DP2010-33

Financ ial Cr isis in As ia:
Its Genes is, Sever ity a nd Impact on
Poverty a nd Hu nger*
Katsushi S. IMAI
Raghav GAIHA
Ganesh THA PA
Samuel Kobina ANNIM
November 12, 2010

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Financial Crisis in Asia: Its Genesis, Severity and Impact on Poverty and
HungerKatsushi S. Imai
University of Manchester, United Kingdom &Research Institute for Economics & Business Administration (RIEB), Kobe University, Japan
Raghav Gaiha
Massachusetts Institute of Technology, United States of America & University of Delhi,
India
Ganesh Thapa
International Fund for Agricultural Development, Italy&
Samuel Kobina Annim
University of Manchester, United Kingdom
12th November 2010
Summary
Building on the recent literature on finance, growth and hunger, we haveexamined the experience of Asian countries over the last five decades, using
dynamic panel models. Although the results are mixed, depending on thespecification and variables used, there is some evidence favouring a positive roleof finance in growth of GDP and agricultural value added. While financial
development reduces income inequality, the effects on hunger are not so robust.
Although microfinance has considerable potential for ameliorating deprivation,
the contraction of credit and risk aversion of investors, together with a faltering
global recovery, underlie gloomy prospects for the poor in Asia.

Key Words: Finance, Economic Development, Agriculture, Inequality, Poverty, Asia
JEL Code: C33, E44, G01, I32, O15
This study is funded by IFAD (International Fund for Agricultural Development). We are grateful toThomas Elhaut, Director of Asia and the Pacific Division, IFAD, for his support and guidance
throughout this study. The first author thanks generous research support from RIEB, Kobe University,
during his stay in 2010. Raghbendra Jha’s help with the econometric analysis is greatly appreciated,
as also valuable research assistance by Valentina Camaleonte and Sundeep Vaid. We are indebted toguest editors, Ed Amann and David Lawson, and participants in the Second Annual ESRC
Development Economics Conference “The Effects of the Financial Crisis on Developing Countries”
in Manchester in January 2010 for their useful comments. The present version has benefitted fromvaluable comments from an anonymous referee. The views expressed are, however, those of theauthors’ and do not necessarily represent those of the organisations to which they are affiliated.
Corresponding Author: Katsushi Imai (Dr), Economics, School of Social Sciences, Arthur Lewis
Building, University of Manchester, Oxford Road, Manchester M13 9PL, UK Phone: +44-(0)161275-4827; Fax: +44-(0)161-275-4928; E-mail: Katsushi.Imai@manchester.ac.uk.

1

Financial Crisis in Asia Region: Its Genesis, Severity and Impact
on Poverty and Hunger
I.

Introduction

There has been a surge of studies focusing on the recent financial crisis that erupted in USA
and has rapidly spread to the rest of the world (e.g., IMF, 2008, World Bank, 2008a, ADB,
2008, Arrow, 2008, Krugman, 2008, Phelps, 2008). Indeed, this crisis has turned into a crisisof confidence. Despite extensive interventions by governments and monetary authorities, thesupply of credit has shrunk, stock markets have recorded dramatic losses, and a major
downturn in the global economy has occurred. Commodity prices have eased from recent
peaks and large exchange rate realignments have occurred (ADB, 2008, 2010, IMF, 2008,2010).
The roots of this crisis lay in the subprime mortgage market in USA. Expansionary
monetary policy kept the interest rate low for some years and encouraged borrowing for realestate. Financial institutions offered loans to people who were not capable of repaying themthe subprime borrowers. Such loans were bundled up and turned into investments throughsecuritization. These mortgage securities were also combined with other securities designed
to reduce the risk such as collateralized debt obligations (CDOs) and used to entice moreinvestors. Financial institutions also entered into intricate financial contracts known as credit
derivatives or credit default swaps in order to protect against default. An oversupply ofhomes and rising interest rates caused a decline in housing prices and home loans. Risingdefaults, and declining house prices and lending resulted in losses to those institutions thatheld and sold mortgage-backed securities and credit derivatives. Subsequently, the crisis
spread to non-housing businesses and larger financial institutions not directly connected withmortgage lending. Many had invested in assets derived from mortgage-based securities.
Interbank lending rates rose to reflect higher risk in the financial sector. As interbank lending2

contracted and trust eroded, the credit market failure unravelled. Overseas financial
institutions linked to these markets were sucked into the financial turmoil of USA, UK andthe rest of Europe. Investors started withdrawing from stock markets resulting in huge falls in
valuation. These falls were recorded in both high-income and emerging markets, given trade
and asset linkages. As the contagion spread, further, predictions of a global recession gained
plausibility.
This crisis followed on the heels of the food price crisis that caused riots in many
developing countries (FAO, 2009). In fact, as emphasised by Rodrik (2010), developing
countries have been prone to a series of crises -some financial and others of a different kindwith devastating consequences for the poor. He observes “For too many of these countries,
economic growth in the last two decades relied on a combination of two factors: a natural
rebound from previous financial crises (as in Latin America) or political conflicts and civilwar (as in Africa), and high commodity prices. Neither can be relied on for the productive
transformation that developing countries need” (Rodrik, 2010, no page number).
The main objective of the present study is to deepen our understanding of the severity of
the financial crisis and its implications for growth and poverty reduction in selected Asian
countries. While our focus is on the recent financial crisis, our analysis yields insights into
the channels through which the effects of financial crisis on growth and poverty aretransmitted in developing countries. In fact, there is a body of empirical literature to assess
the effects of financial crises on growth and/or poverty, using micro data sets. Most of thesestudies have confirmed negative impacts of crises on growth and poverty reduction (e.g., theLatin American Crisis (Oscar, 1998), the Asian Financial Crisis (Nixson and Walters, 1999;
Mazumdar and Horton, 2000) the Russian Crisis (Lokshin and Ravallion, 2000)).1 But there
have been few studies to assess the effect of a crisis on growth and poverty or nutrition using
cross-country data over long periods.

3

The financial crisis which began unravelling in 2007 is distinct because it originated in thecrisis of Banking System of the United States, not in developing countries. Its effects weresevere and worldwide, as it raised the spectre of the Great Depression in the 1930s (IMF,
2010). To better understand the implication of the recent financial crisis for economic growthand poverty, the present study carries out cross-country regression analysis for a sample of
Asian countries.
The scheme is as follows. In the next section, an attempt is made to link finance and thereal economy, followed by a brief exposition of the dynamics of the financial crisis. Section
III gives an account of how the crisis unfolded in Asia. Section IV is devoted to a review ofthe literature on finance, growth and poverty. Section V reviews the impact of financial crisison microfinance given the increasingly important role of the latter in reducing poverty in
Asian countries. In the absence of firm empirical evidence, a distillation of recent surveys of
MFIs and other evidence is given. The data, model specifications and econometric results are
discussed in Sections VI and VII, primarily to illustrate how credit influences growth andpoverty reduction. An extension throws new light on various mechanisms that linkcontraction of credit to lowering of crop yields and aggravation of poverty in a sample of
Asian countries. In Section VIII, some concluding observations are made from a broad policy
perspective.

II.

