Cigarette tax pass-through in Vietnam: evidence from retailers’ data
Bibliographic record
Abstract
Purpose This study aims to estimate the pass-through rate of the increases in the excise tax and TCF tax on tobacco in Vietnam. This study seeks to shed light on how the tax burden is split between consumers and producers and inform policy discussions in the country. Using panel micro-level data collected from three waves of a nationwide retailer's survey, this study provides an evidence-based pass-through estimation for tobacco tax in Vietnam and contributes to the understanding of tax policy on smoking and smoking-related issues. Design/methodology/approach Following increases in the excise tax and TCF tax on tobacco in 2019, the differential effect of the tax hike on the “treatment group” (domestic cigarettes) versus the “control group” (illicit cigarettes) using a difference-in-difference (DID) analysis has been studied. The study utilized unique longitudinal retailers’ data on cigarettes prices in Vietnam from 2018 to 2019 to estimate the tax pass-through rate for some of the most popular factory-made cigarette brands. Findings This study found evidence of an over-shifting of cigarette taxes on smokers. Specifically, it discovered that the tax increase is absorbed more by low-priced brand smokers compared to premium brand users due to (1) the limited increase in prices under a pure ad valorem system and (2) the way the Vietnamese currency is denominated. Additionally, there is evidence of cushioning to mitigate price shock on consumers as the real prices increase gradually over the period of one year after the tax change. Originality/value To the best of the authors’ knowledge, this study is the first to collect and analyze a unique panel micro-level data from three waves of a nationwide retailers’ survey, which captures the changes in marketing and pricing strategies of the tobacco industry in Vietnam before and after an increase in excise tax in 2019. The results of this study could be used as a reference for future policymakers in considering increasing taxes on tobacco.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".