This paper proposes a tractable model of Bayesian learning on large random networks where agents choose whether to adopt an innovation. We study the impact of the network structure on learning dynamics and product diffusion. In directed networks, all direct and indirect links contribute to agents' learning. In comparison, learning and welfare are lower in undirected networks and networks with cliques. In a rich class of networks, behavior is described by a small number of differential equations, making the model useful for empirical work.

We propose a new model of flrm reputation that interprets reputation directly as the market belief about product quality. Quality is persistent and is determined endogenously by the flrm’s past investments. We analyse how investment incentives depend on the flrm’s reputation and derive implications for reputational dynamics. We consider three types of consumer learning. When consumers learn about quality through good news, investment incentives are decreasing in reputation, leading to a unique work-shirk equilibrium and convergent dynamics. When consumers learn through bad news, investment incentives are increasing in reputation, leading to a continuum of shirk-work equilibria and divergent dynamics. Finally, when consumers learn through Brownian news and the cost of investment is low, incentives are hump-shaped but a work-shirk equilibrium exists and is essentially unique.

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.

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.