex
(0-1
3.91
4(0
.017
)4.
151
3.92
74.
125
(0.0
13)
4.12
43.
927
4.12
5(sc
ale)
(0.0
13)
(0.0
17)
(0.0
03)
(0.0
64)
0.05
4)
Init
ialG
DP
per
capi
ta-0
.007
-0.0
06-0
.007
-0.0
07-0
.007
-0.0
07-0
.007
(0.0
19)
(0.0
24)
(0.0
17)
(0.0
17)
(0.0
02)
(0.0
36)
(0.0
38)
(con
stan
t20
00U
S$)
108
Dep
ende
ntV
aria
ble:
Gro
wth
inG
DP
Mea
sure
ofV
olat
ilit
yin
Gro
wth
deno
ted
inC
olum
nsco
nsta
nt20
00U
S$P
oole
dO
LS
wit
hP
rais
-Win
sten
pane
l-co
rrec
ted
Poo
led
Ran
do
mR
and
om
stan
dar
der
rors
*G
LS
*E
ffec
tsE
ffec
ts
Ex
pla
nat
ory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Std
Inte
r-W
eigh
ted
Wei
ghte
dW
eigh
ted
Wei
ghte
dW
eigh
ted
Dev
iati
onq
uar
tile
Std
Inte
r-In
ter-
Std
-In
ter-
Dev
iati
onq
uar
tile
qu
arti
leD
evia
tion
quar
tile
Dem
ocra
cyin
dex
(Pol
ity)
0.05
2(0
.492
)0.
077
0.05
10.
059
(0.4
30)
0.02
50.
051
0.05
9(0
.324
)(0
.497
)(0
.672
)(0
.602
)(0
.545
)
Du
mm
yv
aria
ble
for
poli
tica
lcri
sis
-0.6
90-0
.930
-0.6
61-0
.686
-1.1
52-0
.660
-0.6
86
(Pol
ity)
(0.3
66)
(0.2
16)
(0.3
73)
(0.3
52)
(0.0
41)
(0.3
77)
(0.3
56)
Tra
de
asa
shar
eo
fGD
P-0
.019
-0.0
21-0
.019
-0.0
19-0
.023
-0.0
19-0
.019
(0.0
64)
(0.0
41)
(0.0
68)
(0.0
64)
(0.0
01)
(0.1
37)
(0.1
30)
Infl
atio
nra
te(G
DP
defl
ator
)0.
007
(0.6
47)
0.00
60.
007
0.00
6(0
.711
)0.
002
0.00
70.
006
(0.6
86)
(0.6
52)
(0.8
72)
(0.6
27)
(0.6
84)
Gro
ssca
pit
alfo
rmat
ion
assh
are
of
0.07
2(0
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0763
0.07
10.
072
(0.0
41)
0.09
30.
071
0.07
2
GD
P(0
.026
)(0
.045
)(0
.001
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.088
)(0
.082
)
Tot
aly
ears
ofs
choo
ling
0.16
7(0
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)0.
168
0.17
10.
190
(0.4
08)
0.11
50.
171
0.19
0(0
.457
)(0
.465
)(0
.532
)(0
.534
)(0
.482
)(m
ale
and
fem
ale)
Go
ver
nm
entf
inal
cons
umpt
ion
-0.0
45-0
.050
-0.0
45-0
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-0.0
34-0
.045
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50
exp
end
itu
reas
ash
are
ofG
DP
(0.3
33)
(0.2
70)
(0.3
35)
(0.2
80)
(0.2
96)
(0.4
79)
(0.4
30)
109
Dep
ende
ntV
aria
ble:
Gro
wth
inG
DP
Mea
sure
ofV
olat
ilit
yin
Gro
wth
deno
ted
inC
olum
nsco
nsta
nt20
00U
S$P
oole
dO
LS
wit
hP
rais
-Win
sten
pane
l-co
rrec
ted
Poo
led
Ran
do
mR
and
om
stan
dar
der
rors
*G
LS
*E
ffec
tsE
ffec
ts
Ex
pla
nat
ory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Std
Inte
r-W
eigh
ted
Wei
ghte
dW
eigh
ted
Wei
ghte
dW
eigh
ted
Dev
iati
onq
uar
tile
Std
Inte
r-In
ter-
Std
-In
ter-
Dev
iati
onqu
arti
leq
uar
tile
Dev
iati
onq
uar
tile
Eth
no-l
ingu
isti
cfr
acti
onal
izat
ion
inde
x-0
.247
-0.1
96-0
.255
-0.1
950.
115
-0.2
55-0
.195
(0.8
12)
(0.8
51)
(0.8
08)
(0.8
51)
(0.8
96)
(0.8
45)
(0.8
80)
M2
and
quas
i-m
oney
asa
shar
eo
fGD
P-0
.001
-0.0
01-0
.001
-0.0
01-0
.001
-0.0
01-0
.001
(0.5
60)
(0.4
96)
(0.5
69)
(0.5
79)
(0.5
05)
(0.4
33)
(0.4
47)
Aid
per
capi
tadi
sbur
sed
(gra
nts)
0.05
1(0
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051
0.05
10.
052
(0.0
06)
0.05
00.
051
0.05
2(0
.006
)(0
.007
)(0
.000
)(0
.010
)(0
.008
)(c
onst
ant2
005
US$
)
Vol
atil
ity
inai
dp
erca
pita
disb
urse
d-0
.063
-0.0
68-0
.061
-0.0
63-0
.055
-0.0
62-0
.063
(0.0
63)
(0.0
38)
(0.0
60)
(0.0
53)
(0.0
25)
(0.0
83)
(0.0
73)
For
eign
dire
ctin
vest
men
tas
ash
are
of
0.20
2(0
.083
)0.
209
0.20
20.
202
(0.0
86)
0.19
20.
202
0.20
2G
DP
(0.0
71)
(0.0
85)
(0.0
21)
(0.1
04)
(0.1
03)
Tot
ald
ebt
serv
ice
asa
shar
eo
fexp
orts
-0.0
39-0
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-0.0
39-0
.036
-0.0
52-0
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-0.0
36of
good
s,se
rvic
esan
din
com
e(0
.156
)(0
.176
)(0
.162
)(0
.193
)(0
.014
)(0
.150
)(0
.173
)
110
Dep
ende
ntV
aria
ble:
Gro
wth
inG
DP
Mea
sure
ofV
olat
ilit
yin
Gro
wth
deno
ted
inC
olum
ns
cons
tant
2000
US$
Poo
led
OL
Sw
ith
Pra
is-W
inst
enpa
nel-
corr
ecte
dP
oole
dR
and
om
Ran
do
mst
and
ard
erro
rs*
GL
S*
Eff
ects
Eff
ects
Exp
lana
tory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Std
Inte
r-W
eigh
ted
Wei
ghte
dW
eigh
ted
Wei
ghte
dW
eigh
ted
Dev
iati
onqu
arti
leS
tdIn
ter-
Inte
r-S
td-
Inte
r-
Dev
iati
onqu
arti
leq
uar
tile
Dev
iati
onqu
arti
le
Rsq
uar
e0.
471
0.47
90.
471
0.47
4...
...0.
471
0.47
4
No
ofO
bser
vati
ons
117
117
117
117
117
117
117
*Ass
umes
cros
s-se
ctio
nalh
eter
oske
dast
icit
yan
dco
ntem
pora
neou
sco
rrel
atio
nac
ross
pane
ls
Not
e:1.
Dum
my
vari
able
sfo
rea
cho
fthe
5ye
arin
terv
al(f
rom
1961
-20
00)
are
incl
uded
inth
ees
tim
atio
ns,b
utre
sult
sar
eno
trep
orte
dhe
refo
rre
ason
so
fspa
ce.
The
omit
ted
cate
gory
isth
ela
stpe
riod
2001
-200
5.2.
P-v
alue
sar
ein
pare
nthe
ses.
3.W
eigh
ted
regr
essi
ons
are
used
whe
reth
ew
eigh
tis
init
ialG
DP
per
capi
ta.4
.W
hen
wei
ghte
dst
anda
rdde
viat
ion
isus
edan
des
tim
atio
nis
done
usin
gG
LS,
the
coef
fici
ent
onai
dis
posi
tive
and
sign
ific
ant
wit
hp
valu
eo
f0.
000
and
the
coef
fici
ento
nvo
lati
lity
inai
dis
nega
tive
wit
ha
p-va
lue
of0
.016
.
111
A.2
.4.
Gro
wth
Equ
atio
n(W
eigh
ted
Est
imat
es)
-In
clu
din
gF
orei
gnD
irec
tInv
estm
ent
Dep
ende
ntV
aria
ble:
Gro
wth
inG
DP
Mea
sure
ofV
olat
ilit
yin
Gro
wth
deno
ted
inC
olum
nsco
nsta
nt20
00U
S$P
oole
dO
LS
wit
hP
rais
-Win
sten
pane
l-co
rrec
ted
Poo
led
Ran
do
mR
and
om
stan
dar
der
rors
*G
LS
*E
ffec
tsE
ffec
ts
Exp
lana
tory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Std
Inte
r-W
eigh
ted
Wei
ghte
dW
eigh
ted
Wei
ghte
dW
eigh
ted
Dev
iati
onq
uar
tile
Inte
r-In
ter-
Std
-In
ter-
Std
Dev
qu
arti
leq
uar
tile
Dev
iati
onq
uar
tile
Inte
rcep
t54
6.16
258
9.05
951
5.21
354
1.91
839
0.60
751
5.21
354
1.91
8(0
.009
)(0
.004
)(0
.015
)(0
.009
)(0
.017
)(0
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)(0
.023
)
Vol
atil
ity
ingr
owth
-0.0
32-0
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0.02
0-0
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10.
024
0.02
0-0
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(0.6
31)
(0.0
91)
(0.8
45)
(0.4
72)
(0.7
31)
(0.8
69)
(0.5
59)
Dum
my
for
Su
b-S
ahar
anA
fric
a-1
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-1.4
24-1
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-1.3
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-1.4
22-1
.382
(0.0
36)
(0.0
27)
(0.0
28)
(0.0
35)
(0.0
21)
(0.0
80)
(0.0
86)
Dum
my
for
Lat
inA
mer
ica
-3.2
17-3
.278
-3.1
38-3
.239
-2.4
39-3
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-3.2
39(0
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)(0
.007
)(0
.015
)(0
.011
)(0
.023
)(0
.013
)(0
.010
)
En
vir
on
men
talv
ulne
rabi
lity
inde
x(0
-3.
614
3.53
6(0
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)3.
526
3.59
22.
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22)
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63.
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(0.0
28)
(0.0
31)
(0.0
28)
(0.0
65)
(0.0
59)
Init
ial
GD
Pp
erca
pita
0.00
20.
001
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18)
0.00
20.
002
0.00
1(0
.809
)0.
002
0.00
2(0
.573
)(0
.585
)(0
.574
)(0
.583
)(0
.575
)(c
onst
ant2
000
US$
)
11
2
Dep
ende
ntV
aria
ble:
Gro
wth
inG
DP
Mea
sure
ofV
olat
ilit
yin
Gro
wth
deno
ted
inC
olum
nsco
nsta
nt20
00U
S$
Poo
led
OL
Sw
ith
Pra
is-W
inst
enpa
nel-
corr
ecte
dP
oole
dR
and
om
Ran
do
mst
and
ard
erro
rs*
GL
S*
Eff
ects
Eff
ects
Exp
lana
tory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Std
Inte
r-W
eigh
ted
Wei
ghte
dW
eigh
ted
Wei
ghte
dW
eigh
ted
Dev
iati
onq
uar
tile
Inte
r-In
ter-
Std
-In
ter-
Std
Dev
qu
arti
leq
uar
tile
Dev
iati
onq
uar
tile
Dem
ocra
cyin
dex
(Pol
ity)
0.03
60.
