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Financial Inclusion: progress, motivations and impactLi, Linyang

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Chapter 2

Productivity Convergence of

Global Microfinance:

What is the Source of

Convergence?

2.1 Introduction

Since the late 1970s, the poor in emerging economies have increasingly gained access to

financial services offered by so-called microfinance institutions (MFIs). These MFIs have

shown significant growth rates in terms of providing financial services to poor households.

Whereas in 1997 these MFIs had around 10 million clients, in 2010 this number had grown to

over 200 million (Maes and Reed, 2012). According to Lützenkirchen and Weistroffer (2012),


22 Chapter 2

around 1 billion poor people have been directly or indirectly affected by the access to

microfinance.

The enthusiasm for providing financial services to poor people has spawned a dramatic

increase in the number of institutions in the microfinance industry worldwide (Mcintosh and

Wydick, 2005). The accession of newly established instutions to the microfinance industry

poses a question that whether the new (or young) MFIs are catching up with the leading MFIs

in terms of productivity convergence. The concept of productivity convergence implies that,

in the case of global microfinance movement, differentials in factors of production across MFIs,

such as labor, capital and technology, should be reduced overtime. This is due to the fact that

international donors and organizations, such as the World Bank and Microfinance Information

Exchange (MIX), have been promoting the microfinance movement by exchanging MFIs’

operating information and techniques worldwide (Cull et al., 2009). Consequently, operating

strategies of new MFIs across countries should be approaching towards the most developed

(or productive) MFIs, reflected in a convergence of productivity scores (Casu and Girardone,

2010). Therefore, investigating convergence in MFI’s productivity informs the progress of

newly established MFIs improving themselves towards the best-practice in the microfinance

market.

This chapter aims to assess the convergence of productivity for the global microfinance

market and identify the source(s) of productivity convergence from MFI’s daily operations. In

the last decade, the microfinance industry has experienced dramatic evolutions with respect

to daily operations, e.g. focusing on financial sustainability (Hermes et al., 2011; Wagenaar,

2014), modifications of business models, e.g. commercialization (Cull et al., 2007; Mersland

and Strøm, 2010), and upgrading daily operations, e.g. applying technologies (Kapoor et al.,


Productivity Convergence of Global Microfinance 23

2007; Hermes et al., 2011). These evolutions exert profound impacts on MFI’s financial

performance (productivity of generating revenues) and social performance (productivity of

reaching the poor) (Ledgerwood, 2013). Accordingly, we analyze to what extent these

operational changes contribute to the convergence of productivity in the global microfinance

market.

Our analysis applies a number of methodologies, which has never been touched by

previous microfinance studies, aiming to answer three questions: Has there been a

convergence in (financial and social) productivity in the global microfinance market in the last

decade? Is the productivity convergence in the market driven by leading MFIs shifting the

productivity frontier or by lagging MFIs catching up with the leaders? To what extent has the

evolution of daily MFI operations contributed to the producitivity convergence in the global

microfinance market?

Until now, a number of literature measure MFI’s productivity and efficiency

(Gutie rrez-Nieto et al., 2007a, 2007b; Bassem, 2008; Haq et al., 2010; Hermes et al., 2011; Piot-

Lepetit and Nzongang, 2014; Bos and Millone, 2015), but no one investigates the long-term

financial or social productivity converngence in the microfinance market, and the main source

of MFI’s long-term productivity convergence remains unknown. Observing the dynamic

performance of the microfinance market and tracing the source of productivity convergence

bring better understandings on how MFIs are able to reach as many poor clients as possible

with lower prices and better service quality, and also improve institution’s soundness and

management structure to ensure capital buffers to absorb risks (Casu et al., 2004).


24 Chapter 2

We use a balanced panel data set for 171 MFIs active in 59 developing countries, during

the period of 2003–2012. In the first stage, we investigate dynamic patterns of productivity by

applying Intertemporal Data Envelopment Analysis (DEA) and the convergence test.

Based on Intertemporal DEA, we find that the global microfinance market exhibited

overall improvements in both social and financial productivity growth in the ten-year period.

Both social and financial productivity frontiers showed upward shifts, although financial

improvement was substantial and social growth was relatively limited. The convergence test

shows that MFIs that were initially less productive were catching up with leading MFIs, that

is, they were improving their financial and social productivity at a more rapid pace than the

leaders. This finding suggests that evolutions of operating practices are prompting lagging

MFIs to approach the best practices in the market.

In the second stage, we apply a dynamic panel data analysis to trace out the source of

productivity improvement from three recent changes in MFI’s operations: improved operating

efficiency, increased capital intensity and technological change. Results suggest that,

throughout the ten years, capital deepening is consistently the major contributor of

productivity convergence and technological change contributes the least. This empirical

evidence proves that MFIs rely on commercialization to ensure survival and facilitate growth.

contributed the least. This empirical evidence suggests that commercialization may have been

responsible for MFIs’ limited progress in social outreach and substantial improvement in

financial performance during the last decade.

The remainder of this paper is organized as follows. Section 2.2 provides a brief

literature review on microfinance performance and explains why we trace the source of MFI

performance improvement from three changes in operations. Section 2.3 discusses the DEA



we use to analyze patterns of productivity growth and shows how we decompose productivity

changes into technical efficiency changes, changes in capital intensity, and/or technological

change, following the methodology proposed by Los and Timmer (2005). Section 2.4 presents

the results of our empirical analysis and Section 2.5 presents our conclusions.

2.2 Theoretical Background

2.2.1 Microfinance Performance: A Brief Literature Review

The microfinance movement began in Bangladesh in the mid-1970s. Microfinance has become

a household term that is frequently used in press reports about its growth, innovation, and

impact (Ledgerwood, 2013). Recently, the exponential growth of the microfinance industry has

prompted research on the efficiency and productivity of MFIs, using parametric methods such

as Stochastic Frontier Analysis (SFA) and non-parametric methods such as DEA.

DEA has become popular among researchers because it can be estimated without

imposing specific restrictions or functional forms (Fare et al., 1994). Early studies used DEA to

evaluate MFI performance and identify determinants of institutions’ efficiency scores.

Nghiem et al. (2006) used DEA to investigate the efficiency of 46 microfinance schemes

in Vietnam. The authors assessed technical efficiency and scale efficiency by employing two

inputs, that is, labor cost and non-labor costs, and three outputs, that is, number of savers,

number of borrowers, and number of groups. Their main finding was that age and location of

the scheme are primary determinants of efficiency.

Gutiérrez-Nieto et al. (2007) used DEA to test the efficiency of 30 MFIs in 21 Latin

American countries. First, they used different combinations of inputs and outputs in DEA to


26 Chapter 2

show that the choice of DEA specification is relevant to efficiency assessment. Next, by

conducting principal component analysis on DEA efficiency scores, they showed that MFI

performance is influenced by the location of the MFI (country effect) and the institution’s

ownership status (NGO or non-NGO). Gutiérrez-Nieto et al. (2009) went one step further to

examine MFI performance in both the institutions’ social mission (reaching poor clients) and

financial mission (generating revenues). The authors presented a novel indicator to measure

the extent to which MFIs reach the poor and then used DEA to estimate the financial and social

efficiency of MFIs according to various combinations of inputs and outputs. Their analysis

shows that MFIs tend to place financial self-sufficiency ahead of social outreach, because they

need to ensure their survival in the long run. It also showed that NGOs are more efficient than

non-NGOs, because they serve more poor clients at lower costs.

Haq et al. (2010) examined the cost efficiency of 39 MFIs in Africa, Asia, and Latin

America. The authors were the first to use DEA to test MFI efficiency according to both the

production approach and the intermediation approach. While the former approach specifies

that MFIs produce deposits and loans by using assets, capital, and personnel, the latter

recognizes that MFIs transform deposits and loanable funds into credit. Results show that

NGOs are more efficient according to the production approach, because they place stronger

emphasis than commercialized MFIs on providing services to poor people. According to the

intermediation approach, commercialized MFIs outperform NGOs.

Recently, researchers have used SFA to assess MFI performance because the method

has the advantage of controlling for measurement errors and other random effects.

Researchers have also shifted their attention from assessing MFI performance only to

examining whether MFIs can ensure efficiency in the process of realizing their social objectives.



Hermes et al. (2011) use SFA to calculate the cost efficiency of 435 MFIs from 1997–2007.

The main aim of their study is to examine the existence of a trade-off between financial

sustainability of MFIs and measures of social outreach. Cost efficiency specifies the cost

difference between an MFI and the best-practice MFI, when the institutions are providing

services to poor clients; the authors interpret such efficiency as indicator of MFI financial

sustainability. They conduct regressions based on cost efficiency scores and find that MFI cost

efficiency is negatively related to measures of social outreach, that is, average loan balance and

number of female borrowers. These results indicate that extending an MFI’s social outreach is

negatively related to improving its financial performance.

Servin et al. (2012) use SFA to investigate a sample of 315 MFIs operating in Latin

America during the period of 2003–2009. They focus on technical efficiency and compare it

among NGOs, cooperative, and credit unions, non-bank financial intermediaries (NBFIs), and

banks. Results show that NGOs and cooperatives exhibit lower technical efficiencies than

banks and NBFIs because they have a lower technology level. However, because NGOs and

cooperatives focus on social goals and lack the proper incentive mechanisms to promote

performance, they incur higher inefficiencies than NBFIs and banks.

Hudon and Traca (2011) pay special attention to the relationship between MFI

efficiency and subsidy. Their research is motivated by the debate on whether subsidy harms

or benefits MFI development. One point of view is that subsidy enables MFIs to build

infrastructure and develop operating strategies, both of which ensure their survival and

growth. However, another perspective is that institutions rely too heavily on subsidy and have

fewer incentive to improve operations, leading to lower efficiency and worse performance.

Based on data from 100 MFIs, Hudon and Traca find that increasing subsidy intensity


28 Chapter 2

enhances MFI productivity, but the promotion role of subsidy functions only within a certain

range. If the subsidy intensity goes beyond a particular threshold, productivity is lowered.

With regard to the above review of studies of efficiency and performance of MFIs, we

note two important limitations in existing literature.

