Bibliographic record
Abstract
Since the economic reform of China in the late 70s, China is going through a rapid economic development, however, the proportion of consumption in the national economy is declining over the past 30 years. In this paper, we first describe the current facts of China’s falling consumption rate. Then we propose the factors that lead to this decline and divide them to two categories. One category consists of structural factors, which corresponds to the declining proportion of the disposable income in GDP. The other category consists of behavior factors, and they describe how much the consumption rate is affected by consumer behavior. The research proposed in this paper is based on fundamental statistical data, using time series analysis on Chinese economy as well as international and inter-provincial panel-data analysis. To study on this problem, our research is comprised of two steps. In the first step, we focus on the change of China’s disposable income per GDP. In the second step, we study on the change of household consumption as percentage of disposable income. Key words: Consumption Rate; Economic Structure; National Income DistributionResume: Depuis la reforme economique a la fin des annees 70, la Chine est en train de connaitre un developpement economique rapide, cependant, la proportion de la consommation dans l'economie nationale est en declin au cours des 30 dernieres annees. Dans cet article, nous decrivons d'abord les circonstances actuelles de la baisse du taux de la consommation de la Chine. Nous exposons ensuite des facteurs qui conduisent a ce declin et les divisons en deux categories. Une categorie se compose des facteurs structurels, ce qui correspond a la proportion decroissante du revenu disponible dans le PIB. L'autre categorie comprend des facteurs de comportement, et ils decrivent a quel point le taux de consommation est affecte par le comportement des consommateurs. La recherche proposee dans le present article est basee sur des donnees statistiques fondamentales, en utilisant l'analyse des series chronologiques sur l'economie chinoise et l’analyse des donnees de panel internationales et inter-provinciales. Afin d’etudier ce probleme, notre recherche est composee de deux etapes. Dans la premiere etape, nous nous concentrons sur le changement du revenu disponible par unite de PIB de la Chine. Dans la deuxieme etape, nous etudions le changement de la consommation des menages en pourcentage du revenu disponible. Mots cles: Taux de consommation; Structure economique; Distribution du revenu national
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".