Modelización de la autosuficiencia de las instituciones microfinancieras mediante regresión logística basada en análisis de componentes principales

• Autores: Irimia Diéguez, Ana, Blanco Oliver, Antonio, Oliver Alfonso, María Dolores
• Formato: artículo
• Fecha de Publicación: 2016
• Institución: Universidad ESAN
• Repositorio: ESAN-Institucional
• Lenguaje: español
• OAI Identifier: oai:repositorio.esan.edu.pe:20.500.12640/2611
• Enlace del recurso: https://revistas.esan.edu.pe/index.php/jefas/article/view/151; https://hdl.handle.net/20.500.12640/2611; https://doi.org/10.1016/j.jefas.2015.12.002
• Nivel de acceso: acceso abierto
• Materia: Microfinance; Operational sustainability; Theory of organisational change; Self-sufficiency; Microfinanzas; Sostenibilidad operativa; Teoría del cambio organizacional; Autosuficiencia; https://purl.org/pe-repo/ocde/ford#5.02.04

The analysis of the factors that influence sustainability is the key to achieving it. Based on the Theory of Resources and Capabilities (Grant, 1991), a management model that determines the explanatory factors of the sustainability of microfinance institutions (MFI) is developed. The empirical model is constructed by applying a principal component and logistic regression analysis using a sample of 313 MFI, with 31 finance variables, grouped into 6 components/factors that are theoretically associated with self-sufficiency. The results obtained showed a significant and positive relationship between size and theefficiency-productivity of the MFI and their sustainability, with the credit risk factor having an inverse relationship as regards that sustainability. Thus, it may be suggested that the MFI that wish to conti-nue developing their activity using a self-sufficiency approach must promote a management strategy oriented towards: (1) an increase in efficiency-productivity, (2) the exhaustive control of credit risk and,(3) the increase in size in order to achieve economies of scale. The predictive capacity of the model is high, with an area under the ROC curve of 89.7%.