This dissertation contains three chapters that each investigate a different aspect of government policy and policymaking. The first chapter analyzes the problem faced by partisan gerrymanderers. Standard intuitions for optimal gerrymandering involve concentrating one's extreme opponents in "unwinnable" districts ("packing") and spreading one's supporters evenly over "winnable" districts ("cracking"). These intuitions come from models with either no uncertainty about voter preferences, or in which there are only two voter types. In contrast, this chapter characterizes the solution to a problem in which a gerrymanderer observes a noisy signal of voter preferences from a continuous distribution and creates N districts of equal size to maximize the expected number of districts which she wins. Under mild regularity conditions we show that cracking is never optimal--one's most ardent supporters should be grouped together. Moreover, for sufficiently precise signals the optimal solution involves creating a district which matches extreme "Republicans" with extreme "Democrats," then continuing to match toward the center of the signal distribution.

The second chapter investigates the potential for gerrymandering to influence the reelection chance of incumbents. The probability that an incumbent in the United States House of Representatives is reelected has risen dramatically over the last half-century; it now stands at more than 98%. A number of authors and commentators claim that this rise is due to an increase in bipartisan gerrymandering in favor of incumbents. Using a regression discontinuity approach, we find evidence of the opposite effect. All else equal, redistricting has reduced the probability of incumbent reelection over time. The timing of this effect is consistent with the hypothesis that legal constraints on gerrymandering, such as the Voting Rights Act, have become tighter over time. Incumbent gerrymandering may well be a contributor to incumbent reelection rates, but it is less so than in the past. The third chapter turns more towards policy and analyzes a model in which incentives in one period on one task can affect output more broadly through learning. If agents can invest in human or organizational capital, then output will increase both before and after short-term incentives. This chapter develops a model of these effects and then evaluates its predictions using data from hospitals in Britain during a series of limited-time performance incentives offered by the government. Empirically, these policies increase performance not only during the incentivized periods but also before and after, matching the predictions of our model. This chapter also examines performance along non-incentivized dimensions of quality of care and finds little evidence of classical effort substitution.