literature review of microfinance banks

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• Published: 20 April 2022

Does microfinance foster the development of its clients? A bibliometric analysis and systematic literature review

• João Paulo Coelho Ribeiro 1 ,
• Fábio Duarte   ORCID: orcid.org/0000-0002-4919-0736 2 &
• Ana Paula Matias Gama 3  

Financial Innovation volume  8 , Article number:  34 ( 2022 ) Cite this article

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This paper conducts a scientometric analysis and systematic literature review to identify the trends in microfinance outcomes from the perspective of their recipients, specifically more vulnerable people, while also focusing on the demand side. Applying the keywords “co-occurrence networks” and “citation networks,” we examined 524 studies indexed on the ISI Web of Science database between 2012 and March 2021. The subsequent content analysis of bibliometric-coupled articles concerns the main research topics in this field: the socioeconomic outcomes of microfinance, the dichotomy between social performance and the mission drift of microfinance institutions, and how entrepreneurship and financial innovation, specifically through crowdfunding, mitigate poverty and empower the more vulnerable. The findings reinforce the idea that microfinance constitutes a distinct field of development thinking, and indicate that a more holistic approach should be adopted to boost microfinance outcomes through a better understanding of their beneficiaries. The trends in this field will help policymakers, regulators, and academics to examine the nuts and bolts of microfinance and identify the most relevant areas of intervention.

This study conducts a scientometric analysis and systematic literature review to identify the trends in microfinance outcomes from the perspective of their recipients

A Bibliometric analysis were conducted to examine 524 studies indexed on the ISI Web of Science database between 2012 and March 2021

A content analysis of 11 ABS ranked articles (rank 4 or 4*) were conducted to stablish trends of research

The findings suggest that a holistic approach should be adopted to boost microfinance outcomes through a better understanding of their beneficiaries

Introduction

Microcredit has emerged as an innovative tool for fighting poverty in underdeveloped countries (Mustafa et al. 2018 ). Positive experiences suggest that it constitutes an agile, flexible, and cost-effective financial instrument for entrepreneurship projects that otherwise suffer from bank credit rationing (Stiglitz 1990 ). Combining microcredit, microsavings, and microinsurance, microfinance “can help low-income people reduce risk, improve management, raise productivity, obtain higher returns on investments, increase their incomes, and improve the quality of their lives and those of their dependents” (Robinson 2001 : 9).

The promise of microcredit to eradicate global poverty has proven overly ambitious, as poverty results from a wide number of factors. Nevertheless, at least theoretically, providing poor people with financial resources to start their own businesses can help them increase their income and purchasing power, even if starting and running a successful business is not a simple task. Furthermore, if microcredit loans do not create financial wealth, they should then be classified simply as a “mechanism for transferring resources to the poor” (Khandker 1998 : 7).

The implementation of microfinance and its potential as a tool for fighting social and financial asymmetries is an expanding research topic. However, while microfinance may have grown into a worldwide industry, scholars have expressed doubt about its actual impact on the recipients (e.g., Morduch 1999 ). The lack of true profit-generating potential of financed ventures (Bradley et al. 2012 ), high interest rates (Webb et al. 2013 ), and the lack of management and entrepreneurial skills (Evers and Mehmet 1994 ) raise substantial doubts about the outcomes of microfinance for recipients. Furthermore, the current empirical literature casts doubt on the ability of microfinance to generate multidimensional outcomes such as empowerment, education, health, and nutrition (Khavul et al. 2013 ; Miller et al. 2012 ). Therefore, this study seeks to examine the trends in the outcomes of microfinance for its clients, particularly for more vulnerable people (e.g., women, self-employed, older adults, low-income, and refugees), by focusing on the demand dimension of microfinance. To present the prevailing state of research on microfinance and its benefits for clients, we apply a scientometric analysis, which enables us to trace the anatomy and analyze the knowledge of this research topic. Thus, we address three research goals: identifying the current trends in the outputs of the microfinance literature in terms of dates, journals, authors, affiliated countries, and institutions; examining the most influential studies and themes in this field; and discussing the intellectual structures of the outcomes of microfinance research and the underlying trends.

This approach identified five clusters using keyword analysis and knowledge maps: (1) the socioeconomic outcomes of microfinance, (2) the conflict between social performance and the mission drift of microfinance institutions; (3) group lending, social networks, and social capital; (4) poverty alleviation through entrepreneurial activities and the impact of innovative services, especially crowdfunding; and (5) gender and new thematic frontiers.

Muhammad Yunus argues that poor people possess natural abilities to run businesses, and that their own subsistence reflects the capacities of their survival skills (Yunus 1998 ). However, to set up new businesses, poor entrepreneurs need to find alternative financial resources due to their general exclusion from the traditional banking system because of their lack of collateral (Stiglitz 1990 ), limited property rights (Webb et al. 2013 ), and the high transaction costs incurred by small-scale bank loans (Chliova et al. 2015 ; Ghatak 1999 ; Weiss and Montgomery 2005 ). Ongoing and established relations between lenders and borrowers often generate trust and reduce the risk of credit rationing (Stiglitz 1990 ); however, this inherently does not apply to most potential microcredit beneficiaries, as they lack any credit history (Tang et al. 2017 , 2018 ). Hence, Yunus ( 1994 ) identifies the provision of credit as a key factor for overcoming poverty through innovative approaches to providing credit to the poor as encapsulating a potential solution. Therefore, as microfinance-related articles have been published, literature reviews have appeared on several microfinance-related themes. Table 1 summarizes these studies.

Brau and Woller ( 2004 ) surveyed 350 articles related to microfinance institutions (MFIs) sustainability, products and services, management practices, client targeting, regulations and policies, and impact assessment before calling for further research into microfinance practices as a means of combatting poverty around the world. Based on 71 research papers (peer-reviewed journals, university publications, reports by development organizations, and conference publications) on the performance of MFIs, Roy and Goswami ( 2013 ) propose that microfinance researchers, practitioners, and rating agencies consider other dimensions for assessing MFI performance besides the financial aspect, particularly considering measures for social performance, outreach, and sustainability. García-Pérez et al. ( 2017 ) carried out a systematic literature review of 475 articles on microfinance, resulting in their classification of sustainability research under four perspectives: economic, environmental, social, and governance. They report that the economic and social fields have received the most attention, with authors having researched the interrelationships and considered a broader variety of subjects in those areas than in the environmental or governance fields. Fall et al. ( 2018 ) performed a meta-regression analysis of the performance of 38 MFIs before demonstrating that the mean technical efficiency (MTE) of MFIs has increased over time. However, research estimating social efficiency generated lower MTE levels than that for financial efficiency, which may explain why the African microfinance sector has poor performance. Hermes and Hudon ( 2018 ) also studied MFIs while focusing on the determinants of social and financial performance. From a study including 169 articles, they concluded that the most important determinants of MFI performance addressed by the literature are their own respective characteristics (such as the size, age, and type of organization), their funding sources, the quality of their corporate governance policies, and the characteristics of their external environment (such as the prevailing macroeconomic, institutional, and political conditions). However, they report mixed empirical findings, which may stem from a multidimensional perspective of performance. They suggest that outreach, gender, and rural measures should be adopted to measure the social performance of MFIs more holistically. Akter et al. ( 2021 ) have also recently addressed this dual nature of MFI performance (i.e., spanning the financial and social dimensions). After applying bibliometric data to 1252 Scopus-indexed articles, the authors convey how the hot topic research themes related to microfinance cover poverty alleviation, group lending, and credit scoring, whereas the financial performance aspect has been gaining greater attention from recent research evaluating MFI performance.

Copestake et al. ( 2016 : 290) review three decades of microfinance doctoral research, referring to this as a “distinct field of development thinking,” describing the “mainstream narrative of progressive inclusion of poor people and their livelihoods into a globally integrated and regulated financial system, largely in the private sector but also strategically subsidised by government and aid agencies.” The authors identify a critical counterpoint to this narrative of development thinking by emphasizing the specific negative effects of financial integration on poverty and inequality. By compiling a series of studies, they suggest that the performance of microfinance depends on socio-cultural norms, regulation, and management practices, which might further explain the mixed empirical evidence on the impact of microfinance.

Deploying a scientometric analysis of 1874 papers on microfinance, Gutiérrez-Nieto and Serrano-Cinca ( 2019 ) focus on the most cited 5% in this pool and classify the resulting 94 papers as institutionalist (when more oriented toward MFIs), welfarist (when more oriented toward microfinance clients), and generalist (otherwise). Based on chronological analysis, these authors report that, having previously covered innovations in microcredit practices and their impacts (the first research stage), as well as the peculiarities of MFI (second stage), current research primarily targets certain concerns over MFI mission drift and the role of microfinance in fostering financial inclusion. Somewhat interrelated with Gutiérrez-Nieto and Serrano-Cinca ( 2019 ), Zaby ( 2019 ) sets out an overall picture of the state of the art in the microfinance literature coupled with the main schools of thought. This author adopts science mapping to examine 4,409 Scopus-index articles explicitly related to microfinance (Zaby 2019 : 1), and correspondingly identifies three thematic research clusters: (1) the institutional aspects of microfinance, (2) the application of sophisticated research methods to evaluate the impacts of microfinance, and (3) ground-breaking microfinance literature related more generally to social justice. Nogueira et al. ( 2020 ) also report how MFI performance-related issues represent one of the most commonly approached fields of research. Based on 2168 articles indexed in the Web of Science, these authors point out how financial inclusion and entrepreneurship are hot topics related to microfinance. The authors then conclude in favor of the relevance of studying entrepreneurship in order to better understand the beneficiaries of microfinance.

Duvendack et al. ( 2011 : 2) argue that “no study robustly shows any strong impact of microfinance” on the well-being of its beneficiaries. After analyzing 58 papers, these authors identified cases with both poor methodology and data and concluded that most studies advanced no reliable evidence regarding the impact of microfinance. Van Rooyen et al. ( 2012 ) also focus on the impact of microfinance on poor people in their systematic review of studies conducted in sub-Saharan Africa. They report that microfinance has a modestly positive impact, but also occasionally results in the deterioration of the situations faced by beneficiaries. This framework indicates that academics and practitioners should closely consider the beneficiaries of microfinance rather than the overall performance of MFIs. This research gap prevents us from reaching any conclusions about the value of microfinance, particularly microcredit, as a tool for mitigating poverty and financial and social exclusion, nor regarding whether their multidimensional outcomes extend beyond the creation of wealth.

Only a few studies have hitherto focused on the impact of microfinance on the poor and on their well-being (e.g., Duvendack et al. 2011 ; Van Rooyen et al. 2012 ). This gap led us to combine bibliometric and content analysis to compile current literature and provide a roadmap of trends for future research into the outcomes of microfinance for recipients with a particular demand-side focus.

Therefore, this study makes several contributions to the literature. In particular, the results of the knowledge maps convey how more traditional topics, such as the focus of microfinance institutions, may potentially shift gradually over time and with the move from social to financial performance, increasing the risk of mission drift, and the advantages of group lending for creating social networks to overcome access to capital-related problems still attracts research interest. Furthermore, emerging trends relate to strategies for overcoming poverty and enhancing socioeconomic development. Entrepreneurship is a powerful tool that strengthens the financial and non-financial outcomes of microfinance. In addition, the scope of microfinance outreach is changing due to the emergence of crowdfunding platforms, particularly prosocial platforms (e.g., KIVA: https://www.kiva.org/ ) that boost women empowerment and gender equalities, stimulating the liberalization of financial systems at a global level and potentially prompting a more financially and socially inclusive system.

The structure of this paper is as follows: Sect.  2 sets out the research methodology design, and Sect.  6 details the bibliometric analysis that systematizes the publication trends, the most prolific journals, authors, and affiliated institutions, as well as the most influential studies and subjects in the field. Section  12 provides the content analysis based on bibliometric coupling, and Sect.  18 outlines and discusses the new trends in the microfinance literature, before Sect.  23 presents our conclusions.

Research methodology

Data and research criteria.

This study applies bibliometric and content analytical procedures to the selected papers, focusing on the outcomes of microfinance for their recipients (demand side), based on information collected from the Web of Science (WoS), Footnote 1 a database that “contains thousands of academic publications along with bibliographic information on their authors, affiliations, and citations” (Ferreira et al. 2019 : 186). We limited our research to articles published after 2011, as that was the last year with systematic literature reviews of this field, following the studies by Duvendack et al. ( 2011 ) and Van Rooyen et al. ( 2012 ; see Table 1 ). Our search of the field adopted the keywords (“microfinance*” OR “micro finance” OR “micro-finance*” OR “microcredit*” OR “micro credit*” OR “micro-credit*”) AND NOT (“microbank*” OR “micro bank*” OR “micro-bank*” OR “microfinance institution*” OR “micro finance institution*” OR “micro-finance institution*” OR “mfi*”) AND (“performance*” OR “success*” OR “outreach*” OR “impact*” OR “impacts*”) as entered in the WoS database. We then screened the articles based on titles, keywords, and abstracts to establish a database of 796 articles with the data collected in April 2021 spanning the period between 01:2012 and 03:2021. Footnote 2 Table 2 provides a comprehensive summary of the criteria used to collect the WoS data.

In accordance with our objective of analyzing the literature on the outcomes of microfinance for recipients, the more vulnerable people (demand side), we carried out a screening process of these documents involving the reading of the abstracts and, in case of doubt, we examined the documents in full length, which led to the exclusion of 272 purely institutional articles, that is, those concentrating solely on the financial performance of MFIs (e.g., Gutiérrez-Nieto and Serrano-Cinca, 2019 ). Nevertheless, this screening process did not exclude studies focusing on the social performance of MFIs, as these usually reach out to women, rural, vulnerable, and marginalized populations. This process was undertaken independently by two of the authors before verification by the third author. Thus, the bibliometric analysis examined 524 articles with detailed content analysis and then applied more detailed analysis to 47 of them in keeping with their common linkage to other documents in the network, based on the bibliometric coupling methodology. Furthermore, we undertook an additional context analysis of the most recent articles published between January 2018 and March 2021, ranked by the Association of Business Schools (ABS). This analysis concentrated on 11 articles published in elite journals (ABS 4*) and top journals (ABS 4). These journals generally publish the greatest advances in their respective fields and generate the highest citation impact factors within their field of knowledge. Figure  1 provides a comprehensive summary of the data analysis process.

Data retrieval process

Therefore, this study combines bibliometric analysis and a systematic literature review. Based on quantitative literature analysis, bibliometrics represents a study method from the library and information sciences (Huang and Ho 2011 ) and, according to Sengupta ( 1992 : 76), “is a sort of measuring technique by which interconnected aspects of written communications can be qualified.” Narin et al. ( 1994 : 65) refer to “bibliometrics and, in particular, evaluative bibliometrics,” which “uses counts of publications, patents, and citations to develop science and technology performance indicators.” This type of analysis emerged in order to deal with constantly growing bodies of knowledge and incorporates three major dimensions: measuring a particular scientific activity, its impacts as conveyed by the total number of article citations, and the links among articles (Narin et al. 1994 ), thus tracing the anatomy of the knowledge existing in a research field with regard to a specific topic.

Our study applied VOSviewer Footnote 3 software version 1.6.8 to analyze the publishing trends and most prolific journals, disciplines, authors, institutions, countries, studies, and subjects. This analysis is mainly derived from the number of published articles, total citations, and occurrences. To complement the analysis of the most influential studies, we performed co-citation analysis to systematize the most fundamental articles published between 1:2012 and 3:2021. Introduced by Small ( 1973 ) and developed by White and Griffith ( 1981 ) and White and McCain ( 1998 ), co-citation analysis is one of the most common bibliometric methods for unveiling similarities among the cited articles (Small 1973 ). By applying this tool via VOSviewer, we were able to highlight the main studies guiding the research over the last decade. The fractional counting methodology was used to analyze the most influential subjects, correcting the number of occurrences of each keyword in accordance with the total number of (key)words used in the title, abstract, or keyword list for the same article (Xu et al. 2018 ). The fractional counting method is more suitable than the full counting method (Narin et al. 1994 ): “When full counting is used to construct a bibliometric network, each link resulting from an action has a full weight of one, which means that the overall weight of an action is equal to the number of links resulting from the action. On the other hand, when fractional counting is used, each link has a fractional weight such that the overall weight of an action equals one” (Perianes-Rodriguez et al. 2016 : 1180). In so doing, the relationship between two keywords becomes closer when articles provide fewer keywords. Thus, Van Eck and Waltman ( 2014 ) recommend the fractional counting method, as this overcomes the potential for bias created by highly cited articles with long reference lists or more keywords, leading to misinterpretations.

