U.S. labor markets are increasingly diverse and persistently unequal between genders, races and ethnicities, skill levels, and age groups. We use a structural model to decompose the observed differences in labor market outcomes across demographic groups in terms of underlying wedges in fundamentals. Of particular interest is the potential role of discrimination, either taste-based or statistical. Our model is a version of the Diamond-Mortensen-Pissarides model extended to include a life cycle, learning by doing, a nonparticipation state, and informational frictions. The model exhibits group-specific wedges in initial human capital, returns to experience, matching efficiencies, and job separation rates. We use the model to reverse engineer group-specific wedges that we then feed back into the model to assess the fraction of various disparities they account for. Applying this methodology to 1998–2018 U.S. data, we show that differences in initial human capital, returns to experience, and job separation rates account for most of the demographic disparities; wedges in matching efficiencies play a secondary role. Our results suggest a minor aggregate impact of taste-based discrimination in hiring and an important role for statistical discrimination affecting particularly female groups and Black males. Our approach is macro, structural, unified, and comprehensive.
Drivers of Financial Globalisation: The Role of Informational Frictions
