Download Determinants of Microfinance Loan Performance and Fluctuation Over the Business Cycle

Determinants of Microfinance Loan Performance and
Fluctuation Over the Business Cycle
Jordan Hitchcock
Haverford College
Economics Department Thesis
Advisor: Vladimir Kontorovich
Spring 2014
Abstract
This paper examines fluctuations in microfinance loan performance over the business
cycle. Specifically, this paper studies how MFI loan delinquency rates change with
variation in yearly GDP growth. Furthermore, this paper also studies the correlation
between non-performing loans and GDP according to MFI status as a non- or for-profit
organization. The results presented in this paper indicate that MFI loan performance
reacts procyclically to the business cycle, although the effect is not as strong as
experience by other types of lending institutions. In addition, for-profit MFI loan
performance is estimated to be more sensitive to the business cycle than non-profit MFIs.
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Acknowledgments
I would to thank Haverford College and the Economics Department for their support. The
professors of the Economics Department have shown exceeding support and
encouragement during the writing process. In particular, I would like to thank Vladimir
Kontorovich for his suggestions, comments and advice. Finally, I would like to express
my appreciation for the support and advice of fellow seniors, friends and family.
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Table of Contents
Introduction
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1. Literature Review
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1.1 Determinants of Non-Performing Loans at Traditional Banks
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1.2 Macroeconomic Influences
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1.3 The Business Cycle and MFI Loan Quality
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2. Methodology
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3. Data Review
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4. Results
29
4.2 Non-Profit vs. For-Profit MFIs
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5. Conclusion
35
References
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Appendix
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Introduction
Microfinance institutions (MFI) provide important banking services to the poorest
sections of the world’s population. MFIs provide financial tools for the poorest of the
poor to finance new investments and smooth consumption, but crucial for their ability to
sustainably alleviate poverty is consistent financial performance in negative economic
climates. Consistent and efficient lender operations are important from both financially
and socially oriented perspectives. The main goal of this paper is to determine whether
MFIs are able to maintain a consistent lending environment over the business cycle by
looking at loan performance and delinquency rates. Specifically, the goal of this paper is
to examine whether MFI loan performance displays the same procyclical fluctuations as
commercial banks over the business cycle.
There are several reasons that loan performance over the business cycle is
important for the health of microfinance institutions. First, sustainable poverty alleviation
is one of the most highly lauded characteristics of MFIs. However, in order to become
sustainable and break away from reliance on philanthropy and government aid, an MFI
needs to acquire outside funding sources. From a financial perspective, consistent loan
performance is a key factor in MFI sustainability. In 2010, roughly a third of MFI
financing came from in the form of debt (Sapundzhieva 2011). Debt financing is
especially important for NGO’s and non-banking financial institutions (NBFI) since there
are often limits on their ability to mobilize deposits as lending capital. Debt funding
comes from a variety of sources such as governments and NGO’s, however the largest
source of debt funding comes from financial institutions such as commercial banks
(Sapundzhieva 2011). MFIs that rely on debt as a source of financing must make regular
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payments to their lenders. As a result, MFIs are in a better position to negotiate a lower
interest rate if they can display that revenue streams are consistent and that there is little
risk of illiquidity or insolvency.
Independent loan performance over the business cycle is also an indicator of
social outreach. Many MFIs have either an explicit or implicit social goal as well as a
financial objective. In addition to providing entrepreneurial capital, consumption
smoothing is another mechanism through which MFIs can alleviate poverty. Accordingly,
it is important that borrowers are able to find credit during periods of distress (Murdoch
1998). If loan repayment is not consistent through the business cycle, it becomes more
likely that an MFI will face significant loan write-offs and will subsequently constrict
credit growth. Under such a scenario, poor potential borrowers may be faced with low
loan supply and be unable to start new business ventures or protect against shocks in
income.
Up through the mid-2000s, MFIs were commonly thought to operate
independently from international and domestic economic activity. MFIs were considered
resilient to domestic and international economic shocks. Despite economic depressions
and crises in several South Asian countries, MFIs often performed quite well. McGuire
and Conroy (1998) examine MFI data from nine countries from 1996 to 1998. They reach
several interesting conclusions that indicate strong MFI resilience. First, MFIs in poorer
countries were less affected than those in richer countries. Moreover, MFIs that served
poorer sections of the income distribution were better off than MFIs that served wealthier
clients. Additionally, MFIs did not hike up interest rates as severely as commercial banks
during the East Asian Crisis.
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Jansson (2001) also finds that MFIs are resilient to economic downturns. He
studies loan portfolio growth, return on assets, and the percent of loans that have been in
delinquency for greater than thirty days for fourteen MFIs in Columbia, Bolivia and Peru.
Jansson finds that MFIs perform significantly better in each category compared to
commercial banks for the years 1997 to 2000.
Patten and Rosengard (2001) examine micro-lending at Indonesia’s Bank Rakyat
during the East Asian financial crisis. The authors note that during the crisis, microloan
delinquency rates remained very low. During both the monetary crisis and drought,
microloan repayment rates remain better than 97 percent. It should be noted that the loan
portfolio growth leveled off during the crisis, although the main reason for a lack of
strong loan growth appears to have been a result of lower loan demand; repeat borrowers
were more likely to delay taking out a new loan during the crisis in the face of uncertain
business activity. The conclusion drawn by Patten and Rosengard asserts that the strong
performance of micro-lending in Indonesia during the crisis indicates that MFIs have a
cushioning effect for the poor during periods of economic depression and uncertainty.
Despite earlier literature indicating that MFIs operate independently from many
macroeconomic influences, more recent literature provides evidence that MFIs may
experience the same exposure as other types of banking institutions. Increasingly
comprehensive data collection capabilities in recent years have allowed researchers to
broaden the scope of empirical research. Contrary to the traditional view that MFIs
performed well in any economic climate, the new empirical research has shown
indication that MFIs do not display consistent performance independently from the
business cycle.
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The objective of this paper is to determine how MFIs loans are affected by the
business cycle. The empirical estimations presented here examine GDP growth or decline
as a determinant of MFI non-performing loans. In addition to determining whether GDP
fluctuations have a statistical and economic impact on MFI loan performance, this paper
compares the results estimated here to the general literature on non-performing loans at
other types of lending institutions.
The following section reviews the literature regarding non-performing loans at
MFIs and other types of lending institutions. Following the literature review, the
empirical methodology and data is discussed. Finally, estimation results are review, and
the paper concludes with a summary of the findings.
