Empirical Analysis of Kaldor’s Growth Law: The Sample of OECD Countries
Bibliographic record
Abstract
Nicholas Kaldor has made important contributions to forming and developing the foundation of Post Keynesian economics. The industrial growth model is a fundamental contribution of Nicholas Kaldor to economic theory. Kaldor's growth law has a significant place in the economic growth literature. Kaldor laws explore the relationships between the industrial sector, economic growth, and labor productivity. According to Kaldor's law of growth, the economy's growth rate is positively related to the increase in production in the manufacturing industry sector. Kaldor stated that the engine of economic growth is the industrial sector. In this study, the validity of Kaldor's first law was analyzed using the panel data method for 38 OECD countries (the USA, Canada, the UK, Australia, Belgium, Austria, Chile, Czech Republic, Colombia, Estonia, Costa Rica, Denmark, Germany, Finland, Hungary, France, Ireland, Greece, Iceland, Israel, Portugal, Italy, Lithuania, Korea Rep., Latvia, Luxembourg, Mexico, Norway, Netherlands, New Zealand, Japan, Poland, Slovakia, Switzerland, Slovenia, Sweden, Spain, and Türkiye). 1997-2020 annual data were used in the study. According to the analysis, Kaldor law is valid for OECD countries. This result revealed in the analysis reveals that the industrial sector is very important for economic growth. Therefore, since the industrial sector is so important in economic growth, policies that will encourage the development of the industrial sector should be implemented.
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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 | 0.000 | 0.000 |
| 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 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".