microfinance institutions and financial inclusion

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.

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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).

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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.

https://www.hioa.no/eng/content/download/146067/4103186/file/Wkshp18Call.pdf






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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).

https://doi.org/10.1080/00036846.2017.1368990

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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.

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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).

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%20Financial%20Stability_1.pdf, accessed 25 February 2016

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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: [email protected]

mailto:[email protected]

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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.

http://www.superbancos.gob.ec/

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).


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


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).


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.


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.


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.


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.


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


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


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


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.


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.


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.


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.


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


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)


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


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


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


Public Investments PublicMFI debt provided by public investors as a percentage


Foreign Private Investments FopriMFI debt provided by foreign private investors as a


Foreign Public Investments FopuMFI debt provided by foreign public investors as a


Domestic Private Investment LopriMFI debt provided by Domestic private investors as a


Domestic Public Investment LopuMFI debt provided by Domestic public investors as a


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


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)


_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)


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)


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


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)


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)


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)


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


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


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.

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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: [email protected] Email: [email protected]

https://doi.org/10.1080/00036846.2017.1368990



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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).

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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.

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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).

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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.

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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.


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.


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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 –

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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.


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.

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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.

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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.

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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.


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.

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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

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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.


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


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


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


* 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)


(-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)


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)



_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


* 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)


(-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)


(-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)


(-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)


(-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)


(-2.75)

PRODUCTIVITY 0,0000 0,0000 0,0000 0.0000842**

(0,33) (1,23) (0,68) (2,38)


(-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)


(-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)


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)



_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


* 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)


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)


_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)


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)


_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

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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.

.

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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: [email protected] Email: [email protected]



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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

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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.

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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).

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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)

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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

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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.


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.


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.

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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.


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.


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.


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


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.


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.

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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)



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)



Financial Inclusion Variables

SHAREBORROWERS08 Number of borrowers from commercial banks

divided by adult population in 2008



BORROWERGROWTH0407 Borrowers compound annual growth rate between

2004 and 2007.



SHARELOANS08Number of loans otstanding at commercial banks

divided by adult population in 2008



LOANSGROWTH0407Loan accounts compound annual growth rate between

2004 and 2007.



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.



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


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)


164



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



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



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



Financial Inclusion Variables

SHAREBORROWERS Number of borrowers from commercial banks divided by

adult population in the last pre-crisis year



BORROWERGROWTH Borrowers compound annual growth rate in the last three

years before the crisis



SHARELOANS Number of loans outstanding at commercial banks divided

by adult population in the last pre-crisis year



LOANSGROWTH Loan accounts compound annual growth rate in the last

three years before the crisis




CREDITGROWTH Real outstanding loans (commercial banks) compound

annual growth rate in the last three years before the crisis.



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


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


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)


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


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


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


LNGDPPERCAPITA 52 8.35 8.30 1.15 5.66 10.82

KAOPEN 52 0.52 0.43 0.39 0.00 1.00


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


*Indicate significance at 5% level

167


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


*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)


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


* 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


1 2 3 4

Dependent Variable:

SHAREBORROWERS -0.459 -0.801***

(-1.21) (-2.95)

BORROWERGROWTH 0.565* -0.0205

(1.73) (-0.10)


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)


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


* 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


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)


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)


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


* 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


1 2 3 4

Dependent Variable:

SHAREBORROWERS -0.155 -0.875**

(-0.31) (-2.16)

BORROWERGROWTH -0.0893 0.00829

(-0.41) (0.03)


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)


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


* 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)


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


* 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


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)


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)


inclusion

176

1 2 3 4 5 6 7 8


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


* 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


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)


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)


inclusion

Change in financial

inclusion variableChange in methodology

178

1 2 3 4 5 6 7 8 9 10


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


* 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


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)


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)


inclusion

180

1 2 3 4 5 6 7 8


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


* 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


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)


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)


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


* 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


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)


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)


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


* p<0.10, **p<0.05, *** p<0.01

Changes in independent variables

other than financial inclusion


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


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)


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)


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


* p<0.10, **p<0.05, *** p<0.01

Changes in independent

variables other than financial

inclusion

Change in methodology


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


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)


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)


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


* p<0.10, **p<0.05, *** p<0.01

Changes in independent variables

other than financial inclusion


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


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)


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)


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


* 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


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)


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)


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


* p<0.10, **p<0.05, *** p<0.01

DROPCREDIT


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)


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)


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


* p<0.10, **p<0.05, *** p<0.01

DROPBORROWER


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

[email protected]

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

microfinance institutions and financial inclusion

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