DP2013-10

Fina ncial Crisis in Asia:
Its Genesis, Sev erity a nd Impa ct
on Poverty and Hunger *Katsushi S. IMA I
Raghav GAIH A
G ane sh THAPA
S am ue l Ko bina A NNIM

March 27, 2013

* 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 & 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
27th March 2013

Abstract
Building on the recent literature on finance, growth and hunger, we haveexamined the experience of Asian countries over the period 1960-2010 bydynamic and static panel data models. We have found evidence favouring a
positive role of finance - defined as private credit by banks - on growth of GDP
and agricultural value added. Private credit as well as loans from the World Bank
significantly reduces undernourishment, while remittances and loans frommicrofinance institutions appear to have a negative impact on poverty. Our
empirical evidence shows that growth performance was significantly lower
during the recent global financial crisis than non-crisis periods, though theseverity is much smaller during the recent financial crisis than Asian financial
crisis.
Key Words: Finance, Economic Development, Agriculture, Inequality, Poverty, Asia
JEL Code: C33, E44, G01, I32, O15
The project was initially funded by IFAD (International Fund for Agricultural Development). We are
grateful to Thomas Elhaut, former) Director of Asia and the Pacific Division, IFAD, for his support
and guidance. The first author thanks generous research support from RIEB, Kobe University, during
his stay in 2010. The second acknowledges generous support of Bish Sanyal, Department of urban
Studies, MIT. Raghbendra Jha’s help with the econometric analysis is greatly appreciated, as alsovaluable research assistance by Valentina Camaleonte and Sundeep Vaid. We are indebted to guest
editors, David Lawson and Ed Amann, and participants in the Second Annual ESRC Development
Economics Conference “The Effects of the Financial Crisis on Developing Countries” at Manchester
in January 2010 for their useful comments. The present version has benefitted from valuablecomments from an anonymous referee. The views expressed are, however, those of the authors’ anddo 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.

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Financial Crisis in Asia: Its Genesis, Severity and Impact on Poverty and
HungerI.

Introduction

There are a number of studies that focused on the financial crisis that erupted in USA in 2008
and 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 crisis ofconfidence. Despite extensive interventions by governments and monetary authorities, thesupply of credit shrank, stock markets recorded dramatic losses, and a major downturn
occurred in the global economy. Commodity prices eased from the earlier peaks-especiallyduring 2007-08 which sparked riots in many developing countries- and large exchange rate
realignments occurred shortly after the crisis (ADB, 2008, 2010, FAO, 2009, IMF, 2008,2010. In fact, as emphasised by Rodrik (2010), developing countries have been prone to aseries of crises - some financial and others of a different kind - with devastatingconsequences for the poor. He observes “For too many of these countries, economic growthin the last two decades relied on a combination of two factors: a natural rebound fromprevious financial crises (as in Latin America) or political conflicts and civil war (as inAfrica), and high commodity prices. Neither can be relied on for the productive
transformation that developing countries need” (Rodrik, 2010, no page number).
An observation on the classification of the 2008-09 crisis as financial is pertinent. As thefinancial crisis evolved into an economic crisis through its linkages with the real economy for example, through trade channels - several recent commentators prefer to characterise therecent crisis as economic. As an important analytical concern of our study is comparison ofthis crisis with an earlier Asian financial crisis of 1997-98, we stick with the classification of
the former as financial without overlooking the trade channels.
However, regarding the impact of the financial crisis which started in 2008, there isconsensus about the extent to which it affected economic growth and poverty in the2

developing world. Two studies in this volume offer insightful accounts. Indonesia fared
reasonably well in the financial crisis of 2008/2009 (Mc Culloch and Grover, 2013). The
macroeconomic shock it suffered was much less than those of neighbouring countries andslowed slightly its growth rate. There was little evidence of sub-groups which wereparticularly badly affected, although the impact of the crisis on migrant workers isunderstated by the data. A surprising result is that the period between August 2008 andFebruary 2009 saw large increases in real wages for employees over 25. Although real wages
in mining, agriculture and public utilities fell, reflecting the collapse in commodity prices,
wages in industry, construction and transport, and communications increased quickly.
There are several reasons why Indonesia was affected mildly. One is structural - Indonesia, as
a large country, is much less dependent on international trade than most other countries in theregion. The large drop in exports and imports therefore had a commensurately smaller effecton the domestic economy. Moreover, the government’s macroeconomic management of thiscrisis was good. Confidence was restored to the market, limiting the fall in the value of thecurrency, and hastening its early recovery. Briefly, the Indonesian experience has usefulbroader lessons about the impact of the crisis. First, the nature of the shock was confined toexport sectors, particularly commodities and manufacturing. Second, monetary management
prevented a long-lasting shock to the exchange rate, while a long period of prudential budget
management had created the fiscal space for Indonesia to respond.
During the global recession in 2008-2009, Sub-Saharan Africa (SSA)’s GDP growth fell
substantially. But, fortunately, the decline was much lower than in the rest of the world.
Besides, SSA showed greater resilience during the present crisis than in previous ones (Fosu,
2013). Some conjectures are offered. First, the global environment has changed significantly,
with emerging developing countries like China and India currently assuming a much larger
portion of Africa’s trade than previously. As these economies were less adversely affected by3

the crisis, SSA would be similarly so, via the trade shock channel. Second, economic andpolitical governance have improved for SSA as a whole, enabling measures to mitigate theeconomic impact of the crisis. Such instruments include fiscal thrust or accommodating
monetary policy, and in the political context, constraints on the executive, among others. In
conclusion, although SSA was hurt by the recent economic crisis, it has weathered the storm
much better than previously without seriously disrupting the pre-crisis steady march toward
economic and human development.
Set against these and other important recent contributions, the main objective of thepresent 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 ismainly on the recent global financial crisis, our econometric analysis is designed to yieldinsights into the channels through which the effects of financial crisis on growth and poverty
were transmitted in developing countries. Comparison will be made between the recent
global financial crisis and the Asian Financial Crisis in 1997-98 in terms of their severity. In
fact, there is a body of empirical literature to assess the effects of financial crises on growthand/or poverty, using micro data sets. Most of these studies have confirmed negative impactsof crises on growth and poverty reduction (e.g., the Latin American Crisis (Oscar, 1998), theAsian Financial Crisis (Nixson and Walters, 1999; Mazumdar and Horton, 2000) the Russian
Crisis (Lokshin and Ravallion, 2000)).1 But, to our knowledge, there have been no studies toassess the effects of the recent crisis on growth and poverty using a detailed data set. To
better understand the implications of the recent financial crisis for economic growth andpoverty, the present study carries out cross-country regression analysis for a sample of Asian
countries in 1960-2010.

1

However, using panel data, Stillman and Thomas (2008) found a weak impact of the Russian crisis
on nutritional status.

4

The scheme is as follows. The next section sets the stage for our analysis, by linking
finance and the real economy, and through a brief exposition of the dynamics of the financial
crisis and how the crisis unfolded in Asia. The impact of the financial crisis on microfinance given the latter’s increasingly important role in reducing poverty in Asian countries - is also
reviewed. Section III is devoted to a review of the literature on finance, growth and poverty.
The data, model specifications and results are discussed in Sections IV and V, primarily toillustrate how credit influences growth and poverty reduction. In Section VI, concluding
observations are made from a broad policy perspective.

II.

Backgrounds

Linkages between Finance and Real Economy
While the linkages between finance and real economy remain contentious, various studies
have focused on the following routes (IMF, 2008). The first is through a financial accelerator
that amplifies the effects of financial cycles on the real economy specifically, its effects on
the value of collateral and thereby expansion of credit. Another is through lenders’ balance
sheets and the relationship between bank capital and aggregate credit. When bank capital iseroded, banks become reluctant to lend and are forced to deleverage. A third but overlappingwith the first linkage is the variation in the role of the financial accelerator with the financial
system (arm’s length financing as opposed to relationship banking). In other words,
households and producers can substitute away from banks to markets (ibid, 2008). No lessimportant are the trade linkages focused on contraction of exports to developed countries thatwere badly hit by the financial crisis.
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 of5

capital, 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, 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 arm’s length financial systems in which a greater share of intermediation
occurs through financial markets rather than through traditional relationship-based (and bankdominated) activities. Thus, arm’s length financial systems are more prone to financial crises.
The channels through which the financial crisis impacted on growth and poverty indeveloping countries are diverse (Lin and Martin, 2010). These include changes in capital
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)6

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, 2011). Finally, as the poor are more creditconstrained, contraction of microcredit and more stringent selection criteria are likely to hurt
the poor more.2

How did the Crisis Unfold in Asia?
The crisis manifested itself in emerging Asia in late 2008, and was initially expected toworsen in response to slackening demand from advanced economies and growing tensions inregional financial markets. Later assessments, however, pointed to a strong recovery led by
China, India and other emerging Asian economies (ADB, 2010, IMF, 2010).
Table 1 and Appendices 1 and 2 largely confirm the view that Asia was not affected asmuch by the global financial crisis in 2008 as by the Asian Financial Crisis in 1997-98.
Table 1 reviews the changes of GDP per capita growth, poverty head count ratio, private
credit as a share of GDP, and export share in GDP before and after the Asian financial crisis
as well as the global financial crisis for East Asia and Pacific (EAP), South Asia (SA) and2

For assessment of poverty alleviation role of microfinance from micro and macro perspectives, see
Imai, Arun and Annim (2010) and Imai et al. (2012).