Finance and Real Economy

While this linkage remains contentious, various studies have focused on the following. The
first is through a financial accelerator that amplifies the effects of financial cycles on the real
economy, through its effects on the value of collateral and thereby expansion of credit.
Another is through lenders’ balance sheets and the relationship between bank capital andaggregate credit. When bank capital is eroded, banks become reluctant to lend and are forced4

to deleverage. A third but overlapping with the first linkage is the variation in the role of thefinancial accelerator with the financial system (arm’s length financing as opposed torelationship banking). In other words, households and producers can substitute away frombanks to markets (IMF, 2008).
The dynamics of the financial crisis could be delineated as follows: the procyclicalbehaviour of bank leverage – changes during upturns and downturns is crucial tounderstanding how banking stress translates into a reduced credit supply, a higher cost ofcapital, and a flattening of economic activity. More specifically, the key issue is: when banks
overextend their balance sheets during booms, on the back of higher asset values and lower
perceived risks, financial imbalances build up, economic activity is further boosted that inturn also further boosts asset values, reduces perceived risk, fostering further lending andeconomic expansion. Under such conditions, a financial shock that either increases risks or
reduces yields prompts a cycle of deleveraging, with a sharp reduction in bank lending asbank capital falls, leading to an economic slowdown that feeds into a further reduction incredit supply. The procyclicality of bank leverage is greater when banks are more exposed tofluctuations in the market value of assets-for example, through their holdings of securities
and their repurchase. IMF (2008) confirms that commercial banks tend to be more procyclicalwhen operating in more arm’s length financial systems, where a greater share ofintermediation occurs through financial markets rather than through traditional relationshipbased (and bank dominated) activities. Thus, more arm’s length financial systems are more
prone to financial crises. In fact, lack of information about the value and risk of many
securitised products, and about the losses subsequently associated with these products,
amplified the present crisis.
The channels through which the financial crisis impacted on growth and poverty indeveloping countries are diverse (Lin and Martin, 2010). These include changes in capital5

flows, commodity prices, remittances, interest rates, risk premia, and trade opportunities. The
channels through which rural poor were impacted are even more complex, with linkages
involving commodity prices, wage rates and employment likely to be particularly important.
To elaborate selectively, the effects of changes in commodity prices are complex.
Declines in the prices of staple foods typically reduce poverty in developing countries, as thepoor spend a large share of their incomes on these foods, and many poor in rural areas
including small farmers are net buyers of these foods (Ivanic and Martin, 2008). Declines in
the prices of some higher income-elastic foods such as dairy products, however, increase
poverty by lowering the incomes of small producers who produce and sell these commodities
but are unable to afford them. Declines in the prices of cash crops (e.g. cotton, coffee, rubber)are, however, more likely to increase poverty as farmers in developing countries are net
sellers of these goods and the poor spend only small shares of their incomes on them.
A related observation is that income reductions increase not just poverty but also
nutritional deprivation. Through a lower demand for calories, proteins and fats, andconsequently lower intake, productivity is lowered and employment in rural labour markets ishampered. Thus nutrition-poverty traps emerge (Dasgupta and Ray, 1986, and Jha et al.
2009). Evidence also suggests that large sections of rural poor are also more vulnerable toshocks and crises than the non-poor, and shocks propel them into long spells of poverty(Gaiha and Imai, 2009, Dercon and Christiansen, 2010). Finally, as the poor are more creditconstrained, contraction of MFIs’ loan portfolio and more stringent selection criteria arelikely to hurt the poor more.2

III.

Financial Crisis in Asia

How did the Crisis Unfold in Asia?

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The crisis manifested in emerging Asia in early 2008, and was expected to worsen in
response to slackening demand from advanced economies and growing tensions in regional
financial markets. More recent assessments, however, point to a strong recovery led by
China, India and other emerging Asian economies (ADB, 2010, IMF, 2010). A selective
review of the evidence is given below.
Growth in China eased to 101/2 per cent in the first half of 2008, from 12 per cent in 2007,
partly because of slowing exports. Investment and consumption, however, maintained their
momentum. On the other hand, in India, growth in the second quarter slowed to 8 per cent, on
the back of weakening investment, while private consumption and exports held up better thanfeared, with signs of the latter registering a sharp drop in October, 2008.3 In fact, exports fell
sharply in other Asian countries too, including South Korea, China, Japan and Taiwan.4 In theso-called NIEs and ASEAN economies, activity decelerated. Domestic demand softened as aresult of surge in food and fuel prices, and investment plans were scaled down. Vietnam, forexample, underwent a sharp correction as the demand boom caused by large capital inflows
eroded.
Financial markets weakened due to a pessimistic global outlook and investor risk appetite
declined following the September turbulence. Equity markets that had a bull run during 200507-prices, for instance, more than quadrupling in China and tripling in India- plummeted. In
some countries, borrowing spreads shot up for banks relying on wholesale funding.
Current accounts began to show strains as well, largely due to rising import bills forcommodities and slowing export growth, while capital account and exchange rate
developments varied.

Capital inflows to China remained strong, as reflected in thecontinuing surge of foreign reserves; capital flows to other countries in the region became
more volatile, particularly to those running large external deficits. Consequently, theircurrencies came under pressure, prompting central banks to intervene (e.g. India, Pakistan

7and Vietnam). While the Chinese renmimbi and the ASEAN currencies appreciated, theSouth Asian and NIE’s currencies weakened.
Headline CPI inflation soared in many countries in the first half of 2008, with slight
reductions in a few. In China, headline CPI inflation declined from its peak of 81/2 per cent inApril, 2008, as food supply improved. In India, CPI inflation jumped to 9 per cent in August,
2008.

Prospects
Contrary to apprehensions, domestic –demand driven economies of China, India andIndonesia led to a slow but faltering global economic recovery. Indeed, the short-term
outlook for Asia remains positive, with growth expected to settle at more sustainable but stillhigh levels. Ample global liquidity, on the one hand, and the relatively robust growth and low
public debt in Asia, on the other hand, are likely to fuel capital flows to this region.
Reflecting the slowing of export growth and strong domestic demand, Asia’s current accountsurplus is expected to fall to 3 per cent of the regional GDP in 2010 and 2011, from about 5
per cent in 2007, making a modest contribution to reduction of global imbalances.
With external demand from advanced economies unlikely to return to pre-crisis levels in
the near future, Asia will need stronger domestic demand to maintain robust growth. The
normalisation of policy measures in Asia, therefore, must be accompanied by continued
measures to reinforce private domestic consumption and investment (IMF, 2010).

IV.

Review of Cross-Country Studies on Finance, Growth and Poverty

There is a vast literature on this theme with valuable insights from cross-country data overtime. We will concentrate largely on two (Beck et al. 2007, and Claessens and Feijen 2006),
with brief comments on a few other important contributions. Beck et al. (2007) examine the8effects of financial development on poverty through two channels: aggregate growth, andchanges in the distribution of income. Instead of examining the finance-growth link, they
offer an assessment of the impact of financial development on changes in the distribution ofincome and changes in both relative and absolute poverty. Specifically, the variablesconsidered are (i) the Gini coefficient of income distribution; (ii) income share of the poor,
measured as the income share of the poorest quintile relative to total national income; and(iii) the share of the population living on less than $1 per day. Using GMM panel estimatorfor dynamic models, greater financial development is associated with poverty reduction. In
fact, 60 per cent of the impact of financial development on the poorest quintile works throughaggregate growth and 40 per cent through reduction in income inequality5.
Claessens and Feijen (2006) identify specific channels through which financial
development impacts on undernourishment6. Using data from 1980-2003 and relying on IV
estimation for robustness, they show that private credit has a large negative effect onundernourishment through higher agricultural productivity in general and higher livestock,crop and cereal yields in particular. To a large extent higher agricultural productivity due tofinancial development is mediated by greater fertilizer and tractor use. Besides, thedistribution of banking outlets makes a difference.

