Appraising Cross-National Income Inequality Databases : An Introduction
In response to a growing interest in comparing inequality levels and trends across countries, several cross-national inequality databases are now available. These databases differ considerably in purpose, coverage, data sources, inclusion and exclu...
| Main Authors: | , , |
|---|---|
| Format: | Working Paper |
| Language: | English en_US |
| Published: |
World Bank, Washington, DC
2015
|
| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/2015/11/25384034/appraising-cross-national-income-inequality-databases-introduction http://hdl.handle.net/10986/23451 |
| Summary: | In response to a growing interest in
comparing inequality levels and trends across countries,
several cross-national inequality databases are now
available. These databases differ considerably in purpose,
coverage, data sources, inclusion and exclusion criteria,
and quality of documentation. A special issue of the Journal
of Economic Inequality, which this paper introduces, is
devoted to an assessment of the merits and shortcomings of
eight such databases. Five of these sets are
microdata-based: CEPALSTAT, Income Distribution Database,
Luxembourg Income Study, PovcalNet, and Socio-Economic
Database for Latin America and the Caribbean. Two are based
on secondary sources: All the Ginis and the World Income
Inequality Database; and one is generated entirely through
multiple-imputation methods: the Standardized World Income
Inequality Database. Although there is much agreement across
these databases, there is also a nontrivial share of
country/year cells for which substantial discrepancies
exist. In some cases, different databases would lead users
to radically different conclusions about inequality dynamics
in certain countries and periods. The methodological
differences that lead to these discrepancies often appear to
be driven by a fundamental trade-off between a wish for
broader coverage on the one hand, and for greater
comparability on the other hand. These differences across
databases place considerable responsibility on both
producers and users: on the former, to better document and
explain their assumptions and procedures, and on the latter,
to understand the data they are using, rather than merely
taking them as true because available. |
|---|