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Decentralized Matching with Transfers: Experimental and Noncooperative Analyses
Last registered on February 24, 2020

Pre-Trial

Trial Information
General Information
Title
Decentralized Matching with Transfers: Experimental and Noncooperative Analyses
RCT ID
AEARCTR-0005488
Initial registration date
February 21, 2020
Last updated
February 24, 2020 10:11 AM EST
Location(s)
Region
Primary Investigator
Affiliation
University of Oregon
Other Primary Investigator(s)
PI Affiliation
Shanghai University of Finance and Economics
PI Affiliation
Michigan State University
Additional Trial Information
Status
On going
Start date
2019-05-14
End date
2021-06-30
Secondary IDs
Abstract
We conduct one of the first laboratory experiments and noncooperative analyses of the decentralized matching market with transfers (Koopmans and Beckmann, 1957; Shapley and Shubik, 1972; Becker, 1973). Some theoretical predictions align with but some differ from experimental evidence. Stable matching, which coincides with efficient matching in this setting, is the most frequent outcome. Theoretically neither factor should matter, but experimentally whether equal split is in the core and whether efficient matching is assortative determine the rate of matching, efficient matching, and surplus achieved. We also study the bargaining process and categorize the reasons why participants end up unmatched. Finally and most interestingly, for the matched ones, experimental payoffs coincide with the equilibrium payoffs of a multiplayer extension of Rubinstein (1982) bargaining model, providing the cooperative game with a noncooperative foundation.
External Link(s)
Registration Citation
Citation
He, Simin , JIABIN WU and Hanzhe Zhang. 2020. "Decentralized Matching with Transfers: Experimental and Noncooperative Analyses." AEA RCT Registry. February 24. https://doi.org/10.1257/rct.5488-1.0.
Experimental Details
Interventions
Intervention(s)
In our matching experiment, the main intervention is the features of the surplus configurations. In total we have four different configurations. The configurations differ in two ways: the “assortativity level” and “whether equal split is in the core”. Surplus configuration is either assortative or non-assortative, and equal split is either in the core or not. This gives in total four (two by two) different types of configurations. We call them positive assortative (assortative, equal split in the core), negative assortative (assortative, equal split not in the core), mixed equal (non-assortative, equal split in the core) and mixed nonequal (non-assortative, equal split not in the core), respectively.

The secondary intervention is the balance of the matching market. We have in total two balance conditions: The balanced one has 3 subjects on each side of the market; the non-balanced one has 3 versus 4 subjects on each side of the market. For both the balanced and non-balanced markets, we perform the main intervention within the balance type.
Intervention Start Date
2019-05-14
Intervention End Date
2021-06-30
Primary Outcomes
Primary Outcomes (end points)
There are three main outcomes variables: 1. At market level, the rate of matching; 2. At market level, the rate of stable matching; 3. At market level, the rate of surplus achieved. 4. At individual level, the payoff gained if matched.
Primary Outcomes (explanation)
1. The rate of matching equals to the number of matched pairs divided by the number of maximal matched pairs (3 pairs in all cases); 2. The rate of stable matching equals to the number of stable matched pairs divided by the number of maximal matched pairs (3 pairs in all cases); 3. The rate of surplus achieved equals to the surplus achieved from the matched pairs divided by the total maximal surplus. 4. The payoffs of two individuals in a matched pair are determined by the division of surplus proposed and accepted.
Secondary Outcomes
Secondary Outcomes (end points)
1. Number of proposals 2. Detailed information of all the proposals by individual and matching group level, including the time a proposal is made/rejected/accepted; the proposed division of surplus of each proposal; and whether it is rejected/accepted by which player.
Secondary Outcomes (explanation)
1. At each market, the number of proposals indicates how active the markets are. 2. The detailed information of all the proposals enables us to construct different measures to understand the market behavior.
Experimental Design
Experimental Design
In our experimental design, we use four different surplus configurations. Each surplus configuration represents a different matching market. We vary the configurations in two dimensions: (1) whether the stable matching pattern is assortative, and (2) whether equal split is in the core. We also design the surpluses in a way that the maximum total surplus that all agents can obtain is 200, the average total surplus that all agents can obtain is 180 if they are all matched randomly, and the minimum total surplus they can obtain is 160, which is constant across all four markets. Maximum total surplus is obtained only under stable matching. Hence, the rate of stable matching serves as a measure of efficiency.

We employ a within-subject treatment design. All the subjects play the four different matching markets (surplus configurations), but they play them in different orders. According to the Latin square method, we have in total four different treatment, which differ in the order of markets played by the subjects. The four treatments orders are described below.

1. Positive assortative, negative assortative, mixed equal, mixed nonequal.
2. mixed nonequal, positive assortative, negative assortative, mixed equal.
3. Mixed equal, mixed nonequal, positive assortative, negative assortative.
4. Negative assortative, mixed equal, mixed nonequal, positive assortative.
Experimental Design Details
Not available
Randomization Method
Within each experiment session, multiple treatment orders are implemented by randomization; the randomization is pre-determined.

Subjects who sign up for the experiment receive a seat number randomly before entering the laboratory; the seat number determines the order of the four payoff configurations subjects will experience.
Randomization Unit
Individual-level randomization
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
We are aiming to collect about 36 subjects in each of the 4 treatment orders for the balanced markets, and 42 subjects for each of the 4 treatment orders for the non-balanced markets.
Sample size: planned number of observations
About 300 individuals, recruited via the subject pool of the Economic Lab of the Shanghai University of Finance and Economics.
Sample size (or number of clusters) by treatment arms
6-7 matching markets (36-42 subjects) per treatment arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Michigan State University
IRB Approval Date
2019-04-18
IRB Approval Number
STUDY00002261
IRB Name
University of Oregon
IRB Approval Date
2019-04-16
IRB Approval Number
03282019.024