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...
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详细
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2018/08/01
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工作文件（编号系列）
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129600
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1
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1
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2018/08/22
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Disclosed
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What Can We (Machine) Learn About Welfare Dynamics from Cross-sectional Data?
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Socio-Economic Database for Latin America and the Caribbean