Promoting the wellbeing of Africans through policy relevant research on population and health

1. Assessing the impact of free primary education policy on access and schooling outcomes in Kenya Authors: Moses Ngware Moses Oketch Alex Ezeh African Population and Health Research Center Promoting the wellbeing of Africans through policy relevant research on population and health

2. Outline • Background • Purpose • Design • Results • Conclusion

3. Background 1 • Kenya Introduced FPE in 2003 • FPE has the following elements • Universal coverage • Universal eligibility • Aims of FPE: • Improve enrolment by increasing public expenditure to education • Increase education attainment and reduce overall poverty by mitigating against intergenerational transfer of low human capital.

4. Background 2 3. Improve the quality of public education • NB: The introduction of FPE coincided with the political transition in Kenya. • FPE is a vehicle to realising UPE, which is an EFA-MDG

5. Purpose • How did different population groups respond to FPE? • Did FPE change enrolment patterns? • Did the patterns differ by population groups? • If the patterns were different, what explains it? • How does enrolment patterns of different population groups relate with children’s indulgence in risky behaviour?

6. Study Design (Case study) • A longitudinal household survey • Nested in NUHDSS that tracks approx. 60,000 individuals – 2 sites • Education research program household longitudinal survey- 4 sites NB: 1. DSS existed before the FPE. 2. This motivated the study of assessing how different groups responded to FPE since DSS was in 2 slums of Nairobi, ERP was designed to cover 2 additional nonslum sites to provide a different population group.

7. Study Design (Case study) Cont…. What did the design enabled us to do? To compare how slum population groups and nonslum population groups responded to the introduction of FPE. By doing so we are able to assess how the poor and the less poor responded to the policy.

8. Study sample • Sampling methods: • Purposive: • ERP household survey : In each site households were identified based on CBS cluster enumeration • 7405 households • 13,257 individuals aged 5-19 • Twice every year • Cont ….

9. Study sample cont’d….. DSS household survey: nested We have about 60,000 individuals living in about 21,000 households We collect data every four months (120 days), so thrice a year We are currently in round 17 of data collection

10. Population in slums

11. Study instruments Parent/guardian questionnaire Household Characteristic questionnaire Child schooling History questionnaire (or Update) Chid Behaviour questionnaire (12 yrs and above) Movement forms (in-migration and out-migration)

12. Data entry

13. Datasets generated Cross-sectional datasets Longitudinal datasets/panel datasets Qualitative data

14. Methods of data analysis Descriptive analysis Regression analysis (OLS, logit, probit) Qualitative analysis

15. Results A revisit to the questions: Did FPE change enrolment patterns? Did the patterns differ by population groups? If the patterns were different, what explains it? How does enrolment patterns of different population groups relate with children’s indulgence in risky behaviour?

16. Trend in school enrolment 2000-2007: slums

17. Trend in school enrolment 2000-2007: non-slums

18. Results Cont.. (slum model) • The odds of enrolling in a public school are high in Viwandani than Korogocho • More of slum least poor households are attending public schools (OR=1.37) • compared to the poorest • Pupils from male headed households have low odds of enrolling in a public school

19. Result cont.. (non-slum model) • The odds of enrolling in a public school is high in Harambee than Jericho • Less of non-slum least poor households are attending public schools (OR=0.16) • compared to the poorest • Pupils from male headed households have low odds of enrolling in a public school

20. Results Con’t… Despite FPE, children from poorer households are still less likely to be enrolled compared to the less poor Children living in non-slum locations are more likely to enroll Children from female-headed HH are more likely to enroll Children from smaller in sized households had a better chance of enrolling. Even among the poor slum residence, those children from households where the head had more education were more likely to enroll

21. Results Con’t… Orphan type matter more than orphanhood in school enrolment Maternal orphans were more associated with negative attitude towards schooling and had lowest attendance

22. Conclusion Slum residents schooling patterns show that they have not responded to FPE as would have been expected In spite increased public expenditure in public schooling, the less poor remain more represented in the public school system than the poor, i.e. the odds of the poor enrolling in public schools is higher relative to the less poor. Policy engagement with the government has led the MOE in Kenya to acknowledge that FPE has not included the slum residents as was intended. With 60% of Nairobi residents living in slums, no wonder Nairobi province registers the lowest enrolment in public schools in spite of Nairobi being overall a wealthy urban province.