DP2010-14

Microf inanc e a nd H ouseh old Poverty
Red uction: New evidence fr om Ind ia *Katsushi S. IMAI
Thankom ARUN
Samuel Kobina ANNIM
April 21, 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 Household Poverty Reduction: New evidence from India
Authors:
Katsushi S. Imai *Economics, School of Social Science, University of Manchester, UK
Thankom Arun
Institute of Development and Policy Management, School of Environment andDevelopment, University of Manchester & Lancashire Business School, University ofCentral Lancashire, UK&
Samuel Kobina Annim
Economics, School of Social Science, University of Manchester, UK
Key Words: Microfinance, Poverty, Evaluation, India, Propensity Score Matching
JEL Classification: C21, I30, I38, O16, R51

Acknowledgements:
This study is based on the national-level household data in India provided by the EDA researchteam in India (www.edarural.com) who coordinated and undertook a national level microfinanceimpact study for the SIDBI Foundation for Micro Credit. We are grateful to Frances Sinha whoallowed us to share the data and her unpublished working papers. We have also benefited fromcomments from Raghav Gaiha, David Hulme, Takahiro Sato, participants in seminars at
University of Manchester and four anonymous referees. Support from RIEB, University of Kobe
for the first author is greatly appreciated. The views expressed are those of the authors and they
bear full responsibility for any deficiencies that remain.
* Corresponding Author
Dr Katsushi Imai
Economics, School of Social Sciences,
Arthur Lewis Building, Oxford Road,
Manchester M13 9 PL, UK
Phone: +44–(0)161-275-4827
Email: Katsushi.Imai@manchester.ac.uk

Microfinance and Household Poverty Reduction: New evidence from India
Abstract
The objective of the present study is to examine whether household access to microfinancereduces poverty. Using national household data from India, treatment effects model is employed
to estimate the poverty-reducing effects of MFIs loans for productive purposes, such as
investment in agriculture or non-farm businesses on household poverty levels. These models take
into account the endogenous binary treatment effects and sample selection bias associated withaccess to MFIs. Despite some limitations, such as those arising from potential unobservable
important determinants of access to MFIs, significant positive effect of MFI productive loans on
multidimensional welfare indicator has been confirmed. The significance of ‘treatment effects’
coefficients have been verified by both Tobit and Propensity Score Matching models. In addition,
we found that loans for productive purposes were more important for poverty reduction in ruralthan in urban areas. However in urban areas, simple access to MFIs has larger averagepoverty-reducing effects than the access to loans from MFIs for productive purposes. This leads
to exploring service delivery opportunities that provide an additional avenue to monitor theusage of loans to enhance the outreach.

1

I.

Introduction

The expansion of microfinance sector is based on the concept that poor households are affectedby lack of access to, and inadequate provision of financial services. This attempt to reduce therate of financial exclusion among the poor was seen as an alternative solution for the failures inagricultural lending and rural credit assistance practices marred by substantial subsidies, urban
biased credit allocation, higher transaction costs, high default rates, corrupt practices andmisaligned incentives (Arun et al., 2005). Despite the exceptional growth of the microfinancesector during the last three decades in serving around 40 million clients, most parts of thedeveloping world would still remain characterised by huge demand for micro financial services.
There is a projection about the potential of this market to grow to $250-$300 billion in the near
future from the existing loan portfolio of $17 billion in mid-2006 (Ehrbeck, 2006). The concept
and practice of microfinance have changed dramatically over the last decade and themicrofinance sector is increasingly adopting a financial systems approach, either by operating oncommercial lines or by systematically reducing reliance on interest rate subsidies and/or aid
agency financial support (Hulme and Arun 2009). The financial systems approach supports theargument that microfinance institutions should aim for sustainable financial services to low
income people, which may risk undermining the potential of institutional innovation for poverty
reduction and social empowerment. According to Cull et al. (2009), the argument thatmicrofinance institutions should seek profits has an appealing ‘win-win’ resonance, admitting
little trade-off between social and commercial objectives.
Irrespective of the renewed emphasis on the financial systems approach, over the years,
many Micro Finance Institution (MFIs) have developed a range of services to address the2requirements of the poor, such as the Income Generation for Vulnerable Group Development(IGVGD) programme of BRAC, Bangladesh. Despite the widely held belief among policy
makers that microfinance has a relatively small impact on poverty at macro level, some recent
studies have shown its significant effect on poverty using household survey data. Using the panel
data at both participant and household levels in Bangladesh, Khandker (2005) confirms thatmicrofinance programmes have a sustained impact in reducing poverty among the participants,
especially for female participants and a positive spill over effect at village level. This study
suggests that microfinance programmes not only help the poor or redistribute income but also
contribute to national economic growth. However, some studies have shown that MFIs have notreached the poorest of the poor in Asian countries (Weiss and Montgomery, 2005) or in Bolivia(Mosley 2001). The challenge in serving the poorest of the poor is to identify who might benefit
from stand-alone financial services or from non-financial services with or without finance,
before participating in market-oriented finance (Meyer 2002). In Bangladesh, Rutherford (2003)
found that despite the widespread presence of MFIs, their share of total money management
activities is relatively small. This indicates the need for microfinance institutions to move awayfrom being product-based organizations to reflect the heterogeneity of the demand structure forfinancial services/products by poor.
The relationship between microfinance and poverty is still in question and this paper
provides some new empirical evidence on the poverty-reducing effects of MFIs. The existingstudies on the impact of microfinance provide inconclusive results ranging from a substantial
positive impact in Bangladesh to ‘zero’ effect in northern Thailand (Cull et al., 2009). This study
argues that the future innovations in the microfinance sector will be reflective to the fresh3

understandings of the financial lives of the poor households. To capture the multi-dimensional
aspect of poverty, such as basic needs, wealth, type of housing, job security, sanitation and foodsecurity, the current study uses Index Based Ranking1 (IBR) Indicators based on a national-level
household survey to examine the role of microfinance in poverty reduction in India.
In India, despite recent economic growth at national level2, poverty remains a serious
problem for policy-makers because the high economic growth is mainly driven by few sectors inurban areas, such as industry and service sectors3. The incidence of poverty in India is estimatedby quinquennial large sample surveys on household consumption and expenditure and, according
to the Uniform Recall Period (URP) consumption distribution data, poverty stands at 28.3 percent in rural areas, 25.7 per cent in urban areas and 27.5 per cent for the country as a whole(Government of India, 2010). Although the proportion of persons below the poverty line has
declined from around 36 per cent of the population in 1993-94 to 28 per cent in 2004-05, poverty
reduction remains the country’s major challenge in the 21st century.
Until the early 1990s, financial services were provided through a variety of state sponsoredinstitutions, which resulted in impressive achievements in expanding access to credit particularly
among the rural poor (Mosley and Arun 2003). Although many of these commercial bankbranches in rural areas were unprofitable, they played a positive role in financial savings and1

In spite of well established concerns on IBR class of poverty measures such as subjectivity,
substitutability and complementary issues of multi-dimensional poverty and stochastic dominance, we
remain resolute on its reliability based on some earlier wealth ranking studies including Adams et al.
(1997) and Pradhan and Ravillion (2000).
2
For example, real GDP grew by 9.7 % in 2007, 9.2% in 2008, and 6.7% in 2009.
3
The average annual output growth rates in industry and services sectors in the period 1994-2004 are
5.6% and 8.2% respectively, while that in the agricultural sector is 2.0% (based on World Bank Data in2005 taken from http://devdata.worldbank.org/AAG/ind_aag.pdf. The poverty head count ratio has beenmuch higher in rural areas than in urban areas (e.g. Deaton and Kozel 2005 and Sen and Himanshu 2004).

4

reducing poverty. This is evident from the fact that during the period 1951-1991 the financial
institutions' total share in rural household debt increased from 8.8 per cent to 53.3 per cent andthe role of money lenders declined significantly (Mosley and Arun 2003; Basu and Srivastava
2005). However, despite the vast network of banking and cooperative finance institutions andstrong micro components in various programmes, the performance of the formal financial sector
still fails to adequately reach out to, or reflect and respond to the requirements of the poor.
In the 1990s, MFIs became increasingly important in India mainly due to their better
access to local knowledge and information at community level and their use of peer groupmonitoring. For example, microfinance programmes involving SHGs (Self-Help Groups), which
are based on the existing banking network in delivering financial services to the poor, havebecome increasingly important in India due to their flexible nature (Mosley and Arun 2003).
SHGs are built on the traditional institution of ROSCA (Rotating Savings and Credit
Associations) and provide access to both savings and credit for the asset-less poor. A recent
study in Pune district in Maharashtra showed that while the targeting performance of
microfinance through SHGs was unsatisfactory in terms of income, it was satisfactory in termsof caste (social division based on descent or birth), landlessness and illiteracy and thus facilitatedthe empowerment of women (Gaiha and Nandhi 2007). This study also found that loans wereused largely for children's health and education and argued against restricting the impact
assessment of microfinance to conventional economic criteria alone.
Despite MFIs’ increasing involvement in poverty reduction in India, there have been
relatively few studies that empirically evaluate their impact at the national level. The present
study aims to provide evidence on the relationship between role of MFIs and its impact on5

poverty in India using a large-scale household data set which was collected with the intention ofassessing the impact of microfinance. In our study, poverty is defined by the ‘IBR (Indexed
Based Ranking) Indicator’, a composite indicator that captures various aspects of wellbeing,
including land holdings, salaried income sources, livestock, transport assets, housing, and accessto sanitation facilities4. Our broad research question is - whether access to MFIs and loans for
productive purposes reduces poverty. A simple comparison of the average of the IBR indicatorfor households with access to MFIs and those without is not appropriate. Firstly, MFIs are notrandomly distributed due to endogenous programme placement where MFIs target poor
households or poor households tend to take loans from, or save at MFIs (EDA Rural Systems2005). Furthermore, there are self-selection problems associated with participation in
microfinance programmes. That is, within the area where microfinance is available, individuals
with similar characteristics (e.g. education or age) might have different levels of entrepreneurial
spirit or ability, which may lead to different probabilities of their participating in the scheme.
Hence it is necessary to take into account self-selection problems or the endogeneity associatedwith participation in microfinance programmes.
To address at least partly the sample selection problem, we apply treatment effects model,
a version of the Heckman sample selection model (Heckman, 1979). We have carried out
robustness test by using propensity score matching (PSM).5 We also use Tobit estimation toestimate the effect of size of productive loan on poverty. Tobit model is meant to account for left
censoring associated with unobserved sample. Other robustness checks explored include (1)4

See Sinha (2009) for the conceptual framework of IBR indicator.

