Product Transparency and Consumer Behavior: Evidence from Drug Checking

Last registered on March 26, 2025
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Pre-Trial

Trial Information

General Information

Title
Product Transparency and Consumer Behavior: Evidence from Drug Checking
RCT ID
AEARCTR-0015540
Initial registration date
March 21, 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
March 26, 2025, 9:23 AM EDT

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

Locations

Country
Switzerland
Region
Country
Germany
Region

Primary Investigator

Name
Anna Costello
Affiliation
University of Chicago
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Other Primary Investigator(s)

PI Name
Christian Friedrich
PI Affiliation
University of Mannheim
Contact Investigator
PI Name
Gerrit von Zedlitz
PI Affiliation
University of Mannheim
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Additional Trial Information

Status
In development
Start date
2025-03-22
End date
2026-03-31
Keywords
Behavior, Crime, Violence, & Conflict, Health
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Consumer goods transactions in black markets are hampered with significant information asymmetry about product quality. This is because products are illegal and therefore unregulated, and market discipline is inefficient because competition and transparency are low. As a result, suppliers have incentives to shirk on quality, which can be particularly problematic for the trade of high risk goods. In this study, we explore the recreational drug market, where poor information about product quality can lead to acute toxicity and death. Specifically, we ask whether product-level transparency, provided through drug checking initiatives, can lead to safer drug consumption and fewer adverse health outcomes. We partner with nightclubs across Switzerland who provide drug checking stations available for patrons at their club. The drug checking booth accepts a drug that the patron has purchased and plans to consume, take a small sample of the drug, and tests and reports the exact chemical composition of the drug; this practice should largely eliminate information frictions about product quality in this high risk market. Our intervention randomly allocates drug checking "tickets" to individuals entering the club (treatment group). Due to constrained capacity of drug checking tests, we randomly decline drug checking to a control group of club patrons. We then use RFID technology to track movements throughout the club that are intended to capture drug consumption and other risky behavior. Finally, we administer a survey to club patrons to assess user experiences and behaviors. Our study seeks to assess the effectiveness of product transparency in reducing harm in the illicit drug market.
External Link(s)

Registration Citation

Citation
Costello, Anna, Christian Friedrich and Gerrit von Zedlitz. 2025. "Product Transparency and Consumer Behavior: Evidence from Drug Checking." AEA RCT Registry. March 26. https://doi.org/10.1257/rct.15540-1.0
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Experimental Details

Interventions

Intervention(s)
The treatment group will receive a "ticket" (in the form of a positive RFID bracelet) that allows entry to the drug checking station. Upon seeking drug checking, the administrator will scan the bracelet to identify whether the individual is in the treatment group.

The control group will receive a control RFID bracelet, which does not allow entry to the drug checking station. Upon seeking drug checking, the administrator will scan the bracelet and inform this control group that due to capacity constraints, they will not receive drug checking on that night.

All patrons will have an RFID bracelet, allowing us to track movements of both treatment and control groups.
Intervention Start Date
2025-03-22
Intervention End Date
2026-03-31

Primary Outcomes

Primary Outcomes (end points)
Drug consumption
Self-assessed wellbeing
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Perceived acceptance of drug use
Perceived ease of receiving help
Safer use behavior
Effects of consumption
Intentions to change drug-related behavior
Secondary Outcomes (explanation)
Safer use behaviors will be constructed from proxies for polydrug use, resting, alcohol consumption, and hydration.

Experimental Design

Experimental Design
Based on the number of drug checking tests available each night, we will randomly allocate the option to take tests to a treatment group (those that receive the ability to get their drug tested) and a control group (those who will be turned down for drug checking, due to capacity constraints). We will allocate treatment at entry to the club, by handing out admission bracelets, which have been coded with an RFID chip. Randomization will be allocated based on a random number generator based on the patrons' entry order. Each patron will be made aware of the drug checking booth at entry.
At the booth, the bracelet will be checked to identify whether the patron is allowed to get their drug checked. Drugs will subsequently be checked and reported for the treatment group.
RFID technology, including antennas, will be distributed throughout the club to track movements. Movements will be captured and recorded.
At exit, patrons will be asked to take an exit survey, whereby they report their experience with drugs, consumption, and other risky behavior.
Experimental Design Details
Not available
Randomization Method
Random number.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
4,000 individuals
note that treatment take-up is voluntary and likely to be relatively low, around 5%-10%
Sample size: planned number of observations
4,000 individuals note that treatment take-up is voluntary and likely to be relatively low, around 5%-10%
Sample size (or number of clusters) by treatment arms
1,000 control, 3,000 treated
note that treatment take-up is voluntary and likely to be relatively low, around 5%-10%
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
All minimum detectable effect sizes based on a 10% significance level, 80% power level, expecting take-up among control observations to be 50 individuals, and take-up among treatment observations to be 150 individuals: Consumption (extensive margin), all observations available: Control: 100% take drugs, standard deviation = 0, minimum detectable effect size for Treatment: 5% of the individuals stop taking drugs (95% take drugs, standard deviation = 0.22). Consumption (intensive margin) and self-assessed wellbeing, both asked on a 5-point Likert scale, assuming we can incentivize 50% of patrons to take our survey (e.g., by handing out small monetary incentives), i.e., 25 control observations, 75 treatment observations: Standard deviation = 1.1. Minimum detectable effect size is a change of 0.64 points on the 5-point scale.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Chicago Social and Behavioral Sciences IRB
IRB Approval Date
2025-03-10
IRB Approval Number
IRB25-0368
IRB Name
University of Mannheim Ethics Committee
IRB Approval Date
2025-02-19
IRB Approval Number
11/2025
Analysis Plan

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