Following the bibliometric analysis, we performed a systematic literature review to systematize the state of the art and to determine trends and possible research gaps based on the content analysis of clusters. Detailed content analysis was performed in the cases of bibliographically coupled articles—articles sharing a common link to other documents in the network. Bibliographic coupling establishes relationships between articles based on citation similarities and deems two articles to be bibliographically coupled whenever there is a third article cited by both these articles (Kessler 1963 ). Based on a dataset of 524 articles, we deployed VOSviewer to generate bibliometric maps based on the visualization of similarities technique. Of the 524 published articles in our refined dataset, this software reports that only 47 articles were coupled by the same item of reference, with at least 25 citations.

• Bibliometric analysis

Annual publication trends

Figure  2 illustrates the trends displayed by the 524 WoS-indexed articles in the field of microfinance outcomes (i.e., demand side) since 2012.

Publication trend of 524 published articles, indexed to WoS, between 1:2012 and 3:2021

The figure indicates an upsurge in publications from 27 papers in 2012 to 84 in 2020. Footnote 4 This trend in publications stems from the increasing number of scholars challenging the proposed benefits of microcredit as a salient tool for addressing credit constraints and poverty (e.g., Angelucci et al. 2015 ; Banerjee et al. 2015a ; Bocher et al. 2017 ; Tarozzi et al. 2015 ), especially when based on entrepreneurial activities (e.g., Alvarez and Barney 2014 ). The Nobel Prize awarded to Banarjee, Duflo, and Kremer in 2019 for their work on different strategies to mitigate poverty also justifies the rise in research related to the ability of microfinance/microcredit to generate positive outcomes, such as empowerment and education, beyond mere wealth creation.

Prolific journals and subjects

Table 3 depicts the list of the most prominent journals publishing on issues related to the demand side of microfinance, and hence the microfinance recipients. A total of 252 journals were included in the 524 articles analyzed. The most prolific journals (two of them ex aequo with nine published articles, three with six published articles, and five with five published articles) have published 179 of the articles studied (34.2% of the total). Almost all of these 179 articles appear in ABS-ranked journals, mainly in ABS 3 (according to the ranking published in 2018) by the Chartered Association of Business Schools. Footnote 5 These findings illustrate how research on the microfinance field primarily engages quality journals of business and management. The Journal of Development Studies represents the most productive journal, having published 31 articles, followed closely by World Development with 30 articles. Together, both journals published 11.4% of the articles analyzed.

Figure  3 displays the 10 main fields of journals publishing microfinance research since 2012. The most representative areas are economics , business , and management (which includes business finance), with 379 articles (i.e., 72.32% of the total articles). This figure indicates how the analysis of the outcomes of microfinance (on the demand side) has especially adopted an economic perspective. Despite the prominent position of Development Studies in publishing research on this topic (80 articles), the journal still only represented 15.27% of the total articles. The relevance of microcredit for society as a whole remains only a marginal issue and is scarcely addressed in the literature. More studies from the fields of health, business ethics, sociology, and psychology would be worthwhile to generate a better understanding of the effectiveness of microfinance in promoting the Sustainable Development Goals (SDG) of the United Nations 2030 Agenda, specifically eradicating poverty (SDG 1), promoting health and well-being (SDG 3), gender equality (SDG 5), and reducing inequalities (SDG 10), in addition to the economic objective of decent work and growth (SDG8).

Top 10 subject areas in microfinance (demand side) research in the 524 published articles, indexed to WoS between 1:2012 and 3:2021

Prolific authors, affiliated institutions, and countries

Tables 4 and 5 display the top 10 authors, institutions, and countries publishing on microfinance (demand side) outcomes since 2012 in WoS-indexed journals by number of publications and citations. Abdullah Al Mamum provides the list detailed in Table 4 , with nine published articles. His research mainly targets the effectiveness of microcredit and training programs to combat poverty and promote the sustainable growth of micro-enterprises in rural areas in Malaysia. However, Ester Duflo stands out as the most prolific author based on total citations—412 citations (Table 5 ) with three published articles. Ester Duflo and her research team, Michael Kremer and Abhijit Banarjee, won the Nobel Prize for Economics in 2019 for research on fighting global poverty over the preceding two decades, contributing to transforming development economics into a flourishing field of research. In the field of microfinance, Duflo conducted experimental research in less developed countries to evaluate the impact of training programs on microfinance outreach, especially on health and empowering women. Dean Karlan emerged as the second most prolific author based on both the total number of published articles (Table 4 ) and the total number of citations (Table 5 ), with six published articles (equal to Ariana Szafarz) and 381 citations, 32 more than Johnathan Zinman, with four articles published with Karlan. The expansion of microcredit, the use of loans, and repayment incentives constitute the main topics in the experimental research undertaken by Dean Karlan and Johnathan Zinman. Erica Field and Rohini Pande attained three publications with a total of 179 citations. Based on randomized experiments in India, these authors have been working on the default risk of microborrowers and the repayment requirements that best suit the needs of the poor. Ariana Szafarz represents one of the six authors with over 100 citations divided across six published articles, mainly approaching the topics of social and financial performance, gender, and empowerment. This evidence suggests that, despite the prevalence of articles from the fields of economics, business, and management (as pointed out in Fig.  3 ), the most prolific authors focus on topics within the scope of development studies. Experimental researchers seem to capture the enthusiasm of their target communities, mainly in less developed countries such as Bangladesh, India, Morocco, and Malaysia.

The institution with the most articles published on this aspect of microfinance (Table 4 ) is the University of Groningen (Netherlands) with 11 published articles, followed by the World Bank (United States) with 10, and MIT (United States) and Yale University (United States) with 9 each. MIT is the most prolific institution, based on total citations (968 citations). Yale University and Harvard University (United States) are among the top three with 440 and 300 total citations, respectively. Together, the articles published by members of these institutions received 1,708 citations, accounting for over 57% of the total citations generated by our dataset of WoS-indexed articles. The most prolific institutions all have locations in the United States and are responsible for the highest number of published articles (145) and total citations (2,990).

Citation analysis

Citation analysis is the best method for mapping the influence of a research paper. Citation counts encompass the number of citations that a paper received over a period of time. Thus, a more influential and productive paper is cited most frequently. We use VOSviewer to determine the most influential papers on microfinance outcomes. Table 6 displays the 10 most cited articles locally and globally. The local citations reflect the number of times a paper is cited by others within a sample size of 524 papers, whereas global citations measure the number of times a paper is cited by other works across all databases, including other areas and research fields.

According to global citations (local citations), Banerjee et al. ( 2015b ) are at the top of the list with 295(72) citations, followed by Banerjee et al. ( 2015a ) and Bruton et al. ( 2013 ) with 226(53) and 157(8) citations, respectively. Banerjee et al. ( 2015a , b ) are the most prominent papers paving the way for further research on microfinance outcomes. These studies provide theoretical support for the use of a randomized experimental methodology to measure the causal effects of microcredit on community development, namely on the livelihood of microentrepreneurs.

The number of citations reflects the popularity of a paper. To measure this prestige, we use the total link strength based on the fractional counting method, which indicates the number of times a paper is cited by highly cited papers. Thus, a highly cited paper could not also be a prestige paper. The total link strength is a composite measure that encompasses both popularity and prestige. Table 7 lists the top 15 papers based on the total link strength. The results differed from those of the citation count. When the top 10 papers were compared based on citations (global and local) with the total link strength (co-citations), only 5 papers (Angelucci et al. 2015 ; Attanasio et al. 2015 ; Banerjee et al. 2015a , b ; Crépon et al. 2015 ) are among the top 15 papers based on total strength links (co-citations). Co-citation refers to the number of times two articles are co-cited by an article in the database. The more often articles are co-cited, the greater the link strength (i.e., the more similar the domains under study).

Table 7 shows the studies that mostly guide the research in the last decade, which includes several articles published before 2012. Pitt and Khandker ( 1998 ), with the highest number of co-citations(total link strength) 76(546), is the most influential study in the recent literature. This study provides an evaluation of the group lending program of the Grameen Bank (and similar ones) in Bangladesh, showing that these programs have a significant effect on the well-being of poor households; their effect on education, health, labor supply, and consumption is greater when targeting women. Khandker ( 2005 ) is the third most influential study in this ranking, with 63(411) co-citations (total link strength) in our dataset. This study examines the effects of microfinance on poverty reduction in Bangladesh, at both the individual and aggregate levels, finding that microfinance contributed to poverty reduction, especially for female participants, in line with Pitt and Khandker ( 1998 ), concluding that microfinance boosts local economic growth at the village level. Morduch ( 1999 ) is the fourth most co-cited author in our sample statistics articles with 524 articles and 55(417) total link strengths. The author promotes an evaluation of innovative mechanisms beyond group-lending contracts, raising doubts about the effectiveness of microcredit programs in fighting poverty compared to traditional credit programs. Armendáriz and Morduch ( 2010 ) is the seventh most influential study according to this ranking, with 49(394) co-citations and total link strength.

These authors conducted extensive research on general topics that question the economic problems of microfinance, why such programs are needed, and why financial resources do not flow naturally to the poor. Karlan and Zinman ( 2011 ), with 46(437) co-citations(total link strength), and Karlan and Zinman ( 2010 ) and Stiglitz ( 1990 ), both with 44 co-citations and 325 and 437 total link strengths, respectively, are the ninth and tenth ( ex aequo ) most influential studies. Karlan and Zinman ( 2011 , 2010 ) adopted experimental research methodologies to analyze microcredit programs in the Philippines and South Africa, respectively. Karlan and Zinman ( 2011 ) found that microcredit may serve to increase the ability to cope with risk, strengthen community ties, and increase access to informal credit, but under channels different from those often proposed. The results of Karlan and Zinman ( 2010 ) corroborate the presence of binding liquidity constraints in South Africa and suggest that expanding the credit supply improves welfare. Stiglitz ( 1990 ) also analyzed the success of the Grameen Bank, suggesting that peer monitoring is largely responsible for the financial performance of the microcredit program in Bangladesh. Banerjee et al. ( 2015a ), with 70(605), Crépon et al. ( 2015 ) with 52(517), and Banerjee et al. ( 2015b ) with 51(398), Attanasio et al. ( 2015 ) with 48(517), and Angelucci et al. ( 2015 ) with 43(409) co-citations(total link strength), all published after 2012, also assume a prominent place in this ranking.

Keyword analysis

Table 8 reports the top 15 keywords in the 524 articles selected by the study methodology and published between 1:2012 and 3:2021 that attain at least 20 occurrences. This table’s right column reports the number of links a given keyword obtains with another keyword based on the total link strength. “Microfinance” is the most frequent keyword, with 320 occurrences (281 total link strength), indicating that this word acts as a termed concept in the literature. The words “microcredit,” “impact,” and “poverty” are also three of the most frequently cited words with 199(187), 154(148) and 138(136) occurrences (total link strength), respectively, suggesting that scholars are focusing on microfinance/microcredit outcomes, especially approaching these as tools for development and intervention with the potential to lift people out of poverty. The emerging topics of “gender/women,” “entrepreneurship,” and “empowerment” emphasize how the literature is increasingly evaluating the effects of microfinance/microcredit across various dimensions beyond the financing facet.

Figure 4 displays the most influential subjects based on the keyword occurrence networks. Footnote 6 These keywords are either extracted from the title and abstract of each article or sourced directly from the article keyword lists (Van Eck and Waltman 2014 ). To establish this network, we applied VOSviewer software and the fractional counting method, which considers the number of keywords (key), to explore the most relevant themes in microfinance outcomes. This figure also confirms that “microfinance” is widely interconnected with “microcredit,” “poverty,” and “impact.” These results again corroborate how researchers examine microfinance/microcredit as a tool to eradicate poverty in greater depth, especially through entrepreneurial activities.

Network of keyword occurrences in the 524 articles selected from the study sample, covering the period between1:2012 and 03:2021 according to the fractional counting method

Content analysis

We deploy bibliometric analysis to explore the most relevant documents in this field of research. To identify the most influential publications, we applied VOSviewer to perform bibliometric coupling with a threshold of 25 citations for our analysis, yielding 47 articles out of a total of 524 with at least 25 citations, coupled into five clusters. Figure 5 depicts the knowledge map of the most-cited microfinance articles resulting from the fractional counting method. In a network, these nodes may be aggregated into clusters in which the weighting of edges is higher between the nodes within one cluster than those with another cluster. Thus, the VOSviewer algorithm returned five distinct clusters, with 11 documents in Cluster 1, 10 documents in Cluster 2, 9 documents in Cluster 3, 9 documents in Cluster 4, and 8 documents in Cluster 5. Footnote 7 Table 9 portrays the 48 papers in the five clusters. We subsequently carried out a content analysis with careful examination of the papers in each cluster to determine their common theme.

Knowledge map of the top articles cited by cluster according to the fractional counting method, based on 524 studies selected between 1:2012 and 3:2021

Cluster 1: socioeconomic outcomes of microfinance

This cluster comprised 11 studies focusing on the impacts of microfinance programs on socioeconomic outcomes with randomized experimental evaluations, questioning the influential role of microcredit on poor households. Banerjee et al. ( 2015b ) report that group lending programs in India increase the take-up of microcredit with a positive impact on small business investment and profits as well as on the expenditure of durable goods, but only over a short period. They also did not encounter any significant effects of group microcredit lending on health, education, or women’s empowerment. Banerjee et al. ( 2015a ), Angelucci et al. ( 2015 ), and Tarozzi et al. ( 2015 ) raised doubts about the transformative impacts of microcredit as a development tool. Angelucci et al. ( 2015 ) and Tarozzi et al. ( 2015 ) provide evidence that the effectiveness of microfinance is modest, with little or no evidence of any effectiveness in promoting micro-entrepreneurship, income, the labor market, consumption, social status, subjective well-being, schooling, or empowerment, despite affording a substantial increase in access to credit. Microcredit increases borrowing, which is mainly used for investment and risk management. However, this increased access to credit leads to only modest increases in female decision making, trust, and business size, with little effect on overcoming debt traps (Angelucci et al., 2015 ).

Crépon et al. ( 2015 ) suggest that the effects of microcredit are mainly derived from borrower characteristics rather than from externalities. Microcredit access leads to a significant rise in investment in the assets applied to self-employment activities and an increase in profits among households with higher abilities to borrow. Ngo and Wahhaj ( 2012 ) also demonstrate how access to microloans can lead to positive outcomes for intra-household decision-making and the welfare of women depending on their starting point conditions. They convey how women only benefit from microcredit when they are able to use the credit to invest in profitable joint activities, and when a large proportion of the household budget goes to the consumption of public goods. Otherwise, women borrowers may experience a decline in welfare.

Bruhn and Love ( 2014 ) document the remarkable effects of microcredit on labor markets and income levels, especially among individuals located in areas with lower pre-existing bank penetration and those with low incomes. Arouri et al. ( 2015 ) also provide evidence that access to microcredit, internal remittances, and social allowances can help households strengthen their resilience to natural disasters. Kaboski and Townsend ( 2012 ) indicate that microcredit lines might increase total short-term credit, consumption, agricultural investment, income growth (from business and labor), and wages, but decrease overall asset growth. Schicks ( 2014 ) provides measures for policymakers to address the over-indebtedness potentially arising from microcredit. Analyzing the loan-related sacrifices that borrowers report, the author identifies how male microborrowers are more likely to be over-indebted than women and that over-indebtedness is lower for borrowers with good levels of debt literacy. Based on a case study of microfinance trials, Allcott ( 2015 ) suggested that default rates may depend on the size of the trial samples. This study of program evaluations based on randomized control trials draws attention to the systematically biased out-of-sample predictions of program evaluations, even after many replications.

