The Crowding-out Effect of Tobacco Expenditure on Health Expenditure: Evidence From a Lower-Middle-Income Country
Bibliographic record
Abstract
INTRODUCTION: Poor people have remarkably lower health expenditures than rich people in Vietnam. According to the 2016 Vietnam Household Living Standard Survey (VHLSS), per capita health expenditure of the top quintile households is around 6 times higher than that of the bottom quintile households. AIMS AND METHODS: We analyze economic inequalities in health expenditure using the concentration index approach and data from the VHLSS 2010-2016. Next, we use the instrumental-variable regression analysis to examine the crowding-out effect of tobacco expenditure on health expenditure. Finally, we use decomposition analysis to explore whether economic inequality in tobacco expenditure is associated with an economic inequality in health expenditure. RESULTS: We find a crowding-out effect of tobacco expenditure on health expenditure of households. The share of health expenditure of households with tobacco spending is 0.78% lower than that of households without tobacco spending. It is estimated that a one-VND increase in tobacco expenditure results in a 0.18 Vietnamese Dong (VND) (95% CI: -0.30 to -0.06) decrease in health expenditure. There is a negative association between economic inequality in tobacco expenditure and economic inequality in health expenditure. This means that if the poor consume less tobacco, their expenditure on health can be increased, resulting in a decrease in inequality in health expenditure. CONCLUSIONS: Findings from this study suggest that reducing tobacco expenditure could improve health care of the poor and reduce inequality in health care in Vietnam. Our study recommends that the government continuously increase the tobacco tax in order to effectively reduce tobacco consumption. IMPLICATIONS: Empirical studies show mixed results on the effect of tobacco expenditure on health expenditure. We find a crowding-out effect of tobacco expenditure on health expenditure of poor households in Vietnam. It implies that if the poor reduce their expenditure on tobacco, economic inequality in health expenditure can be reduced. Our findings suggest that reducing tobacco consumption in poor households can increase their health expenditure, therefore, decreasing inequality in health expenditure. Different policies to reduce tobacco consumption such as tobacco taxation, smoke-free areas, and tobacco advertisement bans should be strengthened.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".