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Green Microfinance: Characteristics of Microfinance Institutions Involved in Environmental Management

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Abstract

In recent years, development practice has seen that microfinance institutions (MFIs) are starting to consider their environmental bottom line in addition to their financial and social objectives. Yet, little is known about the characteristics of institutions involved in environmental management. This paper empirically identifies the characteristics of these MFIs for the first time using a sample of 160 microfinance institutions worldwide. Basing our analysis on various econometric tests, we find that larger MFIs and MFIs registered as banks tend to perform better in environmental policy and environmental risk assessment. Furthermore, more mature MFIs tend to have better environmental performances, in particular in providing green microcredit and environmental non-financial services. On the other hand, financial performance is not significantly related to environmental performance, suggesting that ‘green’ MFIs are not more or less profitable than other microfinance institutions.

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Notes

  1. The Equator Principles are a banking industry framework for addressing environmental and social risks in project financing.

  2. Moore (2001) uses 16 indicators of social performance divided between the six stakeholder groups that were considered; employees, customers, shareholders, suppliers, community, and environment.

  3. Cochran and Wood (1984) use a specific reputation index, the «combined Moskowitz list», to measure social responsibility and performance.

  4. Pioneers include England and Germany (Boyer and Laffont 1997) or Brazil.

  5. The MIX Market (www.mixmarket.com) is a website that provides access to operational, financial, and social performance information on more than 2,000 MFIs, covering 92 million borrowers globally. Being one of the most exhaustive databases of MFIs worldwide, the MIX Market is used as a source of data for many microfinance studies, such as Cull et al. (2009), Dorfleitner et al. (2012), or D’Espalier et al. (2013). However, the MIX data have limitations that need to be acknowledged. Indeed, the MIX only gathers data for institutions that consider themselves as MFIs and that expect a benefit from voluntary reporting to this database. The data set is thus likely to under-represent smaller microfinance providers as well as other institutions providing financial services to low-income people, such as development banks, postal banks, rural banks, or savings and credit cooperatives.

  6. In order to test if our sample was statistically different from that of the MIX, it would have been interesting to conduct some probit tests and eventually correct for selection bias through Heckman’s method. Unfortunately, the 2009 MFI Benchmarks MIX data only presents mean indicators and does not provide the whole database, which prevented us from conducting a Heckman correction.

  7. For instance, if an MFI stated that it had an environmental policy, we asked for the year when the policy was adopted. If an MFI declared that it was offering green microcredit for clean energy technologies, we asked what the type of technology promoted was and how many loans they had disbursed in the past year.

  8. We also reviewed the MFIs websites and annual reports to check for the information provided. However, these sources are quite limited since most MFIs do not communicate yet on their environmental management practices.

  9. Correlations do not exceed 0.8, the level at which collinearity problems appear (Kennedy 1982).

  10. Operational self-sufficiency is widely used as a proxy for financial performance in the microfinance sector (Cull et al. 2009; Lensink and Mersland 2009; Mersland and Strom 2010).

  11. We also checked the error term and found no autocorrelation or heteroskedasticity, which could have reflected a problem of endogeneity (cf section “Data Analysis for Global MEPI Scores”).

  12. We also tested a regression where we controlled for country-level environmental performance. In this perspective, we used the environmental performance index (EPI) developed by Yale researchers and largely used by international organisations such as the UNDP (in its human development index) and the G20. This index ranks countries on performance indicators gauging government policies on environmental public health and ecosystem vitality. This variable, however, was not significantly related to MEPI scores and was not contributing to the explanatory power of our model. We thus decided to remove it.

  13. It could have been interesting to also include Social Performance Indicators (SPI) scores as a control variable. One could indeed assume that MFIs with a high social performance (as it is measured by the SPI) would be likely to have a higher environmental performance. However, it was not possible to include such control variable since, after coordinating with CERISE, we identified that only 24 MFIs from our sample of 160 had conducted an SPI assessment in 2010.

  14. The groups were defined along the MIX peer group definition: small MFIs are those with less than 10,000 active borrowers; medium MFIs are those with 10,000 to 30,000 active borrowers; and large MFIs are MFIs with more than 30,000 active borrowers.

  15. The ecological footprint dimension relates to all internal actions made by the MFI to reduce the direct environmental impact of its operational activities, such as conducting an environmental audit, setting up specific objectives to reduce energy consumption, carbon emissions, or waste releases, raising employees’ awareness on good practices, and including environmental performance indicators in annual reports.

  16. According to UNFCCC (2012): 82.57 % of all registered CDM projects are located in Asia and the Pacific, while only 2.14 % are implemented in Africa.

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Acknowledgments

We would like to thank Isabelle Agier, Bert d’Espallier, Manuel Hensmans, Marc Labie, Kevin Jackson, Pierre-Guillaume Méon, Roy Mersland, Ariane Szafarz, Hubert Tchakouté-Tchuigoua, and Philip Verwimp for their valuable comments on earlier versions. We are also very grateful to the 160 MFIs that responded to the survey. Finally, Marion Allet is thankful to PlaNet Finance and ANRT for their financial support. This research has been partly carried out in the framework of an Interuniversity Attraction Pole on Social Enterprise funded by the Belgian Science Policy Office.

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Authors and Affiliations

  1. Université Libre de Bruxelles, SBS-EM, CEB, CERMi, 42, Av. Roosevelt, CP 114/02, 1050, Bruxelles, Belgium

    Marion Allet & Marek Hudon

  2. Université Paris 1 Panthéon-Sorbonne, IEDES, UMR 201, Développement et Sociétés, Paris, France

    Marion Allet

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  1. Marion Allet
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Correspondence to Marion Allet.

Appendix

Appendix

See Tables 6, 7, 8, 9.

Table 6 Microfinance Environmental Performance Index (MEPI)
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Table 7 Descriptive statistics
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Table 8 Correlations between environmental performance and determinant variables (Pearson correlation coefficients, bilateral tests)
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Table 9 Correlation matrix of explanatory variables (Pearson correlation coefficients, bilateral tests)
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Allet, M., Hudon, M. Green Microfinance: Characteristics of Microfinance Institutions Involved in Environmental Management. J Bus Ethics 126, 395–414 (2015). https://doi.org/10.1007/s10551-013-1942-5

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