Post on 08-Dec-2021
MICROFINANCE INSTITUTIONS
AND FINANCIAL INCLUSION
Dissertation
At the Frankfurt School of Finance and Management
Supervised by
Prof. Dr. Adalbert Winkler (Frankfurt School of Finance and Management)
Prof. Dr. Michael Schröder (ZEW – Leibniz Centre for European Economic Research)
Prof. Dr. Øystein Strøm (Oslo Business School)
Submitted by
Tania Lorena López Urresta
Disputation date: July 31st, 2019
Doctoral Programme
Frankfurt, Germany
December 2019
TABLE OF CONTENT
INTRODUCTION ..................................................................................................................... 5
THE DEBT STRUCTURE OF MICROFINANCE INSTITUTIONS – DOES IT STILL
FOLLOW THE LIFE-CYCLE THEORY? ............................................................................. 17
THE CHALLENGE OF RURAL FINANCIAL INCLUSION – EVIDENCE FROM
MICROFINANCE ................................................................................................................... 72
DOES FINANCIAL INCLUSION MITIGATE CREDIT BOOM-BUST CYCLES?.......... 127
STATEMENT OF CERTIFICATION .................................................................................. 189
3
Dedicada a mis padres como muestra de mi infinita
gratitud por sus esfuerzos y sacrificios
4
ACKNOWLEDGEMENTS
First and foremost, I would like to express my sincere appreciation and gratitude to my
advisor Prof. Adalbert Winkler for his continuous support during my PhD studies and related
research. I thank Adalbert, not only for sharing his knowledge and constructive feedback
academically and professionally, but also for his patience, encouragement and motivation
while going through tough times of the PHD. I also appreciate his principles and hardworking
attitude, which have inspired my personal life.
Besides my advisor, a very special gratitude also goes to the rest of my reading committee
members: Prof. Michael Schröder and Prof. Øystein Strøm who have enriched my work with
insightful comments and suggestions during the final stages.
Special thanks to my family: my parents and brothers for their unconditional love and for
teaching me that the greatest goals deserve sacrifices. They have even celebrated my littlest
achievements, but have also given the strength in tough moments of my life. Last but not the
least; I would like to express how grateful I am to my husband who has been my best
company and best friend. I cannot thank him enough for his practical and emotional support,
for his understanding, but, above all, for motivating me to keep going when challenges
seemed too difficult to be overcame.
5
INTRODUCTION
Over the last decades, microfinance, i.e. the provision of financial services to
microbusinesses and low-income households, has become an integral part of the financial
inclusion agenda. The latter is based on the idea that broadening access to and increasing the
use of formal financial services reduce transactions costs and allow the poor to take
advantage of profitable investment and welfare enhancing consumption smoothing
opportunities (Buera et al. 2015, Demirgüc-Kunt et al. 2018). Some studies (Collier et al.
2009) even suggest that an efficient management of household finances is more important for
poor households than for medium- or high-income households.
Microfinance institutions (MFIs) are widely seen as key drivers of financial inclusion, also
because they operate at the edges of both the formal financial sector (MFI banks, credit
unions and non-bank financial intermediaries) and the informal financial sector (Non-
Governmental Organizations (NGOs)). Accordingly, they often represent the institutional
backbone for expanding financial inclusion. More recently the terms microfinance and
financial inclusion have either been used as quasi synonyms (ADB 2019) or “microfinance”
has quietly been crowded out and replaced by the term “financial inclusion” (Taylor 2012,
Schmidt 2017).1
This PhD thesis contributes with three papers to the literature on microfinance institutions
and financial inclusion. First, we study the development of the MFI funding structure over
time by testing a modified version of the life-cycle hypothesis (LCH) of the MFI capital
structure. Second, we analyze the challenges MFIs face in expanding financial inclusion in
1 Partly, this change in terminology might also have been driven by developments that have cast doubts on the
view that microfinance has only positive impacts on client income and welfare, such as over-indebtedness crises
in several microfinance markets (Chen et al. 2010, CGAP 2010, Wagner and Winkler 2013,,Schicks 2014) and
findings suggesting that the impact of microfinance on client income and welfare is marginal at best (Bruhn and
Love 2014, Banerjee et al. 2015, Beck 2015).
6
rural areas. Finally, we assess the impact of higher and rising levels of financial inclusion on
the depth of credit busts in financial crisis episodes, accounting for the size of the credit
boom in the pre-crisis period. The three papers reflect the development of the debate within
the microfinance and financial inclusion industry over the last decade. The first paper is still
rooted in the microfinance literature as it has developed since the 1980s (Morduch 1999),
even though the topic, the capital structure of MFIs, has emerged in the 1990s only when
microfinance commercialized and MFIs got access to private capital. The second paper,
focusing on credit access in rural areas, continues to have a microfinance orientation but the
financial inclusion aspect gains prominence given the lack of progress in broadening access
to financial services in rural areas. The thesis ends with a paper which goes back to the very
early microfinance literature, namely the broadening of the use of credit. However, it takes a
pure financial inclusion perspective by discussing financial stability aspects of inclusion
within a credit boom-bust cycle framework.
Paper 1: The Debt Structure of Microfinance Institutions – Does It Follow the Life -Cycle
Theory?
This single-authored paper tests a modified life-cycle hypothesis of the MFI capital structure.
The original life-cycle hypothesis of MFI funding (Fehr and Hishigsuren, 2006) suggests that
MFIs in the first stage of their life cycle, often established as NGOs, fund themselves
predominantly by highly risk tolerant public sector funds, e.g. grants and subsidized loans
from donor agencies or development organizations. Over time, however, when gaining size,
developing a track record of profitability, and possibly transforming to a regulated financial
institution, such as a Non-bank financial intermediary or a microfinance bank – MFIs
increasingly access to private debt and equity markets (D’Espallier et al., 2017). Accordingly,
the original life-cycle hypothesis relies on a rather simple picture of the MFI investor
7
universe with just two players: private and public investors characterized by rather
antagonistic investment objectives. Moreover, it reflects an NGO-based narrative implying
that MFIs start as NGOs, mature and might then transform into licensed financial institutions.
The modification of the life-cycle hypothesis is primarily motivated by developments in the
MFI investor universe over the last thirty years, notably the emergence of comparatively
large private-sector led microfinance investment vehicles (MIVs) in mature economies and
the rising amount of funding provided by some domestic governments and agencies with the
goal of fostering domestic private sector development (Gul et al. 2017). However, it also
reflects that the NGO transformation narrative holds for a small minority of MFIs only
(D’Espallier et al., 2017). Most MFIs founded as NGOs remain NGOs, while many of the
MFIs established over the last two decades started as NBFIs or even microfinance banks from
scratch. These developments might explain why the traditional life-cycle theory has had
limited empirical success (Bogan 2012).
The paper contributes to the empirical literature on the development of MFI capital structures
by differentiating between foreign and domestic sources of debt on the one hand, and public
and private sources on the other hand. Taking into account the investment objectives by
foreign private-sector led MIVs and domestic governments, we investigate whether (1) the
share of foreign-private debt in total debt rises, and whether (2) the share of foreign-public
debt in total debt falls, while the share of public domestic debt rises, when MFIs expand.
Moreover, we test whether the share of foreign-private debt rises and the share of domestic-
public debt falls when MFIs become larger and balance financial sustainability with a good
performance on social objectives (i.e. higher depth of outreach expressed by a lower average
loan size and a higher share of female borrowers). We test the validity of the hypotheses
based on a unique, manually collected dataset that provides detailed information about the
share of debt issued by 57 Ecuadorian MFIs to foreign and local as well as private and public
8
investors over the period 2005-2014. To the best of our knowledge, this is the first paper
providing information on the evolving MFI debt structure by distinguishing between four
categories of MFI debt and analyzing this evolution over an extended period of time.
Results from panel fixed effect regressions show that the debt structure of MFIs is largely
driven by changes in size as most other variables do not show significant coefficients.
Concretely, when MFIs become larger the share of foreign, notably foreign-private debt rises.
This provides support for the view that access to foreign-private investment is key for MFIs
funding their growth process over time. Indeed, results point towards a substitution effect
within private capital markets for growing MFIs: foreign private funding, for example by
MIVs, becomes more, domestic private funding less important with rising MFI size. As a
result, the share of private funding as a whole is not affected by rising size.
By contrast, we are unable to find evidence supporting a substitution effect within public
sector funding, i.e. that expanding MFIs issue a larger share of debt to domestic-public
investors and reduce their exposure to foreign-public investors. Neither debt share is
significantly linked to rising MFI size.
Earlier versions of the paper were presented at the V European Microfinance Research
Conference in Portsmouth in June 2017, at the 15th INFINITI Conference on International
Finance in Valencia in June 2017 and at the 8th International Research Workshop in
Microfinance in Oslo in September 2018. Moreover, it was accepted but not presented at the
EEFS 16th Annual Conference in Ljubljana in June 2017 and at the 34th International
Symposium on Money, Banking and Finance in Paris in July 2017. Recently, the paper has
been submitted to the Journal of Business Research.
9
Paper 2: The Challenge of Rural Financial Inclusion – Evidence from Microfinance
In this paper, co-authored with Adalbert Winkler, we focus on the urban-rural dimension
when analyzing whether MFIs aiming for a higher depth of outreach record a lower level of
financial sustainability. Concretely, based on a sample of 772 MFIs (2,470 observations)
reporting to Mixmarket over the period 2008-2013, we test whether MFIs serving a higher
share of rural borrowers are less sustainable than MFIs focusing on urban clients. Moreover,
we analyze whether MFIs with a higher percentage of rural borrowers are less able to exploit
sustainability-enhancing effects of learning, economies of scale and productivity.
The paper is motivated by the observation that access and use of formal financial sector
services has predominantly expanded in urban areas, while the rural population is still
underserved (Beck and Brown 2011, Raghunathan et al. 2011, Allen et al. 2012, Swamy
2014). The lack of progress in rural compared to urban financial inclusion is widely attributed
to greater challenges financial institutions face when serving rural clients. These challenges
include higher transaction costs, higher risks and a more unfavorable contracting environment
(Conning and Udry 2007, Meyer 2011). However, empirical evidence, notably cross-country
empirical evidence on this is scarce, largely due to a lack of data.
Our results show that in principle MFIs with a higher share of rural borrowers do not show a
lower level of sustainability than their peers who focus on urban clients. Thus, MFIs have
demonstrated that lending activities in rural areas can be organized in a sustainable way.
Results also indicate that small-scale MFIs and MFIs with comparatively low levels of loan
officer productivity are – in relative terms – more sustainable when operating in rural
compared to urban areas. We interpret this finding as support for the view that operating in
rural areas also has some advantages, as MFIs can exploit a higher degree of social capital
10
compared to urban areas leading to lower transaction costs. Thus, as long as operations are
relatively small-scale and confined to a certain region, conducting these operations is a
relatively low-cost activity. At the same time, this implies that MFIs with a stronger focus on
rural clients cannot make use of economies of scale and loan officer productivity effects to
the same degree as MFIs focusing on urban areas. Our analysis also provides evidence for
this.
From a policy perspective, our results suggest that promoting the spread of small financial
institutions dedicated to rural activities offers a promising avenue to expand financial
inclusion in rural areas (Chaves and Gonzalez-Vega 1996, Bubna and Chowdhry 2010, Kislat
et al. 2013).
The paper was presented at the IV European Microfinance Research Conference at the
University of Geneva in June 2015, at the 28th Australasian Finance and Banking Conference
in Sydney in December 2015, at the INFINITI conference in Dublin in June 2016, and at the
33rd GdRE International Symposium on Money, Banking and Finance in Clermont-Ferrand
in July 2016. It has been published in the Journal of Applied Economics (2018, 50(14), 1555-
1577, doi.org/10.1080/00036846.2017.1368990).
Paper 3: Does financial inclusion mitigate credit boom-bust cycles? -
Does a higher level of financial inclusion and more rapid progress in financial inclusion in a
pre-crisis period mitigate credit boom-bust cycles by making the bust in the crisis period less
severe? Is financial inclusion itself subject to a boom-bust pattern? These questions are
addressed in the last paper of the thesis, again co-authored with Adalbert Winkler. The paper
is motivated by claims according to which policies making financial sectors more inclusive
would also make them more stable (GPFI 2012, Rahman 2014, Dema 2015).
11
Against this background, we contribute to the literature on the financial inclusion-stability
nexus (Sahay et al. 2015, Čihák et al. 2016, Han and Melecky 2017) by testing whether
financial inclusion mitigates credit boom-bust cycles characterizing financial crises.
Concretely, we analyze whether – given a crisis – a higher level of financial inclusion or
stronger progress in financial inclusion in the pre-crisis period yield a benefit in the form of a
less pronounced drop in credit growth, controlling for the size of the pre-crisis credit boom.
Moreover, we explore whether financial inclusion itself is subject to a boom-bust pattern, i.e.
whether stronger borrower growth in a pre-crisis period is associated with a deeper fall in
borrower growth in a crisis.
Our analysis is based on two country samples. The first sample covers up to 81 countries and
the global financial crisis period; the second sample is based in 51 country specific financial
crisis episodes over the period 2004-2017. As our focus is on credit, we measure the level of
financial inclusion by the share of the population which has a loan outstanding at commercial
banks, and progress in financial inclusion by the growth rate in the number of borrowers in
the pre-crisis period. Data is taken from IMF’s Financial Access Survey (FAS).
Results provide some support for the view that more inclusive banking sectors record less
pronounced declines in credit and borrower growth in times of crisis. However, we also find
that higher borrower growth rates in pre-crisis periods are mainly unrelated to the depth of
the credit bust following a crisis. If significant, coefficients point toward an effect that
reinforces the credit boom-bust cycle. Finally, there is mixed evidence whether countries with
higher borrower growth rates in a pre-crisis period record a greater drop in borrower growth
in crisis times, i.e. there is no clear-cut evidence on whether financial inclusion itself is
subject to boom-bust phenomena.
12
We conclude from this that in a crisis, countries seem to benefit from a higher level of
financial inclusion by recording a less pronounced bust in credit and borrower growth. This
supports the view that higher levels of financial inclusion make financial systems more
resilient in a crisis period. However, rapid progress in financial inclusion has no mitigating
effect on credit developments in a crisis, given pre-crisis credit developments. Thus, for many
developing countries, where reaching higher levels of financial inclusion represents an
important policy objective, our results suggest that managing progress in financial inclusion
represents a challenge if easier access to credit and higher borrower growth rates are
associated with rising credit growth, a key indicator of looming financial instability. Well-
designed policies should account for this by finding ways to expand financial inclusion
without contributing to credit booms.
The paper was presented at the European Microfinance Week in Luxembourg in November
2015, at the 9th Portuguese Finance Network conference in Covilhã in June 2016, at the 2nd
Microfinance and Rural Finance Conference, Financial Inclusion and Emerging Markets
Finance in Aberystwyth in July 2016, at the 2nd International Workshop P2P Financial
Systems in London in September 2016, at the International Conference on Financial Cycles,
Systemic Risk, Interconnectedness, and Policy Options for Resilience, organized by the
Asian Development Bank in Sydney in September 2016, at the Workshop on Banking and
Institutions in May 2017 at the Bank of Finland in Helsinki and the 34th International
Conference of the French Finance Association in Valence (France) in 2017. It has been
published in the Journal of Financial Stability (2019, Volume 43, 116-129
doi.org/10.1016/j.jfs.2019.06.001).
13
References
Allen, F., Demirgüç-Kunt, A., Klapper, L. F., Martinez Peria, M. S. (2012). The foundations
of financial inclusion: Understanding ownership and use of formal accounts. World Bank
Policy Research Working Paper No. 6290, Washington D.C.
Asian Development Bank (ADB) (2019), Financial Inclusion and Microfinance,
https://www.adb.org/sectors/finance/issues/financial-inclusion-microfinance, accessed 25
February 2019.
Banerjee, A., Karlan, D., Zinman, J. (2015), Six Randomized Evaluations of Microcredit:
Introduction and Further Steps. American Economic Journal: Applied Economics, 7(1), 1–21
Beck, T., Brown, M. (2011). Which households use banks? Evidence from the transition
economies. ECB Working Paper No. 1295, Frankfurt a.M.
Beck, T. (2015), Microfinance: A Critical Literature Survey, IEG Working Paper 2015/No. 4,
Washington DC.
Bogan, V. L. (2012), Capital Structure and Sustainability: An Empirical Study of
Microfinance Institutions. The Review of Economics and Statistics, 94:1045-1058
Bubna, A., Chowdhry, B. (2010). Franchising Microfinance. Review of Finance, 14 (3), 451-
476.
Buera, F.J., Kaboski, J.P., Shin, Y. (2015), Entrepreneurship and Financial Frictions: A
Macrodevelopment Perspective, Annual Rev. Econ. 2015. 7:409–436
Bruhn, M., Love, I. (2014), The real impact of improved access to finance: Evidence from
Mexico. The Journal of Finance, 69(3), 1347-1376.
CGAP (2010), Andhra Pradesh 2010: Global Implications of the Crisis in Indian
Microfinance, CGAP Focus Note No. 67, Washington D.C.
14
Chaves, R.A., Gonzelz-Vega, C. (1996). The Design of Successful Rural Financial
Intermediaries: Evidence from Indonesia. World Development, 24(1), 65-78.
Chen, G., Rasmussen, S., Reille, X. (2010), Growth and Vulnerabilities in Microfinance.
CGAP Focus Note No. 61, Washington D.C.
Collier, B., Skees, J., & Barnett, B. (2009). Weather index insurance and climate change:
opportunities and challenges in lower income countries. The Geneva Papers on Risk and
Insurance-Issues and Practice, 34(3), 401-424.
Conning, J., Udry, C. (2007). Rural Financial Markets in Developing Countries. The
Handbook of Agricultural Economics, Vol. 3, Agricultural Development: Farmers, Farm
Production and Farm Markets, edited by Evenson, R.F., Pingali, P., Schultz, T.P., Elsevier,
2857–2908.
Čihák, M., Mare, D.D., Melecky, M. (2016), The Nexus of Financial Inclusion and Financial
Stability, World Bank Policy Research Working Paper 7722, Washington DC.
Dema, E. (2015). Managing the Twin Responsibilities of Financial Inclusion and Financial
Stability. Alliance for Financial Inclusion Viewpoints No. 2, http://www.afi-
global.org/sites/default/files/publications/afi_viewpoints_2_final.pdf , accessed 25 February
2016.
Demirgüç-Kunt, A., Klapper, L., Singer, D. Van Oudheusden, P. (2018). The Global Findex
Database 2014, Measuring Financial Inclusion and the Fintech Revolution, The World Bank,
Washington DC.
D’Espallier, B., Goedecke, J., Hudon, M., Mersland, R. (2017), From NGOs to banks: Does
institutional transformation alter the business model of microfinance institutions? World
Development, 89:19-33
15
Fehr, D., & Hishigsuren, G. (2006). Raising capital for microfinance: Sources of funding and
opportunities for equity financing. Journal of Developmental Entrepreneurship, 11(02), 133-
143.
Global Partnership for Financial Inclusion (GPFI), 2012. Financial Inclusion – A Pathway to
Financial Stability? Understanding the Linkages – Issues Paper, Basel,
https://www.gpfi.org/sites/default/files/documents/GPFI%20SSBs%20Conference%20%20Is
sues%20Paper%203%20Financial%20Inclusion%20%E2%80%93%20A%20Pathway%20to
%20Financial%20Stability_1.pdf, accessed 25 February 2016
Gul, F. A., Podder, J., & Shahriar, A. Z. M. (2017). Performance of microfinance institutions:
: Does Government Ideology Matter?. World Development, 100, 1-15.
Han, R., Melecky, M. (2017). Broader use of saving products among people can make
deposit funding of the banking system more resilient. Journal of International Financial
Markets, Institutions and Money, 47: 89-102.
Kislat, C., Menkhoff, L., Neuberger, D. (2013). The use of collateral in formal and informal
lending. Kiel Working Paper No. 1879
Meyer, R.L. (2011). Subsidies as an Instrument in Agricultural Development Finance:
Review. Joint Discussion Paper of the Joint Donor CABFIN Initiative. Washington D.C.
Morduch, J. (1999), The Microfinance Promise. Journal of Economic Literature 37: 1569–
1614
Raghunathan, U. K., Escalante, C. L., Dorfman, J. H., Ames, G. C., Houston, J. E. (2011).
The effect of agriculture on repayment efficiency: a look at MFI borrowing groups.
Agricultural Economics, 42(4), 465-474.
16
Rahman, A. (2014). The Mutually-Supportive Relationship Between Financial Inclusion and
Financial Stability, Alliance for Financial Inclusion Viewpoints No. 1, http://www.afi-
global.org/sites/default/files/publications/afivp1-11.pdf , accessed 12 February 2016.
Sahay, R:, Čihák , M., N'Diaye, P., Barajas, A., Mitra, S., Kyobe, A., Mooi, Y.N., Yousefi R.
(2015). Financial Inclusion: Can it Meet Multiple Macroeconomic Goals?, IMF Staff
Discussion Note 15/17, Washington DC.
https://www.imf.org/external/pubs/ft/sdn/2015/sdn1517.pdf
Schicks, J. (2014), Over-Indebtedness in Microfinance – An Empirical Analysis of Related
Factors on the Borrower Level, World Development, 54: 301–324
Schmidt, R. H. (2017). Microfinance-once and today (No. 48). SAFE White Paper, No. 48,
Goethe University Frankfurt, SAFE - Sustainable Architecture for Finance in Europe,
Frankfurt a. M.
Swamy, V. (2014). Financial Inclusion, Gender Dimension, and Economic Impact on Poor
Households. World Development, 56(1), 1-15.
Taylor, M. (2012). The Antinomies of ‘Financial Inclusion’: Debt, Distress and the Workings
of Indian Microfinance. Journal of Agrarian Change, 12(4), 601-610.
Wagner, C., Winkler, A., 2013. The vulnerability of microfinance to financial turmoil–
evidence from the global financial crisis. World Dev. 51, 71–90.
17
The Debt Structure of Microfinance Institutions –
Does It Follow the Life-Cycle Theory?
Tania López*
February 2019
Abstract
We modify the life-cycle hypothesis of the debt structure of
microfinance institutions (MFIs) accounting for changes in the
microfinance investor universe over the last three decades. We
test its implications based on a unique dataset covering 57
Ecuadorian MFIs over the period 2005-2014 distinguishing
between origin (foreign vs domestic) and nature (private vs
public) of MFI funding. Regression results show that MFI debt
structure changes are related to changes in MFI size, with
foreign-private debt becoming more and local private debt
becoming less important with rising size. Thus, there is a
substitution effect within private capital markets for growing
MFIs. By contrast, the evidence does not support the notion that
expanding MFIs increasingly obtain funding from domestic-
public investors while reducing their exposure to foreign-public
investors.
JEL classification: F34, G11, G21, O18, O16
Keywords: Microfinance, capital structure, debt financing, debt diversification
* Research Associate, Centre for Development Finance, Frankfurt School of Finance &
Management, Adickesallee 32-34, 60322 Frankfurt am Main, Germany, Tel.: +49 (0)69 /
154008-750, Email: t.lopez@fs.de
18
1. Introduction
Microfinance plays a major role in facilitating access to financial services by microbusinesses
and poor people under-served by formal financial institutions in developing countries.
According to the 2015 State of the Microcredit Summit Campaign Report (Maes and Reed,
2012), microfinance institutions (MFIs) have reached 211 million people worldwide by
offering savings and credit services.
Initially, nascent MFIs were largely funded by the public sector, i.e. by donor agencies,
governments and development organizations (Hudon, 2007). They provided capital in the
form of grants and subsidized loans to support the social mission of microfinance, i.e.
reducing poverty and promoting microbusiness development (Helms, 2006; Goodman, 2006;
Dieckmann et al., 2007). However, since the early 2000s the funding base has widened
substantially as private capital discovered microfinance as an investment object (De Sousa-
Shields and Frankiewicz, 2004).
This development is broadly in line with the life-cycle hypothesis (LCH) of MFI funding
(Fehr and Hishigsuren, 2006) suggesting that MFIs in the first stage of their life cycle,
operating as small-scale, credit-granting NGOs, fund themselves predominantly by highly
risk tolerant public sector funds. Over time, however, when gaining size, developing a track
record of profitability, and possibly transforming to a regulated financial institution, MFIs
were expected to increasingly access private debt and equity markets (Cull et al., 2009;
D’Espallier et al., 2017).2
2 The life-cycle hypothesis is largely concerned about MFI equity, i.e. equity investors. However, the vast
majority of MFI non-deposit funding is in the form of debt (Sapundzhieva, 2011; Lahaye et al., 2012). Thus,
analyzing the capital structure of MFIs implies analyzing the debt structure of MFIs.
19
However, studies based on MFI-level datasets provide only limited support to the life-cycle
theory as it has been difficult to establish a clear link between the (evolution of the) capital
structure of MFIs and their degree of maturity, size or profitability (Bogan, 2012). This might
reflect the fact that the LCH portrays a rather simple picture of the MFI investor universe
with just two players, namely risk-tolerant and development oriented public and risk-averse
and return-oriented private investors.3 However, many private investors, mainly in the form
of specialized microfinance investment vehicles (MIVs, Goodman, 2006), are also influenced
by social and development objectives (Ivatury and Abrams, 2005; Martins and Winkler,
2013; Dorfleitner et al., 2017). Moreover, the original LCH assumes that maturing and
expanding MFIs increasingly tap local debt markets only, i.e. that MFI development and
funding becomes a part of national financial development strategies (Hudon, 2007; Mersland
et al., 2011). In the real world, however, MFI funding has expanded substantially due to the
emergence of a class of socially responsible private, foreign investors (SRIs), largely funding
the above mentioned MIVs. These investors aim for a positive financial return but are also
motivated by broader social or development goals. In addition, local governments have
discovered MFIs as institutions they can use to channel funds dedicated to private sector
development, notably support of micro- and small businesses (Gul et al. 2017). Finally, the
original LCH is based on a narrative that MFIs start as NGOs, expand and mature, and then
transform into licensed financial institutions. The empirical evidence suggests, however, that
this narrative holds for a small minority of MFIs only (D’Espallier et al., 2017). Many of the
MFIs operating today have never been NGOs but started as non-bank financial
intermediaries, cooperatives or banks from scratch. As in particular the latter institutions take
deposits, they might not need (large) support of public investors in the early days of operation
and might be less inclined to take on private non-deposit debt when expanding operations.
3 By contrast, the life-cycle theory is rich with regard to the forms of debt funding. Fehr and Hishigsuren (2006)
name five forms of debt private investors might provide to MFIs, namely loans, guarantee funds, bonds,
securization and inter-bank loans.
20
Against this background, the paper makes two contributions to the literature on MFI capital
structure. First, we present a modified version of the life-cycle hypothesis that reflects the
changes in the MFI investor universe over the last thirty years. Concretely, we acknowledge
the difference between foreign and domestic investors because foreign investors, private as
well as public, might be driven by other investment motives than domestic investors. Second,
we test the validity of the modified hypothesis based on a unique dataset that provides
detailed information about the share of debt issued by 57 Ecuadorian MFIs to foreign and
domestic as well as private and public investors over the period 2005-2014. To our
knowledge, this is the first paper providing information on the evolving MFI capital structure
by distinguishing between four categories of MFI debt and analyzing this evolution over an
extended period of time.4
We find some support for the modified life-cycle as the share of foreign, notably foreign-
private debt rises when MFIs become larger in terms of assets. Theyprovide support for the
view that access to foreign-private investment has been key for MFIs funding their growth
process.
The paper is structured as follows. After a literature review (section 2), we discuss the
importance of distinguishing not only between private and public, but also between foreign
and local investors. Based on this, we derive three hypotheses reflecting a modified version
of the LCH of the MFI capital structure (Section 3). Section 4 introduces the data and
methodology (Section 4), followed by results (Section 5) and robustness checks (Section 6).
A discussion of our findings and conclusions (section 7) end the paper.
4 Most of the previous work focuses on models treating debt either as a uniform category or distinguishes
between origin (private versus public) of funds only (Cobb et al., 2016; Mersland and Urgeghe, 2013). The lack
of data appears to be one of the main obstacles studying debt heterogeneity (Rauh and Sufi, 2010).
21
2. The life-cycle theory and the MFI capital structure – a literature review
Microfinance has been one of the most researched areas of development finance over the last
decades (Morduch 1999, Armendáriz and Morduch 2010, Beck 2015). It has been dominated
by two issues, namely impact (Banerjee et al. 2015) and the sustainability-outreach trade-off
(Von Pischke, 1996; Zeller and Meyer, 2002; Hermes and Lensink, 2011). By contrast, the
MFI capital and debt structure became a research topic in the mid-2000s only. It was
triggered by the LCH on the development of MFI capital structures (Fehr and Hishigsuren,
2006).5 According to this hypothesis, early stage MFIs, usually operating as NGOs and at a
small scale, fund their activities by tapping highly risk tolerant funds in the form of grants
and subsidized loans from the public sector, notably donor agencies and development
organizations. Over time, when MFIs mature and become larger they start accessing private
debt markets. In a mature stage, when MFIs have achieved a certain size, they transform into
regulated financial institutions and issue debt and equity on the open capital market (Figure
1).
- Insert Figure 1 about here -
Following up on the LCH, the literature also addresses the questions whether the capital
structure impacts MFI financial and social performance (D’Espallier et al., 2013; Bogan,
2012; Kar, 2012; Hudon and Traca, 2011; Kyereboah-Coleman, 2007) and which are the
determinants of the MFI capital structure (Cobb et al., 2016, Tchuigoua, 2015; Tchuigoua,
5 This contrasts with the literature on the capital structure of firms which recorded a boom after the seminal
contribution by Modigliani and Miller (1958) with many studies analyzing the determinants and development of
the capital structure of non-financial firms (Ferri and Jones, 1979; Titman and Wessels, 1988; Rajan and
Zingales, 1995). Somewhat similar to microfinance, the capital structure of financial institutions, particularly
banks, has been a much less researched area (Berger and Di Patti, 2006; Berger et al., 2008; Gropp and Heider,
2010), possibly because of regulations and rules that impose certain restrictions on the way banks fund their
operations (i.e. minimum capital requirements).
22
2014; Mersland and Urgeghe, 2013;). Results suggest that MFIs with a higher share of grants
and donations show lower levels of operational self-sufficiency and efficiency (Bogan,
2012).6 At the same time, the provision of subsidized funds is associated with a better social
performance of MFIs (Mersland and Urgeghe, 2013; D’Espallier et al., 2013).7 There is also
evidence that a higher degree of leverage is positively associated with larger breadth of
outreach and better financial performance, while the evidence on depth of outreach is mixed
(Conning, 1999; Kyereboah-Coleman, 2007; Kar, 2012). Turning to the determinants of the
MFI capital structure, the institutional framework prevailing in a country has been found to
influence the ability of MFIs to access external funding (Tchuigoua, 2014). By contrast,
having a rating seems to play a rather modest role in explaining cross-MFI differences in
capital structure (Tchuigoua, 2015).8 Moreover, the type of investor makes a difference as
public donors seem to be largely concerned about the risk level when making an investment
decision. This contradicts the original LCH portraying the public sector as a highly risk
tolerant source of funds. Finally, private as well as public providers of debt appear to prefer
larger MFIs.
As debt is the most important form of MFI funding, substantially exceeding grants and equity
(Zhao and Lounsbury, 2016), some studies explore the determinants of debt heterogeneity in
greater detail. Mersland and Urgeghe (2013), distinguishing between commercial and
subsidized debt provided by MIVs, find that MFIs with a better financial performance are
more likely to tap commercial MIV funding while the probability of having access to
subsidized funding rises with MFIs showing a better social performance, for example in form
6 Endogeneity concerns loom large in these kind of analyses as financial performance is arguably a key driver of
the capital structure, an issue taken up in the second strand of the literature.
7 “Smart” subsidies, below a certain threshold, might even have a positive effect on staff productivity (Hudon
and Traca, 2011).
8 There is also evidence that the impact of ratings depends on the rating agency providing the assessment
(Hartarska and Nadolnyak, 2008).
23
of serving a larger share of female borrowers. Distinguishing between debt provided by
private and public funders, Cobb et al (2016) find that in normal times the absolute level of
private debt is positively associated with MFI size and financial performance, while the
amount of public debt shows no relationship with size and is negatively linked with financial
performance. Thus, private and public investors perform the investment strategies suggested
by the LCH. However, in times of uncertainty results suggest that the investment logics of
private and public funders converge, as public investors put almost as much emphasis on risk
as private investors when risks are perceived as high.
3. The development of the MFI investor base and its implications for the life-cycle
hypothesis of the MFI capital structure
The LCH is based on a simple picture of the MFI investor universe composed of two players,
private and public investors, driven by seemingly antagonistic investment objectives. Public
investors want to promote development and are ready to accept substantial risks to foster their
development goals, while private investors are risk averse and purely return-oriented.9
However, many private investors, notably private foreign investors, are also influenced by
social motives. Risk-return considerations remain relevant as private investors focus on a
core of 100-200 established and profitable MFIs while ignoring most of the remaining 10,000
MFIs operating worldwide (Symbiotics, 2016; Von Stauffenberg and Rosas, 2011). However,
within the financial sustainability constraint they seem to allocate funds also based on MFI
social performance (Dorfleitner et al., 2017).10
This might reflect the fact that many MIVs
represent public-private-partnerships (PPP), with governments or development banks
9 By contrast, the life-cycle theory is rich with regard to the forms of debt funding. Fehr and Hishigsuren (2006)
name five forms of debt private investors for MFIs. 10
For instance, foreign funding may have longer maturities and lower collateral requirements because investors
aim at alleviating the outreach-sustainability trade-off of the receiving MFIs (Deshpande et al., 2007).
24
providing the risky tranches (Hudon, 2007; Moretto and Scola, 2017). Moreover, as MIVs
fund MFIs in many countries they are able to exploit diversification opportunities, i.e. a
rather risky MFI might be seen as investable as its inclusion in the MIV investment portfolio
contributes to a reduction in risk (Krauss and Walter, 2009). Thus, foreign private investors
are likely to be less risk averse than private local investors. In addition, it can be expected
that foreign private investors, given their development mission, respond positively to MFIs’
social performance record while such an effect is likely to be less pronounced for local
private investors guided by a stronger focus on financial returns.
Debt origin might also matter when projecting the contribution of public funding for MFIs.
The traditional LCH suggests that public investors will provide the risky start-up capital
driven by development and poverty alleviation motives. However, this is much more likely to
hold for foreign public than for local public investors, as the former can diversify risks across
MFIs and countries (Cobb et al., 2016) while the latter are unable to do so. Thus, in line with
the original LCH foreign public investors might exit or reduce their share of funding when
MFIs mature and become sustainable in order to reinvest funds with a larger development
impact in other MFIs or other development projects worldwide. By contrast, local public
investors discover MFIs as an investment object after they have reached a certain size and
level of profitability, as this reduces the likelihood of losses and defaults to the disadvantage
of local taxpayers. Thus, many funds set up by local governments to fight poverty and foster
development are more likely to invest funds in reliable partners when channeling resources to
the respective target groups. In doing so they are driven by broad development goals, such as
private sector development and economic growth. As a result, the share of funding provided
by local public investors is likely to increase when MFIs have matured (Gul et al., 2017),
contradicting the predictions of the original LCH for public funding at large. At the same
time, given the overall development orientation, the share of funding by domestic public
25
investors is likely to rise when MFIs focus on larger and more growth-oriented borrowers
suggesting a negative link with depth-of-outreach indicators.
Overall, the analysis calls for modifications of the LCH in the form of differentiating between
foreign and local sources of funds on the one hand, and public and private sources on the
other hand. Based on these modifications we test the following hypotheses:
H1: When MFIs expand, the share of foreign-private debt in total debt rises.
H2: When MFIs expand, the share of foreign-public debt in total debt falls, while the share
of public domestic debt rises.
H3: The share of foreign-private debt rises and the share of domestic-public debt falls
when MFIs become larger and balance financial sustainability with a good
performance on social objectives (i.e. higher depth of outreach expressed by a lower
average loan size and a higher share of female borrowers).
4. Data and Methodology
We test these hypotheses by making use of a unique dataset that provides detailed
information about the debt structure of Ecuadorian MFIs.11
Ecuador is a vibrant microfinance
market where MFIs have passed through the various stages of the MFI life-cycle showing a
rise in size and aiming at balancing financial and social performance objectives, some of
them transformed into regulated institutions within the country’s legal framework.
11
With the exception of social performance indicators all MFI data are taken from AFS, SBS and SEPS. Social
performance indicators are from Mixmarket – a platform that comprises the largest data on microfinance
activities at global level – and Red Financiera Rural (RFR), the largest Ecuadorian microfinance network. About
twenty observations of social indicators represent estimates using extrapolation in order to fill gaps created by
missing values. Rating data are from Microfinanza (http://www.microfinanzarating.com) and Microrate
(http://www.microrate.com).
26
Moreover, Ecuadorian MFIs have been able to tap international as well as local debt markets
by issuing debt to public as well as private investors (Estrella & Cordovez, 2003). Domestic
funding has been supported by a solid business environment for microfinance operations
(EIU, 2010). For example, since 2000, the Red Financiera Rural (RFR), a non-profit national
network pursues the goal of financial inclusion by means of training, institution building,
transparency, self-regulation, among others. MFI funding by foreign investors has been
facilitated by the official dollarization status of the Ecuadorian economy12
as it reduces
foreign currency risks associated with cross-border lending (“original sin”) to zero
(Eichengreen and Hausmann, 1999). In 2015, MFIs in Ecuador were the third largest
recipients of funds provided by Micro Investment Vehicles (MIVs) accounting for a 6.3
percent share of total MIV’s claims (Symbiotics, 2016). Finally, lending by the public sector
increased substantially after the 2007 election of a left-wing government as MFIs are widely
used as institutions to channel public funds in the fight against poverty (Weisbrot et al., 2013;
Gul et al., 2017) as well as providing emergency lending in the aftermath of the global
financial crisis (GFC). Thus, Ecuador represents a good case study to assess whether and how
debt structures change in the course of time.
We manually collect data from 57 MFIs, including 6 MFI banks, 15 NGOs and 36
cooperatives (Table 1), capturing the development of debt structures over the period 2005-
2014. We do so as the most common sources of MFI data, i.e. the MIX database and rating
reports (Tchuigoua, 2016) lack the necessary detail to make them useful for studying the
development of MFI debt structures. We make use of two sources: (1) annual audited
financial statements (AFS) of MFIs downloaded either from the Mixmarket website or
directly from the respective MFI website, and (2) information reported by MFIs to the
12
In 2000, Ecuador officially adopted the U.S. dollar as the local currency.
27
national supervisory authorities SBS and SEPS.13
AFS provide detailed information on MFI
borrowings from individual lenders, which allows us to identify the legal status, nature and
origin of more than 100 different investors, for example by accessing information from their
respective webpages. Information from SBS and SEPS already categorizes debt by investor
type, i.e. Local Financial Institutions, Foreign Financial Institutions, Local Entities from the
same Banking Group, Foreign Entities from the same Banking Group, Local Public Financial
Entities, Foreign Multilateral Organisms, and Local Public Entities.
- Insert Table 1 about here -
Combining these data sources and classifying MFI debt by its origin (local versus
international) and its nature (private versus public), there are four subclasses of MFI debt:
1. Debt issued to Foreign private investors (Fopri), represented by private investment
funds, also known as microfinance investment vehicles (MIVs), international banks,
crowdfunding platforms, associations, and others.14
13
In Ecuador, microfinance banks are supervised by the Superintendence of Banks and Insurances (SBS)
(http://www.superbancos.gob.ec). Credit unions and NGOs are supervised by the Superintendence of Solidarity
and Popular Economy (SEPS) (http://www.seps.gob.ec/estadisticas) which was created in 2011. Before 2011,
the SBS was also supervising large cooperatives which passed later under the SEPS. However, smaller
cooperatives and NGOs were not subject to any regulation.