7

Sub-Saharan Africa (SSA).3 These figures for selected countries are shown in Appendices 1
and 2.
(Table 1 to be inserted around here)

The first panel of Table 1 shows that economic growth slowed down in 2008 in EAP, SA
and SSA, most notably in SA. While SSA’s growth rate reduced to -0.5% in 2009, both EAPand SA made a sharp recovery in 2009-10 (World Bank, 2012). This V-shaped recovery issimilar in the case of Asian financial crisis for EAP and SA, while the damage during thecrisis (broadly inferred from the figures of GDP per capita in 1998 and 2008) was muchseverer for EAP in 1998 (than in 2008) and for SA in 2008 (than in 1998). On the other hand,poverty headcount at US$2 a day steadily declined after the crisis with the exception of SSA
where it only marginally declined from 77.5% in 1996 to 77.4% in 1999.
Following Fosu (2013), Mc Culloch and Grover (2013) and others, the present study takes
into account both “trade effects” and “finance effects” associated with the crisis. Table 1
focuses on both private credit as well as export - defined as shares of GDP. It is striking tofind that private credit was not much affected, or even increased during both crises with the
exceptions of EAP in which private credit reduced by 2.8% in 2008 from the pre-crisis level(with a sharp recovery in 2009 to the level much higher than those in pre-crisis years), and
SSA where it reduced by 8% in 2008 and recovered to the pre-crisis level in 2010.4 Exports
were not much affected either with the exceptions of SSA in 1998 and EAP in 2008. It isnoted that EAP reduced its export share in GDP further down to 34.5% in 2009 (from 46.2%
in 2006).

3

Only the countries classified by the World Bank as developing are included. The selection of the regions is
guided by the fact that these regions still have a large number of the poor. The figures for other regions will befurnished on request.
4
Other aggregate credit indicators (e.g. financial deposit or credit given by broader financial institutions) show
similar trends.

8Appendices 1 and 2 break down the figures in Table 1 for selected Asian countries. As thespace is limited, only a few observations on the 2008-09 crisis are made below. Financial
markets weakened due to a pessimistic global outlook and investor risk appetite declined
following the turbulence in September in 2008 and plummeting of equity markets. Current
accounts began to show strains as well in these countries, largely due to rising import bills forcommodities and slowing export growth. In consequence, India’s growth rate fell to 3.4% in2008 (down from 8.2% in 2007) and China’s fell to 9.0% in 2008 (from 13.6% in 2007),
while export share declined after the crisis only in China5 (Appendix 2). Growth and export
figures vary among other Asian countries, while the negative impact was generally muchsmaller in 2008 than in 1998. It is notable that Bangladesh, Indonesia, Vietnam and Lao PDR
kept relatively high growth figures before and after the 2008 crisis, while Pakistan, Malaysia,
the Philippines, Thailand, Cambodia, Kazakhstan, and Kyrgyz Republic had one year ofnegative growth rate after the crisis with varying stability. Apart from China, Malaysia,
Pakistan, the Philippines, Nepal and Kazakhstan saw declining exports after the 2008 crisis.
However, most of the countries have seen broadly steady decline in poverty after the 2008
crisis. This is in sharp contrast to the Asian financial crisis which increased poverty inIndonesia and the Philippines. Only countries which experienced contraction of credit in2008 were China, Sri Lanka, Malaysia and Myanmar.
Impact of Financial Crisis on MicrofinanceMicrofinance allows poor people to protect, diversify and increase their incomes.
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). Using data of 655
microfinance institutions across 80 countries in 2000-2009, Wagner and Winkler (2012)
5

Growth rate of merchandise exports in China plummeted from 28.9 per cent in 2007 to 13.7 per centin 2008 (ADB, 2010).

9

showed that the crisis considerably reduced credit growth of microfinance institutions - mostnotably in Eastern Europe and Central Asia - and suggested that microfinance is vulnerable tocrisis. To the extent that there is contraction of credit, and concomitant reduction in rural
credit, the implications for the rural poor are likely to be serious. Even though interest rates
have fallen to stimulate demand for credit, there is a strong reluctance to lend in anenvironment lacking trust. So, effectively, contraction of credit implies higher interest rates
and shorter maturities. If these observations have general validity, it follows that the demandfor credit would be reduced especially in the target groups of microfinance institutions(MFIs), and poverty may increase through financial constraints on raising agricultural
productivity. Vulnerability of low income households may also get aggravated because of
their failure to smooth consumption. On the other hand, the loan portfolio of MFIs may shift
in favour of wealthier clients. Moreover, financial viability may erode because of moral
hazard and adverse selection. A major priority therefore is to inject more capital into thefinancial system-especially MFIs (Microcredit Summit Campaign, 2008). Associated with thevulnerability of MFIs is the larger risk of mission drift and abandonment of their social
objectives. Given the evidence on microfinance reducing poverty across developing countries(Imai et al., 2012), we will also consider the impact of microfinance in our econometricanalysis.

III.

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 on Beck et al. (2007) and Claessens and Feijen (2006) with briefcomments on a few other important contributions. Beck et al. (2007) examine the effects offinancial development on poverty through two channels: aggregate growth, and changes inthe distribution of income. Instead of examining the finance-growth link, they offer an

10assessment of the impact of financial development on changes in the distribution of income
and changes in both relative and absolute poverty. Specifically, the variables considered are(i) the Gini coefficient of income distribution; (ii) income share of the poor, measured as theincome share of the poorest quintile relative to total national income; and (iii) the share of the
population living on less than US$1 per day. Using GMM panel estimator for dynamic
models, they show that 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 inequality6.
Claessens and Feijen (2006) identify specific channels through which financial
development impacts on undernourishment7. 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. The present study builds upon Beck et al.
(2007) and Claessens and Feijen (2006), among others.

IV.

Data and Models for Finance, Growth and Hunger in Asia

Here the objective is to analyse the relationships between finance, growth and hunger/
poverty in selected Asian countries. The analysis is based on a panel of 9 countries(Bangladesh, China, India, Indonesia, Malaysia, Pakistan, Philippines, Thailand, andVietnam)8 over the period 1960 to 2010, based on dynamic and static panel estimations.
6

Honohan (2003) shows that a 10 per cent increase in private credit to GDP reduces poverty by 2.5-3
percent.
7
Undernourishment is defined as “the condition of people whose dietary energy consumption iscontinuously below a minimum dietary energy requirement for maintaining a healthy life and carryingout a light physical activity” (FAOSTAT, 2008).
8
Selection of the countries is guided by data availability.

11

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) 2012 (World
Bank, 2012), World Bank’s Finance Data (based on Beck and Demirgüç-Kunt 2009; Beck etal., 2000), The UNU-WIDER World Income Inequality Database (WIID) (UNU-WIDER,
2008), and Barro-Lee’s (2011) data on education. One of the data constraints in addressing
our research questions is that while annual data on most of the key economic and financial
variables are available for 9 countries (except Vietnam for which most of the variables startfrom 1985-1990) in 1960-2010, the data on inequality and poverty are available only for
those years in which 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 growth in the period 1960-2010, with a few missing
observations. 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 are available roughly once or sometimes twice in 5-year periods. If more than one
estimate is available in one period, the average is used. These poverty and inequality data arematched with the 5-year averages of finance and economic variables. One of the advantagesof applying two different time schedules is that we can use the predicted values of financedata based on annual panel data for 5- year panel, whereby inequality or undernourishment isestimated by the aggregated finance data based on predictions on annual basis. This approach
would at least partially address the issue of endogeneity of finance in the inequality or
undernourishment equation. For annual data, we have specified a dynamic panel data model,
drawing upon Blundell and Bond (1998) - an extension of Arellano and Bond (1991) to

12

estimate GDP per capita, agricultural value added per worker and various finance variables.
For 5- year average data, we used static panel data models, namely, fixed effects or random
effects models given a few missing observations in poverty and inequality data as well as
reduced number of observations as a result of averaging annual data.
Appendix 3 lists the definitions and descriptive statistics of the variables as well as data
sources. We have taken three different measures of finance - (i) logarithm of the share ofprivate credit in GDP; (ii) log of the share of private credit through (formal) money deposit
banks as a share of GDP (the narrow definition of private credit), and (iii) log of Financial
System Deposits in GDP, but we have mainly presented the results of (i). Because these are
aggregative measures in nature, we have also used three additional finance or finance-related
variables: total gross loan portfolio (GLP) of microfinance institutions (MFIs) aggregated foreach country, loans from the World Bank groups (IBRD loans and IDA credits), andremittances (net remittance inflows as a share of GDP). For inequality, we use the income
Gini coefficient. Undernourishment is the share of population below minimum level of
dietary energy consumption. Poverty is defined as poverty headcount ratio at $2 a day9. Other
variables used in the analysis are defined in Appendix 3.

