Measuring anomalies in cigarette sales using official data from Spanish provinces: Are the anomalies detected by the Empty Pack Surveys (EPSs) used by Transnational Tobacco Companies (TTCs) the only anomalies?

Abstract

Introduction There is a literature that questions the veracity of the studies commissioned by transnational tobacco companies (TTCs) to measure the illicit tobacco trade. Furthermore, there are studies that have indicated that the empty pack surveys (EPSs) ordered by TTCs overestimate the size of this trade. This study simultaneously analyzed whether the EPSs established in each of the 47 Spanish provinces were accurate and measured anomalies observed in provinces where sales exceed expected values. Methods To achieve the objectives of this study, provincial data on cigarette sales, prices and GDP per capita were used. These data were modeled with machine learning techniques that are widely used to detect anomalies in other areas. Results The magnitude of the average anomaly in provinces where sales are higher than their expected values exceeds 40%, while the average anomaly in provinces where sales are lower than their expected values (as detected by the EPSs) is <15%. Furthermore, the results reveal that there is a clear geographical pattern to the provinces in which sales below reasonable values are observed. In addition, the values provided by the EPSs in Spain, as indicated in the previous literature, are slightly overestimated. Finally, some regions bordering other countries or that are highly influenced by tourism have observed sales that are higher than their expected values. Conclusions Cooperation between countries in their tobacco control policies can have better effects than policies developed based on information from a single country. The lack of control over the transactions of tourists and the inhabitants of bordering countries can cause important anomalies that distort the understanding of tobacco consumption that governments have based on official data.

Keywords

• empty pack survey
• cigarette sales
• illicit trade
• machine learning