14 The list of lenders included in the AFS is not completely consistent across institutions. For instance, some
institutions report the Asset Manager (which might manage public and private funds) as the final lender while
others report the Investment Fund. To minimize the bias related to different forms of reporting, we proceed in
the following ways: i) when the fund name is specified, we identify its nature and categorize it in the respective
type ii) when the Asset Manager is listed, we engage in additional research to identify the managed fund. If this
leads to a result, we list the Asset Manager to the respective category the fund belongs to, iii) when the Asset
Manager is listed, but we are unable to identify the investment funds, we consider the respective debt as private
given that Assets Managers are in general private institutions.
28
2. Debt issued to Foreign public investors (Fopu) represented by multilateral and
bilateral development institutions as well as governmental organizations for
international cooperation.
3. Debt issued to Domestic private investors (Dopri) which mainly include local banks,
other MFIs, local microfinance networks and second-floor cooperatives.15
4. Debt issued to Domestic public investors (Dopu), i.e. projects, programs and
institutions funded by municipalities or the national government.
Descriptive statistics (Table 2) show that the average MFI age is 20 years old (median: 16
years). Thus, our sample is dominated by mature institutions, defined by MixMarket as MFIs
with more than eight years of operations (Mature). 5.2% and 11.9% of the observations refer
to MFIs defined as New (up to 4 years of operation) and Young (between 5 and 8 years of
operation), respectively. In total we have 9 (15) institutions in the sample passing the stage
from new to young (young to mature), and 8 institutions passing through the complete life
cycle. Despite the dominance of mature institutions, the sector has advanced rapidly over the
observation period. Mean (median) portfolio growth stands at 30% (25%). Moreover, most
MFIs are profitable with a mean (median) ROA of 2% (1.3%).
Focusing on the capital structure, MFIs record on average an equity ratio of 25% (median:
18%). About 45% of total assets (median 57%) are funded by deposits. This is largely driven
by MFIs operating as cooperatives which traditionally operate a business model that relies on
member deposits for funding lending activities (the deposit/asset ratio for cooperatives
amounts to 66% on average, compared to 50% for banks and 2.5% for NGOs). Non-deposit
debt accounts for 25% of total assets on average. This percentage is higher for NGOs as they
depend significantly on non-deposit debt (46% of their total assets on average). However,
15
Second floor cooperatives are institutions whose members are other cooperatives.
29
MFI banks which have access to deposits (50% of total assets on average) also depend to a
substantial extent on non-deposit debt (28% of total assets on average).
- Insert Table 2 about here -
In absolute amounts, debt issued to foreign private investors is by far the largest source of
funding for MFIs in the country (Figure 2). In line with the global trend, it has seen strong
growth, rising from about USD 100 million in 2005 to more than USD 400 million in 2014.
However, seventy-nine percent of foreign private MFI debt outstanding in 2014 is issued by
seven MFIs only, while twelve MFIs of the sample do not record any debt issuance to foreign
private investors over the observation period. Thus, the Ecuadorian case illustrates the global
finding according to which foreign private investors, notably MIVs, focus on a few, large and
creditworthy MFIs. Thirty-one MFIs of the sample do not issue any debt to foreign public
investors. By contrast, domestic funding can be found in the financial statements of almost all
MFIs at least once; only 4 (0) MFIs do not show financial statements without disclosing
domestic private (public) debt over the observation period. Thus, funding from domestic
sources is much more widely distributed than foreign funding. 16
Domestic public entities have become the second largest source of funding. Debt issued to
this investor type rises from USD 15 million in 2005 to about USD 140 million in 2014. By
contrast, debt provided by domestic private and foreign public investors has been rather
stable during the observation period. Volumes provided are also much smaller, reaching a
maximum of about USD 50 million.
16
Table 2 also reveals that for each debt category there is at least one MFI that funds its debt from the respective
source only. This is indicated by a maximum value of 1 for each debt category. Moreover, for each debt
category there is also at least one MFI that does not issue debt to the respective source, indicated by zero as the
minimum value.
30
- Insert Figure 2 about here -
Debt patterns are different when focusing on the shares of the respective investor types in
total MFI debt. Descriptive statistics reveal that on average MFIs fund about 42% of their
debt from foreign and 54% from domestic sources suggesting that foreign funding remains
important even in mature microfinance markets such as Ecuador. Private sources account for
the bulk (63%) of total funding, while on average the share of public funds in total debt
amounts to 34%.17
The dominance of private debt is in line with the traditional LCH given
that many MFIs in the sample represent mature institutions. However, developments over
time show that the average shares of private and public debt have been converging; while on
average domestic debt has gained in importance relative to foreign debt (Figure 3).
- Insert Figure 3 about here -
When distinguishing between the four subcategories of investors (foreign-private (Fopri),
foreign-public (Fopu), domestic-private (Dopri) and domestic-public (Dopu)), we find that
the share of domestic public funding in MFI debt, which is below 20% in the first years of the
observation period, starts rising significantly after 2009, reflecting the larger availability of
government funds referred to above (Figure 4). In 2014, the average share of Ecuadorian MFI
debt funded by domestic public sources amounts to about 40%, while the share of foreign
public funding in total debt is always below ten percent and has declined to a trough of only
17
The dominance of private debt is even larger when comparing the medians (72% versus 28%).
31
2% in 2014 on average. Private funding is dominated by funding from foreign sources.
However, the shares of private foreign as well as private domestic debt in total MFI debt
decline given the strong rise in the share of domestic public funding.
- Insert Figure 4 about here -
New MFIs, i.e. MFIs which have operated for less than 4 years, fund about 44% of debt with
funds provided by domestic private investors (Figure 5), while they do not access funds from
foreign public sources. Thus, as indicated before, the more recently established MFIs do not
rely on donors, foreign governments and development institutions to start their operations.
For young and mature MFIs the share of debt funded by foreign public sources is low, i.e.
below 5%, which is in line with the view donors do not provide a substantial part of funding
for established MFIs. By contrast, the average share of foreign private funding rises from just
above 25% for new MFIs to about 45% for young MFIs and a 40% for mature ones. This is
consistent with the modified LCH predicting that expanding MFIs increasingly tap foreign-
private capital to fund this expansion. By contrast, the average share of debt funded by
domestic public sources is close to 30% for all MFIs independently of their age.
- Insert Figure 5 about here -
The development of average shares might sketch a distorted picture given that many MFIs do
not tap all sources of debt. Calculating means and medians excluding those MFIs never
issuing debt to the respective investor types shows the share of foreign private debt rises to an
32
average of 61% (median: 65%) when focusing on only those MFIs with proven access to
foreign private debt. This is substantially above the 38% average share recorded for the
sample as a whole. Moreover, MFIs with access to foreign private funding basically do not
issue domestic private debt as the respective share is 3% only (median: 0%). A similar picture
emerges when focusing only on MFIs issuing at least some of their debt to domestic public
investors. They show on average a share of domestic public debt which is substantially higher
(mean: 46%, median: 43%) than for the sample as a whole (mean: 29%, median: 15%).
Moreover, they are – on average – also more active on the domestic private debt market
(mean: 0.28, median 0.17) than the sample as a whole. Thus, MFIs borrowing from domestic
public creditors record – on average – a substantial share of domestic private debt as well.
- Insert Table 3 about here -
Correlation analysis (Table 4) reveals that older MFIs record lower shares of foreign and
private debt, while observing higher shares of public and domestic public debt. This is mainly
driven by falling shares of foreign-private and rising shares of domestic-public debt. The
latter also correlates positively with MFI size. By contrast, size correlates positively with
foreign, foreign private and domestic public debt, while there is negative correlation between
size and private and domestic private debt. Thus, correlation coefficients are in line with
hypotheses 1 and 2 reflecting the modified LCH with regard to foreign-private and domestic-
public debt. Moreover, higher depth of outreach expressed by a lower average loan size is
linked to a lower share of public, notably local public debt, but a higher share of foreign
(private) debt. By contrast, the share of female borrowers does not correlate significantly with
either foreign or domestic debt shares, while there is a positive correlation with (domestic)
33
private debt and a negative correlation with (domestic) public debt. Thus, correlation
coefficients do not unambiguously support the view expressed in hypothesis 3 that foreign
private debt is positively related to social performance. Finally, there is little significant
correlation between debt shares and MFI profitability (RoA), portfolio quality (PAR30) and
asset growth (growth).
Debt structures correlate strongly with MFI type. Cooperatives show significantly larger
(smaller) shares of domestic (private) debt, as the cooperative dummy has a significant
negative (positive) correlation with the share of foreign private (local public) investment. By
contrast, MFI banks and NGOs record higher foreign and private shares in total debt than
cooperatives.
- Insert Table 4 about here -
We continue exploring the relationship between the MFI life cycle and debt structure by
running a fixed effects panel model with the respective debt ratios as dependent variables.
The fixed effects panel regression is the appropriate model as our hypotheses focus on
changes in the debt structure over time and when MFIs change size over time: as MFIs
become larger, their debt structure is expected to change.18
The fixed effects specification
also has the advantage of minimizing endogeneity and omitted variable bias concerns.19
At
18 Moreover, a Hausman specification test rejects the random effects model in favor of the fixed effects model at
a 5% level.
19 Endogeneity is a common issue on capital structure studies. This is mainly related to potential reverse
causality. The MFI literature itself suggests that the choice of capital structure affects performance, while at the
same time performance determines the access to specific sources of funding.
34
the same time, it exposes the analysis of the impact of rising age on the debt structure to
substantial multicollinearity issues. Against this background, we analyze the modified LCH
by focusing on size serving as the variable characterizing the respective MFI’s position in the
life-cycle, i.e. we associate MFIs with rising size with MFIs moving ahead in the life-cycle.
MFIs showing a steady or even declining size are MFIs who have reached a mature state.
We follow the approach taken in the empirical literature on the determinants of corporate
debt (e.g. Johnson, 1997; Cantillo and Wright, 2000; Ferreira and Matos, 2008) and estimate
the following model:
Debt ratio it = β0 + β1 Sizeit + β2Zit + β3 (Sizeit * Zit) + γ i + δtTt+ε it
Debt ratio has a non-negative value constrained between zero and one and represents the
share of outstanding debt in total debt of MFI i issued to a specific group of investors (i.e.
foreign, local, private, public) in year t. Among the explanatory variables reflecting MFI-
specific characteristics, we focus on MFI size, as it is the variable representing the MFI’s
position in the life cycle. However, following the analysis s in section 2, we also look at other
MFI characteristics possibly influencing the MFI debt structure. These variables, i.e. asset
growth leverage, profitability, risk, breadth of outreach (average loan size and share of female
borrowers) as well as the dummy rating, are represented by the vector Z (Table 5 lists all
variables used in the regression). We also make use of these characteristics by creating
introducing interaction terms between size and the relevant MFI characteristics, notably size,
profitability, portfolio quality and outreach. Finally, γi denotes the unobservable MFI fixed
effects, T year dummies and εit is the disturbance term. To mitigate endogeneity concerns, we
employ lagged values of all explanatory variables.
35
- Insert Table 5 about here -
We estimate regression (1) for the share of debt held by foreign investors (Foreign) and the
share of debt held by private investors (Private), i.e. we test the original LCH.20
We continue
by exploiting the level of disaggregation of the data in terms of debt origin and nature. Thus,
we test the modified LCH and run regression (1) for the share of debt held by i) foreign
private investors (Fopri), ii) foreign public investors (Fopu), iii) domestic private investors
(Dopri), and iv) domestic public investors (Dopu). We run both tests in two steps: Initially,
we do not control for any interaction terms between size and MFI characteristics. In a second
step, we introduce interaction terms one by one in order to test whether the relationship
between MFI size and debt structure is moderated by MFI characteristics.
In line with hypotheses 1 and 2, we expect that size shows a positive coefficient for foreign-
private and domestic-public debt, but a negative coefficient for foreign-public debt. With
regard to hypothesis 3 we expect that MFIs with rising size and a rising depth of outreach,
(expressed by declining average loan size and rising shares of female borrowers) show a
rising share of debt issued to foreign-private investors and a lower share of debt issued to
domestic-public investors.
5. Results
Our baseline regressions provide some support for the modified LCH (Table 6).
20 We also run regressions for the counterparties of foreign and public (local and private). As our dependent
variables are proportions of total debt, when having only two categories, the results for the second category are
the same than for the first regression, but with different sign. Thus, we only present the results for foreign and
private as their investments have been the most significant in the Ecuadorian market in absolute numbers.
36
- Insert Table 6 about here -
First, we find that MFI debt structures change over time largely driven by changes in MFI
size as most other variables do not show significant coefficients. Second, results support
hypothesis 1 as MFIs gaining size record a rising share of foreign private debt. Third, we are
unable to confirm hypothesis 2 as size is neither significant in explaining changes in foreign-
public nor in domestic-public debt shares. Fourth, the share of domestic private debt falls
with rising MFI size. This points towards a substitution effect within private capital markets
for growing MFIs: foreign private funding, for example by MIVs, becomes more, domestic
private funding less important with rising MFI size. As a result, the share of private funding
as a whole is not affected by rising size.
Turning to the control variables, we find that changes in these variables are not significantly
associated with changes in the debt structure. There are three exceptions to this. First, rising
growth and declining average loan size are associated with a rise in foreign investment. This
result lends further support to the notion that MFIs expanding their operations with the target
group, the latter indicated by a falling average loan size, rely on foreign investors funding this
expansion. While this is consistent with hypothesis 3, there is no direct support for the
hypothesis as the effect only holds for foreign funding as a whole but not for funding
provided by foreign-private investors. Second, MFIs with a rising share of female borrowers
record a fall in the share of local private debt. This is in line with the view that private local
investors are not primarily focused on the social mission of MFIs but on their financial
performance. Finally, there is evidence that rising credit risk, expressed by a rising portfolio
at risk over thirty days (PAR30) is associated with a rise in private, notably foreign-private
37
debt, but with a fall in the share of domestic public debt. The result supports the view
expressed in the modified LCH that domestic public funds are invested in safe institutions,
contradicting the widely held view that public MFI investors are more risk tolerant than
private investors. By contrast, foreign-private investors seem to be more risk-tolerant, which
is consistent with the notion that they are ready to support MFIs expanding operations and
hence fostering financial inclusion even if this implies a – measured – decline in portfolio
quality (Martins and Winkler 2013).
We shed some more light on the relationship between the MFI life-cycle and debt structure
by introducing interaction effects between size and other MFI characteristics (Table 7).21
When interacting size with the return on assets (RoA) results reveal that the shares of private
debt and local private debt fall. Thus, expanding MFIs with rising profitability rely less on
private, notably domestic-private debt, which is inconsistent with the logics of the original
life-cycle hypothesis. By contrast, it is consistent with the modified LCH pointing to foreign-
private and domestic-public debt as alternative sources of funding for well-performing and
expanding MFIs. However, we are unable to provide direct support for the modified LCH as
the respective interaction terms are not significant for foreign-private and domestic-public
debt. The latter is a general pattern as it also holds for most interaction terms linking rising
size with depth of outreach indicators. An exception is the interaction term between size and
female borrowers in the foreign-private debt estimation showing that foreign private debt
falls when MFIs become larger and serve more female borrowers. This clearly rejects
hypothesis 3 as good performance on social objectives, represented by a rising share of
female borrowers, does not raise but lower the share of foreign private debt for MFIs with a
rising size.
21
The table reports the coefficients of the variables employed in the interaction terms only. Full results,
available on request, do not show significant changes for the remaining variables compared to the baseline
results reported in Table 10.
38
- Insert Table 7 about here -
6. Robustness Checks
We run two major robustness checks. First, we include the lagged dependent variable as a
covariate since the current shares of debt are likely to be affected by their past levels. Thus,
we run a dynamic panel model applying the generalized method of moments estimator
(GMM) (Arellano and Bond, 1991; Arellano and Bover, 1995) to address the problem of
autocorrelation. Second, we run a two-stage Heckman-selection model to account for the fact
that changes in debt shares are only possible if MFIs do have access to the respective sources
of funds. For instance, there are several MFIs which do not tap foreign funds, notably
foreign-private funds, possibly because they are not rated or operate as cooperatives. We test
for this by running a selection equation with the dummy rating and the dummy cooperative
as exclusion restriction variables. Results of the selection equation (see Annex 5) show that
the dummies rating and cooperative have a significant influence on the probability of MFIs
having access to foreign debt. By contrast, they are not significant in explaining access to
domestic debt (results not shown), which probably reflects the fact that 53 out of 57
institutions have access to domestic private debt and all MFIs are able to secure funding from
the domestic public sector. Thus, we report the results of the second-stage equation for
foreign debt shares only. When interpreting these results, it has to be noted that the Heckman
model takes a cross-sectional perspective, i.e. it does not consider changes in variables within
39
MFIs over time as our baseline fixed effects panel regression does. Accordingly, the
coefficients indicate whether MFI debt structures are different for MFIs with different sizes
rather than the response of MFI debt structures to changes in MFI size as postulated in the
original and modified LCH.
Results of the dynamic panel model indicates that the lagged dependent variable is
significantly positive in half of the model specifications suggesting that the actual shares of
debt are influenced by their past levels (Table 8).22
Confirming the baseline result, MFI debt
structure changes appear to be driven by changes in MFI size as the share of foreign,
particularly foreign-private debt, rises with larger size. This supports hypothesis 1. However,
the evidence rejects hypothesis 2 as foreign public debt does not fall and domestic public debt
even falls when MFIs increase in size.
- Insert Table 8 about here -
Interestingly, GMM results show substantially more significant coefficients for other control
variables than baseline results. This holds in particular for the relationship between control
variables and foreign private as well as domestic public debt. While the share of foreign
private debt continues to be driven by rising growth of assets and a lower average loan size,
the same effects can now be found for the share of foreign private debt. Moreover, the rating
dummy is now positively associated with the share of foreign private debt. Overall, the result
for foreign private debt is consistent with hypothesis 1 and 3, i.e. the share of foreign-private
debt rises when MFIs become larger and record a deeper outreach. For domestic public debt
22
The Arellano-Bond test for second order correlation AR(2) provides no evidence of serial correlation as pr > z
is higher than 0.05. The Hansen test does not reject the null hypothesis of over-identified restrictions, so the lags
we use in the model are considered valid instruments.
40
the GMM specification reveals a negative relationship with the return on assets, i.e. rising
MFI profitability is linked to a declining share of local public debt, which again rejects
hypothesis 2. At the same time, there is evidence supporting the line of reasoning underlying
hypothesis 3 as we find that the share of public debt increases when the MFI becomes less
focused on social performance. Coefficients of average loan size and female borrowers
indicate a higher share of domestic public debt the higher (lower) average loan size (the share
of female borrowers served). Finally, most GMM results with interaction terms are
insignificant, i.e. the robustness check confirms that there is little evidence directly
supporting hypothesis 3. The only significant result is for the interaction term between size
and average loan size suggesting that MFIs with deeper depth of outreach have larger shares
of private debt, but this share is decreasing when institutions become larger.
- Insert Table 9 about here -
Turning to the Heckman selection model (Table 10) results confirm that size plays an
important role in driving MFI debt structures: larger MFIs show a higher share of foreign-
private debt. This supports hypothesis 1. Moreover, results lend support to hypothesis 2: the
share of foreign public debt declines with rising size. With regard to control variables, results
provide evidence supporting hypothesis 3, as the share of foreign as well as foreign private
debt responds positively to MFIs becoming more profitable and more socially oriented, while
this is not or less the case of foreign public debt.
- Insert Table 10 about here -
41
However, foreign and foreign private debt shares decline when the share of female borrowers
rise. Testing explicitly for the effect of rising size and rising profitability and rising depth of
outreach via interaction terms again fail to provide significant coefficients. The exception is
the interaction term between size and portfolio at risk which indicates that larger MFIs with
lower portfolio qualities have to fund their operations with a significantly lower share of
foreign private debt.
- Insert Table 11 about here -
7. Conclusions
We contribute to the literature on the MFI capital structure by developing and testing a
modified life-cycle hypothesis. While the original hypothesis predicts that private and
domestic sources will account for an increasing share of debt issued by MFIs over their life-
time, the modified hypothesis accounts for the fact that over the last thirty years a new class
of foreign-private investors emerged which provide funding to MFIs based on social criteria
given financial sustainability. Moreover, the modified hypothesis acknowledges that MFIs
have become a convenient outlet for the domestic public sector channeling funds to (micro)
businesses and hence supporting overall private sector development, i.e. these funds are not
necessarily invested to boost poverty alleviation and empowerment, key goals of
microfinance, but business growth and employment. Accordingly, the modified LCH predicts
42
that foreign-private funding will rise when MFIs expand and show a good performance with
regard to the sustainability-outreach trade-off. By contrast, the public share in MFI debt
might not fall over the MFI life-cycle even though foreign-public debt (as a share of total
debt) declines over time, as local-public debt is likely to rise.
We test these hypotheses based on a novel dataset covering 57 Ecuadorian MFIs over the
period 2005 and 2014. Most importantly, the dataset provides information for the share of
foreign-public, foreign-private, local-public and local-private debt issued by MFIs over time.
Results from panel fixed effect regressions show that the debt structure of MFIs is largely
driven by changes in size. Concretely, when MFIs become larger the share of foreign, notably
foreign-private debt rises. This provides support for the view that access to foreign-private
investment is key for MFIs funding their growth process over time. Indeed, results point
towards a substitution effect within private capital markets for growing MFIs: foreign private
funding, for example by MIVs, becomes more, domestic private funding less important with
rising MFI size. As a result, the share of private funding as a whole is not affected by rising
size.
By contrast, we are unable to find evidence supporting a substitution effect within public
sector funding, i.e. that expanding MFIs issue a larger share of debt to domestic-public
investors and reduce their exposure to foreign-public investors. Neither debt share is
significantly linked to rising MFI size. Finally, there is inconclusive evidence with regard to
foreign-private debt and domestic-public debt shares and changes in depth of outreach. As
stand-alone variables, some specifications show that a declining average loan size and a
rising share of female borrowers are associated with a rising share of foreign-private and
falling share of domestic-public debt. While this is consistent with hypothesis 3, interaction
terms linking size and social performance either fail to be significant or show a result
rejecting the hypothesis.
43
We conclude with a note of caution. Although we present a novel dataset to analyze the
relevance of the life-cycle hypothesis on the development of MFIs debt structure, it is a
dataset representing a country case study. Thus, we are unable to generalize the findings for
the global microfinance market. More research is needed for testing whether distinguishing
between four sources of debt along the lines of origin and nature is useful for understanding
the development of MFI debt structures.
44
Acknowledgements
I would like to thank Tobias Berg, Øystein Strøm, Adalbert Winkler and the participants of
the V European Microfinance Research Conference held in Portsmouth 12-14 June 2017, the
15th INFINITI Conference on International Finance, Valencia, 11-12 June 2017 and the 8th
International Research Workshop in Microfinance in Oslo in September 2018 for helpful
comments and suggestions on earlier versions of this paper.
45
Figure 1: Evolution of MFI financing
I II
Start-Up
Operational
self-
sufficiency
NGO NGO NGO Licensed FI NGO Licensed FI
Donor
Grant and Soft Loans X X X X X X
Private
Commercial Loans X X X X X
Guarantee Funds X X X X X
Bonds X X X X
Securitization X X X X
Inter-bank borrowing X X
Equity
Quasi-equity X X X X
Commercial Equity X X
STAGES
III IV
Comercial level ReturnSinancial Self-sufficiency
Source: Fehr and Hishigsuren (2006)
46
Figure 2: Total Debt Amount in the Ecuadorian Microfinance Market according to the
source of funds
Source: author’ calculations.
Figure 3: Evolution of the MFIs Debt Structure in the Ecuadorian Microfinance Market
according to its origin and nature (average proportions)
0,00
0,10
0,20
0,30
0,40
0,50
0,60
0,70
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
Foreign Domestic
0,00
0,10
0,20
0,30
0,40
0,50
0,60
0,70
0,80
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
Private Public
Source: author’ calculations.
47
Figure 4: Evolution of the MFIs Debt Structure in the Ecuadorian Microfinance Market
according to subcategories (average share)
Source: author’s calculations.
Figure 5: Debt Structure of the Ecuadorian MFIs according to their age (averages)
Source: author’ calculations.
48
Table 1. List of MFIs by Legal Status
Banks NGOs
1 Banco Solidario 7 CCC 15 Fundacion Espoir
2 Codesarrollo 8 Cesol Acj 16 Fundamic
3 D-miro 9 Cepesiu 17 Insotec
4 Procredit 10 Fodemi 18 Ucade Ambato
5 Finca 11 Eclof 19 Ucade Guaranda
6 Coop Nacional 12 Faces 20 Ucade Latacunga
13 FED 21 Ucade Santo Domingo
14 F. Alternativa
Credit Union / Cooperative
22 CACMU 34 COAC Chone 46 COAC Pallatanga
23 CACPE Pastaza Ltda. 35 COAC Fondvida 47 COAC Riobamba
24 CACPE Zamora 36 COAC Fernando Daquilema 48 COAC Sac Aiet
25 CACPECO Ltda 37 COAC Guaranda 49 COAC San Antonio
26 COAC 4 De Oct 38 COAC Jardin Azuayo 50 COAC San Gabriel
27 COAC 29 De Octubre 39 COAC Kullki Wasi 51 COAC SAN JOSE
28 COAC 23 De Julio 40 COAC La Benefica 52 COAC Santa Ana
29 COAC Accion Rural 41 COAC Lucha Campesina 53 COAC Santa Anita
30 COAC Ambato 42 COAC Luz Del Valle 54 COAC Tulcan
31 COAC Artesanos 43 COAC MCCH 55 COAC Virgen Del Cisne
32 COAC Atuntaqui 44 COAC Mushuc Runa 56 Coprogreso
33 COAC Chibuleo 45 COAC Padre Vicente Ponce 57 Union El Ejido
Source: authors’ compilation
49
Table 2. Descriptive Statistics
Variable Obs Mean Median Std0, Dev0, Min Max
Foreign 447 0.42 0.39 0.38 0 1
Domestic 447 0.54 0.54 0.38 0 1
Private 447 0.63 0.71 0.36 0 1
Public 447 0.34 0.23 0.34 0 1
Fopri 447 0.38 0.30 0.37 0 1
Fopu 447 0.04 0.00 0.15 0 1
Dopri 447 0.25 0.09 0.32 0 1
Dopu 447 0.29 0.15 0.34 0 1
Foreign 368 0.43 0.40 0.38 0 1
Domestic 368 0.53 0.54 0.38 0 1
Private 368 0.62 0.70 0.36 0 1
Public 368 0.34 0.23 0.34 0 1
Fopri 368 0.38 0.30 0.37 0 1
Fopu 368 0.05 0.00 0.15 0 1
Dopri 368 0.24 0.09 0.32 0 1
Dopu 368 0.29 0.15 0.33 0 1
AGE 458 20.5 16.0 13.6 0 54
ASSETS (mn) 451 48.4 11.1 95.4 0.4 746.0
LOG_ASSETS 451 16.35 16.23 1.68 12.85 20.43
GROWTH 449 0.30 0.25 0.27 -0.25 1.99
LEV 451 4.49 4.62 2.50 0.02 13.18
EQ_ASSETS 451 0.25 0.18 0.19 0.07 0.98
DEPOSITS_ASSETS 451 0.45 0.6 0.3 0 0.91
D_Rating 458 0.51 1.0 0.50 0 1
S_Rating 458 1.60 2.0 1.64 0 4
BANK 454 0.10 0 0.30 0 1
COOP 454 0.59 1 0.49 0 1
NGO 454 0.31 0 0.46 0 1
ROA 448 0.02 0.01 0.03 -0.23 0.15
PAR30 451 0.04 0.03 0.02 0.00 0.16
Borrowers 438 16,322 5,340 34,173 379 395,047
LOG_BORR 438 8.77 8.58 1.32 5.94 12.89
AV_LOAN_GNI 451 0.51 0.44 0.36 0.05 2.09
FEMALE 451 0.56 0.51 0.16 0.22 1.00
Social Indicators
Dependent Variables
For MATURE MFIs ( > 8 years)
MFI's Institutional Variables
Financial Indicators
Source: authors’ compilation
50
Table 3. Means and Medians for Subsamples based on access to debt sources
Firms with some Fopri Fopu Dopri Dopu
Fopri (n=45)
Mean 0.61 0.18 0.03 0.35
Median 0.65 0.07 0.00 0.31
Fopu (n=26)
Mean 0.03 0.18 0.03 0.03
Median 0.00 0.07 0.00 0.00
Dopri (n=53)
Mean 0.15 0.17 0.34 0.16
Median 0.07 0.05 0.23 0.07
Dopu (n=57)
Mean 0.08 0.09 0.28 0.46
Median 0.03 0.02 0.17 0.43
Shares of debt held by
Source: authors’ compilation
51
Table 4. Correlation Matrix
Variable 1 2 3 4 5 6 7 8 9 10 11 12
1 Foreign 1
2 Domestic -0.8740* 1
3 Private 0.5289* -0.3478* 1
4 Public -0.4291* 0.5197* -0.8522* 1
5 Fopri 0.9255* -0.8103* 0.6194* -0.5346* 1
6 Fopu 0.2275* -0.1951* -0.2135* 0.2557* -0.1582* 1
7 Dopri -0.4865* 0.5554* 0.3999* -0.3329* -0.4719* -0.055 1
8 Dopu -0.5356* 0.6141* -0.7772* 0.9099* -0.4784* -0.1662* -0.3142* 1
9 AGE -0.2999* 0.2594* -0.3585* 0.3346* -0.3085* 0.0113 -0.0424 0.3377* 1
10 LOG_ASSETS 0.1431* -0.1874* -0.1945* 0.1515* 0.1622* -0.0437 -0.4077* 0.1712* 0.1870* 1
11 GROWTH 0.0049 0.0465 0.1190* -0.0672 0.0402 -0.0908 0.0866 -0.0297 -0.1328* -0.2372* 1
12 LEV -0.0598 0.0296 -0.1828* 0.1584* -0.0038 -0.1463* -0.2009* 0.2225* 0.0035 0.5374* 0.0588 1
13 EQ_ASSETS 0.0414 -0.0037 0.1870* -0.1541* -0.0751 0.3012* 0.2977* -0.2839* -0.0341 -0.5229* -0.0527 -0.8324*
14 DEPOSITS_ASSETS -0.3320* 0.2246* -0.3671* 0.2694* -0.2667* -0.1797* -0.1008* 0.3498* 0.1188* 0.4540* -0.074 0.6782*
15 ROA 0.0928 -0.0703 0.0857 -0.0661 0.0243 0.1797* 0.0679 -0.1428* 0.0193 -0.1588* 0.0717 -0.3534*
16 PAR30 0.017 0.0566 0.0409 0.0393 0.0238 -0.017 0.0182 0.0484 -0.1968* 0.0162 -0.0229 0.1972*
17 LOG_BORR 0.3277* -0.3726* 0.0561 -0.1136* 0.3444* -0.0333 -0.3399* -0.1045* 0.0621 0.8714* -0.1898* 0.3447*
18 AV_LOAN_GNI -0.1936* 0.2263* -0.3868* 0.4445* -0.2017* 0.0139 -0.1990* 0.4475* 0.2570* 0.5818* -0.1286* 0.4485*
19 FEMALE 0.0437 -0.0161 0.3454* -0.3312* 0.0505 -0.016 0.3289* -0.3315* -0.2336* -0.4080* 0.0834 -0.4167*
20 D_Rating 0.1656* -0.2122* -0.1051* 0.0548 0.2024* -0.0888 -0.3542* 0.0914 0.2017* 0.5823* -0.1052* 0.1981*
21 S_Rating 0.1484* -0.2216* -0.1261* 0.0475 0.1795* -0.0746 -0.3512* 0.0775 0.2282* 0.6677* -0.1550* 0.2388*
22 BANK 0.2428* -0.3962* 0.0389 -0.2174* 0.2355* 0.0261 -0.2311* -0.2340* -0.0863 0.4818* -0.2040* 0.1599*
23 COOP -0.3504* 0.3829* -0.3103* 0.3677* -0.2803* -0.1908* -0.0191 0.4576* 0.1127* 0.1155* 0.0512 0.4741*
24 NGO 0.2125* -0.1440* 0.3067* -0.2481* 0.1422* 0.1872* 0.1761* -0.3334* -0.0631 -0.4455* 0.0814 -0.6141*
52
Variable 13 14 15 16 17 18 19 20 21 22 23 24
13 EQ_ASSETS 1
14 DEPOSITS_ASSETS -0.6658* 1
15 ROA 0.4554* -0.3214* 1
16 PAR30 -0.1301* 0.1888* -0.2443* 1
17 LOG_BORR -0.3191* 0.1511* -0.0563 -0.0348 1
18 AV_LOAN_GNI -0.4113* 0.5798* -0.1517* 0.0345 0.2255* 1
19 FEMALE 0.4522* -0.5812* 0.1418* -0.1536* -0.0597 -0.5737* 1
20 D_Rating -0.2858* 0.1475* -0.0921 -0.1703* 0.5943* 0.3528* -0.1598* 1
21 S_Rating -0.3041* 0.2081* -0.0936* -0.1955* 0.6597* 0.4135* -0.2007* 0.9691* 1
22 BANK -0.1400* 0.0409 -0.0976* -0.0891 0.5610* 0.0533 -0.0066 0.3354* 0.4234* 1
23 COOP -0.5338* 0.8140* -0.2877* 0.1904* -0.2095* 0.4839* -0.5147* -0.0515 -0.0508 -0.4042* 1
24 NGO 0.6647* -0.8983* 0.3730* -0.1442* -0.1532* -0.5535* 0.5552* -0.1660* -0.2247* -0.2281* -0.7984* 1 Source: authors’ compilation
53
Table 5. List of Variables
VARIABLES CODE DESCRIPTION SOURCE
DEBT STRUCTURE
Foreign Investments ForeignMFI debt provided by foreign investors as a percentage
of total debt AFS, SBS
Domestic Investments DomesticMFI debt provided by Domestic investors as a
percentage of total debt AFS, SBS
Private Investments PrivateMFI debt provided by private investors as a percentage
of total debt AFS, SBS
Public Investments PublicMFI debt provided by public investors as a percentage
of total debt AFS, SBS
Foreign Private Investments FopriMFI debt provided by foreign private investors as a
percentage of total debt AFS, SBS
Foreign Public Investments FopuMFI debt provided by foreign public investors as a
percentage of total debt AFS, SBS
Domestic Private Investment LopriMFI debt provided by Domestic private investors as a
percentage of total debt AFS, SBS
Domestic Public Investment LopuMFI debt provided by Domestic public investors as a
percentage of total debt AFS, SBS
MFIs VARIABLES
Institutional characteristics
Age AGE Number of years since inceptionAFS, MFI's Webpage, Mix
Market
Size LOG_ASSETS Natural Logarithm of total assetsAFS, SBS, Mix Market,
RFR, SEPS
Growth GROWTH Year-to-year percentage change in gross loan portfolioAFS, RFR, SBS, SEPS,
Mix Market
Leverage LEV Total Debt divided by Total EquityAFS, RFR, SBS, SEPS,
Mix Market
Dummy Rating D_RatingDummy equaling 1 if MFI received a rating in that
year, 0 otherwiseMix Market, SBS, SEPS
Rating Score S_Rating
Value between 1 and 4 assigned in line with the Rating
Grade Comparability Table for SMRAs0, In this case,
having 4 the best rated MFIs0,
Mix Market, SBS, SEPS,
Microfinanza SRL,
Microrate
Financial Performance
Profitability ROAReturn on Assets calculated: (Net Operating Income,
less Taxes)/Assets average
AFS, RFR, SBS, SEPS,
Mix Market
Risk PAR 30 Portfolio at risk > 30 days/Gross Loan Portfolio
Social Performance
Depth of Outreach AV_LOAN_GNI Average loan size as percentage of GNI percapita Mix Market, RFR
Targeting of women FEMALETotal women served by the MFI as a percentage of
total borrowersMix Market, RFR
54
Table 6. Panel Data Fixed Effects - Baseline Regression Results
Foreign PrivateForeign-
private
Foreign-
public
Domestic-
private
Domestic-
public
VARIABLES Fopri Fopu Dopri Dopu
L.LOG_ASSETS 0.0966*** -0.0547 0.0817*** 0.0149 -0.136*** 0.0407
(3.28) (-1.34) (2.95) (1.27) (-2.83) (1.19)
L.GROWTH 0.114** 0.0253 0.0813* 0.0328 -0.056 -0.0456
(2.11) (0.45) (1.73) (0.82) (-1.21) (-0.84)
L.DEBT/EQ 0.00317 0.0102 -0.00245 0.00562 0.0127 -0.0176
(0.28) (0.87) (-0.22) (1.49) (0.98) (-1.60)
L.ROA -0.292 0.0559 -0.218 -0.074 0.274 -0.0387
(-0.86) (0.13) (-0.70) (-0.35) (0.63) (-0.11)
L.PAR 30 1.048 1.881** 1.434* -0.386 0.447 -1.667**
(1.43) (2.26) (1.69) (-1.19) (0.58) (-2.21)
L.AV_LOAN_GNI -0.152** 0.045 -0.0655 -0.0865 0.11 0.113
(-2.28) (0.36) (-0.67) (-1.21) (1.29) (1.02)
L.FEMALE 0.102 -0.124 0.122 -0.0197 -0.246* 0.291
(0.71) (-0.71) (0.89) (-0.35) (-1.71) (1.53)
L.D_Rating 0.0292 0.0514 0.0275 0.00165 0.0239 -0.0546
(0.74) (1.20) (0.65) (0.13) (0.74) (-1.34)
Time Dummies YES YES YES YES YES YES
_cons -1.187** 1.481** -1.027** -0.161 2.508*** -0.462
(-2.46) (2.30) (-2.30) (-0.80) (3.35) (-0.81)
N 388 388 388 388 388 388
R-squared 0.8159 0.6766 0.8152 0.658 0.6733 0.6849
This table reports the estimated coefficients of the panel fixed effects model presented in equation (1). The
dependent variable is the proportion of total debt funded by Foreign Investors (Column 1) or the proportion of
total debt funded by Private Investors (Column 2) during the period from 2005 to 2014. Columns 3 to 6 show
the results for the baseline when considering the level of debt for four different categories as dependent variable:
Fopri, Fopu, Dopri and Dopu respectively. Our focus variable is age represented by a dummy variable equal 1 if
an MFI is mature. Our focus and control variables are lagged. Year dummies are included as indicated,
reference category is 2009. T-statistics are provided in parentheses.
55
Table 7. Panel Data Fixed Effects - Baseline Regression Results with interaction terms
Foreign Private Fopri Fopu Dopri Dopu
A: Profitability
L.ASSETS*ROA 0.0145 -0.418* -0.0599 0.0745 -0.357* 0.309
(0.06) (-1.77) (-0.25) (0.62) (-1.67) (1.41)
L.LOG_ASSETS 0.0961*** -0.0424 0.0834*** 0.0127 -0.126** 0.0316
(3.16) (-1.03) (2.91) (1.05) (-2.56) (0.90)
L.ROA -0.51 6.328* 0.683 -1.194 5.644* -4.677
(-0.13) (1.73) (0.19) (-0.63) (1.66) (-1.41)
B: Risk
L.ASSETS*PAR 0.0000286 0.000117 -0.00000832 0.000037 0.000125 -0.000136
(0.29) (0.88) (-0.08) (0.57) (1.01) (-1.12)
L.LOG_ASSETS 0.0968*** -0.0537 0.0816*** 0.0152 -0.135*** 0.0396
(3.25) (-1.32) (2.93) (1.29) (-2.81) (1.16)
L.PAR 30 1.061 1.933** 1.430* -0.369 0.5020 -1.728**
(1.42) (2.31) (1.66) (-1.14) (0.65) (-2.28)
C: Depth of outreach (loan size)
L.ASSETS*AV_LOAN_GNI 0.0251 0.0378 0.0507 -0.0256 -0.0129 -0.0207
(0.99) (0.85) (1.44) (-0.92) (-0.40) (-0.60)
L.LOG_ASSETS 0.0939*** -0.0586 0.0763** 0.0176 -0.135*** 0.0429
(3.05) (-1.57) (2.52) (1.56) (-2.79) (1.32)
L.AV_LOAN_GNI -0.601 -0.6310 -0.972 0.371 0.3410 0.483
(-1.26) (-0.79) (-1.61) (0.82) (0.54) (0.73)
D: Depth of outreach (number of female borrowers)
L.ASSETS*FEMALE -0.102 -0.0765 -0.143** 0.0414 0.0667 0.0453
(-1.63) (-1.02) (-2.36) (1.11) (0.97) (0.66)
L.LOG_ASSETS 0.159*** -0.00744 0.170*** -0.0107 -0.177** 0.0127
(2.95) (-0.11) (3.26) (-0.40) (-2.09) (0.22)
L.FEMALE 1.655* 1.0430 2.305** -0.65 -1.2630 -0.4
(1.71) (0.90) (2.48) (-1.11) (-1.16) (-0.39)
Control Variables YES YES YES YES YES YES
Time Dummies YES YES YES YES YES YES
N 388 388 388 388 388 388
This table reports the estimated coefficients of the panel fixed effects model presented in equation (1). Columns
1 and 2 considered as the dependent variable: the proportion of total debt funded by Foreign Investors and the
proportion of total debt funded by Private Investors during the period from 2005 to 2014. Columns 3 to 6 show
the results for the baseline when considering the level of debt for four different categories as dependent variable:
Fopri, Fopu, Dopri and Dopu respectively. All explanatory variables are lagged. The focus variable is Age
represented by a dummy variable equal 1 if an MFI is mature. The panels shows the results of the interaction
terms between age and profitability (A), age and profitability (B), age and risk (C), age and breadth of outreach
(D), age and depth of outreach (E) and age and female borrowers (F). Control variables and year dummies with
2009 as reference category are included. T-statistics are provided in parentheses.
