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We study the relationship between teachers' stereotypes and students' high school choice in a setting where students can enroll in more or less demanding high school tracks. We show that teachers with negative stereotypes toward immigrants, as measured by an Implicit Association Test (IAT), are more likely to recommend lower-tier tracks to immigrant students, relative to natives with similar ability and socioeconomic background. Implicit stereotypes may thus prevent immigrant students from nurturing and developing their academic talents.

In response to the COVID-19 outbreak, the governments of most countries ordered the closure of schools, potentially exacerbating existing learning gaps. This paper evaluates the effectiveness of an intervention implemented in Italian middle schools that provides free individual tutoring online to disadvantaged students during lock-down. Tutors are university students who volunteer for 3 to 6 hours per week. They were randomly assigned to middle school students, from a list of potential beneficiaries compiled by school principals. Using original survey data collected from students, parents, teachers and tutors, we find that the program substantially increased students' academic performance (by 0.26 SD on average) and that it significantly improved their socio-emotional skills, aspirations, and psychological well-being. Effects are stronger for children from lower socioeconomic status and, in the case of psychological well-being, for immigrant children.

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.

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, the probability of having children, 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.

I study whether exposure to teachers' stereotypes, as measured by the Gender-Science Implicit Association Test, affects student achievement. I provide evidence that the gender gap in math performance substantially increases when students are assigned to teachers with stronger gender stereotypes. Teachers' stereotypes induce girls to underperform in math and self-select into less demanding high-schools, following the track recommendation of their teachers. These effects are at least partially driven by a lower self-confidence on own math ability of girls exposed to gender biased teachers. The findings are consistent with the hypothesis that stereotypes impair the test performance of ability-stigmatized groups, who end up failing to achieve their full potential.

In this paper, we focus on a project aimed at fostering coding and social skills of girls called Girls Code It Better implemented in Italy. We analyze gender gaps in academic interests and perception of barriers to achieve own career goals, as well as how girls applying to the coding clubs differ from those that decide not to apply. First, we show that there are substantial gaps in academic interests since middle school, with girls being less interested in STEM compared to boys despite the higher willingness to attend university. Girls are also more likely to perceive their own gender and their ability as a barrier to achieve their educational goals. Second, we show that girls who self-select into a coding club are different compared to other girls: in our sample, we can rule out a substantial differences in parental education and occupation that may affect take-up, but girls applying to coding clubs have higher interest in pursuing STEM and they are more likely to perceive own gender as a barrier for their educational goals. Programs aimed at increasing girls’ interest in STEM may be effective in closing the gender gap if they manage to “hack” gender stereotypes and perceived barriers of high-achieving girls.