1. Using IPUMS data from the 1999 Kenya Census to explore internal migration David Owen *, Elisa Brey + and John Oucho *, University of Warwick * and Universidad Complutense Madrid +

2. Outline Background information on Kenya Demographic and socio-economic profile Data source Geographical patterns of migration Modelling migration differentials Conclusions

3. Historical context While there were no formal administrative boundaries before the colonial period, ethnic territoriality existed. Kenya contains considerable ethnic diversity. The coterminous nature of administrative and ethnic boundaries reflect regional balkanization of the country, which makes regional development and internal migration closely interrelated. Colonial administration introduced modern forms of production and a monetised dual economy consisting of (a) commercial plantation and annual cash crop farming and (b) urban areas Commercial agriculture was established in the former “White (after independence “Kenya”) Highlands”. Much of the country outside these areas is classified as “dry” and much less suitable for agriculture. While land appropriation occurred in these highlands, the peripheries became the reservoir of cheap unskilled labour. Four types of internal migration emerged: rural-rural, rural-urban, urban- urban and urban-rural (incl. return)

4. Patterns of Internal migration Overall, the pattern of internal migration at the provincial scale has remained consistent over time, and can be summarised thus:  Net out migration provinces: Nyanza, Western, North Eastern and Eastern  Net in-migration: Rift Valley, Nairobi and Coast Internal migration reflect of regional inequality within the country: Better endowed areas receive migrants from poorer endowed areas (SID, 2004)

5. Provincial divisions of Kenya

6. Demographic overview Kenya has experienced extremely rapid population growth since independence; from 6.6 million in 1962 to 35 million (projected) in 2008. The major force has been natural change, driven by mortality rates falling faster than fertility rates. The TPFR for Kenya was still 4 during 2000-2005. The Kenyan population is thus very young; 42% were aged under 15 in 2002. Life expectancy was 50 for women and 49 for men during 2000- 2005, and declined in the late 20 th century. In 2001, 15% of 15-49 year olds were infected with HIV/AIDS. There is a marked urban/rural contrast. The annual average population growth rate was 3 per cent between 1980 and 2000, but urban areas grew at 6.6% p.a. while the rural population grew by 1.8% p.a. A third of the population lived in urban areas in 2000. In 1997, 49% of the urban population and 53% of the rural population lived in poverty, as defined by the World Bank.

7. Demographic features of Kenya (IPUMS) The Kenyan population is extremely youthful, with the pyramid shape typical of a rapidly-growing population. Slowing population growth is apparent in the younger age groups The percentage single declines quickly with age, but the percentage widowed starts to increase from the late 20s (perhaps reflecting AIDS/HIV mortality) The percentage of the population in polygamous households increases with age

8. Educational profile of adults Despite the youthful nature of the population and the strong emphasis on education in Kenya, the average length of time in education amongst adults (aged 15+) is low a fifth had received no schooling and 80 per cent had no more than primary school level education Just over 1 per cent had been to college or completed university.

9. Participation in the labour market The overall employment rate for people aged 15+ was 74.1%. Agricultural employment is extremely important as is working for family businesses Most of those unemployed believed that there was no work available Of the quarter of adults inactive, the largest component was those still in education, reflecting the youthful profile of the population 81.9% of people aged 15+ were economically active The overall unemployment rate was 9.5%.

10. Population distribution and ethnic composition The most densely populated parts of the country lie in the south-west and centre. The ethnic composition of the country is complex. The main groups are Kikuyu (22%), Luhya (14%), Luo (13%), Kalenjin (12%) and Kamba (11%). The Luo tend to concentrate near Lake Victoria while the Kikuyu are more likely to be found in the “White Highlands”.

11. Population change by province The population of Kenya is predominantly located in the western and southern provinces. Population density is highest in Nairobi, Western, Nyanza and Central. Much of the dry east and north-east has very low population density. Nairobi’s population is growing extremely rapidly – 40% between 1999 and 2008, but this rate of increase.was slower than in the two preceding decades. The Rift Valley and Western provinces also experienced high rates of population growth over the last 3 decades, but most provinces outside the capital grew slower than the national average. Province % population change person/sq. km. share of national population 1979-891989-991999-2008 Nairobi60.061.841.844428.7 Central32.919.55.229711.2 Coast36.236.024.2378.8 Eastern38.622.917.83415.5 North Eastern-0.6159.146.6114.0 Nyanza32.625.216.031514.5 Rift Valley53.740.325.35024.9 Western38.832.029.552012.4 Kenya39.933.822.460100.0

