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MICROFINANCE AND WOMEN’S EMPOWERMENT
Mary La Rocque
International Relations Honors Thesis
New York University
Spring 2015
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Table of Contents
Table of Contents ........................................................................................................................................ 1
Abstract ....................................................................................................................................................... 2
Introduction ................................................................................................................................................ 3
Microfinance Industry ............................................................................................................................. 4
Literature Review .................................................................................................................................... 8
Theory ....................................................................................................................................................... 10
Method ...................................................................................................................................................... 13
Hypotheses ................................................................................................................................................ 15
Data ........................................................................................................................................................... 16
Results ....................................................................................................................................................... 20
Results of First Dependent Variable: Ratio of Girls/Boys in Secondary Education ............................. 21
Results of Second Dependent Variable: Girl’s Enrollment in Secondary Education ........................... 25
Results of Third Dependent Variable: Women’s Financial Independence .......................................... 29
Conclusion ................................................................................................................................................. 32
Works Cited ............................................................................................................................................... 34
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Abstract
Bridging the gap between socially responsible investment bankers, World Bank officials, and non-‐profit
workers, the idea of microfinance has become paramount to the development industry. However one of
the important caveats of the industry is its large gender disparity as almost 73% of all microfinance
borrowers are women. A fact claimed by many to show microfinance’s intense emphasis on women’s
empowerment. The purpose of this study is to test this claim and analyze the effects of microfinance on
women’s empowerment. Through the use of traditional OLS regression on cross-‐sectional time series
data from MixMarket, WDI and DHS, this study examines the extent in which microfinance empowers
women using measures of female enrollment in secondary school and the proportion of women with
financial independence. Despite the claims that microfinance empowers women, the results of this
study show limited, and in a few cases, negative effects of microfinance on women’s development
indicators. There are three proposed reasons for this lack of significant results: a) microfinance causes
negative incentives encouraging girls to be employed in small family businesses instead of enrolling in
school, b) the effects of microfinance follows a generational effect and is therefore not displayed in the
limited years of this study and c) in the majority of countries enrollment rates are already fairly high
which causes little variation to be explained within the values.
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Introduction
The idea started out so simple: invest in the poor and let the classic economic process lead the
way. By providing start-‐up financial capital to budding entrepreneurs, microfinance would create new
businesses, generate income, and bring jobs to small communities; one little investment would
dramatically change the lives of individuals throughout the world. This simple ground-‐making model has
now become an essential tool for alleviating poverty with a global gross loan portfolio of over $78 billion
dollars in 2011 reaching 94 million borrowers. Of these borrowers almost 73% are women showcasing
the large gender disparity in the industry (Microfinance Barometer 2013 / Convergences). In general,
microfinance institutes (MFIs) predominately target female business owners as women generally have
higher repayment rates than men. In order to justify female targeting many MFIs argue that microloans
are a positive way to enact women’s empowerment and will have positive repercussions for the entire
community (Susan Cheston “Empowering Women through Microfinance”). Allegedly as woman and
households increase their primary income, education enrollment rates rise as parents have more money
for school fees and other expenses.
Debated by economists, politicians, and philanthropists alike; microcredit has been praised and
critiqued for its ability to empower women through access to financial capital. By increasing access to
financial capital and investing resources in thousands of women-‐run businesses, microfinance provides
women with greater opportunities to earn income treating them as economic agents in traditionally
male-‐dominated societies. Through this method microfinance allows women to gain financial
independence which can lead to increased education enrollment along with other investment in social
development. Although the topic has been widely researched through case studies with mixed results,
there have been few global comprehensive studies measuring the effectiveness of the model. The
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purpose of this research study is to test the claim that microfinance encourages women’s
empowerment and growth in education. Through the use of traditional OLS regression, this study will
analyze if a highly developed microfinance sector encourages an increase in female secondary school
enrollment rates. The model will also test the claim that microfinance will lead to a proportionally higher
increase in girl’s enrollment as compared to boys. Afterwards an additional study will be conducted
using household survey data from the DHS database to determine if microfinance leads to an increase in
women’s financial independence. By conducting this investigation, the study will be able to determine
whether or not microfinance affects women’s empowerment and is therefore an effective development
strategy. As microfinance continues to gain importance in the development sphere, the results of this
study could have important implications for economic development policies.
Microfinance Industry
2006 Nobel Peace prize winner Muhammad Yunus is credited as the founder and “inventor” of
the microcredit model. (Nunez y Allejo 1). Muhammad Yunus’ Grameen bank started their programs in
the 1970’s as a way to reach the poorer sectors of society who were marginalized from receiving access
to the traditional methods of banking. Previously, the banking sector shied away from approaching
lower income households because many households lacked credit history and were risky. In order to
circumvent that problem, Yunus worked with groups of 15-‐20 women using the idea of solidarity as a
guarantee for the loans instead of credit history. Each member of the group became financially viable
for the others; essentially, if one member could not pay her monthly rate the other members of the
group would have to cover her part. The loans were used to create small entrepreneurial projects which
would then generate revenue further stimulating and growing the economy of these communities
(Olsen 504). Following the success of Grameen bank, other NGO’s and Microfinance institutions (MFIs)
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expanded throughout the developing world and by early 2000’s close to 70% of developing economies
would have microfinance institutions (Olsen 510).
As the sector continued to develop, competition within the industry led many MFI’s to begin to
innovate and differentiate their products. Many organizations began to offer other services in addition
to financial capital. One example of this is the organization ProMujer. Arguing that in many cases the
indebtedness of the borrowers is caused by the onset of an illness, ProMujer combines their
microfinance loans with simple primary health care. Along with the financial capital, all borrowers
receive free BMI tests, pap smears, and access to other preventive health care tests (Promujer 2013).
Meanwhile other MFI’s started to deviate from the traditional “group-‐lending model” offering individual
loans to less risky borrowers.
