We use cookies to provide you with the best possible website experience. This includes cookies that are necessary for the operation of the site, as well as cookies used for anonymous statistics, comfort settings, or displaying personalized content. You can decide which categories you want to allow. Please note that depending on your settings, some features of the website may not be available.

Cookie settings

These necessary cookies are required to enable the core functionality of the website. Opting out of these cookies is not possible.

cb-enable
This cookie stores the user's cookie consent status for the current domain. Expiry: 1 year.
laravel_session
Stores the session ID to recognize the user when the page reloads and to restore their login session. Expiry: 2 hours.
XSRF-TOKEN
Provides CSRF protection for forms. Expiry: 2 hours.
IZA Discussion Paper No. 17751
March 2025
Seeing Stereotypes
Elisa Baldazzi, Pietro Biroli, Marina Della Giusta, Florent Dubois

Reliance on stereotypes is a persistent feature of human decision-making and has been extensively documented in educational setting, where it can shape students' confidence, performance, and long-term human capital accumulation. While effective techniques exist to mitigate these negative effects, a crucial first step is to establish whether teachers can recognize stereotypes in their environment. We introduce the Stereotype Identification Test (SIT), a novel survey tool that asks teachers to evaluate and comment on the presence of stereotypes in images randomly drawn from school textbooks. Their responses are systematically linked to established measures of implicit bias (Implicit Association Test, IAT) and explicit bias (survey scales on teaching stereotypes and social values). Our findings demonstrate that the SIT is a valid and reliable measure of stereotype recognition. Teachers' ability to recognize stereotypes is linked to trainable traits such as implicit bias awareness and inclusive teaching practices. Moreover, providing personalized feedback on implicit bias improves SIT scores by 0.25 standard deviations, reinforcing the idea that stereotype recognition is malleable and can be enhanced through targeted interventions.

Communications
Mark Fallak
mark.fallak@liser.lu
+352 585-855-526
World of Labour
Olga Nottmeyer
olga.nottmeyer@liser.lu
+352 585-855-501
Network Coordination
Christina Gathmann
christina.gathmann@liser.lu

The IZA@LISER Network is a global community of scholars dedicated to excellence in labor economics and related fields, now coordinated at the Luxembourg Institute of Socio-Economic Research (LISER) following its transition from Bonn.

About IZA@LISER Network
Contact
IZA Network (Current Site Operator):

Luxembourg Institute of Socio-Economic Research (LISER)
11, Porte des Sciences
Maison des Sciences Humaines
L-4366 Esch-sur-Alzette / Belval, Luxembourg

IZA Institute (In Liquidation):

Forschungsinstitut zur Zukunft der Arbeit GmbH i. L.
Schaumburg-Lippe-Str. 5-9, 53113 Bonn. Germany
Phone: +49 228 3894-0 | Fax: +49 228 3894-510
E-Mail: info@iza.org | Web: www.iza.org
Represented by: Martin T. Clemens (Liquidator)