Simone Balestra
Last Name
Balestra
First name
Simone
Email
simone.balestra@unisg.ch
Phone
+41 71 224 2318
10 results
Now showing 1 - 10 of 10
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Item type:Publication, High-ability influencers? The heterogeneous effects of gifted classmatesType:journal articleJournal:The Journal of Human ResourcesScopus© Citations 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Peers with special needs: Effects and policiesType:journal articleJournal:Review of Economics and StatisticsScopus© Citations 23 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Summer-born struggle: The effect of school starting age on health, education, and workType:journal articleJournal:Health EconomicsVolume:29Issue:5DOI:10.1002/hec.4005Scopus© Citations 18 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The impact of high school exit exams on graduation rates and achievement(2018) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The development of non-cognitive skills in adolescence(2018); Backes-Gellner, UschiType:journal articleJournal:Economics LettersVolume:163 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Heterogeneous returns to education over the wage distribution: Who profits the most?(2017); Backes-Gellner, UschiType:journal articleJournal:Labour EconomicsVolume:44 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, When a door closes, a window opens? Long‐term labor market effects of involuntary separations(2017); Backes-Gellner, UschiType:journal articleJournal:German Economic ReviewVolume:18Issue:1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Earth is Not Flat: A New World of High-Dimensional Peer Effects(2022); The majority of recent peer-effect studies in education have focused on the effect of one particular type of peers on classmates. This view fails to take into account the reality that peer effects are heterogeneous for students with different characteristics, and that there are at least as many peer effect functions as there are types of peers. In this paper, we develop a general empirical framework that accounts for systematic interactions between peer types and nonlinearities of peer effects. We use machine-learning methods to (i) understand which dimensions of peer characteristics are the most predictive of academic success,(ii) estimate high-dimensional peer effects functions, and (iii) investigate performance-improving classroom allocation through policy-relevant simulations. First, we find that students’ own characteristics are the most predictive of academic success, and that the most predictive peer effects are generated by students with special needs, low-achieving students, and male students. Second, we show that peer effects traditionally reported by the literature likely miss important nonlinearities in the distribution of peer proportions. Third, we determine that classroom compositions that are the most balanced in students’ characteristics are the best ways to reach maximal aggregated school performance.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Misclassification in linear-in-means models: Theory and application to peer effects estimationType:ArticleJournal:Labour EconomicsVolume:102