When Product Markets Become Collective Traps: The Case of Social Media

Last registered on June 16, 2025
View Trial History

Pre-Trial

Trial Information

General Information

Title
When Product Markets Become Collective Traps: The Case of Social Media
RCT ID
AEARCTR-0016196
Initial registration date
June 10, 2025

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 16, 2025, 12:08 PM EDT

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

Locations

Country
United States of America
Region

Primary Investigator

Name
Leonardo Bursztyn
Affiliation
University of Chicago
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Christopher Roth
PI Affiliation
University of Cologne, Max Planck Institute for Research on Collective Goods, CESifo, NHH, and CEPR
Contact Investigator
PI Name
Benjamin Handel
PI Affiliation
University of California Berkeley and NBER
Contact Investigator
PI Name
Rafael Jiménez-Durán
PI Affiliation
Bocconi University, IGIER, Stigler Center, and CESifo
Contact Investigator

Additional Trial Information

Status
Completed
Start date
2023-07-06
End date
2024-04-10
Keywords
Welfare
Additional Keywords
JEL code(s)
D83, D91, P16, J15
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
Individuals might experience negative utility from not consuming a popular product. With such externalities to non-users, standard consumer surplus measures, which take aggregate consumption as given, fail to appropriately capture consumer welfare. We propose an approach to account for these externalities and apply it to estimate consumer welfare from two social media platforms: TikTok and Instagram. Incentivized experiments with college students indicate positive welfare based on the standard measure, but negative welfare when accounting for these non-user externalities. Our findings highlight the existence of product market traps, where active users of a platform prefer it not to exist.
External Link(s)

Registration Citation

Citation
Bursztyn, Leonardo et al. 2025. "When Product Markets Become Collective Traps: The Case of Social Media." AEA RCT Registry. June 16. https://doi.org/10.1257/rct.16196-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We conducted three pre-registered studies that can be found on AsPredicted (#137878, #142247, and #144630).

For AsPredicted #142247:
Treatment Arm A (Instagram): Eliciting WTA to deactivate Instagram for four weeks.
Treatment Arm B (Maps): Identical elicitation for navigation apps (Google Maps, Apple Maps, Waze)

For AsPredicted #137878:
All respondents complete the same WTA elicitation survey for hypothetical TikTok deactivation scenarios.

For AsPredicted #144630:
Single‐wave survey presenting three binary choice questions about luxury goods and iPhone release schedules.
Intervention (Hidden)
For AsPredicted #142247:
Random selection of eligible students from each arm asked to actually deactivate the assigned app(s) for four weeks.

For AsPredicted #137878:
Random selection to actually deactivate TikTok for four weeks.

For AsPredicted #144630:
None.
Intervention Start Date
2023-07-06
Intervention End Date
2024-04-10

Primary Outcomes

Primary Outcomes (end points)
For AsPredicted #142247:
Individual consumer surplus (WTA to deactivate)
Collective consumer surplus (WTA to deactivate all participants’ apps/accounts)

For AsPredicted #137878:
Individual‐level welfare (WTA to deactivate own TikTok)
Aggregate welfare (WTA to deactivate all participants’ TikToks)

For AsPredicted #144630:
No-luxury world (dummy=1 if respondent prefers a world without any luxury products)
Biennial iPhone release (dummy=1 if prefers new iPhone every other year vs. annually)
No-iPhone world (dummy=1 if prefers a world without any iPhones)
Primary Outcomes (explanation)
For AsPredicted #142247:
Midpoints of MPL ranges (Allcott & Kessler 2019); alternative triangulation variants.

For AsPredicted #137878:
Midpoints of MPL ranges per Allcott & Kessler (2019); alternative endpoint distributions.

For AsPredicted #144630:
Dummy variables.

Secondary Outcomes

Secondary Outcomes (end points)
For AsPredicted #142247:
Individual-alone surplus (WTA if everyone else also deactivates)
Fraction of respondents in “coordination problem” (positive individual, negative collective) and “prisoner’s dilemma” (coordination + positive individual-alone).
Inverse demand curves from MPL; ranking preferences across deactivation scenarios.

For AsPredicted #137878:
WTA if all others deactivate (individual-alone welfare)
Fraction with positive WTA in “all-deactivate” scenario

For AsPredicted #144630:
None.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
For AsPredicted #142247:
Two‐arm hypothetical‐valuation survey. Primary analysis: one-sided t-tests comparing individual vs. aggregate surplus, and aggregate vs. 0.

For AsPredicted #137878:
Single‐arm hypothetical‐valuation survey. Primary analysis: one-sided t-tests comparing individual vs. aggregate surplus, and aggregate vs. 0.

For AsPredicted #144630:
Cross‐sectional descriptive survey of preferences; no random assignment.
Experimental Design Details
Randomization Method
For AsPredicted #142247:
Respondents are randomized into one of two MPL-elicitation surveys on Qualtrics: either they are randomized into treatment arm A (MPL-elicitation for Instagram) or B (MPL-elicitation for Maps); lottery randomly chose respondents for individual-level deactivation. No other control condition.

For AsPredicted #137878:
Respondents are not randomized at the survey stage as there is only one treatment arm (MPL-elicitation for TikTok); lottery randomly chose respondents for individual-level deactivation. No other control condition.

For AsPredicted #144630:
None.
Randomization Unit
Individual respondent for all three studies.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A
Sample size: planned number of observations
For AsPredicted #142247: 500 respondents. For AsPredicted #137878: 1,000 respondents. For AsPredicted #144630: 500 respondents.
Sample size (or number of clusters) by treatment arms
For AsPredicted #142247:
Half assigned to Instagram survey and half assigned to Maps survey.

For AsPredicted #137878:
Single arm.

For AsPredicted #144630:
Single arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Chicago Social and Behavioral Sciences Institutional Review Board
IRB Approval Date
2023-05-23
IRB Approval Number
IRB23-0797
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

There is information in this trial unavailable to the public. Use the button below to request access.

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Intervention

Is the intervention completed?
No
Data Collection Complete
Yes
Data Collection Completion Date
Final Sample Size: Number of Clusters (Unit of Randomization)
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
Final Sample Size (or Number of Clusters) by Treatment Arms
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials