Pengaruh Fertilitas Terhadap Partisipasi Tenaga Kerja Perempuan
Bibliographic record
Abstract
Indonesia, like other countries in the world, is experiencing a downward trend in the total fertility rate and an increasing trend in the female labor force participation rate. This study looks at the effect of female fertility on job supply in Indonesia by using IFLS data and the instrumental variable (IV) estimation technique first introduced by Angrist and Evans (1996; 1998) conducted in the United States. This study shows how parental preferences are related to different sexes of children as identification of fertility towards women's participation in the labor market. The results of this study indicate that fertility with the approach of the number of children owned and then instrumented by looking at the sex of the child resulted in a decrease in the supply of female labor. The magnitude of the effect on the working age group is that there is a decrease in work participation of around 52-54 percent and a decrease in overall work hours of around 23 hours / week.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".