Heterogeneous Impact of Microcredit:
Revisiting the Evidence from the Randomized Experimentin Hyderabad, India

Eduardo LucioMay 2013

Heterogeneous Impact of Microcredit:
Revisiting the Evidence from the Randomized Experimentin Hyderabad, India

A Thesis Submitted
to the Graduate School of Public Policy,
The University of Tokyo
in partial fulfilment of the requirements
for the Degree

Masters of Public PolicybyEduardo Lucio

Advisor: Professor Yasuyuki Sawada

May 2013

Abstract

The study revisits the experimental data used in the paper “The Miracle of Microfinance?
Evidence from a Randomized Evaluation” by Banerjee, Duflo, Glennerster, and Kinnan (2010).
The analysis identifies three household types in the sample and the varying impacts of
microcredit on each group. The analysis showed that even in the short-run, microcredit does
affect different aspects of human development previously considered as non-responsive whensample households were analyzed as a whole. The analysis shows fragmented positive impact of
microcredit to specific household types in aspects of new business creation, investment indurables, health, and women empowerment. The most disadvantaged household group in thesample benefits from microcredit with new business creation and lesser severity ofsickness/accident. While the relatively better-off household groups benefit with the expansion inbusiness and household durables spending, higher sense of financial betterment, and stronger
women empowerment for education related spending. Overall and on the sub-cluster level, theevidence show no significant short-run impact on total household income, general healthexpenditure, overall women empowerment, and actual investments in education.

Keywords: microcredit, randomized-controlled trial

Table of Contents
Section

Page Number

Outline of Thesisi

List of Tables and Figure

ii

1. Background1

2. Experimental Design and the Microcredit Product4

3. Does Household Heterogeneity Matter?

5

4. Partitioning and Identification of Household Grouping

6

4.1 Clustering Results
5. Impact Assessment on Different Household Groups

912

5.1 Business Start-up and Entrepreneurial Performance12

5.2 Income, Consumption, and Expenditure13

6. Impact on the Indicators of Social Well-Being

16

6.1 Health Outcomes17

6.2 Women Empowerment and Education

18

7. Conclusion and Policy Recommendation

20

References

22

Data Source

24

Appendix25Outline of Thesis
Section 1 briefly discusses the developments in microcredit since its inception. It also provides
an overview of the current issues and debates surrounding microfinance program evaluation
methodologies and existing empirical evidences on the effectiveness of microcredit. The section
likewise presents the motivation and contribution of this study.
Section 2 presents the details of the microcredit intervention and experimental design
implemented in Banerjee et.al (2010).
Section 3 reviews existing studies in microfinance program evaluation that relates the varying
effects of microcredit on different borrower types. It provides a short summary of the findings
for each experiment that considers heterogeneity of the microfinance client.
Section 4 discusses the procedure which the study uses for identifying the different household
types in the dataset. It shows the specification and the methodology for identifying clusters in thesurvey and census data. Meanwhile, Section 4.1 presents and discusses the results of theclustering.
Section 5 presents the impact estimates of microcredit on the different sub-groups formed in
Section 4. It starts with a description of the intent-to-treat equation to be estimated, and proceedswith the presentation and interpretation of outputs using variables reflecting the different aspectsof entrepreneurial performance (Section 5.1) and the various indicators related to householdincome, consumption, and expenditure (Section 5.2). The discussion of results from the different
equations is compared with the output of Banerjee et.al (2010).
Section 6 continuous the discussion of the intent-to-treat estimates in the previous section, but
focuses more on the different areas of household well-being. Section 6.1 provides a discussion of
the estimates for health related variables, while Section 6.2 shows the output for variables related
to women empowerment and education.
Section 7 wraps up the findings of the whole study and provides a brief recommendation on how
microcredit could be designed to address the specific needs of the different borrowers.

iList of Tables and Figure
Figure 1: Baseline and Endline Clustering

11

Table 1: Control-Treatment Distribution

11

Appendix
Balancing Tables and Descriptive Statistics:
Table A.1: Baseline Balancing Table

25

Table A.2: Endline Balancing Table

26

Table A.3: Intra-Cluster Baseline Balancing Table

27

Table A.4: Intra-Cluster Endline Balancing Table

29

Impact Assessment Estimates:
Business Start and Performance
Table B.1: New Business Start

30

Table B.2: Closed Business

30

Table B.3: Business Profit

30

Table B.4: Business Revenue

31

Table B.5: Business Cost

31

Table B.6: Wages Paid to Non-Household Members

31

Table B.7: Number of Employees (Non-Household Members)

31

Table B.8: Business Operation with Help from Household Members?

32

Table B.9: Total Working Hours Rendered by Membersof Households in the last week (total man-hours)

32

Table B.10: Number of Casual Workers (Non-Household Members)

32

Table B.11: Average Hours Rendered by the Person Most Responsible in the Business

32

ii

Table B.12: Number of Days Rendered by the Person Most Responsible in the Business 33
Table B.13: Business Inventory

33

Table B.14: Business Asset Spending for the Past Year

33

Table B.15: Poultry: Number of Birds Owned

33

Table B.16: Livestock: Number of Cows Owned

34

Table B.17: Livestock: Number of Goats, Sheep, and Pigs Owned

34

Table B.18: Livestock: Number of Other Large Animals Owned

34

Income and Spending
Table C.1: Impact on Household Income: Work Earnings

35

Table C.2: “How do you feel about your overall financial situation?”

35

Table C.3: Total Household Spending

35

Table C.4: Food Spending

35

Table C.5: Temptation Goods Spending

36

Table C.6: Celebration Spending (excluding Wedding)36

Table C.7: Household Asset Spending

36

Table C.8: Household Repair Spending Amounting to More than Rs.500

36

Table C.9: House Waterproofing

37

Table C.10: Shared Latrine

37

Health Indicators and Health Shock Coping Mechanisms
Table D.1: Medical Related Spending

38

Table D.2: Health Expenditure due to Sickness or Accident

38

Table D.3: “Did the sickness affect your work or school?”38

Table D.4: Number of Days Missed in School or Work due to Sickness/Accident

38

iii

Table D.5: Coping with Health Shock though Other Funding Sources

39

Table D.6: Coping with Health Shock though Gifts

39

Table D.7: Coping with Health Shock though Borrowing from Moneylenders

39

Table D.8: Coping with Health Shock though Other Financing Mechanisms

39

Table D.9: Coping with Health Shock though Borrowing from Other MFIs

40

Table D.10: Coping with Health Shock though Borrowing from Relatives and Friends

40

Table D.11: Coping with Health Shock though Borrowing from Spandana

40

Impact on the Occurrence of Other Shocks
Table E.1: Incidence of Property Shock41

Table E.2: Incidence of Job/Business Loss

41

Indicators of Women Empowerment
Table F.1: All Decisions

42

Table F.2: Women Decision on Education and Education Related Spending

42

Table F.3: Women Decision on Food Expenditure

42

Table F.4: Women Decision on Health42

Table F.5: Women Decision on Clothing

43

Table F.6: Women Decision on Home Improvement

43

Table F.7: Women Decision on Spending on Durables

43

Table F.8: Women Decision on Investments in Gold and Silver

43

Education
Table G.1: Education Spending

44

Table G.2: Number of Enrolled Individuals in the Household

44iv

1. Background
Microcredit has evolved significantly since its inception more than 30 years ago. Starting as
small collateral-free loans given to groups of female borrowers in a rural village in Bangladesh
by Mohammad Yunus, it has now spurred global a movement -- mobilizing funds fromindividuals and global organizations towards a common goal of eradicating poverty. The idea of
microcredit is simple and inspiring: by lending small amount of money to poor female borrowers,
it empowers them, enables them to start small businesses, expand their income, and improvehousehold’s investments in health and education. The simplicity of such solution, highlighted bysome anecdotal evidences of microcredit’s effectiveness, created a momentum which brought
private foundations, aid donors, and public sector together in supporting these institutions inproviding small credits to poor households.
In the 2013 Microfinance Summit, the focus has turned into formulating strategies in broadening
the reach of microfinance 1 as an inclusive and effective poverty reduction tool. The summit
highlights the importance of forging public-private partnerships and strengthening collaborations
between government agencies, financial regulators, and global organizations. The summit
believes that microfinance institutions create an effective bridge in delivering education, healthprograms, and livelihood trainings to the marginalized individuals which can eventually end
global poverty.
With all the hypes propelling massive efforts and support into microcredit, how effective is it in
actually improving the lives of the poor? Is every dollar and time allocated for the program worth
spending? Ironically, empirical evidences still lack consensus. Despite the strong support that the
program receives, results from academic researches remain fragmented and polarized. These
different views about the effectiveness of microcredit have consequently sparked long exchangesof ideas between researchers in the field of development economics. Among the most prominent
discussions in the area is between economists who support the effectiveness of microcredit indelivering what it promised to bring, and those who believe otherwise2.
The most popular debate perhaps is between Mark Pitt and Shahidur Khandker (Brown
University and World Bank, respectively) and Jonathan Murdoch and David Roodman (NewYork University and Center for Global Development). In 1998, Pitt and Khandker published oneof the highly cited empirical studies upholding the effectiveness of microcredit. The study, basedon a quasi-experimental methodology applied in Bangladesh, shows that microcredit is effectivein bringing positive impact to poor households. The paper shows that the program strongly and1

The term microfinance refers to a set of products and services that cater to individuals who are underserved by the
traditional financial market. The more popular microfinancing tools include microcredit, microsavings, andmicroinsurance.
2
Goldberg (2005), Odell (2010), and Duvendack et.al (2011) provide a comprehensive review of the different
microfinance impact assessment studies available up to 2011. These papers illustrate the progression of impact
evaluation approaches in the years covered, together with varying results that each study unfolds.

1

positively improves household income, school participation of girls, and health of children inborrowing households, especially when loans are given to women.
The validity of the results from Pitt and Khandker (1998) was questioned in Morduch (1998).
Using a simpler statistical methodology applied on the same data, Morduch (1998) foundinsignificant effects of microcredit on poverty and other human development indicators.
However, the estimates show that microcredit allows households to have better consumption
smoothing.
Khandker released a follow-up study in 2003 to further strengthen the findings of Pitt andKhander (1998). Using extended dataset from the same intervention in Bangladesh, the results
show stronger impacts of microcredit. Khandker (2003) concluded that for longer time periods,
there is a substantial reduction in poverty rates in areas with access to microcredit.
Few years after, these three studies was jointly revisited and re-estimated in Morduch andRoodman (2009). Using the same dataset, they found weak overall impact of microcredit;
undermining the foundation of the empirical evidence supporting the ‘miracle’ whichmicrocredit claims to bring. The study of Roodman and Morduch suggests that although there
are lots of anecdotal stories on the success of microcredit in improving the lives of the poor,
there is not enough evidence to substantiate such claims. Roodman even suggests that access to
microcredit to the poor should be discouraged and institutional support to microcredit institutions
should be reduced. Microcredit brings similar effect as any ordinary loan. A small loan might
help, but anything in excess is harmful and reduces borrower’s freedom. There is no guarantee.
According to Roodman, access to such service can make the already risky lives of the poor muchriskier. Because of the inherent risks carried by borrowers and microcredit companies,
government and development institutions should instead promote the use of other microfinancing
tools such as the microsavings and microinsurance (Ramnath, 2012).
In late 2000s, a number of new procedures for microcredit impact evaluation studies have started
to come up. The randomized control trials (RCTs), a method that is commonly used in medical
studies, have started to be used for evaluating microcredit effectiveness. The results of thesestudies are mixed. Although most findings do not contradict the findings of Pitt and Khandkher(1998) and Khandker (2003), they do not strongly support them either. Most paper would
suggest that microcredit positively impacts some aspects of business creation and humandevelopment, but the total effect on poverty reduction is not as ‘miraculous’ as normativeknowledge suggests. Most empirical evidences provide no conclusive support of microcredit
being the panacea for poverty, at least in the short term.
The discussion between the supporters and critics is still ongoing. But the main argument thatthese debates highlights is the need for a continued development in microcredit program
evaluation. For the amount of support that microcredit is getting, all the hypes about its
effectiveness would be better substantiated by sufficient evidence.
2

The paper “The Miracle of Microfinance? Evidence from a Randomized Evaluation” by
Banerjee, Duflo, Glennerster, and Kinnan (2010) is one of the first microfinance impact
assessment studies which use RCT methodology. It concluded that access to microcredit
generally brings positive impact when introduced to the poor. Empirical evidence shows thatmicrocredit brings positive impact on business creation and allows increase in consumption
spending and investments. However, estimates from Banerjee et.al (2010) suggest that the
introduction of microcredit do not provide any significant improvement in other aspects of social
well-being such as women empowerment, health, and education.
This paper revisits the same set of data from Banerjee et.al (2010) and dwells further into theheterogeneous impact of microcredit. The contribution of this research comes by looking into theeffect of microcredit on the different types of households within the sample. The paper augments
the results of the original study by complementing existing program evaluation techniques with
exploratory tools used in other disciplines. As Duvendack et.al (2011) mentions, there is stillmuch room for exploring microfinance evaluation methodologies. Employed experimental
techniques can be further enhanced by combining other quantitative and qualitative tools to gain
better understanding of the underlying dynamics operating microcredit and its resulting
outcomes.
It has been noted by some economists that the differences in characteristics across households
could mask the treatment-effect. This research deals with this issue by partitioning observed
households and classifying them into more homogeneous groupings. Through this, the paper wasable to identify the differing aspects which microcredit addresses for each household type. The
findings in the analysis illustrate how households respond differently with the introduction of
microcredit in terms of business creation, investment and consumption behaviour, healthexpenditure, and education.
The results show that microcredit does create impact in some human development indicatorseven in the short-run. Aside from the generally positive effect on business creation andinvestments in household durables, estimates from the analysis show that microcredit also
provides significant positive impact in other aspects of human development such as household
health, time spent for leisure, and women empowerment in education related decisions. It must
be noted however, that such substantial effect is fragmented and varies on each household type.
Microcredit best addresses different needs for specific household types.

3

2. Experimental Design and the Microcredit Product3
The microcredit impact assessment studied by Banerjee et.al (2010) uses 52 pairs of slum areas
where no existing microfinance institution is operating. These areas were considered bySpandana, a large microfinance organization, as locations where they intend to start operation.
Sample areas were selected based on the criteria of having sufficient potential borrowers but withno present microcredit company covering the specific market. The matching of the 52 pairs wasdone based on the similarities of each slum area in terms of the per capita consumption, debt
holding of households, and business ownership. Then, one area on each pair was randomly
assigned in the treatment group, while the other one remains in the control group. Spandanatargeted borrowers who are relatively poor, but not identified as the “poorest of the poor.”
Prior to the introduction of the microcredit in treatment areas, a baseline survey was done in2005 involving 2,440 households. The survey collected information on household characteristics,
education, employment, asset ownership, business ownership and operations, borrowing, andhousehold savings. Between 2006 and 2007, Spandana started operating in the 52 new markets
under the treatment areas. Between the same periods, other microfinance companies also started
offering microcredit products both in the treatment and control areas. To measure the effect of
the intervention, an endline census involving 6,798 households was done in sample areas fifteen
to eighteen months after the introduction of Spandana.
Similar to a traditional microcredit facility, Spandana’s product was primarily targeted towards
groups of 6 to 10 female borrowers within the age range of 18 to 59 years old. Borrowers must
be residing in the same area for at least one year, have valid identification, and at least 80 percentof the group members are house owners. Borrowers are free to use the money in whichever way
they want as long as they can ensure repayment. The initial loan size amounts to Rs. 10,000, witha typical loan term of 50 weeks, and an interest rate of 12 percent (non-declining balance). The
group is qualified to avail for a second loan only after full repayment of the first loan by all
members of the group. Typical loan amount for the second round of borrowing is between Rs.
10,000 to Rs. 12,000, but may reach up to Rs. 20,000.

3

See Banerjee et.al(2010) for a more detailed description of the experiment.

4

3. Does Household Heterogeneity Matter?
The heterogeneity of household characteristics is implicit within any large community such as inHyderabad, India4. It has been noted in some RCT-based microfinance impact evaluations thattreatment effects can be different depending on the household characteristics. Microcredit
evaluations of Crepon et.al (2011), Karlan and Zinman (2010), and Banerjee et.al (2010)
considered the variations in household characteristics in determining the heterogeneous effectbrought by microcredit. The study of Crepon et.al (2011) in rural areas of Morocco revealed thatexisting business owners prior to the intervention decrease their consumption of durables andnon-durables as they build up their capital to further expand their business. Meanwhile, nonbusiness owners increase spending in food and durables, while there is no discernible effect on
business outcomes. Karlan and Zinman (2010) studied the effect of microcredit for previously
rejected borrowers in Manila, Philippines. They estimated the varying intervention impacts
conditional on the gender and income of borrowers. The results show that there is a significant
increase in profits of household businesses if the borrower is a male, while there is no significant
effect if the borrower is a female. In addition, they also found out that there is a larger
improvement in profit for households who belong to the higher income group, than to those in
the lower income group.
Banerjee et.al (2010) explored the heterogeneous impact of the microcredit in Hyderabad, India
conditional on each household’s existing business or their propensity to become business owner.
The results show that households who are business owners increase their investments in durable
goods while their consumption of non-durables did not differ. Meanwhile, for those who do not
own a business but have high propensity to start a new one, access to microcredit allowed themto increase their investment in durable goods and reduce their consumption of non-durable
commodities. This can be associated to the process of building up initial investment to engage in
new proprietary venture. Lastly, households who are not existing business owners and have lowtendency to start a business increased their consumption of non-durables. Microcredit enabled
them to adjust their consumption pattern through borrowing against their future income. In allcases, welfare effect is not clear. The expansion of current business and the increase ininvestment in durables do not guarantee improved business profitability or sustainability.
Similarly, increasing consumption today may or may not be beneficial for the household in the
future.
These studies have highlighted the fact that microcredit can bring varying impacts to different
borrower profile. This research extends the study of Banerjee et.al (2010). The study explores theembedded heterogeneity in the sample households through the different demographic
characteristics, and assesses the magnitude of impact of microcredit for each identified subgroup.

4

The 2011 census estimated a total population of 7,749,334 individuals residing in Hyderabad, making it the fourthmost populous city in India. (http://www.censusindia.gov.in)5

As mentioned by Odell (2010), though there have been vast improvements in evaluation
procedures, most procedures only measure average impacts. Hence, even if the program brought
positive effects to half of program recipients, but had negative impact to the other half, theaverage effect will most likely be insignificant. Therefore, the evaluation measures may suggestthat the microfinance program is ineffective. This paper attempts to bridge such deficiency andshow that the microcredit intervention does create positive welfare impact even in the short run
to specific household types.

4. Partitioning and Identification of Household Grouping
Using the information collected from the baseline survey and endline census of Banerjee et.al(2010), the research attempts to identify different household groupings through an exploratory
multivariate approach commonly used in identifying underlying patterns in large datasets. We
apply clustering algorithm on the dataset after recognizing the similarities or dissimilarities
between households. The basic objective of the cluster analysis is to form groupings by keeping
observations in each group similar to each other, while keeping every group dissimilar from theother group (Sharma, 1996).
It is recognized that creating partitions using the dataset’s intrinsic heterogeneity is heuristic andexploratory in nature. Consistent clusters may or may not be formed given the sample’scharacteristics. However, applying the procedure on a randomized experiment may provide
useful information on the verification of the correctness of the sampling implementation.
Assuming we can produce interpretable household groupings, then clusters in both the baseline
and endline dataset must also be similarly formed. There must also be treatment-control balancein most non time-varying variables even on the sub-cluster level.
The exploratory clustering was implemented using a set of variables reflecting the household’s
education level, standard of living, wealth, financial stability, income source, and access tofinancial services. The following household head information was used: age, gender, indicatorfor literacy, highest academic achievement, and type of work. In addition, the following
household level information were also included: number of household members, ownership ofother land in the village, indicator for house ownership, latrine type, monthly expenditure,indicator for health expenditure more than Rs. 500, and access to formal savings and insurance.
Typical clustering procedure cannot analyze datasets with missing information. In the case ofhousehold surveys, missing values and responses like “refuse to answer” and “don’t know” isvery common. The usual solution to accommodate cluster analysis even with such problem iseither to fill in the value for the missing information (imputation) or to drop the observation withmissing value (marginalization) (Wagstaff, 2004). Both procedures pose some limitations for thedataset that we are using. Replacement values generated by data imputation are only as reliable
as the assumptions used in creating these values. Meanwhile, marginalization is not an option
6

since we aim to preserve and assign each household unit in the sample into more homogenous
groupings. Therefore, the research performed a clustering procedure similar to Wagstaff (2004),
where it used the partially observed data to create soft constraints which enabled clustering withmissing values.
We implemented the clustering by initially measuring ‘distances’ between each household using
the set of observed characteristics in the dataset. We use the Gower’s dissimilarity measure forthis since it can jointly handle different types of variables, including objects with missing
information.

Consider a pair of households  and  in the dataset. Each household possess a set of  selectedcharacteristics which we decided to use for clustering. These information in the dataset were
defined such that it can be categorized to either represent qualitative (binary) or quantitative(ordinal and continuous) variables. Then, for each ,  = 1 …  and = 1 … , we can use theGower’s similarity measure
to assign a pairwise distance between each household. Gower(1971) prescribed the following measure:
=
where



    





 



  = 1 if the value of the kth variable is observed for both pairs, and   = 0 ,otherwise (i.e. missing for at least one of the pairs).

  is the distance between the household i and j using the kth observed characteristic,
defined by:
 =
1−

  =   ,"  −   ",[max' ( − min' (] 

 ,- , .,/0,0

1

The resulting  matrix will then contain the pairwise similarity measure between households.
Let 2 be this matrix. The Gower’s dissimilarity matrix can be computed as 3=' 445 − 2(4/7(Pekalska and Duin 2005).5
After which, the identified dissimilarity matrix 3 was used as an input to form groupings using a
specific algorithm. The paper used the Ward’s linkage method 6 to create groupings. Thisalgorithm forms clusters by initially joining two objects which least increase the group’s error

5

The transformation from similarity matrix to dissimilarity matrix before proceeding to the clustering algorithm isdue primarily on the limitation of the computational software used for the analysis. Either way, the resulting
clustering presented interpretable partitions.
6
Also known as minimum-variance method or error-sum-of-squares method in some multivariate textbooks.

