Bibliographic record
Abstract
This paper revisits demographic dividend issues after almost 2 decades of debate. In 1998, David Bloom and I used a convergence model to estimate the impact of demographic-transition-driven age structure effects and calculated what the literature has come to call the “demographic dividend.” These early estimates seem to be similar to those coming from more recent overlapping generation models, when properly estimated. Research has shown that the demographic dividend is not simply a labor participation rate effect, but also a growth effect. Life-cycle savings, investment deepening, foreign capital flows, and schooling have all been greatly affected by the demographic transition. The paper discusses just how much of these positive growth effects are based on accelerating human capital accumulation induced by demand-side quality–quantity trade-offs versus a co-movement between demographic transitions and public schooling supply-side expansions. Since emigration has been driven in part by demography, it has wasted some of the demographic dividend by brain drain. In addition, within-country rural–urban migrations have also been driven in part by demographic transitions with different spatial timing. Finally, the paper shows how lifetime—not just annual—income inequality has been influenced by demographic transitions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.005 | 0.015 |
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; both teacher heads agree on what is shown here.
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".