56
Table 8. Dynamic Panel Data – GMM Results
Foreign PrivateForeign-
private
Foreign-
public
Domestic-
private
Domestic-
public
VARIABLES Fopri Fopu Dopri Dopu
L.Dependent Variable 0.408*** 0.244 0.263* 0.316 1.107*** 0.242
(2.62) (1.11) (1.69) (1.58) (3.63) (1.17)
L.LOG_ASSETS 0.0661** 0.0166 0.0721** -0.000575 -0.00224 -0.0416*
(2.06) (0.42) (2.17) (-0.13) (-0.09) (-1.81)
L.GROWTH 0.140* 0.0474 0.121* 0.00715 -0.114** 0.0402
(1.73) (0.63) (1.92) (0.36) (-1.99) (0.90)
L.DEBT/EQ -0.0128 0.00791 -0.00256 -0.00245 0.00599 -0.00148
(-1.35) (0.78) (-0.28) (-1.01) (0.88) (-0.16)
L.ROA 0.0534 0.816* -0.0231 0.00949 0.463 -0.678***
(0.12) (1.83) (-0.07) (0.07) (0.92) (-2.65)
L.PAR 30 0.72 1.579 1.206 0.0125 1.227 -0.809
(0.76) (1.38) (1.11) (0.10) (1.64) (-1.35)
L.AV_LOAN_GNI -0.200* -0.323 -0.273** 0.000849 0.0105 0.374**
(-1.72) (-1.59) (-2.32) (0.03) (0.22) (2.49)
L.FEMALE 0.0121 0.372** 0.0288 -0.01 0.0429 -0.254**
(0.08) (2.01) (0.16) (-0.18) (0.26) (-2.22)
L.D_Rating 0.0342 0.0384 0.0837* -0.00482 0.038 -0.019
(0.92) (0.95) (1.77) (-0.55) (0.97) (-0.59)
Time Dummies YES YES YES YES YES YES
_cons -0.78 0.03 -0.877* 0.04 -0.06 0.876**
(-1.63) -0.06 (-1.72) -0.39 (-0.15) -2.28
N 386 386 386 386 386 386
AR(1) Pr > z 0.006 0.038 0.017 0.115 0.016 0.038
AR(2) Pr > z 0.356 0.411 0.651 0.367 0.128 0.689
Hansen Test 0.215 0.392 0.39 0.696 0.31 0.556 This table reports the estimated coefficients of the GMM regression model. The dependent variable is the
proportion of total debt funded by Foreign Investors (Column 1) or the proportion of total debt funded by
Private Investors (Column 2) during the period from 2005 to 2014. Columns 3 to 6 show the results for the
baseline when considering the level of debt for four different categories as dependent variable: Fopri, Fopu,
Dopri and Dopu respectively. Our focus variable is age represented by a dummy variable equal 1 if an MFI is
mature. Our focus and control variables are instrumented by their lags. Year dummies are included as indicated,
reference category is 2009. T-statistics are provided in parentheses.
57
Table 9. Dynamic Panel Data – GMM Results with interaction terms
Foreign Private Fopri Fopu Dopri Dopu
A: Profitability
L.ASSETS*ROA 0.0145 -0.418* -0.0599 0.0745 -0.357* 0.309
(0.06) (-1.77) (-0.25) (0.62) (-1.67) (1.41)
L.LOG_ASSETS 0.0961*** -0.0424 0.0834*** 0.0127 -0.126** 0.0316
(3.16) (-1.03) (2.91) (1.05) (-2.56) (0.90)
L.ROA -0.51 6.328* 0.683 -1.194 5.644* -4.677
(-0.13) (1.73) (0.19) (-0.63) (1.66) (-1.41)
B: Risk
L.ASSETS*PAR 0.0000286 0.000117 -0.00000832 0.000037 0.000125 -0.000136
(0.29) (0.88) (-0.08) (0.57) (1.01) (-1.12)
L.LOG_ASSETS 0.0968*** -0.0537 0.0816*** 0.0152 -0.135*** 0.0396
(3.25) (-1.32) (2.93) (1.29) (-2.81) (1.16)
L.PAR 30 1.061 1.933** 1.430* -0.369 0.5020 -1.728**
(1.42) (2.31) (1.66) (-1.14) (0.65) (-2.28)
C: Depth of outreach (loan size)
L.ASSETS*AV_LOAN_GNI 0.0251 0.0378 0.0507 -0.0256 -0.0129 -0.0207
(0.99) (0.85) (1.44) (-0.92) (-0.40) (-0.60)
L.LOG_ASSETS 0.0939*** -0.0586 0.0763** 0.0176 -0.135*** 0.0429
(3.05) (-1.57) (2.52) (1.56) (-2.79) (1.32)
L.AV_LOAN_GNI -0.601 -0.6310 -0.972 0.371 0.3410 0.483
(-1.26) (-0.79) (-1.61) (0.82) (0.54) (0.73)
D: Depth of outreach (number of female borrowers)
L.ASSETS*FEMALE -0.102 -0.0765 -0.143** 0.0414 0.0667 0.0453
(-1.63) (-1.02) (-2.36) (1.11) (0.97) (0.66)
L.LOG_ASSETS 0.159*** -0.00744 0.170*** -0.0107 -0.177** 0.0127
(2.95) (-0.11) (3.26) (-0.40) (-2.09) (0.22)
L.FEMALE 1.655* 1.0430 2.305** -0.65 -1.2630 -0.4
(1.71) (0.90) (2.48) (-1.11) (-1.16) (-0.39)
Control Variables YES YES YES YES YES YES
Time Dummies YES YES YES YES YES YES
N 388 388 388 388 388 388
This table reports the estimated coefficients of the GMM regression model. Columns 1 and 2 considered as the
dependent variable: the proportion of total debt funded by Foreign Investors and the proportion of total debt
funded by Private Investors during the period from 2005 to 2014. Columns 3 to 6 show the results for the
baseline when considering the level of debt for four different categories as dependent variable: Fopri, Fopu,
Dopri and Dopu respectively. The focus variable is Age represented by a dummy variable equal 1 if an MFI is
mature. Our focus and control variables are instrumented by their lags. The panels shows the results of the
interaction terms between age and size (A), age and profitability (B), age and risk (C), age and breadth of
outreach (D), age and depth of outreach (E) and age and female borrowers (F). Year dummies with 2009 as
reference category are included. T-statistics are provided in parentheses.
58
Table 10. Heckman Model
HECKMAN 1 2 3
Foreign Fopri Fopu
L.LOG_ASSETS 0.0488 0.0720*** -0.0233*
(1.60) (3.11) (-1.87)
L.GROWTH -0.208* -0.124 -0.0839*
(-1.72) (-1.35) (-1.72)
L.DEBT/EQ 0.0253 0.0294** -0.00409
(1.48) (2.27) (-0.58)
L.ROA 1.759** 1066.00 0.693**
(2.03) (1.62) (2.00)
L.PAR 30 -3.466** -2.889*** -0.577
(-2.57) (-2.82) (-1.04)
L.AV_LOAN_GNI -0.249** -0.390*** 0.141***
(-1.98) (-4.08) (2.72)
L.FEMALE -0.457** -0.423*** -0.0348
(-2.34) (-2.86) (-0.44)
Time Dummies YES YES YES
_cons 0.472 -0.0359 0.508**
(0.90) (-0.09) (2.39)
N 387 387 387
This table reports the estimated coefficients of the second-stage Heckman Model. The dependent variable is the
proportion of total debt funded by Foreign Investors (Column 1) during the period from 2005 to 2014. Columns
2 and 3 include the subcategories of proportion of debt related to Fopri and Fopu as dependent variable. All
explanatory variables are lagged, The main variable of interest is Age represented by a dummy variable equal 1
if an MFI is mature. Control variables and year dummies are also considered, reference category is 2009. T-
statistics are provided in parentheses.
59
Table 11. Heckman Model with interaction terms
Foreign Fopri Fopu
A: Profitability
L.ASSETS*ROA -0.223 -0.000281 -0.223
(-0.37) (-0.00) (-0.88)
L.LOG_ASSETS 0.0523 0.0720*** -0.0198
(1.63) (2.97) (-1.51)
L.ROA 5.082 1.07 4.011
(0.56) (0.16) (1.06)
B: Risk
L.ASSETS*PAR -0.00153* -0.00138** -0.000152
(-1.68) (-1.99) (-0.38)
L.LOG_ASSETS 0.0573* 0.0797*** -0.0224*
(1.85) (3.40) (-1.78)
L.PAR 30 -4.107*** -3.466*** -0.641
(-2.93) (-3.26) (-1.11)
C: Depth of outreach (loan size)
L.ASSETS*AV_LOAN_GNI 0.00233 -0.016 0.0183
(0.06) (-0.53) (1.03)
L.LOG_ASSETS 0.048 0.0770*** -0.0290**
(1.45) (2.94) (-2.15)
L.AV_LOAN_GNI -0.293 -0.0914 -0.201
(-0.41) (-0.16) (-0.60)
D: Depth of outreach (number of female borrowers)
L.ASSETS*FEMALE 0.102 0.0947 0.00679
(1.05) (1.29) (0.16)
L.LOG_ASSETS -0.0134 0.014 -0.0274
(-0.20) (0.28) (-0.95)
L.FEMALE -2.023 -1.884* -0.14
(-1.35) (-1.65) (-0.21)
Control Variables YES YES YES
Time Dummies YES YES YES
N 388 388 388 This table reports the estimated coefficients of the second-stage Heckman Model. Columns 1 considered as the
dependent variable: the proportion of total debt funded by Foreign Investors during the period from 2005 to
2014. Columns 1 to 2 include the subcategories of proportion of debt related to Fopri and Fopu as dependent
variable. All explanatory variables are lagged. The focus variable is Age represented by a dummy variable equal
1 if an MFI is mature. The panels shows the results of the interaction terms between age and size (A), age and
profitability (B), age and risk (C), age and breadth of outreach (D), age and depth of outreach (E) and age and
female borrowers (F). Control variables and year dummies with 2009 as reference category are included. T-
statistics are provided in parentheses.
60
ANNEXES
61
Table A1. Foreign Private Investors
Cooperatives/NGOs/Foundations MIVs/ Investment Funds
· ADA · ASN Novib Fonds
· ALTERFIN · Bank im Bistum Essen
· Catholic Relief Services · Blue orchard
· Eclof · Calvert Social Investment
· ETIMOS · Cresud
· Fundacion Caixa Catalunya · Credit Suisse Microfinance
· Grameen Trust · Deutsche Bank
· HIVOS · Dexia Microcredit fund
· MCE Microcredit Enterprises · DWM Developing World Market
· Oikocredit · Envest
· Finethic Microfinance
Banks · Fondo Saint-Honore
· Banco Bilbao Kutza · GCMC Global Commercial Microfinance
Consortium (DB)
· BBVA CODESPA · Geneva global
· BCC Credito Cooperativo · Global Partnerships
· Citibank · Gray Ghost Microfinance Fund
· LEHMAN · Impulse
· Rabobank · INCOFIN
· VDK Spaarbank · Kolibri kapital
· LLB EMF Microfinance
· Locfund
Associations · Microvest
· COLAC (Confederación Latinoamericana de COACs)
· MIPRO
· FOGAL (Fondo de Garantia Latinoamericana) · Planet Finance
· ICCREA Banca Spa · ResponsAbility
· Raiffeisen Landesbank Sudtirol AG · SNS Institutional Microfinance Fund
· Swisscontact · Symbiotics
· Triodos
Holdings
· FINCA Capital fund Others
· ProCredit Holding · Smith Barney
· Vision fund · Pettelaar Effectenbewaarbedrjf N.V
· World Vision · PPS Carnegie Consult ((Investment Advisory
Services)
23 Source: authors’ compilation
23
The table presents MFI lenders mentioned in the AFS. In some cases, the investment fund name and its
distributor name are included in the table. We include both names as there are Assets Managers distributing
more than one fund.
62
Table A2. Foreign Public Investors (Multilateral, Bilateral and Development
Institutions)
· AECI-ICO (Agencia Espanola de Cooperacion) · IIC (Interamerican Investment Corporation)
· ACNUR. (UNHCR) The UN Refugee Agency · IFC (International Finance Corporation)
· BIO (Belgium Investment Company for
Developing Countries)· ICO (Instituto de Credito Oficial)
· CAF (Corporacion Andina de Fomento) · KfW (German development bank)
· CDC Ixis Am· LA - CIF S.A. (Latin American Challenge
Investment Fund)
· EBRD (European Bank for Reconstruction and
Development)
· OPIC (Overseas Private Investment
Corporation)
· ELF Emergency Liquidity Fund
· IDB (Interamerican Development Bank)
Source: authors’ compilation
Table A3. Domestic Private Investors
Banks/Financial Institutions NGOs
· Banco Amazonas · CEPESIU
· Banco de Guayaquil
· CEPAM (Centro Ecuatoriano para la
Promoción y Acción de la Mujer)
· Banco del Pacifico · Fundacion M.A.R.C.O.
· Banco Jaramillo Arteaga · Fund. Promocion Humana
· Banco Pichincha · UCADE
· Banco Proamerica
· Banco Solidario Second-Floor Institutions/Networks
· Codesarrollo
· FECOAC (Federacion de Cooperativas
de Ahorro y Credito)
· Coopromic · Financoop (Caja Central Cooperativa)
· Jardin Azuayo
· UCACNOR (Unión de Cooperativas
de Ahorro y Crédito del Norte)
· Produbanco · RFR (Red Financiera Rural)
Private Companies Others
· GMAC del Ecuador
· CODEMIC (Corporacion para el Desarrollo
de la Microempresa)
· Repsol YPF · Fondo Soy
· Microempresas Rurales
· Mision Salesiana
Source: authors’ compilation
63
TableA4. Domestic Public Investors
· CFN (Corporacion Financiera Nacional)
· PNFPEES (Programa Nacional de Finanzas
Populares)
· Banco Ecuatoriano de la Vivienda · Prog. Cred. Prod. Solidario
· Fondo Programa de proteccion social · Prolocal
· Fondo Proquito Migrantes · Proquito
· Fonlocal · PSNM Prog. Sistema Nac. de Microfinanzas
Source: authors’ compilation
64
Table A5: Heckman selection equation
HECKMAN
Foreign Fopri Fopu
L.D_Rating -0.0105 -0.0105 -0.0105
(-0.05) (-0.05) (-0.05)
L.COOP -0.835*** -0.835*** -0.835***
(-3.36) (-3.36) (-3.36)
L.LOG_ASSETS 0.292*** 0.292*** 0.292***
(3.51) (3.51) (3.51)
L.AGE -0.0174*** -0.0174*** -0.0174***
(-3.07) (-3.07) (-3.07)
L.GROWTH 1.603*** 1.603*** 1.603***
(4.13) (4.13) (4.13)
L.DEBT/EQ -0.149*** -0.149*** -0.149***
(-3.12) (-3.12) (-3.12)
L.ROA -9.072** -9.072** -9.072**
(-2.41) (-2.41) (-2.41)
L.PAR 30 9.772*** 9.772*** 9.772***
(2.73) (2.73) (2.73)
L.AV_LOAN_GNI -0.675** -0.675** -0.675**
(-2.17) (-2.17) (-2.17)
L.FEMALE -0.546 -0.546 -0.546
(-0.88) (-0.88) (-0.88)
_cons -2.583* -2.583* -2.583*
(-1.85) (-1.85) (-1.85)
mills
lambda -0.458*** -0.347*** -0.111**
(-3.42) (-3.42) (-1.99)
N 387 387 387
This table reports the estimated coefficients of the first-stage Heckman Model. Columns 1 considered as the
dependent variable: the proportion of total debt funded by Foreign Investors during the period from 2005 to
2014. Columns 1 to 2 include the subcategories of proportion of debt related to Fopri and Fopu as dependent
variable. All explanatory variables are lagged. The exclusion restriction variables are: Rating and legal status as
Cooperative represented by a dummy variable equal 1 if an MFI has a rating or if it is a Cooperative
respectively. T-statistics are provided in parentheses.
7. REFERENCES
65
Arellano, M., & Bond, S. (1991). Some tests of specification for panel data:Monte Carlo
evidence and an application to employment equations. Review of Economic Studies, 58, 277–
297.
Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of
error-components models. Journal of econometrics, 68(1), 29-51.
Armendáriz, B., & Morduch, J. (2010). The economics of microfinance. MIT press.
Banerjee, A., Duflo, E., Glennerster, R., & Kinnan, C. (2015). The miracle of microfinance?
Evidence from a randomized evaluation. American Economic Journal: Applied
Economics, 7(1), 22-53.
Beck, T. (2015), Microfinance – A Critical Literature Survey. World Bank Independent
Evaluation Group Working Paper No. 4, Washington, DC.
Berger, A. N., DeYoung, R., Flannery, M. J., Lee, D., & Öztekin, Ö. (2008). How do large
banking organizations manage their capital ratios? Journal of Financial Services Research,
34(2-3), 123-149.
Berger, A. N., & Di Patti, E. B. (2006). Capital structure and firm performance: A new
approach to testing agency theory and an application to the banking industry. Journal of
Banking & Finance, 30(4), 1065-1102.
Bogan, V. L. (2012). Capital structure and sustainability: An empirical study of microfinance
institutions. Review of Economics and Statistics, 94(4), 1045-1058.
Cantillo, M., & Wright, J. (2000). How do firms choose their lenders? An empirical
investigation. The Review of Financial Studies, 13(1), 155-189.
66
Cobb, J. A., Wry, T., & Zhao, E. Y. (2016). Funding financial inclusion: Institutional logics
and the contextual contingency of funding for microfinance organizations. Academy of
Management Journal, 59(6), 2103-2131.
Conning, J. (1999). Outreach, sustainability and leverage in monitored and peer-monitored
lending. Journal of Development Economics, 60(1), 51-77.
Cull, R., Demirgüç-Kunt, A., & Morduch, J. (2009). Microfinance meets the market.
Contemporary Studies in Economic and Financial Analysis, 92, 1-30.
De Sousa-Shields, M., Frankiewicz, C. (2004). Financing microfinance institutions: the
context for transitions to private capital. Micro Report, 32.
Deshpande, R., Nestor, C., & Abrams, J. (2007). MFI capital structure decision making: a call
for greater awareness. World Bank, Washington, DC.
Dieckmann, R., Speyer, B., & Walter, N. (2007). Microfinance: An emerging investment
opportunity. Deutsche Bank Research. Current Issues. Frankfurt.
Dominicé, R. (2012). Microfinance Investments. Symbiotics. Retrieved from
https://symbioticsgroup.com/wp-
content/uploads/2015/08/Microfinance_Investment_Book_web.pdf
Dorfleitner, G., Röhe, M., & Renier, N. (2017). The access of microfinance institutions to
debt capital: An empirical investigation of microfinance investment vehicles. The Quarterly
Review of Economics and Finance, 65, 1-15.
D’Espallier, B., Goedecke, J., Hudon, M., & Mersland, R. (2017). From NGOs to banks:
Does institutional transformation alter the business model of microfinance institutions?.
World Development, 89, 19-33.
D’Espallier, B., Hudon, M., & Szafarz, A. (2013). Unsubsidized microfinance institutions.
Economics letters, 120(2), 174-176.
67
Eichengreen, B., & Hausmann, R. (1999). Exchange rates and financial fragility (No. w7418).
National bureau of economic research. Cambridge, MA.
Economist Intelligence Unit, (2010). Global Microscope on the Microfinance Business
Environment: An Index and Study by the Economist Intelligence Unit. Inter-American
Development Bank. Retrieved from
http://idbdocs.iadb.org/wsdocs/getdocument.aspx?docnum=35379430
Estrella, H. J., & Cordovez, J. (2003). Microfinanzas en la economía ecuatoriana: una
alternativa para el desarrollo. Microfinanzas en la economía ecuatoriana, 11. Quito.
Fehr, D., & Hishigsuren, G. (2006). Raising capital for microfinance: Sources of funding and
opportunities for equity financing. Journal of Developmental Entrepreneurship, 11(02), 133-
143.
Ferreira, M. A., & Matos, P. (2008). The colors of investors’ money: The role of institutional
investors around the world. Journal of Financial Economics, 88(3), 499-533.
Ferri, M. G., & Jones, W. H. (1979). Determinants of financial structure: a new
methodological approach. The Journal of Finance, 34(3), 631-644.
Goodman, P. (2006). Microfinance investment funds: Objectives, players, potential. In
Microfinance investment funds (pp. 11-45). Springer, Berlin, Heidelberg.
Gropp, R., & Heider, F. (2010). The Determinants of Bank Capital Structure. Review of
Finance, 14(4), 587-622.
Gul, F. A., Podder, J., & Shahriar, A. Z. M. (2017). Performance of microfinance institutions:
: Does Government Ideology Matter?. World Development, 100, 1-15.
Hartarska, V., & Nadolnyak, D. (2008). Does rating help microfinance institutions raise
funds? Cross-country evidence. International Review of Economics & Finance, 17(4), 558-
571.
68
Helms, B. (2006). Access for all: building inclusive financial systems. World Bank.
Washington, DC. Retrieved from
https://openknowledge.worldbank.org/bitstream/handle/10986/6973/350310REV0Access0for
0All01OFFICIAL0USE1.pdf?sequence=1
Hermes, N., & Lensink, R. (2011). Microfinance: its impact, outreach, and sustainability.
World development, 39(6), 875-881.
Hudon, M. (2007). Use of Donor Funds in the Financing of MFIs, CEB Working Paper
07/020, Université Libre de Bruxelles.
Hudon, M., & Traca, D. (2011). On the efficiency effects of subsidies in microfinance: An
empirical inquiry. World Development, 39(6), 966-973.
Ivatury, G., & Abrams, J. (2005). The Market for Foreign Investment in Microfinance:
Opportunities and Challenges. CGAP Focus Note, 30. Washington, DC. Retrieved from
https://www.cgap.org/sites/default/files/researches/documents/CGAP-Focus-Note-The-
Market-for-Foreign-Investment-in-Microfinance-Opportunities-and-Challenges-Aug-2005.pdf
Johnson, S. A. (1997). An empirical analysis of the determinants of corporate debt ownership
structure. Journal of Financial and Quantitative Analysis, 32(01), 47-69.
Kar, A. K. (2012). Does capital and financing structure have any relevance to the performance
of microfinance institutions?. International Review of Applied Economics, 26(3), 329-348.
Krauss, N., & Walter, I. (2009). Can microfinance reduce portfolio volatility?. Economic
Development and Cultural Change, 58(1), 85-110.
Kyereboach-Coleman A., (2007). The impact of capital structure on the performance of
microfinance institutions. The Journal of Risk Finance, 8(1), 56-71.
Lahaye, E., Rizvanolli, R., & Dashi, E. (2012). Current trends in cross-border funding for
microfinance. CGAP. Washington, DC . Retrieved from
69
https://www.cgap.org/sites/default/files/researches/documents/Brief-Current-Trends-in-Cross-
Border-Funding-for-Microfinance-Nov-2012.pdf
Maes, J. P., & Reed, L. R. (2012). State of the microcredit summit campaign report 2012.
Microcredit Summit Campaign. Washington, DC.
Martins, F., & Winkler, A. (2013). Foreign ownership in Latin American microfinance
institutions: evidence and impact. Journal of Business Economics, 83(6), 665-702.
Mersland, R., Randøy, T., & Strøm, R. Ø. (2011). The impact of international influence on
microbanks’ performance: A global survey. International Business Review, 20(2), 163-176.
Mersland, R., & Urgeghe, L. (2013). International debt financing and performance of
microfinance institutions. Strategic Change, 22(1‐2), 17-29.
MicroRate, (2012). The State of Microfinance Investment 2012- MicroRate’s 7th Annual
Survey and Analysis of MIVs. MicroRate, Luminis.
Modigliani, F., & Miller, M. H. (1958). The cost of capital, corporation finance and the theory
of investment. The American Economic Review, 48(3), 261-297.
Morduch, J. (1999). The microfinance promise. Journal of Economic Literature, 37(4), 1569-
1614.
Moretto, L., Scola, B. (2017). Development Finance Institutions and Financial Inclusion:
From Institution-Building to Market Development, Focus Note 105, Washington D.C.: CGAP
Rajan, R. G., & Zingales, L. (1995). What do we know about capital structure? Some
evidence from international data. The Journal of Finance, 50(5), 1421-1460
Rauh, J. D., & Sufi, A. (2010). Capital structure and debt structure. The Review of Financial
Studies, 23(12), 4242-4280.
70
Sapundzhieva, R., (2011). Funding Microfinance-a Focus on Debt Financing. MicroBanking
Bulletin. MIX. Washington, DC. Retrieved from
https://www.themix.org/sites/default/files/publications/MBB-Funding%20Microfinance-
%20A%20focus%20on%20debt%20financing_0.pdf
Symbiotics (2016). 2016 Symbiotics MIV Survey: Market Data & Peer Group. Analysis.
Geneva.
Tchuigoua, H. T. (2014). Institutional framework and capital structure of microfinance
institutions. Journal of Business Research, 67(10), 2185-2197.
Tchuigoua, H. T. (2015). Capital structure of microfinance institutions. Journal of Financial
Services Research, 47(3), 313-340.
Tchuigoua, H. T. (2016). Buffer capital in microfinance institutions. Journal of Business
Research, 69(9), 3523-3537.
Titman, S., & Wessels, R. (1988). The determinants of capital structure choice. The Journal of
Finance, 43(1), 1-19.
Von Pischke, J. D. (1996). Measuring the trade‐off between outreach and sustainability of
microenterprise lenders. Journal of International Development, 8(2), 225-239.
Von Stauffenberg, D., & Rosas, D. (2011). Role Reversal Revisited. Are Public Development
Institutions Still Crowding-out Private Investment in Microfinance?. Washington DC:
MicroRate.
Weisbrot, M., Johnston, J., & Lefebvre, S. (2013). Ecuador’s new deal: reforming and
regulating the financial sector. Center for Economic and Policy Research. February.
Zeller, M., & Meyer, R. L. (Eds.). (2002). The triangle of microfinance: Financial
sustainability, outreach, and impact. Intl Food Policy Res Inst. Baltimore: Johns Hopkins
University Press.
71
Zhao, E.Y., Lounsbury, M. (2016). An institutional logics approach to social
entrepreneurship: Market logic, religious diversity, and resource acquisition by microfinance
organizations. Journal of Business Venturing 31:643-662.
72
The following article is published in the Journal of Applied Economics (2018, 50(14), 1555-
1577, doi.org/10.1080/00036846.2017.1368990).
The Challenge of Rural Financial Inclusion
–
Evidence from Microfinance
Tania López and Adalbert Winkler*
Abstract
Financial inclusion is said to foster development and growth. However, progress in financial
inclusion has been slow in rural areas where poverty is most pronounced. This is often
attributed to higher transaction costs, higher risks and a more unfavorable contracting
environment which makes it more difficult for financial institutions to achieve and maintain
sustainability in rural compared to urban areas. Based on data covering 772 microfinance
institutions (MFIs) over the period 2008-2013 we test whether rural financial inclusion,
notably lending to rural borrowers, is hampered by stronger sustainability challenges than
inclusion in urban markets. Our results suggest that a higher share of rural borrowers has no
direct effect on MFI sustainability. However, we find that MFIs with a higher share of rural
borrowers are less able to exploit economies of scale and productivity effects. Thus, our
results provide support for the view that sustainability challenges make it more difficult to
achieve progress in financial inclusion in rural than in urban areas.
JEL classification: G21, O18, R51
Key words: Financial inclusion, rural lending, microfinance, sustainability
Corresponding author:
Adalbert Winkler Tania López
Academic Head Research Associate
Centre for Development Finance Centre for Development Finance
Frankfurt School of Finance & Frankfurt School of Finance &
Management Management
Adickesallee 32-34 Adickesallee 32-34
60322 Frankfurt am Main, Germany 60322 Frankfurt am Main, Germany
Email: a.winkler@fs.de Email: t.lopez@fs.de
73
1. Introduction
Financial inclusion, i.e. access to and use of formal financial sector services, has been a key
theme in development economics over the last years (Demirgüç-Kunt and Klapper 2012,
Allen et al. 2012, Kumar et al. 2015, Sahay et al. 2015). Policy makers hope that rising levels
of financial inclusion will help to reduce poverty and inequality and to raise growth (UNCDF
2015).1
Substantial progress has been made in raising financial inclusion levels as globally the
number of unbanked people dropped by 20 percent to two billion in the period 2011 – 2014
(Demirguc-Kunt et al. 2015). However, the evidence also shows that access and use of formal
financial sector services has predominantly expanded in urban areas, while the rural
population is still underserved (Schreiner and Colombet 2001, Charitonenko and Campio
2003, Honohan 2008, Beck and Brown 2011, Raghunathan et al. 2011, Allen et al. 2012,
Swamy 2014). This represents a major policy challenge as 80 percent of the poor live in rural
areas (World Bank 2016). Thus, for financial inclusion to make a more significant
contribution to poverty reduction, it has to become more pronounced in rural areas.
The lack of progress in rural compared to urban financial inclusion is widely attributed to
greater challenges financial institutions face serving rural clients while meeting the
sustainability constraint, i.e. operating on a cost covering basis. Concretely, higher transaction
costs, higher risks and a more unfavorable contracting environment have been identified as
factors that make the trade-off between outreach and sustainability more severe in rural than
in urban areas (Conning and Udry 2007, Meyer 2011). However, empirical evidence, notably
cross-country empirical evidence on this is scarce, largely due to a lack of data.
1 However, the empirical evidence on the impact of financial inclusion on end-development goals is mixed
(Duvendack et al. 2011, Banerjee et al. 2015). This also holds for studies focusing on the impact of financial
inclusion in rural areas (see e.g. Burgess and Pande 2005 and Mazumder and Lu 2015).
74
Microfinance institutions (MFIs) have been major players in the drive for financial inclusion
over the last decade. Indeed, recent advances in financial inclusion reflect to a considerable
extent the expansion of microfinance. This expansion has been well documented by data
compilation within the industry, notably by MixMarket and the Microcredit Summit
Campaign (2015).2 According to the former, the number of borrowers (depositors) served by
MFIs has risen from 18.8 million (10.1 million) in 2004 to 112.6 million (100.5 million) in
2014.3 It is reasonable to assume that the bulk of these borrowers (depositors) had previously
relied exclusively on products offered by the informal financial sector.4 Thus, MFIs have
shown that it is possible to expand financial inclusion within the sustainability constraint.
Learning, scale and productivity effects have been identified as key factors allowing MFIs to
successfully manage the sustainability-outreach trade-off (Hardy et al. 2002, Mersland and
Strøm 2009, Caudill et al. 2009, D’Espallier et al. 2017).
We contribute to the financial inclusion literature by analyzing the sustainability-outreach
trade-off MFIs face when focusing on the urban-rural depth of outreach dimension.
Concretely, we run pooled OLS and instrumentable variable (IV) regressions testing whether
MFIs serving a higher share of rural borrowers are less sustainable than MFIs focusing on
urban clients. Moreover, we analyze whether the expansion of financial inclusion is limited by
a lower potential of MFIs in exploiting learning, scale and productivity effects when serving
rural compared to urban borrowers. Our analysis is based on a dataset covering 772 MFIs
operating in 80 countries in the period 2008-2013. These MFIs provide detailed information
on their client base, most importantly by reporting the number of urban and rural borrowers
2 For details on the microfinance datasets and their key characteristics see Bauchet and Morduch (2013).
Comprehensive cross-country data collection efforts directly addressing progress in financial inclusion have
started only recently. For example, the Findex Database provides data for 2011 and 2014 only.
3 The number of MFIs on which this information is based has increased from 302 institutions in 2004 to 1064
institutions in 2014 (Mix Market 2005, 2016). Borrower expansion has been even larger when relying on
information provided by the Microcredit Summit Campaign (2015).
4 On the linkages between microfinance and the informal sector see Guérin et al. (2011), Madestam (2014) and
Islam et al. (2015). Overall the evidence suggests that microfinance can serve as a substitute but also as a
complement to the use of informal financial sector services.
75
they serve. Accordingly, we proxy an MFIs’ contribution to financial inclusion in rural areas
by the share of rural borrowers in total borrowers.5
The paper is structured as follows: After a literature review (section 2), we introduce the data
and the methodology used (section 3). This is followed by a presentation of results (section 4)
and robustness checks (section 5). A discussion of our findings and conclusions (section 6)
end the paper.
2. Sustainability challenges of rural microfinance – a literature review
Poverty is widespread in rural areas while financial inclusion is low. Thus, raising the number
of people served in rural areas is a key goal of MFIs when pursuing their social mission. MFIs
serving a higher share of rural borrowers are said to record a higher “depth of outreach” than
MFIs focusing on urban clients. However, deeper outreach likely implies larger sustainability
challenges.6
The sustainability challenges related to rural lending can be summarized as follows: First,
rural areas are characterized by a lower population density and a less developed infrastructure,
raising transaction costs and operating expenses (Caudill et al. 2009). Second, farming, an
important entrepreneurial activity in rural areas, has features that limit the applicability of the
standard MFI loan product. Lending to farmers implies accounting for seasonality and the
5 Financial inclusion is often measured by the degree of account ownership, i.e. by the share of people with a
sight or term deposit held at a formal financial institution, as many people are banked without necessarily taking
a loan. However, Mixmarket does not provide any information on the rural-urban split with regard to depositors.
Moreover, in many countries MFIs operating as NGOs are usually restricted by regulatory measures in providing
deposit services. Finally, there is some evidence suggesting that “loans and mortgages appear to be better drivers
for financial inclusion than saving products” (Clamara et al. 2014), which justifies the focus on lending and
borrowing in explaining differences in financial inclusion levels between rural and urban areas.
6 Accordingly, MFIs focusing on rural borrowers are confronted with an outreach-sustainability trade-off that
characterizes microfinance operations also with regard to reaching the poorest of the poor – usually captured by
the average loan size as a percentage of GDP / GNI – or with regard to reaching female borrowers (Hermes et.
al. 2011). However, results of some studies raise doubts as to whether such a trade-off exists (see e.g. Louis et al.
2013).
76
need for comparatively larger loans with longer maturities which runs counter to the
microfinance tradition of granting short-term, small installment loans without grace periods
(Armendáriz and Morduch 2010, Field et al. 2013, Di Benedetta et al. 2015). In addition,
agricultural activities are exposed to weather and climate risks that are largely absent in urban
lending (Moll 2005, Meyer 2011). Third, rural environments are characterized by problems of
asymmetric information and contract enforcement that are more difficult and more costly to
address than in urban environments. This may be due to the inherent characteristics of
agricultural and farming activities (Conning and Udry 2007) or a lack of political support in
issuing and implementing legislation facilitating contract enforcement in rural areas (Giné
2011). As a result, MFIs either have to have intimate knowledge about the rural areas they are
operating in or have to invest substantially in screening and monitoring when entering rural
areas. The former limits the potential for growth, while the latter aggravates the trade-off
between outreach and sustainability compared to urban areas. Finally, raising financial
inclusion in rural areas might face a demand problem as rural activities record lower
profitability than petty trade and other activities of urban MFI clients (Harper 2012, Falco and
Haywood 2016). Moreover, at any given interest rate rural clients might be less willing than
their urban peers to take up loans due to a higher degree of risk aversion (Duflo et al. 2011,
Dupas et al. 2012, Kremer et al. 2013) or higher transaction costs (Dehem and Hudon 2013).7
Thus, rural clients might be less able and willing than their urban peers to serve comparatively
high-priced loans.
However, there are also arguments suggesting that MFI lending in rural areas might face a
less severe outreach-sustainability trade-off than in urban areas. For example, MFIs might be
able to exploit the higher stock of “social capital” prevailing in rural areas (DeYoung et al.
7 A lower level of demand for formal financial sector services among rural compared to urban borrowers might
also reflect cost advantages of the informal sector, which consists not only of moneylenders, but also of family,
friends, suppliers, ROSCAs and other informal financial sector arrangements (Guérin et al. 2011). These cost
advantages explain the co-existence of formal and informal financial sector activities (Kochar 1997, Giné 2011),
implying that formal financial sector loan demand might be lower than commonly perceived (Collins et al. 2009,
Meyer 2011, Dupas et al. 2012, Lønborg and Rasmussen 2014).
77
2012) which reduces transaction and risk costs. This is likely to hold in particular for MFIs
with intimate knowledge of the local communities they serve (Chaves and Gonzalez-Vega
1996, Sriram 2005, Conning and Udry 2007, Ledgerwood and Wilson 2013, Bos and Millone
2015). The sustainability advantage of rural lending might be enhanced by lower factor costs,
i.e. wages, prevailing in rural compared to urban areas (Freire-Gibb and Nielsen 2014).
However, these advantages come at a price, namely a markedly lower ability to exploit
learning as well as economies of scale and productivity effects than in urban areas. Learning
effects are small, as within their local communities loan officers already know their local
customers. Moreover, the local community is inherently limited which hampers the ability of
the respective MFIs to exploit economies of scale effects by raising the scale of activities and
by pushing loan officer productivity, i.e. the number of borrowers served by one loan officer.
Attempts by loan officers to expand their customer base beyond the local community is more
costly in rural than in urban areas as local information acts as a more decisive “entry barrier”
to credit markets (Chaves and Gonzalez-Vega 1996). Overall this suggests that MFIs
operating in rural areas are confronted with a significantly more severe outreach-sustainability
trade-off as rural MFIs receive a smaller sustainability boost than their urban peers when
expanding their customer base by becoming more mature, larger and more productive.
The empirical evidence on the sustainability impact of rural activities performed by MFIs is
scarce. Partly, this can be explained by a lack of readily available data. While data on other
depth of outreach indicators, such as average loan size and female borrowers, have been
available for quite some time, data on the extent of rural lending has been provided on a
reasonable scale in recent years only (see section 3). Thus, earlier cross-country studies on the
sustainability-outreach trade-off MFIs face either control for rural orientation by including a
rural variable derived from self-constructed data (Mersland and Strøm 2009) or rely on data
compiled via special collection efforts (Buchenau and Meyer 2007, Caudill et al, 2009, Weber
78
and Musshoff 2012, Epstein and Yuthas 2013).8 Results do not provide support for the view
that a stronger rural focus is detrimental to MFI sustainability. While rural MFIs are found to
be less efficient (Bos and Millone 2015), dummy variables reflecting the degree of rural
orientation either show coefficients that are not significant in explaining MFI performance
(Mersland and Strøm 2009, Caudill et al. 2009) or even suggest that a stronger rural focus is
associated with higher portfolio quality (Raghunathan et al. 2011, Epstein and Yuthas 2013)
and lower operational expenses for personnel (Roberts 2013). Vanroose and D’Espallier
(2013) report results according to which MFIs operating in rural areas show a significantly
higher degree of sustainability than urban MFIs. However, the former serve fewer borrowers
and grant larger loans, i.e. show lower breadth and depth of outreach than the latter. In a
mature economy setting DeYoung et al. (2012) find that small US community banks serving
rural areas are sustainable.
