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Can High School Counselors Help the Economics Pipeline?

icpsr_189563

Melissa Gentry, Jonathan Meer, Danila Serra (2023). AEA Papers and Proceedings

DOI: https://doi.org/10.1257/pandp.20231121

field value
venue AEA Papers and Proceedings
paper https://doi.org/10.1257/pandp.20231121
replication data openICPSR 189563
data license CC BY 4.0 (openICPSR project page 189563 (maintainer-read 2026-07-20)) — “This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License.”
regression datasets (in this corpus) 2
graduated / eligible / exported 2 / 11 / 24 (policy claims_manual_regression_cap)
routed / gap / residue (at source) 11 / 0 / 0

Claims & tasks

Every claim the paper makes in its main body about a parameter, joined to the benchmark task (dataset · coefficient) that captures it. The effect, s.e. and p are the captured regression's own reported values (the paper's Stata figures, carrying the paper's SE method); the row shows the paper's verbatim quote (page · exhibit), so you can confirm the regression matches what the paper says. The prominence column is the within-study inclusion signal (⭐ headline → primary → secondary). The sensitivity column is the privacy level of the most-sensitive variable in the regression (🔴 high / 🟠 medium / ⚪ low).

# prominence sensitivity claim effect s.e. p task instance (dataset · coef)
C1 ⭐ primary 🔴 high “The results show that the likelihood of a high-achieving student selecting economics on their application increased by 2.6 percentage points for all such students (a 33 percent increase), by 2.9 percentage points (46 percent) for women, and by 4.7 percentage points (66 percent) for URM students.” — p. 4
“For this group, we see an economically and statistically meaningful increase in student interest in the economics major.” — p. 4
Table 2, col 5 (All top (5))		 +0.026 0.011 0.015 Linear Regression reg_5 · workshop_2020
n=14107 · d=4
C2 primary 🔴 high These coefficients are very small and not statistically significant.
p. 4 · Table 2, col 1 (All (1))		 +0.002 0.006 0.684 Linear Regression reg_1 · workshop_2020
n=42442 · d=5

Regression datasets

The 2 regression dataset(s) graduated into the benchmark corpus from this study's replication package — each backs one or more claims above. Expand each for its features, response, (sound, data-independent) public bounds, and reproduction grade.

icpsr_189563_reg_1 — original_econ ~ workshop_2020 + yr_2020 + female + URM + top_perform

Sensitivity proposal: 🔴 high maximum across columns (advisory; reviewed manually). Reproduction grade: ✅ match (benchmark-eligible).

Features

name description type sensitivity bound lo bound hi estimate s.e.
workshop_2020 Received counselor economics workshop treatment continuous ⚪ low -1.0 1.0 +0.002394 0.00587
yr_2020 Application year 2020 cohort indicator continuous ⚪ low -1.0 1.0 +0.003119 0.0042
female Student gender indicator (female) continuous 🟠 medium -1.0 1.0 -0.04945 0.00382
URM Underrepresented minority race/ethnicity status continuous 🔴 high -1.0 1.0 -0.007725 0.00316
top_perform High-achieving/top-performing applicant indicator continuous 🟠 medium -1.0 1.0 -0.0005348 0.003

Response

name description type sensitivity bound lo bound hi estimate s.e.
original_econ Selected economics as intended major continuous 🟠 medium -1.0 1.0 — —

Public bounds (data-independent) sourced from: binary indicator; academic major choice is education-related; binary indicator; based on academic performance/test scores; binary indicator; race/ethnicity classification is high sensitivity; binary indicator; sex/gender is medium sensitivity; binary indicator; treatment/arm variable; binary indicator; wave/round/time variable.

n = 42,442 samples.

icpsr_189563_reg_5 — original_econ ~ workshop_2020 + yr_2020 + female + URM

Sensitivity proposal: 🔴 high maximum across columns (advisory; reviewed manually). Reproduction grade: ✅ match (benchmark-eligible).

Features

name description type sensitivity bound lo bound hi estimate s.e.
workshop_2020 Received counselor economics workshop treatment continuous ⚪ low -1.0 1.0 +0.02578 0.0105
yr_2020 Application year 2020 cohort indicator continuous ⚪ low -1.0 1.0 -0.00621 0.00767
female Student gender indicator (female) continuous 🟠 medium -1.0 1.0 -0.0378 0.00619
URM Underrepresented minority race/ethnicity status continuous 🔴 high -1.0 1.0 -0.01445 0.00703

Response

name description type sensitivity bound lo bound hi estimate s.e.
original_econ Selected economics as intended major continuous 🟠 medium -1.0 1.0 — —

Public bounds (data-independent) sourced from: binary indicator; academic major choice is education-related; binary indicator; race/ethnicity classification is high sensitivity; binary indicator; sex/gender is medium sensitivity; binary indicator; treatment/arm variable; binary indicator; wave/round/time variable.

n = 14,107 samples.