Do Banks and Microfinance Institutions Compete? Microevidence from Madagascar

Abstract

In recent years, both microfinance institutions (MFIs) and banks across the world have been converging towards the financing of small enterprises with high financing needs. This paper scrutinizes whether banks and MFIs compete each other as a result of recent transformations in both industries. In doing so, we study whether the loan strategy of a microfinance institution is shaped by the local presence of a bank. Specifically, we investigate whether bank proximity influences loan conditions provided by one of the largest microfinance institutions in Madagascar. We employ an original panel dataset of 32,374 loans granted to 14,834 borrowers over the period 2008-2014. We find that the closer a bank is located to a given MFI borrower, the larger the loan obtained and the less collateral required. These results are insensitive to several robustness tests for possible endogeneity of distance, sample selection issue, and alternative specifications. In addition, findings are stronger for larger and more established (older) firms in line with our hypothesis.

Provisioning over the business cycle: Some insights from the microfinance industry

“…The coefficient of real GDP growth, our main indicator of business cycles, is not significant in this subgroup. The rationale is that profit-oriented MFIs nowadays compete with banks by targeting clients who are also more exposed to economic fluctuations (Baraton & Léon, 2019;Cull et al, 2014;Vanroose & d'Espallier, 2013). Our finding is also in line with Wagner and Winkler (2013), who find microfinance institutions to follow similar cyclical patterns as conventional banks.…”

Microfinance and Small Business Development in a Transitional Economy: Insights from Borrowers’ Relations with Microfinance Organisations in Kazakhstan

“…Borrowers have often been approached as the rule-takers in the lending relationship (Schäfer, Siliverstovs, & Terberger, 2010), but it is increasingly clear that MFOs have to take borrower preferences into account in order to thrive and grow (Baraton & Léon, 2019) and that borrowers strategically negotiate between different MFOs and different lending arrangements in order to meet their own (changing) interests (Cohen, 2002). Extensive research on lending to women in developing countries shows how men and women circumvent the empowerment aspirations of donors and MFOs to meet their gendered interests (Cervantes, Montoya, & Ponce, 2017).…”

Proximity‐based screening tools and credit rationing: Lessons from a Cameroonian greenfield microfinance institution

“…The geographical distance between the borrowers' business location and the MFI branch may also determine loan term contracts. The closer the distance, the more likely the borrower is to interact with the MFI, which will allow the MFI to produce soft information (Baraton & Léon, 2021).…”

“…In the relationship lending literature, this variable is measured by the shortest traveling time (Degryse & Ongena, 2005), a unit capturing the travel time between the MFI branch and the borrower's location. Given that computing the shortest traveling time in a context of developing countries like Cameroon is not an easy task, we proxy proximity with the geographic distance in kilometers between the borrower's business location and the MFI branch, as used in recent studies (e.g., Baraton & Léon, 2021; Beck et al, 2018; Pedrosa & Do, 2011). In the cities of Douala and Yaoundé, C‐GMFI has several branches where customers can carry out day‐to‐day operations, such as cash transfers, deposits, and withdrawals.…”

“…In addition, previous studies on the topic have been conducted in different contexts and used loan level data from credit‐only MFIs. Indeed, studies using loan‐level data and analyzing credit availability are centered on credit‐only microlenders in Latin America and the Caribbean (Agier & Szafarz, 2013), Mozambique (Behr et al, 2011), Madagascar (Baraton & Léon, 2021), and France (Cozarenco & Szafarz, 2018). Our study is based on a unique loan‐level dataset of more than 5500 loans approved by a Cameroonian MFI (henceforth C‐GMFI) that adopted the greenfield business model to expand its financial services.…”