Microcredit has been referenced as a relevant tool for addressing credit constraints and promoting entrepreneurial activities. However, empirical studies have returned conflicting results, casting doubt on the strength of microcredit not only in financial outcomes but also in its actual ability to enhance several dimensions of human development. Stressing the research findings that indicate the need to consider the context of microcredit program deployment, we suggest paying particular attention to the development setting, as some studies demonstrate how microcredit programs are more effective in contexts where the credit markets have failed (i.e., poor settings), while others propose that microcredit intervention is boosted by environments with higher levels of social, economic, and institutional development. Research on this domain constitutes a fruitful field of research.

Cluster 2: Social performance or mission drift?

This cluster encompasses 10 studies. The focus of this cluster is access to microfinance, usually addressed in the literature as an indicator of MFI social performance (mission locked-in versus mission drift). Vanroose and D’Espallier ( 2013 ) report that MFIs reach more poor clients and prove more profitable in countries where access to the traditional financial system remains low. The results suggest that MFIs offset market failures in the traditional banking sector and flourish best when the formal financial sector is absent. However, MFIs have also shown remarkable social performance in countries with well-developed financial systems, as this pushes MFIs down the market and makes mission drift less likely. Cornée and Szafarz ( 2014 ) also provide evidence that banks offer advantageous credit terms for social projects. In turn, borrowers are motivated to repay loans, thus reducing the probability of default. Louis et al. ( 2013 ) and Lebovics et al. ( 2016 ) provide evidence that these dimensions of performance, and thus the social and financial aspects, are not mutually exclusive. Over a short time frame, there are positive relationships between social efficiency and financial performance (Lebovics et al. 2016 ; Louis et al. 2013 ). However, D’Espallier et al. ( 2013 ) adduce evidence pointing in the opposite direction and propose that microfinance faces a mission drift with the lack of subsidies, worsening the social performance of MFIs. Dealing with this trade-off has involved the implementation of several strategies, including charging higher interest rates, targeting less poor individuals, or reducing the proportion of female borrowers in order to compensate for public non-subsidization.

Bocher et al. ( 2017 ) demonstrate that individuals owning land and with larger households and/or savings experience a greater probability of getting microcredit. These results may indicate that some MFIs do not target the poorest of the poor. Canales ( 2014 ) examines how MFIs balance the pressures to pursue financial efficiency with the need to remain responsive to local needs. The authors document how MFI branches allowed discretionary diversity and decentralized flexibility through relational embeddedness to cater to local needs tend to achieve better performance. Thus, microcredit committees may yield substantial benefits for organizations and unbackable local individuals, for example, when dealing with missed repayments. Augustine ( 2012 ) proposes that the transparency of MFIs’ corporate governance policies is more important than their orientation, concluding that transparent declarations of their social orientation increase their performance. This may occur because public statements about MFI orientation generate commitments to the target community.

Among these clusters, the studies conducted by Al-Azzam et al. ( 2012 ) and Van Gool et al. ( 2012 ) are somewhat collateral to the main topic of MFI social performance. Van Gool et al. ( 2012 ) analyze whether the credit scoring system adopted in retail banking is appropriate for the microfinance industry, especially with regard to its social concerns, and reported that all the benefits of credit scoring models are commercially related. However, they also suggest that credit scoring may serve social concerns, for instance, by modelling information about indebtedness in order to avoid debt traps. Al-Azzam et al. ( 2012 ) focus on the effects of screening, peer monitoring, group pressure, and social ties on borrowing group repayment behaviors. The authors provide evidence that social ties built on religious attitudes and beliefs improve repayment performance. Thus, this study straddles the frontier with Cluster 3.

The trade-off between MFI outreach and profitability remains controversial. Several studies report that MFI shifts over time from social to financial performance as a result of both the costs of microfinance market contracts and the high fixed costs associated with small loans. Recent studies also reinforce that the national context also has a relevant impact on MFI performance. Consequently, several strategies have emerged to improve profitability, including increasing loan amounts, charging high-interest rates, public subsidization, and gaining efficiency through new technologies. Hence, the trade-off between outreach and sustainability continues to attract the research community studying governance and new organizational strategies, such as legal status, to improve MFI social and financial performance.

Cluster 3: group lending, social networks, and social capital

The third cluster involves nine studies focusing on group lending, social networks, and social capital, and how these relate to credit access and loan repayment. Group lending has the ability to build up social networks outside of the family (Attanasio et al. 2015 ), promoting social interactions that increase repayment rates (de Quidt et al. 2016 ), even in the absence of any collateral (Feigenberg and Pande 2013 ). One concern here is that the grace period might restrict social networks among group members, thus increasing default rates by lowering the effectiveness of informal insurance (Field et al. 2013 ).

Social capital is based on a “pre-existing connection between group members” (Banerjee 2013 : 496). Group members hold better information about each other than the respective MFI; they are therefore not only in a better position to screen and monitor the actions of each group member but also to punish those who default, for example, by withdrawing social capital from them (Banerjee 2013 ). Thus, group meetings increase social capital and networks and reduce the monitoring costs of lenders, which may encourage recourse to formal insurance, reducing the bail-in costs in case of default (de Quidt et al. 2016 ). According to the authors, by also functioning on an individual liability basis, group lending might facilitate increases in repayment rates depending on the social capital and networks developed within those groups. Group lending also displays the ability to increase both borrowing and entrepreneurship, as such an approach reduces the discouragement experienced by some individuals who are uncomfortable with borrowing on an individual basis but are willing to borrow in groups and share the liabilities, especially women with lower levels of education (Attanasio et al. 2015 ).

Wei et al. ( 2016 ) indicate how credit scoring models encapsulating client social networks—their social score—might provide a means of raising access to microcredit as an alternative to group lending. However, Yuan and Xu ( 2015 : 232) drew attention to how poorer households “are limited by social networks and they have no financial means to invest in their social capital to expand their social network.” Donou-Adonsou and Sylwester ( 2016 ) examine the relationships between financial development and poverty reduction, a topic on the frontier with Cluster 1. Gabor and Brooks ( 2017 ) seem to approach the frontier with Cluster 4, as they analyze the growing importance of digital-based programs for fostering financial inclusion in the fintech era.

Group-lending mechanisms are still attracting the attention of scholars. The social cohesion characterizing borrowing groups explains the effectiveness of the screening and monitoring stages that reflect in the repayment rates as well as in the outcomes of loans made for business purposes. Furthermore, this requires a deeper understanding of where group lending contexts generate advantages over individual contracts, for example, in developing countries where social capital often implies investments that poor people are not able to attain.

Cluster 4: poverty alleviation, entrepreneurial activities, and financial service innovations

Cluster 4 includes nine studies that focus on the contribution of entrepreneurial activities and financial service innovations to poverty alleviation. The literature posits that entrepreneurship represents a crucial pathway for alleviating poverty (Bruton et al. 2013 ) arising from socioeconomic and technological growth and development (Zahra and Wright 2016 ), which requires an industrialized approach to offset the multiple market failures prevailing in developing economies (Alvarez et al. 2015 ). This might explain why microcredit generally has stronger socioeconomic impacts (especially for the empowerment of women) in more challenging contexts and when targeting client entrepreneurs (Chliova et al. 2015 ). However, not all entrepreneurial activities lead to sustainable economic growth. For example, self-employment opportunities in sectors requiring low levels of human capital tend to perpetuate abject poverty (Alvarez and Barney 2014 ). Significant economic growth and poverty alleviation depend on the ability to discover and create new business opportunities based on more effective utilization of human capital, property rights, and financial capital (Alvarez and Barney 2014 ; Alvarez et al. 2015 ). In fact, local development (Diniz et al. 2012 ) and entrepreneurial success (Josefy et al. 2017 ) depend on the ability to mobilize resources, including financial capital. However, to be effective, an increase in financial resources requires accompanying financial education.

Formal credit markets and even traditional microfinance sources for encouraging investment, innovation, and launching new ventures may no longer be sufficient to overcome the persistent societal challenges of poor countries (Zahra and Wright 2016 ). According to these authors, peer-to-peer lending and crowdfunding may provide a solution for financial, social, and environmental wealth. “Crowdfunding refers to the practice of raising funds for a venture or project from dispersed funders typically using the Internet as a channel of operation” (Josefy et al. 2017 : 163). The availability of funds for promoting microenterprises is expanding rapidly through crowdfunding platforms, such as Kiva, which provides a greater audience of lenders for microenterprises’ signaling autonomy, competitive aggressiveness, and risk-taking (Moss et al. 2015 ). The success of loan campaigns on crowdfunding platforms also depends on contextual community attributes, such as the cultural values of the target audience that shape the level of interest the projects generate in the crowd (Josefy et al. 2017 ).

Information and communication technology (ICT) seems to be an alternative for supporting financial inclusion and fostering social inclusion (Diniz et al. 2012 ). By examining an ICT-based platform, Berger and Nakata ( 2013 ) analyze the socio-technical characteristics that technological solutions may have to successfully implement financial service innovations in the field of microfinance. According to these authors, these innovations tend to produce better results when they are congruent with the unique surrounding socio-human, regulatory, and market conditions.

The literature references entrepreneurship, particularly in deprived environments, as the only option to earn money due to the absence of any other market participation. In such contexts, microcredit enhances entrepreneurial activities through the issuance of small and unsecured loans. Scholars still raise concerns about the effectiveness of such programs, mainly due to the lack of profits generated by the financed ventures to pay the costs of loans and ensure loan repayment. The lack of management skills is an additional issue pointed out by researchers. Recently, new finance alternatives have emerged, especially crowdfunding, which deploys online platforms to allow entrepreneurs to connect with prospective crowd funders—the crowd—who finance new entrepreneurial ventures by lending small amounts. Empirical studies in this area are still in their infancy, but strengthen the perspective that crowdfunding may democratize entrepreneurial finance, particularly among the more vulnerable, and help break the poverty cycle.

Cluster 5: gender and thematic frontiers

The final cluster included eight studies. This cluster covers the impacts of microcredit targeting the vulnerable, with some articles focusing specifically on women. Thus, in this cluster, we encounter several studies bordering on the frontier with other clusters, such as Cluster 1 (e.g., Duvendack and Palmer-Jones 2012 ; Roodman and Morduch 2014 ), Cluster 3 (e.g., Willy and Holm-Müller 2013 ; Mallick 2013 ; Mendes-Da-Silva et al. 2016 ), and Cluster 4 (Deininger and Liu 2013 ; Barasinska and Schäfer 2014 ; Mendes-Da-Silva et al. 2016 ; Gleasure and Feller 2016 ).

Duvendack and Palmer-Jones ( 2012 ) and Roodman and Morduch ( 2014 ) re-examined previous studies, specifically those developed by Pitt and Khandker ( 1998 ), questioning the evidence they reported after studying Bangladesh microcredit programs. Both studies raise doubts about the microcredit outcomes identified by Pitt and Kandker. However, Duvendack and Palmer-Jones ( 2012 ) corroborate the positive effects of microcredit for vulnerable women. Gleasure and Feller ( 2016 : 110) conducted a meta-triangulation analysis of crowdfunding research. Their results suggest that crowdfunding generates new opportunities and describing how these “present genuinely new ideas and behaviours” and not “simply a migration of established practices into a new domain.” For example, crowdfunding may solve some of the discrimination problems faced by women in traditional credit markets, as the study found no gender effects on the likelihood of receiving funds. Deininger and Liu ( 2013 ) report that a combination of microcredit and self-help group initiatives (including training and capacity-building programs) produces positive pro-poor effects, especially by promoting the empowerment of women and health and improving consumption and income diversification in the short term.

Mallick ( 2013 : 179) examines whether continued support for poor individuals, which includes “management assistance, a subsistence allowance, health care facilities, and support for building social networks,” plays a crucial role in borrowing decisions. The authors indeed conclude that this “big push” affords the extremely poor access to microfinance. This effect is higher for larger households and for households with male heads, and increases with the average levels of education and income in the household. Social capital also plays an important role in borrowing decisions, in keeping with several of the findings systematized in Cluster 3. Mendes-Da-Silva et al. ( 2016 ) also support the notion that entrepreneurs’ social networks might play a central role in funding, especially on crowdfunding platforms. Willy and Holm-Müller ( 2013 ) examined the effects of social influence, social capital, and credit access in the agricultural sector and demonstrated how they represent significantly positive predictors of farm soil conservation.

Scholars have identified how entrepreneurship represents one path to the empowerment of women, particularly in developing countries, although empirical evidence indicates a mixed range of outcomes. Some studies stress that microcredits/microfinance endows women with great control over the operations of their ventures and household resources, thus fostering their empowerment. Others argue that microfinance programs do not take into account the cultural and social context of their deployment and thus, in some ways, sustain the existing hierarchy of classes, increasing tensions among household members and providing new forms of dominance over women. Recent research posits that new technologies extending basic financial services have a large effect at a relatively low cost and are susceptible to deepening through knowledge transfers in the form of financial literacy.

Mapping the trends

This section discusses the most recent and influential articles on microfinance topics published in the last three years (2018–3:2021) and ranked on ABS with a classification of 4 or 4*, yielding a total of 11 articles. Footnote 8 As they are more recent, these articles have been cited less often and therefore excluded from the bibliographic coupling analysis carried out in Sect. 4 . We also identified the most relevant emerging topics in the field.

Emerging trends

Table 10 systematizes the scope and main findings of all the articles published in ABS (4 or 4*)-ranked journals in the field of microfinance. Recent studies have promoted new approaches to examining the socioeconomic impacts of microfinance at both the macro (Buera et al. 2021 ; Duflo 2020 ) and micro (Burke et al. 2019 ; Singh et al. 2021 ) levels. The theme of MFI mission drift or mission lock-in is still at the fore in most recent literature (Alon et al. 2020 ), as well as the benefits to the group and joint lending (Attanasio et al. 2019 ), and reputation, social capital, and network (Li and Martin 2019 ). ABS (4 and 4*)-ranked journals have also published papers on somewhat underexplored topics on the frontiers of some clusters, such as alternative programs for promoting social changes (Kim et al. 2019 ), the impact of microcredits on subjective well-being (Bhuiyan and Ivlevs 2019 ), and the roles of cultural institutions (Drori et al. 2018 ) and government regulation (Tantri 2018 ) in the microfinance performance returns.

After analyzing the keywords of the most influential studies published between 1:2018 and 3:2021 (whether or not ABS ranked), Table 11 presents the most recent trends on microfinance literature, with “microfinance,” “microcredit,” “impact,” and “poverty” still representing the keywords with the most occurrences. Comparing Tables 8 with 11 , we observe that roughly half of the occurrences of these keywords relate to articles published since 2018. “Gender/women,” “entrepreneurship,” “performance,” and “empowerment” are trending topics, gaining in importance in the microfinance literature over the last three years.

Entrepreneurship and performance

Microfinance appears as an instrument that promotes access to capital for impoverished individuals otherwise excluded from financial systems and gaining popularity as a means of enhancing entrepreneurial activities (Yunus 1998 ), enabling vulnerable people to engage in market transactions and end subsistence-based livelihoods. Consequently, entrepreneurship among individuals living in poverty settings represents a more important outcome than much traditional entrepreneurship research in developed countries.

However, the empirical literature is inconclusive about the ability of microfinance to enhance the financial standing of vulnerable people (Khavul et al. 2013 ). This ambiguity is strengthened when coupled with other development outcomes, specifically the capabilities of the poor across several facets of human development (e.g., empowerment, education, health). Thus, researchers perceive that a key aspect for continuing scrutiny derives from the effectiveness or otherwise of microfinance, justifying the emergence of an increasing number of papers on this domain. Furthermore, some authors maintain that the context of microfinance deployment, hence the national context and specific features, impact the outcomes of such tools (Crépon et al. 2015 ; Weiss and Montgomery 2005 ), particularly in environments where credit markets have failed. Hence, the performance effect of microfinance is greater in developing countries (Chliova et al. 2015 ). Meanwhile, other authors emphasize the synergetic relationships between institutional and socioeconomic developments as outcomes that microfinance can achieve. However, it remains unclear whether microfinance aligns with supplementary or complementary outcomes.

