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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. - PowerPoint PPT Presentation
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Promoting the wellbeing of Africans through policy relevant research on population and health
African Population and Health Research Center
Authors: Moses NgwareMoses Oketch
Alex Ezeh
Assessing the impact of free primary education policy on access and schooling outcomes in Kenya
Outline
• Background
• Purpose
• Design
• Results
• Conclusion
Background 1• Kenya Introduced FPE in 2003
FPE has the following elements1. Universal coverage2. Universal eligibility
• Aims of FPE:1. Improve enrolment by increasing
public expenditure to education 2. Increase education attainment and
reduce overall poverty by mitigating against intergenerational transfer of low human capital.
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
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?
Study Design (Case study)•A longitudinal household surveyNested in NUHDSS that tracks
approx. 60,000 individuals – 2 sitesEducation research program
household longitudinal survey- 4 sitesNB: 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.
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.
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 ….
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
Population in slums
Korogocho Viwandani
12 10 8 6 4 2 0 2 4 6 8 10 12
0-4
10-14
20-24
30-34
40-44
50-54
60-64
70-74
80-8485+
12 10 8 6 4 2 0 2 4 6 8 10 12
Male
Percent of Total Population
Female Male Female
Percent of Total Population
Study instruments
Parent/guardian questionnaireHousehold Characteristic questionnaire
Child schooling History questionnaire (or Update)
Chid Behaviour questionnaire (12 yrs and above)
Movement forms (in-migration and out-migration)
Data entry
Datasets generated
Cross-sectional datasetsLongitudinal datasets/panel datasets
Qualitative data
Methods of data analysis
Descriptive analysisRegression analysis (OLS, logit, probit)
Qualitative analysis
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?
Trend in school enrolment 2000-2007: slums
Trend in school enrolment 2000-2007: slums
52.4750.81
49.31
64.96 65.64
60.7358.17
56.73
25
30
35
40
45
50
55
60
65
70
2000 2001 2002 2003 2004 2005 2006 2007
Year
Perc
enta
ge
Slum % Public
Slum % Private
Trend in school enrolment 2000-2007: non-slums9.
73
10.4
6
11.7
2
13.4
2
16.0
5
18.8
9
19.1
3
20.4
3
0
20
40
60
80
100
2000 2001 2002 2003 2004 2005 2006 2007
Year
Per
cent
age
Non-slum % Public Non-slum % Private
Trend in school enrolment 2000-2007: non-slums
Results Cont.. (slum model)OR SE
HHS (coef) 0.05
0.01
ATHEA (Coef)
0.06
0.02
HHW: Poorest
2 1.19
0.1
3 1.03
0.09
4 1.13
0.1
Least poor 1.37
0.12
SITE : KOCH
VIWA
1.94
0.13
HHG: Male 0.80
0.05
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
Result cont.. (non-slum model)OR SE
HHS (coef) 0.23
0.05
ATHEA (Coef)
0.30
0.07
HHW: Poorest
2 0.48
0.18
3 0.31
0.11
4 0.25
0.09
Least poor 0.16
0.06
SITE : Jericho
Harambee
1.55
0.34
HHG: Male 0.68
0.014
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
Results Con’t…Despite FPE, children from poorer households are still
less likely to be enrolled compared to the less poorChildren living in non-slum locations are more likely to
enrollChildren from female-headed HH are more likely to
enrollChildren 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
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
ConclusionSlum 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.