We go beyond this literature in three important aspects. First, we focus on the sustainability
implications of rural lending based on a large MFI sample covering an extended period.
Second, we do not stop at testing the sustainability impact of a stronger rural focus as such but
also explore the factors that account for the impact of a stronger rural orientation on MFI
sustainability. Concretely, we test whether the impact of rural lending varies with MFI
experience and size as well as with the productivity of the loan officers employed. Third, we
explicitly account for the endogeneity of the degree of rural lending and sustainability by
employing an instrumental variable approach.
8 Given this lack of data, some cross-country studies make use of the share of the rural population (e.g. Assefa et
al. 2013) or the share of agriculture in GDP (Ahlin et al. 2011) to account for potential rural-urban differences.
Results show that MFIs operating in more rural environments record a significantly higher portfolio quality and
charge lower interest rates than their urban counterparts. However, there is no significant effect on sustainability.
79
3. Data and Methodology
We base our analysis on a sample of 772 MFIs (2,470 observations) reporting to Mixmarket
over the period 2008-2013. The sample size reflects that only about 1,600 of the 2,585
reporting MFIs provide information on the relative importance of their urban and rural
activities. Moreover, in the baseline regression we exclude a) MFIs with low quality reporting
standards, i.e. MFIs rated by Mixmarket with only one and two diamonds; b) MFIs with no
information about their legal form and other key control variables – Table 1 lists all variables
used in the regression –, and c) MFIs located in countries where information about
macroeconomic and structural developments is available to a limited extent only. We also
winsorize the data with regard to our dependent variable, operational self-sustainability
(OSS), by excluding observations exceeding +/- 3 standard deviations from the mean. After
these adjustments the dataset consists of only seven reporting MFIs for the period 2004-2007;
thus we limit the observation period to 2008-2013.
- Insert Table 1 about here -
Table 2 provides information about the MFI distribution with regard to region and legal form.
In total, MFIs are located in 80 countries, most of them in Latin America and The Caribbean.
Non-bank financial institutions (NBFIs) and NGOs account for the bulk of the institutions. In
addition there are 110 credit unions, 69 banks and 21 rural banks.
- Insert Table 2 about here -
80
We measure MFI sustainability by Operational Self-Sufficiency (OSS). OSS is a measure of
financial performance widely used in the microfinance literature (Alhin et al., 2011, Vanroose
and D’Espallier, 2013; Strøm et al., 2014). By excluding non-operating revenues and
donations, a value larger than 1 indicates that the respective MFI has financial revenues from
operations exceeding financial, operational and impairment loss expenses. Rural financial
inclusion is measured as the percentage of rural borrowers served by an MFI (RURAL). A
higher percentage indicates that the respective MFI is more active in fostering financial
inclusion in rural areas.
Descriptive statistics (Table 3) show that MFIs are sustainable on average, with a mean
(median) OSS of 115% (112%). On average, MFIs serve rural and urban borrowers to an
almost equal extent, with the mean and median shares of rural borrowers at 51% and 54%
respectively. 225 MFIs in the sample exclusively lend to rural (108) or urban (117) borrowers.
The average size of MFIs, expressed by total assets, is USD 68.4 million. The distribution is
highly skewed as the median size is only USD 10.2 million. Thus, the sample includes some
very large MFIs, the largest institution holding assets in the amount of USD 6.1 billion. The
same phenomenon can be observed for the number of borrowers, with the median (mean) MFI
serving about 13,300 (107,400) borrowers. Substantial cross-MFI differences also exist with
regard to loan officer productivity, business concentration, the latter measured as the share of
the gross loan portfolio in total assets, and age. In terms of depth of outreach, the share of
female borrowers amounts to 65% of total borrowers on average. Mean and median of the
average loan size, expressed as a percentage of GNI per-capita, are 51% and 26%
respectively, as some institutions issue considerably larger loans than their peers.
- Insert Table 3 about here –
81
Correlation analysis (Table 4) reveals that a higher share of rural borrowers is positively
associated with OSS. Moreover, smaller MFIs and NGOs are more inclined to serve rural
borrowers. Contradicting expectations MFIs with higher loan officer productivity show a
higher share of rural borrowers in their portfolio, while there is no correlation between MFI
age and rural orientation. The share of rural borrowers is also positively correlated with
business concentration (GLP) and the share of female borrowers but negatively correlated
with loan size. Finally, countries with stronger growth, lower GDP per capita and a higher
share of the rural population are populated by MFIs serving a higher share of rural borrowers.
- Insert Table 4 about here -
We explore whether sustainability constraints are more severe for MFIs serving a higher share
of rural borrowers which would explain why financial inclusion is less pronounced in rural
than in urban areas. The literature review leads to two hypotheses guiding our analysis:
Hypothesis 1: MFIs with a higher percentage of rural borrowers are less sustainable than their
urban peers.
Hypothesis 2: MFIs with a higher percentage of rural borrowers are less able to exploit
sustainability-enhancing effects of learning, economies of scale and productivity.
We test the validity of the hypotheses by estimating the following pooled OLS model:9
(1) OSSi,j,t = α + β1RURALi,j,t + β2Zi,j,t + β3Xj,t+ui,j,t
where OSSi,j,t is the level of operational self-sufficiency for MFI i located in country j in year
t;10
RURALi,j,t is the percentage of rural borrowers;11
Zi,j,t is a matrix of MFI-specific controls;
9 The Hausman test rejects a random effects model. We run a panel fixed effects model as a robustness check
which avoids the unobservable variable bias but is unable to account for time-invariant control variables.
82
Xj,t is a set of country macroeconomic and structural variables. Finally, we include year and
regional dummies.
Given the trade-off between rural outreach and sustainability discussed in the literature (Von
Pischke, 1996; Zeller and Meyer, 2002; Olivares-Polanco, 2005; Cull and Morduch 2007,
Hermes et al. 2011) and expressed in hypothesis 1, we expect a negative sign for the
coefficient β1. We also include interaction terms between the share of rural borrowers as well
as MFI age, size and loan officer productivity (AGE*RURAL, SIZE*RURAL,
PRODUCT*RURAL).12
The coefficients of these terms provide answers about the validity of
hypothesis 2. We expect negative signs given that a higher share of rural borrowers is likely to
dampen the positive sustainability effects of older, larger and more productive MFIs observed
in urban areas.
We include several controls to account for MFI heterogeneity likely to influence OSS such as
age, size, productivity, institutional type, business concentration, female borrowers and
average loan size. AGE represents the number of years the MFI has been operating,13
SIZE is
measured as MFI total assets in USD expressed in natural logs, PRODUCTIVITY accounts for
the number of loans outstanding per loan officer. Given learning, economies of scale and
productivity effects we expect all of them to be positively associated with OSS (Woller 2000,
Hardy et al. 2002, Paxton 2007, Rosenberg 2009, Ahlin et al. 2011; Hartarska et al. 2013,
Vanroose and D’Espallier, 2013; Strøm et al. 2014, Wijesiri et al. 2015). In addition, we
include a dummy which takes the value of 1 when the MFI operates as an NGO (NGO). In
contrast to other institutional forms, i.e. banks, non-bank financial intermediaries (NBFIs),
10
As a robustness check, we replace OSS by the return on assets (RoA) as dependent variable measuring MFI
financial performance.
11 The Mixmarket dataset also contains information about the share of rural loans in total loans and the share of
rural lending in total gross loan portfolio. Both variables are highly correlated with the share of rural borrowers.
12 For the sake of completeness we also run regressions with interaction terms between the share of rural
borrowers and the NGO dummy (NGO), the share of the Gross Loan Portfolio in Total Assets (GLP), the share
of female borrowers (FEMALE) and average loan size as a percentage of GNI per capita (LOANSIZE).
13 For AGE, we also control for possible non-linear effects by including AGE squared as a control variable.
83
credit unions and rural banks, NGOs are usually not subject to specific financial sector
regulation and often founded and run with a stronger focus on outreach than on sustainability.
Thus, we expect a negative coefficient.14
MFI sustainability might also be related to the degree of business concentration, i.e. the share
of total loans in total assets (GLP). The variable signals the importance of lending operations
in MFI activity. We expect a positive coefficient, also because portfolio yields are usually
substantially above yields on alternative assets. Finally, we control for two MFI variables that
serve as additional indicators for the degree of MFI outreach, i.e. the percentage of female
borrowers in total borrowers (FEMALE) and the average loan size expressed as a percentage
of GNI per capita (LOANSIZE) (Paxton, 2007; Cull and Morduch 2007; Caudill et al., 2009;
Strøm et al. 2014, Quayes 2015). Given the assumed trade-off between sustainability and
outreach, we expect a negative coefficient for the percentage of female borrowers and – as
smaller loan size as a percentage of GNI per-capita indicates lending operations with poorer
clients – a positive coefficient for the loan size variable.15
The country context has been found to be an important factor affecting MFI sustainability
(Ahlin et al. 2011). Thus, we control for country and macroeconomic characteristics such as
the level of GDP per capita expressed in purchasing power parity (GDP PPP)16
, real GDP
growth, foreign direct investment net inflows as a percentage of GDP (FDI-GDP), inflation,
financial development (i.e. the private sector credit to GDP ratio), the business climate
(number of procedures needed to start a business), as well as political stability and the
14
Previous studies provide mixed evidence on whether and to what extent MFI performance depends on
governance mechanisms associated with different legal forms, in particular whether NGOs underperform in
terms of efficiency and sustainability (Mersland and Strøm 2009, Servin et al. 2012, Barry and Tacneng 2014).
15 We refrain from accounting for the quality of the loan portfolio (PAR30 or write-offs), the yield on the gross
loan portfolio or financial and operating expenses (as a percentage of total assets) as additional independent
variables due to endogeneity concerns. Most of them are alternative MFI performance indicators rather than
factors explaining MFI performance.
16 Given the focus of our study, it might be surprising that we do not control for the importance of the rural
sector within a country’s economy, for example by including the agriculture value-added to GDP ratio or the
percentage of the rural population in the regressions. However, both variables are highly (negatively) correlated
with GDP PPP. Thus, we refrain from including them in order to limit multicollinearity concerns.
84
absence of violence. All country data is retrieved from World Bank datasets. Finally, we make
use of country and time fixed effects (2008 serving as the reference category). We use robust
standard errors, in the OLS regression clustered on MFI level to address potential
heteroscedasticity.
Running a pooled OLS regression suffers from a possible endogeneity bias, as MFI
sustainability acts as a binding constraint on MFI activities. Accordingly, if rural activities
were to threaten sustainability MFIs – at least in the medium to long run – would have to
respond by reducing these activities. Another source of endogeneity can come from the
possibility that both sustainability and the share of rural borrowers might be determined by an
omitted third factor.
Against this background we go beyond a pooled OLS regression and also run IV regressions.
We address the endogeneity issue by instrumenting the share of rural borrowers by a
population density index (DENSITY) that measures the population density of the city the MFI
headquarter is located in to the population density of the most densely populated city of the
country.17
For example, MFI Fundenuse from Nicaragua has its headquarters in Ocotal, which
has a population density of 491.3 people per square kilometer. The most densely populated
city in the country is Managua with a density of 4054.5 people. Thus, the index value is 0.12.
By contrast, MFI Financiera Fama has its headquarters in Managua, which leads to an index
value of 1. Our instrument captures the idea that headquarter choice reveals the objective
function of MFIs along the outreach-sustainability trade-off. Concretely, MFIs with
headquarters in less dense cities are expected to run operations with a higher share of rural
borrowers than MFIs with headquarters in densely populated cities as the former are better
able than the latter to achieve sustainability despite serving a higher share of rural borrowers.
Salim (2013), Monne er al. (2016) and Vanroose (2016) provide support for this kind of
17 In most countries the city with the highest population density is also the city with the largest population.
Exceptions are Azerbaijan, Bolivia, Colombia, Pakistan and the Philippines.
85
reasoning in a national context when analyzing branch location decisions of MFIs within a
country. We apply this logic to headquarter location decisions within a cross-country panel.
The first stage equation (Table 5) confirms that the instrument is significantly negatively
related to the share of rural borrowers, i.e. MFIs headquartered in more densely populated
cities (relative to the most densely populated city in the respective country) serve a
significantly lower share of rural borrowers. The relevance of our instrument is confirmed by
the F-statistic which is much larger than the rule-of-thumb value of 10 in case of a single
endogenous regressor.
- Insert Table 5 about here -
4. Results
Tables 6 reports results of the pooled OLS regression while Table 7 presents the two-stage
least squares estimates, instrumenting for the share of rural borrowers by the population
density index. The first regression shows results without interaction terms, while regressions 2
– 4 include one by one the interaction terms between the share of rural borrowers and MFI
age, size and productivity, respectively.
- Insert Tables 6 and 7 about here -
The evidence clearly rejects hypothesis 1. Serving a higher share of rural borrowers is not
associated with lower MFI sustainability. There is no specification, neither in the pooled OLS
nor in the IV regressions, showing a significant negative sign of the RURAL coefficient. This
86
suggests that per se financial inclusion in rural areas is not hampered by sustainability
concerns. By contrast, results provide support for hypothesis 2 as most interaction terms show
significantly negative coefficients. Only for AGE the pooled OLS estimate shows an
insignificant coefficient. Thus, rural financial inclusion is constrained by sustainability
concerns when MFIs exploit learning, economies of scale and productivity effects.18
Regressions including RURAL interaction terms with SIZE and PRODUCTIVITY also show
significantly positive coefficients for RURAL as a stand-alone variable. Thus, when MFIs are
comparatively small and rather unproductive, a higher share of rural borrowers boosts
sustainability. Negative sustainability effects of a higher share of rural borrowers associated
with size and productivity become dominant, however, when MFIs reach a size (level of
productivity) close to (somewhat above the) the sample mean.
Finally, estimations with interaction terms (columns 2 – 4) provide for a higher significance
level (age) and larger coefficients for the respective MFI controls applying to MFIs focusing
on urban borrowers only compared to the estimation without interaction terms (column 1).
Again, this lends support to hypothesis 2 as the sustainability effects of the urban vs. rural
distinction become visible when linking the distinction to other MFI characteristics, notably
age, size and productivity.
5. Robustness checks
We run a series of checks to test the robustness of our results. Concretely, we test whether our
results are robust to (1) changes in the sample of MFIs covered, (2) a change in the proxy for
sustainability, and (3) changes in the methodology.
18
We find similar evidence when assessing the role of business concentration as well as female borrowers on
MFI sustainability as the respective interaction terms with RURAL are significant and negative. By contrast, the
importance of institutional type and loan size for MFI sustainability are largely unaffected when introducing
interaction terms. Results are available from the authors on request.
87
The first category of robustness checks relates to changes in the sample. We do this in two
ways. First, we apply a stricter outlier definition, i.e. we exclude MFIs with the smallest (5th
percentile) and the largest (95th percentile) OSS. Second, we expand the sample by also
including MFIs rated with one and two diamonds only by Mixmarket. The latter modification
is motivated by the argument that the enlarged sample might be more representative for the
industry at large than our baseline sample. Estimates put the total number of MFIs operating
worldwide at about 10,000 (Responsability 2013), most of them showing substantial deficits
in management, reporting and accounting (Fitch 2008). Thus, the MFI population is better
represented when including institutions rated with one or two diamonds than by focusing on
institutions rated with three or more diamonds only.
In a second set of robustness checks we replace OSS with the return on assets (RoA) as a
benchmark for MFI sustainability. While the return on assets does not take into account any
subsidies MFIs receive via grants, a clear disadvantage compared to OSS, the indicator is also
free from the many challenges which a proper subsidy adjustment is subject to (Cull 2015).
Thus, the RoA specification is a useful robustness check as it indicates whether and to what
extent the baseline results might be driven by the way subsidies are accounted for.
Our last set of robustness checks involves changes in the econometric methodology.
Concretely we (a) perform a probit analysis, i.e. we ask whether MFIs serving a higher share
of rural borrowers have a lower probability in reaching operational self-sufficiency, i.e. an
OSS value larger than 1, and (b) a panel fixed effects model, i.e. we ask whether increasing
the share of rural borrowers over time has an impact on MFI sustainability independently
from differences across MFIs.19
The probit analysis is motivated by the fact that MFIs pursue
the double bottom line, i.e. they strive to maximize social goals while remaining sustainable
19 We also run a dynamic panel data approach, i.e. we include one-period-lagged OSS as an independent variable
following Arellano and Bover (1995) and Blundell and Bond (1998) to control for endogeneity. We estimate our
model using the two-stepGMM specification with the finite sample correction derived by Windmeijer (2005)
where standard errors are robust to heteroskedasticity and to panel specific autocorrelation. Following Köhler
(2015), we treat all MFI variables as endogenous, while the country variables are treated as exogenous.
88
(Hartarska et al. 2013). This suggests that MFIs might forego an expansion of rural financial
inclusion only if it significantly lowers the probability of achieving self-sufficiency. The
probit analysis captures this. The panel fixed effects model represents an alternative approach
to deal with the unobserved variable bias and informs about whether moving towards a
stronger rural orientation over time leads to significant sustainability challenges. However, as
time invariant factors drop out the model does not allow for any cross-section interpretation of
the results.
Overall, the results of the robustness checks – available from the authors on request – reveal
three important insights:20
First, we robustly do not find any direct negative effect of a higher degree of rural orientation
on MFI sustainability. This clearly rejects hypothesis 1.
Second, the evidence on a higher share of rural borrowers affecting the size of possible
learning effects is mixed at best. While there are some checks showing a weakly significant
negative coefficient for the interaction term between RURAL and AGE, coefficients fail to be
significant in other estimations.
Third, with the exception of the panel fixed effect regressions there is robust evidence that the
sustainability of MFIs serving a higher share of rural borrowers receives a lower boost from
higher loan officer productivity than the sustainability of MFIs serving urban clients.
Moreover, a majority of checks show a significant negative coefficient for the SIZE*RURAL
interaction term. Overall, these results lend support for hypothesis 2: A higher share of rural
borrowers makes it more difficult for MFIs to exploit sustainability enhancing economies of
scale and productivity effects.
20
Both panel fixed specifications have low explanatory power. This might reflect the fact that many MFIs record
little variation over time in the percentage of rural borrowers served (the year-to-year change in the share of rural
borrowers within an MFI is limited to less than +/- 1% in 1,432 observations (318 MFIs)). Thus, our main
variable has characteristics of a time invariant variable. As a result, fixed effect estimates are imprecise and carry
large standard errors (Allison, 2009).
89
6. Discussion and conclusion
Our analysis has two major results. First, in line with the literature reviewed in section 2, we
find that in principle MFIs with a higher share of rural borrowers do not show a lower level of
sustainability than their peers who focus on urban clients. Thus, MFIs have demonstrated that
lending activities in rural areas can be organized in a sustainable way. Second, there is
evidence that MFIs with a stronger focus on rural clients cannot make use of economies of
scale and productivity effects to the same degree as MFIs focusing on urban areas. Thus,
expansion of financial inclusion in rural areas is more difficult than in urban areas as
sustainability concerns put a stricter limit on the breadth of outreach for MFIs focusing on
rural borrowers.
Against the background of the literature reviewed in section 2, our analysis provides support
for both views on the link between rural orientation and MFI sustainability: Small-scale MFIs
and MFIs with comparatively low levels of loan officer productivity receive a sustainability
boost when operating in rural areas, possibly reflecting low transaction costs related to social
capital. However, expanding financial inclusion in rural areas is more difficult than in urban
areas. This is in line with arguments claiming that higher transaction, risk and contract design
costs hamper the ability of rural MFIs to take advantage of economies of scale and
productivity effects which foster MFI sustainability in urban areas.
From a policy perspective our results suggest that promoting the spread of small financial
institutions dedicated to rural activities, including larger networks with semi-autonomous
local institutions, offers a promising avenue to expand financial inclusion in rural areas
(Chaves and Gonzalez-Vega 1996, Bubna and Chowdhry 2010, Kislat et al. 2013). Moreover,
the use of modern technologies, such as mobile banking, might lower the sustainability
disadvantages of expanding rural MFIs by reducing transaction costs, even though the effect
is likely to be substantially smaller for credit than for other financial services, such as
90
payments and transfers (Allen et al. 2014).21
Alternatively, the private sector and/or
governments and the public sector at large might succeed in reducing costs by innovations in
credit contract design and enforcement.22
Theory and empirical evidence suggest that
advances in this field can lead to substantial progress in financial inclusion (Armendáriz and
Morduch 2010, Cull et al. 2009).23
Overall, we conclude from our analysis that progress in financial inclusion is more difficult to
achieve in rural than in urban areas. This holds even though MFIs, important drivers of
financial inclusion in the past, are not subject to a negative sustainability effect of rural
lending per se. However, MFIs serving a larger share of rural borrowers are less able to
exploit sustainability enhancing economies of scale and productivity effects than their urban
peers. As there are no easy ways addressing the more severe trade-off between breadth of
outreach and sustainability for rural MFIs, levels of financial inclusion in rural areas are likely
to remain below those observed in urban areas.
21
However, up to now, there is “no real evidence of MFIs reaching customers in new geographies and/or lower
income segments through m-banking” (Hanouch and Rotman, 2013).
22 Giné (2011) argues that contract enforcement costs represent the major hurdle for a massive expansion in rural
financial inclusion. As a word of caution, however, history (Kranton and Swamy 1999) and the recent experience
in some microfinance markets (Chen et al. 2010) also indicate that new contracting formats might raise severe
financial stability issues leading to overindebtedness and crisis. Since the latter is usually followed by a decline
in financial inclusion, these episodes serve as a reminder that rapid advances in financial inclusion triggered by
contract innovations might be difficult to sustain.
23 The sustainability-outreach trade-off could also be mitigated by governments and donors returning to the
policy approach of the 1960s and 1970s which massively relied on subsidized rural credit (see e.g. Meyer 2011).
However, this is unlikely to happen, as most studies fail to show transformative impacts of financial inclusion on
end-development goals, such as income levels, poverty alleviation or empowerment. Even if these funds were
forthcoming, negative incentive effects could hamper their impact on supply conditions and hence on the
expansion of rural financial inclusion, as documented in the financial repression literature (Conning and Udry
2007).
91
Acknowledgements
We thank two anonymous referees, Wiebke Bartz, Judith Mader, Jan-Egbert Sturm and
participants of the IV European Microfinance Research Conference, held in Geneva 1-3 June
2015, the 28th
Australasian Finance and Banking Conference, Sydney, 16-18 December 2015,
the INFINITI conference, Dublin, 13-14 June 2016, the 33rd GdRE International Symposium
on Money, Banking and Finance, Clermont-Ferrand, 7-8 July 2016 for helpful comments and
suggestions on earlier versions of this paper.
92
References
Ahlin, C., Lin, J., Maio, M. (2011). Where does microfinance flourish? Microfinance
institution performance in macroeconomic context. Journal of Development Economics,
95(2), 105-120.
Allen, F., Demirgüç-Kunt, A., Klapper, L. F., Martinez Peria, M. S. (2012). The foundations
of financial inclusion: Understanding ownership and use of formal accounts. World Bank
Policy Research Working Paper No. 6290, Washington D.C..
Allen, F., Carletti, E., Cull, R., Qian, J., Senbet, L., Valenzuela, P. (2014). The African
Financial Development and Financial Inclusion Gaps. Journal of African Economies, 23(5):
614-642.
Allison, P. D. (2009). Fixed effects regression models (Vol. 160). SAGE, Los Angeles et al.
Arellano, M., Bover, O. (1995). Another look at the instrumental variable estimation of error-
components models. Journal of Econometrics. 68, 29–51.
Armendáriz, B., Morduch, J. (2010). The economics of microfinance. MIT press, Cambridge
and London.
Assefa, E., Hermes, N., Meesters, A. (2013). Competition and the performance of
microfinance institutions. Applied Financial Economics, 23(9), 767-782.
Banerjee, A., Karlan, D., Zinman, J. (2015). Six Randomized Evaluations of Microcredit:
Introduction and Further Steps. American Economic Journal: Applied Economics, 7(1), 1–21
Barry, T.A., Tacneng, R. (2014). The Impact of Governance and Institutional Quality on MFI
Outreach and Financial Performance in Sub-Saharan Africa. World Development, 58(1), 1-20.
Bauchet, J., Morduch, J. (2013). Selective knowledge: Reporting biases in microfinance data.
In: Haase, D. (ed.). The Credibility of Microcredit, Studies of Impact and Performance, Brill,
Leiden, The Netherlands, 52-82.
93
Beck, T., Brown, M. (2011). Which households use banks? Evidence from the transition
economies. ECB Working Paper No. 1295, Frankfurt a.M.
Blundell, R., Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel
data models. Journal of Econometrics, 87, 115–143.
Bos, J. W., Millone, M. (2015). Practice What You Preach: Microfinance Business Models
and Operational Efficiency. World Development, 70, 28-42
Bubna, A., Chowdhry, B. (2010). Franchising Microfinance. Review of Finance, 14 (3), 451-
476.
Buchenau, J., Meyer, R. L. (2007, March). Introducing rural finance into an urban
microfinance institution: The example of Banco ProCredit El Salvador. In International
Conference on Rural Finance Research: Moving Research Results into Policies and Practice,
Food and Agriculture Organization of the United Nations, Rome, March. 19-21.
Burgess, R., Pande, R. (2005). Do Rural Banks Matter? Evidence from the Indian Social
Banking Experiment. American Economic Review, 95(3), 780- 795.
Caudill, S. B., Gropper, D. M., Hartarska, V. (2009). Which microfinance institutions are
becoming more cost effective with time? Evidence from a mixture model. Journal of Money,
Credit and Banking, 41(4), 651-672.
Charitonenko, S., Campion, A. (2003). Expanding Commercial Microfinance in Rural Areas:
Constraints and Opportunities. In Paving the Way Forward: An International Conference on
Best Practice on Rural Finance. Washington, D.C., June (Vol. 2).
Chaves, R.A., Gonzelz-Vega, C. (1996). The Design of Successful Rural Financial
Intermediaries: Evidence from Indonesia. World Development, 24(1), 65-78.
Chen, G., Rasmussen, S., Reille, X. (2010). Growth and Vulnerabilities in Microfinance.
CGAP Focus Note No. 61, Washington D.C.
94
Clamara, N., Peña, X., Tuesta, D. (2014). Factors that Matter for Financial Inclusion:
Evidence from Peru. BBVA Research Working Paper No. 14/09, Madrid.
Collins, D., Morduch, J., Rutherford, S., Ruthven, O. (2009). Portfolios of the Poor, Princeton
University Press, New Jersey.
Conning, J., Udry, C. (2007). Rural Financial Markets in Developing Countries. The
Handbook of Agricultural Economics, Vol. 3, Agricultural Development: Farmers, Farm
Production and Farm Markets, edited by Evenson, R.F., Pingali, P., Schultz, T.P., Elsevier,
2857–2908.
Cull, R., Morduch, J. (2007). Financial performance and outreach: a global analysis of leading
microbanks. The Economic Journal, 117(517), F107-F133.
Cull, R., Demirgüç-Kunt, A., Morduch, J. (2009). Microfinance Meets the Market. Journal of
Economic Perspectives, 23(1), 167-92.
Cull, R. (2015). Does Microfinance Still Hold Promise for Reaching the Poor? Facts and (A
Little) Speculation - Presentation, www.worldbank.org
D’Espallier, B., Goedecke, J., Hudon, M., Mersland, R. (2017), From NGOs to banks: Does
institutional transformation alter the business model of microfinance institutions? World
Development, 89:19-33
Dehem, T., Hudon, M. (2013). Microfinance from the Clients’ Perspective: An Empirical
Enquiry into Transaction Costs in Urban and Rural India. Oxford Development Studies. 41,
117-132.
Demirgüç-Kunt, A., Klapper, L. F. (2012). Measuring financial inclusion: The global findex
database. World Bank Policy Research Working Paper No.6025, Washington D.C.
95
Demirguc-Kunt, A., Klapper, L., Singer, D., Van Oudheusden, P. (2015). The Global Findex
Database 2014: measuring financial inclusion around the world. World Bank Policy Research
Working Paper No. 7255. Washington, D.C.
DeYoung, R., Glennon, D., Nigro, P., Spong, K. (2012). Small Business Lending and Social
Capital: Are Rural Relationships Different? CBE Working Paper No. 2012-1, KU Center for
Banking Excellence, University of Kansas School of Business
Di Benedetta, P., Lieberman, I.W., Ard, L. (2015). Corporate governance in microfinance
institutions. The World Bank, Washington D.C.
Duflo, E., Kremer, M., and Robinson, J. (2011). Nudging Farmers to Use Fertilizer: Theory
and Experimental Evidence from Kenya. American Economic Review, 101(6), 2350–2390
Dupas, P., Green, S., Keats, A. Robinson, J. (2012). Challenges in Banking the Rural Poor:
Evidence from Kenya’s Western Province, NBER Working Paper No. 17851, Cambridge
MA.
Duvendack M., Palmer-Jones, R., Copestake J. G., Hooper L., Loke Y., Rao N. (2011). What
is the evidence of the impact of microfinance on the well-being of poor people? London:
EPPI-Centre, Social Science Research Unit, Institute of Education, University of London.
Epstein, M. J., Yuthas, K. (2013). Rural Microfinance and Client Retention: Evidence From
Malawi. Journal of Developmental Entrepreneurship, 18(01).
Falco, P., Haywood, L. (2016). Entrepreneurship versus joblessness: Explaining the rise in
self-employment. Journal of Development Economics, 118, 245-265
Field, E., Pande, R., Papp, J., Rigol, N. (2013). Does the Classic Microfinance Model
Discourage Entrepreneurship among the Poor? Experimental Evidence from India,
American Economic Review, 103(6), 2196-2226)
96
Fitch (2008), The Microfinance Sector: Its Success Could be its Biggest Risk, Special Report,
http://www.microfinancegateway.org/sites/default/files/mfg-en-paper-the-microfinance-
sector-its-success-could-be-its-biggest-risk-jun-2008.pdf, accessed 26 March 2015
Freire-Gibb, L.C., Nielsen, K. (2014). Entrepreneurship Within Urban and Rural Areas:
Creative People and Social Networks. Regional Studies, 48(1), 139-153,
Giné, X. (2011). Access to capital in rural Thailand: An estimated model of formal vs.
informal credit. Journal of Development Economics, 96(1), 16-29.
Guérin, I., Morvant, S., Servet, J.-M. (2011). Understanding the diversity and complexity of
demand for microfinance services: lessons from informal finance. In: Handbook of
Microfinance, edited by Armendáriz, B., Labie M., Londres : World Scientific, 101-122.
Hanouch, M. and Rotman, S. 2013. Microfinance and Mobile Banking: Blurring the Lines?
CGAP Focus Note No. 88, Washington D.C.
Hardy, D.C., Holder, P., Prokopenko, V. (2002). Microfinance Institutions and Public Policy.
IMF Working Paper 02/159, Washington D.C.
Harper, M. (2012). Microfinance Interest Rates and Client Returns. Journal of Agrarian
Change, 12(4), 564-574.
Hartarska, V., Shen, X., Mersland, R. (2013). Scale economies and input price elasticities in
microfinance institutions. Journal of Banking and Finance, 37(1), 118-131
Hermes, N., Lensink, R., Meesters, A. (2011). Outreach and Efficiency of Microfinance
Institutions. World Development, 39(6), 938-948.
Honohan, P. (2008). Cross-country variation in household access to financial services.
Journal of Banking and Finance, 32(11), 2493-2500.
97
Islam, A., Nguyen, C., Smyth, R. (2015). Does microfinance change informal lending in
village economies? Evidence from Bangladesh. Journal of Banking and Finance, 50, 141-
156.
Kochar, A. (1997). An empirical investigation of rationing constraints in rural credit markets
in India. Journal of Development Economics, 53(2), 339-371.
Kislat, C., Menkhoff, L., Neuberger, D. (2013). The use of collateral in formal and informal
lending. Kiel Working Paper No. 1879
Köhler, M. (2015). Which banks are more risky? The impact of business models on bank
stability. Journal of Financial Stability, 16, 195–212.
Kranton, R.E., Swamy, A.V. (1999). The hazards of piecemeal reform: british civil courts and
the credit market in colonial India. Journal of Development Economics, 58, 1-24.
Kremer, M., Lee, J., Robinson, J., Rostapshova, O. (2013). Behavioral Biases and Firm
Behavior: Evidence from Kenyan Retail Shops, American Economic Review: Papers &
Proceedings, 103(3), 362–368
Kumar, A., Narain, S., Rubbani, S. (2015). World Bank Lending for Financial Inclusion:
Lessons from Reviews of Selected Projects. IEG Working Paper Serie No. 2015/1,
https://openknowledge.worldbank.org/handle/10986/21796
Ledgerwood, J., Wilson, K. (2013). Community-based financial services: a spectrum of
providers. Enterprise Development and Microfinance, 24(2), 91-103.
Lønborg, J.H., Rasmussen, O.D. (2014). Can Microfinance Reach the Poorest: Evidence from
a Community-Managed Microfinance Intervention, World Development, 64, 460–472
Louis, P., Seret, A., Baesens, B. (2013). Financial Efficiency and Social Impact of
Microfinance Institutions Using Self-Organizing Maps. World Development, 46, 197–210
98
Madestam, A., (2014). Informal finance: A theory of moneylenders. Journal of Development
Economics, 107, 157-174.
Mazumder, M.S.U., Lu, W. (2015). What Impact Does Microfinance Have on Rural
Livelihood? A Comparison of Governmental and Non-Governmental Microfinance Programs
in Bangladesh. World Development, 68, 336-354.
Mersland, R., Strøm, R. Ø. (2009). Performance and governance in microfinance institutions.
Journal of Banking and Finance, 33(4), 662-669.
Meyer, R.L. (2011). Subsidies as an Instrument in Agricultural Development Finance:
Review. Joint Discussion Paper of the Joint Donor CABFIN Initiative. Washington D.C.
Microcredit Summit Campaign (2015). Report 2015. https://stateofthecampaign.org/read-the-
full-2015-report/
Mix Market (2005). 2004 Annual MFI Benchmarks,
https://www.themix.org/publications/mix-microfinance-world/2005/12/2004-annual-mfi-
benchmarks
Mix Market (2016). Global Outreach & Financial Performance Benchmark Report - 2014,
https://www.themix.org/sites/default/files/publications/mix_global_regional_benchmark_repo
rt_2014_0.pdf
Moll, H. A. (2005). Microfinance and rural development: A long-term perspective. Journal of
Microfinance/ESR Review, 7(2), 13-31.
Monne, J., Louche, C., Villa, C. (2016). Rational Herding toward the Poor: Evidence from
Location Decisions of Microfinance Institutions within Pakistan, World Development, 84,
266-281
Olivares-Polanco, F. (2005). Commercializing microfinance and deepening outreach?
Empirical evidence from Latin America. Journal of Microfinance/ESR Review, 7(2), 47-69.
99
Paxton, J. (2007), Technical efficiency in a semi-formal financial sector: The case of Mexico,
Oxford Bulletin of Economics and Statistics, 69, 57-74
Quayes, S. (2015). Outreach and performance of microfinance institutions: a panel analysis.
Applied Economics, 47(18): 1909-1925.
Raghunathan, U. K., Escalante, C. L., Dorfman, J. H., Ames, G. C., Houston, J. E. (2011).
The effect of agriculture on repayment efficiency: a look at MFI borrowing groups.
Agricultural Economics, 42(4), 465-474.
Responsability (2013). Microfinance Market Outlook 2014 – No “sudden stop”: demand for
microfinance soars, http://www.responsability.com/funding/data/docs/en/1872/rA-
Microfinance-Market-Outlook-2014.pdf
Roberts, P. W. (2013). The profit orientation of microfinance institutions and effective
interest rates. World Development, 41, 120-131.
Rosenberg, R. (2009). Measuring Results of Microfinance Institutions, Minimum Indicators
That Donors and Investors Should Track. Consultative Group to Assist the Poor/The World
Bank, http://www.cgap.org/sites/default/files/CGAP-Technical-Guide-Measuring-Results-of-
Microfinance-Institutions-Minimum-Indicators-That-Donors-and-Investors-Should-Track-Jul-
2009.pdf, accessed 30 March 2015.
Sahay, M. R., Cihak, M., N'Diaye, M. P., Barajas, M. A., Pena, M. D. A., Bi, R., Svirydzenka,
K. (2015). Rethinking Financial Deepening: Stability and Growth in Emerging Markets (No.
15-18). International Monetary Fund.
Salim, M.M. (2013). Revealed objective functions of Microfinance Institutions: Evidence
from Bangladesh. Journal of Development Economics, 104, 34-55
Schreiner, M., Colombet, H. H. (2001). From urban to rural: Lessons for microfinance from
Argentina. Development policy review, 19(3), 339-354.
100
Servin, R., Lensink, R., van den Berg, M. (2012). Ownership and technical efficiency of
microfinance institutions: Empirical evidence from Latin America. Journal of Banking and
Finance, 36, 2136-2144.
Sriram, M. (2005). Information Asymmetry and Trust: A Framework for Studying
Microfinance in India, Vikalpa, 30(4), 77- 85
Strøm, R. Ø., D’Espallier, B., Mersland, R. (2014). Female leadership, performance, and
governance in microfinance institutions. Journal of Banking and Finance, 42, 60-75.
Swamy, V. (2014). Financial Inclusion, Gender Dimension, and Economic Impact on Poor
Households. World Development, 56(1), 1-15.
UNCDF (2015). Inclusive Finance – Increasing Access to Financial Services, New York.
Vanroose, A., D’Espallier, B. (2013). Do microfinance institutions accomplish their mission?
Evidence from the relationship between traditional financial sector development and
microfinance institutions’ outreach and performance. Applied Economics, 45(15), 1965-1982.
Vanroose, A. (2016). Which local factors drive the regional expansion of microfinance
institutions? Evidence from Peru. http://www.inesad.edu.bo/bcde2010/contributed/e32_15.pdf
Von Pischke, J. D. (1996). Measuring the trade-off between outreach and sustainability of
microenterprise lenders. Journal of International Development, 8(2), 225-239.
Weber, R., Musshoff, O. (2012). Is agricultural microcredit really more risky? Evidence from
Tanzania. Agricultural Finance Review, 72(3), 416-435.
Wijesiri, M. Viganò, L; Meoli, M. (2015). Efficiency of microfinance institutions in Sri
Lanka: a two-stage double bootstrap DEA approach. Economic Modelling. 47, 74-83
Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step
GMM estimators. Journal of Econometrics, 126, 25–51.
101
Woller, G., (2000). Efficiency in microfinance institutions. Microbanking Bulletin, 4, 3-8.
World Bank (2016). Taking on inequality. Washington DC
Zeller, M., Meyer, R. L. (2002). The triangle of microfinance (No. 40). Food Policy Statement
No. 40, International Food Policy Research Institute (IFPRI), Washington D.C.
