Interrelationships Among Government Participation, Population and Growth of per Capita Income: Inquiry on Top Twenty Income-Holding Countries in the World
Bibliographic record
Abstract
The literature on growth in economics encompasses two main facets of thinking: the applicability of diminishing productivity of capital, as has been in the neoclassical growth model with exogenous technological progress, and the applicability of non-diminishing productivity of capital, as has been in the endogenous growth models. The main conclusion of the former is the cross-country convergence to a common steady state while that of the latter is non-convergence. The tremendous history of the growth of the world’s so-called developed economies in the 1980s, diverging with the so-called backward economies, has nullified the applicability of the neoclassical growth model and justified non-steady state positive per capita growth of income and consumption through endogenous technological progress in terms of knowledge capital, human capital, good public institutions, etc. The present study aims to examine whether per capita income growth is explained by the size of government intervention coupled with the working population size in the world’s top twenty countries in terms of aggregate income. With the theoretical setup of the model and using empirical tools, such as cointegration, error correction and causality in a vector autoregression structure, this study reveals that eighteen countries maintain long-run relationships among per capita income growth, government participation, population and the interaction factors between government intervention and population, excepting Germany and Canada. Further, in the short run, for eleven countries on the list, there are instances in which public institutions associated with the population and the interaction term have a causal influence on the growth of per capita income. The empirical results relating to income growth, thus, have sustainability implications.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".