There is an ongoing debate about the economic effects of individualism. We establish that individualism leads to better educational and labor market outcomes. Using data from the largest international adult skill assessment, we identify the effects of individualism by exploiting variation between migrants at the origin country, origin language, and person level. Migrants from more individualistic cultures have higher cognitive skills and larger skill gains over time. They also invest more in their skills over the life-cycle, as they acquire more years of schooling and are more likely to participate in adult education activities. In fact, individualism is more important in explaining adult skill formation than any other cultural trait that has been emphasized in previous literature. In the labor market, more individualistic migrants earn higher wages and are less often unemployed. We show that our results cannot be explained by selective migration or omitted origin-country variables.

The Roy-Borjas model predicts that international migrants are less educated than nonmigrants because the returns to education are generally higher in developing (migrant-sending) than in developed (migrant-receiving) countries. However, empirical evidence often shows the opposite. Using the case of Mexico-U.S. migration, we show that this inconsistency between predictions and empirical evidence can be resolved when the human capital of migrants is assessed using a two-dimensional measure of occupational skills rather than by educational attainment. Thus, focusing on a single skill dimension when investigating migrant selection can lead to misleading conclusions about the underlying economic incentives and behavioral models of migration.

Job training is widely regarded as crucial for protecting workers from automation, yet there is a lack of empirical evidence to support this belief. Using internationally harmonized data from over 90,000 workers across 37 industrialized countries, we construct an individual-level measure of automation risk based on tasks performed at work. Our analysis reveals substantial within-occupation variation in automation risk, overlooked by existing occupation-level measures. To assess whether job training mitigates automation risk, we exploit within-occupation and within-industry variation. Additionally, we employ entropy balancing to re-weight workers without job training based on a rich set of background characteristics, including tested numeracy skills as a proxy for unobserved ability. We find that job training reduces workers’ automation risk by 4.7 percentage points, equivalent to 10 percent of the average automation risk. The training-induced reduction in automation risk accounts for one-fifth of the wage returns to job training. Job training is effective in reducing automation risk and increasing wages across nearly all countries, underscoring the external validity of our findings. Women tend to benefit more from training than men, with the advantage becoming particularly pronounced at older ages.

Job training is widely regarded as crucial for protecting workers from automation, yet there is a lack of empirical evidence to support this belief. Using internationally harmonized data from over 90,000 workers across 37 industrialized countries, we construct an individual-level measure of automation risk based on tasks performed at work. Our analysis reveals substantial within-occupation variation in automation risk, overlooked by existing occupation-level measures. To assess whether job training mitigates automation risk, we exploit within-occupation and within-industry variation. Additionally, we employ entropy balancing to re-weight workers without job training based on a rich set of background characteristics, including tested numeracy skills as a proxy for unobserved ability. We find that job training reduces workers’ automation risk by 4.7 percentage points, equivalent to 10 percent of the average automation risk. The training-induced reduction in automation risk accounts for one-fifth of the wage returns to job training. Job training is effective in reducing automation risk and increasing wages across nearly all countries, underscoring the external validity of our findings. Women tend to benefit more from training than men, with the advantage becoming particularly pronounced at older ages.

We analyze how skill transferability and the local industry mix affect the adjustment costs of workers hit by a trade shock. Using German administrative data and novel measures of economic distance we construct an index of labor market absorptiveness that captures the degree to which workers from a particular industry are able to reallocate into other jobs. Among manufacturing workers, we find that the earnings loss associated with increased import exposure is much higher for those who live in the least absorptive regions. We conclude that the local industry composition plays an important role in the adjustment processes of workers.

We study a mentoring program that aims to improve the labor-market prospects of school-attending adolescents from disadvantaged families by offering them a university-student mentor. Our RCT investigates program effectiveness on three outcome dimensions that are highly predictive of later labor-market success: math grades, patience/social skills, and labor-market orientation. For low-SES adolescents, the mentoring increases a combined index of the outcomes by over half a standard deviation after one year, with significant increases in each dimension. Part of the treatment effect is mediated by establishing mentors as attachment figures who provide guidance for the future. Effects on grades and labor-market orientation, but not on patience/social skills, persist three years after program start. By that time, the mentoring also improves early realizations of school-to-work transitions for low-SES adolescents. The mentoring is not effective for higher-SES adolescents. The results show that substituting lacking family support by other adults can help disadvantaged children at adolescent age.

Establishment closures have lasting negative consequences for the workers displaced from their jobs. We study how these consequences vary with the amount of skill mismatch that workers experience after job displacement. Developing new measures of occupational skill redundancy and skill shortage, we analyze the work histories of individuals in Germany between 1975 and 2010. We estimate difference-in-differences models, using a sample of displaced workers who are matched to statistically similar non-displaced workers. We find that displacements increase the probability of occupation change eleven-fold. Moreover, the magnitude of post-displacement earnings losses strongly depends on the type of skill mismatch that workers experience in such job switches. Whereas skill shortages are associated with relatively quick returns to the earnings trajectories that displaced workers would have experienced absent displacement, skill redundancy sets displaced workers on paths with permanently lower earnings. We show that these differences can be attributed to differences in mismatch after displacement, and not to intrinsic differences between workers making different post-displacement career choices.