Publications

We develop a state-space model with a transition equation that takes the form of a functional vector autoregression (VAR) and stacks macroeconomic aggregates and a cross-sectional density. The measurement equation captures the error in estimating log densities from repeated cross-sectional samples. The log densities and their transition kernels are approximated by sieves, which leads to a finite-dimensional VAR for macroeconomic aggregates and sieve coefficients. With this model, we study the dynamics of technology shocks, GDP (gross domestic product), employment, and the earnings distribution. We find that spillovers between aggregate and distributional dynamics are generally small, that a positive technology shock tends to decrease inequality, and that a shock that raises earnings inequality leads to a small and insignificant GDP response.

We study agents who are more likely to remember some experiences than others but update beliefs as if the experiences they remember are the only ones that occurred. To understand the long-run effects of selective memory, we propose selective-memory equilibrium. We show that if the agent’s behavior converges, their limit strategy is a selective-memory equilibrium, and we provide a sufficient condition for behavior to converge. We use this equilibrium concept to explore the consequences of several well-documented biases. We also show that there is a close connection between selective-memory equilibria and the outcomes of misspecified learning.

We propose a method for identifying exposure to changes in trade policy based on asset prices that has several advantages over standard measures: it encompasses all avenues of exposure, it is natively firm-level, it yields estimates for both goods and service producers, and it can be used to study reductions in difficult-to-quantify non-tariff-barriers in a way that controls naturally for broader macroeconomic shocks. Applying our method to two well-studied US trade liberalizations provides new insight into service-sector responses and reveals dramatically different outcomes among small versus large firms, even within narrow industries.

We propose a theory of gradualism in the implementation of good policies, suitable for environments featuring time consistency. We downplay the role of the initial period by allowing agents both to wait for future agents to start equilibrium play and to restart the equilibrium by ignoring past history. The allocation gradually transits toward one that weighs both short- and long-term concerns, stopping short of the Ramsey outcome but greatly improving upon Markovian equilibria. We use the theory to account for the slow emergence of both climate policies and the reduction of global tariff rates.

We propose a new adaptive hypothesis test for inequality (e.g., monotonicity, convexity) and equality (e.g., parametric, semiparametric) restrictions on a structural function in a nonparametric instrumental variables (NPIV) model. Our test statistic is based on a modified leave-one-out sample analog of a quadratic distance between the restricted and unrestricted sieve two-stage least squares estimators. We provide computationally simple, data-driven choices of sieve tuning parameters and Bonferroni adjusted chi-squared critical values. Our test adapts to the unknown smoothness of alternative functions in the presence of unknown degree of endogeneity and unknown strength of the instruments. It attains the adaptive minimax rate of testing in L². That is, the sum of the supremum of type I error over the composite null and the supremum of type II error over nonparametric alternative models cannot be minimized by any other tests for NPIV models of unknown regularities. Confidence sets in L² are obtained by inverting the adaptive test. Simulations confirm that, across different strength of instruments and sample sizes, our adaptive test controls size and its finite-sample power greatly exceeds existing non-adaptive tests for monotonicity and parametric restrictions in NPIV models. Empirical applications to test for shape restrictions of differentiated products demand and of Engel curves are presented.

A signal is privacy‐preserving with respect to a collection of privacy sets if the posterior probability assigned to every privacy set remains unchanged conditional on any signal realization. We characterize the privacy‐preserving signals for arbitrary state space and arbitrary privacy sets. A signal is privacy‐preserving if and only if it is a garbling of a reordered quantile signal. Furthermore, distributions of posterior means induced by privacy‐preserving signals are exactly mean‐preserving contractions of that induced by the quantile signal. We discuss the economic implications of our characterization for statistical discrimination, the revelation of sensitive information in auctions and price discrimination.

We develop a new equilibrium model in which households’ labour supply choices form the link between sorting on the marriage market and sorting on the labour market. We first show that in theory, the nature of home production—whether partners’ hours are complements or substitutes—shapes equilibrium labour supply as well as marriage and labour market sorting. We then estimate our model using German data to empirically assess the nature of home production, and find that spouses’ home hours are complements. We investigate to what extent complementarity in home hours drives sorting and inequality. We find that home production complementarity strengthens positive marriage sorting and reduces the gender gap in hours and in labour sorting. This puts significant downward pressure on the gender wage gap and on within-household income inequality, but fuels between-household inequality. Our estimated model sheds new light on the sources of inequality in today’s Germany, and—by identifying important shifts in home production technology toward more complementarity—on the evolution of inequality over time.

