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Correlating Social Mobility and Economic Outcomes

Author

Listed:
  • Maia Güell

    (University of Edinburgh, CEPR, FEDEA and IZA.)

  • Michele Pellizzari

    (University of Geneva, CEPR, LIVES and IZA.)

  • Giovanni Pica

    (Università degli Studi di Milano, LdA, CSEF and and Centro Luca D’Agliano)

  • José V. Rodríguez Mora

    (University of Edinburgh and CEPR.)

Abstract

We construct comparable measures of intergenerational mobility (IM) for 103 Italian provinces using the recent methodology of Güell, Rodríguez Mora and Telmer (2007; 2014) and explore their correlation with a variety of social and economic outcomes. We find that higher IM is positively associated with economic activity, education and social capital, and negatively correlated with inequality. Moreover, there is no clear pattern of correlation with other socio-political variables. These results are qualitatively similar to Chetty, Hendren, Kline, and Saez (2014), with the important difference that Italy is a highly centralized state where institutions and policies are ‘de jure’ the same in all provinces. This suggests that something beyond institutional and policy differences also shapes intergenerational mobility.

Suggested Citation

  • Maia Güell & Michele Pellizzari & Giovanni Pica & José V. Rodríguez Mora, 2015. "Correlating Social Mobility and Economic Outcomes," CSEF Working Papers 394, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy, revised 27 Jul 2016.
  • Handle: RePEc:sef:csefwp:394
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    References listed on IDEAS

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    1. Kenneth A. Couch & Thomas A. Dunn, 1997. "Intergenerational Correlations in Labor Market Status: A Comparison of the United States and Germany," Journal of Human Resources, University of Wisconsin Press, vol. 32(1), pages 210-232.
    2. Maia Güell & José V. Rodríguez Mora & Christopher I. Telmer, 2015. "The Informational Content of Surnames, the Evolution of Intergenerational Mobility, and Assortative Mating," Review of Economic Studies, Oxford University Press, vol. 82(2), pages 693-735.
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    More about this item

    Keywords

    Surnames; intergenerational mobility; cross-sectional data analysis.;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General

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