DP2010-24

Does Microfinance Red uce Poverty in
Bangladesh? New Evidence fromHousehold Panel Data*
Katsushi S. IMAI
Md. Shafiul AZAM
Revised November 1, 2011

* 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.

Does Microfinance Reduce Poverty in Bangladesh? New Evidence fromHousehold Panel Data*
Katsushi S. Imai
Economics, School of Social Sciences, University of Manchester, UK and
Research Institute for Economics & Business Administration (RIEB), Kobe University, Japan&
Md. Shafiul Azam
Economics, School of Social Sciences, University of Manchester, UK
23rd June 2011

Abstract
The purpose of the present study is to examine whether loans from microfinance institutions(MFI) reduce poverty in Bangladesh drawing upon the nationally representative household
panel data covering 4 rounds from 1997 to 2004. Our aim is to estimate the effects of general
microfinance loans as well as loans for productive purposes on household income, foodconsumption and women’s Body Mass Index (BMI). It has been found by different versions ofhousehold fixed-effects model that overall effects of MFI’s loans on income and foodconsumption in 1997-2004 were positive and that the purpose of the loan is important inpredicting which household welfare indicator is improved. As a supplementary analysis, wehave carried out Difference-In-Difference Propensity Score Matching (DID-PSM) and haveconfirmed a positive impact of MFI’s general loans on food consumption’s growth in 19992004. It can be concluded that loans provided by MFIs had significant poverty reducing effects
particularly on income and consumption in Bangladesh.
Key words: microfinance, MFI (microfinance institution), microcredit, poverty, Bangladesh
JEL codes: C30, C31, G21, I32*Corresponding Author: Katsushi Imai (Dr) Department of Economics, School of Social Sciences
University of Manchester, 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

Acknowledgements
We acknowledge valuable comments and advice from Samuel Annim, Thankom Arun, Raghav Gaiha,
Shoji Nishijima, Takahiro Sato, seminar participants at Kobe University and two anonymous referees.
The first author wishes to thank RIEB, Kobe University for generous research support during his stayin 2010. Only the authors are responsible for any errors.

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Does Microfinance Reduce Poverty in Bangladesh? New Evidence fromHousehold Panel Data*
I. Introduction
The idea that microfinance helps poor people build businesses, increase their income and exit
poverty has turned into a global movement, so called ‘microfinance revolution’, to fightagainst poverty over the last three decades. This is reflected in the significant increase ofdonor countries’ investment in microfinance sector in recent years. The poor tend to have
limited access to services from formal financial institutions in less developed countries due
to, for example, (i) the lack of physical collateral; (ii) the cumbersome procedure to starttransaction with formal banks, which would discourage those without education fromapproaching the banks; and (iii) lack of supply of credit in the rural areas related to urban
biased banking networks and credit allocations. The hallmark of microfinance revolution is
the system of group lending based on the joint liability or ‘social capital’ of groups which
would guarantee to repay loans.1 Here the poor with no physical collateral are allowed toform a group to gain access to credit and the repayment rate is kept high because of, forexample, mutual monitoring, sanction against non-repayment of the member or incentives toretain the individual reputation or credit within a community (e.g. Armendáriz and Morduch,
2005, Besley and Coate, 1995, Ahlin and Townsend, 2007).
The last thirty years witnessed a phenomenal growth in microfinance sector serving about
40 million clients with an outstanding loan portfolio of US$17 billion in mid-2006 and the
projected market size could be around US$250-300 billion in near future (Ehrbeck, 2006).
However, the argument that microfinance responds to the derived demand for borrowing tosupport self-employment and small business has come under intense scrutiny in recent years.
Even the hard core of pro-microfinance researchers now broadly agree that attention should
be drawn to both supply and demand sides of microfinance in order for the sector to have a2

noticeable poverty reducing effects. As Robert Pollin (2007, p.2) notes, micro-enterprises“need a vibrant, well functioning domestic market itself that encompasses enough people
with enough money to buy what these enterprises have to sell”. Moreover, as noted by
Bateman and Chang (2009), microfinance neglects the crucial role of scale economies and it
produces an oversupply of inefficient micro-enterprises that could undermine thedevelopment of more efficient small and medium industries (SMEs) that would be potentiallyable to reduce unit costs and register productivity growth in the long run. However, shifting
the donor’s fund away from very small groups or enterprises (target of microfinanceinstitutions) to SMEs could imply the neglect of the very poor who are credit constrained.
The development agencies of donor countries or government will have to make sure whether
the benefits to programme participants are sustainable and large enough to make a dent in thepoverty of participants and society at large.
Bangladesh has recorded a modest 4-6 % growth within a stable macroeconomic
framework in recent years. The poverty trend has shown a consistent decline in poverty
incidence over the years, especially in rural areas. However, aggregate poverty rates still
remain dauntingly high. According to the estimates based on the Household Income andExpenditure Surveys (HIES) of the Bangladesh Bureau of Statistics, poverty head count ratio
declined from 58.8 % in 1991 to 48.9 % per cent in 2000 and it further declined to 40.0 % in
2005. So poverty has declined on average just above one percentage point a year since the1990s. The observed improvement holds true for the distributionally sensitive poverty
measures: the poverty gap ratio reduced from 17.2 % to 12.9 % and the squared poverty gap
ratio from 6.8 % to 4.6 % during the same period from 2000 to 2005. This indicates that thesituation of the poorest also improved during this period, though there existed the very poor
as well as inequality among the poor even in 2005. The head count ratio in rural area reduced
from 52.3 % in 2000 to 43.8 % in 2005. However, the absolute number of people living

3

below poverty line was in fact on the rise – a staggering 56 million people were found to bepoor in 2005. The corresponding figure was 55 million in 2000. Similarly, hard core poverty
remains almost same during the period 2000-2005 (18.8 % and 18.7 % in 2000 and 2005
respectively). So poverty reduction remains the most daunting challenge for Bangladesh.
Bangladesh, the birthplace of microfinance, is credited with the largest and most vibrantmicrocredit sector in the world. Microcredit programmes are implemented in Bangladesh bya host of formal financial institutions, specialized government organizations and semi-formal
financial institutions (nearly 1000 NGOs-MFIs). Furthermore, with a view to coordinating
the flow of funds to appropriate use and NGOs-MFI activities, the Palli Karma Sahayak
Foundation (the Bengali acronym PKSF and can be translated into English as “Rural
Employment Support Foundation”) came into being in 1990. The growth in the MFI sector,
in terms of the number of MFIs as well as total membership, was phenomenal during the
1990s and after 2000. The effective coverage would be around 17.32 million borrowers. The
total amount is 24.25 million due to overlapping – one borrower taking loan from more than
one MFI and the extent of overlapping may be as high as 40% (PKSF, 2006). Out of 17.32
million borrowers covered by micro credit programmes, about 62% were below the poverty
line, that is, 10.74 million poor borrowers were covered by MFI programmes. Out of
estimated 56 million poor people in 2005, 29.26 million (53% of 56 million) were supposed
to be economically active and potential target of microfinance operations. Therefore, there
was still scope for further extending the coverage of microcredit programmes in 2005 to an
approximate 18.52 million borrowers who were poor and economically active but notcovered by MFI programmes (Ahmed, 2007).
The main purpose of the present study is to test whether microfinance reduces poverty in
Bangladesh drawing upon a nationally representative household panel data covering 4 rounds,
1997-98, 1998-99, 1999-2000, and 2004-05. Special attention is drawn to the issue of sample

4selection or endogeneity associated with participation in microfinance, by applying different
versions of household fixed effects model as well as difference in difference and propensity
score matching (DID-PSM)2 to the sample which consists of participants and non-participantsof microfinance programmes.
The rest of the paper is organized as follows. The next section surveys the literature on
poverty and microfinance in Bangladesh. Section III describes briefly the survey design anddata. Section IV emphasises the underlying intuition of econometric models and Section V
summarizes the econometric results and findings. The final section offers concluding
observations.

