Expectations Under Incomplete Information

Last registered on May 03, 2022

View Trial History

Pre-Trial

Trial Information

General Information

Title

Expectations Under Incomplete Information

RCT ID

AEARCTR-0009329

Initial registration date

April 29, 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

May 03, 2022, 9:39 AM EDT

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

Locations

Country

United States of America

Region

Primary Investigator

Name

Glen Waddell

Affiliation

University of Oregon

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Kyle Raze

PI Affiliation

University of Oregon

Contact Investigator

Additional Trial Information

Status

In development

Start date

2022-05-02

End date

2022-07-01

Keywords

Education, Gender, Labor

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

We consider how expectations about an individual’s mastery of a skill depend on a signal of the individual's mastery of a different skill, and whether this expectation differs across race and gender. To this end, we have designed a survey task in which research subjects make an incentivized guess about a math test score given information on a reading test score (and vice versa), when race and gender are evident in a photograph of a test taker.

External Link(s)

Registration Citation

Citation

Raze, Kyle and Glen Waddell. 2022. "Expectations Under Incomplete Information." AEA RCT Registry. May 03. https://doi.org/10.1257/rct.9329-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2022-05-02

Intervention End Date

2022-07-01

Primary Outcomes

Primary Outcomes (end points)

The primary outcome is a subject's guess about an individual's test score on the math or reading section of the SAT. Guesses are constrained to the domain of SAT section scores (all multiples of 10 between 200 and 800).

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

Subjects will see 10 randomly selected individuals with a score on either the math or reading section of the SAT and then be asked to guess each individual's score on "the other" section. All subjects will see a fixed number of individuals from 8 race-by-gender groups, but the order in which these individuals appear will be randomized.

Information about test scores, race, and gender will be drawn from publicly available data on the 1997 cohort of the National Longitudinal Survey of Youth. Each individual will be represented by a randomly selected portrait from the Chicago Face Database (Ma et al., 2015) and a randomly selected name from a list of common names associated with that race and gender.

At the end of the experiment, one of the individuals will be chosen at random and the payment a subject receives will be based on how close the subject's guess was to that individual's other score.

Experimental Design Details

Randomization Method

Randomization will be done on a shinyapps.io server using R.

Randomization Unit

Randomizing scores shown to subjects (within-subject).

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

We plan to recruit between 200 and 500 subjects.

Sample size: planned number of observations

Each subject will make 10 guesses, yielding between 200*10 = 2000 and 500*10 = 5000 subject-period observations.

Sample size (or number of clusters) by treatment arms

We expect between 400 and 1000 observations for the white male stimulus, between 400 and 1000 for the white female stimulus, between 200 and 500 for the Black male stimulus, between 200 and 500 for the Black female stimulus, between 200 and 500 for the Latino male stimulus, between 200 and 500 for the Latina female stimulus, between 200 and 500 for the Asian male stimulus, and between 200 and 500 for the Asian female stimulus.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB