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.

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

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.

This proposal consists of three interconnected projects that study how social networks shape the behavior of 1) scientists, 2) politicians, and 3) citizens by using the structure of networks for identification. The first project investigates how social media networks shape knowledge production in academia. Through a comprehensive dataset of economists and their Twitter activity, the project provides insights into the information acquisition process of scientists and how scientists’ networks impact the direction of science. The project will examine the causal effect of social media usage on research output, collaboration, citation patterns, and careers. Moreover, the project will highlight inefficiencies in the scientific process that arise through scientific fads and herd behavior. The second project will generate some of the first large-scale empirical analyses of the role of congressional staffers in the policy-making process. Based on comprehensive data on congressional staffers, their networks, and the activities of congress members, the project will generate pathbreaking insights into the impact of staffers on congressional voting behavior as well as policy priorities. The project will isolate the effect of staffers using a network-based identification strategy that leverages the educational ties of staffers. In this way, the research project will break new ground in the field of political economy by opening the black box of the policy-making process. The third project focuses on the political effects of social media and the effectiveness of algorithmic interventions by investigating the impact of sharing limits on the social media platform WhatsApp. The project will analyze how this change to information flows on a network affects conflict events, believes, and election results in the world's largest democracy: India. The research project thereby addresses a gap in the field of social media research by shedding light on an often-proposed policy intervention

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.