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The nonlinearity of the relationship between competition and the dual performance of regulated microfinance institutions in Peru

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Abstract

The objective of this study is to determine whether a nonlinear relationship exists between competition and outreach, as well as, between competition and financial sustainability of Peruvian regulated microfinance institutions (MFIs) from 2003 to 2019. We consider three different competition measures reflecting market power, the geographical presence of MFIs, and market concentration. Our findings are as follows: Market concentration does not affect financial sustainability and outreach, whereas market power has a nonlinear U-shaped relationship with financial sustainability and depth of outreach and a negative linear relationship with outreach breadth. Furthermore, the geographic presence of MFIs has a nonlinear U-shaped relationship with financial sustainability and depth of outreach, while it has a nonlinear inverted U-shaped relationship with outreach breadth. These findings reveal differentiated effects of competition on the performance of MFIs that depend on the level of their market power and their geographic presence in the market. Given the high market power and low geographic presence, on average, of Peruvian MFIs, we find that competition negatively affects their financial sustainability and positively affects their outreach. This study brings to the debate on the effects of competition on MFI performance a new interpretation of these effects based on empirical evidence that reconciles previous empirical results.

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Fig. 1

Source: SBS. Own elaboration

Fig. 2

Source: SBS. Own elaboration

Fig. 3

Source: SBS. Own elaboration

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. There are non-governmental organizations (NGOs) with microcredit programs and cooperatives specializing in microfinance, which are also part of the Peruvian microfinance system. While NGOs are unregulated entities, cooperatives are in the process of being incorporated into supervision and regulation. The most important of both types of entities voluntarily report their financial information to the MIX Market database. According to this information, in 2019, these institutions were responsible for 6% of all the microcredit granted by the Peruvian microfinance system. Given their reduced participation in the national supply of microcredit, our analysis incorporates regulated MFIs only.

  2. Resolution SBS 1276–2002, which approved the regulations for the entry of MFIs into the Lima market.

  3. See SBS (2003).

  4. Legislative Decree 1028/2008, which amends Article 290 of Law 28,677, General Law of the Financial System and the Insurance System and Organic Law on the Superintendency of Banking, Insurance, and Private Pension Fund Administrators.

  5. Inefficiencies in a firm’s operations prevent it from maximizing profits and/or minimizing costs.

  6. For Al-Azzam and Parmeter (2021), the higher this percentage, the less competition because the expansion of the number of MFI branches increases market concentration. We consider this interpretation to be inadequate because competition drives MFIs to expand their branch networks.

  7. In increasingly concentrated markets, firms can engage in collusive conduct and possess the capacity to establish a price above their marginal cost (Leon 2014).

  8. An alternative interpretation of the relationship between competition and concentration is provided by Philippon (2019), according to whom, in industries with efficient and high-productivity firms, it is possible to observe high levels of competition in conjunction with high market concentration; that is, higher market concentration leads to higher competition. However, evidence shows that concentration and average market power have increased in the regulated microfinance industry in Peru (Aguilar and Portilla 2020; Huayta et al. 2017). Similarly, there has been no major technological change in this industry that would improve its efficiency (Aguilar and Portilla 2019). Therefore, it is more appropriate to consider that the relationship between competition and concentration is negative in an analysis of the Peruvian microfinance sector.

  9. There are other non-parametric approaches to estimate the firm’s inefficiency. However, the main disadvantage of non-parametric approaches, such as data envelopment analysis (DEA), is that they attribute any deviation from the best performance to inefficiency and do not consider other factors, such as idiosyncratic shocks or measurement errors. On the other hand, the major disadvantage of SFA is that imposes a functional form (Khalily et al. 2014). Considering both arguments, we use SFA. Finally, for a detailed presentation on the topic of SFA, see Kumbhakar and Lovell (2003).

  10. We estimate an alternative profit frontier that, unlike the standard one, considers that financial intermediation institutions (in this case, MFIs) do not act in a perfectly competitive market and, thus, the price of their product is not given; rather, it can be established through product fixing. Therefore, we include the product and the price of inputs as exogenous factors in the profit frontier (Berger and Mester 1997; Humphrey and Pulley 1997). Under this assumption, we use profits as an endogenous variable and the same set of explanatory variables as in the case of the cost frontier.

  11. In this study, we follow the intermediation approach of Benston et al. (1982), according to which financial intermediaries produce loans from a combination of the following inputs: loanable funds, labor, and physical infrastructure. The production of loans is measured by the monetary value of the loans offered, while the total costs include both operating and financial costs. We must mention that in the particular case of MFIs, given the greater risk to which their loan portfolios are exposed due to the type of loans they grant (to highly unstable businesses and/or productive units), it is necessary to incorporate the cost derived from the risk of default into the total cost.

  12. The definition of the variables employed in the cost and profit frontiers are detailed in Table A1. Moreover, Table A2 presents the descriptive statistics for these variables. Finally, the estimates of the cost and profit frontiers are described in Tables A3 and A4, respectively.

  13. In the case of negative profits, we use the following estimation: \({TP}_{AD}=(TP) (profit\, efficiency)\).

  14. In addition, the U.S. Department of Justice and the Federal Trade Commission (2010) classify markets into three types according to HHI level. Unconcentrated markets: HHI below 1500; moderately concentrated markets: HHI between 1500 and 2500; highly concentrated markets: HHI above 2500.

  15. OSS is the ratio of financial revenue to total cost (operating costs plus financial costs and provisions).

  16. We use the following equation to rescale the variables:

    $${f(x)}_{t}=\frac{(a-b)({x}_{t}-d)}{e-d}+b,$$

    where \(f(x)\) is the rescaled variable, a is the maximum value of the new scale, b is the minimum value of the new scale, e is the maximum value of the original variable, d is the minimum value of the original variable, and xt is the variable to be rescaled.

  17. Instrumental variable (IV) estimator with fixed effects using the generalized method of moments (GMM).

  18. Our results are robust to the use of a higher or lower lag. Moreover, we employed the Newey–West standard errors to correct for the presence of heteroscedasticity and autocorrelation.

  19. The financial documents published by the SBS include the following: Balance Sheet, Income Statement, Financial Indicators, Distribution of Offices by Geographic Area, Staff by Job Category, and Number of Borrowers with Direct Credit.

  20. At the beginning, we had data for 44 MFIs: 13 CMACs, two banks specializing in microfinance, 13 CRACs, and 14 EDPYMEs. However, we excluded those entities that were taken over at an early stage because they provided only a small number of observations and represented less than 3% of total loans in the Peruvian microfinance market. This left a panel with 37 MFIs.

  21. See the section ''Peruvian regulated microfinance industry''.

  22. See footnote 20.

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Acknowledgements

We would like to acknowledge the financial support provided by the Pontificia Universidad Católica del Perú to carry out this research.

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Pontificia Universidad Católica del Perú.

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All authors contributed to the conception and design of the study. Material preparation and data collection were performed by JP and analysis by GA and JP. The first draft of the manuscript was written by both authors. Also, both authors read and approved the final version of the manuscript.

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Correspondence to Giovanna Aguilar.

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Aguilar, G., Portilla, J. The nonlinearity of the relationship between competition and the dual performance of regulated microfinance institutions in Peru. SN Bus Econ 3, 128 (2023). https://doi.org/10.1007/s43546-023-00506-4

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