Abstract
In this article, we analyze the way in which interest rates interact with financial performance in the MFI context. To that end, we use structural equation modeling, as it can measure both direct and indirect effects between variables. We found that interest rates are a significant mediator variable between financial performance and environment (corruption, the rule of law and government inefficiency), MFI size, and operating expense. The originality of this work lies in the methodology used. Although previous studies analyze the effect of interest rates on the financial performance of MFIs, our methodology captures the mediation effect of this variable. Finally, we state that interest rates play an essential role in the poverty-alleviating mission of MFIs, such that they are a significant indirect driver of financial performance.
Highlights
The primary objective of MFIs is to alleviate poverty through a combination of small loans and other financial services, such as savings accounts, training, health services, networking, and peer support
When we tested the direct effects, we found that the only factors that are significant estimators of financial performance are operating expenses and real yield
ARTÍCULO ORIGINAL ACEPTADO when we incorporate the yield on gross loan portfolio, we find that the effect of the MFIs environment and size on financial performance is mediated through interest rates
Summary
The primary objective of MFIs is to alleviate poverty through a combination of small loans and other financial services, such as savings accounts, training, health services, networking, and peer support. We use Structural Equation Modeling (SEM) to test whether capital structure, environment (corruption, the rule of law and government inefficiency), operating efficiency and MFI size have an indirect effect on financial performance (measured as ROE, ROA and OSS), with interest rate as the mediator channel or variable. To test the model proposed, first, we must perform a confirmatory factor analysis to verify the validity of the constructs and to evaluate the model using a double mediation technique with SEM At this regard, SEM is the most recommended method for mediation analysis, because it allows separating the measurement errors of the mediator and dependent variables. It allows flexibility to estimate and compare different models using sophisticated goodness-of-fit statistics (Danner, Hagemann and Fiedler, 2015)
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