Increasing consumer surplus through a novel product testing mechanism

Last registered on December 07, 2020

View Trial History

Pre-Trial

Trial Information

General Information

Title

How a novel mechanism for product testing organizations improves markets with asymmetric information

RCT ID

AEARCTR-0006685

First published

October 30, 2020, 9:05 AM EDT

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

Last updated

December 07, 2020, 9:44 AM EST

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

Locations

Country

Germany

Region

Primary Investigator

Name

Ulrike Vollstaedt

Affiliation

University of Michigan

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

Completed

Start date

2017-01-10

End date

2020-11-24

Keywords

Behavior, Firms & Productivity, Lab

Additional Keywords

asymmetric information, product quality, product test, information disclosure, unraveling, mechanism design, experiment

JEL code(s)

C72, C91, D82, L15

Secondary IDs

Abstract

Sellers are often better informed about product quality than buyers. Product testing organizations like Consumer Reports (US) or Stiftung Warentest (Germany) can reduce this asymmetry by providing credible information. Due to limited capacities, however, the sample of tested product models – often bestsellers – may lead to suboptimal information. We propose a novel mechanism, and develop a game to derive testable predictions. We show theoretically that a unique Nash equilibrium exists in which our mechanism leads to optimal information, thus to the consumer surplus of a world of complete information, while selecting bestsellers does not. Subsequently, we confirm experimentally that our new mechanism increases consumer surplus.

External Link(s)

Registration Citation

Citation

Vollstaedt, Ulrike. 2020. "How a novel mechanism for product testing organizations improves markets with asymmetric information." AEA RCT Registry. December 07. https://doi.org/10.1257/rct.6685-2.0

Former Citation

Vollstaedt, Ulrike. 2020. "How a novel mechanism for product testing organizations improves markets with asymmetric information." AEA RCT Registry. December 07. https://www.socialscienceregistry.org/trials/6685/history/81045

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2017-01-10

Intervention End Date

2018-12-14

Primary Outcomes

Primary Outcomes (end points)

consumer surplus

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

We design four experimental treatments. The first two represent currently used product model selection mechanisms (called BESTSELLERS), the latter two represent our new product model selection mechanism (called SELLERS APPLY).

Experimental Design Details

BESTSELLERS-WORST CASE: To model a scenario in which the market functions extremely poorly, we include the worst-case-scenario regarding the bestselling product models, i.e., the bestsellers are chosen to be the product models farthest away from the globally non-dominated ones.

BESTSELLERS-RANDOM: We add an intermediate scenario regarding the bestselling product models. More specifically, bestsellers are chosen randomly among all product models. We include this treatment to investigate whether our new mechanisms outperforms chance.

SELLERS APPLY-LYING POSS: We include this treatment to provide sellers with the option of stating false qualities towards the product testing organization when applying to be tested, thus the name LYING POSS(ible).

SELLERS APPLY-TRUTH: We include this treatment to investigate an ideal setting for our new mechanism in which sellers can apply to be tested while technically not being able to state false qualities towards the product testing organization.

Randomization Method

randomization done by a computer

Randomization Unit

experimental session as one level of randomization (regarding which treatment), individual as a second level of randomization (regarding whether an a person participates as a seller or buyer, and with which ID)

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

25 experimental sessions

Sample size: planned number of observations

575 participants

Sample size (or number of clusters) by treatment arms

Bestsellers-WorstCase: 5 experimental sessions, Bestsellers-Random: 5 experimental sessions, SellersApply-LyingPoss: 5 experimental sessions, SellersApply-Truth: 10 experimental sessions

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

Supporting Documents and Materials

IRB