Robust Correlates of Growth Spells: Do Inequality and Redistribution Matter?

Andros Kourtellos () and Charalambos Tsangarides
Additional contact information
Andros Kourtellos: Department of Economics, University of Cyprus, Cyprus; The Rimini Centre for Economic Analysis, Italy

Working Paper series from Rimini Centre for Economic Analysis

Abstract: Theoretical and empirical evidence on inequality and growth provides only partial guidance about the relationship. Much of the problem stems from model uncertainty – about what covariates to use, the relationships between them, and the transmission mechanisms – which is often not properly accounted for. This paper investigates the relationship between growth, redistribution, and the duration of growth spells in the presence of a rich set of alternative determinants and theories using model averaging for duration models to account for model uncertainty. We find that lower net inequality is robustly correlated with longer growth spells, while redistribution appears generally benign for growth spells, but the effect is not robust. In addition, there is no evidence of nonlinearities in the inequality-redistribution-growth spell relationship. Finally, lower initial income, higher secondary education and higher FDI are associated with longer spell duration.

Date: 2015-05
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.rcea.org/RePEc/pdf/wp15-20.pdf (application/pdf)

Related works:
Journal Article: Robust Correlates of Growth Spells: Do Inequality and Redistribution Matter? (2022)
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:rim:rimwps:15-20

Access Statistics for this paper

More papers in Working Paper series from Rimini Centre for Economic Analysis Contact information at EDIRC.
Bibliographic data for series maintained by Marco Savioli ().