does regulatory supervision curtail microfinance profitability and outreach?
TRANSCRIPT
World Development Vol. 39, No. 6, pp. 949–965, 2011� 2011 Elsevier Ltd. All rights reserved
0305-750X/$ - see front matter
www.elsevier.com/locate/worlddevdoi:10.1016/j.worlddev.2009.10.016
Does Regulatory Supervision Curtail Microfinance
Profitability and Outreach?
ROBERT CULL, ASLI DEMIRGUC-KUNT *
World Bank, Washington, DC, USA
and
JONATHAN MORDUCH *
New York University, USA
Summary. — Regulation allows microfinance institutions to take deposits and expand their banking functions, but complying with reg-ulation can be costly. We examine implications for institutions’ profitability and their outreach to small-scale borrowers and women,using a newly-constructed dataset on 245 leading institutions. Controlling for the non-random assignment of supervision via treatmenteffects and instrumental variables regressions, we find evidence consistent with the hypothesis that profit-oriented microfinance institu-tions respond to supervision by maintaining profit rates but curtailing outreach to women and customers that are costly to reach. Insti-tutions with a weaker commercial focus instead tend to reduce profitability but maintain outreach.� 2011 Elsevier Ltd. All rights reserved.
Key words — microfinance, regulation, cross-country regression, global
* The views are those of the authors and not necessarily those of the World
Bank or its affiliate institutions. The Microfinance Information eXchange,
Inc. (MIX) provided the data through an agreement with the World Bank
Research Department. Confidentiality of institution-level data has been
maintained. We thank Isabelle Barres, Joao Fonseca, and Peter Wall of
the MIX for their substantial efforts in assembling both the adjusted data
and the qualitative information on MFIs for us. Varun Kshirsagar and
Mircea Trandafir provided expert assistance with the research. Final re-
1. INTRODUCTION
Microfinance institutions now reach well over 100 millionclients and achieve impressive repayment rates on loans (Cullet al., 2009). The rapid growth of microfinance has broughtincreasing calls for regulation, but complying with prudentialregulations and the associated supervision can be especiallycostly for microfinance institutions. The best empirical esti-mates of the costs of such regulation come not from micro-finance or other financial institutions operating indeveloping countries, but from banks in industrialized coun-tries. For example, by one estimate, the costs of complyingwith regulation in the US are sizable, equal to 12–13% ofbanks’ non-interest expenses (Elliehausen, 1998; Thornton,1993). We expect that such costs would be higher for MFIs,and Christen, Lyman, and Rosenberg (CLR) (2003) speculatethat compliance with prudential regulations could cost amicrofinance institution (MFI) 5% of assets in the first yearand 1% or more thereafter.
In discussing tradeoffs in regulation of microfinance, Chris-ten et al. (2003, p. 3) draw an important distinction betweenprudential and non-prudential regulation. According to theirdefinition, regulation is prudential when “it is aimed specifi-cally at protecting the financial system as a whole as well asprotecting the safety of small deposits in individual institu-tions.” The assets of microfinance institutions remain substan-tially less than those of formal providers of financial services,most notably banks, and thus they do not yet pose a risk to thestability of the overall financial system in most countries.However, an increasing share of microfinance institutions takedeposits from the public, and many of the depositors are rela-tively poor. Protecting the safety of those deposits provides arationale for improved regulation and supervision of microfi-nance institutions, and thus CLR argue that prudential regu-lations should generally be triggered when an MFI acceptsretail deposits from the general public.
949
There are multiple reasons why costs associated with thiskind of regulation are likely to be higher for microfinanceinstitutions. First, regulatory costs exhibit economies of scaleand thus smaller banks face higher average costs than largerbanks in complying with regulations (Murphy, 1980; Schroe-der, 1985; Elliehausen & Kurtz, 1988). Moreover, the start-up costs of regulation display more pronounced scale econo-mies than ongoing costs, because they have a large indivisiblecomponent which requires the same amount of time and ex-pense regardless of the scale of bank lending activities. Again,these estimates of scale economies are for US banks. Formicrofinance institutions in developing countries that havenever faced regulation, the costs are likely to be even higher.Moreover, frequent reporting to a supervisory authority aboutits financial position is substantially more difficult for an MFIthat specializes in very small transactions than for other finan-cial intermediaries such as banks (Christen et al., 2003).
A second reason why the costs of compliance with pruden-tial regulation might be especially onerous for microfinanceinstitutions stems from the high share of skilled labor costs in-volved. Studies indicate that most of the costs of complyingwith new banking regulations in industrialized countries arefor labor (Elliehausen & Kurtz, 1988; Elliehausen & Lowery,1995; Schroeder, 1985), and a substantial component of thoselabor costs are managerial and legal expenses—to monitor
vision accepted: October 30, 2009.
950 WORLD DEVELOPMENT
employee compliance, coordinate compliance reviews withregulators, and keep abreast of regulatory changes, regulatorinterpretations, and court decisions (Elliehausen, 1998). Suchskilled labor is likely to be in short supply in many MFIsand costly to acquire. Even the less skilled administrative workassociated with preparing regular reports for supervisors islikely to be done in headquarters, which could mean less staffin the field working directly with clients.
Third, microlending inherently involves making small loansto large numbers of borrowers. Because the administrativecosts per dollar lent are much higher for small loans thanfor large ones, the interest rates necessary to cover all costs(including costs of funds and loan losses) are much higherfor MFI loans than for conventional bank loans. Fortunately,the returns to capital can also be high for small, capital-starved businesses, and so high interest rates can be paid(see, e.g., De Mel, McKenzie, & Woodruff, 2006; McKenzieand Woodruff, 2007). At the same time, any factor that causescosts to go up, including the costs associated with complyingwith prudential regulation, is likely to force MFIs to raiseeither interest rates or loan sizes to maintain the same levelof profitability. Increases on either dimension could result inthe exclusion of some potential borrowers.
We investigate the impact of prudential regulation on theprofitability and financial self-sustainability of microfinanceinstitutions, with an eye on the channels through which im-pacts work. For example, if profit-oriented MFIs find waysto absorb the costs of prudential regulation that leave theirprofits unchanged, it would be of interest to see whether thesecosts are absorbed by changing the business orientation andcurtailing outreach to smaller borrowers and women becausereaching those market segments can be costlier per dollar lent.We also examine whether prudential regulation reduces theshare of employees who work in the field. Finally, if prudentialregulation imposes costs, we also need to investigate whatthose costs buy. In particular, we look at whether regulationis associated with improved loan quality.
These issues have been under-studied largely due to lack ofdata. We build on a subset of the MixMarket dataset, whichprovides unusually high-quality financial information for abroad range of institutions world-wide. The data we use cover346 institutions in 67 countries, most from 2003 or 2004 (the2008 round, collected in volume 17 of the Microbanking Bulle-tin, includes 1406 institutions). The MFIs included in the Mix-Market are among the largest in the world, and theirwillingness to submit their financial information to the Micro-finance Information eXchange, Inc. (MIX) indicates a com-mitment to achieving financial self-sufficiency. We expectthat these would be the MFIs best positioned to absorb thecosts of prudential regulation. If we find evidence of trade-offsassociated with such regulation in this group of MFIs, we ex-pect that the effects would be even more pronounced for smal-ler institutions not included in our database.
Most MFIs face some form of non-prudential regulation.These regulations can include rules governing MFI formationand operations, consumer protection, fraud prevention, estab-lishing credit information services, secured transactions, inter-est rate limits, foreign ownership limitations, and tax andaccounting issues (Christen et al., 2003). Prudential regulationsare less common and are imposed when system-wide concernsare justified or protecting small depositors is an issue.
Previous research on microfinance regulation and prudentialsupervision focuses on the relationship between financial per-formance and regulation, treating outreach as a secondaryconcern. Hartarska (2005) finds that regulated MFIs in Cen-tral and Eastern Europe and the Newly Independent States
have lower return on assets relative to others, and weak evi-dence that the breadth of outreach may be related to regula-tion. After controlling for the endogeneity of regulation,Hartarska and Nadolnyak (2007) find that regulation has noimpact on financial performance and weak evidence that reg-ulated MFIs serve less poor borrowers. Mersland and Strøm(2009) use an endogenous equations approach to find that reg-ulation does not have a significant impact on financial or so-cial performance, where regulation is measured by aregulation dummy variable.
These previous efforts to study the effect of regulation arebased on a binary regulation indicator variable. Based oninformation from the MIX database collected for the Micro-banking Bulletin publication, which we cross-referenced withinformation from other sites, we construct a measure ofwhether an MFI faces prudential regulation of the sort de-scribed in Christen et al. (2003). We construct two variablesthat are the focus of the analysis, a dummy variable equal toone if an MFI faces onsite supervision, and another equal toone if that supervision occurs at regular intervals. Within thesame country, we find that some MFIs face onsite supervisionwhile others do not, depending on their ownership structure,funding sources, activities, and organizational charter. Toour knowledge, this is the first dataset that allows for with-in-country variation regarding MFI regulation and supervi-sion. The results highlight the role of the enforcement ofregulation, and not just whether laws are on the books or not.
Our results suggest that microfinance institutions subjectedto more regular—and presumably more rigorous—supervisionare not less profitable compared to others despite the highercosts of supervision. We also observe that this type of supervi-sion is associated with larger average loan sizes and less lend-ing to women, hence indicating a reduced outreach tosegments of the population that are costlier to serve. We alsofind that controlling for the non-random assignment of super-vision is important in obtaining these results.
