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Field
Abstract
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Before
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.
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After
Researchers typically categorize discrimination as either taste-based or belief-based discrimination based on either accurate or inaccurate beliefs. I argue, that 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. 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. In order to have a closer look at discrimination based on inaccurate beliefs I suggest a careful analysis of the formation of these inaccuracies. In particular 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.
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Trial Start Date
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Before
August 08, 2021
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After
December 01, 2021
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Trial End Date
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Before
September 30, 2021
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After
December 31, 2021
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Last Published
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Before
August 02, 2021 05:31 PM
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After
October 20, 2021 11:08 AM
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Intervention (Public)
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Before
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.
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After
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Intervention Start Date
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Before
August 08, 2021
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After
December 01, 2021
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Intervention End Date
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Before
September 30, 2021
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After
December 31, 2021
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Primary Outcomes (End Points)
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Before
"Information acquisition" and "Discrimination"
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After
"Discrimination", "information acquisition" and "information processing"
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Field
Primary Outcomes (Explanation)
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Before
"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.)
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After
"Discrimination": I measure the frequencies with which workers of different subgroups are 'hired' and to what extent hiring is consistent with the seen signals in each treatment.
"Information acquisition": I measure how many signals employers gather in each treatment and whether or not specific signal requests depend on the interaction of the treatment group and the previous signal(s).
"Information processing": I measure to what extent hiring decisions are consistent with previously seen information signals between treatments.
(I check and account for learning and fatigue effects by dropping observations in which decision times significantly deviate from the median.)
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Experimental Design (Public)
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Before
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.
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After
I set up a hiring situation in which 'employers' are repeatedly asked to select one of two potential 'workers'.
Employers are randomly allocated to the groups "neutral" and "real". In group 'neutral' any information about whether or not the workers belong to a minority group is hidden.
In each hiring decision, employers first receive a randomly drawn piece of information about the two workers. They may then choose to gather additional information signals before hiring one of the two workers.
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Randomization Method
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Before
Randomization done in office by a computer
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After
Randomization into groups "neutral" and "real" is done by a computer
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Planned Number of Observations
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Before
10,000
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After
~10000 decisions
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Sample size (or number of clusters) by treatment arms
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Before
100 individuals in group real, 100 individuals in group neutral, same distribution in all 4 experiments
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After
250 individuals in group real, 250 individuals in group neutral
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Intervention (Hidden)
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Before
Please check Section Experimental Design for more information about the experiment.
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After
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Secondary Outcomes (End Points)
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Before
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After
Decision times
Beliefs
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Secondary Outcomes (Explanation)
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Before
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After
"Decision times": I measure the time taken to make a hiring decision.
"Beliefs": I measure the subjective probabilities regarding the scores of particular worker subgroups (blacks, whites, hispanics, asians) before and after an aggregate information update
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Building on Existing Work
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Before
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After
No
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