Inequality of the crowding-out effect of tobacco expenditure in Colombia
Bibliographic record
Abstract
In recent decades, policy initiatives involving increases in the tobacco tax have increased pressure on budget allocations in poor households. In this study, we examine this issue in the context of the expansion of the social welfare state that has taken place over the last two decades in several emerging economies. This study explores the case of Colombia between 1997 and 2011. In this period, the budget share of the poorest expenditure quintile devoted to tobacco products of smokers' households doubled. We analyse the differences between the poorest and richest quintiles concerning the changes in budget shares, fixing a reference population over time to avoid demographic composition confounders. We find no evidence of crowding-out of education or healthcare expenditures. This is likely to be the result of free universal access to health insurance and basic education for the poor. For higher-income households, tobacco crowds out expenditures on entertainment, leisure activities, and luxury expenditures. This finding should reassure policymakers who are keen to impose tobacco taxes as an element of their public health policy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".