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.

翻译：我们提出了一种适用于具有未观测固定效应的纵向数据的多重插补随机套索方法改进方案，该方法能够在存在复杂缺失性、高维性和多重共线性的情况下提供稳健的变量选择。我们将其应用于一个全面、平衡的全球面板数据集，该数据集包含213家微金融机构在六年期间的136项特征，以数据驱动的方式识别微金融机构的社会与财务成功因素。我们发现员工结构对微金融机构的成功具有重要性，并发现盈利能力是财务成功的最重要决定因素。我们的结果表明，财务可持续性与服务广度可以同时提升，而与服务深度的关系则更为复杂。