First, none of the previous studies explicitly captures the convergence of financial and

social productivity of the microfinance market over the long term. Studies are restricted by

their data to assessing MFI performance within a limited time period. Moreover, because

previous research tends to calculate MFI efficiency scores annually, it is impossible to observe

the changing patterns of MFI performance; annually calculated efficiency scores are

incomparable across time. In contrast, we use Intertemporal DEA, which is able to present

every historical change in MFI performance. More importantly, our substantially larger

balanced panel dataset, covering 171 MFIs around the world from 2003–2012, enables us to

explicitly capture the dynamic changes of MFI performance within a much longer period than

previous research.

Second, previous studies did not identify the source of MFI productivity convergence.

We are interested in how the three most important changes in MFI daily operations—increased

operating efficiency, capital deepening, and application of technological innovations—affect

MFI producitivity convergence. On the one hand, they are the most remarkable trends in

recent evolution of the microfinance market (Helms, 2006; Ledgerwood, 2013); on the other

hand, they are general operating responses of MFIs to the changing environment of the

microfinance market, that is, commercialization and increased competition (Rhyne and Otero,

2006; Armendàriz and Morduch, 2010; Hermes et al., 2011).



Below, we briefly discuss the ways in which operational changes influence MFI

performance, and further affect productivity convergence in the microfinance market.

2.2.2 Operating Efficiency

In reaction to the changing environment in the microfinance market, MFIs are improving their

sustainability by promoting their operating efficiency. Operating efficiency denotes the daily

challenge of bringing in enough money to cover costs of operations and funds (Armendàriz

and Morduch, 2010; Prior and Argandoña, 2009). Increasing operational efficiency has become

a hot topic in microfinance literature, because many MFIs face relatively high operating or

administrative expenses compared with their revenues. With the rapidly growing number of

MFIs and commercial banks involved in lending to the poor, the microfinance market has

become increasingly competitive; this competition has forced MFIs to reduce costs and

improve financial sustainability (Rhyne and Otero, 2006).

The daily operations of MFIs are costly because the institutions engage in numerous

small financial transactions. Each transaction requires face-to-face interaction with poor

borrowers to evaluate their creditworthiness and monitor their use of credit (Fernando, 2006).

MFIs also bear the financial burden of obtaining funds. Most institutions, especially those that

are small-scale or newly established, rely solely on donations that are often limited to daily

operations. Because they are not allowed to collect deposits, MFIs must pay high interest for

borrowing from commercial sources to lend to the poor (Armendàriz and Morduch, 2010).


30 Chapter 2

Learning from leading MFIs is the primary way for less-developed institutions to

improve operational efficiency3 (Caudill et al., 2009). Leading MFIs are more cost-effective

because they have been offering financial services to clients for a longer time; their practical

experience ensures both financial self-sufficiency and outreach. Less-efficient institutions

establish connections with leading institutions; these relationships enable laggards to improve

themselves through technological transfer (Gonzalez-Vega et al., 1996). For instance, laggards

may mimic the designs and operations of leaders by improving management structures,

rearranging operational procedures, and installing better corporate governance systems

(Hudon and Sandberg, 2013; D’Espallier et al., 2016). According to Caudill et al. (2009),

learning from leaders is particularly important for MFIs that are seeking to improve cost

efficiency and financial productivity. By adopting experience in lending practices from market

pioneers (e.g., how to use lending strategies and deal with default risks of poor clients), lagging

MFIs can reduce the effort and spending associated with trial and error.

2.2.3 Capital Deepening

The second trend in MFI development is capital deepening, which denotes that MFIs increase

their capital intensity per unit of labor by accessing a larger volume of external investment

(D’Espallier et al., 2016).

3 Internal organization improvement can be an alternative explanation of operating efficiency change.

Here we stress the importance of learning effect because, unlike other well-developed industries, the

microfinance industry drew worldwide attention in the late 1990s and most MFIs were recently

established in the last decade. One particular feature of the microfinance is that institutions need to

provide financial services to poor clients based on spatialized strategies, such as group lending. With

limited initial funding from donors to ensure survival and promote growth in a short time, newly

established MFIs generally learn from market leaders to adopt the strategies of serving the poor, instead

of improving themselves from internal organizations, which is more costly on time and funding.

Accordingly, we interpret the learning effect is the primary source of operating efficiency change in our

analysis.



In the competitive market, increasing capital intensity enhances MFI productivity and

performance by several means. For instance, because credit agents play a crucial role in

ensuring the profitability and sustainability of the MFIs they work for, MFIs are investing

increasing amounts of funds in monetary incentives to motivate staff and promote

competitiveness in the market (Aubert et al., 2009). More and more institutions are using a

system of financial rewards for employee performance. In a survey of 147 MFIs, McKim and

Hughart (2005) found that the percentage of MFIs using staff incentive schemes increased from

6% in 1999 to 63% in 2003. The magnitude of the incentives is quite large; the average incentive

payment represents more than one-quarter of a credit agent’s fixed salary. This trend of

allocating funding to staff rewards is supported by the finding that MFIs that have applied the

schemes exhibit substantial improvement in productivity of their credit agents and overall

financial performance (Aubert et al., 2009).

Capital deepening also improves MFI competitiveness when managers allocate more

funding to educate and train staff. Although the microfinance industry has become

increasingly professional, inadequate management skills and lack of experience are still

bottlenecks in the growth of MFIs, especially in newly established institutions (CGAP, 2009).

Given the increasing competition in the microfinance market, investment in staff training in

issues such as dealing with delinquent clients, time management, operational risk

management, accounting principles, and other soft skills, is crucial to improving operational

management and promoting competitiveness (Sarker, 2013).

MFIs are strongly motivated to increase their capital intensities to ensure survival and

better performance in the competitive market; sufficient capital flows from investors such as

commercial banks and microfinance investment vehicles (MIVs) provide MFIs with plenty of


32 Chapter 2

opportunities to deepen capital. In the last decades, a growing number of commercial banks,

such as Citigroup, Deutsche Bank and HSBC (Hermes et al., 2011) have become interested in

investing in MFIs. This entry of commercial banks has been encouraged by the success of first

movers into the market, which has shown that the poor can be both bankable and profitable

(Helms, 2006). Moreover, by investing in the microfinance market, commercial banks

demonstrate their corporate social responsibility of helping the poor (Hermes et al., 2011).

2.2.4 Technological Innovations

The third major change for MFIs is their increasing incorporation of technological innovations

into daily operations.

Technologies applied by MFIs range from acquiring software that supports internal

systems to installing hardware that serves clients. In daily operations, managers use internal

information systems to track transactions and client information. Network connections

between branches allow institutions to exchange information and monitor transactions. Credit

scoring systems are used by loan officers to evaluate credit applicants’ creditworthiness and

repayment abilities according to their individual characteristics (Helms, 2006).

On the client side, MFIs provide services through ATMs, chip cards, point-of-sale (POS)

devices, and mobile banking. These services not only benefit customers by providing

convenience and lower transaction costs but also enable MFIs to reach more clients in remote

areas, reduce operational costs, and increase financial security (Rhyne and Otero, 2006).

Several factors motivate MFIs to deploy technologies to boost performance. First, by

using well-designed information systems, network connections, and credit scoring systems,

MFIs are better able to capture accurate information on customers, monitor their use of credit,

and reduce default risks. They can also produce more reliable reports on institutional



performance and increase information disclosure with investors. This improved transparency

attracts more external funding from investors because the investors can better identify the

institution’s progress and improvement (Rhyne and Otero, 2006).

Second, wide application of technology in daily operations reduces operating costs,

which is particularly important as the microfinance market becomes increasingly competitive.

Technology simplifies and standardizes transaction procedures and reduces unnecessary

effort and time spent by personnel. Ketley et al. (2005) investigate a number of emerging

economies and show that a teller costs five times more than an ATM in each transaction.

Third, the incorporation of technology in daily operations expands financial access for

poor clients. By increasing transparency and reducing operating costs, MFIs attract more

external funding and increase their revenues. This strengthened financial basis enhances their

ability to reach poorer clients, with whom transactions are more costly (Conning, 1999).

Moreover, technology promotes streamlined and automated transaction processes that enable

MFIs to offer services in remote areas, using ATMs and POS devices in place of expensive

branch locations (Helms, 2006).

By incorporating technology, MFIs enhance both their financial and social performance.

2.3 Data and Methodology

2.3.1 Data

We use a balanced panel containing 171 MFIs around the world to construct a global

production frontier, using data for the period of 2003–2012. The data are drawn from the MIX

Market Internet database. Use of a balanced panel data set allows us to analyze the continuous


34 Chapter 2

dynamics of MFI efficiency over time. Many studies of MFI efficiency are based on unbalanced

panels from the same MIX Market database.4 Although the unbalanced database provides a

wealth of data for a long period (the data dates from the mid-1990s), MFI data availability is

rather irregular, resulting in gaps in time series data. Moreover, if we use an unbalanced

sample, some MFIs may be newly established during the ten-year period, which interfere our

analysis with respect to the source of the shift of productivity frontiers. For instance, a newly

established MFI during the ten-year period may have sufficient initial asset and offer services

to a large number of clients. In the extreme case, this newly established MFI may drive the

frontier upward, but the shift can only be explained by the new MFI’s sufficient initial funding

rather than the operational progress made in the microfinance market. In order to avoid the

interfere in the unbalanced panel data and trace the source of productivity frontier shift from

MFIs’ daily operations, we use balanced panel in our analysis.

2.3.2 Intertemporal Data Envelopment Analysis

We apply DEA to determine the global production frontier. An important feature of

DEA is that MFI productivity can be estimated without imposing specific restrictions or a

particular functional form (Fare et al., 1994), because DEA conducts a piece-wise linear

function over the data based on linear programming methods (Coelli, 1996; Los et al., 2005).

Thus, the productivity of each MFI is determined entirely by the combination of inputs, which

is reflected in the MFI’s operating technology, and the productivity frontier is driven by MFIs

that attain the highest productivity based on their input combinations (or operating

technology).

4 See Appendix Table 2.A.2 for a description of the data for the MFIs in our data set, compared with the

same data for the full sample of all MFIs reporting to the Mix Market data base. See Appendix Table

2.A.1 for definitions, explanations, and data sources for all variables used in the empirical analysis.



