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.
Highlights
The ideal of equality of opportunity has fascinated mankind for centuries
We propose a method that builds on a data-driven approach and exploits two machine learning algorithms to estimate inequality of opportunity consistent with Roemer’s original theory
The number of parameters one needs to estimate when applying a latent class model is, a function of the number of classes, the number of circumstances considered, and the number of values each circumstance can take. This implies that the choice of circumstances considered, which is arbitrary, will affect the result. This weakness of the latent types approach highlights that a proper method that aims to estimate inequality of opportunity needs to comprise both, the identification of types based on observed circumstances, and a variable selection criterion that would select the most appropriate set of the many observable circumstances
Summary
The ideal of equality of opportunity has fascinated mankind for centuries. Its popularity among people from both sides of the political spectrum probably derives from the fact that it encompasses and balances two aspects; equality of outcomes and freedom of choice. Recent contributions have proposed approaches to improve the empirical specification of the underlying models, finding consistent econometric methods to identify the relevant circumstances, and eventually estimate inequality of opportunity Within this literature, Li Donni et al (2015) and Brunori et al (2018a) propose data-driven approaches to identify Roemerian types (i.e. sets of individuals characterized by identical circumstances). Controlling for the sample size, albeit the number of types increases by roughly 75%, the level of inequality of opportunity in 2016 is just around 7% higher than in 1992 During this period Germany experienced first a slow decrease in inequality of opportunity after reunification, and a sudden rise coinciding with rising income inequality and the implementation of the Hartz-reforms, a set of substantial changes to the German labour market and welfare benefits system that had persistent repercussions for German society. Thenceforth, inequality of opportunity stayed at this relatively high level and followed a stable trend, with a slight decrease from 2010 onward
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