V.

Impact of Financial Crisis on MicrofinanceMicrofinance allows poor people to protect, diversify and increase sources of their income.
Microfinance also mitigates vulnerability to extreme fluctuations that are a feature of their
daily existence. Loans, savings, and insurance smooth out income fluctuations and stabilize
consumption levels even during lean periods (Littlefield et al. 2003).
There is little hard evidence on the impact of the current financial turmoil on microfinance.
To the extent that there is contraction of credit, and the concomitant reduction in rural credit,9the implications for the rural poor are likely to be serious. Even though interest rates havefallen to stimulate demand for credit, there is a strong reluctance to lend in an environment
lacking trust. So, effectively, contraction of credit implies higher interest rates and shorter
maturities. If these observations have general validity, it follows that the demand for credit
would be reduced especially in the target groups of MFIs, and poverty may increase throughfinancial constraints on raising agricultural productivity. Vulnerability of low income
households may also get aggravated because of their failure to smooth consumption. On theother hand, the loan portfolio of MFIs may shift in favour of wealthier clients. Moreover, thefinancial viability may erode because of moral hazard and adverse selection. A major priority
therefore is to inject more capital into the financial system-especially MFIs. That theseconcerns have emerged as major priorities is reflected in a recent survey conducted by theMicrocredit Summit Campaign, reported in Micro-credit Summit e-news, vol. 6, issue 2:
October, 2008). A summary of the responses to the questions asked is given below.
The concerns stem from a tightening money market, higher cost of funds, and drying up of
foreign funds. Higher rates of interest are resulting in repayment difficulties and reduction inborrowing. Consumption of food is reduced in the event incomes cannot be supplemented.
MFIs are being forced to be more cost-effective or else are likely to be wiped out. What isindeed most worrying is the pessimism of investors in microfinance. Few, if any, concrete
strategies are identified to deal with the financial turmoil.
A more recent survey (CSFI, 2009), based on 430 respondents from 82 countries,
including observers, regulators, investors and practitioners, throws new light on many ofthese issues. Going by the aggregate of responses, the concerns about credit risk and too little
funding moved centre-stage. The fact that much funding is in non-local currency has added toforeign currency risk owing to volatility in the foreign exchange markets. All these risks
taken together are reflected in more serious concerns about erosion of profitability. “Many10respondents saw a vicious circle here: the recession creating a worse business environment,
leading to mounting delinquencies and shrinking markets, leading to declining profitability,
loss of investor confidence, and cutbacks in funding , and so on” (CSFI, 2009, p.7).
Associated with the vulnerability of MFIs is the larger risk of mission drift andabandonment of their social objectives. There was a mixed response to how well prepared
were the MFIs to handle these risks. Barely 5 per cent of the respondents acknowledged thatthey were well prepared and 13 per cent confessed that they were ill-prepared. The rest gave
a mixed response. Among the Asian respondents, however, the concerns about these risks
were more muted: liquidity and credit risks figured in their top ten, but not in theconcentrated form of other regional respondents. There were also concerns about mission
drift and political interference.
In sum, even though perceptions delineated here may well be more negative thanwarranted by the ground reality, it would be a mistake to set them aside completely.
Supplementary evidence comes from simulations in Imai et al (2010b). The results
simulate the effects of hypothetical reductions in gross loan portfolio (GLP) of MFIs, GDPper capita and share of domestic credit in GDP on the head-count index of poverty. Although
the overall rise in poverty is low (about 3.45 per cent in the mild recession scenario), it is by
no means negligible. It must be emphasised, however, that the cumulative effect of the global
slowdown reflected in sluggishness of investment on poverty may well be larger.

VI.

Data and Models for Finance, Growth and Hunger in Asia

Here the objective is to analyse the relationships between finance, growth and hunger inselected Asian countries. The analysis is based on a panel of 9 countries over the period 1960
to 2006, based on a dynamic panel estimation strategy that builds upon the recent literature
reviewed above. First, a description of the data used is given. This is followed by an

11exposition of the models estimated. In a subsequent section, the results are discussed,
followed by some concluding observations from a broad policy perspective.

Data
All the models are estimated with the finance, poverty and inequality data at the country level.
The data sets created are based on World Bank Development Indicators (WDI) 2008 (World
Bank, 2008b), FAO-STAT (FAO, 2008), World Bank’s Finance Data (based on Beck et al.
(2000), The UNU-WIDER World Income Inequality Database (WIID) (UNU-WIDER, 2008),
and Barro-Lee’s (2000) data on education.
One of the data constraints in addressing our research questions is that while annual dataon most of the key economic and financial variables are available for 9 countries (except
Vietnam for which most of the variables start from 1985-1990) in 1960-2006, the data on
inequality and poverty are available only for few years, when a national income or
expenditure survey or a census were carried out. Hence we use annual panel data for 8 or 9
countries to examine the links between financial growth and economic or agricultural growthin the period 1960-2006, with a few missing observations. We have specified a dynamic
panel data model, drawing upon Blundell and Bond (1998) - an extension of Arellano andBond (1991). To investigate the relationship between finance and inequality or poverty, we
use the panel data aggregated at 5- year intervals since 1960 (along the lines of Barro and Lee(2000) or the empirical macroeconomics literature to test growth theories). For all countries
except Vietnam, inequality data from UNU-WIDER’s WIID and undernutrition data from
WDI (Classens and Feijen, 2006) are available roughly once or sometimes twice in 5 year
periods. If more than one estimate is available in one period, the average is used7. Thesepoverty and inequality data are matched with the 5- year averages of finance and economic
variables. One of the advantages of applying two different time schedules is that we can use

12the predicted values of finance data based on annual panel data for the 5 year-panel, whereby
inequality or undernourishment is estimated by the aggregated finance data based on
predictions on an annual basis. This approach would at least partially address the issue ofendogeneity of finance in the inequality or undernourishment equation.
Appendix 1 summarises the definitions of variables, descriptive statistics and data sources.
We take three different measures of finance-(i) logarithm of the share of private credit in
GDP; (ii) log of the share of private credit through (formal) money deposit banks as a shareof GDP (the narrow definition of private credit), and (iii) log of Financial System Deposits in
GDP. For inequality, we use the income Gini coefficient. Poverty is treated as synonymouswith the prevalence of undernourishment, as in Classens and Feijen (2006). Other variables
used in the analysis are defined in the appendix.