We develop a dynamic model of input–output networks that incorporates adjustment costs of changing inputs. Our closed-form solution for the dynamics of the economy shows that temporary shocks to upstream sectors, whose output travels through long supply chains, have disproportionately significant welfare impact compared to affected sectors’ Domar weights. We conduct a spectral analysis of the U.S. production network and reveal that the welfare impact of temporary sectoral shocks can be represented by a low-dimensional, 3-factor structure.

We develop a framework for quantifying barriers to labor force participation (LFP) and entrepreneurship faced by women in India. We find substantial barriers to LFP, and higher costs of expanding businesses through hiring workers for women entrepreneurs. However, there is one area where female entrepreneurs have an advantage: the hiring of female workers. We show that this is not driven by the sectoral composition of female employment. Consistent with this pattern, policies promoting female entrepreneurship can significantly increase female LFP even without explicitly targeting female LFP. Counterfactual simulations indicate that removing all excess barriers faced by women entrepreneurs would substantially increase the fraction of female‐owned firms, female LFP, earnings, and generate substantial gains for the economy. These gains are due to higher LFP, higher real wages and profits, and reallocation: low productivity male‐owned firms previously sheltered from female competition are replaced by higher productivity female‐owned firms previously excluded from the economy.

This paper studies school choice and information frictions in Haiti. Through a randomized control trial, we assess the impact of disclosing school-level test score information on learning outcomes, prices, and market shares. We find evidence that in markets where information was disclosed, students attending private schools increased test scores. The results also suggest private schools with higher baseline test scores increased their market share as well as their fees when the disclosure policy is implemented. While prices and test scores were not significantly correlated in the baseline survey, they exhibited a significant and positive correlation in treatment markets after information disclosure. These results underscore the potential of information provision to enhance market efficiency and improve children’s welfare in context such as Haiti.

We contribute to the public debate on immigration policy in the United States by providing a long-term, empirical perspective. We develop a novel method of linking individuals across historical Census waves to trace the lives of millions of immigrants in the past and compare their outcomes with immigrants today. We document that upward mobility is just as possible for immigrants today as it was in the early 20th century, and that children of immigrant parents catch up to and frequently exceed the economic outcomes of the children of US-born parents. By our measures, immigrants as a group are no more likely to be incarcerated than those born in the United States, and they assimilate into American culture today at rates comparable to historical standards. Attitudes toward immigrants today are more positive than a century ago, albeit more polarized by political party.

Poor entrepreneurs must frequently choose between business investment and children's education. To examine this trade-off, we exploit experimental variation in short-run microenterprise growth among a sample of Indian households and track schooling and business out-comes over eleven years. Treated households, who experience higher initial microenterprise growth, are on average one-third more likely to send children to college. However, educational investment and schooling gains are concentrated among literate-parent households, whose enterprises eventually stagnate. In contrast, illiterate-parent households experience long-run business gains but declines in children's education. Our findings suggest that microenterprise growth has the potential to reduce relative intergenerational educational mobility.

We analyze representativeness in a COVID-19 serological study with randomized participation incentives. We find large participation gaps by race and income when incentives are lower. High incentives increase participation rates for all groups but increase them more among under-represented groups. High incentives restore representativeness on race and income and also on health variables likely to be correlated with seropositivity, such as the uninsured rate, hospitalization rates, and an aggregate COVID-19 risk index.

A monopolist platform uses data to match heterogeneous consumers with multiproduct sellers. The consumers can purchase the products on the platform or search off the platform. The platform sells targeted ads to sellers that recommend their products to consumers and reveals information to consumers about their match values. The revenue- optimal mechanism is a managed advertising campaign that matches products and preferences efficiently. In equilibrium, sellers offer higher qualities at lower unit prices on than off platform. The platform exploits its information advantage to increase its bargaining power vis-à-vis the sellers. Finally, privacy-respecting data-governance rules can lead to welfare gains for consumers.

We study noisy aggregation of dispersed information in financial markets without imposing parametric restrictions on preferences, information, and return distributions. We provide a general characterization of asset returns by means of a risk-neutral probability measure that features excess weight on tail risks. Moreover, we link excess weight on tail risks to observable moments such as forecast dispersion and accuracy, and argue that it provides a unified explanation for several prominent cross-sectional return anomalies. Simple calibrations suggest the model can account for a significant fraction of empirical returns to skewness, returns to disagreement, and interaction effects between the two.