Tepper School of Business
Carnegie Mellon University
My research is in the fields of public finance, macroeconomics, and finance. It focuses on imperfect information issues, inequality, and financial inclusion.
asztutma at andrew dot cmu dot edu
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, the corrective component of taxes is large, especially at the top of the income distribution.
How should income taxes account for heterogeneity in elasticities of taxable income? We address this question with a test that passes if and only if there exists a weighted utilitarian planner for whom taxes are locally optimal. Our test incorporates standard sufficient statistics and a novel ingredient: the variance of elasticities conditional on income. Theoretically, we show that the test fails when these variances are sufficiently high. Empirically, we find they are indeed large in a panel of US tax returns. We thereby conclude, without taking a stance on redistributive preferences, that there are welfare-improving tax reforms.
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
Testing Rational Inattention with Experimental Auction