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Assessing inequality using percentile shares

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  • Ben Jann

    (University of Bern)

Abstract

At least since Thomas Piketty’s best-selling Capital in the Twenty- First Century (2014, Cambridge, MA: The Belknap Press), percentile shares have become a popular approach for analyzing distributional inequalities. In their work on the development of top incomes, Piketty and collaborators typically report top-percentage shares, using varying percentages as thresholds (top 10%, top 1%, top 0.1%, etc.). However, analysis of percentile shares at other positions in the distribution may also be of interest. In this article, I present a new command, pshare, that estimates percentile shares from individual-level data and displays the results using histograms or stacked bar charts. Copyright 2016 by StataCorp LP.

Suggested Citation

  • Ben Jann, 2016. "Assessing inequality using percentile shares," Stata Journal, StataCorp LLC, vol. 16(2), pages 264-300, June.
    • Handle: RePEc:tsj:stataj:y:16:y:2016:i:2:p:264-300
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    Cited by:

    1. James B. Davies & Nicole M. Fortin & Thomas Lemieux, 2017. "Wealth inequality: Theory, measurement and decomposition," Canadian Journal of Economics, Canadian Economics Association, vol. 50(5), pages 1224-1261, December.
    2. M. Mesut Badur & Kazi Sohag & Shawkat Hammoudeh & Gazi Salah Uddin, 2023. "Costs of economic growth: new insights on wealth and income inequalities in the post-communist countries," Post-Communist Economies, Taylor & Francis Journals, vol. 35(8), pages 830-855, November.
    3. Mark C. Strazicich & Peter A. Groothuis & Tiberiu S.V. Ungureanu, 2024. "A Rising Tide Raises all Boats: The changing distribution of salaries in the NBA over time," Working Papers 24-20, Department of Economics, Appalachian State University.
    4. Anastasios Evgenidis & Apostolos Fasianos, 2021. "Unconventional Monetary Policy and Wealth Inequalities in Great Britain," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(1), pages 115-175, February.
    5. Bornmann, Lutz & Leydesdorff, Loet, 2017. "Skewness of citation impact data and covariates of citation distributions: A large-scale empirical analysis based on Web of Science data," Journal of Informetrics, Elsevier, vol. 11(1), pages 164-175.
    6. Anand, Ishan & Kumar, Rishabh, 2022. "The sky and the stratosphere: concentrated wealth in India during the ‘lost decade’," SocArXiv 726c8, Center for Open Science.
    7. Peñasco, Cristina & Anadón, Laura Díaz, 2023. "Assessing the effectiveness of energy efficiency measures in the residential sector gas consumption through dynamic treatment effects: Evidence from England and Wales," Energy Economics, Elsevier, vol. 117(C).
    8. Gerson Pech & Catarina Delgado, 2020. "Percentile and stochastic-based approach to the comparison of the number of citations of articles indexed in different bibliographic databases," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 223-252, April.
    9. Mussa, Richard, 2017. "To Err is Human: Inconsistencies in Food Conversion Factors and Inequality in Malawi," MPRA Paper 75981, University Library of Munich, Germany.
    10. M. Mesut Badur & Md. Monirul Islam & Kazi Sohag, 2023. "Globalization–Income Inequality Nexus in the Post-Soviet Countries: Analysis of Heterogeneous Dataset Using the Quantiles via Moments Approach," Mathematics, MDPI, vol. 11(7), pages 1-29, March.
    11. Oliver Hümbelin, 2016. "Ungleichheit und Umverteilung über das Steuersystem. Eine Analyse der Verteilungseffekte von direkten Steuern und steuerlichen Abzügen mit Steuerdaten des Kantons Aargau (2001-2011)," University of Bern Social Sciences Working Papers 23, University of Bern, Department of Social Sciences.
    12. Senia Nhamo & Edinah Mudimu, 2020. "Shifting from deductions to credits: Unpacking the distributional effects of medical expenditure considerations in South Africa," WIDER Working Paper Series wp-2020-30, World Institute for Development Economic Research (UNU-WIDER).
    13. Charles Beach, 2023. "Quantile Tool Box Measures for Empirical Analysis and for Testing Distributional Comparisons in Direct Distribution-Free Fashion," Working Paper 1508, Economics Department, Queen's University.

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    JEL classification:

    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement

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