Inaccurate Beliefs & Bias-Motivated Updating

Last registered on January 21, 2022

Pre-Trial

Trial Information

General Information

Title
Inaccurate Beliefs & Bias-Motivated Updating
RCT ID
AEARCTR-0008849
Initial registration date
January 21, 2022

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
January 21, 2022, 3:45 PM EST

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

Locations

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Primary Investigator

Affiliation
Harvard University

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2021-04-01
End date
2022-07-01
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
An important question in understanding the dynamics of discriminatory behavior is why statistical discrimination based on inaccurate beliefs occurs and -- perhaps more importantly -- why it persists when correcting information is available. For example, Americans hold inaccurate beliefs about household income by race, even though accurate information is readily available through the Census Bureau. Aigner & Cain (1977) assume such ``mistaken behavior" will not persist in competitive markets. However, if inaccurate priors about group productivity are driven by bias rather than a lack of information, they may be resistant to updating in the face of accurate information that contradicts a stereotype about that group. A recent hiring experiment showed that not all participants fully updated their inaccurate beliefs about the productivity of stigmatized groups after receiving correcting information, leading to persistent discrimination. This proposed experiment investigates the source of this non-Bayesian updating.
External Link(s)

Registration Citation

Citation
Rackstraw, Emma. 2022. "Inaccurate Beliefs & Bias-Motivated Updating." AEA RCT Registry. January 21. https://doi.org/10.1257/rct.8849
Experimental Details

Interventions

Intervention(s)
In Part I of the experiment, I gathered information about different groups’ productivity in a stereotyped domain task: a math test.

In Part II of the experiment, I have a different set of participants – heretofore referred to as “employers” – engage in a task where they decide how much they’d be willing to pay the Part I workers for their work.

Part II employers see 8 anonymized "resumes” containing information about a worker's group identities and they are be asked to make an incentive-compatible wage offer using a Becker-Degroot-Marschak mechanism for payment. Group identities include education, age, beverage preferences, and either gender or race. The “control” arm of the experiment will end here.

The main treatment arm of the experiment (the “motivated reasoning” arm) will then induce random variation in the direction of signals (i.e. whether a participant receives a high or low signal of each group’s productivity) by providing the average score for each group among a small subsample of Part I workers. Then I measure wage offers to 8 additional workers in a second hiring task. Comparing the updating process of employers who receive randomly positive versus randomly negative signals about stigmatized group productivity will isolate any asymmetric (bias-motivated) updating.

In addition, in a smaller treatment arm of the experiment (the “accurate information” arm), employers will receive an information intervention with the actual group-level productivity averages among all Part I workers. This will test whether (and for whom) motivated updating disappears when there is no uncertainty about average group productivity.

Finally, all Part II employers will complete a relevant Implicit Association Test to measure implicit biases, attitudinal questions to measure explicit biases, and a demographic survey. This will allow me to measure to what extent any asymmetric updating correlates with measured explicit and implicit biases. The location of the IAT within the survey is randomly assigned to test whether the intervention affects IAT scores.
Intervention Start Date
2022-02-01
Intervention End Date
2022-04-29

Primary Outcomes

Primary Outcomes (end points)
belief updating & implied likelihood ratio, wage offers, posterior beliefs
Primary Outcomes (explanation)
ImpliedLikelihoodRatio = (omega * eta_hat)/(tau_hat + omega * eta_hat)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Pre-intervention, individuals will make wage offers to individuals based on "resumes" that include group identity information. I will also elicit beliefs. In the treatment group, I will then provide either a noisy signal (with randomly generated noise) of group productivity or an accurate signal of group productivity. I will then measure how wage offers and beliefs change post-information.
Experimental Design Details
Not available
Randomization Method
randomization done by random number generator embedded within Qualtrics
Randomization Unit
individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
250 individuals in Part I (math test)
1760 individuals in Part II including pilots
Sample size: planned number of observations
250 individuals in Part I (math test) 1760 individuals in Part II including pilots
Sample size (or number of clusters) by treatment arms
250 individuals in Part I (math test)
1760 individuals in Part II including pilots, with 880 in noisy treatment 440 in accurate treatment and 440 in control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
Harvard University
IRB Approval Date
2021-01-21
IRB Approval Number
IRB20-1957
Analysis Plan

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