Research

Abstract Informed voters are essential for government accountability, and social networks are an important avenue through which voters acquire political information. However, U.S. House of Representatives districts do not necessarily align with social networks. This misalignment potentially impacts the ease with which voters learn about their representatives, by altering the chance of encountering friends who provide relevant political information. I study whether the alignment between district boundaries and social networks affects voter knowledge, turnout, and campaign contributions in congressional elections. Using Facebook’s Social Connectedness Index and an event study design, I find that an increase in the share of friends living in the same district increases voters’ knowledge about their representative. For example, a 10 percentage point increase in this share raises the probability that a voter knows their representative’s party by 3.3 percentage points; this represents a 5% increase over the mean. Additionally, a higher share of friends in the same district increases voter turnout in House elections and shifts campaign contributions towards own-district House candidates. I use a model of information diffusion to simulate the share of informed voters under counterfactual district maps, creating a framework to evaluate the informational effects of alternative maps. These findings suggest that aligning political boundaries with social networks can enhance democratic engagement.

Proponents of participatory democracy praise Liquid Democracy: decisions are taken by referendum, but voters delegate their votes freely. When better informed voters are present, delegation can increase the probability of a correct decision. However, delegation must be used sparely because it reduces the information aggregated through voting. In two different experiments, we find that delegation underperforms both universal majority voting and the simpler option of abstention. In a tightly controlled lab experiment where the subjects’ precision of information is conveyed in precise mathematical terms and very salient, the result is due to overdelegation. In a perceptual task run online where the precision of information is not known precisely, delegation remains very high and again underperforms both majority voting and abstention. In addition, subjects substantially overestimate the precision of the better informed voters, underlining that Liquid Democracy is fragile to multiple sources of noise. The paper makes an innovative methodological contribution by combining two very different experimental procedures: the study of voting rules would benefit from complementing controlled experiments with known precision of information with tests under ambiguity, a realistic assumption in many voting situations.

Recent research suggests that the share of US households living on less than $2/person/day is high and rising. We reexamine such extreme poverty by linking SIPP and CPS data to administrative tax and program data. We find that more than 90% of those reported to be in extreme poverty are not, once we include in-kind transfers, replace survey reports of earnings and transfer receipt with administrative records, and account for ownership of substantial assets. More than half of all misclassified households have incomes from the administrative data above the poverty line, and many have middle-class measures of material well-being.

In a world where the majority and the minority group have equal distributions of talent, where candidates are objectively and accurately evaluated, and no discrimination occurs, the underrepresentation of the minority group in prestigious positions is nonethless highly sticky. The reasons are intuitive. If the sample of candidates from the minority group is numerically smaller, at equal distribution of talent, the most qualified candidate is more likely to belong to the majority sample, mirroring its larger numerical size. If future samples of candidates respond to the realized selection in the expected direction–increasing if the selection came from the sample, decreasing or increasing less if it did not–the higher probability of success in the majority sample will persist. We capture this process with a well-known statistical model: the Polya urn. The richness of existing results and the streamlined model allow us to study and compare different policy interventions. A simple app (https://caron.shinyapps.io/Women-Men-Polya-Urns/) allows readers to run their own experiments. Two robust results are that temporary affirmative actions interventions have long -term equalizing effects, and that any decline in the quality of selected candidates is self-correcting, even while the intervention lasts.