Lacking time series data on MFIs’ performance before andafter supervision, our empirical strategy is to start with arough classification of the extent to which MFIs in our sampleare profit-oriented based on their sources of funding. Wehypothesize that greater reliance on commercial sources (likedeposits) than on non-commercial sources (like donations)would lead an MFI to be more profit-oriented. While it is truethat commercially funded (profit oriented) MFIs tend to besupervised more often than non-commercially funded ones,there is still variation in supervision within each group. Thisis the variation we exploit in the paper: we compare the super-vised with the unsupervised MFIs in each sub-group (commer-cially oriented and non-commercially oriented). We argue thatthis is a fair test because MFIs in the commercially fundedgroup are facing similar incentives to make profits. Similarly,the MFIs that are not commercially funded face weaker incen-tives to be profitable, so comparing the supervised and unsu-pervised within that group is also a fair test.
The rest of the paper is organized as follows. In Section 2 wedescribe our data and present the relationships between MFIcharacteristics (e.g., size and lending methodology), MFI per-formance (profitability and outreach), and our regulatory vari-ables. Those relationships help to form the profiles forregulated versus un-regulated MFIs. Because the profiles inSection 2 indicate strongly that the assignment of regulationand supervision to MFIs is non-random, we discuss estimationtechniques that can account for selection effects in Section 3.We present regression results in Section 4, robustness checksbased on split-sample tests in Section 5, and offer concludingremarks in Section 6.
DOES REGULATORY SUPERVISION CURTAIL MICROFINANCE PROFITABILITY AND OUTREACH? 951
2. DATA
The analysis relies on data from 346 microfinance institu-tions (MFIs) in 67 developing countries that were collectedby the Microfinance Information eXchange (or the MIX), anot-for-profit private organization that aims to promote infor-mation exchange in the microfinance industry. 1 There are 540observations in our database because some MFIs report infor-mation for multiple years. In the regressions that follow, weuse only the most recent observation from each MFI becausethe error terms from observations from the same MFI arelikely to be correlated, leading to an artificial reduction inthe standard errors of the estimated coefficients. Qualitativeresults are similar when we include all observations in theregressions and cluster standard errors at the MFI level. Allbut one of the most recent observations are from 2003 or2004. 2
Participation by microfinance institutions in the MIX is vol-untary, and thus the sample is skewed toward institutions thathave stressed financial objectives and profitability. These insti-tutions also tend to be large by the standard of MFIs, covering16.1 million active microfinance borrowers with a combinedtotal of $2.8 billion in assets. Most of the clients (10 million)are found in the top 20 largest institutions. Honohan (2004)finds that the largest 30 MFIs account for more than three-quarters of the customers of 2,572 MFIs that report to theMicrocredit Summit. While we cannot be certain, it seemshighly likely that our sample covers a significant proportionof the customers in that database. The relatively large, profit-
Table 1. Regulat
Panel A: Summary statistics
Regulation (from MBB database)Regular reporting (constructed here)Onsite supervision (constructed here)Regular onsite supervision (constructed here)Country Obs Onsite
supervision (%)Regular onsitesupervision (%)
Panel B: Distribution by country and region, selected variables
Albania 2 100 50Armenia 5 0 0Azerbaijan 3 0 0Bangladesh 4 0 0Bolivia 9 100 22Bosnia and Herz. 10 0 0Brazil 5* 20 0Bulgaria 2 0 0Ecuador 16 100 100Egypt, Arab Rep. 3 0 0El Salvador 3 100 0Ethiopia 15 100 100Georgia 6 0 0Ghana 18 67 67India 7 100 100Indonesia 12 92 92Jordan 4 0 0Kazakhstan 2 0 0
Region Obs
East Asia & Pacific 38 MEurope & Central Asia 52Latin America & Caribbean 71*
* One less observation for regular onsite supervision.
able MFIs in our dataset are likely to be in the best position toabsorb the costs of prudential supervision. If we find evidencethat they decrease outreach to help absorb those costs, wewould presume that the smaller MFIs not covered in our data-set would face even more severe tensions.
The data are collected for publication in the MicrobankingBulletin (MBB) and have been adjusted to help ensure compa-rability across institutions when measuring profitability. 3 Inaddition to standard entries from the balance sheet and in-come statements, the dataset contains qualitative informationon the lending style employed by the MFI (group versus indi-vidual-based lending), the range of services it offers, its profitstatus, ownership structure, charter status, and sources offunds. Many of these serve as important controls in the regres-sions that follow.
The key variables in the analysis summarize whether anMFI faces prudential supervision. As already pointed out,most MFIs face some form of non-prudential regulation,and thus it is not surprising that the average for the regulatorydummy variable that is included in the MBB and our dataset is0.85 (Table 1). 4 It also comes as no surprise that, due to itsuniformity, the simple regulation variable cannot explain sub-stantial variation in MFI profitability or outreach. What isneeded is a variable that better summarizes variation in thecost of complying with regulation. Toward that end, we offerthree dummy variables indicating whether (1) an MFI faces aregular reporting requirement to a regulatory authority; (2)the MFI faces onsite supervision; and (3) onsite supervisionoccurs at regular intervals. Because MFIs that face onsite
ory variables
Observations Mean
225 0.85220 0.68245 0.51244 0.38
Country Obs Onsitesupervision (%)
Regular onsitesupervision (%)
Kenya 2 100 50Kyrgyz Republic 1 100 0
Malawi 2 0 0Mexico 4 75 0
Mongolia 1 100 100Nepal 1 100 0
Nicaragua 10 10 0Pakistan 14 0 0
Peru 24 63 63Philippines 24 38 38
Russian Federation 15 73 0Serbia and Montenegro 3 0 0
South Africa 1 100 0Tajikistan 2 100 0Tanzania 5 20 20Thailand 1 0 0Uganda 8 63 13Ukraine 1 0 0
Region Obs
iddle East & North Africa 7South Asia 26
Sub-Saharan Africa 51
952 WORLD DEVELOPMENT
supervision also have a regular reporting requirement tosupervisory authorities, the set of MFIs under supervisionare a subset of those with reporting requirements. Similarly,MFIs that face regular onsite supervision are a subset of thosethat face any onsite supervision. Moving from the least to themost stringent type of supervision, 68% of our sample have areporting requirement, 51% face onsite supervision, and 38%face onsite supervision at regularly scheduled intervals.
To construct our regulatory variables, we use the legal (orcharter) status of each MFI. That variable classifies institu-tions into one of five types: banks, rural banks, credit unionsand cooperatives, non-bank financial institutions (NBFIs),and non-governmental organizations (NGOs). We thenlooked at the description of the regulation faced by MFIsfor each country as described on the MIX website. A quickperusal of those country pages indicates that the stringencyof regulation faced by MFIs within a given country often de-pends on their legal status. In instances when we could notdetermine whether an institutional type faced a reportingrequirement, onsite supervision, or regular onsite supervisionfrom the MIX regulatory descriptions, we checked the web-sites of the regulatory authorities themselves. In the end wewere able to obtain data for the onsite supervision variablesfor 245 MFIs, and 220 MFIs for the reporting requirementvariable. Table 1 also provides statistics at the country levelfor the shares of MFIs facing onsite supervision and regularonsite supervision. The regional composition of the sampleis provided at the bottom of the table.
Table 2. Sample comparison, super
Variable Faces onsitesupervision
NGO dummy 0.112(0.31)
Obs. 125Accepts deposits dummy 0.74
(0.43)Obs. 124
Individual-based lender 0.51(0.50)
Obs. 125Total assets ($US millions) 55.0
(317.0)Obs. 117
Financial self-sufficiency 1.05(0.31)
Obs. 113Age 11.32
(9.3)Obs. 125
Average loan size(relative to per capita income of bottom quartile)
3.3(4.9)
Obs. 125% Women borrowers 63.59%
(27.4%)Obs. 84
% of Staff in head office 45.7%(40.0%)Obs. 68
% of Staff that are loan officers 52.1%(18.8%)
Obs. 120Operating expenses/gross loan portfolio 21.7%
(20.8%)Obs. 124
Comparing the characteristics of supervised and unsuper-vised institutions indicates strongly that the assignment of pru-dential supervision is non-random. In Table 2, we compareMFIs that face onsite supervision with those that do not be-cause that regulatory variable splits our sample roughly inhalf. More detailed descriptions of the distributions and con-struction of all of the variables used in the analysis are foundin Appendix A. Christen et al. (2003) argue that prudentialsupervision should be triggered when an MFI accepts retaildeposits from the public and in our sample 74% of those thataccept deposits face onsite supervision compared with only14% for those that do not accept deposits. A much highershare of those with onsite supervision are non-NGOs that lendto individuals (rather than groups), make larger loans, lendless to women, and have a higher share of staff concentratedin the head office (and thus fewer staff with contact with clientsin the field). Supervised MFIs also tend to be larger (in termsof assets), and a bit older and more profitable (in terms ofFinancial Self Sufficiency) than unsupervised MFIs. However,these differences are not significant for total assets or FSS. Theprofile that emerges is that more commercially-oriented MFIstend to be supervised, while the more outreach-oriented MFIsare not.
These summary statistics foreshadow our main regressionresults. Namely, while there is no significant difference in prof-itability, there is significantly less outreach (larger loan sizesand less lending to women) for supervised MFIs than unsuper-vised ones. These simple statistics are, therefore, also
vised versus unsupervised MFIs
Does not face onsitesupervision
Difference inmeans (t test significance at 95 CI)
0.76(0.42)
Obs. 120
Yes
0.14(0.35)
Obs. 120
Yes
0.2(0.40)
Obs. 120
Yes
10.1(36.2)
Obs. 113
No
1.01(0.37)
Obs. 115
No
8.71(4.96)
Obs. 120
Yes
1.35(2.55)
Obs. 117
Yes
73.1%(29.4%)
Obs. 118
Yes
32.5%(32.4%)Obs.96
Yes
58.7%(14.5%)Obs.119
Yes
38.8%(117.1%)Obs. 119
No
DOES REGULATORY SUPERVISION CURTAIL MICROFINANCE PROFITABILITY AND OUTREACH? 953
consistent with the idea that supervised MFIs are compelled tocurtail outreach to maintain profitability.
Operating expense ratios for supervised institutions tend tobe lower than for the unsupervised. At first blush this mightseem to contradict an important part of our story, namely thatcomplying with regulation and supervision is costly for MFIs.However, operating expense ratios tend to be higher for insti-tutions that make smaller loans and lend more to women pre-cisely because those market segments are harder to reach(Cull, Demirguc�-Kunt, & Morduch, 2007; Cull, Demirguc�-Kunt, & Morduch, 2009; Gonzalez, 2007). Table 2 therefore,indicates that the costs of complying with onsite supervisionare not large enough to push the cost profiles of the more com-mercially-oriented MFIs beyond those of the outreach-ori-ented MFIs. Yet this does not necessarily indicate that thecosts of complying with supervision are negligible or that com-mercially-oriented MFIs do not seek to defray those costs bymaking larger loans and lending less to women. In any event,the operating cost levels for the two groups are not signifi-cantly different from one another.
3. ESTIMATION TECHNIQUE
Estimating the effects of regulation and supervision on MFIoutreach and profitability calls for a technique that can ac-count for the non-random assignment of supervision high-lighted in the prior section. We opt for treatment effectsregression which considers the effect on an endogenously cho-sen binary treatment (in this case, the choice to regulate andsupervise an MFI) on another endogenous continuous vari-able (in this case, indicators of MFI profitability and out-reach), conditional on two sets of independent variables. Thefirst set of independent variables is used to estimate a selectionequation that describes the supervisory choice. Informationfrom the selection equation is then used in the financial devel-opment regression. The key difficulty is in finding an appropri-ate set of exogenous variables for use in the selection equation.As a check on our results, we also offer instrumental variablesregressions.
In many Heckman-type selection models, the dependentvariable is observable only for those individuals (or house-holds or MFIs) that received the treatment. In this analysis,indicators of profitability and outreach are observable forMFIs that do and do not face supervision. Treatment effectsmodels are, therefore, estimated in which:
Y i ¼ aþ bX i þ dZi þ ei; ð1Þwhere Y is an indicator of profitability or outreach and X is amatrix of control variables describing an MFI’s size, lendingtechnology, business orientation, and region. Z is the endoge-nous treatment variable indicating whether or not MFI i facessupervision. As is typical in this literature, the decision tosupervise is modeled as the outcome of an unobserved latentvariable Z*, which is a function of exogenous covariates Wand a random component u:
Z�i ¼ cW i þ ui: ð2ÞThe researcher observes:
Zi ¼ 1; if Z�i > 0;
Zi ¼ 0; otherwise:ð3Þ
Because the selection of MFIs for supervision is non-random,and because the error term of the model that summarizes thischoice (namely, 2) could be correlated with the error term in
the regression of interest (namely, 1), one must search for aset of valid instruments. These instruments should be highlycorrelated with the endogenous regressor (the supervisorydummy), but contemporaneously uncorrelated with the errorterm in (1) (i.e., truly exogenous).
We use three variables as instruments in the regressions thatfollow. The first is a dummy variable indicating whether largeand medium-sized banks have annual (or more frequent) on-site supervision (“Big Bank Supervision”). The variable isbased on survey responses from bank supervisors in more thanone hundred countries that were collected by Barth, Caprio,and Levine (2006). 5 We view this variable as a measure of acountry’s general propensity to regulate the banking indus-try. 6 In countries with a high propensity to regulate banks,we expect that MFIs will also be more likely to face regulationand supervision. The variable is exogenous in that the propen-sity to regulate banks existed before the MFIs arrived. Evenhad MFIs and large banks arrived contemporaneously, it ishighly unlikely that the regulation of MFIs would have influ-enced regulation of banks, owing to the small size of the MFIs.
As a robustness check on our main results, we replace theBig Bank Supervision dummy variable with an index of theofficial powers of bank supervisors, also developed by Barthet al. (2006). That index, which is described in detail in Appen-dix B, is based on 12 questions about the powers granted tosupervisors in monitoring and disciplining banks. Thoughthe qualitative results of those models are similar to those thatuse Big Bank Supervision as an instrument, we have 40% fewerobservations for the Official Supervisory Powers (“OS”) index,and thus significance levels tend to be lower, model conver-gence is sometimes problematic, and excludability is harderto demonstrate. 7 To conserve space, we do not present thoseresults below.
Because the Big Bank Supervision dummy variable and theOS index do not explain sufficient variation in the assignmentof supervision, neither can serve as the only instrument in ourregressions. 8 In part, this is because both are country-levelvariables and, as discussed above, there is substantial within-country variation in the types of MFIs that face supervision.We also, therefore, need instruments that provide MFI-spe-cific information.
Our first MFI-level instrument is a dummy variable indicat-ing whether each MFI is organized as a non-governmentalorganization (NGO) or a non-bank financial institution(NBFI). MFIs with NGO/NBFI charters tend to have objec-tives and funding arrangements that differ from those of morecommercially-oriented MFIs (such as banks or credit unions).In particular, NGO/NBFI-based MFIs place greater emphasison outreach and rely relatively heavily on donated funds tosubsidize those efforts (Cull et al., 2009). Because NGO-basedMFIs were designed to be somewhat less commercially-ori-ented from their inception, we expect that there would be lessneed for supervision aimed at ensuring the quality of their as-set portfolios. 9
In the regressions, we use a dummy variable indicating thatan MFI was not organized as a NGO/NBFI (non-NGO/NBFIstatus) as an instrument. We recognize that our grouping issomewhat arbitrary in that we are lumping together banks,cooperatives, and credit unions and differences in their organi-zational structures might give rise to different incentives. How-ever, we have relatively few cooperatives and credit unions inthe sample and the results of the regressions are very similarwhen they are dropped. 10 As expected, non-NGO/NBFI statusis strongly positively linked to our primary supervisory dum-my variable, onsite supervision at regular intervals (correlation.40, p-value 0.000). Because charter status was determined at
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the outset of the creation of each MFI, prior to and withoutsubstantial consideration for whether the MFI would facesupervision, non-NGO/NBFI status can be viewed as exoge-nous. In some specifications, we interact non-NGO/NBFI sta-tus with the Big Bank Supervision variable or the OSindex. 11 The intuition behind the interaction is that supervi-sion should be especially likely for non-NGO/NBFI MFIs lo-cated in countries where the general propensity to regulate andsupervise providers of financial services is high.
As noted by Christen et al. (2003), prudential supervisionshould generally be triggered when an MFI accepts retaildeposits from the general public so as to safeguard the savingsof relatively poor depositors. We, therefore, use a dummy var-iable indicating whether an MFI accepts retail deposits (sav-ings dummy) as our second MFI-level instrument. 12 Likenon-NGO/NBFI status, we presume that the decision to acceptdeposits was, in most instances, taken at the outset of the cre-ation of the MFI. Also similar to non-NGO/NBFI status, weinteract the savings dummy with the Big Banks Supervision var-iable and the OS index. MFIs that accept deposits and operatein countries with a high propensity to regulate should beamong those most likely to face supervision. 13
Most of the control variables in the X matrix in Eqn. (1) arethe same as those used in other studies of MFI performanceand outreach (Ahlin and Lin, 2006; Cull et al., 2007). Onenew variable is Premium, the difference between the interest
Table 3. The effect of regulatory supervision o
OLS (White SE) Treatm
Regular onsite supervision �0.176*** �(3.60) (
Premium 0.808*** 0.(3.83) (
Capital costs to assets �1.477*** �1(3.56) (
Labor costs to assets �1.684*** �1(4.91) (
Village bank 0.055 0(1.12) (
Solidarity �0.055 �(1.34) (0
Average loan size 0.003 0(0.40) (
Size indicator 0.004 �(0.13) (
Log of age 0.029 0(0.75) (
Observations 167R2 0.3799Prob > chi 0Prob > F 0.000
LR test of independent equations: Prob > chi 0Anderson LR statistic(identification/IV relevance): Chi-sq P-val
Excludability
Instrument Non-NBBig bank
Significant in OLS? P =
Notes: The regressions also include inflation, real GDP growth, the KKMconstant. Observations where premium, FSS, or average loan size (relative todropped from the regressions. Rural banks are also excluded from the sample. Treplaces the regular onsite supervision variable. The hope is that the coefficient*, **, and *** represent statistical significance at the 10%, 5%, and 1% level r
rates an MFI charges its borrowers and the rate it pays onits own liabilities. The interest rate charged on loans equals to-tal interest revenue divided by the gross loan portfolio. Be-cause loan losses are not netted out of the interest revenues,this measure is intended to capture the ex ante interest ratecharged by the lender rather than the ex post interest rate real-ized on the portfolio. Interest paid on liabilities equals totalinterest payments divided by commercial liabilities. All elseequal, a higher premium is presumably associated with greaterprofitability.