7

sum of squares. The procedure will continue joining pairs of objects and clusters until all objects
are joined in one group.
To illustrate how the households are combined while minimizing the increase in the error sum ofsquares within the combined objects, assume we only have two clusters of households, 8 and 9with their respective within cluster 22:s :?> ‖@ ,  ∈ 822:; =  ‖=> − =>?C ‖@ ,  ∈ 922:B =  ‖=C − =CCombining these two clusters to form one bigger cluster, cluster C, we get?E ‖@ , . ∈ F
22:D =  ‖=E − =E?E = 'G =?> + I =?C (/ 'G + I (,  is the number of objects in cluster .
where =

Note that the total sum of squares from the entire household dataset is equal to the sum ofsquares errors once all the observations are formed in one cluster (Timm, 2002).
22: = J‖= − =?‖@ = K

By letting 22:E be the total sum of squares, and 22:> + 22:C the within cluster sum ofsquares, the marginal increase in the error sum of squares should not be greater than the between
cluster sum of squares. The incremental between cluster sum of squares 'LMNN( by joining
clusters 8 and 9 is:?> ‖@ + I ‖=C − =?C ‖@ = OLMNNGI = G ‖=> − =G I?> − =?C ‖@P ‖=G + I

For the initial condition where there is only one household in each cluster, this formula becomes@
>C/2, or the average of the square of the pairwise distances between households a and b fromthe dissimilarity matrix 3. By combining these two most similar households in the dataset, we@
will have a new incremental sum of squares proximity measure R>C = >C/2 . Combining?E .The increase in the error sum of squares
households a and b in one cluster will give a mean =from the union of these two households in one group, and its succeeding merging with otherclusters, can be computed using the Lance-William formula (Timm, 2002):

RES = ['T + G (R>S + S + C RCS − S R>C /'G + I + T (8

Where, R>C is the proximity measure between households a and b; c is the newly formed cluster

after joining a and b, and RES , R>S , and RCS are the distances between clusters c and d, a and d,
and b and d, respectively.
The clustering algorithm will continue merging different partitions until all the households arecontained in one group. The increase in the error sum of squares as pairs of clusters are
combined is illustrated in a “dendogram.” The number of clusters to be retained is based on thesudden “jump” in the error sum of squares within the group after joining specific clusters.

4.1 Clustering Results
The clustering procedure was applied on both the baseline and endline datasets to compare how
household groupings have changed between the fifteen to eighteen months interval of the twosurveys7. The cluster procedure produced somewhat similar groupings between the two periods.
Table A.1 to A.4 presents the descriptive statistics, treatment-control balance, and tests fordissimilarities between each of the clusters formed in the baseline survey and endline census.
Three groupings were retained for each time period. Tables A.1 and A.2 show the treatmentcontrol balance for the entire baseline sample and endline census, and for their respective subclusters. We can see from the table that households from the control and treatment areas in bothtime periods are almost similar in most of the non-time varying characteristics. The same holds
true for the treatment-control balance for each of the sub-clusters both in the baseline and endline.
Tables A.3 and A.4 display the between-cluster t-tests for the baseline and endline datasets. We
see in the baseline clustering that the third cluster contains most of the households who areheaded by females, and are generally older compared to the other two groups. Similarly, thisgroup receives the most financial aid among the three clusters. In terms of educational attainment,
the first and second group have higher literacy rate and completed higher grade level comparedto the third group. Among all the differences, the distinguishing factor of the first cluster is it isheaded by male individuals who are educated, but with low financial stability partly due to low
rate of property and house ownership and are employed in less stable income sources (i.e. salary
workers and casual labourers). In addition, these households contain household members who
are relatively sicklier compared to the second group. The second sub-cluster of households issimilar to the first in terms of the demographics, however, this group possess healthier household7

It could have been ideal if the dataset forms a perfect panel. If the dataset was formed in such a way, then theanalysis could have proceeded by performing the clustering using the baseline data, then preserving the clusteringresults and matching the grouping with the exact household in the endline. With such, we can perform anotherexploratory clustering using the endline values and compare how households either stayed in the same group, or
moved to another cluster. It would be interesting to explore and identify specific household characteristics which
affect such dynamics. However, due of the adjustments done by Banerjee et.al (2010) to accommodate for the lowloan take-up, the panel was not perfectly formed.

9members, more stable income source (wage earners and business owners), and higher rate ofownership of houses and other land in the village.
Meanwhile, for the endline dataset, the first cluster combined the households who have almost
similar profile as in the third cluster in the baseline (See Table A.4). These are female headedhouseholds, with lower level of educational training, and low level property and houseownership. The second and third clusters in the endline are mostly similar with each other interms of demographic profile (mostly male headed households). However, the households insecond sub-cluster have marginally higher index for property and house ownership compared tothe third sub-cluster. Lastly, between these two groups, the second group contain householdswith less healthy members.
To summarize, the formed sub-clusters in the baseline survey and endline census can begenerally characterized into the following descriptions: (1) households headed by educatedindividuals (mostly males) with less stable financial condition, and with sickly members (2)those headed by educated individuals (predominantly males as well) with financially stablecondition, and relatively healthy household members and by (3) those led by older individuals
with low level of literacy (mostly women) and relatively unstable income source. For the sake of
convenience, we will label these groupings as educated and sickly households (thus, ES),
educated and healthy households (thus, EH), and poor and low level of literacy (thus, PL)
households, respectively. The household group labels for the analyses in the paper were judged
mainly on the differences in the average values of the set of variables of the endline clustering.
Figure 1 and Table 1 show the distribution of the treatment and control households for eachhousehold clusters and the dendogram to visualize the aggregation of each household into
different groups. The table shows an almost balanced distribution of households between thethree groups both for the baseline and endline dataset. The resulting distribution is suggestive of
the effectiveness of the randomization design implemented in Banerjee et.al (2010). The control
and treatment groups seemed to be almost balanced in terms of number of households between
all the sub-groupings.

10

Figure 1:
1 Baseline and Endline Clustering

Table 1: Control-Treatment
ent Household Distribution
Baseline Clustering
Household Type
Control
Treatment
ES Households
638
604
EH Households
321358
PL Households
261
258
Total
1,220
1,220
Endline Clustering
Household Type
Control
Treatment
PL Households
1,035
ES Households
1,191
EH Households
1,008
Total
3,234

11

Total
1,242
679
519
2,440
Total
1,123
1,373
1,068
3,564

2,158
2,564
2,076
6,798

5. Impact Assessment on Different Household Groups
We employ a similar procedure used in Banerjee et.al (2010) in determining the varying impactof microfinance on the different household groups identified. This section compares the averages
of a number of variables of interest between the treatment and control areas for each householdgroup. We discuss the intent to treat (ITT) estimates for specific household groups, and relatethese to the results of the original paper.
The estimation for the ITT is specified as follows:,UUVWXY = ZUUVWXY + [UUVWXY ∗ ]^/ ,UUVWXY + _ ,UUVWXYsuch that {ab, cN, cd} ∊ dd/ R^ , ]^/ ,UUVWXY is an indicator for households residing intreated area, and [UUVWXY is the ITT effect for each household type identified in the previous
section.
It was mentioned in Banerjee et.al (2010) that the actual microcredit take-up in treatment areas
was considerably low to identify plausible treatment effects. Hence, the endline sampling was
adjusted such that it will accommodate more of those who have higher propensity to borrow. As
a result, the Spandana borrowers were oversampled. The authors corrected this by assigningweights to adjust for the oversampling.
The estimates for the microfinance impact on the different household groups involving variables
related to business, income and spending, household health, other shocks, indicators of women
empowerment, and education are presented in Tables B.1 to G.2. All estimates are weighted toaccount for the oversampling of Spandana borrowers, and the standard errors reported arecluster-adjusted at the slum area level.

5.1 Business Start-up and Entrepreneurial Performance
Banerjee et.al (2010) estimated that over the entire sample households, those residing in treatedareas are more likely to start a new business than the comparison group. However, factoring in
the differences of household types, we discover that the magnitude of impact to start a businessalso vary for every sub-clusters. Table B.1 reveals that among the three groups, only the mostdisadvantaged household group receives a statistically significant impact in terms of businesscreation.
The PL household receives the biggest impact. Estimates report that 17.7 percent of the PL
households in treatment areas opened a new business within fifteen to eighteen months after the
introduction of microcredit. This is 6.6 percent higher than the rate of opening a new business insimilar household type in control areas. For the ES and EH household type, although notstatistically significant, the direction of estimates are also consistent. The ES household group12

show that 12.7 percent of those who live in control area ventured into a new business, while thestart-up rate is 1.7 percent higher in treatment area. Lastly, 12.4 percent of the EH household
residing in control area reported opening a new business, while it is 2.3 percent higher forhouseholds in the treatment area.
The scale of business operation of enterprises which were not newly established can be classified
into two general categories. Looking into the average values of business profit, revenue and cost,
wages, employee, and inventory (Tables B.3 to B.13), it can be deduced that existing businesses
operated by the ES and EH households are much larger in scale compared to those owned by the
PL households. Relating this to the other aspects of business operations, the results show paralleloutcomes with the original paper. Businesses which were not newly opened a year ago did not
create statistically discernible impact on profitability, revenue, or total cost for all the three types
of households. However, the reduction of credit constraint in treatment areas created aninteresting and varying effect on the behaviour of households with existing businesses.
The general direction of the estimates in Tables B.6 to B.9 suggest that access to microcredit
could have prompted the PL business owners to reduce current business spending in preparation
for buying more business assets to expand the scale of operation. Wages paid by PL businessowners in treatment areas were substantially lower than the comparison group (Table B.6). The
average wage expenditure to non-household members were Rs.5,404 lower than the average ofthe control of Rs.7,200. However, the reduction in manpower may not be driven by the decline in
the number of hired workers. In fact, Table B.7 shows that the manpower employed by PL typebusiness owners does not systematically differ from the control group. It could be associated to
the reason that business owners may actually be asking workers to work lesser hours. In
exchange, businesses hire more casual workers (Table B.10) and owners render more of his/her
personal time in lieu of the savings from the reduced wages paid to regular employees. Although,the estimates are insignificant, the most responsible person in businesses owned by PL
households allot 1.27 hours more everyday and 0.22 days more for each week than in thecomparison group (Table B.11 and Table B.12).
Meanwhile, larger businesses owned by ES households were enabled with the access to
microcredit to invest in more business assets, thus improving the efficiency of business
operations and bringing reduced cost and enhancing profitability. Table B.14 shows that ES
households considerably invested in more business assets after the introduction of microcredit in
the area. Such businesses purchased an average of Rs. 1,408 in productive assets compared with
the Rs. 480 spending in the treatment group 8 . Further, although the estimate in Table B.3(column 3) is not statistically significant, this additional business assets investment could havecontributed to better business operation (i.e. reduced cost, Table B.5 Column 3) and driven profit
to reach almost thrice than the similar household group residing in control areas. Businesses by
8

Banerjee et.al (2010) similarly detected significant increase in overall spending for business durables for the wholesample households. They estimated a 127 percent higher business investment in treatment areas. The estimate forthis paper shows that this effect is captured mainly by the businesses owned by ES households.

13

ES households reported an average profit of Rs. 2,914, higher by Rs. 1,885 in comparison to theaverage of Rs. 1,028 in the control group. This is reasonable as ES households could have beenlong constrained in expanding and improving their business operation caused by the immediate
financing needs of its sickly members.
The outcomes for the existing business owned by EH households tell a related story. Since thesehouseholds are the relatively financially unrestrained compared to the other two groups even
without the microcredit, the intervention brought no substantial impact in their business
operation. One interesting finding is shown in the fourth column of Tables B.9 and B.10. The
additional funding opportunities brought by the intervention to the EH households allow
household members to enjoy more leisure time (7.8 hours free time in a week) compared to thecontrol group. The reduction of the rendered “free” man-hours by household members issubstituted by the additional, albeit statistically insignificant, 1.6 unit of casual worker. Further,
we can see from Table B.15 that EH households in treatment area significantly raise theirlivestock agriculture holdings. The poultry stock of EH households in treatment areas is almost
twice the number of poultry in the control areas. An average of 3.4 birds is owned by such typeof household with the presence of intervention, in contrast to the 1.7 birds owned by those
without the presence of Spandana.
In summary, microcredit brought some positive impacts not only in business start-up, but also in
business expansion, and reallocation of business resources. By household types, the microcredit
prompted PL households to start a new business, ES households to augment financing source toinvest in more business asset and expand existing business operations, and EH households toincrease the capacity of their livestock agriculture business and allow household members toenjoy more leisure. Consistent with Banerjee et.al (2010), the welfare effect of the creation of
new business and expansion of existing enterprises remain unclear in the short run. The evidence
in the short run does not strongly suggest substantially stronger profitability for households withhigher access to microcredit.

