2009 Kenya Population and Housing Census “Counting Our People for Implementation of Vision 2030” Volume III Population Dynamics

Abstract

Demographic information is the backbone of all socio-economic planning in any country. The collection, analysis and dissemination of accurate demographic information enables policy makers plan for the future development of a country. Censuses are the prime sources for determining the components of population change particularly at the sub-national levels. This report presents the analysis and the results of the population dynamics of Kenya using the 2009 Kenya Population and Housing Census. The analysis and results are presented in 8 chapters. Chapter 1 presents the general introduction providing the census background, objectives, general methodology of the analysis, definition of key terms and assessment of the data quality. Analysis of the coverage indicates that the census count may have been higher by nearly 1.1 million people, with net error higher for females compared to males. Districts in North Eastern Province and Turkana in the North Rift Valley appeared to have more persons enumerated than expected. Information on age and sex is important in the analysis of any demographic data therefore all modern censuses collect information pertaining to the age and sex of the individuals. However, data often contain errors because some people do not know their actual age and others do not report their age accurately. It is critical to evaluate the reported age and sex composition. Various techniques have been developed for checking such inconsistencies. These include those that checks age and sex ratios, among others. The analysis indicated that on average, the sex ratios at national level do not reflect any serious irregularities in reporting, but North Eastern Province showed irregularities in reporting at all ages. While there has been some improvement in age reporting since 1979, analysis at regional level revealed that North Eastern Province reflects highly inaccurate reporting, followed by Nairobi Province. Western Province fell in the category of accurate reporting. Chapter 2 describes the population size, growth, structure and distribution. A total population of 38,610,097 was enumerated of which 19,192,378 were males and 19,417,719 were females. The 2009 population size was slightly over seven fold compared to that of 1948. Kenya‟s population has always been dominated by children; however, the proportion of children has gradually declined from 48.0 percent in 1969 to about 43.0 percent in 2009.The youth age 15-24 years share, has remained just at about one fifth while the persons in age 25-34 have increased from about 12.0 percent in 1969 to nearly 15.0 percent in 2009. The elderly (age 60 years and above) constitute 5.0 percent of the total population. The results revealed that population distribution is generally uneven. Nairobi County continues to have the largest share of the population followed by Kakamega, Kiambu and Nakuru. Nairobi and Mombasa (largest urban centres) have the highest population densities of 4515 and 4291persons per square kilometre, respectively. Vihiga County recorded the next highest density of 1045 persons per square kilometre. Isiolo, Marsabit and Tana River are the least sparsely populated areas. Currently, 68.0 percent of Kenya‟s population lives in slightly over one tenth (12.0 percent) of the land area. The population living in 50.0 percent of the total land area declined from about 92.0 percent in 1999, to about 90.0 percent in 2009. The urban population in Kenya increased from 5.4 million in 1999 to about 12 million in 2009. At the same time, the proportion of urban population to the total population rose from 19.0 percent in 1999 to 31.3 percent in 2009. About 15.0 percent of the urban population lives in informal settlements and Nairobi, the capital city contributes the larger share of 62.0 percent. Chapter 3 describes nuptiality patterns. Nuptiality which refers to the frequency of marriages between persons of opposite sexes, involves rights and obligations fixed by law or custom. It is a key determinant of fertility. In the context of the family, marriage represents the initial step in furthering group survival and family expansion. Marriage in Kenya is still nearly universal while divorce and separation is still low. One of the key indicators of marriage is the timing of first marriage. Two indicators are used namely:-percent married at ages 15-19 and singulate mean age at marriage (SMAM). The proportion married at age 15-19 captures the extent of very young marriages, while SMAM represents a summary indicator of the entire age distribution of first marriages. The proportion married in the age bracket 15-19 declined from about 19.0 percent in 1989 to about 15.0 percent in 2009 among females, while it increased from 2.0 percent to 3.0 percent among males. SMAM is about 26.7 years for males and 22.5 years for females indicating that in general young people are postponing entry into marriage in the recent past. Chapter 4 describes fertility patterns. The total fertility rate is about 4.8 but there exists wide regional differentials. The observed lifetime fertility indicates that the average number of children ever born is declining for most of the regions except North Eastern Province. Chapter 5 describes mortality patterns. Approximately 54 out every 1000 children born die before reaching their first birthday while 79 out of every 1000 children born do not reach their fifth birthday. High mortality rates occur in North Eastern and Nyanza provinces where infant mortality rate is still above 100 per 1000 live births. Adult mortality indicators show that about 35.0 percent of adult males age 15 do not reach the retirement age (60 years) while 31.0 percent of females age 15 do not reach retirement age. Indicators of mortality due to pregnancy related conditions show that nearly 495 women out of every 100,000 live births die every year. Chapter 6 provides information on migration trends in Kenya. Analysis of lifetime in migration show that the patterns of migration are changing. Contrary to the usual trend, the 2009 Census data shows that even though Nairobi, Coast and Rift Valley still remain in-migration areas, the rate of change of migration inflow dropped considerably in Coast and Rift Valley while that of Nairobi slightly increased. The volume of female migration has increased in most regions. The reasons for changing migration patterns are still undetermined and therefore more studies are required to understand migration trends in Kenya. Chapter 7 provides an overview of the future population structure in Kenya. Starting with a population of 38.5 million in 2010, it is projected that the population of Kenya will reach about 44.2 million by 2015, about 50.3 million by 2020, about 57.0 million by 2025, and about 63.9 million by 2030. These projections imply that resources must be put in place to provide services for the increasing large number of the youth expected to reach peak size in 2030. Similarly more adults are expected to survive hence the change in disease burdens. Chapter 8 provides conclusions and recommendations. The estimated crude birth rate is about 38.4 per 1000 population while the crude death rate is 10.4 per 1000 population, giving natural growth rate as 3.0 percent per annum. If the growth rate is unchanged, then the population will double in the next 23 years. This is due to mortality decline and unchanging fertility levels in a number of Arid and Semi-Arid counties. Therefore efforts must be made to reduce the high fertility in the Arid and Semi-Arid parts of the country. There is need to focus interventions to reduce the high childhood and maternal mortality in areas where infant mortality is well above 100 per 1000 child births. These areas have equally high maternal mortality rates. The analysis of the 2009 Census indicates that a number of errors occurred which may have produced an over count and perhaps distorted some of the estimates of population change. The data quality depends on fieldwork quality, which in turn depends on careful questionnaire design, thorough training, and continuous supervision. This should be the focus of future censuses.