Cheap Credit, Lending Operations, and
International Politics: The Case of Global
Microfinance
MARK J. GARMAISE and GABRIEL NATIVIDAD∗
ABSTRACT
The provision of subsidized credit to financial institutions is an important and fre-quently used policy tool of governments and central banks. To assess its effectiveness, we exploit changes in international bilateral political relationships that generate shocks to the cost of financing for microfinance institutions (MFIs). MFIs that experi-ence politically driven reductions in total borrowing costs hire more staff and increase administrative expenses. Cheap credit leads to greater profitability for MFIs and pro-motes a shift toward noncommercial loans but has no effect on total overall lending. Instead, the additional resources are either directed to promoting future growth or dissipated.
THE PROVISION OF CREDITto financial institutions at below-market interest rates is one of the primary tools used by governments and central banks to regu-late and stimuregu-late the broad economy. When cheap financing is provided, the benefits of injecting capital must be balanced against the costs of distorting market incentives for financial institutions to operate efficiently. Despite the widespread use of low-priced credit, however, empirically analyzing its conse-quences is difficult. The allocation of subsidized financing may be endogenously related to countrywide economic conditions or to unobserved characteristics of the financial institutions receiving the influx of capital. In this paper, we inves-tigate the effects of below-market credit on the operations, lending activities, and performance of microfinance institutions (MFIs) around the world. We em-ploy an empirical strategy based on changing bilateral international political relations between the host country of an MFI and the nations of its lenders, as measured by the similarity of their United Nations (U.N.) voting patterns. Com-paring MFIs in the same country, we show that those experiencing improved political relations between their home nation and the states of their lenders enjoy improvements in the terms of lending; these changes are plausibly unrelated to the characteristics of the MFIs. In this sense, shifting political
∗Garmaise is at UCLA Anderson. Natividad is at NYU Stern. We thank Damian von Stauffenberg and MicroRate for access to the data, Cam Harvey (the Editor), two anonymous refer-ees, Viral Acharya, Ray Fisman, Matthias Kahl, Justin Murfin, and audiences at NYU Stern, UCLA Anderson, the NBER, Drexel, the International Society for New Institutional Economics, the Pe-ruvian Banking Superintendency (SBS), the PePe-ruvian Central Bank, and Universidad de Piura for useful comments. Natividad acknowledges the financial support of the Berkley Center at NYU Stern.
DOI: 10.1111/jofi.12045
relationships may be viewed as an exogenous source of variation in the provi-sion of cheap credit. The subsidized financing that is supplied is in many cases provided at rates below those of U.S. government bonds of equivalent maturity. Given the international pervasiveness of below-market credit for financial institutions, which has been accentuated in the past few years, it is important
to understand its effects.1 In particular, it is useful to know to what extent
the subsidy generates additional lending and to what degree it is dissipated or captured by managers and employees at the financial institutions. Our study analyzes MFIs, small lending institutions that resemble banks except that they typically finance themselves with loans, not deposits. They are a rising form of financial intermediary in many developing markets. We use political shocks to the supply of credit to gauge the impact of subsidized financing by studying three basic issues. First, we consider the effect of cheap financing on the hiring and operations of MFIs. We show that MFIs receiving low-cost funds increase the number of credit officers and especially augment their noncredit staff. They also have higher administrative expenses. Second, we assess the impact of below-market financing on the quantity and composition of lending
by financial institutions (Bernanke and Blinder (1988), Gertler and Gilchrist
(1993), Kashyap and Stein (1995), Den Haan, Sumner, and Yamashiro (2007)).
We show that, similar to its effects on banks in developed markets, cheap credit has little impact on the total amount of lending, but it does lead to a shift in favor of noncommercial loans (i.e., loans made to directly support the personal consumption of borrowers, as opposed to income-generating commercial loans to small enterprises). Third, we analyze the effect of the financial subsidy on
the performance of lending institutions (Saunders, Strock, and Travlos (1990),
Hovakimian and Kane (2000), Acharya and Yorulmazer (2008)). We find that
subsidized MFIs enjoy higher gross margins, but exhibit no change in their propensity to make nonperforming loans. We also find no evidence that the subsidy leads MFIs to charge lower rates to their customers.
The influence of politics on global capital flows is an increasingly important question, but it has received relatively little attention in empirical work. It is clear that for developing countries this is a first-order question as the supply of critical loans from multilateral organizations is likely affected by international political connections. Even in developed economies political proximity can
facil-itate bailouts (Faccio, Masulis, and McConnell (2006)) and political connections
can be valuable to individual firms (Faccio (2006)). Given the role of
globaliza-tion in explaining financial instituglobaliza-tions’ lending activities (e.g., Cetorelli and
Goldberg (2012)), understanding the political economy of cross-country links
is particularly important. Our results from a large sample of lenders from 47 countries and borrowers in 28 emerging markets allow us to quantify the significant impact of international political relationships on loan terms and to
1Low-priced credit may take the form of reduced cost for government-guaranteed deposits
describe the eventual implications of this credit for the financial institutions that receive it.
Our analysis makes use of a unique proprietary data source with both op-erating and loan-level financial information on MFIs. The global microfinance market has achieved considerable size—recent estimates place it as at least
$65 billion in loans to 93 million borrowers2—and it is fast growing. Moreover,
the potential social impact of access to credit for the poor borrowers who make
use of microfinance can be profound (Morduch (2000) and Pitt et al. (2003)).
Un-derstanding how and whether to subsidize microfinance is thus an important question with ramifications for millions of poor borrowers worldwide. As recent controversies and crises in microfinance have revealed, it is an issue on which there is little consensus. Microfinance has begun to receive sustained scholarly
attention examining its impact (Karlan and Zinman (2008,2010), Gin´e et al.
(2010), Kaboski and Townsend (2011)). Most of this work focused on the effects
of microfinance on the ultimate borrowers. Our study, by contrast, investigates the operations and financing of the MFIs themselves. While MFIs are increas-ingly important in their own right, in their basic lending activities they also share many features in common with banks. Our analysis therefore has policy implications for the subsidization of financial institutions more broadly.
We first establish that an increased similarity in the voting patterns of two countries in the U.N. General Assembly is associated with reduced interest rates and greater quantities of loans between lenders and MFIs in those coun-tries in the following year. Specifically, we make use of a well-known bilateral
measure from the political science literature (Signorino and Ritter (1999)) that
captures the “macro” affinity between countries in regressions explaining “mi-cro” terms at the loan and MFI level. We find that the interest rate a lender charges an MFI decreases and the loan amount increases when the lender’s nation and the MFI’s host country become politically closer. In this test, we in-clude MFI-lender pair fixed effects, and we use MFI-year fixed effects to control for all unobserved changes in the MFI and in economic conditions as well as the demand for credit more broadly. This finding indicates that an improvement in the affinity between the country of the lender and the MFI’s host country leads to the provision of more financing on better terms.
