Economics of Inequality
Paper Session
Sunday, Jan. 3, 2021 3:45 PM - 5:45 PM (EST)
- Chair: Susan Athey, Stanford University
Mentoring and the Dynamics of Affirmative Action
Abstract
We study the workforce composition that emerges when same-group mentoring lowers education costs. Our continuous-time overlapping-generations model considers a majority and a minority group of identically distributed talent. Under sufficiently decreasing returns to mentoring, and in high-skill sectors, a patient social planner enforces an over-representation of minority workers relative to their population share. Such a composition never arises endogenously as a steady state, and thus requires persistent government intervention. As such, the surplus-maximizing policy goes beyond fairness objectives and qualitatively differs from leading models of workforce imbalance, with implications for the ``glass ceiling effect'' and the design of affirmative action.The Impact of Automation and Inequality across Europe
Abstract
Existing research suggests that automation has the potential to impact inequality through two channels, either by changing the relative wage returns for different sets of tasks or by changing the composition of employment. This paper measures the relative importance of these two channels for a sample of European countries by decomposing the effects of a set of characteristics along these two dimensions using the structure of earnings survey (SES) and data for 2002 and 2014 Firpo et al. (2018). The approach isolates changes in the earnings distribution to identify the component that is due to changes in composition and to changes in the wage structure. We find that the overall contribution of the risk of automation on inequality effects only a few countries. However, when decomposing these effects, the composition effect explains a large part of automation related inequality, while there is a wage effect in some countries, the effect is not nearly as large. These results confirm that the way in which technology is increasing inequality is largely due to the fact that there is a growing wage dispersion between jobs that are resilient to automation and those that are not.Discussant(s)
Maya Rossin-Slater
,
Stanford University
Susan Athey
,
Stanford University
David Deming
,
Harvard University
JEL Classifications
- J3 - Wages, Compensation, and Labor Costs
- J7 - Labor Discrimination