Consumer Demand and Market Competition with Time-Intensive Goods

Last registered on January 12, 2026
View Trial History

Pre-Trial

Trial Information

General Information

Title
Consumer Demand and Market Competition with Time-Intensive Goods
RCT ID
AEARCTR-0016397
Initial registration date
January 09, 2026

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
January 12, 2026, 8:07 AM EST

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
John List
Affiliation
University of Chicago
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Joseph Goodman
PI Affiliation
Compass Lexecon
Contact Investigator
PI Name
Lancelot Henry de Frahan
PI Affiliation
Kenneth C. Griffin Department of Economics, University of Chicago
Contact Investigator
PI Name
Justin Holz
PI Affiliation
Gerald R. Ford School of Public Policy, University of Michigan
Contact Investigator
PI Name
Evan McKay
PI Affiliation
Compass Lexecon
Contact Investigator
PI Name
Niall McMenamin
PI Affiliation
Compass Lexecon
Contact Investigator
PI Name
Magne Mogstad
PI Affiliation
Kenneth C. Griffin Department of Economics, University of Chicago, NBER, and Statistics Norway
Contact Investigator
PI Name
Sally Sadoff
PI Affiliation
Rady School of Management, University of California San Diego
Contact Investigator
PI Name
Hal Sider
PI Affiliation
Compass Lexecon
Contact Investigator

Additional Trial Information

Status
Completed
Start date
2023-05-01
End date
2023-07-01
Keywords
Behavior, Firms & Productivity
Additional Keywords
multi-sided platforms, ``time allocation"
JEL code(s)
C93, D12, L40, L41
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
We leverage Becker’s time allocation theory to examine consumer demand and market competition for time-intensive goods. The Beckerian model predicts higher diversion ratios for goods with substantial time shares and those with high time costs relative to monetary prices. Applying this model to data from two field experiments, we analyze demand for Facebook and Instagram, focusing on substitution patterns across online activities and offline time use. Our findings indicate that users exhibit low elasticity to ad load, the primary user cost, and that time shares and time costs significantly influence diversion ratios. We explore the implications for user costs and benefits on these platforms and assess the potential impact of a Federal Trade Commission-proposed de-merger of Facebook and Instagram.
External Link(s)

Registration Citation

Citation
Goodman, Joseph et al. 2026. "Consumer Demand and Market Competition with Time-Intensive Goods." AEA RCT Registry. January 12. https://doi.org/10.1257/rct.16397-1.0
Sponsors & Partners

Sponsors

Partner

Name
Compass-Lexecon
Type
private_company
URL
https://www.compasslexecon.com
Experimental Details

Interventions

Intervention(s)
We conducted two concurrent experiments. In one, participants received $4 per hour for reducing their average daily Instagram usage below a personalized baseline. In the other, participants received the same incentive for reducing Facebook usage below their respective baseline.
Intervention (Hidden)
Intervention Start Date
2023-06-04
Intervention End Date
2023-07-01

Primary Outcomes

Primary Outcomes (end points)
Daily minutes spent on personal social networking (PSN), non-PSN and offline. Also, time spent on individual apps, like Facebook, Instagram, Snapchat, TikTok, YouTube, etc.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We recruited participants with ads on Facebook and Instagram, third-party publishers, and online panels maintained by firms for internet-based market research. Interested individuals were directed to a screening survey to confirm eligibility. Criteria included being 18 or older, residing in the U.S., primarily using Facebook or Instagram on an Android device (with sole access to that device), and having an average daily usage of at least 15 minutes on Facebook or 10 minutes on Instagram over the prior 28 days, based on Meta’s internal data. Eligible participants were instructed to download a RealityMine app from the Google Play Store, developed for passive device monitoring, and received $5 upon installation. They were guided to enable activity tracking, which recorded time spent on all apps on their Android device. Participants who successfully installed the app, enabled tracking, and could be matched to Meta’s data via email and Facebook or Instagram IDs were compensated an additional $10, provided their actual usage met the daily criteria.

We conducted two concurrent experiments: one for Facebook and one for Instagram and participants were first assigned to either the Facebook or Instagram experiment. Within each experiment, we then randomly allocated participants to either the treatment or control group. Participants in the treatment groups of both the Facebook and Instagram experiments received $4 per hour (prorated for partial hours) for reducing engagement below their compensation baseline, which we define as the average daily minutes of use of the week with the highest average daily use from the four weeks before the treatment period, rounded up to the nearest quarter hour. Participants in the control groups received compensation for continued data provision via their device monitor but faced no financial incentives to reduce engagement.

We collected data on daily minutes spent on phone applications using the RealityMine Android app, recorded participants using over 35,000 distinct apps during the experiment.
Experimental Design Details
Randomization Method
Blocked randomization by baseline usage of Facebook, Instagram and Snapchat. We followed a re-randomization approach to ensure balance across demographics, baseline usage of other apps and app categories, baseline Facebook friend count, and baseline Instagram follower and following counts.
Randomization Unit
Individual (user) level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
In total, the Facebook experiment included 3,500 participants, and the Instagram experiment included 2,768 participants.
Sample size: planned number of observations
In total, the Facebook experiment included 3,500 participants, and the Instagram experiment included 2,768 participants.
Sample size (or number of clusters) by treatment arms
Facebook experiment: 1750 to treatment and 1750 to control. Instagram experiment: 1384 to treatment and 1384 to control.
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 & Behavioral Sciences (SBS) IRB
IRB Approval Date
2026-01-08
IRB Approval Number
IRB25-1391
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.

Request Information

Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials