Intergenerational Transfer, Human Capital and Long-term Growth in China under the One Child Policy
Bibliographic record
Abstract
We argue that the demographic changes caused by the one child policy (OCP) may not harm China's long-term growth.This attributes to the higher human capital induced by the intergenerational transfer arrangement under China's poor-functioning formal social security system.Parents raise their children and depend on them for support when they reach an advanced age.The decrease in the number of children prompted by the OCP resulted in parents investing more in their children's educations to ensure retirement consumption.In addition, decreased childcare costs strengthen educational investment through an income effect.Using a calibrated model, a benchmark with the OCP is compared to three counterfactual experiments without the OCP.The output under the OCP is expected to be about 4 percent higher than it would be without the OCP in 2025 under moderate estimates.The output gain comes from a greatly increased educational investment driven by fewer children (11.4 years of schooling rather than 8.1).Our model sheds new light on the prospects of China's long-term growth by emphasizing the OCP's growth enhancing role through human capital formation under the intergenerational transfer arrangement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".