Literature Review
1.1 Determinants of Non-Performing Loans at Traditional Banks
Before examining the existing literature on non-performing loans (NPL), it is
necessary to define several terms within the context of this paper and the related
literature. The terms “traditional bank” and “traditional lenders” are used within the
scope of this paper to identify non-MFIs from MFIs. In other words, “traditional banks”
refers to financial intermediaries and lending institutions that are not MFIs. Within the
category of traditional banks there are of course numerous types of lenders. For example,
lenders may target specific business sectors or consumers. However, much of the
literature on loan performance does not segregate between the types of lender, but rather
looks at an aggregate metric of loan performance. While much of the empirical work
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reviewed here uses aggregate loan data, it should be noted that the data primarily are
drawn from a conglomerate of commercial, retail and housing loans.
Furthermore, comparison across different types of lenders is the most appropriate
benchmark for the empirical analysis of MFIs. Borrowers that take out microloans may
do so for a number of reasons—entrepreneurial, housing, consumption smoothing, life
events or natural disasters among other reasons. In order to analyze loan performance
based on the type of loan, it would be necessary to have loan-level data from MFIs. Since
such data is not available, this paper follows the literature on aggregated loan
performance.
The phrase non-performing loans (NPL) is a catch-all term that refers to all loans
that have not been repaid according to schedule. The exact definition varies from study to
study, and there is no defined formula for NPL. Typically NPL are measured as some
ratio of the sum of loans in arrears plus loans in default over the loan portfolio. In other
words, it is usually the percent of the gross loan portfolio that has missed a payment or
has defaulted.
Given the general definition of NPL above, there are two primary values that
constitute NPL—loans in arrears and loans in default. Classifying late loans is relatively
straightforward and unambiguous. If a borrower has missed a payment for a
predetermined amount of time, that loan is said to be in arrears. For example, 90-day
portfolio-at-risk (PAR90) is the value of the loans that are at least 90 days overdue. There
is, however, some variation in the categorization of arrears across empirical work that
renders direct comparison between studies problematic. Banking institutions may be
required to report portfolio-at-risk for different time intervals according to their
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regulatory agencies. For example, banks in one country may be required to report PAR60
while banks in another country are required to report PAR270. Despite the differences in
portfolio at risk, the literature generally finds the benefits associated with cross-country
estimation to be informative.
In addition to loans in arrears, loans in default are also an important component of
NPL. However, in contrast to late loans, there is more judgment involved when declaring
that a loan has defaulted. The write-off ratio (WOR) is an accounting term that is widely
used in empirical estimations to represent defaulted loans. Fortunately, both the Financial
Accounting Standards Board (FASB) and the International Financial Reporting Standards
(IFRS) have the same definition for the write-off of impaired financial instruments. The
IFRS are a set of accounting guidelines that have been widely adopted internationally.
The IFRS are created and published by the International Accounting Standards Board
(IASB). According to the IASB, out of a total of 130 jurisdictions—i.e. countries and the
EU—105 jurisdictions require IFRS for most or all of publically listed companies and
financial institutions. Fourteen other jurisdictions permit the use of IFRS, and several
more require IFRS only for financial institutions (Analysis of the IFRS jurisdiction
profiles 2014). The United States is among the countries that do not employ IFRS
practices. Instead, the U.S. follows the US Generally Accepted Accounting Principles
(US GAAP) that are maintained by the FASB. Although the US has its own set of
accounting guidelines, there has been a concerted effort to reconcile IFRS and US GAAP
over the years. Accordingly, both the IFRS and US GAAP define write-off of financial
assets as “a direct reduction of the amortized cost of a financial asset resulting from
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uncollectibility” (IASB/FASB Staff Paper 2011). Furthermore, the standards include
guidance on write-offs:
“A financial asset is considered uncollectible if the entity has no
reasonable expectation of recovery. Therefore, an entity shall write off a
financial asset or part of a financial asset in the period in which the entity
has no reasonable expectation of recovery of the financial asset (or part of
the financial asset).” (IASB/FASB Staff Paper 2011)
There are several points that should be highlighted in the definition and guidance
above. First, loans should be written off if the bank does not have a reasonable
expectation of recollection. In addition, banks should include write-offs as soon as the
bank realizes that a loan is not going to be repaid. Finally, an entire loan does not need to
be written off, only the portion that is not expected to be collected.
Given the context and definitions provided above, the next few sections of this
paper examine previous literature regarding the determinants of non-performing loans.
1.2 Macroeconomic Influences
The primary focus of this paper is to determine how MFI loan performance is
affected over the business cycle. Looking at traditional banks, there is a fairly well
supported procyclical relationship between loan performance and the business cycle
(Espinoza and Prasad 2010; Jimenez and Saurina 2006; Klein 2013). The general
relationship between loan repayment and economic fluctuations is fairly intuitive; during
good economic times wages and wealth increase while unemployment declines, and
during bad times income and wealth decline while unemployment rises. As one might
expect, the percent of non-performing loans is low when average incomes and revenues
are rising and unemployment is low. Conversely, loans tend to perform poorly when
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unemployment is high and average revenues and incomes are not growing. As a result,
loan performance is procyclical (meaning that NPL display a counter-cyclical
relationship to the business cycle).
There is also evidence that indicates loan officer behavior affects loan
performance over the business cycle. Under the institutional memory hypothesis (Berger
and Udell 2003), the ability of loan officers deteriorates during the growth phase of the
business cycle. As loan officers’ judgment deteriorates, credit standards of lenders eases.
As a result, risky borrowers receive loans. Many of the low credit-quality borrowers
subsequently default contemporaneously with the decline in the business cycle. Berger
and Udell (2003) test the institutional memory hypothesis empirically over a twenty-one
year period and find evidence that indicates loan officers do ease credit standards during
growth periods of the business cycle.
The relationship between loan performance and the business cycle is spurious and
causation runs both ways. This results in an amplification effect during good times and a
depression effect during bad times. Bernanke and Gertler (1989) and Bernanke, Gertler
and Gilchrist (1998) set a model for the endogenous relationship by examining net worth
and agency costs. Under the economic model, Bernanke, Gertler and Gilchrist assert that
borrowers’ net worth and asymmetric information between lenders and borrowers are
important drivers for financing costs. During bad economic periods when net worth is
low, financing agency costs increase as lenders must commit more resources towards
researching and monitoring borrowers’ credit worthiness. However, during good
economic times borrowers are able to post more resources as collateral, and lenders can
commit fewer resources towards credit due diligence. The result is that high net worth
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reduces external financing inefficiencies and lowers the cost of borrowing. Over the
business cycle, borrowers’ net worth increases and decreases procyclically. The result of
these fluctuations is that external financing becomes more expensive during bad
economic periods, which amplifies the real business cycle.
