Socioeconomic differences in the impact of prices and taxes on tobacco use in low- and middle-income countries–A systematic review
Bibliographic record
Abstract
There is indisputable evidence that increases in taxes that raise tobacco prices reduce tobacco use. Consumption taxes on manufactured tobacco products, however, can be regressive in socioeconomic status (e.g., when the ratio of tax paid to income is lower for higher-income groups than for lower-income groups). Nevertheless, if the poor or less educated are more price responsive, a change in tobacco tax may be progressive in socioeconomic status. Existing reviews clearly indicate that populations with lower income or education are more responsive to tobacco tax and price changes than higher-income and more educated populations in high-income countries. Research pertaining to low- and middle-income countries was, however, limited and inconclusive. We conducted a review of quantitative studies that examined if socioeconomic status modified the association between prices and taxes and tobacco use in low- and middle-income countries. We searched two electronic databases, two search engines, and two working paper repositories. At least two reviewers independently screened articles for inclusion, extracted detailed characteristics, and assessed the risk of bias of each included study. Thirty-two studies met our inclusion criteria. Overall, we found that the evidence in low- and middle-income countries was too limited and methodologically weak to make any conclusive statements. Our review highlights a number of data and methodological limitations in existing studies. The most important limitation was the lack of formal assessment of socioeconomic differences in price responsiveness. Only seven of 32 studies assessed statistically whether own-price effects were modified by socioeconomic status. Many modelling studies have examined the distributional effect of a tax increase on tobacco use, while assuming a strong own-price elasticity gradient in income. The poor were generally assumed to be more responsive to price by a factor of two to five, relative to the wealthy. Although there are theoretical reasons to expect poorer individuals to be more responsive to monetary prices than wealthy ones in low- and middle-income countries, our review provides little empirical support.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".