Getting Rich and Eating Out: Consumption of Food Away from Home in Urban China
Bibliographic record
Abstract
The overall goal of this study is to better understand food‐away‐from‐home (FAFH) consumption in urban China. We use national statistical sources and our own data to examine the trends in FAFH during the late reform period and to analyze the determinants of FAFH demand, examining how different groups of consumers have participated in this new area of consumption. Besides the normal Tobit model for total food expenditure away from home, a system of multivariate Tobit equations was estimated simultaneously for three categories of foods consumed outside of the home. The results show that the rapid increase of FAFH demand, a rise that is fueled by higher incomes, is changing consumption patterns in China's post‐reform urban economy. We also use our findings to illustrate how omission of accounting for FAFH trends by China's official statisticians has affected the reported trends in national meat supply and demand statistics. La présente étude visait à mieux comprendre le phénomène de la consommation de repas à l'extérieur en Chine urbaine. Nous avons utilisé des données de sources nationales et nos propres données pour étudier le phénomène au cours de la dernière réforme et pour analyser les déterminants de la demande de repas à l'extérieur en examinant la participation de divers groupes de consommateurs à ce nouveau créneau. Outre le modèle Tobit simple pour évaluer les dépenses totales de repas à l'extérieur, nous avons estimé simultanément un système d'équations Tobit à plusieurs variables pour trois catégories d'aliments consommés à l'extérieur du foyer. Les résultats ont montré que la croissance rapide de la demande de repas à l'extérieur, alimentée par une hausse des revenus, est en train de modifier les habitudes de consommation dans l'économie urbaine de la Chine post‐réformiste. Nous avons également utilisé nos résultats pour illustrer de quelle façon le fait que les statisticiens officiels de la Chine ne tiennent pas compte des tendances de consommation de repas à l'extérieur a une influence sur l'évaluation des tendances dans les données nationales de l'offre et de la demande de viande.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".