Abstract
Late school entry is driven by several factors, one of the key ones being the cost barrier to schooling. Policies such as free primary education (FPE) that advocate for universal coverage are therefore partly aimed at removing the cost barrier. The Kenyan Government, like many in sub-Saharan Africa (SSA), introduced FPE in 2003 with the aim of universalising access to schooling, which is one of the eight United Nations Millennium Development Goals (MDGs) it signed up to achieve. Based on a case study of four sites in Nairobi, the aim of this paper is to assess whether the FPE policy has affected late enrolment. The data used were collected by the African Population and Health Research Center (APHRC) and comprise a sub-sample of 4,325 first-graders during 2000–2005. The paper applies a probit model to assess the impact of the policy on the basis of marginal effects on the predicted probability of late enrolment. The results show that the FPE policy reduces the probability of late enrolment by 14 per cent. The reduction in probability of late enrolment was greater among children residing in slums (16 per cent) than those in non-slums (9 per cent). The main implication of the findings for policy makers is that cost barriers are a likely cause of over-age enrolment.
Résumé
L’impact de la politique d’enseignement primaire gratuit sur la scolarisation tardive dans les écoles primaires urbaines au Kenya – La scolarisation tardive est due à plusieurs facteurs, l’un des principaux étant l’obstacle des frais scolaires. Plusieurs politiques, telle celle de l’enseignement primaire gratuit (free primary education, FPE) qui favorise la généralisation de la scolarisation, visent donc entre autres à éliminer la barrière des coûts. Le gouvernement kényan, comme beaucoup d’autres en Afrique subsaharienne, a introduit le FPE en 2003 dans le but de généraliser l’accès à la scolarité, qui constitue l’un des huit Objectifs du Millénaire pour le développement (OMD) que le gouvernement s’est engagé à atteindre. À partir d’une étude de cas menée dans quatre établissements de Nairobi, les auteurs se proposent d’évaluer si la politique du FPE influe sur la scolarisation tardive. Les données exploitées ont été collectées par le centre de recherche sur la population et la santé en Afrique (African Population and Health Research Center, APHRC) et comportent un sous-échantillon de 4325 élèves inscrits en première année primaire sur la période 2000–2005. Les auteurs appliquent un modèle probit pour évaluer l’impact de la politique à partir des effets marginaux sur la probabilité anticipée de scolarisation tardive. Les résultats révèlent que la politique du FPE réduit de 14 pour cent la probabilité de scolarisation tardive. Cette réduction est plus importante chez les enfants vivant dans les bidonvilles (16 pour cent) que chez les autres élèves (9 pour cent). La principale conclusion des résultats pour les décideurs établit que l’obstacle financier est une cause probable de la scolarisation tardive.
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Notes
Low resources are partly to blame for the low sustainability of the UPE polices of the 1960s and 1970s.
The gross enrolment ratio (GER) is defined by the UNESCO Institute for Statistics (UIS) as the “total enrolment in a specific level of education, regardless of age, expressed as a percentage of the eligible official school-age population corresponding to the same level of education in a given school year” (UIS 2009, p. 9). The net enrolment ratio (NER) is defined as the “enrolment of the official age group for a given level of education expressed as a percentage of the corresponding population” (ibid., p. 10).
This term refers to students from poor families who are exempted from having to pay for their lunch.
The Nairobi Urban Health and Demographic Surveillance System (DSS), a platform established by the African Population and Health Research Centre (APHRC), “collects data on two informal settlements – Korogocho and Viwandani – in Nairobi City every four months on issues ranging from household dynamics to fertility and mortality, migration and livelihood as well as on causes of death, using a verbal autopsy” (Emina et al. 2011).
The Pearson correlation measures the linear relationship between two variables X and Y. Value +1 stands for total positive correlation, 0 is no correlation, and −1 signifies negative correlation.
Principal component analysis (PCA) converts a set of observations of potentially correlated variables into a set of values of linearly uncorrelated variables (“principal components”).
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Acknowledgement
We acknowledge the important contributions of APHRC staff who participated at various stages of the development of this paper including at the data collection stage and in processing, and in reviewing the literature. We are also grateful to our partners including the City Council of Nairobi’s Department of Education for providing useful information. Funding for this study was provided by The William and Flora Hewlett Foundation through the Education Research Programme at APHRC. However the views presented in this paper are only those of the authors and are not necessarily shared by those mentioned.
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Ngware, M.W., Oketch, M., Ezeh, A.C. et al. The effect of free primary education policy on late school entry in urban primary schools in Kenya. Int Rev Educ 59, 603–625 (2013). https://doi.org/10.1007/s11159-013-9389-6
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DOI: https://doi.org/10.1007/s11159-013-9389-6