We next aggregate across all of an MFI’s current borrowing relationships and define its “average political shock” to be the weighted change it experi-ences in its political affinity across its full set of lenders, where the weights reflect the amounts borrowed. Controlling for country-year fixed effects, we find that MFIs with improved political affinities pay a lower average cost of debt funding. MFIs that suffer from declining affinities with their current lenders receive less subsidized financing from them, but they substitute market-rate financing with new lenders for the lost credit. As a result, an increase in po-litical affinity is associated with an overall lower cost of credit, but has no significant effect on the total quantity of financing. A one-standard-deviation greater (i.e., more positive) political shock leads to a 88-basis-point decrease in
the average weighted cost of financing for an MFI. Along with the loan-level results, this finding suggests that our affinity measure is a suitable proxy for studying the effects of a positive financing supply shock on the hiring, lending, and performance of MFIs.
Using this empirical strategy, we show that MFIs hire more credit officers after they benefit from a positive political shock, and they particularly increase their noncredit staff. These MFIs also have higher administrative expenses and set aside greater amounts for loan loss provisions.
We find that a reduced cost of credit does not affect the total quantity of lending by an MFI, but it does lead to a shift toward noncommercial lending,
as has been documented in the United States (Gertler and Gilchrist (1993),
Kashyap and Stein (1995), Den Haan, Sumner, and Yamashiro (2007)).
Previ-ous research, however, has found it difficult to disentangle credit supply and demand effects, as subsidized financing is often provided in distressed macroe-conomic periods. Our empirical design, through its focus on the political shocks to specific MFIs in a given country, is able to control for any general nation-wide economic factors. Our results therefore provide clear evidence that the supply of commercial loans is less sensitive to a financial institution’s cost of funds than the supply of noncommercial loans. This is consistent with the no-tion that commercial loans are relano-tionship-based and thus less sensitive to a bank’s cost of funds.
MFIs receiving below-market funds do experience increased gross margins, as expected, but most other performance indicators are unchanged, includ-ing portfolio quality and the average rate charged to customers. There is no evidence of a trickle-down effect from the lower cost of credit received by MFIs. Overall, these results provide a new perspective on both financial institution efficiency and the credit channel in a growing sector of the world economy that is largely understudied. While cheap credit is not associated with a significant increase in total loans, it does lead to more noncommercial lending. There is some evidence that subsidized MFIs use their additional resources to plan for the future by building their credit staff and increasing loan loss reserves. There is also evidence, however, that some of the proceeds are dissipated in the hiring of noncredit staff and through higher administrative expenses. The real effects linked to dissipation appear to occur more quickly in response to a positive credit shock than those suggestive of planning for future growth. Overall, these findings help elucidate the functioning of the credit channel in emerging markets.
The rest of the paper is organized as follows. SectionIdescribes the microfi-nance setting and the data we use in the study. Our empirical specification is
detailed in SectionII. SectionIIIdiscusses our results. SectionIVconcludes.
I. Empirical Setting
The global microfinance market consists of lending institutions that provide
small loans to poor borrowers.3In this section, we briefly review the evidence
on the financing of MFIs and describe our data sources.
A. Financing MFIs
Relatively little is known about how MFIs finance their lending activities
(Jansson (2003)). While anecdotal evidence suggests that MFIs receive capital
injections from social entrepreneurs, non-governmental organizations, govern-ments, and donors, the economic terms on which MFIs receive capital to invest in a lending portfolio have remained largely unknown due to lack of data. The MFI industry relies directly on subsidized financing for its everyday activities
such as lending (Morduch (2000)). The stated social goals of MFIs facilitate
their obtaining capital at below-market interest rates from many of their fund providers. MFIs, in turn, do not give away funds for free to their borrowers, as they incur various costs to select, serve, and monitor their clients. Although MFIs differ in the degree of their social orientation, access to finance and prof-itability are crucial to all MFIs, enabling them to accomplish their expansion goals in their untraditional, underserved segments of the financial services market.
B. Data
Our main data source is a database of audited financial statements and selected operating variables on MFIs provided to us by MicroRate, a leading microfinance rating agency. The data cover 133 MFIs over the period 1997 to 2009. MicroRate collects information directly from MFIs, visiting their head-quarters and branches as part of its evaluation services. The MFIs are drawn from 28 countries in Africa and Latin America. The MFIs that are evaluated by MicroRate likely represent a more successful and perhaps more commercially oriented sector of the overall microfinance market.
The MicroRate database provides audited information on both the financing
and lending activities of the MFIs. Table I shows some summary statistics.
The median portfolio of loans granted by an MFI is $7 million, and the median amount of financing received by an MFI in a given year is $0.6 million per
3Detailed descriptions of the microfinance industry are becoming more widely available. See
Table I
Summary Statistics
The unit of observation is a microfinance institution (MFI) in a given year. Average interest rate is the quantity-weighted average of nominal interest rates on outstanding loans received by MFIs, translated into a dollar interest rate using forward exchange rates from Datastream. Total loans received is the sum over all outstanding loans expressed in millions of dollars. Continuation loans received sums over the loans of existing relationships of the MFI in millions of dollars. Weighted change in political affinity is constructed using U.N. voting data compiled by Voeten and Merdzanovic (2009) and available athttp://dvn.iq.harvard.edu/dvn/dv/Voeten. Operational, credit, and performance variables are compiled by MicroRate from its client MFIs. Total staff include credit staff and noncredit staff. Administrative expenses are expressed as a fraction of the total loan portfolio of the MFI. Total loan portfolio is the sum in thousands of dollars of the loans an MFI makes to its clients. Share of commercial loans is the sum of all microcommerce loans and small business loans made by the MFI divided by the sum of commercial loans and noncommercial loans made by the MFI. Provision for loan loss is in thousands of dollars. Gross margin is interest and fee income minus interest and fee expense divided by the size of the loan portfolio in the previous year. Average rate charged to clients is defined as interest and fee income over total loan portfolio. Average loan size is the ratio of the total loan portfolio of the MFI divided by the number of loans. Portfolio quality is the fraction of the portfolio composed of loans with fewer than 30 days past due. Leverage is total liabilities over total equity.
Variable Median Mean Std. Dev. Min. Max. # Obs.
Average interest rate 0.08 0.07 0.06 −0.21 0.31 825
Total loans received (in logs) 8.47 8.33 1.87 0.88 12.51 825 Continuation loans received (in logs) 8.00 6.85 3.67 0.00 12.45 825 Weighted change in political affinity 0.00 −0.01 0.08 −0.54 0.68 825
Total staff (in logs) 4.76 4.77 1.18 1.39 8.63 812
Number of credit staff (in logs) 3.91 3.92 1.23 0.00 8.16 803 Number of noncredit staff (in logs) 4.19 4.16 1.20 0.69 7.66 803
Admin. expenses/portfolio 0.08 0.97 22.95 0.00 622.33 735
Total loan portfolio (in logs) 8.86 8.95 1.63 4.04 13.00 825
Share of commercial loans 0.81 0.67 0.36 0.00 1.00 735
No. of commercial loans (in logs) 8.41 6.87 4.01 0.00 12.44 824 Provision for loan loss/portfolio 0.03 0.08 1.18 −0.01 32.00 735
Gross margin 0.24 0.29 0.17 0.03 1.13 736
Average rate charged to clients 0.28 0.32 0.15 0.07 1.25 825 Average loan size ($000, in logs) −0.48 −0.62 0.88 −3.11 1.34 795
Portfolio quality 0.95 0.84 0.28 0.00 1.00 811
Leverage 2.12 2.41 1.86 0.02 16.66 815
lender, with a median of five lenders per MFI. Forty-nine percent of the loan financing obtained by MFIs is from foreign lenders, and these lenders represent 54% of the distinct institutions lending to MFIs.
MFIs target microscale entrepreneurs and poor borrowers as their main customers. The median size of a loan originated by an MFI is $620, and the median number of clients served in a given year is 13,950. The overall quality of MFI investments is high, with a median of 0.95 for the fraction of loans with fewer than 30 days past due.
The U.N. voting data we use to construct a measure of political affinity are
call votes of all countries in the U.N. General Assembly over the entire sample period.
II. Empirical Specification
A. Financing Terms and Political Affinity
Many lenders to the microfinance industry choose to supply funds for non-market motives, and these motives may also affect the terms (e.g., price or quantity) of a loan given to an MFI. In particular, we test whether an improve-ment in the political ties between the country of the lender and the country of the borrower leads to improved financing terms. The terms a lender offers an
MFI in periodtcan be modeled as
LoanT ermt=a∗St−1+b∗U+ǫt, (1)
whereSt−1measures the political affinity between the host nations of the MFI
and the lender,U is a set of lender-specific characteristics (e.g.,U may describe
the lender’s propensity to supply finance for a philanthropic motive), andǫtis
an error term.
We presume it is difficult to observeU, which may raise an issue from an
econometric standpoint if the correlation between St−1 andU is nonzero. For
example, consider an MFI in a developing nation that receives funding both from the national aid agency of a European government and from a bank in a neighboring country. The MFI’s host nation is likely to have a higher political affinity with the neighboring state than with the European country. On the other hand, the European aid agency may be extending the loan for
charitable reasons (highU), while the neighboring bank may be purely profit
maximizing (lowU). In this case, the correlation betweenSandU is negative
and estimating a version of (1) without controls will lead to inappropriate
parameter estimates ifUis unobservable. Our empirical specifications account
for such an effect.
B. Measuring Political Affinity
We adopt the popular Signorino and Ritter (1999) S variable as our
mea-sure of affinity between countries. This variable is a summary meamea-sure that describes the similarity between the voting patterns of two countries in the
U.N. General Assembly. For a given countryIvoting on resolutionr, letPrI =1
if the country votes “Yes,” PI
r =0 if it votes “Abstain,” and PrI = −1 if it votes
“No.” For countriesIand Jin yeart, the affinity measureSis defined as
SI,J,t=1−
2rR=1|PI r −PrJ|
2R , (2)
where Ris the total number of resolutions in yeart. (Resolutions for which at
least one of the countries casts no vote are excluded.) The measureStherefore
patterns between the two countries. Note that,SI,I,t=1: the affinity between a country and itself is always one.
Votes in the General Assembly rarely have direct political implications (un-like those in the Security Council), and thus may be viewed as a reasonable measure of the true preferences of states, since strategic considerations in Gen-eral Assembly voting are typically quite slight. This approach has been applied
in studies of multilateral organizations (Andersen, Harr, and Tarp (2006)).
Fol-lowing the political science literature, we therefore view Sas a time-varying
measure of the true affinity between two countries.
C. The Impact of Political Shifts on Lender–Borrower Relationships
To test if political affinity affects the loan contracts offered to MFIs, we
ana-lyze the financing terms of new loans provided by lender j(based in countryJ)
to MFIi(based in countryI). Following (1), we estimate
LoanT ermi,j,t=α+β∗(SI,J,t−1)+γ∗controlsi,t+δi,j+λi,t+σi,j,t, (3)
where LoanT ermi,j,t is either the interest rate or the amount of the loan
pro-vided by lender j to MFI i in year t, SI,J,t−1 measures the political affinity
between countries I and J in the previous year,controlsi,tis a vector of
loan-level controls, δi,j is a fixed effect for the relationship between the MFI and
the lender,λi,t is an MFI-year fixed effect, andσi,j,t is an error term. We
es-timate robust standard errors double clustering at both the level of the MFI
and the country of the lender (Petersen (2009)). We are primarily interested
in the coefficientβ, which details the effect ofSon financing terms. Loans are
provided throughout the year, so, to avoid regressing loan characteristics on a political affinity measure calculated using subsequent votes, we always relate loan terms in the current year to voting patterns in the previous year.
Equation(3)makes use of MFI-lender relationship fixed effects, and therefore
describes for a given relationship how changes in political affinity at the level of
two countries affect interest rates (lender-specific unobservables such asU in
(1) are absorbed in these fixed effects). The inclusion of MFI-year fixed effects (subsuming country-year fixed effects) ensures that the estimated impact of
S is unrelated to any general political or economic phenomena occurring in
the MFI’s country. Moreover, these fixed effects also control for any changes to the MFI’s overall condition in a year. This specification therefore focuses only on differences among the various terms offered to a given MFI by its different lenders in a given year. We analyze how these terms change with the shifting political affiliations of the various lenders’ countries to the MFI home nation.
from its Dutch lender. The specification (3) with loan quantities as the depen-dent variable provides evidence on the impact of affinity on quantity. The fact that much of the motivation for lending to MFIs is not market driven suggests that it is reasonable to suppose that political considerations may play a role in determining both the interest rates charged and the quantity supplied.
D. Political Changes and Financing Terms for an MFI
Aggregating the loan-level effects ofSsuggests that an MFI’s overall
financ-ing terms may depend on changes in the political relationships between its lenders’ countries and the MFI’s host nation. Changes in the composition of the set of lenders to an MFI require that care be taken in measuring an MFI’s average political affinity. For example, suppose that a Bolivian MFI borrows in period one from a Dutch aid agency with a relatively low affinity at a low inter-est rate (due to the philanthropic motives of the lender). If political relations between Bolivia and the Netherlands suffer, the subsidized loan will no longer be made available in period two. Instead the MFI will receive financing in the second period from a Mexican bank (with a relatively high affinity). This loan is made on market terms and carries a higher rate.
In this case, a straight regression of rates on political affinity will mislead-ingly show a positive relationship because it ignores the changing character-istics of the MFI’s lenders (i.e., the switch from the Dutch aid agency to the Mexican bank). The loan-level specification (3) includes fixed effects for each MFI-lender relationship, but an MFI-level specification cannot include these fixed effects. Instead, we adopt a different strategy and implicitly control for unobserved lender characteristics by regressing the change in loan terms on the change in the political affinity experienced by the MFI with its current set of lenders. In this example, the negative change in Bolivia’s relationship with the Netherlands would thus predict a worsening in the MFI’s loan terms between periods one and two. This prediction is made without reference to the new relationship the Bolivian MFI forms with the Mexican bank in period two. Any future change in Bolivia’s relationship with Mexico, however, would be used to predict the subsequent change in the MFI’s financing terms.