II. The Literature Review on Poverty and Microfinance
Despite the data limitations and methodological problems, e.g. on dealing with the sample
selection bias associated with microfinance participation, there are a few rigorous studies toassess the impact of microfinance on poverty. The findings of a set of studies summarised by
Hulme and Mosley (1996) are somewhat provocative: households with initial higher income
above the poverty line benefited from microfinance and enjoyed sizeable positive impacts,
while poorer households below the poverty line did not. A majority of those with their initial
income below the poverty line actually ended up with less incremental income after obtaining
microcredit, as compared to a control group which did not get any loans from MFI. Pitt andKhandker (1998) carried out a survey in 1991/92 involving about 1800 households in
Bangladesh and found that for every 100 taka borrowed by a woman, household consumption
expenditure increased by 18 taka. For a male borrower, the figure was 11 taka. They
estimated the poverty reducing effect of three major microfinance institutions in Bangladesh
namely –Bangladesh Rural Advancement Committee (BRAC), Grameen Bank, andBangladesh Rural Development Board (BRDB). Moderate and ultra poverty was reduced by5about 15 % and 25 % for households who were BRAC members for up to three years. Similar
results were found for Grameen Bank and BRDB members.3
Drawing upon the follow up survey in 1998/99, Khandker (2005) found resounding results
at both micro and aggregate levels: microcredit continued to contribute to reducing poverty
among poor borrowers and within local economy. The impact appears to be greater forhouseholds who were initially extremely poor (18 percentage point drop in extreme poverty
in seven years) compared to moderate poor households (8.5 percentage point drop). These
results differ from earlier evidence that pointed to moderate poor borrowers having benefited
more than extremely poor borrowers who tended to have a number of constraints (e.g. fewer
income sources, worse health and education) which prevent them from investing the loan in ahigh-return activity (Wood and Sharif 1997). The finding that better off households
benefiting more was also borne out by detailed case-study evidence (Farashuddin, andAmin,1998) and by comparing participants of credit programmes who cater to different
socio-economic groups (Montgomery et al., 1996).
The general conclusions of Pitt and Khandker (1998) and Khandker (2005) about the
impact of microcredit on poverty include: (i) microcredit was effective in reducing poverty
generally, (ii) this is especially true when borrowers were women, and (iii) the extremely
poor benefited most in 1998/99. Consumption data from 1072 households in one district ofBangladesh were used to show that the largest effect on poverty occurs when a moderatepoor BRAC client borrows more than tk10,000 (US$200) in cumulative loans (Zaman, 1998).
In other words, there may be a threshold level of credit above which a household gains mostin terms of increases in income.
Using the same household data set in 1991/2 used by Pitt and Khandker (1998) and
Morduch (1998) and overcoming the limitations of the previous studies (e.g. the problem
related to identification for the former and the problem not taking account of endogenous

6

programme placement for the latter), Chemin (2008) applied the propensity score matching(PSM) technique to evaluate the impact of participation in microfinance programmes on anumber of outcome indicators. He found that microfinance had a positive impact onparticipants’ expenditure, supply of labour and male/female school enrolment. The present
study attempts to extend Chemin (2008) in the following three ways. First, we have used
more recent and rich data for a panel of households in Bangladesh from 1997 to 2005 andhave examined the effects of microfinance participation on household welfare, in terms of log
per capita household income, food consumption and women’s body mass index (BMI).
Second, while we also apply PSM to each cross-sectional component of the panel, we utilise
the longitudinal nature of the data by applying different versions of household fixed effects
model as well as DID-PSM. Third, we have focused on the effects of purpose of loans fromMFIs (i.e. productive purposes or not)4on per capita household income, food consumption
and women’s BMI.
The relationship between poverty and microfinance is unclear outside Bangladesh. Tworecent studies that attempted to overcome the sample selection problem by using randomized
sample selection methods also came up with not so resounding evidence in favour of
microcredit. Banerjee, Duflo, Glennerster and Kinnan (2010) did not find much strong
average impact; i.e., the impact on measures of health, education, or women’s decisionmaking among the slum dwellers in the city of Hyderabad, India was negligible. The study by
Karlan and Zinman (2009) took a similar method to the Philippines, with a focus on the
traditional microcredit for small business investment. Profits rise, but largely for men andparticularly for men with higher incomes. Moreover, the increases in profits appear to arise
from business contractions that yielded smaller, lower-cost (and more profitable) enterprises.
Imai et al. (2010), however, found that the loans from MFI for productive purposes reduced

7

significantly multifaceted household poverty, which was defined in terms of assets,
employment, health facilities, and food security, using the survey data in India in 2000.
Given the inconclusive and ambiguous nature of evaluation outcomes and increasing
involvement of MFIs both in terms of number of institutions and resources in poverty
reduction efforts, it is important to have a deeper look into the relationship between
microfinance or microcredit and poverty. The present study aims to provide new evidence on
the impact of microfinance on poverty in rural Bangladesh using a large and nationally
representative panel data. The indicator for wellbeing/poverty is (i) per capita householdincome, (ii) per capita food consumption, and (iii) women’s BMI. This paper seeks to answer
the question – whether access to loans from MFIs for general purposes (or loans from MFIs
for productive purposes) reduced poverty in rural Bangladesh. Ideally, the impact of
microfinance should be ascertained by a counterfactual approach - what would have
happened to a person who took a loan from a MFI if she or he had not done so. However,such a counterfactual is never observed in reality. The easiest and intuitive method is tocompare the welfare or income of borrowers and non-borrowers. But such comparison is
problematic for a number of reasons. First, MFIs are not distributed across regions randomly
due to endogenous programme placement where MFIs generally target poorer households, orthe constituent of core group of clientele for the services of MFIs are poorer households.
Second, there is a self selection problem, that is, whether an individual participates in the
MFI programme is determined by herself, not by chance. That is, within the area where the
MFI programme is available, individuals sharing similar socio-cultural backgrounds (e.g.,
education, age or religion) might have different levels of entrepreneurial skills and latentability leading to different probabilities to their participating in a certain programme. Hence,
it is essential to take into account the endogeneity or self-selection problems in assessing theimpact of microfinance.

8III. Design of Survey and Data(a)

Details of Survey

The four-round panel survey was carried out by the Bangladesh Institute of Development
Studies (BIDS) for Bangladesh Rural Employment Support Foundation (PKSF) with funding
from the World Bank. All four rounds of the survey were conducted during the DecemberFebruary period in 1997-98, 1998-99, 1999-2000, and 2004-05. The survey covered a sample
of 13 PKSF’s Partner Organization (PO) and over 3000 households in each round distributed
evenly throughout Bangladesh so as to obtain a nationally representative data set for theevaluation of microfinance programmes in the country (different districts spanning 91
villages from around 23 thanas).
A sample of villages under each of the selected MFI was drawn through stratified
random sampling. The stratification was based on the presence or absence of microfinanceactivities. The non-programme or control villages were selected from the neighbouring
villages. At each PO, six to eight villages were selected depending on the availability ofcontrol villages. In selecting survey households, the universe of households in the programmevillages drawn from the census was grouped according to their eligibility status. A household
is said to be eligible if it owns 50 decimals (half an acre) or less of cultivable land.
Participation status of the household is defined using the net borrowing from a MFI. If ahousehold is not a participant in a given round, the net borrowing is zero for that household.
From the village census list, 34 households were drawn from each of the programme andnon-programme villages. The proportion of eligible and non-eligible households was kept ataround 12:5 and the sample size within the programme and control villages was determinedaccordingly. The ratio is chosen to reflect the average participants to non-participants ratio of
the population in the village. This is the largest and the most comprehensive data of its kindso far in Bangladesh collected with detailed information on a number of socio economic

9variables, including household demographics, consumption, assets and income, health andeducation and participation in microcredit programmes.
(b)

Descriptive Statistics and Definition of Variables

The study uses two different definitions of access to Microfinance Institutions: first, whether
a household is a client of any MFI and takes loans for general purposes or not, and second,
whether a household has actually taken a loan from any MFI for productive purpose or not.
The first definition is used to observe the effect of taking general loans from MFI on percapita household income, food consumption and women’s BMI and thus on poverty. It isnoted that unlike the first, third and fourth rounds, the second round consumption data arehighly aggregated and not comparable with the other rounds. In case of BMI, comprehensive
data are available only for the first and the last rounds, while the data on household income
are available for all four rounds. The second is concerned with whether the household hastaken loans for productive activities (and has an outstanding balance of loans at the time ofsurvey) leading to an increase in production, for example, starting a small business or otherself employment activities, like small scale poultry or cattle rearing. Loans used for
consumption or other non-productive activities like marriage or dowry are excluded from thiscategory.
Online Appendix 1 provides the descriptive statistics of the variables for the sample
households with access to loans from MFI and for those without. As shown by the number ofobservations, more than a half of the sample household have access to MFI loans. About ahalf of them have access to loans from MFI for productive purposes. In general, there is arelatively negligible difference between the descriptive statistics of each variable for thehouseholds with and without access to loans from MFIs (or with access to loans from MFIs
for productive purposes) and for those without.

10The average household size is about 6 for both categories of households. Heads of thehouseholds are categorised into four groups depending on their educational level – illiterate,completing primary education, secondary education, or higher education. Similarly,
occupation of the head of the households is grouped into six distinct categories – farmers,
agricultural wage labourers, non-agricultural wage labourers, small business, professionals(which comprises teachers, lawyers, doctors and other salaried employees), and others(beggars, students, retired persons, disabled, unemployed etc.). However, per capita income isgenerally higher for those who do not take MFI loan or participate in MFI programmes. This
does not necessarily imply that taking loans from MFIs reduces per capita income due to theaforementioned sample selection bias. About 93 per cent of the households are male headed,mainly due to the sample design where households in a village are selected randomly eventhough a majority of the MFI clients are female.

IV. Methodologies(1)

Panel Data Model

Fixed Effects (FE) ModelFirst, we have applied different versions of household fixed effects model to take account of
the amount of MFI’s general or productive loans whilst PSM or DID-PSM can consider only
binary classification of participation status. The standard fixed effect model is estimated as:
Wit = β 0 + X it β 1 + Lit β 2 +Yt β 3 + µ i + ε it(5)

where Wit is the outcome variable (namely, log household income per capita, log foodconsumption per capita or women’s BMI), X it is a vector of variables of household andsocio-economic characteristics as well as other control variables, and Lit is either a total
amount of MFI’s loans or a vector consisting of MFI’s productive and non-productive loans
11(sum of which is equal to the total amount of MFI’s loans). We are interested in the sign ofcoefficient of Lit , β 2 which represents the effects of MFI’s loan on the outcome variable.5
Yt is a vector of year dummies to take account of time specific effects, µ i is a householdspecific unobservable fixed effect (e.g. unobserved entrepreneurship), and ε it is an error
term, i.i.d. (see, e.g. Greene, 2003). For the income equation, we use household
characteristics ( X it ), such as arable land and its square, age of the household head and its
square, household size, sex of the household head, education of the head, occupational
categories, and whether a household has access to electricity. For the equations for foodconsumption and BMI, we replace arable land and its square by a set of prices (for rice,
potatoes and milk).

