International Productivity Differences and Roles of Domestic Investment, FDI and Trade
Bibliographic record
Abstract
ABSTRACT This paper calculates Theil’s entropy index to measure the extent of productivity differences across 92 countries for the period from 1970 to 2003. While there is evidence of increasing differences in productivity across these countries, we observe different patterns when we group the countries by income levels. These differences seem to be decreasing among middle income developing and developed countries, whereas they seem to be widening among low and high income developing countries. The results of our multivariate time series analysis also suggest that FDI increases productivity differences among low and high income developing countries, whereas GDI reduces these differences among low income countries in the long-run. Granger causality test results indicate that while an increase in GDI leads to a decline in growth of trade, a higher growth of trade appears to be important for attracting FDI to middle income countries. Furthermore, a reduction in productivity differences and a higher FDI growth lead to higher growth of trade in developed countries.
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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) | 0.000 | 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) | 0.000 | 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".