5.2 Income, Consumption, and Expenditure
One of the main arguments of microfinance advocates is that even through small credits,households can be lifted away from poverty. It allows them to expand their earnings bypromoting self-employment, and use this to eventually augment their investments in business,health, education, and other aspects of human development. Interestingly, evidences in the shortrun, show a reduction in the household work earnings (Table C.1). Although the results are notstatistically different from zero (as in Banerjee et.al (2010)), the direction show that the overall
average work earnings are lower by 13 percent for the treatment area when benchmarked against
the comparison group. On a subgroup level, it is lower by 13 percent for the PL households, 17
percent for the ES households, and 8 percent for the EH households. The estimate is insightful if
14

we connect these results with the previous section. We saw in Table B.9 that, among existing
business owners, the intervention allowed the three groups to spend more leisure time rather thanspend time working in the household business. Assuming we can generalize this to include evennon-business owners, this may suggest that the availability of microcredit drove household
members to work less in wage industries, involve themselves more in their own activity, or start
their own business. This result is similar to the findings of Crepon et.al (2011) where they foundthat borrowers in sampled area Morocco tend to supply less wage labour, and instead, consume
more leisure. Similarly, with the intervention, households gain more liberty with their time since
they know that there is an available funding source in case of an immediate need. Looking at the
fourth column of Table C.2, we also see that the presence of microcredit brings a significant
increase in the sense of financial improvement especially with the EH group. The EH households
feel 0.13 points financially better compared to the control group. The coefficients for the PL and
ES households are likewise positive, although insignificant. Despite this, the issue of whether or
not this will translate to an improved household’s earnings in the long run remains unclear.
Tables C.3 to C.8 summarizes the intent to treat estimates brought by the Spandana intervention
on aspects of total household spending and its subcomponents. In summary, these tables showthat microcredit do not provide any significant impact in augmenting total household spending
for all the three household types. The result of the analysis in this paper is consistent with
Banerjee et.al (2010). They were able to find a minute and statistically insignificant increase intotal per capita expenditure and spending on non-durables, but statistically significant increase in
the spending for household durables. For this analysis, the estimates for total household spending,food spending, temptation goods spending, and celebration spending are all insignificant for all
the three household groups. However, the model detected a small increment in the total
household spending (2.9 percent, Table C.3 column 1) and decent reduction in the consumptionof temptation goods9 (9.1 percent, Table C.5 column 1) for the entire households in the treatmentgroup. For the latter, the introduction of microcredit is often seen to act as a discipline
mechanism for all the types of households (Banerjee et.al 2010).
Another interesting finding is shown in the differences in the signs of the estimates in celebration
spending (Table C.6). Despite that estimates are all insignificant, the introduction of microcredit
facility allowed poorer households to enjoy celebrations more. In contrast to the previous notionthat treatment households are more likely to spend less on social events than households in thecontrol group (on the average) because they tend to save more10, the estimates here show that the
behaviour depends on the initial level of household’s financial capacity. The PL households can
now spend 8.2 percent more for these occasions compared to their counterparts without
microcredit intervention11. On the contrary, the signs for the ES and EH households are both
9

“Temptation goods spending” is the computed as the sum of monthly spending for outside meals, pan, tobacco,
intoxicants, lottery tickets, and gambling.
10
Banerjee et.al (2010) and Crepon et.al (2011).
11
It might also be important to note that a large share of female headed households is in the PL grouping. Theremight be causation between having a female household head and spending more with celebrations.

15

consistent with the previous findings. While the PL households are now “liberated” to enjoy
celebrations more, the two “wealthier” households can afford celebration spending even before
the intervention. Therefore, they would rather save these funds for business expansion and otherproductive activities.
Continuing with the other aspects of consumption, results in Table C.7 show that spending forhousehold durables significantly increased with the intervention on the aggregate level. With thepartitions in household types, we can see that among the three subgroups, the ES households
receive the most impact. ES households in treatment area spend 28.9 percent more on the
average than those in the control group. Household spending for such household types in thetreatment group averages Rs.11,848 compared to the comparison group’s average of Rs.9,214.
However, there is no discernible effect for the other subgroups.
We now look into how the expansion in household spending translated to an improvement in theliving standards for those with access to Spandana. This research uses two indicators in thequestionnaire to reflect the living standards. Tables C.9 and C.10 show the intent to treat
estimates for indicators of house waterproofing and shared latrine. 12 The results are notstatistically significant in terms of total and for each identified household types. However, it isinspiring to see that all the estimates point to the same direction: the availability of microcredit
improves the quality of living of the households in the area. Households living in the treatmentarea have higher propensity to have better waterproofed houses and have lesser tendency to use ashared latrine compared to the comparison group.

6. Impact on the Indicators of Social Well-Being
On top of the claimed impacts of microcredit in promoting entrepreneurial activities,
improvements in income, and expansion in consumption levels, supporters of microcredit havelong praised its ability to create ripples that benefits areas affecting human development. Amongthe most reputed aspects of microfinance is its ability to improve health, women empowerment,
and education. These indirect impacts of microfinance to health have been noted well in a
number of existing empirical researches. Assuming that microfinance is effective in increasing
household income, this would create improvements in the borrower’s living standards, watersupply, and sanitation, and eventually translate to improvements in household health13. In termsof microcredit, Banerjee et.al (2010) mentions that availability of such borrowing facility relaxes
the credit constraint of households, thereby allowing them to increase investment in health,household durables, and education.
12

House waterproofing is defined as an ordinal variable: i.e. 0-house is not waterproofed, 1-Some rooms arewaterproofed, 2-All rooms are waterproofed. Similarly, shared latrine is an indicator variable: 1-shared latrine, 0otherwise. The author assumed that, everything else remaining the same, it is more hygienic to use a private latrine
than a shared latrine.
13
See Begum et.al (2000), Morduch and Haley (2002), Pitt et.al (2003)

16

Leatherman and Dunford (2009) believes that these indirect impacts generate positive feedbacks
as well to the microfinance institution for two main reasons: First, this ensures the financial
security of the household and the client, and second, it helps support the long-term viability andgrowth of the microfinance institutions.

6.1 Health Outcomes
Prior to the propagation of the RCT-based impact evaluation, studies supporting the
microfinance’s impact on health and nutrition is much lesser than the findings supporting
microfinance as a tool for poverty alleviation. Although these existing evidences are few, theseare generally positive (Wright 2000, Morduch and Haley 2002).
Some of the latest RCT-based impact evaluations, however, provide mix results to support suchclaims. For instance, Banerjee et.al (2010) showed that there is no statistically significant effecton health spending in the short-run in areas where microcredit intervention was provided. Karlan
and Zinman (2010) detected both positive and negative impacts (but statistically insignificant) of
microcredit on health. In particular, treated applicants expressed having “very good” health but
generally higher mental stress compared to the controlled group. Meanwhile, Crepon et.al (2011)
in their experiment in rural Morocco showed a plausible increase in health spending with theintroduction of microcredit.
Among the three household types, only the PL and ES households possess much of theinformation about health events costing more than Rs. 50014. Tables D.1 to D.4 summarizes theresults of the intent to treat estimates for health related spending and the variables suggesting theadverse effects of these health events to work and school absenteeism.
Table D.1 shows that in general, microcredit does not create impact to health related
expenditures of households. The estimates for the whole group and all the three subgroups havevery low statistical significance. However, for health related expenditures conditional on
sickness or accident (Table D.2), we see a strong positive effect of the intervention to the PL
households. The PL households in treatment area reported Rs. 1,365 lower medical expenses lastyear compared to the control group. If we interpret the medical cost as an indicator of the scaleof the health event, then the estimates show that microcredit lessens the severity of the sickness
or accident for the PL household. However, there is no statistically discernible effect for the ESgroup. For the entire sample, these also (weakly) translate to reduction of expenditure by anaverage of Rs. 353 (p-value of 0.46). The reduction in the degree of health shocks of the sickly
households also translated to a slight reduction in work/school absenteeism (Table D.4). Theintervention lessened the number of days missed in school or work by 3 days (p-value: 0.32) for
14

Recall that one of the main distinguishing factors between the ES and EH household is the occurrence of health
events which amounted to more than Rs.500 in the past year. Hence, the name ES: Educated and Sickly and ES:
Educated and Healthy households.

17the entire households, 4 days (p-value: 0.30) for the ES households, and 1 day (p-value: 0.78) for
the PL households.
What drove this improvement in the health condition of the sickly households? If we look in theestimates from the previous section on consumption and expenditure (Tables C.5, C.9, and C.10),
the intervention caused a slight improvement in hygiene facilities, living standards (i.e.
waterproofing of houses), and reduction in the consumption of temptation goods. The
improvements in these three areas may not be statistically substantial in comparison to thecontrol group, but its impact on health, especially for the poor household, is significant.

6.2 Women Empowerment and Education
Providing women more control over household income has been found to promote more
spending on education and health (Lundberg et.al 1997, Duflo 2003). The microcredit productintroduced in the experiment was designed to cater primarily to female borrowers with a goal of
empowering and enabling them to be more involved in household decision making. We saw inthe last section that the introduction of microcredit does not impact the total household healthspending for all household types. Further, despite the explicit targeting of the products towards
woman, Banerjee et.al (2010) found no discernible effect on women’s empowerment in the short
run for all aspects of household decision making. While microcredit does not seem to strongly
impact household income, the effect also remains unclear for the aspect of health, women’s
empowerment, and education.
Tables F.1, F.2, and G.1 to G.2 summarize the intent to treat estimates for indicators for women
empowerment and education. The results in Table F.1 show the effect of microcredit on theoverall household decision making 15 . Considering all the sampled households in the dataset(column 1), the estimates shows a consistent result with Banerjee et.al. (2010). The estimates do
not seem to show that women in the treatment group are more empowered in terms of the overall
decision making compared to the control group. Looking further into the different household
types, we find more interesting findings. Among the three types of households, the relatively“well-off” EH household (Column 4) show a slight improvement in the women decision making.
The estimate shows that EH households in treatment areas have 0.24 points higher (p-value:0.19) overall decision making index compared to the control group. Meanwhile there is almost
no perceptible effect for the other groups.
Another variable of interest is the indicator for women’s decision making on education related
spending which shows significance for the EH household (Table F.2, column 4). Women in EH
households achieve greater empowerment for education related decision making with the15

Involves aggregating the indicators of female primary decision maker for food, education, clothing, durables,
health, investments in gold and silver, and home improvement related spending.

18

introduction of the microcredit. However, there is still no statistically significant effect for othertwo subgroups and when estimated using the entire sample. The estimates for education spending
and enrolment rates (Table G.1 and G.2, respectively) also show consistent values. Bothindicators are relatively more significant for the EH group (column 4) in comparison to the PL
and ES household. The education spending of EH household is higher by Rs. 147 in contrast to
the comparison (p-value: 0.20). In a similar manner, enrolment rate is 0.06 person higher in EH
household compared to the household of the same type in the control area (p-value: 0.29).
The other decision making indicators are shown in Tables F.3 to F.8. Although all the otherestimates are statistically not different from zero, it is worth noting that the values for EH
households are almost always positive and with decently low p-values16. On the contrary, PL
households get most estimates with negative sign and relatively low level of significance17.
The results are stimulating as these suggest that a higher level of financial stability, education,and more stable household health condition is imperative to create a household environment
where microcredit intervention can bring better impact to women empowerment and education
related indicators

16

ES household’s p-values for women’s decision making: food spending - 0.45, health spending – 0.28, clothing:
0.14.
17
PL household’s p-values for women’s decision making: home improvement – 0.17, and durables spending – 0.22.

