Please fill out this short user survey of only 3 questions in order to help us improve the site. We appreciate your feedback!
Fairness preferences in the face of limited information
Last registered on September 20, 2017


Trial Information
General Information
Fairness preferences in the face of limited information
Initial registration date
September 20, 2017
Last updated
September 20, 2017 3:16 PM EDT
Primary Investigator
NHH Norwegian School of Economics
Other Primary Investigator(s)
Additional Trial Information
In development
Start date
End date
Secondary IDs
Recent experimental studies show that behavior in distributional situations can be traced back to a variety of fairness ideals and that the majority of people can be characterized as meritocrats distinguishing between personal factors (effort and talent) and impersonal factors (luck) when it comes to the attribution of responsibility for an outcome. However, in many situations the extend to which an achievement is determined by an individual's performance or luck is not directly observable and often remains unknown. In this project, we therefore aim to answer the following research question: What happens to people's willingness to redistribute earnings if there exists uncertainty about the role of luck and merit in the production of these earnings?
Registration Citation
de Haan, Thomas. 2017. "Fairness preferences in the face of limited information." AEA RCT Registry. September 20. https://doi.org/10.1257/rct.2445-1.0.
Former Citation
de Haan, Thomas. 2017. "Fairness preferences in the face of limited information." AEA RCT Registry. September 20. http://www.socialscienceregistry.org/trials/2445/history/21623.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Our main outcome variable of interest is the degree of inequality implemented by the spectators, deciding how to distribute points earned by pairs of workers via Amazon Mturk. (See the uploaded preplan for more details)
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The experiment will consist of two parts. In the first part workers recruited via Amazon Mturk will work on a real effort task. In the second part, spectators recruited from the first years student population at the NHH Norwegian School of Economics will decide on how certain amounts of bonus payments will be distributed over two randomly paired Mturk workers from the first part of the experiment. The first part: Real effort task conducted by Mturk workers. Workers will be recruited via the Amazon Mturk account of the Choice Lab research group. The task the workers will perform is a qualtrix implementation of the ‘encription task’ introduced for experiments by Benndorf, V., Rau, H.A., and Sölch, C. (2014). In this task workers are shown three letters and at the bottom of the screen all letters of the alphabet, in random order, with below each letter a random number between 100 and 1000. For the three letters shown, the workers must fill out the corresponding number. After this, they can continue to the next screen where they will receive a new three letters. Workers will have exactly 15 minutes to work on the task. Workers will earn points depending on how many times they correctly filled out the three codes on a screen. Next to this, for each worker individually, an integer ‘random factor’ will be drawn from a normal distribution with mean 0 and standard deviation of 20. The sum of the worker’s task performance and the random factor will add to be the ‘production’ that each Mturk worker will bring to the second part of the experiment. Each Mturker who finishes at least 20 will earn 2 dollar as a fixed fee and workers earn a bonus depending on a number of factors being; their performance and random factor, a random other worker’s performance and random factor and the distribution decision made by a spectator. The Mturk workers are informed about this procedure.

The second part: Distributional choices by spectators. The second part of the design will be conducted at the Norwegian School of economics on the 27th 28th and 29th of September. Students from the first year course “Business Ethics” will be recruited via the course.All first year students at the NHH are expected to follow the Business Ethics course and participation in the experiment is made a compulsory part of the course. They will be instructed that the experiment results will be used for actual research purposes, that they will receive a payment for participating in the experiment, which will depend in part on their decisions, and the experiment will take place in a separate time and location compared to the classes for the course. The participants will be asked to perform a number of tasks.

Experiment sequence

• Participants will read instructions and answer a comprehension questionnaire

• Participants will answer incentivized questions regarding two Bayesian updating scenario’s, a ‘disease test’ scenario and the ‘taxi cab color’ scenario. We expect participants updating with a high degree of 'base-rate neglect' to show a quantitatively (and possibly) qualitatively different treatment effect. This task will pre-classify participants along the base-rate weighting dimension.

• Participants are then informed about the encryption task Mturk workers performed in the week before the experiment. The participants will be shown a histogram distribution of the amount of correct encriptions the different workers managed to complete in the given time.

• Participants will then receive information on the productions (in the each treatment) and performances and random factors (in the Full Information treatment) regarding a set of 10 randomly chosen pair of Mturk workers from the first part of the experiment. For each pair, the participants will be asked to make a distributional decision, splitting the total production points over the two Mturk workers.

• Participants in the Limited Information treatment are then elicited their beliefs regarding the individual performances of the 10 pairs they made a decision for.

• After this participants will make a number of further, non-incentivized choices that will be used for purposes of the course.

• The Participants will fill out a post-experimental questionnaire which will include questions on background variables (age, gender, political preferences). After this participants will be payed out and leave the experiment.

We plan to recruit around 800 workers on Mturk to work on the task to match with the approximately 450 expected participants for the spectator decisions.
Experimental Design Details
Randomization Method
Randomisation of the 'random factor' in the Mturk point production is done via the computer. Matching of Mturk worker pairs is done via computer randomisation.
Treatment randomisation is done via the fact that spectator participants when entering the lab will randomly draw a table number.
Randomization Unit
Random factor (number drawn from a discrete normal distribution)
Mturk to Spectator matching. The unit here is a list of Mturk pairs per Spectator
Treatment unit (2 treatments)
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
No clusters
Sample size: planned number of observations
We expect between 400 and 500 spectator decisionmakers
Sample size (or number of clusters) by treatment arms
2 treatments, between 200 and 250 spectator decisionmakers per treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
see uploaded pre-analysis plan for the specific details. Using a simulation apporach based on results form previous expeiments and the decision model from Cappelen et al (2007,AER) we calculate a power of 95% assuming 450 decisionmaking Spectators and sufficient variance in Mturk real effort task performance and drawn random factor.
IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan
Analysis Plan Documents
Pre-analysis plan Fairness and Limited Information September 2017

MD5: 748aca49a4890aca8a81f5e91942ce1a

SHA1: 78158910b544822de0ab89dd0645573383a2a1dd

Uploaded At: September 20, 2017

Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)