Stereotypes are often at the origin of biased behaviour and can contribute to the widening of socioeconomic inequalities in diverse societies. This is especially true in the schooling context, where both teachers and students may hold negative stereotypes towards certain groups. The overarching goal of this proposal is to study the formation of stereotypes and test policies designed to mitigate educational inequalities, building on insights from behavioural economics and causal machine learning techniques. The proposed research combines several innovative aspects: (i) cutting edge datasets merging administrative data with newly collected surveys, including psychological measures and incentivized experiments (ii) quasi-natural experiments to shed light on the determinants of stereotypes and biased behaviour, and (iii) randomized controlled trials to test scalable and cost-effective policies. SOFIA is composed of three workpackages (WP), focusing respectively on evidence from Italy, Finland, and Chile. WP1 provides innovative evidence on the role of selective memory in the formation of gender stereotypes for adolescents and teachers (Project A), and evidence on how causal machine learning techniques can be used to mitigate inequalities (Project B). WP2 focuses on the implications of exposure to immigrants on the development of stereotypes and inter-ethnic relationships (Project C) and on how to improve social cohesion through innovative interventions that exploit behavioural insights (Project D). WP3 investigates the role of self-stereotypes in explaining limited access to opportunities in education (Project E). The proposal speaks to the policy debate on how to effectively mitigate discrimination to foster educational achievements of disadvantaged or underrepresented groups. It is my hope that the combination of innovative solutions inspired by behavioural insights and solid evidence generated through credible empirical strategies will help inform this debate

Given the uncertainty shrouding even the most promising research projects, information plays a key role in the organization of science. The project develops foundational tools in the organizational economics oGiven the uncertainty shrouding even the most promising research projects, information plays a key role in the organization of science. The project develops foundational tools in the organizational economics of science, through two inter-linked packages: I. Comparing Information Researchers select questions to work on, choose sites for testing, and trim samples when analysing data. Editors select referees from a pool of potential reviewers. This package develops a general approach for comparing information structures, such as the selected experiments described above, for decision problems with certain properties. Once decision problems are restricted to a class satisfying properties such as monotonicity, more information structures can be compared than in Blackwell’s classic approach. The proposal presents some ideas for developing general characterization results and for applying the new method to the comparison of experiments produced in a social context. II. Supporting Science This package develops a framework for the design of science subsidy schemes with imperfect verification. Researchers have some noisy information about the chances of success of their project and go through a costly selection process. Research proposals are evaluated by reviewers with imperfect expertise. The aim is to characterize the optimal mix of push and pull incentives for financing knowledge creation and aligning researchers incentives with social objectives. A subproject develops a structural methodology for estimating research funding models and applies the method to a unique dataset from the Research Council of Norway (RCN) covering all fields of research and containing detailed information about applicants (whether they are awarded a grant or not) as well as evaluators.

In a recovery problem, we are interested in recovering structure from data that contains a mix of combinatorial structure and random noise. In a robust recovery problem, the data may contain adversarial perturbations as well. A series of recent results in theoretical computer science has led to algorithms based on the convex optimization technique of Semidefinite Programming for several recovery problems motivated by unsupervised machine learning. Can those algorithms be made robust? Sparsifiers are compressed representations of graphs that speed up certain algorithms. The recent proof of the Kadison-Singer conjecture by Marcus, Spielman and Srivastava (MSS) shows that certain kinds of sparsifiers exist, but the proof does not provide an explicit construction. Dynamics and population protocols are simple models of distributed computing that were introduced to study sensor networks and other lightweight distributed systems, and have also been used to model naturally occurring networks. What can and cannot be computed in such models is largely open. We propose an ambitious unifying approach to go beyond the state of the art in these three domains, and provide: robust recovery algorithms for the problems mentioned above; a new connection between sparsifiers and the Szemeredi Regularity Lemma and explicit constructions of the sparsifiers resulting from the MSS work; and an understanding of the ability of simple distributed algorithms to solve community detection problems and to deal with noise and faults. The unification is provided by a common underpinning of spectral methods, random matrix theory, and convex optimization. Such tools are used in technically similar but conceptually very different ways in the three domains. By pursuing these goals together, we will make it more likely that an idea that is natural and simple in one context will translate to an idea that is deep and unexpected in another, increasing the chances of a breakthrough.

In the context of a growing knowledge economy the competitiveness of global cities is crucially affected by their ability to nurture and attract talented workers. In this global race the European Union is lagging behind in comparison with the US and other Anglo-Saxon economies. The diffusion of anti-immigration sentiments is also worrisome because the recovery from the Great Recession of 2008 is far from being in sight for many EU regions while the access to a diverse set of skills via high skilled immigrants could boost the innovative sectors needed for economic growth. Therefore it is important to provide robust evidence on the economic effects of High Skilled Immigration (HSI) in order to justify policies for their attraction. GOTaM cities aims at understanding how talents are attracted to cities and how they impact on their innovative performance and prosperity. While building on existing literature the project will make several contributions by addressing some unexplored questions and empirical shortcomings: a. GOTaM will focus on HSI in different geographical contexts (i.e EU, US, China and Brazil): the existing evidence is biased towards the US which leaves a lack of understanding about HSI in other areas; b. It will use individual data and focus on city/region level effects: most empirical literature relies on aggregate data at country level; c. It will investigate the qualitative effects of migration: whether HIS enhances the technological and economic diversification of cities by bringing new knowledge to those places; d. It will build a unique comprehensive dataset (and related methodologies) on migrant inventors and scientists which will help the scholarly community and policy makers to carry out informed empirical analysis: the findings so far are disputed also because carried out on specific cases or ethnic groups (e.g. Russian mathematicians); The findings of GOTaM will serve as a basis for inspiring new and more effective immigration policies.

The key aim of the LOSS project is to unravel: How economic hardship affects support for socially conservative political agendas aimed at restricting the rights of marginalised groups (ethnic, linguistic and religious minorities, migrants, LGBTQIA+ and women), and how local and national policy contexts affect this relationship. Many European societies have recently experienced growing prejudice towards marginalised groups and the rise in support for far-right parties advocating to restrict the rights of these groups. These developments threaten the cohesion of national and local communities across Europe. While aggregate level evidence suggests that financial crises generally coincide with increased support for far-right parties, we do not understand why this happens. By developing a groundbreaking interdisciplinary theoretical framework that integrates insights about the role of loss from political science, sociology, social psychology and behavioural economics, the LOSS project contends that experiences of economic hardship translate into specific narratives of loss that in turn trigger support for social conservative political agendas. To empirically examine the importance of narratives of loss, the LOSS project employs an innovative multi-method empirical approach combining qualitative and quantitative research methodologies. To uncover the role of context, the project compares five European countries (Italy, Germany, the Netherlands, Sweden, and the United Kingdom) that vary in the level of compensation provided for economic hardship, and local contexts within these countries.