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Financial Sustainability of For-Profit Versus Non-Profit Microfinance Organizations Following a Scandal

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Abstract

Why do some organizations suffer more than others in the wake of an industry scandal? Although ex-ante greater opportunistic behavior of organizations is one factor, we argue that ex-post greater targeting of organizations is another important factor. Using the context of microfinance organizations (MFOs), we examine why the financial sustainability of for-profit and non-profit organizations may be heterogeneously affected following a scandal. Leveraging the 2010 Indian microfinance scandal as our research setting and analyzing longitudinal data, we find a substantial decline in the financial sustainability of Indian MFOs relative to their counterparts within the rest of South Asia. Compared to Indian non-profit MFOs, Indian for-profit MFOs suffered substantially more. Intriguingly, these results hold not only in the full sample, but also in the matched sample of comparable for-profit and non-profit MFOs. Further analysis reveals that the adverse impact on for-profit MFOs was much bigger in the scandal’s epicenter. Our findings suggest that some organizations may suffer more than others not only due to their engagement in actual malfeasance but also due to their greater targeting by the social control agents. We discuss the implications of this study for social enterprise managers and policymakers.

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Notes

  1. The Grameen Bank I model relied on organizing borrowers into groups to address the issue of information asymmetry by turning an individual liability into a group liability (Sriram, 2010). Most for-profit MFOs in India largely followed the Grameen Bank I model because of its scalability, while non-profits followed the SHG model because of its focus on member savings.

  2. The concept of legitimacy has generated a large body of literature (Suddaby et al., 2017). By legitimacy, we refer to a generalized perception that the actions of an entity are deemed appropriate within some socially constructed system of norms and beliefs (Suchman, 1995: 573–574).

  3. We recognize that the two reasons—greater opportunism by for-profit MFOs pre-scandal and greater targeting of for-profit MFOs post-scandal—may be correlated. Therefore, to test Hypothesis 2, one must control for opportunism. We address this issue empirically by analyzing comparable for-profit and non-profit MFOs, which are matched on pre-scandal organizational characteristics (e.g., non-repayment risk and interest rates).

  4. One may employ a pre vs. post estimation approach by examining the post- minus pre-scandal change in the organizational outcomes of only Indian MFOs. But such estimates may be biased due to unobserved factors (e.g., an economic shock in the South Asia region) unrelated to the scandal that may change organizational outcomes over time (Dunning, 2012; Shadish et al., 2002). Therefore, we employ a difference-in-differences (DID) estimation approach by using the change over time in the control group (i.e., ROSA MFOs) as the counterfactual for the change over time in the treated group (i.e., Indian MFOs). The DID estimation may entail challenges of comparability since organizations in different countries may vary due to several features (e.g., institutions). Because our analyses include organization fixed effects (as we document in detail later), the potential concern of the lack of comparability is reduced but not eliminated. Therefore, we also conduct a pre vs. post estimation to ensure that the results are not susceptible to the identifying assumptions of either the DID approach or the pre vs. post estimation approach.

  5. For a more granular analysis, we construct three binary indicators Before, During, and After. Before is set to one for the period before the scandal (i.e., year < 2010), During is set to one for the period during the immediate years following the scandal (i.e., year = 2010, 2011, 2012), and After is set to one for the period after the legitimacy loss from the scandal had subsided (i.e., year > 2012).

  6. Table S1 (in the online supplementary material available at https://shorturl.at/qzDMO) shows the pairwise correlations. We follow the recommendation of Kalnins (2018: 2377) to ensure that the results do not suffer from any Type 1 errors due to multicollinearity. Thus, we re-estimate all specifications by including organization fixed effects but excluding all other control variables. The interpretation of all of the main results remains qualitatively similar.

  7. The results remain similar if we use Poisson pseudo maximum likelihood (PPML) estimation instead of OLS. The robustness of the results across OLS and PPML allay the concern that the findings may be susceptible to specific functional form assumptions (Correia et al., 2020).

  8. Table S2 shows qualitatively similar results if PPML is used instead of OLS. We also employed the bootstrapping approach because it provides more accurate inferences when the data may not follow restrictive distributional assumptions (Cameron & Trivedi, 2010). The results remain similar when a bootstrapping approach (with 1,000 replications), based on random draws of observations with replacement, estimates the significance of coefficients with bias-corrected confidence intervals.

