Reciprocating Preferences in Matching Markets
Last registered on June 17, 2021


Trial Information
General Information
Reciprocating Preferences in Matching Markets
Initial registration date
June 17, 2021
Last updated
June 17, 2021 2:34 PM EDT
Primary Investigator
Max Planck Institute for Innovation and Competetion & MGSE LMU Munich
Other Primary Investigator(s)
PI Affiliation
LMU Munich, Department of Economics
Additional Trial Information
In development
Start date
End date
Secondary IDs
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
Opitz, Timm and Christoph Schwaiger. 2021. "Reciprocating Preferences in Matching Markets." AEA RCT Registry. June 17.
Experimental Details
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 Start Date
Intervention End Date
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
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.]
IRB Name
Ethics Committee -Department Economics LMU Munich
IRB Approval Date
IRB Approval Number
Analysis Plan

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