Eqn. (1) also includes labor costs and capital costs, bothmeasured relative to total assets. 14 Both variables should benegatively associated with profitability. Solidarity is a dummyvariable equal to one if an MFI makes joint liability loans tosolidarity groups. Loans are made to individuals, but thegroup, which has between 3 and 10 members depending onthe institution and location, shoulders responsibility for aloan if a member cannot repay. Village Bank is a dummyvariable equal to one if the MFI does village banking, whereeach branch forms a single, large group and is given a degreeof self-governance (this kind of arrangement was pioneeredby FINCA and is now employed by organizations like ProMujer and Freedom from Hunger). MFIs that make stan-dard bilateral loans to individuals (the so-called individual-based lenders), therefore, form the omitted category in ourregressions.
n MFI financial self-sustainability (FSS)
Dependent variable: FSS
ent effects IV Treatment effects
0.045 0.078 0.0070.53) (0.70) (0.06)
733*** 0.738*** 0.745***
3.54) (3.63) (3.59).315*** �1.158** �1.330***
2.66) (2.50) (2.68).754*** �1.525*** �1.7404.12) (4.23) (4.08).102* 0.152*** 0.104*
1.80) (2.69) (1.82)0.031 �0.006 �0.030.466) (0.15) (0.70).001 0.009 0.001
0.14) (1.19) (0.13)0.0001 �0.019 �0.0080.04) (0.65) (0.23).047 0.048 0.041
1.38) (1.37) (1.19)
154 154 154
.0000 0.00000.0000
.2607 0.25270.0000
FI/NGO �s supervision
Non-NBFI/NGO � Big bankssupervision
Savings Dummy �Big banks supervision
0.743 P = 0.743 P = 0.311
measure of institutional development, regional dummy variables, and athe bottom quintile) ranked above the 99th or below the 1st percentile arehe excludability test is based on an OLS regression in which the instrumenton the instrument is insignificant, and thus it can be viewed as excludable.espectively.
DOES REGULATORY SUPERVISION CURTAIL MICROFINANCE PROFITABILITY AND OUTREACH? 955
Additional MFI-level controls include Age (measured inyears since inception), Size (as measured by total assets),and Average Loan Size (relative to GNP per capita). We expectall three variables to be positively linked to MFI profitabil-ity—age and size because they reflect how well establishedthe MFI is, and average loan size because making relativelylarge loans to fewer customers is likely to be more efficientthan making large numbers of small loans (as discussedabove). The size indicator is also included to control for econ-omies of scale that larger MFIs might enjoy.
Country-level control variables include Inflation, real GDPGrowth, and a measure of broad institutional development cre-ated by Kaufmann, Kraay, and Mastruzzi (2007). We expectthe KKM Institutional Development and GDP Growth variablesto be positively linked to MFI profitability, and Inflation tohave a negative relationship. Finally, region is a matrix ofdummy variables for each main region of the developingworld, with “Latin America and the Caribbean” as the omit-ted category.
4. RESULTS: OLS VERSUS TREATMENT EFFECTSREGRESSIONS
Due to the non-random assignment of supervision to MFIsdiscussed in Section 2, this section highlights the differences inresults from estimation techniques that account for selectioneffects (treatment effects and IV regressions) with those fromone that does not (OLS). We discuss regressions that explainprofitability, outreach, and staffing in separate sub-sections.
(a) Profitability
The OLS regression in Table 3 (model 1) indicates that, asexpected, there is a negative relationship between FinancialSelf-Sufficiency (FSS) and our supervisory variables. 15 Weuse FSS instead of operational self-sufficiency (OSS), anothercommonly used measure of MFI performance, because FSSbetter incorporates making a return on capital that is marketrelated and commensurate with the risks involved. 16 Thus, theadditional costs of complying with prudential supervision areassociated with reduced financial self-sufficiency. Significancelevels and the magnitudes of some coefficients are slightlyhigher for the regular onsite supervision dummy variable thanfor the simple onsite supervision variable, a pattern consistentwith regular supervision being more stringent, and thus cost-lier. The associations are also economically large: MFIs facingregular onsite supervision have FSS levels .18 lower than otherMFIs. The sample mean for FSS is 1.03. Because the qualita-tive results for the simple onsite dummy and the regular onsitedummy are so similar, we report results only for the regularonsite supervision variable in the tables to conserve space.
When we control for the non-random assignment of pruden-tial supervision via treatment effects and IV regression (models2–4 in Table 3), there is no significant relationship between thesupervision variables and FSS. This suggests that those MFIsthat are both outreach-oriented and supervised cannot or donot adjust to the increase in costs imposed by supervision,and are driving the negative significant coefficient for supervi-sion in the OLS regression. On the flip side, the treatment ef-fects and IV regressions suggest that the profit-oriented MFIsthat are likely to be selected for supervision find ways to main-tain their profitability. Below we test whether those methodsinclude making larger loans and lending less to women.Although our instruments are all highly significant in the selec-tion equations and of the sign that we predicted, we cannot re-
ject the hypothesis that the errors for the selection equationare uncorrelated with those for the FSS regression based onthe likelihood ratio test near the bottom of the treatment ef-fects regressions. 17 Both of the instruments used in Table 3are excludable in that they are not significant when they re-place the supervision variable in the simple OLS regression.
Regarding the control variables in the FSS regressions, theinterest premium is positive and highly significant across allspecifications. Labor and capital costs are negative and signif-icant across specifications. None of the additional MFI-levelcontrols (age, size, average loan size) is significant. However,village banks tend to have higher FSS in some of the treatmenteffects and IV regressions.
Inflation is negative and significant in most regressions, inline with findings from Ahlin and Lin (2006) that the macro-economic context is a key determinant of MFI performance. 18
Like those authors, we also find that real growth is positiveand more highly correlated with FSS than inflation. Ashypothesized, the KKM measure of institutional developmentis positive and significant in three of the regressions.
Qualitative results are similar for regressions in Table 4where the dependent variable is return on assets (ROA). 19
Again, the supervision variables are negative and significantin the OLS regression, but insignificant in the treatment effectsand IV regressions. The results for the control variables aresimilar to those for the FSS regressions, although the inflationcoefficient is no longer significant and the growth variabletends to be less significant. The ROA regressions indicatemore strongly than the FSS regressions that profitability islower for MFIs located in Sub-Saharan Africa, East Asia,and the Middle East and North Africa. The instruments areagain excludable in that they are not significant when includedin the simple OLS regressions. We again cannot reject thehypothesis that the errors for the selection equation are uncor-related with those for the ROA regression based on the likeli-hood ratio test near the bottom of the treatment effectsregressions.
(b) Outreach
In the previous sub-section we found no significant relation-ship between supervision and MFI profitability when we con-trolled for the non-random assignment of supervision viatreatment effects or IV regressions. Here we test whether MFIsmaintain profitability while absorbing the additional costs ofsupervision by curtailing outreach, specifically whether theymake larger loans and fewer loans to women.
In the OLS regression in Table 5, the supervisory variable isnegatively associated with average loan size measured relativeto the income of the bottom quintile in each country, thoughthe result is not significant. As in the profitability regressions,results change when we account for the non-random assign-ment of supervision using treatment effects or IV regressions(models 2–4). Those regressions indicate a positive link be-tween supervision and loan size (significant in models 2 and3). 20 The magnitude of the supervisory coefficients is alsoquite large. Regular onsite supervision is associated with in-creases in loan sizes almost two times the average income ofthe lowest quintile in model 2. Coefficients are even larger inthe IV regression (model 3). 21
Comparisons between the OLS and treatment effects regres-sions suggest that those MFIs that are both outreach-orientedand supervised are driving the negative, nearly significant coef-ficient between supervision and average loan size in the OLSregression. Finally, our instruments are all highly significantin the selection equations and of the sign that we predicted,
Table 4. The effect of regulatory supervision on MFI adjusted return on assets
Dependent variable: ROA
OLS (white SE) Treatment effects IV Treatment effects
Regular onsite supervision �0.058*** �0.026 0.005 0.0007(5.07) (1.14) (0.19) (0.02)
Premium 0.337*** 0.347*** 0.348*** 0.353***
(4.96) (6.56) (4.85) (6.54)Capital costs to assets �0.433*** �0.452*** �0.411*** �0.463***
(3.25) (3.57) (2.58) (3.59)Labor costs to assets �0.761*** �0.818*** �0.756*** �0.820***
(4.71) (7.52) (4.83) (7.43)Village bank 0.006 0.016 0.029 0.017
(0.38) (1.12) (1.38) (1.18)Solidarity 0.007 0.010 0.017 0.011
(0.55) (0.98) (1.16) (1.02)Average loan size 0.001 0.0003 0.002 0.00004
(0.37) (0.13) (1.35) (0.02)Size indicator 0.001 �0.002 �0.007 �0.005
(1.42) (0.27) (0.91) (0.52)Log of age �0.004 �0.001 �0.001 �0.003
(0.47) (0.19) (0.17) (0.42)
Observations 167 154 154 154R2 0.5069 0.4879Prob > chi 0.0000 0.0000Prob > F 0.0000 0.0000
LR test of independent equations: Prob > chi 0.2634 0.2050Anderson LR statistic (identification/IV relevance): Chi-sq P-val
0.0000
Excludability
Instrument Non-NBFI/NGO �Big banks supervision
Non-NBFI/NGO �Big banks supervision
Savings dummy �Big banks supervision
Significant in OLS? P = 0.417 P = 0.417 P = 0.538
Notes: The regressions also include inflation, real GDP growth, the KKM measure of institutional development, regional dummy variables, and aconstant. Observations where premium, adjusted ROA, or average loan size (relative to the bottom quintile) ranked above the 99th or below the 1stpercentile are dropped from the regressions. Rural banks are also excluded from the sample. The excludability test is based on an OLS regression in whichthe instrument replaces the regular onsite supervision variable. The hope is that the coefficient on the instrument is insignificant, and thus it can be viewedas excludable. *, **, and *** represent statistical significance at the 10%, 5%, and 1% level respectively.
956 WORLD DEVELOPMENT
and we can reject the hypothesis that the errors for the selec-tion equation are uncorrelated with those for the average loansize regression based on the likelihood ratio test near the bot-tom of the treatment effects regressions. Thus, some adjust-ment for the non-random assignment of supervision isnecessary.