014
(0.8
58)
0.04
50.
037
(0.6
29)
-0.0
030.
045
0.03
7(0
.648
)(0
.560
)(0
.963
)(0
.619
)(0
.681
)
Du
mm
yva
riab
lefo
rpo
liti
cal
cris
is-0
.697
-0.4
70-0
.802
-0.7
11-0
.974
-0.8
02-0
.711
(Pol
ity)
(0.3
67)
(0.5
42)
(0.2
81)
(0.3
36)
(0.0
74)
(0.2
30)
(0.2
84)
Tra
de
asa
shar
eo
fGD
P-0
.012
-0.0
11-0
.012
-0.0
12-0
.018
-0.0
12-0
.012
(0.1
96)
(0.2
50)
(0.2
11)
(0.1
97)
(0.0
20)
(0.3
25)
(0.3
10)
Infl
atio
nra
te(G
DP
defl
ator
)0.
002
0.00
2(0
.885
)0.
001
0.00
2(0
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)0.
005
(0.6
58)
0.00
10.
002
(0.8
95)
(0.9
60)
(0.9
62)
(0.8
72)
Gro
ssca
pita
lfor
mat
ion
assh
are
of
0.05
30.
049
(0.1
59)
0.05
60.
053
(0.1
32)
0.08
9(0
.001
)0.
056
0.05
3G
DP
(0.1
36)
(0.1
16)
(0.1
48)
(0.1
69)
To
taly
ears
ofs
choo
ling
-0.1
39-0
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-0.1
21-0
.144
-0.1
80-0
.121
-0.1
44(0
.572
)(0
.618
)(0
.633
)(0
.567
)(0
.393
)(0
.664
)(0
.600
)(m
ale
and
fem
ale)
Gov
ernm
entf
inal
cons
umpt
ion
-0.0
55-0
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57-0
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-0.0
10-0
.057
-0.0
54ex
pend
itur
eas
ash
are
ofG
DP
(0.2
10)
(0.2
29)
(0.1
94)
(0.2
18)
(0.7
50)
(0.3
17)
(0.3
39)
113
Dep
ende
ntV
aria
ble
:G
row
thin
GD
PM
easu
reo
fVol
atil
ity
inG
row
thde
note
din
Col
umns
cons
tant
2000
US
$P
oole
dO
LS
wit
hP
rais
-Win
sten
pane
l-co
rrec
ted
Poo
led
Ran
do
mR
and
om
stan
dar
der
rors
*G
LS
*E
ffec
tsE
ffec
ts
Exp
lana
tory
var
iab
les
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Std
Inte
r-W
eigh
ted
Wei
ghte
dW
eigh
ted
Wei
ghte
dW
eigh
ted
Dev
iati
onqu
arti
leIn
ter-
Inte
r-S
td-
Inte
r-S
tdD
evqu
arti
leq
uar
tile
Dev
iati
onqu
arti
le
Eth
no-l
ingu
isti
cfr
acti
onal
izat
ion
-0.8
86-0
.826
-0.7
81-0
.827
-0.4
29-0
.781
-0.8
27in
dex
(0.4
08)
(0.4
30)
(0.4
70)
(0.4
38)
(0.6
34)
(0.5
28)
(0.4
99)
M2
and
quas
i-m
oney
asa
shar
eo
f-0
.002
-0.0
01-0
.001
-0.0
01-0
.001
-0.0
01-0
.001
GD
P(0
.320
)(0
.361
)(0
.325
)(0
.310
)(0
.317
)(0
.324
)(0
.315
)
Aid
per
capi
tadi
sbur
sed
(gra
nts)
0.05
30.
052
(0.0
01)
0.05
20.
053
(0.0
01)
0.04
6(0
.000
)0.
052
0.05
3(0
.001
)(0
.002
)(0
.006
)(0
.005
)(c
onst
ant2
005
US$
)
Vol
atil
ity
inai
dp
erca
pita
disb
urse
d-0
.090
-0.0
81-0
.093
-0.0
92-0
.066
-0.0
93-0
.092
(0.0
04)
(0.0
08)
(0.0
03)
(0.0
03)
(0.0
01)
(0.0
01)
(0.0
01)
For
eign
dire
ctIn
vest
men
tas
ash
are
0.14
10.
143
(0.1
28)
0.13
40.
143
(0.1
27)
0.10
8(0
.154
)0.
134
0.14
3o
fGD
P(0
.136
)(0
.158
)(0
.159
)(0
.131
)
Tot
alde
btse
rvic
eas
ash
are
of
-0.0
41-0
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-0.0
40-0
.043
-0.0
54-0
.040
-0.0
43ex
port
sof
good
s,se
rvic
esan
din
com
e(0
.116
)(0
.092
)(0
.123
)(0
.101
)(0
.020
)(0
.167
)(0
.142
)
114
Dep
ende
ntV
aria
ble:
Gro
wth
inG
DP
Mea
sure
ofV
olat
ilit
yin
Gro
wth
deno
ted
inC
olum
nsco
nsta
nt20
00U
S$
Poo
led
OL
Sw
ith
Pra
is-W
inst
enpa
nel-
corr
ecte
dP
oole
dR
and
om
Ran
dom
stan
dar
der
rors
*G
LS
*E
ffec
tsE
ffec
ts
Exp
lana
tory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Std
Inte
r-W
eigh
ted
Wei
ghte
dW
eigh
ted
Wei
ghte
dW
eigh
ted
Dev
iati
onqu
arti
leIn
ter-
Inte
r-S
td-
Inte
r-S
tdD
evqu
arti
lequ
arti
leD
evia
tion
quar
tile
Rsq
uar
e0.
585
0.59
30.
584
0.58
5'"
0.58
40.
585
No
ofO
bser
vati
ons
117
117
117
117
117
117
117
Hau
sman
test
(Fve
rsio
n)'"
...'"
...'"
0.71
03.
180
Fte
stst
atis
tic
and
pva
lue
(0.5
51)
(0.0
28)
*Ass
umes
cros
s-se
ctio
nalh
eter
oske
dast
icit
yan
dco
ntem
pora
neou
sco
rrel
atio
nac
ross
pane
ls
Not
e:1.
Dum
my
vari
able
sfo
rea
cho
fthe
5ye
arin
terv
al(f
rom
1961
-20
00)
are
incl
uded
inth
ees
tim
atio
ns,
butr
esul
tsar
eno
tre
port
edhe
refo
rre
ason
so
fspa
ce.
The
omit
ted
cate
gory
isth
ela
stpe
riod
2001
-200
5.2.
P-v
alue
sar
ein
pare
nthe
ses.
3.W
eigh
ted
regr
essi
ons
are
used
whe
reth
ew
eigh
tis
init
ialG
DP
per
capi
ta.4
.W
hen
wei
ghte
dst
anda
rdde
viat
ion
isus
edan
des
tim
atio
nis
done
usin
gG
LS
,th
eco
effi
cien
ton
aid
ispo
siti
vean
dsi
gnif
ican
tw
ithp
valu
eo
f0.
001
and
the
coef
fici
ent o
nvo
lati
lity
inai
dis
nega
tive
wit
ha
p-va
lue
of0
.001
.5.
For
the
Ran
dom
effe
cts
mod
el,
the
Fve
rsio
no
fthe
Hau
sman
test
isre
port
edin
orde
rto
test
for
endo
gene
ity
of
the
rand
omer
ror
term
wit
hex
plan
ator
yva
riab
les.
At
a1%
leve
l,th
enu
llth
atth
era
ndom
effe
cts
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fici
ents
are
effi
cien
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ed(s
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ages
290-
291
inW
oolr
idge
for
ade
scri
ptio
no
fthe
test
;tr
ansf
orm
edva
riab
les
from
the
fixe
def
fect
sm
odel
are
aid,
gros
sdo
mes
ticin
vest
men
tand
M2
asa
shar
eo
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115
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atil
ity
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row
thE
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ion
(Unw
eigh
ted
Est
imat
es)
Dep
ende
ntV
aria
ble:
Vol
atil
ity
Mea
sure
ofV
olat
ilit
yin
Gro
wth
deno
ted
inC
olum
nsin
Gro
wth
inG
DP
cons
tant
2000
US$
Poo
led
OL
SP
oole
dO
LS
wit
hP
rais
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sten
pane
l-co
rrec
ted
Poo
led
Ran
do
mst
and
ard
erro
rs*
GL
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Eff
ects
Exp
lana
tory
var
iab
les
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Std
Inte
r-S
tdIn
ter-
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ghte
dW
eigh
ted
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ghte
dW
eigh
ted
Dev
iati
onq
uar
tile
Dev
iati
onq
uar
tile
Std
Dev
Inte
r-In
ter-
Inte
r-q
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arti
leq
uar
tile
Inte
rcep
t-0
.808
00.
1024
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00)
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02)
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00)
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01)
(0.0
02)
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11)
Du
mm
yfo
rS
ub
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aran
Afr
ica
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719
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519
0.00
630.
2241
0.75
121.
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1.12
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5044
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18)
(0.2
80)
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95)
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59)
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46)
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rL
atin
Am
eric
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.195
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.455
)(0
.276
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.480
)
Env
iron
men
tal
vuln
erab
ilit
y-2
.250
7-1
.458
2-5
.348
4-4
.887
2-5
.029
1-5
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6-4
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1-5
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dex
(0-1
scal
e)(0
.028
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)(0
.082
)(0
.001
)(0
.014
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Init
ial
GD
Pp
erca
pita
(con
stan
t0.
0006
0.00
08-0
.006
9-0
.006
8-0
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4-0
.003
6-0
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2-0
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00U
S$)
(0.0
00)
(0.0
00)
(0.0
10)
(0.0
50)
(0.0
75)
(0.1
89)
(0.1
32)
(0.3
82)
Dem
ocra
cyin
dex
(Pol
ity)
-0.0
049
-0.0
480
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826
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944
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664
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159
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31)
(0.0
02)
(0.0
21)
(0.0
06)
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00)
(0.0
28)
11
6
Dep
ende
ntV
aria
ble:
Vol
atil
ity
Mea
sure
ofV
olat
ilit
yin
Gro
wth
deno
ted
inC
olum
nsin
Gro
wth
inG
DP
cons
tant
2000
US$
Poo
led
OL
SP
oole
dO
LS
wit
hP
rais
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sten
pane
l-co
rrec
ted
Poo
led
Ran
dom
stan
dar
der
rors
*G
LS*
Eff
ects
Exp
lana
tory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)
Std
Inte
r-S
tdIn
ter-
Wei
ghte
dW
eigh
ted
Wei
ghte
dW
eigh
ted
Dev
iati
onqu
arti
leD
evia
tion
quar
tile
Std
Dev
Inte
r-In
ter-
Inte
r-qu
arti
lequ
arti
lequ
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le
Dum
my
vari
able
for
poli
tica
l-0
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00.
4660
0.85
891.
3287
-0.2
422
-0.2
290
-0.2
398
-0.2
290
cris
is(P
olit
y)(0
.985
)(0
.499
)(0
.374
)(0
.227
)(0
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)(0
.772
)(0
.691
)(0
.849
)
Tra
deas
ash
are
ofG
DP
-0.0
002
-0.0
083
0.04
550.
0466
0.03
530.
0445
0.03
130.
0445
(0.9
70)
(0.2
40)
(0.0
00)
(0.0
19)
(0.0
00)
(0.0
04)
(0.0
03)
(0.0
31)
Infl
atio
nra
te(G
DP
defl
ator
)0.
