Market Inequality and Redistribution in Latin America and the Caribbean

Morgan, Jana; Kelly, Nathan J.

doi:10.1017/s0022381613000509

Cited by 47 publications

(38 citation statements)

References 36 publications

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“…Many of the more recent income inequality studies use the SWIID Gini coefficient (see, for example, Cole, 2013;Herzer and Nunnenkamp, 2015;Morgan and Kelly, 2013). However, this index has the limitation that it is estimated, and estimates may be biased (for several reasons).…”

Section: Data and Descriptive Statisticsmentioning

confidence: 99%

Unions and Income Inequality: A Heterogeneous Panel Co‐integration and Causality Analysis

Herzer

2016

Labour

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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 Zusammenfassung/ AbstractAlthough a large body of research has examined the effects of unions on the wage distribution, surprisingly little attention has been devoted to the effects of unions on the distribution of income. This paper examines the long-run relationship between unionization and income inequality for a sample of 20 countries. Using heterogeneous panel cointegration techniques, we find that (i) unions have, on average, a negative long-run effect on income inequality, (ii) there is considerable heterogeneity in the effects of unionization on income inequality across countries (in about a third of cases the effect is positive), and (iii) long-run causality runs in both directions, suggesting that, on average, an increase in unionization reduces income inequality and that, in turn, higher inequality leads to lower unionization rates.

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Section: Data and Descriptive Statisticsmentioning

confidence: 99%

Unions and Income Inequality: A Heterogeneous Panel Co‐integration and Causality Analysis

Herzer

2016

Labour

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“…24 The SWIID combines information from the LIS and WIID data to create an improved data set with greater coverage than the LIS data and greater comparability than the WIID data. The logic behind the methodology underlying the SWIID can be summarized as follows (see also Morgan and Kelly 2013). The synchronization process for the SWIID starts by utilizing inequality data from both the LIS and the WIID.…”

Section: Data and Preliminary Evidencementioning

confidence: 99%

“…Many of the more recent income inequality studies use the SWIID Gini coefficient (see, e.g., Desbordes and Verardi 2012;Cole 2013;Morgan and Kelly 2013). However, this index has the limitation that it is estimated, and estimates may be biased (for several reasons).…”

Section: Data and Preliminary Evidencementioning

confidence: 99%

Income Inequality and Health: Evidence from Developed and Developing Countries

Herzer

Nunnenkamp

2015

Economics

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We assess the effect of income inequality on life expectancy by performing separate estimations for developed and developing countries. Our empirical analysis challenges the widely held view that inequality matters more for health in richer countries than for health in poorer countries. Employing panel cointegration and conventional panel regressions, we find that income inequality slightly increases life expectancy in developed countries. By contrast, the effect on life expectancy is significantly negative in developing countries. Even though the quantitative effects are small, the contrast between the two country groups proves to be robust to modifications in measurement, specification and methodological choices. Nunnenkamp (2015). Income Inequality and Health: Evidence from Developed and Developing Countries. Economics: The Open-Access, Open-Assessment E-Journal, 9 (2015-4): 1-57. http:// dx.doi.org/10.5018/economics-ejournal.ja. www.economics-ejournal.org 2 JEL I14 C23 IntroductionIncome disparities have widened in various developed and developing countries during the process of economic globalization. Critics have called for redistributive policy interventions, not only for reasons of fairness but also to avoid economic and social costs of wide income gaps. Impaired health could add considerably to such costs. Pickett (2006: 1768) conclude from their comprehensive review of the literature that the large majority of more than 150 empirical studies suggest that "health is less good in societies where income differences are bigger." 1 In particular, this applies to international cross-country studies. Moreover, the literature suggests that the case for health-related redistribution is particularly strong for developed countries. According to Wilkinson (1996: 4), the distribution of income is "one of the most powerful influences on the health of whole populations in the developed world to have come to light." 2 Lynch et al. (1998Lynch et al. ( : 1074 reckoned that the loss of life from income inequality in the United States "is comparable to the combined loss of life from lung cancer, diabetes, motor vehicle crashes, human immunodeficiency virus (HIV) infection, suicide, and homicide in 1995." The so-called Whitehall studies on British civil servants showed "that, even among people who are not poor, there is a social gradient in mortality that runs from the bottom to the top of society" (Marmot 2003: S10). 3 More recently, the meta analysis of Kondo et al. (2009) reveals a modest adverse effect of income inequality on health. 4 Zheng (2012) finds that, while income inequality did not have an instantaneous effect on mortality risk in the United States, long-term _________________________ 1 See also Lynch et al. (2004) who review almost 100 studies addressing the question of whether more unequal societies are less healthy. Informative reviews of the relevant literature are also presented by Judge et al. (1998), Wagstaff and van Doorslaer (2000), Deaton (2003), and Subramanian and Kawachi (2004). 2 I...

