As super-diversity becomes an integral part of the modern youth's everyday reality, it is of vital importance for them to learn how to embrace differences, appreciate and value ‘otherness’, and develop positive relations with people of diverse backgrounds. Although the literature suggests that adolescence is an especially crucial period for the development of inter-ethnic attitudes, we still lack a clear understanding of how youth change their inter-ethnic attitudes across this developmental stage and which factors contribute to these changes. To address this important gap in knowledge and go beyond the state of the art, this project will analyze whether youth follow different developmental trajectories of inter-ethnic attitudes across adolescence and provide a comprehensive understanding of the role of school context in shaping these trajectories. The focus of this ambitious project is on attitudes in general, rather than only prejudice, since it will not only find ways to prevent negative outgroup dispositions, but also identify factors that might place youth on positive attitudinal trajectories during this critical developmental period. The project will utilize a mixed-method research design integrating quantitative (i.e., longitudinal large-scale panel data) and qualitative (i.e., semi-structured interviews and citizen science) approaches. The project will not limit its analysis to ethnic majority youth but will also focus on adolescents of various minority backgrounds (including newly arrived Ukrainian refugee adolescents) across different national contexts. New knowledge in this area will make a unique contribution to the theory advancement as well as provide an avenue for developing more personalized and effective programs and interventions aimed to facilitate positive inter-ethnic interactions among youth. The host is the perfect match for the successful realization of the project and provides a highly appropriate environment for the fellow's career growth.

Over the past two decades, regression discontinuity (RD) designs have become one of empirical economics' most popular strategies for estimating causal effects from observational data. In such designs, units are assigned to the treatment group if and only if a special covariate, or running variable, falls above a known cutoff value. Under mild conditions, those units close to the cutoff are as good as randomly assigned to receive the treatment, which provides a simple and transparent source of identification of the treatment's causal effect. This project extends the range of methodological tools available to applied researchers working with data from RD designs. It is divided into three parts. The first part develops methods for incorporating covariates and group structures into the analysis of RD designs by adapting modern machine learning methods and empirical Bayes approaches. The second part considers RD designs with a discrete running variable. It shows that current state-of-the-art inference procedures are likely to be misleading in such settings, and develops new confidence intervals for causal effects. The third part develops methods for estimation and inference that account for manipulation in RD designs. Here manipulation refers to any strategic action taken by the actors within the respective institutional context that leads to observational units on different sides of the cutoff being non-comparable. The part develops a general framework for manipulation with corresponding nonparametric methods for estimation and inference, and considers various extensions. Given the huge popularity of RD designs, and the proposal's focus on practical methods, this project has the potential to have a sizable impact on empirical economic research in a number of policy relevant areas, including education and public finance; but also on other branches of science where researchers commonly work with observational data, such as sociology or epidemiology.