Tables
Table 1: List of Variables
Variables Variable Description Source
Dependent Variables
Performance
OSSFinancial Revenue / (Financial Expense + Impairment Loss +
Operating Expense)Mix Market
ROA (Net Operating Income minus Taxes)/ Assets, average Mix Market
Explanatory Variables
RURAL Number of Rural Borrowers / Total Number of Active Borrowers Mix Market
MFI data
AGE Number of years operating Mix Market
SIZE Natural Logarithm MFI's Total Assets Mix Market
PRODUCTIVITY Number of Loans Outstanding / Number of Loan Officers Mix Market
NGO Dummy Variable (1 if NGO, 0 all other legal forms) Mix Market
GLP Gross Loan Portfolio/ Total Assets Mix Market
FEMALENumber of Active Borrowers who are women / Number of Active
BorrowersMix Market
LOANSIZE Average Loan Balance per Borrower/ GNI per capita Mix Market
DENSITYDensity of the MFI's headquarter divided by the density of the most
densely populated city in the respective country
Own
Calculation*
Country level data
Macroeconomic and Structural Indicators
GROWTH Real GDP per capita growth World Bank
INFLATION Inflation consumer prices (annual %) World Bank
PRIVY Domestic credit to private sector by banks (% of GDP) World Bank
FDI Foreign direct investment, net inflows (BoP, current US$) World Bank
GDPPC GDP per capita based on purchasing power parity (PPP) World Bank
INDUSTRY Industry, value added (% of GDP) World Bank
RURALPOPGROWTH Rural population growth (annual %) World Bank
SPREAD Interest rate spread (lending rate minus deposit rate, %)
AGRICULTURE Agriculture, value added (% of GDP) World Bank
RURALPOP Rural population (% of total population) World Bank
STABILITY
Political stability and absence of violence/terrorism Index (−2.5 to
2.5). -2.5 higher likelihood of political instability/violence, 2.5 higher
likelihood of political stability and absence of violence
World Bank
* Densities are calculated in the following way: Population of the respective cities divided by land area in square kilometers (last year
available). Original data has been taken from www.citypopulation.de, wikipedia and other sources.
Source: authors’ compilation
103
Table 2: Sample Distribution
Region # Countries # MFIs # Observations
Africa 25 113 225
East Asia and the Pacific 8 75 199
Eastern Europe and Central Asia 19 138 482
Latin America and The Caribbean 18 271 978
Middle East and North Africa 4 15 64
South Asia 6 160 522
TOTAL 80 772 2470
Legal Status # MFIs # Observations
NGO 286 973
Bank 67 194
Credit Union / Cooperative 108 306
NBFI 290 940
Rural Bank 21 57
TOTAL 772 2470
Source: authors’ compilations.
104
Table 3: Descriptive Statistics
Variables Obs Mean Median Std. Dev. Min Max
OSS 2544 1,15 1,12 0,31 -0,10 3,61
ROA 2528 0,01 0,02 0,09 -1,11 0,37
RURAL 2544 0,51 0,54 0,33 0 1
DENSITY 2461 0,55 0,52 0,41 0,00 1,00
AGE 2470 14,47 13,00 9,33 0 61
ASSETS 2544 68.400.000 10.200.000 254.000.000 25.998 6.130.000.000
SIZE (ln Assets) 2544 16,26 16,14 1,83 10,17 22,54
PRODUCTIVITY 2544 352,80 280,50 422,02 1,00 8905,00
NGO 2544 0,40 0,00 0,49 0 1
GLP 2544 0,80 0,82 0,19 0 3
FEMALE 2544 0,65 0,65 0,26 0,00 1,00
LOANSIZE 2544 0,51 0,26 0,80 0,02 15,71
GROWTH 2544 4,62 4,74 3,10 -14,80 20,10
INFLATION 2544 6,71 5,72 4,32 -3,70 27,28
PRIVY 2544 39,93 37,44 20,30 3,92 151,85
FDI 2544 3,69 2,61 4,23 -1,52 85,37
GDPPC 2544 7552,00 6160,80 4952,81 583,25 24114,09
PROCEDURES 2544 9,41 9,00 3,36 2,00 18,00
STABILITY 2544 -0,72 -0,69 0,67 -2,81 1,13
Source: authors’ compilations.
Table 4: Correlation matrix
1 2 3 4 5 6 7 8 9 10 11 12
1 OSS 1
2 ROA 0.7284* 1
3 RURAL 0.0553* 0.0360 1
4 DENSITY -0.0415* -0.0350 -0.1534* 1
5 AGE 0.0581* 0.1061* -0.0277 0.0145 1
6 AGE SQ: 0.0635* 0.0716* -0.0316 -0.0028 0.9308* 1
7 Assets 0.0584* 0.0391* -0.0170 0.0812* 0.1324* 0.0972* 1
8 SIZE 0.1219* 0.1507* -0.0589* 0.1281* 0.2726* 0.2131* 0.5113* 1
9 PRODUCTIVITY 0.0189 0.0195 0.0783* -0.0858* 0.1716* 0.1892* 0.2586* 0.1230* 1
10 NGO -0.0238 0.0175 0.0743* -0.0399* 0.1788* 0.0924* -0.1338* -0.2659* 0.0357 1
11 GLP 0.1161* 0.1327* 0.0598* -0.1147* -0.0223 -0.0367 0.0229 0.0630* 0.1151* 0.0457* 1
12 FEMALE -0.0643* -0.0561* 0.1325* -0.0852* -0.0699* -0.0604* -0.0819* -0.1117* 0.1566* 0.2833* 0.1013* 1
13 LOANSIZE 0.0664* 0.0310 -0.1294* 0.1367* 0.0237 0.0245 0.1071* 0.1466* -0.1353* -0.2071* -0.1023* -0.3628*
14 GROWTH 0.0878* 0.0427* 0.0945* -0.0474* -0.0255 0.0082 0.0686* 0.0371 0.0844* 0.0220 0.0631* 0.1487*
15 INFLATION -0.0058 -0.0705* 0.1109* -0.0669* -0.1765* -0.1288* -0.0125 -0.0881* 0.0898* 0.0200 -0.0293 0.2343*
16 PRIVY 0.0746* 0.0358 0.0423* -0.1040* 0.0039 -0.0118 0.0979* 0.0151 0.0674* 0.1078* 0.1626* 0.1692*
17 FDI 0.0805* 0.0249 -0.0495* 0.1929* -0.0748* -0.0815* 0.0810* -0.0193 -0.0730* -0.0439* -0.0198 -0.1221*
18 GDPPC 0.1397* 0.1215* -0.2016* -0.0849* 0.0514* 0.0459* 0.0283 0.0453* -0.0824* -0.1221* 0.0827* -0.2843*
19 PROCEDURES -0.0364 -0.0225 0.0435* -0.2126* 0.0785* 0.0913* -0.0072 0.0377 0.1130* 0.1668* 0.1213* 0.2118*
20 STABILITY 0.0814* 0.0765* -0.0769* 0.2303* 0.0636* 0.0225 0.0369 -0.0740* -0.0576* 0.0131 0.0598* -0.2392*
21 Dummy2009 -0.0591* -0.0357 0.0093 0.0111 -0.0263 -0.0142 -0.0301 -0.0218 -0.0022 0.0184 -0.0552* -0.0099
22 Dummy2010 -0.0223 -0.0118 0.0385 -0.0177 -0.0345 -0.0250 -0.0072 -0.0498* -0.0005 0.0103 -0.0203 0.0230
23 Dummy2011 0.0025 0.0012 0.0214 -0.0265 0.0089 0.0044 -0.0092 -0.0168 0.0121 0.0083 0.0010 0.0213
24 Dummy2012 0.0134 -0.0026 -0.0176 0.0006 0.0104 0.0085 -0.0115 -0.0019 0.0137 0.0008 0.0088 -0.0117
25 Dummy2013 0.0531* 0.0276 -0.0124 0.0150 0.0868* 0.0688* 0.0814* 0.1184* -0.0141 -0.0496* 0.0409* -0.0129
106
13 14 15 16 17 18 19 20 21 22 23 24 25
13 LOANSIZE 1
14 GROWTH 0.0273 1
15 INFLATION -0.0331 0.2376* 1
16 PRIVY -0.1597* 0.0263 -0.0533* 1
17 FDI 0.1420* 0.1673* 0.0817* 0.0497* 1
18 GDPPC -0.1325* -0.1637* -0.2673* 0.1959* 0.0840* 1
19 PROCEDURES -0.0806* 0.0522* 0.1229* 0.1287* -0.2395* -0.1937* 1
20 STABILITY 0.1302* -0.0784* -0.2800* 0.2108* 0.3624* 0.3240* -0.1628* 1
21 Dummy2009 -0.0281 -0.3398* -0.0902* -0.0606* -0.0730* -0.0876* 0.0933* -0.0453* 1
22 Dummy2010 -0.0318 0.1493* -0.0722* -0.0122 -0.0604* -0.0596* -0.0096 -0.0328 -0.1963* 1
23 Dummy2011 -0.0292 0.1086* 0.0617* -0.0067 0.0033 -0.0159 -0.0268 -0.0561* -0.2049* -0.2472* 1
24 Dummy2012 0.0290 -0.0148 -0.1148* 0.0373 0.0222 0.0299 -0.0215 0.0088 -0.1935* -0.2334* -0.2438* 1
25 Dummy2013 0.0660* -0.0165 -0.1550* 0.0789* 0.0119 0.1633* -0.1007* 0.1237* -0.1804* -0.2176* -0.2272* -0.2146* 1
* indicates significance at 5% level
Source: authors’ compilations.
Table 5: IV Regressions – First Stage Results
RURAL
DENSITY -0.150***
(-9.23)
NGO 0.0872***
(5,10)
AGE 0,0005
(0,25)
AGE 2 0,0000
(-0.33)
SIZE 0.0158***
(3,70)
PRODUCTIVITY 0.0000364***
(3,17)
GLP 0,0387
(1,15)
FEMALE -0.101***
(-2.67)
LOANSIZE -0.0630***
(-5.20)
GROWTH -0,0009
(-0.37)
INFLATION 0,0010
(0,40)
PRIVY 0,0007
(0,41)
FDI 0,0021
(0,80)
GDPPC 0,0000
(0,19)
PROCEDURES 0.0109*
(1,88)
STABILITY -0.0585*
(-1.72)
COUNTRY FIXED EFFECTS Yes
TIME DUMMIES INCLUDED Yes
_cons 0,1220
(0,63)
N 2413
R-squared 0,3426
t statistics in parentheses
* p<0.10, **p<0.05, *** p<0.01
(Weak id) SW F( 1, 2314)
F statistic 85,26
108
This table reports the first-stage regression estimates, instrumenting for the share of rural borrowers (RURAL) by
a population density index as defined in the text. The dependent variable is the share of rural borrowers during
the period from 2008 to 2013. Our main variable of interest (the instrument) is the population density index
(DENSITY) as defined in the text. We control for a set of MFI characteristics, macroeconomic and structural
country indicators as well as country and year dummies (reference category is 2008). Robust standard errors are
provided in parentheses.
109
Table 6: Pooled OLS - Baseline Regression Results
1 2 3 4
RURAL 0,0329 0,121 0.686*** 0.0795**
(1,09) (1,51) (2,68) (1,97)
MFI controls
AGE -0,0015 0,0031 -0,0012 -0,0017
(-0.42) (0,59) (-0.33) (-0.47)
AGE 2 0,0001 0,0000 0,0001 0,0001
(0,92) (-0.07) (0,87) (0,97)
AGE*RURAL -0,0097
(-1.11)
AGE2*RURAL 0,0002
(0,87)
SIZE 0.0201*** 0.0203*** 0.0405*** 0.0191***
(2,79) (2,81) (4,25) (2,63)
SIZE*RURAL -0.0410***
(-2.62)
PRODUCTIVITY 0,0000 0,0000 0,0000 0.000105*
(0,31) (0,35) (0,38) (1,93)
PRODUCT*RURAL -0.000134*
(-1.94)
NGO 0,0425 0,0441 0.0518* 0.0464*
(1,60) (1,64) (1,93) (1,75)
GLP 0,1300 0,1300 0,1270 0,1280
(1,53) (1,53) (1,52) (1,49)
FEMALE 0,0114 0,0042 0,0057 0,0087
(0,21) (0,08) (0,11) (0,16)
LOANSIZE 0,0230 0,0229 0,0227 0.0261*
(1,60) (1,57) (1,60) (1,75)
Macroeconomic and country controls
GROWTH 0.00750*** 0.00751*** 0.00749*** 0.00758***
(3,02) (3,03) (3,01) (3,07)
INFLATION -0,0021 -0,0021 -0,0021 -0,0022
(-1.02) (-1.04) (-1.05) (-1.06)
PRIVY -0,0008 -0,0007 -0,0010 -0,0007
(-0.37) (-0.37) (-0.47) (-0.33)
FDI 0,0058 0,0059 0,0058 0,0058
(1,33) (1,34) (1,33) (1,32)
GDPPC 0,0000 0,0000 0,0000 0,0000
(0,85) (0,86) (0,84) (0,87)
PROCEDURES 0,0027 0,0021 0,0023 0,0022
(0,52) (0,40) (0,44) (0,43)
STABILITY 0,0173 0,0192 0,0179 0,0181
(0,58) (0,64) (0,60) (0,61)
COUNTRY FIXED EFFECTS Yes Yes Yes Yes
TIME DUMMIES INCLUDED Yes Yes Yes Yes
_cons 0.496** 0.456** 0,1760 0.491**
(2,49) (2,28) (0,79) (2,45)
N 2470 2470 2470 2470
R-squared 0,227 0,228 0,232 0,229
t statistics in parentheses
* p<0.10, **p<0.05, *** p<0.01
OSS
110
This table reports the estimated coefficients of the pooled OLS model presented in equation (1). The dependent
variable is operational self-sustainability OSS during the period from 2008 to 2013. Our main variables of
interest are rural borrowers expressed as a share of total borrowers (RURAL) as well as interaction terms
involving rural borrowers. Column 1 displays results without interaction terms, columns 2, 3 and 4 show the
results when including an interaction term of rural borrowers with age, size, and productivity respectively. We
control for a set of MFI characteristics, macroeconomic and structural country indicators as well as country and
year dummies (reference category is 2008). Robust standard errors clustered at the MFI level are provided in
parentheses.
111
Table 7: IV Regressions – Baseline Results
1 2 3 4
RURAL 0,103 0,0545 2.595*** 0.203*
(1,01) (0,19) (2,80) (1,80)
MFI controls
AGE -0,0018 -0,0098 -0,0004 -0,0022
(-0.74) (-0.73) (-0.17) (-0.90)
AGE 2 0,0001 0.000456* 0,0001 0.0000815*
(1,50) (1,70) (1,20) (1,67)
AGE*RURAL 0,0212
(0,78)
AGE2*RURAL -0.000940*
(-1.66)
SIZE 0.0196*** 0.0186*** 0.100*** 0.0174***
(4,12) (3,70) (3,17) (3,59)
SIZE*RURAL -0.160***
(-2.66)
PRODUCTIVITY 0,0000 0,0000 0,0000 0.000236**
(0,28) (1,42) (0,77) (2,12)
PRODUCT*RURAL -0.000319**
(-2.09)
NGO 0.0395** 0.0422** 0.0780*** 0.0491***
(2,24) (2,33) (3,15) (2,70)
GLP 0.122* 0.121* 0.117* 0.116*
(1,82) (1,78) (1,81) (1,69)
FEMALE 0,0116 0,0122 -0,0108 0,0041
(0,29) (0,28) (-0.25) (0,10)
LOANSIZE 0.0265** 0.0295** 0,0213 0.0334**
(2,05) (2,23) (1,64) (2,42)
Macroeconomic and country controls
GROWTH 0.00733** 0.00750** 0.00706** 0.00752**
(2,41) (2,46) (2,25) (2,49)
INFLATION -0,0024 -0,0027 -0,0024 -0,0026
(-1.03) (-1.13) (-1.01) (-1.11)
PRIVY -0,0009 -0,0003 -0,0015 -0,0006
(-0.38) (-0.13) (-0.68) (-0.29)
FDI 0,0057 0,0055 0,0059 0,0057
(1,12) (1,08) (1,19) (1,11)
GDPPC 0,0000 0,0000 0,0000 0,0000
(0,73) (0,50) (0,59) (0,77)
PROCEDURES 0,0008 -0,0004 0,0002 -0,0002
(0,14) (-0.06) (0,03) (-0.03)
STABILITY 0,0230 0,0283 0,0229 0,0243
(0,65) (0,78) (0,64) (0,69)
COUNTRY FIXED EFFECTS Yes Yes Yes Yes
TIME DUMMIES INCLUDED Yes Yes Yes Yes
_cons 0.518*** 0.571** -0,7360 0.506***
(2,69) (2,52) (-1.45) (2,60)
N 2413 2413 2413 2413
R-squared 0,2223 0,189 0,181 0,2201
t statistics in parentheses
* p<0.10, **p<0.05, *** p<0.01
OSS
112
This table reports the two-stage least squares estimates, instrumenting for the share of rural borrowers by a
population density index as defined in the text. The dependent variable is operational self-sustainability OSS
during the period from 2008 to 2013. Our main variables of interest are rural borrowers expressed as a share of
total borrowers (RURAL) as well as interaction terms involving rural borrowers. Column 1 displays results
without interaction terms; columns 2, 3 and 4 show the results including the interaction of rural borrowers with
age, size, and productivity respectively. We control for a set of MFI characteristics, macroeconomic and
structural country indicators as well as country and year dummies (reference category is 2008). Robust standard
errors are provided in parentheses.
113
Appendix
(referred to in the text as “results available from the authors on request”)
Table A1: Robustness Check – Sample without 5th
and 95th
percentile
1 2 3 4
RURAL 0.00739 0.0797** 0.165 0.0457*
(0.41) (2.07) (0.98) (1.87)
MFI controls
AGE 0.000612 0.00433 0.000732 0.000576
(0.36) (1.63) (0.44) (0.35)
AGE 2 0.00000646 -0.0000559 0.00000495 0.00000789
(0.20) (-1.06) (0.15) (0.24)
AGE*RURAL -0.00769*
(-1.74)
AGE2*RURAL 0.000131
(1.36)
SIZE 0.0193*** 0.0197*** 0.0242*** 0.0185***
(5.02) (5.13) (4.52) (4.78)
SIZE*RURAL -0.00982
(-0.95)
PRODUCTIVITY -0.000000631 0.000000220 -0.000000344 0.0000787***
(-0.04) (0.01) (-0.02) (2.64)
PRODUCT*RURAL -0.000108**
(-2.49)
N 2222 2222 2222 2222
R-squared 0,228 0,230 0,229 0,233
PANEL A: OLS MODEL
OSS
1 2 3 4
RURAL 0.232*** 0.402** 0,143 0.288***
(3,19) (2,48) (0,29) (3,68)
MFI controls
AGE 0,0006 0,0085 0,0005 0,0005
(0,40) (1,23) (0,35) (0,38)
AGE 2 0,0000 -0,0001 0,0000 0,0000
(0,30) (-0.65) (0,33) (0,38)
AGE*RURAL -0,0160
(-1.16)
AGE2*RURAL 0,0002
(0,67)
SIZE 0.0175*** 0.0182*** 0,0146 0.0161***
(5,75) (5,87) (0,90) (5,32)
SIZE*RURAL 0,0056
(0,18)
PRODUCTIVITY 0,0000 0,0000 0,0000 0.000124*
(-0.80) (-0.20) (-0.81) (1,81)
PRODUCT*RURAL -0.000180**
(-1.97)
N 2172 2172 2172 2172
R-squared 0,1113 0,1063 0,1089 0,1223
PANEL B: IV MODEL
OSS
114
Panel A reports the estimated coefficients of the pooled OLS model presented in equation (1) excluding the 5th
and 95th percentile of our dependent variable. Panel B reports the two-stage least squares estimates,
instrumenting for the share of rural borrowers by a population density index as defined in the text. The
dependent variable is operational self-sustainability OSS during the period from 2008 to 2013. Column 1
displays results without interaction terms; columns 2, 3 and 4 show the results including the interaction of rural
borrowers with age, size, and productivity respectively. We control for a set of MFI characteristics,
macroeconomic and structural country indicators as well as year and country dummies (not reported). Robust
standard errors are provided in parentheses.
115
Table A2: Robustness Check – Diamonds from 1 to 5
1 2 3 4
RURAL 0.0346 0.0963 0.569** 0.0752**
(1.30) (1.41) (2.57) (2.13)
MFI controls
AGE 0.00140 0.00520 0.00159 0.00123
(0.43) (1.00) (0.48) (0.38)
AGE 2 0.0000107 -0.0000757 0.00000863 0.0000155
(0.15) (-0.59) (0.12) (0.21)
AGE*RURAL -0.00853
(-1.09)
AGE2*RURAL 0.000197
(1.09)
SIZE 0.0171*** 0.0173*** 0.0335*** 0.0164**
(2.67) (2.72) (3.76) (2.56)
SIZE*RURAL -0.0340**
(-2.46)
PRODUCTIVITY 0.0000124 0.0000115 0.0000126 0.0000918**
(0.82) (0.75) (0.89) (2.21)
PRODUCT*RURAL -0.000118**
(-1.98)
N 3119 3119 3119 3119
R-squared 0,181 0,182 0,185 0,183
OSS
PANEL A: OLS MODEL
1 2 3 4
RURAL 0.294*** 0.430* 2.064** 0.466***
(3.15) (1.75) (2.33) (3.59)
MFI controls
AGE 0.000366 0.00544 0.00102 -0.000466
(0.16) (0.46) (0.43) (-0.20)
AGE 2 0.0000290 0.0000454 0.0000210 0.0000520
(0.59) (0.20) (0.42) (1.03)
AGE*RURAL -0.00931
(-0.38)
AGE2*RURAL -0.0000696
(-0.15)
SIZE 0.0137*** 0.0132*** 0.0704** 0.0109**
(3.08) (2.91) (2.39) (2.36)
SIZE*RURAL -0.115**
(-1.99)
PRODUCTIVITY 0.00000357 0.0000126 0.00000620 0.000409*
(0.34) (1.06) (0.66) (1.89)
PRODUCT*RURAL -0.000598*
(-1.80)
N 3003 3003 3003 3003
R-squared 0.1307 0.1222 0.1263 0.1115
PANEL B: IV MODEL
OSS
116
Panel A reports the estimated coefficients of the pooled OLS model presented in equation (1) adding MFIs rated
with 1 and 2 diamonds by Mixmarket. Panel B reports the two-stage least squares estimates, instrumenting for
the share of rural borrowers by a population density index as defined in the text. The dependent variable is
operational self-sustainability OSS during the period from 2008 to 2013. Column 1 displays results without
interaction terms; columns 2, 3 and 4 show the results including the interaction of rural borrowers with age, size,
and productivity respectively. We control for a set of MFI characteristics, macroeconomic and structural country
indicators as well as year and country dummies (not reported). Robust standard errors are provided in
parentheses.
117
Table A3: Robustness Check – RoA
1 2 3 4
RURAL 0.00721 0.0702** 0.281*** 0.0189
(0.88) (2.27) (3.11) (1.56)
MFI controls
AGE 0.00165 0.00498** 0.00179 0.00160
(1.43) (2.44) (1.55) (1.41)
AGE 2 -0.0000258 -0.0000823** -0.0000272 -0.0000249
(-1.28) (-2.22) (-1.35) (-1.25)
AGE*RURAL -0.00701**
(-2.18)
AGE2*RURAL 0.000121*
(1.92)
SIZE 0.00668*** 0.00689*** 0.0153*** 0.00643***
(3.64) (3.69) (4.16) (3.50)
SIZE*RURAL -0.0172***
(-3.15)
PRODUCTIVITY 0.00000262 0.00000334 0.00000298 0.0000270
(0.45) (0.56) (0.57) (1.30)
PRODUCT*RURAL -0.0000337
(-1.37)
N 2454 2454 2454 2454
R-squared 0,192 0,200 0,204 0,193
PANEL A: OLS MODEL
ROA
1 2 3 4
RURAL 0,0337 0,121 0.723*** 0.0698**
(1,20) (1,25) (2,87) (1,97)
MFI controls
AGE 0.00166* 0,0056 0.00202** 0.00154*
(1,95) (1,42) (2,30) (1,80)
AGE 2 -0.0000260* -0,0001 -0.0000294* 0,0000
(-1.73) (-0.98) (-1.93) (-1.54)
AGE*RURAL -0,0078
(-0.94)
AGE2*RURAL 0,0001
(0,43)
SIZE 0.00640*** 0.00658*** 0.0288*** 0.00560***
(4,76) (4,87) (3,60) (3,99)
SIZE*RURAL -0.0442***
(-2.75)
PRODUCTIVITY 0,0000 0,0000 0,0000 0.0000842**
(0,33) (1,23) (0,68) (2,38)
PRODUCT*RURAL -0.000114**
(-2.22)
N 2398 2398 2398 2398
R-squared 0,1856 0,1836 0,1729 0,1775
PANEL B: IV MODEL
ROA
118
Panel A reports the estimated coefficients of the pooled OLS model presented in equation (1) replacing OSS by
the Return on Assets (RoA) as the dependent variable. Panel B reports the two-stage least squares estimates,
instrumenting for the share of rural borrowers by a population density index as defined in the text. Column 1
displays results without interaction terms; columns 2, 3 and 4 show the results including the interaction of rural
borrowers with age, size, and productivity respectively. We control for a set of MFI characteristics,
macroeconomic and structural country indicators as well as year and country dummies (not reported). Robust
standard errors are provided in parentheses.
Table A4: Robustness Check – Probit Regression
1 2 3 4
RURAL 0,17 0.702** 2.804** 0.534**
(1,19) (2,14) (2,10) (2,53)
MFI controls
AGE 0,0057 0,0320 0,0078 0,0021
(0,39) (1,53) (0,53) (0,14)
AGE 2 0,0001 -0,0003 0,0001 0,0002
(0,33) (-0.55) (0,23) (0,61)
AGE*RURAL -0,0564
(-1.49)
AGE2*RURAL 0,0008
(0,83)
SIZE 0.145*** 0.146*** 0.231*** 0.136***
(4,55) (4,56) (4,57) (4,16)
SIZE*RURAL -0.169**
(-1.98)
PRODUCTIVITY 0,0000 0,0000 0,0000 0.000837**
(-0.11) (0,01) (0,02) (2,12)
PRODUCT*RURAL -0.00112**
(-2.34)
NGO 0,0704 0,0695 0,0986 0,0968
(0,60) (0,59) (0,83) (0,82)
GLP 0,5240 0,5170 0,5120 0,4720
(1,40) (1,38) (1,38) (1,24)
FEMALE 0,0499 0,0089 0,0181 0,0116
(0,20) (0,04) (0,07) (0,05)
LOANSIZE 0.220* 0.219* 0.207* 0.253**
(1,93) (1,92) (1,91) (2,13)
Macroeconomic and country controls
GROWTH 0.0285** 0.0286** 0.0279** 0.0292**
(2,05) (2,05) (1,99) (2,09)
INFLATION 0,0008 0,0016 0,0011 0,0007
(0,06) (0,13) (0,09) (0,05)
PRIVY 0,0089 0,0088 0,0080 0,0100
(0,88) (0,87) (0,79) (0,98)
FDI 0,0055 0,0064 0,0057 0,0060
(0,36) (0,42) (0,39) (0,40)
GDPPC -0,0001 -0,0001 -0,0001 -0,0001
(-1.25) (-1.25) (-1.28) (-1.28)
PROCEDURES 0,0279 0,0231 0,0266 0,0227
(0,65) (0,54) (0,63) (0,53)
STABILITY 0,1720 0,1820 0,1680 0,1810
(0,80) (0,85) (0,78) (0,84)
COUNTRY FIXED EFFECTS Yes Yes Yes Yes
TIME DUMMIES INCLUDED Yes Yes Yes Yes
_cons 2.008* 1,7900 0,7180 1.958*
(1,72) (1,52) (0,59) (1,66)
N 2392 2392 2392 2392
R-squared 0,188 0,191 0,191 0,194
t statistics in parentheses
* p<0.10, **p<0.05, *** p<0.01
DUMMY OSS
120
This table reports the estimated coefficients of a probit model following the setup of equation (1). The dependent
variable is a dummy variable for operational self-sustainability (DummyOSS) which is equal to 1 when a MFI
reaches an OSS equal to and higher than 1 and 0 otherwise. The observation is 2008 to 2013. Our main variables
of interest are rural borrowers expressed as a share of total borrowers (RURAL) as well as interaction terms
involving rural borrowers. Column 1 displays results without interaction terms, columns 2, 3 and 4 show the
results when including an interaction term of rural borrowers with age, size, and productivity respectively. We
control for a set of MFI characteristics, macroeconomic and structural country indicators as well as country and
year dummies (reference category is 2008). Robust standard errors clustered at the MFI level are provided in
parentheses.
.
121
Table A5: Robustness Check: Panel Analysis Fixed Effects (static)
1 2 3 4
RURAL -0,00367 0,146 0,0664 0,0144
(-0.10) (1,40) (0,19) (0,32)
MFI controls
AGE -0,2200 -0,1930 -0,2220 -0,2220
(-1.05) (-0.83) (-1.07) (-1.06)
AGE 2 -0.000411** -0.000528*** -0.000410** -0.000402**
(-2.26) (-2.62) (-2.26) (-2.20)
AGE*RURAL -0,0164
(-1.52)
AGE2*RURAL 0,0003
(1,25)
SIZE 0.113*** 0.112*** 0.115*** 0.112***
(5,44) (5,43) (5,11) (5,40)
SIZE*RURAL -0,0044
(-0.21)
PRODUCTIVITY 0,0000 0,0000 0,0000 0,0000
(0,23) (0,32) (0,23) (0,61)
PRODUCT*RURAL 0,0000
(-0.71)
NGO -0,0336 -0,0383 -0,0338 -0,0340
(-1.09) (-1.10) (-1.09) (-1.10)
GLP 0,0382 0,0389 0,0379 0,0361
(0,55) (0,56) (0,54) (0,52)
FEMALE 0,0557 0,0550 0,0552 0,0560
(0,85) (0,85) (0,85) (0,86)
LOANSIZE -0,0117 -0,0139 -0,0117 -0,0105
(-0.57) (-0.68) (-0.57) (-0.51)
Macroeconomic and country controls
GROWTH 0.00709*** 0.00718*** 0.00708*** 0.00709***
(3,48) (3,49) (3,48) (3,49)
INFLATION 0,0009 0,0008 0,0009 0,0009
(0,53) (0,47) (0,53) (0,53)
PRIVY -0,0004 -0,0004 -0,0003 -0,0004
(-0.19) (-0.23) (-0.19) (-0.19)
FDI 0.00338* 0.00350* 0.00338* 0.00337*
(1,75) (1,83) (1,75) (1,74)
GDPPC 0,0000 0,0000 0,0000 0,0000
(-1.45) (-1.48) (-1.46) (-1.45)
PROCEDURES 0,0032 0,0028 0,0032 0,0030
(0,57) (0,51) (0,57) (0,54)
STABILITY 0,0056 0,0069 0,0053 0,0060
(0,19) (0,23) (0,18) (0,20)
TIME DUMMIES INCLUDED Yes Yes Yes Yes
_cons 2,0270 1,7680 2,0280 2,0570
(0,82) (0,64) (0,82) (0,83)
N 2470 2470 2470 2470
R-sq:
within 0,095 0,098 0,095 0,095
between 0,001 0,001 0,001 0,001
overall 0,002 0,002 0,002 0,002
OSS
122
This table reports the estimated coefficients of the fixed effects model presented in equation (1). The dependent
variable is operational self-sustainability OSS during the period from 2008 to 2013. Column 1 displays results
without interaction terms; columns 2, 3 and 4 show the results including the interaction of rural borrowers with
age, size, and productivity respectively. We control for a set of MFI characteristics, macroeconomic and
structural country indicators as well as year dummies (not reported). Robust standard errors are provided in
parentheses
123
Table A6: Robustness Check – Panel Analysis Fixed Effects (dynamic))
1 2 3 4
L.OSS 0.661*** 0.664*** 0.667*** 0.662***
(10.30) (11.89) (10.92) (10.85)
RURAL 0.0275 0.0359 0.5020 0.0497
(0.31) (0.25) (0.68) (0.55)
MFI controls
AGE -0.0078 -0.0029 -0.0088 -0.0098
(-1.02) (-0.28) (-1.16) (-1.35)
AGE 2 0.0002 0.0001 0.0002 0.0003
(1.12) (0.31) (1.33) (1.47)
AGE*RURAL -0.0017
(-0.11)
AGE2*RURAL 0.0001
(0.24)
SIZE 0.0121 0.0123 0.0191 0.0126
(0.80) (0.98) (0.71) (0.85)
SIZE*RURAL -0.0276
(-0.64)
PRODUCTIVITY 0.0000 0.0000 0.0000 0.0000
(-0.71) (-0.98) (-0.77) (0.25)
PRODUCT*RURAL -0.0001
(-0.56)
NGO -0.0342 -0.0249 -0.0484 -0.0401
(-0.52) (-0.36) (-0.80) (-0.62)
GLP 0.1500 0.1330 0.1610 0.1080
(1.18) (1.18) (1.55) (0.94)
FEMALE -0.1010 -0.0206 -0.0985 -0.1770
(-0.54) (-0.13) (-0.48) (-0.99)
LOANSIZE -0.0345 -0.0232 -0.0347 -0.0482
(-0.84) (-0.61) (-0.77) (-1.27)
Macroeconomic and country controls
GROWTH 0.0033 0.0031 0.00360* 0.00355*
(1.51) (1.39) (1.74) (1.66)
INFLATION 0.0013 0.0005 0.0007 0.0020
(0.40) (0.17) (0.21) (0.68)
PRIVY 0.0002 0.0001 0.0002 0.0004
(0.44) (0.11) (0.43) (0.74)
FDI 0.0002 0.0005 0.0010 0.0003
(0.21) (0.44) (0.71) (0.23)
GDPPC 0.0000 0.0000 0.0000 0.0000
(-0.56) (-0.08) (-0.49) (-0.91)
PROCEDURES -0.0020 -0.0029 -0.0014 -0.0015
(-0.76) (-1.19) (-0.55) (-0.57)
STABILITY 0.0193 0.0153 0.0166 0.0183
(1.06) (0.94) (0.91) (1.01)
TIME DUMMIES INCLUDED Yes Yes Yes Yes
_cons 0.2520 0.1630 0.1090 0.3240
(1.17) (0.84) (0.27) (1.58)
N 1491 1491 1491 1491
AR(1) 0.0000 0 0 0
AR(2) 0.870 0.872 0.883 0.896
Sargan test 0.001 0.002 0.002 0.003
Hansen test 0.423 0.579 0.506 0.402
OSS
124
This table reports the estimated coefficients of a two-step system GMM model following the setup of equation
(1). The dependent variable is operational self-sustainability OSS during the period from 2008 to 2013. Our main
variables of interest are rural borrowers expressed as a share of total borrowers (RURAL) as well as interaction
terms involving rural borrowers. We include one-period-lagged OSS as an independent variable. Column 1
displays results without interaction terms, columns 2, 3 and 4 show the results when including an interaction
term of rural borrowers with age, size, and productivity respectively. We control for a set of MFI characteristics
which are treated as endogenous, i.e. we use their second lags as instruments. We also control for
macroeconomic and structural country indicators which are treated as exogenous. We include year dummies
(reference category is 2008). AR tests for the first and second order autocorrelation in the residuals as well
Sargan and Hansen tests are reported, the latter indicating the validity of the instruments. Robust standard errors
clustered at the MFI level are provided in parentheses.
.
125
Table A7: Pooled OLS and IV Regressions –
Interaction terms with other MFI characteristics
1 2 3 4
RURAL 0,0474 0,0931 0,101 0,049
(1,29) (0,57) (1,21) (1,44)
MFI controls
NGO 0,0628 0,04 0,04 0,04
(1,58) -1,6000 -1,6100 -1,5700
NGO*RURAL -0,0381
(-0.63)
GLP 0,1290 0,1700 0,1300 0,1280
(1,52) (1,39) (1,53) (1,51)
GLP*RURAL -0,0773
(-0.38)
FEMALE 0,0095 0,0108 0,0669 0,0009
(0,17) (0,20) (0,92) (0,02)
FEMALE*RURAL -0,1030
(-0.97)
LOANSIZE 0,0230 0,0231 0.0248* 0,0290
(1,58) (1,60) (1,66) (1,52)
LOANSIZE*RURAL (0,04)
(-0.79)
N 2470 2470 2470 2470
R-squared 0,227 0,227 0,227 0,227
OSS
PANEL A: OLS MODEL
1 2 3 4
RURAL 0,00171 1.837** 1.160** 0,116
(0,01) (2,00) (2,24) (1,20)
MFI controls
NGO -0,0924 0,03 0.0359* 0.0389**
(-1.04) -1,3600 -1,8800 -2,2200
NGO*RURAL 0,2460
(1,48)
GLP 0.128* 1.216** 0.112* 0.120*
(1,92) (2,24) (1,68) (1,80)
GLP*RURAL -2.127*
(-1.96)
FEMALE 0,0258 0,0058 0.829** 0,0058
(0,63) (0,13) (2,23) (0,13)
FEMALE*RURAL -1.502**
(-2.26)
LOANSIZE 0.0257** 0.0349** 0.0597** 0,0305
(2,09) (2,18) (2,48) (1,46)
LOANSIZE*RURAL -0,0248
(-0.32)
N 2413 2413 2413 2413
R-squared 0,2042 0,0645 0,0927 0,2225
PANEL B: IV MODEL
OSS
126
Panel A reports the estimated coefficients of the pooled OLS model presented in equation (1). Panel B reports
the two-stage least squares estimates, instrumenting for the share of rural borrowers by a population index as
defined in the text. The dependent variable is operational self-sustainability OSS during the period from 2008 to
2013. Our main variables of interest are rural borrowers expressed as a share of total borrowers as well as
interaction terms involving rural borrowers. Columns 1- 4 show the results when including an interaction term of
rural borrowers with the NGO dummy (NGO), business concentration (the share of the Gross Loan Portfolio in
Total Assets, GLP), the share of female borrowers (FEMALE) and average loan size as a percentage of GNI per
capita (LOANSIZE). We control for a set of MFI characteristics, macroeconomic and structural country
indicators as well as country and year dummies (not reported). Robust standard errors are provided in
parentheses.
127
Does financial inclusion mitigate credit boom-bust cycles?
Tania López and Adalbert Winkler*
Abstract
Following up on claims that high and rising levels of financial inclusion
might contribute to financial stability, we test whether level and progress in
financial inclusion has an effect on the magnitude of a financial bust after a
crisis. We do this for the global financial crisis and a sample of crisis
episodes covering the period 2004 – 2017. We find some evidence that
countries with more inclusive banking sectors show less pronounced credit
busts in times of financial turbulence. However, higher borrower growth
rates in the years preceding a crisis have no mitigating effect on the depth
of the bust. Thus, it remains a policy challenge to expand financial
inclusion without contributing a potentially destabilizing credit boom.
JEL classification: G01, G21, O16
Key words: Financial inclusion, credit boom-bust cycles, financial crisis
Corresponding author:
Adalbert Winkler Tania López
Academic Head Research Associate
Centre for Development Finance Centre for Development Finance
Frankfurt School of Finance & Frankfurt School of Finance &
Management Management
Adickesallee 32-34 Adickesallee 32-34
60322 Frankfurt am Main, Germany 60322 Frankfurt am Main, Germany
Email: a.winkler@fs.de Email: t.lopez@fs.de
128
1. Introduction
Raising financial inclusion, i.e. the number of individuals and firms using formal financial
sector services (Demirgüc-Kunt 2014) has become a key objective in the post-2015
Development Agenda (GPFI 2016). This is somewhat paradoxical, as only a few years earlier
the global financial system had been on the brink of collapse, only saved by massive
interventions of governments and central banks (Laeven and Valencia 2018). The paradox
can be resolved by arguing that higher levels of financial inclusion yield substantial growth
benefits for individuals as well as for the economy as a whole. If these benefits outweigh the
costs associated with instability, raising financial inclusion from the low levels recorded in
many developing and emerging market economies represents a valid policy objective.
However, some observers go one step further. They argue that, by achieving higher levels of
financial inclusion, developing countries could also make their financial systems more stable.
Thus, under “well-designed financial policies” (Dema 2015), a vigorous expansion of
financial inclusion might create a win-win situation in which countries can gain in terms of
growth and stability (GPFI 2012, Rahman 2014).
Over the last years several attempts have been made to capture the stability-enhancing effects
of financial inclusion (Sahay et al. 2015, Čihák et al. 2016, Han and Melecky 2017). We
contribute to this literature by testing whether financial inclusion mitigates credit boom-bust
cycles characterizing financial crises (Mendoza and Terrones 2012, Schularick and Taylor
2012, Feldkircher 2014, Babecký et al 2014, Alessi and Detken 2017, Richter et al. 2017).