0010
0.00
240.
0084
0.00
850.
0151
0.02
460.
0051
0.02
46(0
.094
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Gov
ernm
entf
inal
cons
umpt
ion
0.00
960.
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0.17
760.
2534
0.12
220.
1706
0.09
060.
1706
expe
ndit
ure
asa
shar
eof
GD
P(0
.776
)(0
.027
)(0
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)(0
.016
)(0
.038
)(0
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)(0
.103
)(0
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)
Eth
no-I
ingu
isti
cfr
acti
onal
izat
ion
0.37
910.
4345
-3.7
003
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926
-4.6
321
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075
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138
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075
inde
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M2
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quas
i-m
oney
asa
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e0.
0001
0.00
001
0.03
490.
0484
0.00
540.
0062
-0.0
025
0.00
62o
fGD
P(0
.828
)(0
.974
)(0
.295
)(0
.319
)(0
.850
)(0
.876
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)(0
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)
Inde
xo
fsec
tora
ldi
vers
ific
atio
n8.
5763
7.07
3017
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922
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816
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722
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117
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622
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1(0
.00
ofG
DP
(0.0
20)
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30)
(0.0
02)
(0.0
03)
(0.0
00)
(0.0
00)
(0.0
00)
9)
117
Dep
ende
ntV
aria
ble:
Vol
atil
ity
Mea
sure
ofV
olat
ilit
yin
Gro
wth
deno
ted
inC
olum
nsin
Gro
wth
inG
DP
cons
tant
2000
US$
Pool
edO
LS
Poo
led
OL
Sw
ith
Pra
is-W
inst
enpa
nel-
corr
ecte
dP
oole
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ando
mst
anda
rder
rors
*G
LS*
Eff
ects
Exp
lana
tory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)
Std
Inte
r-S
tdIn
ter-
Wei
ghte
dW
eigh
ted
Wei
ghte
dW
eigh
ted
Dev
iati
onqu
arti
leD
evia
tion
quar
tile
Std
Dev
Inte
r-In
ter-
Inte
r-qu
arti
lequ
arti
lequ
arti
le
Sha
reo
fser
vice
sin
GD
P-0
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9-0
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5-0
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8-0
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2-0
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0-0
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9-0
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2-0
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9(0
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.119
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Log
ofto
talp
opul
atio
n0.
2867
0.20
332.
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2164
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9589
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Fue
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oft
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-0.0
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rts
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99)
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75)
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23)
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port
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0.13
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Sha
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tura
lexp
orts
in-0
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80.
0057
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100
0.00
10-0
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9-0
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4-0
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1-0
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4m
erch
andi
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port
s(0
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)
Sha
reo
fman
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turi
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port
s-0
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8-0
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50.
0158
0.04
200.
0073
0.01
260.
0156
0.01
26in
mer
chan
dise
expo
rts
(0.0
93)
(0.2
59)
(0.3
00)
(0.0
47)
(0.5
21)
(0.4
23)
(0.1
45)
(0.6
16)
Sho
ckin
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trol
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pric
e0.
0408
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0)0.
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0.12
160.
1447
0.08
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0.08
190.
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01)
(0.0
00)
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00)
(0.0
00)
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02)
(0.0
00)
(0.0
29)
118
Dep
ende
ntV
aria
ble:
Vol
atil
ity
Mea
sure
ofV
olat
ilit
yin
Gro
wth
deno
ted
inC
olum
nsin
Gro
wth
inG
DP
cons
tant
2000
US$
Poo
led
OL
SP
oole
dO
LS
wit
hP
rais
-Win
sten
pane
l-co
rrec
ted
Poo
led
Ran
do
mst
and
ard
erro
rs*
GL
S*
Eff
ects
Exp
lana
tory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)
Std
Inte
r-S
tdIn
ter-
Wei
ghte
dW
eigh
ted
Wei
ghte
dW
eigh
ted
Dev
iati
onq
uar
tile
Dev
iati
onq
uar
tile
Std
Dev
Inte
r-In
ter-
Inte
r-q
uar
tile
qu
arti
leq
uar
tile
Sho
ckin
nom
inal
exch
ange
rate
-0.0
005
0.01
72-0
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0-0
.872
00-0
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2-0
.270
9-0
.140
3-0
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9(0
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)(0
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)(0
.000
)(0
.070
)(0
.143
)(0
.320
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)(0
.352
)
Vol
atil
ity
info
odpr
oduc
tion
0.12
390.
1136
0.05
170.
0221
0.03
90-0
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6-0
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8-0
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6in
dex
(0.0
02)
(0.0
23)
(0.3
53)
(0.7
79)
(0.3
79)
(0.9
93)
(0.8
21)
(0.9
94)
Sho
ckin
net
fina
ncia
lfl
ows
-0.0
001
-0.0
001
-0.0
000
-0.0
005
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003
-0.0
005
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005
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005
(0.4
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82)
(0.0
00)
(0.5
81)
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41)
(0.4
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39)
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00)
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0.02
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90.
0072
0.00
810.
0105
0.00
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port
sto
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ort
s(0
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)(0
.488
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)
Aid
per
capi
tadi
sbur
sed
.....
,-0
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9-0
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3-0
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4-0
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1-0
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8-0
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1(g
rant
s)(c
onst
ant
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)(0
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Vol
atil
ity
inai
dp
erca
pita
......
0.08
410.
1336
0.06
540.
0896
0.06
790.
0896
disb
urse
d(0
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Tot
alde
btse
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eas
ash
are
of
-0.0
003
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165
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372
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696
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582
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774
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457
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774
expo
rts
ofgo
ods,
serv
ices
and
(0.9
84)
(0.3
23)
(0.2
09)
(0.0
74)
(0.0
11)
(0.0
18)
(0.0
29)
(0.0
51)
inco
me
11
9
Dep
ende
ntV
aria
ble:
Vol
atil
ity
Mea
sure
ofV
olat
ilit
yin
Gro
wth
deno
ted
inC
olum
nsin
Gro
wth
inG
DP
cons
tant
2000
US$
Poo
led
OL
SP
oole
dO
LS
wit
hP
rais
-Win
sten
pane
l-co
rrec
ted
Poo
led
Ran
do
mst
and
ard
erro
rs*
GL
S*
Eff
ects
Exp
lana
tory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)
Std
Inte
r-S
tdIn
ter-
Wei
ghte
dW
eigh
ted
Wei
ghte
dW
eigh
ted
Dev
iati
onqu
arti
leD
evia
tion
quar
tile
Std
Dev
Inte
r-In
ter-
Inte
r-qu
arti
lequ
arti
leq
uar
tile
Rsq
uare
0.29
10.
267
0.59
90.
556
0.59
90.
492
...0.
492
No
ofO
bser
vati
ons
339
339
8989
8989
8989
Not
e:1.
Dum
my
vari
able
sfo
rea
cho
fthe
5ye
arin
terv
al(f
rom
1961
-20
00)
are
incl
uded
inth
ees
tim
atio
ns,b
utre
sult
sar
eno
trep
orte
dhe
refo
rre
ason
so
fspa
ce.
The
omit
ted
cate
gory
isth
ela
stpe
riod
2001
-200
5.2.
P-v
alue
sar
ein
pare
nthe
ses.
3.T
heIn
dex
of
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tora
lD
iver
sifi
cati
ono
fG
DP
isa
Her
find
ahl
inde
xo
fco
ncen
trat
ion
whe
rehi
gher
valu
esin
dica
tes
less
erdi
vers
ific
atio
n.4.
Whe
nw
eigh
ted
stan
dard
devi
atio
nis
used
and
esti
mat
ion
isdo
neus
ing
GL
S,
the
coef
fici
ent
onai
dis
nega
tive
and
sign
ific
ant
wit
hp
valu
eo
f0.
007
and
the
coef
fici
ent
onvo
lati
lity
inai
dis
posi
tive
wit
ha
p-va
lue
of0
.044
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hen
the
esti
mat
ion
isdo
neus
ing
aR
ando
mE
ffec
tsm
odel
,th
eco
effi
cien
ton
aid
isne
gati
vean
dsi
gnif
ican
twit
hp
valu
eo
f0.0
36an
dth
eco
effi
cien
ton
vola
tili
tyin
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siti
vew
ith
ap
valu
eo
f0.0
76.
120
A.2
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Vol
atil
ity
inG
row
thE
quat
ion
(Wei
ghte
dE
stim
ates
)
Dep
ende
ntV
aria
ble:
Vol
atil
ity
Mea
sure
ofV
olat
ilit
yin
Gro
wth
deno
ted
inC
olum
nsin
Gro
wth
inG
DP
cons
tant
2000
US$
Poo
led
OL
SP
oole
dO
LS
wit
hP
rais
-Win
sten
pane
l-co
rrec
ted
Poo
led
GL
S*
Ran
dom
stan
dar
der
rors
*E
ffec
ts
Ex
pla
nat
ory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)
Std
Inte
r-S
tdIn
ter-
Wei
ghte
dW
eigh
ted
Inte
r-q
uar
tile
Wei
ghte
dS
tdD
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tion
qu
arti
leD
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tion
quar
tile
Std
Dev
Inte
r-D
evia
tion
quar
tile
Inte
r-qu
arti
le
Inte
rcep
t33
5.41
2650
3.19
82-4
06.1
886
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7.28
9039
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9-3
27.4
45-1
196.
542
-518
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4-4
06.1
886
(0.5
97)
(0.4
46)
(0.1
47)
(0.0
12)
(0.8
72)
(0.3
57)
(0.0
0)(0
.006
)(0
.311
)
Du
mm
yfo
rS
ub
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aran
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056
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941
0.58
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3540
1.03
160.
7289
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70)
0.80
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.921
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.530
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.119
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.106
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Du
mm
yfo
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atin
Am
eric
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1618
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.644
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.237
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.732
)(0
.578
)(0
.700
)
Env
iron
men
talv
ulne
rabi
lity
-3.5
424
-2.8
898
-5.0
510
-4.6
489
-3.0
073
-2.0
405
-5.4
900
-2.2
801
-5.0
510
inde
x(0
.025
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.078
)(0
.005
)(0
.035
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.017
)(0
.239
)(0
.004
)(0
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)(0
.048
)
(0-1
scal
e)
Init
ialG
DP
per
cap
ita
0.00
120.
0014
-0.0
100
-0.0
153
-0.0
043
-0.0
082
-0.0
142
-0.0
089
-0.0
099
(0.0
00)
(0.0
00)
(0.0
01)
(0.0
00)
(0.0
32)
(0.0
05)
(0.0
00)
(0.0
00)
(0.0
03)
(con
stan
t20
00U
S$)
121
Dep
end
ent
Var
iabl
e:V
olat
ilit
yM
easu
reo
fVol
atil
ity
inG
row
thde
note
din
Col
um
ns
inG
row
thin
GD
Pco
nsta
nt20
00U
S$P
oole
dO
LS
Poo
led
OL
Sw
ith
Pra
is-W
inst
enp
anel
-cor
rect
edP
oole
dG
LS*
Ran
dom
stan
dard
erro
rs*
Eff
ects
Exp
lan
ator
yva
riab
les
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Std
Inte
r-St
dIn
ter-
Wei
ghte
dW
eigh
ted
Inte
r-q
uar
tile
Wei
ghte
dSt
dD
evia
tion
qu
arti
leD
evia
tion
qu
arti
leSt
dD
evIn
ter-
Dev
iati
onq
uar
tile
Inte
r-q
uar
tile
Dem
ocra
cyin
dex
(Pol
ity)
-0.1
192
-0.2
008
-0.2
048
-0.4
600
-0.1
521
-0.2
738
-0.4
312
-0.2
640
-0.2
048
(0.0
30)
(0.0
00)
(0.0
13)
(0.0
00)
(0.0
10)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
59)
Du
mm
yva
riab
lefo
rpo
liti
cal
-2.1
547
-1.1
445
1.35
892.