Excludability of our instruments is, however, a somewhatthornier issue for the average loan size regressions than itwas in the profitability regressions. For example, the interac-tion between the savings dummy and big banks’ supervision issignificant in the simple OLS regression at the p = .01 level,indicating that it is unsuitable for use as an instrument. Theinteraction between non-NGO/NBFI and big banks’ supervisionis insignificant, but the p-value is a bit too close for comfort(.12). However, recognizing that excludability is a problemin the average loans size regressions, there are some in whichthe problem is less severe, and the results support the notionthat profit-oriented MFIs that face onsite supervision extendsubstantially larger loans than those that do not face supervi-sion.
Results for the control variables in Table 5 are different thanin the profitability regressions. The MFI size indicator, basedon total assets, is strongly positively associated with largerloan size. Premium is negative and highly significant indicating
that MFIs require a wider margin between lending and bor-rowing interest rates to make small loans. All else equal, vil-lage banks and, to a lesser extent, solidarity group lenderstend to extend smaller loans. Regional dummies also play amore important role in the loan size regressions than in theprofitability regressions. Negative significant coefficients formany regions indicate that loan sizes tend to be larger in LatinAmerica and the Caribbean, the omitted region in the regres-sions. Coefficients for the other regions indicate that loan sizesare smallest in Eastern Europe and Central Asia, South Asia,and the Middle East and North Africa.
The results for the share of lending to women provide strongsupport for the notion that profit-oriented MFIs that face on-site supervision curtail outreach (Table 6). In the OLS regres-sion, there is a negative relationship between onsitesupervision and the share of lending to women. In the treat-ment effects and IV regressions, there is also a significant neg-ative relationship between supervision and lending to women,but the coefficients are much larger (in absolute value) than inthe OLS regression. Again, our instruments are significant inthe selection equations and of the sign that we predicted.Moreover, none of our instruments are significant (at thep = .10 level) when they replace the supervision variable inthe OLS regression, which indicates that they are excludable,
Table 5. The effect of regulatory supervision on MFI average loan size (relative to income of bottom quintile)
Dependent variable: average loan size/income per capita of bottom quartile
OLS (white SE) Treatment effects IV Treatment effects
Regular onsite supervision �1.072 1.751*** 4.293* 0.952(1.55) (2.85) (1.73) (1.02)
Premium �7.191*** �7.498*** �8.681*** �7.007***
(3.91) (4.63) (3.42) (3.72)Capital costs to assets �2.827 �0.032 2.469 �1.792
(0.94) (0.01) (0.51) (0.38)Labor costs to assets 1.247 2.792 8.956* 2.066
(0.41) (0.78) (1.77) (0.51)Village bank �1.215*** �1.058** �0.156 �1.303**
(3.76) (2.14) (0.22) (2.41)Solidarity �0.625 �0.549 0.163 �0.445
(1.53) (1.45) (0.27) (1.09)Size indicator 1.251*** 1.041*** 1.080** 1.198***
(3.41) (3.21) (2.13) (3.50)Log of age �0.181 0.163 0.013 �0.112
(0.69) (0.52) (0.04) (0.34)
Observations 167 154 154 154R2 0.3811 0.3586Prob > chi 0.0000 0.0000Prob > F 0.0000 0.0000
LR test of independent equations: Prob > chi 0.0000 0.004Anderson LR statistic (identification/IV relevance): Chi-sq P-val
0.0000
Excludability
Instrument Non-NBFI/NGO �Big banks supervision
Non-NBFI/NGO �Big banks supervision
Savings dummy �Big banks supervision
Significant in OLS? P = 0.121 P = 0.121 P = 0.001
Notes: The regressions also include inflation, real GDP growth, the KKM measure of institutional development, regional dummy variables, and aconstant. Observations where premium, FSS, or average loan size (relative to the bottom quintile) ranked above the 99th or below the 1st percentile aredropped from the regressions. Rural banks are also excluded from the sample. The excludability test is based on an OLS regression in which the instrumentreplaces the regular onsite supervision variable. The hope is that the coefficient on the instrument is insignificant, and thus it can be viewed as excludable. *,**, and *** represent statistical significance at the 10%, 5%, and 1% level respectively.
DOES REGULATORY SUPERVISION CURTAIL MICROFINANCE PROFITABILITY AND OUTREACH? 957
though the p-value is .11 for one of them. For model 2, whichuses big banks’ supervision x non-NGO/NBFI as an instrument,we can also reject (at the p = .05 level) the hypothesis that theerrors from the selection equation are uncorrelated with thosefrom the share of lending to women regression based on thelikelihood ratio test near the bottom of the treatment effectsregressions.
The relative importance of the control variables is differentin the women’s lending share regressions than in the averageloan size regressions. MFI size is not significantly associatedwith the share of lending to women. However, lending methodis important; solidarity group lenders and village banks devote7–20% points more of their loans to women than do individ-ual-based lenders based on the coefficients in Table 6. The pre-mium variable is also highly significant indicating that theinterest rates charged on loans to women are relatively high.Finally, the share of lending to women tends to be higherfor MFIs in South Asia than those in other regions. In all,the results for both average loan size and the share of lendingto women indicate that, once we control for its non-randomassignment, prudential supervision is associated with less out-reach.
(c) Staffing
As noted in the introduction, the administrative work asso-ciated with preparing regular reports for supervisors is likely
to be done in headquarters resulting in less staff working di-rectly with clients in the field. To test that proposition, weuse the percentage of staff located in headquarters as a depen-dent variable in the regressions in Table 7. We find that regularonsite supervision is positively associated with the share ofstaff located in headquarters. In the treatment effects and IVregressions, that relationship is even stronger.
Though the results point in the predicted direction, we mustacknowledge that we cannot reject the hypothesis that the er-rors from the selection equation and the % headquarters staffequation are independent based on the likelihood ratio for thetreatment effects regressions, and big banks’ supervision � non-NGO/NBFI is significant in the OLS regression, indicating thatit is an unsuitable instrument. However, the regressions pro-vide some evidence consistent with the idea that supervisedfirms re-deploy personnel away from the field to comply withregulation. This re-deployment could also contribute to re-duced outreach.
As a final exercise, we used the share of the loan portfoliothat was delinquent at least thirty days as a dependent vari-able. Though our results to this point are consistent with theidea that prudential supervision imposes costs on MFIs, itmight also be true that it helps ensure the safety of deposits,which could be an important goal given that MFIs tend tohave small depositors as customers. In OLS regressions, therelationship between regular onsite supervision and delinquentportfolio share is positive and significant, indicating perhaps
Table 6. The effect of regulatory supervision on fraction of borrowers that are women
Dependent variable: fraction women borrowers
OLS (white SE) Treatment effects IV Treatment effects
Regular onsite supervision �0.172*** �0.320*** �0.366*** �0.350***
(3.01) (3.56) (2.56) (2.22)
Premium 0.507*** 0.530*** 0.557*** 0.525***
(2.77) (2.90) (2.66) (2.80)Capital costs to assets 0.072 0.015 �0.102 0.035
(0.17) (0.03) (0.21) (0.07)Labor costs to assets �0.272 �0.1038 �0.495 �0.154
(0.62) (0.24) (1.06) (0.35)Village bank 0.159*** 0.189*** 0.177*** 0.195***
(2.89) (3.79) (3.16) (3.75)Solidarity 0.070* 0.085** 0.071 0.087**
(1.72) (2.10) (1.60) (2.14)Size indicator �0.017 �0.0005 �0.004 �0.003
(0.54) (0.02) (0.14) (0.10)Log of age 0.003 0.009 0.001 0.009
(0.08) (0.28) (0.04) (0.26)
Observations 134 121 121 121R2 0.4265 0.3795Prob > chi 0.0000Prob > F 0.0000 0.0000
LR test of independent equations: Prob > chi 0.0438 0.6157Anderson LR statistic (identification/IV relevance): Chi-sq P-val
0.0000
Excludability
Instrument Non-NBFI/NGO �Big banks supervision
Non-NBFI/NGO �Big banks supervision
Savings dummy �Big banks supervision
Significant in OLS? P = 0.111 P = 0.111 P = 0.306
Notes: The regressions also include inflation, real GDP growth, the KKM measure of institutional development, regional dummy variables, and aconstant. Observations where premium, FSS, or average loan size (relative to the bottom quintile) ranked above the 99th or below the 1st percentile aredropped from the regressions. Rural banks are also excluded from the sample. The excludability test is based on an OLS regression in which the instrumentreplaces the regular onsite supervision variable. The hope is that the coefficient on the instrument is insignificant, and thus it can be viewed as excludable. *,**, and *** represent statistical significance at the 10%, 5%, and 1% level respectively.
958 WORLD DEVELOPMENT
that supervised MFIs tend to take on more credit risk thanothers. In the treatment effects and IV regressions, the delin-quent portfolio share variable is no longer significant, suggest-ing that prudential supervision can contribute to improvedportfolio quality. However, data from only 37 MFIs enterthose regressions, which pass neither the simple excludabilitytest nor the likelihood ratio test indicating that the errors fromthe selection equation and the equation of interest are inde-pendent. We do not, therefore, present those results in the ta-bles and are reluctant to draw conclusions about the stabilitybenefits of the prudential supervision, which is a topic betterleft to future research.
5. SPLIT-SAMPLE ROBUSTNESS CHECKS
The ideal empirical test would involve otherwise identicalMFIs; some subjected to prudential supervision, others not.In practice, such an experiment is not possible, and thus thetreatment effects regressions are designed to approximate it.The differences between the OLS and treatment effects regres-sions in the previous section provide an indication that super-vision has a causal effect on the trade-off between profitabilityand outreach. For a number of those regressions our instru-ments also satisfy standard statistical tests of excludability.Still, one cannot help but worry that the treatment effectsand IV regressions are picking up a distinction between types
of MFIs. Those with a relatively high-profitability, low-out-reach profile might also tend to be supervised. Supervisionmight just be a part of, rather than a cause of, the profile.
