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Does Microfinance Need Infrastructure? *

Nurmukhammad YUSUPOV1

January 5, 2011

Abstract

Microfinance by and large implies small loans to support entrepreneurial ventures of the poor. Entrepreneurial success is closely interlinked with the quality of accessible infrastructure. Thus, success of microfinance programs also depends on the infrastructure in which the borrowing microentre-preneurs operate. This paper examines empirical relationships between the infrastructure variables of the economy and performance of microfinance institutions (MFIs). The overall results show insignificant relationships which are supportive of the hypothesis in the microfinance literature that MFIs primarily support the informal economy agents.

Keywords: microfinance, infrastructure, entrepreneurship, micro-entrepreneurship

JEL Codes:

* I would like to thank Christian Ahlin for methodological advice and allowing me to use the database compiled for Ahlin et al. (2010). I am also grateful to Gnoudanfoly Amadou Soro for research assistance. All the remaining errors and omissions are mine. 1 Chaire Banques Populaires, Audencia Nantes School of Management, 8 Route De La Jonelière, 44300 Nantes, France. nyusupov@audencia.com

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1. Introduction

Microfinance involves provision of small loans to support

entrepreneurial endeavours of the poor. As a rule, these microenterprises

employ primitive production technologies and take the form of life-style

ventures. Typical examples are catering, cleaning and various household

services among others. Such small businesses, like any other forms of

entrepreneurship, can greatly benefit from the overall economic

infrastructure2. However, modern institutional microfinance emerged in the

1970s in South-East Asia and Latin America in countries where financial

markets were relatively inefficient and the credit default risk was

exacerbated by the lack of sound infrastructure (Bhole and Ogden, 2010).

Nevertheless, many early MFIs demonstrated impressive financial results

which even triggered the interest of commercial investors.

Thus, a natural question emerges whether microfinance institutions

benefit from good infrastructure in the economy through better business

environment for their borrowers. Little is known about such relationships.

How different infrastructure variables affect the performance of

microfinance interventions? How does performance of MFIs vary across

countries with different levels of infrastructure? In order to answer these

questions, this paper analyzes the social performance and the financial

2 The benefits of infrastructure for businesses is conventionally shown through increase in pro-ductivity. See for example Aschauer (1989).

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results of MFIs in the context of economic infrastructure available across

countries.

The paper relates to two strands of literature. First, it is an empirical

study of the relationship between infrastructure variables and

microenrepreneurial activity. Importance of infrastructure on

entrepreneurship in general has already been postulated in academic

research. Van de Ven (1993) argues infrastructure conditions for

entrepreneurship should not be treated as externalities. The focus of

entrepreneurship research exclusively on the characteristics and

behaviours of individual entrepreneurs makes it deficient. A common bias is

to attribute innovations to a particular individual entrepreneur, who is

credited with the innovation, but change and innovation occurs in the

context of an established culture and institutional environment. This context

must be created before an enterprise can be born (Kimberly, 1980). Positive

effects of infrastructure on entrepreneurial activity have been documented

in a number of studies both in the context of high technology industries

(Carlton, 1983; Flynn, 1993) as well as rural and village economies

(Binswanger et al., 1993; Jacoby, 2000).

In a somewhat broader context, the paper is related to empirical cross-

country studies on MFIs and the environments under which they operate.

Macroeconomic conditions, according to Ahlin et al. (2010), may affect MFI

performance differently. On one side, economic growth can open up many

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new profitable investment opportunities for microentrepreneurs. This can

reflect in higher demand for microloans. Economic decline, in turn, can

increase poverty and unemployment, both of which are believed to

potentially increase demand for microloans. On the other side, a growing

economy can lower demand for microloans by expanding mainstream

financial institutions' operations to small borrowers. However, if the

economy is in stagnation business opportunities may decline as well,

leading to lower demand for microloans. Finally, if microfinance

interventions are targeted at informal economy macroeconomic conditions

may as well be irrelevant. This latter point is emphasized in Krauss and

Walter (2009) which detects low correlation between the performance of

MFIs and that of traditional commercial banks across a number of countries.

A similar result is reported in Gonzalez (2007) who finds that financial

performance of MFIs, measured in portfolios at risk, is independent of

macroeconomic conditions. At the same time however, Vanroose (2008)

argues that MFIs in more developed countries appear to perform better in

terms of outreach. Hermes and Meesters (2010)3 also argue that the

performance of MFIs is “clearly and robustly associated” with the macro

conditions these institutions are confronted with.

The role of macroeconomic environment has been studied in a number

of other papers focusing on specific countries. Fernando (2003) relates

3 Hermes and Meesters (2010) offer fairly extensive review of the empirical studies of MFI performance in the context of macroeconomic environment.

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MFIs' problems in Brazil to high inflation rates. Sharma (2004) argues that

success of MFIs in India and Nepal is largely attributed to favourable

macroeconomic conditions while setbacks are due to political instability.

Patten et al. (2001) study Bank Rakyat Indonesia and explain positive

results for the microbanking division of the bank during the Asian crisis by

pointing out that microborrowers were less affected as they were more

dependent on domestic goods and operated in rural areas. Thus, they were

not significantly susceptible to currency fluctuations that made imports

more expensive and were less exposed to shocks from foreign markets.

Patten et al. (2001) also argue that microborrowers were more disciplined

in repayment as they were in need of continued access to microloans.

