How School Matching Designs Affect Inequality: Experimental Evidence

Last registered on February 09, 2023


Trial Information

General Information

How School Matching Designs Affect Inequality: Experimental Evidence
Initial registration date
December 21, 2022

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
January 03, 2023, 4:44 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
February 09, 2023, 4:08 PM EST

Last updated is the most recent time when changes to the trial's registration were published.



Primary Investigator

Düsseldorf Institute for Competition Economics - Heinrich-Heine-University Düsseldorf

Other Primary Investigator(s)

PI Affiliation
Düsseldorf Institute for Competition Economics - Heinrich-Heine-University Düsseldorf

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Education has long-lasting effects in life outcomes of individuals. For some time now, market designers investigate matching mechanisms for school allocation, aiming to enhance fairness and ensure equal opportunities for children. Widely discussed are the Immediate-Acceptance (IA) and the Deferred-Acceptance (DA) mechanism, where the latter one has been agreed upon as more desirable due to its strategy-proofness which makes truthful preference submission a dominant strategy.
However, lab and field investigations have revealed that often applicants do not understand the strategy-proofness of the mechanism. This becomes especially problematic if applicants differ in submission strategies correlating with their socio-economic status (SES). The intended fairness might actually end up in an even higher socio-economic gap in educational outcomes.
In an online experiment, parents matched in groups of 16 with induced preferences over 4 schools have to choose a school ranking. After explaining them either the IA or DA mechanism we ask them to submit an application form for a school choice problem in which they compete over the 16 seats at the 4 schools. We pre-select parents by SES. In an additional treatment variation, we examine whether advice on the submission strategy (‘skip-the-middle school’ in IA and ‘submit the true preference order’ in DA) affects the socio-economic gap in school admissions. To gain a deeper understanding of what drives parents’ choices, we additionally elicit survey measures including, e.g., cognitive ability, beliefs about other parents’ behavior and returns to school quality, or trust in institutions.
External Link(s)

Registration Citation

Hermes, Henning and Sabrina Herzog. 2023. "How School Matching Designs Affect Inequality: Experimental Evidence." AEA RCT Registry. February 09.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Truth-telling rates, skip-the-middle rates
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We employ a between-subject design with four treatments. In each treatment group we sort parents of low and high socio-economic status (SES) in equal proportions.
Experimental Design Details
Subjects face a school choice problem, in which they and 15 other participants are asked to submit an application form to a centralized school admission authority. The problem consists of four schools with four seats each. Subjects are randomly matched in groups with an equal proportion of low and high SES parents in each group of 16. Preferences over schools are induced by the payoffs subjects receive when they get admitted at a school. There are four types of parents with different but correlated preference orderings over schools. We randomly assign two low and two high SES parents to each type.
We have two treatment variations: First, treatments vary by the school choice mechanism that is applied to find the admission of applicants to schools, the manipulable Immediate-Acceptance (IA) mechanism and the strategy-proof Deferred-Acceptance (DA) mechanism. As second treatment variation, we add a strategic advice on the optimal preference submission strategy. In the IA mechanism, we advise to skip-the-middle school in order to get a higher chance at the third-preferred school and not end up in the least preferred school. In the DA mechanism, we advise to submit preferences on the application form in the truly preferred order. Doing this, we aim to explore whether advise is able to help to decrease a socio-economic gap in school admissions, that we expect to find when comparing low and high SES parents in either one of the mechanisms.
Additional to the school choice problem, we ask subjects in all treatments to answer a questionnaire that elicits the following measures: subjects' strategies, loss-aversion, altruism, risk preferences, patience, trust in general and trust in institutions, level-k thinking, beliefs about returns to school quality, malleability of children, importance of school quality and importance of the decision in this study, and some demographics. Before subjects are faced with the school choice problem, we let them do a fluid IQ test (similar to Ravens) to measure cognitive ability. All measures serve to understand what drives parents' decisions on their preference submissions.
Randomization Method
computerized random assignment
Randomization Unit
socio-econcomic status, individual
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
see "Sample size"
Sample size: planned number of observations
960 parents in three treatments
Sample size (or number of clusters) by treatment arms
240 in Deferred-Acceptance without advice; 240 in Deferred-Acceptance with advice; 240 in Immediate-Acceptance without advice; 240 in Immediate-Acceptance with advice (always 120/240 low-SES parents and 120/240 high-SES parents)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
With 960 parents as decision makers, that is, 240 decision makers per treatment with 120 having a low SES and 120 having a high SES, we can detect the following effect sizes at conventional levels of power (two-sided chi^2 test, significance level 5%, power 80%): For truth-telling rates as well as skip-the-middle rates, we can detect effect sizes of around 15-20 percentage points when comparing treatments, socio-economic status and advice variation.

Institutional Review Boards (IRBs)

IRB Name
Gesellschaft für experimentelle Wirtschaftsforschung e.V.
IRB Approval Date
IRB Approval Number
No. k5BydCEu
Analysis Plan

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Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials