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.

2

3

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.

4

5

Table of Contents

Introduction 6

1. Literature Review

9

1.1 Determinants of Non-Performing Loans at Traditional Banks

9

1.2 Macroeconomic Influences

12

1.3 The Business Cycle and MFI Loan Quality

16

2. Methodology 20

3. Data Review 26

4. Results 29

4.2 Non-Profit vs. For-Profit MFIs

32

5. Conclusion 35

References 37

Appendix 40

6

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

7

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.

8

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.

9

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

10

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

11

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

12

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

13

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

14

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

15

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 GDP1. 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.

1 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)

16

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,

17

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

18

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

19

(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 write-

offs 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

20

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

21

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 + 𝛽1𝑁𝑃𝐿𝑡−1 + 𝛽2𝐺𝐷𝑃 + 𝛽3𝐺𝐷𝑃𝑡−1 + 𝛽4𝑈𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 + 𝛽5𝐼𝑛𝑓𝑙𝑎𝑡𝑖𝑜𝑛

+ 𝛽6𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒𝑅𝑎𝑡𝑒 + 𝑀𝐹𝐼𝐹𝐸

Where: 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

22

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

23

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.

24

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 + 𝛽1𝜒 + 𝛽2∆𝑔𝑙𝑝 + 𝛽3𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑅𝑎𝑡𝑒 + 𝛽4𝐸𝑞𝑢𝑖𝑡𝑦𝑡𝑜𝐴𝑠𝑠𝑒𝑡𝑠 + 𝛽5𝑅𝑂𝐸

+ 𝛽6𝐿𝑜𝑎𝑛𝑠𝑡𝑜𝐴𝑠𝑠𝑒𝑡𝑠 + 𝛽7𝑀𝐹𝐼𝐹𝐸

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

25

(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.

26

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 MFI-

specific 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

27

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 country-

level 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.

28

Table 1. Observation Frequency by Region

Region Frequency Africa 10 East Asia and the Pacific 188 Eastern Europe and Central Asia 314 Latin America and The Caribbean 560 Middle East and North Africa 64 Total 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

Variable Obs. Mean Std. 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

29

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 R2 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

30

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

31

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 Growth -0.030*** -0.024*** -0.026*** -0.047***

(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 R2 Overall 0.2128 0.2191 0.0793 0.4190 * .10 significance, ** .05, ***.01

32

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.

33

The data presented in tables 5 and 6 (in the appendix) support the idea that non-

profit 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

34

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 for-

profit 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 drift2.

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 “up-stream” 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.

35

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

36

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.

37

References

Analysis of the IFRS jurisdictional profiles. April14, 2014. IFRS. <www.ifrs.org/Use-around-the-world/Pages/Analysis-of-the-IFRS-jurisdictional-profiles.aspx> Accessed April 30, 2014.

Bank for International Settlements. (2014). “Glossary”.

www.bis.org/statistics/bankstatsguide_glossary.pdf. Beck, R., Jakubik, P., Piloiu, A. (2013) “Non-Performing Loans: What Matters in

Addition to the Economic Cycle?” European Central Bank. Working Paper Series No 1515. February 2013.

Berger, A., Udell, G. (2003) “The institutional memory hypothesis and the procyclicality

of bank lending behaviour” Bank for International Settlements. Working Papers (Piloiu, 2013)No. 125.

B. Bernanke and M. Gertler. (1989) “Agency Costs, Net Worth, and Business

Fluctuations”, American Economic Review, Vol. 79, No. 1, pp. 14-31. Bernanke, B., Gertler, M., Gilchrist, S. (1998) “The Financial Accelerator in a

Quantitative Business Cycle Framework”, NBER Working Papers 6455.

Di Bella, G. (2011) “The Impact of the Global Financial Crisis on Microfinance and Policy Implicaitons” IMF Working Papers 11/175.

Glen, J., and Mondragón-Vélez, C. (2011) “Business Cycle Effects on Commercial Bank

Loan Portfolio Performance in Developing Economies” International Finance Corporation, World Bank Group.

Gonzalez, A. (2007) “Resilience of Microfinance Institutions to National Macroeconomic

Events: An Econometric Analysis of MFI asset quality” Microfinance Information Exchange. Mix discussion Paper No. 1.

Espinoza, R., and Prasad, A. (2010) “Nonperforming Loans in the GCC Banking

System and their M acroeconom ic Effects” IM F W orking Papers 10/224. Foos, D., Norden, L., and Weber, M. (2010) “Loan growth and riskiness of banks”

Journal of Banking and Finance 34 2929-2940.

38

IASB/FASB Staff Paper. “Financial Instruments: Impairment”. Agenda Paper 9 (IASB)/78(FASB). February 14, 2011.

International Financial Statistics (IFS). Country Tables. Nominal Effective Exchange

Rate, Consumer Price Index. Washington, D.C.: International Monetary Fund. Accessed April 30, 2014.

Janda, K., Svarovska, B. (2009). “Investing into Microfinance Investment Funds”.

Institute of Economic Studies, Charles University in Prague. IES Working Paper 32/2009.

Jansson, T. (2001) “Microfinance: From Village to Wall Street” Inter-American

Development Bank. Sustainable Development Department Best Practices Series, No MSM-113.

Jimenez, G., and Saurina, J. (2006) “Credit Cycles, Credit Risk, and Prudential

Regulation” International Journal of Central Banking. Banco de España. Keeton, W. (1999) “Does Faster Loan Growth Lead to Higher Loan Losses?” Federal

Reserve Bank of Kansas City. Economic Review, Quarter II. Keeton, W. and C. Morris. 1987. “Why Do Banks’ Loan Losses Differ?” Federal Reserve

Bank of Kansas City, Economic Review, May, pp. 3–21.

Klein, N. (2013) “Non-Performing Loans in CESEE: Determinants and Impact on Macroeconomic Performance” IMF Working Papers 13/72.

Krauss, N., and Walter, I. (2008). “Can Microfinance Reduce Portfolio Volatility?”

Working Paper. Laeven, L., and Majnoni, G. (2003) “Loan loss provisioning and economic slowdowns:

too much too late?” Journal of Financial Intermediation. 12 178-197. Marconi, R., and Mosley, P. (2005) “Bolivia during the global crisis 1998-2004: towards

a ‘macroeconomics of microfinance.” Sheffield Economic Research Paper Series. No 2005007.

McGuire, P., and Conroy, J. (1998), “Effects on Microfinance of the 1997- 1998 Asian Financial Crisis,” Foundation for Development Cooperation, Brisbane, Australia, November.

39

Microfinance Information Exchange (MIX). Basic MIX MFI Data Set. Washington, D.C. Accessed March 6, 2014.

Murdoch, J. (1998). “Does Microfinance Really Help the Poor? New Evidence from Flagship Programs in Bengladesh” Hoover Institution, Stanford University. <www.nyu.edu/projects/morduch/documents/microfinance/Does_Microfinance_Really_Help.pdf>.

Nkusu, M. (2011) “Nonperforming Loans and Macrofinancial Vulnerabilities in

Advanced Economies” IMF Working Papers 11/161. Patten, R., Rosengard, J., and Johnston, D. (2001) “Microfinance success amidst

macroeconomic failure: the experience of Bank Rakyat Indonesia during the east Asian crisis” World Development, vol. 29 no.6, 1057-1069.

Salas, V., and J. Saurina, 2002, “Credit Risk in Two Institutional Regimes: Spanish

Commercial and Savings Banks,” Journal of Financial Services Research, Vol. 22, pp. 203–224.

Sapundzhieva, R. (2011) “Funding Microfinance – a Focus on Debt” Mix Market. Mix

Martket Newsletter. <www.themix.org/publications/microbanking-bulletin/2011/11/microfinance-funding-microfinance-debt-financing>.

Wagner, C., Winkler, A. (2012). “The vulnerability of microfinance to financial turmoil –

evidence from the financial crisis”. Frankfurt School of Finance and Management. World Databank. Inflation, consumer prices (annual %) and Unemployment, total (% of

total labor force). The World Bank Group. Accessed May 4, 2014.

40

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 648 648 648 648 R2 Within 0.1137 0.1137 0.1859 0.3010 R2 Between 0.4282 0.5367 0.2245 0.7413 R2 Overall 0.3094 0.3610 0.2138 0.5550

41

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 488 488 488 488 R2 Within 0.2278 0.2136 0.0791 0.2773 R2 Between 0.1063 0.0387 0.0022 0.2768 R2 Overall 0.1385 0.0900 0.0091 0.2699

42

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

Correlation Matix

PAR

30

PAR

90

WO

R

WO

R90

GD

P

Une

mpl

oym

ent

Infla

tion

NEE

R

Chn

g. L

oan

Port.

Inte

rest

Rat

e

Equi

ty/a

sset

RO

E

PAR30 1

PAR90 0.96 1

WOR 0.18 0.13 1

WOR90 0.81 0.82 0.68 1

GDP -0.16 -0.14 -0.05 -0.13 1

Unemployment

0.01 -0.01 -0.06 -0.04 0.02 1

Inflation -0.07 -0.04 -0.12 -0.10 0.15 -0.20 1

NEER -0.04 0.00 -0.07 -0.04 0.21 0.23 -0.22 1

Chng. Loan Portfolio

-0.27 -0.25 -0.21 -0.31 0.18 -0.01 0.00 0.02 1

Interest Rate -0.06 -0.13 0.20 0.02 -0.05 -0.22 -0.13 -0.34 0.08 1

Equity/Assets 0.08 0.05 0.04 0.06 -0.04 0.03 -0.07 -0.10 -0.08 0.16 1

ROE -0.17 -0.13 -0.11 -0.16 0.07 -0.04 0.08 -0.02 0.11 0.04 0.01 1