A New Data Set Measuring Income Inequality

Deininger, Klaus; Squire, Lyn

doi:10.1093/wber/10.3.565

Cited by 2,158 publications

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“…Deininger and Squire (1996), in the context of their dataset, suggest adding 6.6 Gini points to Gini coe¢ cients based on consumption to obtain the corresponding income Gini coe¢ cients. In this study, as described in the following subsection, all our inequality estimates are made directly using quantile share data.…”

Section: Data Compilationmentioning

confidence: 99%

Global Interpersonal Inequality: Trends and Measurement

2014

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This paper discusses different approaches to the measurement of global interpersonal in equality. Trends in global interpersonal inequality during are measured using data from UNU-WIDER's World Income Inequality Database. In order to better understand the trends, global interpersonal inequality is decomposed into within-country and between-country inequality. The paper illustrates that the relationship between global interpersonal inequality and these constituent components is a complex one. In particular, we demonstrate that the changes in China s and India s income distributions over the past 30 years have simultaneously caused inequality to rise domestically in those countries, while tending to reduce global inter-personal inequality. In light of these findings, we reflect on the meaning and policy relevance of global vis-à-vis domestic inequality measures.Keywords: global interpersonal inequality, inequality, inequality measurement JEL classification: D31, D63, E01, O15 Acknowledgements: We are grateful to seminar participants at the universities of Helsinki, Oxford, Bielefeld, and Beijing Normal University, for their helpful comments on earlier versions of this paper. All the errors are ours. Global interpersonal inequality Trends and measurementMiguel Niño-Zarazúa y , Laurence Roope z and Finn Tarp x ABSTRACT This paper discusses di¤erent approaches to the measurement of global interpersonal inequality. Trends in global interpersonal inequality during 1975-2005 are measured using data from UNU-WIDER's World Income Inequality Database. In order to better understand the trends, global interpersonal inequality is decomposed into within-country and between-country inequality. The paper illustrates that the relationship between global interpersonal inequality and these constituent components is a complex one. In particular, we demonstrate that the changes in China's and India's income distributions over the past 30 years have simultaneously caused inequality to rise domestically in those countries, while tending to reduce global interpersonal inequality. In light of these …ndings, we re ‡ect on the meaning and policy relevance of global vis-à-vis domestic inequality measures.

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Section: Data Compilationmentioning

confidence: 99%

Global Interpersonal Inequality: Trends and Measurement

2014

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Section: Overviewmentioning

confidence: 99%

“…The quality controls used to filter information from the primary to the secondary datasets eliminate unreliable data and inequality observations that are not representative of the whole country. Deininger and Squire (1996) used three quality controls:…”

Section: Secondary Datasetsmentioning

confidence: 99%

“…Yet, the study of income inequality has been seriously limited by data constraints. The introduction of a cross-country inequality dataset by the World Bank (Deininger and Squire, 1996) has complemented the recent literature and has itself launched a series of influential econometric studies.While at the core of most recent work in the area, the structure of the inequality dataset compiled by Deininger and Squire, henceforth DS, recently has been criticized by Atkinson and Brandolini (2001), henceforth AB. These criticisms also extend implicitly to the recent extension of the DS data in the World Income Inequality Dataset (WIID).…”

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The Construction and Interpretation of Combined Cross-Section and Time-Series Inequality Datasets

François

Rojas‐Romagosa

2005

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes.You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. licence. www.econstor.eu Please send questions and/or remarks of nonscientific nature to driessen@tinbergen.nl. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicatedMost TI discussion papers can be downloaded at http://www.tinbergen.nl. The Construction and Interpretation of Combined Cross-Section and Time-Series Inequality Datasets Joseph F. Francois Tinbergen Institute (Erasmus University Rotterdam) and CEPR Hugo Rojas-Romagosa Tinbergen Institute (Erasmus University Rotterdam) August 2005Abstract: The inequality dataset compiled in the 1990s by the World Bank and extended by the UN has been both widely used and strongly criticized. The criticisms raise questions about conclusions drawn from secondary inequality datasets in general. We develop techniques to deal with national and international comparability problems intrinsic to such datasets. The result is a new dataset of consistent inequality series, allowing us to explore problems of measurement error. In addition, the new data allow us to perform parametric non-linear estimation of Lorenz curves from grouped data. This in turn allows us to estimate the entire income distribution, computing alternative inequality indexes and poverty estimates. Finally, we have used our broadly comparable dataset to examine international patterns of inequality and poverty.Keywords: Income distribution datasets, inequality trends, Lorenz curve estimation, poverty estimation JEL codes: D31, C80, O15We acknowledge support from the EU research and training network (RTN) "Trade, Industrialization, and Development," as well as research support from DFID and the World Bank. All errors are of course our own.Address correspondence to: J. Francois, Tinbergen Institute, Erasmus University Rotterdam, Burg Oudlaan 50-H8-18, 3000DR Rotterdam, NETHERLANDS. Email: francois@few.eur.nl. Data are available at www.intereconomics.com/francois/data.html. OverviewThere is a sizeable literature regarding the interaction between income inequality and other economic variables, such as growth, poverty, trade and ...

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“…an index of distribution of income ranging from 0 to 1, where a score of 1 corresponds to the most unequal income distribution (24,28) rural water access rural water (percent)…”

Section: Features Of Cartmentioning

confidence: 99%

Determinants of National Diarrheal Disease Burden

Green

Small

Casman

2009

Environ. Sci. Technol.

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Diarrheal illness is a leading cause of child mortality in developing nations. Previous longitudinal studies have attempted to identify the factors that contribute to child mortality, but few have examined the determinants of diarrheal illness at a country level. Here we demonstrate the use of Classification and Regression Trees (CART) to predict diarrheal illness from a 192-country data set of country-level attributes and compare the performance of CART with a linear regression model. The CART model identifies improvements in rural sanitation as the most important spending priority for reducing diarrheal illness. We estimate that reducing unmet rural sanitation need worldwide by 65% would save the equivalent of 1.2 million lives annually.

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A New Data Set Measuring Income Inequality

Cited by 2,158 publications

References 19 publications

Global Interpersonal Inequality: Trends and Measurement

Global Interpersonal Inequality: Trends and Measurement

The Construction and Interpretation of Combined Cross-Section and Time-Series Inequality Datasets

Determinants of National Diarrheal Disease Burden

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