We propose a model of strategic experimentation on social networks in which forward-looking agents learn from their own and neighbors’ successes. In equilibrium, private discovery is followed by social diffusion. Social learning crowds out own experimentation, so total information decreases with network density; we determine density thresholds below which agents’ asymptotic learning is perfect. By contrast, agent welfare is single peaked in network density and achieves a second-best benchmark level at intermediate levels that strike a balance between discovery and diffusion. (JEL D82, D83, D86, O31, O33, Z13)

We propose a simple model of racial bias in hiring that encompasses three major theories: taste-based discrimination, screening discrimination, and complementary production. We derive a test that can distinguish these theories based on the mean and variance of workers’ productivity under managers of different pairs of races. We apply this test to study discrimination at a major U.S. retailer using data from 48,755 newly hired commission-based salespeople. White, black, and Hispanic managers within the same store are significantly more likely to hire workers of their own race, consistent with all three theories. For black–Hispanic pairs, productivity variance is lower for same-race pairs than cross-race pairs, implying that screening discrimination dominates. For white–Hispanic pairs, mean productivity is higher for same-race pairs, indicating a combination of screening discrimination and complementary production. For white–black pairs, biased hiring implies the presence of discrimination, but productivity results suggest the effects of the three forms of discrimination offset one another.