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
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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:
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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:
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National Science Foundation (SES-22049356);
Alfred P. Sloan Foundation (Sloan research fellowship (Kolesar));
National Science Foundation (SES-2049250.)
Scope of Project
Subject Terms:
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Linear regression;
bias;
treatment effects
JEL Classification:
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C14 Semiparametric and Nonparametric Methods: General
C21 Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
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):
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experimental data;
observational data
Collection Notes:
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This project reanalyses datasets from 9 replication packages.
Methodology
Data Source:
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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
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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