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Appraising cross-national income inequality databases: An introduction

机译:评估跨国收入不平等数据库:简介

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In response to a growing interest in comparing inequality levels and trends across countries, a number of cross-national inequality databases are now available. These databases differ considerably in purpose, coverage, data sources, inclusion and exclusion criteria, and quality of documentation. This special issue reviews and compares eight such databases. Five are microdata-based: CEPALSTAT, Income Distribution Database (EDD), LIS, PovcalNet, and Socio-Economic Database for Latin America and the Caribbean (SED-LAC); two are based on secondary sources: "All the Ginis" (ATG) and the World Income Inequality Database (WIID); and one is generated entirely through multiple-imputation methods: the Standardized World Income Inequality Database (SWIID). Although there is much agreement across these databases, there is also a non-trivial 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. 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 it as true because available.
机译:为了响应人们对比较各国之间不平等水平和趋势的日益增长的兴趣,现在可以使用许多跨国不平等数据库。这些数据库在目的,覆盖范围,数据源,包含和排除标准以及文档质量方面存在很大差异。本期特刊复习并比较了八个此类数据库。五个是基于微数据的:CEPALSTAT,收入分配数据库(EDD),LIS,PovcalNet和拉丁美洲和加勒比海社会经济数据库(SED-LAC);其中两个是基于辅助资源的:“所有吉尼斯人”(ATG)和世界收入不平等数据库(WIID);其中一个完全通过多种计算方法生成:标准化的世界收入不平等数据库(SWIID)。尽管这些数据库之间存在很多共识,但在国家/年单元格中也存在不小的份额,存在巨大差异。在某些情况下,不同的数据库会导致用户对某些国家和时期的不平等动态得出根本不同的结论。导致这些差异的方法上的差异通常似乎是由基本的权衡驱动的,一方面要扩大覆盖面,另一方面要有更大的可比性。数据库之间的这些差异对生产者和用户都负有相当大的责任:前者要更好地记录和解释他们的假设和程序,而后者要了解他们正在使用的数据,而不仅仅是因为可用而将其视为真实。

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