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Record W270985840

Dynamic Effects of the Chinese GDP and Number of Higher Education Based on Cointegrating1/L'ÉTUDE DES EFFETS DYNAMIQUES DU PIB CHINOIS SUR L'ÉDUCATION SUPÉRIEURE BASÉE SUR LA COINTÉGRATION

2010· article· fr· W270985840 on OpenAlexvenueno aff
Fei-xue Huang, Cheng Li

Bibliographic record

VenueCanadian social science · 2010
Typearticle
Languagefr
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsCointegrationError correction modelHumanitiesGranger causalityPolitical scienceEconometricsMathematicsEconomicsPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Abstract: This study's objective was to the issue for the impact between regular higher education scale and GDP in China. We integrate Unit Root Test, Cointegration Test, Vector Error Correction Model (VECM), Variance Decomposition, etc. The sample is to use the annual data of GDP and number of Students Enrollment of Regular Institutions from 1952 to2004. Empirical results show that there is co-integration relation between GDP and number of Students Enrollment of Regular Institutions and economic growth can affect higher education scale and the contribution of education to economic growth is increasing gradually. To achieve good interaction between higher education and economic growth, the advice is that make scientific policy of regular higher education scale's expansion. Key Word: Gross Domestic Product (GDP); Granger causality test; Vector Error Correction Model; Cointegration Test; number of Students Enrollment of Regular Institutions Resume: L'objectif de cette etude etait d'examiner l'impact entre l'echelle de l'education superieure et le PIB en Chine en utilisant test de racine unitaire, test de cointegration, modele vectoriel de correction d'erreurs (MVCE), decomposition des ecarts, etc. L'echantillon utilise sont les donnees annuelles du PIB et le nombre des etudiants inscrits dans les institutions universitaires de 1952 a 2004. Les resultats empiriques montrent qu'il y a une relation de co-integration entre le PIB et le nombre des etudiants inscrits dans les institutions universitaires, que la croissance economique peut affecter l'echelle de l'education superieure et que la contribution de l'education a la croissance economique est en en train d'augementer progressivement. Pour obtenir une bonne interaction entre l'education superieure et la croissance economique, les auteurs conseillent d'etablir une politique scientifique de l'expansion reguliere de l'echelle de l'education superieure. Mots-cles: Produit interieur brut (PIB); test de causalite de Granger; Modele vectoriel a correction d'erreur; test de cointegration; le nombre des etudiantes inscrits dans les institutions 1. INTRODUCTION Foreign scholar Schultz expounded the concept, investment and formation ways of human capital, and the key role of human capital in economic growth systematically in 1990, and pointed out that education was the main way in forming human capital (Theodore, 1990). Yan Wang (2003) and so on measured human capital stock in China from 1978 to 1999, and then applied the Douglas function to test the data during this time. The results showed that the rapid accumulation of human capital had significant contribution to the economic growth and welfare improvement and total factor growth in gross domestic product still played an important role (WANG & YAO, 2003). Paul Glewwe and Hanan G. (2004) taking Vietnam for an example studied the extent of restrictions of household wealth in developing countries to investment in education, used the function of economic growth to study the relationship between wealth in the Vietnamese family and education needs and found that between wealth and educational needs there was a strong positive correlation (Paul & Hanan, 2004). Chinese scholar Cai Zeng-zheng (1999) used the data of the world's 194 countries and regions to study the contribution of education in 1965-1990 to economic growth. First, he modeled all the functions and spillover effects of education and then estimated their contributions to economic growth. The main findings showed that the contribution of education to economic growth was tremendous and substantive, not only the spillover effect was positive but also quite substantial; The education sector comparing with the rest of the economy had low productivity; The role of Education to economic growth in the process of economic development displayed weak at first, then strong and a little of abating at last (CAI, 1999). Ye Mao-lin, Zheng Xiao-qi, Wang bin (2003) argued that education played an important role in economic growth, which was an important factor in promoting economic growth; Economic growth, in turn, could support the education and contribute to its development. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.007
GPT teacher head0.277
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

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Citations0
Published2010
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