Data and Code for: What’s in a Name? Can Name-Blind Evaluation Reduce Bias in AP Course Recommendation?

Principal Investigator(s):  View help for Principal Investigator(s) Dania V. Francis, University of Massachusetts Boston; Angela C. M. de Oliveira, University of Massachusetts-Amherst; Carey Dimmitt, University of Massachusetts-Amherst

Version:  View help for Version V1

Name File Type Size Last Modified
LICENSE.txt text/plain 15.2 KB 05/01/2024 07:25:PM
README.pdf application/pdf 187.6 KB 05/01/2024 09:49:PM
SCAP_Variable_Codebook.pdf application/pdf 116 KB 05/01/2024 11:05:PM
dvf_scap_bld_001_v02.dta application/x-stata-dta 311.5 KB 05/01/2024 07:42:PM
dvf_scap_bld_002_v03.do text/plain 5 KB 05/01/2024 08:42:PM
dvf_scap_mod_004_v06.do text/plain 2 KB 05/01/2024 10:09:PM
scap_master.do text/plain 862 bytes 05/01/2024 09:50:PM

Project Citation: 

Francis, Dania V., de Oliveira, Angela C. M., and Dimmitt, Carey. Data and Code for: What’s in a Name? Can Name-Blind Evaluation Reduce Bias in AP Course Recommendation? Nashville, TN: American Economic Association [publisher], 2024. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-05-13. https://doi.org/10.3886/E201921V1

Project Description

Summary:  View help for Summary Using a variation of a correspondence audit study, we show that, even after controlling for how prepared a candidate seems, White males are more likely to be recommended for AP Calculus. In this setting, name-blind review does not improve the likelihood of recommendation for any race/gender group and can actually be harmful rather than simply bias-reducing. This is the data and code for replication of the results in the paper.
Funding Sources:  View help for Funding Sources J-PAL North America (GR-2107)

Scope of Project

Subject Terms:  View help for Subject Terms racial discrimination; sex discrimination; secondary education
JEL Classification:  View help for JEL Classification
      D90 Micro-Based Behavioral Economics: General
      I24 Education and Inequality
      J15 Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
      J16 Economics of Gender; Non-labor Discrimination
Time Period(s):  View help for Time Period(s) 11/2023 – 12/2023 (Fall 2023)
Collection Date(s):  View help for Collection Date(s) 11/2023 – 12/2023 (Fall 2023)
Universe:  View help for Universe School counselors at the high school level in the United States
Data Type(s):  View help for Data Type(s) program source code; survey data

Methodology

Response Rate:  View help for Response Rate We distributed the survey to 8,323 email addresses. Of those, 896 bounced back, leaving us with 7,427 successful emails sent. We received clicks from 907 unique individuals into the survey. Of those, 715 agreed to the informed consent, completed the transcript evaluations and demographic information surveys for a response rate of 9.6%. 
Sampling:  View help for Sampling Participants were recruited through email from 14 states that represent a convenience sample of states where we were able to collect direct school counselor emailsf rom public sources online: Alabama, Alaska, Arkansas, Georgia,Hawaii, Illinois, Massachusetts, Mississippi, North Dakota,New Hampshire, Oregon, Pennsylvania, Virginia, and Wyoming.
Collection Mode(s):  View help for Collection Mode(s) web-based survey

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Published Versions

V1 [2024-05-13]

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