Model Specifications
We estimate two dynamic panel models in which the dependent variables comprise GDP per
capita and finance, and three static panel models for inequality, undernourishment andpoverty. A variable on finance predicted by the dynamic model is used as one of the
explanatory variables for static models.
(a) Model for GDP per capita
Following Guariglia and Poncet (2008), we specify the following relation:
9

Use of poverty headcount ratio at $1.25 or poverty gaps for both thresholds have resulted inlead tobroadly similar results.

13

∑

(1)

where i and t denote country and year, respectively,
is its j-thlag.10is GDP per capita growth andis a proxy variable for various variables on finance (e.g. private credit,

MFI’s total gross loan portfolio, public debt, remittances).

, the sum of export and importas a share of GDP, is supposed to capture the trade openness effect.

is a vector ofcontrol variables, such as, education (log of the share of main working age population (25years or above) with primary education or above), log of government education in GDP tomeasure size of government, and log of CPI (Consumer Price Index).

and, dummy variables for the Asian Financial Crisis (taking 1 for 1997 and 1998
0 otherwise ) and for the Global Financial Crisis (taking 1 for 2008 or 2009 and 0 otherwise)
are included to see the effects of crises on the growth performance 11 .
specific unobservable effect (e.g. social and cultural factors)

andis the country
is an error term,

independent, and identically distributed (or i.i.d.). To see how the effect (or slope) oforon growth is significantly different during the Asian financial crisis or the global financial
crisis (in comparison with non-crisis periods), we have included the interacted terms with(or

) as specified by equation (1)’. This specification is usefulto address “trade effects” and “finance effects” of the crisis (Fosu, 2013) indirectly. That is, if
the interaction terms were negative and statistically significant, the effect of trade or financeon economic growth was supposed to be dampened during the crisis periods.
∑

(1)’

10

As an extension, we have applied the same specification for agricultural value added per worker
given the importance of agricultural sector in these Asian countries.
11
We can include the time effect term in the two-way error model (Baltagi, 2005), but to see theimpact of crises clearly, we have introduced two dummy variables instead. Inclusion of time effects
will not change the main results.

14

A version of equation (1) or (1)’ can be written by replacing all the explanatory variables(except lagged dependent variables) by a vector of(.

)

with log of lagged per capita GDP on the right hand side. 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)12

Two issues have to be resolved: one is endogeneity of the regressors and the second is thecorrelation between (Assuming that) and () (e.g. see Baltagi, 2005, Chapter 8).

is not serially correlated and that the regressors inare 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 conditions. Such a system gives consistent results under the assumptions
that there is no second order serial correlation and the instruments are uncorrelated with the12

As an extension, we have also implemented the case with the first and second lagged dependent variables insome cases, depending on the results of serial correlation tests and significance of coefficient estimates of thelagged dependent variables.

15

error terms. Validity of instruments is tested by Sargan’s J test and the second order serial
correlation of the residuals. The Blundell-Bond system GMM estimator is used in the present
study. This estimator is useful to address the problem of potentially endogenous regressors(e.g. finance in equation (1)). In the system equation, endogenous variables can be treatedsimilarly 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 for the level equation in the system. We try
the cases (i) where the endogeneity is not taken into account, and (ii) where some endogenous
variables (after instrumenting) are included. In this model, we try the cases where finance andtrade 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 equation is also estimated by the Blundell-Bond system GMM estimator.

(c)Model for Inequality, Undernourishment and Poverty

Due to the small number of observations for 5-years average data and missing observations,
we have applied static panel models, namely, fixed-effects or random effects model.
̂(5)

The dependent variable is the Gini index of income and denoted as. Here t’ stands for 5-

year averages (1960-64, 1961-65, … , 2005-9), which is distinguished from t, or annual data.
̂is 5- year average of log of finance (i.e., private credit, total gross loan portfolio of16

MFIs, public debt, remittances) predicted by the equation (4).

is a vector of control

variables, such as the share of working age population with primary education or above,
population growth, and dependency ratio (the share of population below 15 years old or 65
years old), all in log.13 In this case,andis defined as a dummy variable for 1995-99

is for 2005-9 for simplicity. We have also used the same specification

for undernourishment and poverty.

V.

Econometric Results

The results of the models specified above are discussed here. Table 2 reports the selected
results where a dependent variable is log GDP per capita growth and ‘log finance’ is definedas private credit as a share of GDP (Cases 1-3), private credit given by banking sectors as ashare of GDP (which follows a narrower definition as detailed in Appendix 3) (Case 4), total
Gross Loan Portfolio of MFIs (Case 5), public debt to the World Bank groups (Cases 6-8)
and net flow of remittances (Case 9). Only a summary of the results is given below.
(Table 2 to be inserted around here)

When we adopt a broader definition of finance, or private credit/GDP, the sign of financeis positive but not statistically significant (Cases 1-3). In Cases 1 and 2, credit or trade shareis not treated as endogenous variables and in the latter, trade share or credit is interacted withcrisis dummies. In Case 3 credit and trade are treated as endogenous variables. The first lagof log GDP per capita is positive and significant, while the second is negative and significant,suggesting persistence of growth with some adjustment in 2 years. It is noted that GDP per
capita was significantly lower during the Asian financial crisis as well as during the recent
global financial crisis than the levels in non-crisis years. However, we can also find that

13

Trade share has been dropped because of the high level of correlation with education.

17

growth performances of Asian countries were more severely affected by Asian financial crisis
than the recent global financial crisis as the dummy variable for the former has a higher
absolute value of coefficient estimate in all the cases.
The interaction of the 1997-8 dummy and trade share is negative and significant, which
implies that the positive effect (or elasticity) of trade (0.0173) became negative (-0.022)
during the Asian Financial Crisis (Case 2). When we adopt the narrow definition of private
credit restricting it to the credit given by banks, it is positive and significant. This signifies
the role of banking sectors in promoting growth (Case 4). The coefficient of gross loan
portfolio of MFIs is positive but not statistically significant (Case 5). ‘Loans from the WorldBank group’ is statistically insignificant regardless of the specifications (Cases 6-8). Tradeshare is positive and significant in general in these cases, but, as in Case 7, the negative
coefficient estimate of the 1997-8 dummy interacted with trade share implies that the effectof trade openness became negative during the Asian financial crisis. This was presumably
because the countries more open to the rest of the economy are likely to be more vulnerable
to the crisis. However, this did not happen during the global financial crisis. The interaction
term of the 2008-9 dummy and loans from the World Bank group is found to be positive andsignificant, while the interaction between the 1997-8 dummy and the World Bank loans is
negative and significant in Case 7. That is, the effect of loans from the World Bank group issignificantly higher during the recent global financial crisis, but lower during the Asianfinancial crisis relative to non-crisis years. This may suggest that the effectiveness of WorldBank loans has improved significantly during the recent financial crisis in enabling
borrowing countries to achieve higher economic growth. Contrary to Imai et al. (2011), thecoefficient estimate of remittances is negative and significant (Case 9) 14. Overall, Table 2
shows that (i) growth performance of Asian countries was affected by the global financial
14

Difference was mainly due to difference in specifications as well as sample countries and years
included.

18

crisis, and (ii) finance did not affect growth except in a few cases. Trade openness or
education is a more important determinant of the growth. However, the effect of trade does
not appear to be influenced by the global financial crisis. Also, government expenditure ispositive and significant only in Case 9.
We have applied exactly the same specifications to agricultural value added per worker15.
The results are very similar to those in Table 2, but we observe a few differences. First, both
first and second lagged dependent variables are positive and significant, suggesting the strong
persistence in agricultural value added. Second, private credit by banks as well as remittances
cease to be significant. That is, finance does not much affect the growth performance ofagricultural sectors. Third, government expenditure is positive and significant in all the cases.
Table 3 contains the results of the finance equation using different definitions of finance.
We have tried the specification, as in the equation (4), as well as the one without crisis
dummies in which GDP per capita and trade share are treated as exogenous variables in aparsimonious specification. Key results of the latter are summarised at the bottom of Table 3.
First, it is striking to find that private credit as a share of GDP during the Asian financial
crisis as well as global financial crisis was significantly higher than in non-crisis periods inCase 1. It is often argued that credit supply tends to immediately contract after the crisis, but
this claim has to be carefully re-examined. This is supported by Appendices 1 and 2.
Appendices 1 and 2 show that 13 out of the 17 Asian countries experienced an increase in
private credit in 1998 as well as in 2008 from the pre-crisis levels of private credit. However,heterogeneity of the crisis impact among different countries should not be ignored because
China or Malaysia experienced a drop in private credit in 2008. It is also notable that crisis
dummies are positive, but not statistically significant if we adopt a narrow definition ofprivate credit (Case 2). Microfinance and remittances significantly increased even during the15

The results will be furnished on request.