Model Specifications
We estimate four dynamic models in which the dependent variable, (a) GDP per capita or
agricultural value added per capita, (b) finance, (c) inequality or (d) undernourishment isseparately estimated. A variable on finance is used as one of the explanatory variables for (a),(c) and (d).
(a) Model for GDP or Agricultural Value Added
Following Guariglia and Poncet (2008), we specify the following relation:(1)
where i and t denote country and year, respectively; ∆Yit is GDP per capita growth and
Finance it is a proxy variable for finance, Control it is a vector of control variables,
country specific unobservable effect (e.g. social and cultural factors),is theis the time effect andis an error term, independent, and identically distributed (or i.i.d.). The log of lagged per
capita GDP is included in Control it to control for convergence. Other controls include log of13

share of population with more than primary education, log of government expenditure in
GDP (to measure size of government), log of CPI (Consumer Price Index), and log of trade asa share of GDP (measure of openness). In a variant, the dependent variable is agriculturalvalue added per capita.
A version of equation (1) can be written as

with the log of lagged per capita GDP on the right hand side and the rest of the explanatory
variables as a vector,. Estimating (1) (with log of lagged per capita GDP) is thus

equivalent to estimating the following standard dynamic panel data model:(2)
GMM panel estimator relies on first-differencing the estimating equation (and thus country
fixed effects will be eliminated) and appropriate lags of the right side variables as
instruments.
(3)8
Two issues have to be resolved: one is endogeneity of the regressors and the second is thecorrelation between

and(e.g. see Baltagi, 2005, Chapter 8).

Assuming that ε it is not serially correlated and that the regressors in Xit are weakly
exogenous, the generalized method-of-moments (GMM) first difference estimator (e.g.
Arellano and Bond, 1991) can be used. Alternatively, we could use the lagged differences ofall explanatory variables as instruments for the level equation and combine the difference
equation (3) and the level equation (2) in a system whereby the panel estimators use
instrument variables based on previous realisations of the explanatory variables as theinternal instruments, using the Blundell-Bond (1998) system GMM estimator based on
additional moment conditions9. Such a system gives consistent results under the assumptions
that there is no second order serial correlation and the instruments are uncorrelated with the14error terms. Validity of instruments is tested by Sargan’s J test and the second order serial
correlation of the residuals. The Blundell-Bond (1998) system GMM estimator is used in thepresent study. We use the heteroscedasticity-robust variance-covariance estimator for allcases.
The Blundell-Bond (1998) system GMM estimator is useful to address the problem ofpotentially endogenous regressors (e.g. Finance in equation (1)). In the system equation,
endogenous variables can be treated similarly to lagged dependent variables. The second
lagged levels of endogenous variables could be specified as instruments for difference
equation. The first lagged differences of those variables could also be used as instruments forthe level equation in the system.
We try the cases (i) where the endogeneity is not taken into account, and (ii) where someendogenous variables (after instrumenting) are included. In this model, we try the cases
where finance and trade share are treated as endogenous variables.
(b)

Model for Financial Development

While there is a huge empirical literature on the determinants of finance, we use a simple
specification, following Baltagi et al.’s (2009) where finance is estimated by a dynamic panel
model in which trade openness and financial openness are used as explanatory variables.
(4)
This is estimated by the Blundell-Bond system GMM estimator.

(c)Model for Inequality

Likewise, inequality is estimated by a dynamic panel model using the Blundell-Bond system
GMM estimator applied to 5- year panel data.
(5)15

The dependent variable is the Gini index of income.

is log of private credit (valueof credit by financial intermediaries to the private sector) divided by GDP,
Financial System Deposits in GDP.

or log of, a vector of control variables including log of initial

years of schooling, log of growth rate of the GDP deflator, and log of trade share. Financeand trade share are treated as endogenous variables in some specifications.
(d)Model for Undernourishment

In the regression of prevalence of undernourishment, we use the same specification as for theinequality equation except that we include log of population growth and log of share ofworking age population (that is, share of the age group between 15-65, or active population in
the total population) in

VII.

.

Econometric Results

The results of the models specified above are discussed here. Table 1 reports 6 cases; Cases 1
and 2 for the broad definition of private credit, Cases 3 and 4 for the narrow definition ofprivate credit through banks, and Cases 5 and 6 for financial system deposits. Cases 2, 4, and6 are those in which finance and trade openness are treated as endogenous in the system.
These six cases (based on three definitions of finance and whether some of the explanatory
variables are endogenized in the system) will be tried for all the other models.
(Table 1 to be inserted around here)
Somewhat surprisingly, the coefficient estimate of finance is negative and significant inCase 1, which is contradictory to the predictions of positive role of financial development on
economic growth (e.g., through financial intermediation or facilitation of industrial or
agricultural investment). However, it ceases to be significant once it is endogenized in the16system. Finance, defined as financial system deposits, is, however, positive and significant inCases 5 and 6.
As a sensitivity test, we have run the regression with the same specification by dropping
Malaysia10. As shown in the last panel, in Case 2 where finance is treated as an endogenous
variable, it has a significant positive coefficient (at the 10% level), while the coefficientestimate in Case 1 ceases to be significant. The coefficient estimates are not significant inCase 3 or Case 4. However, they are highly significant in Case 5 and Case 6, as in thecorresponding case with Malaysia. Incidentally, in Case 6, finance has a significant positivecoefficient with a much higher z value. The rest of the coefficient estimates are more or lessthe same in the cases without Malaysia and are, therefore, not shown here.
Education, defined as the share of the population with primary education or above, ispositively associated with GDP per capita. Also, size of government measured by share ofgovernment spending of GDP leads to higher GDP per capita. The coefficient estimate of CPI
is positive in all cases except Case 1. Trade share is positive and significant regardless ofwhether it is endogenized in the system. Tests for the second order serial correlation of theresiduals (m2) show that there is no second order serial correlation except in Case 1. The
results for the Sargan test validate our specification as over-identifying restrictions are valid
for all the cases.
In Table 2, we estimate the determinants of agricultural value added per capita using thesame specification. Private credit is negative and significant at the 5% level in Case 2 and at
the 10% level in Case 1. However, the coefficient estimate of financial system deposit ispositive and highly significant in Cases 5 and 6.
(Table 2 to be inserted around here)17

We have carried out again a sensitivity test without Malaysia. The coefficient estimates offinance are still negative in Case 1 to Case 4, but they are no longer significant in any of thesecases, which suggests that Malaysia seems to have driven the negative and significant (at the
10% level) coefficients in Cases 1, 2 and 4. Cases 5 and 6 with Malaysia still show a positiveand significant coefficient for finance. Thus the evidence on the role of finance in agricultural
growth is mixed.
Table 3 contains the results of the finance equation. Cases 1 and 2, and Cases 3 and 4, andCases 5 and 6 relate to three different finance measures. Two cases are tried for eachdefinition according to whether trade openness is treated as an endogenous variable or not.
Higher GDP per capita is significantly associated with financial development (at the 10%
level) in all cases except Case 2. This is consistent with Baltagi et al. (2009). However, trade
openness is not significant in any of the six cases.
(Table 3 to be inserted around here)

This is in sharp contrast to Baltagi et al. (2009) who found a positive and significant
coefficient estimate for both trade openness and financial openness. It is noted, however, thatthey use data for 31 countries including advanced countries (e.g. US, UK, Japan), middle
income countries (e.g. Brazil) and low income countries (e.g. Zimbabwe) for 1980-1996. The
use of different data sets would partly explain the differences between the results. The Sargan
tests and tests for serial correlations validate our specification.
Tables 4 and 5 report the results based on a dynamic panel data model where thedependent variables are the Gini coefficient and the prevalence of undernourishment. Basedon the regression results in Cases 2, 4 and 6 in Table 4, the predicted values of three financeindicators are derived for the entire period on an annual basis. These predicted values areaggregated at 5-year intervals and are used as alternatives to the actual values. The merit of18

this approach is that it addresses partially the endogeneity problem of finance. It also
increases the number of observations by making out-of-sample forecast if there are somemissing observations. 12 cases are tried. Cases 1, 3, ..., 11 (odd numbers) are the cases whereendogeneity is not taken into account, while Cases 2, 4, ..., 12 (even numbers) are thosewhere the endogeneity of potentially endogenous variables (e.g. trade openness) is considered.
Cases 1 to 4, Cases 5 to 8 and Cases 9 to 12 are for three different measures of finance, broadand narrow definitions of private credit and financial system deposit (each of which isrelative to GDP). Cases 3 and 4, Cases 7 and 8 and Cases 11 and 12 are based on predictedfinance measures. Only key results are summarized below.
(Tables 4 and 5 to be inserted around here)

