AK growth models: new evidence based on fractional integration and breaking trends
Bibliographic record
Abstract
According to AK growth models, permanent changes in investment rates have permanent effects on a country’s rate of economic growth. Jones (Quarterly Journal of Economics, 1995, 110, 495-525) finds strong evidence against this prediction studying the time series properties of GDP growth rates and investment output ratios in fifteen OECD countries for the period 1950-1988. In this paper, we test the same hypothesis in four OECD countries using a longer span of data (1870-2002 for Canada, the UK and the US and 1885-2002 for Japan). Moreover, instead of using classic approaches, which are based on stationary I (0) or unit roots I (1) processes, we use methodologies based on fractional integration. After examining the order of integration of GDP growth rates and non-residential investment rations for these countries, we do not find much evidence against the “growth effects” prediction of AK models. In fact, we only find clear evidence against this theory for the UK case.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".