19

Asian financial crisis. We do not find a positive and significant link between GDP per capita
and finance (except case 4), but once we drop the crisis dummies and treat GDP per capita
and trade share as exogenous variables, the coefficient estimate of GDP per capita becomessignificant and positive for private credit and MFI loans. It is negative and significant for theloans from World Bank groups as poor countries tend to obtain loans in difficult times.
Consistent with Baltagi et al. (2009), trade openness shows a positive and significant estimate
in the parsimonious specification. Trade share is also positive and significant for loans fromWorld Bank groups, but negative and significant for microfinance loans regardless of thespecifications. The reasons are not clear, but we conjecture that the latter is affected by
Bangladesh with a high value of MFI loans and a low level of trade openness.
Table 4 reports the results for the Gini coefficient as well as undernourishment while
Table 5 presents those for poverty headcount ratio at US$2 based on fixed-effects or random
effects model. The choice of the model is guided by the Hausman test. To avoid cluttering thetext, we focus only on key results associated with the effects of (predicted) finance on thedependent variables. Our explanations below draw mainly upon the cases preferred by the
results of Hausman tests (shown in bold).
(Table 4 to be inserted around here)

In Table 4 inequality measured by the Gini coefficient is not significantly affected byfinance variables if we focus on the cases chosen by Hausman tests. However, if we take theresults in Cases 3 and 6 based on random-effects models, we find that inequality tends to besignificantly reduced by either MFI loans (Case 3) or loans from the World Bank group (Case6) presumably because these mainly target the poor. It is not certain whether education
reduces inequality as the results are sensitive to specification. GDP deflator is not statisticallysignificant in the cases chosen by Hausman tests. Crisis dummy variables are statistically20insignificant, suggesting that inequality levels during the crisis periods were not muchdifferent from those in the non-crisis periods.
On the other hand, undernourishment is negatively and significantly associated withpredicted values of private credit (Case 10) and loans from the World Bank groups (Case 12).
The coefficient of remittances is positive, but not significant. Population growth tends toincrease undernourishment (Case 13). No significant effects are observed for the Asianfinancial crisis dummy. The global financial crisis dummies have been dropped due tomissing variables of undernourishment in recent years.
(Tables 4 to be inserted around here)

Table 5 shows a set of results in which poverty headcount ratio at US$2 is estimated byusing the same specification used for Gini or undernourishment. Most of the finance variables
are statistically insignificant, but ‘remittances’ is negative and significant at the 10% level,
which is consistent with poverty reducing roles of international remittances (Imai et al. 2011).
In this case, remittances are likely to reduce poverty headcount at US$2 by 0.9% in response
to a 10% increase in remittances ceteris paribus. If we apply the same model to headcountratio at US$1.25, the coefficient estimate is -0.175 and is significant at the 5% level. That is,poverty headcount at US$1.25 is reduced by 1.75% in response to a 10% increase inremittances.16 ‘MFI loans’ seems to play an important role in reducing poverty at US$2 withthe coefficient estimate of -0.203 and t value of 1.503. A 10% increase in MFI loan portfolio
tends to reduce poverty by 2.03%.
GDP deflator is positive and significant in Case 6. In this case, price increase has apoverty increasing effect. A higher dependency ratio tends to be associated with a higher
level of poverty. It is noted that the global financial crisis is positive and significant in Cases

16

The results for poverty headcount ratio at US$1.25 will be furnished on request.

21

1, 2, 8, 9, and 10. That is, poverty levels during the crisis are generally lower than those in the
non-crisis periods ceteris paribus.

VI.

Concluding Observations

Building on the recent literature on finance, growth and hunger, we have examined theexperience of Asian countries over the period 1960-2010 by dynamic and static panel data
models. We have first examined the effect of various forms of finance, such as private credit,
loans from microfinance institutions (MFIs), loans from the World Bank group, andremittances. In the cases where finance is defined as private credit given by banks, we foundevidence favouring a positive role of finance on growth of GDP and agricultural value added.
While the literature suggests that GDP growth also causes financial development (e.g. Becket al., 2007), we do not find clear evidence of a reverse causality between GDP growth andfinancial development. Financial development does not much reduce the Gini coefficient ofincome distribution, but it reduces undernourishment, if financial development is defined asshare of private credit in GDP or as loans from the World Bank, the results of which areconsistent with the role of financial development in reducing undernourishment. While it is
not clear whether aggregate finance reduces poverty, remittances as well as loans frommicrofinance institutions seem to have some poverty reducing roles.
We have also examined whether growth, finance, and undernourishment or poverty
change during the Asian financial crisis and the recent global financial crisis. Our analysis
suggests that ‘trade effects’ or ‘finance effects’ are different during the crisis periods. Whilst
we find a significant ‘trade effect’ for private credit as well as loans from the World Bank
group during the Asian financial crisis in 1997-98, that is, the effect of trade openness on
growth was dampened during the Asian financial crisis, such an effect was not observed
during the recent global financial crisis in 2008-2009. On the ‘finance effects’, these were22

negative and significant for the loans from the World Bank group during the Asian financial
crisis. But the effect of loans from the World Bank group was significantly higher during therecent global financial crisis in 2008-9. It is also noted that poverty levels are generally lower
in the period of global financial and economic crisis than in other periods other things being
equal.
These results are broadly consistent with other analyses in this special volume However,
our empirical analysis using cross-country panel data in Asia over the last five decades showsthat growth performance was significantly lower during the recent global financial crisis thanin non-crisis periods, though the severity was much considerably lower during the former
relative to the Asian financial crisis. Remittances as well as microfinance also helped reducepoverty to some extent. However, this does not imply that poverty or undernourishment after
the crisis is not important as the poverty rate is still high in many countries-particularly inSouth Asian countries including India, Bangladesh, Pakistan, or Nepal.

References
ADB (Asian Development Bank). 2008. Navigating the Global Storm: A Policy Brief on the
Global Financial Crisis. Manila: ADB.
ADB (Asian Development Bank). 2010. Outlook 2010. Manila: ADB.
Arellano M, Bond S. 1991. Some tests of specification for panel data: Monte Carlo evidence
and an application to employment equations. Review of Economic Studies 58: 277-297.
Arrow K. 2008. Risky Business, The Guardian, 15 October,
http://www.guardian.co.uk/commentisfree/cifamerica/2008/oct/15/kenneth-arroweconomy-crisis.

23

Baltagi BH. 2005. Econometric Analysis of Panel Data, Third Edition. Chichester: John
Wiley & Sons Ltd.
Baltagi BH, Demetriades PO, Law S H. 2009. Financial development and openness:
Evidence from panel data. Journal of Development Economics 89(2): 285-296.
Barro RJ, Lee J. 2000. International Data on Educational Attainment: Updates and
Implications. CID Working Paper No. 42, April 2000. Cambridge, MA: Harvard
University.
Beck T, Demirgüç-Kunt A. 2009. Financial Institutions and Markets Across Countries andover Time: Data and Analysis. World Bank Policy Research Working Paper No. 4943,
May 2009. Washington DC: World Bank.
Beck T, Demirgüç-Kunt A., Levine R. 2007. Finance, Inequality and the Poor. Journal of
Economic Growth 12(1), 27-49.
Beck T., Demirgüç-Kunt A., Levine R. 2000. A New Database on Financial Development
and Structure. World Bank Economic Review 14, 597-605.
Blundell R, Bond S. 1998. Initial conditions and moment restrictions in dynamic panel-0data
models. Journal of Econometrics 87: 115-143.
Claessens S, Feijen E. 2006. Finance and Hunger: Empirical Evidence of the Agricultural
Productivity Channel. World Bank Policy Research Working Paper 4080, December 2006,
Washington DC: World Bank.
Dasgupta P, Ray, D. 1986. Inequality as a determinant of malnutrition and unemployment:
Theory. Economic Journal 96: 1011–1034.
Dercon S, Christiaensen L. 2011. Consumption risk, technology adoption and poverty traps:
evidence from Ethiopia. Journal of Development Economics 96(2): 159-173.
FAO. 2008. FAO-STAT, Rome: FAO. http://faostat.fao.org/default.aspx.
FAO. 2009. The State of Food Insecurity in the World. Rome: FAO.

24

Fosu AK. 2013. The Global Financial Crisis and Development: Whither Africa?. Draft.
Gaiha R, Imai K. 2009. Measuring Vulnerability and Poverty in Rural India. Vulnerability in
Developing Countries, Naudé W, Santos-Paulino A, McGillivray M. (Eds.) United
Nations University press: Tokyo, 13-54.
Guariglia A, Poncet S. 2008. Could financial distortions be no impediment to economic
growth after all? Evidence from China. Journal of Comparative Economics 36(4), 633-657.
Honohan P. 2003. Financial Development, Growth and Poverty: How Close are the Links?.
World Bank Policy Research Working Paper 3203, Washington DC: World Bank.
Imai, KS, Arun T, Annim, SK. 2010. Microfinance and Household Poverty Reduction: New
evidence from India. World Development 38(12), 1760-1774.
Imai, KS, Gaiha, R, Thapa G. 2010. Is the Millennium Development Goal of Poverty Still
Achievable? Role of Institutions, Finance and Openness. Oxford Development Studies
38(3): 309-337.
Imai KS, Gaiha R, Ali A, Kaicker N. 2011. Remittances, Growth and Poverty: New Evidence
from Asian Countries. RIEB Discussion Paper Series DP2011-30, Kobe: Kobe University.
Imai KS, Gaiha R., Thapa G., Annim, SK. 2012. Microfinance and Poverty: A Macro
Perspective. World Development 40(8): 1675–1689.
IMF (International Monetary Fund). 2008. World Economic Outlook. October 2008,
Washington DC: IMF.
IMF (International Monetary Fund). 2010. World Economic Outlook. October 2010,
Washington DC: IMF.
Ivanic M, Martin W. 2008. Implications of higher global food prices for poverty in lowincome countries-super-1. Agricultural Economics 39(s1): 405-416.
Jha R., Gaiha R., Sharma A. 2009. Calorie and Micronutrient Deprivation and Poverty
Nutrition Traps in Rural India. World Development 37(5): 982–991.

