Estimating Price Elasticities When There is Smuggling: The Sensitivity of Smoking to Price in Canada
Bibliographic record
Abstract
A central parameter for evaluating tax policies is the price elasticity of demand for cigarettes. But in many countries this parameter is difficult to estimate reliably due to widespread smuggling, which significantly biases estimates using legal sales data. An excellent example is Canada, where widespread smuggling in the early 1990s, in response to large tax increases, biases upwards the response of legal cigarette sales to price. We surmount this problem through two approaches: excluding the provinces and years where smuggling was greatest; and using household level expenditure data on smoking, where there is a downward bias to estimated elasticities from smuggling. These two approaches yield a tightly estimated elasticity in the range of -0.45 to -0.47. We also show that the sensitivity of smoking to price is much larger among lower income Canadians. In the context of recent behavioral models of smoking, whereby higher taxes reduce unwanted smoking among price sensitive populations, this finding suggests that cigarette taxes may not be as regressive as previously suggested. Finally, we show that price increases on cigarettes do not increase, and may actually decrease, consumption of alcohol; as a result, smuggling of cigarettes may have raised consumption of alcohol as well.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".