Fixed Effects Model with PSM (FE-PSM)
Initial household characteristics as well as pre-existing socio-economic and area attributes arelikely to influence the programme placement and the subsequent growth paths of outcome
variables. Controlling for these potential sources of selection bias would bring us a morecredible estimate of the policy effect. To deal with these sources of bias, we need to control
for the initial conditions as well as time-varying factors that would influence the programme
placement and growth rates. One possible way of correcting for these biases is to use PSM toselect appropriate counterfactuals from the sampled non-participants (Ravallion and Chen
2005, Chen et al. 2008). Matching methods or PSM will construct the control groups that are
as similar as possible except for the access to microfinance programmes. PSM will trim thesample of control group with propensity scores that do not overlap with those for thetreatment groups. More specifically, we carry out PSM for each round and match theparticipating households with non-participating ones.6 Second, we drop all the households
which are not matched, or outside the common support region.7 In order to control for the12

initial conditions and any time-varying factors, we have carried out the fixed-effects modelfor the reconstructed panel data in which participating households have been matched with
controls.

Fixed Effects (FE) Model with control for initial characteristics
In the FE or FE-PSM estimation, some of the explanatory variables have either a linear timetrend or little variation over time and they are swept away in the process of first-differencing.
However, these variables may have a significant effect on the change in outcome variables
and eliminating them from the model might bias the policy effect. To circumvent thisproblem, using only the data of the first and last rounds, we implement an alternate version of
the fixed effects model where initial characteristics of households (e.g. age of household head
and its square; household size; sex of head of the household; education; occupation; access toelectricity) are used along with the first differenced variables. The purpose of these models is
also to correct any possible bias due to pre-existing initial heterogeneity of households andtime-varying factors.

(2)

DID-PSM

There is a huge empirical literature where the policy effects are estimated by PSM. The
method is applied, in many cases, to cross-sectional data because of the limitations of IV
models (e.g. assuming linearity; requiring a valid instrument; sensitivity of the results tospecifications). PSM matches a participating household in MFIs with a non-participatinghousehold by using the propensity score, the estimated probability of participating in the
microfinance programmes. We can then obtain average treatment effect (ATT) of the policy
by comparing the averages of outcome variables for participants and non-participants. In
PSM, the first stage specifies a function matching the proximity of one household to another13

in terms of household characteristics and then households are grouped to minimize thedistance between matched cases in the second stage (Foster, 2003). Rosenbaum and Rubin(1983) proposed statistical matching using the propensity score, the predicted probability thatan individual receives the treatment of interest to make comparisons between individuals withthe treatment and those without. Models and methodological issues for propensity scorematching estimation are discussed in details, for example, by Becker and Ichino (2002),
Dehejia (2005), Dehejia and Wahba (2002), Heckman et al. (1997), Ravallion (2008), Smith
and Todd (2005), and Todd (2008).8 In the first stage logit model of PSM9, we include thesame set of explanatory variables which we use for the panel data model.
While there are some advantages in using PSM to estimate the impact of policy, thederived impact depends on the variables used for matching and the quantity and quality ofavailable data and the procedure to eliminate any sample selection bias based on observables(Ravallion, 2008). If there are important unobservable variables in the model, the bias is still
likely to remain in the estimates. For example, if the selection bias based on unobservablescounteracts that based on observables, then eliminating only the latter bias may increase
aggregate bias. The replication studies comparing non-experimental evaluations, such as
PSM, with experiments for the same programmes do not appear to have found such an
example in practice (Heckman et al., 1997, Ravallion, 2008). However, there may besystematic differences between participants and non-participant outcomes even after
conditioning on household’s observable characteristics, which could lead to the violation of
the identification condition required for PSM (Smith and Todd, 2005). Because bias cannot
be completely eliminated if there are important unobservable variables in the model, theresults of PSM for cross-sectional data will have to be interpreted with caution. Therefore, thepresent study reports the PSM results in Online Appendix 3 and provide only a summary of
the results.

14

To overcome the limitations of PSM using cross-sectional data, we apply the DID-PSM
method which utilises the longitudinal nature of the data. The DID-PSM estimator requires,as specified by (Smith and Todd, 2005), thatE (W0ti − W0ti' pi ( X i ), Di = 1) = E (W0ti − W0ti' p i ( X i ), Di = 0)

where t and t’ are time periods (where t=1 and t’=0, or after and before the programme). W0ti
is the outcome at time t for non-participant, pi ( X i ) is a propensity score, the probability ofparticipation at time t, and Di is whether a household participated in a microfinanceprogramme between t’ and t (1 if participated, 0 otherwise). In the PSM applied to the crosssectional data, the mean of the outcome of a household at a particular point of time iscompared between participants and non-participants conditional on the probability ofparticipation estimated by observable household characteristics, whilst in the DID-PSM, thetime-series or temporal change of outcome of a household is compared at time t (after the
programme) conditional on the propensity score. The results of the latter are not subject to theexistence of unobservable household characteristics in the model. In our context, DID-PSM
implies that PSM is applied to ‘the first difference’ (from t’ to t) of the outcome variable (e.g.
log per capita income) of a household with access to MFI loans at t, but not at t’, the previous
round, is compared with that of a household with the same characteristics (with respect to thepropensity score), but without any access to MFI loans at both t’ and t, along the lines ofSmith and Todd (2005).

V. Results(1) Results of Fixed-effects models
Table 1 reports the results of different versions of fixed effects models where we estimate
either the effect of MFI’s general loan or that of MFI’s productive loan and non-productive

15

loans on log household income per capita, log food consumption per capita, and BMI of afemale member.
(Table 1 to be inserted around here)

The results of the simple fixed effects model show that MFI’s general loan tends tosignificantly increase household income (the columns (1)). If MFI’s loan is disaggregatedinto the productive component and the non-productive component in the column (4), it isfound that the positive effect of the total loan is associated only with the productive
component. In fact, the non-productive component tends to reduce household income percapita. We have obtained a very similar pattern of the results in case of fixed effects modelwith PSM and fixed effects model with control for initial characteristics (the columns (2), (3),(5) and (6)). That is, our results are robust to use of alternative models in which initialhousehold characteristics are taken account of. However, the magnitude of these effects is not
so large – even 100% increase of net change of total loan raises household income per capita
only by 0.51% to 0.54% on average ceteris paribus. However, 100% increase of net changeof productive loan raises household income per capita by 0.69% to 1.09% on average ceterisparibus.
On the contrary, the non-productive component is positive and significant and the
productive component is non-significant in the case where food consumption is estimated(columns (7) and (10)). The results are once again similar in the cases where alternative
versions of fixed-effects model are used (columns (8), (9), (11) and (12)). MFI’s general loan
has a significant and positive effect on food consumption. 100% increase of net change of
total loan raises household food consumption per capita by 0.52% to 1.02% on averageceteris paribus. On the other hand, 100% increase of net change of non-productive loan

16

increases household food consumption per capita by 0.74% to 1.11% on average ceterisparibus.
While the aggregate component of MFI loans is not a significant determinant of women’s
BMI in any version of fixed-effects models (columns (13), (14) and (15)), non-productiveloans show a significant and positive effect on women’s BMI as in the case of foodconsumption(columns (16), (17) and (18)). The absolute impact seems substantial as, forexample, 10% increase of change in non-productive loans raises women’s BMI by 0.44 pointin columns (16) and (17), while it is only 0.017 in column (18) where initial household
characteristics are included in the model. The results are different from those of PSM and
DID-PSM to be discussed in the next sub-section, but it is conjectured that having access to alarger amount of the non-productive component of MFI loans, rather than simply accessing
MFI’s loans, is important in raising BMI for women.

