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Age at first marriage in Malawi: a Bayesian multilevel analysis using a discrete time-to-event model


  • Samuel Manda
  • Renate Meyer


The paper presents a hierarchical discrete time survival model for the analysis of the 2000 Malawi Demographic and Health Survey data to assess the determinants of transition to marriage among women in Malawi. The model explicitly accounts for the unobserved heterogeneity by using family and community random effects with cross-level correlation structure. A nonparametric technique is used to model the base-line discrete hazard dynamically. Parameters of the model are computed by using a Markov chain Monte Carlo algorithm. The results show that rising age at marriage is a combination of birth cohort and education effects, depends considerably on the family and to some extent on the community in which a woman resides and the correlation between family and community random effects is negative. These results confirm a downward trend in teenage marriage and that raising women's education levels in sub-Saharan Africa has the beneficial effect of increasing age at marriage, and by implication reducing total fertility rates. The negative correlation between family and community random effects has policy implications in that targeting communities with an intervention to increase age at first marriage may not necessarily yield reduced fertility levels in individual families. A campaign that is geared towards individual families would achieve the desired goals. Overall, the findings point to the need for the Government in Malawi to enact public policies which are geared at vastly improving women's education at higher levels. The variation in marriage rates over families poses problems in delivering the policy, since particular policies must be devised for specific groups of families to accomplish the required social and health objectives. Copyright 2005 Royal Statistical Society.

Suggested Citation

  • Samuel Manda & Renate Meyer, 2005. "Age at first marriage in Malawi: a Bayesian multilevel analysis using a discrete time-to-event model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 439-455.
  • Handle: RePEc:bla:jorssa:v:168:y:2005:i:2:p:439-455

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    Cited by:

    1. Guillaume Horny & Rute Mendes & Gerard J. van den Berg, 2012. "Job Durations With Worker- and Firm-Specific Effects: MCMC Estimation With Longitudinal Employer--Employee Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 468-480, March.
    2. Luca Zanin & Rosalba Radice & Giampiero Marra, 2015. "Modelling the impact of women’s education on fertility in Malawi," Journal of Population Economics, Springer;European Society for Population Economics, vol. 28(1), pages 89-111, January.
    3. Guillaume Horny & Rute Mendes & Gerard J. Van den Berg, 2006. "Job mobility in Portugal: a Bayesian study with matched worker-firm data," Working Papers of BETA 2006-32, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    4. Hee-Koung Joeng & Ming-Hui Chen & Sangwook Kang, 2016. "Proportional exponentiated link transformed hazards (ELTH) models for discrete time survival data with application," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(1), pages 38-62, January.
    5. Stephen Gyimah, 2009. "Cohort Differences in Women’s Educational Attainment and the Transition to First Marriage in Ghana," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 28(4), pages 455-471, August.
    6. Stephen Gyimah & Alex Ezeh & J. Fotso, 2012. "Frailty models with applications to the study of infant deaths on birth timing in Ghana and Kenya," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(5), pages 1505-1521, August.
    7. L. J. Welty & R. D. Peng & S. L. Zeger & F. Dominici, 2009. "Bayesian Distributed Lag Models: Estimating Effects of Particulate Matter Air Pollution on Daily Mortality," Biometrics, The International Biometric Society, vol. 65(1), pages 282-291, March.
    8. Mika Ueyama & Futoshi Yamauchi, 2009. "Marriage behavior response to prime-age adult mortality: evidence from malawi," Demography, Springer;Population Association of America (PAA), vol. 46(1), pages 43-63, February.
    9. Ezra Gayawan & Samson B. Adebayo, 2013. "A Bayesian semiparametric multilevel survival modelling of age at first birth in Nigeria," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(45), pages 1339-1372, June.
    10. Shih, Joanna H. & Lu, Shou-En, 2009. "Semiparametric estimation of a nested random effects model for the analysis of multi-level clustered failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3864-3871, September.
    11. Guillaume Horny, 2009. "Inference in mixed proportional hazard models with K random effects," Statistical Papers, Springer, vol. 50(3), pages 481-499, June.
    12. Wilmer Ríos Piñerez, 2016. "Diferencias de género en la edad del primer matrimonio: una evidencia desde los modelos de búsqueda marital para Colombia," REVISTA CIFE, UNIVERSIDAD SANTO TOMÁS, March.

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