JOURNAL ARTICLE
Making a Smooth Exit? Menthol Bans and Cigarette Sales in Massachusetts.
Published In: Marketing Science (INFORMS), 2024, v. 43, n. 3. P. 564 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Goli, Ali; Mummalaneni, Simha; Chintagunta, Pradeep K. 3 of 3
Abstract
This article analyzes the effectiveness of the Massachusetts statewide ban on menthol-flavored cigarettes, implemented in June 2020, by examining retail sales data and modeling consumer behavior. It finds that while the ban reduced in-state menthol cigarette sales by about 54% among Massachusetts residents, nearly half of menthol demand persisted through cross-border shopping in neighboring states, particularly New Hampshire, which has lower cigarette prices. A structural nested logit model accounting for heterogeneity in prices, distances from borders, and menthol preferences predicts that a nationwide menthol ban would yield substantially greater reductions in menthol and overall cigarette consumption than a state-specific ban by eliminating cross-border purchases. Additionally, the model suggests that imposing a menthol excise tax in Massachusetts could reduce menthol consumption while increasing state tax revenues by up to 14%, contrasting with the revenue losses caused by the ban. The study also highlights that the menthol ban disproportionately affects Black consumers, who have stronger preferences for menthol cigarettes and are more likely to incur travel costs to purchase them out of state. These findings imply that policymakers aiming to reduce menthol cigarette use should consider the limitations of state-level bans due to cross-border shopping and weigh alternative policies such as taxes or nationwide bans.
Additional Information
- Source:Marketing Science (INFORMS). 2024/05, Vol. 43, Issue 3, p564
- Document Type:Article
- Subject Area:Geography and Cartography
- Publication Date:2024
- ISSN:0732-2399
- DOI:10.1287/mksc.2022.0361
- Accession Number:177188313
- Copyright Statement:Copyright of Marketing Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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