Marconi and Mosley (2006) study MFIs in Bolivia during the crisis of 1998-

2004 and argue that declining macroeconomic conditions led to increase in

defaults bankrupting some, mainly the commercial, MFIs. Government's

bail-out of troubled MFIs weakened their incentives to behave prudently

and only worsened the situation.

Another class of variables potentially affecting operations of MFIs is the

institutional environment that can both open new possibilities and create

constraints on entrepreneurial ventures. Hartarska and Nadolnyak (2007)

and Cull et al. (2010f) produce mixed results on this account.

Since the accumulation of physical and human capital are regarded as

the core components of economic development (Wennekers et al. 2005), in

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this paper, I consider variables that can be viewed as measures of physical

and social infrastructure. First, for physical infrastructure extant in the

economy I use electricity consumption per capita, the quantity and quality

of roads, access to telephone lines and internet. Second, I consider social

infrastructure variables which are used as a proxy measure for the

aggregate quality of human capital. These include measures of hospital beds,

labor force, level of population’s literacy and girl to boy ratio in schools.

Overall results, obtained using the empirical methodology of Ahlin et al.

(2010), are indicative of weak and insignificant dependence of the

performance of MFIs on these variables providing support to the view in the

literature that microfinance supports primitive production technologies

within the informal economy that leverages on the infrastructure to a

minimum extent.

The paper is structured as follows. The following section describes the

data used for this study. Section 3 discusses econometric specification and

methodological details. Section 4 covers the regression results and Section 5

concludes the paper. All the output tables are included in the appendices.

2. The Data

Data for thus study is collected from two sources. The MFI data that is

used in this version of the paper is that used by Ahlin et al. (2010). It comes

from Microfinance Information Exchange (MIX), a large web portal that

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collects information from individual MFIs on a voluntary basis. The MFI data

was collected in the summer of 2009. At that time, MIX’s publicly accessible

online database (mixmarket.org) contained information on more than 1400

MFIs.

As the data is reported to the MIX on a voluntary basis, the portal has

developed a diamond system of classification based on the reliability of

provided data and is used as the principle MFI database in empirical cross-

country studies (Ahlin et al., 2010; Cull et al., 2010; etc.). The MFIs with the

highest quality information are classified as 5-diamond MFIs while the ones

with the poorest quality of data are labelled as 1-diamond institutions.

Because the quality of data provided in the MIX is heterogeneous across

institutions one must be selective in deciding which MFIs to include in the

study. The final selection, thus, is based on the availability and the quality of

the data provided by MFIs voluntarily. It has been argued in the literature

that such a sample can be viewed as a random sample of the best MFIs but

not a random MFI sample per se (see Gonzales, 2007).

First, following the common practice in microfinance research, the

database includes MFIs with four and five diamonds on the MIX. These MFIs

have provided sufficient amount of data as well as third-party audits of their

financial statements. Second, the database includes only institutions that

were founded no later than 2004 to be able to have at least four

observations on each individual MFI. Third, the MFIs with less than 80% of

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their assets involved in microfinance are excluded so that the results of the

study are indicative of microfinancing activity. Due to lack of available

country data such countries as Afghanistan, East Timor, Kosovo, Palestine,

and Serbia and Montenegro are excluded from the database. Finally, the

database contains 373 MFIs from 77 countries, that includes 2278

observations.

As the dependent variable I analyze the following measures of MFI

performance: portfolio at risk over 30 days (PAR-30), profit margin,

operational self sufficiency (OSS), return on assets (ROA), cost per borrower

(CPB). These are conventional measure of the financial performance of any

financial institution. However, since MFIs, in addition to having to perform

financially, are driven by the mission to offer inclusive financial services to

the less advantaged social groups such as women and poor I use the

following variables to measure social performance of MFIs: number of

female borrowers, share of small loans in the loan portfolio, borrowers per

staff (BPS) and average loan size (ALS). Following the methodology of Ahlin

et al. (2010) I control for the MFI age and the number of borrowers in the

previous year. Definitions and descriptive statistics are given in Table 1.

[ INSERT TABLE 1 ABOUT HERE ]

The focal performance indicator is operational self-sufficiency defined as

the ratio of annual financial revenue to annual total expense where total

expense includes financial expenses, loan loss provision expenses as well as

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operating expenses. Operational self-sufficiency ratio greater than 100%

indicates that the MFI generates sufficient revenue to cover its costs. Profit

margin, another important indicator of financial performance, is defined as

profits divided by the total financial revenue in a given year. Return on

assets is computed as net financial results divided by the value of assets.

Portfolio at risk is computed as value of loans at-risk, that is with

repayments past due date, of over 30 days over average gross loan portfolio.

Cost per borrower is given by operating expense over average number of

active borrowers in 2005 terms. Borrowers per staff is the number of

borrowers divided by the staff number. Average loan size is derived by

dividing the average gross loan portfolio by the average number of active

borrowers in 2005 terms. The MFI age is the number of years since its

inception and until the given year.

Financial revenue versus financial costs stems from the interest markup.

It equals the difference between the average interest rate (financial revenue

per dollar loaned) and the average cost of capital (the financial expenses per

dollar loaned). Financial revenue per dollar loaned equals interest revenue

from loans plus revenue from other investments, all divided by the value of

the loan portfolio.