In recent decades there are two trends that have radically changed the sector. For one, the
industry has seen a shift towards commercialization of the sector. Due to the continuing growth and
success of the field, many new for-‐profit banks started to enter into the sector in early 2000’s while
many former NGO’s were converting into more regulated Non-‐banking Microfinance institutions
(NBMFI) (Servin 4). Former NGO’s began to convert to NBFI’s in order “to reap the benefits of being a
step closer to the formal sector. Often, these advantages include access to commercial capital, and as a
result, less reliance [and greater sustainability] upon state subsidies or philanthropic donations.” (Olsen
505). However, consequently the companies have switched their ownership to a shareholder structure
directing operations to focus more on profitability (Das 2009). Yet, many argue that the shift towards
commercialization of the market is highly beneficial as it increases the spread and access of banking
throughout the region and creates more competition, which encourages increased efficiency of the
organizations. In fact, in a study conducted by Roselia Servin, Marrit van den Berg, and Robert Lensink it
was revealed that banks and NBFI’s are statistically more technologically efficient at operating than
NGO’s and cooperatives which explains the reason behind the success of the more formalized
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organizations in the sector (Servin 8). On the other hand, this alteration of the industry has generated a
lot of controversy. Many critics, such as Pamela Das, argue that “while profit making in microfinance has
its positive effects, it can also lead to exploitation and over indebtedness” of the poor (Das 2009). The
debilitating high interest rates of some formalized organizations have been seen as exploitative of the
poor. For example, the former-‐NGO-‐turned NBFI Compartamos in Mexico has in the past charged
interest rates of up to 100% on their microloans taking advantage of the many clients who do not have
access to other banking services (Das 2009). Although the company has argued that the high level of
interest is needed to fund the administrative costs of the services, a recent study cited by the Economist
stated that “demand for microcredit is more price elastic than had been thought. Cut the interest rate
by 10 percentage points and more people will take out a loan whilst existing borrowers will increase the
size of their loans. The effect of this extra demand equaled the cost of lowering the interest rate, so by
cutting rates Compartamos could earn just as much profit” (Schumpeter, 2013). Furthermore, the
commercialization of the sector is seen as evidence of a shift in focus away from the poorer sectors to
the middle class. For example, BancoSol the leading MFI in Bolivia has clearly opened its borders to a
wider sector of clients. “Although BancoSol claims to still be committed to exclusively servicing the poor,
twenty percent of its clients are not considered poor and eighty percent are not exclusively under the
poverty line” (Das 2009). However, this change in focus can easily be seen as necessary for the
sustainability of the organizations. It is statistically more cost efficient to loan to the middle class. The
middle class generally takes out larger amounts in their loans and requires less attention from the staff
than clients of the poorer classes. According to Roselia Servin, Marrit van den Berg, and Robert Lensink,
“Although NGOs have the lowest cost per loan, their small loan sizes give them the highest cost per US
dollar lent” (Servin 6). Yet, despite the cost efficiency of loaning to the middle class, critics argue that
this defeats the purpose of microfinance since the organizations are no longer focusing on alleviating
poverty.
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In conjunction with this shift in commercialization of the sector, another radical change was the
rise of regulation and government involvement in microfinance. Many national governments started to
implement new reforms and regulations in order to start monitoring this growing sector (Olsen 507). For
example, in 2006 the Bangladeshi government established the Microcredit Regulatory Authority in order
to monitor growth within the sector. “With regards to microfinance regulation, government
involvement in microfinance can be a positive change in that improved regulation can strengthen the
sector by advancing technical capacity, increasing competition and, in turn, improving the quality and
price of microfinance products” (Olsen 502). However, alternatively the regulations will increase the
production costs of the MFI’s, which can be passed down towards the clients (Olsen 502). A study
conducted by Morduch, Demirgüç-‐Kunt and Cull in 2009 attempted to measure the effects of the
newfound regulation on microfinance operations. The results of the study showed that the effectiveness
of the microfinance model is greatly impacted by the regulatory environment. For example as
microfinance regulation is increased within a country, banks and cooperatives will respond by
maintaining their profit levels and decreasing the impact levels while NGO’s and smaller enterprises will
decrease their profit levels but maintain their influence and poverty alleviation impact levels (Morduch
2009).
Today the microfinance industry has grown to unprecedented levels. This model has grown to
global gross loan portfolio of over $78 billion dollars in 2011 reaching 94 million borrowers. There are
over 1000 Microfinance institutions that report on the MIX market database: the largest source of
microfinance data available. Yet of those 1000 institutions, roughly 10% of the MFI’s are extremely large
representing over 77% of the market. For this reason it is clear that commercialization of the industry
has led to increased consolidation and a strong shift away from the “small NGO” outreach of the past.
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Literature Review
While initially most of the research for microfinance was focused on small case studies and
positive PR-‐driven client testimonials, in the past decade there has been more emphasis placed on
rigorous studies. In 2010 economists Banjeree and Duflo tested the effectiveness of microfinance
through a randomized controlled trial conducted in India. In the study a microfinance institution, and
research partner, slowly expanded operations into randomly chosen villages while the other villages
were also monitored as the control group. The study showed that “the main objective of microfinance
seemed to have been achieved. It was not miraculous but it was working…on the other hand we [they]
found no evidence that women were feeling more empowered or that education levels increased”
(Banjeree & Duflo 115). Not surprisingly, these results were met with ample criticism from the
microfinance community. One main critique was that the trial only occurred over a 15 month period,
which may not be sufficient time for a radical transformation of values.
Recently, other RCT studies have been conducted each showing similar limited results of
microfinance. In one study, Dean Karlan and Jonathan Zimmerman partnered with a large MFI First
Macro in the Philippines. In First Macro’s model information such as age, income, and number of years
at a job were calculated and utilized to determine “creditworthiness”. This information was fed into a
computer and based off the results the potential borrower was either accepted or rejected. Utilizing this
approach, Karlan and Zimmerman looked at the “maybe” clients that weren’t automatically accepted
but remained close to that acceptance line and randomly chose whether or not these clients would
receive a loan. The results of the study were conflicted and limited. On one hand, looking at all of the
applicants in aggregate the results were limited. Although business profits were 10% higher than those
who did not receive loans, this result was not significant. On the other hand, it was shown that specific
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segments of clientele were more adept at operations than others and therefore were more positively
affected by the loan. Clients that were wealthier at the onset proved more adept at using the money
effectively. Furthermore, in devastating news for the microfinance industry, men were seen as more
productive than woman and had three times the increase in business profits (Karlan 77). In essence, the
results of research on microfinance showed limited and conflicting results. Microfinance was not seen to
be the “golden bullet” and the “one-‐time solution” to poverty. While it can have positive results, it is
shown that this effect is limited to the wealthier as well as more entrepreneurial minded individuals.
However it is important to note that all of these studies were limited in scope and focused on short-‐
term effects of microfinance. For example, Karlan’s study only looked at a period of two years. For this
reason, this particular study will be of great value to the field. Not only will it look at the global effects of
microfinance, the study will use a longer time frame.
While Banjeree and Duflo piloted the use of RCT analysis in microfinance analysis, other
researches have focused on regression analysis to analyze its effectiveness. A study by Lensink, Van den
Berg, and Servin on “Ownership and Technical Efficiency of Microfinance Institutions: Empirical Evidence
from Latin America” (Servin 6) demonstrated that the different types of microfinance organizations and
their ownership structures have a direct impact on the influence and impact of said institutions on
poverty alleviation. As banks and larger cooperative microfinance institutions are focused profit making
enterprises they are more technically efficient than NGO’s and non-‐banking finance institutions. This
efficiency increases their ability to outreach to more clients and increase impact. On the other hand, a
study conducted by Morduch Demirgüç-‐Kunt and Cull in 2009 showed that these results will be greatly
impacted by the regulatory environment of the microfinance sector. For example as microfinance
regulation is increased within a country, banks and cooperatives will respond by maintaining their profit
levels and decreasing the impact levels while NGO’s and smaller enterprises will have decreased profit
levels but will maintain their influence and poverty alleviation impact levels (Morduch 2009).
