The Sacrifices of Micro-Borrowers in Ghana – A Customer-Protection Perspective on Measuring Over-Indebtedness

Abstract

This article measures the over-indebtedness of micro-borrowers. It defines over-indebtedness from a customer-protection perspective, based on the borrowers' sacrifices. We analyse the sacrifices borrowers experience and find that 30 per cent of borrowers are over-indebted. We then test risk management indicators of debt problems as predictors of over-indebtedness. Over-indebtedness is strongly related to delinquency and the debt-to-income ratio but not to debt amounts or to multiple borrowing. Although our model correctly predicts 72.6 per cent of cases, even the best indicators identify only a few over-indebted borrowers. Risk management is not enough to protect customers against over-indebtedness but the industry needs to measure customer experiences directly.

Acknowledgements

The author is grateful to the Marie-Christine-Adam Foundation for its financial support. She would like to thank the Independent Evaluation Department of KfW Entwicklungsbank and the ACCION Center for Financial Inclusion's Smart Campaign for their contribution to study design and for financing the empirical research project. She is indebted to the Ghanaian microfinance institutions that made this data collection possible by sharing both their data and access to their clients. Valuable comments were provided by Prof. Silvia Prina from Case Western Reserve University, Prof. Marc Labie from University of Mons, Prof. Marek Hudon, Prof. Ariane Szafarz, Prof. Khalid Sekkat and Prof. Francois Rycx from ULB and Prof. Oscar Bernal from University of Namur. Further thanks go to the seminar and conference participants at the 2nd European Research Conference in Microfinance, the Academy of Management Annual Meeting, the Global Microcredit Summit, the University of Namur, Université Protestante du Congo and ULB.

Notes

An Online Appendix is available for this article which can be accessed via the online version of this journal available at http://dx.doi.org/10.1080/10.1080/00220388.2013.775421

1. In this article, the term ‘prediction’ refers to the ability of easily available indicators to foretell, that is, ‘predict’, how much over-indebtedness one would find in a sample if the survey data were available, that is, required to measure over-indebtedness precisely. The article takes the perspective of MFIs and regulators that have access to data as commonly provided by the lenders' MIS. Prediction does not refer to an inter-temporal prediction of trends but to the prediction of the results a more precise measurement would deliver.

2. Another measure of relative debt burden would be the debt-to-asset ratio. However, this measure is less relevant in a low-asset environment such as microfinance. Also, assets are mostly illiquid and do not facilitate repayment. We have tested the indicator in our main model and while it does not change the other findings the debt-to-asset ratio itself is not significant.

3. ‘Once in past year’, ‘1–3 times in past year’, ‘>3 times but not often’, or ‘frequently in past year’. For a respondent cutting down on their food at several points for a week at a time, instead of every individual day, each week would count as one occurrence.

4. ‘Easily acceptable’, ‘only just acceptable’, ‘not really acceptable’, or ‘not acceptable’. In this article we summarise the first two categories under ‘acceptable’ and the latter two under ‘unacceptable’ or ‘not acceptable’.

5. The measurement only counts unacceptable sacrifices and thus does not include assets sales or loan swaps that borrowers simply employ as liquidity management tools. Nevertheless, the calibration of this measurement might have to be different in a different cultural context.

6. For details on the relationship between over-indebtedness and negative loan impact see Schicks (2013).

7. Details on marginal effects corresponding to Column (4) are available on request.