12. Socio-economic differentials by province Two diagrams from the “Pulling Apart” report (Society for International Development, 2004) summarise socio- economic differentials in Kenya at the Provincial scale. Overall, Nairobi and Central provinces are most developed and North Eastern least developed. In 7 of the 8 provinces, more than half the population live below the poverty line. The extremes were 73.1% in North Eastern and 35.3% in Central province. In the capital, Western and Rift Valley provinces, this is accompanied by a high level of income inequality. Life expectancy is highest in Central and lowest in Nyanza province. The Human Development Index is derived from life expectancy at birth, adult literacy rates and per capita income. It is much higher in Nairobi than in the rest of the country and lowest in Coast and Nyanza provinces.

13. The data source The analyses presented here are based on the analysis of data from the IPUMS 5% sample of households from the 1999 Population Census of Kenya collected by the National Bureau of Statistics. The data is made available by the Minnesota Population Center of the University of Minnesota – see: https://international.ipums.org/international/ The data set consists of anonymised census returns for 1,407,547 persons present on Census day (persons travelling were not included). The smallest geographical unit in the data set is the local government district, of which there were 69 at the time of the Census (there are regular revisions to the administrative geography to take account of population increase). The variables available include age, gender, household structure, education, employment and residence in an urban or rural location. The IPUMS data set contains a number of variables useful for migration analysis. It records the (1999) district of birth and residence one year ago for each individual. The latter includes residence outside Kenya, allowing international migrants to be identified. In addition, the length of time for which an individual has lived in the district of enumeration is also recorded.

14. Poverty map of Kenya, 1999 Levels of poverty are relatively high across the country The most prosperous areas are the “White Highlands” in the centre of the country High rates of poverty in the heavily populated west and in the rural east

15. Migration and household status Overall, 8% of people aged 15 and over had moved district in the year before the Census. 1.5% had moved from another country Single people were more likely to have been internal migrants than household heads There was little difference for international migrants

16. Characteristics of inter-district migrants In Kenya, around 5% of people moved district during 1998-9 migration rates are highest for 16-24 year olds, declining with age. There is little difference by gender. International migrants fromed only 1.3% of the population In Nairobi, 16.5% of the population had moved from another district within the previous year. The % migrating increased after the age of 50, with a fifth of those aged over 70 having moved 2.4% were international migrants

17. Age at migration The data set includes a question (for migrants) on how long a person has lived in their current locality. With the age question, this was used to calculate age at migration. The number of persons declines as the length of time increases, first rapidly then more slowly (partly due to mobility, partly due to fewer older people) The most common age at which people moved to their current district was 20. People mainly migrate in their late teens and twenties Missing data was common for this question

18. Age and gender of migrants Migrants are most common in the15 to 29 age range, with peak occurring for 20- 24 year olds There is a strong gender contrast; female migrants are much more likely to be aged under 25 than males However, women aged over 25 are less likely to be migrants than men. The mean age of non-migrants was 33.1 years compared with 28.8 years for migrants.

19. District-level migration rates

20. Ranking of districts by in-migration rate The population of Nairobi and Mombasa were most likely to have migrated during the previous year, while the in-migration rate was least for Wajir, in the north-east and Marsabit in the north. International migration was highest in dustricts close to the border, such as Mandera in the north-east.

21. Migration and urbanisation The percentage of people moving between districts is higher the more urbanised the district. Migrants as a percentage of the population are highest in Nairobi, but in the more rural areas of the country, there is considerable variation.

22. Migration, age and sex ratios Migration rates tend to increase with median age. The median ages of the population of Nairobi and Mombasa are much higher than other districts, while migration rates are also higher The percentage of the population migrating into a district over the year before the Census is higher where the ratio of males to females is highest

23. Inter-provincial migration, 1998-9, percentages of aged people 16+ by province of residence 1998 The rate of out-migration was highest for Nairobi. Nairobi was the major destination fro migrants from other provinces. Otherwise, out-migrants were most likely to go to neighbouring provinces The destination of international migrants was least likely to be in the North-East, but a quarter went to the Rift Valley, with Nyanza and Nairobi the next most popular destinations. Nairobi Central Province Coast Province Eastern Province North- Eastern Province Nyanza Province Rift Valley Province Western Province Residents 1998 (000s) Nairobi92.01.80.71.10.11.71.61.01288.6 Central Province2.096.10.20.30.00.11.10.12134.8 Coast Province0.90.297.40.40.00.40.30.21315.2 Eastern Province2.30.90.795.50.1 0.50.02464.5 North-Eastern Province1.00.10.80.597.10.10.20.1403.7 Nyanza Province1.50.30.40.10.095.91.50.42223.3 Rift Valley Province0.70.50.10.20.00.497.70.43406.2 Western Province1.90.4 0.10.00.62.094.61687.4 Abroad16.27.412.115.21.317.824.06.0225.2 Residents 1999 (000s)1426.52145.41361.62426.3398.32229.43509.81651.715148.9

