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Title: Using a bayesian structural time-series model to infer the causal impact on cigarette sales of partial and total bans on public smoking
Authors: Pinilla, Jaime 
Negrín, Miguel 
González-López-Valcárcel, Beatriz 
Vázquez-Polo, Francisco José 
UNESCO Clasification: 5309 Organización industrial y políticas gubernamentales
Keywords: Smoking ban
Economic policy
Bayesian structural time series
Tobacco control
Issue Date: 2018
Publisher: 0021-4027
Journal: Jahrbucher für Nationalokonomie und Statistik 
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.
ISSN: 0021-4027
DOI: 10.1515/jbnst-2017-0125
Source: Jahrbucher fur Nationalokonomie und Statistik[ISSN 0021-4027],v. 238, p. 423-439
Appears in Collections:Artículos
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