Our bibliometric analysis demonstrates that when designing programs, microfinance institutions should focus on borrower characteristics instead of standard credit contracts; otherwise, credit only worsens problems of over-indebtedness. To achieve win–win propositions, in addition to credit, microfinance interventions should also involve education and training programs to boost the capabilities of less advantaged citizens to start, maintain, and grow their own ventures. This seems particularly relevant in less developed entrepreneurial ecosystems as well as in regions where the economic development model is based on intensive (low-educated) human capital that is more exposed to persistent poverty traps and anemic economic growth. By achieving successful entrepreneurial outcomes, educated and trained entrepreneurs increase their financial and non-financial outcomes. In sum, our findings shed light on the powerful interwoven effects of knowledge, credit, and entrepreneurship in lifting poor entrepreneurs out of poverty, particularly in deprived settings.

Empowerment and gender

Gender inequalities constitute one of the greatest barriers to human development (Conceição 2019 ), especially in developing countries (Ojong et al. 2021 ). In these countries, women may face additional challenges in obtaining education and a well-paid job, in addition to working an average of three times more often in unpaid and domestic activities than men (UN Women 2020 ). Scholars have emphasized how entrepreneurship provides a pathway to empower women, stressing that microfinance is a reliable tool that leverages its effects primarily through business activities.

The strength of microfinance as a development intervention tool to transform social and economic structures relies on its potential ability to lift people out of poverty (Yunus 1998 ) by running small ventures that generate financial resources to increase entrepreneurs’ financial well-being (Mckernan 2002 ). However, beyond wealth creation, this approach forecasts a capacity for microfinance to boost the livelihood of recipients across several dimensions (Buckley 1997 ; Miller et al. 2012 ). Hence, this places great emphasis on non-financial human development outcomes, specifically women empowerment (Hermes and Lensink 2011 ), which is particularly relevant in poor settings, as the constraints women face regarding market participation constitute a form of dominance and control over women. Women empowerment emerges as a multidimensional concept (Weber and Ahmad 2014 ) that, besides access to credit, also includes income, contribution to household expenditure, health, education, control over resources, participation in community and household decision-making, social mobility and freedom of movement, and self-worth (Kabeer 2001 ; Noponen 2003 ). Therefore, when considering these dimensions, any substantial increase in access to credit certainly does not automatically promote subjective well-being or empowerment (Angelucci et al. 2015 ; Tarozzi et al. 2015 ). Nevertheless, studies suggest that the provision of small loans to women enables them to more effectively mitigate gender barriers by running their own businesses, increasing their mobility outside the household, and achieving the ability to make decisions (Todd 1996 ). In addition, through economic activities, household income increases, improving their standard of living, and consequently enhancing the education of their children and leading them to adopt more preventive health practices (Yunus 1998 ).

The mission to promote the empowerment of women through the provision of small loans also depends on training programs and the ability of MFIs to understand the characteristics of female borrowers (Hunt and Kasynathan 2001 ). Thus, MFIs must design and implement internal policies to mitigate gender biases based on the conditions of female borrowers at the outset. Promoting the participation of women in decision-making processes in higher loan cycles, for example, will spread women’s empowerment (Swain and Wallentin 2009 ) and positively increase the abilities of female borrowers to decide how to use their loans (Weber and Ahmad 2014 ). Hence, recent research suggests a more holistic approach to answering the extent to which microfinance meets sustainable development goals, for example, eradicating poverty, reducing inequalities, and boosting sustainable development.

In fact, the outreach of microfinance itself is changing with the emergence of fintech, namely prosocial crowdfunding platforms. Fintech has had a noteworthy impact on the financial system by reducing operating costs, providing higher quality services, and increasing user satisfaction (Kou et al. 2021 ). In the context of microcredit, prosocial crowdfunding platforms act as socially oriented digital marketplaces, particularly targeting poor settings (Meyskens and Bird 2015 ), where lenders provide credit access to impoverished people underserved by the banking industry, facilitating the liberalization of the financial sector at a global level. In turn, this boosts more inclusive financial and social systems (Dupas and Robinson 2013 ) that generate large effects at relatively low costs.

To be fruitful, the crowdfunding platform design cannot ignore the decision dynamics underlying not only traditional e-commerce platforms but also fintech. Commercial digital platform users base their judgments and decisions on trustworthy reviews. Likewise, we posit that prosocial lenders will increasingly drive digital funding decisions on systematized crowd reviews on borrowers and MFI. Thus, as in many financial applications (see Li et al. 2021 ), detecting clusters of financial and social-environment data (such as borrowers’ social capital and MFIs’ financial and social performance) will be critical for inferring lenders’ behavior and maximizing the performance of crowdfunding platforms and their outcomes. This might constitute a new application case for the so-called data-driven opinion dynamics model (see Zha et al. 2020 ), because financial technologies provide important advantages in processing big data into more meaningful, cheaper, worldwide, and more secure data than conventional methods (Lee and Shin 2018 ).

Thus, we might expect these topics to guide future research, providing a starting point for returning practical implications for policymakers, academics, players in crowdfunding markets, and microentrepreneurs.

Conclusions and implications

Poverty remains a key global challenge. According to the World Bank forecast, the total number in poverty is due to rise for the first time in over two decades, from 119 to 124 million by the end of 2021. In this context, microfinance has emerged as an innovative and sustainable poverty alleviation tool to serve more vulnerable people, particularly in developing countries. However, some scholars have challenged its proposed benefits (e.g., Chliova et al. 2015 ; Morduch 1999 ). Through the application of bibliometric methods, this paper reviews the most recent literature on the trends in the outcomes for microfinance recipients, thus focusing on the demand side. The study examines 524 articles collected from the Web of Science database published between 1:2012 and 3:2021.

Based on keywords, co-occurrences, and links between citations to obtain knowledge maps, the findings demonstrate that in both the theoretical domain and the empirical, research still casts doubt on the capacity of microfinance to generate positive outcomes beyond wealth creation, particularly in terms of empowerment, education, and health (Cluster 1). Further studies in this domain should consider the macro-context when undertaking empirical research; otherwise, policies designed based on such limited evidence may yield unexpected outcomes contrary to the forecast socioeconomic goals. Furthermore, entrepreneurship, through granting small loans (microcredits), represents a precondition for individuals living in poverty starting small businesses and the most efficient strategy for leaving behind subsistence-based lives. However, as lack of management skills may hamper the survival of these businesses, providing finance literacy training has a positive impact on the performance of such small ventures (Cluster 4). Nowadays, the reach of microfinance is changing due to the emergence of crowdfunding, as the crowd of lenders provides prompt credit access to start-ups launched by impoverished microentrepreneurs and empowering women (Cluster 5). This research is still in its infancy, but by sharing risks worldwide, informal lenders can extend credit through small loans, thus democratizing entrepreneurial finance to boost new ventures. The group lending methodology remains an efficient instrument for overcoming the lack of access to financial resources by building up social networks in the community (Cluster 3). Therefore, research in this field should examine how new screening models and credit social models, along with soft information, leverage the financial performance of new ventures and enhance financial inclusion and foster the social inclusion of such individuals. This study also identifies the role of MFIs in addressing market failures in the traditional banking sector, stressing the idea that MFIs gradually shift over time from social performance (outreach to the poor) to financial performance (Cluster 2). Thus, taking into account the recognized role played by microfinance and MFIs in the process of socio-economic transformation, public policy must consider the need to compensate for the market’s financial performance gap in the poorest economies by subsidizing credit activities to avoid mission drift effects. This needs to be accompanied by a transformation of MFI corporate governance policies to ensure transparency in their operations and selection of microfinance recipients. Overall, this study corroborates that microfinance is a distinct field of development thinking that requires a more holistic approach to overcome poverty and boost economic and human development at the global level.

As with any bibliometric analysis, this study has some limitations. As we gathered bibliometric data from the WoS database, we may have missed studies listed only in other databases (e.g., Scopus). Furthermore, some research domains within microfinance and microcredit may rely more on citations than others, which may reduce the scope of the outputs within clusters. Finally, early career researchers may not fare well in citation and co-citation studies even when producing seminal research, which may reduce the impact of their studies as measured using tools deployed to gather bibliometric data.

One of the greatest advantages of the Web of Science database, compared with PubMed, Scopus or Google Scholar, is its timeline coverage in terms of quality research production (Falagas et al., 2008 ). This aggregates research information from five indexed databases: Science Citation Index Expanded (SCI Exp.), Social Sciences Citation Index (SSCI), Arts and Humanities Citation Index (A&HCI), and the index of Chemistry and Current Chemical Reactions (Goodman 2005 ). The SCI Exp. includes articles published since 1900; and with SSCI and A&HCI dating back to 1956 and 1975, respectively (Meho and Yang 2007 ).

We would acknowledge how searches based on a set of keywords include certain limitations (e.g., Costa et al, 2016 ). One way of improving the selection process in a systematic literature review involves adopting a Preferred Reporting Item for Systematic Review and Meta-Analysis – PRISMA (Moher 2009 ).

VOSviewer is a program developed for constructing and viewing bibliometric maps based on the visualization of similarities (VOS) technique (Van Eck and Waltman, 2009 ).

The year 2021 reflects only the publications until March.

https://charteredabs.org/academic-journal-guide-2018-view/ (accessed in April 2021).

The following similar keywords were merged: “programs” and “credit programs” (to “credit programs”); and, “gender” and “women” (to “gender/women”).

VOSviewer software only reports the name of the first author.

Paul et al. ( 2017 ) examine the most influential papers in the last four year. Spasojevic et al. ( 2018 ) also examine the papers ranked as class ABDC. Furthermore, Gutiérrez-Nieto and Serrano-Cinca ( 2019 ) select the top 5% articles for analysis as the excellent highly cited papers.

Abbreviations

Arts and Humanities Citation Index

Association of business schools

Information and communication technology

Microfinance institutions

Mean technical efficiency

Preferred reporting item for systematic review and meta-analysis

Science citation index expanded

Social enterprises

Social Sciences Citation Index

Sustainable development goal

Visualization of similarities

Web of science

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We acknowledge the financial support of Fundação para a Ciência e a Tecnologia (UBI PTDC/EGE/OGE/31246/2017; UIDB/ 04630/2020; UIDB/04728/2020; UIDB/04105/2020).

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Ribeiro, J.P.C., Duarte, F. & Gama, A.P.M. Does microfinance foster the development of its clients? A bibliometric analysis and systematic literature review. Financ Innov 8 , 34 (2022). https://doi.org/10.1186/s40854-022-00340-x

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International Journal of Emerging Markets

ISSN : 1746-8809

Article publication date: 8 July 2021

Issue publication date: 11 August 2023

The authors present a systematic literature review on microfinance institutions’ (MFIs) effect on poverty and how they can ensure their sustainability. The purpose of this article is to review the effect of MFIs on poverty in South Asian countries. The analysis and review of the selected corpus of literature also provide avenues for future research.

Design/methodology/approach

A total of 95 papers from 49 journals in 4 academic libraries and publishers were systematically studied and classified. The authors define the keywords and the inclusion/exclusion criteria for the identification of papers. The review includes an analysis of the selected papers that give insights about publications with respect to themes, number of themes covered in individual publications, nations, scope, methodology, number of methods used and publication trend.

The literature indicates the positive effect of microfinance on poverty but with a varying degree on various categories of poor. The relation between poverty and microfinance is, however, dependent on the nation under the scanner. While sustainability and outreach co-exist, their trade-off is still a matter of debate.

Originality/value

This is the first systematic literature review on MFIs’ effect on poverty in South Asian nations. Additionally, the authors discuss the literature on the trade-off between sustainability and outreach for MFIs.

• Microfinance
• Sustainability
• Systematic literature review

Gupta, P.K. and Sharma, S. (2023), "Literature review on effect of microfinance institutions on poverty in South Asian countries and their sustainability", International Journal of Emerging Markets , Vol. 18 No. 8, pp. 1827-1845. https://doi.org/10.1108/IJOEM-07-2020-0861

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Article Contents

1. introduction, 2. model and hypotheses, 3. institutional background, 5. where does procredit locate new branches, 6. what is the impact of procredit on financial inclusion, 7. robustness checks, 8. conclusions, microfinance banks and financial inclusion *.

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Martin Brown, Benjamin Guin, Karolin Kirschenmann, Microfinance Banks and Financial Inclusion , Review of Finance , Volume 20, Issue 3, May 2016, Pages 907–946, https://doi.org/10.1093/rof/rfv026

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We examine how the geographical proximity to a microfinance bank affects financial inclusion. We study the expansion of the branch network of ProCredit banks in South-East Europe between 2006 and 2010. We report three main findings: First, ProCredit is more likely to open a new branch in areas with a large share of low-income households. Second, in locations where ProCredit opens a new branch the share of banked households increases more than in locations where it does not open a new branch. Third, this increase is particularly strong among low-income households, older households, and households which rely on transfer income.

Financial services for the poor are increasingly provided by commercially orientated, deposit taking microfinance banks (MFBs). Among the 485 largest microfinance institutions worldwide, 377 (78%) are regulated deposit taking institutions, among which 240 are profit seeking. 1 In 2011, these large regulated commercial microfinance institutions boasted a combined asset volume of 85 billion USD. The role of commercial MFBs is especially important in emerging economies. In Eastern Europe and Central Asia, for example, 98 of the 101 largest microfinance providers are regulated and 67 of these institutions are profit-seeking MFBs. In this region alone, commercial MFBs together hold a total asset volume of over 14 billion USD.

International donors and development banks support commercial MFBs through subsidized credit lines and equity participation. This support is rationalized by the conjecture that MFBs offer financial services to households which are not served by “ordinary” retail banks (RBs). In emerging economies, however, RBs with large branch networks often provide a broad coverage of financial services across the country. For example in Albania, a country with a population of 2.8 million, the largest RB boasted 102 branches in 2010. The widespread access to ordinary RBs gives rise to the question whether public funding of MFBs is warranted in emerging economies.

In this paper we examine to what extent MFBs foster financial inclusion in emerging economies. We study how the geographical proximity to a new MFB branch affects the use of bank accounts by low-income households in South-East Europe. Our analysis is based on four countries in which the major MFB in the region—ProCredit Bank—expanded its branch network substantially in recent years: Albania, Bulgaria, Macedonia, and Serbia. Our main data source is the EBRD Life in Transition Survey (LITS). This survey provides information on the use of bank accounts, socioeconomic characteristics and geographical location of over 8,000 households in our four countries in 2006 and 2010. We geocode the location of households in the survey and match this data to information on the branch network of ProCredit Bank in 2006 and 2010, as well as the branch network of the major RBs in each country. As the main RBs have large branch networks in all four countries we study the additional effect that new ProCredit branches have in regions which are already served by at least one RB.

Our empirical analysis is guided by hypotheses derived from a model which examines households’ decisions to open bank accounts in a framework where heterogeneous banks choose the location of their branch networks. First, we examine whether ProCredit is more likely to open new branches in regions with a large economically active population as well as a large share of low-income households (location effect). Second, we assess the impact of new ProCredit branches on the share of banked households in the proximity (volume effect) in a difference-in-difference framework. We assign households in regions where ProCredit opens a new bank branch between 2006 and 2010 to a treated group and households in regions where ProCredit does not open a branch to the control group. Households that are surveyed in 2006 constitute the pre-treatment observations while households surveyed in 2010 constitute the post-treatment observations. Third, we conduct subsample analyses in order to study whether the estimated difference-in-difference effect is larger for low-income households compared with high-income households (composition effect).

Our results suggest that ProCredit promotes financial inclusion in South-East Europe. First, we find that ProCredit is more likely to open new branches in regions with strong economic activity, a high population density, and a larger presence of RB branches, and also in regions which have a large share of low-income households. Second, we show that in those locations where ProCredit opens a new branch the share of households with a bank account increases significantly more between 2006 and 2010 than in locations where ProCredit does not open a new branch. The economic magnitude of this effect is significant: Our multivariate estimates indicate that ProCredit leads to a 16–21 percentage point increase in the use of bank accounts. A placebo test in which we replace ProCredit in each country by a RB that showed a similar branch expansion between 2006 and 2010 indicates that our findings are specific to ProCredit. Third, our subsample analyses point to a particularly strong effect of new ProCredit branches on the use of bank accounts among low-income households, older households, and households that rely on transfer income.