There are several other macroeconomic factors related to the state of the economy
that have also displayed correlation with NPL. Inflation has been shown in some cases to
be significantly correlated with NPL, although competing economic effects make the
direction of the correlation ambiguous. For fixed rate loans, a modest uptick in the rate of
inflation can reduce the real cost of interest. A key assumption under the hypothesis that
inflation can reduce the real cost of repayment is that wages must not be sticky. If wages
are sticky and wage increases lag inflation, the rise in the cost of living could put upward
pressure on NPL. Deflation can make repayment harder as well by increasing the real
cost of repayment. Furthermore, high inflation or hyperinflation is often occurs during
periods of instability and may be associated with a high level of non-performing loans.
Empirically, Klein (2013) does find a significant positive relationship between inflation
and non-performing loans, but other studies have not estimated a significant relationship
between inflation and loan performance.
The exchange rate can also affect the ability of borrowers to service their debt.
Movements in the exchange rate can have both a direct and an indirect effect on loan
performance. Borrowers, particularly those in developing countries where credit may be
limited or expensive, may have an incentive to borrow in a foreign currency. The
exchange rate has a direct effect on borrowers if their income or revenue is in the
domestic currency but their debt obligations are in a foreign currency. Under such a
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scenario, a depreciation of the domestic currency would make the cost of loan repayment
greater when expressed in the domestic currency. Beck, Jakubik and Piloiu (2013) study
the direct effect that foreign exchange rate changes can have on loan repayments in
countries with a high level of foreign denominated debt. The authors proxy the degree of
unhedged lending in foreign currencies using a set of dummy variables indicating the
ratio of international claims to GDP 1. By interacting the nominal effective exchange rate
with the degree of unhedged lending in foreign currencies, the authors find that
depreciation of the domestic currency negatively affects loan performance (Beck, Jakubik
and Piloiu 2013). In other words, there is significant correlation between non-performing
loans and depreciation of the domestic currency in countries with high level of foreign
currency lending. This indicates that it is more difficult for borrowers to repay foreign
denominated debts if the domestic currency under goes depreciation.
In addition to the direct effect described above, the exchange rate can also
indirectly affect loan repayment by stimulating the economy. When a country experience
depreciation of it currency, exports become relatively cheaper abroad and imports
become relatively more expensive. This reaction manifests itself through an increasing
trade balance, and activity within the domestic economy will increase. The indirect
stimulation effect on the economy will show itself as an increase in GDP.
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The Banks for International Settlements defines international claims as the “sum of
cross-border claims in any currency and local claims of foreign affiliates denominated in
non-local currencies” (Bank for International Settlements 2014)
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1.3 The Business Cycle and MFI Loan Quality
There have been a few previous papers that have studied the effect of the business
cycle on MFI loan quality. In the nineties and into the mid-2000s, MFIs were considered
resilient to economic downturns (Jansson 2001; Janda and Svarovska 2009). Despite a
lack of strong evidence, both the financial return and social outreach objectives were
considered robust to economic volatility.
Several theories have been presented in the literature that try to explain why MFI
loan performance might show resilience to economic contractions. The general argument
presented in the literature is that MFIs serve a different market and are structured
differently than traditional bank, which means that MFIs follow a different set of
incentives.
One possible explanation is that producers and consumers go “down market”
during bad economic times (Jansson 2001). The main idea behind “down market”
movement is that individuals and companies are not willing to pay a premium for brand
name products when income is low. Instead, harsh economic climates dictate saving by
going “down market” to generic producers. As a result, the loss in demand caused by
losing customers who can no longer afford products is buffered by new customers
moving “down market”, and micro- and small-businesses may not experience a severe
reduction in demand.
Ownership and governance may also affect vulnerability to economic cycles.
Many of the benchmark statistics on non-performing loans at traditional banks use data
from public companies. The lack of a dominant long-term investor base could pressure
bank management into chasing short-term gains and liquidity strategies. MFIs, however,
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are almost entirely privately held organizations. This means that MFIs can afford to
follow long-term strategies that may come at the expense of short-lasting spurts in
performance (Krauss and Walter 2008). In other words, MFIs are primarily private
companies whereas the governance structure for many of the benchmark companies is
public and requires managers to chain performance to quarterly reports.
Krauss and Walter (2008) also present the idea that borrower-lender interactions
are different for MFIs. Although difficult to put into quantifiable terms, these “soft
factors” could influence repayment rates. Unlike traditional banks, MFIs typically collect
weekly or biweekly repayment installments in group settings. Marconi and Mosley
(2005) note that loan repayment rates seem to be higher under the “village-banking”
model. The village-banking model typically assumes creation of education and support
circles where lenders and borrowers discuss how to overcome problems that may face the
community. These types of lender-borrower interactions not only allow lenders to help
borrowers work through repayment problems, but they also allow lenders to gain intimate
knowledge regarding who the safe borrowers are within a community.
Despite initial studies that showed economic resilience, literature that uses more
recent data suggests that MFIs are not as robust to economic shocks as once thought.
Krauss and Walter (2008) study the correlation between GDP growth and several
accounting metrics, including 30-day portfolio-at-risk (PAR30). As with other studies
that use data from the Microfinance Information Exchange (MIX), PAR30 is the ratio of
loans outstanding with one or more installments of principle more than 30 days overdue
to the gross loan portfolio. Krauss and Walter find that PAR30 is significantly negatively
correlated with contemporaneous GDP growth. In other words, the amount of loans more
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than 30 days over due decreases in years with high GDP growth, and it increases in years
with low or negative GDP growth. Di Bella (2011) runs a very similar set of regressions
using observations from a later time period and also finds similar results both in
significance and magnitude. Krauss and Walter (2008) and Di Bella (2011) find that a ten
percent fall in GDP will lead to 2.7 percentage point and 2.8 percentage point increase in
PAR30 respectively.
Krauss and Walter (2008) also provide interesting insight into the relative
performance of MFs and traditional banks. The authors include data from public
commercial banks that are located in the same countries as the MFIs. Although MFIs do
display significant correlation with GDP, the authors are able to show that MFIs are less
correlated in magnitude than public commercial banks in the same country. According to
their estimations, PAR30 for commercial banks is over twice as sensitive to changes in
GDP compared to their MFI counterparts.
