DP2010-30

Microf inanc e a nd Poverty-A Macro Perspective*
Katsushi S. IMAI
Raghav GAIHA
Ganesh THA PA
Samuel Kobina ANNIM
October 27, 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.

Microfinance and Poverty -A Macro Perspective
Katsushi S. Imai*
Economics, School of Social Sciences, University of Manchester, UK &Research Institute for Economics and Business Administration, Kobe University, Japan
Raghav Gaiha
Massachusetts Institute of Technology, USA &Faculty of Management Studies, University of Delhi, India
Ganesh Thapa
International Fund for Agricultural Development, Italy&
Samuel Kobina Annim
Economics, School of Social Sciences, University of Manchester, UK
27th October 2010
Abstract
This paper tests the hypothesis that microfinance reduces poverty at macro level
using cross-country and panel data, based on the Microfinance Information
Exchange (MIX) data on MFIs and the new World Bank poverty estimates. Taking
account of the endogeneity associated with loans from MFIs, our econometricanalysis shows that a country with higher MFIs’ gross loan portfolio tends to havelower levels of FGT class of poverty indices. Contrary to recent micro evidence
based on randomised evaluations pointing to no or weak effect on poverty, there isrobust confirmation of the poverty reducing role of microfinance. Significantly,
microfinance loans are negatively associated with not only the poverty headcountratio, but also with the poverty gap and squared poverty gap, implying that even thepoorest benefit from them. The case for channelling funds from development financeinstitutions and governments of developing countries into MFIs is thus reinforced.
Our assessment has added significance as the tide seems to be turning against
microfinance as a means of poverty alleviation.
*Corresponding Author:
Katsushi Imai (Dr), Department of Economics, School of Social Sciences, University ofManchester, Arthur Lewis Building, Oxford Road, Manchester M13 9PL, UK
Phone: +44-(0)161-275-4827; Fax: +44-(0)161-275-4928, E-mail:Katsushi.Imai@manchester.ac.uk
Acknowledgement
This study is funded by IFAD (International Fund for Agricultural Development). We are gratefulto Thomas 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, and valuable comments from Shoji Nishijima, Takahiro Satoand seminar participants at Kobe University. We have also benefited from the comments ofThankom Arun and M.D. Azam. The views expressed are, however, personal and not necessarilyof the organisations to which we are affiliated.

1

Microfinance and Poverty -A Macro Perspective
I.

Introduction

Most of the recent studies of the impact of microfinance on poverty or income have reliedon micro-level evidence based on household data or entrepreneurial data (e.g. Hulme andMosley, 1996, Mosley, 2001, Khandker, 2005, Imai, Arun and Annim, 2010, Imai and
Azam, 2010). Due to the scarcity of reliable macro data on microfinance, macro-levelstudies of the impact of microfinance on poverty are rather limited. However, there are a
few recent works that investigate the relationship between the macro economy andmicrofinance activities and/or performance, such as Ahlin et al. (2010), Ahlin and Lin(2006) and Kai and Hamori (2009), among others. The thrust of these studies is either toexamine the environmental context in which microfinance operates, or investigate thepotential effect of microfinance on key macroeconomic variables, such as gross domestic
product or inequality. The findings of a significant relationship between operations of
microfinance institutions (MFIs) and the macro economy corroborates the recent
evidence based on household data sets which posits that microfinance has a poverty
reducing effect (e.g. Khandker, 2005, Gaiha and Nandhi, 2009, Imai, Arun and Annim,
2010).
But this consensus over the impact of microfinance on poverty has come under deep
scrutiny in recent randomised evaluations of microfinance (e.g. Banerjee et al. 2010,
Karlan and Zinman, 2009, Feigenberg et al. 2010). Some have, in fact, questioned theimpacts in terms of poverty reduction, promotion of gender equality, and reduction inchild mortality. Indeed, a contrary view that microfinance is oversold is gaining2

credibility 1 . Not only are these studies flawed in a few respects but also a trifle
misleading. Our analysis with cross-country data (including a panel) point to robust
poverty reducing effects of microfinance, as elaborated below.
The challenges for empirical macro studies of microfinance include (a) identifying anappropriate measure of microfinance activities, in terms of ‘availability’ or ‘intensity’; (b)identifying the effects of ‘performance’, distinguished from ‘presence’ and ‘scale’ of
microfinance on macro indicators; and (c) examining the robustness of coefficientestimates related to microfinance. Building on the small but emerging literature on
analysing the impacts of microfinance from a macro perspective, the present study aims
to examine the relationship between MFI’s gross loan portfolio and FGT class of poverty
indices2. The results would be useful for development agencies, governments and otherinvestors, as new insights into microfinance’s potential role in poverty reduction may
emerge. Our counterfactual simulations illustrate the likely effects of reduction in MFIs’
gross loan portfolio, GDP per capita and domestic credit given by the banking sector as aproportion of GDP, as a consequence of the global recession sparked by the financial
crisis in 2008.
This paper tests the hypothesis that microfinance reduces poverty using cross-country
data, including a panel. More specifically, we examine whether a country with higher
MFI’s gross loan portfolio has lower poverty, after controlling for other factors
associated with poverty (e.g. GDP per capita, an index of financial development andregional dummies), and taking account of the endogeneity associated with MFI’s gross1

For a comprehensive but somewhat agnostic appraisal of recent evidence, see Rosenberg (2010).
The Foster-Greer-Thorbecke class of poverty indices comprise the headcount ratio, incomepoverty gap and a distributionally sensitive poverty index that assigns highest weights to thepoorest (Foster et al. 1984).

2

3

loan portfolio.
The rest of the paper is organized as follows. The next section summarizes the recent
evidence of the effects of microfinance on poverty in developing countries. Section III
provides a brief explanation of the data which the present study draws upon. Econometricspecifications and are discussed in Section IV. The main results and simulations are given
in Sections V and VI respectively. The final section offers some concluding observations.

II.

Recent Studies of Poverty and Microfinance: Is Microfinance Oversold?

(a)

Randomised Trials

Let us first review the evidence adduced in favour of the proposition that microfinance(or microcredit) is oversold as a poverty reducing policy instrument3. Until recently, moststudies found that microcredit was responsible for important economic and social benefits.
These include lifting large numbers-especially women- out of poverty, financing of
microenterprises that raised incomes, better health and education and social
empowerment 4 . Armendariz and Morduch (2005), however, are sceptical of theseoutcomes as the separation of causal effects of microcredit from selection effects istypically unsatisfactory. One way of separating these effects is to carry out randomised
trials.
Let us therefore review two recent studies using randomised trials to test microfinanceimpact. Essentially, the procedure involves a large enough group of subjects that is
randomly divided into those who get a loan and others who do not. If the beneficiaries
3

This review draws upon but is not confined to the appraisal of recent evidence in Rosenberg(2010).
4
There is a surfeit of definitions of empowerment. One that is widely used is that women’s
empowerment is about the process by which those who have been denied the ability to makestrategic life choices acquire such ability (Kabeer, 1998).

4

experience better outcomes than the control group (identical in all respects other thanbeing given a loan), these outcomes are then attributed to the loans.
Two studies (Banerjee et al. 2009, and Karlan and Zinman, 2009) focused on
microcredit clients over a short period of 12-18 months found no evidence ofimprovements in household income or consumption, although other benefits showed up5.
As the Banerjee et al. (2009) study is far more detailed and insightful, our review
concentrates on it.
In 2005, 52 of 104 neighbourhoods in Hyderabad (the fifth largest city in India) wererandomly assigned for opening of an MFI branch by one of the fastest growing MFIs in
the area, Spandana, while the remainder were not. 15-18 months later, a household
survey was conducted in an average of 65 households in each slum, a total of 6850
households. In the mean time, other MFIs had also started their operations in both
treatment and comparison households, but the probability of receiving an MFI loan was
still 8.3 percentage points higher in treatment areas than in comparison areas.
This study examines the effect on both outcomes that directly relate to poverty such as
consumption, new business creation, business income as well as measures of other humandevelopment outcomes such as education, health and women’s empowerment.
(1) Households in treatment areas are 13.3 percentage points more likely to report being
Spandana borrowers-18.6 % versus 5.3 %. The difference in the percentage ofhouseholds saying that they borrow from any MFI is 8.3 percentage points, so somehouseholds borrowing from Spandana in treatment areas would have borrowed fromanother MFI in the absence of the intervention.
5

Banerjee et al. (2009) focus on urban slums in Hyderabad while Karlan and Zinman (2009)
analyse loan applicants in the Philippines.

5(2) While the absolute level of total MFI borrowing is not very high, it is almost 50 per
cent higher in treatment than in comparison areas. Treatment households also reportsignificantly more borrowing from MFIs than comparison households.
(3) 1 in 5 of the additional MFI loans in treatment areas is associated with the opening ofa new business: 1.7 percentage points more new businesses due to 8.3 percentage point
more MFI loans.
(4) Business owners in treatment areas report more monthly business profits thanbusiness owners in comparison areas.
(5) Averaged over old business owners, new entrepreneurs, and non-entrepreneurs, there
is no significant difference in total household expenditure(6) However, there are shifts in the composition of expenditure: households in treatmentareas spend more per capita per month on durables than do households in comparison
areas. Further, focusing on spending on durable goods used in household business, thedifference is more striking: households in treatment areas on average spend more than
twice as much on durables used in a household business.
(7) Increase in durables spending was partially offset by reduced spending on temptation
goods: alcohol, tobacco, betel leaves, gambling and food consumed outside the home.
(8) Women in treatment areas were no more likely to make decisions about householdspending, investment, savings or education than in comparison areas.
(9) A common finding of many studies is that women spend more on child health andeducation. However, there is no effect on health or education outcomes.
(10) Households who have an old business significantly increase their durable spending,averaged over borrowers and non borrowers in treatment versus comparison areas.

6(11) Households who do not have an old business, and have the lowest propensity to starta business, do not increase durable spending at all. However, moving to the 75th
percentile of propensity to become an entrepreneur results in significant increase indurables spending.
(12) However, households who do not have an old business and have the lowest
propensity to start a business show a large and significant increase in nondurables
spending.
(13) Existing business owners see a large and significant increase in business profits.

In sum, microcredit does have significant effects on business outcomes and thecomposition of household expenditure. However, these effects differ for different
households, in a way consistent with the fact that a household wishing to start a new
business must pay a fixed cost to do so. But there is no discernible effect on education,
health or women’s empowerment. So microcredit is not the ‘miracle” that is claimed on
its behalf, but it does allow households to borrow, invest, and create and expand new
businesses. But this conclusion must be qualified. Does the fact that comparison groupalso has MFIs mute the effects on health, education and women’s empowerment? Nosatisfactory answer is offered by the authors. What further undermines the validity of theconclusions is the relatively short period over which improvements in living standards
and empowerment are presumed to show up.
Another randomised trial comes close to validating the larger benefits thatmicrofinance is claimed to confer. Feigenberg et al. (2010) offers the first experimental
evidence on the economic returns to social interaction in the context of microfinance. The

7

experiments were conducted in West Bengal (another Indian state).
Random variation in the frequency of mandatory meetings across first-time borrowergroups generates exogenous and persistent changes in clients’ social ties. Resulting
increases in social interaction among clients more than a year later are associated withimprovements in informal risk-sharing and reductions in default. A second field
experiment among a subset of clients yields direct evidence that more frequent interaction
increases economic cooperation among clients. The results indicate that group lending is
successful in achieving low rates of default without collateral not only because it
harnesses existing social capital, as has been emphasised in the literature, but also
because it builds new social capital among participants.
Development programmes can create social ties and enhance social capital among
members of a highly localised community in a strikingly short time. Close neighbours
from similar socio-economic backgrounds got to know each other well enough tocooperate with only the outside stimulus of microfinance meetings.
The findings support the idea that complementarities in social capital acquisition
create the possibility of multiple equilibria. This suggests potentially large gains frompolicies which facilitate interaction and help coordinate social capital investments,
especially in low-income countries where formal risk-sharing arrangements remain
limited. By broadening and strengthening social networks the group-lending model usedby MFI may provide an important impetus for the economic development of poor
communities and the empowerment of women.

8

(b)

Financial Diaries

In a remarkable new book by Collins et al. (2009), referred to as Portfolios hereafter,
results of year-long financial diaries were collected about twice a month from hundredsof rural and urban households in India, Bangladesh, and South Africa. The main insights
in support of the general proposition that financial services are critical survival tools forthe poor are summarised below:(1) As economic poverty is not just a matter of low incomes but also of irregular anduncertain incomes, credit and savings have a vital role in consumption smoothing and indealing with health and other contingencies. Over the year, the average Diary household
used 8 to 10 different types of financial instruments.
(2) Although the poor combine informal sources of financing (e.g. friends, relatives) withmicrofinance, the latter is preferred because it is considered more reliable.
This is corroborated by other evidence about customers arriving in droves to enlist for
microfinance, propelled by word of mouth; and by high repayment rates (Rosenberg,2010)6.
(3) If microcredit is used for smoothing consumption and contingencies that could impair
productive capacity of the poor, as Portfolios reveal, its poverty alleviating role is intact.

(c) Other Evidence
Let us now turn to other evidence-based on detailed econometric analysis of household
surveys- that corroborates the poverty alleviating role of microfinance. Our review is
6

Although Rosenberg (2010) asserts that the high repayment rates are primarily because of theneed for continued access to financial services and not due to peer pressure, it is unclear whether
this assertion is backed with irrefutable evidence.

9

confined to India and Bangladesh.
Imai, Arun and Annim (2010) analysed the impact of access to MFIs and MFI loans
on household poverty in India, drawing upon a national-level cross-sectional household
data for India in 2000 collected by EDA rural systems. They found that access to MFIs
and MFI loans significantly reduced poverty. They used an Index Based Ranking (IBR)
Indicator, which reflects multi-dimensional aspects of poverty, covering aspects of foodsecurity, assets, health, employment and agricultural activities.7 To address the issue ofendogeneity, the treatment effects model, a version of the Heckman sample selection
model, a Tobit model and a propensity score matching (PSM) model were used toestimate poverty-reducing effects of access to MFIs and loans used for productive
purposes, such as investment in agriculture or non-farm businesses. They found that forhouseholds in rural areas, a larger poverty reducing effect of MFIs is observed whenaccess to MFIs is defined as taking loans from MFIs for productive purposes than in thecase of simply having access to MFIs. In urban areas, on the contrary, simple access to
MFIs has larger average poverty-reducing effects than taking loans from MFIs for
productive purposes. That is, clients’ intended use of loans is important in determining
poverty reduction outcomes. This implies that it would be important for development
partners and other stakeholders to develop a consistent framework to monitor the usageof loan with adequate flexibility to capture different levels of participation of households.
Imai and Azam (2010) have recently analysed the effects of microfinance on poverty,
drawing upon panel data of households in Bangladesh. The data are based on the fourround panel survey which was carried out by the Bangladesh Institute of Development
7

It is noted that Imai, Arun and Annim (2010) did not define poverty in terms of income orconsumption because of lack of data.

10

Studies (BIDS) for Bangladesh Rural Employment Support Foundation (PKSF, Bengali
acronym) with funding from the World Bank. All four rounds of the survey wereconducted during the December-February period in 1997-98, 1998-99, 1999-2000, and2004-05. It covered over 3000 households in each round distributed evenly throughout
Bangladesh so as to obtain a nationally representative data set for the evaluation of
microfinance programmes in the country. A sample of villages under each of the selected
MFI was drawn through stratified random sampling and control groups were selectedfrom the neighbouring villages without any MFI. Imai and Azam (2010) have applied
treatment effects model and propensity score matching where (a) ‘the treatment’ is either
whether a household had access to loans from MFI for general purposes or whether ahousehold obtained loans from MFI for productive purposes and (b) a dependent variable
is per capita household income. They report that simple access of a household to MFI did
not significantly increase per capita household income, while loans for productive
purposes did, consistent with Imai, Arun and Annim’s (2010) finding for rural India.
In sum, microfinance, particularly loans for productive purposes, reduced poverty
significantly in both India and Bangladesh. 8 There is, however, a risk of overstating thecase for productive use of loans without examining inter-temporal implications ofconsumption smoothing and coping with contingencies.
In what follows, our analyses with cross-country data -including a panel- robustly
show that a country with higher MFIs’ gross loan portfolio tends to have lower levels of
FGT class of poverty indices, corroborating the poverty reducing role of microfinance. It
is notable that microfinance loans are negatively associated with not only the poverty
8

See Imai et al. (2010) and Imai and Azam (2010) for other evidence of the relationship between
microfinance and poverty at the household level.

11headcount ratio, but also with the poverty gap and squared poverty gap, implying thateven the poorest benefit from them.

III.

Data

The present study analyses the role of microfinance – volume/scale of activities (notperformance/quality)- on poverty, using cross-sectional data covering 48 countries in thedeveloping regions for 20079. The cross- sectional data are supplemented by a two-period(2003 and 2007) panel covering 61 countries.10 This is based on the data generated byMicrofinance Information Exchange (2010) or MIX and the World Development
Indicators 2010 (World Bank, 2010). It is noted that relatively few studies have used ameasure of microfinance operations (volume/scale) in a country, based on the MIX data.
Also, the present study uses new World Bank poverty estimates, released in 2008 (Chenand Ravallion, 2008 and Ravallion, Chen, and Sangraula, 2008). These poverty estimatesare based on the poverty line of US$1.25 (based on PPP- purchasing power parity) per
day in 2005, and cover a wider range of countries than the previous estimates (based on apoverty line of $1.08 on 1993 PPP). While there are many studies based on the latter,
those based on the more recent poverty estimates are still few.11 Also, as noted earlier, wehave used the FGT class of poverty indices.
With a view to measuring microfinance activities in a country, we rely mainly on
gross loan portfolio (GLP) given that it measures actual funds disbursed to households. In
supplementary exercises, we replace it by GLP/borrowers. Total GLP of MFIs,

9

See Appendix 1 for the list of the countries.
Appendix 4 lists the countries included in the panel data estimations. Because the number ofcountries for 2003 is greater than 2007, the panel covers a larger number of countries.
11
Exceptions include Imai, Gaiha and Thapa (2010).
10

12

aggregated for each country, is adjusted for write-offs and inflation. This is a benchmark
indicator generated by MIX. Standardization of raw data facilitates meaningfulcomparison of benchmark indicators (MIX, 2010). Other variables in the poverty
equation include gross domestic product per capita, domestic credit as a share of GDP
and regional dummies.12 While a robust inverse relationship between poverty and GDP
per capita is confirmed in extant literature, share of domestic credit in GDP has a more
complex role partly because financial development is both a cause and result of growth. Itis, however, plausible that when financial development is low there may be a mutually
reinforcing relationship between it and microfinance. Finally, as poverty is conditioned
on many unobservable regional characteristics (e.g. resilience to natural shocks), regionaldummies are used.

IV.

Specification of Models and Estimation

Our analysis is based on the data for 2007 (for cross-sectional estimations), and 2003 and2007 (for panel data estimations), not only because extensive and reliable historical dataon microfinance do not exist 13 but also because international poverty estimates areavailable only for one or two specific years for most of the countries. 14 As a result,country panel data of poverty are highly unbalanced, as shown in Appendix 4.
We apply both OLS (Ordinary Least Squares) and IV (Instrumental Variable) modelor 2SLS (Two Stage Least Squares) to estimate the effect of gross loan portfolio of MFIs

12

See Table 1 for descriptive statistics of these variables. These will be discussed in Section IV.
MIX data date back to 1994, but not until 2002 most MFIs were not keen on submitting theirrecords for public use.
14
This is because the construction of international poverty estimates has to rely on nation-widehousehold expenditure or income surveys which are carried out once or twice in a decade formost of the developing countries.

13

13

on poverty. 2SLS involves two stages: gross loan portfolio of MFIs is estimated by an
instrumental variable and other covariates in the first stage and in the second poverty
head count ratio is estimated by the predicted gross loan portfolio and covariates. The useof IV is necessary because gross loan portfolio of microfinance is likely to be endogenous
in the poverty equation. Here the endogeneity is associated with the bi-casual relationshipbetween gross loan portfolio and poverty levels in a country. This reverse causality frompoverty to gross loan portfolio may arise, for example, if poverty-oriented development
partners and governments provide more funds to MFIs located in poorer countries.
We also argue that the number of active borrowers is likely to be endogenous in apoverty equation in association with possible measurement errors. In view of theemerging literature on microfinance clients’ multiple membership, the issue of
measurement error is plausible since the number of active borrowers over time can movein opposite directions from the perspective of the country and the MFI. Srinivasan (2009)and Wright and Rippey (2003) discuss the issue of ‘client’s debt hang’ that is likely toemerge from multiple borrowing. In this event, the expected poverty-reducing effect as aresult of changes in the number of active borrowers of microfinance institutions will be
uncertain. We circumvent this difficulty by using an IV measure of GLP/borrowers. Thisis used as an alternative to an IV measure of GLP.
With the usual data constraint in finding a valid instrument that satisfies ‘an exclusion
restriction’, that is, correlates with gross loan portfolio/number of active borrowers but
not poverty, this papers uses lag of five-years average of gross loan portfolio (number of14

active borrowers) weighted by the number of MFIs for every country 15 . The unit ofanalysis for the econometric exercise is the country.
Equations (1) and (2) below describe respectively the structural and reduced form ofleast squares used in estimating the relationship between gross loan portfolio and poverty.
Pov = β + β GLP + β GDPPC + β Domestic Crd + β REG + u
i
0
1
i
2
i
5
i
6
i
i

(1)

GLP = π + π GLPMF + π GLPMF+ π X +υNOABMF i
i
0
1
i
2
3 i
i

(2 )

where ‘Pov’ indicates poverty head count ratio (or poverty gap; squared poverty gap);‘GLP’ represents gross loan portfolio; ‘GDPPC’ denotes gross domestic product percapita (at 2000 constant USD prices); ‘Domcred’ indicates domestic credit of banks as aproportion of GDP; ‘REG’ is a vector of regional dummies with Latin America andCaribbean being the reference region; Equation (2) is the reduced form which tests thepresence of endogeneity and suitability of our instruments. ‘GLPMF’ is the weighted
five-year average lag of gross loan portfolio (which is weighted by the number of MFIs
for each country); ‘NOABMF’ is the weighted five-year average lag of number of active
borrowers (which is also weighted by the number of MFIs for each country) and X is thevector of all the other explanatory variables considered in equation (1). The respective
independently and identically distributed (i.i.d.) error terms for the two equations aredenoted by ‘u’ and ‘υ’.
Two variants of Equations (1) and (2) are estimated. The first variant examines theeffect of log of GLP and the second case examines that of log GLP per borrower(GLP/NOAB). The first case focuses on the effect of total amount of loans from MFIs on
15

This index passes the statistical validity of a valid instrument as it shows a high correlation withgross loan portfolio and a low correlation with the poverty headcount ratio (with the coefficient ofcorrelation being 0.8 for the former and 0.1 for the latter respectively).

15

poverty in a country. An increase of MFI loans may be accompanied by an increase in
number of borrowers or an increase in the amount of loans each borrower receives. The
latter is used in the second variant. Corresponding to these, the IV estimation uses log of
GLPMF for the first case and log of [GLPMF/NOABMF] for the second case.
In addition to the cross-sectional estimations, we generate panel data for 2003 and2007 for all the variables16 and estimate linear panel models. This construction enables us
to examine the robustness of our coefficients as the panel data estimation takes account ofchanges of variables over time and unobservable country or regional-level effects. Our
aim is to further examine the hypothesis that higher gross loan portfolio leads to poverty
reduction at macro level.
One of the important limitations pointed out by micro-studies is that microfinance or
microcredit does not necessarily reach the poorest of the poor (e.g. Morduch, 1999). To
further investigate this from the macro perspective, we examine the effects of gross loan
portfolio on poverty gap (which measures depth of poverty) and squared poverty gap(which measures severity of poverty).

V.

Results

Figures 1 to 3 below, describe the patterns and trends in size and outreach of the
microfinance industry using real gross loan portfolio (after adjusting inflation), number of
MFIs and active borrowers. Overall, the compound growth rate of the median gross loan
portfolio increases for all regions over the period 2005 to 2009. However, there arevariations (steep and gentle) in the year-by-year upward slopes, while in one instance16

Due to data constraints on the international poverty data, we took averages for the period 2000
to 2003 and 2004 to 2007 for poverty.

16

(Eastern Europe and Central Asia), a downward trend is observed. In particular, the slopefor 2007 to 2008 is either gently increasing or sloping downwards. An interpretation of
the trend over this period will need to take cognizance of the potential adverse effect of
the global financial crisis on the microfinance industry.
Until 2007, the largest MFIs were located in Latin America and the Caribbean (LAC).
However, in 2008, MFIs in Middle East and North Africa (MENA) experienced a sharp
increase in their gross loan portfolio. Comparison of the patterns and trends of gross loan
portfolio with the greater and sharp increase in number of active borrowers in South Asia
(Figure 2) would trigger a number of questions, especially when using either of these
indicators as a measure of microfinance operations in a country. Two reasons can berespectively surmised for the greater and sharp increase in South Asia’s number of active
borrowers. Firstly, one can argue that by virtue of population size of countries in thisregion, it is by no means surprising that MFIs are able to reach out to more clients (scaleof outreach). Secondly, differences in the mission of MFIs as a result of country (regional)
level influences can account for the variation in the scale of outreach (number of clients).
Thus, MFI’s with outreach focus (poverty-reducing) are likely to reach out to moreclients.
Numbers of MFIs in different regions and over time present another challenge inchoosing an index to measure microfinance operations in a country. Figure 3 shows that
LAC consistently (since 2005) has the highest number of MFIs in spite of its relatively
smaller number of active borrowers, compared to South Asia (SA) and MENA.
Table 1 provides a summary statistics of the variables used in the regression analyses. We
report both mean and median of each variable for 2003 and 2007 for the respective

