Evidence map›Paper›PMID 32779278›Full record

ArticleHealth economics2020

Mostly harmless regulation? Electronic cigarettes, public policy, and consumer welfare.

Donald S Kenkel, Sida Peng, Michael F Pesko, Hua Wang

Abstract read
In one paragraph

Article in Health economics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Trial
  2. E-CIGARETTE FLAVOR RESTRICTIONS' EFFECTS ON TOBACCO PRODUCT SALES.American journal of health economics · 2026
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Donald S KenkelDepartment of Policy Analysis and Management, Cornell University, Ithaca, NY, USA.
Sida PengOffice of Chief Economist, Microsoft Research, Redmond, WA, USA.
Michael F PeskoDepartment of Economics, Georgia State University, Atlanta, GA, USA.ORCID 0000-0002-9247-9703
Hua WangDepartment of Policy Analysis and Management, Cornell University, Ithaca, NY, USA.

Funding

Monitoring and Assessing the Impact of Tax and Price Policies on US Tobacco UseU01CA154248 · NCI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI CHALOUPKA, FRANK JOSEPH · 2011 to 2015
$6.6M
The Impacts of Vaping Regulations on Perceptions, Access, Prices, and Tobacco UseR01DA045016 · NIDA · UNIVERSITY OF MISSOURI-COLUMBIA · PI Michael Pesko · 2018 to 2026
$4.2M
NCI NIH HHS U01 CA154248NIDA NIH HHS R01 DA045016
6 · The paper itself

Abstract

Electronic cigarettes are a less harmful alternative to combustible cigarettes. We analyze data on e-cigarette choices in an online experimental market. Our data and mixed logit model capture two sources of consumer optimization errors: overestimates of the relative risks of e-cigarettes and present bias. Our novel data and policy analysis make three contributions. First, our predictions about e-cigarette use under counterfactual policy scenarios provide new information about current regulatory tradeoffs. Second, we provide empirical evidence about the role consumer optimization errors play in tobacco product choices. Third, we contribute to behavioral welfare analysis of policies that address individual optimization errors. Compared with standard cost-benefit analysis, our behavioral welfare economics analysis leads to much larger estimates of the costs of policies that discourage e-cigarette use or the benefits of policies that encourage e-cigarette use.

Indexed as

Electronic Nicotine Delivery SystemsTobacco ProductsHumansPublic Policybehavioral welfare analysisconsumer optimizationelectronic cigaretteselectronic nicotine delivery systemstobacco control

Identifiers

PMID32779278
PMCPMC7876166

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.