5

For brevity, the PSM results are provided only in Appendices 2 and 3.

6

decomposition of the IBR index into perception of income level and food security6 and (2)examining whether poverty reducing effects of productive loan would be observed in the casewhere it is replaced by total loan. In all instances, we observe that microfinance has a significant
positive effect on poverty reduction.
The treatment effects model estimates the probit model with the same specification as in
the first stage of PSM. In the second stage, the IBR indicator, our proxy for poverty, is estimatedby OLS while sample selection is corrected by using estimates of the probability of participationin microfinance programmes. The model is fitted by a full maximum likelihood (Maddala, 1983).
The merits of the treatment effects model over PSM include that (i) the degree of sample
selection bias is explicitly taken into account and (ii) the determinants of the dependent variablein the second stage are identified. However, the treatment effects model imposes strong
distributional assumptions for the functions in both stages and the final results are highly
sensitive to the choice of explanatory variables and the instrument. The presence of unobservablevariables would also affect the results as in PSM. Given these limitations, applying different
models is useful as each model serves to check the robustness of the results derived by the other.
The rest of the paper is organised as follows. Section II summarises the survey design anddata. Section III describes the econometric intuition underlying treatment effects and Tobit
model. Section IV provides the econometric results and main findings. The concluding remarks
are given in the final section.

6

These two components are deemed only candidates for decomposition analysis given the data
limitations, e.g. insensitivity of other components in IBR, such as land-holding or household access tosanitation facilities, to microfinance access or loan amount. The choice of these proxies was also guided
by the data generation process since each provides either subjective or objective view points of wellbeing.

7II. Survey Design and Data 7
Details of Survey
The original survey was carried out by EDA Systems for SIDBI (Small Industries Development
Bank of India) in 2001 as a part of SIDBI’s impact assessment study of its micro financeprogramme. This cross-sectional socio-economic research was undertaken to assess, on anational scale, the development impact of MFI programmes. The study covered a sample of 20
SIDBI's partner Micro Finance Institutions (MFIs) and 5260 households distributed acrossdifferent and diverse regions of India, including both clients and non-clients (EDA Rural
Systems 2005; SIDBI 2005). Our study is based on the cross-sectional data set for thesehouseholds.
The hypothesis of our study is:(1) access to microfinance institutions (MFIs) andproductive loan reduces poverty and (2) amount of productive loan has a poverty reducing effect.
Five types of MFI were selected as representative of 31 MFIs in SFMC8’s list of current partners- representing different regions and models of microfinance (Self Help Group (SHG), Grameen,
Individual Banking and sector/enterprise specific cooperatives), age, outreach to members andrange of services. At each MFI, two to four sample areas (villages or urban wards) were
purposefully selected to represent a typical area of the MFI in terms of the socio-economic
context and range of MFI programmes. Within each sample area, a stratified random sample ofclients, non-clients and dropouts was drawn using wealth ranking as a basis for stratification
7
8

This section is based on EDA Rural Systems (2002, 2005), SIDBI (2005) and Sinha (2009).
It stands for SIDBI (Small Industries Development Bank of India) Foundation for Micro Credit.

8

(EDA Rural Systems 2002, 2005). The ratio of non-client households to MFI client households
was set at 1:2.75 for most of the villages. This ratio was chosen to reflect the average non-client
to client ratio of the population in the village or the urban wards where microfinanceprogrammes were in operation. For each group of clients in the programme area, an appropriate
number of non-client households with similar characteristics (based on wealth, social group or
female-headedness) were chosen in the same program area as a comparison group.

Index Based Ranking (IBR) Indicators
Index Based Ranking (IBR) Indicators were created to overcome any limitations of the income
or consumption based poverty measures and to capture non-income or multi-dimensional
dimensions of poverty, such as basic needs, wealth, type of housing, job or employment security,
sanitation, and food security (Sinha 2009). A score index, such as IBR, is useful to capture
various dimensions of poverty because of its higher practicality (e.g. less costly than those for
expenditure surveys; based on less-sensitive /obtrusive and simpler questions) and higher
reliability due to lower risk of falsification or error. Respondents are asked about their quality oflife in several dimensions and then IBR indicators are created as a weighted sum of scores fordifferent categories with a maximum score of 60.
The actual scoring is based on quantitative observations of trained researchers using
common criteria. The dimensions include (i) agriculture (e.g. area in acres, value of crop soldlast year in rupees, and, as a proxy for food security, the number of months the stock of cropwould meet family needs); (ii) employment (e.g. regularity of income, type of employment permanent or ad hoc, binary classification of income level, number of people employed); (iii)9animal husbandry (the number of buffalos, cows, goats, pigs, and poultry); (iv) transport andhousehold assets (e.g. the number of bicycles, rickshaws, two or four wheelers; ownership offridge, TV, or phone); (v) house ownership and housing type (owned, rented, or homeless; housesize - large, medium, or small, electrical connection); and (vi) sanitation (with or without accessto public, shared or own toilet (inside or not), with or without bath, inside or outside). The IBR
indicator thus reflects income or employment or business characteristics, basic needs such as
food security, the availability of sanitation facilities, housing and asset characteristics.
Households are grouped into five categories, namely ‘very poor’ (with an IBR indicator of 8 or
less; 5.1% of the total sample of 5260), ‘poor’ (IBR - 9-18; 23.6%), ‘moderately poor orborderline’ (IBR - 19-29; 33.5%), ‘self-sufficient’ (IBR - 30-40; 33.5%), and ‘surplus’ (IBR 41-60 (Sinha, 2009). Thus, the very poor or the poor have relatively insecure agricultural
income, few animal or household assets, relying on casual labour, and lower level of sanitation.
Incidentally, the share of ‘the poor’ and ‘the very poor’ (28.7%) in our study matches, thepoverty head count ratio for all India in 2004-5 based on the national poverty line applied to the
National Sample Survey data (Himanshu, 2007).

Descriptive Statistics and Definitions of the Variables
The present study employs two different definitions of access to MFIs; (a) whether a household
is a client of any MFI (“MFI_Access”) or not, and (b) whether a household has taken a loan fromMFI for a productive activity (“MFI_Productive”). The first definition is used to observe the10effect of simply accessing MFI on poverty. 9 The second is concerned with whether thehousehold has taken loans for productive activities (and has an outstanding balance of thoseloans at the time of survey), leading to an increase in production, e.g. buying inputs for
agriculture or investment in non-farm business, such as repairing a shop. This is based onborrowers’ broad perception of the use of loans taken from MFIs. In this category, the loan used
for self consumption or non-productive purposes is excluded. The binary classification of‘whether the household used the MFI loans for productive purposes’ is based solely on the
respondents' perception of the nature of their loans and thus the possibility cannot be ruled outthat loans were actually used for other purposes. Thus, caution is needed in interpreting theresults.
Appendix 1 provides descriptive statistics of the variables for the sample households withaccess to MFIs and for those without. As shown by the number of observations in two columns(third and sixth), about three quarters of the sample households have access to MFIs in both ruraland urban areas.

About a half of them has access to loans from MFI for productive purposes.

In general, there is a relatively small difference between the descriptive statistics of each variable
for the households with access to MFIs (or with access to MFI loans for productive purposes)
and for those without, except in a few cases (e.g. there are higher proportions of larger
households with lower dependency ratios and households with non-farm business opportunities
among those receiving MFI loans than among those without). That is partly because of thedesign of the sample survey where households with relatively similar characteristics are chosen
in each village. The higher proportion of female-headed households probably indicates that MFIs
9

‘Being a client’ means that any member of the household has either savings or loan account with MFIs
at the time of survey.

11use sex of the head of household for targeting female/poorer clients. For most rural households,
the household head is either illiterate or ‘completed primary school’ only, while all of those inurban areas completed only primary school.
A household typically has about five members. About 30% of the sample households
belong to a Scheduled Caste or Scheduled Tribe (population groupings based on descent or birthand are explicitly recognized by the Constitution of India). The proportion of Hindus is relatively
higher in urban areas, while that of Muslims is relatively higher in rural areas. Other religions
include Christianity and Sikhism. We created a variable on ‘business availability’, theavailability of non-farm business opportunities for households. It is assumed that more business
opportunities will increase the demand for microfinance. This is proxied by the proportion ofhouseholds engaged in non-farm business in a village. As expected, it is higher in urban areas.
The average IBR indicator of households in rural areas is lower than in urban areas, implyingthat poverty is more severe in rural areas. The IBR indicator is higher for those with access to
MFIs (or those with access to MFI loans for productive purposes) than those without. However,this may not necessarily imply that access to MFIs reduces poverty due to the possible sample
selection biases. The next section will address the methodologies by which the treatment effects
and Tobit models take account of sample selection biases and censoring respectively.