Default costs: to measure default costs I resort to two financial

indicators. The loan loss expense rate is the provision for bad loans

measured as the share of the average annual loan portfolio. Additionally,

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portfolio at risk of over 30 days measured in the share of the loan portfolio

that is behind schedule with payments for over thirty days. As a rule, this

variable is used as an early signal of possible default problems.

Operating cost: operating costs are taken in per-dollar-lent terms

measured as the ratio of annual operating costs over the year-average size

of the loan portfolio. This can be decomposed into the per-borrower

operating cost and the reciprocal of the average loan size. Thus, lower

operating costs in per dollar-lent terms can be indicative of the lower

operating costs per borrower or larger average loan sizes.

Baseline MFI control variables also include institutional type and age of

the MFIs assuming that the year of foundation marks the start of MFI’s

operations. A larger set of MFI controls can include a decomposition of asset

size into: the number of borrowers, the average loan size, and the ratio of

assets to loan portfolio. The latter may proxy for overhead. The product of

the three quantities forms the MFI's assets.

Country infrastructure level data are collected through the online

database of World Development Indicators as of January 2010. Following

the literature on the links between infrastructure and entrepreneurship I

distinguish two broad types of infrastructure variables. First, I focus on

physical infrastructure. This is usually decomposed into energy, transport

and telecommunications.

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For energy, I use electricity consumption per capita defined by the

reported electric power consumption in kWh per capita of population. For

transport I opt for roads paved which is the share of total length of roads in

percents that have been covered by pavement. Additionally I use roads total

which is the total length of the extant roads within the country.

Telecoms are represented in my analysis by two variables: phones and

web users. The former is the number of subscribers to mobile and fixed-line

telephone subscribers per 100 people of the population, while the latter is

the number of internet users per 100 people.

Second, I collect data on the so called social infrastructure that measure

the characteristics of the non-physical environment under which

microfinance borrowers dwell and operate. Measures of human capital

investment, research and development capital and health services (Jahan

and McCleery, 2005) are usually included in the notion of social

infrastructure in the development literature4. I use the following variables

to measure the social infrastructure which can be beneficial for the quality

of human capital: urban population, hospital beds and literacy rate. Urban

population is the percentage of total population living in urban

4 There exist alternative definitions of the social infrastructure in the literature. For example, Hall and Jones (1999) define it as the institutions and government policies that determine the economic environment within which individuals accumulate skills and firms accumulate capital and produce output. Their proposed measure of social infrastructure as the average of an index of the extent to which property rights and contracts are enforced and respected in a country and the degree of openness to international trade shows strong association between social infrastructure and productivity. A so-called “entrepreneurial social infrastructure” which includes three elements: symbolic diversity, resource mobilization, and quality of networks is discussed in Flora and Flora (1993).

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administrative units. Hospital beds is the number of hospital beds computed

as per 1,000 people. Labor force is the total labor force total of a given

country persons. Literacy rate is the share of the population of adults, aged

15 and above, that have received formal education. Finally, I opt for the ratio

of boys to girls in primary and secondary education to control for possible

gender empowerment and discrimination within the country.

3. Estimation Methodology

To estimate the effects of available infrastructure on the financial

performance of MFIs I resort to the uneven panel data analysis similar to

that employed by Ahlin et al (2010). Let yijt be the observation of the

dependent variable for MFI I from country j in year t. Let Mit denote the

vector of MFI-specific control variables in year t and Xjt denote that of

infrastructure variables characterising individual countries where MFIs

operate. The baseline specification takes the following form:

ijtjtXitMijt XMy 0

I use the MFI’s age and age squared as control variables due to potential

endogeneity concerns. I also perform tests using a larger set of MFI controls

consisting additionally of logs: number of borrowers, average loan size, and

assets per loans. To alleviate endogeneity concerns each of these is lagged

by one year, i.e. corresponds to the final date of year t − 1. For the moment I

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disregard institutional types5 as I hypothesize that MFIs primarily lend to

small entrepreneurial ventures which benefit from better infrastructure

regardless of what type of MFI is financing them. This is somewhat in

contrast to the methodology of Ahlin et al. (2010).

To address the outlier issue I estimate conditional median functions

rather than conditional mean functions. This approach tends to suffer less

from outliers than least squares. The reported coefficients are based on

median regressions, which minimize the sum of absolute residuals rather

than the sum of squared residuals and tends to be less exposed to outlier

problems than least squares. Additional two regression methods are used to

ensure robustness. First is the “robust regression” that drops extreme

outliers and then iterates using weighted least squares with weights

negatively related to residual size, until the weights and coefficient

estimates converge. Second, OLS is run with truncated samples with

{0,1,2,3,4,5}% of the sample is eliminated at the top and bottom of the

dependent variable. According to Ahlin et al (2010) “these three approaches

need not give the same results; however, when the results do coincide, it

increases confidence that results are not being driven by outliers”. I do not

report the results of the truncated sample OLS regressions because they do

not significantly alter the existing results. Therefore, the reported results

can be regarded as not affected by possible outliers.

5 In the future versions of the paper I shall also introduce institutional types into account as well because non-profit players may deliberately have lower profit margins.

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To address the possibility of error correlation within MFIs and outlier

problems I do the following. To address the former I bootstrap standard

errors and confidence intervals for both the median and robust regressions,

clustering the bootstrap by MFIs, and estimate standard errors.

