Martin Nybom
Jan Stuhler
Mattia Fochesato
Sam Bowles
Linda Wu
Tzu-Ting Yang
Thomas Piketty
Malka Guillot
Jonathan Goupille-Lebret
Bertrand Garbinti
Antoine Bozio
Hakki Yazici
Slavík Ctirad
Kina Özlem
Tilman Graff
Tilman Graff
Yuri Ostrovsky
Martin Munk
Anton Heil
Maitreesh Ghatak
Robin Burgess
Oriana Bandiera
Claire Balboni
Jonna Olsson
Richard Foltyn
Minjie Deng
Iiyana Kuziemko
Elisa Jácome
Juan Pablo Rud
Bridget Hofmann
Sumaiya Rahman
Martin Nybom
Stephen Machin
Hans van Kippersluis
Anne C. Gielen
Espen Bratberg
Jo Blanden
Adrian Adermon
Maximilian Hell
Robert Manduca
Robert Manduca
Marta Morazzoni
Aadesh Gupta
David Wengrow
Damian Phelan
Amanda Dahlstrand
Andrea Guariso
Erika Deserranno
Lukas Hensel
Stefano Caria
Vrinda Mittal
Ararat Gocmen
Clara Martínez-Toledano
Yves Steinebach
Breno Sampaio
Joana Naritomi
Diogo Britto
François Gerard
Filippo Pallotti
Heather Sarsons
Kristóf Madarász
Anna Becker
Lucas Conwell
Michela Carlana
Katja Seim
Joao Granja
Jason Sockin
Todd Schoellman
Paolo Martellini
UCL Policy Lab
Natalia Ramondo
Javier Cravino
Vanessa Alviarez
Hugo Reis
Pedro Carneiro
Raul Santaeulalia-Llopis
Diego Restuccia
Chaoran Chen
Brad J. Hershbein
Claudia Macaluso
Chen Yeh
Xuan Tam
Xin Tang
Marina M. Tavares
Adrian Peralta-Alva
Carlos Carillo-Tudela
Felix Koenig
Joze Sambt
Ronald Lee
James Sefton
David McCarthy
Bledi Taska
Carter Braxton
Alp Simsek
Plamen T. Nenov
Gabriel Chodorow-Reich
Virgiliu Midrigan
Corina Boar
Sauro Mocetti
Guglielmo Barone
Martin Nybom
Jan Stuhler
Mattia Fochesato
Sam Bowles
Linda Wu
Tzu-Ting Yang
Thomas Piketty
Malka Guillot
Jonathan Goupille-Lebret
Bertrand Garbinti
Antoine Bozio
Hakki Yazici
Slavík Ctirad
Kina Özlem
Tilman Graff
Tilman Graff
Yuri Ostrovsky
Martin Munk
Anton Heil
Maitreesh Ghatak
Robin Burgess
Oriana Bandiera
Claire Balboni
Jonna Olsson
Richard Foltyn
Minjie Deng
Iiyana Kuziemko
Elisa Jácome
Juan Pablo Rud
Bridget Hofmann
Sumaiya Rahman
Martin Nybom
Stephen Machin
Hans van Kippersluis
Anne C. Gielen
Espen Bratberg
Jo Blanden
Adrian Adermon
Maximilian Hell
Robert Manduca
Robert Manduca
Marta Morazzoni
Aadesh Gupta
David Wengrow
Damian Phelan
Amanda Dahlstrand
Andrea Guariso
Erika Deserranno
Lukas Hensel
Stefano Caria
Vrinda Mittal
Ararat Gocmen
Clara Martínez-Toledano
Yves Steinebach
Breno Sampaio
Joana Naritomi
Diogo Britto
François Gerard
Filippo Pallotti
Heather Sarsons
Kristóf Madarász
Anna Becker
Lucas Conwell
Michela Carlana
Katja Seim
Joao Granja
Jason Sockin
Todd Schoellman
Paolo Martellini
UCL Policy Lab
Natalia Ramondo
Javier Cravino
Vanessa Alviarez
Hugo Reis
Pedro Carneiro
Raul Santaeulalia-Llopis
Diego Restuccia
Chaoran Chen
Brad J. Hershbein
Claudia Macaluso
Chen Yeh
Xuan Tam
Xin Tang
Marina M. Tavares
Adrian Peralta-Alva
Carlos Carillo-Tudela
Felix Koenig
Joze Sambt
Ronald Lee
James Sefton
David McCarthy
Bledi Taska
Carter Braxton
Alp Simsek
Plamen T. Nenov
Gabriel Chodorow-Reich
Virgiliu Midrigan
Corina Boar
Sauro Mocetti
Guglielmo Barone

