Gaming or Gambling? On Selection Neglect and Loot Boxes

Last registered on August 02, 2021

Pre-Trial

Trial Information

General Information

Title
Gaming or Gambling? On Selection Neglect and Loot Boxes
RCT ID
AEARCTR-0008021
Initial registration date
July 30, 2021
Last updated
August 02, 2021, 1:44 PM EDT

Locations

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Primary Investigator

Affiliation
Central European University

Other Primary Investigator(s)

PI Affiliation
University of Konstanz
PI Affiliation
Heinrich-Heine-University Düsseldorf
PI Affiliation
Heinrich-Heine-University Düsseldorf

Additional Trial Information

Status
In development
Start date
2021-08-03
End date
2021-10-31
Secondary IDs
Abstract
Nowadays, many successful video games feature "loot boxes" that, just like gambling, offer random rewards to be used in-game. In 2020 alone such loot boxes generated $15 billion of worldwide revenue. There is an increasing public concern, however, that video game developers design loot boxes in a way to make gamers "overpay" for the typically small chance of getting the reward. We single out two features of loot boxes that might result in gamers overestimating the probability of winning the reward and, consequently, paying too much. First, developers typically do not disclose the odds, but provide gamers only with an interval-censored distribution of rewards. Second, in many games there is a public announcement whenever someone wins a reward, which results gamers observing a selected sample of the reward distribution. In a controlled laboratory experiment, we systematically study the effect of either feature, as well as combinations thereof, on the willigness-to-pay for (monetary) lotteries.
External Link(s)

Registration Citation

Citation
Cordes, Simon et al. 2021. "Gaming or Gambling? On Selection Neglect and Loot Boxes." AEA RCT Registry. August 02. https://doi.org/10.1257/rct.8021-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
We study the willingness-to-pay for (and beliefs about) monetary lotteries across four treatments that vary how much information subjects receive on the reward distribution.
Intervention Start Date
2021-08-03
Intervention End Date
2021-10-31

Primary Outcomes

Primary Outcomes (end points)
A subject's willingness-to-pay for different lotteries.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
A subject's belief about the probability to win the maximum reward. (Applies only for the treatments without full disclosure of the odds.)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In each treatment, subjects state their willingness-to-pay for 10 different monetary lotteries. All lotteries have the following structure: with probability 0.99 - p, a subject gets an amount of x > 0; with probability 0.01, a subject gets an amount of y > x; and with probability p, a subject gets nothing. In each decision, subjects learn the feasible outcomes, x, y and 0, but we vary across four treatments how much information they receive on the corresponding probabilities.

Treatment "FULL:" subjects observe the full reward distribution; that is, they learn the probabilities of receiving of the three possible outcomes.

Treatment "CENSORED:" subjects only learn the probability, 1 - p, of receiving at least an amount of x.

Treatment "FULL-BEST:" subjects observe the full reward distribution and, in addition, the five highest outcomes in a random sample of 250 draws (from the underlying distribution).

Treatment "CENSORED-BEST:" subjects learn the probability, 1 - p, of receiving at least an amount of x, and, in addition, they observe the five highest outcomes in a random sample of 250 draws (from the underlying distribution).

In CENSORED and CENSORED-BEST, we further ask subjects to state their belief about the probability with which the first lottery that they had to price during the experiment pays the maximum amount.
Experimental Design Details
Not available
Randomization Method
Randomization is done by the computer.
Randomization Unit
Individual.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
400 individuals.
Sample size: planned number of observations
4000 willingness-to-pay statements, and 200 belief statements.
Sample size (or number of clusters) by treatment arms
100 individuals per treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
German Association for Experimental Economic Research e.V.
IRB Approval Date
2021-07-30
IRB Approval Number
No. bK8MbiCk