The studies described above show evidence that loan performance is correlated
with the business cycle. However, 30-day portfolio at risk is a very sensitive definition
for NPL since a loan only needs to be in arrears for 30 day in order to fall into this
category. Most of the literature regarding commercial banks measures long-term portfolio
at risk and default rates. Gonzalez (2007) provides a more comprehensive examination of
the MFI loan portfolio profile over the business cycle. Gonzalez uses gross national
income (GNI) per capita as a proxy for the business cycle. Most other studies use the
year-over-year GDP growth rate as the independent variable, but GNI per capita and
GDP are very similar and do not differ widely. Furthermore, Gonzalez compares GNI per
capita to four different measures of loan performance: PAR30, PAR90, write-off ratio
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(WOR), and the loan loss rate. The first three variables fall under the definitions given
previously. The final variable, the loan loss rate (LLR), is calculated as the ratio of writeoffs less any amount that is recovered during the period over the gross loan portfolio. For
example, if a loan is not expected to be collected at the beginning of the year, it would be
recorded as a write-off. However, if part of the loan is subsequently paid off or if the MFI
is able to collect collateral, the loan loss rate would be reduced by the net recovered
amount. Under this scenario, WOR would strictly increase over the period as the MFI
recognizes write-offs, but LLR would be reduced during the course of the year as
borrowers that the MFI previously expected to default instead recover or collateral is
collected. Since microloans are rarely secured by collateral, WOR and LLR tend to be
similar in practice.
In agreement with Krauss and Walter (2008) and Di Bella (2011), Gonzalez
(2007) finds that PAR30 is significantly correlated with the business cycle. However,
none of the other dependent variables tested show significant correlation with the
business cycle. By showing that PAR30 displays co-movement with the business cycle
while other metrics do not, Gonzalez is able to reconcile the evidence presented by
Krauss and Walter and Di Bella with the traditional view that MFI loan performance is
largely independent of economic volatility. Although it is abundantly clear that 30-day
portfolio at risk is sensitive to the business cycle, there is not much evidence that the
literature on commercial banks, which uses long-term portfolio at risk and default rates,
can be generalized to MFIs.
The conclusion taken from the literature suggests that while small shocks to the
economy have a strong impact on short-term portfolio at risk, severely over-due loans
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and defaults are not a product of economic volatility. In other words, MFI borrowers
often miss payments for short periods of time when the economy is bad, but it takes a
strong negative shock in order for borrowers to become severely delinquent or default. If
MFI default rates do not display highly significant correlation with the business cycle,
that would represent a departure from literature on traditional bank loans. However, the
impact of the business cycle on MFI loan quality has not been robustly tested. This paper
aims to broaden the scope of MFIs in its empirical estimation and provide more robust
evidence on the relationship between the business cycle and MFI loan quality.
2. Methodology
The affect of the business cycle on non-performing loans is the primary point of
examination in this paper. The empirical estimation methodologies presented here draw
from the literature on non-performing loans at traditional banks in order to determine if
the same set of NPL determinants apply equally to MFIs.
There are four primary measures of delinquent loans that are used as dependent
variables this study: 30-day portfolio at risk (PAR30), 90-day portfolio at risk (PAR90),
write-off ratio (WOR) and the sum of PAR90 and WOR (WOR90). PAR30, PAR90 and
WOR fall under the definitions given in the previous section. Since loans only need to be
in arrears for 30 days, PAR30 is the most sensitive measure of non-performing loans.
WOR90 is the sum of the write-off ratio plus 90-day portfolio at risk. Much of the
literature defines non-performing loans as the total amount of loans in arrears (usually
loans that have been in arrears for 90 days although the time period varies across studies)
plus the amount of loans written off. Accordingly, WOR90 is included in order to provide
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a closer metric of comparison to previous literature. The objective of testing several
measures of non-performing loans is to determine how NPL vary across a spectrum of
sensitivity.
The first regression represented by Model 1 looks at macroeconomic determinants
of NPL. The estimator takes the form presented below:
Model 1:
𝑁𝑃𝐿 = 𝛽0 + 𝛽1 𝑁𝑃𝐿𝑡−1 + 𝛽2 𝐺𝐷𝑃 + 𝛽3 𝐺𝐷𝑃𝑡−1 + 𝛽4 𝑈𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 + 𝛽5 𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛
Where:
+ 𝛽6 𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒𝑅𝑎𝑡𝑒 + 𝑀𝐹𝐼𝐹𝐸
NPL is the dependent variable measuring relative level of non-performing loans.
(Either PAR30, PAR90, WOR or WOR90)
NPLt-1 is the lagged level of the dependent variable.
GDP is measured as the percent growth of GDP
GDPt-1 is the lagged percent growth of GDP
Unemployment is the unemployment rate
Inflation is the inflation rate as measured by the consumer price index
Exchange Rate is the nominal effective exchange rate for a given country
MFIFE represents MFI level fixed effects
The primary variable of interest is GDP growth. Lagged GDP growth is also
included as an explanatory variable since there may be a time difference between changes
in GDP and the resulting change in NPL. Salas and Saurina (2002) and Beck (2013) both
estimate that lagged GDP growth in addition to contemporaneous GDP growth is
correlated with NPL for traditional banks.
The economic reasoning behind the unemployment rate is fairly intuitive as well.
A high unemployment rate indicates that there are many people without an income. Klein
(2013) estimates that unemployment is positively correlated with traditional bank NPL
under some specifications, although the relationship is not robust across estimation
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techniques. Accordingly, unemployment is expected to be positively correlated with
NPL, assuming that the relationship is significant.
The effects of inflation are somewhat ambiguous (Klein 2013). Modest increases
in inflation could reduce the real cost of outstanding loans, meaning that repayments
become a smaller burden on borrowers. Under this scenario, inflation would have a
negative effect on NPL. However, if wages are sticky, real income may not increase as
quickly as the cost of living, meaning that loans become more difficult to repay.
Furthermore, excessively high inflation is often associated with tough economic
conditions. Therefore, if wages are sufficiently sticky or inflation is associated with
economic uncertainty, NPLs would display negative correlation with inflation. Given the
two effects in opposite directions, the effect of inflation on NPL is unclear. The inflation
statistic used here is the consumer price index for each country respectively. For MFI
loans made to individuals, the CPI is a natural measure of inflation since it estimates
prices faced by consumers. Although businesses face a different set of prices than
individuals, the CPI is a suitable measure of inflation for MFI loans used to start or
support a business as well. Businesses that are run by MFI borrowers often sell their
products or services directly to consumers. For example, a typical business might sell
produce or offer mechanical repair services. Since these types of businesses constitute the
penultimate stage in the product chain before the consumer, it would be reasonable to
assume that they face similar costs compared to the end user.
The foreign exchange rate can have both an indirect and direct effect on loan
performance. The indirect effect is the consequence of increased economic activity when
the domestic currency falls in value and the trade balance increases. Since a depreciation
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of the domestic currency (increase in the nominal effective exchange rate) increases
economic activity, the exchange rate is expected to have a negative influence on NPL
through the indirect effect. Domestic currency depreciation can also have a direct affect
on loan repayments if loans are issued in a foreign currency. Since a depreciation of the
domestic currency would make foreign-denominated loans more difficult to repay, the
direct effect would result in positive correlation with NPL (Beck, Jakubik and Piloiu
2013).