19

7. Conclusion and Policy Recommendations
Using the experimental data from Hyderabad, India, the paper was able to identify three types ofhousehold in the sample and the corresponding impact that microcredit brings on each group.
The analysis showed that even in the short-run, microcredit does affect different aspects ofhuman development previously considered as non-responsive when sample households wereanalyzed as a whole. In addition, the analysis shows fragmented positive impact of microcredit tospecific household types in aspects of new business creation, investment in durables, health, andwomen empowerment.
Poor households headed by individuals with low level of education and unstable employment
benefit from microcredit by allowing them to acquire the initial capital needed to start a new
business. Further, there is a substantial reduction in the sickness/accident related expenditures
brought by the decline in the consumption of temptation goods and relative improvement insanitation and living standards (i.e. shared latrine and house waterproofing) of households intreatment areas. Similarly, households who have sickly members but with educated head isbenefited by microcredit by allowing them to increase their investment in household andbusiness durables which could have not been available due to the constraints brought by thefrequent sickness/accident related expenses in the family. Lastly, households who are relatively
healthy and headed by an educated individual feel financially better-off and have stronger
women empowerment in education related decisions. However, these do not significantly
translate to an increase in household income nor to actual expansion in education related
spending in the short run. Overall and on the sub-cluster level, microcredit brings no significant
impact on total household income, general health expenditure, overall women empowerment,
and actual investments in education in the short run.
Over the years, there have been vast developments in the design and utilization of microcredit in
the market. From the traditional design of microcredit, some lending institutions have started
incorporating enhancements by combining the product with health seminars, education programs,
livelihood trainings, and other schemes 18. More recently, development organizations who aretargeting ultra-poor individuals apply a ‘graduation’ approach for poverty alleviation 19 . The‘graduation’ approach combines asset-transfer programs, livelihood trainings, and other skills
development seminars in jumpstarting the ultra-poor to enable them to access microcredit andeventually graduate from extreme poverty.
The results of the analysis suggest that parallel strategies may work even for borrowers who do
not belong to the ‘poorest-of-the-poor.’ By identifying which aspects of the borrower are the18

For example, see Leatherman and Dunford. (2009)
Ultra-poor individuals refer to those who are at the very bottom of the socioeconomic ladder. These are people
who are so economically disadvantaged that even microcredit providers deem them to be too risky to be microloan
clients. Organizations implementing the ultra-poor targeting programs include the Bangladesh-based BRAC
(http://tup.brac.net/)andtheConsultativeGroup
toAssistthe
Poor(CGAP)(http://www.cgap.org/about/programs/cgap-ford-foundation-graduation-program).
19

20

most and least influenced by the microcredit, better targeting can be done to achieve stronger
impacts. For instance, borrower with profiles who are most likely to start a new business begiven supplementary livelihood trainings on how to make the proprietary ventures moresustainable and efficient. Similarly, existing business owners who have profiles that suggest
higher tendency to expand business ventures can be offered auxiliary assistance by the
microcredit institution. Microcredit companies could expand in the business of ‘micro-leasing’,
where livelihood tools and equipment could be acquired on leasing terms, thereby promoting
flexibility and enabling more efficient business operations and better profitability without theinitial hurdle of large acquisition cost of business assets. Likewise, borrower types who are more
likely to be empowered with education related decisions could be provided options to initiate
actual investments through educational loans. As households take a ‘step-up’ in terms ofborrowing profile, their financing needs may be changing as well. By including these different
borrower characteristics in lending decisions, microcredit institutions can be more dynamic andeffective in helping their clients. By offering borrowers various sets of auxiliary products
through client profiling, microcredit impact could be enhanced. Additional products and services
could aid where the effectiveness of traditional program lacks.

21

References
Banerjee, A., E. Duflo, R. Glennerster, and C. Kinnan. (2010). The Miracle of Microfinance?
Evidence from a Randomized Evaluation. MIT Working Paper
Begum S., M. Bhuiyan, I. Davis, J. Ogusky, and E. Ziemba (undated). Microcredit: Is it good for
health? http://www.microfinancegateway.org/gm/document-1.9.25035/280.pdf.
Crepon B., F. Devoto, E. Duflo, and W. Pariente. (2011). Impact of Microcredit in Rural Areasof Morocco: Evidence from a Randomized Evaluation. http://economics.mit.edu/files/6659
Duflo, E., (2003). Grandmothers and Granddaughters: Old Age Pensions and Intra-Household
Allocation in South Africa. World Bank Economic Review 17. pp 1-25.
Duvendack, M., R. Palmer-Jones, J. Copestake, L. Hooper, Y. Loke, and N. Rao (2011). What is
the Evidence of the Impact of Microfinance on the Well-being of Poor People? EPPI-Centre,Social Science Research Unit, Institute of Education, University of London. ISBN:978-1907345-19-7
Goldberg, N. (2005).” Measuring the Impact of Microfinance: Taking Stock of What We Know”.
Grameen Foundation.
Gower, J. (1971). A General Coefficient of Similarity and Some of its Properties. Biometrics 27,
pp. 857-874. December
Karlan, D. and J. Zinman. (2010). Expanding Credit Access: Using Randomized Supply
Decisions to Estimate the Impacts. Review of Financial Studies.
http://karlan.yale.edu/p/Karlan&Zinman_ExpandingCreditAccess_jan2008.pdf
Khandker S. (2003). Microfinance and Poverty: Evidence using Panel Data from Bangladesh.
World Bank Policy Research Working Paper No. 2945.
Leatherman, S., and C. Dunford. (2009). Linking Health to Microfinance to Reduce Poverty.
World
HealthOrganization.
http://www.who.int/bulletin/volumes/88/6/09-071464/en/.
November.
Lundberg, S., R. Pollak, and T. Wales. (1997). Do Husbands and Wives Pool Their Resources?
Evidence From the United Kingdom Child Benefit. Journal of Human Resources 32, pp 463-480.
Morduch J. (1998). Does Microfinance Really Help the Poor? New Evidence from Flagship
Programs in Bangladesh. http://www.nyu.edu/projects/morduch/documents/microfinance/Does_
Microfinance_Really_Help.pdf
Morduch, J. and B. Haley. (2002). Analysis of the Effects of Microfinance on Poverty Reduction.
NYU Wagner Working Paper Series No. 1014.

22

Odell, K. (2010). Measuring the Impact of Microfinance: Taking Another Look. Grameen
Foundation Publication Series. http://www.microfinancegateway.org/gm/document1.1.5403/measuring-the-impact-of-microfinance-taking-another-look.pdf .
Pekalska, E. and R.Duin. (2005). The Dissimilarity Representation for Pattern Recognition:
Foundations and Applications. World Scientific.
Pitt, M. and S. Khandkher. (1998). The Impact of Group-Based Credit Programs on Poor
Households in Bangladesh: Does the Gender of Participants Matter?, Journal of Political
Economy, vol. 106 no. 5
Pitt, M., S. Khandkher, O. Chowdhury, and D. Millimet. (2003). Credit Programs for the Poorand the Health Status of Children in Rural Bangladesh. International Economic Review Vol.44
No.1, February.
Ramnath, N. (2012). What Ails Microfinance?. Forbes India.
http://forbesindia.com/article/special/what-ails-microfinance/32220/1#ixzz2PrXJDCHK
Roodman, D., (2012). Think Again: Microfinance. Foreign Policy.
http://www.foreignpolicy.com/articles/2012/02/01/think_again_microfinance. February 1.
Roodman, D. (2009). What Do We Really Know About Microfinance’s Impact?. CGAP
Microfinance Gateway. http://www.microfinancegateway.org/p/site/m/template.rc/1.26.11408/.
August 31.
Roodman, D. and J. Morduch. (2009). The Impact of Microcredit on the Poor in Bangladesh:
Revisiting the Evidence. Center for Global Development. Working Paper Number 174
Sharma, S. (1996). Applied Multivariate Techniques. 1996, John Wiley & Sons, Inc.
Timm, N. (2002) Applied Multivariate Analysis. Springer-Verlag New York, Inc.
Wagstaff, K. (2004). Clustering with Missing Values: No Imputation Required. Classification,
Clustering, and Data Mining Applications (Proceedings of the Meeting of the International
Federation of Classification Societies). pp 649-658.
Wright, G. (2000). Microfinance Systems: Designing Quality Financial Services for the Poor.
Zed Books Ltd. London & New York, and The University Press Limited, Dhaka.

23

Data Source:
Abhijit Banerjee; Esther Duflo; Rachel Glennerster ; Cynthia Kinnan, Replication Data Set(Baseline) for ‘"Measuring the impact of microfinance in Hyderabad, India" (2010)’,
http://hdl.handle.net/1902.1/11389 UNF:5:7llipBUQ4zNQHjfYYJVqwA== MacArthur Data
Consolidation Project [Distributor] V5 [Version]
Abhijit Banerjee; Esther Duflo; Rachel Glennerster ; Cynthia Kinnan, Replication Data Set(Endline) for ‘"Measuring the impact of microfinance in Hyderabad, India" (2010)’,
http://www.centre-for-microfinance.org/wp-content/uploads/attachments/csy/3528/Spandana.rar

24

Appendix
Table A: Balancing Tables and Descriptive Statistics
Table A.1 Baseline Balancing Table
Control
Mean

Treatment
Mean

Gender

1.19

1.19

Age

44.93

Entire Baseline Sample

Standard
Error

P-value

0.00

0.01580.7563

44.65

0.27

0.53490.6091Diff.

Is the Household Head Literate?

1.57

1.58

-0.01

0.02250.6738Highest Grade Completed

7.79

7.71

0.09

0.17530.6114Work Type

4.03

4.00

0.04

0.06430.5772132.01

97.16

34.85

22.6733

0.1244

Financial Aid
Number of Household Members

5.04

5.13

-0.10

0.06990.1739Other Land in City

1.93

1.91

0.02

0.0112

0.0942Other Land in Village

1.85

1.81

0.03

0.01520.0395

House Ownership

1.41

1.45

-0.04

0.03750.23714734.69

4986.50-251.82

166.8035

0.1313

1.89
Control
Mean

1.87
Treatment
Mean

0.01

0.0132Standard
Error

0.3841
P-valueMonthly Expenditure
Did the household receive any assistancefrom the government?
Subcluster 1:
1: ES Household

Diff.

Gender

1.04

1.04

0.00

0.01120.8436

Age

42.11

42.59

-0.48

0.6971

0.4897Is the Household Head Literate?

1.58

1.56

0.01

0.03360.7158Highest Grade Completed

7.61

7.34

0.27

0.24280.2702Work Type

4.18

4.18

0.00

0.0806

0.9943Financial Aid

47.43

23.31

24.12

17.22260.1616

Number of Household Members

5.02

5.15

-0.13

0.09760.1717

Other Land in City

1.95

1.94

0.00

0.01300.7209Other Land in Village

1.92

1.91

0.01

0.01580.6866

House Ownership

1.45

1.48

-0.03

0.04920.49054103.964468.27-364.31196.9372

0.0646

1.90
ControlMean

1.89
Treatment
Mean

0.01

0.0175
Standard
Error

0.6125P-value

Gender

1.00

1.00

0.00

0.00280.3177

Age

43.36

41.72

1.64

0.88800.0651Monthly Expenditure
Did the household receive any assistancefrom the government?
Subcluster 2:
2: EH Household

Diff.

Is the Household Head Literate?

1.88

1.90

-0.02

0.0266

0.5033Highest Grade Completed

8.25

8.21

0.05

0.27130.8679Work Type

3.91

3.77

0.14

0.11910.2388205.35166.2839.07

58.8715

0.5071

Financial Aid
Number of Household Members

4.98

5.05

-0.07

0.12500.5644

Other Land in City

1.89

1.85

0.04

0.02570.1048Other Land in Village

1.68

1.58

0.10

0.03700.0051

House Ownership

1.38

1.50

-0.12

0.07890.12565776.125470.57

305.55335.7035

0.3631

1.87

1.87

0.00

0.02600.9751Monthly Expenditure
Did the household receive any assistancefrom the government?