(2) Results of DID-PSM 10
The results of logit model reported in Online Appendix 2 reflect the determinants of access to
MFI’s general loans, or access to MFI’s loans for productive purposes. Corresponding to
PSM or DID-PSM, we present the results of logit model for all the rounds as well as eachround. To briefly summarise the results, the coefficient estimate of age of head of thehousehold is positive and significant in all cases, which implies that a household with anolder head is more likely to have a member taking a general loan from MFIs, but thestatistically significant and negative coefficient estimate of ‘age squared’ suggests a nonlinear effect of age of the head. In the participation equation, the coefficient estimate of sex of
the head of the household, whether a head is female or not, is positive and significant in a
majority of the cases. Given that microfinance targets women, the result implies that ahousehold headed by a woman is more likely to have a participant in the MFI programmes

17than a male headed household. The coefficient estimate of distance from the nearest Upzilla(a business hub) is negative and significant only in the first column. A negative sign indicatesthat a household living closer to the nearest town with Upzilla is more likely to access MFI’sloans than those who live far away. This makes sense because Upzilla provides banking,marketing and other essential services for micro businesses and enterprises to market theirproducts. The number of village money lenders is negative and either statistically significant
in a few cases.
The coefficient estimates of the education dummies are all negative and significant except
for most of the cases of primary education. This means the reference category, i.e., illiteratehouseholds, are more likely to have a member of participating in microfinance programmes.
Coefficient estimates of different occupational categories reveal that a household whosehead’s occupation is the non-agricultural wage labourer or runs the small business is more
likely to have a member of participating in microfinance programmes. This makes sense as
these two categories form the core clientele of MFIs.
Table 2 presents the results of DID-PSM. It is noted that in DID-PSM the first differenceof the dependent variable (e.g. log of household income per capita) of the households whichaccessed the MFI loans in the present round, but not in the previous round, is compared withthat of the household which did not access MFI loans in either the previous round or thepresent round and had similar characteristics in terms of the propensity score. Because boththe objective variable is in log term, policy effect (or the average treatment effects) denotes
the growth of household income per capita (or food consumption/ women’s BMI) achievedby accessing general loans (or productive loans). While DID-PSM is superior to PSM incorrecting for sample selection biases, because of the relatively small sample size oftreatment groups in our context, the estimated average treatment effect tends to be generally
non-significant. Case (a) is the case where DID-PSM is applied to see if household access to18

MFI’s general loans increases the growth of household income per capita, food consumption,
and women’s BMI. Only a significant policy effect is observed for food consumption percapita growth from 1999-2000 to 2004-5. That is, a household which had access to MFI loans
in 2004-5, but not in 1999-2000 had 10.4% a higher per capita food consumption growth on
average than the household with the same characteristics (in terms of propensity score) which
did not access to MFI loans in either of these years. It is noted that the former (newparticipants in 2004-5) did not see increase in their household income in 2004-5, or actually
decreased per capita income growth by 5.9%, though it is statistically non-significant. That is,
it is probably safe to conclude that the new participants in 2004-05 used microfinance loans
for food consumption, but not for income-increasing activities. In Case (b), we focus onhousehold access to MFI’s productive loans, but the policy effects are non-significant. It is
non-significant for the case of food consumption growth from 1999-2000 to 2004-5.
(Table 2 to be inserted around here)

Though the average treatment effects are statistically non-significant except one case in
Table 2, it is generally observed that the effects of both general loan and productive loans onhousehold income growth turned negative in the last period (1999-2000 to 2004-5), while
those on food consumption remained positive in the last period. To see why, we havedisaggregated DID-PSM by income groups based on the household per capita income of thefirst round of each pair for 0-25%, 25-50%, 50-75% and 75-100%. The average treatment
effects of general loan on food consumption growth of relatively poor income groups (0-25%
and 25-50%) are consistently positive (with the effects statistically significant for the 25-50%
group for both ‘1997-8 to 1999-2000’ and ‘1999-2000 to 2004-5’), while the effects on
relatively richer income groups (50-75% and 75-100%) become negative and non-significant
for ‘1999-2000 to 2004-5’. That is, the effect of MFI loans in increasing food consumption

19

growth was strong for poorer groups, confirming the poverty-reducing role of MFI’s generalloan. Second, the effects of MFI’s general loans on household income are non-significant formost of the groups for all the cases (‘1997-8 to 1998-9’, ‘1998-9 to 1999-2000’and ‘19992000 to 2004-5’) except one case where the loans had a significant and negative average
impact on income growth of the 50-75% group for ‘1999-2000 to 2004-5’. That is, thenegative (and non-significant) effect of general MFI loans on income growth in the last round
Table 2 is mainly associated with their negative effect on the non-poor households.11

VI. Concluding Observations
The main purpose of the present study is to examine whether microfinance reduced poverty –defined in terms of household income, food consumption and women’s BMI - in Bangladesh
drawing upon the nationally representative household panel data covering 4 rounds, 1997-98,
1998-99, 1999-2000, and 2004-05. Special attention was given to the issue of endogeneity by
applying different versions of fixed effects model as well as DID-PSM proposed by Smith
and Todd (2005), following a recent contribution by Chemin (2008) who thoroughly analysed
the Bangladesh household data in 1991-92 for the participants and non-participants of
microfinance programmes. Another contribution of the present study is that it distinguishes
between the effects of different purposes of loans from microfinance institutions (MFIs) on
household income, i.e., whether loans were used for the purposes of enhancing agricultural
productivity or for general purposes, such as consumption.
We applied household fixed-effects models - with or without control for initial household
characteristics - to the panel data in order to estimate the effects of amount of aggregate MFIloans as well as their subcomponents, the productive and non-productive loans. A positiveand significant effect of the aggregate component of MFI loans is found for both householdincome and food consumption, but this positive effect is due to the positive effect of the20

productive component in case of income, and that of the non-productive component in caseof food consumption. That is, income poverty tends to be alleviated by offering productiveloans for households and consumption poverty is likely to be reduced by non-productiveloans. It is also found that MFI’s non-productive loans will reduce BMI. These results arebroadly consistent with the past studies which have confirmed poverty reducing effects of
microfinance programmes in Bangladesh (e.g. Pitt and Khandker, 1998, Khandker, 2005 andChemin, 2008). DID-PSM confirms that the households which accessed MFI’s general loans
in 2004-05, but not in 1999-2000, had a higher food consumption growth than those which
did not access to microfinance loans in either of these years. The effect on women’s BMI is
non-significant and mostly negative.
It can be concluded that loans provided by microfinance institutions had significant
poverty reducing effects particularly on income and consumption in Bangladesh, which isconsistent with earlier studies, for example, Pitt and Khandker (1998), Khandker (2005),
Chemin (2008) and Pitt (2011 a, b). More evidence is needed, however, to confirm our results,
for example, by randomised control trials (RCTs) 12 or econometric estimations using more
recent household data.

21

Table 1 Panel Data Models for Income, Food Consumption and women’s BMI
(1)

(2)

Initial Age of the head of the hh

Household size
Initial Household size
Sex of head of household(female or not)Initial Sex of head of household

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

log food consumption per capita

FixedEffects
with
PSM

FixedEffects:Controlfor initial
Characteristics

FixedEffects

FixedEffects
with
PSM

FixedEffects:Controlfor initial
Characteristics

FixedEffects

FixedEffects
with
PSM

FixedEffects:Controlfor initial
Characteristics

FixedEffects

FixedEffects
with
PSM

FixedEffects:Controlfor initial
Characteristics

0.0051* 1(2.37)
0.318(0.90)-0.152(-0.86)0.00146(0.86)-

0.0054*(2.46)

0.0054(1.59)

0.284(0.80)-0.135(-0.75)0.0010(0.58)-4.71e06(0.95)-0.024**(-4.56)-0.301**(-5.01)-

0.021*(1.66)
0.704(0.95)-0.360(-0.97)0.0015(0.56)
0.023*(2.43)-6.02e-06

0.0069**(3.22)-0.0054*(-2.22)0.321(0.91)-0.153(-0.86)0.00145(0.86)-

0.0072**(3.30)-0.0053*(-2.17)0.284(0.80)-

0.0109**(3.08)-0.0079+(-1.78)0.0207(1.61)
0.683(0.92)-0.350(-0.94)0.0014(0.52)
0.024(2.51)

0.0052+(1.88)-0.00217(-1.10)-0.0102**(2.77)**-

0.00213(0.77)
0.00747*(2.24)-0.00222(-1.12)0.00177(0.63)
0.00742*(2.21)-

0.00053(0.01)
0.0111*(2.35)--0.00159(-0.78)-0.00083(-0.29)0.0073(0.78)-5.69e-06(-0.82)-0.0002(2.10)-0.037**(-5.34)0.059(6.34)**-0.357**(-4.42)-0.260**(2.56)**

2.05e-07(0.036)0.0504**(8.79)
0.00629(0.092)-0.0048+(1.70)-0.00154(-0.76)-9.44e07(-0.17)0.0501**(8.66)
0.00829(0.12)-

Age_squared

Initial Age_squared(4)

FixedEffects

Model Chosen
Explanatory Variables
MFI loan amount (aggregate)(log)
MFI's productive loan amount(log)
MFI's non-productive loan amount(log)
Arable land area(log)
Initial Arable land area(log)
Arable land area2(log)
Initial Arable land area2(log)
Age of the head of the hh(3)

log household income per capita

Dependent Variable-5.54e-06(-1.13)-0.025**(-4.73)-0.308**(-5.17)-(0.87)-0.0002*(-2.02)-0.037**(-5.30)0.059**(6.28)-0.348**(-4.30)0.059(6.28)**-5.58e-06(-1.14)-0.0251**(-4.75)-0.313**(-5.25)--0.135(-0.76)0.0010(0.58)-4.71e-06(0.96)-0.024**(-4.59)-0.306**(-5.09)-22-0.00081(-0.28)0.0079(0.78)-1.79e-06(-0.25)-0.0001(-1.07)0.0336**(4.54)
0.1753**(17.36)
0.0619(0.74)
0.009(0.08)

3.71e-07(0.07)-7.75e07(0.14)

0.0505**(8.82)

0.0503**(8.69)

0.0120(0.18)-0.0023(-0.03)-1.26-06(-0.17)-0.0001(-0.99)0.0350**(4.73)
0.1738**(17.18)0.0679(0.81)
0.0139(0.13)