Beck, Jakubik and Piloiu (2013) estimate that currency depreciation has an overall
negative effect on NPL, although significance varies depending on the specification of
the model. This indicates that the international competitive (indirect) channel dominates.
Interestingly, when countries are divided up with a proxy for the amount of unhedged
lending in foreign currencies, the direct balance sheet affect dominates and currency
depreciation leads to greater NPL. Since MFIs lend to the “poorest of the poor”, there is
not much evidence that MFIs loans are paid out or collected in a foreign currency.
Accordingly, the indirect international competitiveness channel is expected to dominate
MFI loan performance, meaning that correlation is expected to be negative.
Finally, a Wald test rejects the null hypothesis of no autocorrelation, so a lagged
dependent variable is included to correct for serial correlation. Furthermore, it should be
noted that this model uses MFI specific fixed effects. Fixed effects are used to eliminate
any time invariant differences between MFIs, such as lending or collection techniques,
that could influence rates of non-performing loans. Standard errors robust to
heteroskedasticity are also used.
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Since loan performance is also influenced by bank and borrower characteristics,
the second model estimated in this paper includes bank-specific control factors. The
second specification follows the form:
Model 2
𝑁𝑃𝐿 = 𝛽0 + 𝛽1 𝜒 + 𝛽2 ∆𝑔𝑙𝑝 + 𝛽3 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑅𝑎𝑡𝑒 + 𝛽4 𝐸𝑞𝑢𝑖𝑡𝑦𝑡𝑜𝐴𝑠𝑠𝑒𝑡𝑠 + 𝛽5 𝑅𝑂𝐸
+ 𝛽6 𝐿𝑜𝑎𝑛𝑠𝑡𝑜𝐴𝑠𝑠𝑒𝑡𝑠 + 𝛽7 𝑀𝐹𝐼𝐹𝐸
Where:
X Represents a vector of the variables from the first equation
∆glp Percent growth in gross loan portfolio.
InterestRate Average interest rate charged by the MFI calculated as the nominal
yield on the gross loan portfolio.
EquitytoAssets The ratio of owners equity to total assets
ROE Return on equity (net income/owners’ equity)
LoanstoAssets Gross loan portfolio over total assets
MFIFE MFI level fixed effects
The first term (X) represents a vector of the independent variables presented in the first
model. The economic reasoning behind each of these variables remains the same for this
model as well.
The second term (∆glp) is calculated as the percent growth in the total loan
portfolio. Keeton (1999) provides evidence that growth in the loan portfolio often results
in greater loan losses. The primary cause presented by Keeton is that banks loan officers
typically become negligent or overworked when the loan portfolio expands. Furthermore,
loan growth often occurs during growth periods of the business cycle. This means that
loan officers may also relax credit standards. The result is that borrowers’ credit
standards decrease and NPL increase. Foos, Norden and Weber (2010) also estimate that
high loan growth can have a negative effect on loan performance. Jimenez and Saurina
24
(2006) also provide further evidence that rapid loan growth can lead to deterioration in
loan quality. Given the relationship between loan growth and NPL, the coefficient for
∆glp is expected to be negative.
The interest rate is used as a proxy to account for the credit worthiness of
borrowers. It is assumed that banks will charge a higher interest rate in order to
compensate for a perceived increase in the risk of default. Accordingly, a higher interest
rate would indicate that a bank expects a greater number of defaults. Jimenez and Saurina
(2006) and Beck (2013) both find a positive relationship between interest rate and NPL,
indicated that a high interest rate is associated with greater delinquent loans. In agreement
with previous literature, the interest rate coefficient is expected to be positive in this
estimation as well. The interest rate used in the estimations presented here is calculated as
the revenue generated from loan operations over the loan portfolio. This measure is not a
perfect proxy for the average interest rate since it includes fees as well as interest
payments, however it is reasonable to assume that is a fairly accurate substitute.
EquitytoAssets is included to control for problems resulting from moral hazard.
The “moral hazard” hypothesis discussed by Keeton and Morris (1987) asserts that moral
hazard conditions (i.e. a low stake in bank represented by low equity) result in greater
non-performing loans. Keeton and Morris show that loans do tend to perform more
poorly for banks that have a relatively low equity-to-assets ratio. Salas and Saurina
(2002) and Klein (2013) also show that the moral hazard hypothesis holds prominent
results. The EquitytoAssets statistic used in this model is calculated as the ratio of
owners’ equity to total assets. Given the effects of the moral hazard hypothesis, the
coefficient is expected to be negative.
25
Management characteristics can also influence loan performance. Return on
Equity (ROE) is a measure of profitability and is calculated as the ratio of net income to
equity. Since banks aim to maximize profitability, ROE is a benchmark for management
skill (Klein 2013). Banks that are able to profitably manage a loan portfolio can be
expected to identify good borrowers from bad more readily than unprofitable banks.
Klein (2013) does indeed find that the more profitable banks as measured by ROE have
fewer delinquent loans.
As with the first model, a lagged dependent variable is included to avoid serial
correlation, MFI fixed effects are used to control for time-invariant characteristics and
robust standard errors are used.
3. Data Review
This paper uses panel data over the ten-year period from 2003 to 2012. All MFIspecific data come from the Microfinance Information Exchange (MIX). The MIX
reports data for over 2500 MFIs, however since every MFI does not report full statistics
to the MIX, only 231 MFIs are used in this paper.
This research follows many other scholar works that have used MFI data from the
MIX to conduct empirical research. There are, however, several caveats that should be
mentioned in association with the MIX. First, data listed on the MIX is self-reported by
the MFIs. Although the MIX takes some auditing measures to ensure that the data they
release is accurate, every MFI does not undergo auditing review. Furthermore, since the
data is self-reported, a given MFI may choose not to release data during years of poor
performance in order to inflate its appearance. Consequently, the data provided by the
26
MIX may not accurately reflect MFIs as a whole. Although self-selection may limit
interpretation of the MIX data, it is reasonable to assume that the data reported by the
MIX is representative of the subsection of the best global MFIs. The assumption that the
information reported by the MIX provides a reasonable representation of the best MFIs is
in line with numerous other academic studies.
Data on GDP growth comes from the World Bank. As with other the countrylevel statistics, many of the countries used in this sample are very poor and may not have
robust reporting agencies. Consequently, the margin for error may be larger than it is for
developed countries. The World Bank also provides the unemployment and inflation
data. The consumer price index is used in this paper.
