Developing democracies are experiencing unprecedented increases in primary and secondary schooling. To identify education's long-run political effects, we use a difference-in-differences design that leverages variation across local government areas and gender in the intensity of Nigeria's 1976 universal primary education reform—one of Africa's largest ever educational expansions—to instrument for education. We find large increases in basic civic and political engagement: better educated citizens are more attentive to politics, more likely to vote, and more involved in community associations. The effects are largest among minority groups and in fractionalized areas, without increasing support for political violence or own-group identification.

We theoretically and empirically study an incomplete information model of social learning. Agents initially guess the binary state of the world after observing a private signal. In subsequent rounds, agents observe their network neighbors' previous guesses before guessing again. Agents are drawn from a mixture of learning types—Bayesian, who face incomplete information about others' types, and DeGroot, who average their neighbors' previous period guesses and follow the majority. We study (1) learning features of both types of agents in our incomplete information model; (2) what network structures lead to failures of asymptotic learning; (3) whether realistic networks exhibit such structures. We conducted lab experiments with 665 subjects in Indian villages and 350 students from ITAM in Mexico. We perform a reduced‐form analysis and then structurally estimate the mixing parameter, finding the share of Bayesian agents to be 10% and 50% in the Indian‐villager and Mexican‐student samples, respectively.

Politicians rely on political brokers to buy votes throughout much of the developing world. We investigate how social networks facilitate these vote-buying exchanges. Our conceptual framework suggests brokers should be particularly well-placed within the network to learn about non-copartisans’ reciprocity in order to target transfers effectively. As a result, parties should recruit brokers who are central among non-copartisans. We combine village network data from brokers and citizens with broker reports of vote buying, allowing us to use broker and citizen fixed effects. We show that networks diffuse information about citizens to brokers who leverage it to target transfers. In particular, among those citizens who are not registered to their party, brokers target reciprocal citizens about whom they can learn more through their network, and these citizens are more likely to support the brokers’ party. Moreover, recruited brokers are significantly more central than other citizens among non-copartisans, but not among copartisans. These results highlight the importance of information diffusion through social networks for vote buying, broker recruitment, and ultimately for political outcomes.