III. Methodology
We use the treatment effects model for the effect of access to MFIs and productive loans onpoverty reduction. While this approach addresses sample selection issues, we check forrobustness using Propensity Score Matching and report its findings in Appendices 2 and 3.

12

Secondly, we apply Tobit regression to investigate the poverty reducing effect of productive loan
amount.

(1) Treatment effects Model
Our main hypothesis is that access to microfinance institutions (MFIs) reduces poverty as
defined by the IBR indicators. Because we have only cross-sectional data, we can compare IBR
indicators of households with access to MFIs and those without, as long as MFIs are randomly
distributed across the sample. However, we cannot simply statistically compare the average of
IBR indicators for those with access to MFIs and those without because of the sample selection
bias. The sample selection problem may arise from (1) self selection where the households
themselves decide whether or not to participate in MFI programmes, which depends on
observable and unobservable household characteristics, and/or (2) endogenous program
placement where those who implement microfinance programmes select (a group of) householdswith specific characteristics (e.g. high poverty rates or reasonably good credit records dependingon the programme specifications). Heckman Sample Selection Model could be used tocompensate for sample selection bias or the endogeneity associated with household access to
MFIs.
We employ the treatment effects’ model version of the Heckman sample selection model(Heckman, 1979), which estimates the effect of an endogenous binary treatment. This enables us
to compensate for sample selection bias associated with access to MFIs. In the first stage, accessto MFI is estimated by a probit model. In the second, we estimate the IBR indicator by various
household characteristics and a dummy variable on whether the household participates in the MF

13

programme after controlling for the inverse Mill’s ratio which reflects the degree of sample
selection bias. The instrument used is the availability of formal banks10 at the village level(proxy for the level of local financial services) which determines the demand for microfinance,
but would not directly affect the poverty level of the household.
The merit of the treatment effects model is that sample selection bias is explicitly
estimated by using the results of the probit model. However, its weak aspects include (i) strong
assumptions being imposed on distributions of the error terms in the first and the second stages,(ii) the results being sensitive to the choice of explanatory variables and instruments, and (iii)
valid instruments rarely found in non-experimental data.
The selection mechanism by the probit model above can be more explicitly specified as(e.g. Greene, 2003):
D *i = γX i + u i

(3)’
and
D *i = 1 if D *i = γX i + u i > 0
D *i = 0 otherwise

where

Pr{D i = 1 X i } = Φ ( γ ′X i )Pr{D i = 0 X i } =1 − Φ ( γ ′X i )and
D *i = 1 if D *i = γX i + u i > 0

10

Hausman test has been carried out to compare the coefficient estimates of treatment effects model andthose of OLS to test the validity of ’availability of formal banks’ as an instrument. The instrument isdeemed valid on the ground that its coefficient estimate is statistically significant in the treatment effects
model and the difference of coefficient estimates of these two models are also significant as shown by
Hausman test.

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D *i is a latent variable. In our case, D i equals 1 if a household has access to MFIs and 0

otherwise, X i is a vector of household characteristics and the instrument for the participation
equation, that is, the proportion of households with access to formal banks, Φ , denotes thestandard normal cumulative distribution function.
The linear outcome regression model in the second stage is specified below to examine thedeterminants of poverty, proxied by IBR (index based ranking) score or Wi . That is,

Wi = β′Z i + θD i + ε i(4)(u ε ) ~ bivariate normal [0, 0,1, σ , ρ] .
iiεwhere θ is the average net wealth benefit of participating in MF programmes. Z i is the same as
X i except that it does not include instruments for the MFI participation equation.
Using a formula for the joint density of bivariate normally distributed variables, theexpected IBR indicator for those with access to MFIs (or clients) is expressed as:
E[Wi D i = 1 ]= β′Zi + θ + E[ε i D i = 1 ]= β′Zi + θ + ρσεφ(γ ′X i )Φ(γ ′X i )(5)where φ is the standard normal density function. The ratio of φ and Φ is called the inverse
Mill’s ratio.
The expected IBR for non-clients is:
E [Wi Di = 0 ]= β ′Z i + E [ε i Di = 0 ]= β ′Z i − ρσ εφ (γ ′X i )1 − Φ(γ ′X i )

15

(6)

The expected effect of poverty reduction associated with MFI access is computed as (Greene,
2003, 787-789):
E[Wi D i = 1 ]− E[Wi D i = 0 ] = θ + ρσ εφ(γ ′X i )Φ (γ ′X i )[1 − Φ (γ ′X i )](7)

If ρ is positive (negative), the coefficient estimate θ of using OLS is biased upward(downward) and the sample selection term will correct this. Since σ ε is positive, the sign andsignificance of the estimate of ρσ ε (usually denoted as β λ ) will show whether any selection
bias exists. To estimate the parameters of this model, the likelihood function given by Maddala(1983, 122) is used where the bivariate normal function is reduced to the univariate function andthe correlation ρ . The predicted values of (5) and (6) are derived and compared by the standard t
test to examine whether the average treatment effect or poverty reducing effect is significant.

(2)

Tobit ModelIn our bid to estimate the effect of productive loan amount on household poverty, non-zerovalues occur only when the former has been accessed by a household. This generates a censored
sample in which Maddala (1983) and Amemiya (1984) argue that estimating least squares on the
reduced sample leads to biased and inconsistent results. The other alternative of categorizing thedependent variable into a binary outcome, masks actual predictions since the use of either logit
or probit reveals estimates premised on the probability that the dependent variable lies above a16certain threshold. Tobit (hybrid between probit and least squares) uses information on all
observations. The model takes the form:

(8)

where

is the dependent variable,of unknown coefficients,

is the vector of independent variables,is the vector

represents the independently distributed error term. Underlying theestimation of equation (8), is a latent variablewhich is assumed to be linearly related to thevector of independent variables. In effect we calculate the normalized coefficients which needs
to be multiplied by the standard error to ascertain the actual sort for

estimates.

IV. Results(1)Treatment Effect Model

We first provide the probit results for the treatment effects model to investigate the impacts ofaccess to MFIs and productive loans on poverty. Because of the fundamental differences ofenvironment, industrial structures, household characteristics and activities between urban andrural areas, we first derive the estimations for total households and then for urban areas and rural
areas separately. The results of the probit model imply the sort of characteristics which are thekey determinants underlying access to, and use of, microfinance services.
The estimation results of the probit model in Table 1 are generally intuitive in the case of
all households where the dependent variable is ‘MFI_Access’ (i.e. Case A-1). A household withan older household head is more likely to be an MFI client, but the negative coefficient of the17age square suggests a non-linear effect, which is significant for both total and rural households.
Also, a household with a female head is more likely to be a client, which reflects the fact thatmicrofinance programmes target women. Education variables are not significant. Dependency
ratio has a negative and significant effect. The coefficient estimate of ‘business availability’ ispositive and significant in Cases A-1 (total) and A-3 (rural areas). If a household deals withformal banks, it is less likely to be an MFI client. This is significant in Cases A-1 and A-3. The
coefficient estimates of loans from formal banks, money lenders, friends and relatives arenegative, which reflects the fact that those who cannot obtain loans, or can only obtain smallerloans11, tend to use MFI services. The availability of formal banks is positive and significant inurban areas and negative and significant in rural areas. That is, households in areas where formalbanks are not available are more (less) likely to be MFI clients in rural (urban) areas.12
However, in Case B-1 where ‘MFI_Productive’ is estimated, a few differences areobserved. The coefficient estimate of ‘Female’ (headedness) is negative in Case B-1 (total) andCase B-3 (rural areas), that is, a household with a male head is more likely to take a loan for
productive purposes. This may reflect the fact that, although microfinance focuses on women,
male-headed households are more likely to take loans for productive purposes. The coefficientestimates of variables on ‘Education’ are positive and significant. Households with moreeducated heads are more likely to take MFI loans for productive purposes, while education does
11

Average loan size for the current study is about USD600, compared with global average of about
USD530 (MIX, 2009).
12
We estimated the treatment effects model based on the probit without the variables on access to otherfinancial services for both ‘MFI-Access’ and ‘MFI-Productive’ noting that these may not be exogenous.
The coefficient estimates of variables show similar results in the cases without the variables on access toother financial services. The final results of the treatment effects model and PSM model are also similar.
However, this has a shortcoming of not controlling for the variables on other financial services and thus
we decided to present the cases with these variables.

18

not matter for simple access to MFI. The coefficient estimates of ‘Caste_dum’ (dummy for caste)
are negative and significant in Case B-1 and Case B-3. That is, households which do not belong
to Scheduled Castes or Scheduled Tribes are more likely to be MFI clients, suggesting theexclusion of socially disadvantaged groups from MFI loans for productive purposes. The
availability of non-farm business is highly significant in all cases as this increases the demand
for loans for productive purposes. In rural areas transactions with formal banks and loans frommoney lenders show positive and significant signs, that is, other financial services serve ascomplements to MFI loans for productive services. On the other hand, the coefficient estimate ofloans from formal banks is negative and significant in Case B-2 for urban areas. That is, those
who cannot get loans from the formal banks tend to obtain MFI loans for productive purposes inurban areas. Formal bank availability at village level is negative and significant in Case B-1(total) and Case B-3 (rural areas). Rural households living in a village with more difficult accessto formal banks are more likely to take MFI loans.

