Correspondence experiments probe for discrimination by manipulating employer perceptions of applicant characteristics. We consider the gains from dynamically adapting the number and characteristics of fictitious applications to the sequence of employer responses received so far. Calibrating the...

Governments around the world redistribute to distressed areas by conditioning taxes and transfers on location. We show that when poor households are spatially concentrated, transfers from one location to another can yield equity gains that outweigh their efficiency costs, even when income-based...

We study trends in income inequality across U.S. states and counties 1960-2019 using a mix of administrative and survey data sources. Both states and counties have diverged in terms of per-capita pre-tax incomes since the late 1990s, with transfers serving to dampen this divergence. County...

We study the results of a massive nationwide correspondence experiment sending more than 83,000 fictitious applications with randomized characteristics to geographically dispersed jobs posted by 108 of the largest U.S. employers. Distinctively Black names reduce the probability of employer...

We develop a theoretically grounded extension of the two-way fixed effects model of Abowd et al. (1999) that allows firms to differ both in the wages they offer new hires and the wages required to poach their employees. Expected hiring wages are modeled as the sum of a worker fixed effect, a...

We develop an empirical Bayes ranking procedure that assigns ordinal grades to noisy measurements, balancing the information content of the assigned grades against the expected frequency of ranking errors. Applying the method to a massive correspondence experiment, we grade the race and gender...

We review the literature on firm-level drivers of labor market inequality. There is strong evidence from a variety of fields that standard measures of productivity – like output per worker or total factor productivity – vary substantially across firms, even within narrowly-defined...