8137
-0.4
493
0.01
172.
3306
(0.0
14)
-0.4
596
1.35
89
cris
is(P
olit
y)(0
.123
)(0
.429
)(0
.261
)(0
.012
)(0
.511
)(0
.988
)(0
.372
)(0
.227
)
Tra
de
asa
shar
eo
fGD
P-0
.000
1-0
.007
20.
0479
0.04
540.
0359
0.03
780.
0376
(0.0
00)
0.03
210.
0479
(0.9
89)
(0.3
40)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
01)
Infl
atio
nra
te(G
DP
defl
ator
)0.
0000
0.00
150.
0029
-0.0
062
0.00
440.
0052
-0.0
045
-0.0
038
0.00
29(0
.966
)(0
.068
)(0
.872
)(0
.739
)(0
.743
)(0
.748
)(0
.789
)(0
.771
)(0
.888
)
Gov
ern
men
tfi
nal
cons
umpt
ion
0.05
770.
2702
0.18
420.
2586
0.06
120.
1013
0.23
86(0
.000
)0.
0748
0.18
42
exp
end
itu
reas
ash
are
ofG
DP
(0.2
77)
(0.0
00)
(0.0
07)
(0.0
02)
(0.2
05)
(0.1
00)
(0.0
89)
(0.0
41)
Eth
no-I
ingu
isti
c4.
5379
4.16
30-4
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8-7
.027
6-4
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1-5
.789
6-6
.306
4-5
.162
8-4
.539
8
frac
tion
aliz
atio
nin
dex
(0.0
00)
(0.0
02)
(0.0
03)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
58)
M2
and
qu
asi-
mon
eyas
ash
are
0.00
000.
0001
0.01
501
0.04
58-0
.023
0-0
.025
50.
0382
(0.1
84)
-0.0
133
0.01
50
ofG
DP
(0.9
78)
(0.9
58)
(0.6
22)
(0.2
15)
(0.3
17)
(0.3
80)
(0.5
22)
(0.7
06)
122
Dep
ende
ntV
aria
ble:
Vol
atil
ity
Mea
sure
ofV
olat
ilit
yin
Gro
wth
deno
ted
inC
olum
nsin
Gro
wth
inG
DP
cons
tant
2000
US$
Poo
led
OL
SP
oole
dO
LS
wit
hP
rais
-Win
sten
pane
l-co
rrec
ted
Poo
led
GL
S*
Ran
do
mst
and
ard
erro
rs*
Eff
ects
Ex
pla
nat
ory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)
Std
Inte
r-S
tdIn
ter-
Wei
ghte
dW
eigh
ted
Inte
r-q
uar
tile
Wei
ghte
dS
tdD
evia
tion
qu
arti
leD
evia
tion
qu
arti
leS
tdD
evIn
ter-
Dev
iati
onq
uar
tile
Inte
r-qu
arti
le
Ind
exo
fsec
tora
ldi
vers
ific
atio
n9.
6564
-18.
1019
23.8
578
32.4
834
15.9
869
23.5
222
30.3
544
19.3
559
23.8
578
ofG
DP
(0.1
94)
(0.0
20)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
03)
Sh
are
ofse
rvic
esin
GD
P-0
.102
30.
0129
-0.0
826
-0.1
246
-0.0
448
-0.0
736
-0.1
112
-0.0
700
-0.0
826
(0.0
37)
(0.8
00)
(0.0
06)
(0.0
01)
(0.0
37)
(0.0
09)
(0.0
00)
(0.0
00)
(0.0
44)
Log
ofto
tal
popu
lati
on-0
.064
0-0
.440
32.
4762
3.88
431.
3995
2.25
723.
2625
1.78
672.
4762
(0.9
02)
(0.4
16)
(0.0
00)
(0.0
00)
(0.0
02)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
21)
Fu
elex
port
sas
%o
ftot
al-0
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4-0
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40.
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-0.0
043
0.02
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5-0
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40.
0197
0.01
12m
erch
andi
seex
port
s(0
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)(0
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)(0
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)(0
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)(0
.122
)(0
.448
)(0
.781
)(0
.082
)(0
.603
)
Fu
elim
port
sas
%o
fto
tal
-0.0
523
-0.0
781
0.10
470.
0624
0.14
110.
1231
0.07
09(0
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)0.
1405
0.10
47m
erch
andi
seim
port
s(0
.101
)(0
.019
)(0
.001
)(0
.090
)(0
.000
)(0
.000
)(0
.000
)(0
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)
Sh
are
ofa
gri
cult
ura
lex
port
sin
0.00
120.
0307
-0.0
064
-0.0
004
-0.0
049
-0.0
069
0.00
45(0
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)-0
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5-0
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4m
erch
andi
seex
port
s(0
.931
)(0
.028
)(0
.474
)(0
.968
)(0
.450
)(0
.384
)(0
.814
)(0
.593
)
Sh
are
ofm
anu
fact
uri
ng
-0.0
369
-0.0
255
0.01
850.
0411
0.00
050.
0011
0.04
90(0
.001
)0.
0147
0.01
85ex
port
sin
mer
chan
dise
expo
rts
(0.0
06)
(0.0
67)
(0.1
80)
(0.0
20)
(0.9
61)
(0.9
33)
(0.0
85)
(0.3
67)
123
Dep
ende
ntV
aria
ble:
Vol
atil
ity
Mea
sure
ofV
olat
ilit
yin
Gro
wth
deno
ted
inC
olum
nsin
Gro
wth
inG
DP
cons
tant
2000
US
$P
oole
dO
LS
Poo
led
OL
Sw
ith
Pra
is-W
inst
enpa
nel-
corr
ecte
dP
oole
dG
LS
*R
and
om
stan
dar
der
rors
*E
ffec
ts
Exp
lana
tory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)
Std
Inte
r-S
tdIn
ter-
Wei
ghte
dW
eigh
ted
Inte
r-q
uar
tile
Wei
ghte
dS
tdD
evia
tion
qu
arti
leD
evia
tion
qu
arti
leS
tdD
evIn
ter-
Dev
iati
onq
uar
tile
Inte
r-q
uar
tile
Sho
ckin
cru
de
petr
oleu
m0.
1244
0.63
19-1
.110
5-1
.607
60.
0029
-1.0
503
-1.2
928
-0.7
552
-1.1
105
pric
e(0
.000
)(0
.082
)(0
.000
)(0
.000
)(0
.712
)(0
.002
)(0
.001
)(0
.002
)(0
.029
)
Sho
ckin
nom
inal
exch
ange
rate
-0.0
308
-0.0
161
-0.5
465
-0.5
805
-0.1
955
-0.1
694
-0.4
614
-0.1
554
-0.5
465
(0.0
38)
(0.2
94)
(0.0
04)
(0.0
14)
(0.1
39)
(0.3
30)
(0.0
13)
(0.2
09)
(0.0
25)
Vol
atil
ity
info
odpr
oduc
tion
0.15
960.
0893
0.00
69-0
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4-0
.745
80.
0038
-0.0
677
-0.0
118
0.00
69in
dex
(0.0
11)
(0.1
69)
(0.8
90)
(0.4
28)
(0.0
01)
(0.9
34)
(0.1
75)
(0.7
04)
(0.9
10)
Sho
ckin
net
fina
ncia
lfl
ows
-0.0
002
-0.0
002
-0.0
000
-0.0
004
-0.0
002
-0.0
004
-0.0
006
-0.0
004
-0.0
000
(0.0
09)
(0.0
24)
(0.9
86)
(0.5
67)
(0.6
07)
(0.4
91)
(0.3
87)
(0.4
23)
(0.9
84)
Sho
ckin
rati
oo
fm
erch
andi
se0.
1245
0.06
52-0
.013
4-0
.027
10.
0029
-0.0
042
-0.0
119
0.00
60-0
.013
4
expo
rts
toim
port
s(0
.000
)(0
.018
)(0
.251
)(0
.043
)(0
.712
)(0
.655
)(0
.305
)(0
.355
)(0
.416
)
Aid
per
capi
tadi
sbur
sed
......
-0.0
559
-0.0
431
-0.0
316
-0.0
221
-0.0
354
-0.0
100
-0.0
559
(gra
nts)
(0.0
10)
(0.0
91)
(0.0
34)
(0.2
51)
(0.0
98)
(0.4
44)
(0.0
37)
(con
stan
t20
05U
S$)
12
4
Dep
ende
ntV
aria
ble:
Vol
atil
ity
Mea
sure
ofV
olat
ilit
yin
Gro
wth
deno
ted
inC
olum
nsin
Gro
wth
inG
DP
cons
tant
2000
US$
Poo
led
OL
SP
oole
dO
LS
wit
hP
rais
-Win
sten
pane
l-co
rrec
ted
Poo
led
GL
S*
Ran
dom
stan
dar
der
rors
*E
ffec
ts
Exp
lana
tory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)
Std
Inte
r-S
tdIn
ter-
Wei
ghte
dW
eigh
ted
Inte
r-qu
arti
leW
eigh
ted
Std
Dev
iati
onqu
arti
leD
evia
tion
quar
tile
Std
Dev
Inte
r-D
evia
tion
quar
tile
Inte
r-qu
arti
le
Vol
atil
ity
inai
dp
erca
pita
......
0.08
080.
1066
0.05
370.
0539
0.1
017
(0.0
05)
0.04
420.
0808
disb
urse
d(0
.032
)(0
.033
)(0
.033
)(0
.119
)(0
.086
)(0
.044
)
Tot
alde
btse
rvic
eas
ash
are
of
-0.0
056
-0.0
038
-0.0
310
-0.0
403
-0.0
573
-0.0
666
-0.0
197
-0.0
510
-0.0
310
expo
rts
ofg
oods
,ser
vice
san
d(0
.726
)(0
.818
)(0
.325
)(0
.186
)(0
.004
)(0
.005
)(0
.423
)(0
.001
)(0
.364
)in
com
e
Rsq
uar
e0.
806
0.76
70.
767
0.80
00.
696
0.63
7...
...0.
767
No
ofO
bser
vati
ons
339
339
8989
8989
8989
89
Hau
sman
test
(Fve
rsio
n)...
.....
...
....
......
...0.
540
Fte
stst
atis
tic
and
pva
lue
(0.6
58)
Not
e:1.
Dum
my
vari
able
sfo
rea
cho
fthe
5ye
arin
terv
al(f
rom
1961
-20
00)
are
incl
uded
inth
ees
tim
atio
ns,b
utre
sult
sar
eno
trep
orte
dhe
refo
rre
ason
so
fspa
ce.
The
omit
ted
cate
gory
isth
ela
stpe
riod
2001
-200
5.2.
P-v
alue
sar
ein
pare
nthe
ses.
3.T
heIn
dex
of
Sec
tora
lD
iver
sifi
cati
ono
fG
DP
isa
Her
find
ahl
inde
xo
fco
ncen
trat
ion
whe
rehi
gher
valu
esin
dica
tes
less
erdi
vers
ific
atio
n.4.
Wei
ghte
dre
gres
sion
sar
eus
edw
here
the
wei
ghti
sin
itia
lGD
Ppe
rca
pita
.5.
