female and male migration patterns into the urban … · 2009. 11. 30. · ligaya batten phd...
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FEMALE AND MALE MIGRATION PATTERNS INTO THE URBAN SLUMS OF NAIROBI, 1996 - 2006: EVIDENCE OF FEMINISATION OF MIGRATION?
Ligaya BattenPhD StudentCentre for Population StudiesLondon School of Hygiene and Tropical Medicine
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GENERAL BACKGROUND
• Population growth and urbanisation in sub-Saharan Africa
• Mainly due to Rural to Urban Migration and Natural Increase
• Negative outcomes related to urbanisation in SSA:– Population pressure on services in ill-equipped cities (such as
housing, health and education) and economic opportunities often leads to:
• Slum formation – poor quality housing, lack of sanitation, lack of access to clean water and health services.
• Unemployment and growth in the informal labour market –poverty, precarious livelihoods
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GENERAL BACKGROUND• Phenomenon of female autonomous migration emerging
from previously male dominated process• Evidence of autonomous female migration in South-East
Asia and Latin America, West Africa, South Africa • Causes of feminisation of migration
– Household poverty, fragile ecosystems– Less marriage, better female education– Increase in family and refugee migration
• Consequences of feminisation of migration– Change of gender roles in the family and labour market– Potential knock on effect of reducing fertility
• But no evidence on trends, causes and consequences of sex composition of migration in African slums yet
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STUDY SETTING
• High Rural-Urban migration (esp. Nairobi)
• Over half urban population living in slums
• Rel. high education• Informal Sector• Poverty
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STUDY SETTING (cont.)
Source: APHRC 2002
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STUDY SITE
APHRC (African Population and Health Research Centre)Two urban slums –Viwandani and KorogochoPopulation ≈60,000Area ≈ 1km2EmploymentFertilityHighly mobile population
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DATA• Nairobi Urban Health Demographic Surveillance Site
(NUHDSS)– Who?
• No sampling – ALL residents– When?
• Initial Census in August 2002• Every 4 month• I will use data from 01 January 2003 – 31 December
2007– What is collected in the main DSS?
• Demographic data (births, deaths, in and out migration)
• Socio-Economic data (marriage, education, employment, assets)
• Health Data (morbidity, vaccinations, verbal autopsy)
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DATA• Nairobi Urban Health Demographic Surveillance Site
(NUHDSS)
• Nested surveys:– Migration history
• Who?– >= 12 years old– 14000 sampled 11487 responses
• When?– September 2006 - April 2007
• What is collected?– 11 year migration history calendar (every month)– Detailed cross-sectional questionnaire
– Birth histories and marital histories collected periodically
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Timeline of Available Data1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
NUHDSS
Data
N=112003
Birth History*
N=17532
Migration
History
N=12634
Employment
History^
N=12634
*Birth histories collected retrospectively as part of the main NUHDSS
^ Time period covered (in retrospect)Year during which data collection occurredTime period covered in retrospect
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Aims
1. Define migrant typologies and assess differences between female and male migrant types.
2. Assess whether or not there has been a trend of feminisation of migration between 1996 and 2006.
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METHODS• Basic descriptive analysisAim 1• Sequence Analysis
– Descriptive Analysis of Sequences– Compare sub-groups– Create typologies
• Logistic Regression• Multinomial logistic regressionAim 2• Mantel-Haenzel test for trend
– sex ratio of migrants over time– sex ratio of autonomous migrants over time– sex ratio of economic migrants over time
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Definition of Variables• Outcomes:
– Migrant (Long term, recent, serial, circular)– Autonomous/Associational– Economic/Non-economic
• Explanatory variables:– Sex– Study site, age, education level, ethnicity,
marital status, socio-economic status, relationship to household head
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RESULTS
i. Descriptive Results
ii. Migrant typologies
iii. Feminization of migration?
