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Upward and downward bias when measuring inequality of opportunity

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  • Paolo Brunori

    () (University of Florence
    University of Bari)

  • Vito Peragine

    () (University of Bari)

  • Laura Serlenga

    () (University of Bari and IZA)

Abstract

Estimates of the level of inequality of opportunity have traditionally been proposed as lower bounds due to the downward bias resulting from the partial observability of circumstances that affect individual outcome. We show that such estimates may also suffer from upward bias as a consequence of sampling variance. The magnitude of the latter distortion depends on both the empirical strategy used and the observed sample. We suggest that, although neglected in empirical contributions, the upward bias may be significant and challenge the interpretation of inequality of opportunity estimates as lower bounds. We propose a simple criterion to select the best specification that balances the two sources of bias. Our method is based on cross-validation and can easily be implemented with survey data. To show how this method can improve the reliability of inequality of opportunity measurement, we provide an empirical illustration based on income data from 31 European countries. Our evidence shows that estimates of inequality of opportunity are sensitive to model selection. Alternative specifications lead to significant differences in the absolute level of inequality of opportunity and to the re-ranking of a number of countries, which confirms the need for an objective criterion to select the best econometric model when measuring inequality of opportunity.

Suggested Citation

  • Paolo Brunori & Vito Peragine & Laura Serlenga, 2019. "Upward and downward bias when measuring inequality of opportunity," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 52(4), pages 635-661, April.
  • Handle: RePEc:spr:sochwe:v:52:y:2019:i:4:d:10.1007_s00355-018-1165-x
    DOI: 10.1007/s00355-018-1165-x
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    References listed on IDEAS

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    Cited by:

    1. Paul Hufe & Andreas Peichl, 2020. "Beyond Equal Rights: Equality of Opportunity in Political Participation," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(3), pages 477-511, September.
    2. Juan C. Palomino & Gustavo A. Marrero & Juan G. Rodríguez, 2019. "Channels of Inequality of Opportunity: The Role of Education and Occupation in Europe," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 143(3), pages 1045-1074, June.
    3. Paolo Brunori & Guido Neidhoefer, 2020. "The Evolution of Inequality of Opportunity in Germany: A Machine Learning Approach," SERIES 01-2020, Dipartimento di Economia e Finanza - Università degli Studi di Bari "Aldo Moro", revised Jan 2020.
    4. Xavier Ramos & Dirk Van de gaer, 2017. "Is inequality of opportunity robust to the measurement approach?," Working Papers 450, ECINEQ, Society for the Study of Economic Inequality.
    5. Paolo Brunori & Paul Hufe & Daniel Gerszon Mahler, 2017. "The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees," Working Papers - Economics wp2017_18.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    6. Leonardo Gasparini & Irene Brambilla & Andrés César & Guillermo Falcone & Carlo Lombardo, 2020. "The Risk of Automation in Argentina," CEDLAS, Working Papers 0260, CEDLAS, Universidad Nacional de La Plata.
    7. Toshiaki Aizawa, 2020. "Trajectory of inequality of opportunity in child height growth: Evidence from the Young Lives study," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 42(7), pages 165-202.
    8. Vincenzo Carrieri & Apostolos Davillas & Andrew M. Jones, 2020. "A latent class approach to inequity in health using biomarker data," Health Economics, John Wiley & Sons, Ltd., vol. 29(7), pages 808-826, July.
    9. Brunori, Paolo & Neidhöfer, Guido, 2020. "The evolution of inequality of opportunity in Germany: A machine learning approach," ZEW Discussion Papers 20-013, ZEW - Leibniz Centre for European Economic Research.
    10. Rafael Carranza, 2020. "Upper and lower bound estimates of inequality of opportunity: A cross-national comparison for Europe," Working Papers 511, ECINEQ, Society for the Study of Economic Inequality.
    11. Yuzhakov, Vladimir (Южаков, Владимир) & Dobrolyubova, Elena (Добролюбова, Елена) & Alexandrov, Oleg (Александров, Олег) & Klochkova, E (Клочкова, Е.), 2015. "Improving the Efficiency of the Public Service and Optimization of the Number of its Personnel Structure [Повышение Эффективности Государственной Службы И Оптимизация Численности Ее Кадрового Соста," Published Papers mn36, Russian Presidential Academy of National Economy and Public Administration.

    More about this item

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D3 - Microeconomics - - Distribution
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement

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