I also decompose the regressors into a within-MFI median (e.g. the

electricity consumption per capita for the years the MFI data is collected)

and a deviation from this median. Then I estimate a variation on the

baseline specification that separates within-MFI and between-MFI variation

for the independent variables. By isolating within-MFI variation in the

estimation one can control for unobserved MFI (or country) attributes that

may be correlated with the infrastructure variables and important for MFI

performance. For example, it may be that more profitable or profit-driven

MFIs choose to locate in countries with better infrastructure. On the back

side of the coin, however, within-MFI variation only picks up high frequency

relationships between the variables. For example, it fails to directly address

the question of whether MFIs in countries with consistently better

infrastructure have an easier time achieving self-sufficiency than those who

are not.

4. Results

Regressions are performed using Stata 10. Regressions were performed

using all three methods discussed in the previous section with three

specifications: with all infrastructure variables, with physical infrastructure

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variables and with social infrastructure variables. Table 2 reports baseline

results. The first table reports the results of regressions using all

infrastructure variables, while the additional panels, i.e. Table 2a, contains

the results for separate regressions using physical and social infrastructure

variables.

[ INSERT TABLE 2 ABOUT HERE ]

The results indicate that neither financial performance indicators nor

social performance measures are predicted by the chosen set of predictors.

A notable exception is the cost per borrower for which roads paved, urban

population and access to telecommunications appear to be significant

predictors. In case of physical infrastructure, there is naturally more

significance in predictive power for financial results. For example, roads

paved is statistically significant almost for all 5 financial performance

variables. This can be attributed to the ease of accessing supplies and

consumers for the borrowing microentrepreneurs as well as the ease of

MFIs’ access to their clients. At the same time, social performance measures

seem to be less significantly dependent on the characteristics of the physical

infrastructure. Notably, the number of phone users is a significant predictor,

with a negative sign, for the share of small loans. That is countries with

more telephones prompt microloans to be smaller on average. This is

consistent with the emerging literature that shows how mobile phones in

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particular and telecommunication technology in general is enabling MFIs to

increase their outreach and lend to more disadvantaged.

Social infrastructure appears to be less strong predictor of MFI

performance. Notable exceptions are urban population and hospital beds.

However, often their significance is above 10% significance level. Banking

with women appears to be the most well predicted using the chosen set of

variables. Notably, MFIs in countries with higher literacy rates, higher urban

population and higher ratio of girls to boys in school are evidently lending

more to female borrowers.

[ INSERT TABLE 3 ABOUT HERE ]

These results largely hold for within and between variation regressions

shown in Table 3. The overall results generally imply that infrastructure

does not significantly affect the performance of MFIs both on social and

financial fronts. This results supports the increasingly popular view the

literature that microfinance interventions are targeted at the informal

economy and primitive technology for which macroeconomic conditions

and advanced infrastructure may be irrelevant (Krauss and Walter, 2009).

5. Conclusion

This paper reports the results of an empirical analysis of the relationship

between country infrastructure variable and performance of MFIs operating

there. The overall results suggest relative independence of the performance

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of MFIs, both in terms of their financial and social bottomlines, from the

available infrastructure. This result supports the increasingly popular view

in the literature that microfinance interventions are targeted at the informal

economy and primitive technology for which macroeconomic conditions

and advanced infrastructure may be irrelevant (Krauss and Walter, 2009).

In a broader context, the paper contributes to the empirical literature on

cross-country studies of MFIs and the macro environments under which

they operate (Ahlin et al., 2010; Gonzalez, 2007; Hermes and Meesters,

2010).

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References

Ahlin, Christophe, Jocelyn S. Lin and Michael Maio (2010). Where Does Microfinance Flourish? Microfinance Institution Performance in Macroeconomic Context. Journal of Development Economics, forth-coming

Aschauer, David (1989). Is public expenditure productive? Journal of Monetary Economics, 23(2), pages 177-200

Bhole, Bharat and Sean Ogden (2010). Group lending and individual lending with strategic default. Journal of Development Economics, vol. 91, issue 2, pages 348-363

Binswanger, Hans P., Shahidur R. Khandker and Mark R. Rosenzweig (1993) How infrastructure and financial institutions affect agricultural output and investment in India. Journal of Development Economics, 41(2), pages 337-366

Carlton, D. W. (1983). The Location and employment choices of new firms: an econometric model with discrete and continuous endogenous variables, Review of Economics and Statistics, 65 (4), pages 440-449.

Cull, Robert, Asli Demirguc-Kunt and Jonathan Morduch (2010). Does regulatory supervision curtail microfinance profitability and outreach? World Development, forthcoming, also available as World Bank Policy Research Working Paper No 4948

Fernando, Nimal (2003). The changing face of microfinance: Transformation of NGOs into regulated financial institutions. Mimeo, Asian Development Bank

Flora, Cornelia Butler and Jan L. Flora (1993). Entrepreneurial Social Infrastructure: A Necessary Ingredient. The Annals of the American Academy of Political and Social Science, 529(1), pages 48-58.