Specifically, we calculate the average weighted change in political affinity
it=
j(SI,J,t−SI,J,t−1)li,j,t
jli,j,t
, (4)
where i is an MFI identifier,tis the year,li,j,t is the dollar value of the loan
extended by lender j (in country J) to MFI i (in country I) in year t. The
variable i,t describes the weighted average political shock experienced by
MFIi in yeart, where the weights are given by the loan amounts it receives
from each current lender.
If variation in lagged S is associated with variation in credit terms, then
i,twill have an impact on the change in the average financing terms on new
MFI-year level gives
LoanT ermsi,t+1−LoanT ermsi,t=η1+θ1∗ i,t+κ1∗controlsi,t+1
+µi+νI,t+1+ξi,t+1, (5)
where controlsi,t+1 is a vector of MFI-level first-differenced controls, µi is an
MFI-level fixed effect,νI,t+1is a country-year fixed effect at the level of the MFI’s
country I, andξi,t+1is an error term. Robust standard errors are clustered at
the level of the MFI.
This empirical method controls for the changing composition of the lender pool by holding the set of lenders fixed for the purposes of calculating the change in political affinity. A shift in lenders simply changes the reference point for
calculating , but the change in political affinity is measured each period
with respect to the current group of lenders. This allows for the appropriate aggregation of loan-level political shifts into a measure for the MFI as a whole. This approach ignores variation in an MFI’s average political affinity that arises from its reoptimizing over its set of lenders. Instead, we take as fixed the current set of lender countries and measure how the relationships between the MFI host country and the countries of its lenders change over the year. We also do not account for the relationships between the MFI and the full set of potential lenders from which it does not currently borrow; we focus exclusively on its present group of lenders and implicitly argue that shocks to its relationships with these lenders matter more to the MFI than shocks to its
relationships with other possible lenders.4
As a variation on the previous example, if Bolivian MFI Areceives most of
its loans from a Mexican lender, and Bolivian MFIBreceives most of its loans
from a Dutch lender, then an improvement in Mexico–Bolivia affinity and a decline in Netherlands–Bolivia affinity would be predicted to lead to better
financing terms for MFIA(which has experienced a positive political shock) and
worse financing terms for MFI B(which has experienced a negative political
shock).
We are interested in the implications that subsidized credit may have for the scale of operations, lending policies, and performance of MFIs. Unlike the loan data that are reported as flows through the year, the MFI characteristics are stock variables that are usually reported at year-end. Thus, political affinity in a given year may have an impact on year-end MFI characteristics for the same
year or in the subsequent year. For example, political affinitySt−1may increase
both MFI characteristicsYt−1andYt. As a result, the change in political affinity
(St−St−1) should have a positive effect on (Yt+1−Yt−1). We therefore estimate equations of the form
MFIcharacteristici,t+1=ψ+χ∗( i,t)+ρ∗controlsi,t+τi+υI,t+φi,t,
(6) whereMFIcharacteristici,t+1=MFIcharacteristici,t+1−MFIcharacter-istici,t−1,
τi is an MFI fixed effect, νI,t is a country-year fixed effect at the level of the
MFI’s country I, andφi,t is an error term. For robustness, and to decompose the timing of the effects, we also consider results using only first-differenced MFI characteristics as dependent variables.
We view i,tas a proxy for the provision of subsidized credit in this
reduced-form equation. This approach allows us to estimate the causal effect of subsi-dized financing on the operations and investment of an MFI. We are essentially contrasting MFIs in a given country that experienced a positive political shock (due to the nationalities of their lenders) in the previous year from those in the same country that experienced a negative political shock in the previous year. Given that MFIs are small organizations with a median loan portfolio of $7 million, they are unlikely to influence the diplomatic stances of their host na-tions. We therefore argue that variation in the international relations between states may be viewed as plausibly exogenous from the perspective of any given MFI.
E. Country-Year Fixed Effects
All the equations(3),(5), and(6)include country-year fixed effects for each
MFI. (In the case of equation(3), the country-year fixed effects are subsumed
in the MFI-year fixed effects.) We therefore control for any unobserved changes occurring over time in the MFI’s home state. For example, the country-year fixed effects control for any nationwide impact of an economic crisis, changes in property rights, freedom of the press, general political character, macroeco-nomic condition of the country, etc. Identification in our empirical specifications arises solely from changes in the bilateral relationships between an MFI’s coun-try and the nations of its lenders. In our running example, any broad impact of a national election on Bolivia’s economic performance and governance will be net-ted out by the country-year interaction fixed effects. Our approach essentially contrasts multiple Bolivian MFIs in the same year that have been differentially affected by changes in Bolivia’s relations with their lenders’ countries.
III. Results
A. Political Affinity, Lender-MFI Relationships, and Loan Terms
Given the nonmarket motivation of many loans to MFIs, it is plausible that the changing political affiliations between countries may affect loan terms. Approximately 15% of the loans in our sample are made at U.S. dollar interest rates below those of U.S. government securities of equivalent maturity. We refer to these debt contracts as “social loans.” In our first test, we relate the
provision of social loans to changing affinity: we estimate equation (3) with
Table II
International Affinity and the Supply of Credit in Loan Relationships
The table reports regressions of loan-level terms on political affinity, following equation(3). The dependent variables are all based on loan contract terms. Social loan is a dummy equal to one for whether the interest rate of the loan is lower than the United States’ risk-free interest rate (U.S. Treasury bonds matching the maturity of the MFI loans). Interest rate is expressed in U.S. dollars using forward exchange rates from Datastream and adjusted by maturity subtracting from it the U.S. Treasury bond rate matching the maturity of the MFI loans. The loan-level controls include the number of semesters in which the MFI and its lender have had a loan relationship, the age of the MFI expressed in years, as well as an unreported constant. Fixed effects for each MFI-lender pair (totaling 1,670 fixed effects) and for each MFI-year combination (totaling 816 fixed effects) are included. Standard errors are heteroskedasticity-robust and clustered separately by MFI and by country of lender. Robustt-statistics based on double-clustered standard errors are reported in parentheses.
Dependent Variable
Social Loan Interest Rate Quantity
0/1 1=100% $ in logs
(1) (2) (3)
St−1 0.333∗∗∗ −0.040∗∗ 2.156∗∗∗
(3.19) (−2.02) (2.92)
Relationship −0.015 0.006∗∗∗ 0.007
(−1.26) (2.94) (0.41)
Age of MFI 0.018∗∗∗ −0.011∗∗∗ 0.221∗∗∗
(3.00) (−11.53) (22.60)
MFI×Lender pair fixed effects Yes Yes Yes
MFI×Year fixed effects Yes Yes Yes
R2 0.64 0.61 0.69
Sample size 13,265 13,265 13,265
Number of clusters (MFI) 130 130 130
Number of clusters (country of lender) 47 47 47
∗∗∗p<0.01,∗∗p<0.05,∗p<0.1.
