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Original Articles

Measuring Income Polarization for Twenty European Countries, 2004–13: A Shapley Growth-Redistribution Decomposition

, &
Pages 477-499 | Published online: 19 Jul 2017
 

Abstract

Income polarization adds to the literature on income distribution by providing information on the poles of the distribution of income, but little is known about this issue in Europe. This article explores income polarization and its determinants for twenty European countries over the period 2004–13 based on EU-SILC microdata and Shapley decomposition. The results suggest that income polarization is rather low in Europe; it rose in West-EU15 countries during 2004–08, but declined afterwards. The opposite development is witnessed for Central and Eastern European new member states. Moreover, in most cases, market income induced higher polarization, while tax-benefit systems were polarization-reducing.

JEL Classification:

ACKNOWLEDGMENTS

Earlier versions of this article were presented at the 22nd International Research Seminar of the Foundation for International Studies on Social Security (2015), Hong Kong, China; the Research Seminar at the Department of Economics (2015), Leiden, the Netherlands; and the Dutch Economist Day (2015), Amsterdam, the Netherlands. The authors thank all the participants and Egbert Jongen, Lieke Kools, Olaf van Vliet, Bea Cantillon, Jekaterina Navicke, Rod Hick, Time Goedemé, and Albert Jan Hummel for useful suggestions and comments. Support from the Instituut Gak, China Scholarship Council, China Postdoctoral Science Foundation (Project 2016M591645), and National Natural Science Foundation of China (Project 71133004, 71373186) are acknowledged. This study is based on data from Eurostat, EU-SILC. The results and conclusions are ours and acknowledged. This study is based on data from Eurostat, EU-SILC. The results and conclusions are ours and not those of Eurostat, the European Commission, or any of the national statistical authorities whose data have been used.

SUPPLEMENTARY MATERIAL

Supplemental data for this article can be accessed on the publisher’s website.

Notes

1. The α is bounded [0, 1.6] to satisfy the axioms imposed on the ER and other intuitive properties of the measure (Esteban and Ray Citation1994).

2. To construct the identification ingredient, Duclos, Esteban, and Ray (Citation2004) first operationalized a Gaussian kernel: gvi=1h12πexp12viuh2. A Gaussian kernel function has a symmetric bell-curve shape. The bandwidth h is a free parameter controlling the width of the bell, and thus exhibits strong influence on the result. 1h12π expresses the height of the curve’s peak. u is the expected value of v, indicating the position of the peak center. fˆvi then is the average of the sum of gvi using the income distance vivj of two individuals instead of viu.

3. One can find decomposition of income polarization by population in the literature. For example, Araar (Citation2008) and Gradín (Citation2000) decompose the polarization by population group. Araar (Citation2008) decomposes income polarization by income source as well, but only for China and Nigeria.

4. The Appendix is added to the online version of the article and is also available via the authors’ personal Web pages at https://www.universiteitleiden.nl/en/staffmembers/jinxian-wang and https://www.universiteitleiden.nl/en/staffmembers/koen-caminada.

5. The Appendix is added to the online version of the article and is also available via the authors’ personal Web pages at https://www.universiteitleiden.nl/en/staffmembers/jinxian-wang and https://www.universiteitleiden.nl/en/staffmembers/koen-caminada.

6. In addition to the DER indicator (α = 0.5), income polarization is estimated utilizing different indicators (FW, EGR) and the DER indicator with different values of α (α = 0.25, α = 0.75, and α = 1). Although the magnitudes of the polarization indicators are different using different indicators or different values of α, the overall trends of income polarization estimated by the DER indicator (α = 0.5) are robust. Detailed information is presented in Tables B1–B3 and Figures B1 in Appendix B and Table C1 in Appendix C. The Appendix is added to the online version of the article and is also available via the authors’ personal Web pages at https://www.universiteitleiden.nl/en/staffmembers/jinxian-wang and https://www.universiteitleiden.nl/en/staffmembers/koen-caminada.

7. The Appendix is added to the online version of the article and is also available via the authors’ personal Web pages at https://www.universiteitleiden.nl/en/staffmembers/jinxian-wang and https://www.universiteitleiden.nl/en/staffmembers/koen-caminada.

Additional information

Notes on contributors

Jinxian Wang

Jinxian Wang is an Assistant Professor in the Department of Economics at Leiden University, Leiden, the Netherlands. Koen Caminada is a Professor of Empirical Analysis of Tax and Social Policy and an Academic Director of the Institute of Tax Law and Economics at Leiden University, Leiden, the Netherlands. Chen Wang is a Lecturer in the School of Urban & Regional Science, Shanghai University of Finance & Economics, Shanghai, China.

Koen Caminada

Jinxian Wang is an Assistant Professor in the Department of Economics at Leiden University, Leiden, the Netherlands. Koen Caminada is a Professor of Empirical Analysis of Tax and Social Policy and an Academic Director of the Institute of Tax Law and Economics at Leiden University, Leiden, the Netherlands. Chen Wang is a Lecturer in the School of Urban & Regional Science, Shanghai University of Finance & Economics, Shanghai, China.

Chen Wang

Jinxian Wang is an Assistant Professor in the Department of Economics at Leiden University, Leiden, the Netherlands. Koen Caminada is a Professor of Empirical Analysis of Tax and Social Policy and an Academic Director of the Institute of Tax Law and Economics at Leiden University, Leiden, the Netherlands. Chen Wang is a Lecturer in the School of Urban & Regional Science, Shanghai University of Finance & Economics, Shanghai, China.

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