Revisiting the effects of cigarette taxation on smoking outcomes

Abstract This study reassesses the efficacy of cigarette taxation in curtailing smoking by leveraging recent advancements in the difference-in-differences (DiD) literature to account for heterogeneous treatment effects. Using data from the Behavioral Risk Factor Surveillance System Selected Metropolitan/Micropolitan Area Risk Trend (BRFSS SMART) for the sample periods 2004-2010 and 2015-2020, the study reveals three key findings. Firstly, the TWFE estimate for the 2004-2010 sample is only 48% of the average treatment effect on the treated (ATT) estimate obtained through the DiD framework. Secondly, event-study-type estimates demonstrate a gradual increase in magnitude following the treatment year, highlighting dynamic treatment effects overlooked by the TWFE estimate. Third, the ATT estimate for the 2015-2020 sample is approximately 66% of the ATT estimate for the 2004-2010 sample. Overall, the study underscores the potential bias toward zero in elasticity estimates when relying solely on TWFE models.

Authors

Institutions

Publication Details

Journal
Empirical Economics
Published
2024-12-31
DOI
https://doi.org/10.1007/s00181-024-02674-0
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Revisiting the effects of cigarette taxation on smoking outcomes

Vinish Shrestha
Empirical Economics
Advanced Causal Inference Techniques
article

Revisiting the effects of cigarette taxation on smoking outcomes

Vinish Shrestha
article en

Abstract

Abstract This study reassesses the efficacy of cigarette taxation in curtailing smoking by leveraging recent advancements in the difference-in-differences (DiD) literature to account for heterogeneous treatment effects. Using data from the Behavioral Risk Factor Surveillance System Selected Metropolitan/Micropolitan Area Risk Trend (BRFSS SMART) for the sample periods 2004-2010 and 2015-2020, the study reveals three key findings. Firstly, the TWFE estimate for the 2004-2010 sample is only 48% of the average treatment effect on the treated (ATT) estimate obtained through the DiD framework. Secondly, event-study-type estimates demonstrate a gradual increase in magnitude following the treatment year, highlighting dynamic treatment effects overlooked by the TWFE estimate. Third, the ATT estimate for the 2015-2020 sample is approximately 66% of the ATT estimate for the 2004-2010 sample. Overall, the study underscores the potential bias toward zero in elasticity estimates when relying solely on TWFE models.

Empirical EconomicsVol. 68(3)
Towson University (US)
Good health and well-being
Openalex Percentile: Top 75%
Advanced Causal Inference Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Revisiting the effects of cigarette taxation on smoking outcomes — Vinish Shrestha · Empirical Economics (2024) | TGRS Research Map | TGRS