Internal labor markets are increasingly important for matching workers to jobs within organizations. We present evidence from a randomized trial that compares matching workers to jobs using the deferred acceptance (DA) algorithm to the traditional manager‐directed matching process. Our setting is the U.S. Army's internal labor market, which matches over 14,000 officers to units annually. We find that DA reduces administrative burden and increases match quality as measured by reduced justified envy, increased truthful preference reporting, and officers' and units' preferences over their matches. The overall impact of DA on officer retention and performance in the two years after officers started their new jobs is limited by strategic preference coordination between officers and units. However, DA leads to significant improvements in officer retention and promotions in markets with inexperienced managers. Our findings suggest that cross‐market communication between agents in internal labor markets can attenuate the benefits of strategyproof matching algorithms.

Abstract This paper reports the results of two randomized field experiments, each offering different populations of Chicago youth a supported summer job. The program consistently reduces violent-crime arrests, even after the summer, without improving employment, schooling, or other arrests; if anything, property crime increases over two to three years. Using a new machine learning method, we uncover heterogeneity in employment impacts that standard methods would miss, describe who benefits, and leverage the heterogeneity to explore mechanisms. We conclude that brief youth employment programs can generate important behavioral change, but for different outcomes, youth, and reasons than those most often considered in the literature.

To estimate treatment heterogeneity in two randomized controlled trials of a youth summer jobs program, we implement Wager and Athey's (2015) causal forest algorithm. We provide a step-by-step explanation targeted at applied researchers of how the algorithm predicts treatment effects based on observables. We then explore how useful the predicted heterogeneity is in practice by testing whether youth with larger predicted treatment effects actually respond more in a hold-out sample. Our application highlights some limitations of the causal forest, but it also suggests that the method can identify treatment heterogeneity for some outcomes that more standard interaction approaches would have missed.

Abstract Relative intergenerational mobility declined for cohorts born around 1960 compared to those born around 1950. The former entered the labor market after the rise in inequality around 1980, while the latter entered the labor market earlier. We show that the rank-rank slope rose from 0.25 to 0.36 and the intergenerational elasticity increased from 0.28 to 0.45. These increases are more pronounced for men than for women. Increases in returns to schooling and in the gradient in the likelihood of marriage by parent income are contributors to increased intergenerational persistence.

Improving academic outcomes for economically disadvantaged students has proven challenging, particularly for children at older ages. We present two large-scale randomized controlled trials of a high-dosage tutoring program delivered to secondary school students in Chicago. One innovation is to use paraprofessional tutors to hold down cost, thereby increasing scalability. Participating in math tutoring increases math test scores by 0.18 to 0.40 standard deviations, and increases math and nonmath course grades. These effects persist into future years. The data are consistent with increased personalization of instruction as a mechanism. The benefit-cost ratio is comparable to many successful early childhood programs. (JEL H75, I21, I24, I26, I32, J13, J15)