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A cautionary tale on using panel data estimators to measure program impacts

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  • Wichman, Casey J.
  • Ferraro, Paul J.

Abstract

We compare experimental and nonexperimental estimates from a social and informational messaging experiment. Our results show that applying a fixed effects estimator in conjunction with matching to pre-process nonexperimental comparison groups cannot replicate an experimental benchmark, despite parallel pre-intervention trends and good covariate balance. The results are a stark reminder about the role of untestable assumptions–in our case, conditional bias stability–in drawing causal inferences from observational data, and the dangers of relying on single studies to justify program scaling-up or canceling.

Suggested Citation

  • Wichman, Casey J. & Ferraro, Paul J., 2017. "A cautionary tale on using panel data estimators to measure program impacts," Economics Letters, Elsevier, vol. 151(C), pages 82-90.
    • Handle: RePEc:eee:ecolet:v:151:y:2017:i:c:p:82-90
    DOI: 10.1016/j.econlet.2016.11.029
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    References listed on IDEAS

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    7. Wichman, Casey J. & Taylor, Laura O. & von Haefen, Roger H., 2016. "Conservation policies: Who responds to price and who responds to prescription?," Journal of Environmental Economics and Management, Elsevier, vol. 79(C), pages 114-134.
    8. Dehejia, Rajeev, 2005. "Practical propensity score matching: a reply to Smith and Todd," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 355-364.
    9. Paul J. Ferraro & Michael K. Price, 2013. "Using Nonpecuniary Strategies to Influence Behavior: Evidence from a Large-Scale Field Experiment," The Review of Economics and Statistics, MIT Press, vol. 95(1), pages 64-73, March.
    10. Sekhon, Jasjeet S., 2011. "Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i07).
    11. Ferraro, Paul J. & Miranda, Juan José, 2014. "The performance of non-experimental designs in the evaluation of environmental programs: A design-replication study using a large-scale randomized experiment as a benchmark," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 344-365.
    12. Kosuke Imai & Gary King & Elizabeth A. Stuart, 2008. "Misunderstandings between experimentalists and observationalists about causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 481-502, April.
    13. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
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    Citations

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    Cited by:

    1. Nemati, Mehdi & Penn, Jerrod, 2020. "The impact of information-based interventions on conservation behavior: A meta-analysis," Resource and Energy Economics, Elsevier, vol. 62(C).
    2. Hamilton, Timothy L. & Wichman, Casey J., 2018. "Bicycle infrastructure and traffic congestion: Evidence from DC's Capital Bikeshare," Journal of Environmental Economics and Management, Elsevier, vol. 87(C), pages 72-93.
    3. Jaime Torres, Mónica M. & Carlsson, Fredrik, 2018. "Direct and spillover effects of a social information campaign on residential water-savings," Journal of Environmental Economics and Management, Elsevier, vol. 92(C), pages 222-243.
    4. Jared Coopersmith & Thomas D. Cook & Jelena Zurovac & Duncan Chaplin & Lauren V. Forrow, 2022. "Internal And External Validity Of The Comparative Interrupted Time‐Series Design: A Meta‐Analysis," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 41(1), pages 252-277, January.
    5. Wichman, Casey J., 2017. "Information provision and consumer behavior: A natural experiment in billing frequency," Journal of Public Economics, Elsevier, vol. 152(C), pages 13-33.
    6. Pratt, Bryan, 2023. "A fine is more than a price: Evidence from drought restrictions," Journal of Environmental Economics and Management, Elsevier, vol. 119(C).
    7. Brandon Cunningham & Jacob LaRiviere & Casey J. Wichman, 2021. "Clustered into control: Heterogeneous causal impacts of water infrastructure failure," Economic Inquiry, Western Economic Association International, vol. 59(3), pages 1417-1439, July.
    8. Brent, Daniel A. & Ward, Michael B., 2019. "Price perceptions in water demand," Journal of Environmental Economics and Management, Elsevier, vol. 98(C).
    9. Vivian C. Wong & Peter M. Steiner & Kylie L. Anglin, 2018. "What Can Be Learned From Empirical Evaluations of Nonexperimental Methods?," Evaluation Review, , vol. 42(2), pages 147-175, April.

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    More about this item

    Keywords

    Design replication; Program evaluation; Matching; Panel data; Water conservation;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • H42 - Public Economics - - Publicly Provided Goods - - - Publicly Provided Private Goods
    • Q25 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Water

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