Labor Studies Program Meeting
February 22, 2013
Raven Molloy and Christopher Smith, Federal Reserve Board of Governors, and Abigail Wozniak, University of Notre Dame and NBER
Molloy, Smith, and Wozniak examine explanations for the secular decline in interstate migration since the 1980s. After showing that demographic and socioeconomic factors can account for little of this decrease, they present evidence suggesting that it is related to a downward trend in labor market transitions - that is, a decline in the fraction of workers moving from job to job, changing industry, and changing occupation - that occurred over the same period. They explore a number of reasons why these flows have diminished over time, including changes in the distribution of job opportunities across space, polarization in the labor market, concerns of dual-career households, and changing returns to labor market transitions. They find little empirical support for all but the last of these hypotheses. Specifically, using data from three cohorts of the National Longitudinal Surveys, they find that wage gains associated with employer transitions have fallen, which may have depressed general labor market transitions and, consequently, long-distance migration.
Jesse Gregory, University of Michigan
Following Hurricane Katrina, the Louisiana Road Home program provided cash grants directly to individual homeowners to offset repair costs and to encourage rebuilding. Gregory develops a dynamic discrete choice model of New Orleans homeowners' post-Katrina choices regarding residential locations, home repairs, home sales, and amounts to borrow or save, and derives and implements a maximum-likelihood estimator for the model's structural parameters. Using simulations, he finds that the Road Home program significantly increased the fraction of homes rebuilt within four years of Katrina, mostly by relaxing financing constraints for borrowing constrained households who would have strongly preferred to rebuild even in the absence of a subsidy if the associated costs could have been spread out over time. He also finds that location preferences are highly heterogeneous, and most households are far enough from the margin with respect to their preferred location that even large location subsidies induce few households to change locations. These findings suggest that disaster-related subsidies to dangerous locations generate substantially smaller economic distortions than would be predicted by spatial equilibrium models with homogeneous agents.
Patrick Kline, University of California at Berkeley and NBER, and Melissa Tartari, Yale University
Kline and Tartari study the impact of the Connecticut Jobs First (JF) experiment on the labor supply and program participation decisions of a sample of welfare applicants and recipients. They develop a model incorporating under-reporting decisions, labor supply constraints, and heterogeneity in welfare stigma, hassle effects, and fixed costs of work. The model rationalizes the large empirical impacts of the JF experiment on the distribution of administrative earnings, despite the absence of bunching at a program-induced notch in agents' budget sets. The researchers show that the model places nonparametric restrictions on experimental impacts that can be used to develop bounds on the magnitude of a variety of allowable intensive and extensive margin responses to reform. They find that, in addition to incentivizing work at the extensive margin, the JF experiment induced a substantial welfare "opt-in" response among women with relatively high earnings potential.
Gary Chamberlain, Harvard University and NBER
Chamberlain studies predictive effects of teachers and schools on test scores in fourth-through-eighth grade and on outcomes later in life, such as college attendance and earnings. These predictive effects predict the fraction of a classroom attending college at age 20, given the test score for a different classroom in the same school with the same teacher, and given the test score for a classroom in the same school with a different teacher. Chamberlain would like to have predictive effects that condition on averages over many classrooms, with and without the same teacher. he sets up a factor model which, under certain assumptions, makes this feasible. He uses administrative school district data in combination with tax data to calculate estimates and do inference.
Gordon Dahl, University of California at San Diego and NBER; Dan-Olof Rooth and Magnus Carlsson, Linnaeus University; and Bjorn Ockert, Institute for Evaluation of Labour Market and Education Policy
How schooling affects cognitive skills is a fundamental question for studies of human capital and labor markets. While scores on cognitive ability tests are positively associated with schooling, it has proven difficult to ascertain whether this relationship is causal. Moreover, the effect of schooling is difficult to separate from the confounding factors of age at test date, relative age within a classroom, season of birth, and cohort effects. Dahl, Rooth, Carlsson, and Ockert use a fundamentally different identification approach than earlier authors. They exploit conditionally random variation in the assigned test date for a battery of cognitive tests which almost all 18-year-old males were required to take in preparation for military service in Sweden. Both age at test date and number of days spent in school vary randomly across individuals, after controlling for date of birth, parish, and expected graduation date. They find that an extra 10 days of school instruction raises cognitive scores on crystallized intelligence tests (synonym and technical comprehension tests) by approximately one percent of a standard deviation, whereas extra non-school days have almost no effect. The benefit of additional school days is homogeneous, with similar effects based on past grades in school, parental education, and father's earnings. In contrast, test scores on fluid intelligence tests (spatial and logic tests) do not increase with additional days of schooling, but do increase modestly with age.
Edward Lazear and Kathryn Shaw, Stanford University and NBER, and Christopher Stanton, University of Utah
Using a company-based data set on the productivity of technology-based services workers, Lazear, Shaw, and Stanton find that supervisor effects are large, and in particular that the choice of boss matters. In fact, there is substantial variation in boss quality as measured by the effect on worker productivity. Replacing a boss who is in the lower 10 percent of boss quality with one who is in the upper 10 percent of boss quality increases a team's total output by about the same amount as adding one worker to a nine-member team. This implies that the average boss is about 1.75 times as productive as the average worker. Second, the boss's primary activity is teaching skills that will persist. Third, efficient assignment allocates the better bosses to the better workers because good bosses increase the productivity of high quality workers by more than that of low quality workers.