Whe
nw
eigh
ted
stan
dard
devi
atio
nis
used
and
esti
mat
ion
isdo
neus
ing
GL
S,th
eco
effi
cien
ton
aid
isne
gati
vean
dsi
gnif
ican
tw
ith
pva
lue
of
0.02
1an
dth
eco
effi
cien
ton
vola
tili
tyin
aid
ispo
siti
vew
ith
ap-
valu
eo
f0.
026.
6.W
hen
wei
ghte
dst
anda
rdde
viat
ion
isus
edan
des
tim
atio
nis
done
usin
ga
Ran
dom
Eff
ects
mod
el,
the
coef
fici
ento
nai
dis
nega
tive
and
mar
gina
lly
sign
ific
antw
ith
pva
lue
of0
.107
and
the
coef
fici
ento
nvo
lati
lity
inai
dis
posi
tive
wit
ha
p-va
lue
of0
.067
.7.
Whe
n
125
wei
ghte
din
ter-
quar
tile
isus
edan
des
tim
atio
nis
done
usin
ga
Ran
dom
Eff
ects
mod
el,
the
coef
fici
ento
nai
dis
nega
tive
and
not
sign
ific
antw
ith
ap-
valu
eo
f0.4
03an
dth
eco
effi
cien
ton
vola
tili
tyin
aid
ispo
siti
vew
ith
ap-
valu
eo
f0.1
72.
8.F
orth
eR
ando
mef
fect
sm
odel
,th
eF
vers
ion
oft
heH
ausm
ante
stis
repo
rted
inor
der
tote
stfo
ren
doge
neit
yo
fth
era
ndom
erro
rte
rmw
ith
expl
anat
ory
vari
able
s.A
ta
1%le
vel,
the
null
that
the
rand
omef
fect
sco
effi
cien
tsar
eef
fici
ent
cann
otbe
reje
cted
(see
Pag
es29
0-29
1in
Woo
lrid
gefo
ra
desc
ript
ion
oft
hete
st;
tran
sfor
med
vari
able
sfr
omth
efi
xed
effe
cts
mod
elar
eai
d,gr
oss
dom
esti
cin
vest
men
tand
M2
asa
shar
eo
fGD
P).
126
A.2
.7.
Vol
atil
ity
inG
row
thE
quat
ion
(Wei
ghte
dE
stim
ates
)-
Big
ger
Sam
ple
Dep
ende
ntV
aria
ble:
Vol
atil
ity
inM
easu
reo
fVol
atil
ity
inG
row
thde
note
din
Col
umns
Gro
wth
inG
DP
cons
tant
2000
US
$P
oole
dO
LS
Poo
led
OL
Sw
ith
Pra
is-W
inst
enpa
nel
corr
ecte
dP
oole
dG
LS
*R
and
om
stan
dar
der
rors
*E
ffec
ts
Ex
pla
nat
ory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)
Std
Inte
r-S
tdIn
ter-
Wei
ghte
Wei
ghte
dIn
ter-
Wei
ghte
dS
tdD
evia
tion
qu
arti
leD
evia
tion
qu
arti
led
Std
Inte
r-q
uar
tile
Inte
r-D
evia
tion
Dev
qu
arti
leq
uar
tile
Inte
rcep
t88
0.77
714
06.2
3396
.744
-242
.559
170.
158
-61.
814
98.7
6210
1.36
096
.744
(0.0
77)
(0.0
08)
(0.8
31)
(0.6
51)
(0.4
75)
(0.8
38)
(0.8
09)
(0.6
46)
(0.8
53)
Dum
my
for
Sub
-Sah
aran
Afr
ica
-2.8
47-3
.188
-1.2
26-1
.609
-0.4
81-0
.482
-0.8
27-0
.327
-1.2
26(0
.000
)(0
.000
)(0
.201
)(0
.094
)(0
.337
)(0
.416
)(0
.286
)(0
.511
)(0
.230
)
Dum
my
for
Lat
inA
mer
ica
-2.6
53-1
.640
1.01
7(0
.651
)2.
201
-1.5
90-0
.806
0.56
9-0
.472
1.01
7(0
.000
)(0
.006
)(0
.329
)(0
.122
)(0
.431
)(0
.760
)(0
.563
0(0
.648
)
En
vir
on
men
talv
ulne
rabi
lity
inde
x-5
.258
-5.7
16-0
.106
-2.4
36-2
.773
-4.6
70-4
.039
-5.1
47-0
.106
(0-1
scal
e)(0
.000
)(0
.000
)(0
.973
)(0
.480
)(0
.080
)(0
.006
)(0
.083
)(0
.000
)(0
.974
)
Init
ialG
DP
per
capi
ta0.
001
0.00
1-0
.003
-0.0
070.
0000
3-0
.003
-0.0
02-0
.002
-0.0
03
(0.0
00)
(0.0
00)
(0.6
41)
(0.2
67)
(0.9
91)
(0.1
71)
(0.5
98)
(0.3
92)
(0.5
37)
(con
stan
t20
00U
S$)
Dem
ocra
cyin
dex
(Pol
ity)
-0.1
23-0
.182
-0.3
68-0
.491
0.01
6-0
.013
-0.2
91-0
.063
-0.3
68(0
.011
)(0
.000
)(0
.014
)(0
.002
)(0
.821
)(0
.861
)(0
.008
)(0
.248
)(0
.009
)
127
Dep
ende
ntV
aria
ble:
Vol
atil
ity
inM
easu
reo
fVol
atil
ity
inG
row
thde
note
din
Col
umns
Gro
wth
inG
DP
cons
tant
2000
US$
Poo
led
OL
SP
oole
dO
LS
wit
hP
rais
-Win
sten
pane
lco
rrec
ted
Poo
led
GL
S*
Ran
do
mst
and
ard
erro
rs*
Eff
ects
Exp
lana
tory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)
Std
Inte
r-S
tdIn
ter-
Wei
ghte
Wei
ghte
dIn
ter-
Wei
ghte
dS
tdD
evia
tion
qu
arti
leD
evia
tion
qu
arti
led
Std
Inte
r-qu
arti
leIn
ter-
Dev
iati
onD
evq
uar
tile
qu
arti
le
Dum
my
Var
iabl
efo
rpo
liti
cal
cris
is0.
910
0.48
46.
381
(0.0
00)
7.10
00.
675
0.79
53.
760
0.86
8(0
.038
)6.
381
(Pol
ity)
(0.3
35)
(0.6
28)
(0.0
00)
(0.2
19)
(0.1
30)
(0.0
00)
(0.0
00)
Tra
de
asa
shar
eo
fGD
P-0
.006
-0.0
180.
003
(0.8
67)
0.Q
18-0
.001
0.00
60.
014
0.00
5(0
.408
)0.
003
(0.3
54)
(0.0
08)
(0.3
41)
(0.9
12)
(0.4
35)
(0.2
88)
(0.8
48)
Infl
atio
nra
te(G
DP
defl
ator
)0.
001
0.00
2-0
.001
-0.0
060.
008
0.00
6-0
.003
0.00
3(0
.739
)-0
.001
(0.4
40)
(0.0
11)
(0.9
65)
(0.7
35)
(0.4
22)
(0.6
37)
(0.7
98)
(0.9
70)
Go
ver
nm
ent
fina
lco
nsum
ptio
n0.
021
0.19
50.
022
(0.8
01)
0.13
3-0
.052
0.01
20.
022
-0.0
420.
022
expe
ndit
ure
asa
shar
eo
fGD
P(0
.624
)(0
.000
)(0
.135
)(0
.207
)(0
.783
)(0
.711
)(0
.216
)(0
.804
)
Eth
no-l
ingu
isti
cfr
acti
onal
izat
ion
4.09
93.
328
3.23
3(0
.199
)3.
095
-2.6
06-3
.372
-0.4
65-2
.973
3.23
3in
dex
(0.0
00)
(0.0
01)
(0.2
55)
(0.0
26)
(0.0
03)
(0.7
76)
(0.0
01)
(0.1
23)
M2
and
quas
i-m
oney
asa
shar
eo
f0.
0001
0.00
001
0.00
4(0
.409
)0.
009
-0.0
005
0.00
10.
007
0.00
2(0
.110
)0.
004
GD
P(0
.885
)(0
.991
)(0
.178
)(0
.844
)(0
.643
)(0
.296
)(0
.698
)
Inde
xo
fse
ctor
aldi
vers
ific
atio
no
f4.
240
-21.
681
6.79
0(0
.432
)15
.824
6.52
312
.294
15.9
208.
811
(0.0
07)
6.79
0G
DP
(0.4
75)
(0.0
01)
(0.1
00)
(0.0
88)
(0.0
00)
(0.0
16)
(0.4
44)
128
Dep
end
ent
Var
iab
le:
Vol
atil
ity
inM
easu
reo
fVol
atil
ity
inG
row
thd
enot
edin
Col
um
ns
Gro
wth
inG
DP
con
stan
t20
00U
S$
Poo
led
OL
SP
oole
dO
LS
wit
hP
rais
-Win
sten
pan
elco
rrec
ted
Poo
led
GL
S*
Ran
dom
stan
dar
der
rors
*E
ffec
ts
Exp
lan
ator
yva
riab
les
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Std
Inte
r-S
tdIn
ter-
Wei
ghte
Wei
ghte
dIn
ter-
Wei
ghte
dSt
dD
evia
tion
qu
arti
leD
evia
tion
qu
arti
led
Std
Inte
r-q
uar
tile
Inte
r-D
evia
tion
Dev
qu
arti
lequ
arti
le
Sh
are
ofs
ervi
ces
inG
DP
-0.0
140.
111
-0.0
95-0
.087
-0.0
34-0
.036
-0.0
89-0
.033
-0.0
95(0
.712
)(0
.007
)(0
.003
)(0
.015
)(0
.073
)(0
.101
)(0
.003
)(0
.056
)(0
.047
)
Log
oft
otal
pop
ula
tion
-0.6
08-1
.341
-0.3
010.
672
-0.2
790.
300
0.80
10.
037
(0.9
44)
-0.3
01(0
.134
)(0
.002
)(0
.804
)(0
.584
)(0
.673
)(0
.663
)(0
.420
)(0
.821
)
Sh
ock
incr
ude
pet
role
um
pri
ce0.
349
0.90
30.
217
(0.7
09)
-0.2
890.
272
0.03
9-0
.253
0.17
5(0
.528
)0.
217
(0.1
82)
(0.0
01)
(0.6
32)
(0.4
04)
(0.9
14)
(0.5
93)
(0.7
50)
Sh
ock
inn
omin
alex
chan
gera
te-0
.013
-0.0
03-0
.048
-0.1
580.
035
-0.0
30-0
.113
-0.0
07-0
.048
(0.1
89)
(0.7
64)
(0.7
60)
(0.1
39)
(0.7
40)
(0.6
02)
(0.2
28)
(0.8
95)
(0.7
20)
Vol
atil
ity
info
odp
rod
uct
ion
ind
ex0.
103
0.01
10.
338
(0.0
00)
0.20
40.
136
0.05
20.
149
0.07
4(0
.028
)0.
338
(0.0
67)
(0.8
51)
(0.0
12)
(0.0
02)
(0.2
08)
(0.0
15)
(0.0
00)
Sh
ock
inn
etfi
nan
cial
flow
s-0
.000
2-0
.000
2-0
.000
4-0
.000
4-0
.000
1-0
.000
1-0
.000
3-0
.000
1-0
.000
4(0
.010
)(0
.053
)(0
.480
)(0
.531
)(0
.641
)(0
.718
)(0
.585
)(0
.699
)(0
.534
)
Sh
ock
inra
tio
of
mer
chan
dis
e0.