When using DEA, we assume constant returns to scale5 with two inputs producing a

single output. The two inputs are the capital and labor of the MFI. In the evaluation of financial

productivity, the output is the MFI’s gross loan portfolio; in the evaluation of social

productivity, the output is the MFI’s number of active borrowers6. Figure 2.1 illustrates an

example of the productivity frontier. The horizontal axis depicts the capital intensity (i.e., total

capital divided by labor). The vertical axis denotes the MFI’s financial productivity (gross loan

portfolio divided by labor) and social productivity (number of active borrowers divided by

labor). The two pentagrams (M1 and M2) on the frontier are two MFIs with ‘best-practice’

performance, that is, the highest (financial or social) productivity at current capital intensity

level. The feasible range of productivity for MFIs with different input combinations lies below

the frontier.

One important feature of our estimation of the global productivity frontier is that we

take into account historical statistics of productivity for all MFIs. Specifically, when we

construct the productivity frontier at year n, we adopt each MFI’s statistics of input and output

in previous years (before year n) as observations to estimate the frontier; this approach is

known as Intertemporal DEA (Tulkens et al., 1995). Therefore, we construct the productivity

5 The two basic DEA models are CCR (Charnes et al., 1978) and BCC (Banker et al., 1984) with constant

returns to scale (CRS) and variable returns to scale (VRS), respectively. We assume CRS in our analysis

because it better suits the decomposition analysis of MFI’s productivity growth than the VRS model

(Ouellette and Vierstraete, 2004). As noted by Grifell-Tatje and Lovell (1995), VRS models may lead to

a bad evaluation of Malmquist productivity index (a popular index for decomposition analysis) even in

the presence of a non-CRS technology, a problem that is not shared by CRS models in the presence of a

CRS technology. 6 We identify MFI’s social performance by investigating its outreach to poor. In the literature, borrowers

per loan officer is a standard measure of outreach to the poor. MFIs are established to provide financial

services to people living under the poverty line, and It is reasonable to assume that people borrowing

from MFIs are poor people. Therefore, we use borrower per labor to identify MFI’s breadth of outreach,

referring to MFI’s capability of serving large numbers of poor people (Schreiner, 2002; Brau & Woller,

2004; Louis et al., 2013).


36 Chapter 2

frontier of year n according to the most efficient observations in the period from the first year

in our data (2003) to year n, i.e. 𝑛 ∈ [2004, 2012].

Figure 2.1 The Production Frontier of MFIs

There are three reasons for our use of Intertemporal DEA. First, by adopting historical

statistics we ensure that the productivity frontier lines (for each year) do not shrink back when

they are displayed in sequence, suggesting that “technological regress” never occurs (Los et

al., 2005). By excluding the possibility of technological regress, we reflect reality: Along with

the accumulation of knowledge, technological progress is achieved on the basis of current

technology and the loss of technology or knowledge never occurs in practice.

Second, in line with Basu and Weil (1998), we assume that immediate transfer of

technology and knowledge is possible around the globe, indicating that less productive MFIs

are able to learn technological innovation from leading institutions in the industry. Basu and



Weil (1998) emphasize that the “learning from leaders” effect takes place when followers are

associated with comparable capital intensity with the industry leader. Because of the learning

process, the historical statistics of MFI input combinations are essential for estimating the

global productivity frontier; all established technology is accessible such that lagging MFIs can

make improvements (Timmer and Los, 2005).

Finally and most importantly, by adopting the productivity history of MFIs, we reveal

their development paths, because any historical change of labor productivity can be

decomposed into technical efficiency change, capital deepening, and technological

improvement. Therefore, this approach allows us to identify the sources of MFI productivity

improvement and facilitate the development of the microfinance industry.

2.3.3 Convergence of Productivity in the Microfinance Industry

In this section, we present the convergence test that we used to investigate the productivity

dynamics of the microfinance industry. The convergence test was first used to investigate the

evolution of macroeconomic growth (GDP) across counties and the dynamic distribution of

household income across regions (Baumol, 1986; Barro, 1991; Barro, et al., 1992). Recently,

researchers have begun to explore the efficiency and competition of firms and banks in certain

regions (Casu and Girardone, 2010; Andries, 2014; Mamatzakis, 2008; Matthews; 2010). In a

growing number of studies, concepts of β -convergence and σ -convergence are the most

frequently applied and tested. Our research is the first attempt to adopt both β-convergence

and σ-convergence in the microfinance industry to provide an overview of MFI productivity

development from 2003–2012.

Specifically, β-convergence captures the ‘catching-up’ effect of MFIs by testing the

correlation between the change of productivity growth rate and the initial level of productivity


38 Chapter 2

growth rate. If the change of growth rate is negatively correlated with the initial growth rate,

the implication is that initially less progressive MFIs are accelerating and growing more

rapidly than institutions with initially high growth rates, suggesting that a catching-up effect

occurs, and MFIs overall are agglomerating to similar growth rates of productivity. However,

the test of β-convergence cannot reveal details of productivity convergence and may lead to

misunderstanding. For instance, if lagging MFIs are accelerating their productivity growth

rates and surpassing those of leading MFIs, change in productivity growth is still negatively

related to the initial productivity growth rate, but divergence occurs. Therefore, we adopt the

notion of σ-convergence, which complements the β-convergence test by investigating the

dispersion of productivity growth rate over time. Convergence is confirmed when the

dispersion is diminished during the specified period and the productivity growth of all MFIs

moves to the average level of growth rate (Weill, 2009). Thus, our research employs both β-

convergence and σ-convergence to shed light on the dynamics of MFI productivity.

2.3.3.1 𝛽-convergence test

Model (2.1) is used to examine the β-convergence MFIs’ productivity growth:

ln 𝑃𝑖,𝑇 − ln𝑃𝑖,𝑇−1 = 𝛼1 + 𝛽1 ln 𝑃𝑖,𝑇−1 + 𝛾1(ln 𝑃𝑖,𝑇−1 − ln𝑃𝑖,𝑇−2) + 𝜃𝑍𝑖,𝑇 + 𝜀1, (2.1)

where 𝑃𝑖,𝑇 denotes the financial or social productivity of the MFI 𝑖 in year 𝑇 and 𝑃𝑖,𝑇−1 is the

productivity of MFI 𝑖 in year T-1; i = 1, 2…171 and T = 1, 2…10. The difference of logarithm of

productivity between two years indicates the change of productivity growth rate (ln 𝑃𝑖,𝑇 −

ln𝑃𝑖,𝑇−1). If the variation of MFIs’ productivity growth is negatively correlated with their

initial level of productivity growth rate (ln 𝑃𝑖,𝑇−1), MFIs with low initial productivity growth



rates show a higher speed of boosting productivity than those with high initial productivity

growth rates. In other words, a negative 𝛽1 is interpreted as the incidence of absolute catch-up

effects of slowly growing MFIs, and the larger the absolute value of the parameter, the greater

the tendency of convergence. The vector 𝑍𝑖,𝑇 contains variables of MFIs’ individual

characteristics.

2.3.3.2 𝜎-convergence test

Model (2.2) specifies the estimation of 𝜎-convergence, which complements the investigation

of β-convergence because it displays the dispersion of productivity growth rate (Weill, 2009);

𝑆𝑖,𝑇 = ln(𝑃𝑖,𝑇) − ln(��𝑇), where ��𝑇 is the average productivity growth rate of all MFIs in our

sample at year T, 𝛼, 𝜎 and 𝛾 are parameters to be estimated, and 𝜀 is the residual. By

investigating the distance to the mean level of productivity over time, 𝜎 convergence exhibits

the variation of the gap of the productivity growth rate; a significantly negative 𝜎 implies a

narrowing dispersion. Therefore, convergence is captured only when it meets the conditions

of both β convergence and 𝜎 convergence:

ln 𝑆𝑖,𝑇 − ln 𝑆𝑖,𝑇−1 = 𝛼 + 𝜎 ln 𝑆𝑖,𝑇−1 + 𝛾(ln 𝑆𝑖,𝑇−1 − ln 𝑆𝑖,𝑇−2) + 𝜀. (2.2)

2.3.4 Sources of Productivity Convergence

To investigate sources of financial productivity convergence and social productivity

convergence, we follow the theory of Kumar et al. (2002) to decompose each MFI’s

productivity growth into three components. We then employ the approach from Los et al.

(2005) to estimate the effort of each estimated component in contributing to the productivity

convergence of the microfinance industry in the ten-year period.


40 Chapter 2

2.3.4.1 Decomposition of productivity growth

Figure 2.2 illustrates an example of decomposing an MFI’s productivity growth from Year 0

to Year 1. In Figure 2.2, the global productivity frontiers in Year 0 and Year 1 are labeled F(0)

and F(1) respectively. They represent the most productive institutions in the two-year period.

Between the two years, the global productivity frontier shifts upward. As a result, the

maximum attainable productivity level increases for the majority of capital intensity. M0 and

M1 are the locations of the observed MFI in Year 0 and Year 1. In the investigated period, the

productivity of the MFI also increases but stays below the maximum attainable productivity

in both years.

Figure 2.2 Decomposition of an MFI’s Productivity Convergence



According to Kumar et al.’s (2002) theory, the labor productivity growth of MFIs can

be decomposed into their movement towards the frontier, their movement along the frontier,

and the shift of the frontier, all of which are calculated according to the following equations:

𝑃1

𝑃0= (

𝑃1

𝑃𝑑∗𝑃𝑎

𝑃0) ∗ (

𝑃𝑐

𝑃𝑎∗𝑃𝑑

𝑃𝑏)

1

2 ∗ (

𝑃𝑏

𝑃𝑎∗𝑃𝑑

𝑃𝑐)

1

2 (2.3)

or:

(1 + ��) = (1 + ��𝐿)(1 + ��𝐶)(1 + ��𝐼) (2.4)

Equation (2.3) 7 shows details of the decomposition of productivity growth based on

Figure 2.2. Equation (2.4) shows corresponding elements of Equation (2.3) with simplified

symbols. On the left side, �� is the growth rate of productivity of Year 1 to Year 0. On the right

side, (1 + ��𝐿) is the operating efficiency ratio between Year 1 and Year 0; ��𝐿 implies the

change of the operating efficiency as well as the change of an MFI’s vertical distance towards

the productivity frontier. Because the frontier depicts all maximum attainable productivity for

MFIs, narrowing vertical distance to the frontier line implies that, ceteris-paribus, lagging MFIs

7 The left side of the equation denotes the productivity ratio of Year 1 to Year 0. On the right side, the

first term measures the operating efficiency change by calculating the Farrell Efficiency Index, where Pa

and Pd represent the maximum values of productivity (best practice) in Year 0 and Year 1 given MFI’s

capital labor ratios in respective years. According to Kumar and Russell (2002), measuring the capital

intensity change and technological change needs to adopt the ‘Fisher ideal’ decomposition because the

vertical shift of the frontier can be observed both for vertical and horizontal movements from M0 to M1.