Thus, we do not analyze whether more inclusive banking sectors are less likely to experience
a crisis but whether – given a crisis – a higher level of financial inclusion or stronger progress
in financial inclusion in the pre-crisis period yield a benefit in the form of a less pronounced
drop in credit growth, controlling for the size of the pre-crisis credit boom. Moreover, we
explore whether financial inclusion itself is subject to a boom-bust pattern, i.e. whether
129
stronger borrower growth in a pre-crisis period is associated with a deeper fall in borrower
growth in a crisis. Our analysis is based on two samples covering the global financial crisis
and 51 crisis episodes over the period 2004-2017. As our focus is on credit, we measure the
level of financial inclusion by the share of the population which has a loan outstanding at
commercial banks, and progress in financial inclusion by the growth rate in the number of
borrowers in the pre-crisis period.
Results provide some support for the view that more inclusive banking sectors record less
pronounced declines in credit and borrower growth in times of crisis. However, we also find
that higher borrower growth rates in pre-crisis periods are mainly unrelated to the depth of
the credit bust following a crisis. If significant, coefficients point toward an effect that
reinforces the credit boom-bust cycle. Finally, there is mixed evidence whether countries with
higher borrower growth rates in a pre-crisis period record a greater drop in borrower growth
in crisis times, i.e. with regard to boom-bust phenomena for financial inclusion itself.
We conclude from this that in a crisis, countries seem to benefit from a higher level of
financial inclusion by recording a less pronounced bust in credit and borrower growth. This
supports the view that higher levels of financial inclusion make financial systems more
resilient in a crisis period. However, rapid progress in financial inclusion has no mitigating
effect on credit developments in a crisis, given pre-crisis credit developments. Thus, for many
developing countries, where reaching higher levels of financial inclusion represents an
important policy objective, our results suggest that managing progress in financial inclusion
represents a challenge if easier access to credit and higher borrower growth rates are
associated with rising credit growth, a key indicator of looming financial instability. Well-
designed policies should account for this by finding ways to expand financial inclusion
without contributing to credit booms.
130
2. Related literature
Financial inclusion ranks high on the global development agenda, as there is evidence
demonstrating that the poor make substantial use of informal finance in managing their daily
lives (Collins et al. 2009). However, informal finance is unreliable and expensive. Hence,
replacing informal with formal financial sector services is likely to raise the income and
welfare of the poor.1 This makes inclusive finance an area where finance is still seen as
unambiguously beneficial (Zingales 2015).
More recently, the policy case for financial inclusion has also been made based on the
argument that a higher level of financial inclusion might deliver financial stability benefits
(Hannig and Jansen 2010, GFPI 2012). For example, countries with banking sectors
extending loans and offering deposits to a larger share of the population are likely to reap
stability-enhancing diversification effects (Diamond 1984, Khan 2011, Cull et al. 2012).2
Consistent with this, a study of Chilean banks shows that the quality of loan portfolios based
on many small loans is found to behave less cyclically than the quality of portfolios
composed of a smaller number of large loans (Adasme et al. 2006). Cross-country evidence
reveals that a higher level of financial inclusion, measured as the share of SME loans in the
volume of outstanding loans issued by commercial banks, is associated with a higher degree
1 Having said this, theory and empirical evidence suggest that the interplay between the formal and the informal
financial sector is not only characterized by substitution but also by complementarity (see e.g. Guérin et al.
2012, Madestam 2014). Thus, switching from informal to formal finance might not always enhance client
welfare (Guérin et al. 2013). In a similar vein, the long-held consensus view on a positive relationship between
finance and growth has recently been qualified, as new empirical evidence suggests that the relationship
between finance and growth might be non-linear and/or subject to the concrete form of finance, i.e. household or
business finance (Arcand et al. 2015, Beck, R. et al. 2014, Beck, T. et al. 2014, Beck 2015, Cecchetti and
Kharroubi 2012, Manganelli and Popov 2013, Rioja and Valev 2004, Rousseau and Wachtel 2011, Sassi and
Gasmi 2014).
2 However, credit risk diversification might not always reduce but could even increase financial stability risks
(Battiston et al 2012). At least with regard to international diversification of banks, the empirical evidence on
the diversification-stability nexus is mixed (Gulamhussen et al. 2014).
131
of banking sector stability, captured by the Z-score and the non-performing loan ratio
(Morgan and Portines 2014). Turning to the deposit side, Han and Melecky (2017) show that
the maximum size of deposit withdrawals in a period of turmoil, i.e. 2007 – 2010, is
significantly negatively related to the share of people using formal savings products. In line
with this, there is evidence that retail deposits were more stable than wholesale deposits
during the global financial crisis (Huang and Ratnovski 2011, Craig and Dinger 2013,
Gertler, Kiyotaki and Prestipino 2016, Baselga-Pascual et al. 2015).3
However, theory also suggests that moving towards a higher level of financial inclusion
might be associated with financial instability. For example, lending standards might decline
as banks engage in credit activities with new, unknown borrowers (Dell’Ariccia and Marquez
2006). This effect is reinforced when loan officers perform a less stringent credit analysis in
good times, characterized by strong growth and optimism (Becker et al. 2016, Brown et al.
2016). Accordingly, while banking sectors with a higher level of inclusion might be more
stable, the process of becoming more inclusive raises the policy challenge of keeping credit
growth at sustainable levels.4
The years preceding the global financial crisis provide some anecdotal evidence for these
concerns. In Eastern Europe the number of borrowers soared in the early 2000s as consumer
and business credit expanded rapidly (Arcalean et al. 2007, Klapper et al. 2013), but the
countries recorded a severe credit crunch after 2008. Several crises in microfinance markets,
3 There is also evidence that the poor show a more stable deposit behavior than richer clients (Abakaeva and
Glisovic 2009).
4 The financial stability implications of a rapid rise in the use of credit can be compared to those of a rapid rise
in financial innovation (Beck et al. 2015). While bank loans do not represent a new product, they are “new” for
recently included customers. They might also be new for institutions such as microfinance institutions that
explicitly aim at raising the level of financial inclusion. These new players might underestimate the risks
associated with established products “because of the lack of data on the default and performance records” (Boz
and Mendoza 2014) and lack of prior financial experience or financial literacy among their customers (Klapper
et al. 2013). These concerns are at the heart of debates on proper supervisory and regulatory frameworks for
new service providers such as microfinance institutions or mobile money operators (Dittus and Klein 2011,
Khiaonarong, T. 2014, Mehrotra and Yetman 2015, GPFI 2016)
132
such as in Bosnia and Herzegovina, Morocco and Nicaragua can be linked to fast borrower
and credit growth in the pre-crisis years (Chen et al. 2010). Finally, US subprime lending
triggered the global financial crisis, as the massive “democratization” of credit (Greenspan
1997, Gramlich 2007, Reinhart and Rogoff 2008, Rajan 2010) became associated with a
“credit tsunami” (Mishkin 2011) in the pre-crisis years. In all cases, the crisis years saw a
reversal in credit growth as well as in borrower growth. The latter suggests that financial
inclusion itself might follow a boom-bust pattern similar to the one firmly identified for credit
volumes.
More recent econometric studies also raise doubts that financial inclusion has a strictly
positive impact on stability. For example, the relationship might be non-linear and moderated
by the quality of banking supervision, as in countries with a low supervisory quality, more
inclusion is associated with lower Z-scores, i.e. more instable banks (Sahay et al. 2015).
Based on a detailed cross-country correlation analysis, including various dimensions and
measures of inclusion and stability Čihák et al. (2016) find more evidence for a trade-off than
for synergies between the two concepts. In terms of crisis resilience, there is basically no link
between inclusion and stability. Some findings even suggest that strong progress in inclusion
with regard to credit might undermine stability.
We pick up on this theme and build on the empirical evidence (Mendoza and Terrones 2012,
Schularick and Taylor 2012, Babecký et al. 2014) according to which rapid credit growth is
“the single best predictor of financial instability” (Jordá et al. 2011). In doing so, we are
aware that credit booms, in particular in developing countries (Meng and Gonzalez 2017), do
not necessarily have to end in a crisis. However, many of them do, with the crisis exhibiting a
severe credit crunch.5 Against this background, we test whether financial inclusion impacts
5 Crises periods might also be associated with massive deposit withdrawals, the indicator of instability used by
Han and Melecky (2017). However, deposit withdrawals on the retail level have become rare events given
133
the magnitude of a credit bust in a financial crisis. Given our focus on credit we measure
financial inclusion by the use of credit, i.e. by the share of borrowers in the adult population
and by the rate of growth in the number of borrowers. Thus, our analysis is guided by three
hypotheses:
H1: A higher level of financial inclusion, i.e. a higher share of borrowers in the
population, mitigates the size of the credit bust, in terms of credit volumes and numbers of
borrowers, in a financial crisis.
H2: Rapid progress in financial inclusion, depicted by high growth rates in the number of
borrowers in pre-crisis times, does not mitigate the credit bust in a crisis. Indeed – coupled
with rapid credit growth – it might even lead to a greater drop in credit growth.
H3: Financial inclusion itself follows a boom-bust cycle, i.e. higher borrower growth rates
in a pre-crisis period are associated with a larger drop in borrower growth in a crisis.
3. Data and empirical strategy
We base our analysis on the IMF’s Financial Access Survey (FAS) database which compiles
data from financial institutions, i.e. from the supply side of financial services (Mialou 2015).6
The database covers 189 economies over the period 2004-2017 and provides detailed
information on loan and deposit volumes as well as number of borrowers and depositors, also
for subgroups like SMEs and households, served by banks, credit unions, microfinance
deposit insurance and lender of last resort activities by central banks. For example, the global financial crisis
was characterized by a severe credit crunch in many countries but with few exceptions (Shin 2009) saw
basically no retail deposit withdrawals. Thus, we focus on the impact of financial inclusion on credit rather than
deposit developments in crisis periods.
6 Special surveys of households and businesses, i.e. the demand side of financial services, are highly costly.
Thus, most of them lack the time dimension. The most encompassing survey, the Findex Database (Demirgüç-
Kunt et al. 2018), provides data for 2011, 2014 and 2017 only, which implies that it cannot be used to study the
impact of (changes in) the level of financial inclusion on the degree of financial instability experienced by a
country in the global financial crisis . For an overview of measures of financial inclusion see Beck (2016).
134
institutions and other intermediaries. However, for many categories information is available
only for more recent years. For example, there are only 21 (40) countries reporting any data
on the number of SME (household) borrowers from commercial banks before 2009. Data on
borrowers from financial institutions other than commercial banks is also very incomplete;
for example, only 23 countries report the number of borrowers from cooperatives. Against
this background and given our focus on credit we measure financial inclusion by the number
of borrowers from commercial banks.
- Insert Table 1 about here -
Our analysis has two parts. First, we employ the global financial crisis as a “testing ground”
(Čihák and Schaeck 2010) for our hypotheses, i.e. we take the view that all countries
experienced a crisis in 2008/2009 given the “global” nature of the crisis (Global Financial
Crisis sample). Thus, the number of countries in the GFC sample is limited by data
availability only, mainly by the availability of financial inclusion data.7 As low levels of
participation in the formal financial sector are an issue mainly in developing countries, the
associated data collection efforts are greater in these countries than in mature economies.
Accordingly, the majority of the 81 countries in the GFC sample (Table 1, Panel A) are
developing countries, i.e. low and lower-middle income economies in Africa, Asia and Latin
America. Argentina, Brazil, Columbia, Malaysia and Thailand are key representatives of
emerging markets, while the group of advanced economies is only represented by Belgium,
Estonia, Israel, Italy, Latvia, Portugal, San Marino, Singapore and the United Kingdom.
7 Drop-outs unrelated to financial inclusion are rare and mainly driven by a lack of data on capital account
openness, bank concentration and bank liquidity.
135
It can be argued that despite the global character of the 2008/2009 crisis many countries did
not experience a financial crisis themselves. For example, the Laeven and Valencia (2018)
crisis database lists only 27 countries with a crisis in 2008 and/or 2009. Thus, we construct a
second sample covering all country-specific crisis episodes over the period 2004 – 2017
(Table 1, Panel B). In addition to the episodes identified by Laeven and Valencia (2018) we
also include countries engaging in in a stability-oriented program with the IMF (Table 1,
Panel B). Accounting for data availability this leads to a sample of 52 crisis/program
observations involving 40 countries. Compared to the GFC sample, the IMF sample is more
homogenous in terms of economic development, as it predominantly consists of lower and
upper-middle income countries.
We run pooled OLS regressions, applying robust standard errors with the depth of the credit
crunch (DROPCREDIT) and the depth of the decline in the growth rate of borrowers between
the last pre-crisis year and the year after the outbreak of the crisis (DROPBORROWER) as
the dependent variables, and the level and growth of financial inclusion in the pre-crisis
period as the independent variables of interest.8 Figure 1 illustrates the approach for the
global financial crisis. It shows the cross-country averages for real credit growth over the
period 2005 – 2016. In 2007, the last year before the crisis, mean credit growth is above 20%;
it drops to around 3.3% in 2009, the year after the Lehman default. Thus, the GFC sample
average of DROPCREDIT is 18 percentage points.
- Insert Figure 1 about here -
8 We denote all variables employed in the IMF crisis sample without subscripts, while variables employed in the
GFC sample carry subscripts referring to the respective years for which they were calculated or taken.
136
We test whether DROPCREDIT is mitigated by SHAREBORROWERS, i.e. the level of
financial inclusion in the year before the crisis (hypothesis 1),9 and whether it is influenced
by stronger progress in financial inclusion measured by the compound annual bank borrower
growth rate (BORROWERGROWTH) in the pre-crisis period (hypothesis 2). We define the
pre-crisis period as the four years preceding the crisis. Thus, in the GFC analysis we calculate
BORROWERGROWTH for the years 2004-2007.
In all regressions we control for pre-crisis credit growth, i.e. the compound annual growth
rate of real outstanding loans issued by commercial banks (CREDITGROWTH10
), and a
matrix of banking sector and economic control variables (Xit, see Table 2 for a description of
all variables used). Including CREDITGROWTH is motivated by the credit boom-bust
literature firmly establishing a link between the size of the boom and the depth of the bust.11
The selection of banking and economic control variables largely follows Han and Melecky
(2017) and other studies of financial crisis episodes (Lane and Milesi-Ferretti 2011). Given
the relatively small sample sizes involved, we aim at keeping the number of controls as low
as possible.
We account for the state of play in the banking sector by controlling for the Z-Score, bank
concentration and the loans-to-deposit-ratio. We expect a higher Z-score to be associated with
a less pronounced credit crunch in crisis times as countries with stronger banking sectors are
likely to be more resilient to boom-bust phenomena in a crisis (Caprio et al. 2014, Vazquez
9 We opt for the 2008 level of financial inclusion for the GFC sample, as the crisis started with the default of
Lehman Brothers on 15 September 2008. Thus, the share of borrowers in the total adult population at end-2008
is likely to represent a better proxy for the pre-crisis level of financial inclusion than the respective share for
end-2007, as the immediate effect of the crisis on the number of borrowers can be assumed to be substantially
weaker than on volumes (which is why we choose end-2007 values for the remaining pre-crisis variables). Our
baseline results do not change when employing the 2007 level of financial inclusion as the main independent
variable.
10 Concretely, we take the nominal values for outstanding loans by commercial banks and deflate them with the
CPI. Based on this we calculate the compound annual growth rate for the pre-crisis period.
11 The same approach is also taken in studies explaining the depth of the recession following the Lehman default
by making the strength of GDP growth in the pre-crisis period an explanatory variable (Lane and Milesi-Ferretti
2011).
137
and Federico 2015). The effect of bank concentration (CONCENTRATION), defined as the
share of total assets in the banking system held by the three largest banks, is theoretically
ambiguous (Beck 2008). However, a number of recent studies show results supporting the
concentration-stability hypothesis (see e.g. Baselga-Pascual et al. 2015, Bretschger et al.
2012, Tabak et al. 2012). Thus, we expect a negative coefficient while banking sectors with a
larger loan-to-deposit ratio are likely to show a deeper fall in credit growth in crisis times
(Caprio et al. 2014, Richter et al. 2017). Key characteristics of the respective economies are
captured by GDP per capita (GDPPERCAPITA) and capital account openness (KAOPEN).
The global financial crisis was triggered by mature economies. Thus, we expect a positive
coefficient for GDP per capita in the GFC analysis. There is also evidence that credit boom-
bust cycles are often triggered by capital flow reversals,12
which implies that countries with a
more open capital account, measured by the KAOPEN index (Chinn and Ito 2008), should
show a more pronounced credit bust in crisis times.13
We include all variables in the
following OLS model which we run by applying robust standard errors (equation 1).
(1) DROPCREDITi = β1 + β2 INCLUSIONi + β3 CREDITGROWTHi
+ β4Xi + εi
We also analyze whether financial inclusion itself is subject to a boom-bust cycle pattern, i.e.
we ask whether the drop in borrower growth in the respective crisis episodes
(DROPBORROWER) is significantly linked to the level and the growth rate of financial
inclusion in the pre-crisis period. We calculate DROPBORROWER in the same way as
12
For the global financial crisis, this transmission mechanism is stressed by Dooley and Hutchinson (2009) and
Claessens et al. (2010).
13 We opt for the de jure openness index compiled by Chinn and Ito, as the de facto openness index constructed
by Lane and Milesi-Ferretti (2007) is available only up to 2011.
138
DROPCREDIT, i.e. it represents the difference between the borrower growth rate countries
observed before entering the crisis and the borrower growth rate at the end of first year after
the outbreak of the crisis, with higher values indicating a higher drop. The analysis is
motivated by the anecdotal evidence reviewed in section 2, indicating that borrower growth
falls significantly in times of crisis, suggesting that financial turmoil puts an end to or even
reverses progress made in financial inclusion in the pre-crisis period.
Figure 2 illustrates this for the GFC analysis. As for credit, it shows a pronounced boom-bust
pattern, as the pre-crisis years record strong borrower growth which drops substantially in the
crisis 2008/2009.
- Insert Figure 2 about here -
The boom-bust hypothesis 3 receives support if in equation (2) the compound annual growth
rate of borrowers from commercial banks in the pre-crisis period (BORRGROWTH) has a
significant positive coefficient (hypothesis 3). For sake of completeness, we also test whether
countries with a higher SHAREBORROWERS show a less pronounced drop in borrower
growth during the crisis.
(2) DROPBORROWERi = β 1 + β 2 INCLUSIONi + β3 CREDITGROWTHi + + β4Xi] +
εi
Descriptive statistics for the GFC sample (Table 3, Panel A) show that countries on average
experience an 18 percentage point drop in credit and a 21 percentage point drop in borrower
growth between 2007 and 2009. There is substantial cross-country variance: the deepest fall
139
in credit (borrower) growth amounts to 75 (159) percentage points while some countries see
even higher credit (borrower) growth in the crisis period than between 2004 and 2007. With
regard to the pre-crisis period, the distribution of pre-crisis borrower growth is skewed, as
mean growth (27%) is substantially above median growth (17%), indicating that some
countries recorded a very rapid expansion in the number of borrowers. By contrast, pre-crisis
credit growth (CREDITGROWTH0407) has been more homogenous across countries,
supporting the view that there is a global financial cycle (Rey 2015). Finally, the positive
difference between mean and median for SHAREBORROWERS08 indicates that many
countries in the sample record comparatively low levels of financial inclusion.
In the IMF sample, on average the drop in credit growth and pre-crisis growth rates of credit
volumes are at about the same magnitude as in the GFC sample. By contrast, the drop in
borrower growth in the crisis and pre-crisis borrower growth are substantially smaller than in
the GFC sample ((9 versus 21 for DROPBORROWER, 17 versus 27 for BORROWER-
GROWTH). Thus, on average financial inclusion expands less rapidly in pre-crisis periods
but also records a less severe drop in a crisis in the IMF than in the GFC sample. Pre-crisis
levels of financial inclusion (SHAREBORROWERS) are about the same on average (0.18),
but more homogenous in the IMF sample with a standard deviation of 0.14 versus 0.19 in the
GFC sample. This is likely to reflect the greater homogeneity of the IMF sample with regard
to GDP per capita levels. For the remaining control variables, descriptive statistics are similar
in both samples.
- Insert Table 3 about here -
140
Correlation analysis shows that credit and inclusion developments are strongly linked to each
other in the GFC sample (Table 4, Panel A).14
Drops in credit and borrower growth as well as
pre-crisis credit and borrower growth are positively correlated. Moreover, both drop variables
show significant positive correlations with pre-crisis credit and borrower growth. By contrast,
there is no significant correlation between pre-crisis financial inclusion levels, i.e. the share
of borrowers, and the drops in credit and borrower growth in the crisis. Finally, as expected,
there is a strong positive correlation between the level of financial inclusion and GDP per
capita, while stronger borrower growth in the pre-crisis period is negatively associated with
per capita income supporting the idea of catching-up effects in financial inclusion (Demirgüç-
Kunt et al 2015).
Correlations are fairly similar in the IMF sample (Table 4, Panel B). Major exceptions refer
to the drop in borrower growth in a crisis. On the one hand, there is no significant correlation
between the depth in borrower drop in the crisis and pre-crisis borrower and credit growth
respectively. By contrast, a higher pre-crisis level of financial inclusion is significantly
associated with a smaller drop in borrower growth in the crisis.
- Insert Table 4 about here -
4. Results
Table 5 reports our results on the impact on the drop in credit and borrower growth in a crisis
of pre-crisis levels and pre-crisis growth in financial inclusion. Panel A presents evidence for
the GFC, Panel B for the IMF crisis sample.
14
We test for multicollinearity among independent variables in the baseline regressions. All variance inflation
factors are below 2.35 (panel) and 3.38 (cross-country regressions), suggesting that the coefficients are not
poorly estimated due to multicollinearity.
141
- Insert Table 5 about here -
We find that the level of financial inclusion, SHAREBORROWERS(08), has no direct
impact on the depth of post-crisis credit crunches in either sample (Table 5, Panels A and B,
column 1), even though the coefficient barely misses significance at the 10 percent level in
the GFC specification. By contrast, a higher level of inclusion is associated with a smaller
drop in borrower growth during a crisis in the IMF sample (Table 5, Panel B, column 3).
Higher pre-crisis borrower growth is related to a larger drop in borrower growth in the GFC
sample (Table 5, Panel A, column 4) and a larger drop in credit growth in the IMF sample
(Table 5, Panel B, column 2). Overall, results provide strong support for hypothesis 2, as
stronger pre-crisis progress in financial inclusion never has a mitigating impact on the drop in
credit or borrower growth in a crisis. There is even some evidence that higher pre-crisis
borrower growth intensifies the bust in credit (IMF sample) and in borrower growth (GFC
sample) if a crisis occurs. The latter result also supports hypothesis 3; however, the overall
evidence with regard to boom-bust phenomena in financial inclusion itself is mixed as there
is no significant relationship between pre-crisis borrower growth and the drop in borrower
growth in a crisis in the IMF sample,. Finally, there is little support for hypothesis 1, as
higher pre-crisis levels of financial inclusion as such are found to have a mitigating impact on
developments in a crisis for borrower growth only in the IMF sample.
We expand our baseline regression and include interaction terms between the financial
inclusion variables and pre-crisis credit growth (Table 6), i.e. we link the pre-crisis level of
progress in financial inclusion to pre-crisis credit growth (SHARE*CREDITGROWTH,
BORR*CREDITGROWTH). This allows us to explore a possible mechanism of how the pre-
142
crisis state of play with regard to financial inclusion might influence the drop in credit and
borrower growth during a crisis. Concretely, the coefficients provide information on whether
the bust in credit or borrower growth in crisis periods is less pronounced if the growth of
credit prior to the crisis is distributed more broadly. This is of policy relevance as sign and
significance of the interaction term provide an answer to the question whether – everything
else being equal – level of and progress in financial inclusion should be part of the risk
assessment that policymakers engage in when observing strong credit growth.
Results for the GFC sample indicate that a higher pre-crisis share of borrowers significantly
mitigates the destabilizing impact of a stronger pre-crisis credit boom on credit and borrower
developments in the crisis (Table 6, Panel A, columns 1 and 3). While this is not the case for
the IMF sample, a higher level of inclusion continues to directly limit the drop in borrower
growth (Table 6, Panel B, column 3). For pre-crisis borrower growth, the GFC sample shows
again that higher pre-crisis borrower growth is associated with a deeper drop of borrower
growth in the crisis (Table 6, Panel A, column 4), while the IMF sample continues to show
the same effect for the drop in credit growth. However, in the latter case the effect is now
expressed via the interaction term, as stronger pre-crisis borrower growth reinforces the
boom-bust relationship for credit (Table 6, Panel B, column 2) while the direct effect of
stronger pre-crisis borrower growth on the drop in credit growth captured in Table 5 turns
insignificant.
- Insert Table 6 about here -
In all specifications reported in Tables 5 and 6 either higher pre-crisis credit growth or higher
pre-crisis borrower growth impacts the drop in credit or borrower growth in a crisis. Thus,
143
our analysis provides broad support for the boom-bust pattern of credit developments
identified in the financial crisis literature. For the GFC sample we also robustly find that a
higher degree of capital account openness has a positive impact on the drop in growth during
a crisis, indicating that capital flows and credit growth are closely related. However, there is
no such effect for the IMF sample.
Combining the results reported in Tables 5 and 6, we find some support for hypothesis 1:
countries benefit in crisis times from a higher level of financial inclusion, as this either
directly or indirectly, i.e. by dampening the destabilizing effects of pre-crisis credit growth,
mitigates the depth of busts in credit and borrower growth in a crisis. By contrast, neither in
the GFC nor in the IMF sample is there any evidence suggesting that stronger progress in
financial inclusion before a crisis has a mitigating impact on developments in a crisis. If
significant, stronger pre-crisis borrower growth even has a destabilizing impact on credit
(IMF sample) and borrower growth (GFC sample) in the crisis. Both results support
hypothesis 2. Finally, we find mixed evidence with regard to hypothesis 3 which is supported
by the GFC evidence, while for the IMF sample we are unable to identify a boom-bust cycle
in financial inclusion itself.
5. Robustness checks
We run a series of checks to test the robustness of our results (available from the authors on
request, Tables A1 – A4 in the Appendix). They focus on the direct effects of financial
inclusion, i.e. the specification without interaction terms. Concretely, we test whether our
results are robust to a) changes in the independent variables used, b) changes in the proxy of
financial inclusion, c) changes in the sample and d) changes in the econometric methodology.
144
Depending on data availability, some of the tests could be conducted for the GFC or the IMF
sample only.
We start by testing for possible non-linear effects of SHAREBORROWERS on the depth of
the credit and borrower crunch. Moreover, we account for the possibility that credit and
borrower growth developments reflect the activities of foreign banks operating in host
countries (Claessens and van Horen 2015, 2016). Thus, we expand the KAOPEN variable by
multiplying the capital account openness index with the share of assets held by foreign banks
in the respective host country banking sectors.
A higher level of financial inclusion might be positively associated with policy efforts to
maintain financial stability when a crisis hits, as “greater financial inclusion … is associated
with more costly financial crises” (Čihák et al. 2016, p. 11).15
Thus, our baseline results could
reflect intervention and stabilization measures taken by the respective authorities during a
crisis which we did not control for (Calderon and Schaeck 2016). We test for this by making
use of information provided by Laeven and Valencia (2018) and including a variable which
accounts for the number of different stabilization measures authorities employed in the GFC,
with a higher number indicating a more thorough intervention. Finally, we address the
possibility that busts in credit and borrower growth in crisis times largely reflect demand
effects and control for the respective drops in GDP growth in crisis times.
We also test whether results are robust to changes in the indicator measuring financial
inclusion. To this end, we replace the number of borrowers with the number of loan accounts
reported in the IMF FAS dataset. For the GFC sample, we also make use of the Honohan
15
The former Governor of the Bank of Kenya expresses the link as follows: “With enhanced financial inclusion
comes the need to step up existing frameworks on consumer protection and deposit protection, while exploring
emerging issues on competition and interoperability.” (Ndungu 2012).
145
index of financial inclusion (Honohan 2008) which focuses on household access to finance.16
An additional robustness check for the GFC involves a change in the sample. It is motivated
by the evidence that the inclusion-stability nexus might be different in mature compared to
developing and emerging market economies (Čihák et al. 2016). As the GFC was triggered in
advanced economies showing high levels of financial inclusion, we test whether our results
are driven by this country group and run our regressions excluding countries with an
advanced economy status as defined by the IMF.
Our last set of tests involves changes in the econometric approach. As a first step, partly
motivated by the small sample sizes, in particular for the IMF sample, we apply a
parsimonious approach, i.e. we simplify our model to the least number of explanatory
variables which capture the structural part of the estimation model.17
Moreover, we run a
Two-Stage Least Squares (2SLS) regression to correct for the possible endogeneity of the
level of financial inclusion, i.e. SHAREBORROWERS. In the first stage regression, we use
population density (population per square kilometer of land area) of the respective pre-crisis
year, i.e. 2007 for the GFC sample, as an instrument18
for SHAREBORROWERS (results not
shown). Finally, influenced by Bekaert et al. (2014), we also orthogonalize pre-crisis
borrower growth by regressing pre-crisis borrower growth on pre-crisis credit growth and
then use the residuals of this regression as the financial inclusion variable. Similarly, we
16
The Honohan index has been compiled only once. Hence, we cannot employ the variable as a substitute for
borrower growth in the pre-GFC period, but only as an alternative to SHAREBORROWERS08. Moreover, the
index is not available for all countries listed in the GFC sample. Thus, the sample size shrinks to 73
observations.
17 The method used for variable selection is stepwise determined by backward selection, with the respective
inclusion variable being locked. We consider a 0.05 significance level for removal from the model. Under the
backward approach, we avoid the so-called suppressor effects. We start fitting the model with all candidate
variables, with the least significant variable being dropped. 18
The choice of the instrument reflects the fact that a higher population density facilitates the provision of
financial services by reducing costs due to the elimination of distances (Scronce 2013) and via economies of
scale effects (Alter and Yontcheva 2015). We find that population density has a strong impact on the 2008 use
of credit, but does not influence the drop in credit or borrower growth rates during the crisis as well as other
covariates. The validity of the instrument is also confirmed when running the test of Olea and Pflueger (2013).
146
extract a credit growth variable that is orthogonal to pre-crisis borrower growth by regressing
pre-crisis credit growth on pre-crisis borrower growth. We then use the residuals of this
regression as the credit growth variable (Tables A3 and A4 in the Appendix).19
Most of the checks suggest that our results are robust. This holds in particular for the GFC
sample. With regard to the IMF sample, some robustness checks indicate that a higher share
of borrowers significantly mitigates the drop in credit growth in a crisis, thereby providing
additional support for hypothesis 1. For the remaining specifications results are fairly robust.
6. Discussion and conclusions
Does financial inclusion dampen the depth of a credit bust in a financial crisis? We find some
evidence that this is the case with regard to the level of financial inclusion. By contrast,
stronger advances in financial inclusion during a pre-crisis period do not mitigate the drop in
credit and borrower growth in a crisis. Thus, credit boom-bust cycles are not different when
the pre-crisis credit boom reflects a “democratization of credit” in the form of a rising growth
rate of bank borrowers. Rapid credit growth “kills” (Kraft and Jankov 2005, Sahay 2015)
even if it is associated with strong advances in financial inclusion. Finally, our results are
inconclusive on the question whether financial inclusion itself exhibits a boom-bust cycle
pattern, as the GFC sample results support this notion while the results for the IMF sample
fail to do so.
19
As a somewhat broader check of the results for the IMF sample we also run a fixed effects panel regression
with annual credit growth as the dependent variable and the level and growth of financial inclusion serving as
independent variables which we interact with a crisis dummy marking the years of turmoil. Running a panel
regression increases the number of observations substantially; however, it comes at the price of a different
research question to be tested, as the regression explores whether crisis periods exhibit significantly different
relationships between financial inclusion and credit growth compared to normal periods. We find that countries
exhibiting a higher level of financial inclusion exhibit a significantly less negative impact of a crisis on credit
growth, while higher borrower growth has neither a stabilizing nor a destabilizing impact in crisis times. Results
are available from the authors on request.
147
Efforts to foster financial inclusion are mainly taken in developing and emerging market
economies as levels of financial inclusion in these economies is substantially below those
recorded for advanced economies. For policymakers in these countries our results have two
implications. First, raising the level of inclusion is a goal worth pursuing, not only because of
the potential growth and poverty mitigating effects of financial inclusion but also because our
analysis suggests that having a higher level of inclusion mitigates credit boom-bust cycles in
a crisis. This supports findings of the earlier literature on the inclusion-stability nexus
suggesting a positive link between both concepts. Second, raising the level of financial
inclusion, for example by facilitating access to credit and thus raising the borrower growth
rate, remains a challenging task, as ““promoting credit for all at all cost can lead to greater
financial and economic instability” (Demirgüc-Kunt 2014, 349), with “at all cost” concretely
meaning “at the cost of rapid credit growth”. Our results imply that rapid borrower growth is
no excuse for ignoring the stability risks associated with rapid credit growth, as higher
borrower growth rates do not mitigate the drop in credit growth related to strong credit
growth if a crisis hits.
The latter implication is in line with the more recent literature on the inclusion-stability nexus
questioning the strictly positive link found in the earlier studies. Policymakers in developing
countries aiming for higher levels of financial inclusion are well advised to stay focused on
credit growth developments in their stability risk assessments, even if credit growth seems to
reflect strong progress in broadening the use of credit. In addition, such a risk assessment is
also useful for financial inclusion motives, as our results for the GFC suggest that progress in
financial inclusion achieved in a pre-crisis period might be easily reversed in a crisis. Thus,
well-designed financial inclusion policies can be defined as policies fostering a broader use
of credit without contributing to a potentially destabilizing credit boom.
148
Acknowledgements
We thank Ata Can Bertay, Judith Mader, Arnaud Mehl, Øystein Strøm and participants of the
European Microfinance Week 2015, held in Luxembourg 18-20 November 2015, the 9th
Portuguese Finance Network conference, held at the University of Beira Interior, 22-24 June
2016, the 2nd Microfinance and Rural Finance Conference, Financial Inclusion and
Emerging Markets Finance,held at the School of Management and Business, Aberystwyth
University, 5-6 July 2016, the 2nd International Workshop P2P Financial Systems, held at
University College London, 8-9 September 2016, the International Conference on Financial
Cycles, Systemic Risk, Inter-connectedness, and Policy Options for Resilience, organized by
the Asian Development Bank, held in Sydney 8-9 September 2016, the Workshop on
Banking and Institutions May 15-16, 2017 Bank of Finland, Helsinki and the 34th
International Conference of the French Finance Association, 31 May –2 June 2017, Valence
(France) for helpful comments and suggestions on earlier versions of this paper.
References
Abakaeva, J., Glisovic, J. (2009). Are Deposits a Stable Source of Funding for Microfinance
Institutions? CGAP Brief, http://www.cgap.org/sites/default/files/CGAP-Brief-Are-Deposits-
a-Stable-Source-of-Funding-for-Microfinance-Institutions-Jun-2009.pdf, accessed 15
February 2016.
Adasme, O., Majnoni, G., Uribe, M. (2006). Access And Risk - Friends Or Foes? Lessons
From Chile, World Bank Policy Research Working Paper No. 4003, Washington DC
Alessi, L., Detken, C. (2017). Identifying excessive credit growth and leverage. Journal of
Financial Stability,
Alter, A., Yontcheva, B. (2015). Financial Inclusion and Development in the CEMAC, IMF
Working Paper WP/15/235, Washington DC.
Arcalean, C., Calvo-Gonzalez, O., Móré, C., van Rixtel, A., Winkler, A., Zumer, T. (2007).
The Causes and Nature of the Rapid Credit Growth of Bank Credit in the Central, Eastern,
and South-eastern European Countries, In Enoch, C. Ötker-Robe, İ. (eds.). Rapid Credit
Growth in Central and Eastern Europe, Palgrave Macmillan, New York, 13-46.
Arcand, J.-L., Berkes, E. and U. Panizza (2015), ‘Too much finance?’ Journal of Economic
Growth, 20(2): 105-148.
Babecký, J., Havránek, T., Matějů, J., Rusnák, M., Šmídková, K.,Vašíček, B. (2014).
Banking, debt, and currency crises in developed countries: Stylized facts and early warning
indicators. Journal of Financial Stability, 15:1-17.
Baselga-Pascual, L., Trujillo-Ponce, A., Cardone-Riportella, C. (2015). Factors influencing
bank risk in Europe: Evidence from the financial crisis. North American Journal of
Economics and Finance, 34: 138–166
150
Battiston, S., Gatti, D.D., Gallegati, M., Greenwald, B., Stiglitz., J.E. (2012). Default
cascades: When does risk diversification increase stability? Journal of Financial Stability, 8:
138– 149
Beck, T. (2008). Bank Competition and Financial Stability: Friends or Foes? Policy Research
Working Paper No. 4656, The World Bank, Washington, DC.
Beck, R., Georgiadis, G., Straub, R. (2014). The finance and growth nexus revisited.
Economics Letters, 124(3): 382-385.
Beck, T. (2015). Finance and growth – beware the measurement, http://www.
voxeu.org/article/finance-and-growth-beware-measurement, accessed 12 February 2016.
Beck, T. (2016). Financial Inclusion – measuring progress and progress in measuring, mimeo.
Beck, T., Degreyse, H., Kneer, C. (2014). Is more finance better? Disentangling
intermediation and size effects of financial systems. Journal of Financial Stability, 10:50-64.
Beck, T., Senbet, L., Simbanegavi, W. (2015). Financial Inclusion and Innovation in Africa:
An Overview. Journal of African Economies, 24:i3-i11
Becker, B., Bos, M., Roszbach, K. (2016). Bad times, good credit, Swedish House of Finance
Research Paper No. 15-05.
Bekaert, G, Ehrmann, M., Fratzscher, M., Mehl, A. (2014). The Global Crisis and Equity
Market Contagion. The Journal of Finance, 69(6): 2597-2649.
Boz, E., Mendoza, E.G. (2014). Financial innovation, the discovery of risk, and the U.S.
credit crisis. Journal of Monetary Economics, 62(C): 1-22
Bretschger, L., Kappel, V., Werner, T. (2012), Market concentration and the likelihood of
financial crises, Journal of Banking and Finance, 36: 3336–3345
151
Brown, M., Kirschenmann, K., Spycher, T. (2016). Numeracy and on-the-job decision
quality: Evidence from loan officers”, mimeo.
Calderon, C., Schaeck, K. (2016). The effects of government interventions in the financial
sector on banking competition and the evolution of zombie banks. Journal of Financial and
Quantitative Analysis, 51(4): 1391-1436.
Caprio, G., D’Apice, V., Ferri, G., Puopolo, G.W. (2014). Macro-financial determinants of
the great financial crisis: Implications for financial regulation. Journal of Banking & Finance,
44: 114-129.
Cecchetti, S. G. and E. Kharroubi (2012). ‘Reassessing the impact of finance on growth’, BIS
Working Papers No. 381, Basle
Chen, G., Rasmussen, S., Reille, X. (2010). Growth and Vulnerabilities in Microfinance.
CGAP Focus Note No. 61, Washington D.C.
Chinn, M. D., Ito, H. (2008). A new measure of financial openness. Journal of comparative
policy analysis, 10(3): 309-322.
Čihák, M., Schaeck, K. (2010). How well do aggregate prudential ratios identify banking
system problems?. Journal of Financial Stability, 6(3): 130-144.
Čihák, M., Mare, D.D., Melecky, M. (2016), The Nexus of Financial Inclusion and Financial
Stability, World Bank Policy Research Working Paper 7722, Washington DC.
Claessens, S., Dell’Ariccia, G., Igan, D., Laeven, L. (2010). Global linkages and global
policies, Economic Policy, 25(62): 267-293
Claessens, S., van Horen, N. (2015). The impact of the global financial crisis on banking
globalization. IMF Economic Review, 63:868-918.