25

Control
Mean

Treatment
Treatment
Mean

Gender

1.80

1.78

Age

53.74

53.53

Subcluster 3:
3: PL HouseholdStandard
Error

P-value

0.02

0.03590.61940.20

1.20370.8651Diff.

Is the Household Head Literate?

1.18

1.18

0.00

0.03730.9755Highest Grade Completed

6.36

6.56

-0.20

0.59760.7421Work Type

3.61

3.81

-0.20

0.19660.3197248.79173.8574.94

59.67420.2097

Number of Household Members

5.16

5.20

-0.04

0.16300.7851Other Land in City

1.92

1.91

0.01

0.02480.6309Other Land in Village

1.87

1.92

-0.05

0.02680.0929House Ownership

1.36

1.33

0.03

0.07710.71434995.615528.05-532.44444.8325

0.2319

1.87

1.84

0.03

0.0306

0.3499Control
Mean

Treatment
Mean
Mean

Standard
Error

P-value

Gender

1.11

1.10

0.01

0.00740.0999

Age

41.27

41.00

0.26

0.25640.3019Is the Household Head Literate?

1.70

1.71

0.00

0.01250.7447Highest Grade Completed

8.83

8.99

-0.16

0.10110.1113Financial Aid

Monthly Expenditure
Did the household receive any assistancefrom the government?

Table A.2 Endline Balancing Table
Entire Endline Sample

Work Type

Diff.
Diff.

1.99

1.99

0.00

0.01770.8888188.74

192.02-3.29

19.3095

0.8648

Number of Household Members

5.63

5.60

0.03

0.05140.5669Other Land in City

1.94

1.94

0.00

0.00580.7748Financial Aid

Other Land in Village

1.81

1.80

0.01

0.00970.5349House Ownership

1.48

1.41

0.07

0.02360.00566388.866480.51-91.65126.0069

0.46701.39
Control
Mean

1.40
Treatment
Mean

-0.01

0.0120Standard
Error

0.2476
P-value

Gender

1.30

1.26

0.03

0.01940.1074

Age

40.84

40.91

-0.07

0.47300.8853Monthly Expenditure
Did the household receive any assistancefrom the government?
Subcluster 1:
1: PL Household

Diff.
Diff.

Is the Household Head Literate?

1.58

1.58

0.00

0.02280.9591Highest Grade Completed

8.19

8.44

-0.25

0.18190.1641Work Type

2.03

2.06

-0.03

0.03280.4364121.82

159.57-37.7530.11720.2102Financial Aid
Number of Household Members

5.43

5.46

-0.03

0.09820.7493

Other Land in City

1.97

1.95

0.01

0.00840.1681Other Land in Village

1.99

1.99

0.00

0.00390.3713

House Ownership

1.52

1.44

0.08

0.0432

0.06655487.875533.53-45.66179.7519

0.7995

1.39

1.43

-0.04

0.02140.0505Monthly Expenditure
Did the household receive any assistancefrom the government?

26

Control
Mean

Treatment
Mean

Gender

1.02

1.01

Age

41.41

41.54

Subcluster 2:
2: ES HouseholdStandard
Error

P-value

0.00

0.00500.5400-0.13

0.42000.7617

Diff.
Diff.

Is the Household Head Literate?

1.75

1.76

-0.01

0.0194

0.5287Highest Grade Completed

9.11

9.14

-0.03

0.16550.8361Work Type

2.00

1.93

0.07

0.02830.0128243.28229.4313.85

35.36810.6953

Number of Household Members

5.97

5.91

0.06

0.08390.4651Other Land in City

1.93

1.93

-0.01

0.01020.5472Other Land in Village

1.65

1.65

0.00

0.01890.9844

House Ownership

1.44

1.38

0.07

0.03730.07767793.55

7887.90-94.35245.9720

0.7013

1.33
Control
Mean

1.34
Treatment
Mean

-0.01

0.0188
Standard
Error

0.7868P-value

Financial Aid

Monthly Expenditure
Did the household receive any assistancefrom the government?
Subcluster 3:
3: EH Household

Diff.
Diff.

Gender

1.02

1.02

0.00

0.00650.9077

Age

41.53

40.40

1.13

0.44070.0105Is the Household Head Literate?

1.77

1.77

0.01

0.02190.8142Highest Grade Completed

9.01

9.25

-0.24

0.17460.1695Work Type

1.94

1.99

-0.06

0.03140.0713Financial Aid

193.35178.12

15.23

33.41410.6486

Number of Household Members

5.44

5.35

0.08

0.08220.3079Other Land in City

1.93

1.94

-0.01

0.0112

0.3481Other Land in Village

1.80

1.79

0.01

0.01790.5222

House Ownership
Monthly Expenditure
Did the household receive any assistancefrom the government?

1.48

1.43

0.05

0.04270.26955642.30

5651.98-9.68

186.9686

0.9587

1.45

1.46

0.00

0.02220.9126S.E.

p-value

Table A.3 Intra-Cluster Baseline Balancing Table
ES
Household

EH
Household(Subclust.1)(Subclust.2)

Gender

1.04

1.00

0.04

0.01

0.00

Age

42.35

42.55

-0.20

0.56

0.72

Diff.

Highest Grade Completed

7.48

8.23

-0.75

0.18

0.00

Work Type

1.36

1.52

-0.16

0.03

0.00

Financial Aid

35.71

184.69-148.99

30.47

0.00

Number of Household Members

5.08

5.02

0.07

0.08

0.40

Other Land in Village

0.08

0.38

-0.29

0.02

0.00

House Ownership

3.93

4.07

-0.14

0.07

0.04

Latrine Type

3.67

3.61

0.06

0.08

0.44

4281.135615.02-1333.89

192.10

0.00

0.59

0.66

-0.07

0.02

0.00

Monthly Expenditure
Health Indicator27

Has Bank Account?

0.00

0.78

-0.78

0.02

0.00

Has Insurance?

0.15

0.39

-0.24

0.02

0.00

2.57
ES
Household

3.64
PLHousehold

-1.07

0.06

0.00

Is the Household Head Literate?

Diff.

S.E.

p-value(Subclust.1)(subclust.3)

Gender

1.04

1.79

-0.75

0.02

0.00

Age

42.35

53.85

-11.500.68

0.00

Highest Grade Completed

7.48

6.46

1.02

0.32

0.00

Work Type

1.36

1.46

-0.10

0.04

0.01

Financial Aid

35.71

211.61-175.91

31.15

0.00

Number of Household Members

5.08

5.18

-0.10

0.09

0.31

Other Land in Village

0.08

0.10

-0.02

0.02

0.21

House Ownership

3.93

4.30

-0.36

0.07

0.00

Latrine Type

3.67

3.51

0.16

0.09

0.06

4281.135260.29-979.16242.70

0.00

Health Indicator

0.59

0.55

0.05

0.03

0.07

Has Bank Account?

0.00

0.54

-0.54

0.02

0.00

Has Insurance?

0.15

0.25

-0.10

0.02

0.00

2.57
EH
Household1.48
PLHousehold

1.09

0.06

0.00

Monthly Expenditure

Is the Household Head Literate?

(Subclust.2)(Subclust.3)

Diff.

S.E.

p-value

Gender

1.00

1.79

-0.79

0.02

0.00

Age

42.55

53.85

-11.300.73

0.00

Highest Grade Completed

8.23

6.46

1.77

0.33

0.00

Work Type

1.52

1.46

0.06

0.04

0.16

184.69211.61-26.92

41.80

0.52

Financial Aid
Number of Household Members

5.02

5.18

-0.16

0.10

0.11

Other Land in Village

0.38

0.10

0.27

0.02

0.00

House Ownership

4.07

4.30

-0.22

0.08

0.00

Latrine Type

3.61

3.51

0.10

0.09

0.30

Monthly Expenditure5615.025260.29354.73277.170.20

Health Indicator

0.66

0.55

0.12

0.03

0.00

Has Bank Account?

0.78

0.54

0.24

0.03

0.00

Has Insurance?

0.39

0.25

0.14

0.03

0.00

Is the Household Head Literate?

3.64

1.48

2.16

0.06

0.00

28

Table A.4 Intra-Cluster Endline Balancing Table
PL
HouseholdES
HouseholdseholdHou(Subclust.1)(Subclust.2)

DifferenceStandard
Error

P-Value

Gender

1.28

1.02

0.26

0.01

0.00

Age

40.92

41.48

-0.56

0.31

0.07

Is the Household Head Literate?

2.70

3.20

-0.50

0.04

0.00

Highest Grade Completed

8.32

9.13

-0.81

0.12

0.00

Work Type

1.95

2.03

-0.08

0.02

0.00

141.48235.85-94.37

23.43

0.00

5.45

5.94

-0.49

0.06

0.00

Financial Aid
Number of Household Members
Other Land in Village

0.01

0.35

-0.34

0.01

0.00

House Ownership

5.25

5.39

-0.14

0.04

0.00

Latrine Type

3.66

3.91

-0.25

0.02

0.00

Has Bank Account?

0.19

0.90

-0.71

0.01

0.00

Has Insurance?

0.41

0.61

-0.20

0.01

0.00

5511.677844.10-2332.43

152.85

0.00

0.21

0.01

0.21

0.01

0.00

Monthly Household Expenditure
Household Health Indicator

PL
Household

EH
Household(Subclust.1)(Subclust.3)

Difference
DifferenceStandard
Error

P-Value

Gender

1.28

1.02

0.26

0.01

0.00

Age

40.92

40.95

-0.03

0.32

0.93

Is the Household Head Literate?

2.70

3.21

-0.51

0.04

0.00

Highest Grade Completed

8.32

9.13

-0.81

0.13

0.00

Work Type

1.95

2.03

-0.08

0.02

0.00

Financial Aid

141.48185.53-44.06

22.73

0.05

Number of Household Members

5.45

5.39

0.05

0.06

0.41

Other Land in Village

0.01

0.21

-0.20

0.01

0.00

House Ownership

5.25

5.28

-0.02

0.04

0.63

Latrine Type

3.66

3.83

-0.17

0.03

0.00

Has Bank Account?

0.19

0.67

-0.48

0.01

0.00

Has Insurance?

0.41

0.42

-0.01

0.02

0.72

5511.675647.27-135.60

129.83

0.30

0.21
ES
Household

1.00
EH
Household

-0.79

0.01

0.00

Monthly Household Expenditure
Household Health Indicator(Subclust.2)(Subclust.3)

DifferenceStandard
Error

P-Value

Gender

1.02

1.02

-0.01

0.00

0.13

Age

41.48

40.95

0.53

0.30

0.08

Is the Household Head Literate?

3.20

3.21

-0.01

0.04

0.75

Highest Grade Completed

9.13

9.13

0.00

0.12

0.99

Work Type

2.03

2.03

0.00

0.02

0.97

235.85185.53

50.32

24.30

0.04

5.94

5.39

0.54

0.06

0.00

Financial Aid
Number of Household Members

29Other Land in Village

0.35

0.21

0.15

0.01

0.00

House Ownership

5.39

5.28

0.11

0.04

0.01

Latrine Type

3.91

3.83

0.08

0.02

0.00

Has Bank Account?

0.90

0.67

0.23

0.01

0.00

Has Insurance?

