2021
DOI: 10.1111/roiw.12502
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The Evolution of Inequality of Opportunity in Germany: A Machine Learning Approach

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… Show more

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Cited by 18 publications
(10 citation statements)
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“…The exploration of these algorithms for other measurement approaches in the inequality of opportunity literature provides another interesting avenue for future research (Lefranc et al, 2009;Kanbur and Snell, 2018;Pistolesi, 2009;Brunori and Neidhöfer, 2021).…”
Section: Discussionmentioning
confidence: 99%
“…The exploration of these algorithms for other measurement approaches in the inequality of opportunity literature provides another interesting avenue for future research (Lefranc et al, 2009;Kanbur and Snell, 2018;Pistolesi, 2009;Brunori and Neidhöfer, 2021).…”
Section: Discussionmentioning
confidence: 99%
“…Oh et al (2019) analyse variables explaining mental depression by applying various algorithms to national health surveys. Moreover, several studies apply decision tree and tree-based models to surveys to estimate inequality of societies (Brunori et al, 2019;Brunori and Neidhofer, 2020;Han, 2022c;Lefranc and Kundu, 2020).…”
Section: Methodsmentioning
confidence: 99%
“…From the family of tree-based methods we select those based on conditional inference algorithms, which have the advantage of not being biased towards splitting only continuous variables, as other tree-based techniques usually do (Hothorn et al 2006). Furthermore, they have already been used in the IOp framework to select the relevant set of circumstances (Brunori et al 2019;Brunori and Neidhöfer 2020) that define household opportunities.…”
Section: Treesmentioning
confidence: 99%
“…Consequently, the effects from these uncontrolled circumstances will partially overlap with our controlled variables and, therefore, will be collected by our measures. (Brunori et al 2019;Brunori and Neidhöfer 2020). 3 Here, we propose two ML techniques to discretize a highly skewed continuous circumstance, like the inheritances received, and thus generate statistically meaningful types.…”
Section: Introductionmentioning
confidence: 99%