Why has the Employment-Productivity Tradeoff among Industrialized Countries been so strong?
Bibliographic record
Abstract
This paper is motivated by a set of cross-country observations on labor productivity growth among industrial countries over the period 1960-1997.In particular, we show that over this period, the speed of convergence among industrialized countries has decreased substantially while the negative effect of a country's own employment growth (or labor force growth) on labor productivity has increased dramatically.The main contribution of the paper is to show how these observations are consistent with the view that industrialized countries have been undergoing a particularly drastic technological revolution over the recent past.In effect, we show how the process of endogenous technological adoption, following the diffusion of a general purpose technology, can explain these observations by causing the emergence of an AK accumulation phase where demographic factors temporarily become an major determinant of labor productivity growth.Our estimation of the model implies that the AK phase has been in effect since the early to mid-seventies, but that this phase may now be coming to an end.An important contribution of the paper is to analyze growth experiences across advanced industrialized countries within an open economy framework and to evaluate the explanation by estimating a multicountry dynamic general model.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".