Higher education can be a double-edged sword in shaping the black-white earnings gap. It may serve as an equalizer, if black youth benefit more from attending and completing college than white youth. It may also reinforce racial inequality, given that black college-goers are less likely to complete college relative to white students. We employ a novel causal decomposition and a debiased machine learning method to isolate the equalizing and disequalizing effects of college and unveil the sources of these effects. Analyzing data from the NLSY97, we find that among men, a BA degree has a strong equalizing effect on earnings, but this equalizing effect is blunted by a disequalizing effect associated with unequal likelihoods of BA completion. Moreover, a BA degree narrows the male black-white earnings gap not by reducing the influence of class background and pre-college academic ability, but by lessening the “unexplained” penalty of being black in the labor market. To illuminate the policy implications of our findings, we estimate counterfactual earnings gaps under a series of idealized educational interventions. We find that interventions that both boost college attendance and BA completion rates and close racial disparities in these transitions can substantially reduce the black-white earnings gap. 

Where: CGIS Knafel Building, Room K354
(See this link for directions).

When: Wednesday, November 17 at 12:10 - 1:30 pm.
(Bagged lunches available for pick-up at CGIS K354 11:30 - 11:45 am, for the participants who responded to our previous survey. The CGIS cafe on the first floor has been designated as an eating area, and participants may also use outdoor spaces for lunch. Please be present at K354 by 12:10 pm for the presentations.)

Zoom link: https://harvard.zoom.us/j/97004196610?pwd=eGFydkF5RDRjUlk5RVcyTjV6OStUQT09
(For the participants who cannot join the session physically.)

Schedule of the workshop: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009

Looking forward to seeing you all on Wednesday!

Best,
Sooahn