South-East Europe provides an ideal laboratory to study the impact of commercial MFBs on financial inclusion in an emerging economy context. First, despite substantial economic growth over the last decade the use of financial services is still low in the region. In the four countries covered by our analysis the incidence of bank accounts varied between 18% and 55% of households in 2006. 2 Second, between 2006 and 2010 the number of bank branches and the share of households with bank accounts increased substantially in all four countries. Third, in this region we can examine the additional effect of a MFB (ProCredit) on financial inclusion, controlling for the presence of ordinary RBs.

From a policy perspective, emerging Europe provides a highly relevant setting to study the potential benefits of public financial support to commercial MFBs. This region has seen considerable foreign direct investment in the retail banking sector over the past decade (see e.g. Giannetti and Ongena, 2009 ; Haselmann, Pistor, and Vig, 2010 ; Ongena, Popov, and Udell, 2013 ; Claeys and Hainz, 2014 ). Today, international banking groups (e.g. Raiffeisen International, UniCredit) maintain retail networks throughout the region. This raises the question whether public investment in the banking sector, e.g. by supporting MFBs, is necessary in these markets. If the retail networks of international banking groups provide similar banking services as MFBs, then public support of the latter is hardly warranted.

Our paper is related to the empirical literature which explores how the structure of the banking sector affects financial inclusion in developing and emerging economies. 3 Allen et al. (2014) examine the relationship between household proximity to a MFB and household use of financial services in sub-Saharan Africa. 4 Similar to our analysis, they study the expansion of the branch network of a large Kenyan MFB (Equity Bank) between 2006 and 2009. They document that compared with other banks, the MFB is more likely to open branches in districts with low population density. Moreover, they show that new MFB branches in a district are associated with a stronger increase in the use of financial services than new branches of other banks. This effect is, as in our data, especially strong among the low-income population. Our analysis complements that of Allen et al. (2014) in two important ways: First, we confirm the impact of commercial MFBs on financial inclusion in an emerging market context where foreign-owned RBs maintain large branch networks. Second, we show at a more granular level, that even in locations where RBs already have a branch, a new MFB branch can enhance financial inclusion—at least in the initial years after its opening. Our more granular analysis is based on matching the precise geographic coordinates of households and bank branches. This use of geographic coordinates also allows us to control for local economic activity by matching household and bank locations with satellite information on nightlight intensity.

Our findings contribute to the broader discussion on bank-ownership structure and access to finance. Beck, Demirgüç-Kunt, and Martinez Peria (2007) use cross-country aggregate data on branch penetration and number of bank accounts to document that government and foreign ownership of banks is negatively associated with access to finance. Beck, Demirgüç-Kunt, and Martinez Peria (2008) examine cross-country information on product terms of large banks and find that barriers for bank customers are higher where banking systems are predominantly government-owned and lower where there is more foreign bank participation. Allen et al. (2012) study household-level data for 123 countries and provide evidence that the use of financial services, especially among low-income households, is strongly related to the costs of banking services and the geographical proximity to financial service providers. They find that the perceived availability of financial services is positively related to state ownership and negatively related to foreign ownership in the banking sector. Beck and Brown (2013) provide evidence that in emerging Europe financially opaque households (households without formal income sources and pledgeable assets) are at a relative disadvantage in credit markets dominated by foreign banks. We contribute to this literature by documenting how the business models of banks, i.e., a focus on serving low-income households by MFBs, affects financial inclusion in emerging markets.

We also contribute to the ongoing debate on the mission drift of commercial microfinance institutions. Examining income-statement and loan portfolio data for 124 of the largest microfinance institutions worldwide for the period 1999–2002, Cull, Demirgüç-Kunt, and Morduch (2007) find some evidence for a mission drift: Larger and more profitable microfinance institutions have higher average loan sizes and serve a lower share of female clients. Mersland and Strøm (2010) examine data for 379 microfinance institutions from seventy-four countries over the period 2001–08 and also find some evidence for a mission drift: More profitable institutions display higher average loan sizes. Their findings suggest, however, that mission drift may be contained if commercial microfinance providers become more cost-efficient. We contribute to this literature by providing household-level evidence (as opposed to bank-level evidence) on how commercial MFBs affect the use of bank accounts (as opposed to loan take up). Moreover, rather than comparing the outreach of commercial MFBs to that of non-profit microfinance institutions, we compare their outreach with that of RBs. In our view, this is the more relevant comparison for policy makers deciding on whether to support commercial MFBs, especially in emerging economies.

The remainder of this paper is organized as follows. In Section 2, we present a model of household deposit and bank location decisions and derive hypotheses for our empirical analysis. Section 3 describes the institutional setting and Section 4 our data. Sections 5 and 6 present our methodology and main results. Section 7 presents robustness checks and Section 8 concludes.

In this section, we derive our empirical hypotheses from a model which explores the choice of heterogeneous households to open bank accounts. Our model is related to that of Mulligan and Sala-i-Martin (2000) who study the extensive margin of holding bank deposits as opposed to cash money. We extend their framework to model the choice of heterogeneous banks to open branches, depending on the expected number of clients and competition in a region.

2.1 Model Set Up

Households live in one of L regions in the economy. There are n l households in each region l . Each household i has wealth A i ∈ [ A ̲ , A ¯ ] and has to decide whether to hold its wealth in cash or to deposit it in a bank.

Households face a fixed cost φ j > 0 of opening a bank account with bank j . The return to a household from opening an account is increasing in wealth. For simplicity we assume that the return is linear in wealth with R j being the return per unit of wealth from an account with bank j . Households only consider local branches of banks when choosing to open a bank account. That is, we assume that the costs of opening an account at a branch in other regions are prohibitively high even for households with the highest wealth level A ¯ . 5

There are two banks in the economy: a MFB and a RB. Both banks choose which regions l to locate branches in. We assume for simplicity that each bank type j has fixed costs of running a branch β j and earns a fixed (exogenous) profit per client served π j .

We assume that the decisions of banks and households take place in two steps: First, the MFB and the RB decide simultaneously in which regions they open branches. Second, given the available bank branches in their region, households decide whether to open a bank account, and—if both banks are present—at which bank to do so. In the following we solve the model by backward induction.

2.2 The Household Deposit Decision

Based on Conditions (1) and (2), we distinguish four types of households with different demand for bank accounts depending on their wealth level A i ∈ [ A ̲ , A ¯ ] :

Households with very low wealth levels A ¯ ≤ A i < φ MFB R MFB will not open a bank account, no matter which type of bank has a branch in their region (Type 1 households).

Households with low wealth levels φ MFB R MFB ≤ A i < φ RB R RB will only open an account if there is a branch of the MFB in their region (Type 2 households).

Households with moderate wealth levels φ RB R RB ≤ A i < φ RB − φ MFB R RB − R MFB will open an account if either of the banks has a branch in their region, but prefer an account at the MFB (Type 3 households).

Households with high wealth levels φ RB − φ MFB R RB − R MFB < A i ≤ A ¯ will open an account if either of the banks has a branch in their region, but prefer the RB (Type 4 households).

2.3 The Bank Branch Location Decision

The decision to open a branch in a region is determined by the number of expected clients and the fixed costs of opening a branch. As each bank type j has fixed costs of running a branch β j and earns a fixed income per client π j the number of clients required for a branch of bank j in region l to break even must exceed β j π j .

We assume that banks know the total population in each region n l as well as the share of Type 1–Type 4 households in each region δ l , 1 , δ l , 2 , δ l , 3 , δ l , 4 . This implies that banks are fully informed about the wealth distribution in each region l . Banks also know the costs and returns of opening a bank account for households at each bank type. Moreover, we assume that banks are informed about the costs of opening a branch and income per client for both bank types.

Based on Equations (3) and (4) we can calculate the profits of both banks from having a branch in region l :

If both banks are in a region the MFB earns π MFB · ( δ l , 2 + δ l , 3 ) · n l − β MFB while the RB earns π RB · δ l , 4 · n l − β RB .

If the MFB is in a region but the RB is not then the MFB earns π MFB · ( δ l , 2 + δ l , 3 + δ l , 4 ) · n l − β MFB while the RB earns 0.

If the MFB is not in a region but the RB is then the MFB earns 0 while the RB earns π RB · ( δ l , 3 + δ l , 4 ) · n l − β RB .

2.4 Model Results and Empirical Hypotheses

Given the income and cost structure of each bank type π MFB , β MFB , π RB , β RB and the population size of a region n l we derive the following results from our model:

Branch location of the MFB: The MFB is more likely to have a branch in regions with a large economically active population n l among which a large share has a low or moderate wealth level δ l , 2 , δ l , 3 . If the RB is not located in a region the share of high-wealth households δ l , 4 also positively affects the decision of the MFB to open a branch.

Financial inclusion: If a MFB has a branch in a region the share of households with a bank account is higher than if the same region is served just by the RB. The additional account holders are characterized by low levels of wealth (Type 2 households).

Note that we have assumed that the relative costs of opening and maintaining a bank account at a MFB compared with a RB ( φ MFB < φ RB ) are identical for all households. This is likely to be the case for the explicit costs of account opening and maintenance, but less so for the non-financial costs induced by different procedures and “cultural barriers” between bank staff and households. Households may differ substantially in their familiarity with banks and their procedures, e.g. due to age, education, economic activity, or social background. Those households with high non-financial costs of using RBs will be more likely to open an account with a MFB. As a consequence we would expect that—conditional on the income distribution—MFBs locate in areas populated by households with high barriers to using RBs. Moreover, in areas where MFBs locate, these banks not only serve lower-income households, but also households which are less familiar with banks and their procedures.

As we discuss in Section 4, our empirical analysis studies the expansion of the branch network of ProCredit Bank in South-East Europe between 2006 and 2010. We hereby focus our analysis on regions which are already served by at least one RB in 2006 and thus examine the additional effect of a new MFB branch in fostering financial inclusion among households in the initial years after its opening. We study three specific research questions: (i) In which regions does ProCredit open a branch? (ii) Does the share of banked households increase in regions where ProCredit opens a new branch compared with regions where ProCredit does not open a branch? (iii) Which types of households display the largest increase in the incidence of bank accounts in regions where ProCredit locates when compared with regions where it does not locate?

Based on the results of our theoretical model we establish the following two hypotheses:

  Hypothesis 1 (location effect): Given the presence of a RB branch in a region, ProCredit bank is more likely to open a new branch in regions with a large economically active population among which there is a substantial share of households with low income.

  Hypothesis 2 (volume and composition effect): Given the presence of a RB, the share of households with a bank account increases more in regions where ProCredit opens a new branch compared with regions where it does not open a branch. The increase in the share of the banked population is stronger among low-income households than among high-income households. Moreover, the increase in the share of the banked population is stronger among households that face higher non-financial barriers to using RBs.

Our analysis studies the expansion of the branch network of the ProCredit banks in four countries of South-East Europe, Albania, Bulgaria, Macedonia, and Serbia, between 2006 and 2010. ProCredit group consists of twenty-one commercial MFBs in emerging and developing countries in Eastern Europe, Latin America, and Africa. 6 All ProCredit banks operate under a local banking license and are regulated by the local banking supervisory agency. ProCredit Holding that holds a controlling stake in all ProCredit banks is owned by a mix of private and public shareholders. 7 The public shareholders expect the ProCredit banks to operate profitably but are not driven by profit maximization aims. They rather include the social return that ProCredit offers in their profit expectations as well. Besides, ProCredit banks may receive public support through subsidized credit lines from their public shareholders and other international donors. ProCredit views its business model as one of “socially responsible banking that seeks to be transparent, efficient, and profitable on a sustainable basis”. It believes that a “functioning and inclusive financial system makes a contribution to a country’s development” and puts the focus of its efforts on achieving this broader aim.

ProCredit offers a wide range of banking services to small and medium enterprises as well as to low- and middle-income savers. Besides small business loans ProCredit considers deposit facilities to be the most important of its core products. ProCredit values the direct and active contact to its (potential) clients and describes its approach as being the neighborhood bank for ordinary people. This approach implies lowering the barriers for (potential) clients to start a formal bank relationship by offering simple and transparent products, also and especially to underserved target groups. This approach also includes providing a wide range of information for customers on the bank web pages. (Potential) depositors, for instance, are informed that they should know the bank they deposit their money with and are then explained the business and lending model of ProCredit.

In sum, the ProCredit banks differ from RBs in important aspects such as their development-oriented business model, their subsidized funding from public sources, and their active and educational client approach. 8 However, some of the products, including their terms, that they offer might not differ significantly from those that the RBs offer. And (as exemplified by our model in Section 2) MFBs and RBs may also have partially overlapping target customer groups.

We focus our analysis on Albania, Bulgaria, Macedonia, and Serbia over the period 2006–10 for three reasons: First, during this period the ProCredit banks in all four countries expanded their branch networks considerably. As documented by Table AI in the Appendix the number of ProCredit branches increased from 16 to 42 in Albania, from 42 to 87 in Bulgaria, from 16 to 42 in Macedonia, and from 35 to 83 in Serbia. Second, in all of these countries the use of bank accounts by households was low in 2006 (between 18% and 55%), but increased sharply between 2006 and 2010 ( Beck and Brown, 2011 ). Third, for each of these countries we can match bank-branch location data to survey data which provide household-level information on the use of bank accounts in 2006 and 2010. 9

In all four countries the ProCredit banks were founded in the early 2000s 10 and had established a substantial branch network by 2006. However, ProCredit is neither the largest bank (measured by total assets) nor the most accessible bank (as measured by branch network) in any of the countries. Table AI in the Appendix shows that in 2006 the largest RB in Albania (Bulgaria, Macedonia, Serbia) had five (three, three, five) times more branches than ProCredit. Moreover, between 2006 and 2010 these RBs also expanded their branch networks substantially. Table AI in the Appendix also documents that the largest RBs in all four countries are either foreign-owned or state-owned. These conditions allow us to examine the impact of a commercially operated MFB on financial inclusion in a context which is common to many emerging economies: The economy is served by several RBs with large branch networks and many of these banks are controlled by foreign financial institutions or the domestic government.

Our main data source is the EBRD-World Bank LITS which was conducted in 2006 and 2010 as a repeated cross-sectional survey. In each of the countries in our sample fifty to seventy-five Primary Sampling Units (PSUs) were randomly chosen for each survey wave. 11 Then twenty households within each PSU were randomly selected, resulting in 1,000–1,500 observations per country and survey wave.

The first part of the interviews was conducted with the person deemed to have the most knowledge on household issues (household head) and yields information on household composition, housing, expenses, and the use of (financial) services. For the second part of the survey, a randomly selected adult household member (respondent) was interviewed about attitudes and values as well as the personal work history, education, and entrepreneurial activity. For the purpose of our study, we use information from the first part of the survey to obtain indicators of household use of banking services, location, size, and income as well as the gender and age of the household head. From the second part of the survey, we obtain indicators of education, employment status, religion, and social integration. We drop all observations with missing household-level information which leads to a sample of 3,992 household-level observations in 2006 and 4,244 household-level observations in 2010. 12 Table AII in the Appendix provides the definitions of all variables which we employ in our analysis, while Table AIII in the Appendix provides summary statistics of these variables by survey wave.

4.1 Use of Bank Accounts

The main dependent variable in our empirical analyses is the dummy variable Account which indicates whether any member of the household has a bank account. Table I shows that the share of households which hold a bank account varies substantially across regions within each of the four countries. For example, in 2006 19% of the households in Albania have a bank account. However, in some PSUs 70% of the households have a bank account, while in other PSUs none of the surveyed households have an account. By 2010 the share of banked households in Albania increased to 45%. However, even in 2010 there are some regions in the country where none of the surveyed households have an account. Table I shows similar patterns for the share of households with bank accounts in Bulgaria, Macedonia, and Serbia. Thus, while the use of bank accounts increased substantially during our observation period, this development occurred very unevenly within each country.

Use of bank accounts by country in 2006 and 2010

This table reports the share of households with a bank account on the PSU level per country in 2006 (Panel A) and in 2010 (Panel B). Definitions and sources of the variables are provided in Table AII in the Appendix.

