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Are the Operations of Microfinance Institutions Different Across Countries? A Comparative Analysis of Cambodia and the Philippines Using DEA and PCA

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  • Hidenobu Okuda
  • Daiju Aiba

Abstract

Of all the Southeast Asian countries, Cambodia and the Philippines have well-developed microfinance institutions (MFIs). However, the environments in which MFIs operate differ considerably between the two countries. Our study investigates the differences in management characteristics and efficiency of Cambodian and Philippine MFIs during the period of 2009-2015 using Data Envelopment Analysis (DEA) and Principal Component Analysis (PCA) and measures the key management characteristics and efficiency levels of local MFIs. Our study found that Cambodian MFIs tend to target sustainability (profitability) oriented management, and Philippine MFIs tend to target outreach (financial service to the poor) oriented management. Second, MFIs in the Philippines had a tendency to shift toward more outreach-orientated management over the period of our analysis. Third, while there are no clear differences in the capital-intensity of MFI operations between the two countries, over time capital-intensity improved in both. We further examined the relationship between country-specific factors, management characteristics and efficiency. We found that overall efficiency, outreach-orientation, and labor-intensive management were associated with the initial conditions of deposit-to-GDP ratio in the period of our analysis. This suggests that the development paths of MFIs are dependent on the development of traditional financial institutions in the early period of MFI development.

Suggested Citation

  • Hidenobu Okuda & Daiju Aiba, 2020. "Are the Operations of Microfinance Institutions Different Across Countries? A Comparative Analysis of Cambodia and the Philippines Using DEA and PCA," Working Papers 212, JICA Research Institute.
  • Handle: RePEc:jic:wpaper:212
    DOI: 10.18884/00001031
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    Keywords

    Cambodia; the Philippines; MFIs; Operational Characteristics; Data Envelopment Analysis; Principle Component Analysis;
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