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This dataset provides a detailed analysis of smoking trends worldwide, covering essential metrics such as:
- Total smokers and smoking prevalence rates
- Cigarette consumption and brand market share
- Tobacco taxation and smoking ban policies
- Smoking-related deaths and gender-based smoking patterns

Spanning data from 2010 to 2024, this dataset offers valuable insights for health research, policy evaluation, and data-driven decision-making.

📊 Column Descriptions

This dataset is about books. It has 7 rows and is filtered where the book subjects is Tobacco industry-United States-History. It features 9 columns including author, publication date, language, and book publisher.

This dataset is about book series. It has 1 row and is filtered where the books is Tobacco industry and smoking. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This database contains tobacco consumption data from 1970-2015 collected through a systematic search coupled with consultation with country and subject-matter experts. Data quality appraisal was conducted by at least two research team members in duplicate, with greater weight given to official government sources. All data was standardized into units of cigarettes consumed and a detailed accounting of data quality and sourcing was prepared. Data was found for 82 of 214 countries for which searches for national cigarette consumption data were conducted, representing over 95% of global cigarette consumption and 85% of the world’s population. Cigarette consumption fell in most countries over the past three decades but trends in country specific consumption were highly variable. For example, China consumed 2.5 million metric tonnes (MMT) of cigarettes in 2013, more than Russia (0.36 MMT), the United States (0.28 MMT), Indonesia (0.28 MMT), Japan (0.20 MMT), and the next 35 highest consuming countries combined. The US and Japan achieved reductions of more than 0.1 MMT from a decade earlier, whereas Russian consumption plateaued, and Chinese and Indonesian consumption increased by 0.75 MMT and 0.1 MMT, respectively. These data generally concord with modelled country level data from the Institute for Health Metrics and Evaluation and have the additional advantage of not smoothing year-over-year discontinuities that are necessary for robust quasi-experimental impact evaluations. Before this study, publicly available data on cigarette consumption have been limited—either inappropriate for quasi-experimental impact evaluations (modelled data), held privately by companies (proprietary data), or widely dispersed across many national statistical agencies and research organisations (disaggregated data). This new dataset confirms that cigarette consumption has decreased in most countries over the past three decades, but that secular country specific consumption trends are highly variable. The findings underscore the need for more robust processes in data reporting, ideally built into international legal instruments or other mandated processes. To monitor the impact of the WHO Framework Convention on Tobacco Control and other tobacco control interventions, data on national tobacco production, trade, and sales should be routinely collected and openly reported. The first use of this database for a quasi-experimental impact evaluation of the WHO Framework Convention on Tobacco Control is: Hoffman SJ, Poirier MJP, Katwyk SRV, Baral P, Sritharan L. Impact of the WHO Framework Convention on Tobacco Control on global cigarette consumption: quasi-experimental evaluations using interrupted time series analysis and in-sample forecast event modelling. BMJ. 2019 Jun 19;365:l2287. doi: https://doi.org/10.1136/bmj.l2287 Another use of this database was to systematically code and classify longitudinal cigarette consumption trajectories in European countries since 1970 in: Poirier MJ, Lin G, Watson LK, Hoffman SJ. Classifying European cigarette consumption trajectories from 1970 to 2015. Tobacco Control. 2022 Jan. DOI: 10.1136/tobaccocontrol-2021-056627. Statement of Contributions: Conceived the study: GEG, SJH Identified multi-country datasets: GEG, MP Extracted data from multi-country datasets: MP Quality assessment of data: MP, GEG Selection of data for final analysis: MP, GEG Data cleaning and management: MP, GL Internet searches: MP (English, French, Spanish, Portuguese), GEG (English, French), MYS (Chinese), SKA (Persian), SFK (Arabic); AG, EG, BL, MM, YM, NN, EN, HR, KV, CW, and JW (English), GL (English) Identification of key informants: GEG, GP Project Management: LS, JM, MP, SJH, GEG Contacts with Statistical Agencies: MP, GEG, MYS, SKA, SFK, GP, BL, MM, YM, NN, HR, KV, JW, GL Contacts with key informants: GEG, MP, GP, MYS, GP Funding: GEG, SJH SJH: Hoffman, SJ; JM: Mammone J; SRVK: Rogers Van Katwyk, S; LS: Sritharan, L; MT: Tran, M; SAK: Al-Khateeb, S; AG: Grjibovski, A.; EG: Gunn, E; SKA: Kamali-Anaraki, S; BL: Li, B; MM: Mahendren, M; YM: Mansoor, Y; NN: Natt, N; EN: Nwokoro, E; HR: Randhawa, H; MYS: Yunju Song, M; KV: Vercammen, K; CW: Wang, C; JW: Woo, J; MJPP: Poirier, MJP; GEG: Guindon, EG; GP: Paraje, G; GL Gigi Lin Key informants who provided data: Corne van Walbeek (South Africa, Jamaica) Frank Chaloupka (US) Ayda Yurekli (Turkey) Dardo Curti (Uruguay) Bungon Ritthiphakdee (Thailand) Jakub Lobaszewski (Poland) Guillermo Paraje (Chile, Argentina) Key informants who provided useful insights: Carlos Manuel Guerrero López (Mexico) Muhammad Jami Husain (Bangladesh) Nigar Nargis (Bangladesh) Rijo M John (India) Evan Blecher (Nigeria, Indonesia, Philippines, South Africa) Yagya Karki (Nepal) Anne CK Quah (Malaysia) Nery Suarez Lugo (Cuba) Agencies providing assistance: Iranian Tobacco Co. Institut National de la Statistique (Tunisia) HM Revenue & Customs (UK) Eidgenössisches Finanzdepartement EFD/Département...

This dataset is about books. It has 1 row and is filtered where the book is Suing the tobacco and lead pigment industries : government litigation as public health prescription. It features 7 columns including author, publication date, language, and book publisher.

The turnover of the tobacco product manufacturing industry in Bosnia-Herzegovina decreased to 7.9 million euros since the previous year. Therefore, 2019 marks the lowest turnover in this industry during the observed period. Notably, the turnover in this industry is continuously decreasing over the last years.For the purpose of Eurstat Dataset NACE Rev.2 Section K turnover comprises the totals invoiced by the observation unit during the reference period, which corresponds to market sales of goods or services supplied to third parties.Find more statistics on the tobacco product manufacturing industry in Bosnia-Herzegovina with key insights such as production value and number of employees.

This dataset is called the "IIT CDIP collection". "CDIP" stands for "Complex Document Information Processing" and "IIT" stands for "Illinois Institute of Technology" who originally built the dataset. The dataset consists of documents from the states' lawsuit against the tobacco industry in the 1990s. As a result of the settlement of that lawsuit (the "Master Settlement Agreement"), the companies had to make all the documents public in an archive, which currently resides at UCSF, the University of California, San Francisco.IIT used this data to build a dataset of "messy" documents that were challenging for existing systems to process. There is handwriting on the documents, stains, etc. TREC used an automatic text conversion of this dataset in the TREC Legal Track, and we also have the original TIFF scans of the documents. The dataset consists of around 7 million documents, preprocessed with 90s-era OCR, and also the original page scans in TIFF format. See contact information in this record for access to this dataset.

1970-2019. Orzechowski and Walker. Tax Burden on Tobacco. Tax burden data was obtained from the annual compendium on tobacco revenue and industry statistics, The Tax Burden on Tobacco. Data are reported on an annual basis; Data include federal and state-level information regarding taxes applied to the price of a pack of cigarettes.

This dataset is about books. It has 1 row and is filtered where the book is Moral jeopardy : risks of accepting money from the alcohol, tobacco and gambling industries. It features 7 columns including author, publication date, language, and book publisher.

This dataset provides information on 1,405 in United States as of April, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This dataset provides information on 11 in Arkansas, United States as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This dataset provides information about regimes in 10 Indian States (Karnataka, Kerala, Goa, Madhya Pradesh, Gujarat, Haryana, Bihar, West Bengal, Meghalaya, and Nagaland) for the period of 1990-2017. The dataset contains variables that are proxy of 'power' and 'stability' of state-level regimes for successive rounds of elections to legislative assemblies covering 1990-2017 period. These data were collected from the Election Commission of India and State Election Commissions available through their websites. We also used free internet searches for specific information including Wikipedia pages of state assembly elections as well as political leaders. The following points will help better understand the dataset and its strengths and limitations:There are two sheets in the associated MS Excel file. The first one, 'Regime variables' provides data about five variables chronologically for the successive election rounds (covering the 1990-2017 period) for 10 Indian states. These variables include: (1) names of chief ministers including indicating president rule in these states; (2) tenure of these chief ministers including duration of president rule if any; (3) names of political party/parties forming governments in these states; (4) type of governments: a single party majority government, a single party majority government that keeps alliances with other parties, and a coalition (multi-party) government; (5) number of seats (constituencies) won by various political parties in a given election round. The second sheet, 'Abbreviations-Political Parties' provide expansion of short forms used for some of the political parties in the dataset in the first sheet.We curated this dataset as part of the broader project wherein we were interested in assessing the power and stability of state regimes. We used the type of government and the winning part/parties share of total seats as proxy measures for the power of the regime. We calculated the regime switch (how frequently the regime change in successive election rounds) and the average tenure of chief ministers as proxy measures for the stability of the regime. It was not easy to find all the data we needed from one source. We could not locate certain data e.g. data on exact composition of political coalitions especially when frequent changes took place in such coalitions post elections in a few states such as Meghalaya and Goa. So, there are likely to be errors and omissions in data. We tried our best to capture data from authoritative sources as much as possible given the limited time and resources we had.This dataset was produced as part of the broader research project that explored the political economy of tobacco, titled “Deciphering an epidemic of epic proportion: the role of state and tobacco industry in tobacco control in post-liberalised India (1990-2017)”. We thank the DBT/Wellcome Trust India Alliance for funding this project through the Intermediate (Clinical and Public Health) Fellowship awarded to Upendra Bhojani (IA/CPHI/17/1/503346).