“Stereotypes and Self-Stereotypes: Evidence from Teachers’ Gender Bias” – Job Market Paper – Winner of the Best JM Paper Award (UniCredit & Universities – 4th Edition), the 2017 Tarantelli Young Labor Economist Award and the 2017 Best Presentation Award (Unicredit & Universities)
I study the impact of exposure to gender-biased teachers on student achievement and self-confidence. The gender gap in math performance substantially increases when students are quasi-randomly assigned to teachers with stronger stereotypes (as measured by an implicit association test). The effect is driven by lower performance of female students, while there is no impact on males. Those lagging behind the most when assigned to biased teachers are girls from disadvantaged backgrounds and with lower initial level of achievements. Teacher bias induces females to self-select into less demanding high-schools, following the track recommendation of their teachers. Finally, teacher bias has a substantial negative impact on females’ assessment of their own math ability. The paper exploits a unique dataset that I have built combining administrative information on pupils with newly collected questionnaire on students and more than 1.400 teacher. These findings are consistent with the hypothesis that ability-stigmatized groups underperform and fail to achieve their potential.
The appendix is available here.
We study the educational choices of children of immigrants in a tracked school system. We first show that immigrant boys in Italy enroll disproportionately into vocational high schools, as opposed to technical and academically-oriented high schools, compared to natives of similar ability. Immigrant girls, instead, choose similar schools as native ones. We then estimate the impact of a large-scale, randomized intervention providing tutoring and career counseling to high-ability immigrant students. Male treated students increase their probability of enrolling into the high track to the same level of natives, also closing the gap in terms of grade retention. There are no significant effects on immigrant females, who exhibit similar choices and performance as native ones in absence of the intervention. Increases in academic motivation and the resulting changes in teachers’ recommendation regarding high school choice explain a sizable portion of the effect, while the effect of increases in cognitive skills is negligible. Finally, we find positive spillovers on immigrant classmates of treated students, while there is no effect on native classmates.
In this paper, we study the effects of immigration on natives’ marriage, fertility, and family formation across US cities between 1910 and 1930. Instrumenting immigrants’ location decision by interacting pre-existing ethnic settlements with aggregate migration flows, we find that immigration raised marriage rates, fertility, and the propensity to leave the parental house for young native men and women. We show that these effects were driven by the large and positive impact of immigration on native men’s employment and occupational standing, which increased the supply of “marriageable men”. We also explore alternative mechanisms – changes in sex ratios, natives’ cultural responses, and displacement effects of immigrants on female employment – and provide evidence that none of them can account for a quantitatively relevant fraction of our results.
The appendix is available here.
Work in Progress
Can making people aware of their own biases affect their behavior? We study this question in the context of race bias of teachers. We administered the implicit association test (IAT) to a sample of teachers and randomly assigned them to receive feedback on their IAT score before or after term grading. First, we document racial bias in teachers’ grading: when we compare blind and non-blind tests, teach- ers with stronger race bias give lower grades to their immigrant students compared to natives. Second, we will exploit the randomization of the timing of the feedback. Our prediction is that if teachers are unaware of their stereotypes, those who learn that they are negatively biased before term grading will adjust their grades toward immigrants upward compared to those who learn it after. We are waiting for administrative data to complete this analysis.
Women are underrepresented in highly profitable fields as science, technology, engineering, and math (STEM). Most of the gender gap in these fields is due to STEM readiness based on high-school track choice (Card and Payne, 2017). From the school year 2017-18, we offer a course on new technologies (such as 3D printing, web design, and robotics) to a random sample of 700 girls among those who applied in 35 Italian middle schools. The main purpose of this project is to improve access to opportunities for girls to pursue training in these fields. We are collecting baseline information on all students in the schools involved in the project, including perception of stereotypes and friendship networks. At the end of the school year, we will collect endline survey and administrative information on high-school track choice, math performance, and the role of gender stereotypes in influencing individual choices.