Do poverty dynamics explain the shift to an informal private schooling system in the wake of free public primary education in Nairobi slums?
Bibliographic record
Abstract
With the introduction of free primary education (FPE) in Kenya in 2003, it was expected that the burden on poor households in financing primary education would be reduced substantially. This in turn would increase enrolment in public schools and lead to universal primary education. However, studies have shown that a considerable proportion of households in urban slums continue to enrol their children in pro-poor fee charging informal schools. The reasons presented in the available literature to explain this phenomenon of why poor slum-residing households bypass free public education are varied and some are simply speculative. In this paper, we hypothesise that poverty dynamics can partly explain households' decisions on the type of school in which to enrol their children. The analysis is based on longitudinal data collected by the African Population and Health Research Centre (APHRC) using urban demographic surveillance in two slums of Nairobi, Kenya. The data covers the period between 2005 and 2009 with a sample of 6965 pupils spread across 3763 households. Logistic regression methods are applied. The findings reveal that moving in and out of poverty can affect the type of schooling decision a household makes, with one quarter of those moving out of poverty shifting schools. The findings demonstrate that there is both willingness and ability to pay by the slum residents that is driving the utilisation of the private schools. The decisions are not random occurrences, but seem systematic and rational – parents want quality and affordability and a good number of those whose economic situation improved do not seem to believe the public schools, even under FPE, offers quality. The policy implication to be drawn from these findings is that the private schools for the poor should not simply be dismissed as 'informal schools' because it seems they have some features which are attracting parents to choose them and leave the state system. Free primary education policy is being 'rejected' by a good number of parents in the slums and this needs further investigation, as 'excess' demand as suggested in some research papers does not seem to be the only explanation.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".