Flynn, David M. (1993) A critical exploration of sponsorship, infrastructure, and new organizations. Small Business Economics 5, pages 129-156

Gonzalez, Adrian (2007). Resilience of microfinance institutions to national macroeconomic events: An econometric analysis of MFI asset quality, MIX Discussion Paper No.1

Hermes, N. and Meesters, A. (2010) The performance of microfinance institutions: Do macro conditions matter? In The Handbook of Microfinance, ed. By Armendariz, B. and Labie, M.

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Jacoby, Hanan G. (2000) Access to markets and the benefits of rural roads. The Economic Journal, 110(465), pages 713-737

Jahan, Selim and Robert McCleery (2005). Making Infrastructure Work for the Poor – Synthesis Report of Four Country Studies Bangladesh, Senegal, Thailand and Zambia. UNDP Report, available at http://www.undp.org/poverty.

Kimberly, J.R. (1980) Initiation, innovation, and institutionalization in the creation process. Chapter 2. In J.R. Kimberly, R.H. Miles, and Associates, eds., The Organizational Life Cycle, San Francisco: Jossey-Bass.

Krauss, Nicolas and Ingo Walter (2009). Can Microfinance Reduce Portfolio Volatility? Economic Development and Cultural Change, 58(1), pages 85-110

Marconi, R. and P. Mosley (2006). Bolivia during the global crisis 1998-2004: Towards a `macroeconomics of microfinance'. Journal of International Development, 18, pages 237-261

Patten, R.H., J.K. Rosengard and D. Johnston, Jr. (2001). Microfinance success amidst macroeconomic failure: The experience of Bank Rakyat Indonesia during the East Asian crisis. World Development, 29, pages 1057-1069

Sharma M.P. (2004). Community-driven development and scaling-up of microfinance services: Case studies from Nepal and India, Discussion Paper No 178, IFPRI

Van de Ven, Andrew (1993) The development of an infrastructure for entrepreneurship. Journal of Business Venturing, 8, pages 211-230

Vanroose, Annabel (2003) Uneven development of microfinance in Latin America. Mimeo, Solvay Business School, Universite Libre de Bruxelles

Wennekers, Sander, André van Wennekers, Roy Thurik and Paul Reynolds (2005) Nascent Entrepreneurship and the Level of Economic Development. Small Business Economics, 24(3), pages 293-309, 04.

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Appendices

Table 1. Variable Descriptions MFI Variables Description Obs. Mean Std.Dev. Min Max

Operational self-sufficiency (OSS) Financial revenue / (Financial expense + Loan loss provision expense + Operating expense)

2612 118% 53.42% 0 1698.45%

Profit Margin Profit/Financial Revenue Return on Assets (ROA) Return on assets PAR-30 Value of loans at-risk > 30 days /average gross loan portfolio 2429 8.05% 116.04% 0 4860.52%

Cost per Borrower (CPB) Operating expense / average number of active borrowers (constant 2005 international $)

2105 120.32 145.18 0 2589.79

Borrowers per Staff (BPS) Total number of borrowers/Number of staff members

Average loan size (ALS) Average gross loan portfolio / average number of active borrowers (constant 2005 international $)

2463 607.63 783.94 1 9594.09

Age Age of the MFI (years) 2755 10.41 7.56 0 45 Log of (Assets per loans)t-1 Log of Total of all net asset accounts / gross loan portfolio 2314 0.34 0.34 -0.34 3.88 Borrowerst-1 Number of active borrowers (1000s) 2274 62.45 372.36 9 6397.64 Infrastructure Variables Description Obs. Mean Std.Dev. Min Max Electricity consumption per capita Electric power consumption (kWh per capita) 1845 4.93+10 1.74+11 2.11+08 2.32+12 Roads paved Roads, paved (% of total roads) 1057 29.688 26.718 0.8 100 Roads total Roads, total network (km) 1242 108395 234088 790 1980000 Urban population Urban population (% of total) 2126 48.726 19.643 9.4 93.4 Phones Mobile and fixed-line telephone subscribers (per 100 people) 2121 8.020 7.754 0.018 36.498 Web users Internet users (per 100 people) 2112 3.782 4.850 0 27.683 Hospital beds Hospital beds (per 1,000 people) 707 3.075 2.800 0.12 12.13 Labor force Labor force total (persons) 2126 1.79+07 5.79+07 62395.6 7.74+08 Literacy Literacy rate, adult total (% of people ages 15 and above) 317 84.662 20.521 13.985 99.901 Girl to boy ratio Ratio of girls to boys in primary and secondary education (%) 1510 94.336 10.967 49.901 113.328

Note: This table is identical to Table 1a in Ahlin et al. (2010) as for this version of the paper I am using their MFI database. For each variable, statistics are calculated based on the observations included in the regression that has the maximum number of observations and includes this variable. The %-between column gives the between-MFI variance as a fraction of the total variance.