Subjective beliefs and inclusion policies: evidence from college admissions

What is this research about and why did you do it?

Research shows that university enrolment from disadvantaged groups can lag despite financial aid, suggesting that preferential admission policies could be a better way to increase the enrolment of disadvantaged students (Chetty et al, 2020). In fact, admission officers and governments around the world are adopting targeted admission policies to reduce inequalities in higher education. But in this paper, we show that information frictions can curb the effectiveness of these policies in bringing disadvantaged talent to college. We studied this topic to understand how to best design policies that can close the gap in university participation between rich and poor students. 


How did you answer this question?

We analysed data from a Randomised Controlled Trial in Chile, where 128 high-schools were randomly assigned to be part of a preferential admission programme or not.  This allowed us to establish the impacts of this policy on university admissions and enrolments. To understand the role of information frictions, we surveyed students  and found that their beliefs about the likelihood of gaining admission are incorrect. We then used economic modelling to understand how these information frictions interacted with policy effectiveness. The model quantified how the biases in subjective beliefs affected investments in admission credentials, and ultimately who gets admitted to college. 


What did you find?

We found that preferential admissions can increase the admission and enrolment rates of disadvantaged students. But information frictions can curb their effectiveness in bringing disadvantaged talent to college. This is because they can lead overconfident but underprepared students to enter college. Providing high-school students with correct information about their university admission chances when introducing preferential admissions can avoid these distortions and lead to a pool of college entrants that is better-prepared.


This Figure shows results from the model simulations. It plots the effect that biases in subjective beliefs about admission chances have on admissions, as a function of students ability, measured by the SIMCE test score. SIMCE is a standardised test taken at baseline (grade 10). The effect of belief biases is obtained by taking the difference between admissions simulated in the baseline scenario in which students have biases in beliefs and in a counterfactual scenario in which students have no biases in their beliefs. The graph shows that, both with and without preferential admissions, belief biases lead low- and middle-ability students to enrol at a higher rate than the rate at which they would enrol if they had correct beliefs, and vice versa for high-ability students. The over-enrollment of low- and middle-ability students is more pronounced under preferential admissions, suggesting that preferential admission can exacerbate the distortions in the composition of college entrants that arise when high-school students are misinformed about their admission chances.

What implications does this have for the research on wealth concentration or economic inequality?

University education can promote intergenerational mobility, one of the most powerful tools to reduce wealth concentration. But university enrolment is severely unequal across socio-economic lines globally. Our study shows that preferential admission policies can play a central role in reducing inequalities, but their design can be improved to better achieve their intended objectives. Eliminating information frictions by, for example, providing individualised information on admission chances into different programs, in contexts where preferential admissions are in place can improve the match between disadvantaged talent and university.


What are the next steps in your agenda?

I want to understand the long-term effects of preferential admissions on the disadvantaged. I will keep collecting data on the students who participated in this Randomised Controlled Trial as they progress through university and to the labour market.

Citation and related literature

This paper can be cited as follows: Tincani, M., Kosse, F. and Miglino, E. (2022) 'Subjective Beliefs and Inclusion Policies: Evidence from College Admissions.' A working paper is available online.

Related literature:


About the authors