Effect of price and income on the demand for sugar-sweetened beverages in Nigeria: an analysis of household consumption data using an almost ideal demand system (AIDS)
Bibliographic record
Abstract
OBJECTIVES: To estimate the own-price, cross-price and income elasticities for carbonated soft drinks (CSDs), malt drinks, chocolate powder, sachet water and sugar in Nigeria. These elasticities can be used to estimate the potential demand response to the recently-introduced sugar tax in Nigeria. SETTING: The study uses household data from the 2018/2019 Nigeria Living Standards Survey (NLSS). PARTICIPANTS: The NLSS is a national household survey. 21 114 households were included in the final sample for this analysis. PRIMARY AND SECONDARY OUTCOMES: We used Deaton's almost ideal demand system, which controls for the goods' quality, to estimate the effect of price and income changes on the demand for CSDs, chocolate powder, malt drinks, sachet water and sugar. RESULTS: We found that the own-price elasticity (ordered from most to least price-responsive) was -0.99 (p<0.01) for sachet water, -0.76 (p<0.01) for CSDs, -0.72 (p<0.01) for chocolate powder, -0.62 (p<0.01) for sugar and -0.19 (p<0.01) for malt drinks. The cross-price elasticities indicate that malt drinks and chocolate powders are substitutes of CSDs. The income elasticities indicate that all the commodities are normal goods. Sachet water had the highest income elasticity at 0.62 (p<0.01), followed by chocolate powder at 0.54 (p<0.01), CSDs at 0.47 (p<0.01), malt drinks at 0.43 (p<0.01) and sugar at 0.13 (p<0.01). CONCLUSION: Even though the price elasticities for CSDs, malt drinks and chocolate powder are less than one, in absolute terms, they are significantly different from zero. Increases in the sugar-sweetened beverage tax could curb the demand for these beverages, and, in turn, reduce the incidence and prevalence of sugar-attributable diseases.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".