0.61

0.42

0.19

0.01

0.00

7844.105647.272196.82

155.16

0.00

0.01

1.00

-0.99

0.00

0.00

Monthly Household Expenditure
Household Health Indicator

Table B: Business Start and Performance
Table B.
B.1
New Business Start---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
0.0308**0.0655**0.01700.0229(0.0155)(0.0254)(0.0267)(0.0253)
Control Mean

0.1215***0.1114***0.1265***0.1236***(0.0108)(0.0147)(0.0190)(0.0194)---------------------------------------------------------------------------N
3081
774
1361
946
r2
0.00200.00880.00060.0011F
3.9685
6.63750.40720.8209---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table B.2
B.2
Closed Business---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
0.00100.00150.0121-0.0121(0.0140)(0.0131)(0.0214)(0.0178)
Control Mean

0.0495***0.0399***0.0592***0.0486***(0.0104)(0.0101)(0.0147)(0.0148)---------------------------------------------------------------------------N
4538
1551
1617
1370
r2
0.00000.00000.00060.0009F
0.00480.01350.32070.4584---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table B.3
B.3
Business Profit---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-212.2984
344.7672
1885.1095-3275.8327(2201.3537)(1051.6970)(1386.4888)(6615.8344)
Control Mean

3710.3066*
2408.7526***
1028.9131
8285.4007(2037.3124)(322.2765)(1179.9340)(6174.3991)---------------------------------------------------------------------------N
2987
746
1323
918
r2
0.00000.00020.00100.0004F
0.00930.1075
1.8486
0.2452---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.0130

Table B.4
B.4
Business Revenue---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-739.7528
857.3717-875.8013-2024.8327(4318.3774)(2218.9751)(3519.4090)(12402.4006)
Control Mean

13187.2215***7398.9844***12525.2219***
19002.6652*(3819.9569)(1146.0599)(3017.7000)(11314.4041)---------------------------------------------------------------------------N
2987
746
1323
918
r2
0.00000.00030.00010.0000F
0.02930.14930.06190.0267---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table B.5
B.5
Business Cost---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-527.4544
512.6045-2760.9108
1251.0000(2604.8472)(1608.5968)(3442.8645)(6391.5212)
Control Mean

9476.9149***4990.2318***
11496.3088***10717.2645**(2073.7828)(949.1303)(3032.0733)(5168.6597)---------------------------------------------------------------------------N
2987
746
1323
918
r2
0.00000.00020.00090.0001
ar2
F
0.04100.10150.64310.0383---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table B.6
B.6
Wages
Wages Paid to NonNon-Household Members---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-25.8598-5404.8562**
2956.0365-2158.8315(1661.9823)(2533.7363)(2074.3747)(2738.7341)
Control Mean

5613.5844***7200.0000***
4505.1429***6772.8571**(1305.8106)(2510.7559)(1253.3937)(2489.6452)---------------------------------------------------------------------------N
161
29
85
47
r2
0.00000.11820.02660.0174F
0.00024.55042.03070.6214---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table B.
B.7
Number of Employees (Non(Non-Household Members)---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-0.3549-0.0266-0.0498-1.0185(0.4818)(0.5623)(0.7128)(0.9079)
Control Mean

3.2276***2.3158***3.3134***
3.5405***(0.3453)(0.3751)(0.4518)(0.8581)---------------------------------------------------------------------------N
269
44
144
81
r2
0.00310.00000.00010.0269F
0.54260.00220.00491.2585---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01

31

Table B.8
B.8
Business Operation with
with Help from Household Members?
---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-0.0136-0.0106-0.04390.0308(0.0308)(0.0476)(0.0410)(0.0483)
Control Mean

0.3721***0.3830***0.3846***
0.3460***(0.0166)(0.0294)(0.0276)(0.0299)---------------------------------------------------------------------------N
2670
662
1197
811
r2
0.00020.00010.00210.0010F
0.19370.04961.15020.4054---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table
Table B.9
B.9
Total Working
Working Hours Rendered by Members of Households in the last week (total manmanhours)---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-3.3306*-4.4851-0.0181-7.7835**(1.8587)(3.1701)(2.7621)(3.7649)
Control Mean

14.6294***15.2813***13.2222***
16.1829***(1.4812)(2.6712)(1.4228)(3.3179)---------------------------------------------------------------------------N
580
171
257
152
r2
0.00920.01730.00000.0449F
3.21112.00170.00004.2740---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table B.
B.10
Number of Casual Workers (Non(Non-Household Members)---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
0.64680.8965-0.1399
1.5556(0.9729)(2.3009)(1.2966)(1.0864)
Control Mean

3.4516***3.33334.2000***2.6154***(0.5709)(1.8559)(0.9166)(0.6441)---------------------------------------------------------------------------N
69
6
42
21
r2
0.00850.03700.00040.0472
F
0.44200.15180.01162.0504---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table B.11
Average Hours Rendered by the Person Most Responsible in the Business---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
0.5611
1.27100.39770.1498(1.0029)(1.0599)(1.1528)(1.1067)
Control Mean

11.2681***10.2449***
11.3986***
11.9765***(0.6901)(0.7501)(0.7954)(0.7403)---------------------------------------------------------------------------N
2958
740
1308
910
r2
0.00210.01080.00110.0001F
0.31301.43790.11900.0183---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01

32

Table B.
B.12
Number of Days
Days Rendered by the Person Most Responsible in the Business---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
0.15970.21990.08140.1948(0.1620)(0.2222)(0.1735)(0.2259)
Control Mean

6.0541***
5.8635***
6.1420***6.1051***(0.1260)(0.1620)(0.1337)(0.1759)---------------------------------------------------------------------------N
2960
740
1309
911
r2
0.00190.00290.00050.0029F
0.97120.97940.22030.7435---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01

Table B.13
B.13
Business Inventory---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
242.5512-842.0647
997.4657-304.3453(1199.7489)(1010.0898)(2173.6673)(1510.0838)
Control Mean

2755.6280***
1698.6847*
3028.7345***3329.8320***(675.9357)(989.2224)(787.5264)(1062.4739)---------------------------------------------------------------------------N
2387
600
1044
743
r2
0.00000.00140.00020.0001F
0.04090.69500.21060.0406---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table B.14
B.14
Business Asset Spending for the Past Year---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
397.5877*-4.0377927.8006*
120.3781(214.0840)(89.4319)(526.8692)(86.9452)
Control Mean

280.3101***
151.3069**
480.5135***
176.1454***(71.0998)(64.7947)(179.6663)(53.6081)---------------------------------------------------------------------------N
6778
2154
2561
2063
r2
0.00060.00000.00120.0008F
3.44900.00203.10101.9169---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table B.
B.15
Poultry
Poultry:
ultry: Number of Birds Owned---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
0.54880.15310.1302
1.5728**(0.5586)(0.5748)(0.7601)(0.7768)
Control Mean

2.4564***2.2542***
3.0750***1.7857***(0.3183)(0.3371)(0.5008)(0.3560)---------------------------------------------------------------------------N
478
143
212
123
r2
0.00610.00070.00030.0560F
0.96530.07100.02934.0991---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01

33

Table B.16
B.16
Livestock: Number of Cows Owned---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-0.1546-0.05150.2292-0.8677(0.3294)(0.2411)(0.4071)(0.5462)
Control Mean

0.7668***0.5690***0.5570**1.2679**(0.2675)(0.1774)(0.2432)(0.5182)---------------------------------------------------------------------------N
473
140
210
123
r2
0.00130.00040.00300.0222F
0.22030.04560.31702.5235---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01

Table B.17
B.17
Livestock: Number of Goats
Goats, Sheep,
Sheep, and Pigs
Pigs Owned---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-0.08850.2856-0.3366-0.0862(0.2419)(0.3260)(0.4404)(0.3395)
Control Mean

0.7552***
0.6316***0.8500**0.7455***(0.1992)(0.2319)(0.4090)(0.1871)---------------------------------------------------------------------------N
472
138
211
123
r2
0.00040.00820.00430.0006F
0.13380.76720.5841
0.0645---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table B.18
B.18
Livestock: Number of Other Large Animals Owned---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-0.01710.1683-0.0130-0.1993(0.1748)(0.2413)(0.2002)(0.4062)
Control Mean

0.4462***0.4074**0.3718**0.5926(0.1539)(0.1644)(0.1616)(0.3539)---------------------------------------------------------------------------N
461
132
207
122
r2
0.00000.00370.00000.0042F
0.0096
0.48650.00420.2409---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01

34

Table C: Income and Spending
Table
Table C.
C.1
Impact on Household Income: WorkWork Earnings---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-440.9097-378.1599-696.6720-237.0789(504.6112)(440.4463)(652.2173)(520.5053)
Control Mean

3337.1533***
2852.4240***4035.3415***3016.1110***(354.6647)(322.7656)(450.9703)(387.1081)---------------------------------------------------------------------------N
6746
2154
2533
2059
r2
0.00350.00370.00680.0011F
0.76350.7372
1.14100.2075---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table C.
C.2
“How do you feel about your overall financial situation?
situation?”” (Scale of 11-10)---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
0.05650.00470.03190.1345*(0.0615)(0.0715)(0.0834)(0.0689)
Control Mean

3.4264***
3.1967***3.5671***
3.4975***(0.0445)(0.0499)(0.0638)(0.0508)---------------------------------------------------------------------------N
6746
2151
2555
2040
r2
0.00060.00000.00020.0031F
0.84450.00430.14613.8116---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table C.3
Total Household Spending---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
186.5981
110.0690
243.2547
74.1483(225.0563)(269.6501)(338.2384)(260.1021)
Control Mean

6388.8587***5487.8708***7793.5523***5642.3046***(154.3218)(216.0116)(253.5914)(169.2473)---------------------------------------------------------------------------N
6761
2151
2563
2047
r2
0.00030.00020.00040.0001F
0.68740.16660.51720.0813---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table C.4
C.4
Food Spending---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-13.3139-33.7654-29.9886
7.8663(56.6315)(84.3807)(77.3512)(61.9081)
Control Mean

2229.3030***
2049.7852***
2490.3693***2102.7997***(40.5224)(69.1618)(54.9028)(42.4590)---------------------------------------------------------------------------N
6481
2064
2460
1957
r2
0.00000.00020.00010.0000ar2
F0.05530.16010.15030.0161---------------------------------------------------------------------------Cluster-robust standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01

35

Table C.5
Temptation Goods Spending---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-34.8407-17.8240-41.9401-49.5581(23.9595)(27.8308)(32.8773)(32.8110)
Control Mean

381.3000***
314.6456***461.0989***354.6783***(16.3640)(15.3472)(23.4216)(24.2008)---------------------------------------------------------------------------N
6345
2021
2407
1917
r2
0.00100.00030.00110.0025F
2.11450.4102
1.62732.2813---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table C.6
Celebration Spending (excluding(excluding Wedding)Wedding)---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-136.6597
336.9359-288.2456-425.5119(316.3483)(463.8043)(409.6546)(290.2231)
Control Mean

5058.5470***4127.4513***6185.2004***4669.5408***(196.2555)(201.0828)(289.0062)(209.0420)---------------------------------------------------------------------------N
4769
1617
1760
1392
r2
0.00010.00050.00030.0030F
0.1866
0.52770.49512.1496---------------------------------------------------------------------------Cluster-robust standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table C.7
Household Asset Spending---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
1420.7916**
591.4803
2634.0175**
524.0927(706.9785)(737.6425)(1321.4902)(1166.0467)
Control Mean

6661.5614***4253.2995***
9214.5241***
6099.7124***(388.6301)(413.2176)(786.0458)(625.2370)---------------------------------------------------------------------------N
6720
2136
2549
2035
r2
0.00080.00040.00200.0001
F
4.03880.64303.97290.2020---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table C.8
C.8
Household Repair Spending Amounting to More than Rs. 500---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-0.0307-0.0183-0.0221-0.0603*(0.0209)(0.0245)(0.0256)(0.0312)Control Mean

0.5059***0.4762***0.5653***0.4658***(0.0141)(0.0183)(0.0161)(0.0233)---------------------------------------------------------------------------N
6750
2150
2556
2044
r2
0.00090.00030.00050.0037F
2.14650.55550.74583.7320---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01

36

Table C.9
House Waterproofing---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
0.06580.06490.04720.0888(0.0603)(0.0697)(0.0729)(0.0702)
Control Mean

0.9318***0.8482***
0.9722***0.9701***(0.0497)(0.0551)(0.0597)(0.0564)---------------------------------------------------------------------------N
6776
2156
2558
2062
r2
0.00130.00130.00070.0024F
1.19260.86720.41901.5979---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
House waterproofing was defined as a categorical value. 0 – Not waterproof, 1 – Somerooms are waterproofed, 2: All rooms are waterproofed.

