Knowledge that Transforms

We study how Americans respond to idiosyncratic and exogenous changes in household wealth and unearned income. Our analyses combine administrative data on U.S. lottery winners with an event study design. We first examine individual and household earnings responses to these windfall gains, finding significant and sizable wealth and income effects. On average, an extra $1 of unearned income in a given period reduces household labor earnings by about 50 cents, decreases total labor taxes by 10 cents, and increases consumption expenditure by 60 cents. These effects are heterogeneous across the income distribution, with households in higher quartiles of the income distribution reducing their earnings by a larger amount. Next we examine margins of adjustment other than earnings and, in the course of doing so, address a number of important economic questions about how additional wealth or unearned income affect retirement decisions and labor market dynamics, family formation and dissolution, entrepreneurship and self-employment, and geographic mobility and neighborhood choice. Last, we carefully compare our findings to those reported in existing lottery studies. This comparison reveals that existing U.S. studies substantially underestimate wealth and income effects because they use measures that understate the earnings responses and overstate the after-tax wealth changes associated with lottery wins.

How should we measure changes in consumer welfare given observed data on prices and expenditures? This article proposes a nonparametric approach that holds under arbitrary preferences that may depend on observable consumer characteristics, for example, when expenditure shares vary with income. Using total expenditures under a constant set of prices as our money metric for real consumption (welfare), we derive a principled measure of real consumption growth featuring a correction term relative to conventional measures. We show that the correction can be nonparametrically estimated with an algorithm leveraging the observed, cross-sectional relationship between household-level price indices and household characteristics such as income. We demonstrate the accuracy of our algorithm in simulations. Applying our approach to data from the United States, we find that the magnitude of the correction can be large because of the combination of fast growth and lower inflation for income-elastic products. Setting reference prices in 2019, we find that (i) the uncorrected measure underestimates average real consumption per household in 1955 by 11.5%, and (ii) the correction reduces the annual growth rate from 1955 to 2019 by 18 basis points, which is larger than the well-known “expenditure-switching bias” over the same time horizon.

I investigate the consequences of firms’ joint import and export decisions in the context of large devaluations. I provide empirical evidence that large devaluations are characterized by an increase in the aggregate share of imported inputs in total input spending and by reallocation of resources toward import-intensive firms, contrary to what standard quantitative trade models predict. These facts are explained by the expansion of exporters, which are intense importers. I develop a model where firms globally decide their import and export strategies and discipline it to match salient features of the Mexican micro data. After a devaluation, the model reproduces the pattern of low aggregate substitution and firm reallocation observed in the data. Compared with a benchmark without global firms, the model predicts higher growth of total exports and imports and a smaller reduction in the trade deficit.

We estimate the equilibrium effects of a public school grant program administered through school councils in Pakistani villages with multiple public and private schools and clearly defined catchment boundaries. The program was randomized at the village level, allowing us to estimate its causal impact on the market. Four years after the start of the program, test scores were 0.2 standard deviations higher in public schools. We find evidence of an education multiplier: test scores in private schools were also 0.2 standard deviations higher in treated markets. Consistent with standard models of product differentiation, the education multiplier is greater for those private schools that faced a greater threat to their market power. Accounting for private sector responses increases the program’s cost-effectiveness by 85% and affects how a policy maker would target spending. Given that markets with several public and private schools are now pervasive in low- and middle-income countries, prudent policy requires us to account for private sector responses to public policy, both in policies’ design and evaluation.

This article studies the long-run effects of slavery and restrictive Jim Crow institutions on Black Americans’ economic outcomes. We track individual-level census records of each Black family from 1850 to 1940 and extend our analysis to neighborhood-level outcomes in 2000 and surname-based outcomes in 2023. We show that Black families whose ancestors were enslaved until the Civil War have considerably lower education, income, and wealth than Black families whose ancestors were free before the Civil War. The disparities between the two groups have persisted substantially because most families enslaved until the Civil War lived in states with strict Jim Crow regimes after slavery ended. In a regression discontinuity design based on ancestors’ enslavement locations, we show that Jim Crow institutions sharply reduced Black families’ economic progress in the long run.

