Research Database

In the 1960 cohort, American men and women graduated from college at the same rate, and this was true for Whites, Blacks and Hispanics. But in more recent cohorts, women graduate at much higher rates than men. To understand the emerging gender education gap, we formulate and estimate a model of individual and family decision-making where education, labor supply, marriage and fertility are all endogenous. Assuming preferences that are common across ethnic groups and fixed over cohorts, our model explains differences in all endogenous variables by gender/ethnicity for the ‘60-‘80 cohorts based on three exogenous factors: family background, labor market and marriage market constraints. Changes in parental background are a key factor driving the growing gender education gap: Women with college educated mothers get greater utility from college, and are much more likely to graduate themselves. The marriage market also contributes: Women’s chance of getting marriage offers at older ages has increased, enabling them to defer marriage. The labor market is the largest factor: Improvement in women’s labor market return to college in recent cohorts accounts for 50% of the increase in their graduation rate. But the labor market returns to college are still greater for men. Women go to college more because their overall return is greater, after factoring in marriage market returns and their greater utility from college attendance. We predict the recent large increases in women’s graduation rates will cause their children’s graduation rates to increase further. But growth in the aggregate graduation rate will slow substantially, due to significant increases in the share of Hispanics – a group with a low graduation rate – in recent birth cohorts.

We analytically characterize the aggregate productivity loss from allocative distortions in a setting that accounts for the sectoral linkages of production. We show that the effects of distortions and the role of sectoral linkages depend crucially on how substitutable inputs are. We find that the productivity loss is smaller if input substitutability is low. Moreover, with low input substitutability, sectoral linkages do not systematically amplify the effects of distortions. In addition, the impact of the sectors that supply intermediate inputs becomes smaller. We quantify these effects in the context of the distortions caused by market power, using industry-level data for 35 countries. With our benchmark calibration, which accounts for low input substitutability, the median aggregate productivity loss from industry-level markups is 1.3%. To assume instead unit elasticities of substitution (i.e., to use a Cobb-Douglas production function) would lead to overestimating the productivity loss by a factor of 1.8. Sectoral linkages do amplify the cost of markups, but the amplification factor is considerably weaker than with unit elasticities.

We derive an optimal labor income tax rate formula for urban models in which tax rates are determined by traditional forces plus a new term arising from urban forces: house price, migration and agglomeration effects. Based on the earnings distributions and housing costs in large and small US cities, we find that in a benchmark model (i) optimal income tax rates are U-shaped, (ii) urban forces serve to raise optimal tax rates at all income levels and (iii) adopting an optimal tax system induces agents with low skills to leave large, productive cities. While agglomeration effects enter the optimal tax formula, they play almost no quantitative role in shaping optimal labor income tax rates.

We benchmark six global optimization algorithms by comparing their performance on challenging multidimensional test functions as well as on a method of simulated moments estimation of a panel data model of earnings dynamics. Five of the algorithms are from the popular NLopt open-source library: (i) Controlled Random Search with local mutation (CRS), (ii) Improved Stochastic Ranking Evolution Strategy (ISRES), (iii) Multi-Level Single-Linkage (MLSL), (iv) Stochastic Global Optimization (StoGo), and (v) Evolutionary Strategy with Cauchy distribution (ESCH). The sixth algorithm is TikTak, which is a multistart global optimization algorithm used in some recent economic applications. For completeness, we add three popular local algorithms to the comparison—the Nelder-Mead downhill simplex algorithm, the Derivative-Free Nonlinear Least Squares (DFNLS) algorithm, and a popular variant of the Davidon-Fletcher-Powell (DFPMIN) algorithm. To give a detailed comparison of algorithms, we use benchmarking tools recently developed in the optimization literature. We find that the success rate of many optimizers varies dramatically with the characteristics of each problem and the computational budget that is available. Overall, TikTak is the strongest performer both on the test functions and the economic application. The next-best performing optimizers are StoGo for the test functions and MLSL and ISRES for the economic application.

To to get the COVID-19 virus under control, many countries have shut down parts of the economy. Older individuals have the most to gain from slowing virus diffusion. Younger workers in sectors that are shuttered have most to lose. We build a model in which economic activity and disease progression are jointly determined. Individuals differ by age (young, retired), by sector (basic, luxury), and by health status. Disease transmission occurs in the workplace, through consumption, at home, and in hospitals. We study the optimal economic mitigation policy for a government that can redistribute through taxes and transfers, but where taxation distorts labor supply and output. Optimal redistribution and mitigation policies interact, and more modest shutdowns are optimal when redistribution creates tax distortions. A harder but shorter shutdown is preferred as vaccines become available in the first half of 2021.

We specify and calibrate a life-cycle model of labor supply and savings incorporating health shocks and medical treatment decisions. Our model features endogenous wage formation via human capital accumulation, employer-sponsored health insurance, and means-tested social insurance. We use the model to study the effects of health shocks on health, labor supply and earnings, and to assess how health shocks contribute to earnings inequality. We also simulate provision of public insurance to agents who lack employer-sponsored insurance. The public insurance program substantially increases medical usage by the uninsured, leading to improved health and life expectancy, which generates higher Social Security costs. But the program also creates positive labor supply incentives, and substantially reduces costs of social insurance, Medicaid and free care. On balance the net program cost is modest, and all agents in the model are ex ante better off in a balanced budget simulation. In contrast, improving access to Medicaid has perverse labor supply effects, does little to improve health, and makes almost all agents worse off in a balanced budget scenario.

This paper reports the results of a large, school-level randomized controlled trial evaluating a set of three informational interventions for young people choosing high schools in 473 middle schools, serving over 115,000 8th graders. The interventions differed in their level of customization to the student and their mode of delivery (paper or online); all treated schools received identical materials to scaffold the decision-making process. Every intervention reduced likelihood of application to and enrollment in schools with graduation rates below the city median (75 percent). An important channel is their effect on reducing nonoptimal first choice application strategies. Providing a simplified, middle-school specific list of relatively high graduation rate schools had the largest impacts, causing students to enroll in high schools with 1.5-percentage point higher graduation rates. Providing the same information online, however, did not alter students’ choices or enrollment. This appears to be due to low utilization. Online interventions with individual customization, including a recommendation tool and search engine, induced students to enroll in high schools with 1-percentage point higher graduation rates, but with more variance in impact. Together, these results show that successful informational interventions must generate engagement with the material, and this is possible through multiple channels.

This paper studies the implications of household heterogeneity for trade. I develop a model where household heterogeneity is induced via incomplete markets and results in heterogeneous price elasticities. Conditional on exposure to trade, heterogeneous price elasticities imply that different households value price changes differently, and thus rich and poor households experience different gains from trade. I calibrate the model to match bilateral trade flows and micro-facts about household-level expenditure patterns and elasticities. I find gains from trade that are pro-poor and that the average gains from trade are substantially larger than representative agent benchmarks.