Table C.10
C.10
Shared Latrine---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-0.0331-0.0189-0.0444-0.0316(0.0346)(0.0431)(0.0369)(0.0374)
Control Mean

0.4037***0.4397***0.3749***0.4042***(0.0235)(0.0297)(0.0264)(0.0253)---------------------------------------------------------------------------N
6173
1863
2416
1894
r2
0.00120.00040.00220.0010F
0.91610.19271.44590.7125---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.0137

Table D: Health Indicators and Health Shock Coping Mechanisms
Table D.1
Medical Related Spending---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
3.0781-91.7849
69.4210-19.6282(60.6141)(84.4899)(124.1584)(39.2200)
Control Mean

637.1744***711.4412***938.1528***198.0121***(39.9560)(70.3421)(88.3544)(31.2303)---------------------------------------------------------------------------N
6649
2119
2529
2001
r2
0.00000.00070.00020.0007F
0.0026
1.18010.31260.2505---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table D.2
Health Expenditure due to Sickness or Accident---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-353.9208-1365.5661**
243.6254
N/A(487.5482)(637.1221)(633.2684)
N/A
Control Mean

6151.6572***6329.6224***6051.0757***
N/A(293.3255)(531.9778)(362.3646)
N/A---------------------------------------------------------------------------N
6600
2487
4110
N/A
r2
0.00010.00200.0000N/A
ar2
F
0.52704.59390.1480
N/A---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table D.
D.3
Did the sickness affect your work or school?
---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
0.02300.02950.0180N/A(0.0232)(0.0334)(0.0254)
N/A
Control Mean

0.6131***0.6045***0.6194***
N/A(0.0154)(0.0223)(0.0170)
N/A---------------------------------------------------------------------------N
6385
2412
3970
N/A
r2
0.00060.00090.0003
N/A
F
0.97840.78120.5009N/A---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table D.4
Number of Days Missed in School or Work due to Sickness/A
ickness/Accident---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-3.0593-1.2725-4.2370
N/A(3.0889)(4.5782)(4.1682)
N/A
Control Mean

36.6598***
37.0443***
36.4200***
N/A(2.4970)(3.7023)(3.4576)
N/A---------------------------------------------------------------------------N
2860
1117
1743
N/A
r2
0.00040.00010.0007
N/A
F
0.98100.0772
1.0333
N/A---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01

38

Table D.5
D.5
Coping with Health Shock through Other Funding Sources---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-0.0264*-0.0140-0.0328**-0.0029(0.0142)(0.0196)(0.0141)(0.0889)
Control Mean

0.0882***
0.0921***0.0833***0.1444(0.0112)(0.0151)(0.0114)(0.0832)---------------------------------------------------------------------------N
6807
2526
4149
132
r2
0.00250.00060.00430.0000F
3.43600.51045.40790.0010---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table D.6
D.6
Coping with Health Shock through Gifts---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-0.0065-0.0041-0.0076-0.0222(0.0045)(0.0074)(0.0059)(0.0140)
Control Mean

0.0229***0.0251***
0.0216***0.0222(0.0037)(0.0057)(0.0051)(0.0140)---------------------------------------------------------------------------N
6807
2526
4149
132
r2
0.00050.00020.00080.0066F
2.05310.30871.6408.
---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table D.7
D.7
Coping with Health Shock through Borrowing from Moneylenders
oneylenders---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-0.0043-0.0116-0.00160.0250(0.0201)(0.0274)(0.0234)(0.0251)
Control Mean

0.2108***0.2544***0.1871***0.1444***(0.0159)(0.0201)(0.0191)(0.0094)---------------------------------------------------------------------------N
6807
2526
4149
132
r2
0.00000.00020.00000.0010F
0.04490.17790.00480.9889---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table D.8
Coping with Health Shock through Other Financing Mechanisms---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
0.02320.02150.0221
0.0962(0.0200)(0.0271)(0.0238)(0.0910)Control Mean

0.4752***0.4184***0.5100***0.4778***(0.0142)(0.0209)(0.0174)(0.0773)---------------------------------------------------------------------------N
6807
2526
4149
132
r2
0.00050.00050.00050.0077F
1.34930.62760.86291.1166---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01

39

Table D.9
Coping with Health Shock through borrowing from Other MFIs---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-0.0040-0.0048-0.0035-0.0111(0.0056)(0.0069)(0.0077)(0.0070)
Control Mean

0.0173***0.0167***0.0180***0.0111(0.0044)(0.0055)(0.0062)(0.0070)---------------------------------------------------------------------------N
6807
2526
4149
132
r2
0.00030.00040.00020.0033
F
0.51550.49600.2077.
---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table D.10
Relativess and Friends
Coping with Health Shock through Borrowing from Relative---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
0.00880.00800.0110-0.0605(0.0173)(0.0255)(0.0185)(0.0544)
Control Mean

0.1898***0.1958***0.1856***0.2000***(0.0120)(0.0191)(0.0127)(0.0386)---------------------------------------------------------------------------N
6807
2526
4149
132
r2
0.00010.00010.00020.0051F
0.25880.09890.35481.2346---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table D.11
Coping with Health Shock through Borrowing from Spandana---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
0.0076***0.0072**0.0079**0.0000(0.0023)(0.0034)(0.0031)(0.0000)
Control Mean

0.0025***0.00170.0031**0.0000(0.0009)(0.0011)(0.0013)(0.0000)---------------------------------------------------------------------------N
6807
2526
4149
132
r2
0.00240.00250.0023.
F
10.50024.5353
6.4142.
---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01

40

Table E: Impact on the Occurrence Other Shocks
Table E.1
Incidence of Property Shock---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-0.0096-0.0041-0.0181-0.0084(0.0107)(0.0158)(0.0152)(0.0137)
Control Mean

0.1142***0.0881***0.1590***0.0878***(0.0070)(0.0102)(0.0107)(0.0100)---------------------------------------------------------------------------N
6753
2153
2561
2039
r2
0.00020.00010.00060.0002F
0.80800.06661.42210.3768---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table E.2
Incidence of Job/Business loss---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-0.0022-0.0060
0.0011-0.0025(0.0045)(0.0078)(0.0069)(0.0065)
Control Mean

0.0246***
0.0261***0.0261***
0.0213***(0.0035)(0.0059)(0.0053)(0.0047)---------------------------------------------------------------------------N
6741
2152
2554
2035
r2
0.00010.00040.00000.0001F
0.2266
0.58830.02530.1469---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01

41

Table F: Indicators of Women Empowerment
Table F.1
All Decisions---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
0.0751-0.0086
0.01610.2433(0.1423)(0.1816)(0.1715)(0.1860)
Control Mean

2.3008***3.0039***2.1480***1.7523***(0.0945)(0.1191)(0.1337)(0.1159)---------------------------------------------------------------------------N
6746
2145
2559
2042
r2
0.00020.00000.00000.0029F
0.27810.00220.00881.7115---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table F.2
Women Decision on Education and Education Related Spending---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
0.02300.00480.0086
0.0598**(0.0202)(0.0245)(0.0264)(0.0262)
Control Mean

0.2179***0.2718***0.2212***0.1577***(0.0140)(0.0171)(0.0201)(0.0161)---------------------------------------------------------------------------N
6746
2145
2559
2042
r2
0.00070.00000.00010.0059F
1.29600.03910.10675.2257---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table F.3
F.3
Women Decision on Food Expenditure---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
0.03110.02010.03300.0395(0.0408)(0.0431)(0.0419)(0.0523)
Control Mean

0.5742***0.6243***0.5736***
0.5228***(0.0295)(0.0287)(0.0324)(0.0380)---------------------------------------------------------------------------N
6746
2145
2559
2042
r2
0.00100.00040.00110.0016
F
0.58060.21880.6201
0.5682---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table F.4
Women Decision on Health
Health---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
0.02750.01390.03800.0309(0.0231)(0.0316)(0.0287)(0.0289)
Control Mean

0.2846***
0.4087***
0.2363***0.2133***(0.0146)(0.0202)(0.0218)(0.0169)---------------------------------------------------------------------------N
6746
2145
2559
2042
r2
0.00090.00020.00190.0014F
1.41720.1926
1.7614
1.1464---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01

42

Table F.5
Women Decision on Clothing
Clothing---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
0.01830.00960.00120.0492(0.0257)(0.0315)(0.0311)(0.0338)
Control Mean

0.3547***0.4583***0.3440***0.2599***(0.0172)(0.0204)(0.0232)(0.0212)---------------------------------------------------------------------------N
6746
2145
2559
2042
r2
0.00040.00010.00000.0030F
0.50950.09220.00142.1161---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table F.6
F.6
Women Decision on Home Improvement---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-0.0087-0.0345-0.0034
0.0143(0.0171)(0.0254)(0.0249)(0.0200)
Control Mean

0.2291***0.3709***0.1783***0.1426***(0.0112)(0.0190)(0.0186)(0.0127)---------------------------------------------------------------------------N
6746
2145
2559
2042
r2
0.00010.00130.00000.0004
F
0.25801.84370.01840.5124---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table F.7
Women Decision on Spending on Durables---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-0.0134-0.0300-0.02360.0179(0.0151)(0.0247)(0.0207)(0.0226)
Control Mean

0.2737***
0.3961***0.2464***0.1790***(0.0092)(0.0159)(0.0166)(0.0129)---------------------------------------------------------------------------N
6746
2145
2559
2042
r2
0.00020.00100.00080.0005
F
0.79801.47311.30600.6310---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table F.8
Women Decision on Investments in Gold and Silver---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment-0.0028
0.0075-0.0378
0.0317(0.0255)(0.0326)(0.0300)(0.0369)
Control Mean

0.3666***0.4738***0.3482***0.2770***(0.0157)(0.0207)(0.0229)(0.0226)---------------------------------------------------------------------------N
6746
2145
2559
2042
r2
0.00000.00010.00160.0012F
0.01190.05281.58460.7384---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01

43

Table G: Education
Table
Table G.1
Education Spending---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
50.2972
19.7635-9.7750
147.9825(62.4775)(51.3998)(82.4083)(115.9111)
Control Mean

688.4771***489.0369***
849.3271***704.0283***(40.2966)(39.1883)(59.0310)(53.6010)---------------------------------------------------------------------------N
6425
2046
2444
1935
r2
0.00030.00010.00000.0022F
0.64810.14780.01411.6299---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.01
Table G.2
Number of Enrolled Individuals in the Household---------------------------------------------------------------------------(1)
(2)
(3)
(4)
ALL Household PL Household
ES Household
EH Household---------------------------------------------------------------------------Treatment
0.01130.0055-0.0259
0.0611(0.0407)(0.0585)(0.0541)(0.0580)
Control Mean

1.6150***1.4193***
1.6986***
1.7173***(0.0282)(0.0451)(0.0373)(0.0394)---------------------------------------------------------------------------N
6798
2158
2564
2076
r2
0.00000.00000.00010.0006
F
0.07710.00890.2284
1.1095---------------------------------------------------------------------------Cluster-Robust Standard errors in parentheses* p<0.10, ** p<0.05, *** p<0.0144