In Table 4, the Gini coefficient is the dependent variable in all the cases. A main finding isthat the financial development measured by higher levels of deposits is significantly
associated with lower inequality as implied by highly significant (at the 1% level) andnegative coefficient estimates of finance in Cases 9 to 12. It is noted that the coefficientestimate is lower in absolute terms when the endogeneity is taken into consideration. Signs ofcoefficient estimates for finance are negative for the other two definitions of finance in Cases
1 to 8 (and significant at the 10% level in Case 1 and Case 7, and non-significant in the rest).
In sum, finance tends to decrease inequality measured by the Gini coefficient. The
coefficient estimates for schooling years in the initial year are negative and significant. If thecountry has higher levels of education in the early period, it tends to have higher Gini
because only a section of the educated people captures the benefits. Trade openness is not
significant, nor is the GDP deflator. The Sargan tests and tests for serial correlations, which
imply that there is no second order serial correlation, validate our specification in all the cases.

19

Table 5 focuses on the determinants of undernourishment. A few additional explanatory
variables are included for these cases. A main finding is that private credit broadly defined
has a significant negative effect on undernourishment (at the 1% level) in Cases 1 to 4, i.e.,
depending on whether the endogeneity of finance is taken into account, or whether thepredicted or the actual values of private credit are used. Subject to these caveats, this suggeststhat private credit, broadly defined to cover formal and informal banking sectors, plays animportant role in reducing hunger. The negative and significant coefficient estimates (at the
10% level) of narrowly defined private credit (formal banking) in Cases 5 and 7 further
strengthen the poverty or undernourishment reducing roles of finance. However, somecaution is necessary as these coefficients cease to be significant once they are endogenized in
Cases 6 and 8. Financial system deposit is not significant in Cases 8 to 12.
On the results of control variables, trade openness is not significant, while population
growth is positive and significant in increasing the prevalence of undernourishment, as inCases 5 to 11. As working age ratio (i.e., share of people in the working age –group)
increases, the prevalence of undernourishment tends to decrease. The Sargan tests and tests
for serial correlations validate our specification.
In sum, there is support for the view that contraction of credit has adverse effects on the
proportion of undernourished. This is broadly consistent with the past empirical literature to
identify the negative impact of the financial crisis on poverty using micro data (e.g. Oscar,
1998; Nixson and Walters, 1999; Mazumdar and Horton, 2000; Lokshin and Ravallion, 2000).
However, the present study is important as this is one of the first few attempts to examine thelink between credit contraction and poverty or undernourishment from a macro perspective
using the cross-country data. There are also likely to be indirect effects of GDP andagricultural growth deceleration due to credit contraction consistent with, for example, Imai,et al. (2010c). However, as in some cases, the causality runs both ways between GDP and

20

finance, disentangling of the direct and indirect effects of finance on undernutrition is notstraightforward.

VIII. Concluding Observations
Building on the recent literature on finance, growth and hunger, we have examined theexperience of 9 Asian countries over the period 1960-2006 by dynamic panel data models.
Although the results are mixed, depending on the specification and variables used, there issome evidence favouring a positive role of finance on growth of GDP and agricultural value
added. But there is also evidence of a reverse causality between GDP growth and financial
development. In fact, there are a few cases in which the causality runs both ways. In light ofthis complexity, the results of finance on inequality and hunger require cautious interpretation.
Financial development reduces the Gini coefficient of income distribution. Although there is
support for the view that financial development reduces hunger, the results are not so robust.
Specifically, when the endogeneity of trade and finance is taken into account, the negative
effect of financial development on hunger disappears. Whether these results are driven bysome outliers or by a complex two-way dynamics between finance and growth needs further
examination.
While microfinance has the potential to ameliorate some of the worst forms of deprivation,
the contraction of credit in general and risk aversion of investors, together with a looming
global recession, underlie gloomy prospects for the poor in this region.
In conclusion, finance, growth and hunger are linked in complex ways. Our conclusion,
therefore, of credit contraction and deceleration of growth aggravating hunger is plausible but
not so robust.

21

Notes
1. However, using panel data, Stillman and Thomas (2008) found a weak impact of the Russiancrisis on nutritional status.
2. For assessment of poverty alleviation role of microfinance from micro and macro
perspectives, see Imai et al. (2010 a) and Imai et al. (2010b).
3. Growth rate of merchandise exports plummeted from 28.9 per cent in 2007 to 13.7 per cent in2008 (ADB, 2010).
4. Financial Times (11 November, 2008) cites evidence of precipitous falls in exports in thesecountries. China, for example, reported the slowest export growth in four months. South
Korea’s exports in the first 10 days of November fell 26 per cent from the same period a year
earlier. A slowing of the Chinese economy also had a knock-on effect on Taiwan. An exporterof electronic goods, its overall exports fell 8.3 per cent from a year ago. Sales to China andHong Kong fell about 20 per cent. For an updated account confirming a sharp drop in exports
in these countries, see ADB (2010).
5. Honohan (2003) shows that a 10 per cent increase in private credit to GDP reduces poverty by
2.5-3 per cent.
6. Undernourishment is defined as “the condition of people whose dietary energy consumption
is continuously below a minimum dietary energy requirement for maintaining a healthy lifeand carrying out a light physical activity”, (FAOSTAT, 2006).
7. There are a few cases where there are no inequality or undernutrition data in a 5-year interval.
Because the missing observations would seriously limit the dynamic panel estimation where
the lagged dependent variable is used as one of the explanatory variables, we fill these bytaking the weighted average of the observations in the pre and post periods. We did not haveany cases where missing observations repeat for 2 periods.
8. As an extension, we have implemented the case with the first and second lagged

dependent variables in some cases, depending on the results of serial correlation tests
and significance of coefficient estimates of the lagged dependent variables.

22

9. See the application by Guariglia and Poncet (2008) to examine the relation between financeand economic growth in China.
10. We do so because Malaysia is a special case not simply because of its size but also because of
its structural characteristics (e.g. small size, and low share of agriculture in GDP).

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26

Table 1 Results for the Growth Equation (GDP per capita) based on Blundell and Bond(1998) GMM estimation (Dependent Variable: log GDP per capita)
Case 1Case 2

Case 3

Case 4

Case 5

Without

With

Without

With

Without

With

Whether

endogenous

Endogenous

endogenous

endogenous

Endogenous

Endogenous

Endogenous

regressors

Regressors

regressors

regressors

Regressors

Regressors

log(GDP pc)

log(GDP pc)

log(GDP pc)

log(GDP pc)

log(GDP pc)

log(GDP pc)

1.238(23.55)**-0.266(4.59)**

1.287(18.48)**-0.311(4.32)**

1.289(19.01)**-0.312(4.46)**

1.308(18.19)**-0.327(4.45)**

1.254(17.75)**-0.279(3.81)**

1.275(15.75)**-0.294(3.66)**-0.005-0.003(2.32)*(1.20)-0.006-0.002(1.35)(0.29)
0.003

0.002

(5.18)**(2.81)**

Dep. Variable

Explanatory
VariablesL.
L2.
log(private
credit/GDP)log(private credit bybanks/GDP)log(financial system
deposit/GDP)log(share of
population
with primary ed. orabovelog(government
expenditure/GDP)
log(CPI)log(Export+Import/GDP)
Constant

Endogenous(Cases 2, 4
& 6)
Endogenous(Cases 2, 4
& 6)
Endogenous(Cases 2, 4
& 6)
Exogenous

Exogenous
Exogenous

Endogenous

Case 6

0.018

0.008

0.026

0.014

0.022

0.01

(1.30)
0.019(4.18)**-0.005(1.56)(1.12)
0.011(4.36)**-0.001(0.43)(2.07)*
0.012(2.28)*
0.001(0.58)(2.92)**
0.003(1.34)
0.004(3.46)**(1.85)
0.007(2.50)*
0.002(1.06)(1.96)
0.002(1.32)
0.004(2.20)*

0.029(4.24)**
-0.23

0.024
(3.27)**-0.0660.019(3.36)**-0.2020.014(3.03)**
0.017

0.015
(3.43)**-0.0520.013(2.38)*
0.064(3.02)**(1.49)(1.34)(0.28)(0.67)(3.75)**

Observations

294

294

258

258

270

270

Number of Country

8

8

7

7

7

7

(-2.01)*(-1.39)(-1.35)(-1.44)(-1.39)Arellano-Bond Test for Serial Correlation (Z value)m2(-2.10)*Sargan Test of overidentifying restrictions
Ho: overidentifying restrictions are valid2chi (323)=
345.15
Prpb>Chi2

0.19

2

2

2

2

2

chi (459)=496.1

chi (288)=
313.18chi (423)=
429.93chi (300)=
323.97chi (435)=
444.1

0.11

0.14

0.4

0.16

0.37

0.003

0.003

(4.65)**(5.01)**

1. Absolute value of z statistics in parentheses
2. * significant at 5%; ** significant at 1% (based on robust estimators)
3. Blundell and Bond (1998) GMM one-step estimator is applied for all the cases.
Without Malaysialog(private
credit/GDP)
Endogenous-0.0040.003(0.93)(1.79)log(private credit by
Endogenous-0.001banks/GDP)(0.23)
log(financial system
Endogenous
deposit/GDP)

27

0.003
(0.59)

Table 2 Results for the Growth Equation (Agricultural Value Added per capita) basedon Blundell and Bond (1998) GMM estimation (Dependent Variable: log (Agriculturalvalue added per capita))
Case 1Case 2

Case 3

Case 4

Case 5

Case 6

Whether

Without

With

Without

With

Without

With

endogenous

Endogenous

Endogenous

endogenous

endogenous

Endogenous

endogenous

Regressors

Regressors

regressors

regressors

Regressors

regressorslog(Agri VA
pc)log(Agri VA
pc)log(Agri VA
pc)log(Agri VA
pc)log(Agri VA
pc)log(Agri VA
pc)

Dep. Variable

Explanatory Variables
L.

L2.

log(private credit/GDP)

Endogenous(Cases 2, 4
& 6)log(private credit by
Banks /GDP)

Endogenous(Cases 2, 4
& 6)log(financial system

Endogenous

0.72

0.732

0.719

0.736

0.659

0.688

(8.23)**(8.81)**(9.96)**(10.91)**(7.67)**(8.07)**

0.244

0.248

0.255

0.25

0.29

0.288

(3.80)**(3.42)**(4.27)**(4.07)**(3.89)**(3.53)**-0.017-0.016(1.81)(2.00)*-0.013-0.011(1.43)(1.72)
0.006

0.003

deposit/GDP)(4.57)**(2.46)*

0.006

0.012

0.013

0.012

(0.27)(1.04)(3.04)**(2.81)**(2.76)**

0.005-0.002-0.005-0.009-0.008(1.28)(1.78)(0.62)(2.64)**(1.79)(2.73)**-0.001

0.00

0.004

0.005

0.005

0.004

(0.24)(0.06)(1.85)(3.26)**(2.79)**(2.42)*

0.025

0.02

0.00

0.002

-0.008-0.002(3.22)**(3.39)**(0.00)(0.31)(1.12)(0.37)

0.122

0.053

0.132

0.115

0.41

0.271

(0.71)(0.59)(1.10)(1.84)(7.19)**(4.99)**

Observations

284

284

248

248

260

260

Number of Country

8

8

7

7

7

7

(-1.53)(-1.00)(-0.93)(-1.42)(-1.35)log(share of population
with primary ed. or
above

Exogenouslog(government

Exogenouslog(CPI)

Exogenouslog(Export+Import/GDP)

Endogenous

expenditure/GDP)

Constant

0.00

0.002

(0.02)
0.007

Arellano-Bond Tes for Serial Correlation (Z value)(-1.58)m2
Sargan Test of overidentifying restrictions
Ho: overidentifying restrictions are valid

2

2

2

2

chi (278)=chi (409)=chi (290)=chi (421)=

345.15496.1

313.18429.93323.97

444.1

0.19

0.11

0.14

0.4

0.16

0.37

Prpb>Chi2

2. * significant at 5%; ** significant at 1% (based on robust estimators)
3. Blundell and Bond (1998) GMM one-step estimator is applied for all the cases.
Endogenous

log(private credit by

Endogenous2chi (449)=

1. Absolute value of z statistics in parentheses

Without Malaysialog(private credit/GDP)2chi (314)=-0.004(1.00)-0.005(1.35)
0.001

28-0.004banks/GDP)log(financial system(0.24)(1.26)

Endogenous

deposit/GDP)

0.004

0.004

(2.46)*(2.48)*

Table 3 Results for the Finance Equation based on Blundell and Bond (1998) GMM
estimation (Dependent Variable: Finance)
Case 1Case 2

Case 3

Case 4

Case 5

Case 6

Whether

Without

With

Without

endogenous

endogenous

endogenous

endogenous

With
Endogenous

Without
EndogenousWith
endogenousor exogenous

regressors

regressors

regressors

Regressors

Regressors

regressorslog(private
credit/GDP)log(private
credit/GDP)log(private
credit bylog(private
credit bylog(financial
systemlog(financial
systembanks/GDP)

Banks/GDP)

deposit/GDP)

deposit/GDP)

Dep. Variable

Explanatory Variables
L.