25

Krugman,

P.

2008.

The

International

Finance

Multiplier.

New

York

Times,

http://www.princeton.edu/~pkrugman/finmult.pdf.
Lin JY, Martin W. 2010. The financial crisis and its impacts on global agriculture.
Agricultural Economics. 41(s1): 133-144.
Littlefield E., Morduch J., Hashemi S. 2003. Is Microfinance an effective Strategy to reachtheMillennium

Development

Goals?.

Focus

Note

24,

CGAP.

http://www.cgap.org/p/site/c/template.rc/1.9.2568/.
Lokshin M, Ravallion M. 2000. Welfare Impacts of the 1998 Financial Crisis in Russia andthe Response of the Public Safety Net. Economics of Transition 8(2). 269-295.
Mazumdar D, Horton S. 2000. Impact of Asian Financial Crisis on Labour Markets, Income
Distribution and Poverty: A Comparative Study of Five Countries. Indian Journal ofLabour Economics 43(3): 451-65.
Microcredit Summit Campaign. 2008. Addressing the global financial crisis and fluctuating
food and fuel costs. Micro-credit Summit E-news 6(2) October 2008,
http://www.microcreditsummit.org/enews/2008-10_africa.html.
McCulloch N, Grover, A. 2013. Estimating the Impact of a financial Crisis - Combining
Rapid Qualitative studies with Nationally Representative Surveys, Draft.
Nixson F, Walters B. 1999. The Asian Crisis: Causes and Consequences. Manchester School
67(5). 496-523.
Oscar A. 2008. Income Distribution and Poverty through Crisis and Adjustment. CEPAL
Review 96, 97-122.
Phelps E. 2008. What Has Gone Wrong up until Now. Spiegel Online, 11 November
http://www.spiegel.de/international/world/0,1518,590030,00.html.
Rodrik D. 2010. Can Developing Countries Carry the World Economy? Project Syndicate,
http://www.project-syndicate.org/commentary/rodrik49/English.

26

Stillman S., Thomas D. 2008. Nutritional Status during an Economic Crisis: Evidence from
Russia. Economic Journal 118(531): 1385-1417.
UNU-WIDER. 2008. UNU-WIDER World Income Inequality Database, Version 2.0c. May
2008, Helsinki: UNU-WIDER, available fromhttp://www.wider.unu.edu/research/Database/en_GB/database/.
Wagner C, Winkler A. 2012. The vulnerability of microfinance to financial turmoil –evidence from the global financial crisis. Mimeo., Frankfurt School of Finance &Management.
World Bank. 2008a. Global Financial Crisis and Implications for Developing Countries. A
Background Paper for G-20 Finance Ministers’ Meeting, Sao Paulo, Brazil, November 8.
World Bank. 2012. World Development Indicator 2012. Washington D.C.: World Bank.

27

Table 1 Summary of Growth, Poverty, Finance, and Export performances at regional
levels(a) Before and After the Asian Financial Crisis
East
Asia
&
Pacific

South
Asia

SubSaharanAfrica(b) Before and After the Global Financial Crisis
East
Asia
&
Pacific

South
Asia

SubSaharanAfricaGDP per capita growth (%)
1995

8.5

4.8

1.1

2005

8.9

7.0

3.1

1996 (pre-crisis)

7.7

4.8

2.3

2006 (pre-crisis)

10.1

7.0

3.7

1997

6.0

1.8

0.9

2007

11.5

7.4

3.9

1998

1.1

3.6

-0.2

2008

7.7

3.2

2.4

change from the pre-crisis
level

-6.6

-1.2

-2.5

change from the pre-crisis
level

-2.4

-3.8

-1.3

1999

5.2

4.7

-0.1

2009

6.7

6.5

-0.5

2000

6.5

2.4

1.0

2010

8.9

6.6

2.6

-1.3

change from the pre-crisis
level

-1.2

-0.5

-1.1

change from the pre-crisis
level

-1.2

-2.3

Poverty headcount ratio at $2 a day (PPP) (%)*

Poverty headcount ratio at $2 a day (PPP) (%)*

1996 (pre-crisis)

64.0

80.7

77.5

2005 (pre-crisis)

39.0

73.4

74.1

1999

61.7

77.8

77.4

2008

33.2

70.9

69.2

change from the pre-crisis
level

-5.8

-2.5

-4.9

change from the pre-crisis
level

-2.2

-2.9

-0.1

1995

85.3

23.0

62.5

2005

99.9

37.7

62.2

1996 (pre-crisis)

91.1

23.8

59.3

2006 (pre-crisis)

97.1

40.8

64.9

1997

98.8

24.0

58.2

2007

96.7

42.5

66.9

1998

103.6

24.2

57.4

2008

94.3

45.6

56.9

Private Credit/GDP (%)

Private Credit/GDP (%)

change from the pre-crisis
level

12.4

0.4

-1.9

change from the pre-crisis
level

-2.8

4.8

-8.0

1999

100.6

25.8

64.3

2009

114.5

43.5

63.8

2000

98.6

27.7

60.9

2010

116.3

45.8

64.8

1.6

change from the pre-crisis
level

19.2

5.0

-0.1

change from the pre-crisis
level

7.5

3.9

Export (goods and services) /GDP (%)

Export (goods and services) /GDP (%)

1995

27.4

12.5

27.8

2005

44.9

19.1

32.5

1996 (pre-crisis)

27.2

12.1

29.8

2006 (pre-crisis)

46.2

20.5

33.3

1997

29.9

12.4

28.7

2007

45.1

19.9

33.7

1998

32.7

12.8

27.5

2008

42.3

22.3

36.1

change from the pre-crisis
level

5.5

0.7

-2.2

change from the pre-crisis
level

-3.9

1.7

2.8

1999

31.6

13.0

28.3

2009

34.5

19.1

29.9

2000

35.3

14.2

32.3

2010

37.2

20.3

31.1

2.5

change from the pre-crisis
level

-9.0

-0.2

-2.1

change from the pre-crisis
level

8.1

2.1

Source: World Bank (2012). * Poverty headcount ratio at $1.25 a day shows the similar results.

28

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

Definition oflog [Finance]
Explanatory Variables
L.log(GDPpc)

Case 2

Case 3

Case 4

Case 5

Case 6Microfinance(Gross Loan
Portfolio of)

Loansfrom WB
groupsCase 7
Loansfrom WB
groupsCase 8
Loansfrom WB
groupsCase 9

PrivateCredit/GDP

PrivateCredit/GDP

Privatecredit/GDP

PrivateCreditBy Bank/GDP

1.288***(0.0384)-0.307***(0.0367)
0.01761.306***(0.0363)-0.322***(0.0354)0.00499

1.305***(0.0370)-0.327***(0.0356)
0.00618**

1.282***(0.0780)-0.295***(0.0763)-0.0000121

1.330***(0.0406)-0.345***(0.0388)
0.00051.340***(0.0383)-0.355***(0.0366)-0.000173

1.348***(0.0377)-0.370***(0.0367)-0.000589

1.352***(0.0395)-0.367***(0.0382)-0.00348**

Remittanceslog [Finance]

Endogenous

1.278***(0.0388)-0.298***(0.0371)0.0172[Private Credit ;Microfinacne, Public Debt
or Remittances](Cases 3,4,
5, 8 & 9)(0.0156)(0.0153)(0.00946)(0.00311)(0.00157)(0.00173)(0.00169)(0.00125)(0.00152)

Exogenous

0.0377***0.0327***0.0152**0.0202***0.04020.0380***0.0300***0.0209***0.0069(0.0108)0.0156(0.0104)-0.0173(0.0167)
0.0149**(0.00723)-0.0387***(0.00575)-0.0157***(0.00585)-(0.0108)0.0122(0.0103)-0.0179(0.0164)0.0173**(0.00714)
0.192**(0.0973)
0.0783(0.134)-0.0402***(0.0109)-0.0141(0.0218)(0.00627)
0.0192***(0.00684)-0.00205(0.0100)
0.0105**(0.00489)-0.0434***(0.00536)-0.0140**(0.00555)-(0.00625)
0.0151**(0.00677)
0.00216(0.00172)0.0103**(0.00493)-0.0477***(0.00547)-0.0135**(0.00562)-(0.0271)0.0197(0.0148)0.0394***(0.0147)-0.0216*(0.0113)-0.0271***(0.00879)-0.0210***(0.00700)-(0.0111)0.0197*(0.0109)
0.000501(0.00358)
0.0129*(0.00753)-0.0395***(0.00550)-0.0175***(0.00588)-(0.0106)
0.0122(0.0103)0.00147(0.00343)
0.0172**(0.00722)
1.085***(0.254)-0.287(0.180)-0.0906***(0.0158)-0.0337***(0.00893)(0.00627)
0.0166**(0.00696)
0.00495**(0.00241)0.0147***(0.00521)-0.0473***(0.00528)-0.0158***(0.00549)-(0.00710)
0.0235***(0.00753)
0.0126***(0.00287)
0.00666(0.00611)-0.0507***(0.00547)-0.0183***(0.00561)-

