André Sztutman

Assistant Professor
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

Working Papers

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, the corrective component of taxes is large, especially at the top of the income distribution.

Efficiency Criteria, Income Taxation, and Heterogeneous Elasticities,  with John Sturm Becko (Revise and Resubmit, American Economic Review)

When can a utilitarian policymaker reject an income tax schedule without making strong inter- or intra-personal utility comparisons? We show that some Pareto efficient schedules are rejected by merely requiring an upper bound on the curvature of utility with respect to consumption. Taxes can be justified as utilitarian while respecting some such bound if and only if revenues are decreasing and concave in a class of narrowly targeted tax cuts. We reformulate this condition as a sufficient statistics test. The test fails whenever elasticities of taxable income are too heterogeneous within some income level, as we argue is empirically likely.

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

Technological Change and the Cost of Redistribution with John Sturm Becko and Bryant Xia

How does technological change affect the fiscal cost of redistributing income through taxes? We address this question in light of the well-documented heterogeneity in elasticities of labor supply and taxable income. When
the same households who are responsive to wage changes are also responsive to tax changes, a uniform increase in productivity disproportionately shifts more tax-elastic types to higher incomes. As a result, it raises the cost of redistributing income away from the top. We provide novel evidence showing that this sorting mechanism is quantitatively powerful and, in recent decades, outweighs a countervailing mechanism resulting from higher income inequality.

Regulating Information and Competition: Evidence from Fintech SME Loans with Yingju Ma and Robert M. Townsend

Leveraging data from a major lending provider for SMEs in China and an interest rate discount policy, we analyze the presence of selection in lending markets and how it interacts with market power. Our findings reveal a decrease in the average probability of loan repayment following interest rate reductions, indicating advantageous selection. Alternative explanations, such as moral hazard, observable heterogeneity, and dynamic portfolio optimization, cannot fully account for that pattern. Building on our estimated demand elasticities and selection effects, we assess the welfare implications of information-sharing and pro-competition policies, finding that among a restricted simple set of policies, ensuring competition and mandating information to be shared across financial providers results in the largest welfare gains.

What is the Variance of Taxable Income Elasticities? A Bagged Forest Approach  with John Sturm Becko

Social Insurance and Information Design

Older Work

Informationally Efficient Markets Under Rational Inattention

Testing Rational Inattention with Experimental Auction
Data