Education of head of household– completed primary school
Education of head of household– completed secondary school
Education of head of household– completed higher education
Initial education of head
– completed primary school
Initial education of head
– completed secondary school
Initial Education of head
– completed higher education
FarmerAgricultural wage labourer

0.0622(1.57)
0.0834(1.59)-0.0684(-0.64)-0.228**(-3.93)-0.110+(-1.68)

Non-Agricultural wage labourer-0.125*(-2.08)small businessprofessionals
others
Farmer (Initial)
Agricultural wage labourer (Initial)
Non-Agricultural wage labourer(Initial)
small business (Initial)
Professionals (Initial)
Others (Initial)
Whether a household
has electricity or not

0.00533(0.09)-0.0572(-0.87)-0.146*(-2.57)0.0830**(3.12)

0.062(1.53)
0.0771(1.43)-0.063(-0.58)-0.245**(-4.18)-0.132*0.067(1.98)*
0.030(0.56)
0.121*(2.15)
0.431(3.49)**
0.011(1.21)-(1.98)--0.141*-(-2.32)--0.010-(-0.18)--0.068(1.02)-0.161**(-2.80)0.063(0.83)-0.062(0.77)-0.219**(2.94)-0.091(0.92)
0.063(0.65)
0.0731(1.63)

0.0845**(3.15)

0.0611(1.54)
0.0816(1.56)-0.0666(-0.62)-0.225**(-3.89)-0.105(-1.60)-0.121*(-2.01)0.00744(0.13)-0.0526(-0.80)-0.141*(-2.49)0.0840**(3.16)

0.061(1.50)
0.0753(1.40)-0.062(-0.57)-0.243**(-4.14)-0.136+0.069(2.05)*
0.033(0.61)
0.117*(2.10)
0.420(3.41)**
0.012(1.31)-(1.90)--0.136*-(-2.25)--0.008-(-0.14)--0.063(0.95)-0.156**(-2.71)

0.066(0.86)-0.066(0.81)-0.208**(2.80)-0.095(0.96)
0.063(0.65)
0.0703(1.57)

0.0854**(3.18)

23

0.0405(0.88)
0.0123(0.21)
0.156(1.36)-0.0202(-0.30)0.0441(0.94)-0.007(0.12)
0.136(1.17)-0.0211(-0.31)0.162*

0.159*(2.07)(2.01)

0.05410.0458(0.77)(0.68)

0.05630.0518(0.82)-0.0821(-1.07)0.0343(0.51)-0.00926(-0.29)(0.75)-0.0874(-1.130)
0.0238(0.350)-0.00930(-0.28)0.0314(0.88)-0.0750(-1.30)-0.0250(-0.42)-0.1025(-0.81)
0.005(0.53)-0.1735+(-1.93)-0.2096*(-1.97)-0.1548(-1.50)-0.094(-1.06)-0.062(-0.58)0.0039(0.08)

0.0419(0.91)
0.0109(0.18)
0.153(1.34)

0.045(0.97)-0.086(-0.14)0.133(1.15)-0.0220(-0.32)-0.023(-0.33)0.157*

0.154+(2.00)(1.94)

0.04870.0432(0.69)(0.61)

0.05420.0500(0.79)-0.0864(-1.12)0.0304(0.45)(0.72)-0.0917(-1.18)0.0204(0.30)-0.00884(-0.27)-0.009(-0.26)0.0307(0.86)-0.0756(-1.31)-0.0269(-0.45)-0.1023(-0.81)-0.004(0.44)-0.17658(-1.96)-0.2013*(-1.89)+-0.1412(-1.36)-0.094(-1.05)-0.061(-0.57)0.0068(0.14)

Whether a household
has electricity or not (Initial)
price of rice(log)
price of potatoes(log)
price of milk
(log)
Whether in 1998-9
Whether in1999-2000
Whether in 2004-5
Constant
Observations
Number of hhid
Joint Significance0.0936**(5.35)
0.189**(10.44)0.446**(19.06)6.445(59.59)10,3882,669

0.0899**(5.09)
0.190**(10.36)0.443**(18.67)6.471(58.66)
10,0762545

F(20,7699)
39.46**

0.006(0.12)
0.176(0.73)
2,484
1,242

0.102**(5.77)
0.195**(10.70)0.451**(19.24)6.449**(59.72)
10,3882,669

0.0985**(5.50)
0.195**(10.61)0.449**(18.85)6.475(58.78)
10,3882,545F(20,7511)

F(24,2459)

F(21,7698)

38.52**

7.71**

38.01**

0.009(0.17)

0.0275(0.51)

0.0279(0.52)-0.192(0.80)
2,484
1,242-0.0778(-0.38)0.260**(3.60)-0.0287(-0.80)-0.220**(-4.08)-0.275**(-4.71)5.438**(8.44)
5,991
2,634-0.0668(-0.32)0.260**(3.60)-0.027(-0.75)-0.224**(-4.13)-0.279**(-4.72)
5.399(8.26)
5,812
2,519

0.2213(0.80)
0.488**(4.59)-0.064(-1.53)-0.375(4.93)
2,174
1,087-0.0703(-0.35)0.262**(3.63)-0.0271(-0.75)-0.228**(-4.22)-0.283**(-4.85)5.415**(8.40)
5,991
2,634-0.0617(-0.30)0.265**(3.63)-0.026(-0.70)-0.232**(-4.27)-0.288**(-4.85)5.382(8.23)
5,812
2,519

0.2628(0.95)
0.4968**(4.66)-0.063(-1.48)-1.398(5.00)
2,174
1,087

F(21,7510)

F(25,2458)

F(20,3337)

F(20,3273)

F(24,2149)

F(21,3336)

F(21,3272)

F(24,2148)

37.11**

7.79**

8.196**

7.88**

20.71**

7.92**

7.64**

19.77**

Notes 1. t-statistics in parentheses, ** p<0.01, * p<0.05, + p<0.1.

24

Table 1 Panel Data Models for Income, Food Consumption and women’s BMI (Cont.)
(13)

(14)

(15)

Age_squared

Initial Age_squaredHousehold size
Initial Household size
Sex of head of household(female or not)Initial Sex of head of household
Education of head of household– completed primary school
Education of head of household– completed secondary school
Education of head of household– completed higher education
Initial education of head
– completed primary school
Initial education of head
– completed secondary school
Initial Education of head
– completed higher education
FarmerAgricultural wage labourer

Non-Agricultural wage labourer

small businessprofessionals
others
Farmer (Initial)
Agricultural wage labourer (Initial)
Non-Agricultural wage labourer(18)

FixedEffects
with
PSM

FixedEffects:Controlfor initial
Characteristics

FixedEffects

FixedEffects
with
PSM

FixedEffects:Controlfor initial
Characteristics

0.0136(1.09)
0.00382(0.40)-1.59e05(-0.71)-0.074**(-2.66)-0.343(-1.11)0.284(1.42)-0.0322(-0.12)0.498(0.84)-0.251(-0.90)0.0130(1.04)
0.00322(0.33)-1.43e05(-0.63)-0.074**(-2.66)-0.333(-1.06)-0.277(-1.37)0.018(0.07)
0.354(0.59)-0.294(-1.03)0.0004(0.62)-0.00030(0.62)-0.0029(-1.60)-1.55e05(-0.13)0.00002(1.17)-0.0042**(-2.96)0.0039*(2.16)-0.0278+(-1.81)-0.1320**(-4.21)
0.002(0.32)
0.023**(2.45)
0.010(1.04)
0.012(0.56)-1.66e-6(-0.00)0.00509(0.41)
0.0442**(2.87)
0.00319(0.34)-1.43e05(-0.64)-0.074**(-2.66)-0.317(-1.03)-0.291(-1.46)-0.0624(-0.24)0.438(0.74)-0.275(-0.98)0.00555(0.45)
0.0443**(2.78)
0.00261(0.27)-1.28e-05(-0.56)-0.074**(-2.66)-0.308(-0.98)-0.284(-1.41)
0.011(0.04)
0.294(0.49)-0.318(-1.12)0.00017(0.28)
0.0017*(2.30)-0.00030(0.56)-0.0027(-1.48)-1.76e07(-0.15)0.00003(1.07)-0.0041**(-2.94)0.0036*(2.01)-0.0270+(-1.76)-0.1299**(-4.15)0.002(0.26)
0.023**(2.49)
0.010(1.04)
0.013(0.61)-1.71e-6(-0.11)-0.352-0.385--0.392-0.420-(-1.09)(-1.18)-(-1.21)(-1.29)--0.299-0.337--0.346-0.383-(-1.02)(-1.14)-(-1.18)(-1.30)--0.297-0.329--0.324-0.354-(-1.03)-0.691*(-2.13)-0.246(-0.88)-(-1.14)-0.703*(-2.15)-0.281(-0.100)-

0.015(0.97)
0.031(1.72)+
0.018(-1.13)-0.739*(-2.28)-0.288(-1.03)-(-1.23)-0.749*(-2.29)-0.321(-0.14)-0.015(0.94)
0.030(1.70)+
0.019

Explanatory Variables

Initial Age of the head of the hh(17)

FixedEffects
Model Chosen

MFI loan amount (aggregate)(log)
MFI's productive loan amount(log)
MFI's non-productive loan amount(log)
Age of the head of the hh(16)

