On the assessment and use of cross-national income inequality datasets
Frederick Solt ()
The Journal of Economic Inequality, 2015, vol. 13, issue 4, 683-691
Researchers should ensure the data they employ are fit for their purpose, and they should maximize the quality of the data they choose. In this paper, I review how this advice applies to broadly cross-national research on income inequality. I demonstrate that the guidance offered in Jenkins (J. Econ. Inequal., 2015 ) to those pursuing cross-national research runs completely counter to the recommendations found in Atkinson and Brandolini (J. Econ. Lit. 39(3), 771–799, 2001 , 2009 ), the source of the aforementioned advice and the works upon which Jenkins (J. Econ. Inequal., 2015 ) claims its own is based. I then show how the Standardized World Income Inequality Database (SWIID) incorporates Atkinson and Brandolini’s recommendations to provide the most comparable data available for those engaged in broadly cross-national research on income inequality. Copyright Springer Science+Business Media New York 2015
Keywords: Measurement; Cross-national; Income inequality (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (25) Track citations by RSS feed
Downloads: (external link)
Access to full text is restricted to subscribers.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:kap:jecinq:v:13:y:2015:i:4:p:683-691
Ordering information: This journal article can be ordered from
http://www.springer. ... th/journal/10888/PS2
Access Statistics for this article
The Journal of Economic Inequality is currently edited by Stephen Jenkins
More articles in The Journal of Economic Inequality from Springer, Society for the Study of Economic Inequality Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla ().