On the empirics of microfinance institutions and local economic development in Africa

ABSTRACT

The link between finance and growth is a well-researched area, yet this relationship at the local level has received little empirical scrutiny. We trace the operations of Microfinance Institutions (MFIs) in Africa. We examine the role played by MFIs on local economic development using a model capturing both local geography and MFIs dynamics for the periods 1992–2015. Our sample is representative of 2,170 MFIs operating in 144 cities, 154 towns, and 14 villages in Africa. Using night time data to capture economic growth, our key hypothesis is that MFIs drive local economic progress. Conditioning on the geographical location of MFIs, we find that the presence and density of MFIs increase as the growth in light density and the estimate retains its statistical significance. The results suggest that MFIs operations may foster local economic activity.

KEYWORDS:

• MFIs
• local economic development
• luminosity data
• Africa

JEL CLASSIFICATIONS:

• G21
• O16
• QQ56

Acknowledgement

We are grateful to the editor and two anonymous reviewers for suggestions, which greatly improved the manuscript. We especially thank, without implicating, Professor Jerry Parwada for inspiring the idea and subsequent insights and discussions as well as providing part of the data used in this research. We wish also to acknowledge comments from Trust Chakanyuka in gathering parts of the data. We thank Benaisa Chidmi for reading and commenting on the earlier draft of the paper and Ore Koren for sharing updated luminosity data. Parts of this work were written while Nixon S. Chekenya was with Steward Bank Limited. All remaining errors are ours alone. Data are available upon request from the authors.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The Institutional Economics view has its roots in seminal works by Coase (1937, 1937, 1960) and North (1991).

2 Our objective ties closely with that of Hyun (2020:95).

3 Rather than focusing on credit and national outcomes, we analyse how MFIs affect economic activity at local level in cities, towns and villages in Africa. Consequently, our findings are less sensitive to aggregate discontinuities as we exploit heterogeneity among locations. Moreover, our expansive focus on all regions in Africa captures heterogeneity and uncovers evidence of the growth effects of MFIs.

4 The approach puts emphasis on observing changes in variables like household income, nutrition, educational attainment, access to health services and insurance by the poor.

5 The view is supported by international organizations such as the World Bank, International Finance Corporation (IFC), United States Agency for International Development (USAID) and Consultative Group to Assist the Poorest (CGAP).

6 This gives a total of 312 locations for which we have data to perform empirical analyses.

7 We merge our dataset with information on MFIs from the MIX Dataset. The MIX is a premier financial database for institutions focused on the bottom of the market. The dataset includes information about MFI ID, MFI Name, MFI Full Name, country, region, last known legal status and last known profit status. Other variables reported in the dataset (which are not of particular interest to the present study) include social performance, financial performance, and data collection templates. The data can be accessed at https://datacatalog.worldbank.org/search/dataset/0038647.

8 In times when GDP is increasingly being regarded as a poor measure of economic activity and even an unreliable gauge of production especially in developing countries due to several limitations, social scientists are looking for better methods of capturing economic activity (See Fleurbaey 2009; Stiglitz et al. 2010; Martinez 2021 for a survey of limitations of using GDP for capturing economic progress). Luminosity may not be the best measure of economic activity (as nothing is perfect) but in our view it gives us a better start in search of finer proxies compared to GDP (See Chen & Nordhaus 2011; Villa, 2016 and Gibson et al. 2021 for literature which argues that luminosity data is a better measure compared to GDP.

9 The data is accessible at https://data.worldbank.org/.