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Table 2. Baseline (Pooled) Results

PAR-30 Profit Margin ROA OSS CPB Women Small Loans BPS ALS Electricity consumption per capita

- - - - - - - - -

Roads paved -0.00068 (0.00159)

0.00861 (0.00911)

0.00176-d (0.00272)

0.01491 (0.01298)

-3.08079-a (4.64257)

0.00535 (0.01087)

-0.00031 (5.05+10)

2.20338 (2.03889)

-28.00316 (30.17027)

Roads total 7.93-07

(1.30-06) -2.43-06 (7.69-06)

-6.06-07 (3.30-06)

-4.24-06 (0.00001)

0.00051 (0.00400)

-5.02-06 (0.00001)

5.60-06 (6.23+08)

-0.00033 (0.00171)

-0.01406 (0.02070)

Phones -0.00531 (0.01880)

-0.04131 (0.09559)

0.00156 (0.03887)

-0.07007 (0.15951)

29.7416-a (49.79342)

0.00854 (0.10928)

- -16.30989 (20.96036)

556.8846 (458.3978)

Web users -0.00338 (0.00778)

0.01177 (0.03852)

0.00029 (0.01154)

0.02379 (0.05784)

-15.87198-a (20.43357)

0.00176 (0.04377)

0.05024 (1.57+13)

5.41019 (8.41820)

-325.1048d (205.1799)

Hospital beds - - - - - - - - -

Labor force -6.54-09-d (1.30-08)

3.95-08 (7.97-08)

9.21-09 (2.96-08)

7.19-08 (1.22-07)

-0.00001-b (0.00004)

5.07-08 (1.14-07)

-5.53-08 (6609060)

7.58-06 (0.00002)

-8.23-06 (0.00020)

Literacy 0.00959

(0.01462) -0.02325 (0.08586)

-0.00916 (0.03532)

-0.05060 (0.14034)

-2.36188 (46.16179)

-0.05402 (0.11985)

- -4.84472

(18.70024) -263.3367 (275.8175)

Urban population 0.00171

(0.00160) -0.00306 (0.00891)

-0.00215 (0.00455)

-0.0073 (0.01408)

4.43037-a (3.79210)

0.00266 (0.01253)

-0.01137 (2.12+11)

-0.22506 (1.99376)

32.19832 (30.53724)

Girl to boy ratio -0.02812 (0.03997)

0.08666 (0.24231)

0.03136 (0.09633)

0.17678 (0.38757)

-12.42973 (130.2978)

0.16749 (0.34332)

- 16.16234

(52.19697) 457.2799

(686.7105)

Age 0.00287

(0.00757) -0.02402 (0.06441)

-0.00166 (0.01714)

0.00983 (0.10979)

1.79816 (11.28571)

-0.00677 (0.07905)

0.00757 (8.52+12)

0.09098 (12.88433)

-47.11824 (105.7556)

Age2 -0.00010 (0.00038)

0.00135 (0.00292)

0.00019 (0.00075)

0.00097 (0.00524)

-0.25202 (0.58469)

0.00124 (0.00417)

-0.00209 (7.08+11)

0.45033 (0.61799)

5.149226 (5.25399)

Note: Missing predictors are dropped by the software because of collinearity. Each column corresponds to a separate regression, with the dependent variable listed atop the column. Median regression coefficients are reported, with bootstrapped standard errors in parentheses. Significance at 1%, 5%, 10% and 20% is denoted by a, b, c and d res-pectively. Significance in the median regression is denoted by the first letter, significance in the robust regression by the second letter.

22

Table 2a. Baseline (Pooled) Results: Physical vs Social Infrastructure

PAR-30 Profit Margin ROA OSS CPB Women Small Loans BPS ALS Physical Infrastructure

Electricity consumption per capita

-0.00340c (0.00203)

0.00793-c (0.01301)

0.00289 (0.00313)

0.00833-c (0.01529)

13.34401aa (4.46051)

0.02841cd (0.01688)

0.05883c (0.03421)

4.96280d (3.79693)

-90.38691-d (75.07649)

Roads paved -0.00035aa

(0.00007) 0.00167aa (0.00047)

0.00026ba (0.00011)

0.00274aa (0.00069)

-0.17231da (0.12606)

0.00040-d (0.00074)

0.00009 (0.00082)

-0.10178 (0.13008)

0.081364 (2.66497)

Roads total 1.55-08

(2.05-08) -5.22-08-d (1.41-07)

-2.11-08-b (1.88-08)

-7.24-08-b (1.10-07)

0.00005-b (0.00006)

1.11-07-c (9.47-08)

1.88-07-b (3.43-07)

-0.00002-d (0.00003)

0.00020 (0.00067)

Phones -0.00042dd (0.00030)

-0.00287-c (0.00280)

-0.00050 (0.00060)

-0.00529dd (0.00398)

-0.59921 (0.73911)

0.00034 (0.00265)

-0.01639aa (0.00481)

-0.066945 (0.67853)

5.20017 (12.12481)

Web users -0.00037dd (0.00027)

0.00454cb (0.00258)

0.00136bb (0.00062)

0.00729cb (0.00374)

-1.45817c (0.83276)

0.00222 (0.00303)

0.00120 (0.00481)

1.703412ba (0.71583)

-16.09451-a (12.66712)

Age 0.00129dc

(0.00091) 0.01394aa (0.00418)

0.00389aa

(0.00111) 0.01609aa (0.00502)

2.79171b (1.16295)

0.02429aa (0.00665)

0.01989db (0.01317)

0.68738-d (1.24594)

-50.61052ca (29.00359)

Age2 -8.34-06

(0.00003) -0.00032ab (0.00012)

-0.00010aa (0.00003)

-0.00036aa (0.00014)

-0.12085ab (0.032575)

-0.00074aa (0.00024)

-0.00044-c (0.00052)

-0.00635-b (0.03527)

1.49582da (0.92875)

Social Infrastructure

Hospital beds -0.00265 (0.00587)