17

regions. The rationale for reporting the median alongside the mean is to provide a further
justification for the choice of the median as a descriptive statistic (and the need to use thelogarithmic form of variables with high standard deviations (skewness) for the regression
analyses). In view of the heterogeneity of the size of MFIs (gross loan portfolio),
outreach (number of active borrowers), and a nation’s output per head (GDPPC), it isalways prudent to observe the dispersion of the data. Table 1 indicates that the median insome instances is about either a hundredth (East Asia and the Pacific (EAP)) or a tenth(MENA) of the mean. This suggests that the raw data for the mean are likely to beaffected by extreme values.
Figure 1: Trends and Patterns of Real Gross Loan Portfolio
East Asian and PacificEastern Europe and Central Asia

3500000

Latin America and the Cariibbean
7000000

4000000

3000000

6000000
3000000

2500000

5000000
2000000
2000000

Gross Loan Portfolio

4000000

1500000
1000000

1000000
2005

2006

2007

2008

2009

3000000
2005

Middle East and North Africa

2006

2007

2008

2009

2005

South Asia

2006

2007

2008

2009

Sub-Saharan Africa

8000000

14000000

4000000
12000000

6000000
3000000

10000000

4000000

8000000
2000000

2000000

600000004000000

2005
2005

2006

2007

2008

2006

2007

2009

2008

2009

1000000
2005

Year

18

2006

2007

2008

2009

Figure 2: Trends and Patterns of Number of Active Borrower
SSA - Numberof Active
Borrowers
EAP - Number of Active
Borrowers
ECA - Number of Active
Borrowers
LAC - Number of Active
Borrowers
MENA - Number of Active
Borrowerss
SA - Number of Active
Borrowers

50000
45000
40000
35000
30000
25000
20000
15000
10000
5000
0
2005

2006

2007

2008

2009

2008

2009

Year

Figure 3: Trends and Patterns of Number MFIs
400
SSA - Number of
MFIs
EAP - Number of
MFIs
ECA - Number of
MFIs
LAC - Number of
MFIs
MENA - Number
ofMFIsSA - Number of
MFIs

350
300
250
200
150
100
50
0
2005

2006

2007
Year

From the perspective of both numbers of active borrowers and MFIs, microfinanceactivities in SA countries are more intense than in the other regions. At the lower end,
MFI activities in Sub-Saharan Africa (SSA) countries tend to show the lowest values forthe number of active borrowers (as a proxy for MFI operations). As observed from thetrends (Figures 1–3), variations in these indicators over time and across different regions
suggest the need to develop a meaningful index that pulls together all three variables.
In terms of the macro indicators, SSA, as expected, is the poorest region for both19

periods irrespective of the measure (incidence, depth and severity) in question. Over theperiod 2003 - 2007, both the poverty headcount and poverty gap showed a decline ofabout 7 and 4 percentage points, respectively.17 An exception is worth mentioning as oursample showed that poverty levels rose in South Asia over this period.18 Among the less‘worse off’ regions,19 MENA recorded the lowest poverty headcount ratio while LAC
showed the highest output per head (GDPPC) in both years. The poverty headcount ratio
continued to be low (2%) in MENA, while it substantially decreased from 14.5% to 8.9%
in LAC.

17

Poverty data for the panel were constructed by taking averages for 2000-03 and 2004-07. The
data are given in the Appendix 5.
18
It is noted that an overall increase in poverty in South Asia is due to the highly unbalanced
nature of our panel data of poverty. As shown in Appendix 5, while Bangladesh has two
observations, India, Nepal and Sri Lanka have only one either for 2003 or 2007. Despite the fallin poverty headcount for Bangladesh from 57.8% in 2003 to 49.6% in 2007, including countries
with high poverty (e.g. Nepal- 55.1%) in 2007 accounts for the higher figure in 2007.
19
Most of the countries used in the study are either transitional or developing countries. This isbecause MFIs mostly evolve in countries with a high level of deprivation (mainly access tofinance).

20

Table 1 Summary Statistics
Poverty
Headcount

Poverty Gap

SquaredPoverty Gap

2003

2007

2003

2007

2003

2007

4

4

4

5

4

5

5

6

5

Mean

41.33

24.29

12.77

6.41

178.26

44.95

4.86E+08

4.51E+08

Median

42

24.12

11.65

5.82

136.0533.87

6.18E+07

No.

19

17

19

17

19

17

Mean

11.13

5.97

2.98

1.51

24.05

Median

3.09

2

0.66

0.5

No.

15

14

15

Mean

14.48

8.85

Median13.73

No.

Regions

Statistic

EAP

No.

ECA

LAC

MENA

SA

SSA

Total

Gross Loan
Portfolio
2003

No. of Active
Borrowers

2007

2007

Domestic
CreditGross Domestic
Product Per Capita

2003

2007

2003

2007

6

5

6

6

6

5

5

757393

1.12E+06

8.6

14.33

42.74

43.08

652.33880.63

1.22E+07

54395

23147

4

11

28.9

26.75

473.42617.12

20

20

20

20

20

20

19

19

19

19

5.55

6.05E+07

3.11E+08

32291.2

96138.15

7.5

11.5

9.83

37.67

1693.212212.38

0.44

0.25

3.87E+07

2.61E+08

21937.5

69229

6.5

7.5

23.78

35.02

1389.97

1945.64

14

15

14

14

15

14

15

14

15

15

15

15

15

5.8

3.04

74.63

14.57

1.48E+08

8.90E+08

220381744239.2

10.14

21.2

47.78

48.86

2638.873110.35

7.97

4.77

2.54

22.75

6.55

6.06E+07

3.96E+08

112302.5308120

6.5

16

39.98

45.25

2324.452692.17

2

2

2

2

2

2

2

3

2

3

2

3

2

2

3

2

Mean

2

2

0.5

0.5

0.25

0.25

3.74E+07

7.37E+07

84573

262524.33

5.5

7.33

98.18

102.271284.72033.5Median

2

2

0.5

0.5

0.25

0.25

3.74E+07

9.40E+07

84573

105544

5.5

7

98.18

102.271469.942033.5

No.

2

3

2

3

2

3

5

5

5

5

5

5

4

5

4

4

Mean

35.88

48.8

9.98

14.54

153.48225.62

1.90E+08

3.36E+08

2.63E+06

2.33E+06

23

32

47.3

43.38

508.01628.02

Median

35.88

49.64

9.98

13.08

153.48171.095.92E+07

5.62E+07

348934609029

14

31

45.52

49.19

439.51563.47

No.

16

9

16

9

16

9

19

22

19

21

19

22

22

19

22

21

Mean

57.88

51.06

24.54

20.56

730.8

509.6

2.52E+07

1.06E+08

111338.6

239020.67

6.89

7.68

26.52

22.92

515.21

548.2

Median

56.95

54.1

21.48

22.82

463.01520.75

1.46E+07

2.48E+07

42828

65922

7

7.5

16.03

11.32

290.89342.18

No.

58

49

58

50

58

50

65

71

65

70

65

71

68

66

68

66

Mean

27.52

19.03

10.49

6.6

246.38115.74

1.11E+08

3.74E+08

353231.5

532116.4

9.11

13.87

31.31

38.85

1356.421684.62Median

16.65

9.34

6.4

3.27

40.97

10.73

3.70E+07

1.32E+08

54395

105467

7

9

29.55

34.7

821.241081.74212003

No. of MFIs

2003

2007

The results of multivariate regressions are given in Tables 2 to 7 and simulation
outcomes in Table 8. With a view to examining the hypothesis of a relationship between
gross loan portfolio and poverty (incidence, depth and severity), fourteen different cases(i.e., six for cross-sectional regressions and eight for panel data regression) are examinedfor each poverty measure. Six different cases using cross section data for 2007 are given
in Tables 2, 4 and 6 and eight cases are given in Tables 3, 5 and 7, using two-period(2003 and 2007) panel data. In Tables 2, 4 and 6, OLS is applied for columns (1) to (4)and IV for columns (5) and (6). The estimations in columns (1) to (4) are robust(corrected for heteroscedasticity), and examine cases either using GLP with and without
regional dummies, or GLP/NOAB with and without regional dummies. The different
specifications are motivated by policy concerns that underlie each of the cases. In asimilar fashion, the IV (columns (5) and (6) of Tables 2, 4 and 6) and panel (Tables 3, 5
and 7) estimations examine the cases for GLP and GLP/NOAB.
Three measures of the FGT class of poverty indices are used. Thus, Tables 2 and 3
contain all the estimations (OLS, IV, Fixed Effects (FE) and Random Effects (RE)) forthe poverty headcount ratio; Tables 4 and 5 considers the case for the poverty gap (depth)
and Tables 6 and 7 investigates the severity of poverty (squared gap).

