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Estimating and Decomposing Conditional Average Treatment Effects: The Smoking Ban in England

Author

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  • Robson, M.;
  • Doran, T.;
  • Cookson, R.;

Abstract

We develop a practical method for estimating and decomposing conditional average treatment effects using locally-weighted regressions. We illustrate with an application to the smoking ban in England using a regression discontinuity design, based on Health Survey for England data. We estimate average treatment effects conditional on socioeconomic status and decompose these effects by smoking location. Results show, the ban had no effect on the level of active smoking, but significantly reduced average exposure to second-hand smoke among non-smokers by 1.38 hours per week. Our method reveals a complex relationship between socioeconomic status and the effect on passive smoking. Decomposition analysis shows that these effects stem primarily from exposure reductions in pubs, but also from workplace exposure reductions for high socioeconomic status individuals.

Suggested Citation

  • Robson, M.; & Doran, T.; & Cookson, R.;, 2019. "Estimating and Decomposing Conditional Average Treatment Effects: The Smoking Ban in England," Health, Econometrics and Data Group (HEDG) Working Papers 19/20, HEDG, c/o Department of Economics, University of York.
    • Handle: RePEc:yor:hectdg:19/20
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    References listed on IDEAS

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    1. Minsu Chang & Sokbae Lee & Yoon‐Jae Whang, 2015. "Nonparametric tests of conditional treatment effects with an application to single‐sex schooling on academic achievements," Econometrics Journal, Royal Economic Society, vol. 18(3), pages 307-346, October.
    2. Fortin, Nicole & Lemieux, Thomas & Firpo, Sergio, 2011. "Decomposition Methods in Economics," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 1, pages 1-102, Elsevier.
    3. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
    4. José Mata & José A. F. Machado, 2005. "Counterfactual decomposition of changes in wage distributions using quantile regression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 445-465.
    5. Jason Abrevaya & Yu-Chin Hsu & Robert P. Lieli, 2015. "Estimating Conditional Average Treatment Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 485-505, October.
    6. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    7. John Tayu Lee & Stanton A Glantz & Christopher Millett, 2011. "Effect of Smoke-Free Legislation on Adult Smoking Behaviour in England in the 18 Months following Implementation," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-6, June.
    8. Shakeeb Khan & Elie Tamer, 2010. "Irregular Identification, Support Conditions, and Inverse Weight Estimation," Econometrica, Econometric Society, vol. 78(6), pages 2021-2042, November.
    9. Bjorklund, Anders & Moffitt, Robert, 1987. "The Estimation of Wage Gains and Welfare Gains in Self-selection," The Review of Economics and Statistics, MIT Press, vol. 69(1), pages 42-49, February.
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    Cited by:

    1. 'Agoston Reguly, 2021. "Heterogeneous Treatment Effects in Regression Discontinuity Designs," Papers 2106.11640, arXiv.org, revised Oct 2021.

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    More about this item

    Keywords

    health inequality; equity; conditional average treatment effects; regression discontinuity; heterogeneity; smoking ban; lwcate;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs

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