Hence, capital intensity change is calculated as the Fisher index of potential change in labor productivity

resulting from a shift in the capital-labor ratio (Woltjer, 2013), where Pb represents MFI’s potential

output in Year 1 using its capital-labor ratio in Year 0 (0.4), and Pc denotes MFI’s potential output in

Year 0 using its capital-labor ratio in Year 1 (1). The last term measures the technological change also by

calculating the geometric average.

Operating Efficiency

Change

Capital Intensity

Change

Technological

Change


42 Chapter 2

boost their operating efficiency by acquiring and applying available strategies and techniques

from leading MFIs (Los et al., 2005). This element reflects the contribution of “learning” from

best-practice MFIs (Timmer and Los, 2005) that takes place when a lagging MFI has capital

intensity that is similar to that of the leading MFI. Therefore, we name ��𝐿 as the ‘learning

effect;’ it contributes to productivity growth when it is larger than 0.

The second element (1 + ��𝐶) reflects the horizontal movement of MFIs along the

productivity frontier. It is facilitated by the increase of capital intensity among MFIs that locate

on the frontier. MFIs on the frontier have already exhausted their potential improvement of

operating efficiency with current input combinations; by increasing capital intensity, such

MFIs shift to a more advanced technology with a higher maximum attainable productivity

level (Los et al., 2005). Thus, because ��𝐶 measures the contribution of capital deepening to

achieve higher level of productivity, we refer to it as the ‘capital deepening effect.’

The third element (1 + ��𝐼) depicts the shift of the productivity frontier itself. The

vertical movement of the frontier is triggered by the incidence of technological innovation,

which promotes the development of the technological context of the microfinance industry.

The incidence of technological innovation not only endows MFIs on the frontier with higher

productivity, but also benefits lagging MFIs in the industry with the technological spillover

effect, because they can further increase their operating efficiency by learning the innovative

technology. Therefore, a positive ��𝐼 represents the extent of technological innovation that is

contributing to MFI productivity improvement; we refer to it as the ‘innovation effect.’

2.3.4.2 Disaggregation of productivity convergence

In this section, we investigate the contributions of learning, capital deepening, and innovation

to the stimulation of productivity convergence of the microfinance industry. Based on the



decomposition results of each MFI’s productivity growth, we employ the following

regressions designed by Los and Timmer (2005) to evaluate the extent to which the three

components contribute to the productivity dynamics of the whole industry in different periods:

Learning:��𝑖𝐿 − ��𝑀

𝐿 = 𝛼𝐿 + β𝐿(ln𝑃𝑖,0

𝑃𝑖,𝑚− ln

𝑃𝐿,0

𝑃𝐿,𝑚) + 𝜀𝑖,𝐿. (2.5)

Capitalintensity:��𝑖𝐶 − ��𝐿

𝐶 = 𝛼𝐶 + β𝐶(ln𝑃𝑖,𝑚

𝑃𝐿,𝑚) + 𝜀𝑖,𝐶. (2.6)

Innovation:��𝑖𝐼 − ��𝐿

𝐼 = 𝛼𝐼 + β𝐼(ln𝑃𝑖,𝑚

𝑃𝐿,𝑚) + 𝜀𝑖,𝐼. (2.7)

Model (2.5) estimates the contribution of the learning effect to productivity

convergence; (��𝑖𝐿 − ��𝑀

𝐿 ) is the difference of the learning effect contributing to productivity

growth between MFI i and the MFI on the frontier with the same input combination (the

leading MFI). On the right side of the model, 𝑃𝑖,0 is the actual productivity level of MFI i, and

𝑃𝑖,𝑚 is the maximum attainable level of the MFIs’ productivity; 𝑃𝑖,0

𝑃𝑖,𝑚 indicates the growth rate

of the ratio of MFI i’s actual and maximum productivity level, and 𝑃𝐿,0

𝑃𝐿,𝑚 is the growth rate of

the leading MFI’s productivity ratio. Consequently, β𝐿 is the parameter of interest. A negative

β𝐿 suggests learning from leading MFIs’ technology enables lagging MFIs to boost their

operating efficiency and converge to the leading group. A larger absolute value of β𝐿 indicates

that MFIs with lower initial productivity improve more from the learning effect. Model (2.6)

and Model (2.7) are analogous to Model (4); they focus on revealing the contribution of

institutions’ capital deepening, and innovation to the productivity convergence in the industry.


44 Chapter 2

2.4. Results of the DEA and Empirical Estimations

We depict the global frontier of both financial productivity and social productivity every three

years from 2003–2012, using Intertemporal DEA. We map the frontier according to the

locations of MFIs that attain the maximum feasible productivity level in the corresponding

year. We indicate best-practice MFIs by the pentagrams on the productivity frontier. Below

the frontier, we plot the locations of less productive MFIs.

To exhibit MFIs’ developing dynamics more clearly, we classify all MFIs in our sample

into two types of institutions before plotting these observations. In particular, according to

MFI operating targets, we classify them into socially oriented MFIs that focus on providing

financial services and loans to the poor and financially oriented MFIs that emphasize

institutions’ financial sustainability and serve a broad range of clients. To classify MFIs into

these two institutional types, we use the percentage of female borrowers of an MFI, which is a

generally accepted indicator of outreach to the extreme poor8 (Olivares-Polanco, 2005; Paxton,

2007; Hermes et al., 2011). Specifically, we label an MFI in our sample as a socially oriented

institution if its percentage of female borrowers is above the average percentage of female

borrowers of the whole sample (66%). We label an MFI as financially oriented when its

percentage of female borrowers is below the sample average. In each figure, all MFIs below

8 In developing countries, more women are poorer than men, especially in rural areas where cultural

norms make it harder for women to borrow and save (D'Espallier et al., 2011; 2013). Studies often use

the percentage of female borrowers to proxy for MFI’s depth of outreach because female clients are often

considered as the extreme poor people in underdeveloped regions (Cull et al. 2007; Gutie rrez-Nieto et

al. 2009; Hermes et al., 2011; Quayes, 2012).



the frontier are less productive; the squares represent socially oriented MFIs and the stars

represent the financially oriented MFIs.9

In the following sections, we present a more detailed discussion of the DEA results

with regard to the financial and social efficiency of MFIs.

2.4.1 Financial Productivity Frontier

Figure 2.3 presents the dynamics of financial productivity of the MFIs in our sample over the

ten-year period. In 2003, most MFIs crowd around the southwest corner of the figure,

suggesting their relatively low levels of financial productivity and capital intensity in the

initial year. In 2006, the frontier line shows a minimal upward shift. Meanwhile, as the result

of institutional capital deepening, MFIs below the frontier (particularly financially oriented

institutions) show a tendency to move horizontally towards the right direction. In the

following years, while the capital deepening process becomes more pronounced as more MFIs

move further to the right, both financially and socially oriented institutions show progress in

productivity improvement, that is, they move in the direction of the efficient production

frontier. The frontier line shows a remarkable upward shift in the last five years, suggesting

that several technological innovations in the industry took place after 2006.

9 As additional investigations of productivity frontiers, we also list basic information about MFIs

operating on the (financial and social) productivity frontier in the middle year (2007) and the final year

(2012) of the ten-year period; see Tables 2.1 and 2.2.


46 Chapter 2

Figure 2.3 Evolution of the Financial Productivity Frontier, 2003–2012

The evolution of financial productivity over the ten years as shown in Figure 2.3

suggests that most MFIs in the industry experienced a catching-up process vis-à-vis the

leading MFIs (which are located on the production frontier), resulting in productivity

improvements. Macroeconomics researchers refer to this catching-up process as “learning

from leaders.” It involves two phases. First, MFIs experience capital deepening, followed by

productivity advancement (Basu et al. 1998, Los and Timmer, 2005; Timmer and Los, 2005).

With regard to the MFIs in our sample, the first phase is before 2006. During this period,

accumulation of capital intensity increases due to the accumulation of capital, leading to

higher levels of the capital–labor ratio. These higher ratios are a precondition for adopting

advanced technology, which may lead to productivity improvement. In the second phase



(after 2006), the high capital–labor ratio enables MFIs to benefit from technological spillovers

of industry leaders, allowing lagging MFIs to narrow their productivity gap with the leaders

on the frontier.

Figure 2.3 also suggests that the incidence of innovation is highly biased to MFIs with

high capital–labor ratios, because the productivity frontier is convex and the shift of the

frontier is largely driven by the MFIs located on the right-hand side of the figure (i.e., the

capital intensive MFIs).

In comparing the productivity dynamics of financially oriented MFIs with socially

oriented MFIs, Figure 2.3 shows that over the ten-year period, financially oriented institutions

exhibit higher productivity in terms of granting credit to clients than socially oriented

institutions. Among financially oriented institutions, the maximum attainable productivity

level increases from approximately $500,000 USD per unit of labor in 2003 to above $700,000

USD per unit of labor in 2012. This is twice as high as the highest productivity level reached

by socially oriented MFIs with similar capital intensity in the corresponding year. Moreover,

most MFIs operating on the frontier of each year are financially oriented institutions, that is,

financially oriented MFIs also dominate the progress of the maximum attainable financial

productivity within the market.

Although socially oriented MFIs are less capable of improving their financial

productivity than financially oriented MFIs, they also show remarkable performance in terms

of boosting financial productivity over the ten-year period. Socially oriented institutions

double their maximum attainable productivity levels from less than $20,000 USD per unit of

labor to about $45,000 USD per unit of labor over the ten-year period. Moreover, at least one

socially oriented MFI operates on the productivity frontier of each year.


48 Chapter 2

Table 2.1 MFIs on the Financial Frontiers of 2007 and 2012

(1) (2) (3) (4) (5) (6) (7)

Frontiers MFI Name Fiscal Year Capital Per

Worker

Financial

Productivity Ownership Region

Financial

Frontier of 2007

AgroInvest

Serbia 2007 11335.78 506099.20 NBFI EECA

COAC Jardín

Azuayo 2007 117956.10 515766.60

Credit Union/

Cooperative LA

FAFIDESS 2007 428.32 82225.66 NGO LA

Azeri Star 2007 128.15 33800.36 NBFI EECA

Financial

Frontier of 2012

AgroInvest

Serbia 2007 11335.78 506099.20 NBFI EECA

EKI 2008 2176.42 451617.90 NGO EECA

COAC Jardín

Azuayo 2010 100763.00 721230.10

Credit Union/

Cooperative LA

FAFIDESS 2011 61.88 84741.06 NGO LA

COAC Mushuc

Runa 2011 136206.50 739469.90

Credit Union/

Cooperative LA

Notes: Latin America and The Caribbean (LA), Eastern Europe and Central Asia (EECA), South East Asia and the Pacific (SEAP),

and Middle East and Africa (MEA)

Further analysis of the productivity data (see Table 2.1) reveals that MFIs on the

financial productivity frontier of 2007 are all observations from 2007, suggesting that MFIs

were highly successful in boosting their productivity in 2007 (i.e., they updated the entire

frontier of this year). On the frontier of 2012, however, none of the observations is from 2012,

indicating that technological stagnation of productivity took place in this year. The data

suggest that MFIs were updating their maximum attainable financial productivity between

2007 and 2011.

2.4.2 Social Productivity Frontier

Figure 2.4 shows the dynamics of MFIs’ social productivity during the period of 2003–2012.

The figure shows that, in general, the microfinance industry experienced only limited progress

in terms of improving social productivity during this period. An upward shift of the



productivity frontier is observed only for the period before 2006. Since then, the frontier

remains almost unchanged.

Figure 2.4 Evolution of the Social Productivity Frontier, 2003–2012

All MFIs operating on the social productivity frontier are socially oriented institutions,

suggesting that the progress of social productivity is entirely dominated by this type of MFI.

Below the frontier, given a similar level of capital intensity, socially oriented institutions tend

to serve more clients than financially oriented institutions. The gap with respect to social

productivity between the two MFI types has become larger since 2006. This may be attributed

to their distinctive operating strategies. Socially oriented institutions put great emphasis on

providing financial services to the poor. They take measures to serve more clients per unit of


50 Chapter 2

capital input and therefore have been able to climb along the social productivity frontier over

the ten-year period.

In contrast, financially oriented institutions make commercial performance and

financial sustainability their priorities, such that maintaining profitability receives more

attention than serving poor clients. These distinct operating preferences are reflected in Figure

2.4, which shows that although financially oriented institutions have been moving

horizontally to the right over the entire period, they show hardly any movement towards the

efficient production frontier, that is, they have made minimal progress in terms of improving

social productivity.

Moreover, all MFIs operating on the social productivity frontier are located on the left

side of the production frontiers of the various years, suggesting that MFIs with the best social

performance are associated with relatively low levels of capital intensity. This further suggests

that improvements in the social efficiency of MFIs are insensitive to the capital deepening

process. In other words, unlike financial productivity improvement in MFIs, which is

associated with more capital-intensive technology, technological innovation and the

advancement of social productivity take place among MFIs with relatively low capital–labor

ratios. Further analysis of our data (see Table 2.2) shows that the social productivity frontier is

determined mainly by MFI observations from the years 2003 and 2006; a similar picture

emerges when we look at the data for the frontier of 2012. This suggests that the social

productivity frontier hardly shifted over the ten-year period, that is, there was virtually no

technological improvement with regard to the social performance of the MFIs in our sample.



Table 2.2 MFIs on the Social Frontiers of 2007 and 2012

(1) (2) (3) (4) (5) (6) (7)

Frontiers MFI Name Fiscal Year Social

Productivity

Capital Per

Worker Ownership Region

Social Frontier of

2007

RGVN 2003 598 6424.039 NBFI SEAP

Pro Mujer NIC 2003 326 1790.35 NGO LA

Seilanithih 2003 215 630.5692 NBFI SEAP

Bandhan 2006 262 1011.321 NBFI SEAP

GRAINE sarl 2006 715 22023.67 NBFI MEA

Binhminh CDC 2006 173 393.2381 NGO SEAP

Azeri Star 2007 116 128.1538 NBFI EECA

Social Frontier of

2012

RGVN 2003 598 6424.039 NBFI SEAP

Pro Mujer NIC 2003 326 1790.35 NGO LA

Bandhan 2006 262 1011.321 NBFI SEAP

GRAINE sarl 2006 715 22023.67 NBFI MEA

FAFIDESS 2011 173 61.87879 NGO LA

CEP 2011 521 4244.825 NGO SEAP

Notes: Latin America and The Caribbean (LA), Eastern Europe and Central Asia (EECA), South East Asia and the Pacific (SEAP),

and Middle East and Africa (MEA)

The most important change with regard to social productivity, that is, the horizontal

movement towards the right of most MFIs, may stem from the process of commercialization

of the microfinance industry as a whole during recent years. This process of commercialization

includes the phenomenon of non-profit organizations transforming themselves into

institutions characterized by seeking of private funding and emphasizing profitability and

operating efficiency (Christen, 2001; Olivares-Polanco, 2007; Mersland et al., 2009). It also

includes the entrance of commercial (profit-seeking) banks into microfinance. As discussed

above, both practitioners and academics hold the optimistic view that commercialization is an

effective method to realize the dual mission of MFIs, that is, maintaining financial


52 Chapter 2

sustainability and reaching the poor (Rhyne, 1998; Christen et al., 2001; Cull, et al. 2007;

Mersland et al., 2010).

2.4.3 Testing for Financial Productivity Convergence

Table 2.3 displays results of the absolute and conditional β-convergence test on the financial

productivity of 171 MFIs.

In the ten-year period and in two sub-periods, β coefficients (β1 and β2) are consistently

significant with negative signs, suggesting that initially less productive MFIs grew more

rapidly than highly productive MFIs at the initial stage. In other words, MFIs that were far

below the productivity frontier showed a more rapid pace of movement towards the frontier

than those located close to the frontier. This provides evidence of the catching-up effect in the

microfinance industry during the period of 2003–2012.



Table 2.3 𝛃-convergence of Financial Productivity

Independent

Variables

Dependent Variable: ∆ ln𝑷𝒊,𝑻

Convergence without Control (𝜷𝟏) Convergence with Controls (𝜷𝟐)

(1) (2) (3) (4) (5) (6)

03-12 03-07 08-12 03-12 03-07 08-12

ln𝑷𝒊,𝑻−𝟏 (𝛃) -0.068*** -0.052*** -0.055*** -0.102*** -0.088*** -0.095***

(0.01) (0.01) (0.01) (0.01) (0.02) (0.01)

∆ ln𝑷𝒊,𝑻−𝟏 0.086*** 0.0390 0.0590 0.0360 0.0190 -0.0250

(0.03) (0.03) (0.04) (0.02) (0.03) (0.03)

Outreach To The Poor

ALBPBGNI 0.041*** 0.041** 0.023*

(0.01) (0.02) (0.01)

PFB -0.074** -0.0900 -0.094**

(0.04) (0.06) (0.05)

Financial Performance

Write-Off Rate -2.015*** -1.541*** -1.614***

(0.34) (0.52) (0.41)

Return of Equity 0.0100 -0.0240 0.117***

(0.03) (0.03) (0.04)

No. of Clients (Reference Group: Small Outreach)

Medium Outreach 0.0250 0.085*** -0.0330

(0.02) (0.03) (0.02)

Large Outreach 0.046*** 0.072** 0.0230

(0.02) (0.03) (0.02)

Age (Reference Group: New)

Young 0.162*** 0.145** -0.109***

(0.06) (0.06) (0.03)

Mature 0.0230 -0.00600 -0.0590

(0.02) (0.03) (0.04)

Location (Reference Group: SEAP)

LA 0.096*** 0.0590 0.084***

(0.02) (0.04) (0.03)

MEA 0.0160 0.0120 -0.00200

(0.02) (0.04) (0.03)

EECA 0.079*** 0.136*** 0.0210

(0.03) (0.04) (0.03)

Ownership (Reference Group: NGO)

CUCO 0.0200 0.0130 0.0180

(0.02) (0.04) (0.03)

NBFI -0.00800 0.0240 -0.0260

(0.02) (0.03) (0.02)

Bank 0.0100 -0.0420 0.045*

(0.02) (0.04) (0.03)

Constant 0.843*** 0.741*** 0.657*** 1.204*** 1.083*** 1.147***

(0.08) (0.13) (0.10) (0.12) (0.20) (0.16)

No. of Observations 1368 513 855 1231 463 768

𝑅2 0.0810 0.0500 0.0450 0.192 0.170 0.211

Adjusted 𝑅2 0.0800 0.0470 0.0430 0.182 0.140 0.194

F statistics 48.28 10.09 19.47 11.06 4.753 12.43

Notes: ∆ ln𝑷𝒊,𝑻 = ln𝑃𝑖,𝑇 − ln𝑃𝑖,𝑇−1 ; ∆ ln 𝑷𝒊,𝑻−𝟏 = ln𝑃𝑖,𝑇−1 − ln 𝑃𝑖,𝑇−2. Robust Standard Errors in Parentheses (* p < 0.1, ** p < 0.05,

***p < 0.01)


54 Chapter 2

When we split the data into two sub-periods, 2003–2007 and 2008–2012, our results

indicate that the absolute value of both β1 and β2 in the second five-year period are slightly

larger than those in the first-five year period, suggesting that the catching-up effect was more

pronounced in the second half. This finding meets our depictions of the intertemporal frontier

of financial productivity in Figure 2.3, which show that most initially less productive MFIs

have been catching up with MFI leaders since 2006. During the first five-year period, less

productive MFIs were accumulating capital, because technological improvement related to

financial productivity is dependent on capital-intensive production techniques (Woltjer, 2013).

After investing more in capital-intensive techniques, less productive MFIs benefited from

technological spillover from productivity leaders, resulting in productivity convergence.

As explained in Section 2.3, σ-convergence captures the pace with which MFI

productivity growth approaches the average productivity growth rate. The σ-convergence test

is complementary to the test of β-convergence that shows the dynamics of productivity

changes of MFIs over time. Table 4 displays the results of our investigation of the dispersion

of MFI financial productivity towards the sample average. The negative and significant

coefficients for all σ-convergence tests in the table confirm that the dispersion of MFI financial

productivity around the sample average is diminishing. Therefore, both the β-convergence

and σ-convergence tests confirm the incidence of financial productivity convergence in the

microfinance industry during the period of 2003–2012.