107
0.05
1-0
.016
-0.0
30-0
.014
-0.0
24-0
.035
-0.0
12-0
.016
exp
orts
toim
por
ts(0
.000
)(0
.044
)(0
.486
)(0
.115
)(0
.227
)(0
.032
)(0
.024
)(0
.130
)(0
.503
)
129
Dep
ende
ntV
aria
ble:
Vol
atil
ity
inM
easu
reo
fVol
atil
ity
inG
row
thde
note
din
Col
umns
Gro
wth
inG
DP
con
stan
t200
0U
S$P
oole
dO
LS
Poo
led
OL
Sw
ith
Pra
is-W
inst
enpa
nelc
orre
cted
Poo
led
GL
S*
Ran
dom
stan
dar
der
rors
*E
ffec
ts
Exp
lana
tory
vari
able
s(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)(9
)
Std
Inte
r-S
tdIn
ter-
Wei
ghte
Wei
ghte
dIn
ter-
Wei
ghte
dS
tdD
evia
tion
quar
tile
Dev
iati
onqu
arti
led
Std
Inte
r-qu
arti
leIn
ter-
Dev
iati
onD
evqu
arti
lequ
arti
le
Aid
per
capi
tad
isb
urs
ed(g
rant
s)..
,...
-0.0
12-0
.029
-0.0
13-0
.028
-0.0
18-0
.016
-0.0
12(0
.692
)(0
.322
)(0
.384
)(0
.051
)(0
.384
)(0
.132
)(0
.678
)(c
onst
ant2
005
US
$)
Vol
atil
ity
inai
dp
erca
pita
disb
urse
d...
...0.
094
(0.0
21)
0.08
10.
028
0.02
40.
060
0.02
7(0
.064
)0.
0938
(0.0
59)
(0.1
73)
(0.2
89)
(0.0
70)
(0.0
28)
Tot
alde
btse
rvic
eas
ash
are
of
0.00
70.
015
-0.0
64-0
.091
-0.0
004
-0.0
06-0
.052
-0.0
10-0
.064
expo
rts
ofg
oods
,se
rvic
esan
din
com
e(0
.640
)(0
.318
)(0
.091
)(0
.014
)(0
.982
)(0
.780
)(0
.033
)(0
.530
)(0
.088
)
Rsq
uar
e0.
789
0.77
70.
572
0.60
70.
362
0.38
6...
...0.
572
No
ofO
bser
vati
ons
416
416
141
141
141
141
141
141
141
Hau
sman
test
(Fve
rsio
n)..
....
...
......
......
...
2.71
0
Fte
stst
atis
tic
and
pva
lue
(0.0
49)
Not
e:1.
Dum
my
vari
able
sfo
rea
cho
fthe
5ye
arin
terv
al(f
rom
1961
-20
00)
are
incl
uded
inth
ees
tim
atio
ns,
butr
esul
tsar
eno
tre
port
edhe
refo
rre
ason
so
fspa
ce.
The
omit
ted
cate
gory
isth
ela
stpe
riod
2001
-200
5.2.
P-v
alue
sar
ein
pare
nthe
ses.
3.T
heIn
dex
of
Sec
tora
lD
iver
sifi
cati
ono
fG
DP
isa
Her
find
ahl
inde
xo
fco
ncen
trat
ion
whe
rehi
gher
valu
esin
dica
tes
less
erdi
vers
ific
atio
n.4.
Whe
nw
eigh
ted
stan
dard
devi
atio
nis
used
and
esti
mat
ion
isdo
neus
ing
GL
S,th
eco
effi
cien
ton
aid
isne
gati
vean
dno
tsig
nifi
cant
wit
hp
valu
eo
f0.4
52an
dth
eco
effi
cien
ton
vola
tili
tyin
aid
ispo
siti
vew
ith
ap-
valu
eo
f0.3
17.
5.W
hen
wei
ghte
dst
anda
rdde
viat
ion
isus
edan
des
tim
atio
nis
done
usin
ga
Ran
dom
Eff
ects
mod
el,
the
coef
fici
ent
onai
dis
nega
tive
and
not
sign
ific
ant
wit
hp
valu
eo
f0.
398
130
and
the
coef
fici
ento
nvo
lati
lity
inai
dis
posi
tive
wit
ha
p-va
lue
of0
.225
.6.
Whe
nw
eigh
ted
inte
r-qu
arti
leis
used
and
esti
mat
ion
isdo
neus
ing
aR
ando
mE
ffec
tsm
odel
,th
eco
effi
cien
ton
aid
isne
gati
vean
dsi
gnif
ican
twit
ha
p-va
lue
of
0.09
4an
dth
eco
effi
cien
ton
vola
tili
tyin
aid
ispo
siti
vew
ith
ap-
valu
eo
f0.3
40.
7.F
orth
eR
ando
mef
fect
sm
odel
,th
eF
vers
ion
oft
heH
ausm
ante
stis
repo
rted
inor
der
tote
stfo
ren
doge
neit
yo
fthe
rand
omer
ror
term
wit
hex
plan
ator
yva
riab
les.
Ata
1%le
vel,
the
null
that
the
rand
omef
fect
sco
effi
cien
tsar
eef
fici
ent
cann
otbe
reje
cted
(see
Pag
es29
0-29
1in
Woo
lrid
gefo
ra
desc
ript
ion
of
the
test
;tr
ansf
orm
edva
riab
les
from
the
fixe
def
fect
sm
odel
are
aid,
gros
sdo
mes
tic
inve
stm
enta
ndM
2as
ash
are
ofG
DP)
.
131
A.2
.8:
IV(P
oole
d2S
LS
)W
eigh
ted
Sin
gle
Equ
atio
nE
stim
ates
wit
hR
obus
t*S
tand
ard
Err
ors
-Gro
wth
Equ
atio
n
Dep
ende
ntV
aria
ble:
Vol
atil
ity
inG
row
thin
GD
Pco
nsta
ntM
easu
reo
fVol
atil
ity
inG
row
thde
note
din
Col
umns
2000
US$
Ex
pla
nat
ory
vari
able
s(1
)(2
)(3
)(4
)
Std
Dev
iati
onIn
ter-
qu
arti
leW
eigh
ted
Std
Wei
ghte
dIn
ter-
Dev
iati
onq
uar
tile
Inte
rcep
t32
8.86
5(0
.119
)41
1.58
99(0
.095
)28
5.57
9(0
.213
)30
1.79
9(0
.180
)
Vol
atil
ity
inG
row
th-0
.009
(0.9
39)
-0.1
62(0
.469
)0.
085
(0.7
07)
0.05
2(0
.813
)
Du
mm
yfo
rS
ub
-Sah
aran
Afr
ica
-1.2
74(0
.045
)-1
.317
(0.0
37)
-1.3
44(0
.029
)-1
.302
(0.0
36)
Du
mm
yfo
rL
atin
Am
eric
a-3
.879
(0.0
02)
-3.8
98(O
.oI
1)-3
.625
(0.0
37)
-3.7
97(0
.018
)
Env
iron
men
talv
ulne
rabi
lity
inde
x(0
-1sc
ale)
4.27
3(0
.007
)4.
264
(0.0
06)
4.22
7(0
.009
)4.
287
(0.0
08)
Init
ial
GD
Ppe
rca
pit
a-0
.002
(0.5
17)
-0.0
02(0
.459
)-0
.002
(0.4
70)
-0.0
02(0
.473
)
(con
stan
t200
0U
S$)
Dem
ocra
cyin
dex
(Pol
ity)
0.03
7(0
.639
)-0
.004
(0.9
58)
0.05
0(0
.456
)0.
045
(0.4
84)
Du
mm
yva
riab
lefo
rpo
liti
calc
risi
s(P
olit
y)-0
.446
(0.5
88)
0.05
8(0
.954
)-0
.638
(0.4
11)
-0.5
51(0
.461
)
Tra
de
asa
shar
eo
fG
DP
-0.0
10(0
.306
)-0
.008
(0.4
26)
-0.0
09(0
.369
)-0
.010
(0.3
42)
Infl
atio
nra
te(G
DP
defl
ator
)0.
007
(0.5
32)
0.00
7(0
.485
)0.
005
(0.6
46)
0.00
6(0
.586
)
Gro
ssca
pita
lfo
rmat
ion
assh
are
ofG
DP
0.06
4(0
.056
)0.
054
(0.1
46)
0.06
6(0
.056
)0.
066
(0.0
53)
132
Dep
ende
ntV
aria
ble:
Vol
atil
ity
inG
row
thin
GD
Pco
nsta
ntM
easu
reo
fVol
atil
ity
inG
row
thde
note
din
Col
umns
2000
US$
Ex
pla
nat
ory
vari
able
s(1
)(2
)(3
)(4
)
Std
Dev
iati
onIn
ter-
quar
tile
Wei
ghte
dS
tdW
eigh
ted
Inte
r-D
evia
tion
qu
arti
le
To
tal
yars
ofs
choo
ling
0.10
9(0
.628
)0.
125
(0.6
25)
0.15
1(0
.602
)0.
129
(0.6
33)
(mal
ean
dfe
mal
e)
Go
ver
nm
ent
fina
lco
nsum
ptio
nex
pend
itur
eas
ash
are
of
-0.0
39(0
.403
)-0
.040
(0.2
39)
-0.0
44(0
.260
)-0
.040
(0.2
67)
GD
P
Eth
no-l
ingu
isti
cfr
acti
onal
izat
ion
inde
x0.
579
(0.5
75)
0.62
0(0
.521
)0.
748
(0.4
91)
0.63
4(0
.530
)
M2
and
quas
i-m
oney
asa
shar
eo
fGD
P0.
004
(0.4
05)
0.00
5(0
.032
)0.
004
(0.0
66)
0.00
4(0
.072
)
Aid
per
cap
ita
disb
urse
d(g
rant
s)0.
053
(0.0
04)
0.05
4(0
.010
)0.
052
(0.0
15)
0.05
2(0
.011
)
(con
stan
t20
05U
S$)
Vol
atil
ity
inai
dp
erca
pita
disb
urse
d-0
.123
(0.0
49)
-0.1
12(0
.116
)-0
.134
(0.0
76)
-0.1
27(0
.077
)
For
eign
dir
ecti
nves
tmen
tas
ash
are
of
GD
P0.
068
(0.4
35)
0.08
1(0
.364
)0.
065
(0.4
84)
0.06
4(0
.497
)
To
tal
deb
tse
rvic
eas
ash
are
ofe
xpor
tso
fgoo
ds,s
ervi
ces
-0.0
60(0
.010
)-0
.064
(0.0
16)
-0.0
57(0
.043
)-0
.058
(0.0
44)
and
inco
me
Rsq
uar
e(u
nce
nter
ed)
0.63
00.
627
0.61
30.
623
No
of
Obs
erva
tion
s11
111
111
111
1
133
Dep
ende
ntV
aria
ble:
Vol
atil
ity
inG
row
thin
GD
Pco
nsta
ntM
easu
reo
fVol
atil
ity
inG
row
thde
note
din
Col
umns
2000
US$
Exp
lana
tory
vari
able
s(1
)(2
)(3
)(4
)
Std
Dev
iati
onIn
ter-
quar
tile
Wei
ghte
dS
tdW
eigh
ted
Inte
r-D
evia
tion
quar
tile
Han
sen
JS
tati
stic
(pva
lues
inpa
rent
hese
s)17
.334
(0.0
98)
13.4
99(0
.262
)13
.338
(0.2
72)
13.4
04(0
.268
)
*Rob
ust t
oar
bitr
ary
hete
rosk
edas
tici
tyan
dcl
uste
red
by
coun
try.
Not
e:1.
