Essays in Applied Game Theory and Political Economy

Abstract

This dissertation provides a game theoretic study of the incentives

and dynamics of government action. In chapter 1, I study how the political

incentives for providing legal protections to minority workers is

influenced by the mobility of workers across cities. I build a model

that shows how cities that provide insufficient protection to minorities

can do better at attracting workers and end up being larger overall.

To the extent that policy makers care about city size and city growth,

my result identifies an incentive that reinforces the inadequate protection

of minorities in the workplace.

In chapter 2, I build a model to explain electoral cycling, which

is the regularity observed in the data that the longer a party is

in power the more likely it loses the next election. I study an infinite

horizon model of political competition where parties choose between

nominating moderate candidates, who are better at winning elections,

and partisan candidates who energize and grow the base which helps

to win future elections. Hence, parties face a trade-off between winning

current and future elections. In the presence of such a trade-off,

parties win elections by sacrificing their future prospects which

naturally leads to electoral cycling.

In chapter 3, I explore novel consequences of the learning model of

economic voting. Economic voting is the empirical phenomenon that

a strong economy makes incumbent re-election more likely while a weak

economy makes incumbent defeat more likely. Previous work in the literature

explains this phenomenon with voter learning; a strong economy is

taken as a positive signal about the incumbent's policy or competence.

I introduce the idea that voters who learn should also experiment,

that is they should care about how much learning a policy allows when

deciding whom to elect. In particular voters will, at times, prefer

a policy that creates worse outcomes but that allows for more learning.