(Table 1 to be inserted around here)

Based on the regression results of the probit model in Table 1, we estimate treatment effects
models and present the results in Table 2 for the total sample and for urban and rural areas,
separately for the cases where whether the household had access to MFI is estimated in the probit
model (Cases A-1, A-2, and A-3) and for those where the households obtained a loan for any
productive purposes (Cases B-1, B-2, and B-3). The dependent variable is either aggregate
Indexed Based Ranking (IBR) of a household's wellbeing, or a disaggregated component of IBR-

19

namely, perceived income level or food security. Note that higher value of a dependent variablereflects higher wellbeing or lower poverty. Most of the results are similar irrespective of theareas chosen or the definitions of the dependent variable in the first stage.

(Table 2 to be inserted around here)

Most of the coefficient estimates of dependent variables show the expected signs. Householdswith older household heads tend to have higher IBR indicators with some non-linear effects, thatis, the IBR indicator first increases as the household head gets older and then decreases. Femaleheaded households are associated with lower IBR indicators. Both completing primary educationand higher education are associated with higher IBR indicators, and thus lower poverty. Larger
households tend to have higher IBR indicators, but a larger proportion of elderly people orchildren in a household have a counter effect. If the household belongs to a Scheduled Caste or
Scheduled Tribe, it is likely to have a lower IBR. Being Hindu has a positive and significant
effect and being Muslim has a negative effect in the cases for total sample and for rural areas,
while their coefficient estimates are non-significant for urban areas.
The availability of non-farm business opportunities is significantly and positively
associated with a higher IBR Indicator. Variables controlling for access to other sources offinancial services (namely, loans from formal banks, money lenders, friends and relatives) show
positive and significant coefficients. This implies that a household less financially constrained isless likely to be poor. Our results would remain the same if the variables on having access toother financial services were omitted. The positive coefficient for Θ implies that the net benefit

20

of having access to MFI is significant and positive in urban areas even without controlling forsample selection bias.
The last panel of Table 2 shows the treatment effects or the average poverty reducing
effects in accessing MFIs or taking loans for productive purposes. In both instances (access to
MFIs and productive loan) and for both urban and rural areas significant average poverty
reducing effects are observed. Incidentally, the results on the size and sign of the poverty
reducing effects in each case are very similar to those derived by kernel matching for PSM. This
would support our results based on PSM with the caveat that both methodologies have their own
limitations. That is, on average, having access to MFI or taking loans from MFI reduces poverty(see Appendices 2 and 3).13 In each of the cases, the decomposed IBR indicators of perceived
income level and food security show significant average poverty reducing effect.

(2) Tobit Regression Results

The sample for regressing amount of productive loan on well being was restricted only tohouseholds that had access to microfinance institutions and productive loan. The results arepresented in Table 3.

(Tables 3 to be inserted around here)

Given the outcome of the effect of sample selection above, the results emerging from the Tobit
estimation shows a highly significant positive relationship between productive loan amount and

13

See Imai and Arun (2008) for details of the methodologies and results of PSM.

21

households poverty after controlling for socio-economic characteristics. It is noted that thecoefficient estimate of amount of productive loan, though its absolute value is small, is more
highly significant in urban area (at 1 % level) than in rural area (significant only at 10 % level).
The results of other covariates are not much different from the second stage results of Treatment
effects model in Table 2. It has been confirmed that larger amount of productive loan improves
well-being, a finding consistent with the underlying thrust of microfinance evolution. It is notedthat this finding supports the earlier results both from the treatment effects model PSM.
Also as a form of robustness check, we observe a significant poverty reducing effect in thecase of total loans. The results are shown in Table 4.

(Tables 4 to be inserted around here)

A similar pattern of the results are obtained in the cases where we estimate the effects of amountof total loan on poverty. That is, larger amount of productive loan reduces poverty in both urban
and rural areas. It is noted that coefficient estimate of total loan is significant at 1 % level in bothurban and rural areas.

V. Conclusions

Drawing upon a national-level cross-sectional household data set in India in 2001, the present
study analyses the impact of Micro Finance Institutions (MFIs) on household poverty, based on
the Indexed Based Ranking (IBR) Indicator which reflects multi-dimensional aspects of poverty.
The treatment effects model, a version of the Heckman sample selection model, and Tobit model22

are employed to estimate poverty-reducing effects of access to MFIs and loans used for
productive purposes, such as investment in agriculture or non-farm businesses. The propensity
score matching (PSM) model has been also used to check the robustness of the results. Thesemodels compensate for endogenous binary treatment effects or sample selection bias associatedwith access to MFIs. Despite some limitations e.g. arising from potentially unobservable
important determinants of participation in microfinance programmes, significantly both models
confirmed positive effects of MFI access on the multidimensional welfare indicator, a result
which suggests that MFIs play a significant role in poverty reduction. If we consider the results
for rural and urban areas separately, some interesting observations emerge. For households inrural areas, a larger poverty reducing effect of MFIs is observed when access to MFIs is defined
as taking loans from MFIs for productive purposes than in the case 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.
The finding of this study provides further impetus to the existing evidences on the impactof microfinance institutions on the household poverty. In rural areas, while significant poverty
reducing effects are observed in all cases, taking loans for productive purposes has a larger
impact in raising the IBR indicator for those above the poverty threshold. That is, clients’intended use of loans is important in determining poverty reduction outcomes. In the context of‘profit-making poverty reduction’ era, the finding on outreach and productive use of loan for
better impact warrants more policy choices. Although many microfinance institutions have
moved on to reflect the heterogeneity of the demand structure for financial services/products bypoor, there is yet to develop a consistent framework to monitor the usage of loan with adequate

23

flexibility to capture different levels of participating nature of the households. This leads tofurther options in the delivery of services such as the integration of non-financial services solely
or in partnership with other development agencies that provides an additional avenue to monitor
the usage of loans and enhance the outreach. The challenge lies in how to design an optimal mixof delivery options to enhance the impact and outreach that determines the nature and characterof the microfinance institutions in the coming years.

24References

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Cull, R., Demirguc-Kunt, A. & Murdoh, J. (2009). Microfinance Meets the Market. Journal of
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Deaton, A., Kozel, V., (2005). Data and Dogma: the great Indian poverty debate. World Bank
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http://www.enterprise.impact.org.uk/informationresources/casestudies/sidbi.shtml.
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Ehrbeck, T., (2006). Optimizing Capital Supply in Support of Microfinance Industry Growth,
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Hulme, D. & Arun, T. (2009). The Future of Microfinance, Chapter 14 in Hulme, D and Arun, T,(Eds.) Microfinance – A Reader. Routledge, London.
Imai, K., Arun, T. (2008). Does Microfinance Reduce Poverty in India? Economics Discussion
Paper, University of Manchester, 0814.

Khandker, S. R. (2005). Micro-Finance and Poverty: Evidence Using Panel Data from
Bangladesh. The World Bank Economic Review 19(2):263-286.
Maddala, G. S. (1983). Limited-dependent and Qualitative Variables in Econometrics.
Cambridge University Press, Cambridge.
Meyer, R. (2002). Track Record of Financial Institutions in Assisting the Poor in Asia. AsianDevelopment Bank Institute Research Paper 49, Asian Development Bank Institute.

26

MIX (Microfinance Information Exchange). (2009) The Microbanking Bulletin No. 19.
Washington D.C., United States of America.
Mosley, P. (2001). Microfinance and Poverty in Bolivia. Journal of Development Studies, 37(4),101-132.
Mosley, P. & Arun, T., (2003). Improving Access to Rural Finance in India: Supply Side
Constraints. Unpublished Background Paper to the Economic and Sector Work study on
Access to Finance, World Bank, South Asia Finance and Private Sector Development Unit,
Washington D.C.
Pradhan, M., & Ravallion, M. (2000). Measuring Poverty Using Qualitative Perceptions ofConsumption Adequacy. The Review of Economics and Statistics 82(3): 462 – 471.
Rutherford, S. (2003) Money talks: Conversations with Poor Households in Bangladesh about
Managing Money. Journal of Microfinance, 5(2), Winter 2003.
Sen, A. & Himanshu (2004). Poverty and Inequality in India-II: Widening Disparities during. the1990s. Economic and Political Weekly September 25, 4361-4376.
SIDBI (Small Industries Development Bank of India), (2005). Impact Assessment. Retrieved
January 31 2007 http://www.sidbi.com/Micro/impact.htm.
Sinha, F. (2009). Microfinance Self-help Groups in India Living Up to Their Promise? Practical
Action Publishing, Essex, United Kingdom.

Weiss, J. & Montgomery, H. (2005). Great Expectations: Microfinance and Poverty Reduction in
Asia and Latin America. Oxford Development Studies 33(3&4):391-416.

27

Table 1 Results of Probit Model on the Determinants of Access to MicrofinanceCase A: Dep Variable: whether a household has access to a MFI (“MFI_access”)
Case A-1: Total
Coef.

Case A-2: Urban

Case A-3: Rural

Z value1)

Coef.

Z value

Coef.

