For many technologies and behaviours, an agent’s benefit from adopting depends on his contacts adopting, and the benefit to his contacts of adopting depends on their contacts adopting. This paper examines how the architecture of these connections shapes the success or failure of the diffusion of innovations. We start with a standard model of diffusion with the key addition that some agents can coordinate their decisions. This captures the idea that people often talk and make decisions together with friends or family to adopt technologies. We show that insularity of connections, that is, the extent to which agents tend to concentrate their connections to a narrow set of other agents, determines contagion. However, whether insularity helps or hinders depends on the technology being diffused. For technologies that are valuable even without many contacts adopting, we find insular connections hinder adoption, but for technologies that are valuable only when many contacts adopt, insular connections facilitate adoption

Business dynamism—the process of firm entry, growth and exit—lies at the heart of modern endogenous growth models. While productivity differences have traditionally been seen as the main driving forces of business dynamism, a growing body of evidence suggests that customer acquisition is at least as important. In light of this evidence, we propose a novel endogenous growth model in which innovating firms must first acquire customers to sell their products. Estimating our model with aggregate and firm-level data, we find that expansions of firms’ customer bases (market sizes) boost their incentives to innovate and shift resources towards high-growth businesses (“gazelles”). Combined, these effects explain over 40% of aggregate growth and substantially change predictions about the efficacy of growth policies. Finally, we document support for key model predictions using firm-level micro-data

We study the impact of large, successful manufacturing plants on other local producers in China, focusing on “Million-Rouble Plants” built in the 1950s during a brief alliance with the U.S.S.R. The ephemeral geopolitical situation and the locations of allied and enemy airbases provide exogenous variation in plant siting. We find a boom-and-bust pattern: Counties hosting these plants were 80% more productive than control counties in 1982 but 20% less productive by 2010. This decline reflects the performance of local establishments, which exhibit low productivity, limited innovation, and high markup. Specialization hindered spillovers, preventing the emergence of new clusters and local entrepreneurship

Economists have studied the impact of numerous state laws, from welfare rules to voting ID requirements. Yet for all this policy evaluation, what do we know about policy diffusion—how these policies are introduced and spread from state to state? We present a series of facts based on a data set of 602 U.S. state policies spanning the past seven decades. First, proxies of state capacity do not predict a higher likelihood of innovating new policies, but the political leaning of the state does predict a higher likelihood of introducing partisan laws since 1990. Second, the diffusion of policies from 1950 to 2000 is best predicted by proximity—a state is more likely to adopt a policy if nearby states have already done so—as well as similarity in voter policy preferences. Third, since 2000, party alignment has become the strongest predictor of diffusion, and the speed of adoption has increased. Models of learning and correlated preferences can account for the earlier patterns, but the findings for the last two decades indicate a sharply increasing role of party control. We conclude that party polarization has emerged as a key factor recently for policy adoption, plausibly leading to a worse match between state policies and voter preferences