 One size does not fit all: Quantile regression estimates of cross-country risk of poverty in EuropeBruno Bosco Economic Analysis and Policy, 2019, vol. 62, issue C, 280-299 Abstract: Using a macro panel of 31 European countries, this study shows that the application of quantile regression (QR) procedures to the estimation of poverty risk reveals that poverty determinants and cross-country differences in poverty levels are more reliable than those emerging from conditional mean estimations. The extent and significance of interquartile differences of estimated coefficients suggest that economic growth, income distribution, public expenditure, investment, education, and the labor’s share of income – aproxy for functional income distribution or social antagonism – have strong but differentiated effects on poverty reduction. Low-institutional quality exemplified by the high public-sector-corruption perception has a significant concomitant adverse effect on poverty and interacts with economic cofactors in determining interquartile differences of estimated coefficients. Results show that the implementation of a common European Union policy against poverty should consider cross-country interquartile differences and avoid a one-size-fits-all uniform philosophy. Keywords: Poverty; Income; Institutional quality; Panel quantile regression; Europe (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecanpo:v:62:y:2019:i:c:p:280-299 DOI: 10.1016/j.eap.2019.04.003 Access Statistics for this article Economic Analysis and Policy is currently edited by Clevo Wilson More articles in Economic Analysis and Policy from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.