Women's BMI

Dependent Variable25

(Initial)
small business (Initial)
Professionals (Initial)
Others (Initial)
Whether a household
has electricity or not
Whether a household
has electricity or not (Initial)
price of rice(log)
price of potatoes(log)
price of milk
(log)
Whether in 1998-9
Whether in1999-2000
Whether in 2004-5
Constant
Observations
Number of hhid
Joint Significance0.193(1.35)-0.394(-0.43)-0.872*(-2.50)0.371**(2.70)
1.965**(7.21)
20.98(7.18)
3,988
2,444

0.196
(1.36)-0.310(-0.34)-0.870*(-2.25)0.403**(2.89)
1.947**(7.11)**
20.70(7.07)
3,881
2,349(1.02)
0.021(1.32)
0.037(1.96)*
0.017*(2.14)
0.016*(1.81)
0.016(1.81)+-0.043*(-2.51)0.018**(2.77)
1.947**(7.11)**
0.260(5.17)
1532
766

0.189
(1.32)-0.319(-0.35)-0.843*(-2.42)0.379**(2.76)
1.915**(6.99)
20.72(7.10)
3,988
2,444

0.191
(1.34)-0.244(-0.26)-0.842*(-2.41)0.411**(2.95)
1.901**(6.92)**
20.47(7.07)
3,881
2,349(1.07)
0.020(1.25)
0.036(1.93)+
0.016*(2.10)
0.016+(1.76)-0.013(0.30)-0.041*(-2.43)0.019**(2.80)
1.947**(7.11)**
0.251(5.00)
1532
766

F(19,1525)

F(19,1513)

F(24,1507)

F(20,1524)

F(20,1512)

F(25,1506)

28.82**

28.51**

2.91**

27.87**

27.55**

3.01**

Notes 1. t-statistics in parentheses,** p<0.01, * p<0.05, + p<0.1.

26

Table 2 Effects of microfinance loans on growth of household income, food consumption
and BMI (DID- Propensity Score Matching: Kernel Matching*3, 4, *5 )Case (a) Whether a household has access to MFI loans

Model

Log per capita Household Income(mean)

PolicyEffector First difference of Log per capita(A-B)

Household Income
With accesstoWithout access to
MFI loans: A

MFI loans: B

ATT:
Averagetreatment
effect(t value)*1, 2

No. of obs.

1. Growth rate of household income per capita
1997-1998 to 1998-1999

0.09883

0.07449

0.02434(0.38)

1998-1999 to 1999-2000

0.129510.10070.02881(0.43)

1999-2000 to 2004-2005

0.20182

0.26111-0.05929(-1.14)Treat: 140, Control:
1081
Treat: 151, Control:
1270
Treat: 424, Control:
1250

2. Growth rate of food consumption per capita
1997-1998 to 1999-2000-0.31527-0.4055

0.09026(1.49)

1999-2000 to 2004-2005

0.36292

0.258540.10438(2.25)*

Treat: 144, Control:
1061
Treat: 406, Control:
1171

3. Change of BMI of a woman (spouse of household head or household head)
1997-1998 to 2004-2005-0.07358-0.0402-0.03341(-0.27)

Treat: 102, Control:
357

Case (b) Whether a household has access to MFI productive loans
1. Growth rate of household income per capita
1997-1998 to 1998-1999

0.09876

0.068760.03(0.14)

1998-1999 to 1999-2000

0.106250.08882

0.01743(0.33)

1999-2000 to 2004-2005

0.230620.25141-0.02079(-0.45)Treat: 163, Control:
1180
Treat: 187, Control:
1435
Treat: 400, Control:1320

2. Growth rate of food consumption per capita
1997-1998 to 1999-2000-0.31603-0.3980.08197(1.04)

1999-2000 to 2004-2005

0.32356

0.264230.05933(1.13)

Treat: 113, Control:
1092
Treat: 404, Control:
1351

3. Change of BMI of a woman (spouse of household head or household head)
1997-1998 to 2004-2005-0.08243-0.0408*1-0.04159(-1.25)Treat: 97 Control:
404

Notes: . t value is calculated by Bootstrapped Standard Errors for PSM (100 bootstrap replications)*2t values in brackets: ** significant at 1%; * significant at 5%; † significant at 10%.
*3
A common support condition is imposed by dropping treatment observations whose propensity score is higher than the maximum or less than theminimum propensity score of the controls.
*4
The bandwidth for kernel is set 0.05.
*5
The balancing property of explanatory variables is tested by the Stata command pstest. In each case, there is no statistically significant difference for all
the explanatory variables for the treated households and the controls which have been matched.
*6‘0’ stands for the state in which any of the household members did not have any access to MFI general loans or MFI productive loans and ‘1’ is for thestate in which one of the household members had access to either MFI general loans or productive loans.

27Appendix 1: Descriptive Statistics for MFI participants and non-participants
VariableWith access to MFI

Obs

Mean

Without access to MFI

Std. Dev.

Obs

Mean

With access to MFI loan for
Productive Purposes
Std. Dev.

Obs

Mean

Without access to MFI laon for
Productive purposes

Std. Dev.

Obs

Mean

Std. Dev.

Age of Head of HH (age)Pooled

5526

45.40

12.38

4954

47.32

15.50

3675

45.17

12.17

6805

46.93

14.82

Round 1

1545

43.86

12.28

1078

45.42

14.77

1237

43.73

12.07

1386

45.19

14.42

Round 2

1463

44.70

12.41

1170

46.62

14.49

948

44.53

12.11

1685

46.12

14.05

Round 3

1327

45.85

12.47

1312

47.26

14.25

721

45.55

12.32

1918

46.93

13.77

Round 4

1209

47.80

12.03

1420

49.43

17.56

778

47.82

11.79

1851

49.04

16.53

Pooled

5528

0.95

0.22

4957

0.92

0.27

3676

0.95

0.21

6809

0.93

0.26

Round 1

1545

0.96

0.20

1078

0.93

0.25

1237

0.96

0.20

1386

0.94

0.25

Round 2

1463

0.95

0.21

1170

0.94

0.24

948

0.96

0.19

1685

0.94

0.24

Round 3

1327

0.95

0.20

1312

0.93

0.24

721

0.96

0.18

1918

0.94

0.23

Round 4

1209

0.93

0.29

1422

0.89

0.37

779

0.93

0.32

1852

0.90

0.35

Pooled

5528

6.23

2.75

4957

6.27

3.16

3676

6.21

2.83

6809

6.27

3.02

Round 1

1545

5.73

2.20

1078

5.52

2.44

1237

5.66

2.12

1386

5.62

2.45

Round 2

1463

5.88

2.28

1170

5.88

2.60

948

5.82

2.26

1685

5.92

2.52

Round 3

1327

6.08

2.34

1312

6.10

2.66

721

6.08

2.37

1918

6.09

2.55

Round 4

1211

7.42

3.78

1423

7.32

4.08

779

7.66

4.05

1855

7.25

3.90

Pooled

5526

0.98

0.70

4944

0.94

0.77

3676

0.97

0.69

6794

0.96

0.76

Round 1

1545

0.98

0.68

1076

0.88

0.68

1237

0.98

0.68

1384

0.91

0.68

Round 2

1463

0.99

0.67

1169

0.92

0.67

948

0.98

0.66

1684

0.94

0.68

Round 3

1327

0.93

0.66

1311

0.88

0.65

721

0.93

0.66

1917

0.89

0.65

Round 4

1209

1.02

0.81

1414

1.07

0.96

779

1.00

0.78

1844

1.07

0.95

Pooled5528

638.151224.674957

823.702541.043676

664.041420.766809

759.242199.64

Round 1

1545

541.48485.61

1078

579.27623.341237

552.64506.981386

560.90579.90Round 21463

577.15647.301170

677.01771.89948

604.84725.241685

630.84696.55

Round 31327

633.411349.081312

701.10762.11

721

688.431785.16

1918

659.03678.64Sex of Head of HH (sex_hh)

Size of the HH (hh_size)

Dependency ratio (d_ratio)

Per capita Income (pcy)28

Variable

With access to MFI

Obs

Std. Dev.

With access to MFI loan for
Productive Purposes
Obs

Mean

Without access to MFI laon for
Productive purposes

Obs

Mean

Std. Dev.

Mean

Std. Dev.

1211

826.002006.941423

1225.704578.39779

875.712347.421855

1111.764053.84

Pooled

5528

6.17

0.71

4957

6.26

0.86

3676

6.19

0.71

6809

6.22

0.83

Round 1

1545

6.05

0.70

1078

6.02

0.86

1237

6.07

0.70

1386

6.01

0.82

Round 2

1448

6.10

0.76

1154

6.17

0.86

940

6.15

0.73

1662

6.12

0.84

Round 3

1324

6.19

0.67

1302

6.24

0.80

720

6.24

0.66

1906

6.20

0.76

Round 4

1211

6.38

0.66

1423

6.52

0.86

779

6.41

0.67

1855

6.48

0.82

Round 4

Mean

Without access to MFI

Std. Dev.

