Summary
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
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
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101117537 |
| Start date: | 01-07-2024 |
| End date: | 30-06-2029 |
| Total budget - Public funding: | 1 499 875,00 Euro - 1 499 875,00 Euro |
Cordis data
Original description
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
Status
SIGNEDCall topic
ERC-2023-STGUpdate Date
22-11-2024
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