0.0836*(0.0456)-0.0210*(0.0127)-0.000868(0.0253)
0.0714(0.0450)

0.0458*(0.0268)

0.0761***(0.0248)0.0058(0.0931)0.0361(0.0576)

0.00924(0.0185)0.0103**(0.00499)
0.0421(0.0544)0.0772*(0.0399)

0.00311(0.0317)

L2.log(GDPpc)

log(share of
population)with primary ed. orabovelog(government
expenditure/GDP)
log(CPI)

Exogenous
Exogenouslog(Export+Import/GDP)
D 1997-8

Endogenous

D 2008-9

Exogenous

D1997-8*Trade Share

Exogenous

D1997-8* Finance

Exogenous

D2008-9* Trade
Share

Exogenous

D2008-9* Finance

Exogenous

Exogenous

Constant
Observations
Sargan Test of overidentifying restrictions: Ho:overidentifying restrictions are valid
Prpb>Chi2

355

355

355

348

85

312

312

312

259

chi2(385)=
391.30chi2(381)=
388.24chi2(533)=
557.46chi2(526)=
536.99chi2(126)=
121.29chi2(335)=
336.46chi2(331)=
341.85chi2(456)=
485.03chi2(382)=
408.75

0.40

0.38

0.22

0.36

0.80

0.47

0.33

0.17

0.17

29

Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. 3. Blundell and Bond (1998) GMM one-step estimator is applied for all the cases.

30

Table 3 Results for the Finance Equation (Blundell-Bond GMM estimation*2;
Dependent Variable: Finance)
Dep. Variable(in log)
Explanatory Variables
L.
L2.
log(GDP per capita)

Endogenous(Cases 2, 4, & 6)log(Export+Import/GDP)

Endogenous(Cases 3, 4, & 6)

D 1997-8
D 2008-9
Constant
Observations
Number of Country

Case 1Case 2

Case 3

Case 4

Case 5

Privatecredit//GDP

PrivateCredit by
Banks/GDP

Microfinance(Gross Loan
Portfolio of)

Loansfrom WB
groupsRemittances

1.478***(0.0275)-0.530***(0.0253)
0.0116(0.0176)
0.0347*(0.0203)
0.0617***(0.0235)
0.0487**(0.0245)0.0193(0.0669)403
91.075***(0.0356)-0.125***(0.0345)-0.00444(0.0181)0.0179(0.0188)
0.00799(0.0207)0.0243(0.0208)0.183***(0.0586)361
90.717***(0.0673)0.200***(0.0577)
0.252(0.162)-0.580***(0.188)1.438***(0.255)-0.188(0.121)2.834***(0.996)69
81.443***(0.0367)-0.394***(0.0333)-0.345***(0.0409)0.135***(0.0469)
0.039(0.0523)
0.131**(0.0531)0.682***(0.247)323
90.953***(0.0415)0.0121(0.0394)-0.0923(0.0563)
0.0624(0.0619)0.100**(0.0497)-0.0207(0.0499)
0.423**(0.193)253
9Sargan Test of overidentifying restrictions Ho: overidentifying restrictions are validchi2(535)=
chi2(493)=
chi2(94)=chi2(425)=chi2(339)=
700.23552.36
195.92965.09363.15
Prpb>Chi2
0.00
0.03
0.00
0.00
0.18
The coefficient estimates for GDP per capita and trade share in the cases where these are treated as exogenous*3
variables and crisis dummy variables excluded.
log(GDP per capita)
0.0363*0.0006
0.793***-0.429***-0.037
Exogenous(0.0210)(0.0213)(0.256)(0.0511)(0.0649)log(Export+Import/GDP)
0.0533**0.0233-0.699**0.102*
0.0116
Exogenous(0.0237)(0.0188)(0.301)(0.0588)(0.0713)
Notes: 1. Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1.
2.
Blundell and Bond (1998) GMM one-step estimator is applied for all the cases.
3.
A full set of results will be provided on request.

31

Table 4 Results for the Inequality or Undernourishment Equation (Fixed or Random Effects Model)
Case 1Case 2

Case 3

Dep. Variable

Case 4

Case 5

Case 6

Case 7

Case 8

Case 9Case 10Gini Coefficient (in log).

Fixed or Random effects

Fixed

RandomDefinition of log (Finance).

PrivateCredit/GDP

PrivateCredit/GDP

Explanatory Variableslog(share of population-0.0394

with primary ed. or abovelog(GDP deflator)

Fixed
Gross
Loan
PortfolioofMicrofinanceRandomGross
Loan
PortfolioofMicrofinance0.248***

1.831**(0.071)(0.0362)-0.0003

Case 11

Case 12Case 13

Case 14

Undernourishment (in log).

Fixed

RandomFixed

RandomFixed

RandomFixed

RandomFixed

RandomLoansfrom WB
groupsLoansfrom WB
groupsRemittances

PrivateCredit/GDP

PrivateCredit/GDP

Loansfrom WB
groupsLoansfrom WB
groupsRemittances

Remittances

0.306**-0.1050.180***-0.107*0.220***

0.516**

0.438*

0.384*

0.241-0.274-0.318(0.241)(0.120)(0.0757)(0.0420)(0.0543)(0.0489)(0.240)(0.224)(0.224)(0.213)(0.192)(0.238)-0.043**-0.403*-0.06450.00591-0.0509*-0.0251-0.056**-0.140**-0.134**-0.0673-0.0597

0.029

0.145

Remittances(0.016)(0.0197)(0.0955)(0.0562)(0.0265)(0.0278)(0.0182)(0.0234)(0.0531)(0.0525)(0.0549)(0.0558)(0.0600)(0.147)Predicted log(Finance)[Private Credit ,Microfinacne, Public Debt
or Remittances]-0.0011-0.0198-0.260**0.00266

0.0189-0.038***0.00239-0.00718-0.363***-0.349***-0.250***-0.221***0.01880.370***(0.012)(0.0127)(0.0353)(0.0161)(0.0143)(0.0111)(0.0116)(0.0144)(0.0671)(0.0651)(0.0451)(0.0438)(0.0579)(0.0666)

log(Population Growth)-0.0757

0.122

0.483*

0.380**-0.05450.0706-0.208**0.0198

0.234

0.225

0.293

0.298

0.404*-1.065*(0.066)(0.0807)(0.161)(0.192)(0.0706)(0.0830)(0.0915)(0.118)(0.189)(0.186)(0.188)(0.191)(0.229)(0.596)

0.07350.08

0.71-0.3840.145

0.0411

0.284

0.2

0.372

0.376

0.03580.0855-0.301

0.733(0.115)(0.150)(0.453)(0.299)(0.145)(0.172)(0.173)(0.248)(0.370)(0.366)(0.400)(0.408)(0.596)(1.234)

D 1995_9

0.03420.0135-0.207*

0.09310.03410.03140.0324-0.0113-0.0385-0.0373-0.0792-0.0783-0.102-0.272(0.034)(0.0497)(0.0652)(0.0935)(0.0345)(0.0467)(0.0303)(0.0496)(0.0917)(0.0911)(0.0899)(0.0919)(0.0667)(0.213)

D 2005_9-0.03450.00163

0.562**

0.07310.0013-0.0293-0.0372-0.052

-

-

-

-

-

-

(0.044)(0.0591)(0.0668)(0.0575)(0.0487)(0.0543)(0.0445)(0.0605)

3.704

3.908

9.681

3.426

3.251

4.652

3.880

3.979

4.889

4.801

8.501

7.791

2.245

3.448

(0.133)(0.131)(0.749)(0.270)(0.349)(0.262)(0.135)(0.195)(0.470)(0.526)(1.065)(1.059)(0.410)(1.054)

75

75

16

16

63

63

50

50

59

59

58

58

38

38

log (Dependency rate)

Constant

Observations

Hausman Test: Ho:differencein coefficients notchi2(7)=chi2(6)=chi2(7)=chi2(7)=systematic
40.50**
7.86
31.25**
29.92**
Prob>Chi2
0.00
0.25
0.00010.0001Model to be preferred
Fixed-effects
Random-effects
Fixed-effects
Fixed-effects
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1; The cases to be chosen by Hausman test are shown in bold.

32

chi2(6)=
5.43
0.49
Random-effects

chi2(6)=
8.02
0.24
Random-effects

chi2(7)=26.92
0.0003Fixed-effects

Table 5 Results for Poverty Equation for Poverty Headcount Ratio at US$2 (Fixed or Random Effects Model)
Case 1

Fixed-effects or Random
effects model
Definition of log(Finance)
Explanatory Variableslog(share of population
with primary ed. or abovelog(GDP deflator)
log(Finance)[Private Credit ;Microfinacne, Public
Debt or Remittances]log(Population Growth)log (Dependency Rate)
D 1995_9
D 2005_9
D 1995_9*Microfinance
D 2005_9*MicrofinanceConstant

Case 2

Case 3

Case 4

Case 5

Case 6

Case 7

Case 8

Case 9Case 10Fixed

RandomFixed

RandomFixed

RandomFixed

RandomRemittances

Remittances

Fixed

RandomPrivateCredit/GDP

PrivateCredit/GDP

0.744*(0.414)0.0448(0.0922)0.042

Gross Loan
Portfolio of
Microfinance)

Gross Loan
Portfolio of
Microfinance)

Gross Loan
Portfolio of
Microfinance)

Gross Loan
Portfolio of
Microfinance)

Loansfrom WB
groups

Loans
from
WB
groups-0.428(0.281)0.479***(0.145)0.141**-0.0231(1.258)0.632(0.520)
0.312

0.231
(0.837)0.337(0.296)0.145-0.393(2.418)0.352(0.394)-0.0401-2.356**(0.926)
1.063**(0.413)-0.2030.578(0.404)0.0894(0.0943)
0.116-0.102(0.246)
0.386***(0.120)0.228***

1.244***(0.353)0.0697(0.0756)-0.0922*0.0115(0.388)0.259**(0.125)-0.0621(0.0462)(0.0648)(0.271)(0.140)(0.123)(0.135)(0.0681)(0.0652)(0.0517)(0.0839)-0.0632(0.344)2.594***(0.926)-0.0298(0.0959)-0.326**(0.139)-2.436***(0.451)4.041***(0.967)-0.153(0.197)-0.988***(0.230)5.527(2.256)-0.0108(0.291)-0.481(0.477)3.951**(1.562)-0.0747(0.211)-0.262(0.279)-1.733***(0.435)3.161***(0.857)-0.121(0.166)-0.708***(0.199)-0.232(0.343)3.507***(0.955)-0.0428(0.0931)-0.269*(0.140)-1.825***(0.578)3.641***(1.411)-0.025(0.188)-0.651***(0.251)

5.566(0.994)42
90.356(5.571)16
8

2.965(3.047)16
8-0.284(5.545)-2.4(3.249)-0.059(0.329)0.0961(0.172)5.350(2.163)22
80.000585(0.335)2.453**(0.884)-0.0247(0.0931)-0.243(0.143)5.356(0.683)42
9-0.0461(3.038)-1.66(1.310)-0.0126(0.178)0.0698(0.0681)
3.914(2.740)22
8

2.675(1.773)41
90.648(1.748)41
96.275(0.666)40
96.207(1.041)40
9Observations
Number of CodeHausman Test: Ho:differencechi2(7)=chi2(6)=chi2(6)=in coefficients not
systematic
28.21
4.65
11.54*
Prob>Chi20.00020.59
0.07
Model to be preferred
Fixed-effects
Random-effects
Random-effects
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1. The cases to be chosen by Hausman test are shown in bold.

33

chi2(7)=chi2(7)=25.73***
0.0006
Fixed-effects

26.10***
0.0005
Fixed-effects

Appendix 1 Summary of Growth, Poverty, Finance, and Export performances at country levels - before and after the Asian Financial Crisis
China

India

Pakistan

Bangladesh

Sri
Lanka

Nepal

Indonesia

Malaysia

Philippines

Thailand

Vietnam

Cambodia

Lao
PDR

MyanmarBhutanAfghanistan

Kazakhstan

KyrgyzRepublic

GDP per capita growth (%)
1995
1996

9.7
8.9

5.5
5.6

2.3
2.1

2.8
2.5

4.7
3.2

0.9
2.7

6.8
6.1

7.1
7.3

2.3
3.5

8.3
4.8

7.8
7.6

3.5
2.7

4.4
4.5

5.5
4.9

7.0
5.8

.
.

-6.6
2.0

-6.4
5.5

1997
1998

8.2
6.8

2.2
4.3

-1.6
-0.1

3.3
3.2

5.9
4.2

2.5
0.5

3.3
-14.3

4.7
-9.6

2.9
-2.7

-2.5
-11.6

6.5
4.2

3.2
2.8

4.6
1.9

4.1
4.4

3.9
3.2

.
.

3.3
-0.2

8.3
0.6

1999

6.7

5.5

1.1

2.9

3.7

1.9

-0.5

3.6

0.9

3.2

3.2

9.7

5.2

9.6

4.0

.

3.7

2.1

2000

7.5

2.3

1.9

4.0

5.2

3.7

3.6

6.3

2.2

3.5

5.4

6.8

3.9

12.6

4.5

.

10.1

4.2

Poverty headcount ratio at $1.25 a day (PPP) (%)
1995
1996

54.1
36.4

.
.

.
.

.
60.9

.
16.3

.
68.0

.
43.4

2.1
.

.
.

.
2.5

.
.

.
.

.
.

.
.

.
.

.
.

.
5.0

.
.

1997
1998

47.8
48.0

.
.

48.1
.

.
.

.
.

.
.

.
.

0.5
.

21.6
.

.
2.1

.
49.7

.
.

49.3
.

.
.

.
.

.
.

.
.

.
31.8

1999

35.6

.

29.1

.

.

.

47.7

.

.

3.2

.

.

.

.

.

.

.

.

2000

.

.

.

58.6

.

.

.

.

22.5

3.0

.

.

.

.

.

.

.

.

Poverty headcount ratio at $2 a day (PPP)
1995
1996

74.1
65.1

.
.

.
.

.
85.5

.
46.7

.
89.0

.
77.0

11.0
.

.
.

.
14.6

.
.

.
.

.
.

.
.

.
.

.
.

.
18.8

.
.

1997
1998

70.8
69.6

.
.

83.3
.

.
.

.
.

.
.

.
.

6.8
.

43.8
.

.
15.3

.
78.3

.
.

79.9
.

.
.

.
.

.
.

.
.

.
60.8

1999

61.4

.

66.5

.

.

.

81.6

.

.

17.8

.

.

.

.

.

.

.

.

2000

.

.

.

84.4

.

.

.

.

44.8

18.1

.

.

.

.

.

.

.

.

Private Credit/GDP (%)
1995
1996
1997
1998
1999

85.0
90.3
97.6
106.2
111.5

22.8
23.7
23.8
24.0
25.9

24.2
24.7
24.6
25.1
25.5

20.9
21.6
22.8
23.2
23.5

31.1
29.9
29.4
28.7
29.3

22.8
23.2
23.9
28.7
28.9

53.5
55.5
60.8
53.2
20.6

124.4
141.6
158.4
158.5
149.2

37.5
49.0
56.5
43.3
38.5

139.8
147.2
165.7
155.9
131.9

18.5
18.7
19.8
20.1
28.2

3.5
4.7
6.3
5.6
5.7

9.1
9.0
13.0
12.6
8.4

7.6
9.6
10.3
9.7
8.1

8.1
7.0
11.8
10.0
8.7

.
.
.
.
.

7.1
6.3
5.2
6.4
8.2

12.5
8.7
3.5
5.3
5.1

2000

112.3

28.8

22.3

24.7

28.8

30.7

19.9

135.0

36.8

108.3

35.3

6.4

8.9

9.5

9.1

.

11.2

4.2

Export/GDP (%)
1995
1996
1997
1998

20.2
20.1
21.8
20.3

11.0
10.5
10.8
11.2

16.7
16.9
16.1
16.5

10.9
11.1
12.0
13.3

35.6
35.0
36.5
36.2

25.0
22.8
26.3
22.8

26.3
25.8
27.9
53.0

94.1
91.6
93.3
115.7

36.4
40.5
49.0
44.8

41.8
39.3
48.0
58.9

32.8
40.9
43.1
44.8

31.2
25.4
33.6
31.2

23.2
22.7
23.9
36.5

0.8
0.7
0.6
0.4

40.0
37.3
38.3
35.1

.
.
.
.

39.0
35.3
34.9
30.3

29.5
30.7
38.3
36.5

1999

20.2

11.7

15.4

13.2

35.5

22.8

35.5

121.3

45.5

58.3

50.0

40.5

35.9

0.3

32.9

.

42.5

42.2

2000

23.3

13.2

13.4

14.0

39.0

23.3

41.0

119.8

51.4

66.8

55.0

49.8

30.0

0.5

30.5

.

56.6

41.8

34

Source: World Bank (2012).

