Heterogeneous Demand for Food Diversity: A Quantile Regression Analysis
Bibliographic record
Abstract
Poverty and inequality studies frequently use the quantile regression approach to provide results on the impact of determinants at different points of the distribution of a dependent variable. To innovate diversity research this paper uses quantile regressions to identify determinants at different points of the food diversity distribution. Regional and household level differences in demand for food diversity are analysed based on a pooled sample of Canadian data of the Family Food Expenditure Surveys of Statistics Canada. Simple OLS regressions show that the determinants of Canadian diversity demand are similar to those of other developed countries. However, with quantile regressions significantly different effects of independent variables on diversity across quantiles are observed. In most cases, low diversified households, especially those in the lower 10% quantile of the distribution, are much more affected by key determinants such as household size, (real) income and age than higher-diversified households. Results further reveal that the demand for food diversity is not stable over time but is lower in 1996 and 2001 than in 1984. The diversity decline over time is higher for households in the middle quantiles compared to the moderate decline for households located at the ends of the diversity distribution. In Armuts- und Ungleichheitsstudien sind Quantil-Regressionen eine häufig angewandte Methode um den Einfluss von Determinanten an verschiedenen Punkten der Verteilung einer abhängigen Variablen zu identifizieren. Um die bestehende Lebensmittelvielfaltsliteratur zu erweitern, werden in diesem Beitrag Quantil-Regressionen dafür genutzt, Nachfragedeterminanten an verschiedenen Stellen der Lebensmittelvielfaltsverteilung zu bestimmen. Dabei werden sowohl regionale als auch weitere haushaltsspezifische Unterschiede in der Nachfrage basierend auf einem gepoolten kanadischen Datensatz identifiziert. Ergebnisse einfacher Regressionen bestätigen, dass die Nachfrage nach Lebensmittelvielfalt in Kanada von ähnlichen Determinanten bestimmt ist wie in anderen Ländern. Die Quantil-Regressionen zeigen allerdings, dass je nach Quantil signifikant unterschiedliche Einflüsse auf die Vielfaltsnachfrage vorliegen. In den meisten Fällen sind die Haushalte mit der geringsten Vielfaltsnachfrage, insbesondere solche im unteren 10%-Quantil, am stärksten beeinflusst durch Schlüsselgrößen wie Haushaltsgröße, (Real)einkommen und Alter als Haushalte mit einer höheren Nachfrage nach Vielfalt. Die Ergebnisse zeigen auch, dass die Vielfaltnachfrage über die Zeit nicht konstant ist, sondern 1996 und 2001 niedriger ist als 1984. Der Rückgang in der Vielfaltsnachfrage über die Zeit ist größer bei Haushalten in den mittleren Quantilen im Vergleich Haushalten an den Enden der Vielfaltsverteilung.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".