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The influence of the market mechanism on moral decision making - Evidence from an online experiment
Last registered on August 02, 2018

Pre-Trial

Trial Information
General Information
Title
The influence of the market mechanism on moral decision making - Evidence from an online experiment
RCT ID
AEARCTR-0002707
Initial registration date
July 31, 2018
Last updated
August 02, 2018 2:17 AM EDT
Location(s)
Region
Primary Investigator
Affiliation
University of Bern, Institute for Organization and Human Resources
Other Primary Investigator(s)
PI Affiliation
University of Bern, Institute for Economics
PI Affiliation
University of Bern, Institute for Organization and Human Resources
Additional Trial Information
Status
In development
Start date
2018-07-31
End date
2018-12-31
Secondary IDs
Abstract
Economists debate whether markets have a tendency to erode moral values. While some studies show that subjects tend to decide less morally when being exposed to a market environment, other studies argue that the market mechanism can have the property to promote moral behavior. We add to this discussion by explicitly distinguishing between two moral concepts from philosophy: consequentialism and deontology. According to consequentialism, the morality of an action is evaluated by the consequences it causes. Following deontology, the morality of an action is evaluated by the action itself.
We plan to conduct an online experiment on Amazon Mechanical Turk (MTurk) using oTree to investigate the research question of how exactly the market mechanism influences moral decision making: Subjects in the control treatment (non-market) play a lottery game in the first stage and take a decision in a moral trolley problem in the second stage. Subjects in the experimental treatment (market) play a double auction game in the role of a seller or buyer in the first stage and take a decision in the same moral trolley problem in the second stage.
We then compare the share of consequentialist decisions in the moral trolley problem between the two treatments. We hypothesize that the market interaction promotes the concept of cost-benefit analysis and makes consequences more salient, leading to a higher share of consequentialist decisions in the market treatment.
An answer to the question whether markets lead to more consequentialism would yield an important contribution to the current debate on the influence of markets on morality.
External Link(s)
Registration Citation
Citation
Adrian, Nana, Ann-Kathrin Crede and Jonas Gehrlein. 2018. "The influence of the market mechanism on moral decision making - Evidence from an online experiment." AEA RCT Registry. August 02. https://doi.org/10.1257/rct.2707-1.0.
Former Citation
Adrian, Nana, Ann-Kathrin Crede and Jonas Gehrlein. 2018. "The influence of the market mechanism on moral decision making - Evidence from an online experiment." AEA RCT Registry. August 02. https://www.socialscienceregistry.org/trials/2707/history/32548.
Experimental Details
Interventions
Intervention(s)
Participants of the experiment are recruited via MTurk and run through three parts: In the first part, they play a monetarily incentivized economic game, which differs according to the treatment. In the second part, participants face the same moral trolley problem and are asked how they would decide. In the third part, participants face a questionnaire with a variety of questions.

Intervention Start Date
2018-07-31
Intervention End Date
2018-12-31
Primary Outcomes
Primary Outcomes (end points)
The key outcome variable is the decision in the moral trolley problem.
Primary Outcomes (explanation)
The key outcome variable yields the share of participants actively intervening ("consequentialist" decisions) and the share of participants staying passive ("deontological" decisions).
Secondary Outcomes
Secondary Outcomes (end points)
Other variables of interest are responses in the questionnaire that comes in the third part of the experiment.
Secondary Outcomes (explanation)
The questionnaire collects information on morality, general experiences with markets, Fair Market Ideology (FMI), risk aversion, trust, and socio-demographic variables.
Experimental Design
Experimental Design
The experiment was programmed with oTree and consists of two treatments: The non-market treatment and the market treatment. It will be conducted on MTurk in a between-subject design. In the first part, participants play a monetarily incentivized game which differs according to the treatment. Both games yield the same expected payoff. In the second part, participants have to decide in a moral trolley problem ("Trap Door"): They either stay passive, leading to the death of 3 people. Or they actively intervene, sacrificing the life of one person to save the other 3 people. In the third part, participants are asked to fill in a questionnaire collecting information on morality, general experiences with markets, Fair Market Ideology (FMI), risk aversion, trust, and socio-demographic variables.

Details on games:
In the non-market treatment, participants play a lottery game over 10 rounds and work on a transcription task (to hold duration and cognitive depletion of the first part constant across treatments). In the market treatment, participants play a double auction market game in the role of a seller or buyer over 10 rounds.
Experimental Design Details
Randomization Method
Randomization is done by the experimental software (oTree).
Randomization Unit
Individual
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
700 individuals
Sample size: planned number of observations
Based on a power calculation (t-test, alpha=0.05, power=0.8), we plan to collect n=700 observations. To check our manipulation for robustness, we will collect 120 additional observations where subjects additionally participate in a word-completion task after the first stage. Our hypothesis is that subjects in the market treatment complete the word fragments with more market-related words than subjects in the non-market treatment.
Sample size (or number of clusters) by treatment arms
Based on a power calculation (t-test, alpha=0.05, power=0.8), we plan to collect n=350 observations per treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers