An Analysis of the Determinants of Fertility Differentials Amongst the Poorest Women Population in Kenya
International Journal of Statistical Distributions and Applications
Volume 5, Issue 3, September 2019, Pages: 60-66
Received: Jul. 2, 2019; Accepted: Jul. 27, 2019; Published: Aug. 13, 2019
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Robert Mathenge Mutwiri, Department of Physical sciences, Kirinya University, Kerugoya, Kenya
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Fertility is one of the major elements in population dynamics that has the highest significant contribution towards population size and structure in the world. In Kenya, fertility levels have been on the decline from approximately 8.1 children in 1979 to 3.9 children in 2014 but still, it is considered high compared to the country’s target of 2.6 by 2030. This has potentially negative consequences to the economic growth and development of a country. The main objective of this study is to determine demographic, socio-economic and cultural factors that explain fertility differential among poor women of childbearing age. A binary logistic regression model was fitted to DHS 2014 data using SPSS Version16. The total number of women in childbearing age is based on 7,262 women who have at least one child and whose age ranges from 15 to 49 years. The majority of women were married 4685 (64.5%), followed by never and formally married 1522 (21.0%) and living with partner 1055 (14.5%) respectively). In the analyses, all the variables Region, women educational level, marital status, age at first marriage and age in 5-years group were found to have a significant effect on the total number of children ever born at a significance level of 5%. From the fitted logistic regression model, the estimated odds ratio for the variable region reference category is Nyanza/Western region. The value of the odds ratio exp(β) =1.060775, for the region that the odds of having TCEB greater than or equals to five children for the North Eastern region has 6.0775% more than women in Nyanza/Western Region (OR=1.060775, C.I=0.873716-1.287883) and its effect is statistically significant.
Fertility Levels, Binary Logistic Regression Model, DHS Data, Total Fertility Rate
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Robert Mathenge Mutwiri, An Analysis of the Determinants of Fertility Differentials Amongst the Poorest Women Population in Kenya, International Journal of Statistical Distributions and Applications. Vol. 5, No. 3, 2019, pp. 60-66. doi: 10.11648/j.ijsd.20190503.13
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