Table 2.4 𝝈-Convergence of Financial Productivity at the Global Average

Independent Variables

Dependent Variable: ∆𝑺𝒊,𝑻

Global Average

(1) (2) (3)

03-12 03-07 08-12

𝑺𝒊,𝑻−𝟏(𝝈) -0.056*** -0.057*** -0.056***

(0.01) (0.01) (0.01) ∆𝑺𝒊,𝑻−𝟏 0.054** 0.0450 0.0650

(0.03) (0.03) (0.04)

cons -0.00700 -0.00600 -0.00800

(0.01) (0.01) (0.01)

N 1368 513 855

𝑅2 0.0520 0.0600 0.0470

ad 𝑅2 0.0510 0.0560 0.0450

F 31.86 12.54 19.76

Notes: Robust Standard Errors in Parentheses (* p < 0.1, ** p < 0.05, ***p < 0.01)

2.4.4 Testing for Social Productivity Convergence

Table 2.5 displays β-convergence tests with regard to social productivity. The results are

similar to the β-convergence tests for financial productivity. Both β1 and β2 are statistically

significant with a negative sign, indicating the incidence of β-convergence for the entire ten-

year period as well as for both five-year sub-periods.


56 Chapter 2

Table 2.5 𝛃-Convergence of Social Productivity Growth

Independent

Variables

Dependent Variable: ∆ ln𝑷𝒊,𝑻

Convergence without Control (𝜷𝟏) Convergence with Controls (𝜷𝟐)

(1) (2) (3) (4) (5) (6)

03-12 03-07 08-12 03-12 03-07 08-12

ln𝑷𝒊,𝑻−𝟏 (𝛃) -0.076*** -0.105*** -0.051*** -0.181*** -0.236*** -0.145***

(0.01) (0.02) (0.01) (0.02) (0.03) (0.02)

∆ ln𝑷𝒊,𝑻−𝟏 0.0450 0.081* -0.0280 0.0220 0.0210 -0.0110

(0.04) (0.05) (0.04) (0.03) (0.05) (0.04)

Outreach To The Poor

ALBPBGNI -0.105*** -0.111*** -0.104***

(0.02) (0.04) (0.02)

PFB 0.0390 0.0180 0.0290

(0.03) (0.06) (0.04)

Financial Performance

Write-Off Rate -1.180*** -0.400 -1.091***

(0.32) (0.80) (0.36)

Return of Equity 0.00900 -0.00500 0.057**

(0.03) (0.02) (0.03)

No. of Clients (Reference Group: Small Outreach)

Medium Outreach 0.084*** 0.145*** 0.052**

(0.02) (0.03) (0.02)

Large Outreach 0.100*** 0.187*** 0.061***

(0.02) (0.04) (0.02)

Age (Reference Group: New)

Young 0.228*** 0.266*** -0.299

(0.08) (0.07) (0.31)

Mature 0.067*** 0.060** 0.0500

(0.02) (0.03) (0.05)

Location (Reference Group: SEAP)

LA 0.00100 0.0170 -0.0140

(0.02) (0.03) (0.02)

MEA 0.0150 0.0420 -0.00400

(0.02) (0.04) (0.03)

EECA -0.037* -0.0240 -0.042*

(0.02) (0.04) (0.02)

Ownership (Reference Group: NGO)

CUCO 0.0370 0.086* 0.0210

(0.03) (0.05) (0.03)

NBFI -0.049*** -0.049* -0.049***

(0.02) (0.03) (0.02)

Bank -0.0210 -0.066* 0

(0.02) (0.03) (0.02)

Constant 0.361*** 0.534*** 0.218*** 0.870*** 1.101*** 0.719***

(0.06) (0.09) (0.07) (0.10) (0.18) (0.10)

No. of Observations 1368 513 855 1231 463 768

𝑅2 0.0440 0.0780 0.0260 0.175 0.231 0.160

Adjusted 𝑅2 0.0430 0.0750 0.0240 0.164 0.204 0.142

F statistics 21.44 15.15 7.344 7.915 4.212 6.562

Notes: ∆ ln𝑷𝒊,𝑻 = ln𝑃𝑖,𝑇 − ln𝑃𝑖,𝑇−1 ; ∆ ln 𝑷𝒊,𝑻−𝟏 = ln𝑃𝑖,𝑇−1 − ln 𝑃𝑖,𝑇−2. Robust Standard Errors in Parentheses (* p < 0.1, ** p < 0.05,

***p < 0.01)



The results also show that, compared with the magnitude of β-convergence in the first

five years, the catching-up effect was less pronounced from 2008–2012 for both absolute and

conditional convergence. This is contrary to our finding for MFI financial productivity. The

slowdown of the catching-up effect in the second five-year period may be explained by the

commercialization of the microfinance industry. As we argued previously when we discussed

the intertemporal productivity frontiers in Figures 2.3 and 2.4, the process of

commercialization particularly affected social productivity of MFIs during the period of 2008–

2012. Most financially oriented and some socially oriented institutions focused on extending

their business by taking up external funding and taking measures to improve their financial

performance and profitability. This process is reflected by the strong increase of capital–labor

ratios and the improvements in financial productivity. However, as we showed above,

improvements in social productivity are largely insensitive to the capital deepening process of

MFIs, which explains why there was only limited improvement of social productivity during

the second five-year period.

Table 2.6 presents results of the σ-convergence test of MFIs’ social productivity. The

negative and significant coefficients for all σ-convergence tests in the table confirm that the

dispersion of MFI social productivity around the sample average is diminishing. Therefore,

both the β-convergence and σ-convergence tests confirm the incidence of financial

productivity convergence in the microfinance industry during the period of 2003–2012.


58 Chapter 2

Table 2.6 𝝈-Convergence of Financial Productivity at the Global Average


Dependent Variable: ∆𝑺𝒊,𝑻

Global Average

(1) (2) (3)

03-12 03-07 08-12

𝑺𝒊,𝑻−𝟏(𝝈) -0.074*** -0.104*** -0.051***

(0.01) (0.02) (0.01) ∆𝑺𝒊,𝑻−𝟏 0.035 0.086* 0.0330

(0.04) (0.05) (0.04)

cons -0.011* -0.0130 -0.0100

(0.01) (0.01) (0.01)

N 1368 513 855

𝑅2 0.0430 0.0790 0.0260

ad 𝑅2 0.0410 0.0750 0.0240

F 20.88 15.24 7.388

Note: Robust Standard Errors in Parentheses (* p < 0.1, ** p < 0.05, ***p < 0.01)

2.4.5 Decomposition of Financial Productivity Convergence

As the final step in the analysis, we decompose the absolute and conditional convergence of

MFI productivity into operating efficiency change, capital intensity change, and technological

change. In productivity literature, these components are frequently referred to as processes of

learning, capital deepening, and innovation. We investigate the role of each of the three

components in the convergence of both financial and social productivity over the ten-year

period.

Table 2.7 provides evidence of the determinants of financial productivity convergence.

In the ten-year period, the learning effect and capital deepening continuously contributed to

convergence of financial productivity because their coefficients are all negatively significant.

The contribution of the learning effect is the larger for the first five years, suggesting that less

financially productive MFIs benefited more from adopting operating techniques from leading

MFIs to approach the best practices of financial productivity.



Table 2.7 Decomposition of Financial Productivity Convergence


Convergence with Controls

(1) (2) (3)

Learning Capital

Deepening Innovation

𝑙𝑜𝑔 (

𝑃𝑖,0𝑃𝑖,𝑎

𝑃𝐿,0𝑃𝐿,0

⁄ ) -0.521*** -- --

(a)

03-07

(0.09) -- --

𝑙𝑜𝑔(𝑃𝑖,𝑎 𝑃𝐿,𝑎⁄ ) -- -0.916*** -0.575***

-- (0.16) (0.06)

𝑙𝑜𝑔 (



⁄ ) -0.275*** -- --

(b)

08-12

(0.06) -- --

𝑙𝑜𝑔(𝑃𝑖,𝑎 𝑃𝐿,𝑎⁄ )

-- -1.218*** -0.032

-- (0.09) (0.03)


Capital deepening appears to play a dominant role in facilitating financial productivity

convergence throughout the ten-year period. While the learning effect slowed down in the

second half of the period, capital deepening shows a stronger contribution than in the first half.

It may be that during the period of 2003–2007, MFIs lagging in financial productivity

successfully narrowed their productivity gap with leading MFIs. During the second five-year

period, they increased investments to adopt more sophisticated technologies from leading

MFIs. Moreover, the strong efforts related to capital deepening confirm that because of the

commercialization of the microfinance industry in the period of 2008–2012, MFIs aimed at

achieving higher operational financial sustainability and financial productivity by focusing on

more capital-intensive service delivery.

The large absolute values of the coefficients of innovation indicate its importance in

promoting financial productivity convergence in the first five years. Because the absolute

values of innovation in the first five-year period are much larger than those in the second five-

year period and even larger than those in the social productivity test (shown below), we can


60 Chapter 2

conclude that technological breakthroughs exhibited substantial spillover effects on

improving the convergence of financial productivity in the first five years.

The coefficient for innovation in the period of 2008–2012 is insignificant in the

convergence test. This suggests that technological innovation, as a driver of financial

productivity, was rather limited.

In summary, our decomposition of financial productivity reveals that the microfinance

industry as a whole experienced improvement in financial performance. On the one hand, less

productive MFIs were able to catch up with the leading MFIs by learning through

technological spillover effects and by increasing their capital intensity to create potential for

productivity improvements. On the other hand, leading MFIs invested in technological

innovation, leading to the shift of the financial productivity frontier. However, this shift is a

less important factor in describing the productivity dynamics of the microfinance industry,

especially during the second five-year period.

2.4.6 Decomposition of Social Productivity Convergence

Table 2.8 presents the results of our decomposition of social productivity convergence. In the

first five-year period, learning and capital deepening drove the process of convergence. In

particular, investments in capital were the major facilitators of convergence. This is because

assimilating more advanced technology requires appropriate technological capabilities and

sufficient capital support (Nelson et al., 1999; Los et al., 2005).



