To the Depths of the Sunk Cost: an Experiment Revisiting the Elusive Fallacy (Field Study)

Last registered on October 05, 2022


Trial Information

General Information

To the Depths of the Sunk Cost: an Experiment Revisiting the Elusive Fallacy (Field Study)
Initial registration date
September 30, 2022

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
October 05, 2022, 9:50 AM EDT

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



Primary Investigator

University of Warwick

Other Primary Investigator(s)

PI Affiliation
The University of Chicago
PI Affiliation
Columbia University

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Field experimental evidence supporting the sunk cost effect is scarce and suffers from identification issues. We illuminate this topic by designing an experiment that exogenously varies the temporal cost of watching a YouTube video, to see how it affects user engagement. We randomize whether the time until a pre-video ad becomes skippable is shortened, normal, or extended. This allows us to test not only for the existence of the sunk cost effect, but also its asymmetry in the gain/loss region.
External Link(s)

Registration Citation

Beknazar-Yuzbashev, George, Sota Ichiba and Mateusz Stalinski. 2022. "To the Depths of the Sunk Cost: an Experiment Revisiting the Elusive Fallacy (Field Study) ." AEA RCT Registry. October 05.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Video engagement time: we define it to be the time between (1) when the video loads after the ads and (2) the first time the user transitions out of the video page.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
We will look at heterogeneity with respect to the length of the video. We will categorize observations by video length in the following way – shorter than 3 minutes, 3-6 minutes, 6-10 minutes, 10-20 minutes, longer than 20 minutes.

We will look at the subsample of videos where we exclude music videos – which might be intended to be played in the background. We will identify those videos as ones that are more than 10 minutes long, and are in the “music” category.

Additionally, we are going to test whether the duration of exposure to factors affecting the sunk cost changes the strength of the effect. To that end, we compare the effect size in the first two weeks vs. the last two weeks.

Lastly, we will collect data on whether an ad is clicked.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experimental design is hidden until the end of the trial.
Experimental Design Details
The intervention is introduced through a browser extension that all participants install. The extension was designed to improve user experience on social media and we sample participants from the pool of users who previously experienced an intervention where the researchers varied exposure to toxic content on social media.

We rely on a within-subjects design structure, randomizing at the video level. Each person will encounter all three of the treatment conditions. For each YouTube video which contains at least one skippable ad, we randomize the wait time until a skip button appears for the first skippable ad attached to the video (see Intervention section).

Primary hypothesis (existence of the sunk cost effect):
The average video engagement time in the long condition is greater than in the short condition.

Secondary hypothesis (asymmetry in the sunk cost effect):
The difference in average video engagement time between the long condition and the default condition is not equal to the difference in average average video engagement time between the default condition and the short condition.

To test the hypotheses, we will estimate the following regression specifications. First, we will regress the outcome variable on default treatment dummy (beta5) and long treatment dummy (beta10). To test the primary hypothesis, we will conduct a one sided test that beta10>0. To test the secondary hypothesis, we will conduct a likelihood ratio test that beta10 is not equal to 2*beta5. In all regressions, we will use individual fixed effects. Under the assumptions of temporal stability and causal transience, each video can be considered an independent observation. Thus, we do not cluster standard errors at an individual level.

Additionally, we will report the 2SLS specifications, where the sunk time is instrumented with the treatment condition.

In our analysis, we will exclude observations in the following cases: the intervened skippable ad did not occur at the front of the video. In rare cases in which it is clear that the extension incorrectly or incompletely recorded information about viewing a video (as a result of a technical issue), we will drop the relevant observation from the sample.
Randomization Method
Randomization is conducted through the browser extension. For each video, the extension (using JavaScript) generates a random number between 0 and 1. If the number exceeds 2/3, we assign the long treatment (10 seconds wait time). If the number is between 1/3 and 2/3, then we assign the default treatment (5 seconds wait time). Lastly, if the number is below 1/3, we assign the short treatment (0 seconds wait time). Note that the treatment status remains unchanged when the user refreshes or accesses another video within 8 seconds of the ad starting.
Randomization Unit
A YouTube video
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
We recruit our sample for 4 weeks. We expect at least 30,000 observations depending on the frequency with which the participants watch YouTube videos with skippable ads.
Sample size (or number of clusters) by treatment arms
We expect at least 10,000 observations per treatment condition.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
For the primary hypothesis, the MDE is 0.035 standard deviation with 10,000 observations per treatment. This would be economically significant for platforms like YouTube given the number of users, and their active time on the platform.

Institutional Review Boards (IRBs)

IRB Name
Social and Behavioral Sciences Institutional Review Board The University of Chicago
IRB Approval Date
IRB Approval Number
IRB Name
Morningside IRB, Columbia University
IRB Approval Date
IRB Approval Number


Post Trial Information

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

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Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials