What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data?

• Tác giả: Leonardo Lucchetti
• Ngôn ngữ: eng
• Ký hiệu phân loại: 330.15 Schools of economic thought
• Thông tin xuất bản: World Bank, Washington, DC, 2018
• Bộ sưu tập: Tài liệu truy cập mở
• ID: 319276
• Chủ đề: 1. LASSO; 2. MACHINE LEARNING; 3. POVERTY; 4. POVERTY TRANSITIONS; 5. WELFARE DYNAMICS

Tóm tắt

 This paper implements a machine learning approach to estimate intra-generational economic mobility using cross-sectional data. A Least Absolute Shrinkage and Selection Operator (Lasso) procedure is applied to explore poverty dynamics and household-level welfare growth in the absence of panel data sets that follow individuals over time. The method is validated by sampling repeated cross-sections of actual panel data from Peru. In general, the approach performs well at estimating intra-generational poverty transitions
  most of the mobility estimates fall within the 95 percent confidence intervals of poverty mobility from the actual panel data. The validation also confirms that the Lasso regularization procedure performs well at estimating household-level welfare growth between two years. Overall, the results are sufficiently encouraging to estimate economic mobility in settings where panel data are not available or, if they are, to improve panel data when they suffer from serious non-random attrition problems.