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Although research on the effects of microfinance has been quite limited in scope, the effect of
women’s empowerment on economic development is a much more widely researched topic. One facet
of this topic is the addressing the rise in women’s financial independence as an instrument for economic
development in terms of education and health. In a study by Duflo and Udry (2004) in Cote D’Ivoire it
was found that as women’s financial wealth increases the entire household spends a disproportionately
larger share on food and women’s goods than when the husband’s financial wealth increases. This study
proves that households do not act as cohesive units and by providing additional income to women there
is increased nutrition and food expenditure for the household. As healthier children are more likely to
remain in school, it can be claimed that an increase in the financial independence for women will lead to
larger enrollment in education (Kremer and Miguel 2001). Yet the question remains: does financial
independence lead to an increase in girl’s education and women’s empowerment? In fact a study
conducted by Benhassine et al. 2011 in Morocco answered this exact question by researching the effect
of conditional cash transfer’s on girl’s education. It was found that even if the transfer is very small, if
the women was a recipient there was an increased effect on the education of young girls than if the
recipient was male. Building of this analysis it can be theorized that an increase in women’s income
given through a microfinance loan will increase the likelihood of girls receiving education. It is precisely
this relationship that will be tested in this study. By drawing on the work of other researchers, the study
will be able to further analyze the effects of the microfinance model and contribute new insights to the
field.
Theory
As stated previously, the purpose of this study is to determine whether or not an increase in
microfinance would lead to an increase in female empowerment and higher enrollment rates. By looking
at different aspects of microfinance, the paper will be able to determine which characteristics of a
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microfinance sector lead to the largest impact on education levels. One of the overarching arguments
for this paper is importance of financial independence as a mechanism for women’s empowerment. For
this relationship drives the change in the household decision-‐making dynamic allowing for increased
enrollment rates.
When woman borrowers take out microfinance loans to grow their own businesses, this creates
a new set of income that allows the women to be financially independent from their husbands. Since
women and men have different consumption patterns, these women are more likely to invest in food
expenditure and their daughter’s education. For this reason, with the additional income households will
more likely invest in education and nutrition which increases enrollment and attendance rates for
children. Furthermore, as shown by previous studies, it has been found that if the recipient of a money
transfer was a woman there was a larger effect on the education of young girls than if the recipient was
male. Building of this analysis it can be theorized that an increase in women’s income given through a
microfinance loan will increase the likelihood of girls receiving secondary education.
Different aspects and characteristics of microfinance will have predicted different effects on
women’s development and its impact on education enrollment. On one hand, as the gross loan portfolio
of a country increases there is an increased amount of money spent within the country encouraging the
growth of more businesses impacting communities and households. This argument also holds true for
the gross number of active borrowers within a country. However, the marginal effect of adding a new
active borrower will be significantly larger than the marginal effect of increasing the gross loan size. For
every new borrower that is incorporated into the microfinance system, this borrower’s entire network is
expected to benefit. With the investment given through the first loan she generates more income
increasing the likelihood of her children attending school. Additionally, by expanding her business her
suppliers will gain increased business and income which can be used to allow their children to attend
school. This borrower’s entire network will be impacted by microfinance and will gain better knowledge
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of the microfinance industry. On the other hand, the marginal effect of increasing a country’s gross loan
portfolio by one dollar will have less impact, as holding all other variables constant, no new borrowers
and networks are affected. For this study, it is believed that the marginal effect of increasing the natural
logarithm of a country’s gross loan portfolio is less than the effect of increasing the proportion of
borrowers in the population. The theory for this argument is that for every borrower added into the
microfinance system, her entire network is impacted.
Similarly, the effect of increasing the number of microfinance institutions in a country will have
an expected greater impact than simply increasing the number of borrowers. As the microfinance
industry becomes more developed, it will begin to spread through new parts of the country allowing
new networks of women and their children access to these services. By adding one MFI into a rural
village, the village will gain access to financial services as well as knowledge of the microfinance industry
even if only a small portion of the population becomes an actual borrower. Furthermore, even after a
country has a saturated market with microfinance spread throughout all regions, new microfinance
firms will establish themselves within the market through innovation. For example, under the pressures
of high competition, a new MFI would have to create a new product that may increase microfinance’s
accessibility to new sectors of the population impacting entire new networks of people. Essentially as
the microfinance sector increases and becomes more widespread, this will increase the level of
women’s empowerment.
On the other hand, different microfinance institutions will be more adept and efficient at
outreach than others. Larger more technical efficient banks and NBFI’s will have increased economies of
scale and will be able to reach more borrowers increasing their impact. As the commercialization of the
microfinance industry increases, these “technically efficient” MFI’s dominate the market consolidating
their operations and monopolizing the market. For this reason if a country’s microfinance sector is highly
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consolidated it will have a larger impact on women’s empowerment because these larger dominating
banks and NBFI firms are more technically efficient.
Another important aspect within the microfinance environment is the impact of regulation on
microfinance development. Shown through a study by Morduch and Cull in 2009, regulation can have a
differential effect on women’s empowerment dependent on the type of MFI. For example, profit driven
microfinance organizations will respond to increased regulation by cutting costs, maintaining profits,
and lowering impact whereas more impact driven non-‐profits will in many cases reduce profitability but
maintain impact levels. For this reason the KKM regulatory quality index is used to control for overall
governance and legislative regulation. This variable will be a substitute for the specific microfinance
regulation of a country as the data for specific microfinance regulation is unavailable for the majority of
the years and countries within this study. By controlling for the overall governance and legislative
regulation of a country, the true unbiased effects of microfinance will be visible.
Method
This research study will use a two part investigation looking at the effects of microfinance on
education and financial independence. Using the MIX market microfinance database, different aspects
of microfinance development will be measured and compared to the World Bank’s development
indicators on secondary school enrollment for girls as well as the ratio of girls to boys enrolled in
secondary school. This test will use data from 115 countries across an eighteen year period and will
show the effectiveness of microfinance as looked at through a global scope. For the first test a
traditional least squared regression analysis with fixed effect analysis will be conducted to look at
microfinance’s effect on education levels. This regression will be tested for various levels of robustness.
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Furthermore different models of this regression will also be tested. The first model to be tested
will lag the independent variables by one year to determine if there is a delayed impact of microfinance.
The second model looks at taking the natural logarithm of the dependent variables in order to measure
the percentage change of each variable. An additional model divides the countries into two different
tiers (top 50% of GDP per capita and bottom 50% of GDP per capita) to measure the effect of
microfinance on countries within different income levels. Lastly, the final model looks at the interaction
effect of women’s financial independence with microfinance. As it is predicted that a women’s financial
independence given through a loan leads to increased female enrollment, the model addresses the
interaction effect of the two variables.