22

Table 2 Results based on Cross-Sectional Regressions (Dependent Variable: Poverty Head Count Ratio)
OLS
Explanatory Variables
Log [GLP]
Log of GLP/NOAB
Log of GDP per Capita

Domestic Credit

MENA

EAP

ECA

SA

SSA

IV

OLS

With log[GLP]

With log [GLP/NOAB]

With log[GLP]

With log[GLP/NOAB]

Without Regions

With Regions

Without Regions

With Regions

With Regions

With Regions

(1)

(2)

(3)

(4)

(5)

(6)
-

-2.39

-1.87

-

-

-2.14

[-2.52]*[-2.39]*--[-2.70]**

-

-

-

-7.74

-1.79

-

-3.54

-

-

[-4.72]**[-1.31]-[-1.75]+-13.98

-6.61

-9.87

-5.63

-6.69

-5.28

[-8.77]**[-4.46]**[-5.71]**[-4.10]**[-4.42]**[-3.11]**

-0.02

-0.08

-0.10

-0.06

-0.08

-0.07

[-0.46][-1.96]+[-1.98]+[-1.61][-1.74]+[-1.31]

-

-7.49

-

-7.57

-7.72

-9.24

-

[-2.85]**-[-1.97]+[-1.37][-1.43]

-

6.80

-

8.75

6.41

7.97

-

[2.00]+-[3.06]**[1.43][1.64]

-

-9.57

-

-5.59

-9.94

-4.26

-

[-3.94]**-[-2.62]*[-3.64]**[-1.35]

-

24.87

-

25.48

24.28

22.07

-

[6.35]**-[4.79]**[4.50]**[3.21]**

-

21.92

-

26.55

21.05

25.13

-

[2.49]*-[2.90]**[4.01]**[4.74]**

Constant

163.79102.25146.5869.44

108.3979.28

HausmanN[6.95]**
47[4.30]**
47[10.97]**47[4.12]**
47[5.07]**
0.20[0.66]
47[4.70]**
2.84[0.09]
47

0.671

0.876

0.736

0.852

0.876

0.849

F- Statistic

26.62

129.65

35.30

83.14

41.23

33.61

Log-L’hood-180.77-154.85-175.60-159.06-154.96-159.55** Significant at one percent; * significant at five percent; † significant at 10 percent; t-values are in parenthesis

Adj. R

2

23

Table 3 Results based on Panel Data Regressions (Dependent Variable: Poverty Head Count Ratio)
Explanatory Variables
Log of GLP
Log of GLP/NOAB

Pool OLS
GLP
GLP/NOAB
(1)
(2)
-1.71
[-3.06]**-1.85[-1.13]Fixed Effects
GLP
GLP/NOAB
(3)
(4)
-0.41
[-0.49]-0.94[-0.44]

Random Effectsᶿ
GLP
GLP/NOAB
(5)
(6)
-0.76
-1.85
[-1.42][-3.63]**-

Random Effectsᶹ
GLP
GLP/NOAB
(7)
(8)
-2.99
-2.93
[-2.24]*[-2.39]*Log of GDP per Capita

-9.08

-8.09

-12.76-13.25-17.20-9.86-15.30-8.89

Domestic Credit[-5.90]**-0.06[-5.37]**-0.05[-1.91]+0.01[-2.11]*
0.00[-9.03]**0.02[-7.50]**-0.07[-7.06]**-0.01[-5.87]**-0.06[-2.18]*-17.11[-6.44]**
6.69[1.72]+[-1.70]+-18.11[-7.10]**7.94[2.12]*[0.08]-[0.03]-[0.30]-[-1.69]+-[-0.22]-[-1.55]--18.37-15.67

-

-

-

-

-

-

[-5.48]**[-3.78]**

-

-

-

-

-

-

SA

10.40
[1.53]

7.69[1.15]

-

-

-

-

-

-

SSA

16.18

18.60

-

-

-

-

-

-

[3.74]**[4.58]**

-

-

-

-

-

-

MENA
EAP
ECA

2007 Year Dummy

-0.53

-2.28

-

-

-

-

-

-

Constant[-0.29]124.65[-1.04]98.75

118.77121.19157.44129.83151.49108.04[8.17]**[7.37]**[3.29]**[3.52]**[12.12]**[9.74]**[12.95]**[10.63]**Hausman

-

-

-

-

11.33[0.0]125.1[0.0]7.26[0.06]

2.46[0.48]

Theta
N

99

99

99

99

0.76
99

0.83
99

0.76
99

0.77
99

0.856

0.844

-0.993-0.996

-

-

-

-

63.22

56.35

4.39

4.37

-

-

-

-

Adj. R

2

F- Statistic

Log-L’hood-352.75-356.66-246.71-246.77** Significant at one percent; * significant at five percent; † significant at 10 percent; t-values are in parenthesis ; ᶿ nation effect; and ᶹ regional effect24

In column (1) of Table 2, all three specifications using the cross section data showsthat GLP is negatively and significantly associated with a poverty headcount ratio, whichis consistent with our hypothesis that GLP reduces poverty. Because MFI loan is definedin log, we observe that a 1% increase in MFI loan reduces poverty by 0.0214% in thecase of the IV estimation (in column (5) of Table 2).20 The coefficient estimate of log ofgross loan portfolio of MFI is negative and significant at 1% level. As expected, GDP per
capita is negative and shows a 1% statistical significance irrespective of the specificationor the estimation method chosen. Also, consistent with the finance-poverty literature, we
also find that the coefficient estimate of share of domestic credit to GDP is negative andsignificant in some cases (columns (2), (3) and (5) of Table 2).
Columns (2) and (4) explore the potential effect of regional dummies on incidence ofpoverty using log of gross loan portfolio and log of loan per borrower respectively. Logof gross loan portfolio of MFI is negative and significant at the 5% level. The effect oflog of loan per borrower is significant in the IV estimation, but not in the OLS. Inclusionof regional dummies in the poverty headcount equation reveals that ECA, with LAC as
the reference case, has a significant negative coefficient (at the 5% level in three out of
the four cases). Also, SA and SSA dummies are positive with a higher level of statisticalsignificance in all four cases. This implies that SA and SSA have higher poverty
headcount levels relative to LAC. These regression results are consistent with thesummary statistics of Table 1, where both poverty levels and GDPPC shows that SA and
SSA trail LAC.
Columns (5) and (7) present the IV estimation with the aim of resolving the potential
20

This is based on the formulae to interpret the coefficient of semi-log specification (level-log):∆Y= (β/100) % ∆X (Wooldridge, 2009, p.46). Note that poverty headcount ratio is defined inpercentages.

25

endogeneity of microfinance variables in the poverty headcount equation, that is, grossloan portfolio and number of active borrowers. As discussed earlier, the endogeneity may
be due to either bi-causality or measurement errors depending on the variable in question.
For instance, in terms of a bi-causal relationship between gross loan portfolio and poverty
headcount, we allude to the fact that investors who are inclined to poverty reduction
might direct their financial resources to countries and regions where poverty is high.
Appendices 2 and 3 show the correlation matrix and the first stage IV estimation which
offer a justification for the validity of our instruments. Instruments used in the two casesof columns (5) and (6) are weighted five-year lag of gross loan portfolio for the equation
using GLP and weighted five-year lag of gross loan portfolio/ weighted five-year lag of
number of active borrowers for the case of loan per borrower. In the case of loan perborrower, the Hausman test favours IV estimates over OLS, and this validates our use of
the IV model. All the signs of the explanatory remain unchanged. We do not report theSargan test as the instruments are exactly identified.

26

Table 4 Results based on Cross-Sectional Regressions (Dependent Variable: Poverty Gap)
OLS

OLS

With log[GLP]
Explanatory Variables

Log [GLP]
Log of GLP/NOAB
Log of GDP per Capita

Domestic Credit

MENA

EAP

ECA

IV

With log [GLP/NOAB]

With log[GLP]

With log[GLP/NOAB]

Without Regions

With Regions

Without Regions

With Regions

With Regions

With Regions

(1)

(2)

(3)

(4)

(5)

(6)

-1.39
[-2.72]**
-4.79

-1.18
[-2.70]*
-2.14

-2.92
[-3.65]**
-3.42

-0.69
[-0.92]
-1.61

-1.43
[-3.42]**
-2.22

-1.65
[-1.48]-1.41[-6.50]**[-2.91]**[-3.99]**[-2.17]*[-2.77]**[-1.52]-0.04[-1.72]+-0.04[-1.83]+-0.06[-2.35]*-0.03[-1.45]-0.05[-1.87]+-0.03[-1.24]

-

-2.58

-

-2.21

-2.79

-3.12

-

[-1.88]+-[-1.09][-0.94][-0.88]

-

-0.40

-

1.03

-0.76

0.60

-

[-0.20]-[0.65][-0.32][0.23]

-

-4.54

-

-2.36

-4.89

-1.63

-

[-3.82]**-[-2.42]*[-3.39]**[-0.94]

SA

-

5.80

-

7.04

5.26

5.19

SSA

-

[2.97]**
9.46-[2.12]*
12.75[1.85]+
8.66[1.37]
11.97-[2.16]*-[2.64]*[3.12]**[4.11]**

Constant

68.08

45.60

53.54

22.35

51.25

27.71

HausmanN[5.31]**
47
0.602[3.95]**
47
0.798[6.83]**
47
0.602[2.53]*
47
0.736[4.54]**
0.61[0.43]
47
0.796

