Data and Code for: Contamination Bias in Linear Regressions

Principal Investigator(s):  View help for Principal Investigator(s) Paul Goldsmith-Pinkham, Yale University; Peter Hull, Brown University; Michal Kolesar, Princeton University

Version:  View help for Version V1

Name File Type Size Last Modified
  dta 09/13/2024 12:09:PM
  original_data 09/13/2024 12:08:PM
  output 09/13/2024 12:09:PM
0_setup.R text/x-rsrc 413 bytes 09/13/2024 08:08:AM
1_prepare_data.do text/plain 9.4 KB 09/13/2024 08:08:AM
2_prepare_STAR.R text/x-rsrc 1.2 KB 09/13/2024 08:08:AM
3_tables.R text/x-rsrc 8.7 KB 09/13/2024 08:08:AM
4_figures.R text/x-rsrc 9.3 KB 09/13/2024 08:08:AM
LICENSE.txt text/plain 13.8 KB 09/13/2024 08:08:AM
Makefile text/plain 1.3 KB 09/13/2024 08:08:AM

Project Citation: 

Goldsmith-Pinkham, Paul, Hull, Peter, and Kolesar, Michal. Data and Code for: Contamination Bias in Linear Regressions. Nashville, TN: American Economic Association [publisher], 2025. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2025-01-03. https://doi.org/10.3886/E207983V1

Project Description

Summary:  View help for Summary We study regressions with multiple treatments and a set of controls that is flexible enough to purge omitted variable bias. We show that these regressions generally fail to estimate convex averages of heterogeneous treatment effects---instead, estimates of each treatment's effect are contaminated by non-convex averages of the effects of other treatments. We discuss three estimation approaches that avoid such contamination bias, including the targeting of easiest-to-estimate weighted average effects. A re-analysis of nine empirical applications finds economically and statistically meaningful contamination bias in observational studies; contamination bias in experimental studies is more limited due to smaller variability in propensity scores.

Funding Sources:  View help for Funding Sources National Science Foundation (SES-22049356); Alfred P. Sloan Foundation (Sloan research fellowship (Kolesar)); National Science Foundation (SES-2049250.)

Scope of Project

Subject Terms:  View help for Subject Terms Linear regression; bias; treatment effects
JEL Classification:  View help for JEL Classification
      C14 Semiparametric and Nonparametric Methods: General
      C21 Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Data Type(s):  View help for Data Type(s) experimental data; observational data
Collection Notes:  View help for Collection Notes This project reanalyses datasets from 9 replication packages.

Methodology

Data Source:  View help for Data Source Achilles, C., Bain, H. P., Bellott, F., Boyd-Zaharias, J., Finn, J., Folger, J.,
Johnston, J., & Word, E. (2008). Tennessee's Student Teacher Achievement Ratio
(STAR) project. Harvard Dataverse https://doi.org/10.7910/DVN/SIWH9F

Benhassine, N., Devoto, F., Duflo, E., Dupas, P., & Pouliquen, V. (2015).
Turning a shove into a nudge? a “labeled cash transfer” for education. American
Economic Journal: Economic Policy, 7 (3), 86–125.
https://doi.org/10.1257/pol.20130225 Replication files:
https://doi.org/10.3886/E114579V2

Cole, S., Giné, X., Tobacman, J., Topalova, P., Townsend, R., & Vickery, J.
(2013). Barriers to household risk management: Evidence from india. American
Economic Journal: Applied Economics, 5 (1), 104–135.
https://doi.org/10.1257/app.5.1.104 Replication files:
https://doi.org/10.3886/E116379V1

de Mel, S., McKenzie, D., & Woodruff, C. (2013). The demand for, and
consequences of, formalization among informal firms in Sri Lanka. American
Economic Journal: Applied Economics, 5 (2), 122–150.
https://doi.org/10.1257/app.5.2.122 Replication files:
https://doi.org/10.3886/E113847V1

Drexler, A., Fischer, G., & Schoar, A. (2014). Keeping it simple: Financial
literacy and rules of thumb. American Economic Journal: Applied Economics, 6
(2), 1–31. https://doi.org/10.1257/app.6.2.1 Replication files:
https://doi.org/10.3886/E113888V1

Duflo, E., Dupas, P., & Kremer, M. (2015). Education, hiv, and early fertility:
Experimental evidence from kenya. American Economic Review, 105 (9), 2757–2797.
https://doi.org/10.1257/aer.20121607 Replication files:
https://doi.org/10.3886/E112899V1

Fryer, R. G., & Levitt, S. D. (2013). Testing for racial differences in the
mental ability of young children. American Economic Review, 103 (2), 981–1005.
https://doi.org/10.1257/aer.103.2.981 Replication files:
https://doi.org/10.3886/E112609V1

Rim, N., Ba, B., & Rivera, R. (2020). Disparities in police award nominations:
Evidence from chicago. AEA Papers and Proceedings, 110, 447–451.
https://doi.org/10.1257/pandp.20201118 Replication files:
https://doi.org/10.3886/E120749V1

Weisburst, E. K. (2019). Police use of force as an extension of arrests:
Examining disparities across civilian and officer race. AEA Papers and
Proceedings, 109, 152–156. https://doi.org/10.1257/pandp.20191028 Replication
files: http://doi.org/10.3886/E114511V1


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