Models: Ratio of Girls/Boys in Secondary Education 1. Ratio of girls/boys=α+β1(MFI count)+β2(Log Gross Loan/GDP) +β3(Borrowers/Pop)
+β4(Concentration) +β5(Log Aidflow/Pop) +β6(Log GDP/Pop) + β7(Log Pop.) + β8(Quality of Regulation)+β9(Polity)
2. Ratio of girls/boys=α+β1(L(MFI count))+β2(L(Log Gross Loan/GDP)) +β3(L(Borrowers/Pop)) +β4(L(Concentration)) +β5(Log Aidflow/Pop) +β6(Log GDP/Pop) + β7(Log Pop.) + β8(Quality of Regulation)+β9(Polity)
3. Log(Ratio of girls/boys)=α+β1(Log MFI count)+β2(log Gross Loan/GDP) +β3(log Borrowers/Pop) +β4(log Concentration) +β5(Log Aidflow/Pop) +β6(Log GDP/Pop) + β7(Log Pop.) + β8(Quality of Regulation)+β9(Polity)
4. Ratio of girls/boys=α+β1(MFI count)+β2(Log Gross Loan/GDP) +β3(Borrowers/Pop) +β4(Concentration) +β5(Log Aidflow/Pop) +β6(Log GDP/Pop) + β7(Log Pop.) + β8(Quality of Regulation)+β9(Polity) if GDP/pop>$2375 and if GDP/pop<$2375
5. Ratio of girls/boys=α+β1(MFI count*women financial ind.)+β2((Log Gross Loan/GDP)*women financial indp.) +β3((Borrowers/Pop)*women financial indep.) +β4(Concentration*women financial indep.) +β5(Log Aidflow/Pop) +β6(Log GDP/Pop) + β7(Log Pop.) + β8(Quality of Regulation)+β9(Polity)
Models: Girl’s Enrollment Rates Secondary Education 6. Girl’s Enrollment=α+β1(MFI count)+β2(Log Gross Loan/GDP) +β3(Borrowers/Pop)
+β4(Concentration) +β5(Log Aidflow/Pop) +β6(Log GDP/Pop) + β7(Log Pop.) + β8(Quality of Regulation)+β9(Polity)
7. Girl’s Enrollment=α+β1(L(MFI count))+β2(L(Log Gross Loan/GDP)) +β3(L(Borrowers/Pop)) +β4(L(Concentration)) +β5(Log Aidflow/Pop) +β6(Log GDP/Pop) + β7(Log Pop.) + β8(Quality of Regulation)+β9(Polity)
8. Girl’s Enrollment=α+β1(Log MFI count)+β2(log Gross Loan/GDP) +β3(log Borrowers/Pop) +β4(log Concentration) +β5(Log Aidflow/Pop) +β6(Log GDP/Pop) + β7(Log Pop.) + β8(Quality of Regulation)+β9(Polity)
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9. Girl’s Enrollment=α+β1(MFI count)+β2(Log Gross Loan/GDP) +β3(Borrowers/Pop) +β4(Concentration) +β5(Log Aidflow/Pop) +β6(Log GDP/Pop) + β7(Log Pop.) + β8(Quality of Regulation)+β9(Polity) if GDP/pop>$2375 and if GDP/pop<$2375
10. Girl’s Enrollment=α+β1(MFI count*women financial ind.)+β2((Log Gross Loan/GDP)*women financial indp.) +β3((Borrowers/Pop)*women financial indep.) +β4(Concentration*women financial indep.) +β5(Log Aidflow/Pop) +β6(Log GDP/Pop) + β7(Log Pop.) + β8(Quality of Regulation)+β9(Polity)
Subsequently, the study will then look at the effects of microfinance on women’s financial
independence. This project will compare the different levels of microfinance cross-‐nationally as
compared to the results of DHS surveys conducted in each country. The surveys collected asked women
about their financial independence from their husbands and their ability to make financial decisions in
the household. Similarly, different models of this regression were also tested including a lagged effect, a
loglog model, and a tiered approach. In combination both will be used to prove or disprove the
hypothesis that microfinance does in fact encourage female empowerment.
Models: Women’s Financial Independence 11. Women’s financial independence=α+β1(MFI count)+β2(Log Gross Loan/GDP)
+β3(Borrowers/Pop) +β4(Concentration) +β5(Log Aidflow/Pop) +β6(Log GDP/Pop) + β7(Log Pop.) + β8(Quality of Regulation)+β9(Polity)
12. Women’s Financial Independence=α+β1(L(MFI count))+β2(L(Log Gross Loan/GDP)) +β3(L(Borrowers/Pop)) +β4(L(Concentration)) +β5(Log Aidflow/Pop) +β6(Log GDP/Pop) + β7(Log Pop.) + β8(Quality of Regulation)+β9(Polity)
13. Women’s Financial Independence=α+β1(Log MFI count)+β2(log Gross Loan/GDP) +β3(log Borrowers/Pop) +β4(log Concentration) +β5(Log Aidflow/Pop) +β6(Log GDP/Pop) + β7(Log Pop.) + β8(Quality of Regulation)+β9(Polity)
14. Women’s financial independence=α+β1(MFI count)+β2(Log Gross Loan/GDP) +β3(Borrowers/Pop) +β4(Concentration) +β5(Log Aidflow/Pop) +β6(Log GDP/Pop) + β7(Log Pop.) + β8(Quality of Regulation)+β9(Polity) if GDP/pop>$2375 and if GDP/pop<$2375
Hypotheses
After considering this research question the following hypotheses were developed.
Variable Name Hypothesis Number of Microfinance Institutions
H1: As number of microfinance institutions increases there will be an increase in the ratio of girls to boys enrollment for secondary education H2: As number of microfinance institutions increases there will be an increase in girl’s secondary enrollment H3: As number of microfinance institutions increases there will be higher rates
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of women’s financial independence Log of Gross Loan Size as percent of GDP
H1: As log gross loan size increases there will be an increase in the ratio of girls to boys enrollment for secondary education H2: As log gross size increases there will be an increase in girl’s secondary enrollment H3: As log gross size increases there will be higher rates of women’s financial independence
Borrowers as Percentage of Population
H1: As borrowers/pop increases there will be an increase in the ratio of girls to boys enrollment for secondary education H2: As borrowers/pop increases there will be an increase in girl’s secondary enrollment H3: As borrowers/pop increases there will be higher rates of women’s financial independence
Concentration of Industry
H1: As microfinance becomes more concentrated there will be an increase in the ratio of girls to boys enrollment for secondary education H2: As microfinance becomes more concentrated there will be an increase in girl’s secondary enrollment H3: As microfinance becomes more concentrated there will be higher rates of women’s financial independence
Data
The purpose of this investigation is to analyze the effect of microfinance on women’s
empowerment. In order to address this question different aspects of microfinance development will be
studied. The dependent variable will be gross enrollment secondary education levels as measured by
enrollment of girls in secondary school and the ratio of girls/boys enrollment. This variable will then be
analyzed in an OLS regression model to determine to what extent changes in education enrollment can
be explained by changes of different aspects of microfinance. For the second section of this study the
dependent variable will an indicator of women’s financial independence as shown through DHS surveys.
This variable is constructed by calculating the percentage of women surveyed who are either in direct
control of their finances or share the responsibility with their spouse or someone else. This variable does
not include women who stated that another party was in full control of their finances.
Table 1: Summary of Data Statistics
Type of Variable Variable Obs Mean Std. Dev. Min Max
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Independent
Number of Microfinance Institutions 4413 8.722184 11.67068 1 127
Log Gross Loan as percentage of GDP 4302 -‐6.441803 2.619506 -‐17.70777 -‐0.850242
Total Borrowers as Percent of Pop. 4104 0.0144831 0.0239485 0 0.1579849
Concentration 6355 0.6308749 0.321624 0.0335152 1
Variable Obs Mean Std. Dev. Min Max
Dependent
Ratio of Girls/Boys Secondary 3680 94.045 15.75048 31.661 133.069
Girls Enrollment Secondary 2057 57.72228 26.78214 2.26188 100
Women's Financial Independence 6596 40.98008 41.90388 0 98.8
Variable Obs Mean Std. Dev. Min Max
Control
Log Aid Flow per Capita 4697 3.5789 1.371367 -‐2.93463 6.79911
Log GDP per Capita 6266 7.222829 1.104429 3.912867 9.62015
Log Population 6467 15.99491 1.747861 11.47135 21.02882
Quality of Regulation Institutions 6366 -‐0.4015393 0.672039 -‐2.675439 1.644733
Polity 4312 -‐2.973098 6.305691 -‐10 10
The independent variables addressed in this study will look at different characteristics and
aspects of each country’s microfinance sector and their individual effects on education enrollment. For
these variables the MIX market will be used. It is the largest collection of microfinance data available
and is the most frequently used database for microfinance studies. In order to measure women’s
empowerment, this study will utilize data from the world development indicators from the World Bank’s
database. For independent variables the study will look at the total number of borrowers as a
percentage of the population, the total number of microfinance institutions, the natural logarithm of the
gross loan portfolio for each nation as a percentage of GDP, and the concentration of the microfinance
sector (measured through the Herfindahl-‐Hirschman index). In order to account for large differences
between countries statistics were taken as either a percentage or through the natural logarithm of the
value. Graphs are provided below to show trends within the data.