152
Claessens, D., van Horen, N. (2016). The Role of Foreign Banks in Local Credit Booms, in:
Demirgüç-Kunt, A., Evanoff, D.D., Kaufman, G.G. (eds.), The Future of Large,
Internationally Active Banks, World Scientific Studies in International Economics, 273-292
Collins, D., Morduch, J., Rutherford, S., Ruthven, O. (2009). Portfolios of the Poor, Princeton
University Press, New Jersey.
Craig, B.R., Dinger, V. (2013). Deposit market competition, wholesale funding, and bank
risk. Journal of Banking and Finance, 37:3605-3622.
Cull, R., Demirgüç-Kunt, A., Lyman, T. (2012). Financial Inclusion and Stability: What Does
Research Show? CGAP Brief, Washington DC.
Dell’Ariccia, G., Marquez, R. (2006). Lending Booms and Lending Standards. The Journal of
Finance, 61(5): 2511-2546.
Dema, E. (2015). Managing the Twin Responsibilities of Financial Inclusion and Financial
Stability. Alliance for Financial Inclusion Viewpoints No. 2, http://www.afi-
global.org/sites/default/files/publications/afi_viewpoints_2_final.pdf , accessed 25 February
2016.
Demirgüç-Kunt, A. (2014). Presidential Address: Financial Inclusion. Atlantic Economic
Journal, 42:349–356.
Demirgüç-Kunt, A., Klapper, L., Singer, D. Van Oudheusden, P. (2018). The Global Findex
Database 2014, Measuring Financial Inclusion and the Fintech Revolution, The World Bank,
Washington DC.
Diamond, D.W. (1984). Financial Intermediation as Delegated Monitoring. The Review of
Economic Studies, 51(3):393-414
Dittus, P., Klein, M. (2011). On harnessing the potential of financial inclusion. BIS Working
Papers No 347, Basel
153
Dooley, M., Hutchison, M. (2009). Transmission of the U.S. subprime crisis to emerging
markets: Evidence on the decoupling-recoupling hypothesis. Journal of International Money
and Finance, 28(8): 1331-1349
Feldkircher, M. (2014). The determinants of vulnerability to the global financial crisis 2008 to
2009: Credit growth and other sources of risk, Journal of International Money and Finance,
Elsevier, 43(C), 19-49
Gertler, M., Kiyotaki, N., Prestipino, A. (2016). Wholesale Banking and Bank Runs in
Macroeconomic Modelling of Financial Crises, Handbook of Macroeconomics, Vol. 2,
Elsevier, 1345-1425.
Global Partnership for Financial Inclusion (GPFI), 2012. Financial Inclusion – A Pathway to
Financial Stability? Understanding the Linkages – Issues Paper, Basel,
https://www.gpfi.org/sites/default/files/documents/GPFI%20SSBs%20Conference%20%20Iss
ues%20Paper%203%20Financial%20Inclusion%20%E2%80%93%20A%20Pathway%20to%
20Financial%20Stability_1.pdf, accessed 25 February 2016
Global Partnership for Financial Inclusion (GPFI), 2016. Global Standard-Setting Bodies and
Financial Inclusion – The Evolving Landscape. http://www.gpfi.org/sites/default/files/-
documents/GPFI%20White%20Paper%20final%20prepublication%20version%20March%20
2016.pdf, accessed 17 March 2016
Gramlich, E.M. (2007), Subprime Mortgages – America’s Latest Boom and Bust, The Urban
Institute.
Greenspan, A. (1997), Consumer Credit and Financial Modernization, Remarks at the
Economic Development Conference of the Greenlining Institute, San Francisco, California,
October 11, 1997
154
Guérin I., Roesch M., Venkatasubramanian G., D'Espallier B. (2012). Credit from whom and
for what? The diversity of borrowing sources and uses in rural southern India. Journal of
International Development, 24(1). S122-S137
Guérin, I. , Bert D'Espallier, B.. Venkatasubramanian, G. (2013). Debt in Rural South India:
Fragmentation, Social Regulation and Discrimination. The Journal of Development Studies.
49 (9): 1155-1171.
Gulamhussen, M.A., Pinheiro, C., Pozzolo, A.F. (2014). International diversification and risk
of multinational banks: Evidence from the pre-crisis period. Journal of Financial Stability, 13:
30–43.
Han, R., Melecky, M. (2017). Broader use of saving products among people can make deposit
funding of the banking system more resilient. Journal of International Financial Markets,
Institutions and Money, 47: 89-102.
Hannig, A., Jansen, S. (2010). Financial Inclusion and Financial Stability: Current Policy
Issues. ADBI Working Paper Series No. 259.
Honohan, P. (2008). Cross-country variation in household access to financial services,
Journal of Banking & Finance, 32(11): 2493-2500.
Huang, R., Ratnovski, L. (2011). The dark side of bank wholesale funding. Journal of
Financial Intermediation, 20(2): 248–263.
Jordà, Ò., Schularick, M., Taylor, A. M. (2011). Financial crises, credit booms, and external
imbalances: 140 years of lessons. IMF Economic Review, 59(2): 340-378.
Khan, H.R. (2011). Financial inclusion and financial stability: are they two sides of the same
coin? http://www.bis.org/review/r111229f.pdf
Khiaonarong, T. (2014). Oversight Issues in Mobile Payments. IMF Working Paper No.
14/123, Washington D.C.
155
Klapper, L., Lusardi, A., Panos, G.A. (2013). Financial literacy and its consequences:
Evidence from Russia during the financial crisis, Journal of Banking and Finance. 37(10):
3904-3923
Kraft, E., Jankov, L. (2005). Does speed kill? Lending booms and their consequences in
Croatia. Journal of Banking & Finance, 29(1): 105–121
Laeven, L., Valencia, F., (2018). Systemic Banking Crises Revisited, IMF Working Paper
18/206, Washington DC.
Lane, P.R., Milesi-Ferretti, G.M. (2007). The external wealth of nations mark II: Revised and
extended estimates of foreign assets and liabilities, 1970–2004, Journal of International
Economics 73, November, 223-250.
Lane, P.R., Milesi-Ferretti, G.M. (2011). The Cross-Country Incidence of the Global Crisis,
IMF Economic Review, 59(1), 77-110.
Madestam, A. (2014). Informal finance: A theory of moneylenders. Journal of Development
Economics, 107, 157-174
Manganelli, S. and A. Popov (2013), ‘Financial dependence, global growth opportunities, and
growth revisted’, Economics Letters, 120(1), 123-135
Mehrotra, A., Yetman, J. (2015). Financial inclusion – issues for central banks. BIS Quarterly
Review, March: 83-96.
Mendoza, E., Terrones, M. (2012). An Anatomy of Credit Booms and Their Demise. NBER
Working Paper 18379. Cambridge MA.
Meng, C., Gonzalez, R. L. (2017). Credit Booms in Developing Countries: Are They
Different from Those in Advanced and Emerging Market Countries?. Open Economies
Review, 28(3): 547-579.
156
Mialou, A. (2015). The IMF’s Financial Access Survey (FAS), In: IFC Bulletin No 38:
Financial inclusion indicators, Basel, http://www.bis.org/ifc/publ/ifcb38.pdf, accessed 1
March 2016.
Mishkin, F. (2011). Monetary Policy Strategy: Lessons from the Crisis, NBER Working
Paper No. 16755, Cambridge MA.
Morgan, P., Pontines, V. (2014). Financial Stability and Financial Inclusion. Asian
Development Bank Institute Working Papers No. 488, Tokyo.
Ndungu, N. (2012). Balancing Financial Inclusion and Stability,
http://www.cgap.org/blog/balancing-financial-inclusion-and-stability, accessed 25 February
2016.
Olea, J. L. M., & Pflueger, C. (2013). A robust test for weak instruments. Journal of Business
& Economic Statistics, 31(3), 358-369.
Rahman, A. (2014). The Mutually-Supportive Relationship Between Financial Inclusion and
Financial Stability, Alliance for Financial Inclusion Viewpoints No. 1, http://www.afi-
global.org/sites/default/files/publications/afivp1-11.pdf , accessed 12 February 2016.
Rajan, R. (2010), Fault Lines, Princeton University Press, Princeton, New Jersey,
Reinhart, C.M., K.S. Rogoff (2008), Is the 2007 U.S. Sub-Prime Financial Crisis So
Different? An International Historical Comparison, NBER Working Paper No 13761,
Cambridge MA.
Rey, H. (2015). Dilemma not trilemma: the global financial cycle and monetary policy
independence. NBER Working Paper 21162, Cambridge MA.
Richter, B., Schularick, M., and P. Wachtel (2017). When to Lean Against the Wind, CEPR
Discussion Paper DP12188, London.
157
Rioja, F. and N. Valev (2004), ‘Does one size fit all?: a reexamination of the finance and
growth relationship’, Journal of Development Economics, 74(2): 429-447
Rousseau, P. and P. Wachtel (2011), ‘What is happening to the impact of financial deepening
on economic growth?’, Economic Inquiry, 49, 276–288
Sahay, R:, Čihák , M., N'Diaye, P., Barajas, A., Mitra, S., Kyobe, A., Mooi, Y.N., Yousefi R.
(2015). Financial Inclusion: Can it Meet Multiple Macroeconomic Goals?, IMF Staff
Discussion Note 15/17, Washington DC,
https://www.imf.org/external/pubs/ft/sdn/2015/sdn1517.pdf
Sassi, S., Gasmi, A. (2014). The effect of enterprise and household credit on economic
growth: New evidence from European Union countries. Journal of Macroeconomics, 39:226-
231.
Schularick, M., Taylor, A. M. ( 2012). Credit Booms Gone Bust: Monetary Policy, Leverage
Cycles, and Financial Crises, 1870-2008. American Economic Review, 102 (2): 1029–61.
Scronce, E. ( 2013). Population Density and Per Capita Income Can Shape a Country's Path to
Financial Inclusion, http://www.cgap.org/news/population-density-capita-income-shape-
financial-inclusion-path, accessed 29 January 2017.
Shin, H.S. (2009). Reflections on Northern Rock: The Bank Run That Heralded the Global
Financial Crisis. Journal of Economic Perspectives. 23(1): 101-119
Tabak, B.M., Fazio, D.M., Cajueiro, D.C. (2012). The relationship between banking market
competition and risk-taking: Do size and capitalization matter? Journal of Banking &
Finance, 36: 3366–3381
Vazquez, F., Federico, P. (2015). Bank funding structures and risk: Evidence from the global
financial crisis. Journal of Banking & Finance, 61:1-14.
158
Zingales, L. (2015). Presidential Address: Does Finance Benefit Society? The Journal of
Finance, 70(4):1327- 1363.
159
Figure 1: Credit growth – country averages, 2005-2016 (in percent)
Source: IMF FAS, authors’ calculations based on our sample of 81 countries.
Figure 2: Borrower growth – country averages, 2005-2016 (in percent)
Source: IMF FAS, authors’ calculations based on our sample of 81 countries.
160
Table 1: List of Countries
Panel A: GFC sample
AFRICACENTRAL, SOUTH
ASIA AND PACIFIC
1 Botswana 43 Bangladesh 1 Burundi 7 Madagascar
2 Burundi 44 Indonesia 2 Chad 8 Malawi
3 Cabo Verde 45 Malaysia 3 Democratic Republic of Congo 9 Mozambique
4 Chad 46 Maldives 4 Ethiopia 10 Rwanda
5 Democratic Republic of Congo 47 Mongolia 5 Guinea 11 Sierra Leone
6 Equatorial Guinea 48 Myanmar 6 Haiti 12 Tanzania
7 Ethiopia 49 Pakistan
8 Gabon 50 Singapore
9 Ghana 51 Tajikistan 13 Bangladesh 26 Mauritania
10 Guinea 52 Thailand 14 Bolivia 27 Moldova
11 Kenya 15 Cabo Verde 28 Mongolia
12 LesothoMIDDLE EAST AND
NORTH AFRICA 16 Egypt 29 Myanmar
13 Madagascar 53 Algeria 17 El Salvador 30 Nigeria
14 Malawi 54 Egypt 18 Georgia 31 Pakistan
15 Mauritania 55 Israel 19 Ghana 32 Samoa
16 Mozambique 56 Kuwait 20 Guatemala 33 Swaziland
17 Namibia 57 Lebanon 21 Honduras 34 Syrian Arab Republic
18 Nigeria 58 Libya 22 Kenya 35 Tajikistan
19 Rwanda 59 Qatar 23 Kyrgyz Republic 36 Yemen
20 Seychelles 60 Saudi Arabia 24 Indonesia 37 Zambia
21 Sierra Leone 61 Syrian Arab Republic 25 Lesotho
22 Swaziland 62 Tunisia
23 Tanzania 63 Yemen, Republic of
24 Zambia 38 Albania 51 Libya
EASTERN EUROPE
AND CENTRAL ASIA39 Algeria 52 Malaysia
64 Albania 40 Azerbaijan, Republic of 53 Maldives
65 Azerbaijan, Republic of 41 Belize 54 Macedonia, FYR
LATIN AMERICA AND
CARIBBEAN66 Bosnia and Herzegovina 42 Bosnia and Herzegovina 55 Namibia
25 Argentina 67 Estonia 43 Botswana 56 Paraguay
26 Belize 68 Georgia 44 Brazil 57 Peru
27 Bolivia 69 Kyrgyz Republic 45 Colombia 58 Romania
28 Brazil 70 Latvia 46 Costa Rica 59 Suriname
29 Chile 71 Macedonia, FYR 47 Dominican Republic 60 Thailand
30 Colombia 72 Moldova 48 Ecuador 61 Tunisia
31 Costa Rica 73 Poland 49 Gabon 62 Turkey
32 Dominican Republic 74 Romania 50 Lebanon
33 Ecuador 75 Turkey
34 El Salvador
35 Guatemala 63 Argentina 73 Portugal
36 Haiti WESTERN EUROPE 64 Belgium 74 Qatar
37 Honduras 76 Belgium 65 Chile 75 San Marino
38 Paraguay 77 Italy 66 Equatorial Guinea 76 Saudi Arabia
39 Peru 78 Portugal 67 Estonia 77 Seychelles
40 Suriname 79 San Marino 68 Israel 78 Singapore
41 Uruguay 80 United Kingdom 69 Italy 79 United Kingdom
42 Venezuela 70 Kuwait 80 Uruguay
OCEANIA 71 Latvia 81 Venezuela
81 Samoa 72 Poland
Low-income economies ($1,045 or less)
Lower-middle-income economies ($1,046 to $4,125)
Upper-middle-income economies ($4,126 to $12,735)
High-income economies ($12,736 or more)
Source: authors’ compilations
161
Panel B: IMF crisis sample
AFRICA Crisis_Year Crisis_Year
1 Democratic Republic of Congo 2009
2 Ghana 2009
3 Ghana 2014 1 Democratic Republic of Congo 2009
4 Kenya 2015 2 Malawi 2012
5 Lesotho* 2015 3 Mozambique 2015
6 Malawi 2012
7 Mozambique 2015
8 Namibia 2015
9 Nigeria 2009
10 Seychelles 2008
11 Seychelles 2009 4 El Salvador 2009
12 Seychelles 2014 5 Georgia 2008
13 Swaziland 2015 6 Georgia 2012
14 Zambia 2009 7 Ghana 2009
15 Zambia 2015 8 Ghana 2014
9 Guatemala 2009
10 Honduras 2008
11 Honduras 2014
16 Argentina 2013 12 Kenya 2015
17 Argentina 2014 13 Lesotho* 2015
18 Belize 2012 14 Moldova 2010
19 Belize 2013 15 Moldova 2014
20 Brazil 2015 16 Mongolia 2008
21 Colombia 2009 17 Mongolia 2009
22 Costa Rica 2009 18 Myanmar 2012
23 Ecuador 2008 19 Nigeria 2009
24 El Salvador 2009 20 Pakistan 2008
25 Guatemala 2009 21 Pakistan 2013
26 Honduras 2008 22 Swaziland 2015
27 Honduras 2014 23 Zambia 2009
28 Peru 2007 24 Zambia 2015
29 Suriname 2016
30 Maldives 2009 25 Albania 2014
31 Mongolia 2008 26 Azerbaijan, Republic of 2015
32 Mongolia 2009 27 Belize 2012
33 Myanmar 2012 28 Belize 2013
34 Pakistan 2008 29 Bosnia and Herzegovina 2009
35 Pakistan 2013 30 Brazil 2015
31 Colombia 2009
32 Costa Rica 2009
33 Ecuador 2008
36 Tunisia 2013 34 Maldives 2009
35 Macedonia, FYR 2011
36 Namibia 2015
37 Peru 2007
37 Albania 2014 38 Romania 2009
38 Azerbaijan, Republic of 2015 39 Suriname 2016
39 Bosnia and Herzegovina 2009 40 Tunisia 2013
40 Georgia 2008
41 Georgia 2012
42 Latvia 2008
43 Macedonia, FYR 2011
44 Moldova 2010 41 Argentina 2013
45 Moldova 2014 42 Argentina 2014
46 Poland 2009 43 Belgium 2008
47 Romania 2009 44 Italy 2008
45 Latvia 2008
46 Poland 2009
WESTERN EUROPE 47 Portugal 2008
48 Belgium 2008 48 Portugal 2011
49 Italy 2008 49 Seychelles 2008
50 Portugal 2008 50 Seychelles 2009
51 Portugal 2011 51 Seychelles 2014
52 United Kingdom 2008 52 United Kingdom 2008
* Only for regressions with Drop_Borrowers
LATIN AMERICA AND CARIBBEAN
CENTRAL, SOUTH ASIA AND PACIFIC
Lower-middle-income economies ($1,046 to $4,125)
Low-income economies ($1,045 or less)
Upper-middle-income economies ($4,126 to $12,735)
High-income economies ($12,736 or more)
EASTERN EUROPE AND CENTRAL ASIA
MIDDLE EAST AND NORTH AFRICA
Sources: Laeven and Valencia (2018), authors’ compilations
162
Table 2: List of variables
Panel A: GFC sample
VARIABLE DESCRIPTION SOURCE
Financial Stability Indicators
DROPCREDIT0709 The difference between real credit annual growth
rate in the post crisis period (2009) and its value in
the pre-crisis period (2007)
IMF Financial Access Survey
(FAS), authors' calculations
DROPBORROWER0709 The difference between number of borrowers annual
growth rate in the post crisis period (2009) and its
value in the pre-crisis period (2007)
IMF Financial Access Survey
(FAS), authors' calculations
DROPLOAN0709 The difference between number of loan accounts
annual growth rate in the post crisis period (2009)
and its value in the pre-crisis period (2007)
IMF Financial Access Survey
(FAS), authors' calculations
Financial Inclusion Variables
SHAREBORROWERS08 Number of borrowers from commercial banks
divided by adult population in 2008
IMF Financial Access Survey
(FAS), authors' calculations
BORROWERGROWTH0407 Borrowers compound annual growth rate between
2004 and 2007.
IMF Financial Access Survey
(FAS), authors' calculations
SHARELOANS08Number of loans otstanding at commercial banks
divided by adult population in 2008
IMF Financial Access Survey
(FAS), authors' calculations
LOANSGROWTH0407Loan accounts compound annual growth rate between
2004 and 2007.
IMF Financial Access Survey
(FAS), authors' calculations
LNHONOHAN08 Percent of people with access to financial services
(Natural Logarithm)
Honohan, P. (2008)
Pre-crisis Credit Growth
CREDITGROWTH0407 Real outstanding loans (commercial banks)
compound annual growth rate between 2004 and
2007.
IMF Financial Access Survey
(FAS), authors' calculations
Banking Sector Variables
LNZSCORE07 ZSCORE07 (Natural Logarithm) Global Financial Development
Database
CONCENTRATION07 Assets of three largest commercial banks as a share
of total commercial banking assets in 2007
Global Financial Development
Database
LOANSTODEPTS07 The financial resources provided to the private
sector by domestic money banks as a share of total
deposits in 2007
Global Financial Development
Database
Macroeconomic Variables
DROPGDPGRW0709 The difference between the annual GDP growth rate
at market prices based on constant local currency in
the post crisis period (2009) and its value in the pre-
crisis period (2007)
World Development Indicators
163
VARIABLE DESCRIPTION SOURCE
Structural Variables (pre-crisis)
LNGDPPERCAPITA07 Gross domestic product per capita in 2007, current
prices (U.S. dollars) (Natural Logarithm)
IMF WEO Database
KAOPEN07 Chinn-Ito country index measuring a country's
degree of capital account openness updated to 2016
Chinn and Ito (2006)
KAOPEN_FG.BANKS Index built by multiplying Kaopen with the share of
Foreign Bank Assets in Total Banking Assets
INTERVENTION0711 Number of intervention forms by the authorities
stabilizing the fiuancial sector in times of crisis.
Laeven and Valencia (2012)
Source: authors’ compilations.
164
Panel B: IMF crisis sample
VARIABLE DESCRIPTION SOURCE
Financial Stability Indicators
DROPCREDIT The difference between real credit annual growth rate in the
post crisis period, i.e. the year after the outbreak of the
crisis, and its value in the last year of the pre-crisis period
IMF Financial Access Survey
(FAS), authors' calculations
DROPBORROWER The difference between number of borrowers annual
growth rate in the post crisis period, i.e. the year after the
outbreak of the crisis, and its value in the last year of the
pre-crisis period
IMF Financial Access Survey
(FAS), authors' calculations
DROPLOAN The difference between number of loan accounts growth
rate in the post crisis period, i.e. the year after the outbreak
of the crisis, and its value in the last year of the pre-crisis
period
IMF Financial Access Survey
(FAS), authors' calculations
Financial Inclusion Variables
SHAREBORROWERS Number of borrowers from commercial banks divided by
adult population in the last pre-crisis year
IMF Financial Access Survey
(FAS), authors' calculations
BORROWERGROWTH Borrowers compound annual growth rate in the last three
years before the crisis
IMF Financial Access Survey
(FAS), authors' calculations
SHARELOANS Number of loans outstanding at commercial banks divided
by adult population in the last pre-crisis year
IMF Financial Access Survey
(FAS), authors' calculations
LOANSGROWTH Loan accounts compound annual growth rate in the last
three years before the crisis
IMF Financial Access Survey
(FAS), authors' calculations
Pre-crisis Credit Growth
CREDITGROWTH Real outstanding loans (commercial banks) compound
annual growth rate in the last three years before the crisis.
IMF Financial Access Survey
(FAS), authors' calculations
Banking Sector Variables
LNZSCORE ZSCORE (Natural Logarithm) in the last year before the
crisis
Global Financial
Development Database
CONCENTRATION Assets of three largest commercial banks as a share of total
commercial banking assets in the last pre-crisis year
Global Financial
Development Database
LOANSTODEPTS The financial resources provided to the private sector by
domestic money banks as a share of total deposits in the
last pre-crisis year
Global Financial
Development Database
Macroeconomic Variables
DROPGDPGRW The difference between the annual GDP growth rate at
market prices based on constant local currency in the year
after the outbreak of the crisis and its value in the last pre-
crisis year
World Development
Indicators
Structural Variables (pre-crisis)
LNGDPPERCAPITA Gross domestic product per capita in the last year before
the crisis, current prices (U.S. dollars) (Natural Logarithm)
IMF WEO Database
KAOPEN Chinn-Ito country index in the last year before the crisis
(measuring a country's degree of de jurecapital account
openness)
Chinn and Ito (2006)
Source: authors’ compilations.
165
Table 3: Descriptive Statistics
VARIABLE Obs Mean Median Std. Dev. Min Max
PANEL A
(GFC sample)
DROPCREDIT0709 81 0.18 0.18 0.27 (0.88) 0.75
DROPBORROWER0709 60 0.21 0.09 0.39 (0.59) 1.59
SHAREBORROWERS08 81 0.18 0.12 0.19 0.00 0.92
BORROWERGROWTH0407 60 0.27 0.17 0.29 (0.02) 1.58
Pre-crisis Credit Growth
CREDITGROWTH0407 81 0.20 0.15 0.16 (0.04) 0.59
Banking sector variables
LNZSCORE07 81 2.27 2.35 0.73 (0.71) 3.51
CONCENTRATION07 81 0.72 0.74 0.20 0.35 1.00
LOANSTODEPTS07 81 0.91 0.83 0.44 0.26 2.39
Structural Variables
LNGDPPERCAPITA 07 81 8.11 8.23 1.44 5.16 11.31
KAOPEN07 81 0.51 0.41 0.38 0.00 1.00
PANEL B
IMF crisis sample
DROPCREDIT 51 0.19 0.13 0.34 (0.37) 1.58
DROPBORROWER 43 0.06 0.04 0.23 (0.36) 0.73
SHAREBORROWERS 52 0.18 0.16 0.14 0.00 0.55
BORROWERGROWTH 42 0.18 0.11 0.25 (0.20) 1.18
Pre-crisis Credit Growth
CREDITGROWTH 52 0.16 0.14 0.20 (0.75) 0.77
Banking sector variables
LNZSCORE 52 2.26 2.34 0.73 (1.35) 3.37
CONCENTRATION 52 0.68 0.65 0.21 0.35 1.00
LOANSTODEPTS 52 1.01 0.91 0.43 0.35 2.25
Structural Variables
LNGDPPERCAPITA 52 8.35 8.30 1.15 5.66 10.82
KAOPEN 52 0.52 0.43 0.39 0.00 1.00
Source: authors’ compilations.
166
Table 4: Correlation matrix
Panel A: GFC sample
GFC 1 2 3 4 5 6 7 8 9 10
1 DROPCREDIT709 1
2 DROPBORROWER0709 0.4154* 1
0.0010
3 SHAREBORROWERS08 0.0743 -0.1573 1
0.5099 0.2302
4 BORROWERGROWTH0407 0.3149* 0.7868* -0.2394 1
0.0143 0.0000 0.0655
5 CREDITGROWTH0407 0.6333* 0.5147* -0.0022 0.6000* 1
0.0000 0.0000 0.9847 0.0000
6 LNZSCORE07 0.0894 -0.1763 0.2227* -0.2781* -0.1516 1
0.4275 0.1778 0.0456 0.0315 0.1767
7 CONCENTRATION07 -0.1684 -0.1132 -0.0743 -0.0692 0.0362 0.0700 1
0.1328 0.3891 0.5099 0.5991 0.7482 0.5345
8 LOANSTODEPTS07 0.3442* 0.1523 0.2924* 0.0511 0.2922* 0.0644 -0.1538 1
0.0017 0.2455 0.0081 0.6979 0.0081 0.5677 0.1704
9 LNGDPPERCAPITA 07 0.1942 -0.1987 0.6758* -0.3367* 0.0486 0.2465* -0.0914 0.3451* 1
0.0823 0.1280 0.0000 0.0085 0.6665 0.0265 0.4171 0.0016
10 KAOPEN07 0.2238* 0.0838 0.4247* -0.0752 -0.0449 0.2149 -0.1404 0.3565* 0.4923* 1
0.0446 0.5245 0.0001 0.5680 0.6908 0.0541 0.2112 0.0011 0.0000
Source: authors’ compilations.
*Indicate significance at 5% level
167
Panel B: IMF crisis sample
IMF 1 2 3 4 5 6 7 8 9 10
1 DROPCREDITGROWTH 1
2 DROPBORROWERGROWTH 0.3339* 1
0.0307
3 SHAREBORROWERS -0.2542 -0.3236* 1
0.0719 0.0343
4 BORROWERGROWTH 0.6685* 0.2114 -0.1729 1
0 0.1846 0.2736
5 CREDITGROWTH 0.5666* 0.2095 -0.1024 0.6970* 1
0 0.1776 0.4699 0
6 LNZSCORE -0.071 0.1411 0.163 -0.1944 -0.2725 1
0.6205 0.3667 0.2484 0.2173 0.0507
7 CONCENTRATION -0.006 0.084 0.0806 -0.0933 -0.1556 -0.1817 1
0.9669 0.5921 0.57 0.5568 0.2708 0.1973
8 LOANSTODEPTS -0.0371 -0.0601 0.4134* -0.0414 0.1441 0.117 -0.1008 1
0.7959 0.7021 0.0023 0.7945 0.3081 0.4086 0.4773
9 LNGDPPERCAPITA -0.2929* -0.2106 0.7320* -0.4235* -0.2234 0.0758 0.1747 0.3656* 1
0.037 0.1751 0 0.0052 0.1113 0.5935 0.2155 0.0077
10 KAOPEN -0.1661 0.0914 0.3358* -0.0655 -0.0995 0.212 0.0996 0.3868* 0.4447* 1
0.244 0.5599 0.0149 0.6802 0.4826 0.1313 0.4824 0.0046 0.001
Source: authors’ compilations.
*Indicate significance at 5% level
168
Table 5: Credit growth and borrower growth drop in a financial crisis and financial
inclusion
Panel A: GFC sample
1 2 3 4
Dependent Variable:
SHAREBORROWERS08 -0.1660 -0.0666
(-1.58) (-0.53)
BORROWERGROWTH0407 -0.0305 0.961***
(-0.29) (3.58)
Financial Stability Indicators (pre-crisis)
CREDITGROWTH0407 1.068*** 1.123*** 1.377*** 0.20
(8.07) (5.02) (5.19) (0.79)
LNZSCORE07 0.0598* 0.0329 -0.0389 -0.0028
(1.96) (1.05) (-0.58) (-0.08)
CONCENTRATION07 -0.230** -0.2030 -0.2020 -0.0793
(-2.40) (-1.67) (-1.09) (-0.60)
LOANSTODEPTS07 0.0358 0.0712 0.0268 0.0616
(0.75) (1.14) (0.26) (0.78)
Structural Variables
LNGDPPERCAPITA07 0.01 0.00 -0.0872*** (0.01)
(0.61) (0.17) (-2.73) (-0.41)
KAOPEN07 0.132*** 0.136** 0.337** 0.159*
(2.65) (2.16) (2.17) (1.83)
_cons -0.1790 -0.1230 0.726** -0.0496
(-0.82) (-0.52) (2.31) (-0.20)
N 81 60 60 60
R-square 0.5239 0.4821 0.4133 0.65
t statistics in parentheses
* p<0.10, **p<0.05, *** p<0.01
DROPCREDITGROWTH 0709 DROPBORROWERGROWTH0709
This table reports the estimated coefficients of the OLS models presented in equations 1 and 2. The dependent
variables are: the drop in credit growth from 2007 to 2009 (columns 1 and 2) and the drop in borrower growth
from 2007 to 2009 (columns 3 and 4). Our main variables of interest are the share of borrowers in the adult
population in 2008 (columns 1 and 3) and the compound borrower growth rate 2004 to 2007 (columns 2 and 4).
We control for a set of banking sector and structural variables. T-statistics are provided in parentheses.
169
Panel B: IMF crisis sample
1 2 3 4
Dependent Variable:
SHAREBORROWERS -0.459 -0.801***
(-1.21) (-2.95)
BORROWERGROWTH 0.565* -0.0205
(1.73) (-0.10)
Financial Stability Indicators (pre-crisis)
CREDITGROWTH 1.026*** 0.488 0.364** 0.334*
(2.79) (1.33) (2.56) (1.99)
LNZSCORE 0.0816 0.0246 0.103** 0.0789*
(1.29) (0.42) (2.64) (1.83)
CONCENTRATION 0.266 -0.00610 0.227 0.245
(1.00) (-0.02) (1.20) (1.19)
LOANSTODEPTS -0.0152 0.00895 0.000822 -0.0503
(-0.14) (0.07) (0.01) (-0.59)
Structural Variables
LNGDPPERCAPITA -0.00700 -0.0360 0.0163 -0.0458
(-0.10) (-0.66) (0.60) (-1.22)
KAOPEN -0.0759 0.0265 0.0309 0.102
(-0.52) (0.21) (0.30) (1.00)
_cons -0.142 0.207 -0.384 0.0473
(-0.25) (0.51) (-1.43) (0.14)
N 51 41 44 42
R-square 0.4011 0.5000 0.2727 0.1908
t statistics in parentheses
* p<0.10, **p<0.05, *** p<0.01
DROPCREDITGROWTH DROPBORROWERGROWTH
This table reports the estimated coefficients of the OLS models presented in equations 1 and 2. The dependent
variables are: the drop in credit growth (columns 1 and 2) and the drop in borrower growth in the respective
post-crisis periods (columns 3 and 4). Our main variables of interest are the share of borrowers in the adult
population in the last year pre-crisis period (columns 1 and 3) and the compound borrower growth rate in pre-
crisis periods (columns 2 and 4). We control for a set of banking sector and structural variables. T-statistics are
provided in parentheses.
170
Table 6: Credit growth and borrower growth drop in a financial crisis and financial
inclusion (including interaction terms)
Panel A: GFC sample
1 2 3 4
Dependent Variable:
SHAREBORROWERS08 0.0959 0.331**
(0.75) (2.06)
BORROWERGROWTH0407 -0.0200 0.985*
(-0.06) (1.84)
Financial Stability Indicators (pre-crisis)
CREDITGROWTH0407 1.314*** 1.132*** 1.804*** 0.22
(7.53) (3.20) (5.58) (0.50)
SHARE08*CREDITGROWTH -1.662*** -2.563***
(-2.98) (-2.86)
BORR*CREDITGROWTH -0.0308 -0.0718
(-0.04) (-0.05)
LNZSCORE07 0.0527* 0.0330 -0.0362 -0.0027
(1.78) (1.06) (-0.56) (-0.08)
CONCENTRATION07 -0.204** -0.2020 -0.1790 -0.0763
(-2.17) (-1.46) (-1.02) (-0.56)
LOANSTODEPTS07 0.0487 0.0719 0.0032 0.0632
(1.11) (1.24) (0.03) (0.77)
Structural Variables
LNGDPPERCAPITA07 0.0202 0.0040 -0.0779** -0.0125
(0.91) (0.16) (-2.61) (-0.41)
KAOPEN07 0.128*** 0.135** 0.331** 0.158*
(2.83) (2.19) (2.15) (1.82)
_cons -0.2800 -0.1270 0.585* -0.0572
(-1.32) (-0.55) (1.89) (-0.23)
N 81 60 60 60
R-square 0.5547 0.4821 0.4471 0.65
t statistics in parentheses
* p<0.10, **p<0.05, *** p<0.01
DROPCREDITGROWTH 0709 DROPBORROWERGROWTH0709
This table reports the estimated coefficients of the OLS models presented in equations 1 and 2 expanded by
interaction terms between pre-crisis credit growth rates and financial inclusion variables. The dependent
variables are: the drop in credit growth from 2007 to 2009 (columns 1 and 2) and the drop in borrowers’ growth
from 2007 to 2009 (columns 3 and 4). Our main variables of interest are the share of borrowers in the adult
population in 2008 (columns 1 and 3), the compound borrower growth rate 2004 to 2007 (columns 2 and 4) and
the interaction terms. We control for a set of banking sector and structural variables. T-statistics are provided in
parentheses.
171
Panel B: IMF crisis sample
1 2 3 4
Dependent Variable:
SHAREBORROWERS -0.155 -0.875**
(-0.31) (-2.16)
BORROWERGROWTH -0.0893 0.00829
(-0.41) (0.03)
Financial Stability Indicators (pre-crisis)
CREDITGROWTH 1.263** 0.522** 0.319* 0.332*
(2.36) (2.33) (1.78) (1.92)
SHARE*CREDITGROWTH -2.2320 0.436
(-0.87) (0.30)
BORR*CREDITGROWTH 1.391*** -0.0595
(6.71) (-0.21)
LNZSCORE 0.0792 0.0266 0.101** 0.0787*
(1.22) (0.50) (2.57) (1.80)
CONCENTRATION 0.217 -0.0279 0.235 0.245
(0.78) (-0.11) (1.18) (1.17)
LOANSTODEPTS 0.00522 0.0122 0.00412 -0.0501
(0.05) (0.10) (0.04) (-0.58)
Structural Variables
LNGDPPERCAPITA 0.00101 -0.00277 0.0151 -0.0470
(0.02) (-0.05) (0.54) (-1.21)
KAOPEN -0.0787 -0.00818 0.0328 0.103
(-0.53) (-0.07) (0.32) (0.99)
_cons -0.224 -0.0205 -0.372 0.0556
(-0.40) (-0.05) (-1.38) (0.16)
N 51 41 44 42
R-square 0.4185 0.6253 0.2738 0.1913
t statistics in parentheses
* p<0.10, **p<0.05, *** p<0.01
DROPCREDITGROWTH DROPBORROWERGROWTH
This table reports the estimated coefficients of the OLS models presented in equations 1 and 2 expanded by
interaction terms between pre-crisis credit growth rates and financial inclusion variables. The dependent
variables are the drop in credit growth (columns 1 and 2) and the drop in borrower growth in the respective post-
crisis periods (columns 3 and 4). Our main variables of interest are the share of borrowers in the adult
population in the last year pre-crisis period (columns 1 and 3), the compound borrower growth rate in respective
pre-crisis periods (columns 2 and 4) and the interaction terms. We control for a set of banking sector and
structural variables. T-statistics are provided in parentheses.
172
Annexes
173
Table A1: Credit growth and borrower growth drop in a financial crisis and financial inclusion (GFC sample) – Robustness checks
1 2 3 4 5 6 7 8 9 10
Panel AChange in
sample
Dependent Variable:
DROPCREDIT0709 Baseline
Non-
linearities
Index Kaopen*
(Assets held by
Foreign Banks
/Total Assets) Intervention
Drop
GDPGrowth
0709 Honohan Loans
Excluding
advanced
economies Parsimonious
IV approach
(Population
density as
instrument)
SHAREBORROWERS08 -0.1660 -0.3040 -0.1300 -0.1630 -0.1200 -0.2230 -0.0956 0.0075
(-1.58) (-1.19) (-1.11) (-1.44) (-1.51) (-1.32) (-1.27) (0.06)
SHAREBORROWERS08 SQUARED 0.1830
(0.65)
SHARELOANS08 -0.1370
(-1.59)
LNHONOHAN08 -0.0294
(-1.02)
Financial Stability Indicators (pre-crisis)
CREDITGROWTH0407 1.068*** 1.070*** 0.936*** 1.064*** 1.054*** 1.159*** 1.303*** 1.070*** 1.108*** 1.076***
(8.07) (8.06) (8.51) (8.04) (6.80) (7.54) (6.74) (8.34) (8.22) (8.55)
LNZSCORE07 0.0598* 0.0599* 0.0686** 0.0581* 0.0636* 0.0677* 0.0385 0.0456 0.0626** 0.0585**
(1.96) (1.95) (2.26) (1.89) (1.96) (1.97) (1.66) (1.50) (2.00) (2.01)
CONCENTRATION07 -0.230** -0.239** -0.244** -0.226** -0.237** -0.200* (0.08) -0.218** -0.242** -0.231**
(-2.40) (-2.39) (-2.60) (-2.36) (-2.50) (-1.86) (-0.76) (-2.08) (-2.42) (-2.44)
LOANSTODEPTS07 0.0358 0.0403 0.0332 0.0295 0.0334 0.0234 0.0441 0.0941* 0.156*** 0.0332
(0.75) (0.79) (0.70) (0.60) (0.72) (0.50) (0.84) (1.87) (3.20) (0.72)
Changes in independent variables other than financial
inclusion
Change in financial
inclusion variableChange in methodology
174
1 2 3 4 5 6 7 8 9 10
Structural variables
LNGDPPERCAPITA07 0.0140 0.0328 0.0317 0.03 0.04 0.0382 (0.01)
(0.61) (0.62) (0.61) (0.45) (1.48) (0.61) (-0.10)
KAOPEN07 0.132*** 0.138*** 0.135*** 0.140** 0.167*** 0.0501 0.149*** 0.127**
(2.65) (2.76) (2.68) (2.60) (3.35) (0.90) (2.80) (2.48)
KAOPEN_FG.BANKS 0.158**
(2.40)
INTERVENTION0711 0.0163
(0.82)
DROPGDPGRW0709 0.18
(0.36)
_cons (0.18) -0.1700 -0.1220 -0.1530 -0.0880 -0.0512 -0.430* -0.2210 -0.0638 -0.0680
(-0.82) (-0.78) (-0.61) (-0.68) (-0.81) (-0.27) (-1.92) (-0.91) (-0.61) (-0.30)
N 81 81 80 81 81 73 51 73 81 81
R-square 0.524 0.524 0.513 0.526 0.522 0.555 0.606 0.543 0.518 0.516
t statistics in parentheses
* p<0.10, **p<0.05, *** p<0.01
This table reports the estimated coefficients of a number of robustness checks of the OLS model presented in equation 1. The dependent variable is the drop in credit growth from 2007 to 2009.
