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Free Primary Education in Kenya: An Impact Evaluation Using Propensity Score Methods

  • Chapter
Child Welfare in Developing Countries

Abstract

This chapter attempts to evaluate the impact of the free primary education programme in Kenya, which is based on the premise that government intervention can lead to enhanced access to education especially by children from poor parental backgrounds. Primary education system in Kenya has been characterized by high wastage in form of low enrolment, high drop-out rates, grade repetition as well as poor transition from primary to secondary schools. This scenario was attributed to high cost of primary education. To reverse these poor trends in educational achievements, the government initiated free primary education programme in January 2003. This chapter therefore analyzes the impact of the FPE programme using panel data. Results indicate primary school enrolment rate has improved especially for children hailing from higher income categories; an indication that factors that prevent children from poor backgrounds from attending primary school go beyond the inability to pay school fees. Grade progression in primary schools has slightly dwindled. The results also indicate that there still exist constraints hindering children from poorer households from transiting to secondary school. The free primary education programme was found to be progressive, with the relatively poorer households drawing more benefits from the subsidy.

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Notes

  1. 1.

    Programmes introduced nationally without piloting.

  2. 2.

    Exact control group is non-existent since the programme is implemented in all public primary schools throughout the country. Private schools normally attract enrolment from relatively well-off members of the society and thus could not be used as a control group.

  3. 3.

    For nearest neighbour matching, literature suggests the use of non-replacement to reduce the bias (D’Agostino, R.B. 1998). Matching without replacement involves a trade-off between less bias and a better potential match. However, Zhao (2004) has shown that in practice, the difference between the two approaches is often small.

  4. 4.

    Matching was performed using PSMATCH2 STATA routine developed by Leuven (2003).

  5. 5.

    The New York Times, Monday, April 5, 2005 and BBC, Nairobi Wednesday, 14 January, 2004

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Authors and Affiliations

  1. Tegemeo Institute, Egerton University, Kenya

    Milu Muyanga & John Olwande

  2. University of Nairobi, Nairobi, Kenya

    Esther Mueni

  3. Tegemeo Institute, Egerton University, njoro, Kenya

    Stella Wambugu

Authors
  1. Milu Muyanga
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  2. John Olwande
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  3. Esther Mueni
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  4. Stella Wambugu
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Correspondence to Milu Muyanga .

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Editors and Affiliations

  1. Poverty and Economic Policy(PEP) Research Network Université Laval, Pavillon J.-A. DeSève 1025 avenue des Sciences Humaines, Québec City, QC, Canada, G1V 0A6

    John Cockburn

  2. School of Economics University of Nairobi, Harry Thuku Road Gandhi Wing Room 210, P.O Box 30197, 00100, Nairobi, Kenya

    Jane Kabubo-Mariara

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Muyanga, M., Olwande, J., Mueni, E., Wambugu, S. (2010). Free Primary Education in Kenya: An Impact Evaluation Using Propensity Score Methods. In: Cockburn, J., Kabubo-Mariara, J. (eds) Child Welfare in Developing Countries. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6275-1_5

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