  9. Figure S1 shows the estimation of temporal trends year by year. The results reveal negligible evidence of diverging trends pre-scandal in estimated OSS between the Indian and ROSA MFOs, even when estimated separately for the respective strata of for-profits and non-profits.

  10. A granular analysis (see Table S3) reveals similar results for-profit MFOs (Column 2). Column 3 shows that the adverse effect of the scandal on non-profit MFOs was short-lived; the coefficient of After × Indian MFO is statistically indistinguishable from zero (95%CI [-18.8, 5.8], p = 0.295). The inference from the PPML model (Columns 4–6) remains similar to that obtained from OLS model (Columns 1–3).

  11. In principle, one can use a means comparison test (employing a t-test) using the post-scandal data because it is a matched sample. However, we prefer the DID regression model as it uses more information and allows for more precise estimates (Singh & Agarwal, 2011).

  12. We used the common choice for a caliper of 0.2 times the standard deviation of logit of the propensity score.

  13. The longer the list of matching covariates in CEM is, the more difficult it becomes to identify a match (Azoulay, Stuart, & Wang, 2013: 98). Therefore, in CEM, we match for-profits and non-profits on three important variables: OSS (three categories: not self-sustainable if OSS < 100%, self-sustainable if OSS ≥ 100% and < 120%, highly self-sustainable if OSS ≥ 120%), Non-Repayment Risk (three categories: low if non-repayment risk < 1%, medium if risk ≥ 1% and < 5%, high if risk ≥ 5%), and Interest Rate (three categories: low if interest rate < 20%, moderate if interest rate ≥ 20% and < 30%, high if interest rate ≥ 30%), as of the year preceding the scandal.

  14. In a fine-grained temporal analysis (Table S5), we continue to observe a stronger decline for AP-based for-profit MFOs relative to non-AP-based for-profit MFOs immediately following the scandal. The coefficient of After × Indian AP in Column 1 indicates that the AP-based for-profits show evidence of attenuation in the adverse impact after the legitimacy loss from the scandal had subsided.

  15. One may wonder whether the assumption of heterogeneity among MFOs in being located in the epicenter, but homogeneity in everything else, may be implausible. To overcome this concern, we employ the synthetic control method (Abadie et al., 2015; Adbi et al., 2022) by focusing on the sample of Indian organizations and find similar results. Appendix A (in the online supplementary material available at https://shorturl.at/qzDMO) documents the results of this additional analysis.

  16. We thank an anonymous referee for highlighting this point.

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Acknowledgement

I am grateful to Julie Nelson (Section Editor, Economics and Business Ethics) and three anonymous referees for their insightful feedback and comments. I also thank Gautam Ahuja, Chirantan Chatterjee, Prithwiraj Choudhury, Pushan Dutt, Vibha Gaba, Martin Gargiulo, Henrich Greve, Aseem Kaul, Tarun Khanna, Ilze Kivleniece, Matthew Lee, Jiao Luo, Anant Mishra, Kate Odziemkowska, Phanish Puranam, Aruna Ranganathan, Devanshee Shukla, Jasjit Singh, David Tan, Bala Vissa, and Sri Zaheer as well as seminar participants at SMS, AOM, AIB, CCC, INSEAD, Wharton, National University of Singapore Business School, London School of Economics, Indian School of Business Hyderabad, Rotterdam School of Management, and Singapore Management University for valuable suggestions. I am grateful to the leadership team at the industry associations, Sa-Dhan and MFIN, and the senior management of various forprofit and non-profit microfinance organizations in India for sharing their perspectives.

Funding

The author sincerely acknowledges financial support from the National University of Singapore Start-Up Research Grant (R-313-000-143-133) and the Strategy Research Foundation’s 2018 SRF Dissertation Scholarship Award (SRF-2018- DP-9097).

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Adbi, A. Financial Sustainability of For-Profit Versus Non-Profit Microfinance Organizations Following a Scandal. J Bus Ethics 188, 57–74 (2023). https://doi.org/10.1007/s10551-022-05287-8

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