EconPapers    
Economics at your fingertips  
 

Income distribution and economic development: Insights from machine learning

Pushan Dutt and Ilia Tsetlin

Economics and Politics, 2021, vol. 33, issue 1, 1-36

Abstract: We draw upon recent advances that combine causal inferences with machine learning, to show that poverty is the key income distribution measure that matters for development outcomes. In a predictive framework, we first show that LASSO chooses only the headcount measure of poverty from 37 income distribution measures in predicting schooling, institutional quality, and per capita income. Next, causal inferences with post‐LASSO models indicate that poverty matters more strongly for development outcomes than does the Gini coefficient. Finally, instrumental variable estimates in conjunction with post‐LASSO models show that compared to Gini, poverty is more strongly causally associated with schooling and per capita income, but not institutional quality. Our results question the literature's overwhelming focus on the Gini coefficient. At the least, our results imply that the causal link from inequality (as measured by Gini) to development outcomes is tenuous.

Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
https://doi.org/10.1111/ecpo.12157

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:ecopol:v:33:y:2021:i:1:p:1-36

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0954-1985

Access Statistics for this article

Economics and Politics is currently edited by Peter Rosendorff

More articles in Economics and Politics from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().

 
Page updated 2022-10-29
Handle: RePEc:bla:ecopol:v:33:y:2021:i:1:p:1-36