We study the problem of a partisan gerrymanderer who assigns voters to equipopulous districts to maximize his party's expected seat share. The designer faces both aggregate, district‐level uncertainty (how many votes his party will receive) and idiosyncratic, voter‐level uncertainty (which voters will vote for his party). Segregate‐pair districting , where weaker districts contain one type of voter, while stronger districts contain two, is optimal for the gerrymanderer. The optimal form of segregate‐pair districting depends on the designer's popularity and the relative amounts of aggregate and idiosyncratic uncertainty. When idiosyncratic uncertainty dominates, a designer with majority support pairs all voters, while a designer with minority support segregates opposing voters and pairs more favorable voters; these plans resemble uniform districting and “packing‐and‐cracking,” respectively. When aggregate uncertainty dominates, the designer segregates moderate voters and pairs extreme voters; this “matching slices” plan has received some attention in the literature. Estimating the model using precinct‐level returns from recent U.S. House elections shows that, in practice, idiosyncratic uncertainty dominates. We discuss implications for redistricting reform, political polarization, and detecting gerrymandering. Methodologically, we exploit a formal connection between gerrymandering—partitioning voters into districts—and information design—partitioning states of the world into signals.

A principal can restrict an agent's information (the persuasion problem) or discretion (the delegation problem). We study these two problems under standard single‐crossing assumptions on the agent's marginal utility. We show that these problems are equivalent on the set of monotone stochastic mechanisms, implying, in particular, the equivalence of deterministic delegation and monotone partitional persuasion. We also show that the monotonicity restriction is superfluous for linear persuasion and linear delegation, implying their equivalence on the set of all stochastic mechanisms. Finally, using tools from the persuasion literature, we characterize optimal delegation mechanisms, thereby generalizing and extending existing results in the delegation literature.

We consider general Bayesian persuasion problems where the receiver’s utility is single-peaked in a one-dimensional action. We show that a signal that pools at most two states in each realization is always optimal and that such “pairwise” signals are the only solutions under a nonsingularity condition on utilities. Our core results provide conditions under which the induced receiver action is single-dipped or single-peaked on each set of nested signal realizations. We also provide conditions for the optimality of either full disclosure or negative assortative disclosure, where all signal realizations are nested. Methodologically, our results rely on novel duality and complementary slackness theorems. Our analysis extends to a general problem of assigning one-dimensional inputs to productive units, which we call “optimal productive transport.” This problem covers additional applications including matching with peer effects (assigning workers to firms, students to schools, or residents to neighborhoods), robust option pricing (assigning future asset prices to price distributions), and partisan gerrymandering (assigning voters to districts).

Sender, who is either good or bad, wishes to look good at an exogenous deadline. Sender privately observes if and when she can release a public flow of information about her private type. Releasing information earlier exposes to greater scrutiny, but signals credibility. In equilibrium bad Sender releases information later than good Sender. We find empirical support for the dynamic predictions of our model using data on the timing of U.S. presidential scandals and U.S. initial public offerings. In the context of elections, our results suggest that October Surprises are driven by the strategic behaviour of bad Sender.

Kolotilin acknowledges financial support from the Australian Research Council. Zapechelnyuk acknowledges financial support from the Economic and Social Research Council (grant no. ES/N01829X/1)