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

Abstract

The projected size, composition and distribution of the population at different geographic units and points in time form a rational basis not only to gauge the primary needs of the Kenyan population, but also plan for these needs accordingly. This volume presents various components of population projections based on the 2009 Kenya Population and Housing Census in four chapters. Chapter 1 presents the general introduction providing the background to census taking in Kenya, objectives of the 2009 census, general methodology employed in projecting the population, assessment of the data quality, and base population. The cohort component method was used to project both the national and provincial populations while the ratio method of population projection was used at the county and constituency levels where the necessary input information about the components of population change were not readily available. Although there were no irregularities in sex ratios at national level, there were serious irregularities in reporting sex at all ages in North Eastern province with a maximum sex ratio of 202 for those aged 60-64. As recorded in past censuses, terminal digits (0 and 5) were highly preferred with zero having the highest preference, more so by females. It was evident that most respondents avoided reporting age ending with digit 1 followed by digit 9 and thus shifted their ages to end with the most likely terminal adjacent digit zero. The Accuracy Index for the 2009 census was 23.7, which is fairly accurate, as it is on the lower scale of 20-40. Comparatively, the 2009 census had the best Accuracy Index followed by the 1989 census, with the 1979 census recording the worst at 28.1. Once again, data from North Eastern province reflected highly inaccurate reporting with an index of 108 (with 20 as the cut off point) followed by Nairobi province (64) in this category. Only Western province – with an index of 18 – fell in the category of accurate reporting. The rest of the provinces had indices that placed them in the category of inaccurate. Chapter 2 documents the assumptions used in the three components of population change, i.e. fertility, mortality and migration. At both national and provincial levels, it was assumed that the decrease in TFR would continue till the end of the projection period. For example, the TFR was assumed to decline from 4.7 in 2010 to 4.1 in 2030. Similar fertility assumptions were made for each province using national and provincial estimates of TFR from the 2009 Census as inputs. Mortality projections were based on projecting future expectation of life at birth. Expectation of life at birth was assumed to increase from 58.5 for males and 61.6 for females in 2010 to 65.3 and 69.5 in 2030 respectively. This was based on the fact that both the 2008/09 KDHS and the 2009 Census had shown improvement in infant and child mortality. The AIDS-only age-specific mortality rates from the National AIDS Control Council data were used as the input in projecting the effect of AIDS. The result was a continued decline in AIDS-only mortality in future years. For example, by 2030, these computations indicate that there will be minimal impact of HIV and AIDS on the Kenyan population. Like in previous censuses, Kenya continues to record very low volumes of international migration (less than 1%) – hence an insignificant factor in population change, and therefore not incorporated into the national population projections. At sub-national level, the average of the net migration figures in the last three censuses (1989, 1999 and 2009) were used to represent internal migration at the provincial level. It was assumed that these averages would remain constant for the entire projection period. Chapter 3 outlines the results of the population projections at different levels. At national level, population is expected to increase from 38.5 million in 2010, then to 44.2 million by 2015, 50.3 million by 2020, 57.0 million by 2025, and eventually reach 63.9 million by 2030. At provincial level, the population is projected to continuously grow throughout the projection period in all provinces. Provincial rankings by population size are not expected to change - Rift Valley is expected to remain the largest province and North Eastern the smallest. However, Nairobi is expected to experience the highest rate (4.7%) in its population increase – from 3.1 million in 2010 to 8.1 million in 2030. During the same period, Eastern and Western provinces are expected to experience the lowest rates (0.9% and 1.7% respectively) in their population increase. Ranked by their projected population sizes, Nairobi county is expected to continue to be the most populous throughout the projection period. However, none of the next nine most populous counties are expected to maintain their ranks in the 20-year projection period. Among the counties whose ranks are expected to improve include: Nakuru (from 5 to 2); Kiambu (from 4 to 3); Mombasa (from 14 to 7); and Uasin Gishu (from 16 to 9). On the other hand, ranks of some of them are expected to decline: Kakamega (from 2 to 4); Bungoma (from 3 to 5); Meru (from 6 to 10); Machakos (from 9 to 21); and Kitui (from 10 to 24). Nationally, pre-school age population is expected to increase from 3.5 million in 2010 to 5.5 million in 2030; those of primary school age from 8.2 million in 2010 to 13.0 million in 2030; while those of secondary school age from 3.5 million in 2010 to 5.7 million by 2030. At regional level, the rate of increase in the projected school-age population within the projection period is expected to vary: Nairobi province is expected to take lead with its pre-school age population increasing at about five percent and both the primary school age and secondary school age populations increasing by about six percent; the three components of school-age populations in Coast and Rift Valley provinces are expected to increase by about three percent; Eastern province is expected to experience the least rate of increase in the three components – at between 0.2 and 0.5 percent.