0.05427-a (0.21530)

0.01853-a (0.01938)

0.09615-b (0.08658)

-17.8359-b (70.29983)

0.03044 (0.03157)

0.04276-d (0.40658)

13.50939-d (13.80189)

31.97959 (519.2232)

Labor force 4.83-10-d (5.11-10)

1.06-09 (4.85-09)

6.59-10-b (1.18-09)

-3.45-10 (6.23-09)

6.84-07-c (9.68-07)

-4.39-09db (3.14-09)

8.61-10 (1.01-07)

-1.04-06dc (7.91-07)

-5.84-06 (9.29-06)

Literacy -0.00001 (0.00060)

-0.00123 (0.00702)

0.00091 (0.00200)

-0.00286-d (0.00963)

1.38393-b (1.8679)

0.01888aa (0.00696)

0.00772-c (0.08760)

-0.62170 (1.20058)

-9.55442 (21.09166)

Urban population -0.00027-d (0.00036)

0.00215-d (0.00408)

0.00128da (0.00083)

0.00549-c (0.00441)

0.33961 (1.19273)

0.00950aa (0.00277)

0.00178-d (0.03722)

1.88904da (0.75018)

-7.84835-a (11.8903)

Girl to boy ratio 0.00014

(0.00170) -0.00635 (0.01924)

-0.00256-c (0.00526)

-0.00843 (0.02597)

0.07390 (5.41494)

-0.04467a (0.01703)

-0.02227-c (0.28408)

-2.72578-b (2.60551)

16.70603-d (64.26611)

Age -0.00122 (0.00418)

0.02165 (0.04187)

0.01029-d (0.00947)

0.04309 (0.04742)

-5.37876 (6.56098)

0.03880d (0.02608)

0.00201 (0.39876)

1.61594 (8.29869)

-31.29383 (81.2849)

Age2 0.00005

(0.00018) -0.00086 (0.00161)

-0.00036 (0.00038)

-0.00161 (0.00184)

0.01675 (0.26957)

-0.00139d (0.00102)

-0.00015 (0.03103)

0.18695-c (0.35127)

1.04411 (3.23180)

Note: Missing predictors are dropped by the software because of collinearity. Each column corresponds to a separate regression, with the dependent variable listed atop the column. Median regression coefficients are reported, with bootstrapped standard errors in parentheses. Significance at 1%, 5%, 10% and 20% is denoted by a, b, c and d respect-tively. Significance in the median regression is denoted by the first letter, significance in the robust regression by the second letter, and significance using the median p-value of six OLS regressions dropping varying numbers of outliers by the third letter.

23

Table 3a. Within and Between Results

PAR-30 Profit Margin ROA OSS CPB Women Small Loans BPS ALS Physical Infrastructure

Electricity consumption per capita Median

-0.00305c (0.00183)

0.00325 (0.01990)

0.00100 (0.00412)

0.00033 (0.01720)

-4.58669 (4.72370)

0.05052ba (0.02463)

-0.00289 (0.05407)

3.44937-a (4.61936)

-249.8676aa (64.77654)

Electricity consumption per capita Deviation

0.02114db (0.01464)

0.15743dd (0.10301)

0.01710 (0.03685)

0.13148-b (0.13716)

11.52923 (43.60917)

-0.60076aa (0.17099)

-1.10078c (0.64881)

1.70984-c (48.3473)

253.6799-d (400.1247)

Roads paved Median -0.00031aa (0.00008)

0.00174aa (0.00054)

0.00023db (0.00015)

0.00223aa (0.00063)

-0.40962ba (0.19517)

0.00139d (0.00089)

0.00150-c (0.00155)

-0.26738da (0.17950)

-4.16302-a (3.51807)

Roads paved Deviation -0.00033 (0.00057)

0.00423 (0.00622)

0.00142 (0.00163)

0.00060 (0.00782)

-3.16598cc (1.83834)

-0.00091-d (0.00729)

0.00926 (0.01342)

2.17198dc (1.36496)

-30.67589da (20.64487)

Roads total Median 2.77-09

(1.66-08) -2.03-07-d (2.95-07)

-2.48-08-c (5.49-08)

-2.13-07-a (1.69-07)

-0.00008ba (0.00003)

1.94-07da (1.51-07)

5.42-07-a (7.90-07)

0.00005dd (0.00003)

-0.00089ba (0.00044)

Roads total Deviation -5.78-08c (2.85-07)

2.18-06-b (1.47-06)

4.04-08 (4.47-07)

2.43-06dd (1.52-06)

0.00104bb (0.00051)

1.06-06 (1.18-06)

3.48-06 (6.82-06)

-0.00058-a (0.00052)

0.01297bb (0.00584)

Phones Median 0.00039

(0.00035) -0.00235-d (0.00210)

-0.00037-c (0.00064)

-0.00244-d (0.00259)

6.67170aa (0.89365)

-0.01274aa (0.00338)

-0.03550aa (0.00739)

-3.79739aa (0.69451)

117.8692aa (15.64056)

Phones Deviation 0.00135bc (0.00053)

-0.01141-a (0.00918)

-0.00144 (0.00381)

-0.01319 (0.01063)

-3.06398-c (3.82511)

-0.01150-a (0.01352)

-0.00635 (0.01683)

-3.52226ab (1.32493)

