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Retrospective and Prospective Benefit-Cost Analyses of U.S. Anti-Smoking Policies 1

  • Lawrence Jin (a1), Don Kenkel (a2), Feng Liu (a3) and Hua Wang (a4)


Regulatory policies designed to improve societal welfare by “nudging” consumers to make better choices are increasingly popular. The application of benefit-cost analysis (BCA) to this sort of regulation confronts difficult theoretical and applied issues. In this analysis we contribute a worked example of behavioral BCA of U.S. anti-smoking policies. Our conceptual framework extends the standard market-based approach to BCA to allow for individual failures to make lifetime-utility-maximizing choices of cigarette consumption. We discuss how our market-based approach compares to the health benefits approach and the “consumer surplus offset” controversy in recent BCAs of several health-related regulations. We use a dynamic population model to make counterfactual simulations of smoking prevalence rates and cigarette demand over time. In our retrospective BCA the simulation results imply that the overall impact of anti-smoking policies from 1964 to 2010 is to reduce the total cigarette consumption by 28%. At a discount rate of 3% the 1964–present value of the consumer benefits from anti-smoking policies through 2010 is estimated to be $573 billion ($2010). Although we are unable to develop a hard estimate of the policies’ costs, we discuss evidence that suggests the consumer benefits substantially outweigh the costs. We then turn to a prospective BCA of future anti-smoking Food and Drug Administration (FDA) regulations. At a discount rate of 3%, the 2010–present value of the consumer benefits 30 years into the future from a simulated FDA tobacco regulation is estimated to be $100 billion. However, the nature of potential FDA tobacco regulations suggests that they might impose additional costs on consumers that make it less clear that the net benefits of the regulations will be positive.

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Retrospective and Prospective Benefit-Cost Analyses of U.S. Anti-Smoking Policies 1

  • Lawrence Jin (a1), Don Kenkel (a2), Feng Liu (a3) and Hua Wang (a4)


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