MFI, the state of the economy, and the overall demand for credit). Due to the multiple dimensions of fixed effects, we estimate via ordinary least squares rather than using a binary model such as logit. The results, displayed in the
first column of TableII, show that, in a given relationship, an increase in the
lagged political affinity of the home nations of the lender and MFI results in a
significant (t-statistic=3.19) increase in the probability that the loan provided
is a social loan. Reported t-statistics are robust and double-clustered at the
level of the MFI and the country of the lender. This accounts for all within-MFI and within-lender-country correlations. To measure the economic impact, we
consider the variability of S within relationships over time. A
one-standard-deviation increase in within-relationshipSresults in a 10.2% increase in the
Changes in affinity also affect interest rates more broadly. We estimate
equa-tion (3) with the U.S. dollar interest rate premium as the dependent
vari-able and present the results in the second column of Tvari-able II. (We use
for-ward exchange rates from Datastream to convert rates from loans priced in other currencies. The rate premium is calculated by subtracting the maturity-matched U.S. Treasury bond rate from the U.S. dollar rate on the loan.) An increase in last year’s political affinity is associated with a significant decrease
(t-statistic= −2.02) in the interest rate charged. A one-standard-deviation
in-crease in within-relationship S leads to a 2.3% decrease in the interest rate
charged relative to the mean. Greater affinity between the lender’s country and the MFI home nation reduces rates, controlling for all MFI-year effects.
Political shocks also have an impact on the loan quantity. We estimate
equation(3)with the log of the loan amount in U.S. dollars as the dependent
variable. The results, described in the third column of TableII, show that an
increase in last year’s political affinity is associated with a significant increase
(t-statistic=2.92) in the log of the loan quantity supplied. A
one-standard-deviation increase in within-relationship S leads to a 10.3% increase in the
size of the loan. Taken together, these results suggest that positive political shocks lead lenders to supply financing on more favorable terms, at greater quantity, and with lower cost.
The basic intuition for the results in this section is that the supply of finance may be influenced by political factors. Earlier work shows that political ties
affect government capital allocation (Faccio, Masulis, and McConnell (2006)).
The funding of microfinance is often done for nonmarket reasons, so one might expect that political affinity plays a large role in determining loan rates and amounts. The results in this section clearly establish that this is so.
What drives political affinity? The Svariable has become widely accepted
in political science as an effective measure of shared national preferences, but there is relatively little work analyzing its determinants (Sweeney and Keshk
(2005)). To build some intuition for our findings, and to support the introduction
of S to the finance literature, in the Internet Appendix5we investigate three
causes that affect bilateral political arrangements. We show that it is influenced by similarity in left–right political orientation, the participation of a country in
an international conflict, and the extent of bilateral trade. Political affinity S
is available for each country-pair observation every year, and in this sense we view it as a rich and objective measure that captures a wide variety of changes in policy.
B. MFI Cost of Capital and Affinity
The findings in SectionIII.Adescribe how loan terms in a given MFI-lender
relationship are influenced by international political affinity, controlling for any MFI-year effects. This suggests that an MFI’s overall cost of capital may
Table III
International Affinity and the Supply of Credit for MFIs
This table reports regressions of financing variables on an MFI’s weighted change in political affinity, as detailed in equation(5). All dependent and independent variables are expressed as differences, and the observations are at the MFI-year level. Average interest rate is the quantity-weighted average of nominal interest rates on outstanding loans received by MFIs. Total loans received is the sum over all outstanding loans, expressed in millions of dollars. Continuation loans received are those from existing relationships of the MFI. The controls include leverage, portfolio quality, as well as an unreported constant. Fixed effects for each MFI (totaling 118 fixed effects) and for each country-of-MFI×year combination (totaling 161 fixed effects) are included. Standard errors are heteroskedasticity-robust and clustered by MFI. Robustt-statistics based on clustered standard errors are in parentheses.
Dependent Variable (in Differences)
Average Total Loans Continuation Interest Rate Received Loans Received
1=100% $M, in logs $M, in logs
(1) (2) (3)
Weighted change in political affinity −0.104∗∗∗ 0.664 5.691∗∗∗
(−3.73) (0.79) (2.65)
Leverage −0.001 0.010 −0.031
(−0.44) (0.33) (−0.35)
Portfolio quality −0.004 −0.270 1.068
(−0.73) (−1.26) (1.59)
Fixed effects:
MFI Yes Yes Yes
Country of MFI×Year Yes Yes Yes
R2 0.86 0.51 0.45
Sample size 567 567 567
Number of clusters (MFIs) 118 118 118
∗∗∗p<0.01,∗∗p<0.05,∗p<0.1.
in part be determined by variations in political affiliations, which is the topic we consider in this section.
As we describe in Section II.A, an MFI’s average political affinity may be
correlated with unobserved lender characteristics (the composition effect). To control for these unobservables, we therefore regress changes in an MFI’s av-erage interest rate or total loans received on changes in its avav-erage political affinity.
To determine if improved political connections reduce rates at the MFI level,
we estimate equation(5)regressing the first-differenced average interest rate
paid by the MFI (weighted by loan amount) on the weighted change in political affinity, first-differenced leverage (the ratio of total liabilities to total equity), first-differenced portfolio quality, and both MFI-level and country-year fixed effects.
We find, as documented in the first column of Table III, that MFIs that
enjoy significantly lower (t-statistic= −3.73) average interest rates. (Reported
t-statistics are robust and clustered at the MFI level.) A one-standard-deviation
greater (i.e., more positive) political shock is associated with an 88-basis-point decrease in the average rate paid.
This finding is consistent with the results described in TableII: MFIs whose
host countries improve relations with their lenders’ countries receive lower rates and more financing from those lenders. An MFI that is subject to a negative weighted change in political affinity, however, need not experience a decline in total financing received; if capital markets are competitive, then this MFI may be able to replace the lost financing from its former lenders with new financing from new lenders, on market terms. The overall cost of financing for this MFI would increase (as we display in the first column) due to its decreased supply of subsidized financing, but the impact on its total loan supply is not clear.
As we show in the second column of TableIII, the impact of an MFI’s weighted
change in political affinity on its total log of loans received is insignificant
(t-statistic=0.79). This is indirect evidence that MFIs have effective access to
funding on market terms and can apparently substitute market financing for
subsidized financing if required. In the third column of TableIII, we show that
continuation loans (i.e., loans from last year’s lenders) do indeed significantly
increase (t-statistic=2.65) in an MFI’s weighted change in political affinity,
as we would expect from the results in Table II. The overall picture is quite
clear: MFIs that experience a positive political shock enjoy increased financing on better terms from their existing lenders, and this reduces their average cost of capital. MFIs that experience a negative political shock receive reduced subsidized financing from their previous lenders but are able to acquire new financing on market terms that substitutes (at a higher cost) for their lost funding.
The coexistence of subsidized and market financing is apparent in the data. As we describe above, about 15% of the loans are social loans, but in 62% of the MFI-year observations the MFI receives at least one social loan. Political shocks thus serve mainly to change the subsidized/nonsubsidized composition of an MFI’s borrowing and its average interest rate, but not its total amount of financing.
MFIs are small and are very unlikely to influence their home countries’ for-eign policies, so reverse causality is not a concern. Variation in its weighted change in political affinity is thus generated by credibly external political shocks that influence an MFI’s cost of capital. The political shock is unlikely to be correlated with MFI-level unobservables such as quality of investment opportunities, etc. In this sense, MFIs that benefit from a positive political shock have simply experienced good fortune that has given them access to less expensive financing.
captured in our analysis. It may be the case that MFIs experiencing positive political shocks from their current set of lenders also enjoy improved relations with a broader set of potential lenders, and that it is the latter and not the former that drives the improvement in their credit terms. Nonetheless, even with these reasonable concerns in mind, we argue that our empirical strategy exploits plausibly exogenous shocks to an MFI’s cost of credit.
C. Cheap Credit, Personnel, and Slack
The results described in the previous subsection indicate that positive po-litical shifts lead to an MFI’s being supplied with cheaper credit, though not with more financing overall. In this subsection we analyze the implications of this cheap credit for the operations of MFIs. In particular, MFIs with access to below-market financing may have the ability to expend resources to hire staff and expand administrative expenses.
We first consider the effect of cheap credit on the hiring policies of MFIs. We regress the change in the log of total staff on the weighted change in political affinity and we include as control variables the change in the MFI’s leverage and portfolio quality, as well as MFI and country-year fixed effects, as described in specification (6). All changes in real variables contrast the year before the political shock with the year after it to allow time for the financing effect to lead
to changes in firm policies. The results, reported in the first column of TableIV,
show that a positive political shock results in a significant (t-statistic=4.62)
increase in the number of total staff, that is, MFIs make use of subsidized credit to expand their operations by hiring more staff. A one-standard-deviation greater political shock leads to a 11.8% increase in staff.
Does cheap financing lead to greater hiring of credit officers? To analyze this question, we regress the change in the log of the number of credit officers on the weighted change in political affinity and the standard controls. We report
the results in the second column of TableIV. A positive political shock leads to
a significant (t-statistic=1.74) increase in the number of credit officers. A
one-standard-deviation greater political shock is associated with a 8.9% increase in
credit staff. As shown in the third column of TableIV, a positive political shock
also leads to a significant (t-statistic=3.16) increase in noncredit staff: a
one-standard-deviation increase in the political shock generates a 13.7% increase in noncredit staff. The greater impact of low-cost financing on total staff than on credit staff suggests that MFIs may use this cheap money somewhat less productively. That is, below-market credit may offer MFIs some financial slack. To gauge this effect, we regress the change in the ratio of administrative expenses to the total lagged loan portfolio on the weighted change in political affinity and the full set of controls. We find, as documented in the fourth
col-umn of Table IV, that administrative expenses are significantly increasing
(t-statistic=1.79) in the political shock. A one-standard-deviation increase in
Table IV
Cheap Credit, Personnel, and Slack
This table reports regressions of personnel and expenses variables on an MFI’s weighted change in political affinity, as detailed in equation (6). All dependent and independent variables are expressed as differences, and the observations are at the MFI-year level. Variable definitions are in TableI. Fixed effects for each MFI (totaling 118 fixed effects) and for each country-of-MFI×year combination (totaling 161 fixed effects) are included. Standard errors are heteroskedasticity-robust and clustered by MFI. Robustt-statistics based on clustered standard errors are in parentheses.
Dependent Variable (in Differences)
Number of Number of Administrative Credit Noncredit Expenses/ Total Staff Staff Staff Portfolio
(1) (2) (3) (4)
Weighted change in political affinity 1.413∗∗∗ 1.069∗ 1.639∗∗∗ 0.530∗
(4.62) (1.74) (3.16) (1.79)
Leverage 0.002 −0.017 0.036 −0.001
(0.12) (−1.02) (1.52) (−0.13)
Portfolio quality 0.060 0.082 0.028 0.029
(0.77) (0.81) (0.28) (1.14)
Fixed effects:
MFI Yes Yes Yes Yes
Country of MFI×Year Yes Yes Yes Yes
R2 0.38 0.35 0.49 0.61
Sample size 573 568 568 496
Number of clusters (MFIs) 116 114 114 112
∗∗∗p<0.01,∗∗p<0.05,∗p<0.1.
noncredit staff), this finding indicates that some of the benefits from cheap financing may be used by MFIs to operate at less than maximal efficiency.
D. Cheap Credit and Lending Expansion
The results in TableIVestablish that MFIs that receive inexpensive
financ-ing increase their stafffinanc-ing. Do they also expand their lendfinanc-ing? To answer this question, we regress the log of the MFI loan portfolio on the weighted change in political affinity and the usual controls. We report the results in the first
column of TableV. (Here we consider purely the dollar volume of loans—below
we turn to the quality of the loans issued.) We find that the political shock has an insignificant effect on the size of the loan portfolio.
This finding is consistent with the results of Gertler and Gilchrist (1993),
Kashyap and Stein (1995), and Den Haan, Sumner, and Yamashiro (2007), who
Table V
Cheap Credit and Lending Expansion
This table reports regressions of lending operations on an MFI’s weighted change in political affinity, as detailed in equation(6). All dependent and independent variables are expressed as differences, and the observations are at the MFI-year level. Variable definitions are in TableI. Fixed effects for each MFI (totaling 118 fixed effects) and for each country-of-MFI×year combi-nation (totaling 161 fixed effects) are included. Standard errors are heteroskedasticity-robust and clustered by MFI. Robustt-statistics based on clustered standard errors are in parentheses.
Dependent Variable (in Differences)
Share of Number of Provision Total Loan Commercial Commercial for Loss/
Portfolio Loans Loans Portfolio
(1) (2) (3) (4)
Weighted change in political affinity −0.467 −0.671∗∗∗ −0.578 0.182∗ (−0.68) (−2.69) (−0.27) (1.83)
Leverage 0.028 −0.018 0.313 0.003
(1.41) (−1.59) (1.52) (1.62)
Portfolio quality −0.016 0.090 1.679∗ 0.006
(−0.19) (1.39) (2.22) (0.60)
Fixed effects:
MFI Yes Yes Yes Yes
Country of MFI×Year Yes Yes Yes Yes
R2 0.53 0.59 0.54 0.52
Sample size 586 510 584 496
Number of clusters (MFIs) 118 106 118 112
∗∗∗p<0.01,∗∗p<0.05,∗p<0.1.
not (and may actually increase). That is, the provision of noncommercial loans is more sensitive to a bank’s cost of funds.
