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Robust Determinants of Intergenerational Mobility in the Land of Opportunity

Author

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  • Andros Kourtellos

    (Department of Economics, University of Cyprus, CY)

  • Christa Marr

    (Department of Economics, Rollins College, USA)

  • Chih Ming Tan

    (Department of Economics, University of North Dakota, USA)

Abstract

This paper revisits the influential work by Chetty, Hendren, Kline, and Saez (2014) who attempt to explain the variation in intergenerational mobility across commuter zones in the US (i.e., spatial mobility) using nine classes of variables. We employ Bayesian model averaging methods that allow for model uncertainty to identify robust predictors of spatial mobility. In doing so we pay special attention to the specification of model and parameter priors. We also investigate the heterogeneous effects of these predictors on spatial mobility across commuter zones in different average income quintiles. Our findings suggest a more nuance and complex characterization of the spatial mobility process than that proposed by Chetty, Hendren, Kline, and Saez.

Suggested Citation

  • Andros Kourtellos & Christa Marr & Chih Ming Tan, 2014. "Robust Determinants of Intergenerational Mobility in the Land of Opportunity," Working Paper series 20_14, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:20_14
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    Cited by:

    1. Orhan Torul & Oguz Oztunali, 2017. "Intergenerational Educational Mobility in Europe," Working Papers 2017/03, Bogazici University, Department of Economics.
    2. Dodin, Majed & Findeisen, Sebastian & Henkel, Lukas & Sachs, Dominik & Schüle, Paul, 2024. "Social mobility in Germany," Journal of Public Economics, Elsevier, vol. 232(C).
    3. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    4. Jinchao Wang & Changfu Luo, 2022. "Social Mobility and Firms’ Total Factor Productivity: Evidence from China," Sustainability, MDPI, vol. 14(22), pages 1-19, November.

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality

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