Z value+

0.0008-0.00020.3445(0.05)(-0.78)(2.47)

0.0167-0.0003
0.2721(1.90)(-3.12)(3.25)

0.0389-0.7791
0.0937-0.5754-0.7683
0.1259(1.62)(-5.15)(1.00)(-1.13)(-1.46)(0.53)-0.0442-0.1251
0.0054-0.5695-0.0629-0.2874-0.2637
0.5052(-0.86)(-0.93)(0.44)(-5.98)(-1.20)(-4.13)(-2.69)(4.91)-0.1106-1.7400(-1.30)(-1.44)-0.19650.0000(-3.95)(0.71)

3.1300-2.1500(1.53)(-1.16)0.00000.0000(-0.38)(-1.45)

-

-

-

-

-0.2560(-1.73)1.2079(4.88)

Age
Age_square
Female0.0138-0.0003
0.2917(1.80)(-3.22)(4.09)

Primary Education
Higher Education
HhsizeDependency
Caste_dum
Hindu
MuslimBusiness Availability-0.0456-0.0532
0.0116-0.64270.0043-0.2813-0.2696
0.4623(-0.91)(-0.40)(1.08)(-8.03)(0.10)(-4.15)(-2.97)(4.99)

Formal banks (transaction)
Formal banks (loan)-0.1729-0.7160(-4.07)(-0.71)

Money lenders (loan)
Friends/Relatives (loan)-0.1120-1.5200(-0.28)(-1.70)Whether in urban areas-0.0136(-0.25)

**
**

**

**
**
**
**

+

Formal Bank Availability

0.0305(0.26)

0.5640(2.49)

Constant

1.2553(5.86)

1.8643(2.81)

No. of Obs.

5327

Joint SignificanceLog likelihood

LR Chi2(11)=168.16-2987.18

Pseudo R2

0.0325

*

**

*

1385
**

LR Chi2(14)=74.11-756.52**

0.04673942
LR
Chi2(16)=154.32-2216.72

+
**
**

**

**
**
**
**

+

**

0.0272

Case B: Dep Variable: whether a household has taken a loan for productive purposes (“MFI_productive”)
Case B-1: Total

Case B-2: Urban

Case B-3: Rural

Coef.

Z value

Coef.

Z value

Coef.

Z valueAge
Age_square
FemalePrimary Education
Higer Education

0.0030-0.0001-0.10070.12210.5804(0.40)(-1.70)(-1.53)(2.52)(4.58)

0.0047-0.00020.0345-(0.28)(-0.91)(0.27)-

0.0032-0.0001-0.1586
0.10290.5714(0.36)(-1.48)(-2.06)(2.08)(4.45)

HhsizeDependency

0.0161-0.8102(1.56)(-10.30)-0.0246-1.1665(-1.10)(-7.65)0.0278-0.6502(2.37)(-7.00)*Caste_dum-0.1119(-2.60)-0.2173(-2.39)-0.1003(-1.99)

*

+

*
**

**
**

28

**
*

*
*
**

**

Hindu-0.0578(-0.92)(-1.83)+-0.0249(-0.38)-0.7420
1.5476(-1.77)(6.73)+

0.01861.4843(0.20)(14.79)0.02190.00000.00000.00000.0932(0.27)(-1.84)(0.48)(1.27)(0.43)

0.12390.00000.00000.0000-0.5536(2.56)(0.63)(4.36)(1.25)(-3.89)0.2760(0.49)

0.0194(0.08)-0.7196

MuslimBusiness Availability-0.02171.5358(-0.25)(17.01)

Formal banks (transaction)
Formal banks (loan)
Money lenders (loan)
Friends/Relatives (loan)
Whether in urban areas
Formal Bank Availability

0.1123-1.3700
2.09001.7200-0.7122-0.3367(2.73)(-1.32)(4.25)(1.84)(-13.59)(-2.96)

Constant

0.1755(0.85)

No. of Obs.

5327

Joint SignificanceLog likelihood

LR Chi2(17)=788.67-3291.66

Pseudo R2

0.107

**
**

**
+
**
**

**

1385
LR
Chi2(14)=175.90-831.140.0957

**

+

**

3942
LR
Chi2(16)=482.92-2445.7
0.0899Notes: 1) ** = significant at 1% level. * = significant at 5% level. + = significant at 10% level.
2) Education is dropped in case of urban areas as there is no variation in the variable.
3) District Dummy Variables are included, but not shown in this table

29

**
*

**

**

**

Table 2 The Results of Treatment effects Model for Poverty (IBR, Income and Food Security
measures of well being) (The First Stage: whether a household has access to productive assets/ whether ahousehold has loan from MFI for productive purposes is shown in Table 1)
Case A: Dep. (the first-stage probit estimates whether a household has access to a MFI (“MFI_Access”))
Case A-1: Total
IBR
Age
Age_squarePrimary
Education
Higher
Education

Dependency
Caste_dum
Hindu
MuslimFormal banks(loans)Friends/Relatives(loans)
Whetherinurban areas

IncomeFood
Security

0.04780.37280.0225-0.00370.20770.01310.1248(1.84)(2.93) **(2.47)**(-0.34)(3.18) **(2.58)**(3.45)**-0.0009-0.0002-0.0002-0.0016-0.00020.0000-0.0016-0.0002-0.0011*(-3.07)**(-2.82)**(-4.12)**(-0.47)(-1.05)(-2.16)**(0.27)(-2.11)4.7049-0.1394

0.9687-4.53130.0096-0.0834-3.5385(-0.1434)

0.8613(9.54) **(-3.49)**(3.35)**(-4.60) **(0.13)(-1.02)(-5.72) **(-3.21)**(2.70)**

1.16420.1166-1.4746---

0.62290.09930.4781(3.26) **(3.88)**(-9.90)**---(1.73) +(3.41)**(-7.27)**2.07930.33770.1414---1.64090.33730.0888+(4.63)**(0.92)*(4.41)**(0.38)---(1.82)

0.60610.01840.0902

1.06620.02010.00640.44230.0123-1.5068(8.01) **(2.89)**(2.88)**(6.20) **(1.60)(0.44)(5.18) **(1.78)(1.81)-0.9876-0.3283-1.3235

1.9087-0.0299-0.2880-4.3710-0.3307-2.9737(-1.46)(-6.62)**(-2.30)(1.60)(-0.29)(-3.01)**(-4.53) **(-5.85)**(-7.26)**-3.8773

0.0676

1.4962-4.55310.1180-0.0413-3.78850.07482.0096(-12.54) **(2.59)**(11.68)**(-6.67) **(2.38)**(-0.72)(-10.76) **(2.63)**(9.91)**

1.4548-0.2877

0.9492-1.7161-0.62130.0264

1.2874-0.27630.5145(2.68) **(-6.35)**(3.67)**(-0.60)(-3.00)**(0.11)(2.26) *(-6.27)**(1.64)-1.4477-0.4264-0.1867-3.0860-0.8484-0.1017-1.2351-0.3926-0.8063+(-6.88)**(-1.99)*(-7.55)**(-0.62)(-1.00)(-3.79)**(-0.40)(-1.74)6.49790.0947-0.71079.5918-0.4086

0.23927.52050.15250.3766**+(-1.62)**(-3.55)**(1.86)**(2.79)**(0.96)(1.84)(6.21)(9.14)

6.26910.11900.69986.74040.18670.04314.90970.10700.5159(21.22) **(4.94)**(4.15)**(11.56) **(4.47)**(0.88)(12.42) **(3.91)**(2.63)**

36.1392

0.9283-0.483232.9048

1.8732-1.2435

55.4760

0.586510.4186**(-1.55)**(0.52)(1.30)**(1.48)(-0.15)(3.43)

13.57120.6124-1.3035-23.3631-1.96716.614924.6858

1.0819-3.1244(2.54) *(1.36)(-0.57)(-1.75) +(-2.02)**(5.92)**(4.22) **(2.29)**(-0.93)62.6509-0.0020-1.851680.0072

0.5150-0.5091

41.7274

0.1302-6.2080(8.22) **(0.00)(-0.54)(5.75) **(0.51)(-0.44)(4.38) **(0.17)(-1.16)10.10170.1301-2.6641

-

-

-

-

-

-

**

(4.16)**(-17.44)**

-

-

-

-

-

-

(4.87)
Money lenders(loans)IBR

0.0167(9.96)
Formalbanking sector

Income(3.56)**(-2.15)Business
Availability

IBR

0.2210(2.29)
HhsizeIncomeCase A-3: Rural
Food
Security(3.95) **(-1.42)
FemaleCase A-2: Urban
Food
Security(27.24)30(2.67)**(3.98)

Θ

8.5276-1.0189-1.2074

15.07800.1351-0.8633-4.9649-0.9533-6.8136

λ(5.76) **-4.0009(-35.19)**0.6376(-0.54)0.7505(10.45) **-7.1425(0.53)- 0.0562(-25.02)**0.8114(-1.69) +3.6335(-30.28)**0.5958(-17.34)**
4.0938(-4.63) **(41.26)**(0.57)(8.86) **0.38(44.17)**(2.12) **(35.52)**(18.72)**-6.8068(-3.38)1.0151(7.86)**

1.1476(0.54)-10.4329(-2.33)0.8439(2.36)**

8.6273(2.74)

1.0388(7.35)**

4.6300(4.42)**

Constant

No. of Obs.
Joint
Significance0.4351(1.09)

