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

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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Bibliographic Details
Main Author: Lucchetti, Leonardo
Format: Working Paper
Language:English
Published: World Bank, Washington, DC 2018
Subjects:
POVERTY
POVERTY TRANSITIONS
LASSO
MACHINE LEARNING
WELFARE DYNAMICS
SYNTHETIC PANELS
CLASS MOBILITY
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
id okr-10986-30235
recordtype oai_dc
spelling okr-10986-302352021-09-17T05:11:55Z What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data? Lucchetti, Leonardo POVERTY POVERTY TRANSITIONS LASSO MACHINE LEARNING WELFARE DYNAMICS SYNTHETIC PANELS CLASS MOBILITY 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. 2018-08-15T19:29:29Z 2018-08-15T19:29:29Z 2018-08 Working Paper 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 English Policy Research Working Paper;No. 8545 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper Latin America & Caribbean Peru
repository_type Digital Repository
institution_category Foreign Institution
institution Digital Repositories
building World Bank Open Knowledge Repository
collection World Bank
language English
topic POVERTY
POVERTY TRANSITIONS
LASSO
MACHINE LEARNING
WELFARE DYNAMICS
SYNTHETIC PANELS
CLASS MOBILITY
spellingShingle POVERTY
POVERTY TRANSITIONS
LASSO
MACHINE LEARNING
WELFARE DYNAMICS
SYNTHETIC PANELS
CLASS MOBILITY
Lucchetti, Leonardo
What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data?
geographic_facet Latin America & Caribbean
Peru
relation Policy Research Working Paper;No. 8545
description 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.
format Working Paper
author Lucchetti, Leonardo
author_facet Lucchetti, Leonardo
author_sort Lucchetti, Leonardo
title What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data?
title_short What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data?
title_full What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data?
title_fullStr What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data?
title_full_unstemmed What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data?
title_sort what can we (machine) learn about welfare dynamics from cross-sectional data?
publisher World Bank, Washington, DC
publishDate 2018
url 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
_version_ 1764471490910093312

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