The nominal effective exchange rate (NEER) comes from the IMF’s International
Financial Statistics (IFS) database. The effective exchange rate is used rather than a
bilateral exchange rate in order to capture the relationship between the domestic currency
and the currencies of all countries with whom business is conducted. NEER is index to
2005 as the base year.
In total, there are 1136 MFI-year observations for 231 MFIs in 23 countries
spanning ten years. Most of the MFIs are located in Asia, Eastern Europe and Latin
America as shown by in the table below. The sample used for these estimations does not
represent the geographical distribution of all MFIs very well. Africa and South Asia are
both home to many microfinance institutions. However, many of the MFIs in Africa and
South Asia did not report the minimum amount of data to be included in this sample,
perhaps indicating a lower quality of institution or reporting standards.
27
Table 1. Observation Frequency by Region
Region
Frequency
Africa
East Asia and the Pacific
Eastern Europe and Central Asia
Latin America and The Caribbean
Middle East and North Africa
Total
10
188
314
560
64
1136
Table 2 provides summary statistics for the data. The mean value for the four
dependent variables ranges from seven percent to two percent in accordance with the
level of sensitivity. GDP growth over the time period averaged 4.0% with inflation just
under six percent. The mean interest rate is 39% which is quite high in conventional
terms. Although a 39% percent interest rate would be exceedingly high in developed
country, MFI borrowers do not have access to conventional financial institutions. Their
only other option are informal money lenders who generally charge even higher interest
rates. High interest rates are also required to cover the large operating costs that
characterize MFIs.
Table 2. Summary Statistics
Std.
Variable
Obs.
Mean
Dev.
Min
Max
PAR30
1136
0.062
0.079
0.000
0.737
PAR90
1136
0.044
0.070
0.000
0.662
WOR
1136
0.026
0.055
0.000
0.678
WOR90
1136
0.070
0.095
0.000
0.861
GDP
1136
0.040
0.045
-0.148
0.375
Unemployment
1136
8.70
5.28
2.90
36.00
Inflation
1136
5.94
3.59
-0.94
25.23
NEER
1136 101.27
13.16
62.32 133.34
% Chng Loan Portfolio
1136
0.314
0.469
-0.996
4.960
Interest Rate
1136
0.39
0.22
0.00
1.37
Equity / Assets
1136
0.323
0.211
-0.501
1.000
ROE
1136
0.090
0.449
-7.448
3.750
28
4. Results
The results from Model 1 are presented in Table 3. The contemporaneous effect
of GDP on NPL ranges from -.046 to -.245 and is significant at the one percent level in
each case. Lagged GDP also has an effect, although it is not as significant nor as strong in
magnitude as current GDP. As the dependent variable moves from PAR30 to PAR90 to
WOR, the magnitude of GDP lessens. This result falls in line with previous literature
since it takes a greater economic shock to cause default than short-term arrears.
Table 3. Macroeconomic Determinats for Non-Performing Loans, Fixed Effects
(1)
(2)
(3)
(4)
PAR30
PAR90
WOR
WOR90
GDP
-0.219***
-0.193***
-0.046*
-0.245***
(0.00)
(0.00)
(0.05)
(0.00)
GDP t-1
-0.068*
-0.077**
-0.059**
-0.104**
(0.06)
(0.03)
(0.05)
(0.03)
Unemployment
-0.001
-0.001
-0.002
-0.002
(0.58)
(0.66)
(0.11)
(0.29)
Inflation
-0.001
-0.001
-0.001
-0.001
(0.28)
(0.38)
(0.11)
(0.24)
NEER
-0.001*
-0.001*
0.000
-0.000
(0.09)
(0.09)
(0.69)
(0.46)
PAR30 t-1
0.158**
(0.03)
PAR90 t-1
0.181**
(0.04)
WOR t-1
0.124
(0.49)
WOR90 t-1
0.330***
(0.00)
Constant
0.147***
0.120**
0.035
0.123*
(0.01)
(0.01)
(0.27)
(0.05)
Observations
1136
1136
1136
1136
2
R Within
0.0753
0.0831
0.0164
0.1375
R2 Between
0.4020
0.4438
0.2419
0.6872
R2 Overall
0.2236
0.2441
0.1059
0.4274
* .10 significance, ** .05, ***.01
29
NEER is the only other macroeconomic factor that shows any significance. Its
sign is negative, indicating that the indirect competitive channel dominates the direct
balance sheet effect. However, the magnitude is very small, and the currency would have
to undergo significant depreciation in order to result in any meaningful reduction in NPL.
The results from Model 2 are shown in Table 4. The inclusion of banks-specific
factors decreases both the significance and magnitude of GDP and its lag. In the case of
WOR, GDP becomes insignificant altogether. The other macroeconomic factors from
Model 1remain roughly the same. NEER under PAR90 is no longer significant and the
inflation rate for WOR becomes marginally significant, although it is close to zero in
magnitude.
On the bank-specific side, loan portfolio growth shows strong significance with
NPL. However, the sign is negative which runs contrary to literature for traditional banks.
Previous literature on NPL at other types of lending institutions has estimated a positive
correlation between credit growth and non-performing loans. This literature has argued
that as the portfolio grows, officers struggle to monitor existing loans, which leads to a
deterioration in loan quality. However, loan growth often occurs during economic booms.
This means that if loan officers are able to maintain monitoring and credit standards, loan
growth could be negatively correlated with NPL.
