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Income Smoothing, Capital Management and Provisioning Behaviour of Microfinance Institutions: A Study Using Global Panel Data

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Abstract

This article investigates the effects of earnings and capital positions on loan-loss provisioning behaviour of microfinance institutions (MFIs) and whether such provisioning is pro-cyclical. The analysis is based on a global panel database of 1294 MFIs in 103 countries over the period of 1996–2013. Results of static and dynamic model estimations confirm the validity of the income-smoothing hypothesis as earnings positively and significantly affect MFIs’ provisioning behaviour. This indicates increased provisioning during times of making profits than in times of incurring loss. As expected, the study also finds that the impact of business cycle on provisioning is pro-cyclical with some regional variations. However, the study fails to confirm that MFIs’ capital management situation significantly impacts their loan-loss provisioning.

Abstract

On étude les effets que les profits et les positions capitales ont sur le comportement de ravitaillement de perte de prêt des institutions micro financières, et si cet approvisionnement et pro-cyclique. Cette analyse est basée sur un panneau de données prises de 1294 institutions micro financières répandues en 103 pays pendant la période 1996–2013. Les résultats de modèles statiques et dynamiques confirment la validité de l’hypothèse de l’atténuation du revenu, puisque les profits impactent positivement et significativement le comportement de approvisionnement des institutions micro financières. Cela indique plus d’approvisionnement pendant les périodes de bénéfice qu’en périodes de perte. Aussi on trouve que l’impact du cycle économique sur l’approvisionnement est pro-cyclique, avec quelques variations régionales. Cependant, l’étude n’arrive pas à confirmer que la gestion des capitales de part des institutions micro financières ait un impact significatif sur leur ravitaillement de perte de prêt.

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Notes

  1. An economic indicator is ‘pro-cyclical’ if it tends to increase in a growing economy. For example, GDP is pro-cyclical as it increases during economic upturns.

  2. The literature review section provides a brief discussion on related studies in banking.

  3. The total is made up of general and specific provisions.

  4. Mergers and acquisitions can impact the variable computed this way. But, as mergers and acquisitions so far have not been very common in microfinance industries, we do not consider them very significant.

  5. Static analyses have been presented mainly for robustness checks.

  6. In the data set, for instance, 8.42 per cent (109) of MFIs have data for 4 years, whereas 8.19 per cent (106) have 6 years’ data. Year-wise number of observations varies significantly: 23 (1996), 54 (1997), 74 (1998), 107 (1999), 146 (2000), 190 (2001), 279 (2002), 397 (2003), 511 (2004), 607 (2005), 678 (2006), 777 (2007), 863 (2008), 1022 (2009), 1140 (2010), 1173 (2011), 1114 (2012) and 656 (2013).

  7. The available MFI profiles and data are available in the public domain: www.mixmarket.org. Recently, however, many studies have also used data from the rating agencies. Unlike the MIX data, their data are not self-reported.

  8. Out of a total of 9811 observations, the highest 1154 observations are ranked with 5 diamonds, whereas 4942, 3070 and 645 observations are ranked with 4 diamonds, 3 diamonds and 2 diamonds, respectively.

  9. The administration of Co-operatives/Credit Unions is a little different. But the number of these types of MFIs in the sample is insignificant and does not affect whole observations; therefore, we retained them.

  10. The original Arellano-Bond ‘difference GMM’ model transforms the regressors by differencing and uses the generalized method of moments (Hansen, 1982). A potential weakness of this estimator was revealed in later works by Arellano and Bover (1995) and Blundell and Bond (1998). The lagged levels are often rather poor instruments for first differenced variables, especially if the variables are close to a random walk. Their modification of the estimator includes lagged levels as well as lagged differences.

  11. For the MFIs in the MENA region, the capital management hypothesis holds true thoughout.

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  1. University of Helsinki, Helsinki, Finland

    Ashim Kumar Kar

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Kar, A. Income Smoothing, Capital Management and Provisioning Behaviour of Microfinance Institutions: A Study Using Global Panel Data. Eur J Dev Res 29, 108–126 (2017). https://doi.org/10.1057/ejdr.2015.81

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