Obs

Log of per capita income (lpcy))

Productive NGO loans (nl_prod)
Pooled5528

8274.5521739.83

4957

105.41

1822.623676

12585.50

25687.42

6809

0.00

0.00

Round 1

1545

18505.53

37656.36

1078

200.923574.95

1237

23288.32

40838.83

1386

0.00

0.00

Round 21463

4139.04

5639.961170

124.601067.35

948

6541.35

5845.09

1685

0.00

0.00

Round 31327

3396.04

5156.92

1312

44.38

563.57721

6331.195538.54

1918

0.00

0.00

Round 41211

5488.879716.99

1423

71.63

810.70779

8663.6210945.31

1855

0.00

0.00

Pooled5528

2574.08

13832.51

4957

24.77

440.403676

2368.82

15820.98

6809

828.984693.11

Round 1

1545

3267.84

17997.61

1078

11.64

307.841237

2620.46

17846.45

1386

1313.028999.78

Round 21463

2357.83

4113.11

1170

35.25

427.709482329.26

3814.281685

761.202798.14

Round 31327

2328.31

19663.10

1312

7.69

220.83721

2652.60

26393.51

1918

619.002560.33

Round 41211

2238.30

4234.92

1423

41.40

638.83779

1730.95

3664.541855

766.082794.69

Pooled5528

376.263615.704957

848.5720363.00

3676

385.464013.816809

715.1417430.35

Round 1

1545

194.221735.181078

470.964132.291237

188.411496.011386

414.643827.35

Round 21463

396.772712.551170

772.654667.90948

409.322621.241685

650.704205.15

Round 31327

369.172516.35

1312

1562.59

38801.45

721

280.312294.18

1918

1218.9332128.97

Round 41211

596.656329.831423

585.945232.52779

777.597698.031855

512.454718.14Pooled5528

2258.52

10024.06

4957

2970.09

16144.27

3676

2138.7810314.13

6809

2841.2014625.54

Round 1

1545

2515.7511259.95

1078

3300.98

13033.71

1237

2513.4611988.29

1386

3128.5212053.55

Round 21463

2571.429321.901170

3816.1418025.69

948

2226.298493.35

1685

3629.8816134.47

Round 31327

2410.368732.30

1312

3938.7922638.89

721

2381.828815.89

1918

3466.60

19353.00

Round 41211

1516.01

10961.81

1423

1201.906336.88779

1416.4211523.49

1855

1316.877316.42

Non-productive NGO loans (nl_nprod)

Formal bank loans (fbl_tot)

Loans from friends and family (ffl_tot)

29

Variable

With access to MFI

Obs

Mean

Without access to MFI

Std. Dev.

With access to MFI loan for
Productive Purposes
Obs

Mean

Without access to MFI laon for
Productive purposes

Obs

Mean

Std. Dev.

Std. Dev.

818.067443.373676

624.815101.62

Obs

Mean

Std. Dev.

6809

653.226429.936767.78

Loans from village money lenders (vml_tot)
Pooled5528

486.514301.19

4957

Round 11545

561.085036.03

1078

737.067547.031237

608.505477.441386

655.63Round 2

1463

408.362714.251170

1095.08

9792.01

948

515.433205.771685

824.958205.16

Round 31327

351.793588.691312

613.104468.81721

523.484514.05

1918

466.003864.57

Round 41211

642.735420.971423

849.587281.99779

896.086655.581855

695.016423.82

Pooled

5526

7.38

6.03

4956

7.71

6.28

3674

7.40

5.97

6808

7.61

6.25

Round 1

1545

7.18

5.83

1078

8.06

6.58

1237

7.40

5.85

1386

7.67

6.43

Round 2

1463

7.29

5.97

1170

7.83

6.38

948

7.36

5.98

1685

7.62

6.25

Round 3

1327

7.28

5.98

1312

7.79

6.32

721

7.41

6.03

1918

7.57

6.20

Round 4

1209

7.80

6.37

1422

7.28

5.92

777

7.41

6.10

1854

7.57

6.15
0.46

Distance to nearest Upzilla (dist_uz)

whether household has electricity or not (elec_hh)
Pooled

5503

0.27

0.44

4944

0.34

0.47

3662

0.28

0.45

6785

0.32

Round 1

1537

0.25

0.43

1074

0.26

0.44

1231

0.24

0.43

1380

0.26

0.44

Round 2

1455

1.77

0.42

1166

1.71

0.45

944

1.76

0.42

1677

1.73

0.44

Round 3

1326

1.74

0.43

1312

1.66

0.47

721

1.73

0.44

1917

1.69

0.46

Round 4

1203

1.63

0.48

1418

1.56

0.50

775

1.60

0.49

1846

1.59

0.49

30

Appendix 2: Results of logit model on the determinants of participation in microfinance (or access to productive loans)
All 4 rounds
MFI
Productive
accessloan
access

1st round
MFI
Productive
accessloan
access

2nd round
MFI
Productive
accessloan
access

3rd round
MFI
Productive
accessloan
access

4th round
MFI
Productive
accessloan
accessAge of the head of thehh

0.0915**0.0763**0.130**

0.120**

0.105**

0.0844**0.0919**0.0554*0.0515*0.0498*Age_squared(8.542)0.00101**(6.828)0.000856**(6.237)0.00147**(5.699)0.00134**(4.862)0.00116**(3.753)0.000936**(4.003)0.000967**(2.309)0.000622*(2.381)0.000580**(2.133)0.000571*

Household size

Sex of head ofhousehold(female or not)Distance from nearest
Upazilla (a businesshub) *2
No. of village
moneylenders *2
Education of head ofhousehold– completed primaryschool
Education of head ofhousehold– completed secondary
school
Education of head ofhousehold– completed higher
education
Agricultural wage
labourer
Non-Agricultural wage
labourersmall businessprofessionals(-9.234)(-7.486)(-6.695)(-6.085)(-5.219)(-4.038)(-4.171)(-2.566)(-2.700)(-2.450)

0.0240**0.0241**0.00991

0.01210.0185-0.01100.00472

0.01400.0341**0.0402**(3.113)(3.012)(0.502)(0.613)(0.986)(-0.558)(0.250)(0.702)(3.093)(3.534)0.364**

0.317**

0.673**

0.635**

0.444*

0.212

0.198

0.406

0.293+

0.198(3.754)(3.067)(3.116)(2.897)(1.978)(0.889)(0.850)(1.541)(1.907)(1.202)-0.00683*-0.00575-0.00949-0.00741-0.0109-0.00807-0.00780-0.00648

0.000673-0.00158(-2.006)(-1.628)(-1.430)(-1.115)(-1.624)(-1.155)(-1.113)(-0.866)(0.0965)(-0.214)-0.0023+-0.00254+-0.00167-0.000817-0.00092-0.00311-0.00293-0.00404-0.00352-0.00236(-1.735)(-1.836)(-0.649)(-0.316)(-0.353)(-1.131)(-1.055)(-1.333)(-1.280)(-0.812)-0.107+-0.0900

0.101

0.0895-0.0669-0.0690-0.0725-0.0572-0.383**-0.350**(-1.914)(-1.551)(0.892)(0.794)(-0.596)(-0.597)(-0.624)(-0.464)(-3.424)(-2.954)-0.451**-0.389**-0.360**-0.318**-0.455**-0.409**-0.366**-0.341**-0.576**-0.466**(-7.779)(-6.459)(-3.159)(-2.776)(-3.893)(-3.369)(-2.988)(-2.613)(-5.049)(-3.875)-0.894**-0.793**-0.570*-0.614*-1.051**-0.799**-0.502+-0.319-1.240**-1.237**(-6.617)(-5.556)(-2.242)(-2.346)(-3.670)(-2.670)(-1.760)(-1.074)(-4.387)(-3.950)

0.115

0.165

-0.155-0.0676

0.03900.464*

0.05630.137

0.08720.137(0.683)(0.930)(-0.923)(-0.401)(0.227)(2.498)(0.311)(0.699)(0.488)(0.727)0.0255-0.190-0.375+-0.447*0.03260.0458-0.0708-0.2320.739*

0.383(0.142)(-1.001)(-1.928)(-2.279)(0.163)(0.211)(-0.334)(-1.001)(2.011)(1.015)0.375*

0.197

0.162

0.0595

0.220

0.183

0.348+

0.152

0.399+

0.483*(2.132)(1.064)(0.791)(0.292)(1.104)(0.854)(1.676)(0.672)(1.904)(2.223)0.663**

0.630**

0.351+

0.422*

0.643**

0.985**

0.656**

0.692**

0.537**

0.326(3.866)(3.501)(1.917)(2.307)(3.455)(4.962)(3.386)(3.346)(2.775)(1.601)31

others

Whether a household
has electricity or not
Whether in 1998-1999(2nd round)
Whether in 1999-2000(3rd round)
Whether in 2004-2005(4th round)
Constant

Observations
Joint Significant Test

-0.0594-0.155-0.487*-0.492*-0.329-0.145-0.243-0.2441.015**

0.909**(-0.316)(-0.782)(-2.180)(-2.177)(-1.394)(-0.564)(-0.967)(-0.897)(2.991)(2.657)-0.0722-0.0260-0.0463-0.0139-0.0458-0.00125-0.176+-0.128-0.0146

0.0422(-1.491)(-0.519)(-0.463)(-0.139)(-0.454)(-0.0120)(-1.763)(-1.210)(-0.161)(0.444)-0.343**-0.488**

-

-

-

-

-

-

-

-

(-6.004)(-8.397)

-

-

-

-

-

-

-

-

-0.767**-0.915**

-

-

-

-

-

-

-

-

(-13.09)(-15.11)

-

-

-

-

-

-

-

-

-0.618**-0.759**

-

-

-

-

-

-

-

-

(-9.786)(-11.69)

-

-

-

-

-

-

-

-

-2.283-2.066-3.016-3.059-2.864-2.653-2.824-2.550-1.998-2.212(-7.244)(-6.267)(-6.048)(-6.076)(-5.416)(-4.799)(-4.919)(-4.198)(-3.467)(-3.577)

10,360LR Chi2(19)=10,360LR Chi2(19)=2,591
LR Chi2(15)=2,591
LR Chi2(15)=2,588
LR Chi2(15)=2,588
LR Chi2(15)=2,594
LR Chi2(15)=2,594
LR Chi2(15)=2,587
LR Chi2(15)=2,587
LR Chi2(15)=0.00

136.94**0.00

144.70**0.00

140.63**
0.00

101.40**0.00

84.31**
0.00

112.46**0.00

95.81**
0.00

-1722

-1723

-1703

-1622

-1618

-1477

-1644

-1521

643.31**
Prob> Chi2=

0.00

658**
0.00

Log likelihood

-6717

-6379

145.10**7Note z-statistics in parentheses (** p<0.01, * p<0.05, + p<0.1).

