JOURNAL ARTICLE

Evaluating the Effect of Soda Taxes Using a Dynamic Model of Rational Addiction.

  • Published In: Management Science (INFORMS), 2026, v. 72, n. 4. P. 3040 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Kim, Jong Yeob; Ishihara, Masakazu 3 of 3

Abstract

This article examines the impact of soda excise taxes on sugar-sweetened beverage (SSB) consumption in the United States, focusing on the role of addiction to caffeine and sugar. Using the Becker-Murphy model of rational addiction, the authors provide evidence that consumers are forward-looking and rationally addicted to sodas containing caffeine and/or sugar, and they develop a multidimensional dynamic structural model to quantify how soda taxes affect purchases, consumption, and addiction levels. Policy simulations based on real-world soda taxes in Berkeley, CA, and Philadelphia, PA, suggest that a one-cent-per-ounce tax on sugary sodas could reduce sugary soda consumption by about 50%, with sugar playing a larger role than caffeine in substitution patterns. The study finds that accounting for addiction significantly increases the estimated effectiveness of soda taxes and projects that such taxes could reduce obesity-related body mass index (BMI) by approximately 2.8 to 3.1 units per individual annually, leading to potential medical cost savings of $642 to $717 per person. The authors note limitations including assumptions of statewide taxes without cross-border shopping effects and the exclusion of substitution to other sugary products, suggesting areas for future research.

Additional Information

  • Source:Management Science (INFORMS). 2026/04, Vol. 72, Issue 4, p3040
  • Document Type:Article
  • Subject Area:Consumer Health
  • Publication Date:2026
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2021.02422
  • Accession Number:192910460
  • Copyright Statement:Copyright of Management 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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