Model Determination for High-Dimensional Longitudinal Data with Missing Observations: An Application to Microfinance Data

TL;DR

This paper introduces a robust variable selection method for high-dimensional longitudinal data with missing observations, applied to microfinance data to identify key success factors of microfinance institutions.

Contribution

It adapts the multiple imputation random lasso procedure for complex longitudinal data with missingness, high dimensionality, and multicollinearity, demonstrating its effectiveness in a real-world application.

Findings

Staff structure is crucial for MFI success.

Profitability is the key determinant of financial success.

Financial sustainability and outreach breadth can be improved simultaneously.

Abstract

We propose an adaption of the multiple imputation random lasso procedure tailored to longitudinal data with unobserved fixed effects which provides robust variable selection in the presence of complex missingness, high dimensionality and multicollinearity. We apply it to identify social and financial success factors of microfinance institutions (MFIs) in a data-driven way from a comprehensive, balanced, and global panel with 136 characteristics for 213 MFIs over a six-year period. We discover the importance of staff structure for MFI success and find that profitability is the most important determinant of financial success. Our results indicate that financial sustainability and breadth of outreach can be increased simultaneously while the relationship with depth of outreach is more mixed.