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“…Researchers should take particular care not to interpret the SWIID estimates of market‐income inequality, the distribution of income before taxes and transfers are taken into account, as the level of “pre‐government” inequality. A wide range of nonredistributive government policies, from minimum‐wage regulations to public education and job‐training programs, also shape the income distribution (see, e.g., Morgan and Kelly, 2013). Further, households’ decisions regarding savings, employment, and retirement, decisions powerfully shaped by redistributive policy, in turn, shape market‐income inequality: in the presence of robust public pension programs, for example, many households save little for retirement, most elderly households will be without market income, and market‐income inequality will be higher relative to places with less comprehensive public pensions (see, e.g., Jesuit and Mahler, 2010).…”

Section: Using the Swiid For Cross‐national Researchmentioning

confidence: 99%

Measuring Income Inequality Across Countries and Over Time: The Standardized World Income Inequality Database

Solt

2020

Social Science Quarterly

572

131

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Objective. This article documents wide-ranging revisions to the Standardized World Income Inequality Database (SWIID), which seeks to maximize the comparability of income inequality estimates for the broadest possible coverage of countries and years. Methods. Two k-fold crossvalidations, by observation and by country, are used to evaluate the SWIID's success in predicting the Luxembourg Income Study (LIS), recognized in the field as setting the standard for comparability. Results. The cross-validations indicate that the new SWIID's estimates and their uncertainty are even more accurate than previous versions, extending its advantage in comparability over alternate income inequality data sets. Conclusion. Given its superior coverage and comparability, the SWIID remains the optimum source of data for broadly cross-national research on income inequality. From its origins now over a decade ago, the goal of the Standardized World Income Inequality Database (SWIID) has been to provide estimates of income inequality for as many countries and years as possible while ensuring that these estimates are as comparable as the available data allow (see Solt, 2009). That is to say, the SWIID's first priority is breadth of coverage, and its second priority is comparability. The starting point for the SWIID estimates is a data set with the complementary priorities: the Luxembourg Income Study (LIS), which aims to maximize comparability and, given that primary concern, to include as many countries and years as possible. 1 Then, the SWIID routine estimates the relationships between Gini indices based on the LIS and all of the other Ginis available for the same country-years, and it uses these relationships to estimate what the LIS Gini would be in country-years not included in the LIS but available from other sources. This approach has made the SWIID a preferred source of income inequality data for researchers pursuing broadly cross-national work across a wide range of disciplines, including not only economics (e.g., Berg et al.

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Market Inequality and Redistribution in Latin America and the Caribbean

Cited by 47 publications

References 36 publications

Unions and Income Inequality: A Heterogeneous Panel Co‐integration and Causality Analysis

Unions and Income Inequality: A Heterogeneous Panel Co‐integration and Causality Analysis

Income Inequality and Health: Evidence from Developed and Developing Countries

Measuring Income Inequality Across Countries and Over Time: The Standardized World Income Inequality Database

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