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The debate about behavioral economics–the incorporation of insights from psychology into economics–is often framed as a question about the foundational assumptions of economic models. This paper presents a more pragmatic perspective on behavioral economics that focuses on its value for improving empirical predictions and policy decisions. I discuss three ways in which behavioral economics can contribute to public policy: by offering new policy tools, improving predictions about the effects of existing policies, and generating new welfare implications. I illustrate these contributions using applications to retirement savings, labor supply, and neighborhood choice. Behavioral models provide new tools to change behaviors such as savings rates and new counterfactuals to estimate the effects of policies such as income taxation. Behavioral models also provide new prescriptions for optimal policy that can be characterized in a non-paternalistic manner using methods analogous to those in neoclassical models. Model uncertainty does not justify using the neoclassical model; instead, it can provide a new rationale for using behavioral nudges. I conclude that incorporating behavioral features to the extent they help answer core economic questions may be more productive than viewing behavioral economics as a separate subfield that challenges the assumptions of neoclassical models.

We show that the neighborhoods in which children grow up shape their earnings, college attendance rates, and fertility and marriage patterns by studying more than 7 million families who move across commuting zones and counties in the United States. Exploiting variation in the age of children when families move, we find that neighborhoods have significant childhood exposure effects: the outcomes of children whose families move to a better neighborhood—as measured by the outcomes of children already living there—improve linearly in proportion to the amount of time they spend growing up in that area, at a rate of approximately 4% per year of exposure. We distinguish the causal effects of neighborhoods from confounding factors by comparing the outcomes of siblings within families, studying moves triggered by displacement shocks, and exploiting sharp variation in predicted place effects across birth cohorts, genders, and quantiles to implement overidentification tests. The findings show that neighborhoods affect intergenerational mobility primarily through childhood exposure, helping reconcile conflicting results in the prior literature.

We estimate the causal effect of each county in the United States on children’s incomes in adulthood. We first estimate a fixed effects model that is identified by analyzing families who move across counties with children of different ages. We then use these fixed effect estimates to (i) quantify how much places matter for intergenerational mobility, (ii) construct forecasts of the causal effect of growing up in each county that can be used to guide families seeking to move to opportunity, and (iii) characterize which types of areas produce better outcomes. For children growing up in low-income families, each year of childhood exposure to a one standard deviation (std. dev.) better county increases income in adulthood by 0.5%. There is substantial variation in counties’ causal effects even within metro areas. Counties with less concentrated poverty, less income inequality, better schools, a larger share of two-parent families, and lower crime rates tend to produce better outcomes for children in poor families. Boys’ outcomes vary more across areas than girls’ outcomes, and boys have especially negative outcomes in highly segregated areas. Areas that generate better outcomes have higher house prices on average, but our approach uncovers many “opportunity bargains”—places that generate good outcomes but are not very expensive.

I show existing evidence on labor supply behavior places an upper bound on risk aversion in the expected utility model. I derive a formula for the coefficient of relative risk aversion (γ) in terms of the ratio of the income elasticity of labor supply to wage elasticity and degree of complementarity between consumption and labor. I bound the degree of complementarity using data on consumption choices when labor supply varies across states. Using labor supply elasticity estimates, I find a mean estimate of [Formula: see text], then show generating γ > 2 requires that wage increases cause sharper labor supply reductions.

We use administrative records on the incomes of more than 40 million children and their parents to describe three features of intergenerational mobility in the United States. First, we characterize the joint distribution of parent and child income at the national level. The conditional expectation of child income given parent income is linear in percentile ranks. On average, a 10 percentile increase in parent income is associated with a 3.4 percentile increase in a child’s income. Second, intergenerational mobility varies substantially across areas within the United States. For example, the probability that a child reaches the top quintile of the national income distribution starting from a family in the bottom quintile is 4.4% in Charlotte but 12.9% in San Jose. Third, we explore the factors correlated with upward mobility. High mobility areas have (i) less residential segregation, (ii) less income inequality, (iii) better primary schools, (iv) greater social capital, and (v) greater family stability. Although our descriptive analysis does not identify the causal mechanisms that determine upward mobility, the publicly available statistics on intergenerational mobility developed here can facilitate research on such mechanisms.

Martin Feldstein's (1999) widely used taxable income formula for deadweight loss assumes the marginal social cost of evasion and avoidance equals the tax rate. This condition is likely to be violated in practice for two reasons. First, some of the costs of evasion and avoidance are transfers to other agents. Second, some individuals overestimate the costs of evasion and avoidance. In such situations, excess burden depends on a weighted average of the taxable income and total earned income elasticities, with the weight determined by the resource cost of sheltering income from taxation. This generalized formula implies the efficiency cost of taxing high income individuals is not necessarily large despite evidence that their reported incomes are highly sensitive to marginal tax rates.

The Moving to Opportunity (MTO) experiment offered randomly selected families housing vouchers to move from high-poverty housing projects to lower-poverty neighborhoods. We analyze MTO's impacts on children's long-term outcomes using tax data. We find that moving to a lower-poverty neighborhood when young (before age 13) increases college attendance and earnings and reduces single parenthood rates. Moving as an adolescent has slightly negative impacts, perhaps because of disruption effects. The decline in the gains from moving with the age when children move suggests that the duration of exposure to better environments during childhood is an important determinant of children's long-term outcomes.

The debate between structural and reduced-form approaches has generated substantial controversy in applied economics. This article reviews a recent literature in public economics that combines the advantages of reduced-form strategies—transparent and credible identification—with an important advantage of structural models—the ability to make predictions about counterfactual outcomes and welfare. This literature has developed formulas for the welfare consequences of various policies that are functions of reduced-form elasticities rather than structural primitives. I present a general framework that shows how many policy questions can be answered by estimating a small set of sufficient statistics using program-evaluation methods. I use this framework to synthesize the modern literature on taxation, social insurance, and behavioral welfare economics. Finally, I discuss problems in macroeconomics, labor, development, and industrial organization that could be tackled using the sufficient statistic approach.

We show that characterizing the effects of housing on portfolios requires distinguishing between the effects of home equity and mortgage debt. We isolate exogenous variation in home equity and mortgages by using differences across housing markets in house prices and housing supply elasticities as instruments. Increases in property value (holding home equity constant) reduce stockholdings, while increases in home equity wealth (holding property value constant) raise stockholdings. The stock share of liquid wealth would rise by 1 percentage point—6% of the mean stock share—if a household were to spend 10% less on its house, holding fixed wealth.

We estimated rates of “absolute income mobility”—the fraction of children who earn more than their parents—by combining data from U.S. Census and Current Population Survey cross sections with panel data from de-identified tax records. We found that rates of absolute mobility have fallen from approximately 90% for children born in 1940 to 50% for children born in the 1980s. Increasing Gross Domestic Product (GDP) growth rates alone cannot restore absolute mobility to the rates experienced by children born in the 1940s. However, distributing current GDP growth more equally across income groups as in the 1940 birth cohort would reverse more than 70% of the decline in mobility. These results imply that reviving the “American dream” of high rates of absolute mobility would require economic growth that is shared more broadly across the income distribution.