Behavioral considerations in a dynamic matching mechanism

Last registered on February 18, 2021

Pre-Trial

Trial Information

General Information

Title
Behavioral considerations in a dynamic matching mechanism
RCT ID
AEARCTR-0007218
Initial registration date
February 18, 2021

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
February 18, 2021, 6:23 AM EST

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

Locations

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Primary Investigator

Affiliation
Université de Lausanne

Other Primary Investigator(s)

PI Affiliation
Université de Lausanne
PI Affiliation
Université de Lausanne

Additional Trial Information

Status
In development
Start date
2021-02-18
End date
2022-09-30
Secondary IDs
Abstract
We examine the role of confidence about academic ranking and mechanism knowledge on application behavior for postsecondary education. Therefore, we conduct a survey experiment with participants of the French college assignment system. Before applicants decide on their final application and before universities start to make their offers, we ask for applicants’ preferred order of universities. To study confidence, we ask for their belief about their relative GPA rank, and the treatment group receives information on their true GPA rank. To study mechanism knowledge, we provide another treatment group with information on the rules of the mechanism. We investigate the effects of information provision on application behavior, as well as predicted and actual placement.
External Link(s)

Registration Citation

Citation
Hakimov, Rustamdjan, Renke Schmacker and Camille Terrier. 2021. "Behavioral considerations in a dynamic matching mechanism." AEA RCT Registry. February 18. https://doi.org/10.1257/rct.7218
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2021-02-18
Intervention End Date
2021-03-12

Primary Outcomes

Primary Outcomes (end points)
We will estimate the impact of the treatment on application behavior as measured by: (1) the prediction of the final assignment, (2) the submitted list on Parcoursup, (3) the final placement, (4) the date of final acceptance, (5) first offer acceptance, and (6) alignment of the submitted list with elicited preferences.
Primary Outcomes (explanation)
(1) Prediction of final assignment: After the intervention, we ask subjects to select their predicted final assignment from the list of trainings that they aim to apply for on Parcoursup in an incentivized way. We measure the rank of the predicted training on the initial rank-order list and selectivity of the predicted training based on the percent of admitted applicants from 2020.
(2) Submitted list on Parcoursup: Using the administrative data, we assess the length of the submitted list (number of applications) and the proportion not exhausting the entire list. Moreover, we measure the selectivity of the submitted list on Parcoursup based on the percent of admitted applicants from 2020.
(3) Final placement: In the administrative data, we measure the rank of the final outcome in the initial survey rank-order list. Moreover, we measure the selectivity of the final placement based on the percent of admitted applicants from 2020.
(4) Date of final acceptance: In the administrative data, we check the date, on which subjects make their ultimate decision for an offer (unconditional and conditional on having pending offers higher on the ROL).
(5) First offer acceptance: In the administrative data, we check how many subjects accept the first offer.
(6) Alignment of the submitted list with elicited preferences: we compare the number of submitted choices to Parcoursup with the list of trainings in the survey.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In this survey experiment, subjects will be randomized into one of four conditions in a 2x2 design: Grade feedback / No feedback x Mechanism information / No information. In the survey, we ask for participants’ rank order list (ROL) of preferred trainings. We randomize subjects into one of the four treatments and elicit their predicted placement in an incentive-compatible way. Using their personal information, we merge their responses to the administrative Parcoursup data to study their actual application behavior.
Experimental Design Details
Not available
Randomization Method
Computerized randomization using Qualtrics.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
We aim for 6,000 individuals but will collect as many respondents as possible during the period, in which the ads are planned (17 February till 11 of March).
Sample size: planned number of observations
We aim for 6,000 individuals but will collect as many respondents as possible during the period, in which the ads are planned (17 February till 11 of March).
Sample size (or number of clusters) by treatment arms
We aim for 1,500 individuals for each of our four treatment arms. We target a rather large sample size as we can only use individuals for the final analysis who we can match to the administrative data.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Based on previous Parcoursup data, we can calculate the following minimum detectable effect sizes (80% power, alpha=0.05). Regarding the days from first offer until final acceptance, we can detect a minimum effect size of 2.1 days. Regarding the percentage of applicants who do not exhaust their application list, we can detect an effect of 5.4 percentage points. Regarding the proportion who accept their first offer, we can detect an effect of 7.2 percentage points. Regarding the outcomes that rely on the elicited preferences, we cannot calculate an effect size.
IRB

Institutional Review Boards (IRBs)

IRB Name
HEC Ethics Committee
IRB Approval Date
2021-01-25
IRB Approval Number
COSTA
Analysis Plan

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