32

Appendix 3: Effects of microfinance loans on level of household income, food consumption and
BMI (Propensity Score Matching by Kernel Matching) *3, 4, 5:Case (a) Whether a household has access to MFI loans

Model

Log per capita Household Income(mean)or First difference of Log per capita
Household Income
With accesstoWithout access to
MFI loans: B
MFI loans: A

PolicyEffect(A-B)%
change(t value)*1, 2

No. of obs.

ATT:

1. Effect on log household income per capita (level)
1997-1998

6.06366.01730.04634.74(1.46)

1998-1999

6.12803

6.06820.05983

6.17(1.96)*

1999-2000

6.21238

6.17970.03268

3.32(1.33)

2004-2005

6.396026.45781-0.06179-5.99(-2.06)*

Treat: 1344,
Control:1247
Treat: 1127, Control:
1430
Treat: 890, Control:
1692
Treat: 949, Control:
1638

2. Effect on log household food consumption per capita (level)
1997-1998

5.88785.87520.01261.27(0.66)

1999-2000

5.49965.46820.03143.19(1.45)

2004-2005

5.794825.76009

0.03473

3.53(0.74)

Treat: 1344, Control:
1247
Treat: 875, Control:
1660
Treat: 930, Control:
1567

3. Effect on BMI of a woman (level) (spouse of household head or household head)
1997-1998

18.37118.501

-0.13

-0.70

(-1.07)

2004-2005

19.7805

19.9727-0.1922-0.96(-1.11)Treat: 1205, Control:
1057
Treat: 788, Control:
1212

Case (b) Whether a household has access to MFI productive loans
1. Effect on log household income per capita (level)

1997-1998

6.07556.018

0.05755.92(1.62)

1998-1999

6.15386

6.063820.09004

9.42(2.10)*

1999-2000

6.24694

6.179710.06723

6.95(1.18)

2004-2005

6.410036.45213-0.0421-4.12(-2.06)*

Treat: 1223, Control:
1368
Treat: 926, Control:
1631
Treat: 706, Control:
1876
Treat: 763, Control:
1824

2. Effect on log household food consumption per capita (level)
1997-1998

5.89383

5.87255

0.02128

2.15(2.40)*

1999-2000

5.51633

5.469270.04706

4.82(2.09)*

2004-2005

5.811545.776210.03533

3.60(0.74)

Treat: 1223, Control:
1368
Treat: 694, Control:
1541
Treat: 747, Control:
1750

3. Effect on BMI of a woman (level) (spouse of household head or household head)
1997-1998

18.40418.472-0.068-0.37(-0.63)

2004-2005

19.814419.984-0.1696-0.85(-0.92)Treat: 1099, Control:
1163
Treat: 633, Control:
1367

Notes: *1. t value is calculated by BootstrappedStandard Errors for PSM (100 bootstrap
replications)*2t values in brackets: ** significant at 1%; * significant at 5%; † significant at 10%.
*3
A common support condition is imposed by dropping treatment observations whose propensity score is higher than the maximum or less than the minimum
propensity score of the controls.
*4
The bandwidth for kernel is set 0.05.
*5
The balancing property of explanatory variables is tested by the Stata command pstest. In each case, there is no statistically significant difference for all the
explanatory variables for the treated households and the controls which have been matched.

33

Endnotes
1

It is noted that joint liability payment may not be imposed on the group, for example, in case oflending by Grameen Bank, but repayment performance of the group is closely monitored by thecommunities and the Bank. To maintain reputations in the community, a member has an incentive tobuild skills and work hard to keep repaying the installments.
2
We have also briefly summarized the results of propensity score matching (PSM) and report theresults in Appendix 3 bearing in mind its limitations.
3
While Roodman and Morduch (2009) tried to replicate Pitt and Khandker’s (1998) results andquestioned their validity, recent responses from Pitt (2011 a, b) have shown that Roodman and
Morduch’s replication of Pitt and Khandker’s study was wrong because of their econometric errors inrunning Tobit model and omission of target/non-target status and they have argued that the original
results remain valid.
4
While this distinction is important in evaluating microfinance programmes (e.g. Imai et al., 2010),
the results will have to be interpreted with caution because the funds are fungible, that is, there might
be some cases where the borrowers use loans for the purpose which is different from the one initially
specified by the lenders.
5Here, because Lit , while fixed-effects models take partial account of the endogeneity of a loan
amount, this can be instrumented by valid instruments, for example, by using fixed-effects IV
estimator. We have carried out this estimation as a robustness check. As our base variables for our
instruments, we use (i) number of village money lenders and (ii) distance from nearest ‘Upzilla’, thebusiness and administrative hub where most of the local services including marketing and financial
are available. The former proxies the competition of the financial market or the degree of strength of
traditional and large money lenders in the village and this is likely to be negatively associated with the
supply of microfinance lending. The latter is associated with demand for microfinance loans anddemand is likely to be lower if the distance from ‘Upzilla’ is larger. However, these instruments havelittle temporal variation over the years and we have used the interactions between year and each ofthese variables on the assumption that there is a year-to-year variation in the competition of thefinancial market or in the demand for microfinance, which are not associated with the outcome
variables. The coefficient estimate of total loan is positive for income and food consumption andnegative for BMI, but as it is non-significant, we avoid reporting the results of IV model in the text.
They will be provided on request.
6
It is noted that PSM for cross-sectional data is based only on observables and thus it cannot control
for unobservables. We will thus introduce DID-PSM in the next sub-section.
7
We have used the Stata command pscore to identify common support in estimating fixed-effects
PSM model. In carrying out DID-PSM as well as PSM, we have applied a different command,
psmatch2 and thus have obtained different ranges of common support for each round.
8
See Becker and Ichino (2002) for technical details of PSM. Technically, we adopt Kernel Matchingfor PSM and DID-PSM where all treated are matched with a weighted average of all controls withweights that are inversely proportional to the distance between the propensity scores of treated andcontrols. We have also tried Nearest Neighbour Matching to take each treated unit and search for thecontrol unit with the closest propensity score and have obtained broadly similar results. To save thespace, only the results based on Kernel Matching are presented.
9
Use of probit model in the first stage provides very similar results.
10
A summary of policy effects derived by propensity score matching (PSM) applied for each crosssectional component of the panel is given in Appendix 3. Case (a) is the case of access to MFI’s
general loans and Case (b) is of access to MFI’s productive loans. The results for all four rounds areshown for log household income per capita, while those for food consumption are only for the first,
the third and the last rounds and those for women’s BMI for the third and the last rounds due to the
problem of missing observations. In case of income, a similar pattern has been observed for bothaccess to MFI’s general loans and productive loans. That is, positive policy effects in the first threerounds (where a statistically significant and positive effect is found only in 1998-9) turned negativeand significant in the last round in 2004-5. As an extension, we have repeated PSM for four different
income groups: 0-25%, 25-50%, 50-75% and 75%-100%. In 1998-9, we have found a positive and34

significant policy effect only for the poorest group of households (0-25%), which further supports thepolicy effectiveness of microfinance programmes. If we disaggregate PSM by income groups for thelast round, we find for both general and productive loans a positive and significant average treatment
effect on income for the poor group (0-25%) and a negative and significant average treatment effecton the group with the income range of 75-100%. This implies that microfinance programmes
continued to have a poverty-reducing effect for the poorest in 2004-5. On food consumption, theeffect of access to general loans is positive and non-significant, while that of access to productiveloans is positive and significant in 1997-8 and in 1999-2000.The effects of MFI loans on women’s
BMI are non-significant in the first and the last rounds. All these results have to be interpreted withcaution because of the limitations of PSM for cross-sectional data which we discussed in Section IV.
11
It is not clear why the non-poor group (50-75%) which obtained the loans decreased per capita
household income in comparison with (continuous) non-participants with similar characteristics. It
might be related to the failure of investment based on MFI loans and a further investigation is needed
to investigate the variation of the effects of microfinance on income among different income groups.
12
However, it is noted that RCTs are not without problems. See the recent critique by Barrett andCarter (2010).

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