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Factors Affecting Children Ever Born (CEB) in Botswana: Application of Poisson Regression Model

Author Affiliations

  • 1University of Botswana, Private Bag: UB 00705, Gaborone, Botswana
  • 2University of Botswana, Private Bag: UB 00705, Gaborone, Botswana
  • 3University of Botswana, Private Bag: UB 00705, Gaborone, Botswana

Res. J. Mathematical & Statistical Sci., Volume 4, Issue (10), Pages 1-9, November,12 (2016)


The number of children ever born to a particular woman is a measure of her lifetime fertility experience up to the moment at which the data are collected. Fertility is one of the key determinants of population growth and pattern and is essential for planning and achieving sustainable development. This paper attempts to identify the socioeconomic and demographic determinants of number of children ever born (CEB) to women of age 15-49 years using 2007 Botswana Family Health Survey-IV (2007 BFHS IV) data. Poisson regression model is explored to study the impact of potential regressors on fertility. The results indicate that the women living in cities/towns and urban villages had 11.2% and 6.8% lower fertility than women living in rural area; as expected percentage of number of kids was consistently decreasing with decrease of age groups. Women in the age group 45-49 have the higher number of kids than any other lower age groups. Mother’s education negatively affects the average number of children ever born to a woman; women with married status have the highest fertility, with 21.7% more kids than women with never married status. Non-working mothers have more number of children ever born than the working mothers and mothers who watch television at least once a week have lower by 9.9% kids who do not watch television at all. Women/her partner who were currently using condom had a lower fertility by 8.1% compared to those who have never used. On the above findings we recommend that more emphasis is needed on women’s literacy which may take care of other social and economic indicators.


  1. Fagbamigbe, Adeniyi F. and Adebowale. Ayo S. (2014)., Current and predicted fertility using Poisson regression model: evidence from 2008 Nigerian demographic health survey., African Journal of Reproductive Health, 18(1),71-83.
  2. Kirk D. and Pillet B. (1998)., Fertility levels, trends, and differentials in Sub-Saharan Africa in the 1980s and 1990s., Studies in Family Planning, 29(1), 1–22.
  3. Kalipeni E. (1995)., The fertility transition in Africa., Geographical Review, 85(3), 286-300.
  4. Thomas D. and Muvandi I. (1994)., How fast is fertility declining in Botswana and Zimbabwe?., World Bank Discussion Paper, 258, 31.
  5. Freedman R. and Blanc A.K. (1992)., Fertility transition: an update., International Family Planning Perspectives, 18, 72, 44-50.
  6. Famoye F. and Wang W. (1997)., Modeling household fertility decisions with generalized Poisson regression., J Popul Econ, 10, 273-283.
  7. Cross A.R., Obungu W. and Kizito P. (1991)., Evidence of a transition to lower fertility in Kenya., International Family Planning Perspectives, 17, 4-7.
  8. Anderson B. (2003)., Fertility, poverty and gender., Fertility: The Current South African Issues. HSRC Department of Social Development. Available at: (accessed 20 November 2015).
  9. Statistics Botswana (2014)., 2011 Census Analytical Report., published by Statistics Botswana.
  10. Republic of Botswana. (2007)., Botswana Family Health Survey IV (BFHS IV)., report Published by CSO, Gaborone, Botswana.
  11. Scott Long J. (1997)., Regression models for categorical and limited dependent variables., Advanced Quantitative Techniques in the Social Sciences, 7.
  12. Hondroyiannis G. (2004)., Modeling household decisions in Greece., The Social Science Journal, 41, 477-483.
  13. Atella Vincenzo and Rosati Furio C. (2000)., Uncertain about children’s survival and fertility: a test using Indian micro data., Journal of Population Economics, 13(2), 263-278.
  14. Melkersson M. and Rooth D. (2000)., Modeling female fertility using inflated count data models., Journal of Population Economics, 13, (2).
  15. Olfa F. and El-Lahga A.R. (2002)., A socioeconomic analysis of fertility determinants with a count data models: the case of Tunisia., Popline.
  16. Al-Qudsi S. (1998)., Labour participation of Arab women: estimates of the fertility to labour supply link., Applied Economics, 30, 931-941.
  17. Barmby T. and Cigno A. (1990)., A sequential probability model of fertility patterns., Journal of Population Economics, 3, 31-51.
  18. Sobel M.E. and Arminger G. (1992)., Modeling household fertility decisions: a nonlinear simultaneous Probit model., J Am Stat Assoc., 87(417), 38-47.
  19. Fahrmeir L. and Lang S. (2001)., Bayesian inference for generalized additive mixed models based on Markov random field priors., Journal Royal Statistical Society (Series C), 50, 201-220.
  20. Kazembe L.N. (2009)., Modelling individual fertility levels in Malawian women: a spatial semiparametric regression model., Stat Methods Appl,18, 237-255.
  21. Winkelmann R. and Zimmermann K.F. (1994)., Count data models for demographic data., Mathematical Population Studies, 4(3), 205-221.
  22. Poston Jr D.L. and McKibben S.L. (2003)., Using zero-inflated count regression models to estimate the fertility of US women., Journal of Modern Applied Statistical Methods, 2(2), 10.
  23. Cameron A. and Trivedi P. (2013)., Regression analysis of count data., Cambrigde University Press.
  24. Little R.J.A. (1978)., Generalized linear models for cross-classified data from the WFS., World Fertility Survey Technical Bulletins, No. 5.
  25. Rogers W.H. (1991)., Poisson regression with rates., Stata Press, 1.
  26. Poston D.L. (2002)., The statistical modelling of the fertility of Chinese women., J Modern Appl Stat Method, 1, 387-396.
  27. Adebimpe W.O., Asekun-Olarinmoye E., Bamidele J.O. and Abodunrin O. (2011)., Comparative study of socio-demographic determinants and fertility pattern among women and urban communities in south western Nigeria., Continental Journal of Medical Research, 5, 32-40.
  28. Alene G.D. and Worku A. (2008)., Differentials of fertility in North and South Gondar zones, northwest Ethiopia: A comparative cross-sectional study., BMC Public Health, 8, 397-398.
  29. Kibirige J.S. (1997)., Population growth, poverty and health., SocSci Med, 45, 247-259.
  30. Onoja Matthew AKPA and OsayomoreIkpotokin (2012)., Modeling the Determinants of Fertility among Women of Childbearing Age in Nigeria: Analysis Using Generalized Linear Modeling Approach., International Journal of Humanities and Social Science, 2(18), 167-176.
  31. Dwivedi S.N. and Rajaram S. (2004)., Some factors associated with number of children ever born in Uttar Pradesh: A comparative results under multiple regression analysis and multilevel analysis., Indian Journal of Community Medicine, 29(2), 72-76.
  32. Cleland J., Onuoha N. and Timaeus I. (1994)., Fertility change in sub-Saharan Africa: a review of evidence., Locoh T, Hertrich V (eds) The onset of fertility transition in Sub-Saharan Africa, Ordinal editions, Liège,1-20.
  33. Becker G.S. (1981)., A Treatise on the Family., Cambridge, MA: Harvard University Press.
  34. Covas F. and Santos Silva J.M.C. (2000)., A modified hurdle model for completed fertility., Journal of Population Economics, 13(2), 173-188.