To address that concern, this section offers tests that splitour sample so that we can compare the supervised and unsu-pervised within a group of MFIs that are similar on non-supervisory dimensions. We use the “non-commercial fundingratio” to split our sample. That ratio is zero if all funds comefrom either commercial borrowing or deposit-taking. The ra-tio is 1 if the institution draws funds from neither source, in-stead relying on donations, borrowing at below-marketinterest rates (i.e., subsidized loans) or equity. 22 In a compan-ion paper, we find that MFIs with similar non-commercialfunding ratios also tend to have similar profitability and out-reach profiles (Cull et al., 2009). We conjecture that highershares of non-commercial funding coincide with softer budgetconstraints, and greater pursuit of outreach at the expense ofprofitability. By grouping MFIs with similar funding profiles,therefore, we are comparing the effects of prudential supervi-sion for MFIs with similar objectives and incentives.
Table 8, panel A offers summary statistics for our dependentvariables for commercial organizations, MFIs with non-com-mercial funding ratios <50%. If our story is correct, we wouldexpect supervised firms in this group to maintain profitabilitywhile absorbing the costs of supervision by curtailing out-reach. Table 8 shows no significant difference in the averageprofitability (FSS or Adjusted ROA) of commercial MFIs that
Table 7. The effect of regulatory supervision on MFI staff concentration
Dependent variable: staff concentration
OLS (white SE) Treatment effects IV Treatment effects
Regular onsite supervision 0.158* 0.321*** 0.321** 0.388***
(1.85) (2.81) (2.25) (2.82)Premium �0.629* �0.306 �0.315 �0.320
(1.87) (1.09) (1.30) (1.15)Capital costs to assets 1.041 0.613 0.620 0.572
(1.39) (0.97) (1.14) (0.90)Labor costs to assets �0.826 �0.181** �1.136*** �1.146**
(1.16) (2.08) (3.49) (2.02)Village bank �0.070 �0.005 �0.004 �0.005
(0.96) (0.08) (0.08) (0.08)Solidarity �0.118* �0.036 �0.037 �0.035
(1.86) (0.68) (0.77) (0.69)Size indicator �0.159*** �0.173*** �0.175*** �0.174***
(2.71) (3.71) (3.49) (3.77)Log of age �0.513 �0.033 �0.031 �0.040
(1.15) (0.85) (1.01) (1.00)Observations 101 92 92 92R2 0.5210 0.7306Prob > chi 0.0000 0.0000Prob > F 0.0000 0.0000LR test of independentequations: Prob > chi
0.7263 0.375
Anderson LR statistic(identification/IV relevance):Chi-sq P-val
0.0000
Excludability
Instrument Non-NBFI/NGO �Big banks supervision
Non-NBFI/NGO �Big banks supervision
Savings dummy �Big banks supervision
Significant in OLS? P = 0.028 P = 0.028 P = 0.182
Notes: The regressions also include inflation, real GDP growth, the KKM measure of institutional development, regional dummy variables, and aconstant. Observations where premium, FSS, or average loan size (relative to the bottom quintile) ranked above the 99th or below the 1st percentile aredropped from the regressions. Rural banks are also excluded from the sample. The excludability test is based on an OLS regression in which the instrumentreplaces the regular onsite supervision variable. The hope is that the coefficient on the instrument is insignificant, and thus it can be viewed as excludable. *,**, and *** represent statistical significance at the 10%, 5%, and 1% level respectively.
DOES REGULATORY SUPERVISION CURTAIL MICROFINANCE PROFITABILITY AND OUTREACH? 959
face onsite supervision and those that do not. 23 By contrast,the average outreach of commercial MFIs that face onsitesupervision is significantly less than that of those that donot. The difference is also reflected in simple correlation coef-ficients between onsite supervision and outreach measures:0.23 (p = .014) for average loan size; �0.38 (p = .0003) forshare of women borrowers; and 0.44 (p = .0003) for the shareof staff located in headquarters. Within a group of relativelyhomogeneous MFIs that have strong incentives to be profit-able, we, therefore, find evidence consistent with the notionthat supervised MFIs maintain a profitability level similar tounsupervised ones by curtailing outreach.
By contrast, within the group of non-commercially orientedMFIs (those with non-commercial funding ratios P50%), wewould expect that absorbing the costs of supervision wouldbe less likely to result in reduced outreach. And in fact, Table8, panel B shows no significant difference in average loan size,share of women borrowers, or share of staff in headquartersfor supervised and un-supervised non-commercial MFIs. Wewould, however, expect the costs of supervision to be reflectedin reduced profitability. Supervised non-commercial MFIs doin fact have lower average FSS than the unsupervised (0.86versus 1.00), though the difference is not quite significant atconventional levels. The simple correlation between supervi-sion and FSS for non-commercial MFIs is �0.19 (p = .07)for the regular onsite supervision variable and �0.14(p = .19) for onsite supervision. There are not, however, any
significant relationships between the supervisory variablesand adjusted ROA for this group of MFIs. Despite a some-what weak relationship between supervision and profitability,the results in panel B are broadly consistent with our story:supervised non-commercial MFIs do not tend to have pooreroutreach than unsupervised ones, and there is some evidencethat they are less profitable.
Grouping MFIs by the non-commercial funding ratio is areasonable, albeit rough method for examining the effects ofsupervision within a relatively homogeneous pool. However,within the group of commercial (or non-commercial) MFIs,supervised institutions might have systematically differentcharacteristics (size, age, lending methodology) than the unsu-pervised, which could be driving the differences in means thatwe found in Table 8. We, therefore, regress each of our depen-dent variables on the supervision variable and all of the othercontrol variables from the base regressions (Tables 3–7). To beconsistent with the base regressions, we use the regular onsitesupervision variable in Table 9, though results are qualita-tively similar for the onsite supervision variable.
For non-commercial MFIs (bottom row of coefficients) reg-ular onsite supervision is not significant in the average loansize, women borrowers, or headquarters staff specifications.Supervision is, however, strongly negatively linked to FSSand adjusted ROA, much more so than in the simple bi-variatecalculations. For commercial MFIs, supervision is significantand negative in the women borrowers’ regression, and signifi-
Table 8. Sample comparison of commercial organizations* (panel A) and non-commercial organizations* (panel B), supervised versus unsupervised MFIs
Variable Faces onsitesupervision
Does not faceonsite supervision
Difference in means(t test significance at 95 CI)
Panel A
Financial self-sufficiency 1.114(0.289)Obs.86
1.082(0.34)
Obs.34
No
Adjusted return on assets 0.016(.047)
Obs.84
0.005(0.094)Obs.33
No
Average loan size (relative to per
capita income of bottom quartile)3.638
(5.571)Obs.89
1.325(1.885)Obs.34
Yes
% Women borrowers 0.650(0.287)Obs.59
0.854(0.197)Obs.34
Yes
% of Staff in head office 0.507(0.432)Obs.51
0.178(0.141)Obs.24
Yes
Panel B
Financial self-sufficiency 0.863(0.358)Obs.20
1.004(0.434)Obs.71
No
Adjusted return on assets �0.047(0.134)Obs.19
�0.042(0.225)Obs.70
No
Average loan size (relative to per
capita income of bottom quartile)1.869
(1.067)Obs.21
1.420(3.011)Obs.70
No
% Women borrowers 0.602(0.231)Obs.14
0.686(0.310)Obs.71
No
% Staff in head office 0.287(0.165)Obs. 10
0.389(0.378)Obs.59
No
* All MFI’s that have a non-commercial funding ratio of 50% or greater are classified as non-commercial organizations. The others are classified ascommercial organizations. Standard deviations are in parentheses.
Table 9. Split-sample regressions
Financial self-sufficiencyratio
Adjusted returnon assets
Average loan size/incomeat 20th percentile
% of Borrowersthat are women
% of Staff locatedin head-quarters
(1) (2) (3) (4) (5)
Sample: non-commercial funding ratio <.5
Regular onsite supervision �0.096 �0.046*** �1.852 �0.190*** 0.225**
[0.06] [0.01] [1.20] [0.06] [0.10]Observations 93 93 95 68 50R2 0.35 0.52 0.44 0.59 0.80
Sample: non-commercial funding ratio P.5
Regular onsite supervision �0.527**** �0.157*** �0.226 �0.123 0.031[0.12] [0.03] [0.62] [0.11] [0.13]
Observations 68 68 71 63 51R2 0.71 0.76 0.45 0.50 0.43
Notes: The regressions also include interest rate premium, capital and labor costs (relative to assets), MFI size and age, dummy variables for village bankand solidarity group lender, inflation, real GDP growth, the KKM measure of institutional development, regional dummy variables, and a constant.Standard errors are in parentheses. Observations where premium, FSS, or average loan size (relative to the bottom quintile) ranked above the 99th orbelow the 1st percentile are dropped from the regressions. Rural banks are also excluded from the sample. *, **, and *** represent statistical significance atthe 10%, 5%, and 1% level respectively.
960 WORLD DEVELOPMENT
cant and positive in the percentage of staff in headquartersregression, results which reinforce the relationships suggestedby Table 8. The supervision variable is not, however, signifi-cant in the average loan size regression. Supervision is insignif-icant for commercial MFIs in the FSS regression, also
consistent with the bi-variate calculations. Supervision is neg-ative and significant in the ROA regression for commercialMFIs, but much smaller (in absolute value) than for non-com-mercial MFIs. This indicates that supervised commercialMFIs are more successful in maintaining their ROA than
DOES REGULATORY SUPERVISION CURTAIL MICROFINANCE PROFITABILITY AND OUTREACH? 961
supervised non-commercial MFIs, which is also consistentwith our story. By and large, the results from the split-sampletests confirm those from our base regressions.
