Fertility rates and trends in Kenya

Abstract

Nairobi which is the study area, is the largest urban centre and capital of Kenya. It acquired its urban status in 1950. It is located almost at the centre of the country. From North to south, it stretches from 2010'S to 2050'S and from East to West, it stretches from 370 10'E to 360 40'E, thus covering an area of 684 square kilometres. According to the 1979 Kenya population census statistics, Nairobi had a total population of 827,775 people. This was 5.4 per cent of the total annual population of Kenya which was 15,327,061 people. The main objectives of this study were to examine fertility levels between wards in Nairobi in order to establish how low they are, and if fertility differentials exist between wards. The study also attempts to identify the various socio-economic and demographic factors that influence fertility and in what direction. Motivation for this study stemmed from the fact that fertility is the major contributor to the population in the early years of life. This youthful population needs care in terms of provision of the essential services like education, health, food, shelter and transport especially in an urban centre where children depend directly on their parents for these services. There is need to understand the level of total fertility rates in Nairobi so as to assess the contribution of fertility to this youthful population. To achieve these objectives, the 1979 Kenya population census forms the basis of the data used in this study. It was found to be rather detailed unlike the other previous censuses of 1962, 1948 and 1969. Thus, data on fertility and other related socio-economic and demographic factors used in this study were obtained.

The demographic techniques used for fertility estimation are: current fertility, life-time fertility, (P3)2/P2 fertility and the Brass p/F ratio fertility methods. The multiple regression analysis is the statistical technique used in this analysis. This technique is suitable in that it can enter many variables into the analysis and it can establish their absolute and relative effects upon the dependent variable (fertility in this case). The F-statistic test has been used to test the levels of significance and thus confirm the stated hypotheses. This study has revealed that fertility differentials exist between wards in Nairobi. A total fertility rate of 5.47 births was .estimated for Nairobi. At ward level, births ranging from 3.62 to 8.29 was observed in Kilimani and Maisha-Makongeni wards respectively