Dum
my
vari
able
sfo
rea
cho
fthe
5ye
arin
terv
al(f
rom
1961
-20
00)
are
incl
uded
inth
ees
tim
atio
ns,b
utre
sults
are
not
repo
rted
here
for
reas
ons
ofs
pace
.T
heom
itted
cate
gory
isth
ela
stpe
riod
2001
-200
5.2.
P-v
alue
sar
ein
pare
nthe
ses.
3.L
ist
of
inst
rum
ents
are:
colo
nial
dum
my
vari
able
s(S
pain
,F
ranc
e,U
K,
US,
Port
ugal
,N
ethe
rlan
ds);
aid
disb
urse
dat
the
star
to
fth
e5
year
inte
rval
(ini
tial
aid)
;lif
eex
pect
ancy
;in
fant
mor
tali
tyra
te;
dist
ance
from
equa
tor;
anin
tera
ctiv
ebe
twee
nin
itial
aid
and
the
dem
ocra
cyin
dex;
and
afe
wre
gres
sors
from
the
vola
tili
tyin
grow
theq
uati
on(f
ract
iona
liza
tion
inde
x,sh
are
of
serv
ices
,po
pula
tion,
shoc
kto
the
pric
eo
fcru
depe
trol
eum
,vol
atil
ity
info
odpr
oduc
tion
and
shoc
kto
the
rati
oo
fmer
chan
dise
expo
rts
toim
port
s)4.
Whe
na
rand
omef
fect
sm
odel
isfi
tted,
acro
ssal
l4
grow
thvo
lati
lity
mea
sure
sus
ed,
we
find
evid
ence
that
aid
rais
esec
onom
icgr
owth
(all
pva
lues
are
low
erth
an0.
02)
and
that
vola
tili
tyin
aid
redu
ces
econ
omic
grow
th(p
valu
esar
elo
wer
than
0.10
).
13
4
A.2
.9:
IV(P
anel
-2S
LS
)W
eigh
ted
Sin
gle
Equ
atio
nE
stim
ates
wit
hR
obus
t*S
tan
dar
dE
rro
rs-
Vol
atil
ity
inG
row
thE
quat
ion
(Big
ger
Sam
ple)
Dep
ende
ntV
aria
ble:
Vol
atil
ity
inG
row
thin
GD
Pco
nsta
ntM
easu
reo
fVol
atil
ity
inG
row
thde
note
din
Col
umns
2000
US$
Ex
pla
nat
ory
vari
able
s(1
)(2
)(3
)(4
)
Std
Dev
iati
onIn
ter-
qu
arti
leW
eigh
ted
Std
Dev
iati
onW
eigh
ted
Inte
r-qu
arti
le
Inte
rcep
t41
0.21
2(0
.258
)24
8.41
8(0
.537
)77
.414
(0.7
72)
-40.
160
(0.9
05)
Dum
my
for
Su
b-S
ahar
anA
fric
a-0
.817
(0.3
34)
-0.7
51(0
.314
)-0
.782
(0.1
66)
-0.6
09(0
.330
)
Dum
my
for
Lat
inA
mer
ica
-1.1
56(0
.577
)-0
.752
(0.6
35)
-1.4
64(0
.261
)-1
.149
(0.3
92)
En
vir
on
men
talv
ulne
rabi
lity
inde
x(0
-1sc
ale)
-3.9
17(0
.121
)-5
.804
(0.0
26)
-4.1
61(0
.026
)-5
.735
(0.0
09)
Init
ialG
DP
per
capi
ta(c
onst
ant2
000
US
$)-0
.002
(0.5
38)
-0.0
03(0
.307
)-0
.003
(0.3
16)
-0.0
04(0
.208
)
Dem
ocra
cyin
dex
(Pol
ity)
-0.2
23(0
.106
)-0
.278
(0.0
15)
-0.0
56(0
.526
)-0
.071
(0.4
26)
Du
mm
yv
aria
ble
for
poli
tica
lcri
sis
(Pol
ity)
3.74
95(0
.006
)3.
391
(0.0
00)
1.46
5(0
.054
)1.
310
(0.0
28)
Tra
de
asa
shar
eo
fGD
P0.
009
(0.5
07)
0.02
2(0
.082
)-0
.001
(0.9
45)
0.00
6(0
.602
)
Infl
atio
nra
te(G
DP
defl
ator
)0.
003
(0.8
58)
0.00
2(0
.888
)0.
005
(0.6
65)
0.00
3(0
.770
)
Go
ver
nm
ent
fina
lco
nsum
ptio
nex
pend
itur
eas
ash
are
of
-0.0
16(0
.798
)0.
073
(0.2
05)
-0.0
06(0
.890
)0.
049
(0.3
91)
GD
P
Eth
no-l
ingu
isti
cfr
acti
onal
izat
ion
inde
x-1
.115
(0.5
05)
-2.8
63(0
.065
)-1
.970
(0.0
65)
-3.1
70(0
.006
)
135
Dep
ende
ntV
aria
ble:
Vol
atil
ity
inG
row
thin
GD
Pco
nsta
ntM
easu
reo
fVol
atil
ity
inG
row
thde
note
din
Col
umns
2000
US$
Exp
lana
tory
vari
able
s(1
)(2
)(3
)(4
)
Std
Dev
iati
onIn
ter-
qu
arti
leW
eigh
ted
Std
Dev
iati
onW
eigh
ted
Inte
r-qu
arti
le
M2
and
quas
i-m
oney
asa
shar
eo
fGD
P0.
004
(0.1
85)
0.00
7(0
.027
)0.
001
(0.3
89)
0.00
2(0
.255
)
Ind
exo
fsec
tora
ldiv
ersi
fica
tion
ofG
DP
13.0
18(0
.019
)21
.980
(0.0
00)
8.23
1(0
.043
)13
.246
(0.0
04)
Sh
are
ofs
ervi
ces
inG
DP
-0.0
60(0
.035
)-0
.052
(0.0
81)
-0.0
21(0
.303
)-0
.021
(0.3
93)
Log
oft
otal
popu
lati
on0.
533
(0.5
96)
1.25
7(0
.173
)-0
.061
(0.9
32)
0.36
6(0
.638
)
Sho
ckin
crud
ep
etro
leu
mpr
ice
-0.2
68(0
.581
)-0
.685
(0.1
50)
0.14
7(0
.704
)-0
.048
(0.9
14)
Sho
ckin
nom
inal
exch
ange
rate
o.04
4(0
.612
)-0
.105
(0.2
53)
0.07
9(0
.170
)0.
0003
(0.9
96)
Vol
atil
ity
info
odp
rod
uct
ion
inde
x0.
238
(0.0
38)
0.07
0(0
.226
)0.
154
(0.0
20)
0.06
0(0
.278
)
Sho
ckin
net
fina
ncia
lfl
ows
-0.0
003
(0.2
19)
-0.0
002
(0.3
24)
-0.0
001
(0.4
21)
-0.0
001
(0.7
26)
Sho
ckin
rati
oof
mer
chan
dis
eex
port
sto
impo
rts
-0.0
05(0
.807
)-0
.026
(0.1
16)
-0.0
05(0
.681
)-0
.016
(0.2
35)
Aid
per
capi
tadi
sbur
sed
(gra
nts)
(con
stan
t200
5U
S$)
-0.0
25(0
.307
)-0
.049
(0.0
76)
-0.0
24(0
.210
)-0
.044
(0.0
38)
Vol
atil
ity
inai
dp
erca
pit
adi
sbur
sed
0.21
1(0
.009
)0.
160
(0.0
26)
0.10
7(0
.054
)0.
102
(0.0
97)
To
tald
ebt
serv
ice
asa
shar
eo
fexp
orts
ofg
oods
,ser
vice
s-0
.017
(0.5
34)
-0.0
32(0
.210
)-0
.014
(0.4
38)
-0.0
18(0
.308
)
and
inco
me
136
Dep
ende
ntV
aria
ble
:V
olat
ilit
yin
Gro
wth
inG
DP
con
stan
tM
easu
reo
fVol
atil
ity
inG
row
thd
enot
edin
Col
um
ns
2000
US$
Exp
lana
tory
vari
able
s(1
)(2
)(3
)(4
)
Std
Dev
iati
onIn
ter-
qu
arti
leW
eigh
ted
Std
Dev
iati
onW
eigh
ted
Inte
r-qu
arti
le
Rsq
uare
(un
cen
tere
d)
0.77
00.
827
0.62
20.
609
No
ofO
bse
rvat
ion
s13
813
813
813
8
Han
sen
JS
tati
stic
(pva
lues
inp
aren
thes
es)
6.54
9(0
.477
)8.
305
(0.3
06)
10.3
57(0
.169
)13
.517
(0.0
60)
*Rob
ustt
oar
bitr
ary
hete
rosk
edas
tici
tyan
dcl
uste
red
byco
untr
y
Not
e:1.
Dum
my
vari
able
sfo
rea
cho
fthe
5ye
arin
terv
al(f
rom
1961
-20
00)
are
incl
uded
inth
ees
tim
atio
ns,b
utre
sult
sar
eno
trep
orte
dhe
refo
rre
ason
so
fspa
ce.
The
omit
ted
cate
gory
isth
ela
stpe
riod
2001
-200
5.2.
P-v
alue
sar
ein
pare
nthe
ses.
3.L
ist
of
inst
rum
ents
are:
colo
nial
dum
my
vari
able
s(S
pain
,F
ranc
e,U
K,
US,
Por
tuga
l,N
ethe
rlan
ds);
aid
disb
urse
dat
the
star
to
fth
e5
year
inte
rval
(ini
tial
aid)
;li
feex
pect
ancy
;in
fant
mor
tali
tyra
te;
dist
ance
from
equa
tor;
anin
tera
ctiv
ebe
twee
nin
itia
lai
dan
dth
ede
moc
racy
inde
x.4.
Whe
na
rand
omef
fect
sm
odel
isfi
tted
,w
efi
ndev
iden
ceth
atai
dlo
wer
sgr
owth
vola
tili
ty(a
llp
valu
esar
elo
wer
than
0.07
)ex
cept
whe
nst
anda
rdde
viat
ion
isus
edas
agr
owth
vola
tili
tym
easu
re;
and
that
vola
tili
tyin
aid
rais
esvo
lati
lity
ingr
owth
(pva
lues
are
low
erth
an0.
06)
exce
ptw
hen
wei
ghte
din
ter-
quar
tile
isus
ed(p
valu
eis
0.12
9).
137
A.2
.10
3SL
SW
eigh
ted
Est
imat
es
(En
dog
enou
sV
aria
bles
:G
row
than
dV
olat
ilit
yin
Gro
wth
;A
idan
dV
olat
ilit
yin
Aid
)
Dep
ende
ntV
aria
ble:
Vol
atil
ity
inG
row
thM
easu
reo
fVol
atil
ity
inG
row
thde
note
din
Col
umns
inG
DP
cons
tant
2000
US
$(1
)(2
)(3
)4)
Std
Dev
iati
onIn
ter-
quar
tile
Wei
ghte
dSt
dD
evia
tion
Wei
ghte
dIn
ter-
quar
tile
Exp
lana
tory
vari
able
sG
row
thV
olat
ilit
yG
row
thV
olat
ilit
yG
row
thV
olat
ilit
yG
row
thV
olat
ilit
yE
quat
ion
Eq
uat
ion
Equ
atio
nE
qu
atio
nE
quat
ion
Equ
atio
nE
quat
ion
Equ
atio
n
Inte
rcep
t19
0.33
1-7
02.2
9526
4.31
5-5
35.9
3019
7.24
0-4
92.9
4523
2.87
5-4
18.5
43(0
.401
)(0
.389
)(0
.231
)(0
.430
)(0
.356
)(0
.395
)(0
.259
)(0
.471
)
Vol
atil
ity
ingr
owth
0.15
7...
