Statistical Inference via Bootstrapping for Measures of Inequality

Mills, Jeffrey A.; Zandvakili, Sourushe

doi:10.2139/ssrn.112248

Cited by 86 publications

(115 citation statements)

References 9 publications

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“…In the context of inequality and poverty, the bootstrap approach was first applied by Mills and Zandvakili (1997), and its validity has been shown in Biewen (2002). Our results contribute to close an apparent lack of statistical inference in the empirical poverty literature.…”

supporting

confidence: 57%

Poverty in Germany – Statistical Inference and Decomposition

Bönke

Schröder

2011

Jahrbücher Für Nationalökonomie Und Statistik

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supporting

confidence: 57%

Poverty in Germany – Statistical Inference and Decomposition

Bönke

Schröder

2011

Jahrbücher Für Nationalökonomie Und Statistik

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“…This technique is relatively straightforward, yet analytically powerful. Mathematical justifications can be quite sophisticated, the bootstrap method requires no theoretical calculations, applies identically to any income inequality measure, and is available no matter how mathematically complicated the parameter estimate or its asymptotic SE may be (Mills and Zandvakili, 1997). The bootstrap procedure is:…”

Section: The Bootstrap Methodsmentioning

confidence: 99%

“…Recall that the true value of the Gini index for this distribution is 0.5. The first column of Table 4 uses SE of the jackknife with N(0, 1) critical values, the second is based on the percentile-t bootstrap confi- dence interval (Mills and Zandvakili, 1997), the third are confidence intervals based on the regression model. It is evident that the asymptotic bootstrap and jackknife intervals are very similar and both are very much narrower than those computed with the SEs based on regression approach.…”

Section: Comparison Of Confidence Intervalsmentioning

confidence: 99%

“…Some related references are Woo and Yoon (2001), Xu (2007), Davidson (2009), Langel and Tillé (2013), and Yitzhaki and Schechtman (2013). Many authors have proposed using resampling approaches such as jackknife and bootstrap techniques to compute variance for the Gini estimator (Kang and Cho, 1996;Mills and Zandvakili, 1997;Moran, 2006;Palmitesta and Provasi, 2006;Yitzhaki, 1991).…”

Section: Introductionmentioning

confidence: 99%

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A comparative study of the Gini coefficient estimators based on the regression approach

Mirzaei

Borzadaran

Amini

et al. 2017

CSAM

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Resampling approaches were the first techniques employed to compute a variance for the Gini coefficient; however, many authors have shown that an analysis of the Gini coefficient and its corresponding variance can be obtained from a regression model. Despite the simplicity of the regression approach method to compute a standard error for the Gini coefficient, the use of the proposed regression model has been challenging in economics. Therefore in this paper, we focus on a comparative study among the regression approach and resampling techniques. The regression method is shown to overestimate the standard error of the Gini index. The simulations show that the Gini estimator based on the modified regression model is also consistent and asymptotically normal with less divergence from normal distribution than other resampling techniques.

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“…To verify the statistical significance of the inequality measures, the bootstrap method is used (Mills and Zandvakili, 1997). 100 random samples with replacement are drawn from all observations within a 20-year period.…”

mentioning

confidence: 99%

Redistribution and Insurance in the German Welfare State

Bartels¹

2012

Schmollers Jahrbuch

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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 SOEPpapers on Multidisciplinary Panel Data Research at DIW BerlinThis series presents research findings based either directly on data from the German SocioEconomic Panel Study (SOEP) or using SOEP data as part of an internationally comparable data set (e.g. CNEF, ECHP, LIS, LWS, CHER/PACO). SOEP is a truly multidisciplinary household panel study covering a wide range of social and behavioral sciences: economics, sociology, psychology, survey methodology, econometrics and applied statistics, educational science, political science, public health, behavioral genetics, demography, geography, and sport science.The decision to publish a submission in SOEPpapers is made by a board of editors chosen by the DIW Berlin to represent the wide range of disciplines covered by SOEP. There is no external referee process and papers are either accepted or rejected without revision. Papers appear in this series as works in progress and may also appear elsewhere. They often represent preliminary studies and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be requested from the author directly.Any opinions expressed in this series are those of the author(s) and not those of DIW Berlin.Research disseminated by DIW Berlin may include views on public policy issues, but the institute itself takes no institutional policy positions. Abstract. Welfare states redistribute both between individuals reducing annual inequality and over the life-cycle insuring against income risks. But studies measuring redistribution often focus only on a one-year period. Using German SOEP data from 1984 to 2009, long-term inequality over a 20-year period is computed and then decomposed into an inter-and intra-individual component. Results show that annual inequality is higher than long-term inequality, but redistribution is also larger annually. In the long-term, the German welfare state clearly gives priority to insurance over redistribution. This gets even more pronounced at later stages of the life-cycle through the payment of social security pensions.

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Statistical Inference via Bootstrapping for Measures of Inequality

Cited by 86 publications

References 9 publications

Poverty in Germany – Statistical Inference and Decomposition

Poverty in Germany – Statistical Inference and Decomposition

A comparative study of the Gini coefficient estimators based on the regression approach

Redistribution and Insurance in the German Welfare State

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