5076

5087

5079

1382

1381

1381

3694

3706

Wald χ2(17)=3442**

Wald χ2(17)=1482**

Wald χ2(33)=1645**

Wald χ2(14)=801**

Wald χ2(14)=81**

Wald χ2(14)=661**

Wald χ2(16)=1151**

Wald χ2(16)=1080**

Wald χ2(16)=701**

Log likelihood-21145.74- 7761.94
κ-5714.69
Case B: Dep. Variable: Index Based Ranking(the first-stage probit estimates whether a household has taken a loan
for productive purposes (“MFI_productive”))- 2277.25- 1961.65-15359.73- 5461.78- 12784.15

Case B-1: Total

Age

IBR

Income0.2700Dependency

Caste_dum

0.0213-0.0035

0.17510.00690.0812(2.54)*(1.78)**(1.95)+(-0.30)(2.89)**(1.41)(2.68)**-0.0021-0.0001

0.0001-0.0016-0.00020.0000-0.0011-0.0001-0.0004+(-1.91)+(-1.18)Muslim

(-3.02)**(0.20)(-1.01)(-1.44)(0.17)(-1.65)-4.0562-0.02101.03443.24610.0058-0.01174.0785-0.0140

1.5676**(0.07)(-0.13)**(-0.33)(5.68)**---- 0.7161

0.0604-1.6155*(2.16)*(-8.81)****(-0.57)(5.21)**- 1.3695

0.0726-1.4096

Formal banking
sectorbanks(7.56)(2.60)**(-8.91)**---(- 2.02)- 3.4746

0.13430.5714---- 2.1236

0.09740.0213

**

+

-

-

*

(1.38)(0.04)(1.87)(1.15)-(- 2.19)

0.63310.01870.08431.35320.0282-0.01090.45660.01790.1038(8.02) **(3.17)**(2.68)**(7.47) **(1.92)+(-0.71)(5.49) **(2.70)**(2.47)*-5.2513-0.3748-0.36864.30710.3155-0.4903-3.6995-0.3940-1.7730(-7.60) **(-7.59)**(-0.67)(3.32) **(3.06)**(-4.73)**(-4.26) **(-7.09)**(-2.81)**3.93620.08741.47202.81630.04220.06813.75450.0665

1.9660(11.33)**(3.89)**(0.72)(1.11)**(2.43)*(11.52)****(3.62)**(11.00)1.3943-0.1676

0.88210.2542-0.4051-0.1265

1.7074-0.1435

1.0141(2.48) *(-3.98)**(3.61)**(0.08)(-1.64)(-0.49)(3.12) **(-3.38)**(3.37)**-1.2643-0.2928-0.3122-2.0580-0.6312-0.2021-0.8736-0.2482-0.4172+(-5.58)**(-1.01)(-0.63)(-2.40)*(-0.73)(-1.22)(4.51)**(-1.04)

11.5261

0.3885-1.9807

3.1718-0.92620.70087.50860.4787-0.1016(13.92) **(6.84)**(-2.39)*(1.80) +(-6.61)**(5.00)**(6.56) **(8.33)**(-0.11)5.97150.19250.71926.47100.1781

0.06105.29840.19581.0329**(3.57)**(1.16)(16.08)**(7.49)**(6.12)**

2.5681-1.546155.05130.79969.1825**(8.65)**(5.87)**(10.51)30.16280.89480.815935.9048(20.06)
Formal(3.15)**(-1.80)Business
Availability(2.80)**(12.20)
Hindu

Food Security

0.3770(- 3.58)
HhsizeIncome+(-3.66)Higher
Education

IBR

0.0408(-8.23)
Primary
Education

Income0.0110(-3.26)
FemaleIBR

Case B-3: Rural
Food
Security**(4.69)
Age_square

Case B-2: Urban
Food
Security3698

31

(loans)
Money(loans)(3.90) **(1.54)(0.26)(3.54) **(3.13)**(-1.80)+(4.07) **(0.74)(1.35)

16.12210.3274-1.6644-16.4192-2.15086.265923.53410.8363-4.6650+(5.23)**(4.17)**(1.85)+(-1.65)+lenders**(0.79)(-0.76)(-1.17)(-1.88)57.8164(7.33) **0.7378(1.25)-1.2549(-0.40)57.9332(3.94) **-0.4488(-0.38)0.9112(0.73)

45.1227(5.02) **0.5571(0.77)-1.2425(-0.27)8.3650-0.0901-2.1450

-

-

-

-

-

-

**

(18.04)-6.6020(-6.78) **(-2.76)**-0.8349(-14.63)**(-5.22)**
1.9543(1.36)

15.4018(12.30) **0.9678(10.94)**-1.0519(-22.18)**-1.4821(-0.84)-0.8986(-20.11)**-1.1644(-0.69)λ5.4197(9.41) **0.4482(13.41)**- 0.8789(1.05)-8.1994(-11.51) **- 0.6549(13.02)**0.7731(30.09)**2.3222(2.17)

0.4929(19.26)**

1.1413(11.05)**Constant

4.5311(2.75)
5076

0.5364(4.44)**
5078-1.0104(-1.02)5079-3.8102(-0.84)1382

0.0187(0.05)
1381

0.5572(1.46)
1381

4.8925(2.61) **3694

0.6277(4.66)**
3706-0.9498(-0.80)3698

Wald χ2(17)=3184**

Wald χ2(17)=499**

Wald χ2(33)=2166**

Wald χ2(14)=772**

Wald χ2(14)=176**

Wald χ2(14)=523**

Wald χ2(1)=1222**(2.91)
Friends/Relatives(loans)Whetherurban areasinΘ

No. of Obs.
Joint Significance

Wald χ2(16)=581

Wald χ2(16)=567**

Log likelihood-21370.03- 8122.59
κ-5791.19- 2334.57- 2283.85- -15504.9
5686.04- 12961.3
Note ** = significant at 1% level. * = significant at 5% level. + = significant at 10% level and values in parenthesis are thez-values. κ – Number of iterations was restricted to two due to non-convergence. λ - Inverse Mill’s ratio derived by the probit
model)

Whether a household is a client of any MFI (“MFI_access”)
Households with
MFIs

Total (Case A-1)
Urban(Case A-2)
Rural(Case A-3)

3908
1025
2883

Households without
MFIs

1419
360
1059

Average Poverty-Reducing Effect
IBR

S.E.

t value

IncomeS.E.

t value

Food
SecurityS.E.

t value

1.710
2.829
1.273

0.148
0.275
0.119

11.56**
10.28**
10.70**

0.057
0.038
0.051

0.00
0.01
0.00

18.23**
5.71**
14.38**

0.071
0.469
0.104

0.03
0.00
0.04

1.77+
166.08**2.70**

S.E.

t value

0.05
0.01
0.04

12.40**
28.36**
18.82**

Whether a household has taken a loan from MFI or from the group for a productive activity
Househol Household
ds with s without
MFIs
MFIs
Average Poverty-Reducing Effect
Food
IBR
S.E.
t value
Income S.E.
t value

Security

2794
2553
0.00 - 24.47**
2.454 0.148
16.59** - 0.087
525
860
0.01 - 18.95**
1.619 0.275
5.89** - 0.127
2269
1673
0.00 - 16.49**
2.414 0.115
21.07**- 0.07** = significant at 1% level. * = significant at 5% level. + = significant at 10% level.

Total (Case B-1)
Urban (Case B-2)
Rural (Case B-3)

Note

32

0.487
0.221
0.751

Table 3 Results of the Tobit Model - Main Covariate : Amount of Productive Loan
Dependent Variable - Poverty Measured Using Indexed Based Ranking
Explanatory
Variables

Case A-1: Total

0.0001

Income0.0000IBR
0.0001(7.80) **(-2.64)**(3.79)**IBR
Amount of
Prod. Loan
Age
Age_square
FemaleHigher
Education
HhsizeDependency

MuslimFormal banking
sectorFormal banks(loans)Money lenders(loans)Friends/Relatives(loans)
Whether inurban areas

Income0.0000Food
Security
0.0001(6.56) **(-0.03)(0.05)(1.81) +(-3.63)**(4.97)**

0.07280.27290.25100.08970.16210.14950.05880.2915(3.78)**(1.98) *(2.15)*(0.19)(1.98) **(1.67)(4.04)**-0.0010-0.0007-0.0017-0.0005-0.0008

0.0001-0.0007-0.0005-0.0020(-1.41)(-2.28)*(-2.18)*(-0.31)(-1.68)(0.02)(-0.80)(-1.35)(-2.47)*3.5435-0.33592.67402.5485-0.0711-7.0266

3.6331-0.4979

3.0135**(-1.75)(4.71)****(-1.66)(4.23)**(2.79)-0.70420.5849-1.5969(-1.81) *(3.52)**-0.7306**(-0.26)(-1.30)(6.25)-------0.22530.5040-1.5513(-3.88)**

--

--

--

(-0.57)(2.60)**(-3.82)**

1.4685

1.6370-------0.6634

1.5131

1.9674(-0.73)(3.85)**(1.61)------(-0.82)(3.47)**(1.96)

0.64050.10860.24601.26590.06200.29410.44660.11510.2646(7.66) **(3.25)**(2.61)**(7.63) **(1.27)(0.27)(4.44) **(2.51)**(2.78)**-2.2994-0.6410-2.6835

