Population Growth, Technological Adoption and Economic Outcomes: A Theory of Cross-Country Differences for the Information Era
Bibliographic record
Abstract
The object of this paper is to show how population growth, through its interaction with recent technological and organizational developments, can account for many of the cross-country differences in economic outcome observed among industrialized countries over the last 20 years. In particular, our model illustrates how a large decrease in the price of information technology can create a comparative advantage for high population growth economies to jump ahead in the adoption of computer- and skill-intensive models of production as a means to exploiting their relative abundance of human capital versus physical capital. The predictions of the model are that, over the span of the information revolution, industrial countries with higher population growth rates will experience a more pronounced adoption of new technology, a better performance in terms of increased employment rates, a poorer performance in terms of wage growth for less skilled workers, a larger increase in the service sector and a larger increase in the returns to education. We provide preliminary evidence in suport of the theory based on a comparative study of observed developments in the US, UK and Germany since the mid-seventies, complemented by an examination of broad wage and employment changes for 18 OECD countries over the same period.
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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 | 0.005 | 0.001 |
| 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 | 0.001 |
| 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".