While hypothesis testing is a highly formalized activity, hypothesis generation remains largely informal. We propose a systematic procedure to generate novel hypotheses about human behavior, which uses the capacity of machine learning algorithms to notice patterns people might not. We illustrate the procedure with a concrete application: judge decisions about whom to jail. We begin with a striking fact: the defendant’s face alone matters greatly for the judge’s jailing decision. In fact, an algorithm given only the pixels in the defendant’s mug shot accounts for up to half of the predictable variation. We develop a procedure that allows human subjects to interact with this black-box algorithm to produce hypotheses about what in the face influences judge decisions. The procedure generates hypotheses that are both interpretable and novel: they are not explained by demographics (e.g., race) or existing psychology research, nor are they already known (even if tacitly) to people or experts. Though these results are specific, our procedure is general. It provides a way to produce novel, interpretable hypotheses from any high-dimensional data set (e.g., cell phones, satellites, online behavior, news headlines, corporate filings, and high-frequency time series). A central tenet of our article is that hypothesis generation is a valuable activity, and we hope this encourages future work in this largely “prescientific” stage of science.

This article documents the economic relevance of measuring cognitive uncertainty: people’s subjective uncertainty over their ex ante utility-maximizing decision. In a series of experiments on choice under risk, the formation of beliefs, and forecasts of economic variables, we show that cognitive uncertainty predicts various systematic biases in economic decisions. When people are cognitively uncertain—either endogenously or because the problem is designed to be complex—their decisions are heavily attenuated functions of objective probabilities, which gives rise to average behavior that is regressive to an intermediate option. This insight ties together a wide range of empirical regularities in behavioral economics that are typically viewed as distinct phenomena or even as reflecting preferences, including the probability weighting function in choice under risk; base rate insensitivity, conservatism, and sample size effects in belief updating; and predictable overoptimism and -pessimism in forecasts of economic variables. Our results offer a blueprint for how a simple measurement of cognitive uncertainty generates novel insights about what people find complex and how they respond to it.

How does wealth taxation differ from capital income taxation? When the return on investment is equal across individuals, a well-known result is that the two tax systems are equivalent. Motivated by recent empirical evidence documenting persistent return heterogeneity, we revisit this question. With heterogeneity, the two tax systems typically have opposite implications for efficiency and inequality. Under capital income taxation, entrepreneurs who are more productive and therefore generate more income pay higher taxes. Under wealth taxation, entrepreneurs who have similar wealth levels pay similar taxes regardless of their productivity, which expands the tax base, shifts the tax burden toward unproductive entrepreneurs, and raises the savings rate of productive ones. This reallocation increases aggregate productivity and output. In the simulated model parameterized to match the U.S. data, replacing the capital income tax with a wealth tax in a revenue-neutral way delivers a significantly higher average welfare. Turning to optimal taxation, the optimal wealth tax (OWT) is positive and yields large welfare gains by raising efficiency and lowering inequality. In contrast, the optimal capital income tax (OKIT) is negative—a subsidy—and delivers lower welfare gains than OWT, owing to the welfare losses from higher inequality. Furthermore, when the transition path is considered, the gains from OKIT turn into significant welfare losses for existing cohorts, whereas OWT continues to deliver robust welfare gains. These results suggest that moderate wealth taxation may be a more appealing alternative than capital income taxation, which can be significantly more distorting under return heterogeneity than under the equal-returns assumption.

This article investigates gender differences in the job search process in the field and lab. Our analysis is based on rich information on initial job offers and acceptances from undergraduates of Boston University’s Questrom School of Business. We find (i) a clear gender difference in the timing of job offer acceptance, with women accepting jobs substantially earlier than men, and (ii) a sizable gender earnings gap in accepted offers, which narrows in favor of women over the course of the job search period. To understand these patterns, we develop a job search model that incorporates gender differences in risk aversion and overoptimism about prospective offers. We validate the model’s assumptions and predictions using the survey data and present empirical evidence that the job search patterns in the field can be partly explained by the greater risk aversion displayed by women and the higher levels of overoptimism displayed by men. We replicate these findings in a laboratory experiment that features sequential job search and provide direct evidence on the purported mechanisms. Our findings highlight the importance of risk preferences and beliefs for gender differences in job-finding behavior and, consequently, early-career wage gaps among the highly educated.

Reducing the emissions of greenhouse gases may be almost impossible without a green transition—a substantial transformation of consumption and production patterns. To study such transitions, we propose a dynamic model, which differs from the common approach in economics in two ways. First, consumption patterns reflect not just changing prices and taxes, but changing values. Transitions of values and technologies create a dynamic complementarity that can help or hinder a green transition. Second, and unlike fictitious social planners, policy makers in democratic societies cannot commit to future policy paths, as they are subject to regular elections. We show that market failures and government failures can interact to prevent a welfare-increasing green transition from materializing or make an ongoing green transition too slow.