IDEAS home Printed from
   My bibliography  Save this article

Determinants of infant and child mortality in Kenya: an analysis controlling for frailty effects


  • D. Omariba


  • Roderic Beaujot
  • Fernando Rajulton


In this paper, Weibull unobserved heterogeneity (frailty) survival models are utilized to analyze the determinants of infant and child mortality in Kenya. The results of these models are compared to those of standard Weibull survival models. The study particularly examines the extent to which child survival risks continue to vary net of observed factors and the extent to which nonfrailty models are biased due to the violation of the statistical assumption of independence. The data came from the 1998 Kenya Demographic and Health Survey. The results of the standard Weibull survival models clearly show that biodemographic factors are more important in explaining infant mortality, while socioeconomic, sociocultural and hygienic factors are more important in explaining child mortality. Frailty effects are substantial and highly significant both in infancy and in childhood, but the conclusions remain the same as in the nonfrailty models. Copyright Springer Science+Business Media B.V. 2007

Suggested Citation

  • D. Omariba & Roderic Beaujot & Fernando Rajulton, 2007. "Determinants of infant and child mortality in Kenya: an analysis controlling for frailty effects," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 26(3), pages 299-321, June.
  • Handle: RePEc:kap:poprpr:v:26:y:2007:i:3:p:299-321
    DOI: 10.1007/s11113-007-9031-z

    Download full text from publisher

    File URL:
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Yamano, Takashi & Jayne, T. S., 2004. "Measuring the Impacts of Working-Age Adult Mortality on Small-Scale Farm Households in Kenya," World Development, Elsevier, vol. 32(1), pages 91-119, January.
    2. Defo, B.A., 1996. "Areal and Socioeconomic Differentials in Infant and Child Mortality in Cameroon," Papers 96-05, RAND - Reprint Series.
    3. Nyambedha, Erick Otieno & Wandibba, Simiyu & Aagaard-Hansen, Jens, 2001. "Policy implications of the inadequate support systems for orphans in Western Kenya," Health Policy, Elsevier, vol. 58(1), pages 83-96, October.
    4. Sonalde Desai & Soumya Alva, 1998. "Maternal education and child health: Is there a strong causal relationship?," Demography, Springer;Population Association of America (PAA), vol. 35(1), pages 71-81, February.
    5. L. Randall Wray & Stephanie Bell, 2004. "Introduction," Chapters,in: Credit and State Theories of Money, chapter 1 Edward Elgar Publishing.
    6. Mario Cleves & William W. Gould & Roberto G. Gutierrez & Yulia Marchenko, 2010. "An Introduction to Survival Analysis Using Stata," Stata Press books, StataCorp LP, edition 3, number saus3, December.
    7. Defo, Barthélémy Kuate, 1996. "Areal and socioeconomic differentials in infant and child mortality in Cameroon," Social Science & Medicine, Elsevier, vol. 42(3), pages 399-420, February.
    8. Nyambedha, Erick Otieno & Wandibba, Simiyu & Aagaard-Hansen, Jens, 2003. "Changing patterns of orphan care due to the HIV epidemic in western Kenya," Social Science & Medicine, Elsevier, vol. 57(2), pages 301-311, July.
    9. Philippe Robert-Demontrond & R. Ringoot, 2004. "Introduction," Post-Print halshs-00081823, HAL.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Walter Rasugu Omariba & Fernando Rajulton & Roderic Beaujot, 2008. "Correlated mortality risks of siblings in Kenya," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 18(11), pages 311-336, April.
    2. Eva Deuchert & Conny Wunsch, 2010. "Evaluating Nationwide Health Interventions when Standard Before-After Doesn't Work: Malawi's ITN Distribution Program," CESifo Working Paper Series 3036, CESifo Group Munich.
    3. Makate, Marshall & Makate, Clifton, 2016. "The causal effect of increased primary schooling on child mortality in Malawi: Universal primary education as a natural experiment," Social Science & Medicine, Elsevier, vol. 168(C), pages 72-83.
    4. Kundu, Nobinkhor & Chowdhury, J.M. Adeeb Salman & Sikdar, Asaduzzaman, 2013. "Millennium development goals affecting child mortality in Bangladesh: A Vector Error Correction model," MPRA Paper 65211, University Library of Munich, Germany, revised 15 Oct 2013.
    5. Adriaan Kalwij, 2014. "An empirical analysis of the importance of controlling for unobserved heterogeneity when estimating the income-mortality gradient," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 31(30), pages 913-940, October.
    6. Lay, Jann & Robilliard, Anne-Sophie, 2009. "The complementarity of MDG achievements : the case of child mortality in Sub-Saharan Africa," Policy Research Working Paper Series 5062, The World Bank.
    7. Han, Peter & Foltz, Jeremy D., 2013. "The Impacts of Climate Shocks on Child Mortality in Mali," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150395, Agricultural and Applied Economics Association.
    8. Hayley Pierce & Ashley Larsen Gibby & Renata Forste, 2016. "Caregiver Decision-Making: Household Response to Child Illness in Sub-Saharan Africa," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 35(5), pages 581-597, October.
    9. Hanne Preter & Dorien Looy & Dimitri Mortelmans, 2015. "Retirement Timing of Dual-Earner Couples in 11 European Countries? A Comparison of Cox and Shared Frailty Models," Journal of Family and Economic Issues, Springer, vol. 36(3), pages 396-407, September.
    10. Emily Smith-Greenaway & Jenny Trinitapoli, 2014. "Polygynous Contexts, Family Structure, and Infant Mortality in Sub-Saharan Africa," Demography, Springer;Population Association of America (PAA), vol. 51(2), pages 341-366, April.
    11. Cynthia Chen & Jason Chen, 2009. "What is responsible for the response lag of a significant change in discretionary time use: the built environment, family and social obligations, temporal constraints, or a psychological delay factor?," Transportation, Springer, vol. 36(1), pages 27-46, January.


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:poprpr:v:26:y:2007:i:3:p:299-321. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla) or (Rebekah McClure). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.