0.4230-0.4552-2.9371-3.4639-0.9356-2.5483(-2.37)*(-3.07)**0.53724.1021(2.72)**(10.18)****3.5655**(-2.39)*(-3.27)**(0.38)(-1.31)(-0.38)(-4.33)0.45973.86213.43770.4368-6.42313.5079**(2.10)*(-1.47)**(9.60)**(5.28)

1.0837-1.10314.2988-1.9028-1.590238.0534

1.7622-1.2397

4.1503(1.89) +(-5.05)**(6.27)**(-0.77)(-2.46)**(0.16)(3.03) **(-4.72)**(6.14)**-2.2786-2.1329

1.8454-4.6696-2.64448.4982-1.3715-2.3771

1.6284++*(8.53)**(3.22)**(-6.98)**(2.22)*(-1.72)(-3.47)**(0.03)(-1.84)(-6.17)**(1.98)

6.1135-0.0890-3.6678

11.1072-1.29918.80284.92030.6079-4.1004(9.07) **(-0.32)(-4.67)**(7.36) **(-2.72)**(0.84)(6.30) **(1.65)(-5.16)**5.51181.10701.48806.13380.7522

3.94765.03381.37451.4123(17.57) **(8.68)**(4.02)**(10.92) **(4.29)**(1.02)(12.98) **(7.55)**(3.77)**

8.7894

6.6972-14.8170-1.78294.3785-37.6282

26.3306

12.4767-21.2820(1.00)(2.45)**(-1.11)(-0.17)(1.59)(-0.42)(1.36)(2.14)*(-1.41)-10.8819

4.9489-28.6145-29.5721-5.522572.5853

1.8952

11.9165-47.1075(-1.76) +(2.35)*(-3.70)**(-2.41) *(-1.50)(1.61)(0.21)(4.00)**(-5.06)**44.1155

3.3603-9.106453.0438

1.1555-24.7174

36.01123

5.6079-10.0227(5.15) **(1.11)(-0.94)(3.55) **(0.28)(-0.23)(2.92) **(1.35)(-1.02)10.6499

0.8184-16.6977----(-3.17)
Business
Availability

IBR
0.0000(2.75)**(10.56)
Hindu

Income0.00000.2131(-3.48)Caste_dum

Case A-3: Rural
Food
Security
0.0000(3.39) **(7.06)
Primary
Education

Case A-2: Urban
Food
Security
0.000033--

(26.34) **Constant

No. of Obs.

(5.15)**(-18.52)**

--

--

--

--

--

2.4711(1.48)-4.1488(-5.79)**-14.8426(-7.20)**6.4506(1.64)-1.5415(-1.30)-76.3936(-0.31)

5.8872(3.11) **

3718

3730

3722

1022

1022

1022

2696

-4.1900(-4.27)**-15.5068(-7.48)**

2708

2700

Notes: ** = significant at 1% level. * = significant at 5% level. + = significant at 10% level and values in parenthesis are the z-values.

Table 4 Results of Tobit Model – Main Covariate: Total Amount of Loan
Dependent Variable - Poverty Measured Using Indexed Based Ranking
Case A-1: Total
Explanatory
Variables
Total Loan AmountAge
Age_square

FemaleIBR
0.0001(11.93) **Higher Education
HhsizeDependency

0.27660.2511

0.09200.16570.11670.0526

0.2914(2.04) *(2.21)*(0.20)(1.62)(1.48)(4.00)**-0.0007-0.0006-0.0018-0.0007-0.0009

0.0001-0.0003-0.0004-0.0020(-1.05)(-2.11)*(-2.24)*(-0.50)(-1.77)(0.01)(-0.41)(-1.15)(-2.46)**

3.6780-0.3226

2.69762.6800-0.0614-7.0287

3.7270-0.4862

3.0639(4.25)**(3.01)*(-0.22)(-1.30)**(-1.70)(4.77)****

0.6197-1.6742

--

--

--

-0.1595

0.5685-1.6631(3.75)**(-4.06)**

--

--

--

(-0.40)(2.92)**(-4.07)**-0.8692

1.52661.4204-------0.5689

1.7032

1.6472(-0.88)(4.03)**(1.39)------(-0.57)(3.89)**(1.64)

0.57770.08720.2512

1.16790.02900.30550.40130.10670.2627(6.97) **(2.60)**(2.65)**(7.21) **(0.60)(0.28)(4.18) **(2.30)*(2.74)**-2.2461-0.5200-2.8789

0.6284-0.3619-2.9594-3.4276-0.7555-2.8177****(-1.95)(-3.50)**(0.57)(-1.05)(-0.38)(-1.92)(-3.38)**3.24500.37173.91762.6776

0.2771-6.3695

3.34920.4902

4.1716**(1.34)(-1.44)(8.55)**(2.47)**(10.26)**-1.4688

50.01102.2076-1.2157

4.2872****(2.60)**(9.69)**(4.18)

1.5773-1.0418

4.3705-1.1923(-4.27)**(-4.79)**(6.340**(-0.49)(-2.31)*(0.05)(3.71)(-4.60)**(6.28)**-1.4463-1.9592

1.8840-3.3724-2.3550

8.9304-0.6501-2.2553

1.7533**(-5.80)**(2.10)**(-6.43)**(2.250*(-1.27)(-3.14)**(0.01)

5.7153-0.4414-3.0710

11.3539-1.4526

8.8535

4.1074-0.0755-3.2900**(-3.05)**(0.85)(5.24)**(-0.20)(-4.02)**0.70673.95824.89941.30681.5134**(7.16)**(4.01)**(-0.84)**(-1.60)(-3.85)**(7.75)

5.40491.05481.56175.9600**(4.07)**(1.02)(13.02)-6.5335-33.9900-63.2617-11.7440-3.5300(-1.91)(-0.33)(-2.82)**(-1.30)(-0.16)-16.5651

75.2041-87.0042-12.3734-33.4194banking**(8.29)**(4.20)**(10.89)-65.6581-8.3854

1.3260-64.4372(0.07)(-4.90)**-16.0145

102.9103banks(-5.67)

Money(loans)(6.29)-0.6755(17.40)Formal(loans)(-1.60)(-1.76) +(8.60)
FormalsectorFood
Security
0.0000(0.65)(3.82)**(-2.02)Business Availability

Income0.0000(2.32)*

0.0685(2.77)
MuslimIBR
0.0001(6.21) **(2.59)**(9.67)
Hindu

Income0.0000(4.59)**

0.1846(-3.43)
Caste_dum

IBR
0.0001(9.99) **Case A-3: Rural
Food
Security
0.0000(-0.03)(2.96) **(7.41)
Primary Education

Income0.0000(4.78)**

Case A-2: Urban
Food
Security
0.0000(0.18)**(-2.31)*

lenders-84.0651-10.1743

34

Friends/Relatives(loans)
Whether in urban
areas(-8.63) **(-3.22)**(-1.02)(-6.98) **(-3.79)**(0.94)(-4.74) **(-1.63)(-1.71)-49.8155-11.3501-6.6957-21.3312-11.1331-19.7145-72.8628-14.0131-15.2666(-4.10) **(-2.75)**(-0.38)(-1.24)(-2.29)*(-0.16)(-3.48) **(-1.61)(-0.74)10.20960.7404-16.7161

--

--

--

--

--

--

**

(4.65)**(-18.44)**

--

--

--

--

--

--

2.6328-4.0342-15.0037

5.9754-1.5276-88.4633

5.9426-4.1381-15.6778(-0.08)1022**(-4.20)**2708(-7.52)**2700(25.45)Constant(1.59)
3718

No. of Obs.
Note(-5.65)**3730(-7.25)**3722(1.56)
1022(-1.30)1022(3.07)
2696** = significant at 1% level. * = significant at 5% level. + = significant at 10% level and values in parenthesis are the z-values.

35Appendix 1: Descriptive Statistics and Definitions of the Variables
Variable

DefinitionAge

Age of household fead

FemalePrimary

HhsizeDepratioover60(Total)Caste_dum

Hindu

MuslimWithout Access to MFI loan
for productive purposes

Without Access to MFI

Mean

Obs

Mean

S.D.

Obs

Mean

Std. Dev.

Obs

S.D.

Mean

S.D.