La Rocque 18
Graph 1: Boxplot showing the Number of Microfinance Institutions over Time
Graph 2: Boxplot of Log of Gross Loan Size as percentage of GDP over time.
010
2030
4050
MFI
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
0.0
5.1
.15
.2
Gro
ss L
oan/
GD
P
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
La Rocque 19
Graph 3: Boxplot of Total Borrowers as percentage of Population over time
As education enrollment levels are impacted by many different factors, this study will also
control for a list of additional variables. For one, the level of overall governance and legislative
regulation was measured through the KKM index. This control will account for the differential effects of
regulation on the different types of microfinance institutions. Subsequently, an additional factor that
must be controlled for is the global trend towards women’s empowerment and education enrollment.
As the Millennium Development Goals were developed, and long before that, there has been a general
trend in development to encourage women’s empowerment and increase worldwide education. To
control for this value total aid flows for countries as measured by the natural logarithm of net official aid
received per capita were used. Lastly, the model will control for the usual indicators such as natural
logarithm of GDP per capita, natural logarithm of population size, and polity scores to look at the effect
of different political systems.
0.0
5.1
.15
Bor
row
ers
as P
erce
ntag
e of
Pop
ulat
ion
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
La Rocque 20
Results
After accounting for many different variations in models and robustness checks, the results of
this study demonstrate limited, if any, effects of microfinance on women’s empowerment as shown
through any of the three tested dependent variables.
In the first part of the study the effects of microfinance on the proportion of girl’s enrollment
rates over boy’s enrollment rates was tested. Although in a few cases the traditional OLS method
showed significant positive results, after clustering the errors and using a fixed effect analysis all of the
variables lost significance. As the fixed effects model was not significant the variation between
education rates within the countries is dependent on individual country differences rather than the
different levels of microfinance. However it is important to note that the dependent variable was highly
clustered in the upper range showing that in the majority of cases countries already started with high
levels of girl’s enrollment rates. Furthermore the ratio of girls/boys education variable is also impacted
by the gender composition of those within the population group. For this reason the variation between
the countries may not solely be caused by differences in enrollment rates but also by the percentage of
girls within the age group.
In the second part of this study the effects of microfinance on the girl’s enrollment rates was
tested. In this particular case only two variables remained significant after testing for different degrees
of robustness: the gross loan size of the country as a percentage of GDP and the number of borrowers as
a percentage of the population. Both variables were significant at the 90% confidence interval. However,
it is important to note that the number of borrowers as a percentage of the population variable
displayed a negative relationship greatly contradicting the overall hypothesis that microfinance leads to
La Rocque 21
women’s empowerment. In the subsequent model where the independent variables were lagged, this
negative relationship persisted and was significant at 90% level even after following a fixed effects
model. Yet these results were contradicted in the log-‐log model where this variable was found to be
positively correlated with enrollment rates at a 95% significance level. The conflicting results of these
indicators demonstrate the limited effectiveness of microfinance indicators.
In the final part of the study, the effects of microfinance on women’s financial independence
was tested. In this case none of the microfinance variables remained significant after clustering and
using a fixed effects regression. This result remained consistent throughout all the models. The results
for each model is further discussed in the following sections.
Results of First Dependent Variable: Ratio of Girls/Boys in Secondary Education
Table 2: Regression results for Ratio of Girls/Boys in Secondary Education Standard errors are presented in parenthesis.
***=99% CI, **=95% CI< *=90% CI
Type of Test # MFI Log Gross loan port-‐folio/ GDP
Borrower/ Pop
Concent-‐ration
Log GDP per Pop
Log Aid per Pop
Governance Log pop
Polity Obs.
Coef Coef Coef Coef Coef Coef Coef Coef Coef
OLS
OLS
.0656*
(.038)
-‐.100
(.236)
211.65***
(20.816)
-‐4.793***
(1.180)
5.609***
(.360)
-‐4.9 ***
(.50)
7.77***
(.772)
-‐2.40 ***
(.445)
0.286
(.066)
1344
OLS w/o Aid
.0390
(.038)
.0451
(.200)
67.38***
(15.318)
-‐5.55***
(1.113)
7.100 ***
(.361)
N/A 4.91***
(.674)
.006
(.322)
.317 ***
(.059)
1732
Cluster
(Country)
.0656
(.138)
-‐.100
(.912)
211.65***
(78.178)
-‐4.793
(7.221)
5.609 *** (1.96)
-‐4.9 ** (2.1)
7.77**
(3.544)
-‐2.40
(1.73)
0.286
(.250)
1344
Fixed Effects
-‐.0001
(.012)
-‐.134
(.126)
9.199
(6.444)
0.2669
(.528)
-‐.406
(1.29)
-‐.49
(.32)
0.263
(.453)
2.50 **
(1.05)
-‐.014
(.023)
1344
La Rocque 22
Lag
Lag-‐no cluster
.0343
(.0402)
-‐.2786
(.2171)
238.56***
(22.519)
-‐5.509***
(1.195)
5.50 *** (.399)
-‐4.7 *** (.52)
7.877***
(.786)
-‐2.49 *** (.447)
.268 *** (.067)
1312
Lag-‐ cluster
.0343
(.1343)
-‐.2786
(.945)
238.56**
(89.961)
-‐5.509
(7.624)
5.50 *** (2.07)
-‐4.7 ** (2.2)
7.877**
(3.658)
-‐2.49
(1.79)
.268 (.445)
1312
Lag-‐ cluster fixed effect
.0121
(.009)
-‐.1383
(.0857)
14.17***
(4.890)
.5682
(.4384)
-‐2.0*
(1.11)
-‐.45
(.30)
.1110
(.5293)
3.29 *** (1.11)
-‐.009 (.022)
1312
Tiered
Tier 1
Bottom (cluster)
.2635
(.1879)
.9586
(1.296)
311.10***
(91.089)
-‐.9910
(8.638)
-‐.125
(4.42)
-‐.43
(3.3)
14.714***
(4.478)
.2858
(2.98)
1.24**
(.546)
669
Tier 2
Top (cluster)
.0780
(.125)
-‐.330
(.9000)
51.396
(77.044)
-‐2.308
(4.992)
-‐1.85
(3.26)
-‐6.4 *** (2.3)
8.880*
(5.577)
-‐4.46 ** (2.00)
.060
(.313)
675
Interact
Interact (cluster)
.003 *** (.001)
.0206*
(.0107)
-‐.2921
(1.040)
-‐6.570
(6.734)
4.47 ** (2.16)
-‐3.7
(2.3)
9.422**
(4.511)
-‐2.31
(1.85)
.2378
(.250)
1344
Interact (cluster fixed effect)
-‐.0001
(.0001)
-‐.003**
(.0009)
.1737**
(.0837)
.4219
(.5581)
-‐.545 (.993)
-‐.52
(.32)
.3099
(.4388)
3.260***
(1.11)
-‐.013
(.022)
1344
Loglog
Loglog cluster
.0715 ** (.0295)
-‐.0058
(.0178)
.00154
(.0221)
-‐.0277
(.0975)
.070 *** (.022)
-‐.06 ** (.03)
.09222***
(.0325)
-‐.048 *** (.018)
.0047
(.003)
1344
Loglog cluster FE
-‐.001
(.004)
-‐.0011
(.0021)
.0012
(.0031)
.00247
(.0060)
.0027
(.014)
-‐.01
(.00)
.0061
(.0084)
.0238 *
(.013)
-‐.000
(.000)
1344
For the first section of this study the effects of microfinance on the ratio of girls/boys in
secondary school was tested. It was predicted that as the level of microfinance within a country
increases the ratio of girls/boys enrollment would also increase as girl’s enrollment rates would be
disproportionately affected. In looking at the primary model using a traditional OLS regression it is
shown that total number of borrowers as a percentage of the population and the concentration of the
industry is significant within a 99% confidence interval. As the total number of borrowers/pop increases
