MétaCan
Menu
Back to cohort
Record W2492497785

A rational taxation system of bidis and cigarettes to reduce smoking deaths in India

2011· article· en· W2492497785 on OpenAlexaff
Prabhat Jha, G. Emmanuel Guindon, Renu Ann Joseph, Arindam Nandi, Rijo M John, Kavita Rao, Frank J. Chaloupka, Jagdish Kaur, Prakash C. Gupta, M. Govinda Rao

Bibliographic record

VenueEconomic and political weekly/Economic & political weekly · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversity of WaterlooUniversity of Toronto
Fundersnot available
KeywordsConsumption (sociology)ExciseContext (archaeology)Tax revenueHarmRevenueTobacco controlBusinessGovernment (linguistics)EconomicsPublic economicsMedicineGeographyPublic healthPolitical scienceFinance
DOInot available

Abstract

fetched live from OpenAlex

Tobacco smoking of bidis and cigarettes causes about one million deaths a year in India. India's relatively high consumption is due in part to a historically low or no tax on bidis and an inefficient, complex system of taxing cigarettes. In the context of planned tax reforms in India, we provide specific recommendations to raise tobacco taxes and to adopt a simpler and more efficient tax administration that would curb smoking. We estimate that raising the tax as a percentage of retail price from 7% to 33% for bidis and from 43% to 58% for cigarettes would conservatively lead to about 14 million smokers quitting and 27 million children never starting, thereby saving some 69 million years of healthy life over the next 40 years. The increase would also raise about Rs 73 billion or an additional 1.2% of current government revenue, while incurring no or minimal economic harm. Modest action on tobacco taxes in India might well save millions of lives. S moking bidis or cigarettes accounts for nearly one million adult deaths a year, or about 10% of all deaths at all ages (Jha et al 2008). The current patterns of tobacco use in India are a consequence of a significant informal economy, struc - ture of taxation, poor information systems and ineffective regu- lation of tobacco products. Tobacco-attributable deaths have fallen sharply in the last two decades in most high income coun- tries in response to comprehensive tobacco control efforts. Higher taxation of tobacco products is the single most effective intervention to reduce consumption (Jha 2009). Additional com- ponents of comprehensive tobacco control include complete bans on smoking in public places; prominent, graphic warning labels and public education campaigns that warn people about the dangers of tobacco use; comprehensive bans on tobacco advertis- ing, promotion; and support for smokers trying to quit (Jha and Chaloupka 1999). Improved health is a key development goal of the Government of India (GOI). Moreover, GOI has recently begun major reforms of its taxation structures including introduction of value added taxation on goods at the state level and proposes to introduce a goods and services tax at both central and state levels (Rao 2010). Thus, now is an appropriate time to conduct a systematic review of tobacco use and the current taxation structure, and to recom- mend specific reforms. Here, we review the key economic issues related to tobacco use and its regulation in India. Our chief conclusions are that substantially higher and smarter excise taxes of bidis and cigarettes would prevent millions of premature deaths, raise additional revenue, and that higher taxation would incur minimal economic costs. We first review the consumption patterns and health conse - quences of smoking in India, followed by the rationale for taxa- tion and the current chaotic tax structure. We describe our pro- posed tax reforms in detail and provide empirical analyses of the effects that a more rational system of higher taxes would have on consumption, tobacco mortality, and revenue. We discuss three common objections to higher taxes on tobacco, and provide two specific recommendations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.234
Teacher spread0.201 · 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 designTheoretical or conceptual
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

Citations55
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueEconomic and political weekly/Economic & political weeklySame topicFiscal Policy and Economic GrowthFrench-language works237,207