Table 2.8 Decomposition of Social Productivity Convergence


Convergence with Controls

(1) (2) (3)

Learning Capital

Deepening Innovation

𝑙𝑜𝑔 (



⁄ ) -0.498*** -- --

(a)

03-07

(0.10) -- --

𝑙𝑜𝑔(𝑃𝑖,𝑎 𝑃𝐿,𝑎⁄ ) -- -1.029*** 0.034

-- (0.08) (0.03)

𝑙𝑜𝑔 (



⁄ ) -0.320*** -- --

(b)

08-12

(0.09) -- --

𝑙𝑜𝑔(𝑃𝑖,𝑎 𝑃𝐿,𝑎⁄ )

-- -0.779*** -0.032***

-- (0.19) (0.01)


During the second five years, capital deepening remained the most important driver of

social productivity improvements. Although learning explains both absolute and conditional

convergence to some extent, its contribution appears to diminish compared with its

contribution during the period of 2003–2007. As we showed previously, MFIs intensified

investments in capital after 2007. Although this action had a positive impact on financial

productivity improvements, its impact on social productivity improvements was much less,

because social performance is much more dependent on the adoption of new services that

provide technologies (i.e., learning from leading MFIs and/or initiating technological

innovation).

The contribution of technological innovation to social productivity improvements was

relatively small in the second five-year period. However, the negative and significant

coefficient suggests that there was at least some development of new technologies, leading to

an outward shift of the social productivity frontier. Moreover, these new technologies allowed


62 Chapter 2

following MFIs to narrow their productivity gap with leading institutions in the microfinance

industry.

Overall, we find that social productivity convergence took place from 2003–2012 and

was mostly driven by learning from leading MFIs and increasing capital intensity. Because of

the reduced contribution of learning during the period of 2008–2012, convergence slowed

down. Although technological innovations facilitated social productivity convergence after

2007, their contribution remained minimal.

2.5 Summary and Concluding Remarks

In this paper, we first investigated whether and to what extent the changing landscape in

microfinance during the past decade has influenced MFI operations and affected the

institutions’ social and financial performance. We applied DEA using a balanced panel data

set for 171 MFIs active in 59 developing countries over the period of 2003–2012 and analyzed

the patterns of social and financial productivity convergence of MFIs over the entire ten-year

period, as well as two five-year sub-periods (2003–2007 and 2008–2012).

We show the trends in financial and social performance using graph analysis. Our

findings indicate overall improvements in financial and social productivity. With regard to

financial productivity, we find that MFIs were able to make remarkable improvements. During

the first part of the ten-year period, they showed a mostly horizontal movement, that is, capital

intensity rose. Such a process of capital deepening is an important prerequisite to enabling

learning effects from applying technological innovations initiated by leading microfinance

institutions. In our data, we observe such a learning process for later years. Moreover, we

observe technological innovations (i.e., upward shifts of the frontier) taking place in the



industry after 2006. For social productivity, trends are similar but less pronounced. Social

productivity improvements lagged behind those of financial productivity, and technological

innovation took place at the beginning of the ten-year period. This evidence suggests the

incidence of mission drift in the microfinance industry.

We interpret these trends in financial and social performance as being the result of the

commercialization process. A stronger focus on financial performance, resulting in part from

an increased interest in and access to private funding, has led MFIs to change the structure

and organization of their financial service activities. These changes have also shifted client

portfolios from poor to relatively wealthy clients and focused on financial services with higher

profit margins. In our data, we observe evidence for mission drift and a trade-off between

social and financial goals.

Second, we investigated to what extent productivity improvements converged, that is,

whether lagging MFIs moved closer to leading MFIs with regard to financial and social output.

Using tests of β-convergence and σ-convergence, we established that processes of convergence

within the microfinance industry took place from 2003–2012. Thus, lagging MFIs were able to

make more significant improvements in productivity than leading MFIs. This suggests the

occurrence of spillover of successful strategies, which may also be the result of

commercialization within the industry; MFIs have been pressured to look for ways to improve

their performance.

Finally, we used a decomposition analysis of the financial and social productivity

convergence to evaluate the contributions made by improving operating efficiency (i.e.,

learning, increasing output by using existing inputs in a more efficient way), increased capital

intensity (i.e., increasing the units of capital per worker, capital deepening), and/or


64 Chapter 2

technological change (i.e., improved methods to combine capital and labor into outputs). Our

findings largely corroborate the analysis of trends in financial and social productivity

convergence, that is, that capital deepening was the most important contributor to

productivity convergence, followed by learning effects. The contribution of technological

innovation was relatively small, especially for social productivity convergence.

Our analysis provides an innovative way to analyze the impact of major changes in the

microfinance industry in the early 2000s. One weakness of our approach, however, is that our

sample of MFIs is relatively small. Moreover, the sample contains MFIs from several countries

around the globe. This means that one of the main assumptions of our analysis—that

technology is available to all MFIs in the sample—may be violated in practice. This problem

could be resolved, at least to some extent, by incorporating the country context. However, at

this stage of our research, we were unable to sufficiently include this context.

We propose two ways to conduct future research. First, we stress the need for

accounting for country-specific conditions such as institutions, culture, and macroeconomic

conditions. Second, we suggest applying our approach using data for MFIs in a one-country

context, which would make the assumption of the availability of technology to all MFIs in the

sample more acceptable.



2.A Appendix

2.A.1 Variable Definitions and Sources

Table 2.A.1 Variable Definitions and Sources

Variable Abbreviation Definition

Assets -- Total of all net asset accounts

Source: The MIX Market

Personnel Labor Total number of staff members


Gross Loan Portfolio --

All outstanding principals due for all outstanding client loans. This includes current,

delinquent, and renegotiated loans, but not loans that have been written off. It does

not include interest receivable.


Number of Active

Borrowers

The number of individuals or entities who currently have outstanding loan balances

with the MFI or are primarily responsible for repaying any portion of the Loan

Portfolio, Gross. Individuals who have multiple loans with an MFI should be

counted as a single borrower.


Capital Per Worker --

Capital intensity of the institution. Defined as the institution’s total assets divided

by its personnel.

Source: Author’s calculation.

Financial Productivity -- The MFI’s gross loan portfolio divided by its personnel.


Social Productivity -- The total number of an MFI’s active borrowers divided by its personnel.


Non-Bank Financial

Institution NBFI

A dummy variable identifies an institution that provides similar services to those of

a bank, but is licensed under a separate category. The separate license may be the

result of lower capital requirements, limitations on financial service offerings, or

supervision under a different state agency. In some countries this corresponds to a

special category created for microfinance institutions.


Credit Union/ Cooperative CUCO

A dummy variable identifies a non-profit, member-based financial intermediary. It

may offer a range of financial services, including lending and deposit taking, for the

benefit of its members. While not regulated by a state banking supervisory agency,

it may come under the supervision of a regional or national cooperative council.


Non-Government

Organization NGO

A dummy variable identifies an organization registered as a non-profit for tax

purposes or some other legal charter. Its financial services are usually more

restricted and usually do not include deposit taking. These institutions are typically

not regulated by a banking supervisory agency.


Bank Bank

A dummy variable identifies a licensed financial intermediary regulated by a state

banking supervisory agency. It may provide any of a number of financial services,

including deposit taking, lending, payment services, and money transfers.


Latin America and The

Caribbean LA

A dummy variable identifies an MFI locates in the Latin America or the Caribbean.


Eastern Europe and Central

Asia EECA

A dummy variable identifies an MFI locates in the Eastern Europe or the Central

Asia.


(Continued)


66 Chapter 2

Table 2.A.1 Variable Definitions and Sources (Continued)

Variable Abbreviation Definition

South East Asia and the

Pacific SEAP

A dummy variable identifies an MFI located in South East Asia or the Pacific.


Middle East and Africa MEA A dummy variable identifies an MFI located in the Middle East or Africa.


Average Loan Balance Per

Borrower Divided by the

Gross National Income per

Capita

ALBPBGNI

This variable specifies MFI’s average loan size adjusted by the local economic

condition. The average loan size is calculated as the MFI’s Gross Loan Portfolio

divided by its Number of Active Borrowers. The ALBPBGNI is the ratio of average loan

size to the GNI per capita within the country.


Percentage of Female

Borrowers PFB

The number of active borrowers who are females divided by Number of Active

Borrowers.


Write-Off Ratio --

Write-offs are the total amount of loans written off during the period. A write-off is

an accounting procedure that removes the outstanding balance of the loan from the

loan portfolio and from the impairment loss allowance when these loans are

recognized as uncollectable. The Write-Off Ratio is defined as the write offs divided

by the Gross Loan Portfolio.


Return on Equity -- The net operating income divided by the total equity.


Small Outreach --

Scale of outreach identifies the total number of clients an MFI served. Small

Outreach is a dummy that equals 1 when the MFI serves less than 10,000 clients,

otherwise 0.


Medium Outreach --

Medium Outreach is a dummy that equals 1 when the total number of clients the

MFI serving is between 10,000 and 30,000, otherwise 0.


Large Outreach --

Large Outreach is a dummy that equals 1 when the total number of clients the MFI

serving is over 30,000, otherwise 0.


New --

Age dummies specify the length of duration since the establishment of an MFI. New

is a dummy that equals 1 when an MFI has been operating for less than 5 years,

otherwise 0.


Young --

Young is a dummy that equals 1 when the age of an MFI is between 5 and 8 years,

otherwise 0.


Mature --

Mature is a dummy that equals 1 when the age of an MFI is over 8 years, otherwise

0.


Portfolio at Risk > 90 days

Ratio PAR90

Portfolio at Risk is the value of all loans outstanding that have one or more

installments of principal past due more than 90 days. This includes the entire unpaid

principal balance, including both the past due and future installments, but not

accrued interest. It also includes loans that have been restructured or rescheduled.

Portfolio at Risk > 90 days Ratio is the total portfolio at risk divided by the Gross Loan

Portfolio.


Profit Margin

Profit Margin measures how much out of every dollar of sales an MFI actually keeps

in earnings. A higher profit margin indicates a more profitable MFI that has better

control over its costs compared with its competitors. It is defined as the Net

Operating Income divided by the Financial Revenue.


Notes: Table 2.A.1 reports variable definitions and sources for all variables used in the paper. The first column reports the variable

name, the second column gives the variable abbreviation in the text and tables, and the third column reports detailed variable

definition and sources.


2.A

.2 S

um

mar

y S

tati

stic

s

Tab

le 2

.A.2

Su

mm

ary

Sta

tist

ics

of

the

Fu

ll S

amp

le (

Un

bal

ance

d)

and

Cu

rren

t U

sin

g S

amp

le (

Bal

ance

d)

fro

m 2

003

–201

2

(1

) (2

) (3

) (4

)

(5)

(6)

(7)

(8)

F

ull

Sam

ple

(U

nb

alan

ced

)

Usi

ng

Sam

ple

(B

alan

ced

)

Fis

cal

Yea

r 20

03

2006

20

09

2012

2003

20

06

2009

20

12

No

. of

Ob

serv

atio

ns

795

1304

15

81

1308

171

171

171

171

Ass

ets

3.94

*10

6 6.

