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If individuals become aware of their stereotypes, do they change their behavior? We study this question in the context of teachers’ bias in grading immigrants and native children in middle schools. Teachers give lower grades to immigrant students compared to natives who have the same performance on standardized, blindly-graded tests. We then relate differences in grading to teachers’ stereotypes, elicited through an Implicit Association Test (IAT). We find that math teachers with stronger stereotypes give lower grades to immigrants compared to natives with the same performance. Literature teachers do not differentially grade immigrants based on their own stereotypes. Finally, we share teachers’ own IAT score with them, randomizing the timing of disclosure around the date on which they assign term grades. All teachers informed of their stereotypes before term grading increase grades assigned to immigrants. Revealing stereotypes may be a powerful intervention to decrease discrimination, but it may also induce a reaction from individuals who were not acting in a biased way.

Robots have radically changed the demand for skills and the role of workers in production at an unprecedented pace, with little scope for human capital adjustments. This has affected the job stability and the economic perspectives of large parts of the population in all industrialized countries. Recent evidence on the US labor market has shown negative effects of robots on employment and wages. In this study, we examine how exposure to robots and its consequences on job stability and economic uncertainty have affected individual demographic behavior. To establish this relationship, we use data from the American Community Survey and the International Federation of Robotics and we adopt an empirical strategy that relies on regional industry specialization before the advent of robots combined with the growth of robot adoption by industry. We first document the differential effect of robots on the labor market opportunities of men and women. We find that in regions that were more exposed to robots, the gender-income and labor-force-participation gaps declined. We then show that US regions affected by intense robot penetration experienced a decrease in new marriages, and an increase in both divorce and cohabitation. While there was no change in overall fertility rate, marital fertility declined, and there was an increase in out-of-wedlock births. Our findings are consistent with the hypothesis that the changes in labor markets triggered by robot adoption increased uncertainty, reduced the relative marriage-market value of men, and the willingness to commit for the long term.

Education is a key sociological variable in the explanation of health and health disparities. Conventional wisdom emphasizes a life course-human capital perspective with expectations of causal effects that are quasi-linear, large in magnitude for high levels of educational attainment, and reasonably robust in the face of measured and unmeasured explanatory factors. In this paper, we challenge this wisdom by offering an alternative theoretical account and an empirical investigation organized around the role of measured and unmeasured cognitive and non-cognitive skills as confounders in the association between educational attainment and health. Based on longitudinal data from the National Longitudinal Survey of Youth – 1997 spanning mid adolescence through early adulthood, results indicate that a) effects of educational attainment are very vulnerable to issues of omitted variable bias; b) that measured indicators of cognitive and non-cognitive skills account for a significant proportion of the traditionally observed effect of educational attainment; c) that such skills have effects larger than that of even the highest levels of educational attainment when appropriate controls for unmeasured heterogeneity are incorporated; and d) that models that most stringently control for such time-stable abilities show little evidence of a substantive association between educational attainment and health. Implications for theory and research are discussed.

We present an introduction to the NetLogo simulation environment using the Segregation Model presented by Nobel Prize Winner Thomas Schelling in 1978. While reviewing the model, its Netlogo implementation is described step by step, using visual tools accompanied by the code for programming in the Netlogo language. Two extensions to the original model are proposed and programmed. All the models are fully described in the text.