4.2 Proximity to Bank Branches

The LITS data provides information on the village/municipality in which each PSU is located. For the four countries in our sample, we obtain the geographical coordinates of each PSU using Google maps. We obtain geographical information on the branch network of banks in each country in 2002, 2006, and 2010 from the EBRD. We augment this data with hand-collected information from banks’ websites and annual reports. Our branch location information covers five (in Macedonia three) major RBs that together account for more than 50% of the bank branches in each country. 13 For each country we also gather information on the branch network of a RB that is similar to ProCredit in terms of its foreign ownership, size of its branch network in 2006, and the expansion of its branch network between 2006 and 2010 in order to run a placebo test as a robustness check for our results. We specify the exact location of each bank branch in terms of the latitude and longitude again using Google maps. Table AI in the Appendix lists all banks included in our analysis. Our Supplementary Material presents a cartographical overview of the locations of PSUs and bank branches by country in 2006 and 2010.

We measure the proximity between households and bank branches at each point in time with the dummy variables ProCredit close in 2006 (2010) and RBs close in 2006 (2010). These indicators are one if the nearest ProCredit branch or RB branch, respectively, is within a travel distance of 5 km of the center of the PSU in which a household is located in 2006 (2010). We use distance thresholds as opposed to continuous measures of travel distance in order to capture the idea that the fixed costs of opening and maintaining a bank account depend on whether a household is within walking, cycling, or local public transport distance of a bank branch or not. We employ a 5-km threshold as previous research suggests that even corporate clients typically bank with financial institutions that are within this narrow radius ( Petersen and Rajan, 2002 ; Degryse and Ongena, 2005 ). As a robustness test we employ a travel distance cut-off of 10 km (see Section 7.2).

Table II documents the proximity of the households in our sample to a ProCredit branch and RB branch in 2006 and 2010. Given that the LITS is a repeated cross-section survey with changing PSUs per wave we observe households either in 2006 or 2010. Importantly though, for each PSU we observe whether that PSU was close to a particular bank branch in 2006 as well as in 2010.

Proximity of PSUs to ProCredit and RB branches in 2006 and 2010

This table shows the number of PSUs by the proximity to ProCredit branches and RB branches and by the year of the LITS survey (2006 or 2010). Closeness of bank branches is defined by 5 km thresholds. Panel A shows the number of PSUs by RBs close in 2006 or ProCredit close in 2006 (for both LITS 2006 and LITS 2010 observations). Panel B shows the number of PSUs and the number of households (in parentheses) by ProCredit close in 2010 for all PSUs where at least one RB branch and no ProCredit branch was close in 2006 (for both LITS 2006 and LITS 2010 observations). Definitions and sources of all variables are provided Table AII in the Appendix.

Panel A shows the distribution of PSUs in the 2006 and 2010 survey waves depending on which banks were close in 2006. As we want to explore the branch expansion of ProCredit bank between 2006 and 2010 we are primarily interested in the PSUs which are not close to ProCredit in 2006. 14 Our analysis is focused on the 100 PSUs (forty-seven in the 2006 wave and fifty-three in the 2010 wave) that were already close to a RB branch in 2006 but not close to a ProCredit branch. Panel B of Table II shows that among these 100 PSUs fifty-four are close to ProCredit in 2010, while forty-six remain distant from ProCredit. The comparison of the households in these two sets of PSUs allows us to estimate the additional effect of a new ProCredit branch on households’ use of bank accounts given that these households have already access to at least one RB.

As shown in Table II , there are also 151 PSUs (seventy-seven observed in 2006 and seventy-four observed in 2010) which are not close to ProCredit and also not close to a RB in 2006. However, only thirteen of these PSUs are close to a RB branch by 2010, while only three are close to a ProCredit branch by 2010. Thus, it seems that those regions which are not served by either bank type in 2006 are also not served in 2010. These PSUs provide no variation that we could exploit in our empirical analysis.

In this section, we examine the first hypothesis derived from our theoretical model: We study whether ProCredit is more likely to open new branches in regions with a larger economically active population and a higher share of low-income households.

5.1 Methodology

In Model (5) there are two coefficients of primary interest: β 1 captures the relation between the economically active population in the PSU ( ECONPOP PSU ) and the location decision of ProCredit. Coefficient β 2 captures the relation between the share of low-income households in the PSU ( LOWINCOME PSU ) and the location decision of ProCredit.

A key challenge to estimating Model (5) is to obtain accurate measures of our two main explanatory variables: the economic active population and the share of low-income households for the 100 locations (PSUs) we are studying.

As a proxy for local economic activity we use the light intensity at night in the area where each PSU is located. This proxy is based on Henderson, Storeygard, and Weil ( 2011 , 2012 ) who show that satellite nightlights data are a useful measure for economic activity in geographic regions where national accounts data are of poor quality or unavailable. The nightlight indicator is measured on a scale ranging from 0 to 63, whereby a greater value indicates higher light intensity. Matching on the geographic coordinates for the 100 PSUs in our sample we calculate the average nightlight intensity around each location for each year over the period 2002–10. 15 We employ two indicators of nightlight in Model (5): Nightlight 2006 captures the nightlight intensity and thus level of economic activity and population density in 2006, while D.Nightlight (2010–2006) captures the increase in nightlight intensity and thus the increase in economic activity and population density in the location between 2006 and 2010. With these two indicators we can disentangle whether ProCredit locates new branches in regions which already have a large economically active population in 2006 or in regions where the population and economic activity grows faster over our observation period. Our Supplementary Material illustrates the nightlight intensity data for our four countries, as measured in 2010.

In our sample, the nightlight intensity ranges from 0 in very remote and unpopulated areas to sixty-three in the respective capitals and economic hubs. Figure 1 (first graph) depicts the average nightlight intensity over the period 2002–10 for the fifty-four PSUs where ProCredit opens a new branch between 2006 and 2010 and for the forty-six PSUs where it does not. The figure suggests that the level of economic activity is substantially higher in areas where ProCredit opens a new branch. The figure, however, also suggests that the difference in economic activity for regions where ProCredit locates new branches compared with where it does not is constant over our observation period (and even well before our period). This visual inspection provides a first indication that the location decision of ProCredit is based on the level rather than the dynamics of economic activity.

Night light intensity, population density, & RB branches. The figure visualizes summary statistics for the variables capturing night light intensity, population density, and the number of RB branches per PSU for the sample of PSUs which are close to RBs in 2006 and 2010 but not close to ProCredit in 2006. The first graph displays night light intensity over the period 2002–10 distinguishing between PSU in which ProCredit locates between 2006 and 2010 and PSU where ProCredit is not preset in 2010. The second graph displays the correlation between night light intensity in 2006 and population density in 2006. The third graph displays the number of bank branches distinguishing between PSU in which ProCredit locates between 2006 and 2010 and PSU where ProCredit is not close in 2010. The proximity of a PSU to RB/ProCredit branches is defined based on 5 km thresholds. Definitions and sources of the variables are provided in Table AII in the Appendix.

Recent evidence suggests that—in a cross-country context—the accuracy of nightlight imagery as a proxy of economic activity depends strongly on the structure of economic activity and the urban–rural population distribution ( Ghosh et al., 2010 ). In particular, nightlight imagery has been shown to be a less precise indicator for economic activity in regions with a substantial share of agricultural production and rural population. Following Ghosh et al. (2010) we therefore employ additional measures of the population density for each PSU in our sample provided by the LandScan database. 16 The variable Population 2006 (Ln) captures the natural logarithm of the population estimate for a radius of 9 km around the geographic coordinate of a PSU. The variable D.Population (2010–2006) is a dummy variable which takes on the value 1 if the within-country ranking of the PSU in terms of population estimate increased between 2006 and 2010. 17

Figure 1 (second graph) shows that in our sample the level of economic activity in 2006 and the population density in 2006 are highly correlated: The pairwise correlation between Nightlight 2006 and Population 2006 (Ln) is 0.75 ( n  = 100, P  < 0.01). In our baseline estimates of Model (5), we therefore enter the indicators Nightlight 2006 and Population 2006 (Ln) alternatively as measures of the level of the economically active population. In robustness tests, we include Population 2006 as well as the variable Nightlight 2006 (orthogonalized), i.e., the error terms of a regression of Nightlight 2006 PSU  =  α  +   β ·Population 2006 PSU  +   є PSU . We do this to examine whether controlling for population density, non-agricultural production—which would be captured by Nightlight 2006 (orthogonalized)—has an impact on the location decision of ProCredit. The change in economic activity between 2006 and 2010 is hardly correlated with our measure of the change in (relative) population density: The mean (standard deviation) of D.Nightlight (2010–2006) is 1.57 (2.62) for PSUs with D.Population (2010–2006) = 1 and 1.50 (3.25) for PSUs with D.Population (2010–2006) = 0.

Our indicator of the share of low-income households in each location is directly taken from the LITS survey. For each household from each survey wave we obtain an estimate of annual income based on annual expenses data (OECD equivalized per capita). A household is defined as a Low-income household (Middle-income household, High-income household) if it is in the lowest (intermediate, upper) tercile of the income distribution for the respective country in that survey wave. 18 The upper threshold for the first income tercile ranges from 1,141 USD (Bulgaria in 2006) to 2,864 USD (Serbia in 2010). The lower threshold for the third income tercile ranges from 2,160 USD (Bulgaria in 2006) to 4,536 USD (Serbia in 2010). For each PSU we calculate the Share of low-income households as the fraction of the surveyed households in that PSU which are low-income households. The variables Share of middle-income households and Share of high-income households are calculated accordingly.

Our hypothesis for the location effect suggests that we should find a positive relation between our indicators of population and economic activity (Nightlight 2006, D.Nightlight (2010–2006), Population 2006 (Ln), D.Population (2010–2006)) and our dependent variable ProCredit close in 2010. In addition, we should find a positive relation between Share of low-income households and ProCredit close in 2010. However, even if we do observe the expected positive correlations, endogeneity concerns imply that these may not be interpreted in the causal manner suggested by our location hypothesis. In particular, our estimates are likely to be plagued by omitted variable bias: other characteristics of the PSUs in our sample may trigger the location decision of ProCredit and these characteristics may be correlated with economic activity, population density, and the share of low-income households.

We add a vector of PSU-level control variables X PSU to our regression Model (5) in order to mitigate concerns about omitted variable bias. Our main control variables capture the structure of economic activity within a PSU. These indicators are taken from the LITS survey data: Each household reports whether its major source of household income is Wage income, whether it is mainly Self-employed or whether it relies mainly on Transfer income. Based on these individual responses we calculate the share of households in a PSU which report that wage employment is their main income source (Share wage income per PSU). Likewise we calculate the share of households that reports that self-employment is their main income source (Share self-employed per PSU).

We further control for the number of RB branches operating in a location. Note that our sample only includes PSUs which are already close to a RB in 2006. However, within this sample the number of RBs close to a PSU in 2006 (Number of RBs in 2006) as well as the change in this number between 2006 and 2010 (D.Number of RBs (2010–2006)) varies strongly. We control for both variables in order to account for the fact that ProCredit may just be opening up new branches where other banks are also opening up new branches. Figure 1 (third graph) documents that the decision of ProCredit to open new branches between 2006 and 2010 decision is strongly related to Number of RBs in 2006, but hardly to D.Number of RBs (2010–2006). Finally, we add country fixed effects α c to account for differences in the economic and regulatory environment across the four countries in our sample.

5.2 Results

Table III presents multivariate results for the location effect. The specifications presented in Columns (1–4) all include our main variable Share of low-income households and an indicator of economic activity/population density. The four specifications differ, however, in how we account for economic activity and population density during our observation period, and which PSU-level control variables we include. All models are estimated with a linear probability model. 19

Location effect

This table shows the estimates of a linear probability model where the dependent variable is ProCredit close in 2010. The parameters are estimated for PSUs where at least one RB branch was close in 2006 and in 2010 and no ProCredit branch was close in 2006. PSU control variables are Share of middle-income households, Share wage income per PSU, and Share self-employed per PSU. Observations are on the PSU level. Ordinary standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 level, respectively. Definitions and sources of the variables are provided in Table AII in the Appendix.

In Column (1) of Table III we control for population density and economic activity with our nightlight indicators (Nightlight 2006, D.Nightlight (2010–2006)) only. In Column (2) we replace these indicators with our measures of the local population density (Population 2006, D.Population (2010–2006)). In Column (3) we enter Population 2006, D.Population (2010–2006), D.Nightlight (2010–2006), as well as Nightlight 2006 (orthogonalized). This specification allows us to examine whether—for a given population density—non-agricultural economic activity affects the location decision of ProCredit. Column (4) provides a robustness test of Column (3) examining whether non-agricultural economic activity plays a more important role for the location decision of ProCredit in rural versus urban areas. To this end we add the dummy variable Rural (which is 1 for non-urban PSUs) and the interaction term Nightlight 2006 (orthogonalized) * Rural. The Column (1)–(4) models all include PSU-level control variables for the level and sources of regional income: Share of middle-income households, Share wage income per PSU, Share self-employed per PSU. In Column (5) we add our control variables Number of RBs in 2006 and D.Number of RBs (2010–2006) to examine whether ProCredit locates where economic activity is high, or whether the bank just follows other banks.

In line with our location hypothesis Table III results suggest that between 2006 and 2010 ProCredit is more likely to open a new branch in locations with a high Share of low-income households. The economic magnitude of this location effect is sizeable: Column (1)–(5) estimates suggest that a one standard deviation increase in the share of low-income households (0.22) increases the probability of ProCredit entering a location by 11–15 percentage points.

In line with our location hypothesis (and as illustrated by Figure 1 ) Table III results also suggest that ProCredit opens new branches in regions which already have a large, economically active population in 2006. In Column (1) we obtain a statistically and economically significant effect of Nightlight 2006: A one standard deviation increase in nightlight intensity (roughly 18 units) increases the probability of ProCredit opening a branch by 20 percentage points. Similarly, the estimate for Population 2006 (Ln) in Column (2) suggests that a one standard deviation increase in the population density (1.15) increases the probability of ProCredit opening a branch by 15 percentage points. These estimated effects are large compared with the unconditional probability of ProCredit opening a branch in 1 of the 100 PSUs in our sample (54%). By contrast the small and insignificant estimates for D.Nightlight (2010–2006) in Column (1) and D.Population (2010–2006) in Column (2) suggest that the location decision of ProCredit is not significantly related to the change in local economic activity or population density over our observation period. The Column (3)–(4) results show that our main findings for the location effect as presented in Column (2) are robust to accounting for potential effects of agricultural versus non-agricultural activity.

Column (5) estimates in Table III , however, cast some doubt on a causal interpretation of the observed relation between the location decision of ProCredit and the level of economic activity in a PSU (Nightlight 2006, Population 2006 (Ln)). In this model we control for the level and the change in the number of other banks operating in each location. The results show that the location decision of ProCredit is strongly correlated with Number of RBs in 2006: A one standard-deviation increase in Number of RBs in 2006 (11.5) increases the probability of ProCredit opening a branch by 78 percentage points. By contrast, the coefficient of Population 2006 (Ln) loses economic and statistical significance, once we control for the number of other bank branches operating in an area. There are two interpretations of the finding: On the one hand, the location decision of ProCredit may be primarily driven by a strategy of following other banks, rather than of locating in areas with a large, economically active population. On the other hand, the number of other bank branches located in an area may simply be a better indicator of local economic activity than nightlight imagery and local population estimates. In this case, Column (5) results would support our location hypothesis that ProCredit does locate in economically active areas.

In this section we examine whether—as suggested by our second hypothesis—the opening of a ProCredit branch in a location increases the number of banked households, and whether this effect is particularly strong among low-income households.

6.1 Methodology

To estimate the impact of ProCredit on financial inclusion we conduct a household-level analysis. We use a difference-in-difference framework that compares the use of bank accounts by a treated group of households (those in locations where ProCredit opens a new branch between 2006 and 2010) to a control group of households (those in locations where ProCredit does not open a branch between 2006 and 2010).

