Estimating the Impact of School Education on Contraception Use among Adolescents Aged 15–19 in Burkina Faso and Nigeria: Evidence from a Heckman Two-Step Correction Model
Bibliographic record
Abstract
Sexually Transmitted Infections (STIs) still raise serious concerns for adolescents’ sexual health in west-Africandeveloping countries. To this extent, promoting contraception use among sexually active adolescents is a major key to addressing this problem. Yet, the rate of contraception use by adolescents remains surprisingly low in these countries. Using the Demographic Health Survey of Burkina Faso (2014) and Nigeria (2013), this paper examines the influence of school education on contraception use among sexually active male and femaleadolescents aged 15-19 in Burkina Faso and Nigeria. The standard estimates using Probit regressions suggestthat achieving a primary school education increases the probability of a sexually active adolescent to usecontraception by 8.26 percentage points (Burkina Faso) and 17.2 percentage points (Nigeria). This effectincreases to 20.3 percentage points (Burkina Faso) and 34.7 percentage points (Nigeria) for adolescents with asecondary or higher school education. However, these baseline estimates are biased because adolescents’decision to engage into sexual activity is not random. In light of this, a Heckman Correction Model (HCM) hasbeen applied to account for this selection bias. The results show that the Probit regressions underestimate theeffect of education on adolescents’ likelihood to use contraception in Burkina Faso and overestimate this effect in Nigeria. In fact, compared to adolescents with no school education, HCM estimates show that adolescentswith primary and secondary (or higher) school education have respectively 10.2 and 24.4 percentage points morein the use of contraception in Burkina Faso and 15.1 and 34 percentage points in Nigeria. Together, these resultssuggest that the exposure to school education increases contraception use among the adolescents in both BurkinaFaso and Nigeria.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".