Motivated statistical discrimination

Last registered on August 02, 2021

Pre-Trial

Trial Information

General Information

Title
Motivated statistical discrimination
RCT ID
AEARCTR-0007991
Initial registration date
August 02, 2021
Last updated
August 02, 2021, 5:31 PM EDT

Locations

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Primary Investigator

Affiliation
JGU Mainz

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2021-08-08
End date
2021-09-30
Secondary IDs
Abstract
While any kind of discrimination can have fatal consequences for the discriminated, a precise identification of the source of discrimination has important implications for effective policy interventions. Researchers typically categorize discrimination as either taste-based or statistical discrimination based on either accurate or inaccurate beliefs. Bohren et al. (2019) argue, that if discrimination stems from inaccurate beliefs instead of animus towards a particular group, a simple information treatment can mitigate discrimination and increase welfare. I suggest a more careful analysis of the formation of inaccurate beliefs. When an agent does not hold randomly inaccurate beliefs, but instead holds inaccurate beliefs due to motivated reasoning, resulting discrimination looks like inaccurate statistical discrimination when it really is a form of motivated discrimination. I show why it is important to separate motivated discrimination from taste-based or inaccurate statistical discrimination. In particular, I analyze the effect of biased information processing on discriminatory behavior and show that people selectively attend to and interpret information in line with their motives - if they have the necessary 'wiggle room' to do so. In that case, they update their beliefs in direction of their motives and ultimately discriminate based on these beliefs. I also show that limiting this 'wiggle room' can be an effective measure to fight motivated statistical discrimination.
External Link(s)

Registration Citation

Citation
Eyting, Markus. 2021. "Motivated statistical discrimination." AEA RCT Registry. August 02. https://doi.org/10.1257/rct.7991-1.0
Experimental Details

Interventions

Intervention(s)
I set up a hiring situation in which 'employers' are repeatedly asked to select one of two potential 'workers'.
In a series of experiments that each consist of the two groups 'real' and 'neutral', I vary the amount and kind of information the employers are given.
In groups 'neutral' any information about whether or not the worker belongs to a minority group is not shown.
Intervention Start Date
2021-08-08
Intervention End Date
2021-09-30

Primary Outcomes

Primary Outcomes (end points)
"Information acquisition" and "Discrimination"
Primary Outcomes (explanation)
"Information acquisition": In experiments 1-3, I measure how often, how much, when, and for how long they look at each additional information signal.
"Discrimination": In experiments 1-4, I measure the frequencies with which workers of different subgroups are 'hired'.
(I check and account for learning and fatigue effects by dropping observations in which decision times significantly deviate from the median.)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
I set up a hiring situation in which 'employers' are repeatedly asked to select one of two potential 'workers'.
In a series of experiments that each consist of the two groups 'real' and 'neutral', I vary the amount and kind of information the employers are given.
In groups 'neutral' any information about whether or not the worker belongs to a minority group is not shown.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
800 individuals
Sample size: planned number of observations
10,000
Sample size (or number of clusters) by treatment arms
100 individuals in group real, 100 individuals in group neutral, same distribution in all 4 experiments
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Gemeinsame Ethikkommission Wirtschaftswissenschaften der Goethe-Universit├Ąt Frankfurt und der Johannes Gutenberg-Universit├Ąt Mainz
IRB Approval Date
2021-03-23
IRB Approval Number
N/A