35

Appendix 2 Summary of Growth, Poverty, Finance, and Export performances at country levels (before and after the Global Financial Crisis)
Pakistan

Bangladesh

Sri
Lanka

Nepal

Indonesia

Malaysia

Philippines

Thailand

Vietnam

Cambodia

Lao
PDR

MyanmarBhutanAfghanistan

Kazakhstan

KyrgyzRepublic

5.8
4.3
3.8
-0.2
1.7
2.3

4.5
5.3
5.2
5.1
4.6
4.9

5.0
6.5
5.7
4.9
2.6
7.0

1.3
1.3
1.4
4.2
2.5
2.7

4.4
4.3
5.2
4.9
3.5
5.0

3.3
3.9
4.7
3.1
-3.2
5.5

2.8
3.3
4.8
2.4
-0.5
5.8

3.6
4.2
4.2
1.8
-3.0
7.2

7.2
7.0
7.3
5.2
4.2
5.7

11.9
9.5
9.0
5.5
-1.0
4.8

5.5
6.9
5.9
6.0
5.9
6.9

12.9
12.4
11.3
9.5
9.8
9.6

6.0
4.4
15.5
2.8
4.9
5.6

11.3
8.1
8.1
0.6
17.1
5.2

8.7
9.5
7.7
2.0
-1.4
5.8

-1.3
2.0
7.5
7.4
1.7
-2.5

Poverty headcount ratio at $1.25 a day (PPP) (%)
2005
16.3
41.6
22.6
50.5
.
2006
.
.
22.6
.
.
2007
.
.
.
.
7.0
2008
13.1
.
21.0
.
.
2009
.
.
.
.
.
2010
.
32.7
.
43.3
.

.
.
.
.
.
24.8

21.4
28.6
24.2
22.6
20.4
18.1

.
.
0.0
.
0.0
.

.
22.6
.
.
18.4
.

.
1.0
.
0.4
0.4
.

.
21.4
.
16.9
.
.

.
.
32.2
22.8
.
.

.
.
.
33.9
.
.

.
.
.
.
.
.

.
.
10.2
.
.
.

.
.
.
.
.
.

.
0.4
0.2
0.1
0.1
.

22.9
5.9
1.9
6.4
6.2
.

Poverty headcount ratio at $2 a day (PPP)
2005
36.9
75.6
60.3
80.3
2006
.
.
61.0
.
2007
.
.
.
.
2008
29.8
.
60.2
.
2009
.
.
.
.
2010
.
68.7
.
76.5

.
.
29.1
.
.
.

.
.
.
.
.
57.3

53.8
63.4
56.1
54.4
52.7
46.1

.
.
2.9
.
2.3
.

.
45.0
.
.
41.5
.

.
7.6
.
5.0
4.6
.

.
48.1
.
43.4
.
.

.
.
60.1
53.3
.
.

.
.
.
66.0
.
.

.
.
.
.
.
.

.
.
29.8
.
.
.

.
.
.
.
.
.

.
3.3
1.5
0.9
1.1
.

45.8
32.1
29.4
20.7
21.7
.

China

India

GDP per capita growth (%)
2005
2006
2007
2008
2009
2010

10.6
12.1
13.6
9.0
8.6
9.8

7.7
7.7
8.2
3.4
7.6
7.3

Private Credit/GDP (%)
2005
2006
2007
2008
2009
2010

113.3
110.7
107.5
103.7
127.2
130.0

39.4
43.2
44.8
49.0
46.8
49.0

28.6
28.9
29.7
29.8
23.6
21.5

33.8
36.2
37.3
39.2
41.5
47.1

32.9
34.0
33.3
28.7
24.7
26.6

28.4
32.9
37.0
51.7
59.2
55.6

26.4
24.6
25.5
26.6
27.7
29.1

110.8
107.7
105.3
100.3
117.0
114.9

29.1
28.7
28.9
29.1
29.2
29.6

100.7
95.2
113.2
113.0
116.4
116.6

65.9
71.2
93.4
90.2
112.7
125.0

9.0
12.0
18.2
23.5
24.6
27.6

7.4
5.8
6.5
9.5
17.0
20.4

4.7
3.9
3.4
3.1
3.5
4.7

18.2
21.6
24.1
30.4
32.4
43.3

.
4.4
6.8
8.1
9.1
10.5

35.7
47.8
58.9
49.6
50.3
39.3

9.4
11.6
14.0
.
.
.

15.7
15.2
14.2
12.8
12.9
13.6

16.6
19.0
19.8
20.3
19.4
18.4

32.3
30.1
29.1
24.8
21.3
21.7

14.6
13.4
12.9
13.0
12.4
9.8

34.1
31.0
29.4
29.8
24.2
24.6

117.5
116.5
110.0
103.2
96.4
97.3

46.1
46.6
43.3
36.9
32.2
34.8

73.6
73.6
73.4
76.4
68.4
71.3

69.4
73.6
76.9
77.9
68.3
77.5

64.1
68.6
65.3
65.5
49.2
54.1

34.4
40.0
34.2
31.8
30.5
36.3

0.2
0.2
0.1
0.1
0.1
0.1

39.1
62.6
55.0
46.6
64.7
.

25.2
24.2
18.0
15.6
17.0
15.5

53.5
51.2
49.4
57.2
42.0
44.0

38.7
41.7
52.9
53.5
54.7
57.7

Export/GDP (%)
2005
37.1
19.3
2006
39.1
21.1
2007
38.4
20.4
2008
35.0
23.8
2009
26.7
19.8
2010
29.6
21.5
Source: World Bank (2012).

36

Appendix 3 Definitions and Descriptive Statistics of the Variables
Annual Panel Data (1960-2010) for 9 countries
Definition

Source*3

Obs

Mean

Std.
Dev.

Min

Max

log(GDP pc)

log of GDP per capita

WDI

435

6.306

0.880

4.281

8.553

log(Agri VA pc)

log of Agricultureal Value Added per worker

WDI

274

6.393

0.821

5.189

8.807

WDI

434

3.413

1.791

-8.2355.884

Beck & DemirgüçKunt (2009) (B & D)

386

3.810

0.687

1.720

5.179

B&D

273

0.259

1.433

-4.4472.577

MIX data

94

18.075

2.729

9.184

23.026

WDI

346

21.3571.940

11.11224.336468-0.6670.515-2.017-0.049log(private credit/GDP)log(private credit bylog of credit to private sector / GDP*1

log of private credit by deposit money banks / GDP*2banks/GDP)
Remittances
Microfinance

B&D
Net remittance inflows as a share of GDP
log of total gross loans potfolio (GLP) of microfinanceinstitutions
aggregated for each country

Loans from WB groups

IBRD loans and IDA credits (DOD, current US$)log(share of population
with primary ed. or
above

log of share of the population with education

Barro-Leelevel of primary or above.

(2000).

log of share of government espenditure in GDP.

WDI

428

2.269

0.331

1.152

2.933

log of Consumer Price Index.

WDI

372

3.286

1.731

-7.8145.197

log of the share of Export and Import in GDP.

WDI

416

3.742

0.770

1.670

5.395

log(government
expenditure/GDP)log (CPI)
log(Ecport+Import/GDP)37

Appendix 3 - Definitions and Descriptive Statistics of the Variables (Cont.)
5 Years Average Panel Data (1960-2010) for 9 countries
Variable

Source*3

Obs

Mean

Std.
Dev.

Min

Max

UNU-WIDER.

74

3.650

0.181

3.316

4.036

WDI

63

3.015

0.778

0.916

3.932

WDI

52

3.862

0.851

0.947

4.557

Undernourishment

Definitionlog of GINI coefficient of income or consumption at naional
level.
The share of population below minimum level of dietaryenergy consumption (also referred to as prevalence ofundernourishment) which shows the percentage of the
population whose food intake is insufficient to meet dietaryenergy requirements continuously.

Poverty Headcount Ratio(US$1.25)

Poverty headcount ratio at $2 a day (PPP) (% of population)

Poverty Headcount Ratio(US$2.0)

Poverty headcount ratio at $2 a day (PPP) (% of population)

WDI

51

3.128

1.324

-0.6604.336

log(private credit/GDP)*1

log of share of domestic credit provided by

B&D

91

3.608

1.695

-6.2095.767

banking sector in GDP.
log of total gross loans potfolio (GLP) of microfinanceinstitutions

MIX data

25

18.4692.229

15.16022.81176

21.5201.803

16.48624.389

B&D

64

0.309

1.428

-3.6992.448

99-0.6450.514-2.017-0.049GINI

Microfinanceaggregated for each country
Loans from WB groups
Remittances

IBRD loans and IDA credits (DOD, current US$)Net remittance inflows as a share of GDP

log(share of populationlog of average schooling years of people above 15 years old

Barro-Leewith primary ed. or abovein the initial year.

(2011).

Inflation as measured by the annual growth rate

WDI

90

1.926

1.012

-0.697

5.847

3.781

0.764

1.947

5.325

log(GDP deflator)of the GDP implicit deflator.
log(Ecport+Import/GDP)

log of the share of Export and Import in GDP.

WDI

91

log(Population Growth)log of annual popuoation growth

WDI

99

0.640

0.416

-0.6571.227

log (Dependency Burden)

the ratio of dependents--people younger than 15 or

WDI

90

-0.3200.219-0.892-0.035older than 64--to the working-age population--those ages 15-64.
*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 this is a defined narrowly by covering only private credit through deposit money banks.
*3 WDI data are available on http://data.worldbank.org/data-catalog/world-development-indicators. Finance data cited by Beck & Demirgüç-Kunt (2009) are available on
http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:20696167~pagePK:64214825~piPK:64214943~theSitePK:469382,00.html. MIX data are available on:
http://www.mixmarket.org/. Inequality data of UNU-WIDER is available on http://www.wider.unu.edu/research/Database/en_GB/database/. See http://www.barrolee.com/ for Barro-Lee data.

38