What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data?
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-...
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| Format: | Working Paper |
| Language: | English |
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World Bank, Washington, DC
2018
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| Online Access: | http://documents.worldbank.org/curated/en/949841533741579213/What-can-we-machine-learn-about-welfare-dynamics-from-cross-sectional-data http://hdl.handle.net/10986/30235 |
| Summary: | 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. |
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