What can we (machine) learn about welfare dynamics from cross-sectional data? / Leonardo Lucchetti
Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: World Bank E-Library Archive | Policy research working paper ; 8545Verlag: Washington, D.C. : World Bank Group, Poverty and Equity Global Practice, August 2018Beschreibung: 1 Online-Ressource (circa 31 Seiten) : IllustrationenSchlagwörter: Genre/Form: Andere physische Formen: Erscheint auch als: What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data?. Druck-Ausgabe Washington, D.C : The World Bank, 2018DOI: DOI: 10.1596/1813-9450-8545Online-Ressourcen: Zusammenfassung: 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 problemsPPN: PPN: 1031669825Package identifier: Produktsigel: ZDB-1-WBA | ZDB-110-WBODieser Titel hat keine Exemplare
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