Kevin Thom

Migration Experience and Earnings in the Mexican Labor Market

We present a theoretical and empirical analysis of the relationship between U.S. migration experience and earnings in the Mexican labor market. We use our model to analyze the effects of self-selection and endogeneity on OLS estimates of the return to migration experience in the Mexican labor market. Under plausible assumptions, OLS estimates provide a lower bound on the true average return to migration experience among return migrants. Using Mexican Migration Project (MMP) data, we find a return to migration experience of about 2.2 percent per year. Our estimates are robust to the inclusion of proxies for unobserved skill. A comparison with patterns in the 1995 Mexican Population and Dwelling Count suggests that our results are robust across data sets and are driven by a relationship between migration experience and wages, not hours worked. We also explore the plausibility of multiple mechanisms that could explain this relationship. We find the most evidence for the theory that individuals are acquiring occupation-specific work experience in the United States. The return to a year of occupation-specific migration experience is estimated to be as high as 8.7 percent for some occupations.

Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals.

Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11–13% of the variance in educational attainment and 7–10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.Gene discovery and polygenic predictions from a genome-wide association study of educational attainment in 1.1 million individuals.

Temporary Migration and Skill Upgrading: Evidence from Mexican Migrants

This paper explores the extent to which temporary Mexican migrants upgrade their skills while working in the United States. The vast majority of the migration that we observe is undertaken without documents. In contrast to Lacuesta (2006), we find that labor market performance in Mexico is positively related to one’s accumulated migration experience in the United States. Self-selection of high-skilled individuals into migration does not drive this result. We also investigate the possible mechanisms by which migration experience might improve earnings in Mexico. We find support for the notion that migration experience improves labor market outcomes by improving occupation specific skills rather than by inducing higher rates of occupational mobility or entrepreneurship.

Childhood socioeconomic status moderates genetic predisposition for peak smoking

Smoking is the leading cause of preventable disease and death in the U.S., and it is strongly influenced both by genetic predisposition and childhood socioeconomic status (SES). Using genetic variants exhibiting credible and robust associations with smoking, we construct polygenic risk scores (PGS) and evaluate whether childhood SES mediates genetic risk in determining peak-cigarette consumption in adulthood. We find a substantial protective effect of childhood SES for those genetically at risk of smoking: adult smokers who grew up in high-SES households tend to smoke roughly the same amount of cigarettesper day at peak (∼ 23 for low and ∼ 25 for high genetic risk individuals, or about 8%more), while individuals from low-SES backgrounds tend to smoke substantially more ifgenetically at risk (∼ 25 for low and ∼ 32 for high genetic risk individuals, or about 28% more).