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Using a Bayesian Structural Time–Series Model to Infer the Causal Impact on Cigarette Sales of Partial and Total Bans on Public Smoking

Author

Listed:
  • Pinilla Jaime

    (Department of Quantitative Methods, University of Las Palmas de Gran Canaria, 35017Las Palmas de Gran Canaria, Spain)

  • Negrín Miguel

    (Department of Quantitative Methods, University of Las Palmas de Gran Canaria, 35017Las Palmas de Gran Canaria, Spain)

  • González-López-Valcárcel Beatriz

    (Department of Quantitative Methods, University of Las Palmas de Gran Canaria, 35017Las Palmas de Gran Canaria, Spain)

  • Vázquez-Polo Francisco-José

    (Department of Quantitative Methods, University of Las Palmas de Gran Canaria, 35017Las Palmas de Gran Canaria, Spain)

Abstract

The Bayesian structural time series model, used in conjunction with a state–space model, is a novel means of exploring the causal impact of a policy intervention. It extends the widely used difference–in–differences approach to the time series setting and enables several control series to be used to construct the counterfactual. This paper highlights the benefits of using this methodology to estimate the effectiveness of an absolute ban on smoking in public places, compared with a partial ban. In January 2006, the Spanish government enacted a tobacco control law which banned smoking in bars and restaurants, with exceptions depending on the floor space of the premises. In January 2011, further legislation in this area was adopted, removing these exceptions. The data source used for our study was the monthly legal sales of cigarettes in Spain from January 2000 to December 2014. The potential control series were the monthly tourist arrivals from the United Kingdom, the total number of visitors from France, the unemployment rate and the average price of cigarettes. Analysis of the state–space model leads us to conclude that the partial ban was not effective in reducing the tobacco sold in Spain, but that the total ban contributed significantly to reducing cigarette consumption.

Suggested Citation

  • Pinilla Jaime & Negrín Miguel & González-López-Valcárcel Beatriz & Vázquez-Polo Francisco-José, 2018. "Using a Bayesian Structural Time–Series Model to Infer the Causal Impact on Cigarette Sales of Partial and Total Bans on Public Smoking," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 238(5), pages 423-439, September.
  • Handle: RePEc:jns:jbstat:v:238:y:2018:i:5:p:423-439:n:14
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    More about this item

    Keywords

    smoking ban; economic policy; cigarettes; Bayesian structural time series; Tobacco control;
    All these keywords.

    JEL classification:

    • K20 - Law and Economics - - Regulation and Business Law - - - General
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • L66 - Industrial Organization - - Industry Studies: Manufacturing - - - Food; Beverages; Cosmetics; Tobacco
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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