The sign on the equity to asset ratio, which is significant for PAR30 and PAR90,
is also different than what was expected. Based on previous literature regarding
traditional banks, the moral hazard effect was expected to produce negative correlation
between equity/assets and NPL. One possible explanation for a positive coefficient could
be a decreased appetite for risk when equity is low compared to total assets. When the
30
equity-asset ratio is high, the MFI is well capitalized and does not have a lot of
outstanding debt. However, when the equity-asset ratio is low, MFIs may feel pressured
Table 4. Macroeconomic and Banks-Specific Determinats of Non-Performing Loans, Fixed
Effects
(1)
(2)
(3)
(4)
PAR30
PAR90
WOR
WOR90
GDP
-0.155**
-0.143**
0.015
-0.138**
(0.02)
(0.01)
(0.56)
(0.02)
GDP t-1
-0.075**
-0.082**
-0.042*
-0.097**
(0.04)
(0.02)
(0.10)
(0.03)
Unemployment
-0.001
-0.001
-0.002
-0.002
(0.57)
(0.68)
(0.11)
(0.27)
Inflation
-0.001
-0.001
-0.001*
-0.001
(0.31)
(0.42)
(0.07)
(0.21)
NEER
-0.001*
-0.001
0.000
-0.000
(0.10)
(0.10)
(0.91)
(0.39)
Loan Portfolio
-0.030***
-0.024***
-0.026***
-0.047***
Growth
(0.00)
(0.00)
(0.00)
(0.00)
Interest Rate
-0.024
-0.027
-0.011
-0.038
(0.53)
(0.42)
(0.74)
(0.46)
Equity / Assets
0.056**
0.053**
-0.036
0.018
(0.03)
(0.02)
(0.17)
(0.59)
Return on equity
-0.013**
-0.006*
-0.001
-0.009**
(0.02)
(0.05)
(0.89)
(0.05)
PAR30 t-1
0.111
(0.13)
PAR90 t-1
0.138
(0.13)
WOR t-1
0.097
(0.53)
WOR90 t-1
0.280***
(0.00)
Constant
0.145***
0.117***
0.065*
0.150***
(0.00)
(0.01)
(0.08)
(0.01)
Observations
1136
1136
1136
1136
R2 Within
0.1452
0.1372
0.0905
0.2438
R2 Between
0.2572
0.2847
0.0641
0.5713
2
R Overall
0.2128
0.2191
0.0793
0.4190
* .10 significance, ** .05, ***.01
31
to perform well in order to meet debt obligations. Consequently, the MFI’s appetite for
risk may decrease and monitoring may in crease, resulting in few delinquent loans
Return on equity also shows significance for PAR30, PAR90 and WOR90.
Negative correlation corroborates the hypothesis that high quality management, proxied
for by profitability, is able to avoid NPL to a greater extent than poor management. A
possible problem with using ROE as a proxy for management ability is that profitability
may depend more on the economic climate than management’s skill. Positive return on
equity may rely more growth in the economy than on the MFI’s ability to pick and
manage good borrowers. The simple pair-wise correlation between GDP growth and
ROE is low (0.0719), however, indicating that economic booms are only loosely related
to ROE.
4.2 Non-Profit vs. For-Profit MFIs
Splitting up the sample group into non- and for-profit organizations provides
interesting results. One hypothesis states that since MFIs often have a social as well as
financial goal, the incentive structures inherent in the organization differ from traditional
banks that operate solely for a financial bottom line. It would be reasonable to assume the
for-profit MFIs generally place a greater emphasis on financial returns compared to their
non-profit counterparts. In other words, for-profit MFIs might be willing to forgo a
greater degree social impact in order to gain greater financial returns. As such, loan
performance at for-profit MFIs may respond to a set of determinants more similar to
banks than non-profit MFIs.
32
The data presented in tables 5 and 6 (in the appendix) support the idea that nonprofit MFI loan performance is more detached from bank NPL determinants. In Table 5
(non-profit MFIs), contemporaneous GDP is only significant for PAR30 and PAR90
while lagged GDP does not show any significance. On the bank-specific side, loan
portfolio growth is significant for each NPL measure. Equity-asset ratio and interest rate
show marginal significance for WOR and WOR90 respectively.
The interest rate is negatively correlated with WOR90, which does not match
previous literature. Since a higher interest rate is generally charged to riskier borrowers
who have a greater chance of default, it was expected that the interest rate would be
positively correlated with NPL. It is possible that the true interest rate is positively
correlated with NPL but that noise form fees causes negative correlation in the interest
rate proxy used here. Although this may be the case, interest rate is not significant for any
other measure of delinquent loans, and it seems more likely that the interest rate would
not show negative correlation with a larger sample size.
In contrast to the findings for non-profit MFIs, Table 6 shows that a greater
number of statistics are significant for for-profit MFIs. Contemporaneous GDP does not
show any significance, although lagged GDP is significant in three of the four cases. The
effect of lagged GDP is also stronger than in the pooled sample regression (Table 4). The
lack of significant contemporaneous GDP growth could mean that for-profit MFIs do
better at choosing safe borrowers and enforcing repayment over the short-run. The idea
that for-profit MFIs are oriented towards the short-term supports the institutional memory
hypothesis which states that banks effectively “forget” lessons learned in past years and
act according to the current economic climate. During good year banks ease credit
33
standards only to realize greater losses during subsequent years of economic downturn,
and in bad years banks overreact by tightening credit and only lending to the highest
quality borrowers. In line with this hypothesis, for-profit MFIs show significant
correlation with lagged GDP growth.
Unlike any of the other previous estimates, inflation shows highly significant
negative correlation with all four dependent variables. Negative correlation indicates that
inflation decreases the real cost of loans and that income or revenue is not sticky. Without
borrower-specific data, it is difficult to determine why inflation is significant for forprofit MFIs but not non-profits. Generally, differences in the composition of borrowers
drives this result as with other results presented here. A simple two-way mean difference
test (Table 7) shows that the for-profit MFIs used in this sample have higher average loan
size relative to income compared to non-profits. Wealthier borrowers are typically less
expense to service, and higher average loan sizes for for-profit MFIs are consistent with
mission drift 2.
The equity-asset ratio and ROE also show increased significance. The signs
remain the same as in the pooled sample estimate, however the strength of the effect does
increase for both ROE and the equity-asset ratio. The equity-asset ratio indicates
problems of moral hazard when equity is low, and ROE provides evidence that good
bank management decreases the rate of non-performing loans.
2
The mission drift theory states that some MFIs, particularly for-profit MFIs, move “upstream” in order to increase profit. These MFIs forgo the social impact of lending to the
poorest of the poor in order to experience greater financial gain by lending to wealthier
borrowers.
34
5. Conclusion
The primary objective of this paper is to determine how loan performance at MFIs
fluctuates over the business cycle. The results show that MFI loan performance does
change procyclically over the business cycle, especially in the case of 30-day and 90-day
portfolio at risk. Furthermore, differences emerge when MFIs are separated according to
profit status. Non-profit MFIs display limited correlation with GDP, and only
contemporaneous PAR30 and PAR90 show significant correlation. For-profit MFIs do
not show any contemporaneous correlation with GDP, but PAR30, PAR90 and WOR90
are negatively correlated with GDP. The correlation with lagged GDP supports the
institutional memory hypothesis, and suggests that short-term loan incentives dominate
for-profit MFIs.
This paper also provides some insight into MFI-specific factors that influence
NPL rates. Growth of the loan portfolio displays highly significant correlation with
decreases in NPL. It should be noted, however, that simply increasing the size of the loan
portfolio would probably not lead to a decrease in delinquent loans. Instead, association
with periods of economic growth during which the loan portfolio also grows probably
drives correlation. Efficient management, proxied for by return-on-equity, and moral
hazard incentives resulting from low equity-to-assets also display significant correlation
with NPL.
The results estimated in this paper indicate that while MFIs are affected by many
of the same NPL determinants as traditional banks, the effects on loan performance is not
as pronounced. This observation becomes particularly visible when MFIs are split by
35
profit status. Based on the estimations here, for-profit MFIs behave more similarly to
traditional banks than non-profit micro-lending institutions.
Looking ahead, there is significant opportunity to conduct future research into the
mechanisms that affect MFI non-performing loans. In particular, the relationship between
inflation and for-profit MFI loan performance remains obfuscating. There is also a need
to study the effects of MFI-specific factors on loan performance. Although this paper
identifies several characteristics that display significant correlation, more work needs to
be done in order to determine which factors are most important.
In summary, this paper provides evidence that MFI loan performance fluctuates
over the business cycle, although to a lesser extent than traditional banks. Additionally,
MFI profit status plays a significant role in determine loan incentives and performance.
36
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Appendix
Table 5. Macroeconomic and Banks-Specific Determinats of Non-Profit MFIs, Fixed
Effects
(1)
(3)
(5)
(7)
PAR30
PAR90
WOR
WOR90
GDP
-0.218**
-0.186**
0.027
-0.163
(0.03)
(0.04)
(0.63)
(0.12)
GDP t-1
-0.101
-0.106
-0.036
-0.101
(0.17)
(0.14)
(0.45)
(0.18)
Unemployment
0.001
0.001
-0.001
0.000
(0.82)
(0.69)
(0.64)
(0.89)
Inflation
0.001
0.001
-0.000
0.001
(0.40)
(0.31)
(0.75)
(0.43)
NEER
-0.000
-0.000
0.001
0.000
(0.42)
(0.39)
(0.30)
(0.68)
Loan Portfolio Growth
-0.025***
-0.020***
-0.040***
-0.056***
(0.00)
(0.00)
(0.00)
(0.00)
Interest Rate
-0.056
-0.041
-0.040
-0.077*
(0.19)
(0.27)
(0.24)
(0.07)
Equity / Assets
0.034
0.032
-0.041*
-0.013
(0.13)
(0.12)
(0.09)
(0.70)
Return on equity
-0.009
-0.002
0.001
-0.004
(0.18)
(0.44)
(0.82)
(0.18)
PAR30 t-1
0.128
(0.23)
PAR90 t-1
0.173
(0.18)
WOR t-1
0.397*
(0.07)
WOR90 t-1
0.392***
(0.00)
Constant
0.109*
0.079*
0.011
0.067
(0.05)
(0.10)
(0.84)
(0.38)
Observations
R2 Within
R2 Between
R2 Overall
648
0.1137
0.4282
0.3094
648
0.1137
0.5367
0.3610
40
648
0.1859
0.2245
0.2138
648
0.3010
0.7413
0.5550
Table 6. Macroeconomic and Banks-Specific Determinats of For-Profit MFIs, Fixed
Effects
(2)
(4)
(6)
(8)
PAR30
PAR90
WOR
WOR90
GDP
-0.099
-0.096
0.037
-0.069
(0.16)
(0.13)
(0.11)
(0.27)
GDP t-1
-0.092*
-0.101**
-0.027
-0.112**
(0.05)
(0.03)
(0.23)
(0.04)
Unemployment
-0.003
-0.003
-0.000
-0.003
(0.18)
(0.22)
(0.83)
(0.23)
Inflation
-0.003***
-0.003***
-0.002***
-0.004***
(0.01)
(0.01)
(0.00)
(0.00)
NEER
-0.001
-0.001
-0.001*
-0.001
(0.19)
(0.22)
(0.05)
(0.11)
Loan Portfolio Growth
-0.029***
-0.023***
-0.012**
-0.034***
(0.00)
(0.01)
(0.02)
(0.00)
Interest Rate
0.005
-0.014
0.013
-0.011
(0.92)
(0.77)
(0.75)
(0.88)
Equity / Assets
0.118**
0.108**
-0.009
0.110**
(0.05)
(0.04)
(0.78)
(0.04)
Return on equity
-0.066**
-0.055**
-0.014
-0.076***
(0.03)
(0.02)
(0.43)
(0.00)
PAR30 t-1
0.091
(0.37)
PAR90 t-1
0.093
(0.44)
WOR t-1
-0.133
(0.22)
WOR90 t-1
0.168*
(0.07)
Constant
0.168**
0.148**
0.104**
0.207***
(0.02)
(0.03)
(0.03)
(0.01)
Observations
R2 Within
R2 Between
R2 Overall
488
0.2278
0.1063
0.1385
488
0.2136
0.0387
0.0900
41
488
0.0791
0.0022
0.0091
488
0.2773
0.2768
0.2699
Table 7. Two-Way Test of Average Loan Size per Gross National Income Mean
Between For- and Non-Profit MFIs
Group
Obs
Mean
Std. Err. Std. Dev.
95% CI
Non-profit
648
0.473
0.036
0.918
0.402
0.543
For-profit
488
0.636
0.039
0.862
0.560
0.713
Combined
1136
0.543
0.027
0.898
0.491
0.595
Difference
-0.164
0.053
-0.268
-0.060
diff = mean(0) - mean(1)
t = -3.0835
Ho: diff = 0
Satterthwaite's degrees of freedom = 1080.87
Ha: diff < 0
Ha: diff != 0
Ha: diff > 0
Pr(T < t) = 0.0010
Pr(|T| > |t|) = 0.0021
Pr(T > t) = 0.9990
PAR30
PAR90
ROE
Equity/asset
Interest Rate
Chng. Loan
Port.
NEER
Inflation
Unemploym
ent
GDP
WOR90
WOR
PAR90
PAR30
Correlation Matix
1
0.96
1
0.18
0.13
1
0.81
0.82
0.68
1
-0.16
-0.14
-0.05
-0.13
1
0.01
-0.01
-0.06
-0.04
0.02
1
-0.07
-0.04
-0.12
-0.10
0.15
-0.20
1
-0.04
0.00
-0.07
-0.04
0.21
0.23
-0.22
1
-0.27
-0.25
-0.21
-0.31
0.18
-0.01
0.00
0.02
1
-0.06
-0.13
0.20
0.02
-0.05
-0.22
-0.13
-0.34
0.08
1
0.08
0.05
0.04
0.06
-0.04
0.03
-0.07
-0.10
-0.08
0.16
1
-0.17
-0.13
-0.11
-0.16
0.07
-0.04
0.08
-0.02
0.11
0.04
0.01
WOR
WOR90
GDP
Unemployme
nt
Inflation
NEER
Chng. Loan
Portfolio
Interest Rate
Equity/Assets
ROE
42
1