To estimate the differential effect in the use of bank accounts between the treated and control groups we would ideally observe the same households in 2006 and 2010. The LITS data, however, consist of two repeated cross-sections from which we construct a “pooled” panel sample. To the treated group we assign all households in the fifty-four PSUs that were not close to ProCredit in 2006 but close in 2010. The control group then consists of all households in the forty-six PSUs that were not close to ProCredit in both years. Households that are observed in the 2006 wave serve as the pre-treatment observations, while households observed in the 2010 wave serve as the post-treatment observations. As Panel B of Table II shows, our data provide us with a similar number of pre-treatment and post-treatment observations for both the treated and control groups.

In Model (6) the coefficient β 1 captures the increase in account use in the control group. The coefficient β 2 captures the pre-treatment difference in account use (i.e., among households observed in 2006) between the treatment and control group. The coefficient β 3 for the interaction term LITS   2010 * ProCredit close in  2 0 1 0 PSU is our effect of interest in this model. This coefficient captures the difference-in-difference effect in account use between the 2006 and 2010 households comparing the treatment group to the control group. We expect this coefficient to be positive and significant if a new ProCredit branch leads to an increase in the share of banked households (volume effect). Moreover, we expect this coefficient to be especially strong in the subsample of low-income respondents if, as suggested by our model, MFBs foster financial inclusion of low-income households (composition effect).

The identification of the difference-in difference effect crucially depends on the common trend assumption which implies that the increase in bank account use would have been the same in the treatment and control groups in the absence of treatment (i.e., if ProCredit had not opened new bank branches). Unfortunately, we have neither household-level nor PSU-level information on the financial inclusion of households in our sample prior to 2006. Thus, we cannot test the common trend assumption using pre-treatment information (under the assumption that pre-treatment would have behaved the same way as after the treatment). We resort to controlling for all household and PSU characteristics which may affect the use of bank accounts by households in the pre-treatment and post-treatment observations of the treatment and control groups.

The vector of household controls X i accounts for differences in household characteristics between the treatment and control households, in both the pre-treatment observations (2006 LITS wave) and the post-treatment observations (2010 LITS wave). We employ control variables to capture variation in household demand for financial services as well as the transaction costs of using these services. The variable Income measures annual household expenses (in log USD), 21 while the income source of a household is captured by the dummy variables Wage income and Self-employed. University degree indicates whether the respondent has tertiary-level education. We also include Household size, as well as the Age and gender (Female) of the household head. The variables Language and Muslim are measures of social integration. 22 We further control for the ownership of a Car, Computer, or Mobile phone as well as Internet access of the household. These indicators account for differences in the transaction costs of using a bank account, but may also be related to economic activity and household income.

Our analysis in Section 5 documented that the decision of ProCredit to open a new branch is non-random. In estimating Model (6) we are therefore confronted with a potential omitted variable bias: Between 2006 and 2010 ProCredit may have opened branches in locations which experienced structural developments which would have led to an increase in the use of bank accounts (for households with a given socioeconomic profile X i ) even if ProCredit did not locate there. For example, improvements in the infrastructure (better roads, public transport) may have reduced the transaction costs of using a bank account (for all households) and also encouraged ProCredit to locate in a region. Also, changes in the structure of local income sources (e.g., more inward remittance transfers from migrant family members) may have encouraged the use of bank accounts through network effects and also encouraged ProCredit to locate in a region.

We mitigate concerns about omitted variables by including a vector Z P S U of PSU-level control variables already employed in our analysis of the location effect. To be precise we control for all PSU-level characteristics which are included as explanatory variables in Column (4) of Table III . Most noteworthy among these PSU-level controls are the level and the change in the number of RB branches in the PSU, Number of RBs (2006), and D.Number of RBs (2010–2006). We would expect that any structural development in a location that would lead to an increase in the use of bank accounts—in the absence of ProCredit—would be associated with a stronger presence of ordinary RBs in that location. The variables Number of RBs (2006) and D.Number of RBs (2010–2006) thus provide us with indicators of the level and change in the attractiveness of each PSU for banks and directly address the endogeneity concerns alluded to above. Finally, we use country fixed effects α C, or alternatively regional fixed effects α R, to account for aggregate differences in economic conditions which may have affected the use of bank accounts. 23

6.2 Results

Table IV , Columns (1)–(3) present our difference-in-difference estimates for the volume effect based on Model (6). In Column (1) we control for differences in household characteristics and country fixed effects. In Column (2) we replace country fixed effects with regional fixed effects. In Column (3) we add our vector of PSU-level control variables to the Column (1) specification. The explanatory variable of main interest is the interaction term LITS 2010 * ProCredit close in 2010. It captures the difference-in-difference effect and reports the differential increase in the use of bank accounts between 2006 and 2010 for households in areas where ProCredit opens a new branch versus households in areas where ProCredit does not open a branch.

Volume effect and composition effect

This table displays the estimates of a linear probability model where the dependent variable is Account. The parameters are estimated for households located in PSUs where at least one RB branch was close in 2006 and in 2010 and no ProCredit branch was close in 2006. Household control variables are Income, Wage income, Self-employed, University degree, Household size, Age, Female, Language, Muslim, Car, Computer, Mobile phone, Internet. PSU control variables are D.Nightlight (2010–2006), Nightlight 2006 (orthogonalized), Population 2006 (Ln), D.Population (2010–2006), Rural, Nightlight 2006 (orthogonalized) * Rural, Average income per PSU, Share wage income per PSU, Share self-employed per PSU, Number of RBs in 2006, and D.Number of RBs (2010–2006). Region FE corresponds to NUTS 2 regions per country. Observations are on the household level. Standard errors are clustered on the PSU level and are reported in parentheses. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 level, respectively. Definitions and sources of the variables are provided in Table AII in the Appendix.

Table IV documents a strong increase in account use in PSUs where ProCredit opens new branches compared with PSUs where it does not. Controlling for differences in socioeconomic characteristics across households in Columns (1) and (2) the estimated difference-in-difference effect of a new ProCredit branch (LITS 2010 * ProCredit close in 2010) is 16–18 percentage points. Both estimates are significant at the 10% level. In Column (3) we find that controlling for differences in socioeconomic conditions between treated and untreated PSUs strengthens our estimate both in statistical and economic terms: Households in PSUs where ProCredit opens a branch display a 21 percentage point higher increase in account use than households in PSUs where ProCredit does not locate. By comparison the aggregate increase in account use in our sample between 2006 and 2010 is 25 percentage points (see Table I ). 24

The results in Table IV provide evidence of a significant volume effect induced by the expansion of the ProCredit Bank branch network between 2006 and 2010. Our theoretical model suggests that given the presence of a RB in all of the regions where ProCredit expanded this volume effect should be mostly attributed to low-income households. In Table IV , Columns (4)–(6) we examine which households benefit most from the expansion of the ProCredit branch network. We replicate our analysis from Column (3) of Table IV for three subsamples of households: low-income, middle-income, and high-income households. 25

Table IV , Column (4)–(6) results document that our difference-in-difference estimate of the effect of ProCredit is especially strong for the low- and middle-income households. Our estimates for the low-income subsample in Column (4) as well as for the middle-income sample in Column (5) are statistically significant and similar in economic magnitude (21 percentage points) to our full sample results in Column (3). By contrast the estimate for the high-income sample in Column (6) is weaker in terms of economic magnitude (14 percentage points) and is statistically insignificant.

While the absolute magnitude of our difference-in-difference estimate is larger for low- and middle-income households than for high-income households statistical tests cannot reject equality of the subsample estimates. 26 However, a comparison of the relative magnitude of our estimates underscores the substantial differences in the effect of a new ProCredit branch across income groups. The share of banked households is 38% among our low-income subsample. Relative to this share the point estimate of our difference-in-difference effect in this subsample (21.6 percentage points) would imply an increase in the share of banked households by 55%. By comparison, the relative increase in the share of banked households suggested by our estimates is 43% for middle-income households and only 25% for high-income households. Thus, our estimates for the relative impact of a new ProCredit branch on the share of banked households is more than twice as large for low-income households than it is for high-income households. Taken the above results together, the heterogeneous treatment effects observed across income groups in Table IV provide indicative support to our conjecture that the volume effect of new MFB branches may go hand in hand with a composition effect: Low-income households may benefit most.

In Table V we explore further potential heterogeneities in the impact of a ProCredit branch on financial inclusion across different household types. In all seven columns of the table we replicate our preferred specification from Table IV (Column 3) for different subsamples of households. In Columns (1) and (2) we split our sample by the gender of the household head. In Columns (3) and (4) we split our sample by the age of the household head (above or below the median age of 54 years). Finally, in Columns (5)–(7) we split our sample by the main income source of the household: wage income, self-employment, or transfer income (among which the overwhelming majority are pensions).

Volume effect by household head characteristics

This table displays the estimates of a linear probability model where the dependent variable is Account. The parameters are estimated for households located in PSUs where at least one RB branch was close in 2006 and in 2010 and no ProCredit branch was close in 2006. Household control variables are Income, Wage income, Self-employed, University degree, Household size, Age, Female, Language, Muslim, Car, Computer, Mobile phone, Internet. PSU control variables are D.Nightlight (2010–2006), Nightlight 2006 (orthogonalized), Population 2006 (Ln), D.Population (2010–2006), Rural, Nightlight 2006 (orthogonalized) * Rural, Average income per PSU, Share wage income per PSU, Share self-employed per PSU, Number of RBs in 2006, and D.Number of RBs (2010–2006). Observations are on the household level. Standard errors are clustered on the PSU level and are reported in parentheses. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 level, respectively. Definitions and sources of the variables are provided in Table AII in the Appendix.

The column (1)–(2) results suggest no gender difference in the effect of ProCredit on the use of bank accounts. By contrast we find that the impact of ProCredit on financial inclusion does differ by household age and by primary income source. The Column (3)–(7) results show that the difference-in-difference estimate is particularly large for older households (26 percentage points compared with 14 percentage points for younger households) and for households that receive transfer income (29 percentage points compared with 18 percentage points for receivers of wage income and 10 percentage points for the self-employed). Statistical tests cannot reject the equality of the difference-in-difference estimates by household age or income source. 27 However, again the size of the ProCredit effect appears much stronger for older households and those that receive transfer income when comparing the point estimates to the respective subsample shares of banked households. For example the point estimate for households that rely on transfer income (0.289) amounts to 74% of the share of banked households (0.39) in this subsample. By comparison, the point estimate for households that rely on wage income (0.182) amounts to only 33% of the share of banked households (0.57) in this subsample.

Table V results point to an interesting result: In South-East Europe ProCredit seems to have fostered the financial inclusion of a specific demographic group which appears to be underserved by ordinary RBs: elderly households. This result is supported by statements of ProCredit senior management suggesting that ProCredit actively targeted elderly people in South-East Europe who had some savings but no account to help them open a formal account in which to deposit their pensions and to provide them with a way to save for their (grand-)children. 28

The finding that elderly households may be particularly inclined to open an account with a development orientated MFB is in line with the composition effect suggested by our theoretical model: For older households the simple and transparent products provided by MFBs may imply lower non-financial costs of opening and maintaining an account compared with a regular RB. The heterogeneous treatment effects by household income source also support the conjecture that MFBs encourage financial inclusion among households with stronger “cultural barriers” to RBs.

In Table VI we examine whether the expansion of the ProCredit branch network in South-East Europe had an impact on households beyond their use of bank accounts. This analysis is motivated by Bruhn and Love (2014) who show that improved access to financial services can have pronounced effects on real economic outcomes for low-income households. They study the expansion of Banco Azteca in Mexico and show that in regions where Azteca opened up a branch low-income households experienced a decline in unemployment and an increase in income.

Cards and real effects

This table displays the estimates of a linear probability model where the dependent variables are Card, Car, Income, Some self-employed, Some wage income. The parameters are estimated for households located in PSUs where at least one RB branch was close in 2006 and in 2010 and no ProCredit branch was close in 2006. Household control variables are University degree, Household size, Age, Female, Language, Muslim, Computer, Mobile phone, Internet. PSU control variables are D.Nightlight (2010–2006), Nightlight 2006 (orthogonalized), Population 2006, D.Population (2010–2006), Rural, Nightlight 2006 (orthogonalized) * Rural, Average income per PSU, Share wage income per PSU, Share self-employed per PSU, Number of RBs in 2006, and D.Number of RBs (2010–2006). Observations are on the household level. Standard errors are clustered on the PSU level and are reported in parentheses. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 level, respectively. Definitions and sources of the variables are provided in Table AII in the Appendix.

Our conjecture is that the expansion of ProCredit in South-East Europe is unlikely to be associated with similar effects on household income and employment. The reason is that one of the key services of Banco Azteca is to provide credit for durable goods purchases to households and entrepreneurs, whereas ProCredit focuses mainly on providing savings services to households. We therefore expect that household clients of ProCredit are most likely to use payment and savings services to accommodate their existing streams of income and expenses rather than to alter their economic activities. This conjecture is further supported by Table V finding that the impact of ProCredit on financial inclusion is strongest among older households and receivers of transfer income.

Table VI results confirm our expectations: A ProCredit branch has no differential effect on the likelihood of households to use bank cards or to own durable consumption goods. Moreover, ProCredit has no differential effect on income levels or income sources of households. In Table VI we replicate our preferred model from Table IV (Column 3), replacing the dependent variable Account with measures of bank card usage, durable consumption, income levels, and sources of income. In Column (1) the dependent variable Card indicates whether any member of the household has a debit or credit card. In Column (2) the variable Car captures whether some member of the household owns a car. In Column (3) the variable Income measures annual household expenses. In Columns (4) and (5) the variables Some self-employment and Some wage income indicate whether the household yields any income from either of these sources. In all columns we find an insignificant coefficient of our difference-in-difference estimator LITS 2010*ProCredit close in 2010.

7.1 Placebo Test

The analysis so far has shown that the opening of new branches of a commercial MFB can foster financial inclusion beyond what normal RBs do: A ProCredit branch is associated with an increased share of banked households, especially among the low-income and older population.

While our multivariate analysis controlled for the change in the number of RBs located in each PSU, one might still be concerned whether our results are indeed driven by a change in the type of banks operating in a region, e.g., the opening of a MFB branch, as opposed to just an increase in the number of banks competing in a region.

To confirm that our results are institution-specific we replicate our Table III and Table IV results replacing ProCredit with a Placebo bank. In each country we choose a Placebo bank which is similar to ProCredit with respect to its foreign ownership, the number of branches in 2006 and the expansion of its branch network until 2010. Table AI in the Appendix provides information on the chosen banks and their branch networks. 29

We conduct our placebo test on households in 88 PSUs which were (i) close to a RB in 2006, (ii) not close to ProCredit in 2006, and (iii) not close to the Placebo banks in 2006. Among these eighty-eight PSUs, the Placebo banks open new branches in thirty-one PSUs between 2006 and 2010.

The multivariate analysis of the Placebo bank’s location decision in Table VII provides evidence that the location decision of the Placebo bank is similar to that of ProCredit: We find that the Placebo bank also opens new branches in areas with higher economic activity in 2006, and also with a higher share of low-income households. Table VII results suggest that given the presence of established RBs which may already be serving high-income clients, new retail entrants target similar regions as the MFB when they expand their branch networks. However, do these RBs also increase the use of financial services, and foster the financial inclusion of low-income households?

Location effect (Placebo bank)

This table shows the estimates of a linear probability model where the dependent variable is Placebo bank close in 2010. The parameters are estimated for PSUs where at least one RB branch was close in 2006 and in 2010 and no ProCredit branch was close in 2006 and no Placebo bank branch was close in 2006. PSU control variables are Share of middle-income households, Share wage income per PSU and Share self-employed per PSU. Observations are on the PSU level. Ordinary standard errors are reported in parentheses. ***, **, and * denote statistical significance at the 0.01, 0.05 and 0.10, level respectively. Definitions and sources of the variables are provided in Table AII in the Appendix.

