Dynamic Impact Mechanism of Birth Rate and GDP in Liaoning Province1/ MÉCANISME DE L'IMPACT DYNAMIQUE DU TAUX DE NATALITÉ ET DU PIB DANS LA PROVINCE DU LIAONING
Bibliographic record
Abstract
Abstract: This paper's objective is to study the issue of impact mechanism of birth rate between Liaoning province and China integrating unit root test, cointegrating test, vector error correction model (VECM), generalized impulse response function and variance decomposition. After selecting the data of birth rate, infant mortality rate and real GDP per capita of Liaoning province and China, we've found that: (1) The log series of birth rate, infant mortality and real GDP per capita are integrated of order one; (2) Long-term co-integration relationships exist among these three series, and the impacts of infant mortality rate and GDP per capita on birth rate are more significant in Liaoning province; (3) According to VECM, the adjustment of birth rate in Liaoning province is 0.09067971446 while it is 0.463901315 in China, which means self-regulatory ability of birth rate in Liaoning province is relatively weak; (4) According to generalized impulse response function and variance decomposition in the short-term birth rate fluctuation of Liaoning isn't affected by infant mortality rate and GDP per capita while in the national level the effect is great. The conclusions and suggestions are: (1) China is in the period of Demographic window of opportunity nowadays so high-speed economic growth and low birth rate are both necessary. China should improve professional education and training to build the harmonious society; (2) Remove obstacles of labor migration in order that the most active factor of production - labor force can flow into Liaoning freely, therefore, we can improve the allocative efficiency without a larger population. Key words: Population Bonus; Social Security System; Education and Training; Birth Rate; Vector Error Correction Model Resume: L'objectif de cet article est d'etudier le probleme du mecanisme de l'impact du taux de natalite dans la province du Liaoning et en Chine, en integrant le test de racine unitaire, le test de cointegration, le modele a correction d'erreur vectorielle (MCEV), la fonction de reponse impulsionnelle generalisee et la decomposition de la variance. Apres avoir selectionne les donnees du taux de natalite, du taux de mortalite infantile et du PIB reel par habitant de la province de Liaoning et de la Chine, nous avons constate que: (1) Les series de logarithme du taux de natalite, du taux de mortalite infantile et du PIB reel par habitant sont integres dans le premier ordre; (2) Des relations de co-integration a long terme existent entre ces trois series, et les impacts du taux de mortalite infantile et du PIB par habitant sur le taux de natalite sont plus importants dans la province du Liaoning, (3) Selon MCEV, l'ajustement du taux de natalite dans la province de Liaoning est de 0,09067971446, alors qu'il est de 0,463901315 en Chine, ce qui signifie que la capacite d'auto-reglementation du taux de natalite dans la province de Liaoning est relativement faible; (4) Selon la fonction de reponse impulsionnelle generalisee et la decomposition de la variance a court terme, la fluctuation du taux de natalite du Liaoning n'est pas affectee par le taux de mortalite infantile et le PIB par habitant alors qu'au niveau national, l'impact est plus important. Les conclusions et les suggestions sont les suivantes: (1) Aujourd'hui, la Chine est en une periode de fenetre demographique d'opportunite, donc une croissance economique rapide et un taux de natalite faible sont tous necessaires. La Chine devrait ameliorer l'education et la formation professionnelle pour construire une societe harmonieuse; (2) Il faut supprimer les obstacles de la migration du travail afin que le facteur le plus actif de la production - la main-d'oeuvre peut circuler librement dans la province du Liaoning, par consequent, nous pouvons ameliorer l'efficience allocative sans une population plus grande. Mots-cles: bonus de la population; systeme de securite sociale, education et formation; taux de natalite; modele a correction d'erreur vectorielle 1. …
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".