-39.02225-d (51.55309)

Web users Median 0.00005

(0.00053) 0.00439db (0.00306)

0.00146da (0.00103)

0.00579dd (0.00386)

2.91407ca (1.65907)

0.00043 (0.00510)

0.01887c (0.01074)

1.92232dc (1.23681)

69.85398ba (30.02762)

Web users Deviation -0.00143aa (0.00042)

0.00386dc (0.00301)

0.00130-c (0.00104)

0.00733cc (0.00425)

-0.38328 (1.41093)

0.00785db (0.00612)

0.01099 (0.01116)

4.14358aa (1.08814)

7.21160 (18.54807)

Age 0.00170bd (0.00071)

0.02042aa (0.00387)

0.00287aa (0.00089)

0.02548aa (0.00483)

1.75188-d (1.91805)

0.02808aa (0.00680)

0.01567 (0.01395)

5.03885ab (1.64564)

7.48636 (19.83175)

Age2 -0.00002 (0.00002)

-0.00045aa (0.00011)

-0.00007aa (0.00002)

-0.00056aa (0.00014)

-0.04104-c (0.05497)

-0.00086aa (0.00028)

-0.00050 (0.00045)

-0.13945bb (0.06222)

0.36109 (0.68727)

Observations 678 749 592 748 560 605 291 680 725 Social Infrastructure

Hospital beds Median 0.00114

(0.00879) 0.09030-a (0.16965)

0.02297-b (0.03246)

0.13524 (0.20046)

3.24065 (16883.94)

0.00020 (0.03492)

0.09144 (0.33645)

-12.13936 (10.77644)

263.6091 (22601.19)

Hospital beds Deviation 0.00751

(0.04321) -0.07716

(0.090711) -0.04691-a (0.15877)

-0.15979-a (1.63684)

44.21068-a (88335.79)

-0.47219d (0.30482)

-0.054385 (2.15958)

3.883536 (55.38291)

924.2796-b (2528.983)

Labor force Median 2.20-10

(5.30-10) 1.08-09-d (6.74-09)

3.11-10-d (1.86-09)

-5.80-10-b (6.51-09)

1.16-07 (33975.02)

6.97-09 (7.34-09)

2.25-08 (1.61-07)

4.59-07 (1.01-06)

-0.00002-a (0.00039)

24

Labor force Deviation 8.80-09

(1.44-08) -7.73-08 (1.18-07)

4.57-08 (2.69-08)

-1.07-07-d (1.46-07)

-2.89-06 (3774875)

-1.34-07d (8.22-08)

-3.36-07 (3.49-06)

-6.36-06 (0.00002)

0.00019 (0.01869)

Literacy Median 0.00022

(0.000713) -0.00933-b (0.00892)

-0.00258 (0.00278)

-0.01202 (0.01592)

1.23567-a (3428.22)

0.00940 (0.00820)

-0.00663 (0.09870)

-0.33221 (1.33312)

-3.24379 (22.77942)

Literacy Deviation -0.00451 (0.01699)

0.03927-d (0.15580)

0.01783 (0.02323)

0.02809 (0.14500)

14.46363-b (1.42+12)

-0.05911 (0.09229)

-0.06493 (5.76933)

-3451037 (38.71704)

172.1906-c (327.5045)

Urban population Median 0.00020

(0.00039) 0.00380-b (0.00487)

0.00047-c (0.00123)

0.00552 (0.00821)

1.49103 (1206.141)

-0.00328 (0.00367)

-0.01249 (0.08158)

-0.15816 (0.86575)

13.01519-c (35.90799)

Urban population Deviation -0.01933cc (0.01082)

0.06310 (0.13504)

0.02051-c (0.04188)

0.12308-d (0.19124)

-10.38068-b (56970.21)

0.23121b (0.10071)

0.00500 (1.71620)

46.16831c (2717811)

-428.664-c (414.3115)

Girl to boy ratio Median -0.00051 (0.00189)

0.00550 (0.02307)

0.00433 (0.00693)

0.00015 (0.0417)

-1.37363 (10373.23)

-0.01356 (0.02010)

-0.00244 (0.040772)

-1.66100 (3.55513)

31.94503 (109.6724)

Girl to boy ratio Deviation 0.00507-c (0.00466)

0.01086 (0.06315)

-0.01432-a (0.01357)

-0.00687 (0.09829)

7.758183-a (1.19+12)

-0.09345d (0.03605)

-0.02126 (1.23605)

-18.42037b (7.87635)

442.8353ba (211.719)

Age 0.00273-d (0.00264)

0.02855-b (0.02932)

0.00400-d (0.00585)

0.04167-a (0.03261)

-1.59137 (3525.128)

0.00034 (0.02564)

0.06899 (0.173436)

6.03853 (4.69116)

-10.76294 (297.541)

Age2 -0.00004 (0.00008)

-0.00057-c (0.00088)

-0.00007 (0.00018)

-0.00085-b (0.00090)

0.07753 (3.53+09)

-0.00002 (0.00102)

-0.00463 (0.01199)

-0.07085 (0.15193)

1.22866-a (16.11009)

Observations 101 102 99 102 95 89 27 98 104 Note: See Note to Table 2. The “Median” variables are within-MFI medians (calculated from only the observations used in the regression), while the “Deviationi” variables are devi-ations from this median in a given year.