Essays on the econometric analysis of treatment assignment rules and altruistic preferences

This dissertation has two main themes: treatment assignment rules and altruistic preferences. The first two chapters are about comparing different treatment assignment rules using observational data. The third chapter studies how altruistic preferences are affected by markets and incentives. In Chapter 1, I develop a theoretical framework to compare different treatment assignment rules. A treatment assignment rule is a mapping from observed characteristics to binary treatment status. The welfare difference between two given treatment assignment rules is not point identified in general when data are obtained from an observational study or a randomized experiment with imperfect compliance. I characterize the sharp identified region of the welfare difference and obtain bounds under various assumptions on the unobservables with and without instrumental variables. I conduct estimation and inference of the bounds using orthogonalized moment conditions to deal with the presence of infinite-dimensional nuisance parameters. In Chapter 2, I apply the method I proposed in Chapter 1 to examine two applications in economics. First, I study the problem of assigning individuals to job training programs. I calculate the welfare differences between different hypothetical policies using experimental data from the National Job Training Partnership Act Study. Second, I apply the method to study public health insurance policies. Specifically, I calculate the welfare impact of Medicaid expansion using data from the Oregon Health Insurance Experiment. Chapter 3 (joint with Ching-to Albert Ma and Daniel Wiesen) studies how altruistic preferences are changed by markets and incentives using a laboratory experiment. Subjects are asked to choose health care qualities for hypothetical patients in monopoly, duopoly, and quadropoly. Prices, costs, and patient benefits are experimental incentive parameters. We combine a theoretical model of strategic interaction with a nonparametric estimation method and find that markets tend to reduce altruism

Changing preferences: an experiment and estimation of market-incentive effects on altruism

This paper studies how altruistic preferences are changed by markets and incentives. We conduct a laboratory experiment in a within-subject design. Subjects are asked to choose health care qualities for hypothetical patients in monopoly, duopoly, and quadropoly. Prices, costs, and patient benefits are experimental incentive parameters. In monopoly, subjects choose quality to tradeoff between profits and altruistic patient benefits. In duopoly and quadropoly, we model subjects playing a simultaneous-move game. Each subject is uncertain about an opponent's altruism, and competes for patients by choosing qualities. Bayes-Nash equilibria describe subjects' quality decisions as functions of altruism. Using a nonparametric method, we estimate the population altruism distributions from Bayes-Nash equilibrium qualities in different markets and incentive configurations. Markets tend to reduce altruism, although duopoly and quadropoly equilibrium qualities are much higher than those in monopoly. Although markets crowd out altruism, the disciplinary powers of market competition are stronger. Counterfactuals confirm markets change preferences.Accepted manuscrip

Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning

We design and implement an adaptive experiment (a ``contextual bandit'') to learn a targeted treatment assignment policy, where the goal is to use a participant's survey responses to determine which charity to expose them to in a donation solicitation. The design balances two competing objectives: optimizing the outcomes for the subjects in the experiment (``cumulative regret minimization'') and gathering data that will be most useful for policy learning, that is, for learning an assignment rule that will maximize welfare if used after the experiment (``simple regret minimization''). We evaluate alternative experimental designs by collecting pilot data and then conducting a simulation study. Next, we implement our selected algorithm. Finally, we perform a second simulation study anchored to the collected data that evaluates the benefits of the algorithm we chose. Our first result is that the value of a learned policy in this setting is higher when data is collected via a uniform randomization rather than collected adaptively using standard cumulative regret minimization or policy learning algorithms. We propose a simple heuristic for adaptive experimentation that improves upon uniform randomization from the perspective of policy learning at the expense of increasing cumulative regret relative to alternative bandit algorithms. The heuristic modifies an existing contextual bandit algorithm by (i) imposing a lower bound on assignment probabilities that decay slowly so that no arm is discarded too quickly, and (ii) after adaptively collecting data, restricting policy learning to select from arms where sufficient data has been gathered