Changes in the world distribution of output-per-worker 1960-98: how a standard decomposition tells an unorthodox story
Bibliographic record
Abstract
Why have some countries done so much better than others over the recent past? In order to shed new light on this issue, this paper provides a decomposition of the change in the distribution of output-per-worker across countries over the period 1960-98. The main finding of the paper is that most of the change in shape of the world distribution of income between 1960-1998 can be accounted for by a very substantial and previously unrecognized change in the parameters driving the growth process. In particular, we show that the role of capital deepening forces - that is the role of investment rates and population growth in affecting output - increased dramatically over the period 1978-98 versus 1960-78, and that this increase can account for almost all the observed changes in the world distribution. In contrast, we do not find any significant effects coming through non-linear convergence mechanisms or increased importance of education; both of which have played prominent roles in recent discussion of economic performance. Our results therefore highlight that the period 1978-98 was particularly advantageous to countries which strongly favored capital accumulation and hence suggests that research aimed at understanding recent differences in economic performances across countries needs to focus on explaining why the social returns to physical capital accumulation where abnormally high over the period 1978-98.
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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 | 0.001 | 0.002 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".