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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. [email protected]
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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.