Edna B. Chun and Joe R. Feagin issue a challenge to professionals working in higher education to look more critically at the context in which “diversity” work is done, and to look at the systemic underpinnings of racial (and gender) inequality in higher education in general, and specifically at Historically White Colleges and Universities (HWCUs).

Governments around the world use grant and loan programs to ease the financial constraints that contribute to socioeconomic gaps in college completion. A growing body of research assesses the impact of grants; less is known about how loan programs affect persistence and degree completion. We use detailed administrative data from Chile to provide rigorous regression-discontinuity-based evidence on the impacts of loan eligibility for university students who retake the national admission test after their first year of studies.

The economic downturn caused by the current COVID-19 outbreak has substantial implications for gender equality, both during the downturn and the subsequent recovery. Compared to “regular” recessions, which affect men’s employment more severely than women’s employment, the employment drop related to social distancing measures has a large impact on sectors with high female employment shares. In addition, closures of schools and daycare centers have massively increased child care needs, which has a particularly large impact on working mothers.

There is a well-known gender difference in time allocation within the household, which has important implications for gender differences in labor market outcomes. We ask how malleable this gender difference in time allocation is to culture. In particular, we ask if US immigrants allocate tasks differently depending upon the characteristics of the source countries from which they emigrated.

The standard approach to modeling inequality, building on Tinbergen's seminal work, assumes factor-augmenting technologies and technological change biased in favor of skilled workers. Though this approach has been successful in conceptualizing and documenting the race between technology and education, it is restrictive in a number of crucial respects. First, it predicts that technological improvements should increase the real wages of all workers. Second, it requires sizable productivity growth to account for realistic changes in relative wages.