This paper investigates the role played by informational frictions in college and the workplace. We estimate a dynamic structural model of schooling and work decisions, where individuals have imperfect information about their schooling ability and labor market productivity. We take into account the heterogeneity in schooling investments by distinguishing between two- and four-year colleges, graduate school, as well as science and non-science majors for four-year colleges. Individuals may also choose whether to work full-time, part-time, or not at all. A key feature of our approach is to account for correlated learning through college grades and wages, whereby individuals may leave or re-enter college as a result of the arrival of new information on their ability and productivity. Our findings indicate that the elimination of informational frictions would increase the college graduation rate by 9 percentage points, and would increase the college wage premium by 32.7 percentage points through increased sorting on ability.
We examine the extent to which participation in high school athletics has beneficial effects on future education, labor market, and health outcomes. Due to the absence of plausible instruments in observational data, we use recently developed methods that relate selection on observables with selection on unobservables to estimate bounds on the causal effect of athletics participation. We analyze these effects in the US separately for men and women using three different nationally representative longitudinal data sets that each link high school athletics participation with later-life outcomes. We do not find consistent evidence of individual benefits reported in many previous studies—once we have accounted for selection, high school athletes are no more likely to attend college, earn higher wages, or participate in the labor force. However, we do find that men (but not women) who participated in high school athletics are more likely to exercise regularly as adults. Nevertheless, athletes are no less likely to be obese.
This paper investigates the wage returns to schooling and actual early work experiences, and how these returns have changed over the past twenty years. Using the NLSY surveys, we develop and estimate a dynamic model of the joint schooling and work decisions that young men make in early adulthood, and quantify how they affect wages using a generalized Mincerian specification. Our results highlight the need to account for dynamic selection and changes in composition when analyzing changes in wage returns. In particular, we find that ignoring the selectivity of accumulated work experiences results in overstatements of the returns to education.
This paper examines effects of the U.S. Immigration Act of 1990 on STEM (science, technology, engineering, and mathematics) education and labor market outcomes for nativeborn Americans. The Act increased the in-flow and stock of foreign STEM workers in the U.S., potentially altering the relative desirability of STEM fields for natives. The authors examine effects of the policy on STEM degree completion, STEM occupational choice, and employment rates separately for black and white men and women. The novel identification strategy measures exposure to foreign STEM workers of age-18 native cohorts immediately before and after the policy change via geographic dispersion of foreign-born STEM workers in 1980, which predicts subsequent foreign STEM flows. The Act affected natives in three ways: (1) it pushed black men out of STEM majors; (2) it pushed white male STEM graduates out of STEM occupations; and (3) it pushed white female STEM graduates out of the workforce.
I examine the extent to which the returns to college majors are influenced by selective migration and occupational choice across locations in the US. To quantify the role of selection, I develop and estimate an extended Roy model of migration, occupational choice, and earnings where, upon completing their education, individuals choose a location in which to live and an occupation in which to work. In order to estimate this high-dimensional choice model, I make use of machine learning methods that allow for model selection and estimation simultaneously in a non-parametric setting. I find that OLS estimates of the returns to business and STEM majors relative to education majors are upward biased by 15% on average and by as much as 30%. Using estimates of the model, I characterize the migration behavior of different college majors and find that migration flows are twice as sensitive to occupational concentration as they are to wage returns. This finding has important implications for local governments seeking to attract or retain skilled workers.
I examine mechanisms that differentially influence migration behavior in response to labor market shocks between employed and unemployed workers in the US. Over the period of the Great Recession, overall migration rates in the US remained close to their respective long-term trends. However, migration evolved differently by employment status with unemployed workers being more likely to migrate during the recession and employed workers less likely. I estimate a dynamic non-stationary search model of migration, focusing on the role of employment frictions, earnings, and amenities on migration decisions. My results show that employed workers are faced with a large job queuing penalty when moving locations, which results in differing migration incentives for the two groups when faced with adverse labor market shocks. I also find that migration rates were muted because of the national scope of the Great Recession. I show that moving subsidies aimed at mitigating local unemployment are greatly hindered by workers' preferences for amenities.
Residential mobility rates in the U.S. have fallen steadily over the past three decades. While 19.6% of U.S. residents changed residence within the United States in 1985, only 10.6% did so in 2017, its lowest level since 1948 when the Census Bureau began tracking mobility. There is growing concern about its implication for efficient labor and resource allocation, potentially lowering productivity growth. Moreover, given the importance of migration for upward mobility the especially large declines seen in residential mobility among lower skilled workers, with many no longer willing or able to leave declining urban and rural areas, is particularly worrisome, likely contributing to reduced economic mobility and increased inequality.
The cause of the long-term decline in mobility remains largely unexplained. A sizeable and growing number of studies have investigated the role of changes in demographics (aging; delayed family formation), the housing market (negative equity and home-lock; rise and heterogeneity in cost of housing), the labor market (reduced job opportunities during the recession; expansion of telecommuting and flexible work schedules; increased state-level occupational licensing and reduced transferability of seniority across states), government policies (homeowner subsidies; location-based welfare and pension benefits; land use regulations), and changes in cultural values. While many of these changes may have played some role, none seem able to explain the broad-based longer-term decline in mobility seen within each age, gender, race, income, home ownership and employment status over the past decades.
To make progress in analyzing the causes and consequences of reduced migration requires a better understanding of residential location- and mobility-relevant preferences. In this paper we investigate the relative importance of alternative drivers of residential mobility, including job opportunities, neighborhood and housing amenities, social networks and housing and moving costs, using data from two different waves of the Federal Reserve Bank of New York's Survey of Consumer Expectations. We do so using a hypothetical choice methodology, akin to a stated-choice approach, where we present survey respondents a series of hypothetical migration choice scenarios and elicit their expected future mobility choices. The experimental variation generated by these scenarios allows estimation of the distribution of preferences for location and mobility attributes, without concerns about omitted variables and selection biases that hamper analyses based on observed mobility choices alone. We estimate substantial heterogeneity in the willingness-to-pay (WTP) for location and housing amenities across different demographic groups.
How Substitutable are Native- and Foreign-born Workers? Wage Effects from STEM In-flow with John V. Winters.
We study heterogeneity in the effect of immigrant labor supply on native wages in the United States using quasi-experimental variation induced by the Immigration Act of 1990. The Act quickly and substantially increased the number of immigrant workers in STEM occupations. Using CPS data, we find short-run policy effects of reduced wages for more substitutable workers but increased wages for more complementary workers. We analyze long-term effects of the policy using administrative earnings data and find smaller long-run effects.
Beating the Heat: Temperature and Spatial Reallocation over the Long Run with Christos Makridis.
Does temperature affect real economic activity? Using the annual Current Population Survey between 1963 and 2015, we show that there is no association between temperature and earnings, hours, or output after controlling for time-invariant spatial heterogeneity and time-varying demographic factors. These results are robust to five separate sources of micro-data, different sampling horizons, functional forms, spatial measures of temperature, and subsets of the data. This paper studies the relationship between temperature and productivity across space and time. Motivated by these null results, we develop a spatial equilibrium model where temperature can affect not only firm productivity, but also individual locational choice. After calibrating the model, we use it to disentangle the role of reallocation versus actual productivity losses in the U.S. economy between 1980 and 2015. Nearly all of the variation is driven by reallocation. We subsequently use the model to evaluate a counterfactual climate scenario and recover a new spatial equilibrium for the U.S. economy by 2050.