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    Estimating the Magnitude of Illicit Cigarette Trade in Bangladesh: Protocol for a Mixed-Methods Study
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    Abstract:
    The illicit tobacco trade undermines the effectiveness of tobacco tax policies; increases the availability of cheap cigarettes, which, in turn, increases tobacco use and tobacco related deaths; and causes huge revenue losses to governments. There is limited evidence on the extent of illicit tobacco trade particularly cigarettes in Bangladesh. The paper presents the protocol for a mixed-methods study to estimate the extent of illicit cigarette trade in Bangladesh. The study will address three research questions: (a) What proportion of cigarettes sold as retail are illicit? (b) What are the common types of tax avoidance and tax evasion? (c) Can pack examination from the trash recycle market be considered as a new method to assess illicit trade in comparison to that from retailers and streets? Following an observational research method, data will be collected utilizing empty cigarette packs from three sources: (a) retailers; (b) streets; and (c) trash recycle market. In addition, a structured questionnaire will be used to collect information from retailers selling cigarettes. We will select post codes as Primary Sampling Unit (PSU) using a multi-stage random sampling technique. We will randomly select eight districts from eight divisions stratified by those with land border and non-land border; and within each district, we will randomly select ten postcodes, stratified by rural (five) and urban (five) PSU to ensure maximum geographical variation, leading to a total of eighty post codes from eight districts. The analysis will report the proportions of packs that do not comply with the study definition of illicit. Independent estimates of illicit tobacco are rare in low- and middle-income countries such as Bangladesh. Findings will inform efforts by revenue authorities and others to address the effects of illicit trade and counter tobacco industry claims.
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    Stratified Sampling
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    Observational learning
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    Regression discontinuity design
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