PovcalNet, WDI and ‘All the Ginis’: a critical review

Smeeding, Timothy M.; Latner, Jonathan P.

doi:10.1007/s10888-015-9312-4

Cited by 35 publications

(26 citation statements)

References 32 publications

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“…Some recent reviews of existing databases (e.g. Jenkins (2015); Smeeding and Latner (2015)) highlight the need for accessibility, replicability, transparency, quality of documentation and user-friendliness. One of the major motivations for developing GCIP was indeed to be able to provide transparent and replicable data.…”

Section: Aggregation Modulementioning

confidence: 99%

The Global Consumption and Income Project (GCIP): An Overview

2016

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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. We introduce two separate datasets (The Global Consumption Dataset (GCD) and The Global Income Dataset (GID)) making possible an unprecedented portrait of consumption and income of persons over time, within and across countries, around the world. The current benchmark version of the dataset presents estimates of monthly real consumption and income for every percentile of the population (a 'consumption/income profile') for more than 160 countries and more than half a century . We describe the construction of the datasets and demonstrate possible uses by presenting some sample results concerning the distribution of consumption, poverty and inequality in the world. Terms of use: Documents in

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Section: Aggregation Modulementioning

confidence: 99%

The Global Consumption and Income Project (GCIP): An Overview

2016

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“…The LIS database allows scholars to access the microdata, so that income inequality measures and fiscal redistribution (and the partial effect per social programme) can be derived consistently from the underlying data at the individual and household level. LIS microdata seem to be the best available data for describing how income inequality and the redistributive effects of income taxes and social transfers vary across countries and over time (Nolan and Marx, ; Smeeding and Latner, ; Nieuwenhuis, Munzi and Gornick, ). We apply a cross‐national analysis using comparable income surveys for all countries of LIS from 1982–2014.…”

Section: Methodsmentioning

confidence: 99%

Has the redistributive effect of social transfers and taxes changed over time across countries?

Caminada

Goudswaard

Wang

et al. 2019

Int Social Security Review

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In most Member countries of the Organisation for Economic Co‐operation Development (OECD), the income gap between rich and poor has widened over the past decades. This article analyses whether and to what extent income taxes and social transfers have contributed to this trend. Has the redistributive impact of different social programmes changed over time? We use microdata from the LIS Cross National Data Center in Luxembourg for the period 1982–2014 and study both the total population and the working‐age population. In contrast to the results of some other studies, especially by the OECD, we do not find that redistribution has declined. Tax‐benefit systems around 2013 are more effective at reducing income inequality compared to the mid‐1980s and the mid‐1990s, especially among the total population. Changes in social programmes are not a driver of greater income inequality across the countries included in this study.

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“…The LIS database allows scholars to access the microdata, so that income inequality measures and fiscal redistribution (and the partial effect per social program) can be derived consistently from the underlying data at the individual and household level. 6 LIS micro data seems to be the best available data for describing how income inequality and the redistributive effects of taxes and transfers vary across countries (Nolan & Marx, 2009;Smeeding & Latner, 2015). However, country-comparative and trend analyses of income distribution based on LIS gross/net datasets should be done with caution (Gornick et al, 2013;Nieuwenhuis et al, 2016)).…”

Section: Datamentioning

confidence: 99%

“…In the last decades, equivalence scales have been widely used in the literature on income distribution (Figini, 1998). Equivalence scale elasticity 6 The distinctive feature and value-added of LIS is the access it provides to a set of harmonized micro data files supplied by participating statistical agencies at the country level (Ravallion (2015: p. 529): Harmonization of income data increases quality and comparability across nations and across time; see Smeeding & Latner (2015) for a critical review of three other popular data sets which summarize inequality across countries and years (World Development Indicators ('WDI')/'PovcalNet' and 'All the Ginis'). Following Ravallion (2015: p. 529): There are pros and cons of each source.…”

Section: Measurement Issuesmentioning

confidence: 99%

Income Inequality and Fiscal Redistribution in 31 Countries After the Crisis

et al. 2018

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In this paper we analyze fiscal redistribution after the Great Recession. Are welfare states still effective in reducing income inequality? We use recent micro-data from the Luxembourg Income Study (LIS) to examine redistribution from transfers and income taxes, and the several underlying social programs that drive the changes in 31 countries. We employ a budget incidence simulation model to investigate to what extent several social transfers and income taxes reduce income inequality. This paper is a cross country comparison at one moment in time for as many countries as possible and for the most recent data year available. The study is novel because it offers an extensive decomposition of the redistributive effects of social transfer programs. In addition, we present sensitivity analyses, applying various measures of global inequality and different equivalence scale methods. Finally, this study offers a detailed analysis of fiscal redistribution for both the working-age population and the total population. On average we find that social transfers and income taxes reduce the Gini by 31 percent. In most countries pensions are a dominant factor. After performing a number of sensitivity analyses, we conclude that the redistributive impact of the welfare state is still substantial.

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PovcalNet, WDI and ‘All the Ginis’: a critical review

Cited by 35 publications

References 32 publications

The Global Consumption and Income Project (GCIP): An Overview

The Global Consumption and Income Project (GCIP): An Overview

Has the redistributive effect of social transfers and taxes changed over time across countries?

Income Inequality and Fiscal Redistribution in 31 Countries After the Crisis

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