Essays on the Impact of China's One-Child Policy on Economic Development
Bibliographic record
Abstract
My dissertation focuses on the macroeconomic consequences of China's one-child policy.The first chapter examines the effects of China's one-child policy on savings and foreign reserve accumulation.Fertility control increases the saving rate both by altering saving decisions at the household level, and by altering the demographic composition of the population at the aggregate level.As in Song, Storesletten and Zilibotti (2011), government-owned firms are assumed to be less productive but have better access to the credit market compare to entrepreneurial firms.As labor switches from less productive to more productive firms, demand for domestic bank borrowing decreases.As saving increases while demand for loans decreases, domestic savings are invested abroad, generating a foreign surplus.In the second chapter of my dissertation, I provide a theoretical framework for examining the effects of China's one-child policy on its long run economic growth.The model incorporates within family intergenerational transfers and a "quantity/quality" tradeoff.When a population control policy is implemented, parents increase investment in their children's education in order to compensate for reduction in future transfers.As in Galor and Weil (2010), technological progress is assumed to be driven by two forces: the population size and the level of education.With population control, the total population decreases and the average level of education increases.Thus, the overall effect on technological progress is ambiguous without specifying functional forms for technology and human capital.The third chapter provides a quantitative exploration of the model from the second chapter.The calibrated results are consistent with the model, in which population, technological progress, and income per capita move in endogenous cycles.The impact of China's one-child policy depends on the timing of the policy.If the policy is enforced when the population is large enough, hence when the rate of technological progress is high, it increases GDP growth both in the short-run and in the long-run.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".