6. CONCLUSIONS
To date, there has been relatively little discussion, at leastwithin academic circles, and almost no empirical analysis ofthe effects that prudential supervision is likely to have onMFI profitability and outreach. To address these issues, wecombine high-quality balance sheet and income statementdata for 245 leading MFIs with a newly constructed databaseof the type of supervision that each faces.
Strong patterns emerge from the supervisory data indicatingthat its assignment is non-random. Specifically, supervisiontends to be more stringent for commercially-oriented MFIs,non-NGOs that collect deposits from the public, lend to indi-viduals (rather than groups), make larger loans, have propor-tionately fewer female customers, and have a higher share ofstaff concentrated in the head office (and thus fewer staff withcontact with clients in the field).
Searching for appropriate instrumental variables is of coursedifficult because many of the characteristics that define super-vised MFIs are likely to be endogenous in profitability andoutreach regressions. We, therefore, offer three instrumentsfor use in treatment effects and IV regressions that have bear-ing on whether an MFI is regulated but do not directly affectMFI performance. The first is the general propensity to super-vise formal financial institutions in a country as reflected in adummy variable indicating whether large banks face onsitesupervision at regular intervals. The second is a dummy indi-cating that an MFI was not chartered as an NGO or NBFI,
since charter status was determined at the outset of the crea-tion of each MFI, prior to and without substantial consider-ation for whether the MFI would face supervision. Similarly,our third instrument is a dummy indicating whether an MFItakes deposits, because that decision was also presumably ta-ken at the inception of the MFI in most cases.
The selection stage of our treatment effects regressionsconfirms that non-NGO/NBFI’s that take deposits andare located in countries that supervise large banks at regu-lar intervals are most likely to face onsite supervision atregular intervals. Controlling for the non-random assign-ment via those instruments, we find that regular onsitesupervision is positively associated with average loan sizeand negatively associated with the share of lending to wo-men. Though onsite supervision is negatively associatedwith profitability in OLS regressions, we find no significantrelationship between supervision and profitability in treat-ment effects or IV regressions. The pattern of results isconsistent with the idea that profit-oriented MFIs that haveto comply with prudential supervision respond by curtailingtheir outreach to segments of the population that arecostlier to serve. 24 By contrast, MFIs that rely on non-commercial sources of funding (e.g., donations), and thusare less profit-oriented, do not adjust loan sizes or lend lessto women when supervised, but their profitability is signifi-cantly reduced. Split sample tests based on the share of fund-ing that an MFI receives from non-commercial sourcesconfirm this pattern. Though these results are intuitive froman economic perspective, it remains an open question whetherthe benefits of supervision in terms of better protection ofdepositors’ funds and improved stability in the MFI sectoroutweigh the reductions in outreach.
NOTES
1. This is a substantial increase over the MIX database used in Cull et al.
(2007), which contained information from 124 MFIs in 49 countries. Thatdataset was a variant of the so-called MBB 9 database. In this paper, weuse a variant of the MBB 10 database.
2. We have one observation from 2002, 50 from 2003, and 194 from2004.
3. These include adjustments for inflation, the cost of subsidized funding,current-year cash donations to cover operating expenses, donated goodsand services, write-offs, loan loss reserves and provisioning, a reclassifi-cation of some long-term liabilities as equity, and the reversal of anyinterest income accrued on non-performing loans.
4. The institutions included in MicroBanking Bulletin data tend to belarger and more profitable than the broader set of MFIs included inMixmarket data. This difference could account for why 85% of MFIs areregulated in our data while only 67% are regulated in Hartarska andNadolnyak (2007), for example.
5. We use the 2003 version of that dataset because it corresponds mostclosely to the years covered in our data.
6. One might worry that regulation and supervision is more stringent inbetter developed financial systems, and thus our supervisory dummiesreflect financial development more than a propensity to regulate.However, when we control for measures of financial development in ourtreatment effects regressions, qualitative results are almost identical tothose presented below.
7. Hartarska and Nadolnyak (2007) confront similar difficulties whenusing this variable as an instrument.
8. The correlation between Big Bank Supervision and the regular onsitesupervision variable is only 0.05 (p-value 0.411). The correlation betweenthe OS index and regular onsite supervision is 0.21 (p-value 0.004), butagain that variable covers far fewer countries.
9. We recognize that some NBFIs are more commercially oriented thanothers. In robustness checks that we do not report, we split the NBFIs,grouping those with non-profit charter status with NGOs, and those withfor-profit charters with banks and credit unions. Results are qualitativelysimilar to those presented below. When we dropped all NBFIs from thesample, we obtained somewhat similar results, though the loss of so manyobservations reduced significance levels.
10. Dropping credit unions and cooperatives from the regressionsreduces the sample by only 8–13 observations depending on thespecification. Credit unions and cooperatives are mutuals with anobjective to achieve operational sustainability (returning any surplus tomembers), rather than seeking profit in order to remunerate shareholdersas in the case of private banks. Regression results for the sample thatexcludes the credit unions and cooperatives are available from the authors.
11. The correlation between non-NGO/NBFI status*Big Bank Supervision
and regular onsite supervision is 0.51 (p-value 0.000). The correlationbetween non-NGO/NBFI status*OS index and regular onsite supervisionsis 0.43 (p-value 0.000).
962 WORLD DEVELOPMENT
12. The correlation between the savings dummy and regular onsitesupervision is 0.49 (p-value 0.000).
13. The correlation between Savings*Big Banks Supervision and regularonsite supervision is 0.44 (p-value 0.000). The correlation betweenSavings*OS index and regular onsite supervision is 0.48 (p-value 0.000).
14. Capital costs are measured as: (rent + transportation + deprecia-tion + office expenses + other expenses)/total assets. Labor costs are:personnel expenses/total assets.
15. We drop rural banks from the sample in all of the regressions that wepresent. The profile of the rural banks provides strong indications thatthey are not like other banks or credit unions. All face regular onsitesupervision, yet 86% of their borrowers are women and their average loansize is 1.7 times the per capita GDP of the bottom quintile, which is similarto that for un-supervised MFIs (see Table 2). They are also larger (averagesize category 2.2) and older (36.3 years) than most MFIs in the sample,and all are located in a single country, the Philippines. Qualitative resultsare not however substantially different when we include rural banks in thesample. It is unfortunate that we do not have a wider sample of ruralbanks since these are precisely the prudentially supervised MFIs that arelikely to be reaching the poorest clients.
16. By contrast operational self-sufficiency is more concerned withcovering costs and, perhaps, generating some surplus.
17. Since all of the instruments are highly significant in the selectionequation, we do not present them in the tables so as to conserve space.
18. Coefficients for the macroeconomic controls and the regionaldummies are also suppressed in the tables to make them more readable.The full specifications are available from the authors.
19. Like the FSS variable, the income portion of the ROA variable isadjusted to reflect a variety of subsidies. Those subsidies are described inAppendix C.
20. MFI performance measures are from 2003 or 2004, essentially across-section. The stringency of supervision and regulation data is basedon information from the MIX website from 2007 (and other sources, whenthat one proved insufficient). We acknowledge that this could producesome weakness in the results (as in models 3 and 4 in Table 5), though wefind it unlikely that a large share of the MFIs in the sample were changingtheir supervisory status during this period. In any event, we have no wayto check. The Barth, Caprio, and Levine data (used for the big banks’supervision variable and index of official supervisory powers) are alsocross-sectional and more or less contemporaneous with the MFI perfor-mance data, so we are less concerned that there is a major problem,though we acknowledge that if the performance effects of supervisionshow up at a lag, results might be weak.
21. Results are also similar when we replace average loan size relative tothe income of the bottom quintile with average loan size relative to incomeper capita as the dependent variable in the regressions.
22. The “non-commercial funding ratio” is defined as (donations + non-commercial borrowing + equity) divided by total funds. Here, donationsare defined as: donated equity from prior years + donations to subsidizefinancial services + an in-kind subsidy adjustment. Equity is the sum ofpaid-in capital, reserves, and other equity accounts; it does not includeretained earnings or net income. Commercial borrowing refers toborrowing at commercial interest rates (though in practice it can be hardto determine where the market would set those rates). Non-commercialborrowing, in parallel, is borrowing at concessional interest rates(with thesame caveat as above). Total funds are the sum of donations, equity,deposits (both savings and time deposits), commercial borrowing, andnon-commercial borrowing.
23. As in Table 2, we use the onsite supervision variable in Table 8because it provides a more balanced split of the sample.
24. Again, we note that these results do not pertain to rural banks sinceour sample included so few of them and all were from the same country.This is unfortunate since these are precisely the prudentially supervisedMFIs that are likely to be reaching the poorest clients. This is clearly apromising area for further research.
REFERENCES
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Barth, J., Caprio, G., & Levine, R. (2006). Rethinking Bank Regulation:Till Angels Govern. New York: Cambridge University Press.
Christen, R. P., Lyman, T., & Rosenberg, R. (2003). Guiding Principles forRegulation and Supervision of Microfinance. Washington, DC: Con-sultative Group to Assist the Poor.
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Cull, R., Demirguc�-Kunt, A., & Morduch, J. (2009). Microfinance meetsthe market. Journal of Economic Perspectives, Winter, 23(1), 167–192.
De Mel, S., McKenzie, D., & Woodruff, C. (2006). Returns to Capital inMicroenterprises: Evidence from a Field Experiment. World BankMimeo.
Elliehausen, G. (1998). The Cost of Bank Regulation: A Review of theEvidence. (United States Federal Reserve System, Staff Studies Series,Number 171). Washington, DC: Board of Governors of the FederalReserve System.
Elliehausen, G. E., & Lowery, B.R. (1995). The Cost of ImplementingTruth in Savings. Working paper, Board of Governors of the FederalReserve System, Office of the Secretary, Regulatory Planning andReview Section. Washington, DC.