66*1

06

1.20

*10

7 4.

76*1

07

4,

411*

106

1.06

*10

7 2.

56*1

07

4.31

*10

7

(1.7

7*10

7 )

(3.2

1*10

7 )

(5.6

7*10

7 )

(8.7

8*10

8 )

(1

.27*

107 )

(3

.281

07)

(7.3

4*10

7 )

(1.3

5*10

8 )

Lab

or

171.

7 50

5.7

460.

3 46

0.5

29

6.2

565.

1 78

8.5

1,04

6

(796

.9)

(768

4)

(355

2)

(149

2)

(1

,386

) (2

,481

) (2

,467

) (2

,701

)

Gro

ss L

oan

Po

rtfo

lio

1.

07*1

07

2.00

*10

7 4.

88*1

07

7.82

*10

7

1.21

*10

7 3.

24*1

07

7.44

*10

7 1.

36*1

08

(7.0

3*10

7 )

(1.1

60*1

08 )

(5

.16*

108 )

(5

.37*

108 )

(3.3

6*10

7 )

(7.0

7*10

7 )

(1.6

0*10

8 )

(3.0

2*10

8 )

No

. of

Act

ive

Bo

rro

wer

s 34

,939

47

,510

67

,225

82

,962

57,0

41

1046

08

158,

143

181,

887

(231

,014

) (3

16,4

66)

(401

,360

) (4

19,0

36)

(3

44,6

92)

(573

,666

) (7

11,4

07)

(712

,082

)

Cap

ital

Per

Wo

rker

24

,337

24

,016

40

,961

72

,763

17,8

27

22,2

31

34,1

31

33,6

41

(117

,770

) (4

5,69

7)

(252

,934

) (8

31,1

79)

(1

9,97

7)

(22,

399)

(3

5,43

9)

(34,

075)

Fin

anci

al P

rod

uct

ivit

y

50,2

88

72,5

39

194,

806

205,

196

59

,136

90

,786

11

0,87

9 12

8,98

8

(76,

529)

(1

21,1

69)

(3.7

7*10

6 )

(2.2

0*10

6 )

(6

6,77

0)

(87,

385)

(1

02,6

97)

(117

,514

)

So

cial

Pro

du

ctiv

ity

13

2.4

139.

2 12

3.6

133.

9

134.

5 13

9.3

135.

5 12

9.8

(154

.7)

(275

.4)

(124

.4)

(236

.7)

(9

1.86

) (8

6.94

) (7

6.17

) (8

1.67

)

AL

BP

BG

NI

1.16

6 0.

998

3.75

0 2.

575

0.

592

0.58

4 0.

529

0.58

0

(5.7

23)

(4.5

13)

(101

.7)

(29.

99)

(0

.753

) (0

.663

) (0

.575

) (0

.748

)

PF

B

0.66

3 0.

656

0.63

0 0.

642

0.

675

0.67

4 0.

652

0.64

3

(0.3

19)

(0.2

67)

(0.2

71)

(0.2

64)

(0

.262

) (0

.231

) (0

.244

) (0

.240

)

Wri

te-O

ff R

atio

0.

020

0.01

4 0.

0245

0.

074

0.

015

0.01

2 0.

027

0.01

9

(0.0

543)

(0

.033

9)

(0.0

61)

(0.9

84)

(0

.025

) (0

.019

) (0

.040

) (0

.026

)

Ret

urn

on

Eq

uit

y

-0.1

45

3.70

5 -0

.032

1.

485

0.

057

0.20

2 0.

035

0.14

6

(1.7

76)

(89.

68)

(2.4

03)

(39.

15)

(0

.321

) (0

.561

) (0

.351

) (0

.352

)

Sm

all

Ou

trea

ch

0.69

2 0.

631

0.58

6 0.

515

0.

608

0.40

9 0.

333

0.28

1

(0.4

62)

(0.4

83)

(0.4

93)

(0.5

00)

(0

.490

) (0

.493

) (0

.473

) (0

.451

)

Med

ium

Ou

trea

ch

0.17

5 0.

194

0.18

7 0.

200

0.

181

0.25

7 0.

211

0.18

7

(0.3

81)

(0.3

96)

(0.3

90)

(0.4

00)

(0

.386

) (0

.438

) (0

.409

) (0

.391

)

(Con

tin

ued

)



Tab

le 2

.A.2

Su

mm

ary

Sta

tist

ics

of

the

Fu

ll S

amp

le (

Un

bal

ance

d)

and

Cu

rren

t U

sin

g S

amp

le (

Bal

ance

d)

fro

m 2

003

–201

2 (C

on

tin

ued

)

(1

) (2

) (3

) (4

)

(5)

(6)

(7)

(8)

F

ull

Sam

ple

(U

nb

alan

ced

)

Usi

ng

Sam

ple

(B

alan

ced

)

Fis

cal

Yea

r 20

03

2006

20

09

2012

2003

20

06

2009

20

12

No

. of

Ob

serv

atio

ns

795

1304

15

81

1308

171

171

171

171

Lar

ge

Ou

trea

ch

0.13

2 0.

175

0.22

8 0.

286

0.

211

0.33

3 0.

456

0.53

2

(0.3

39)

(0.3

80)

(0.4

19)

(0.4

52)

(0

.409

) (0

.473

) (0

.500

) (0

.500

)

New

0.

209

0.19

5 0.

190

0.07

1

0.49

1 0.

713

0.92

4 0.

988

(0.4

07)

(0.3

96)

(0.3

93)

(0.2

57)

(0

.501

) (0

.453

) (0

.266

) (0

.108

)

Yo

un

g

0.31

4 0.

203

0.16

4 0.

193

0.

181

0.03

51

0 0

(0.4

64)

(0.4

02)

(0.3

71)

(0.3

95)

(0

.386

) (0

.185

) (0

) (0

)

Mat

ure

0.

477

0.60

2 0.

645

0.73

6

0.32

7 0.

251

0.07

6 0.

012

(0.5

00)

(0.4

90)

(0.4

79)

(0.4

41)

(0

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) (0

.435

) (0

.266

) (0

.108

)

LA

0.

214

0.24

0 0.

258

0.29

1

0.38

6 0.

386

0.38

6 0.

386

(0.4

10)

(0.4

27)

(0.4

38)

(0.4

55)

(0

.488

) (0

.488

) (0

.488

) (0

.488

)

ME

A

0.29

2 0.

261

0.29

2 0.

283

0.

170

0.17

0 0.

170

0.17

0

(0.4

55)

(0.4

39)

(0.4

55)

(0.4

51)

(0

.376

) (0

.376

) (0

.376

) (0

.376

)

EU

CA

0.

201

0.19

7 0.

171

0.15

8

0.23

4 0.

234

0.23

4 0.

234

(0.4

01)

(0.3

98)

(0.3

76)

(0.3

65)

(0

.425

) (0

.425

) (0

.425

) (0

.425

)

SE

AP

0.

293

0.30

2 0.

280

0.26

8

0.21

1 0.

211

0.21

1 0.

211

(0.4

55)

(0.4

59)

(0.4

49)

(0.4

43)

(0

.409

) (0

.409

) (0

.409

) (0

.409

)

NG

O

0.40

5 0.

352

0.33

5 0.

344

0.

398

0.39

8 0.

398

0.39

8

(0.4

91)

(0.4

78)

(0.4

72)

(0.4

75)

(0

.491

) (0

.491

) (0

.491

) (0

.491

)

CU

CO

0.

169

0.19

2 0.

188

0.19

3

0.07

02

0.07

0 0.

070

0.07

0

(0.3

75)

(0.3

94)

(0.3

91)

(0.3

95)

(0

.256

) (0

.256

) (0

.256

) (0

.256

)

NB

FI

0.26

8 0.

294

0.32

0 0.

359

0.

374

0.37

4 0.

374

0.37

4

(0.4

43)

(0.4

56)

(0.4

67)

(0.4

80)

(0

.485

) (0

.485

) (0

.485

) (0

.485

)

Ban

k

0.15

8 0.

162

0.15

6 0.

104

0.

158

0.15

8 0.

158

0.15

8

(0.3

65)

(0.3

68)

(0.3

63)

(0.3

05)

(0

.366

) (0

.366

) (0

.366

) (0

.366

)

PA

R90

0.

047

0.04

9 0.

062

0.05

4

0.03

3 0.

027

0.04

8 0.

034

(0.0

99)

(0.0

82)

(0.1

65)

(0.1

06)

(0

.050

) (0

.042

) (0

.057

) (0

.039

)

Pro

fit

Mar

gin

-0

.331

-0

.171

-2

8.65

0.

888

-0

.570

0.

151

0.05

9 0.

127

(3.7

30)

(1.9

32)

(100

6)

(27.

47)

(7

.064

) (0

.256

) (0

.213

) (0

.172

)

No

tes:

Tab

le 2

.A.2

pro

vid

es d

escr

ipti

ve

stat

isti

cs f

or

the

full

sam

ple

pro

vid

ed b

y t

he

MIX

Mar

ket

an

d t

he

sam

ple

use

d in

ou

r p

aper

. Th

e fu

ll s

amp

le i

s an

un

bal

ance

d p

anel

co

ver

ing

15,

371

ob

serv

atio

ns

fro

m 1

995–

2013

. W

e m

ake

the

ori

gin

al s

amp

le i

nto

a b

alan

ced

pan

el c

ov

erin

g 1

71 M

FIs

fro

m 2

003

–201

2. C

olu

mn

s (1

) to

(4)

lis

t th

e m

ean

an

d s

tan

dar

d d

evia

tio

n (

in p

aren

thes

es)

of

each

in

dic

ato

r o

f

the

ori

gin

al s

amp

le f

rom

200

3–2

012,

an

d C

olu

mn

s (5

) to

(8)

lis

t co

rres

po

nd

ing

in

form

atio

n o

f th

e b

alan

ced

sam

ple

fro

m 2

003

–201

2.

68 Chapter 2

university of groningen financial inclusion: progress ... · this chapter aims to assess the...

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