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

DOI

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.

Identifier
DOI https://doi.org/10.15456/jbnst.2018303.125442
Metadata Access https://www.da-ra.de/oaip/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:oai.da-ra.de:658998
Provenance
Creator Pinilla, Jaime; Negrín, Miguel; González-López-Valcárcel, Beatriz; Vázquez-Polo, Francisco-José
Publisher ZBW - Leibniz Informationszentrum Wirtschaft
Publication Year 2018
Rights Creative Commons Attribution 4.0 (CC-BY); Download
OpenAccess true
Contact ZBW - Leibniz Informationszentrum Wirtschaft
Representation
Language English
Resource Type Collection
Discipline Economics; Jurisprudence; Law; Social and Behavioural Sciences