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+
Outline
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Background information on Kenya
Demographic and socio-economic profile
Data source
Geographical patterns of migration
Modelling migration differentials
Conclusions
Historical context
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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, urbanurban and urban-rural (incl. return)
Patterns of Internal migration

Overall, the pattern of internal migration at the
provincial scale has remained consistent over time,
and can be summarised thus:
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
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)
Provincial divisions of Kenya
N.
N. EASTERN
EASTERN
WESTERN
WESTERN
NYANZA
NYANZA
RIFT
RIFT VALLEY
VALLEY
EASTERN
EASTERN
CENTRAL
CENTRAL
NAIROBI
NAIROBI
COAST
COAST
COAST
COAST
Demographic overview
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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 20002005, and declined in the late 20th 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.
Demographic features of Kenya (IPUMS)
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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
Educational profile of adults
1%
1%
No schooling
0%
18%
21%
Some primary completed
Primary (6 yrs) completed
Lower secondary general completed
Secondary, general track completed
20%
Some college completed
University completed
39%
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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.
Participation in the labour market
% aged 15+
0.0
10.0
20.0
30.0
40.0
50.0
EMPLOYED
10.6
At w ork, family holding, agricultural
41.3
1.7
UNEMPLOYED
7.9
Unemployed, not specified
2.7
No w ork available
5.2
INACTIVE
18.1
Housew ork
6.7
0.9
In school
Retirees/pensioners
Inactive, other reasons
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80.0
20.5
At w ork, family holding, not agricultural
Unable to w ork/disabled
70.0
74.1
At w ork
Have job, not at w ork last w eek
60.0
8.7
0.6
1.2
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%.
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”.
Population change by province
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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.
% population change
1979-89
1989-99
1999-2008
person/sq. km.
share of national
population
Nairobi
60.0
61.8
41.8
4442
8.7
Central
32.9
19.5
5.2
297
11.2
Coast
36.2
36.0
24.2
37
8.8
Eastern
38.6
22.9
17.8
34
15.5
North Eastern
-0.6
159.1
46.6
11
4.0
Nyanza
32.6
25.2
16.0
315
14.5
Rift Valley
53.7
40.3
25.3
50
24.9
Western
38.8
32.0
29.5
520
12.4
Kenya
39.9
33.8
22.4
60
100.0
Province
Socio-economic differentials by province
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Two diagrams from the “Pulling Apart”
report (Society for International
Development, 2004) summarise socioeconomic 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.
The data source
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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.
Poverty map of Kenya, 1999
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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
Migration and household status
Type of m igration and type of person
8.0
7.0
% of people aged 15+
6.0
5.0
4.0
3.0
2.0
1.0
0.0
All 15+
Singles
International
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Household heads
Internal inter-district
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
Characteristics of inter-district migrants
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Kenya: Percent migrating across districts, 1998-9
12.0
10.0
% of resident population

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
8.0
6.0
4.0
2.0
0.0
under 16
16-24
25-34
35-49
50-69
Internal
Interrnational
70+
16+
All ages
Male
Female
All ages
Male
Female
All migrants
Nairobi: Percent of residents migrating across districts, 1998-9
30.0
25.0
% of resident population

20.0
15.0
10.0
5.0
0.0
under 16
16-24
25-34
35-49
50-69
Internal
Interrnational
70+
All migrants
16+
Age at migration
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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
Age at m igration
12000
10000
8000
Persons

6000
4000
2000
0
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
46
48
Age
50
52
54
56
58
60
62
64
66
68
70
72
74
76
78
80
82
84
86
88
90
92
94
Age and gender of migrants
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Migrants are most common in the15 to 29 age range, with peak occurring for 2024 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.
District-level migration rates
Nairobi
Mombasa
Laikipia
Kajiado
Bondo
Suba
Kisumu
Narok
Thika
UasinNyando
Rachuonyo
Nakuru
Buret
Lugari
Lamu
Homa Bay
TransMoyale
Siaya
Koibatek
Trans Mara
KENYA
Mbeere
Kiambu
Taita
Isiolo
Machakos
Migori
Samburu
Nyandaura
Central
Nandi
Kericho
Embu
Tana River
Kakamega
Nyamira
Keiyo
Butere/Mu
Teso
Malindi
Busia
Meru
South Kissi
Bomet
Kwale
Nyeri
Baringo
Kuria
Makueni
Kilifi
Vihiga
Maragua
Mt Elgon
Bugoma
Garissa
Muranga
Kirinyaga
Marakwet
Tharaka
Kitui
Meru South
West-Pokot
Mandera
Turkana
Mwingi
Meru North
Marsabit
Wajir
14.0
12.0
10.0
8.0
6.0
4.0
2.0
Internal
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0.0
International
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.
Migrants as % of population aged 16+
Ranking of districts by in-migration rate
In-migration by district
18.0
16.0
Migration and urbanisation
In-mi gration rate and urbanization
18.0
Migrants as a percentage of resident population
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
0.0
0.0
20.0
40.0
60.0
80.0
100.0
% population living in urban areas
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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.
Migration, age and sex ratios
Mi gration rate and median age

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
18.0
16.0
14.0
Percent migrants
12.0
10.0
8.0
6.0
4.0
2.0
0.0
10
15
20
25
Median age
Migration rate and sex ratio
18.0
16.0
14.0
Percent migrants
12.0
10.0
8.0
6.0
4.0
2.0
0.0
800
850
900
950
1000
Sex ratio
1050
1100
1150
1200
Inter-provincial migration, 1998-9, percentages of aged people
16+ by province of residence 1998
Nairobi
Central
Province
Coast
Province
Eastern
Province
NorthEastern
Province
Nyanza
Province
Rift
Valley
Province
Western
Province
Residents
1998
(000s)
Nairobi
92.0
1.8
0.7
1.1
0.1
1.7
1.6
1.0
1288.6
Central Province
2.0
96.1
0.2
0.3
0.0
0.1
1.1
0.1
2134.8
Coast Province
0.9
0.2
97.4
0.4
0.0
0.4
0.3
0.2
1315.2
Eastern Province
2.3
0.9
0.7
95.5
0.1
0.1
0.5
0.0
2464.5
North-Eastern Province
1.0
0.1
0.8
0.5
97.1
0.1
0.2
0.1
403.7
Nyanza Province
1.5
0.3
0.4
0.1
0.0
95.9
1.5
0.4
2223.3
Rift Valley Province
0.7
0.5
0.1
0.2
0.0
0.4
97.7
0.4
3406.2
Western Province
1.9
0.4
0.4
0.1
0.0
0.6
2.0
94.6
1687.4
Abroad
16.2
7.4
12.1
15.2
1.3
17.8
24.0
6.0
225.2
1426.5
2145.4
1361.6
2426.3
398.3
2229.4
3509.8
1651.7
15148.9
Residents 1999 (000s)
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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.
Factors underlying the pattern of inter-district
migration
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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.
Net migration rate and indicators
Net mi gration rate and HIV prevalence
10.0
10.0
8.0
8.0
6.0
4.0
2.0
2
R = 0.0211
0.0
0.0
1.0
2.0
3.0
4.0
-2.0
-4.0
-6.0
-8.0
-10.0
Net migrants aged 16+ as a percentage of resident
population
Net migrants aged 16+ as a percentage of resident
population
Net mi gration rate and population density
4.0
2.0
0.0
0
5
10
15
20
25
R2 = 0.0059 30
90
95
R2 = 0.0142 100
-2.0
-4.0
-6.0
-8.0
-10.0
-12.0
log population density
Unem ploym ent rate
Net mi gration rate and unemployment
Net mi gration rate and employment
10.0
10.0
8.0
8.0
6.0
4.0
R2 = 0.0468
2.0
0.0
0
4
8
12
-2.0
-4.0
-6.0
-8.0
-10.0
-12.0
16
20
24
Net migrants aged 16+ as a percentage of resident
population
Net migrants aged 16+ as a percentage of resident
population
-12.0
6.0
6.0
4.0
2.0
0.0
70
75
80
85
-2.0
-4.0
-6.0
-8.0
-10.0
-12.0
Unem ploym ent rate
Em ploym ent rate
OLS regression model of inter-district migration
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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.
Results of modelling inter-district migration
Coefficientsa
Model
1
(Cons tant)
Dis tance (km)
Des tination
unemployment rate 1999
Des tination HIV
Prevalence Rates (2000)
Des tination employment
rate 1999
Origin unemployment rate
1999
Origin HIV Prevalence
Rates (2000)
Origin employment rate
1999
aged 16-24
aged 25-34
aged 35-49
aged 50-69
Female
Log origin population
Log des tination
population
a. Dependent Variable: count
Uns tandardized
Coefficients
B
Std. Error
-279.188
10.765
-.106
.002
Standardized
Coefficients
Beta
-.222
t
-25.935
-54.072
Sig.
.000
.000
.281
.048
.024
5.890
.000
.268
.034
.032
7.980
.000
-2.430
.051
-.243
-47.370
.000
1.190
.047
.104
25.281
.000
-.422
.031
-.054
-13.793
.000
.148
.044
.015
3.336
.001
41.122
19.408
10.893
5.942
8.180
33.413
1.609
1.627
1.664
1.794
.391
.717
.396
.169
.075
.026
.079
.197
25.562
11.931
6.545
3.312
20.940
46.628
.000
.000
.000
.001
.000
.000
49.247
.780
.320
63.152
.000
Exploring the factors underlying individual migration

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
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.
Regression parameters
B
No families at address
S.E.
Wald
df
Sig.
Exp(B)
0.220
0.006
1283.222
1
0.0000
1.2455
age
-0.011
0.001
115.340
1
0.0000
0.9894
Male
0.083
0.014
33.938
1
0.0000
1.0865
Rural
0.415
0.021
389.672
1
0.0000
1.5150
2137.477
2
0.0000
Ownership3Rec2
Owned, inherited and constructed
-0.875
0.019
2137.427
1
0.0000
0.4169
Owned, purchased=1
-0.286
0.029
94.634
1
0.0000
0.7515
0.053
0.008
41.392
1
0.0000
1.0545
1392.598
3
0.0000
Education
Employ4Rec2
At work and have job, but not at work last week=0
0.585
0.021
803.720
1
0.0000
1.7942
At work, family holding=1
0.029
0.022
1.796
1
0.1803
1.0294
Unemployed or no job available=2
0.568
0.022
645.361
1
0.0000
1.7646
2321.879
68
0.0000
1026.242
1
0.0000
distke
Constant
-2.549
0.080
0.0782
Model results
-2 Log
likelihoo
d
Step
Cox & Snell R
Square
Nagelkerke R
Square
1
154257.8
0.042168
0.095312
2
150615.6
0.054327
0.122794
3
148966.7
0.059781
0.135121
4
147828.4
0.063527
0.143589
5
147411.9
0.064894
0.146679
6
147289.3
0.065296
0.147588
7
147247.9
0.065432
0.147895
8
147214
0.065543
0.148146
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


% migrants
predicted
Step

% non-migrants
predicted
1
0.0
100.0
2
0.0
100.0
3
0.0
100.0
4
0.7
99.9
5
0.7
99.9
6
0.7
99.9
7
0.7
99.9
8
0.7
99.9


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.
District coefficients
Conclusions
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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.