1.096

1.114

1.502

1.498

1.017

0.999

(14.33)**(14.04)**(24.03)**(24.56)**(44.82)**(34.64)**-0.189-0.184-0.571-0.559-0.092-0.077(2.51)*(2.56)*(8.11)**(8.11)**(3.87)**(2.50)*

0.039

0.009

0.064

0.041

0.071

0.04

(2.63)**(0.65)(2.99)**(2.80)**(1.80)(2.37)*

0.025

0.028

-0.008

0.001

0.009

-0.011(0.86)(1.37)(0.29)(0.07)(0.18)(0.28)

0.123

0.238

-0.489-0.324-0.505-0.316L2.

log(GDP per capita)log(Export+Import/GD
P)

Endogenous(Cases 2, 4,& 6)
Endogenous(Cases 3, 4,& 6)

Constant(0.88)(2.08)*(3.09)**(2.91)**(1.88)(2.64)**

Observations

319

319

259

259

271

271

Number of Country

9

9

8

8

8

8

(-0.58)(-2.04)*(-2.04)*(-0.95)(-1.12)

Arellano-Bond Tes for Serial Correlation (Z, Probb>z)m2(-0.53)Sargan Test of overidentifying restrictions
Ho: overidentifying restrictions are valid2chi (347)=chi (441)=

chi (291)=

chi (382)=chi (303)=chi (394)=

383.16470.4

333.31*

419.25356.62*456.33*

0.09

0.16

0.04

0.09

0.02

0.02

2

Prpb>Chi222

1. Absolute value of z statistics in parentheses
2. * significant at 5%; ** significant at 1% (based on robust estimators)
3. Blundell and Bond (1998) GMM one-step estimator is applied for all the cases.

29

2

2

Table 4 Results for the Inequality Equation based on Blundell and Bond (1998) GMM estimation (Dependent variable: Gini Coefficient)
Whether
endogenous
or exogenous(Cases 2, 4, 6, 8,
10 & 12)

Dep. Variable
Explanatory VariablesL.
log(schooling years in
the initial years)log(GDP deflator)

Exogenouslog(private credit/GDP)

Endogenous

predicted log(private credit/GDP)

Endogenous

log(private credit bybanks/GDP)
predicted log(private credit bybanks/GDP)log(financial system
deposit/GDP)
predicted log(financial system
deposit/GDP)
log(Export+Import/GDP)

Endogenous

Constant

Exogenous

Endogenous
Endogenous
Endogenous
Endogenous

Case 1Case 2

Case 3

Case 4

Case 5

Case 6

Case 7

Case 8

Case 9Case 10

Case 11

Case 12

Without
endogenousWith
endogenous

Without
endogenous

With
Endogenous

Without
endogenousWith
endogenous

Without
endogenous

With
Endogenous

Without
endogenousWith
endogenous

Without
endogenousWith
endogenous

regressors

Regressors

regressors

Regressors

regressors

regressors

Regressors

Regressors

regressors

regressors

regressors

regressors

Gini
Coef.

Gini
Coef.

Gini
Coef.

Gini
Coef.

Gini
Coef.

Gini
Coef.

Gini
Coef.

Gini
Coef.

Gini
Coef.

Gini
Coef.

Gini
Coef.

Gini
Coef.

0.451
(2.60)**
0.089(1.97)*
0.018(0.74)-0.033(1.91)
0.07(1.53)
2.082(2.98)**
57
8

0.557
(4.14)**
0.066(2.58)**
0.018(0.94)-0.023(1.25)
0.051(1.59)
1.658(2.95)**
57
8

0.404
(1.68)
0.101(2.03)*
0.021(0.85)-0.046(1.07)
0.088(1.18)
2.295(2.19)*
56
8

0.571(3.87)**
0.066(2.62)**
0.015(0.84)-0.015(0.63)
0.04(0.99)
1.573(2.47)*
56
8

0.255(1.91)
0.132(4.14)**-0.006(0.37)-0.034(1.33)
0.082(0.99)
2.67(5.18)**
45
7

0.375
(2.46)*
0.117(5.17)**
0.001(0.05)-0.015(0.83)
0.05(1.06)
2.229(3.90)**
45
7

0.244
(2.12)*
0.14(4.76)**-0.006(0.40)-0.044(1.74)
0.092(1.06)
2.699(6.01)**
44
7

0.357
(2.38)*
0.119(4.80)**
0(0.03)-0.02(0.98)
0.056(1.12)
2.292(4.11)**
44
7

0.197
(1.58)
0.128(3.56)**-0.008(0.64)-0.029(3.42)**
0.086(1.01)
2.891(5.77)**
48
7

0.316
(2.71)**
0.137(5.11)**-0.011(1.09)-0.016(5.17)**
0.043(0.99)
2.447(5.58)**
48
7

0.155
(1.13)
0.148(3.89)**-0.009(0.63)-0.03(2.58)**
0.091(0.95)
3.046(5.63)**
46
7

0.324
(2.19)*
0.133(4.39)**-0.006(0.51)-0.02(3.64)**
0.054(1.16)
2.417(4.36)**
46
7

Observations
Number of Country
Arellano-Bond Tes for Serial Correlation (Z, Probb>z)m2(1.43)(1.45)(1.43)(1.44)(0.12)(0.04)(-0.32)(-0.42)(0.46)(0.47)(0.04)Sargan Test of overidentifying restrictions
Ho: overidentifying restrictions are valid
2
2
2
2
2
2
2
2
2
2
2
chi (37)=chi (66)=
chi (37)=chi (65)=chi (36)=chi (56)=chi (35)=chi (55)=chi (36)=chi (58)=
chi (35)=
37.61
59.88
41.83
58.07
45.04
59.35
46.9
62.75
40.53
56.31
46.28
Prpb>Chi2
0.35
0.69
0.27
0.72
0.14
0.35
0.09
0.22
0.28
0.54
0.096
1. Absolute value of z statistics in parentheses. 2. * significant at 5%; ** significant at 1% (based on robust estimators) 3. Blundell and Bond (1998) GMM one-step estimator is applied for all the cases.

30(-0.09)2chi (57)=
62.25
0.29

Table 5 Results for the Undernourishment Equation based on Blundell and Bond (1998) GMM estimation (Dependent variable: share of
the undernourished population in the total)
Whether
Case 1endogenous
Without
or exogenous
Endogenous(Cases 2, 4, 6, 8,
10 & 12)Regressors

Case 2

Case 3

Case 4

Case 5

Case 6

Case 7

Case 8

Case 9Case 10

Case 11

Case 12With
endogenous

Without
endogenous

With
Endogenous

Without
endogenousWith
endogenous

Without
endogenousWith
endogenous

Without
endogenousWith
endogenous

Without
endogenousWith
endogenous

regressors

regressors

Regressors

regressors

regressors

regressors

regressors

regressors

regressors

regressors

Regressors

Undernour
ishment

Undernour
ishment

Undernour
ishment

Undernour
ishment

Undernour
ishment

Undernour
ishment

Undernour
ishment

Undernour
ishment

Undernour
ishment

Undernour
ishment

Undernour
ishment

Undernour
ishment

0.661(5.43)**

0.93(22.02)**

0.672(5.27)**

0.935(22.99)**1.016(7.57)**

0.976(40.15)**1.006(7.29)**

0.969(40.66)**0.93(6.48)**

0.996(32.06)**0.925(6.16)**

0.992(29.70)**-0.4750.027-0.463

0.015

0.248

0.022

0.256

0.013

0.218

0.02

0.23

0.017

-0.88

-0.35

-0.83

-0.19

-1.11

-0.34

-1.08

-0.18

-1.04

-0.31

-1.09

-0.24

Exogenous-0.094-1.84-0.104(2.83)**-0.086(2.12)*-0.099(2.91)**-0.075(3.54)**

-0.05
-1.77

-0.076(3.13)**-0.053-1.67-0.072(2.30)*-0.048-1.81-0.073(2.53)*-0.044-1.48

log(private
credit/GDP)

Endogenous-0.397(4.88)**-0.276(8.79)**

-

-

-

-

-

-

-

-

-

-

predicted
log(private
credit/GDP)

Endogenous---0.415(5.05)**-0.287(6.48)**

-

-

-

-

-

-

-

-

Endogenous

-

-

-

-

-0.186(2.44)*-0.078
-1.01

-

-

-

-

-

-

Endogenous

-

-

-

-

-

-

-0.193(2.57)*

-0.08
-1.03

-

-

-

-

Endogenous

-

-

-

-

-

-

-

-

-0.034-1.02-0.012-1.08--

Endogenous

-

-

-

-

-

-

-

-

-

-

-0.05
-1.25

-0.01
-0.37

Endogenous

0.471
-1.19

-0.009
-0.13

0.479
-1.18

0.008
-0.11

-0.106-1.37-0.074-1.19-0.105-1.35-0.077-1.2-0.179-1.92-0.063-1.04-0.171-1.61-0.055-0.79

Dep.
Variable
Explanatory
VariablesL.
log(schoolingyears inthe initial
years)
log(GDPdeflator)log(private
credit bybanks/GDP)
predicted
log(private
credit bybanks/GDP)log(financial
systemdeposit/GDP)
predicted
log(financial
systemdeposit/GDP)
log(Export+Im
port/GDP)

Exogenous

31

log(Population
Growth)Exogenous

0.702
-0.96

0.325
-1.14

0.631
-0.91

0.302
-1.07

0.778
(4.11)**

0.552(3.10)**

0.746(3.98)**

0.551(2.86)**

0.591(2.07)*

0.446
-1.74

0.54
(2.03)*

0.424
-1.56

log (Share ofWorking AgePopulation)

Exogenous-0.623
-1.4
5.748
-1.49
47

-0.937-1.89
2.198(2.34)*
47-0.593
-1.31
5.489
-1.48
47

-0.917-1.82
2.151(2.31)*
47-1.935(3.71)**
1.9(2.06)*
38-1.065(4.68)**
1.678(2.68)**
38-1.887(3.57)**
1.804(2.07)*
37-1.061(4.28)**
1.706(2.51)*
37-1.417(2.34)*
1.738
-1.35
39

-0.789(2.31)*
1.377
-1.63
39

-1.372(2.20)*
1.544
-1.26
38

-0.737(2.07)*
1.323
-1.47
38

Constant

Observations
Number of
Code
8
8
8
8
7
7
7
7
7
7
7
Arellano-Bond Tes for Serial Correlation (Z,Probb>z)m2-0.37(-0.16)-0.55-0.07(-1.11)(-1.23)(-1.12)(-1.21)(-0.99)(-1.39)(-1.00)Sargan Test ofoveridentifying
restrictions
Ho: overidentifying
restrictions are validchi2(18)=
chi2(61)=chi2(18)=
chi2(61)=chi2(18)=
chi2(53)=
chi2(18)=
chi2(52)=
chi2(18)=
chi2(54)=chi2(18)=
39.10**
83.6*
37.83**
82.65*
25.29
52.02
25.87
52.31
25.06
53.47
25.36
Prpb>Chi2
0.003
0.03
0.004
0.03
0.12
0.51
0.103
0.46
0.12
0.49
0.12
1. Absolute value of z statistics in parentheses. 2. * significant at 5%; ** significant at 1% (based on robust estimators)3. Blundell and Bond (1998) GMM one-step estimator is applied for all the cases.

32

7(-1.44)chi2(53)=
53.98
0.44

Appendix 1 Definitions and Descriptive Statistics of the Variables
Annual Panel Data (1960-2006) for 9 countries
Variablelog(GDP pc)

Definitionlog of GDP per capita

SourceObs

MeanStd. Dev.

Min

Max

WDI

399

6.219

0.850

4.281

8.420

log(Agri VA pc)

log of Agricultureal Value Added per capita

FAO-STAT.

388

4.772

0.478

3.779

6.044

log(private credit/GDP)

log of share of domestic credit provided by

WDI

339

3.446

0.839

0.651

5.349

Beck et al.

283-1.2250.693-2.6450.507

295-1.382

1.479-9.596

0.235

359

3.475

0.529

2.230

4.251

384

22.4791.362

19.19626.497banking sector in GDP*1.
log(private credit bylog of private credit by Deposit Money Banks*2banks/GDP)and Other Financial Institutions in GDP .

(2000).

log(financial system

log of Financial System Deposits in GDP.

Beck et al.

deposit/GDP)log(share of population
with primary ed. or abovelog(government(2000).
log of share of the population with education

Barro-Leelevel of primary or above.

(2000).

log of share of government espenditure in GDP.

WDI

expenditure/GDP)

Population below minimum level of dietarylogenergyof Consumer
consumption
Price Index.
(also referred to as prevalence of undernourishment)
WDI
shows the percentage336
of the3.334
population 1.694
whose food intake-7.370is insufficient
5.173
to meet dietary energy reqlog(Ecport+Import/GDP)

log of the share of Export and Import in GDP.

-0.7080.729-2.5400.894*1 Domestic credit provided by the banking sector includes all credit to various sectors on a gross basis, with the exception of credit to the central government, which is net. The banking sector
includes monetary authorities and deposit money banks, as well as other banking institutions where data are available (including institutions that do not accept transferable deposits but do incursuch liabilities as time and savings deposits). Examples of other banking institutions are savings and mortgage loan institutions and building and loan associations.
*2 This is similar to the first definition, but the first definition covers a broader category of banking sector, including monetary authorities, formal and informal banking institutions, while the second
mainly covers private credit through deposit money banks.

33

Appendix 1 - Definitions and Descriptive Statistics of the Variables (Cont.)
log(private credit/GDP)

log of share of domestic credit provided by

Beck et al.

banking sector in GDP.
predicted log(private credit/GDP)

log of share of domestic credit provided bybanks/GDP)
predicted log(private credit bybanks/GDP)log(financial system

log of private credit by Deposit Money Banks

the initial year)
log(GDP deflator)

0.960

5.257

74

3.499

0.785

1.390

5.186

62

-1.2130.685-2.4370.374

61-1.1940.666-2.3470.345

65-1.443

1.695-9.5960.186

63-1.3081.302-7.809

0.175

77

0.671

0.743

-0.491

1.478

WDI

82

1.936

1.053

-0.697

5.847

WDI

82

-0.6710.730-2.3850.885

90-3.9200.358-5.117-3.46190-0.3190.219-0.892-0.035Beck et al.

and Other Financial Institutions in GDP.

(2000).

log of private credit by Deposit Money Banks

Beck et al.

and Other Financial Institutions in GDP, predicted by annual panel.
log of Financial System Deposits in GDP.

(2000).
Beck et al.
(2000).

log of Financial System Deposits in GDP, predicted by annual panel.

deposit/GDP)log(schooling years in

0.872(2000).

deposit/GDP)
predicetd log(financial system

3.451

Beck et al.

banking sector in GDP, predicted by annual panel.
log(private credit by75(2000).

Beck et al.
(2000).

log of average schooling years of people above 15 years oldin the initial year.

Barro-Lee(2000).

Inflation as measured by the annual growth rateof the GDP implicit deflator.

log(Ecport+Import/GDP)

log of the share of Export and Import in GDP.

log(Population Growth)log of annual popuoation growthlog (Share of W orking AgePopulation)

the ratio of dependents--people younger than 15 orWDI

older than 64--to the working-age population--those ages 15-64.

34