In this section, we provide insights on the key demographic indicators produced through the NUHDSS between 2003 and 2009. It is worth noting that the annual trends depicted in the section could not have been produced in the absence of the NUHDSS, given the lack of vital registration data in this setting. The tables, where appropriate or available also include comparative national and subnational indicators based on the Kenya Demographic and Health Surveys (KDHS).
Population Structure and Changes
The NUHDSS followed an average population of about 71,000 people living in about 28,500 households every year between 2003 and 2009. Analysis of the population of the DSA by age shows that, on average, 31% of the total NUHDSS population is aged less than 15 years, 68% is aged 15–64 years, and 1% is aged 65 years and above (Figure 3). There are location differences in population structure by age and by sex. Viwandani has more males aged 15–64 years than does Korogocho, while Korogocho has a slightly larger 0 to 4-year-old category. Likewise, Korogocho has a higher dependency ratio compared to Viwandani.
Relative to the whole of Nairobi, the NUHDSS population is predominantly male. In 2009, the sex ratio (including children) was 125 males to 100 females, but with striking differences by age group and between the two slum areas (Korogocho and Viwandani). Analysis by age and by slum area shows that sex ratio is higher among people aged 25 years and above, and in Viwandani (144 males to 100 females) compared to Korogocho (109 males to 100 females). Korogocho has a larger household size (3.0 people per household on average) compared to Viwandani (2.3 people per household).
Table 1 shows the NUHDSS population from 2003 to 2009 and the population as of January 1 of each year during the period. In general, the NUHDSS population increased by 16% from 67,204 people in 2003 to 78,156 residents in 2009. Population growth is slightly higher (18%) in Viwandani compared to Korogocho (14%). The marked difference between the number of people at the beginning of the year and the total that was monitored by the end of the year shows the extent of population mobility.
Trends in Total Fertility Rate
Table 2 presents trends in the total fertility rate (TFR) from 2003 to 2009. The TFR is a synthetic measure of fertility that gives the average number of births a woman would have by the time she reaches 50 years of age if she were to give birth at the current age-specific fertility rates. Unless otherwise specified, the TFR is for all women aged 15–49 years. The NUHDSS TFRs are based on 1 year while the Demographic and Health Surveys (DHS) covers the past 3 years.
Overall, fertility in the NUHDSS varied between 3.2 children per woman in 2003 and 3.8 per woman in 2004, with an average of 3.4 per woman over the study period. This level of fertility is higher than the TFR for Nairobi, but lower than the national level (4.9 children per woman in 2003 and 4.6 children per woman in 2008). Fertility in Korogocho is about half a child higher than that in Viwandani. Analysis of trends over time shows that fertility has remained constant over the 7-year period and is probably consistent with the general stall observed in Nairobi since the mid-1990’s.11
Trends in Child Mortality Indicators
Child mortality indicators are a useful gauge of a country’s level of health or development, and are components of the physical quality of life index. One of the targets of Millennium Development Goal #4 seeks to reduce under-5-year-old mortality rates (U5M) by two thirds between 1990 and 2015. Table 3 shows trends in infant (under 1 year old) and U5M in the NUHDSS during the period 2003–2009.
Infant mortality rate (IMR), the probability of dying before the first birthday per 1,000 live births, has steadily declined between 2003 (82.3) and 2009 (58.5). This trend in infant mortality is consistent with data from the last two Kenya DHSs, which showed that the national IMR decreased from 75.5 in 2003 to 59.6 in 2008. The IMR is lower in Viwandani compared to Korogocho.
U5M refers to the probability of dying between birth and the fifth birthday per 1,000 live births. U5M is always higher in Korogocho than in Viwandani. Overall, U5M has declined from 113 in 2003 to 79 in 2009. The rates in the two slum settlements are higher than those for any of the other population sub-groups. The trends observed in the DSA are also consistent with the national trends in Kenya. For instance, the U5M for Nairobi declined by 33%, from 95 in 2003 to 64 in 2008–2009. The NUHDSS U5M also declined by 30% during the same period13.
One key feature of the longitudinal NUHDSS is its capacity to understand in-migration as well as out-migration patterns and determinants. We use the routine NUHDSS migration data and the migration and employment histories collected from 12,638 randomly selected participants aged 12 years and above in 2006 to describe characteristics of migrants.
Place of birth for the NUHDSS residents
Table 4 shows the percentage of migrants and non-migrants (those born in the study communities) in the two slum communities, Korogocho and Viwandani, using the 2006 survey data. The majority of individuals are migrants, with the proportion of migrants being higher in Viwandani (95%) than it is in Korogocho (75%). The gender differences in place of birth are very minor.
For the migrants to the study communities, the vast majority were born in rural areas—88% of males and 91% of females in Korogocho, and 92% of males and 96% of females in Viwandani.
Place of origin and age at arrival
Table 5 presents the place of residence for in-migrants just before they moved into the DSA. The majority of migrants were living in rural places before migrating to the DSA (63% of females and 58% of males). The pattern is similar in both areas. Nevertheless, considerable intraurban mobility is also observed—over a third of male and female migrants came from other parts of Nairobi and at least 20% of male and female migrants migrated directly from a non-slum part of Nairobi.
