Demographic Effects on Fertility Among Reproductive Aged Women in Ghana. A Demographic and Health Survey Analysis
Bibliographic record
Abstract
In this paper, we argue that fertility is among the key components in the demographic transition process of many global economies. The study used the 2014 Ghana Demographic and Health Survey. We applied the conventional neo-classical consumer-theoretic approach to model the demand of children. The study aimed to estimate the demographic effects on fertility among women of reproductive age 15-49 in Ghana using the negative binomial regression. The results indicated that the age of women at first births, mother’s years of education, number of unions, wealth level, women occupations, Partners occupation, contraceptive use and intention, marital status, are significant and negatively associated with the number of children ever born. However, current age of the woman, fertility preference, decision maker on contraceptive use, child sex, number of living children and experienced of the death of a son and/or daughter resulting from replacement effect and hoarding effects and residing in rural areas have positively and significantly influence fertility in Ghana. We recorded variations in the effects of some variables in total, urban and rural samples. We conclude that reduction in child mortality, increasing women access of formal education, increasing women power in decision making on contraceptive use, increasing access to and use of contraceptives as well as reducing income disparity between urban and rural population by providing employment opportunities in rural areas are keys policy issues that could help achieve fertility reduction in Ghana.
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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 | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".