[2.99]**
2.80[0.09]
47
0.731

14.70

45.68

14.21

24.50

23.12

16.88

-143.49-124.59-143.47-130.92-124.92-131.40

Adj. R

2

F- Statistic
Log-L’hood** Significant at one percent; * significant at five percent; † significant at 10 percent; t-values are in parenthesis

27

Table 5 Results based on Panel Data Regressions (Dependent Variable: Poverty Gap)
Explanatory Variables
Log of GLP
Log of GLP/NOAB
Log of GDP per Capita
Domestic Credit
MENA

EAP

ECA

GLP
(1)

Pool OLS
GLP/NOAB(2)

Fixed Effects
GLP
GLP/NOAB(3)(4)

Random Effectsᶿ
GLP
GLP/NOAB(5)(6)

Random Effectsᶹ
GLP
GLP/NOAB
(7)
(8)

-1.16
[-3.39]**
-3.00

-0.99
[-1.08]
-2.44

-0.07
[-0.17]
-4.83

-0.32
[-0.28]
-4.61

-0.39
[-1.31]
-6.72

-1.19
[-1.62]
-6.02

-1.19
[-4.03]**
-3.30

-1.35
[-1.90]+-2.66[-3.40]**-0.05[-2.65]**-3.20[-2.77]**-0.04[-1.95]+-2.99[-1.34]0.01[0.19]-[-1.37]0.01[0.15]-[-6.49]**-0.01[-0.25]-[-5.00]**-0.02[-0.58]-[-4.33]**-0.05[-2.28]*-[-3.02]**-0.05[-1.95]+-[-1.94]+[-1.54]

-

-

-

-

-

-

0.02

0.93

-

-

-

-

-

-

[0.01][0.40]

-

-

-

-

-

-

-6.06

-3.64

-

-

-

-

-

-

[-4.86]**[-2.75]**

-

-

-

-

-

-

SA

3.28

2.64

-

-

-

-

-

-

SSA

[0.98]
10.49[0.67]
13.05

-

-

-

-

-

-

[3.19]**[3.88]**

-

-

-

-

-

-

2007 Year Dummy

0.13

-1.17

-

-

-

-

-

-

Constant[0.13]
52.04[-0.97]
32.70

43.12

42.48

63.40

59.89

55.64

37.99

Hausman[4.95]**-[3.43]**-[2.22]*-[2.30]*-[8.92]**
23.63[0.0][9.20]**
7.92[0.05][7.23]**
131.5[0.0][6.30]**
5.06[0.17]

Theta
N
2
Adj. R

99
0.753

99
0.724

99
-1.37999-1.376

0.76
99
-

0.77
99
-

0.82
99
-

0.80
99
-

F- Statistic
25.07
19.21
1.73
1.75
Log-L’hood-301.66-307.26-185.47-185.39** Significant at one percent; * significant at five percent; † significant at 10 percent; t-values are in parenthesis ; ᶿ nation effect; and ᶹ regional effect28

Tables 3, 5 and 7 show the results based on panel estimations for the poverty
headcount ratio, depth and severity, respectively. In Table 3, the poverty headcount ratio
is estimated by the same set of explanatory variables. The number of observation is 99. It
is noted that this estimation is based on highly unbalanced panel data and thus the results
have to be interpreted with caution (see Appendix 4 for the list of countries andfrequencies of observations). A similar pattern of results is observed, that is, gross loan
portfolio of microfinance institutions is negatively associated with incidence of poverty,
after controlling for the effects of other covariates and unobserved heterogeneity.
In Tables 4 and 5, we have replicated both the cross-sectional and panel regressions byreplacing the poverty headcount ratio with the poverty gap. To avoid cluttering the text,
we summarise the key findings. All three cases show significant negative effects of log ofgross loan portfolio of MFIs, implying the potential of microfinance in reducing the depthof poverty. The other explanatory variables show expected signs too. In columns (4) and(6), log of loan per borrower has negative but non-significant coefficients. The results ofIV estimations in column (6) of Table 3 support our hypothesis that GLP reduces poverty
gap but the evidence is weak.
This hypothesis is further corroborated by random effects estimations in columns (7)and (8) of Table 5. In both cases (GLP and loan per borrower), there is a significant
negative effect. Both the Hausman and theta statistics favour random effects over fixed
effects.

29

Table 6 Results based on Cross-Sectional Regressions (Dependent Variable: Squared Poverty Gap)
OLS

OLS

With log[GLP]
Explanatory Variables
Log [GLP]
Log of GLP/NOAB
Log of GDP per Capita

Domestic Credit

MENA

EAP

ECA

IV

With log [GLP/NOAB]

With log[GLP]

With log[GLP/NOAB]

Without Regions

With Regions

Without Regions

With Regions

With Regions

With Regions

(1)

(2)

(3)

(4)

(5)

(6)

-53.39[-2.79]**-106.23-44.57[-2.58]*-32.08-70.99[-2.44]*-80.36-22.70[-0.77]-12.96-51.25[-3.20]**-34.25-56.67[-1.34]-6.03[-4.05]**[-1.19][-2.76]**[-0.48][-1.12][-0.17]-1.69[-2.33]*-1.47[-1.59]-2.11[-2.26]*-1.05[-1.29]-1.54[-1.65]+-1.13[-1.07]

-

-7.30

-

9.77

-13.12-22.70-[-0.15]-[0.14][-0.12][-0.17]--43.71-

11.58-53.52-3.61-[-0.57]-[0.21][-0.59][-0.04]--93.37--13.82-102.71

12.00-[-2.44]*-[-0.53][-1.87]+[0.18]

SA

-

87.04

-

140.18

72.46

74.19

SSA

-

[1.26]
330.34-[1.16]
456.63[0.67]
308.66[0.52]
429.22

1946.61[2.03]*
1225.18

1268.54[2.49]*
332.25[2.91]**
1378.14[3.86]**
522.93[3.95]**[2.84]**[3.98]**[1.03][3.19]**[1.48]

47
0.503

47
0.667

47
0.410

47
0.567

0.30[0.58]
47
0.665

2.42[0.12]
47
0.559

Constant

HausmanN
Adj. R

2

F- Statistic

6.48

10.29

5.19

5.80

12.01

8.53

Log-L’hood-308.32-296.00-312.36-302.17-296.16-302.58** Significant at one percent; * significant at five percent; † significant at 10 percent; t-values are in parenthesis

30

Table 7 Results based on Panel Data Regressions (Dependent Variable: Squared Poverty Gap)
Explanatory Variables
Log of GLP
Log of GLP/NOAB
Log of GDP per Capita
Domestic Credit

GLP

Pool OLS
GLP/NOAB

Fixed Effects
GLP
GLP/NOAB

Random Effectsᶿ
GLP
GLP/NOAB

GLP

Random Effectsᶹ
GLP/NOAB

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

-45.28[-2.92]**-46.98-48.82[-1.32]-20.888.63[0.39]-156.547.29[0.13]-121.04-13.66[-0.98]-188.79-38.46[-1.12]-166.96-48.23[-3.30]**-67.06-58.73[-1.73]+-31.04[-1.42]-2.56[-2.69]**[-0.60]-2.26[-2.11]*[-0.88]0.57[0.27][-0.72]0.54[0.25][-4.10]**-0.91[-0.70][-3.07]**-1.18[-0.89][-1.82]+-2.57[-2.35]*[-0.74]-2.44[-2.12]*

32.74

39.56

-

-

-

-

-

-

[0.47][0.50]

-

-

-

-

-

-

EAP

-1.29

31.88

-

-

-

-

-

-

[-0.01][0.35]

-

-

-

-

-

-

ECA

-146.29-41.80

-

-

-

-

-

-

MENA

[-3.11]**[-0.83]

-

-

-

-

-

-

SA

70.84

32.49

-

-

-

-

-

-

SSA

[0.71]
432.09[0.26]
529.36

-

-

-

-

-

-

2007 Year Dummy[2.91]**
9.38[3.64]**-36.91

-

-

-

-

-

-

Constant[0.20]
1385.10[-0.74]664.791101.60

960.76

1818.63

1688.03

1647.96891.55[3.18]**
99
0.548[1.90]+
99
0.518[1.15]
99-1.653[1.05]
99-1.663[5.57]**
21.13[0.0]
0.73
99
-

[5.77]**
7.58[0.05]
0.74
99
-

[4.50]**
161.8[0.0]
0.74
99
-

[3.14]**
7.86[0.05]
0.79
99
-

8.61

5.45

0.31

0.27

-

-

-

-

HausmanTheta
N
Adj. R

2

F- Statistic

Log-L’hood-688.43-691.59-571.70-571.88** Significant at one percent; * significant at five percent; † significant at 10 percent; t-values are in parenthesis ; ᶿ nation effect; and ᶹ regional effect31

Tables 6 and 7 report the cases where the dependent variable is squared poverty gap.
An examination of the poverty gap and squared poverty gap results show consistent
results in terms of sign and significance for both measures of GLP. Also, in the case ofsquared poverty gap, the Hausman test favours random effects. 21 Broadly, the results
imply that GLP of MFIs benefits not just the poor but also the poorest. In sum, gross loan
portfolio of MFIs is negatively associated with the incidence, depth and severity ofpoverty.

VI. Simulations
That microfinance is impervious to the global recession following the financial crisis isdebatable 22 . Some have argued that the slowdown of the global economy will impact
negatively on microfinance as MFIs are now more closely linked to global financial
markets than before. So there will be (i) a funding or liquidity impact, with greaterrefinancing risks for MFIs, and (ii) an economic impact, with financial performance
affected by lower lending volumes, higher costs of funding, tighter net interest margins,
and greater volatility in foreign exchange losses/gains. Magnoni and Powers (2009), forexample, point out that, over 2009-10, the sector-wide microfinance will grow by some
$28 billion less than anticipated before the crisis. Others are more optimistic. For
example, Littlefield and Kneiding (2009) argue that the microfinance sector will survive
the setbacks because of the strong foundations and vast untapped market of creditworthy

21

In addition to the Hausman test, we calculate theta (measure of the extent of biasedness ofrandom effects’ model) and we observe values close to one (greater than 0.75) for each of threeestimations. This supports the fact that country/regional effects over time are important andcannot be ignored. Use of the theta, which is preferred due to the strong finite assumption
underlying the Hausman Test, further supports our choice of fixed effects model.
22
For a comprehensive review, see Llanto and Badiolo (2010).

32

clients. The effects on the poor through MFIs are, however, largely anecdotal. An attempt
is made below to simulate the likely effects of the global recession.

Table 8 Effect of Decrease in GLP, GDP per capita and Domestic Credit on
Poverty
Overall sample

All Developing Countries

East Asia and the PacificEastern Europe and Central
Asia
Latin America and the
Caribbean

Middle East and North Africa

South Asia

Sub-Saharan Africa

Cases

5%

10%

Effect of Decrease in GLP on Poverty

0.106

0.213

Effect of Decrease in GDP Per Capita on Poverty

0.333

0.666

Effect of Decrease in Domestic Credit on Poverty

0.183

0.366

Net Effect

0.622

1.245

Mean Poverty

18.05118.051

Final Poverty

18.67319.296

Mean Poverty

24.29324.293

Final Poverty

24.91525.538Mean Poverty

5.969

5.969

Final Poverty

6.591

7.214

Mean Poverty

8.846

8.846

Final Poverty

9.468

10.091

Mean Poverty

2.000

2.000

Final Poverty

2.622

3.245

Mean Poverty

48.80148.801

Final Poverty

49.42450.046

Mean Poverty

53.64153.641

Final Poverty

54.26354.886In the context of the recent global recession, we examine the possible slowdown ofpoverty reduction23 as a result of contraction (i) in gross loan portfolio of MFIs, (ii) GDP
per capita and (iii) domestic credit provided by banking institutions. Table 8 reports thesimulation results on the basis of the IV estimation reported in column (5) of Table 2.

23

Imai, Gaiha and Thapa (2010) report a slowdown in the rate of poverty reduction post 2000
relative to the 1990-decade.

33

The calculations24 are premised on the assumption that the coefficient estimate of eachvariable is the same across different regions in the regression and thus the simulated
effects of key variables on poverty are also the same.
The second to fourth rows of Table 8 show that the effects GDP per capita anddomestic credit are higher than those of GLP of MFIs. While this is plausible in view of
the comparative contribution of MFI in an economy, the poverty increasing effect of
0.106% (or 0.233%) due to the 5% (or 10%) decline in GLP points to the need forsustainable flow of on-lending funds. Although the overall rise in poverty is low (about
3.45 per cent in the first scenario of a mild recession), it must be emphasized that this isadditional to other cumulative effects of the global slowdown on poverty (investments,for example, continue to be sluggish).

VI.

Concluding Observations

Recent assessments of impact of microfinance –based largely on randomised trials-haveled to questioning of claims of women’s empowerment and poverty reduction. The socalled ‘magic’ of microfinance has thus come under deep scrutiny and the findings oflittle or weak impacts are beginning to turn the tide against it. Not only are some of thesestudies are faulty in some respects, but the findings also cannot be accepted at face value.
Besides, the faltering global economy has raised serious concerns about the immunity ofthe microfinance sector and its potential for poverty reduction.
From this perspective, the preceding analysis centrred around the hypothesis thatmicrofinance reduces poverty. We carried out tests using the cross-country data in 200724

The simulated effects of GLP and GDPPC are calculated with the semi-log (or ‘level-log’)
specification (∆Y= (β/100) % ∆X), while the effect of domestic credit is based on the ‘levellevel’ specification (∆Y= β ∆X) (see Wooldridge, 2009, p.46 for details).

34and a panel for 2003 and 2007. Taking account of the endogeneity associated with loans
from microfinance institutions (MFIs), there is robust confirmation that microfinanceloans are significantly and negatively associated with poverty, i.e., a country with ahigher MFIs’ gross loan portfolio tends to have lower poverty, after controlling for theeffects of other factors influencing it. The negative relationship remains unchanged when
the poverty headcount ratio is replaced by the poverty gap and squared poverty gap.
These results suggest that microfinance not only reduces the incidence of poverty but also
its depth and severity. The panel results further corroborate these findings.
Other factors that contribute to poverty reduction include GDP per capita and share ofcredit in GDP (as a measure of financial development of an economy). Besides, there are
significant regional effects.
Our simulations point to worsening of poverty in a mild recession scenario with small
reductions in gross loan portfolio, GDP per capita and share of credit in GDP. Thesesimulations are helpful in adding precision to anecdotal evidence about how setbacks to
MFIs hurt the poor. Indeed, sustained flows to MFIs may help avert to some extent
accentuation of poverty as a consequence of the slow and faltering recovery of the global
economy.
In conclusion, assertions that microfinance is ‘oversold’ and lacks the ‘magic’
associated with it are widely off the mark, if not largely mistaken.

35References
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39

APPENDICES
Appendix 1: List of Regions and Nations
Regions

Nations

Regions

Nations

CambodiaNo.
53

East Asia and the Pacific
East Asia and the Pacific
East Asia and the Pacific
East Asia and the PacificMiddle East and North Africa

Sudan

Papua New Guinea
East Timor
Indonesia

54
55
56

Middle East and North Africa
Middle East and North Africa
Middle East and North Africa

Palestine
Yemen
Egypt

East Asia and the Pacific

Laos

57

Middle East and North Africa

JordanEast Asia and the Pacific
East Asia and the Pacific
East Asia and the Pacific
East Asia and the Pacific
East Asia and the PacificEastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Eastern Europe and Central Asia
Latin America and the Caribbean
Latin America and the Caribbean

China, People's Republic of
Samoa
VietnamPhilippines
ThailandUzbekistan
Hungary
Georgia
Tajikistan
Armenia
Montenegro
Kazakhstan
KosovoRussiaMongolia
Kyrgyzstan
Macedonia
Bulgaria
SerbiaRomania
TurkeyMoldova
UkraineCroatia
Albania
Bosnia and Herzegovina
PolandAzerbaijan
Peru
Brazil

58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87

Middle East and North Africa
Middle East and North Africa
Middle East and North Africa
Middle East and North Africa
Middle East and North Africa
South Asia
South Asia
South Asia
South Asia
South Asia
South Asia
Sub-Saharan Africa
Sub-Saharan Africa
Sub-Saharan Africa
Sub-Saharan Africa
Sub-Saharan Africa
Sub-Saharan Africa
Sub-Saharan Africa
Sub-Saharan Africa
Sub-Saharan Africa
Sub-Saharan Africa
Sub-Saharan Africa
Sub-Saharan Africa
Sub-Saharan Africa
Sub-Saharan Africa
Sub-Saharan Africa
Sub-Saharan Africa
Sub-Saharan Africa
Sub-Saharan Africa
Sub-Saharan Africa

Syria
Iraq
Tunisia
MoroccoLebanonBangladesh
India
Nepal
Afghanistan
Pakistan
Sri Lanka
Tanzania
Mozambique
Benin
Kenya
AngolaTogo
UgandaSierra Leone
Gambia, The
SenegalSouth Africa
GuineaCameroon
Mali
Malawi
Cote d'Ivoire (Ivory Coast)
Burkina Faso
Swaziland
Niger

