Non-Monotonicity of Fertility in Human Capital Accumulation and Economic Growth
Bibliographic record
Abstract
This paper investigates the relationship between per-capita human capital investment and the birth rate. Since the consequences of higher fertility (birth rate) on per-capita human capital accumulation (the so-called dilution effect) are not the same (in sign and magnitude) across different groups of countries with different birth rates, we analyze the growth impact of a non-linear dilution-effect. The main predictions of the model (concerning the relationship between population and economic growth rates) are then compared with those of a standard model in which the exogenous birth rate affects linearly and negatively (as postulated by most of the existing theoretical literature) human capital investment at the individual level. By using non-parametric techniques, we find evidence of strong nonlinearities in the total effect of fertility on human capital accumulation. This supports the idea that fertility plays a non-monotonic role in the accumulation of human capital and hence in the growth rate of an economy. The non-monotonic effect of fertility on human capital appears to be valid for OECD, as well as non-OECD countries according to our empirical results.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".