it has a large positive impact on ratio of girls/boys in school whereas as the concentration of the
La Rocque 23
industry decreases this leads to an increase in the ratio of girls/boys enrollment. This last result
contradicts the proposed hypothesis and shows a less concentrated industry has more impact on
women’s empowerment. In this case, this suggests that smaller NGO’s and cooperative institutions are
more efficient at generating women’s empowerment than the larger profit-‐driven institutions. This also
follows the typical economic argument of perfect competition in that large monopolistic MFI’s will be
less accountable to their consumers. With increased competition, the price falls and the quality
increases which in this case is shown through increased impact of microfinance on women’s
empowerment. Furthermore, the total number of microfinance institutions in a country is also
significant at a 90% confidence interval showing that as the number of MFI’s increase there is a positive
impact on the ratio of girls/boys in secondary school. Yet after clustering the errors at a country level,
the significance for many of the variables fails to be robust. Although the borrowers as a percentage of
the population variable is significant at the 95% confidence interval after clustering for errors, after a
fixed effect model is applied even this variable remains insignificant.
Remarkably similar results were obtained throughout the many different variations of the
model. For example, after lagging the independent variables similar results are projected. In this case
before accounting for the clustering of country errors both the number of borrowers as a percentage of
population and the concentration of the industry remain significant. Yet, after conducting a fixed effects
regression only the number of borrowers as a percentage of population remains significant.
In the next model the effects of microfinance were measured under different tiers of countries
those in the top 50% income bracket and those in the lower bracket. In this case it was found that the
only variable to be significant is the number of borrowers as a percentage of the population and this
only applied to the lower income bracket.
La Rocque 24
Subsequently, the interaction effect between women’s financial independence rates and
microfinance was tested. As predicted by the theory, women’s financial independence is the driving
mechanism for increasing the ratio of girls/boys in school. It is predicted that as microfinance increases
in a society more women will become financially independent which will impact the ratio of girls/boys in
school. In this case, the number of borrowers as a percentage of the population remains significant at
the 95% confidence level even within a fixed effects model. However of further interest is the results for
the natural logarithm of gross loan size as percentage of GDP variable. This variable was insignificant in
all other models but yet was significant after accounting for fixed effects in this particular case. This
variable showed that as the gross loan size of a country increases this has a negative effect on the ratio
of girls/boys enrollment. This discrepancy might be caused by correlation between the variables.
In this last model the natural logarithm values of the microfinance indicators were tested
against the natural logarithm of the ratio of girls/boys enrollment rates. As the dependent variable is
clustered in the higher ranges, this leads credence to the assumption that it is marginally more difficult
to increase the ratio of girls/boys enrollment rates as the ratio increases. For example if a country
already has 98% of girls in school as compared to boys it is predictably more difficult to increase that
ratio than it would be for a country with 60% of girls in school compared to boys. It was thought that a
loglog model would be able to address this issue. However in this case none of the microfinance
variables were significant in a fixed effects model.
Throughout the many tested models it is clear that one variable seems to stand out and remains
significant even after accounting for the different robustness checks. The number of borrowers as a
percentage of the population has a statistically significant positive effect on ratio of girls/boys in
enrollment in both the lagged and interaction effect model. However it is important to question these
results due to the nature of the construction of the dependent variable. In this case the dependent
La Rocque 25
variable was highly clustered in the upper range showing that in the majority of cases countries in this
study started with already high levels of girl’s enrollment rates.
Graph 4: Histogram for the Ratio of Girls/Boys Secondary Enrollment
Furthermore the ratio of girls/boys education variable is also impacted by the gender
composition of girls/boys within the population group. For this reason the variation between the
countries may not be caused solely by differences in enrollment rates but also by the gender
composition of the population. This also could have affected the total number of borrowers as a
percentage of population variable as this variable is directly influenced by population size.
Results of Second Dependent Variable: Girl’s Enrollment in Secondary Education
Unlike the other dependent variable, the enrollment rates of girls in secondary school is
unaffected by the gender composition of the population. This variable is constructed by looking at the
percentage of girls in school for each age category. Although there still remains a strong level of
clustering within the data, the variance within the data is significantly larger. For this reason, the results
for this variable show an increased effectiveness of microfinance indicators than that of the former
dependent variable. However, similar to the other dependent variable, many of these effects will fail the
robustness checks.
050
100
150
200
250
Frequ
ency
40 60 80 100 120 140Ratio Girls/Boys Secondary
La Rocque 26
Graph 6: Histogram for Girl’s Enrollment in Secondary School
The first model that was proposed was the traditional OLS method. In this model the gross loan
size, borrowers as a percentage of the population, and the concentration variables were all significant at
the 99% level. Furthermore, as before, the concentration variable follows a negative relationship where
a decrease in concentration increases the effectiveness of microfinance on girl’s enrollment rates.
However, after clustering by country none of the variables remain significant. Certain variables (gross
loan size and borrowers as a percentage of population) regain some level of significance after
accounting for fixed effects and remain significant at a 90% confidence interval. This shows that without
a fixed effects model there is a high level of correlation within the data increasing the noise in the
estimates. After this error noise is absorbed with the fixed effect, it is shown that there is a small effect
of microfinance on girl’s enrollment rates.
020
4060
Frequency
0 20 40 60 80 100Value
La Rocque 27
Table 3: Regression results for Girl’s Enrollment in Secondary Education Standard errors are presented in parenthesis.
***=99% CI, **=95% CI< *=90% CI
Type of Test # MFI Log Gross loans port-‐folio/ GDP
Borrower/ Pop
Concent-‐ration
Log GDP per Pop
Log Aid per Pop
Governance Log pop
Polity Obs.
Coef Coef Coef Coef Coef Coef Coef Coef Coef
OLS
OLS
.0415
(.0758)
-‐1.72 *** (.5198)
155.57 *** (38.5298)
-‐9.845***
(2.591)
21.61 *** (1.27)
6.98 *** (1.3)
-‐2.782
(2.074)
.255
(1.16)
-‐.851 *** (.157)
610
OLS w/o Aid
.0758
(.0615)
-‐2.602 *** (.3501)
123.64***
(22.375)
-‐9.262***
(2.111)
17.71 *** (.660)
N/A .9345
(1.016)
-‐5.54 *** (.618)
-‐.889 *** (.113)
906
Cluster
(Country)
.0415
(.2029)
-‐1.72
(1.527)
155.57
(175.84)
-‐9.845
(8.242)
21.61 *** (4.74)
6.98
(4.3)
-‐2.782
(6.176)
.255
(2.46)
-‐.851*
(.428)
610
Fixed Effects
-‐.056
(.0545)
1.695*
(.8946)
-‐79.48*
(43.28)
-‐4.857
(5.387)
5.81
(6.90)
.158
(1.8)
.4693
(3.994)
-‐33.14 *** (13.0)
-‐.150
(.131)
610
Lag
Lag-‐no cluster
.0338
(.081)
-‐1.46 *** (.507)
131.84***
(42.841)
-‐9.710***
(2.616)
22.11 *** (1.32)
7.26 *** (1.4)
-‐3.574*
(2.16)
.3029
(1.23)
-‐.741 *** (.165)
595
Lag-‐ cluster
.0338
(.192)
-‐1.46
(1.30)
131.84
(173.74)
-‐9.710
(7.683)
22.11 *** (4.49)
7.26 * (4.3)
-‐3.574
(5.951)
.3029
(2.74)
-‐.741
(.459)
595
Lag-‐ cluster fixed effect
.0051
(.0472)
1.127
(.855)
-‐94.75*
(38.207)
-‐5.226
(6.170)
6.38
(6.17)
.097
(1.8)
-‐.1316
(4.320)
-‐31.22 ***
(11.6)
-‐.142
(.136)
595
Tiered
Tier 1
Bottom (cluster)
.3197
(.3408)
-‐1.032
(1.166)
371.14**
(159.53)
-‐.0601
(7.913)
14.41
(9.74)
-‐5.3
(4.1)
-‐2.272
(10.056)
-‐8.036
(5.04)
-‐.0098
(.441)
313
Tier 2
Top (cluster)
.0041
(.137)
-‐2.798*
(1.402)
-‐91.923
(109.92)
-‐21.078***
(5.573)
5.698
(4.01)
8.18*** (2.6)
-‐1.908
(4.215)
2.569
(2.91)
-‐.9917 *** (.337)
297
Tier 2
Top Cluster fixed
-‐.0251
(.0492)
.9650
(.6948)
-‐16.298
(45.356)
-‐11.439*
(5.596)
15.54** (7.21)
1.22 (3.1)
1.154
(6.067)
-‐67.53 *** (15.6)
-‐.2308
(.165)
610
La Rocque 28
effects
Interact
Interact (cluster)
.007**
(.003)
.0081
(.0160)
-‐3.483*
(2.016)
-‐8.690
(7.908)
23.91*** (4.56)
7.51* (4.3)
-‐5.407
(7.201)
-‐.2963
(2.40)
-‐.9010
(.490)
610
Interact (cluster fixed effect)
-‐.0009
(.001)
.0167*
(.010)
.1780
(.6741)
-‐4.716
(5.019)
-‐.223
(4.50)
-‐.23
(1.7)
.0601
(3.636)
-‐28.4*
(16.1)
-‐.1483
(.109)
610
Loglog
Loglog cluster
.2205
(.142)
.0247
(.0596)
-‐.080
(.09993)
.1014
(.2834)
.5334*** (.078)
.083
(.08)
.1385
(.1221)
-‐.1187
(.089)
-‐.0156
(.009)
610
Loglog cluster FE
-‐.053*
(.0284)
-‐.0023
(.0229)
.0504**
(.02323)
.0144
(.1443)
-‐.080
(.172)
0.06
(.05)
-‐.0491
(.0927)
-‐.722*
(.413)
-‐.0028
(.002)
610
In the next model and section of robustness checks, the effects of lagging the independent
variables were addressed. In this case it was also shown that there was high correlation within the data
creating a large level of noise in the results. When clustering the variables at a country level, none of the
variables remained significant. Yet after following a fixed effects model this error was absorbed and the
borrowers as a percentage of the population became significant at the 90% level. Yet, corresponding to
the fixed effect’s OLS model, this variable was negatively correlated with girl’s enrollment rates. This
shows that as the total number of borrowers in a population increases the enrollment rate decreases.
Subsequently, an analysis was conducted on the effect of segregating the data into two tiers:
those in the lower 50% of GDP/capita and those in the top 50%. In this case, there was a significant
negative effect of concentration on the enrollment rates for wealthier countries showing that as a
country becomes less concentrated there is more impact for microfinance on enrollment rates.
However, after accounting for a fixed effects model both this variable and the natural logarithm of gross
loan size/GDP were significant at the 90% confidence level.
La Rocque 29
Similarly, when looking at the interaction effect of women’s financial independence with
microfinance on gross enrollment rates, the only significant variable is the gross loan size which is only
significant at the 90% confidence level.
Lastly, when addressing the dependent variable with a log-‐log model, it is shown both the
number of microfinance institutions and the number of borrowers as a percentage of population is
significant after accounting for a fixed effects regression. In this case a 1% change in the number of
microfinance institutions decreases the enrollment rate by 0.05% within a 90% confidence interval and a
1% change in the number of borrowers as a percentage of the population increases the enrollment rate
by 0.05%. This demonstrates that the relationship between the number of microfinance institutions and
the enrollment rate of girls in secondary education contradicts the predicted results. However in this
case it is only significant at a 90% confidence level and has a limited impact.
Results of Third Dependent Variable: Women’s Financial Independence
Table 4: Regression results for Women’s Financial Independence Standard errors are presented in parenthesis.
***=99% CI, **=95% CI< *=90% CI
Type of Test # MFI Log Gross loans port-‐folio/ GDP
Borrower/ Pop
Concent-‐ration
Log GDP per Pop
Log Aid per Pop
Governance Log pop
Polity Obs.
Coef Coef Coef Coef Coef Coef Coef Coef Coef
OLS
OLS
.0708
(.0849)
3.528 *** (.5248)
-‐633.696 *** (52.833)
-‐14.0250***
(2.7628)
-‐11.8 *** (.988)
4.44 *** (1.1)
23.5548*** (1.723)
4.323 *** (.952)
-‐.9064 *** (.139)
2092
OLS w/o Aid
-‐.0808
(.0826)
2.2539 *** (.4492)
-‐69.798* (39.739)
-‐13.5553*** (2.502)
-‐16.4 *** (.887)
n/a 13.4599*** (1.432)
2.511 *** (.742)
.0900 (.128)
2794
Cluster
(Country)
.0708
(.308)
3.528
(2.505)
-‐633.696 *** (216.02)
-‐14.0250
(12.023)
-‐11.8 ** (5.13)
4.44 (6.5)
23.5548*** (9.08)
4.323 (5.77)
-‐.9064 (.831)
2092
La Rocque 30
Fixed Effects
-‐.0236
(.019)
.0218
(.0924)
-‐15.028
(10.72)
.32318
(.2911)
2.41
(1.78)
-‐.14
(.13)
-‐.7694
(.4779)
2.114
(2.16)
.0365
(.033)
2092
Lag
Lag-‐no cluster
.2484 ***
(.0929)
1.811 ***
(.5025)
-‐500.039 ***
(57.38)
-‐11.97***
(2.84)
-‐13.4 *** (1.01)
5.10*** (1.1)
24.995***
(1.759)
3.55 ***
(.981)
-‐1.02 ***
(.143)
2046
Lag-‐ cluster
.2484
(.3634)
1.811
(2.425)
-‐500.039 *
(254.75)
-‐11.97***
(12.522)
-‐13.4 *** (5.15)
5.10
(6.6)
24.995***
(9.164)
3.55
(5.93)
-‐1.02
(.860)
2046
Lag-‐ cluster fixed effect
-‐.0174
(.0132)
-‐.0437
(.1011)
-‐6.3051
(13.30)
.3548
(.309)
2.55
(2.07)
-‐.13
(.14)
-‐.9026
(.5538)
2.366
(2.81)
.0441
(.037)
2046
Tiered
Tier 1
Bottom (cluster)
.05153
(.2105)
1.894
(1.864)
-‐891.396 *** (297.37)
-‐6.1509
(10.213)
9.996
(6.72)
-‐11.6 **
(5.7)
12.2518
(10.54)
-‐7.553
(4.76)
-‐2.26 *** (.876)
1207
Tier1 bottom cluster/fixed effects
-‐.0507
(.0352)
.1752
(.2139)
-‐32.035
(20.952)
.33604
(.6729)
5.138
(3.27)
-‐.14
(.19)
-‐1.1240
(.7554)
.7094
(2.20)
.0453
(.053)
1207
Tier 2 top cluster
-‐.4517
(.5766)
8.435**
(3.476)
-‐618.20**
(299.42)
-‐32.853**
(14.828)
-‐7.48
(16.4)
15.4
(7.4)
22.2898**
(10.845)
20.81*** (6.33)
.3374
(.964)
885
Tier 2
Top Cluster fixed effects
.0012
(.002)
.0483
(.0538)
-‐.13538
(2.06)
.15776
(.1508)
-‐1.13
(1.02)
.002
(.03)
-‐.1021
(.0902)
-‐.2149
(.865)
-‐.0161
(.015)
885
Loglog
Loglog cluster
.04042
(.0435)
-‐.0109
(.0397)
-‐.00025
(.0479)
.12316
(.1130)
.1251
(.068)
-‐.00
(.04)
.0725
(.1052)
-‐.119 ** (.054)
-‐.005
(.008)
1413
Loglog cluster FE
.0004
(.0059)
.006
(.0067)
-‐.0123
(.0088)
.0071
(.0095)
.0519
(.037)
-‐.00
(.00)
-‐.0195
(.0127)
.0490
(.040)
.0010
(.001)
1413
In the last stage of this analysis, the effects of microfinance were tested against the survey
results for each country’s rate of women’s financial independence. In the first model a traditional OLS
regression was used. In this instance one variable was significant after clustering for errors: the total
number of borrowers as a percentage of population. For this variable there was a negative relationship
at the 99% confidence level in contrast to the predicted hypothesis. However after accounting for a
La Rocque 31
fixed effects regression none of the variables remained significant. This shows that variation of women’s
financial independence is not related to differences in levels of microfinance and that the variation in
women’s financial independence is derived by individual country differences.
Similar to the OLS model, when lagging the independent variables and clustering at country
level, the total number of borrowers as a percentage of population and the concentration of the
industry remains significant. As was the case in the OLS regression, both variables followed negative
relationships significant at the 90% and 99% confidence level. This shows that as the number of
borrowers increases the financial independence of women decreases. However after accounting for a
fixed effects robustness check, this significance fades away. It can therefore be concluded that once
accounting for differences in variation between countries there is no effect of microfinance on financial
independence.
In the next model the effect of microfinance was segmented into two different tiers based off
levels of GDP/capita. In both cases after accounting for fixed effects there was not seen any significant
relationship between microfinance and women’s financial independence. However, in the countries
with a higher GDP/capita there was observed significance in the gross loan size of a country, total
number of borrowers as a percentage of population, and the concentration of the industry before
accounting for fixed effects. Yet as these results fail the robustness check of fixed effects, it can be
concluded that the variation between women’s financial independence is more inherent within
unobserved country differences than different levels of microfinance. In the last model a log-‐log model
was used to address this question. In this case there was no found significance when either clustering
for country errors or conducting a fixed effects model.
La Rocque 32
Conclusion
Overall the results of this study indicate limited or even negative effects of microfinance on
women’s empowerment indicators. On one hand, the first variable tested-‐ the ratio of girls/boys in
secondary school-‐ showed limited if any effects of microfinance. In this case after clustering the errors
and using a fixed effect analysis all of the variables lost significance. Yet as the ratio of girls/boys
education variable is also impacted by the gender composition of girls/boys within the population
group, the variation between the countries may not be caused by differences in enrollment rates but
differences in gender composition.
In the second part of this study the effects of microfinance almost consistently demonstrated
that number of borrowers as a percentage of the population variable displays a negative relationship
with girl’s enrollment rates. This illustrates that as larger percentage of the population engages in
microfinance less girls enroll in secondary school. It is believed that the reason for this conflicting result
is in the nature of the microfinance activities itself. For example as microfinance increases many small
home businesses are started. In many cases these businesses employ women and their daughters. For
this reason microfinance may actually influence the incentives behind enrolling in school and encourage
girls to work in the family business. Lastly, the final model addressing the effects of microfinance on
women’s financial independence showed no significant results after following a fixed effects model. This
finding was consistent in all of the models.
In all, the overall results of this study confirmed the general consensus within the scholarly
community of the limited effects of microfinance on women’s empowerment. This model attempted to
address the question through a new method (by looking at a cross-‐sectional time series regression) as
well as by looking at the effects of women’s financial independence in driving this change in
empowerment. However it was determined that microfinance has a limited or even negative effect on
La Rocque 33
women’s empowerment. There are three proposed reasons for the lack of significant results in this
study. On one hand, it can be argued that microfinance leads to lower girl’s enrollment rates because it
provides a negative incentive for schooling and encourages girls to work in the family business. This
argument would explain the negative relationship shown between the total number of borrowers as a
percentage of the population and girl’s secondary enrollment. Furthermore, an additional issue to be
addressed is the constraints with the data. The Mixmarket dataset is limited to less than 20 years of
data. It can be predicted that it would take generations for microfinance to effect girl’s enrollment rates
as cultural norms against schooling would persist throughout the measured period. Lastly, the final
concern is with the strong clustering inherent within the dependent variable. As stated earlier, in the
majority of the countries the girl’s enrollment rates for secondary education are already at high levels by
1995 (when this dataset begins). Therefore, there is little variation within the dependent variable to be
explained besides a few outlier cases. In conclusion, it can be claimed that future studies addressing this
issue of microfinance and women’s empowerment are needed. With the advent and inclusion of more
microfinance data, as well as by looking at other indicators of women’s empowerment outside of girl’s
education, more significant results may be seen.
La Rocque 34
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