Our data provide a description of the intended use of funds by borrowers, as reported by each MFI. Broadly, loans may be classified as commercial or non-commercial loans. Commercial (i.e., income-generating) loans are those des-ignated for use by microenterprises or small businesses that engage in retail
sales, light manufacturing, or services (e.g., Ssendi and Anderson (2009)).
Non-commercial loans, by contrast, are provided by MFIs to pay for the consump-tion needs of borrowers, including children’s tuiconsump-tion, personal health care costs,
or even wedding expenses (e.g., Ramachandar and Pelto (2009)). Commercial
and noncommercial loans are both important components of microfinance, and MFIs vary in the emphasis that they place on each type of lending (Johnston
and Morduch (2007)).
We consider whether the provision of commercial loans is more sensitive to an MFI’s cost of credit by regressing the ratio of commercial loans to the sum of commercial and noncommercial loans on the weighted change in political affinity and the standard controls. As displayed in the second
col-umn of Table V, MFIs that benefit from a positive political shock
noncommercial loans are more affected by the cheap financing. A one-standard-deviation increase in the political shock is associated with a 5.6 percentage point decline in the proportion of commercial loans, relative to a mean of 67%.
As further evidence on this point, we consider the effect of the weighted change in political affinity on the log of the total supply of commercial loans
(TableV, column 3). We find that the political shock has an insignificant effect
(t-statistic= −0.27) on the supply of commercial loans. These findings
comple-ment the results of Gertler and Gilchrist (1993), Kashyap and Stein (1995),
and Den Haan, Sumner, and Yamashiro (2007) by showing that it is the supply
of noncommercial loans that is most tightly correlated with the cost of funding
for a financial institution. Loutskina and Strahan (2009) also find that a shift
in a bank’s liquidity and cost of deposits primarily affects the composition of the portfolio of loans made by the bank, with the supply of illiquid loans most impacted by these factors.
Our finding provides some evidence on the lending mechanism of the credit
channel (Bernanke and Blinder (1988)) in emerging economies. Previous
stud-ies raise two concerns about results documenting the sensitivity of noncom-mercial loans to banks’ cost of funding. The first is that it is often difficult to disentangle the causal impact of a cut in funding costs from the effects of the economic conditions that prevail when it is granted. Cheap credit is frequently supplied at times of macroeconomic weakness. The second difficulty is that the impact of the lending mechanism must be distinguished from that of the balance sheet mechanism. A reduction in central bank interest rates may si-multaneously provide lenders with cheap financing and increase the value of the collateral held by borrowers, especially for firms.
Our approach is not subject to either of these concerns. Our regressions make use of country-year fixed effects, so they control for current economic conditions and for any changes in borrower balance sheets. While there are certainly dis-tinctions between large banks in developed countries and MFIs, for our sample we present clean evidence of the credit channel portfolio composition effect: subsidized credit to financial institutions results in a shift to noncommercial lending. Our results are consistent with the idea that commercial loans are more driven by relationship effects than noncommercial loans. These rela-tionships should weaken the link between current conditions and commercial lending. The increase in credit officers after a positive political shock that we
describe in Table IVwould also be most useful in increasing noncommercial
loans that do not require a relationship.
While subsidized financing does not generate a short-run increase in total lending, it may encourage medium-term lending by allowing MFIs to build reserves to buffer themselves by setting aside cash flows in the form of loan loss provisions. We regress the change in the ratio of loan loss provisions to the total lagged loan portfolio on the weighted change in political affinity and the usual controls. The loan loss provision ratio is significantly increasing
(t-statistic=1.83) in the political shock, as we describe in the fourth column
percentage point increase in the loan loss provision ratio, relative to a mean of 8%. This reasonably large effect suggests that MFIs benefitting from positive political developments do make a serious effort to build their stock of prudential savings.
Taken together, the results in Tables IV andV present a nuanced picture
of the effects of a politically driven reduction in the cost of credit on an MFI. As for many banks in developed markets, overall lending does not change, but there is a relative increase in loans to noncommercial borrowers. Some of the MFI’s policy changes seem to suggest a measure of planning for future growth:
the number of credit officers is increased and loan loss reserves are bolstered.6
On the other hand, some of the benefits of the inexpensive financing do appear to be dissipated through higher administrative expenses and a pronounced increase in noncredit officer staff.
Of course, MFIs differ from banks in their relatively restricted access to credit lines and central bank funds, so we may expect the lending mechanism to affect MFIs and banks differently. Our results on the credit channel thus have the greatest relevance for countries in which MFIs are an important source of fi-nancing, and for markets in which banks resemble MFIs in having only limited access to credit lines. In such locales, our findings suggest that government-supplied cheap credit may result in small effects on overall lending, a shift to noncommercial loans, a buildup in financial institution loss reserves and personnel, and some inefficiencies due to excess slack.
E. Cheap Credit and Performance
Results described in the previous subsections describe the impact of finan-cial subsidies on the hiring and loan-making activities of MFIs. In this sub-section we consider whether the provision of cheap credit has an impact on performance.
We begin by examining the effect of a political shock on portfolio quality, the dollar-weighted fraction of loans that are fewer than 30 days past due. We regress the change in portfolio quality on the weighted change in political
affin-ity and the standard controls. The results, displayed in column one of TableVI,
show that the political shock has an insignificant effect on portfolio quality.
6Our results contrast with Paravisini’s (2008) finding of a quick (three-month) increase in
Table VI
Cheap Credit and Performance
This table reports regressions of performance variables on an MFI’s weighted change in political affinity, as detailed in equation(6). All dependent and independent variables are expressed as differences, and the observations are at the MFI-year level. Variable definitions are in TableI. Fixed effects for each MFI (totaling 118 fixed effects) and for each country-of-MFI×year combi-nation (totaling 161 fixed effects) are included. Standard errors are heteroskedasticity-robust and clustered by MFI. Robustt-statistics based on clustered standard errors are in parentheses.
Dependent Variable (in Differences)
Portfolio Gross Average Rate Average
Quality Margin Charged Loan Size
(1) (2) (3) (4)
Weighted change in political affinity −0.007 1.543∗ 0.067 −0.814 (−0.03) (1.92) (0.38) (−1.11)
Leverage 0.019 0.010 −0.006 0.021
(1.48) (0.79) (−1.42) (0.96)
Portfolio quality 0.042 −0.002 −0.087
(1.00) (−0.06) (−1.18) Fixed effects:
MFI Yes Yes Yes Yes
Country of MFI×Year Yes Yes Yes Yes
R2 0.42 0.50 0.44 0.50
Sample size 586 496 586 555
Number of clusters (MFIs) 118 112 118 115
∗∗∗p<0.01,∗∗p<0.05,∗p<0.1.
