The One Child Policy and Family Formation in Urban China
Bibliographic record
Abstract
The Chinese government implemented the One Child Policy (OCP) in an attempt to ameliorate the population explosion and its potential negative economic consequences on their infant economy in 1979. Here the consequences of this policy for marital matching and family size decisions are examined. A simple General Equilibrium model demonstrates how constraints on marital output on the quantity of children dimension raises the marginal benefit of increased positive assortative matching, and greater investment in children. These theoretical predictions are examined empirically in a variety of ways. The prediction of intensified positive assortative matching was examined using Distributional Overlap and Stochastic Dominance Tests and provided support for intensified assortative matching amongst the urban population. To support this positive finding, we next examined if the policy was indeed binding. The extent to which parental family size decisions were bound by the OCP were examined using Poisson regression techniques and the results suggest that the OCP principally affected the quantity of children decision by suppressing parental preference for male heirs and they suggest that after the OCP was implemented births beyond the first child are purely accidental among younger mothers. In addition, we also found some evidence of increased educational attainment among children reflecting increased parental investments in children post OCP further supporting the view that the One Child Policy altered significantly familial decisions in urban China.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".