Our main variable of interest is the share of borrowers in the adult population in 2008. Column 1 displays the baseline regression results. Columns 2 to 10 report the results of robustness checks
reflecting changes in the independent variables, changes in the proxy of financial inclusion, changes in the sample and changes in the econometric methodology. Column 2 displays the results
when testing for non-linear effects of the share of borrowers. Columns 3-5 include changes in the control variables, namely (3) replacing the KAOPEN index by an openness index composed of
KAOPEN and the share of assets held by foreign banks in the respective country banking sectors, (4) controlling for stabilizing interventions by the authorities in the crisis period and (5)
controlling for possible demand effects on credit growth triggered by the drop in GDP growth from 2007 to 2009. Columns 6 and 7 replace the variable share of borrowers with (6) the Honohan
index of financial inclusion (Honohan 2008) and (7) the number of loan accounts expressed as share of the adult population in 2008. Column 8 shows results based on a sample that excludes
advanced economies. Column 9 reports the parsimonious estimation with the share of borrowers in 2008 defined as the main variable. Column 10 reports the two stage least squares estimates
instrumenting for share of borrowers in the adult population in 2008 using population density. We control for a set of banking sector and structural variables. T-statistics are provided in
parentheses.
175
1 2 3 4 5 6 7 8
Panel BChange in financial
inclusion variable
Change in
sample
Change in
methodology
Dependent Variable:
DROPCREDIT0709 Baseline
Non-
linearities
Index
Kaopen*(Assets
held by Foreign
Banks /Total
Assets) Intervention
Drop
GDPGrowth
0709 Loans
Excluding
advanced
economies Parsimonious
BORROWERGROWTH0407 -0.0305 -0.1050 -0.0472 (0.03) 0.01 -0.0263 -0.0890
(-0.29) (-0.31) (-0.54) (-0.26) (0.08) (-0.25) (-0.83)
BORROWERGROWTH0407 SQUARED 0.0568
(0.25)
LOANSGROWTH0407 0.0361
(1.23)
Financial Stability Indicators (pre-crisis)
CREDITGROWTH0407 1.123*** 1.138*** 0.999*** 1.120*** 0.974*** 1.320*** 1.072*** 1.240***
(5.02) (4.67) (5.87) (4.83) (3.28) (6.79) (4.66) (5.43)
LNZSCORE07 0.0329 0.0329 0.0532* 0.03 0.04 0.0370 0.0309
(1.05) (1.02) (1.73) (1.00) (1.14) (1.41) (0.96)
CONCENTRATION07 -0.2030 -0.2040 -0.217* (0.20) -0.216* -0.0503 -0.2020 -0.216*
(-1.67) (-1.63) (-1.84) (-1.65) (-1.85) (-0.46) (-1.57) (-1.76)
LOANSTODEPTS07 0.0712 0.0735 0.0826 0.07 0.07 -0.0089 0.0969
(1.14) (1.10) (1.44) (1.11) (1.28) (-0.12) (1.46)
Changes in independent variables other than financial
inclusion
176
1 2 3 4 5 6 7 8
Structural Variables
LNGDPPERCAPITA07 0.0041 0.0083 0.0074 0.01 0.0265 0.0176
(0.17) (0.15) (0.15) (0.19) (1.17) (0.26)
KAOPEN07 0.136** 0.137** 0.134** 0.13 0.0506 0.144** 0.189**
(2.16) (2.12) (2.06) (1.63) (0.85) (2.09) (2.54)
KAOPEN_FG.BANKS 0.268***
(3.15)
INTERVENTION0711 0.00
(0.07)
DROPGDPGRW0709 0.71
(1.05)
_cons -0.1230 -0.1130 -0.1030 -0.1260 -0.1120 -0.3560 -0.1610 0.0230
(-0.52) (-0.49) (-0.49) (-0.53) (-0.87) (-1.64) (-0.57) (0.23)
N 60 60 59 60 60 46 55 60
R-square 0.482 0.483 0.481 0.482 0.493 0.590 0.489 0.465
t statistics in parentheses
* p<0.10, **p<0.05, *** p<0.01
This table reports the estimated coefficients of a number of robustness checks of the OLS model presented in equation 1. The dependent variable is the drop in credit growth from 2007 to 2009.
Our main variable of interest is the compound borrower growth rate 2004-07. Column 1 displays the baseline regression results. Columns 2 to 8 report the results of robustness checks reflecting
changes in the independent variables, changes in the proxy of financial inclusion, changes in the sample and changes in the econometric methodology. Column 2 displays the results when
testing for non-linear effects of the borrower growth rate. Columns 3-5 include changes in the control variables, namely (3) replacing the KAOPEN index by an openness index composed of
KAOPEN and the share of assets held by foreign banks in the respective country banking sectors, (4) controlling for stabilizing interventions by the authorities in the crisis period and (5)
controlling for possible demand effects on credit growth triggered by the drop in GDP growth from 2007 to 2009. Column 6 replaces the variable compound borrower growth rate with the
compound growth rate of the number of loan accounts between 2004 and 2007. Column 7 shows results based on a sample that excludes advanced economies. Column 8 reports the
parsimonious estimation with compound borrower growth rate 2004 to 2007 defined as the main variable. We control for a set of banking sector and structural variables. T-statistics are provided
in parentheses.
177
1 2 3 4 5 6 7 8 9 10
Panel CChange in
sample
Dependent Variable:
DROPBORROWER0709
/DROPLOAN0709 Baseline
Non-
linearities
Index
Kaopen*Share
of Foreign
Banks /Total
Assets Intervention
Drop
GDPGrowth
0709 Honohan Loans
Excluding
advanced
economies Parsimonious
IV
approach
SHAREBORROWERS08 -0.0666 -0.0666 0.0110 -0.0451 -0.322** -0.2000 -0.0841 -0.1680
(-0.53) (-0.10) (0.07) (-0.36) (-2.13) (-0.87) (-0.65) (-0.46)
SHAREBORROWERS08 SQUARED 0.0045
(0.01)
SHARELOANS08 0.1140
(1.07)
LNHONOHAN08 -0.155*
(-2.00)
Financial Stability Indicators (pre-crisis)
CREDITGROWTH0407 1.377*** 1.380*** 1.243*** 1.389*** 1.650*** 1.378*** 0.952** 1.429*** 1.418*** 1.377***
(5.19) (5.15) (4.59) (5.21) (4.73) (4.00) (2.57) (5.25) (5.17) (5.58)
LNZSCORE07 -0.0389 -0.0381 0.0096 -0.0318 -0.0566 -0.0705 0.0704* -0.0360 -0.0344
(-0.58) (-0.56) (0.20) (-0.48) (-0.86) (-1.01) (1.98) (-0.51) (-0.56)
CONCENTRATION07 -0.2020 -0.2020 -0.2310 -0.2030 -0.1460 -0.0151 -0.1750 -0.2300 -0.2040
(-1.09) (-1.03) (-1.19) (-1.09) (-0.75) (-0.07) (-1.32) (-1.16) (-1.20)
LOANSTODEPTS07 0.0268 0.0272 0.0846 0.0403 -0.0029 0.0345 0.0253 0.0113 0.0275
(0.26) (0.25) (0.84) (0.37) (-0.03) (0.34) (0.31) (0.11) (0.28)
Changes in independent variables other than financial
inclusion
Change in financial
inclusion variableChange in methodology
178
1 2 3 4 5 6 7 8 9 10
Structural Variables
LNGDPPERCAPITA07 -0.0872*** -0.203*** -0.168** -0.210*** -0.0670** -0.193** -0.208*** -0.185*
(-2.73) (-2.91) (-2.20) (-2.84) (-2.43) (-2.42) (-2.90) (-1.78)
KAOPEN07 0.337** 0.338** 0.350** 0.302** 0.391** 0.1050 0.321* 0.341** 0.342**
(2.17) (2.17) (2.22) (2.07) (2.35) (1.26) (2.01) (2.46) (2.34)
KAOPEN_FG.BANKS 0.5230
(1.66)
INTERVENTION0711 -0.0490
(-1.29)
DROPGDPGRW0709 -2.179**
(-2.14)
_cons 0.726** 0.732** 0.556* 0.726** 0.1960 0.4640 0.3410 0.737** 0.536** 0.676*
(2.31) (2.41) (1.91) (2.28) (0.82) (1.14) (1.62) (2.38) (2.30) (1.86)
N 60 60 59 60 60 53 51 55 60 60
R-square 0.413 0.414 0.409 0.420 0.420 0.431 0.307 0.413 0.397 0.413
t statistics in parentheses
* p<0.10, **p<0.05, *** p<0.01
This table reports the estimated coefficients of a number of robustness checks of the OLS model presented in equation 2. The dependent variable is the drop in borrower growth from 2007 to
2009. Our main variable of interest is the share of borrowers in the adult population in 2008. Column 1 displays the baseline regression results. Columns 2 to 10 report the results of robustness
checks reflecting changes in the independent variables, changes in the proxy of financial inclusion, changes in the sample and changes in the econometric methodology. Column 2 displays the
results when testing for non-linear effects of the share of borrowers. Columns 3-5 include changes in the control variables, namely (3) replacing the KAOPEN index by an openness index
composed of KAOPEN and the share of assets held by foreign banks in the respective country banking sectors, (4) controlling for stabilizing interventions by the authorities in the crisis period
and (5) controlling for possible demand effects on credit growth triggered by the drop in GDP growth from 2007 to 2009. Columns 6 and 7 replace the variable share of borrowers with (6) the
Honohan index of financial inclusion (Honohan 2008) and (7) the number of loan accounts expressed as share of the adult population in 2008. Column 8 shows results based on a sample that
excludes advanced economies. Column 9 reports the parsimonious estimation with the share of borrowers in 2008 defined as the main variable. Column 10 reports the two stage least squares
estimates instrumenting for share of borrowers in the adult population in 2008 using population density. We control for a set of banking sector and structural variables. T-statistics are provided
in parentheses.
179
1 2 3 4 5 6 7 8
Panel DChange in financial
inclusion variable
Change in
sample
Change in
methodology
Dependent Variable:
DROPBORROWER0709 /DROPLOAN0709 Baseline
Non-
linearities
Index
Kaopen*Share
of Foreign
Banks /Total
Assets Intervention
Drop
GDPGrowth
0709 Loans
Excluding
advanced
economies Parsimonious
BORROWERGROWTH0407 0.961*** 1.169** 0.961*** 0.958*** 0.944*** 0.966*** 1.056***
(3.58) (2.39) (3.74) (3.48) (3.93) (3.51) (5.15)
BORROWERGROWTH0407 SQUARED -0.1580
(-0.43)
LOANSGROWTH0407 0.221***
(4.98)
Financial Stability Indicators (pre-crisis)
CREDITGROWTH0407 0.1990 0.1540 0.1300 0.20 0.31 0.876** 0.2120
(0.79) (0.56) (0.51) (0.77) (1.22) (2.63) (0.81)
LNZSCORE07 -0.0028 -0.0030 0.0212 (0.00) (0.01) 0.0613 0.0013
(-0.08) (-0.09) (0.65) (-0.06) (-0.25) (1.63) (0.04)
CONCENTRATION07 -0.0793 -0.0750 -0.0938 (0.08) (0.06) -0.230* -0.0750
(-0.60) (-0.55) (-0.67) (-0.60) (-0.46) (-1.91) (-0.54)
LOANSTODEPTS07 0.0616 0.0550 0.0884 0.06 0.06 0.0384 0.0493
(0.78) (0.71) (1.13) (0.76) (0.79) (0.43) (0.58)
Changes in independent variables other than financial
inclusion
180
1 2 3 4 5 6 7 8
Structural Variables
LNGDPPERCAPITA07 -0.0124 -0.0237 -0.0066 (0.03) -0.0419 -0.0311
(-0.41) (-0.33) (-0.10) (-0.42) (-1.52) (-0.40)
KAOPEN07 0.159* 0.155* 0.161* 0.157* 0.1330 0.1540 0.153**
(1.83) (1.76) (1.80) (1.80) (1.63) (1.63) (2.20)
KAOPEN_FG.BANKS 0.2480
(1.41)
INTERVENTION0711 (0.01)
(-0.24)
DROPGDPGRW0709 (0.69)
(-0.97)
_cons -0.0496 -0.0846 -0.1460 -0.0481 -0.1180 0.1710 -0.0463 -0.147***
(-0.20) (-0.33) (-0.57) (-0.19) (-0.79) -0.8800 (-0.17) (-2.73)
N 60 60 59 60 60 46 55 60
R-square 0.650 0.653 0.648 0.650 0.654 0.415 0.648 0.640
t statistics in parentheses
* p<0.10, **p<0.05, *** p<0.01
This table reports the estimated coefficients of a number of robustness checks of the OLS model presented in equation 2. The dependent variable is the drop in borrower growth from 2007 to
2009. Our main variable of interest is the compound borrower growth rate 2004-07. Column 1 displays the baseline regression results. Columns 2 to 8 report the results of robustness checks
reflecting changes in the independent variables, changes in the proxy of financial inclusion, changes in the sample and changes in the econometric methodology. Column 2 displays the results
when testing for non-linear effects of the borrower growth rate. Columns 3-5 include changes in the control variables, namely (3) replacing the KAOPEN index by an openness index composed
of KAOPEN and the share of assets held by foreign banks in the respective country banking sectors, (4) controlling for stabilizing interventions by the authorities in the crisis period and (5)
controlling for possible demand effects on credit growth triggered by the drop in GDP growth from 2007 to 2009. Column 6 replaces the variable compound borrower growth rate with the
compound growth rate of the number of loan accounts between 2004 and 2007. Column 7 shows results based on a sample that excludes advanced economies. Column 8 reports the
parsimonious estimation with the compound borrower growth rate 2004 to 2007 defined as the main variable. We control for a set of banking sector and structural variables. T-statistics are
provided in parentheses.
181
Table A2: Credit growth and borrower growth drop in a financial crisis and financial
inclusion (IMF sample) – robustness checks
1 2 3 4 5 6
Panel AChange in financial
inclusion variable
Dependent Variable:
DROPCREDIT Baseline Non-linearities
Drop
GDPGrowth
0709 Loans Parsimonious
IV approach
(Population
density as
instrument)
SHAREBORROWERS -0.459 -1.937** -0.604** -0.492* 0.137
(-1.21) (-2.39) (-2.05) (-1.99) (0.06)
SHAREBORROWERS SQUARED 3.058*
(1.98)
SHARELOANS -0.130
(-1.07)
Financial Stability Indicators (pre-crisis)
CREDITGROWTH 1.026*** 1.000*** 0.916** 0.723*** 0.919*** 1.001***
(2.79) (2.83) (2.37) (3.10) (2.94) (2.89)
LNZSCORE 0.0816 0.104 0.0881 0.125** 0.0678
(1.29) (1.64) (1.58) (2.39) (1.26)
CONCENTRATION 0.266 0.349 0.266 0.243 0.260
(1.00) (1.35) (1.05) (0.75) (1.06)
LOANSTODEPTS -0.0152 0.0410 -0.0908 0.0270 -0.0446
(-0.14) (0.37) (-1.03) (0.26) (-0.30)
Structural variables
LNGDPPERCAPITA -0.00700 -0.00436 -0.00372 -0.0571
(-0.10) (-0.06) (-0.07) (-0.30)
KAOPEN -0.0759 -0.0967 -0.0989 -0.140 -0.0631
(-0.52) (-0.68) (-0.84) (-0.85) (-0.49)
DROPGDPGRW 0.0164**
(2.65)
_cons -0.142 -0.200 -0.121 -0.233 0.132 0.228
(-0.25) (-0.35) (-0.45) (-0.50) (1.65) (0.18)
N 51 51 51 40 51 51
R-square 0.401 0.429 0.459 0.261 0.360 0.376
t statistics in parentheses
* p<0.10, **p<0.05, *** p<0.01
Changes in independent
variables other than financial
inclusion
Change in methodology
This table reports the estimated coefficients of a number of robustness checks of the OLS model presented in equation 1. The
dependent variable is the drop in credit growth in the post-crisis period. Our main variable of interest is the share of
borrowers in the adult population in the last year pre-crisis period. Column 1 displays the baseline regression results.
Columns 2 and 6 report the results of robustness checks reflecting changes in the independent variables, changes in the
proxy of financial inclusion, and changes in the econometric methodology. Column 2 displays the results when testing for
non-linear effects of the share of borrowers. Column 3 includes changes in the control variables, namely controlling for
possible demand effects on credit growth triggered by the drop in GDP growth in the year after the outbreak of the crisis and
its value in the last pre-crisis year. Column 4 replaces the variable share of borrowers with the number of loan accounts
expressed as share of the adult population in the last pre-crisis year. Column 5 reports the parsimonious estimation with the
share of borrowers in the last pre-crisis year defined as the main variable. Column 6 reports the two stage least squares
estimates instrumenting for share of borrowers using population density. We control for a set of banking sector and structural
variables. T-statistics are provided in parentheses.
182
1 2 3 4 5
Panel BChange in financial
inclusion variable
Change in
methodology
Dependent Variable:
DROPCREDIT Baseline Non-linearities
Drop
GDPGrowth
0709 Loans Parsimonious
BORROWERGROWTH 0.565* -0.289 0.612 0.906***
(1.73) (-0.84) (1.65) (3.53)
BORROWERGROWTH SQUARED 0.958***
(3.98)
LOANSGROWTH 0.752*
(1.86)
Financial Stability Indicators (pre-crisis)
CREDITGROWTH 0.488 0.613** 0.354 0.150
(1.33) (2.14) (0.95) (0.51)
LNZSCORE 0.0246 0.0378 0.00416 -0.0186
(0.42) (0.69) (0.09) (-0.24)
CONCENTRATION -0.00610 0.00575 -0.105 -0.0473
(-0.02) (0.02) (-0.56) (-0.12)
LOANSTODEPTS 0.00895 0.0220 -0.0939 -0.0129
(0.07) (0.18) (-1.06) (-0.09)
Structural variables
LNGDPPERCAPITA -0.0360 -0.0208 -0.00930
(-0.66) (-0.39) (-0.16)
KAOPEN 0.0265 0.00962 0.0162 0.0587
(0.21) (0.08) (0.16) (0.40)
DROPGDPGRW 0.0224***
(3.24)
_cons 0.207 0.0839 0.0963 0.106 -0.0104
(0.51) (0.22) (0.44) (0.23) (-0.23)
N 41 41 41 25 41
R-square 0.500 0.585 0.597 0.305 0.447
t statistics in parentheses
* p<0.10, **p<0.05, *** p<0.01
Changes in independent variables
other than financial inclusion
This table reports the estimated coefficients of a number of robustness checks of the OLS model presented in equation 1. The
dependent variable is the drop in credit growth in the post-crisis period. Our main variable of interest is the compound
borrower growth rate in the last three years before the crisis. Column 1 displays the baseline regression results. Columns
2 to 5 report the results of robustness checks reflecting changes in the independent variables, changes in the proxy of
financial inclusion, and changes in the econometric methodology. Column 2 displays the results when testing for non-linear
effects of the borrower growth rate. Column 3 includes changes in the control variables, namely controlling for possible
demand effects on credit growth triggered by the drop in GDP growth in the year after the outbreak of the crisis and its value
in the last pre-crisis year. Column 4 replaces the variable compound borrower growth rate with the compound growth rate of
the number of loan accounts in the last three years before the crisis. Column 5 reports the parsimonious estimation with
compound borrower growth rate in the last three years before the crisis defined as the main variable. We control for a set
of banking sector and structural variables. T-statistics are provided in parentheses.
183
1 2 3 4 5 6
Panel CChange in financial
inclusion variable
Dependent Variable:
DROPBORROWER Baseline Non-linearities
Drop
GDPGrowth
0709 Loans Parsimonious
IV approach
(Population
density as
instrument)
SHAREBORROWERS -0.801*** -0.700 -0.753*** -0.671*** -2.011**
(-2.95) (-0.67) (-3.18) (-2.73) (-2.35)
SHAREBORROWERS SQUARED -0.229
(-0.10)
SHARELOANS -0.139***
(-3.54)
Financial Stability Indicators (pre-crisis)
CREDITGROWTH 0.364** 0.365** 0.252** 0.666** 0.289** 0.457***
(2.56) (2.56) (2.37) (2.25) (2.04) (3.00)
LNZSCORE 0.103** 0.101** 0.0879** 0.0999** 0.0872*** 0.134***
(2.64) (2.14) (2.56) (2.38) (3.21) (2.75)
CONCENTRATION 0.227 0.220 0.189 0.221 0.232
(1.20) (1.06) (1.03) (1.09) (1.21)
LOANSTODEPTS 0.000822 -0.000666 -0.0518 0.165* 0.0786
(0.01) (-0.01) (-0.70) (2.04) (0.68)
Structural variables
LNGDPPERCAPITA 0.0163 0.0162 -0.0925** 0.116
(0.60) (0.60) (-2.41) (1.44)
KAOPEN 0.0309 0.0320 0.0494 0.203** -0.0577
(0.30) (0.30) (0.47) (2.10) (-0.44)
DROPGDPGRW 0.0154**
(2.41)
_cons -0.384 -0.380 -0.168 0.199 -0.0558 -1.115*
(-1.43) (-1.44) (-1.12) (0.83) (-0.67) (-1.75)
N 44 44 44 30 44 44
R-square 0.273 0.273 0.368 0.585 0.219 0.077
t statistics in parentheses
* p<0.10, **p<0.05, *** p<0.01
Changes in independent
variables other than financial
inclusion
Change in methodology
This table reports the estimated coefficients of a number of robustness checks of the OLS model presented in equation 2. The
dependent variable is the drop in borrower growth in the post-crisis period. Our main variable of interest is the share of
borrowers in the adult population in the last year pre-crisis period. Column 1 displays the baseline regression results.
Columns 2 and 6 report the results of robustness checks reflecting changes in the independent variables, changes in the
proxy of financial inclusion, and changes in the econometric methodology. Column 2 displays the results when testing for
non-linear effects of the share of borrowers. Column 3 includes changes in the control variables, namely controlling for
possible demand effects on credit growth triggered by the drop in GDP growth in the year after the outbreak of the crisis and
its value in the last pre-crisis year. Column 4 replaces the variable share of borrowers with the number of loan accounts
expressed as share of the adult population in the last pre-crisis year. Column 5 reports the parsimonious estimation with the
share of borrowers in the last pre-crisis year defined as the main variable. Column 6 reports the two stage least squares
estimates instrumenting for share of borrowers using population density. We control for a set of banking sector and structural
variables. T-statistics are provided in parentheses.
184
1 2 3 4 5
Panel DChange in financial
inclusion variable
Change in
methodology
Dependent Variable:
DROPBORROWER Baseline Non-linearities
Drop
GDPGrowth
0709 Loans Parsimonious
BORROWERGROWTH -0.0205 0.208 0.0585 0.241*
(-0.10) (0.57) (0.42) (1.71)
BORROWERGROWTH SQUARED -0.250
(-0.93)
LOANSGROWTH 0.0767
(0.13)
Financial Stability Indicators (pre-crisis)
CREDITGROWTH 0.334* 0.298 0.243 0.639
(1.99) (1.64) (1.52) (1.67)
LNZSCORE 0.0789* 0.0749 0.0688* 0.0628 0.0554**
(1.83) (1.67) (1.87) (0.95) (2.58)
CONCENTRATION 0.245 0.237 0.190 0.0916
(1.19) (1.15) (0.94) (0.41)
LOANSTODEPTS -0.0503 -0.0513 -0.133* 0.137
(-0.59) (-0.61) (-1.80) (1.54)
Structural variables
LNGDPPERCAPITA -0.0458 -0.0484 -0.105*
(-1.22) (-1.25) (-2.03)
KAOPEN 0.102 0.104 0.0789 0.307***
(1.00) (1.00) (0.76) (3.02)
DROPGDPGRW 0.0134**
(2.20)
_cons 0.0473 0.0714 -0.209 0.382 -0.107*
(0.14) (0.21) (-1.26) (1.30) (-1.69)
N 42 42 42 26 42
R-square 0.1908 0.2024 0.2356 0.6150 0.0801
t statistics in parentheses
* p<0.10, **p<0.05, *** p<0.01
Changes in independent variables
other than financial inclusion
This table reports the estimated coefficients of a number of robustness checks of the OLS model presented in equation 2. The
dependent variable is the drop in borrower growth in the post-crisis period. Our main variable of interest is the compound
borrower growth rate in the last three years before the crisis. Column 1 displays the baseline regression results. Columns
2 to 5 report the results of robustness checks reflecting changes in the independent variables, changes in the proxy of
financial inclusion, and changes in the econometric methodology. Column 2 displays the results when testing for non-linear
effects of the borrower growth rate. Column 3 includes changes in the control variables, namely controlling for possible
demand effects on credit growth triggered by the drop in GDP growth in the year after the outbreak of the crisis and its value
in the last pre-crisis year. Column 4 replaces the variable compound borrower growth rate with the compound growth rate of
the number of loan accounts in the last three years before the crisis. Column 5 reports the parsimonious estimation with
compound borrower growth rate in the last three years before the crisis defined as the main variable. We control for a set
of banking sector and structural variables. T-statistics are provided in parentheses.
185
Table A3: Orthogonalized Regressions (GFC sample)
Panel A: Credit growth drop in the financial crisis and pre-crisis borrower growth
This table reports the estimated coefficients of the OLS model presented in equation (1). The dependent variable is the drop
in credit growth from 2007 to 2009. Our main variable of interest is the compound borrower growth rate 2004-2007. Column
1 presents the results when compound borrower growth rate 2004-2007 is orthogonalized (BORROWERGROWTH0407
ORT) by regressing it on the compound real credit growth rate 2004 to 2007, and then using the residuals of this regression
as our main variable of interest. Column 2 displays the results when introducing orthogonalized pre-crisis
CREDITGROWTH0407 resulting from regressing the compound real credit growth rate 2004 to 2007 on the compound
borrower growth rate 2004 to 2007, and then using the residuals of this regression as a control variable
(CREDITGROWTH0407 ORT.). We control for a set of banking sector and structural variables. T-statistics are provided in
parentheses.
186
Panel B: Drop in borrower growth in the financial crisis and pre-crisis borrower growth
1 2
BORROWERGROWTH0407 ORT. 0.960***
(3.57)
BORROWERGROWTH0407 1.024***
(4.73)
Financial Stability Indicators (pre-crisis)
CREDITGROWTH0407 1.283***
(6.14)
CREDITGROWTH0407 ORT. 0.201
(0.79)
LNZSCORE07 -0.00272 -0.00272
(-0.08) (-0.08)
CONCENTRATION07 -0.0795 -0.0795
(-0.60) (-0.60)
LOANSTODEPTS07 0.0616 0.0616
(0.78) (0.78)
Structural Variables
LNGDPPERCAPITA07 -0.0291 -0.0291
(-0.42) (-0.42)
KAOPEN07 0.159* 0.159*
(1.83) (1.83)
_cons 0.00792 -0.0280
(0.03) (-0.11)
N 60 60
R-square 0.6500 0.6500
t statistics in parentheses
* p<0.10, **p<0.05, *** p<0.01
DROPBORROWER0709
This table reports the estimated coefficients of the OLS model presented in equation (2). The dependent variable
is the drop in borrower growth from 2007 to 2009. Our main variable of interest is the compound borrower
growth rate 2004-2007. Column 1 presents the results when compound borrower growth rate 2004-2007 is
orthogonalized (BORROWERGROWTH0407 ORT) by regressing it on the compound real credit growth rate
2004 to 2007, and then using the residuals of this regression as our main variable of interest. Column 2 displays
the results when introducing orthogonalized pre-crisis CREDITGROWTH0407 resulting from regressing the
compound real credit growth rate 2004 to 2007 on the compound borrower growth rate 2004 to 2007, and then
using the residuals of this regression as a control variable (CREDITGROWTH0407 ORT.). We control for a set
of banking sector and structural variables. T-statistics are provided in parentheses.
187
Table A4: Orthogonalized Regressions (IMF sample)
Panel A: Credit growth drop during a financial crisis and pre-crisis borrower growth
1 2
BORROWERGROWTH ORT. 0.565*
(1.73)
BORROWERGROWTH 0.848***
(3.22)
Financial Stability Indicators (pre-crisis)
CREDITGROWTH 0.940***
(3.14)
CREDITGROWTH ORT. 0.488
(1.33)
LNZSCORE 0.0246 0.0246
(0.42) (0.42)
CONCENTRATION -0.00610 -0.00610
(-0.02) (-0.02)
LOANSTODEPTS 0.00895 0.00895
(0.07) (0.07)
Structural Variables
LNGDPPERCAPITA -0.0360 -0.0360
(-0.66) (-0.66)
KAOPEN 0.0265 0.0265
(0.21) (0.21)
_cons 0.237 0.231
(0.58) (0.57)
N 41 41
R-square 0.5000 0.5000
t statistics in parentheses
* p<0.10, **p<0.05, *** p<0.01
DROPCREDIT
This table reports the estimated coefficients of the OLS model presented in equation (1). The dependent variable is the drop
in credit growth in the post-crisis period. Our main variable of interest is the compound borrower growth rate in the last three
years before the crisis. Column 1 presents the results when compound borrower growth rate in the last three years before the
crisis is orthogonalized (BORROWERGROWTH ORT) by regressing it on the compound real credit growth rate in the last
three years before the crisis, and then using the residuals of this regression as our main variable of interest. Column 2
displays the results when introducing orthogonalized pre-crisis CREDITGROWTH resulting from regressing the compound
real credit growth rate in the pre-crisis period on the compound borrower growth rate in the pre-crisis period, and then using
the residuals of this regression as a control variable (CREDITGROWTH ORT.). We control for a set of banking sector and
structural variables. T-statistics are provided in parentheses.
188
Panel B: Borrower growth drop during a financial crisis and pre-crisis borrower growth
1 2
BORROWERGROWTH ORT. -0.0205
(-0.10)
BORROWERGROWTH 0.174
(1.06)
Financial Stability Indicators (pre-crisis)
CREDITGROWTH 0.317**
(2.21)
CREDITGROWTH ORT. 0.334*
(1.99)
LNZSCORE 0.0789* 0.0789*
(1.83) (1.83)
CONCENTRATION 0.245 0.245
(1.19) (1.19)
LOANSTODEPTS -0.0503 -0.0503
(-0.59) (-0.59)
Structural Variables
LNGDPPERCAPITA -0.0458 -0.0458
(-1.22) (-1.22)
KAOPEN 0.102 0.102
(1.00) (1.00)
_cons 0.0462 0.0637
(0.14) (0.19)
N 42 42
R-square 0.1908 0.1908
t statistics in parentheses
* p<0.10, **p<0.05, *** p<0.01
DROPBORROWER
This table reports the estimated coefficients of the OLS model presented in equation (2). The dependent variable is the drop
in borrower growth in the post-crisis period. Our main variable of interest is the compound borrower growth rate in the last
three years before the crisis. Column 1 presents the results when compound borrower growth rate in the last three years
before the crisis is orthogonalized (BORROWERGROWTH ORT) by regressing it on the compound real credit growth rate
in the last three years before the crisis, and then using the residuals of this regression as our main variable of interest.
Column 2 displays the results when introducing orthogonalized pre-crisis CREDITGROWTH resulting from regressing the
compound real credit growth rate in the pre-crisis period on the compound borrower growth rate in the pre-crisis period, and
then using the residuals of this regression as a control variable (CREDITGROWTH ORT.). We control for a set of banking
sector and structural variables. T-statistics are provided in parentheses.
189
STATEMENT OF CERTIFICATION
I hereby confirm that this dissertation constitutes my own work, produced without aid and
support from persons and/or materials other than the ones listed. All used sources are
indicated as direct or indirect quotations. Quotation marks indicate direct language from
another author. Appropriate credit is given where I have used ideas, expressions or text from
another public or non-public source. The thesis in this form or in any other form has not been
submitted to an examination body.
Frankfurt am Main, December 2019
City and Date Signature
190
CURRICULUM VITAE
TANIA LORENA LÓPEZ URRESTA
t.lopez@fs.de
Date and place of Birth: Tulcán, Ecuador, February 6th, 1981
EDUCATION 11/2011 – 11/2019 FRANKFURT SCHOOL OF FINANCE & MANAGEMENT FS, Frankfurt-
Germany Dr. rer.pol in Economics Policy. (Awarded FS Scholarship) Fields of Interest: Microfinance, Financial Inclusion, Development Finance.
11/2009 – 10/2010 UNIVERSITY OF BERGAMO, Bergamo – Italy
Master in Microfinance (awarded MAE AND ITALIAN MINISTRY OF FOREIGN AFFAIRS Scholarship)
11/2007 – 06/2008 UNIVERSITY OF LOJA (UTPL), Guayaquil-Ecuador Postgraduate in Human Resources Management
07/2006 – 07/2007 LATIN AMERICAN FACULTY OF SOCIAL SCIENCES (FLACSO), Argentina Postgraduate in Constructivism and Education 09/1998 – 05/2005 PONTIFICAL CATHOLIC UNIVERSITY OF ECUADOR (PUCE), Quito-Ecuador
Commercial Engineer with a specialty in Finance (awarded top ten student PUCE scholarship)
09/1998 – 02/2003 Certified Public Accountant
PROFESSIONAL EXPERIENCE 05/2016 – to present FRANKFURT SCHOOL FINANCIAL SERVICES GmbH, Frankfurt - Germany
Investment Manager (Head of Investment Management until 2017) Besides Investment Manager role, perform tasks such as strategic planning and articulating investments’ outlook, chairing the Investment Committee, developing investments processes and templates, coaching, supervising and development of investment managers.
07/2012 – 10/2014 FRANKFURT SCHOOL FINANCIAL SERVICES GmbH, Frankfurt - Germany Investment Manager Latin America, Africa and Asia
Scout market opportunities, pipeline origination, qualitative, social and financial performance analysis, risk analysis, due diligences conduct, investment proposals presentations, contract negotiations, Management of existing investments, negotiation in restructuring cases, workout of problem loans in Latin America.
2011 - 2015 MICROFINANCE RATING AGENCIES AND INVESTMENT VEHICLES External Consultant Due diligence, reports preparation, participation in credit/rating committee.
02/2011 – 09/2011 MICROFINANCE RATING, Quito - Ecuador
Social and Financial Analyst Quantitative and Qualitative analysis of MFIs in Ecuador and Latin America, due diligence process, rating reports preparation, evaluation of credit, market, operational and liquidity risks, social and financial performance evaluation.
191
06/2010 – 09/2010 GRAMEEN BANK, Dhaka - Bangladesh
Internship Program and Research Project Research: “How Liabilities and Capital Structure Affects the Financial Sustainability of the Grameen Bank”
09/2009 – 12/2009 ELOY ALFARO UNIVERSITY, Manta - Ecuador
Financial Management Course Lecturer. More than 200 hours of lectures, course materials and contents preparation, methods of instruction, advise in selection of topics for thesis, students' class work and assignments grading.
07/2007 – 12/2009 ITT FEDERAL SERVICES INTERNATIONAL CORPORATION, Manta - Ecuador
Accounting and Financial Clerk Budget controlling and reporting to headquarter in USA, ledgers and financial statements preparation and verification, bank reconciliations, monthly and annual taxes consolidation, monthly payroll for 180 employees approval.
08/2005 – 01/2006 METREX LOGISTIC SERVICES, Quito - Ecuador
Strategies and Projects Business Developer Business strategies and objectives planning, policies and procedures development for improving business efficiency and fulfilling customer requirements, budgeting and controlling, performance business monitoring.
08/2004 – 06/2005 CONSTRUECUADOR S.A, Quito - Ecuador
Administrative and Human Resources Coordinator Payroll, recruitment, corporate education, salary and incentives management for a company of 50 employees. Lead the general services team. Personnel and company insurances management, supplier’s relationship management.
01/2002 – 07/2003 DINERS CLUB DEL ECUADOR, Quito - Ecuador
Administrative Assistant Fixed assets additions, disposals, controls, retirements, and depreciation, account reconciliations, POC with suppliers, purchase offers and orders analysis.
10/2000 – 12/2001 PONTIFICAL CATHOLIC UNIVERSITY OF ECUADOR (PUCE), Quito - Ecuador
Teacher Assistant Lesson outlines, plans and material preparation for management subjects, preparing and giving examinations, and grading examinations.
OTHER EXPERIENCE 04/2007 – 05/2007 EXXON MOBIL, Quito – Ecuador. Human Resources Assistant 01/2006 – 12/2006 Au pair in America, Atlanta, USA 04/2004 – 06/2004 PUCE, Quito – Ecuador. International Relationships Assistant 09/2003 – 02/2004 SODEXHO, Idaho, USA. In campus food services Supervisor 06/2000 – 10/2000 Ecuadorian Corporation of Coffee, Quito-Ecuador. Accounting Assistant OTHER SKILLS LANGUAGES: Spanish (Mother Tongue), English (Fluent), Italian (Medium), German (B1) COMPUTER SKILLS: Windows 7, Microsoft Office, Lotus, Project; Stata, SPSS PRESENTATIONS IN CONFERENCES AND COURSES 09/2018 Oslo Met, Oslo – Norway. Oslo Workshop on Microfinance
192
06/2017 Portsmouth Business School, Portsmouth – England. 5th European Research Conference on Microfinance
06/2017 University of Valencia, Spain. The 15th INFINITI Conference on International Finance 05/2017 Bank of Finland (BOFIT), Helsinki – Finland. Workshop on Banking and Institutions 09/2016 University College London, London – England. P2P Financial Systems 2016 07/2016 University d'Auvergne, Clermont-Ferrand – France. 33rd International Symposium on
Money, Banking and Finance. 06/2016 University of Beira Interior, Covilha – Portugal. 9th Finance Conference of the
Portuguese Finance Network 12/2015 UNSW Business School, Sydney – Australia. 29th Australasian Finance & Banking
Conference. 06/2015 University of Geneva, Switzerland. 4th European Research Conference on
Microfinance. 08/2014 University of Groningen, Netherlands. Microfinance Experiments: Methods and
Applications Course. 10/2013 Graduate Institute's Center for Finance and Development, Zurich – Switzerland.
Global Financial Inclusion Oikos Academy 07/2012 University Roma Tre. Italy. Institutions for a better development after the financial
crisis. PUBLICATIONS López, T., & Winkler, A. (2017). The challenge of rural financial inclusion–evidence from microfinance. Applied Economics, 1-23. López, T., & Winkler, A. (2019). Does financial inclusion mitigate credit boom-bust cycles? Journal of Financial Stability Volume 43, 116-129. WORKING PAPERS López, T (2019) The Debt Structure of Microfinance Institutions – Does It Follow the Life-Cycle Theory? HONORS
2005 Valedictorian, Gold Button, Graduating Class of over five hundred students, PUCE 2004 Valedictorian of the Faculty of Business Administration and Accounting Sciences, PUCE 1993 – 1998 Second best average grade; Tulcan Tech Superior Institute, Tulcán-Ecuador 1997 – 1998 President of the Provincial High School Students Association, Carchi - Ecuador 1995 - 1996 Student Government Secretary, Tulcán Tech Superior Institute, Tulcán-Ecuador 1992 Valedictorian, Alejandro R. Mera Elementary School, Tulcán-Ecuador OTHER SCHOLARSHIPS 07/2007 FUNDACION CAROLINA. Ecuadorian Representative, Program of Iberoamerican
Young Leaders. Cartagena 07/2005 FUNDACION CAROLINA AND SANTANDER BANK. Ecuadorian Representative,
Program of Immersion in the Spanish Social Reality for the sixty best Iberoamerican Graduated Professionals. Director: Andres Pastrana (Ex-President of Colombia), Spain, Portugal and Belgium
2003 –2004 UNIVERSITY OF IDAHO, American Language & Culture Program Semester, Idaho-USA