Cheaper credit, on the other hand, should be expected to have a direct positive impact on the gross margin (interest and fee income minus interest and fee expense scaled by the size of the lagged loan portfolio), as it directly reduces
expenses. The results, documented in the second column of TableVI, confirm
this hypothesis: the gross margin is significantly increasing (t-statistic=1.92)
in the weighted change in political affinity. A one-standard-deviation greater political shock is associated with a 12.9 percentage point increase in the gross margin.
From a social welfare perspective, it is important to know if a subsidy to MFIs results in lower rates for its borrowers. If rates to borrowers are determined competitively, it is not clear that cheap credit to MFIs will have any influence on the rates they charge. On the other hand, if MFIs seek to maximize borrower welfare subject to meeting a zero-profit constraint, then it might be the case that low-cost financing enables them to offer lower rate loans to their clients. We analyze this issue by regressing the change in the average rate paid by the MFI’s clients on the weighted change in political affinity experienced by
the MFI. As displayed in the third column of TableVI, we find no significant
subsidized MFIs increase or decrease rates overall. The results in TableVshow that there is a shift in borrower composition toward noncommercial clients after the supply of cheap credit, so the pool of borrowers does change. Nonetheless, this finding provides some evidence that there is unlikely to be a clear and sustained trickle-down effect from the subsidy of the MFI to lower rates paid by its borrowers.
In the fourth column of Table VI, we show the results from regressing the
change in the log of the average loan size on the MFI’s weighted change in political affinity. Loan size is often viewed as a proxy for borrower wealth. We find no evidence of a shift toward larger or smaller loans.
F. Robustness Tests
F.1. Timing
As we discuss in Section II.D above, political affinity may have an
im-pact on MFI characteristics in both the same year and the next year, since these characteristics are usually measured at year-end. This motivates our
specification (6) regressingMF Icharacteristici,t+1−MF Icharacteristici,t−1on
i,t, as this approach does not require that we take a strong stand on the
timing of the impact. In this section, we consider the results from a spec-ification in which we modify (6) by changing the dependent variable to
MF Icharacteristici,t−MF Icharacteristici,t−1. This specification provides
ev-idence on how quickly MFIs respond to changes in political relations.
The results are summarized in Table VII (full details are provided in the
Internet Appendix). As shown in the first row of TableVII, a positive political
shock results in a significant immediate increase in total staff, noncredit staff, and administrative expenses. A positive political shock has an insignificant immediate effect on the number of credit staff.
The second row of Table VII shows that a positive political shock has an
immediate negative effect on an MFI’s share of commercial loans and has an insignificant immediate effect on its provisions for future losses. In the third
row of Table VII, we find that the gross margin is immediately increasing in
the political shock, and average loan size is immediately decreasing.
Overall, these results are broadly consistent with those from our main tests, suggesting that some of the impact of a political shock on the operating charac-teristics of MFIs is realized relatively quickly. In general, though, the estimated coefficients on the weighted change in political affinity are lower in this spec-ification, which is consistent with the argument that a significant portion of the effect of politics on real outcomes occurs in the subsequent year. The only dependent variable that varies significantly with the political shock immedi-ately but not in our main tests is average loan size; there is evidence of a quick decline in average loan size that is not sustained over the subsequent year.
Table VII
Cheap Credit, Personnel, Lending, and Performance—Robustness to Immediate Timing
Each entry in this table is from a different regression of personnel, expenses, lending opera-tions, and performance variables on an MFI’s weighted change in political affinity, each regression being fully available in the Internet Appendix. The observations are at the MFI-year level. All dependent and independent variables are expressed in differences, as detailed in equation(6), with first-differenced dependent variables. Variable definitions are in TableI. Standard errors are heteroskedasticity-robust and clustered by MFI. Robustt-statistics based on clustered standard errors are in parentheses.
Dependent Variable (in Differences)
Number of Number of Admin. Credit Noncredit Expenses/
Total Staff Staff Staff Portfolio
Weighted change in political affinity 0.442∗∗ 0.224 0.683∗ 0.136∗∗∗
(2.12) (1.23) (1.84) (2.76)
Dependent Variable (in Differences)
Total Loan Share of Number of Provision Portfolio Commercial Commercial for Loss/
Loans Loans Portfolio
Weighted change in political affinity 0.155 −0.395∗ −0.387 −0.006 (0.97) (−1.81) (−0.42) (−0.32)
Dependent Variable (in Differences)
Portfolio Gross Average Rate Average
Quality Margin Charged Loan Size
Weighted change in political affinity −0.017 0.274∗∗ 0.047 −0.253∗∗ (−0.15) (2.37) (0.78) (−2.01)
∗∗∗p<0.01,∗∗p<0.05,∗p<0.1.
happening quite quickly in response to a positive political shock to financing terms. Real changes suggestive of planning for future growth, such as the hiring of credit officers and an increase in loan provisions, appear to take somewhat longer.
F.2. For-Profit Status
respond to cheap credit by improving their portfolio quality, while no effect is ob-served for not-for-profit MFIs. Overall, for-profit and not-for-profit MFIs appear quite comparable, with perhaps some slight evidence for a greater emphasis on lending discipline in the for-profit MFIs, as suggested by the portfolio quality finding.
There are two plausible explanations for the broad similarity in the results across both groups. First, it may be the case that, for this sample of MFIs, the distinction between for-profit and not-for-profit institutions is not sharp. It is clearly true that even not-for-profits must maintain financial sustainability. Moreover, MFIs receiving ratings from MicroRate tend to be larger and more professional than MFIs generally, as these MFIs are interested in receiving a credit rating and they are therefore quite likely to have a significant com-mercial orientation. Second, given the presence of international donors who support both types of institutions and can offer emergency assistance, both for-profit and not-for-for-profit MFIs may face soft budget constraints that allow them to deviate from full efficiency. Indeed, soft budget constraints apply quite gen-erally to financial institutions in the face of potential bailouts and government
rescues (Kornai, Maskin, and Roland (2003)).
IV. Conclusion
Governments and nonmarket actors intervene in financial markets in many ways. Our understanding of the effects of these interventions, particularly in an international setting, is often quite limited. In this paper, we study the impact of below-market financing on the operations, loan-making activities, and performance of MFIs around the world. We make use of changing cross-country affinities to generate political shocks to the cost of credit for MFIs. In a relationship-level analysis, we demonstrate that an increase in affinity leads to a greater quantity of debt supplied and a reduction in the rate paid by an MFI to a given lender. MFIs that benefit from overall improved relations between their home country and the nations of their previous lenders enjoy a lower cost of funds. We find that MFIs that receive subsidized financing increase their staff size, spend more on administrative expenses, and make greater provision for loan losses. The subsidy does not lead to more total lending, but it is associated with a shift to noncommercial loans.
Microfinance serves as one of the very few existing mechanisms for making credit available to some of the most disadvantaged borrowers in the world. While the need for this financing is pressing, our study suggests that investors who support the social goals of microfinance require patience. The supply of subsidized capital to MFIs does not generate an immediate increase in lending to the poor, but it does promote the organizational expansion that is a crucial precursor to future growth in lending.
