The Trade-off between Child Quantity and Child Quality: An Application of the Kremer-Chen Model to Households in Rural Terengganu, Malaysia
Bibliographic record
Abstract
The idea of the trade-off between child quantity and child quality has long been documented in economics. In the past, this trade-off has been attributed to the rising marginal cost of child quantity and quality. In recent years, this trade-off has been attributed to a declining direct cost of childrearing and an escalating opportunity cost of childrearing induced by rising household income. Exploiting a micro data set from rural Terengganu, we revisit the idea based on this new postulate. Our goal is to investigate whether a rise in household income has a positive impact on child quality and a negative impact on child quantity. In a series of empirical exercises, we find that household income has a positive impact on both child quantity and child quality. We take these findings as evidence that there is no trade-off between child quantity and child quality among rural households in Terengganu, Malaysia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".