Most people in-migrate into the slums when they were in their young adult age for both males and females. In Korogocho, about 22% females and 13% males moved into the slum when aged 15–19 years, while in Viwandani the corresponding percentages were 24% and 14%, respectively. The percentage of migrants increases up to a peak at 20–29 years old for both slums and for both males and females.
Duration of stay in the slum communities
We use the 2006 survey data to examine duration of stay of people who are currently resident in the slum settlements. Migrants in Korogocho stay longer than do those in Viwandani (Tables 5 and 6). Residents in Korogocho have lived in the community for a cumulated average of about 14 years, while those in Viwandani have lived there for about 8 years. This statistics confirms the high mobility in Viwandani as opposed to the relative stability of the population in Korogocho. In both slum communities, males stay longer on average than females do. Other analyses have shown, however, that female migration is more intense than male migration.14 Contrary to conventional thinking about the lack of permanence of residence in slum settlements, these data show that slums provide long-term homes for many people. In Korogocho, 57% of women and 63% of men had lived in the slums for more than 10 years, while in Viwandani the corresponding figures were 25% and 36%, respectively. Caution should be exercised in interpreting these durations because they are based on self-reported data of when people started living in these communities.
In-migration and out-migration rates
The net migration rate is the difference between the in-migration and out-migration rates. A number of studies have shown that Nairobi is a net in-migration region.15
17 Table 7 presents the NUHDSS migration rates from 2003 to 2009, based on the routine NUHDSS migration data. Like in Nairobi, the NUHDSS migration difference (net migration) is positive. Between January 2003 and December 2009, on average, 27.1% of people moved into the DSA every year while about 25.6% moved out of the DSA. The average net annual migration rate for the study period is estimated at 1.1% during the period 2003-2009. The net migration magnitude varied between −8.1% in 2004 to 5.5% in 2009. The high out-migration rate observed in 2004 is due to the demolition exercise that affected households living near high-voltage electricity lines or near railway lines and pipelines. Residents were given a 3-month ultimatum to move out of their structures or face forced eviction and demolition of their houses. During that year, in-migration and out-migration rates reached 33.1% and 41.3%, respectively. Although many structures were demolished and many residents were forced to leave the study area, many residents rebuilt their houses after they realized that the demolition notice was not seriously enforced. In general, in-migration and out-migration rates are higher in Viwandani than in Korogocho, corroborating the fact that the latter retains more of its population. For example in 2004, the in- and out-migration rates were 24% and 24% in Korogocho as compared to 42% and 58% in Viwandani, respectively.
Poverty and Livelihood Status
Given that livelihood prospects are the main attraction for people coming to Nairobi from rural areas (and ending up in slum settlements), it is important to examine what sort of income-generating activities slum residents rely on for their survival. Table 8 presents the economic activity status of residents aged 18 years and above over the last 30 days in 2009. Overall, among people aged 18 years and above, only 24% were involved in a salaried employment of established or stable business, with Korogocho residents being less likely to be in these two relatively stable income generating activities than their counterparts in Viwandani (16%% vs. 30%). For the two slums combined, about half of men and women relied on the two unstable forms of income generation (unestablished business of casual employment) while 27% were economically inactive. About 59% of men were in these unstable livelihood sources while 9% were economically inactive. For women, however, half were economically inactive while 33% were in the two unstable sources of income. So, while many people migrate to Nairobi to look for better livelihood opportunities, most slum residents rely on unstable forms of income. It would be interesting to examine the extent to which migrants transition from the unstable to stable forms of income over time and how these transitions affect future migration decisions.
The NUHDSS data show an acute lack of basic amenities in slum settlements, although some improvement has been recorded over time. For example, the proportion of households with electricity increased from 32% to 40% between 2006 and 2008 (Table 9). The remainder households rely on kerosene for lighting. In 2006, about 6% of households had access to piped water in their homes. This proportion has decreased over time. The vast majority of households (over 90%) buy water from vendors/kiosks where they pay exorbitant prices for water that is often contaminated by refuge and sewer18. From 2006 to 2009, the majority of households had pit latrines shared with other households; about 3% in 2006 and 1% of households in 2009 had private toilets. Most areas of the informal settlements continue to suffer from incredible amounts of litter and waste in open areas and proper garbage disposal remains a huge challenge. In fact, the majority of households either dispose of garbage in public open spaces or in the river (69% in 2006 and 75% in 2009). The remainder of households reports relying on garbage disposal services, pits or dump sites. The vast majority of households (more than 80% from 2006 to 2009) report using kerosene as cooking fuel.
One of the unique features of the NUHDSS data is that it allows assessment of transition in-and-out of poverty for households that were there at two points in time as shown below in Table 10. Overall, there was a reduction in the poverty headcount for all households in 2006 and 2009; the proportion of households below the poverty line (estimated at 2913 Kenyan Shillings) declined from 55% in 2006 to 35% in 2009. Tracking of same households shows that there was greater transition from being poor to non-poor (47%) than there was from being non-poor to poor (30%). This reduction in absolute poverty is in line with the observed decline between 2003 and 200619 as well as the improvements in amenities described in this study.