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-...
Main Author: | Lucchetti, Leonardo |
---|---|
Format: | Working Paper |
Language: | English |
Published: |
World Bank, Washington, DC
2018
|
Subjects: | |
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 |
Similar Items
-
Who Escaped Poverty and Who Was Left Behind? : A Non-Parametric Approach to Explore Welfare Dynamics Using Cross-Sections
by: Lucchetti, Leonardo
Published: (2017) -
Welfare Dynamics in India over a Quarter Century : Poverty, Vulnerability, and Mobility during 1987-2012
by: Dang, Hai-Anh H., et al.
Published: (2020) -
The Interplay of Regional and Ethnic Inequalities in Malaysian Poverty Dynamics
by: Rongen, Gerton, et al.
Published: (2022) -
Machine Learning in International Trade Research : Evaluating the Impact of Trade Agreements
by: Breinlich, Holger, et al.
Published: (2021) -
Is Poverty in Africa Mostly Chronic or Transient? : Evidence from Synthetic Panel Data
by: Dang, Hai-Anh H., et al.
Published: (2017)