Latin America and the Caribbean

Bolivia88

Sub-Saharan Africa

Guinea-Bissau

No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36

40

37
38
39
40

Latin America and the Caribbean

Mexico89

Sub-Saharan Africa

Congo, Republic of theLatin America and the Caribbean

Costa Rica90

Sub-Saharan Africa

Ethiopia

Latin America and the Caribbean

Guatemala91

Sub-Saharan Africa

BurundiLatin America and the Caribbean

Colombia

92

Sub-Saharan Africa

Nigeria

Latin America and the Caribbean

Trinidad and Tobago

93

Sub-Saharan Africa

Latin America and the Caribbean

Venezuela

94

Sub-Saharan Africa

Congo, Democratic Republic of
the
Chad

Latin America and the Caribbean

Haiti

95

Sub-Saharan Africa

Central African Republic

Latin America and the Caribbean

Ecuador96

Sub-Saharan Africa

RwandaLatin America and the Caribbean
Latin America and the Caribbean

Nicaragua
Panama97
98

Sub-Saharan Africa
Sub-Saharan Africa

Ghana
Madagascar

Latin America and the Caribbean
Latin America and the Caribbean
Latin America and the Caribbean

Argentina
Chile
El Salvador

99

Sub-Saharan Africa

ZambiaLatin America and the Caribbean

Paraguay

Latin America and the Caribbean

Honduras

Latin America and the Caribbean

Dominican Republic

41
42
43
44
45
46
47
48
49
50
51
52

Appendix 3: Correlation Matrix
Variables

Poverty
Headcount

Poverty Headcount1

Povertygap

SquaredPoverty Gap

Log of
GLP

Log of
NOAB

Log of
GLP/NOAB

Log of[W.GLP/W.NOAB]

Log of GDP
per Capita

Domestic
CreditLog. W.
GLP

Log of W.
NOAB

Poverty gap

0.96

1

Squared Poverty Gap

0.84

0.94

1

Log of GLP

-0.34

-0.38

-0.37

1

Log of NOAB

0.04

-0.04

-0.11

0.8

1

Log of GLP/NOAB

-0.63

-0.55

-0.43

0.44

-0.19

1

Log of W.GLP/W.NOAB

-0.73

-0.65

-0.53

0.47

0.02

0.83

1

Log of GDP per Capita

-0.79

-0.69

-0.53

0.28

-0.04

0.52

0.61

1

Domestic Credit

-0.31

-0.32

-0.31

0.09

0.11

-0.01

0.06

0.46

1

Log. W. GLP

-0.17

-0.28

-0.36

0.85

0.75

0.28

0.46

0.3

0.16

1

Log of W. NOAB

0.28

0.13

-0.02

0.63

-0.15

-0.06

0.13

0.81

0.82
-0.24
W. – Weighted five-year lag average

41

1Appendix 3: First Stage IV Estimation
GLP
0.78[6.60]**

Log of Weighted lag of Average GLP
Log of Weighted lag of Average GLP/ Weighted lag of Average NoAB

GLP/NOAB

0.83[12.10]**Log of GDP per Capita-0.14[-0.50]0.00[0.48]-0.78[-0.73]-1.55[-1.87]+-0.19[-0.37]-2.01[-2.06]*-0.79[-0.83]
5.82[1.73]+
47
0.548
7.97
-72.10

Domestic Credit
MENA
EAP
ECA
SA
SSA
ConstantN
Adjusted R-Squared
F-Statistic
Log-Likelihood

0.00[1.19]-0.68[-2.70]*0.12[0.62]
0.13[1.10]-0.45[-1.79]+0.08[0.40]
1.34[2.71]**
47
0.923
79.59
-6.27

†

** Significant at one percent; * significant at five percent; significant at 10 percent; t-values are in parenthesis

Appendix 4: List of Nations for Panel Data - Table 5
No.

Nations

Freq.

No.

Nations1

Albania2

32

Kazakhstan

22

Armenia

2

33

Kenya

1

3

Azerbaijan

2

34

Kyrgyz Republic

2

4

Bangladesh

235

Macedonia, FYR

2

5

Benin

1

36

Madagascar

2

6

Bosnia and Herzegovina

2

37

Malawi

1

7

Brazil2

38

Mexico28Bulgaria

1

39

Moldova2

9Burkina Faso

1

40

Mongolia

2

10

Cambodia1

41

Mozambique

1

11

Cameroon

1

42

Nepal

1

12

Chile

2

43

Nicaragua

2

13

China

2

44

Panama1

14

Colombia

2

45

Paraguay

2

15

Congo, Dem. Rep. of

1

46

Peru

2

16

Costa Rica

2

47

Poland2

17

Croatia2

48

Romania2

18

Dominican Republic

2

49

Russia2

19

East Timor

1

50

Rwanda1

20

Ecuador2

51

Sierra Leone

1

42

Freq.

21

Egypt, Arab Rep.

2

52South Africa

1

22

El Salvador

253

Sri Lanka

123

Ethiopia

2

54

Swaziland124

Georgia2

55

Tajikistan

225

Guatemala2

56

Tanzania

1

26

Guinea1

57

Turkey

2

27

Haiti

1

58

Uganda2

28

Honduras

2

59

Ukraine

2

29

India

1

60

Vietnam2

30

Indonesia

161

Zambia2

31

Jordan2

Appendix 5: International Poverty Data - Unbalanced Panel
No.

Year

Nations

Poverty HC

No.

Year

Nations

Poverty HC

1

2003

Albania

2.00

54

2007

Kenya

19.72

2

2007

Albania

2.00

55

2003

Kyrgyz Republic

34.03

3

2003

Armenia

12.20

56

2007

Kyrgyz Republic

12.62

4

2007

Armenia

3.65

57

2003

Macedonia, FYR

2.31

5

2003

Azerbaijan

6.32

58

2007

Macedonia, FYR

2.00

6

2007

Azerbaijan

2.00

59

2003

Madagascar

76.34

7

2003

Bangladesh

57.82

60

2007

Madagascar

67.83

8

2007

Bangladesh

49.64

61

2007

Malawi

73.86

9

2003

Benin

47.33

62

2003

Mexico

4.28

10

2003

Bosnia and Herzegovina

2.00

63

2007

Mexico

2.40

11

2007

Bosnia and Herzegovina

2.00

64

2003

Moldova

25.05

12

2003

Brazil

10.40

65

2007

Moldova

5.26

13

2007

Brazil

8.00

66

2003

Mongolia

15.47

14

2003

Bulgaria

2.32

67

2007

Mongolia

22.38

15

2003

Burkina Faso

56.54

68

2003

Mozambique

74.69

16

2007

Cambodia

33.02

69

2007

Nepal

55.12

17

2003

Cameroon

32.81

70

2003

Nicaragua

19.42

18

2003

Chile

2.00

71

2007

Nicaragua

15.81

19

2007

Chile

2.00

72

2007

Panama

9.34

20

2003

China

28.36

73

2003

Paraguay

17.23

21

2007

China

15.92

74

2007

Paraguay

7.88

22

2003

Colombia

16.07

75

2003

Peru

13.84

23

2007

Colombia

16.01

76

2007

Peru

7.94

24

2007

Congo, Dem. Rep. of

59.22

77

2003

Poland

2.00

25

2003

Costa Rica

4.52

78

2007

Poland

2.00

26

2007

Costa Rica

2.19

79

2003

Romania

3.09

27

2003

Croatia

2.00

80

2007

Romania

2.00

28

2007

Croatia

2.00

81

2003

Russia

2.00

43

29

2003

Dominican Republic

5.27

82

2007

Russia

2.00

30

2007

31

2003

Dominican Republic

4.46

83

2003

Rwanda76.56

East Timor

52.94

84

2003

Sierra Leone

53.37

32

2003

Ecuador

10.49

85

2003

South Africa

26.20

33

2007

Ecuador

7.24

86

2003

Sri Lanka

13.95

34

2003

Egypt, Arab Rep.

2.00

87

2003

Swaziland

62.85

35

2007

Egypt, Arab Rep.

2.00

88

2003

Tajikistan

36.25

36

2003

El Salvador

13.73

89

2007

Tajikistan

21.49

37

2007

El Salvador

8.70

90

2003

Tanzania

88.52

38

2003

Ethiopia

55.58

91

2003

Turkey

2.00

39

2007

Ethiopia

39.04

92

2007

Turkey

2.65

40

2003

Georgia

12.80

93

2003

Uganda

57.37

41

2007

Georgia

13.44

94

2007

Uganda

51.53

42

2003

Guatemala

14.99

95

2003

Ukraine

2.00

43

2007

Guatemala

11.70

96

2007

Ukraine

2.00

44

2003

Guinea

70.13

97

2003

Vietnam

40.05

45

54.9

Haiti

54.90

98

2007

Vietnam

22.82

46

2003

Honduras

18.10

99

2003

Zambia

64.60

47

2007

Honduras

20.19

100

2007

Zambia

64.29

48

2003

India

41.64

49

2007

Indonesia

25.42

50

2003

Jordan

2.00

51

2007

Jordan

2.00

52

2003

Kazakhstan

3.42

53

2007

Kazakhstan

2.00

Source: Authors’ Compilation Based on 2010 World Development Indicators44