The Evolution of Inequality of Opportunity in Germany: A Machine Learning Approach

Paolo Brunori and Guido Neidhöfer
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Paolo Brunori: University of Florence

CEDLAS, Working Papers from CEDLAS, Universidad Nacional de La Plata

Abstract: We show that measures of inequality of opportunity (IOP) fully consistent with Roemer (1998)’s IOP theory can be straightforwardly estimated by adopting a machine learning approach, and apply our novel method to analyse the development of IOP in Germany during the last three decades. Hereby, we take advantage of information contained in 25 waves of the Socio-Economic Panel. Our analysis shows that in Germany IOP declined immediately after reunification, increased in the first decade of the century, and slightly declined again after 2010. Over the entire period, at the top of the distribution we always find individuals that resided in West-Germany before the fall of the Berlin Wall, whose fathers had a high occupational position, and whose mothers had a high educational degree. East-German residents in 1989, with low educated parents, persistently qualify at the bottom.

JEL-codes: D30 D31 D63 (search for similar items in EconPapers)
Pages: 53 pages
Date: 2020-03
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (5)

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Related works:
Journal Article: The Evolution of Inequality of Opportunity in Germany: A Machine Learning Approach (2021)
Working Paper: The Evolution of Inequality of Opportunity in Germany: A Machine Learning Approach (2020)
Working Paper: The Evolution of Inequality of Opportunity in Germany: A Machine Learning Approach (2020)
Working Paper: The Evolution of Inequality of Opportunity in Germany: A Machine Learning Approach (2020)
Working Paper: The evolution of inequality of opportunity in Germany: A machine learning approach (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:dls:wpaper:0259

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