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Record W2928142661 · doi:10.18332/tpc/105199

Economics of Smokeless Tobacco Taxation in Bangladesh

2019· article· en· W2928142661 on OpenAlexfundno aff
Nasiruddin Ahmed

Bibliographic record

VenueTobacco Prevention & Cessation · 2019
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsnot available
FundersThird Health ProgrammeUniversity of WaterlooCanadian Institutes of Health ResearchEuropean Commission
KeywordsSmokeless tobaccoEconomicsBusinessEnvironmental healthTobacco useMedicine

Abstract

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Introduction With 22.0 million (20.6%) adult people using smokeless tobacco (SLT), Bangladesh is one of the largest SLT consuming countries in the world (WHO, GATS, 2017). Of SLT users, 16.2% are men and 24.8% are women. The Global Youth Tobacco Survey (GYTS), 2013 reveals that more students were SLT users (4.5%) than smokers (2.9%). The prevalence of SLT use is alarming because Bangladesh ranks the second (next to India) in 34 high SLT burden countries (Sinha and Yadav, 2017). Objectives and methods The study attempts to examine the present structure of SLT taxes in Bangladesh, and estimate the own-and cross-price elasticities of demand for SLT products with a view to suggesting more appropriate SLT tax pricing strategies for designing effective SLT tax policy in Bangladesh. For examining the SLT tax structure, we have used the secondary data collected from the National Board of Revenue (NBR), and other relevant data. In order to estimate the price elasticities of demand for SLT products, we have used Deaton Model (1997) which exploits price variation over space to estimate price elasticities using household survey data. We have used Household Income and Expenditure Survey (HIES) 2016 data of Bangladesh Bureau of Statistics (BBS) for estimating price elasticities of demand for SLT products. Significance of the study The significance of undertaking this study is due to several reasons. Firstly, SLT has received little attention in terms of quality research and evidence-based policy making in Bangladesh. Secondly, the HIES, 2016 of the BBS shows that on average, the consumption of SLT products accounts for the largest share being 1.4 per cent of the total household budget. Thirdly, SLT holds the potential for increasing the tax revenue of the government as the revenue share of SLT products is only 0.14 per cent of the NBR revenue. Results The government has developed a complex multi-tiered ad valorem SLT tax system, which creates a number of problems. The tax base for SLT is tariff value, which is much lower than the retail price. The overall taxation on SLT remains generally low, making it readily affordable to people especially women. Low SLT price also encourages downward substitution from smoked to SLT and discourages quitting behavior. The estimate of own-price elasticity of demand for SLT products is -0.24 (inelastic demand), which is consistent with the available evidence (Nargis et al, 2014). Rural households are found to be more responsive to change in prices of SLT products than urban households.The poor households are more responsive to the changes in the price of SLT products than the rich households. Conclusions Our findings suggest that using the tax system to increase significantly the prices of SLT products would lead to a substantial reduction in SLT use while increasing government revenue. The tax base for SLT needs to be changed from tariff value to retail price. Measures may be taken to harmonize tax rates across tobacco products to avoid substitution of one tobacco product by another. Policymakers may introduce specific excise system replacing the existing ad valorem for substantially contributing to the revenue collection from SLT products.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.236
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2019
Admission routes1
Has abstractyes

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