Market exchange and diversity

Last registered on April 08, 2023

View Trial History

Pre-Trial

Trial Information

General Information

Title

Market exchange and diversity

RCT ID

AEARCTR-0010862

Initial registration date

January 30, 2023

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 07, 2023, 11:12 AM EST

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

Last updated

April 08, 2023, 11:31 AM EDT

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

Locations

Country

United States of America

Region

Texas

Primary Investigator

Name

Simon Siegenthaler

Affiliation

University of Texas at Dallas

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Sheen Levine

PI Affiliation

PI Name

Bart Wilson

PI Affiliation

Additional Trial Information

Status

In development

Start date

2023-04-18

End date

2023-12-31

Keywords

Behavior, Gender, Lab

Additional Keywords

Specialization, Market, Competition, Homophily, Race

JEL code(s)

D01

Secondary IDs

Prior work

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

Abstract

Individuals specializing in what they can do best is a central component of economic production. However, people often seek partners among those who are superficially like them (e.g., gender, race). Such preferences for homophily can deprive a population of diversity, resulting in segregation. In a market context, such preferences can harm efficiency by limiting the search for exchange partners. We are interested to study mechanisms that drive lack of diversity in a setting where agents can engage in the most foundational market behavior: they can specialize to generate gains from trade through exchange.

External Link(s)

Registration Citation

Citation

Levine, Sheen, Simon Siegenthaler and Bart Wilson. 2023. "Market exchange and diversity." AEA RCT Registry. April 08. https://doi.org/10.1257/rct.10862-3.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

We are running a lab experiment with a setting where participants can discover how to specialize. We study if market efficiency and specialization differ between groups with different race and gender compositions. We are also interested in market size effects.

Update April 8th 2023:
We have decided to run an online experiment on mTurk and/or Prolific instead. There are two main reasons. First, a more diverse subject pool will allow us to recruit people from different ethnicities into our markets. We initially thought this was possible at the UT Dallas lab, but after examining the subject pool in more detail, we learned that the number of white/Caucasian subjects would be limited. We also plan a larger number of treatments than initially planned. Therefore, a larger online subject pool is beneficial. Second, we believe homophily effects may occur more naturally in a general population setting rather than at the laboratory where all participants are students and are used to interacting across ethnicities.

Intervention Start Date

2023-04-18

Intervention End Date

2023-12-31

Primary Outcomes

Primary Outcomes (end points)

Efficiency
Efficiency in the last 14 periods
Degree and speed of specialization
Number of same-race (or gender) versus across-race (or gender) specialized pairs in Mixed treatment
Dropouts during the experiment conditional on race

Primary Outcomes (explanation)

Efficiency is the percentage of realized gains relative to the most efficient outcome (competitive equilibrium).
Specialization is the shift in time devoted to the good for which a participant has a comparative advantage relative to the autarky outcome.
Specialization across race or gender is the degree to which it happens between individuals of different races/genders.
Dropouts/attrition can be a problem for online experiments. For us, it's a possible outcome variable. There are 4 or 8 people in a market, and if some drop out, it hinders specialization, but the market can continue. Dropouts reflect low anticipated earnings, a dislike for the interactions, etc., or randomness. So, if, on average, white (respectively, Asian) participants are more likely to drop out in Mixed than in Homogenous, this is part of the treatment effect we want to capture.

Secondary Outcomes

Secondary Outcomes (end points)

Chat and communication
Image selection

Secondary Outcomes (explanation)

We will analyze the chat people use to enter trading relationships. Specifically, we will analyze how active and sociable people are, and if the discussions are positive/negative.
Image selection is the photograph people select.

Experimental Design

The experimental design is described in the "hidden" field.

Experimental Design Details

In the Baseline (Homogenous) condition, markets are homogenous (i.e., all males, all females, all South-Asian, all white). In the Mixed condition, markets consist of pairs of similar participants. For example, 2 white females and 2 male South Asians are in a market. Gains from trade are larger across dissimilar people. We also plan to increase the market size to 8 people to study the impact of competition and market size on specialization. We also plan to change the observability of actions, i.e., in some treatments people will not see others' production amounts. Otherwise, markets are identical.

Randomization Method

Participants of the UT Dallas LBOE lab subject pool are invited randomly using the recruitment software SONA.

Update: Participants are recruited via Prolific and/or mTurk.

Randomization Unit

Markets (groups of 4 or 8)

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

25-30 markets per treatment. (updated from 20 as the online environment allows us to collect more data.)

Sample size: planned number of observations

100-120 unique participants per treatment, about 500 subjects.

Sample size (or number of clusters) by treatment arms

100-120 participants per treatment.

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

Wilcoxon-Mann-Whitney test: Efficiency in the last 14 periods: minimum detectable effect size is 0.74, the unit is the proportion relative to competitive equilibrium (between 0 and 1), and the expected standard deviation is 0.23. 50% increase over expected baseline efficiency (from 50% to 70%). Specialization: same. Wilcoxon matched-pair test: Number of same-race versus mixed-race pairs in Mixed treatments: minimum detectable effect size is 0.37, the unit is the number of specialized pairs per market (between 0 and 2), and the expected standard deviation is 0.92 for same-race and 0.74 for mixed.

Supporting Documents and Materials

IRB