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

Paolo Brunori and Guido Neidhöfer ()

Review of Income and Wealth, 2021, vol. 67, issue 4, 900-927

Abstract: We show that measures of inequality of opportunity (IOP) fully consistent with the IOP theory of Roemer (1998) can be straightforwardly estimated by adopting a machine learning approach, and apply our method to analyze the development of IOP in Germany during the past 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 who 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.

Date: 2021
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https://doi.org/10.1111/roiw.12502

Related works:
Working Paper: The Evolution of Inequality of Opportunity in Germany: A Machine Learning Approach (2020) Downloads
Working Paper: The Evolution of Inequality of Opportunity in Germany: A Machine Learning Approach (2020) Downloads
Working Paper: The Evolution of Inequality of Opportunity in Germany: A Machine Learning Approach (2020) Downloads
Working Paper: The Evolution of Inequality of Opportunity in Germany: A Machine Learning Approach (2020) Downloads
Working Paper: The evolution of inequality of opportunity in Germany: A machine learning approach (2020) Downloads
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