Table VIII presents the difference-in-difference results for the volume effect (Columns 1–3) and the composition effect (Columns 5–7) of the Placebo bank. In contrast to our results for ProCredit we find no significantly positive coefficient for the difference-in-difference term (LITS 2010 * Placebo bank close in 2010). This suggests that the use of bank accounts does not increase more in areas where the Placebo bank opens a new branch compared with areas where it does not open a new branch. And even though the Placebo bank opens its new branches in areas with a higher share of low-income households, these households do not benefit by experiencing a disproportionate increase in bank accounts.

Volume effect and composition effect (Placebo bank)

This table displays the estimates of a linear probability model where the dependent variable is Account. The parameters are estimated for households located in PSUs where at least one RB branch was close in 2006 and in 2010 and no ProCredit branch was close in 2006 and no Placebo bank branch was close in 2006. Household control variables are Income, Wage income, Self-employed, University degree, Household size, Age, Female, Language, Muslim, Car, Computer, Mobile phone, Internet. PSU control variables are D.Nightlight (2010–2006), Nightlight 2006 (orthogonalized), Population 2006 (Ln), D.Population (2010–2006), Rural, Nightlight 2006 (orthogonalized) * Rural, Average income per PSU, Share wage income per PSU, Share self-employed per PSU, Number of RBs in 2006, and D.Number of RBs (2010–2006). Region FE correspond to NUTS II regions per country. Observations are on the household level. Standard errors are clustered on the PSU level and are reported in parentheses. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 level, respectively. Definitions and sources of the variables are provided in Table AII in the Appendix.

In Column 4 of Table VIII we directly test the volume effect of ProCredit against the volume effect of the Placebo bank. To this end we again look at those PSUs that were close to at least one RB in 2006 and 2010 but not close to ProCredit nor close to the Placebo bank in 2006. We then replicate the analysis of Column (3) but jointly estimate the difference-in-difference effect for ProCredit (LITS 2010 * ProCredit close in 2010) and the Placebo bank (LITS 2010 * Placebo bank close in 2010). This analysis is feasible because there are significant differences in the expansion pattern of the Placebo banks compared with ProCredit among this same sample of eighty-eight PSU: ProCredit opens a branch in twenty-three locations where the Placebo banks do not, while the Placebo banks open a branch in eight locations where ProCredit does not. Column (4) results confirm our previous findings. Even when controlling for the branch expansion of the Placebo bank we still find that the opening of a new ProCredit branch leads to a 18 percentage point increase in the share of households with a bank account. In contrast, we again do not find an effect on account use from new Placebo bank branches.

Summarizing, the Placebo bank results provide evidence that it is not the entrance of any additional bank into a region that increases the use of bank accounts in general and among low- and middle-income households in particular. By contrast, the results substantiate that commercial MFBs such as ProCredit Bank play an important role in deepening access to financial services even in regions in which ordinary RBs already operate large branch networks.

7.2 Expanding the Distance Threshold

In Table IX we examine how our main results displayed in Table IV are affected by extending the distance threshold employed in the empirical analysis. We define “closeness” to a ProCredit branch or a RB branch as households lying within a 10-km (instead of 5-km) radius of the nearest branch. Employing this wider radius increases our sample of PSUs where a RB is close in 2006 but ProCredit is not to 110. Between 2006 and 2010 ProCredit opens a branch within a 10-km radius in 58 of these 110 PSU. Replicating our analysis in Table IV we estimate the difference-in-difference effect of a new ProCredit branch on the use of bank accounts among all households in this sample as well as separately for low-income, middle-income, and high-income households. The results presented in Table IX document a weaker volume effect. In our preferred specification the difference-in-difference estimate for ProCredit (LITS 2010 * ProCredit close in 2010) drops from 21 percentage points (see Table IV , Column 3) to (an imprecisely estimated) 12 percentage points. Estimating the difference-in-difference effect of ProCredit by income group we find a significantly positive effect only for the low-income sample (17 percentage points). The estimated effect is weaker (12 percentage points) and imprecisely estimated for middle-income households, while the estimated effect is zero in the sample of high-income households. These findings suggest—again in line with our theory—that the average impact of a MFB on financial inclusion is weaker the further away households are from the bank. But despite this weaker volume effect, even more distant MFBs exert a disproportionately positive impact on the financial inclusion of low-income households.

Volume effect and composition effect (10 km threshold)

This table displays the estimates of a linear probability model where the dependent variable is Account. The parameters are estimated for households located in PSUs where at least one RB branch was close in 2006 and in 2010 and no ProCredit branch was close in 2006 (within 10 km). Household control variables are Income, Wage income, Self-employed, University degree, Household size, Age, Female, Language, Muslim, Car, Computer, Mobile phone, Internet. PSU control variables are D.Nightlight (2010–2006), Nightlight 2006 (orthogonalized), Population 2006 (Ln), D.Population (2010–2006), Rural, Nightlight 2006 (orthogonalized) * Rural, Average income per PSU, Share wage income per PSU, Share self-employed per PSU, Number of RBs in 2006, and D.Number of RBs (2010–2006). Region FE corresponds to NUTS 2 regions per country. Observations are on the household level. Standard errors are clustered on the PSU level and are reported in parentheses. ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 level, respectively. Definitions and sources of the variables are provided in Table AII in the Appendix.

In this paper we examine how the opening of a branch of a MFB affects the use of bank accounts by households in the vicinity of that branch. We combine household survey data on the use of bank accounts in South-East Europe with the exact geographic location of these households and the branches of the region’s major commercial MFB and the largest RBs. We account for local economic activity and population density by using geocoded imagery data on nightlight intensity. This setting allows us to study the additional effect of a commercial MFB on financial inclusion controlling for the presence of RBs and the economic development at a very local level.

Our results suggest that commercial MFBs contribute significantly to financial inclusion. First, we show that ProCredit is more likely to open new branches in regions with a high share of low-income households. Second, we show that the share of households with a bank account increases significantly more in locations in which ProCredit opened a new branch compared with locations where it did not. Third, subsample analyses point to a particularly strong effect of new ProCredit branches on the use of bank accounts by low-income households, older households, and households that rely on transfer income.

Overall, our findings document a significant impact of ProCredit on financial inclusion among households located close to new branches—at least in the first years after a branch has been opened. Due to the limited observation period we cannot, however, establish whether ProCredit has a significant long-term impact on financial inclusion. One challenge for future research, using follow-up waves of the LITS survey, is to examine whether the effects documented by our analysis hold in the long term. That said we believe that our findings have important implications for policy makers who aim to foster financial inclusion. In particular, they suggest that public support of commercial MFBs may help policy makers achieve objectives for financial inclusion even in emerging markets that are served by large retail branch networks of international banking groups.

Banks per country

This table provides information on the branch networks of the banks considered in the empirical analysis. The first column indicates each bank’s rank per country according to the size of the branch network (year-end 2012). The column Branches in 2006 indicates the number of bank branches in 2006. The column Branches in 2010 indicates the number of branches in 2010. The column Type indicates the bank type (Retail bank, Placebo bank, or Commercial MFI). The last column indicates the bank ownership. The information on the bank branch network was obtained from the websites of the banks, central banks, and from the EBRD. The classification of bank ownership is based on Claessens and Van Horen (2014) .

Variable definitions and sources

This table presents definitions, sources, and the year of observation for all variables used in the empirical analysis.

Summary statistics

This table reports summary statistics of all variables in the years 2006 and 2010. Note that the exponentiated values of ln-transformed variables (age, income, average income per PSU, population 2006) are shown in this table. Definitions and sources of the variables are provided in Table AII .

* We thank three anonymous referees, Ralph De Haas, Lars Norden, Charlotte Ostergaard, Matthias Schündeln, Ulrich Schüwer, Oystein Strom, and Eva Terberger as well as participants at the 2013 AEL Conference, the 2013 Banking Workshop at the University of Muenster, 3rd European Research Conference on Microfinance, the CEPR-EBRD-EBC-RoF Conference on “Understanding Banks in Emerging Markets: Observing, Asking, or Experimenting?”, the EEA-ESEM 2013 Conference, the Nordic Finance Network Young Scholar Workshop as well as seminar participants at the Aalto University School of Business, European Bank for Reconstruction and Development (EBRD), Frankfurt School of Finance & Management, KfW Development Bank, ProCredit Holding, University of Hannover, the University of St Gallen and University of Zurich for helpful comments. We thank the EBRD and Pauline Grosjean, Antti Lehtinen, and Mirko Nikodijevic for providing us with data. We received financial support from KfW Development Bank. This paper was previously circulated under the title “Commercial Microfinance and Household Access to Finance”. Preliminary results from this research project were published as part of a review article on microfinance commercialization and mission drift (Brown, Guin, and Kirschenmann, 2012).

1 Source: www.mixmarket.org . The figures are based on 2011 data for large microfinance institutions (as classified by MIX Market) in Latin America and the Caribbean, Sub-Saharan Africa, North Africa and the Middle East, Eastern Europe and Central Asia, South Asia as well as East Asia and the Pacific.

2 By comparison similar survey data show that in Western Europe more than 95% of all households hold bank accounts ( Beck and Brown, 2011 ).

3 See Karlan and Murdoch (2010) for a comprehensive overview of the empirical literature on access to finance. For recent evidence on the impact of access to saving services see, e.g., Ashraf, Karlan, and Yin (2010) , Brune et al. (2011) , and Dupas and Robinson (2013) .

4 For further recent evidence on access to finance in sub-Saharan Africa see Beck et al. (2010) , Aterido, Beck, and Iacovone (2013) , and Honohan and King (2013) .

5 This is in line with the evidence of Allen et al. (2012) suggesting that geographical distance to financial service providers is a main barrier to households’ use of these services.

6 See http://www.procredit-holding.com for more information. The quotes on ProCredit’s business model are also taken from this web page.

7 As of December 2010, the shareholders are IPC GmbH, ipc-invest GmbH and Co KG, KfW, DOEN, IFC, BIO, FMO, TIAA-CREF, responsAbility, PROPARCO, FUNDASAL, and Omidyar-Tufts Microfinance Fund.

8 At the same time, ProCredit banks are similar to other commercial microfinance banks such as the banks of the Access Group ( http://www.accessholding.com/ ), and also to those institutions of FINCA that have been or are about to be transformed into banks with licenses ( http://www.finca.org/who-we-are/business-model/ ). ProCredit banks differ from other, non-profit, microfinance institutions in their ability to collect savings because they are formal, licensed banks that are regulated and supervised by the national authorities and in their aim to become financially self-sustainable in the long-term.

9 We do not include Bosnia, Romania, and Ukraine due to data limitations. We do not include Croatia in our study because the use of bank accounts was already very high in 2006.

10 Only in Albania a predecessor institution existed before it was renamed ProCredit and became a full-service commercial microfinance bank. In 2010, the majority owner of all four banks with between 80% and 90% of the shares was ProCredit Holding. The remaining shares were held by Commerzbank AG and the EBRD.

11 In each wave PSUs were randomly selected with the probability of selection proportional to PSU size.

12 See http://www.ebrd.com/what-we-do/economic-research-and-data/data/lits.html for details of the LITS survey questionnaire.

13 We have information on the number of all bank branches in each country in 2012 only and therefore base our ranking of banks in terms of the size of their branch networks on these numbers (see Supplementary Material). We resort to including five major retail banks from among the 10 largest retail banks in each country because historical branch opening or location information is not available for all banks. For Macedonia, we resort to the largest three retail banks because they already cover around 50% of the bank branches in the country.

14 We observe 170 PSUs (76 in the 2006 survey wave and 94 in the 2010 wave) that were already close to a ProCredit branch in 2006. All of these PSUs were also close to a retail bank branch in 2006.

15 Our data come from the Version 4 DMSP-OLS Nighttime Lights Time Series using satellite F15 for years 2002–03, satellite F16 for years 2004–09, and satellite F18 for year 2010. Elvidge et al. (2009) , Henderson, Storeygard, and Weil ( 2011 , 2012 ), and Cauwels, Pestalozzi, and Sornette (2014) provide detailed descriptions of the nightlight data and the process how it is derived from the satellite images produced by the US Airforce Defense Meteorological Satellite Program. See also http://ngdc.noaa.gov/eog/ . Since our nightlight data come from different satellites over time and different satellites had different sensor settings, it is important to intercalibrate the nightlight data. Elvidge et al. (2009) point out that the value shift between different satellites is not linear but needs a second order adjustment. Therefore, including year and satellite fixed effects is not enough to correct for the value shifts and make the nightlight data comparable over time. We obtain the 2002–09 parameters from Elvidge et al. (2014) and follow the regression-based calibration process suggested by Elvidge et al. (2009) to calculate the 2010 parameters. Nightlight 2006 (Nightlight 2010) is then measured as the average of the nightlight intensity parameters in a radius of 9 km around any geo location.

16 The LandScan database provides an estimate of the local population based both on spatial analysis and remote imagery data. For details see: http://web.ornl.gov/sci/landscan/ .

17 Our indicator of changes in population estimates over time is based on within-country rankings per period as the quantitative population estimates provided by LandScan are not well comparable over time.

18 The tercile thresholds are given by the following values of Income per country and wave (measured in ln(USD)): Albania 2006: 7.326 (33.33%), 7.874 (66.67%)/2010: 7.568 (33.33%), 8.098 (66.67%); Bulgaria 2006: 7.048 (33.33%), 7.678 (66.67%)/2010: 7.750 (33.33%), 8.220 (66.67%); Macedonia 2006: 7.290 (33.33%), 7.834 (66.67%)/2010: 7.852 (33.33%), 8.308 (66.67%); Serbia 2006: 7.454 (33.33%), 8.027 (66.67%)/2010: 7.976 (33.33%), 8.422 (66.67%).

19 In unreported tests we confirm that our results are robust to using a non-linear (probit) estimation method.

20 In unreported robustness tests we confirm that our results are robust to using a non-linear (probit) estimation method.

21 Income is equivalized at the OECD scale to account for the varying number of adults and children across households.

22 Muslim respondents may also be reluctant to use commercial banking services for religious reasons. Using the LITS 2006 data Grosjean (2011) provides evidence that regions in South-East Europe which were under the influence of the Ottoman Empire show a lower level of financial development.

23 The regional fixed effects are based on the NUTS 2 level classification. A more granular classification (e.g., NUTS 3) is not feasible in our analysis due to the lack of sufficient within-region observations.

24 In unreported robustness tests we establish that the difference-in-difference effect estimated in Table IV is not driven by one particular country in our sample. To this end we replicate the analysis dropping (in separate analyses) each of the four countries. Due to the lower and varying number of observations our estimates vary in economic magnitude and precision but remain qualitatively robust. We also examine whether our estimates are impacted by the composition of retail banks (foreign-owned versus domestic-owned) close to a PSU. We add a variable Foreign share of retail banks (2006) and the interaction term Foreign share of retail banks (2006) * ProCredit close in 2010 to Column (3) in Table IV . We find that the estimated coefficient for our difference-in-difference effect of ProCredit is unaffected by these additional control variables.

25 Note that in our low-income sample we include not only the Type 2 households from our model but also the Type 1 households which are too poor to open an account at any bank. Thus, we will yield conservative estimates for the impact of the microfinance bank on the bankable low-income households (Type 2).

26 We conduct two types of tests to establish whether our difference-in-difference estimate differs significantly across income groups. First, we pool the subsamples of low-income and high-income households and estimate Model (6) including the triple interaction term Low income * LITS 2010 * ProCredit close in 2010 and in order to saturate the model the interaction terms Low income * LITS 2010 and Low income * ProCredit close in 2010. The estimated triple interaction term is positive, but imprecisely estimated (point estimate: 0.047, standard error: 0.111). Second, we simultaneously run the two regressions in Columns (4) and (6) and then use a “Chow” test to test for differences in the estimated difference-in-difference parameter LITS 2010 * ProCredit close in 2010 across the two subsamples. The test statistic ( p  = 0.51) does not reject equality across the two subsamples.

27 We simultaneously run the regressions in Columns (3)–(4) and (5)–(6) of Table V and then use a “Chow” test to test for differences in the estimated difference-in-difference parameter LITS 2010 * ProCredit close in 2010 across the respective subsamples.

28 This information was provided to the authors by the senior management of ProCredit Holding.

29 In unreported robustness tests we replace the chosen set of Placebo banks with an alternative placebo bank for each country and obtain similar findings.

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