Abstract
This study quantifies the social welfare loss caused by market power in Peru’s regulated microfinance industry and analyzes its effect on microfinance institutions’ (MFIs) efficiency from 2003 to 2019. We estimate the efficiency-adjusted Lerner index as a measure of market power and obtain efficiency scores via cost and profit stochastic frontiers estimation using data from a wide panel of MFIs. Additionally, to analyze the effect of market power on the MFI’s efficiency, we estimate a fixed effects model with instrumental variables to correct the endogeneity problem. The results show that the welfare loss due to market power in Peru’s regulated microfinance industry has increased from 0.12% of GDP in 2003 to 0.27% in 2019. It is also found that market power positively affects Peruvian MFIs’ efficiency. Therefore, reducing market power leads to a welfare gain by lowering the social welfare loss (Harberger’s triangle) and a welfare loss due to decreased efficiency in MFIs. However, we find that reducing market power leads to a positive net effect on social welfare due to greater welfare gain than loss.
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Data availability
The datasets generated during and/or analyzed during the current study are available upon reasonable request to the corresponding author.
Notes
Non-Governmental Organizations (NGOs) with microcredit programs and cooperatives specialized in microfinance are also part of the Peruvian microfinance system. The most important of these entities voluntarily report their financial information to the MIX Market database. According to this information, these institutions were responsible for 6% of the total microloans placed by the Peruvian microfinance system in 2019. Given their reduced participation in the national microcredit supply, our analysis only considers regulated MFIs.
See the Appendix for this demonstration.
For a presentation of this literature, see the paper by Fall et al. (2018).
Resolution SBS 1276–2002, which approved the regulations for the entry of MFIs into the Lima market.
Superintendencia de Bancos, Seguros y Administradoras de Fondos de Pensiones (SBS) (2003).
Legislative Decree 1028/2008, which amends Article 290 of Law 28677, General Law on the Financial System and the Insurance System and Organic Law on the Superintendency of Banking, Insurance, and Private Pension Funds Administrators.
It should be noted that we estimate an alternative profit frontier that, unlike the standard one, takes into account that financial intermediation institutions (in this case, MFIs) do not act in a perfectly competitive market, and therefore, the price of their product it is not given; if not, they can establish it by fixing their output. Consequently, we include output and input prices as exogenous variables in the profit frontier (Berger & Mester, 1997; Humphrey & Pulley, 1997). Under this assumption, we use profits as an endogenous variable and the same explanatory variables as in the cost frontier case.
For a detailed presentation on stochastic frontiers, see Kumbhakar and Lovell (2003).
We follow the intermediation approach of Benston et al. (1982), which defines financial intermediaries as firms that produce loans from the combination of the following inputs: loanable funds, labor, and physical infrastructure. This approach defines the cost concept broadly because it includes financial and operating costs within the total cost. In addition, in the particular case of MFIs, given that they finance volatile economic activities, their loan portfolios are exposed to greater credit risk, which implies the constitution of high provisions for expected losses for the entity. Although provisions are not an outflow of resources, they reduce the entities’ capital in accounting terms, constituting a cost for them, so the credit risk cost must be considered part of the total cost. Finally, we measure production as the monetary value of total loans.
In the case of negative profits, we use the following estimation: \({TP}_{AD}=(profits) (PROFIT\_EFFI)\).
See Sect. 2 for details.
Our results are robust with different specification of lags. We use clustered standard errors.
See Table 10: First stage of IV with fixed effects estimation.
The exclusion restriction is difficult to test. The direct influence of these instruments (LIAD,t−7, LIAD,t−8, LIAD,t−9, LIAD,t−10, LIAD,t−11, LIAD,t−12) on contemporary efficiency (EFFIt) is unlike because market power from 7 months ago or even further back cannot influence EFFIt or its influence would be negligible. In any case, the influence of these instruments would be indirect through contemporary market power (LIAD,t).
At the outset, we had data for 44 MFIs: thirteen CMACs, two banks specializing in microfinance, thirteen CRACs, and fourteen EDPYMEs. However, it was decided to exclude those entities that were taken over at an early stage, as 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 of 37 MFIs.
For the Mexican banking sector, Solís and Maudos (2008) detected a social welfare loss representing, on average, 0.34% of GDP from 1993–2005. In the study by Maudos and Fernández de Guevara (2007) on 15 EU banks from 1993–2002, the average welfare loss was 0.49% of GDP for this group of countries. Although these values are not comparable with those we estimate for the Peruvian microfinance industry, they are referential for our analysis.
There are no prior estimations of welfare losses caused by cost and profit inefficiency for the microfinance industry. However, Solís and Maudos (2008) presented such estimates for the Mexican banking industry. They reported welfare losses associated with cost and profit inefficiency, as a percentage of GDP, of 0.02% and 0.07%, respectively. Maudos and Fernández de Guevara (2007) found that, for 15 EU countries, the welfare loss associated with cost inefficiency totaled 0.35% of the GDP of this group of European countries.
We use the xtivreg2 command in Stata to estimate the fixed effects model with instrumental variables. See Schaffer (2005) for more details on the command.
See Baum et al. (2007) for more details on these diagnostic tests for the fixed effects model with instrumental variables.
The estimates of this new alternative profit frontier are described in Table 11.
The main regression table is available on request.
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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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Appendix
Appendix
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1.
Harberger’s triangle
An MFI with market power in the microcredit market maximizes its profit by choosing the price or interest rate (P) that it will charge for its loans (L). The problem to be solved is therefore:
The first-order condition to solve this problem is:
Since \(\frac{\partial TC }{\partial L}\) is the marginal cost (MC), the term on the left of Eq. (13) is the Lerner index (LI) and \(\frac{\partial L}{\partial P}\frac{P}{L}\) is the price elasticity of loans demand (ɛ). Therefore, we obtain from profit maximization:
The net loss of social welfare in Fig. 1 is equivalent to the area of triangle ABC:
Replacing ɛ from Eq. (14), we have:
Replacing LI by \(\frac{{P}^{*}\left(L\right)-MC}{{P}^{*}}\), we have:
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Aguilar, G., Portilla, J. Market power, social welfare, and efficiency in the Peruvian microfinance. Econ Polit 41, 123–152 (2024). https://doi.org/10.1007/s40888-023-00321-y
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DOI: https://doi.org/10.1007/s40888-023-00321-y