0.08
6(0
.590
)...
0.23
5(0
.157
)...
0.19
0...
(0.1
99)
(0.2
50)
Du
mm
yfo
rS
ub
-Sah
aran
Afr
ica
-1.2
34-0
.584
-1.1
62-0
.266
-1.2
96-0
.281
-1.2
470.
116
(0.0
59)
(0.6
24)
(0.0
72)
(0.7
85)
(0.0
49)
(0.7
40)
(0.0
54)
(0.8
90)
Du
mm
yfo
rL
atin
Am
eric
a-3
.621
-1.4
34-4
.085
-0.6
49-3
.639
-1.0
98-4
.056
-0.3
07
(0.0
13)
(0.5
27)
(0.0
03)
(0.7
26)
(0.0
11)
(0.4
95)
(0.0
03)
(0.8
97)
Env
iron
men
talv
uln
erab
ilit
yin
dex
4.13
3-3
.531
4.35
0-2
.624
4.05
5(0
.011
)-4
.806
4.34
3-5
.462
(0.0
10)
(0.3
55)
(0.0
06)
(0.3
86)
(0.0
66)
(0.0
06)
(0.0
36)
(0-1
scal
e)
138
Dep
ende
ntV
aria
ble:
Vol
atil
ity
inG
row
thM
easu
reo
fVol
atil
ity
inG
row
thde
note
din
Col
umns
inG
DP
cons
tant
2000
US$
(1)
(2)
(3)
4)
Std
Dev
iati
onIn
ter-
qu
arti
leW
eigh
ted
Std
Dev
iati
onW
eigh
ted
Inte
r-qu
arti
le
Ex
pla
nat
ory
vari
able
sG
row
thV
olat
ilit
yG
row
thV
olat
ilit
yG
row
thV
olat
ilit
yG
row
thV
olat
ilit
yE
qu
atio
nE
qu
atio
nE
qu
atio
nE
qu
atio
nE
qu
atio
nE
qu
atio
nE
qu
atio
nE
quat
ion
Init
ial
GD
Pp
erca
pita
-0.0
02-0
.021
-0.0
02-0
.015
-0.0
02-0
.012
-0.0
02-0
.010
(0.5
38)
(0.0
39)
(0.5
19)
(0.0
77)
(0.4
33)
(0.0
83)
(0.4
53)
(0.1
50)
(con
stan
t20
00U
S$)
Dem
ocra
cyin
dex
(Pol
ity)
0.12
8-0
.259
0.10
4(0
.262
)-0
.243
0.10
3(0
.212
)-0
.119
0.09
3-0
.110
(0.1
57)
(0.0
69)
(0.0
37)
(0.2
39)
(0.2
45)
(0.2
76)
Du
mm
yva
riab
lefo
rpo
liti
calc
risi
s(P
olit
y)-1
.395
4.47
2-1
.013
3.80
0(0
.000
)-1
.068
2.16
0(0
.010
)-0
.908
1.83
7(0
.122
)(0
.000
)(0
.275
)(0
.161
)(0
.208
)(0
.026
)
Tra
de
asa
shar
eo
fGD
P-0
.011
0.00
6-0
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0.01
7(0
.258
)-0
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0.00
7(0
.584
)-0
.012
0.01
6(0
.272
)(0
.721
)(0
.218
)(0
.270
)(0
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)(0
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)
Infl
atio
nra
te(G
DP
defl
ator
)0.
001
-0.0
080.
003
(0.7
77)
-0.0
10-0
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50
.00
70.
001
0.00
9(0
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)(0
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)(0
.546
)(0
.968
)(0
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)(0
.908
)(0
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)
Gro
ssca
pit
alfo
rmat
ion
assh
are
ofG
DP
0.08
3...
0.07
7...
0.09
0(0
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)...
0.08
6...
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12)
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19)
(0.0
07)
Tot
aly
ears
ofs
choo
ling
0.22
2...
.0.
193
(0.4
00)
...0.
247
(0.3
09)
...0.
215
...(0
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)(0
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)(m
ale
and
fem
ale)
139
Dep
ende
ntV
aria
ble:
Vol
atil
ity
inG
row
thM
easu
reo
fVol
atil
ity
inG
row
thde
note
din
Col
umns
inG
DP
cons
tant
2000
US$
(1)
(2)
(3)
4)
Std
Dev
iati
onIn
ter-
qu
arti
leW
eigh
ted
Std
Dev
iati
onW
eigh
ted
Inte
r-qu
arti
le
Ex
pla
nat
ory
vari
able
sG
row
thV
olat
ilit
yG
row
thV
olat
ilit
yG
row
thV
olat
ilit
yG
row
thV
olat
ilit
yE
quat
ion
Eq
uat
ion
Equ
atio
nE
qu
atio
nE
qu
atio
nE
qu
atio
nE
quat
ion
Equ
atio
n
Go
ver
nm
ent
fina
lco
nsum
ptio
nex
pend
itur
e-0
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0.09
7-0
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0.05
6(0
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0.08
8(0
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)-0
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0.08
6as
ash
are
ofG
DP
(0.1
50)
(0.3
58)
(0.2
07)
(0.1
73)
(0.2
27)
(0.2
54)
Eth
no-I
ingu
isti
cfr
acti
onal
izat
ion
inde
x0.
874
-3.8
390.
485
(0.6
58)
-2.7
710.
891
-3.1
700.
586
-3.1
42(0
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)(0
.136
)(0
.195
)(0
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)(0
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)(0
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)(0
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)
M2
and
quas
i-m
oney
asa
shar
eo
fGD
P0.
004
0.00
50.
003
(0.4
80)
-2.7
71-0
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0.00
3(0
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004
0.00
3(0
.450
)(0
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)(0
.195
)(0
.173
)(0
.453
)(0
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)
Inde
xo
fse
ctor
aldi
vers
ific
atio
no
fGD
P...
9.81
8...
12.1
01...
14.3
78...
18.2
65(0
.365
)(0
.184
)(0
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)(0
.019
)
Sha
reo
fse
rvic
esin
GD
P'"
-0.0
54...
-0.0
53...
-0.0
19...
-0.0
16(0
.346
)(0
.266
)(0
.633
)(0
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)
Log
oft
ota
lpo
pula
tion
'"1.
474
...1.
648
(0.1
28)
...0.
927
(0.3
15)
...1.
338
(0.2
55)
(0.1
49)
Sho
ckin
cru
de
petr
oleu
mpr
ice
'"-0
.095
...-0
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...-0
.234
...-0
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(0.8
99)
(0.5
12)
(0.6
29)
(0.2
50)
140
Dep
ende
ntV
aria
ble:
Vol
atil
ity
inG
row
thM
easu
reo
fVol
atil
ity
inG
row
thde
note
din
Col
umns
inG
DP
con
stan
t20
00U
S$
(1)
(2)
(3)
4)
Std
Dev
iati
onIn
ter-
quar
tile
Wei
ghte
dS
tdD
evia
tion
Wei
ghte
dIn
ter-
quar
tile
Ex
pla
nat
ory
vari
able
sG
row
thV
olat
ilit
yG
row
thV
olat
ilit
yG
row
thV
olat
ilit
yG
row
thV
olat
ilit
yE
quat
ion
Equ
atio
nE
qu
atio
nE
quat
ion
Equ
atio
nE
quat
ion
Eq
uat
ion
Equ
atio
n
Sho
ckin
nom
inal
exch
ange
rate
...0.
040(
0.75
7...
-0.1
67...
0.11
9(0
.193
)...
0.03
1)
(0.1
22)
(0.7
39)
Vol
atil
ity
info
odpr
oduc
tion
inde
x...
0.20
6...
0.06
5(0
.320
)...
0.10
5...
0.02
0(0
.009
)(0
.061
)(0
.720
)
Sho
ckin
net
fina
ncia
lfl
ows
...-0
.000
3...
-0.0
003
...-0
.000
1...
-0.0
001
(0.4
80)
(0.3
98)
(0.6
77)
(0.6
71)
Sho
ckin
rati
oo
fm
erch
andi
seex
port
sto
...0.
028
...-0
.008
...0.
022
(0.1
67)
...0.
007
impo
rts
(0.2
27)
(0.6
59)
(0.6
63)
Aid
per
cap
ita
disb
urse
d(g
rant
s)(c
onst
ant
0.04
8-0
.020
0.05
1(0
.002
)-0
.028
0.04
7(0
.005
)-0
.029
0.05
0-0
.040
2005
US
$)(0
.004
)(0
.520
)(0
.273
)(0
.196
)(0
.003
)(0
.072
)
Vol
atil
ity
inai
dp
erca
pita
disb
urse
d-0
.137
0.34
2-0
.115
0.19
8(0
.011
)-0
.119
0.22
0(0
.001
)-0
.111
0.17
5(0
.018
)(0
.000
)(0
.042
)(0
.024
)(0
.029
)(0
.009
)
Tot
ald
ebt
serv
ice
asa
shar
eo
fexp
orts
of
-0.0
51-0
.003
-0.0
52-0
.015
-0.0
45-0
.024
-0.0
46-0
.030
good
s,se
rvic
esan
din
com
e(0
.043
)(0
.945
)(0
.038
)(0
.670
)(0
.078
)(0
.442
)(0
.067
)(0
.342
)
141
Dep
ende
ntV
aria
ble:
Vol
atil
ity
inG
row
thM
easu
reo
fVol
atil
ity
inG
row
thde
note
din
Col
umns
inG
DP
cons
tant
2000
US$
(1)
(2)
(3)
4)
Std
Dev
iati
onIn
ter-
quar
tile
Wei
ghte
dS
tdD
evia
tion
Wei
ghte
dIn
ter-
quar
tile
Exp
lana
tory
vari
able
sG
row
thV
olat
ilit
yG
row
thV
olat
ilit
yG
row
thV
olat
ilit
yG
row
thV
olat
ilit
yE
quat
ion
Equ
atio
nE
quat
ion
Equ
atio
nE
quat
ion
Equ
atio
nE
quat
ion
Equ
atio
n
For
eign
dire
ctin
vest
men
tas
ash
are
of
0.03
9...
0.04
2(0
.609
)...
0.02
2(0
.790
)...
0.02
4...
GD
P(0
.633
)(0
.776
)
No
ofO
bser
vati
ons
105
105
105
105
105
105
105
105
Join
tte
ston
aid
and
vola
tili
tyin
aid
24.7
7(0
.000
)15
.63
(0.0
04)
18.7
2(0
.000
)15
.180
(0.0
04)
Wal
dte
stst
atis
tic
wit
hp
valu
ein
pare
nthe
ses
Not
e:1.
Dum
my
vari
able
sfo
rea
cho
fthe
5ye
arin
terv
al(f
rom
1961
-20
00)
are
incl
uded
inth
ees
tim
atio
ns,b
utre
sult
sar
eno
trep
orte
dhe
refo
rre
ason
so
fspa
ce.
The
omitt
edca
tego
ryis
the
last
peri
od20
01-2
005.
2.P
-val
ues
are
inpa
rent
hese
s.2.
Lis
to
fin
stru
men
tsar
e:co
loni
aldu
mm
yva
riab
les
(Spa
in,
Fran
ce,
UK
,U
S,P
ortu
gal,
Net
herl
ands
);ai
ddi
sbur
sed
atth
est
art
of
the
5ye
arin
terv
al(i
nitia
lai
d);
life
expe
ctan
cy;
infa
ntm
orta
lity
rate
;di
stan
cefr
omeq
uato
r;an
inte
ract
ive
betw
een
initi
alai
dan
dth
ede
moc
racy
inde
x.
142