Reciprocating Preferences in Matching Markets

Last registered on May 10, 2023

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Pre-Trial

Trial Information

General Information

Title

Reciprocating Preferences in Matching Markets

RCT ID

AEARCTR-0007551

Initial registration date

June 17, 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

June 17, 2021, 2:34 PM EDT

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

Last updated

May 10, 2023, 5:14 PM EDT

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

Locations

Country

Germany

Region

Primary Investigator

Name

Timm Opitz

Affiliation

Max Planck Institute for Innovation and Competetion & MGSE LMU Munich

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Christoph Schwaiger

PI Affiliation

LMU Munich, Department of Economics

Contact Investigator

Additional Trial Information

Status

Completed

Start date

2021-06-17

End date

2022-04-30

Keywords

Other

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

Matching markets intend to form mutually beneficial stable relationships. The stability criterion guarantees that no participant can benefit from breaking up a formed match. Under the deferred acceptance mechanism (DA), matches ought to be stable. This result builds on the assumptions of strict and invariable preference orders under complete information. Empirical evidence on whether preferences actually meet these assumptions is lacking. In a theory-guided laboratory experiment, we test whether agents have reciprocating preferences. We hypothesize that agents prefer to interact with someone who prefers interacting with them. Hence, agents adjust their own preference ranking once they know how other participants ranked them. We document how this affects the stability of the DA, investigate subsequent cooperation behavior in the formed teams and discriminate between belief-based and preference-based explanation for preference changes. This contributes to the literature on the design and robustness of (centralized) matching markets.

External Link(s)

Registration Citation

Citation

Opitz, Timm and Christoph Schwaiger. 2023. "Reciprocating Preferences in Matching Markets." AEA RCT Registry. May 10. https://doi.org/10.1257/rct.7551-2.1

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

We compare behavior under two information structures in a between-subject design In the baseline condition (No-Info), participants never know how their potential (and actual) partners rank them. In the treatment condition (Info), participants do receive the information how they are ranked before submitting their final preference list. This allows them to incorporate this information into their own preferences and gives them the option to adjust behavior in the PGG based on their knowledge of how much their partner wanted to do be matched with them.

Intervention (Hidden)

Intervention Start Date

2021-06-17

Intervention End Date

2022-04-30

Primary Outcomes

Primary Outcomes (end points)

- Stability of the matching outcome [Hypothesis 1 in pre-analysis plan.]
- Behavior in the PGG (unconditional cooperation behavior) [Hypothesis 4 in pre-analysis plan.]

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

- Individual preference changes [Hypothesis 2+3 in pre-analysis plan.]
- Behavior and Beliefs in the PGG (conditional cooperation behavior and beliefs about others' contributions) [Hypothesis 5-7 in pre-analysis plan.]

Secondary Outcomes (explanation)

Experimental Design

The experiment consists of a team-formation process and a PGG that is played within the formed dyads. Teams are formed through a centralized matching mechanism. The underlying preferences of players are based on self-reported questionnaire information of the potential partners. After being matched with one of the potential partners, participants play the PGG with the (known) partner. During the team-formation process, players interact within matching groups. We study a setting of two-sided matching in a one-to-one market. Hence, half of the players within each matching group take the role of proposers, half the role of receivers. Within each experimental session, there will be multiple matching groups, each consisting of 8 participants. To increase statistical power, we reshuffle matching groups 4 times. No proposer will interact with the same receiver twice and vice versa. [Detailed information on the design can be found in the pre-analysis plan.]

Experimental Design Details

Randomization Method

Randomization of (online) laboratory participants by o-Tree (computer).

Randomization Unit

Individual (student).

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

320 students.

Sample size: planned number of observations

320 students. Number of observations depends on outcome (some analysis on the matching group level, other analysis at the individual level + repetitions). [Detailed information on the number of observations for each outcome can be found in the pre-analysis plan.]

Sample size (or number of clusters) by treatment arms

160 students in Info, 160 students in No-Info

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

[Detailed information on the power calculations can be found in the pre-analysis plan.]

Supporting Documents and Materials

IRB