24. Factors underlying the pattern of inter-district migration The preceding maps include international migrants, who tend to be concentrated in border regions and Nairobi. Focussing on internal migration, there is a very low correlation (r=0.17) between in- and out-migration rates Associations between net migration rates (for people aged 16 and over) and a number of potential explanatory variables are weak. The strongest positive associations with net migration are with unemployment and population density; net immigration is higher where the unemployment rate is higher, but also where population density is higher. This suggests the influence of urban size – net migration is highest in the large cities, which also experience high rates of unemployment.

25. Net migration rate and indicators

26. OLS regression model of inter-district migration The dependent variable was the (log) number of migrants (aged 16 +) Independent variables were log of origin and destination population, employment, unemployment and HIV rates at origin and destination, distance and age and gender variables. The model achieved a high degree of fit (adjusted R2=0.62). The most important influence was age, with migration declining with age. The next most important was size of destination Migration declined with distance between origin and destination district The influence of labour market conditions at the origin or destination was not strong.

27. Results of modelling inter-district migration

28. Exploring the factors underlying individual migration Using the IPUMS data, a series of binary logistic regression models were estimated One example of these models is presented here. The dependent variable was whether or not a person had moved districts between 1998 and 1999. Because children do not move independently of their family, the analysis was restricted to single people aged 15 and above, who would be making the decision to migrate. The first step was to calculate correlations between the independent variables, in order to guide the choice of variables for the analysis A forward stepwise estimation procedure was employed, to identify independent variables with significant influences on the migration decision.

29. Regression parameters BS.E.WalddfSig.Exp(B) No families at address0.2200.0061283.22210.00001.2455 age-0.0110.001115.34010.00000.9894 Male0.0830.01433.93810.00001.0865 Rural0.4150.021389.67210.00001.5150 Ownership3Rec2 2137.47720.0000 Owned, inherited and constructed-0.8750.0192137.42710.00000.4169 Owned, purchased=1-0.2860.02994.63410.00000.7515 Education0.0530.00841.39210.00001.0545 Employ4Rec2 1392.59830.0000 At work and have job, but not at work last week=00.5850.021803.72010.00001.7942 At work, family holding=10.0290.0221.79610.18031.0294 Unemployed or no job available=20.5680.022645.36110.00001.7646 distke 2321.879680.0000 Constant-2.5490.0801026.24210.00000.0782

30. Model results The fit of the model is relatively low, but increases as variables are added. The percentage of migrants predicted is very low. The probability of being a migrant is higher for women, but declines with age and level of education. Rural dwellers are more likely to be migrants Those with family-owned property are less likely to migrate Both employees and the unemployed are less likely than those working for family enterprises to be migrants. The probability of being a migrant is highest in Bondo, Kajiado and Narok and lowest in Mandera, Marsabit and Wajir. The probability of being a migrant is relatively high in both Nairobi and Mombasa. Step -2 Log likelihoo d Cox & Snell R Square Nagelkerke R Square 1154257.80.0421680.095312 2150615.60.0543270.122794 3148966.70.0597810.135121 4147828.40.0635270.143589 5147411.90.0648940.146679 6147289.30.0652960.147588 7147247.90.0654320.147895 81472140.0655430.148146 Step % migrants predicted % non-migrants predicted 10.0100.0 20.0100.0 30.0100.0 40.799.9 50.799.9 60.799.9 70.799.9 80.799.9

31. District coefficients

32. Conclusions The IPUMS 1999 Census microdata for Kenya offers the potential to explore the factors underlying migration. With a large sample fraction, it provides a powerful data set for modelling and a resource for generating data for geographical areas. It enables both one-year and lifetime migration to be analysed – this paper has focussed on the former. All the analyses presented here demonstrate that migrants are predominantly male, mainly young and with better education. There are a small number of explanatory variables to include in the models available from the Census or other Kenyan data sources, limiting the analysis of socio-economic influences on migration. More sophisticated models would require integrating more information on the geographical differentials in economic development within Kenya (from the Labour Force Survey, Demographic and Health Survey) and information on ethnicity.