(Total)3908

39.341

12.2411419

41.59914.0722794

39.37712.2962533

40.56713.292(Urban)1025

37.30011.531360

38.78312.704525

37.341

11.475860

37.89712.092(Rural)2883

40.06712.4041059

42.55614.3882269

39.84812.4331673

41.94013.671

Whether a hh head is female(Total)

3908

0.904

0.295

1419

0.929

0.257

2794

0.928

0.258

2533

0.891

0.312

(Urban)

1025

0.898

0.303

360

0.928

0.259

525

0.914

0.280

860

0.900

0.300

(Rural)

2883

0.906

0.291

1059

0.929

0.257

2269

0.932

0.252

1673

0.886

0.317

0.500

2794

0.523

0.500

2533

0.565

0.496

Education of the household head, 1= completed primary school, 0= otherwise.
(Total)HigherObs

With Access to MFI loan
for productive purposesWith Access to MFI

3908

0.552

0.497

1419

0.517

(Urban)*

1025

1.000

0.000

360

1.000

0.000

525

1.000

0.000

860

1.000

0.000

(Rural)

2883

0.393

0.489

1059

0.352

0.478

2269

0.413

0.492

1673

0.341

0.474

Education of the household head, 1= completed higher education, 0=otherwise.
(Total)

3908

0.022

0.146

1419

0.025

0.155

2794

0.015

0.122

2533

0.031

0.173

(Urban)*

1025

0.000

0.000

360

0.000

0.000

525

0.000

0.000

860

0.000

0.000

(Rural)

2883

0.029

0.169

1059

0.033

0.179

2269

0.019

0.135

1673

0.047

0.211

Household size: number of household members(Total)

3908

5.075

2.024

1419

4.913

2.038

2794

5.253

2.053

2533

4.788

1.974

(Urban)

1025

4.780

1.844

360

4.439

1.756

525

4.798

1.942

860

4.626

1.751

(Rural)

2883

5.180

2.075

1059

5.075

2.102

2269

5.358

2.064

1673

4.871

2.075

Dependency Ratio (Ratio of household members under 15 ortothetotal)
3908
0.563
0.253
1419

0.626

0.274

2794

0.536

0.240

2533

0.628

0.273

(Urban)

1025

0.602

0.262

360

0.702

0.276

525

0.553

0.237

860

0.674

0.277

(Rural)

2883

0.549

0.249

1059

0.600

0.269

2269

0.531

0.241

1673

0.605

0.269

Whether a household belongs to scheduled caste or not(Total)

3908

0.685

0.465

1419

0.693

0.462

2794

0.711

0.453

2533

0.660

0.474

(Urban)

1025

0.748

0.434

360

0.792

0.407

525

0.819

0.385

860

0.723

0.448

(Rural)

2883

0.663

0.473

1059

0.659

0.474

2269

0.686

0.464

1673

0.628

0.483

Whether a household head is Hindu or not(Total)

3908

0.769

0.422

1419

0.792

0.406

2794

0.758

0.429

2533

0.794

0.404

(Urban)

1025

0.930

0.256

360

0.911

0.285

525

0.926

0.262

860

0.924

0.264

(Rural)

2883

0.712

0.453

1059

0.752

0.432

2269

0.719

0.450

1673

0.727

0.445

Whether a household head is Muslim or not

36

(Total)Business
Availability

3908

0.115

0.319

1419

0.106

0.308

2794

0.137

0.344

2533

0.085

0.279

(Urban)

1025

0.059

0.235

360

0.086

0.281

525

0.057

0.232

860

0.071

0.257

(Rural)

2883

0.135

0.341

1059

0.113

0.317

2269

0.156

0.363

1673

0.092

0.289

Whether there is a business opportunity available to the household(Total)

3908

0.412

0.264

1419

0.375

0.263

2794

0.447

0.267

2533

0.353

0.252

(Urban)

1025

0.548

0.196

360

0.529

0.196

525

0.605

0.165

860

0.505

0.204

(Rural)

2883

0.364

0.268

1059

0.322

0.262

2269

0.411

0.272

1673

0.274

0.238

0.443

0.497

2794

0.419

0.494

2533

0.377

0.485

Formal Banks

Whether a household has any transaction with the formal bank(transaction)(Total)

3908

0.383

0.486

1419

(Urban)

1025

0.482

0.500

360

0.497

0.501

525

0.531

0.499

860

0.458

0.499

(Rural)

2883

0.348

0.476

1059

0.424

0.494

2269

0.393

0.489

1673

0.335

0.472

Formal Banks

The balance of loan of a household from the formal bank(loan)(Total)

3908

0.002

0.018

1419

0.003

0.019

2794

0.002

0.013

2533

0.003

0.022

(Urban)

1025

0.004

0.027

360

0.006

0.035

525

0.003

0.020

860

0.005

0.034

(Rural)

2883

0.002

0.013

1059

0.002

0.008

2269

0.002

0.011

1673

0.002

0.012

Money lenders

The balance of loan of a household from Money lenders(loan)(Total)

3908

0.011

0.040

1419

0.012

0.061

2794

0.013

0.055

2533

0.009

0.035

(Urban)

1025

0.007

0.022

360

0.006

0.016

525

0.007

0.019

860

0.007

0.022

(Rural)

2883

0.012

0.044

1059

0.014

0.070

2269

0.014

0.060

1673

0.010

0.040

1419

0.006

0.023

2794

0.005

0.023

2533

0.004

0.016

Relatives
friends(loan)

andThe balance of loan of a household from relatives and friends(Total)

3908

0.004

0.019

(Urban)

1025

0.005

0.019

360

0.006

0.024

525

0.007

0.025

860

0.004

0.016

(Rural)

2883

0.004

0.019

1059

0.006

0.023

2269

0.005

0.022

1673

0.004

0.017

Formal Bank Availability (share of the households with access to formal banks at the village level- Excluding microfinance)

Urban_dum(Total)

3908

0.398

0.200

1419

0.402

0.194

2794

0.399

0.206

2533

0.399

0.190

(Urban)

1025

0.494

0.224

360

0.463

0.225

525

0.526

0.230

860

0.462

0.218

(Rural)

2883

0.364

0.178

1059

0.381

0.177

2269

0.369

0.188

1673

0.367

0.164

0.440

1419

0.254

0.435

2794

0.188

0.391

2533

0.340

0.474

Whether a household is in urban areas or not(Total)

IBR indicator

3908

0.262

Indexed Based Ranking of a household's wellbeing(Total)

3718

25.14

11.753

1358

23.52

11.88

2643

25.736

11.257

2433

23.58

12.29

(Urban)1022

34.057

11.229360

30.83612.027523

35.987

10.529859

31.532

11.782(Rural)2696

21.75710.057998

20.87510.6682120

23.207

9.918

1574

19.24510.21137

Appendix 2: Results of Propensity Score Matching: Effects of MFIs in Reducing Poverty(Estimation using Bootstrapped Standard Errors, 100 Rps.) for Total Sample

Whether a household is a client of any MFI (“MFI_access”)
Householdswith MFIs
Nearest Neighbour Matching
Total (Case A-1)
3908
Urban(Case A-2) 1025
Rural (Case A-3) 2883

Households
without MFIs

Average Poverty-Reducing
EffectS.E.

t value

1059
275
772

2.084
4.038
0.769

0.48
0.914
0.574

4.339**
4.420**
1.340

Kernel Matching
Total (Case A-1)
Urban (Case A-2)
Rural (Case A-3)

1419
360
1058

1.705
3.212
1.095

0.287
0.693
0.364

5.932**
4.635**
3.011**

3908
1025
2883

Whether a household has taken a loan from MFI or from thegroup for a productive activity(MFI_Productive)
Householdswith MFIs
Neighbour

NearestMatching
Total (Case B-1)
Urban (Case B-2)
Rural (Case B-3)

2794
525
2269

Households
without MFIs

Average Poverty-Reducing
EffectS.E.

t value

1226
311
868

0.182
0.888
2.488

0.475
1.088
0.501

3.829**
0.816
4.970**

Kernel Matching
Total (Case B-1)
2794
2521
0.292
2.29
7.848**
Urban (Case B-2) 525
840
0.525
1.865
3.553**
Rural (Case B-3) 2269
1669
0.357
2.489
6.973**
Note a ** = significant at 1% level. * = significant at 5% level. + = significant at 10% level.

38

Appendix 3: Results of Propensity Score Matching: Effects of MFIs in Reducing Poverty(Estimation using Bootstrapped Standard Errors, 100 Rps.) for the Poor and the Moderately Poor.
Whether a household is a client of any MFI (“MFI_access”)
Households
Households
Averagewith MFIs
without MFIs
EffectPoverty-Reducing
S.E.

t value

For the Poor
Nearest Neighbour Matching
Total (Case A-1)
1184
Urban(Case A-2)
78
Rural(Case A-3)
1106

351
24
324

0.735
0.603
0.91

0.331
1.108
0.359

2.119*
0.544
2.535*

Kernel Matching
Total (Case A-1)
Urban (Case A-2)
Rural (Case A-3)

495
39
449

0.86
0.682
0.863

0.207
0.939
0.212

4.149**
0.762
4.071**

Nearest Neighbour Matching
Total (Case A-1)
2493
Urban(Case A-2)
397
Rural(Case A-3)
2096

740
127
587

0.767
2.111
1.268

0.394
0.739
0.428

1.948*
2.854**
2.96**

Kernel Matching
Total (Case A-1)
Urban (Case A-2)
Rural (Case A-3)

960
183
775

1.22
1.574
1.186

0.24
0.534
0.287

5.079**
2.950**
4.125**

Whether a household has taken a loan from MFI for a productive activity (MFI_Productive)
Households
Households
Average
Poverty-Reducing
with MFIs
without MFIs
S.E.
Effectt value

1184
78
1106

For the Moderately Poor

2493
397
2093

For the Poor
Nearest Neighbour Matching
Total (Case A-1)
749
Urban(Case A-2)
11
Rural(Case A-3)
738

373
10
384

0.869
0.091
0.956

0.33
2.501
0.358

2.607**
0.036
2.667**

Kernel Matching
Total (Case A-1)
Urban (Case A-2)
Rural (Case A-3)

749
11
738

914
83
805

1.056
-0.619
1.088

0.214
2.031
0.247

4.941**-0.3054.408**

Nearest Neighbour Matching
Total (Case B-1)
1740
Urban(Case B-2)
173
Rural(Case B-3)
1567

794
109
675

1.891
1.827
2.172

0.338
0.77
0.434

5.59**
2.371*
5.001**

Kernel Matching
Total (Case B-1)
Urban (Case B-2)
Rural (Case B-3)

1695
408
1303

2.228
2.046
2.200

0.244
0.482
0.262

9.114**
4.241**
8.385**For the Moderately Poor

1740
173
1567

39