Elliehausen, G. E., & Kurtz, R. D. (1988). Scale economies in compliancecosts for federal consumer credit regulations. Journal of FinancialServices Research, 1(2), 147–159.
Gonzalez, A. (2007). Impact of Credit Information Systems and OtherVariables on the Operating Cost of Microfinance Institutions (MFIs).Microfinance Information Exchange, Inc, Mimeo.
Hartarska, V. (2005). Governance and performance of microfinanceInstitutions in Central and Eastern Europe and the newly independentstates. World Development, 33(10), 1627–1643.
Hartarska, V., & Nadolnyak, D. (2007). Do regulated microfinanceinstitutions achieve better sustainability and outreach? cross-countryevidence. Applied Economics, 39(10–12), 1207–1222.
Honohan, P. (2004). Financial Sector Policy and the Poor: SelectedFindings and Issues. World Bank Working Paper No. 43, Washington,DC: World Bank.
Kaufmann, D., Kraay, A., & Mastruzzi, M. (2007). Governance MattersVI: Governance Indicators of 1996–2006. World Bank Policy ResearchWorking Paper 4280. Washington, DC.
McKenzie, D.J., & Woodruff, C. (2007). Experimental Evidence onReturns to Capital and Access to Finance in Mexico. World Bankmimeo.
Mersland, R., & Strøm, R. Ø. (2009). Performance and Governance inMicrofinance Institutions. Journal of Banking and Finance, forthcom-ing.
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Murphy, N. B. (1980). Economies of scale in the cost of compliance withconsumer protection laws: The case of the implementation of the EqualCredit Opportunity Act of 1974. Journal of Bank Research, 10(4),248–250.
Schroeder, F. J. (1985). Compliance Costs and Consumer Benefits of theElectronic Fund Transfer Act: Recent Survey Evidence. (Board ofGovernors of the Federal Reserve System, Staff Study No. 143).Washington, DC.
APPENDIX A. VARIABLE DESCRIPTI
Variable name Definition
Financial self-sufficiency Adjusted operating reven(financial expense + loanexpense + operating expe
Return on assets adjusted Adjusted net operating intaxes/Average total assets
Average loan size to GNPper capita of the poorest 20%Women borrowers (%) Percentage of borrowers
women.Staff concentration at home office Fraction of total staff bas
office.Loan officers (%) Fraction of total staff tha
contact with the clients.Real gross portfolio yield (Yield on gross portfolio
(nominal) � Inflation rat(1 + Inflation rate)
Premium Real yield � Real InteresCharged to Prime Lende
Annual big bank onsite supervision Dummy = 1 if “How freqonsite inspections conducmedium size banks?” is afrequent.
Non- (NBFI/NGO) Dummy for OrganizationClassified as NGO’s or N
Savings dummy Equal to 1 if the MFI accDeposits
Age Age of the MFI in yearsSize of MFI indicator Size of the loan portfolio
small, 2 for medium andVillage bank lender The MFI does village ban
(as opposed to MFIs wholending or solidarity lend
Solidarity lender The MFI does some solidlending (as opposed to Monly individual lending obank lending).
Capital costs to assets (Rent + transportation+ depreciation + office +assets
Labor costs to assets Personnel expenses/totalKKM Governance Index (KaufmReal GDPgr (%)InflationEastern Europe and Central AsiaAfricaMiddle East and North AfricaSouth AsiaEast Asia and the Pacific
Thornton, G. (1993). Regulatory Burden: The Cost to Community Banks.Study prepared for the Independent Bankers Association of America.Washington, DC.
ON AND SUMMARY STATISTICS
Mean Median Minimum Maximum
ue/Adjustedloss provisionnse)
1.030 1.035 �0.595 2.622
come after �0.018 0.008 �1.658 0.413
2.160 1.269 .00002 37.777
who are 0.690 0.718 0.001 1.0
ed at the home 0.285 0.211 0.001 0.983
t have direct 0.547 0.545 0.062 1.0
e)/0.222 0.201 �0.133 0.949
t Raters
0.170 0.147 �0.212 0.803
uently areted in large andnnual or more
0.755 1 0 1
s that are notBFI’s
0.257 0 0 1
epts Voluntary 0.421 0 0 1
10.485 8 0 48, which is 1 for3 for large.
2.491 3 1 3
k style lendingdo individual
ing).
0 .161 0 0 1
arity styleFIs who do
r do village
0.572 1 0 1
other)/total0.0822 0.064 0.004 0.392
assets 0.101 0.078 0.005 0.461ann et al) �0.489 �0.450 �1.587 1.245
5.863 5.611 �3.094 17.8548.734 6.223 �4.567 51.4610.187 0 0 10.213 0 0 10.057 0 0 10.075 0 0 10.161 0 0 1
964 WORLD DEVELOPMENT
APPENDIX B INDEX OF OFFICIAL SUPERVISORYPOWERS
The questions that are used to calculate the index of officialsupervisory powers are:
1. Does the supervisory agency have the right to meet withexternal auditors to discuss their report without theapproval of the bank?2. Are auditors required by law to communicate directly tothe supervisory agency any presumed involvement of bankdirectors or senior managers in illicit activities, fraud orinsider abuse?3. Can supervisors take legal action against external audi-tors for negligence?4. Can the supervisory authority force a bank to change itsinternal organizational structure?5. Are off-balance sheet items disclosed to supervisors?6. Can the supervisory agency order the bank’s directors ormanagement to constitute provisions to cover actual orpotential losses?7. Can the supervisory agency suspend the directors’ deci-sion to distribute dividends?8. Can the supervisory agency suspend the directors’ deci-sion to distribute bonuses?9. Can the supervisory agency suspend the directors’ deci-sion to distribute management fees?
APPENDIX C. FINANCIAL STATEMENT A
Adjustment Effect on financial sta
Inflation adjustment of equity (minusnet fixed assets)
Increases financial exon income statementoffset by inflation increvaluation of fixed areserve in the balancaccount, reflecting thMFI’s retained earniconsumed by the effeDecreases profitabilitretained earnings.
Reclassification of certain long-termliabilities into equity, and subsequentinflation adjustment
Decreases concessionand increases equity ainflation adjustmentstatement and balanc
Subsidized cost of funds adjustment. Increases financial exstatement to the exteliabilities carry a belointerest. Decreases neincreases subsidy adjon balance sheet.
Subsidy adjustment: current-year cashdonations to cover operating expenses
Reduces operating exstatement (if the MFdonations as operatinIncreases subsidy adjon balance sheet.
In-kind subsidy adjustment (e.g.,donation of goods or services: line staffpaid for by technical assistanceproviders)
Increases operating estatement to the extenreceiving subsidized oor services. Decreaseincreases subsidy adjbalance sheet.
10. Who can legally declare—such that this declarationsupersedes the rights of shareholders—that a bank is insol-vent? (A) bank supervisor; (B) court; (C) deposit insuranceagency; (D) bank restructuring or asset managementagency; (E) other.11. According to the Banking Law, who has authority tointervene—that is, suspend some or all ownershiprights—a problem bank? (A) bank supervisor; (B) court;(C) deposit insurance agency; (D) bank restructuring orasset management agency; (E) other.12. Regarding bank restructuring and reorganization, canthe supervisory agency or any other government agencysupersede shareholder rights or remove and replace man-agement or directors? (A) bank supervisor; (B) court; (C)deposit insurance agency; (D) bank restructuring or assetmanagement agency; (E) other.
For questions 1–9: Yes = 1: No = 0.For questions 10–12: Bank supervisor = 1: Deposit insur-
ance agency = 0.5; Bank restructuring or asset managementagency = 0; 0 otherwise.
The official supervisory powers index is constructed as thesum of these assigned values, with higher values indicatinggreater power.
Source: Barth et al. (2006)
DJUSTMENTS AND THEIR EFFECTS
tements Type of institution most affected byadjustment
pense accounts, to some degreeome account forssets. Generates ae sheet’s equityat portion of thengs that has beencts of inflation.y and “real”
MFIs funded more by equity than byliabilities will be hardest hit, especiallyin high-inflation countries
ary loan accountccount; increases
on incomee sheet.
NGOs that have long-term low-interest “loans” from internationalagencies that function more asdonations than loans
pense on incoment that the MFI’sw-market rate oft income and
ustment account
MFIs with heavily subsidized loans(i.e., large lines of credit fromgovernments or international agenciesat highly subsidized rates)
pense on incomeI recordsg income).
ustment account
NGOs during their start-up phase.The adjustment is relatively lessimportant for mature institutions.
xpense on incomet that the MFI isr donated goods
s net income,ustment on
MFIs using goods or services forwhich they are not paying a market-based cost (i.e., MFIs during theirstart-up phase)
(Continued on next page)
APPENDIX C—(Continued)
Adjustment Effect on financial statements Type of institution most affected byadjustment
Loan loss reserve and provision expenseadjustment
Usually increases loan loss provision expenseon income statement and loan loss reserve onbalance sheet.
MFIs that have unrealistic loan lossprovisioning policies
Write-off adjustment On balance sheet, reduces gross loan portfolioand loan loss reserve by an equal amount, sothat neither the net loan portfolio nor theincome statement is affected. Improves(lowers) portfolio-at-risk ratio.
MFIs that do not write off non-performingloans aggressively enough.
Reversal of interest income accrued on non-performing loans
Reduces financial income and net profit onthe income statement, and equity on thebalance sheet.
MFIs that continue accruing income ondelinquent loans past the point wherecollection becomes unlikely, or that fail toreverse previously accrued income on suchloans
Source: The Microbanking Bulletin, Our Methodology (www.mixmbb.org/en/company/our_methodology.html).
DOES REGULATORY SUPERVISION CURTAIL MICROFINANCE PROFITABILITY AND OUTREACH? 965
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