Job Market Paper
Dynamic Job Market Signaling and Optimal Taxation
How are optimal taxes affected by reputation building and imperfect information in labor markets? In this paper, I build a model of labor markets with incomplete and asymmetric information where job histories play a crucial role in transmitting information about workers' productivity, which allows us to better understand the efficiency and distributive consequences of imperfect monitoring and screening in labor markets, and the tradeoffs the government faces when setting taxes. Optimal taxes are described by generalized versions of standard redistributive and corrective taxation formulas, which depend crucially on labor wedges: the ratios of the marginal contribution to output over the increases in lifetime earnings that result from supplying one extra unit of labor at each period. Combining estimates from the literature and new estimates using data from the Health and Retirement Study, I find that, once career concerns are taken into account, the current tax system may look less redistributive than previously thought.
Income Taxation with Elasticity Heterogeneity with John Sturm
How should income taxes account for differences in households’ tax responses? We address this question with a new test that passes if and only if there exists a utilitarian planner for whom the current tax system is locally optimal. Our test takes as inputs standard sufficient statistics, such as the average elasticity of taxable income and the shape of the income distribution, but also incorporates a novel statistic: the variance of elasticities conditional on income. Indeed, the test fails when this variance is sufficiently high. We then proceed to evaluate our test empirically using the NBER panel of tax returns and providing novel estimates of the variance of ETI by income bracket. We find that our optimality test fails, implying there are welfare-improving tax reforms.
Optimal Credit Scores Under Adverse Selection with Nicole Immorlica and Robert M. Townsend
The increasing availability of data in credit markets may appear to make adverse selection concerns less relevant. However, when there is adverse selection, more information does not necessarily increase welfare. We provide tools for making better use of the data that is collected from potential borrowers, formulating and solving the optimal disclosure problem of an intermediary with commitment that seeks to maximize the probability of successful transactions, weighted by the size of the gains of these transactions. We show that any optimal disclosure policy needs to satisfy some simple conditions in terms of local sufficient statistics. These conditions relate prices to the price elasticities of the expected value of the loans for the investors. Empirically, we apply our method to the data from the Townsend Thai Project, which is a long panel dataset with rich information on credit histories, balance sheets, and income statements, to evaluate whether it can help develop the particularly thin formal rural credit markets in Thailand, finding economically meaningful gains from adopting limited information disclosure policies.
Selected Works in Progress
Social Insurance and Information Design
Changing Taxes for Changing Times with John Sturm
What is the Variance of Taxable Income Elasticities? A Bagged Forest Approach with John Sturm
Optimal Menus, Moral Hazard, and Adverse Selection in Data-Rich Lending Markets with Yingju Ma and Robert M. Townsend