Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The Effects of High School Peers' Gender on College Major, College Performance and Income Terms of use: Documents in AbstractUsing an originally constructed dataset that follows 30,000 Italian individuals from high school to the labor market, we analyze whether the gender composition of peers in high school affected their choice of college major, their academic performance and their labor market income. We exploit the within-school, cohort-by-cohort variation in the gender composition of high school classmates (peers), after controlling for school and teachers fixed effects. We find that male students graduating from classes with a large majority of male peers were more likely to choose "prevalently male" (PM) college majors (Economics, Business and Engineering). However, this impact was partially undone during college through attrition, worse academic performance and change in major. And in the long run it did not produce any difference in income or labor market outcomes. We do not find significant effects of the high school class gender composition on women. Our results are consistent with the fact that individuals are affected by the choice/pressure of the network of friends and with the observation that network size responds to class gender composition more for men than for women. JEL-Codes: I210, J160, J240, J310, Z130.

We investigate the impact of robot adoption on electoral outcomes in 14 Western European countries, between 1993 and 2016. We employ both official election results at the district level and individual-level voting data, combined with party ideology scores from the Manifesto Project. We measure exposure to automation both at the regional level, based on the ex-ante industry specialization of each region, and at the individual level, based on individual characteristics and pre-sample employment patterns in the region of residence. We instrument robot adoption in each country using the pace of robot adoption in other countries. Higher exposure to robot adoption is found to increase support for nationalist and radical-right parties. Unveiling some potential transmission channels, higher robot exposure at the individual level leads to poorer perceived economic conditions and well-being, lower satisfaction with the government and democracy, and a reduction in perceived political self-efficacy.

Mobility within the European Union (EU) brings great opportunities and large overall benefits. Economically stagnant areas, however, may be deprived of talent through emigration, which may harm dynamism and delay political, and economic, change. A significant episode of emigration took place between 2010 and 2014 from Italy following the deep economic recession beginning in 2008 that hit most acutely countries in the southern EU. This period coincided with significant political change in Italy. Combining administrative data on Italian citizens who reside abroad and data on characteristics of city councils, city mayors and local vote, we analyze whether emigration reduced political change. The sudden emigration wave interacted with the pre-existing networks of emigration from Italian municipalities allow us to construct a proxy for emigration that is municipality-specific and independent of local political and economic trends. Using this proxy as an instrument, we find that municipalities with larger emigration rates had smaller shares of young, college educated and women among local politicians. They were also more likely to have had municipal councils dismissed due to inefficiency or corruption, a larger share of vote for status-quo-supporting parties and lower political participation. Migration was also associated with lower firm creation.

I take advantage of a discontinuity in the probability of admission to a highly selective private university to estimate causal returns to investing in elite university education. I use a newly assembled data set that combines individual administrative records about high school attendance, university admission, university attendance, and tax returns. I find a discontinuity in income of 38 log points at the admission cutoff. The fuzzy regression discontinuity estimate for the elite enrollment effect is 58 log points. This should be interpreted as the average treatment effect for students applying to the elite university who are close to the cutoff and chose to enroll. When I take into account the evidence that students enrolling in the elite university tend to make different field choices, the net institutional enrollment premium is 41 log points. Cumulated over 15 years, the net-of-tuition elite premium is €246,991. I explore potential channels explaining the sizeable enrollment effects and I find that students just above the admission cutoff are 15 percentage points more likely to complete a university degree, they are 26 percentage points more likely to graduate on time and attend university with substantially higher quality peers.

The increasing success of populist and radical-right parties is one of the most remarkable developments in the politics of advanced democracies. We investigate the impact of industrial robot adoption on individual voting behavior in 13 western European countries between 1999 and 2015. We argue for the importance of the distributional consequences triggered by automation, which generates winners and losers also within a given geographic area. Analysis that exploits only cross-regional variation in the incidence of robot adoption might miss important facets of this process. In fact, patterns in individual indicators of economic distress and political dissatisfaction are masked in regional-level analysis, but can be clearly detected by exploiting individual-level variation. We argue that traditional measures of individual exposure to automation based on the current occupation of respondents are potentially contaminated by the consequences of automation itself, due to direct and indirect occupational displacement. We introduce a measure of individual exposure to automation that combines three elements: 1) estimates of occupational probabilities based on employment patterns prevailing in the preautomation historical labor market, 2) occupation-specific automatability scores, and 3) the pace of robot adoption in a given country and year. We find that individuals more exposed to automation tend to display higher support for the radical right. This result is robust to controlling for several other drivers of radical-right support identified by earlier literature: nativism, status threat, cultural traditionalism, and globalization. We also find evidence of significant interplay between automation and these other drivers.

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In this study we analyze whether the gender composition of siblings within a family affects the choice of College Major. The question is whether a family environment that is more gender-homogeneous encourages academic choices that are less gender stereotyped. We use the last name and the exact family address contained in a unique dataset covering 30,000 Italian students graduated from high school between 1985 and 2005 to identify siblings. We follow the academic career of these individuals from high school to college graduation. We find that mixed gender siblings within a family tend to choose college majors following a stereotypical gender specialization. Namely, males have higher probability of choosing "male dominated" majors such as Engineering and women higher probability of choosing "female dominated" majors such as Humanities. Same-gender siblings, on the other hand, have higher probability of making non-gender stereotyped choices. This college major choice is not driven by the choice of high school academic curriculum, which appears to be mainly function of geographical proximity to schools.