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DESCRIPTIVE RESULTS
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Age and Gender Structure of Viwandani & Korogocho in Dec 2006, by in-migrant status
Viwandani Korogocho
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Proportions of in-migrants
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Origin of In-Migrants
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Form (In-Migrants)
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Motivations for In-Migration
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Duration of stay0
.25
.5.7
51
0 1 2 3 4 5Duration of stay in the DSA (Years)
95% CI
95% CI
95% CI
95% CI
slumid = VIWANDANI/sex = Male
slumid = VIWANDANI/sex = Female
slumid = KOROGOCHO/sex = Male
slumid = KOROGOCHO/sex = Female
Kaplan-Meier survival estimates
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AIM 1:CREATING MIGRANT
TYPOLOGIES
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0
3000
6000
9000
12000
Nu
mbe
r of
Seq
uen
ces
0 2 4 6 8 10 11Years
Within DSANairobi SlumNairobi Non-SlumOther UrbanRuralOutside Kenya
Migration History Indexplot for Whole SampleLB-LSHTM2
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Slide 23
LB-LSHTM2 insert graphs comparing migrant types
insert economic related graphs as well for IUSSP Ligaya, 08/09/2009
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0
1000
2000
3000
Nu
mbe
r of
Seq
uen
ces
0 2 4 6 8 10 11Years
Within DSANairobi SlumNairobi Non-SlumOther UrbanRuralOutside Kenya
Migration History Indexplot for Males in Korogocho0
1000
2000
3000
Nu
mbe
r of
Seq
uen
ces
0 2 4 6 8 10 11Years
Within DSANairobi SlumNairobi Non-SlumOther UrbanRuralOutside Kenya
Migration History Indexplot for Females in Korogocho
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0
1000
2000
3000
4000
Nu
mbe
r of
Seq
uen
ces
0 2 4 6 8 10 11Years
Within DSANairobi SlumNairobi Non-SlumOther UrbanRuralOutside Kenya
Migration History Indexplot for Males in Viwandani0
1000
2000
3000
4000
Nu
mbe
r of
Seq
uen
ces
0 2 4 6 8 10 11Years
Within DSANairobi SlumNairobi Non-SlumOther UrbanRuralOutside Kenya
Migration History Indexplot for Females in Viwandani
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Descriptive Analysis of SequencesSex Both Sites Korogocho Viwandani
Mean length of stay (months) [Freq]
Male 97.35 [6561] 111.09 [2703] 87.72 [3858]
Female 93.14 [4926] 108.14 [2420] 78.67 [2506]
Total 95.55 [11487] 109.70 [5123] 84.15 [6364]
Mean number of places lived [Freq]
Male 1.63 [6561] 1.37 [2703] 1.82 [3858]
Female 1.65 [4926] 1.40 [2420] 1.90 [2506]
Total 1.64 [11487] 1.38 [5123] 1.85 [6364]
Mean number of residence episodes [Freq]
Male 1.67 [6561] 1.39 [2703] 1.86 [3858]
Female 1.69 [4926] 1.43 [2420] 1.95 [2506]
Total 1.68 [11487] 1.41 [5123] 1.90 [6364]
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Logistic RegressionIndependent Variables Odds Ratio (95% Conf. - Interval)
Sex
Male (ref.) 1.00 -
Female 1.41** (1.27 – 1.58)
Study site
Viwandani (ref.) 1.00 -
Korogocho 0.28** (0.25 – 0.31)
Age group (at time of migration for migrants, 1996 for non-migrants)
0-4 0.01** (0.01 – 0.02)
5-9 0.06** (0.05 – 0.07)
10-14 0.17** (0.14 – 0.21)
15-19 0.77* (0.66 – 0.91)
20-24 (ref.) 1.00 -
25-29 0.56** (0.47 – 0.67)
30-34 0.32** (0.27 – 0.40)
35-39 0.19** (0.15 – 0.25)
40-44 0.19** (0.14 – 0.26)
45-49 0.17** (0.11 – 0.26)
50-54 0.16** (0.10 – 0.27)
55-59 0.19** (0.09 – 0.38)
60+ 0.14** (0.07 – 0.28)
Highest education level reached
No education (ref.) 1.00 -
Primary 2.62** (1.94 – 3.54)
Secondary 2.32** (1.70 – 3.16)
Higher 3.32** (1.70 – 6.48)
** p
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Index plots comparing migration typologies: Long term migrants
0
200
400
600
800
1000
1200
1400
Nu
mbe
r of
Seq
uen
ces
0 1 2 3 4 5 6 7 8 9 10 11Years
Within DSANairobi SlumNairobi Non-SlumOther UrbanRuralOutside Kenya
Long Term Migrants - Male0
200
400
600
800
1000N
um
ber
of S
equ
ence
s
0 1 2 3 4 5 6 7 8 9 10 11Years
Within DSANairobi SlumNairobi Non-SlumOther UrbanRuralOutside Kenya
Long Term Migrants - Female
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Index plots comparing migration typologies: Recent migrants
0
250
500
750
1000
Nu
mbe
r of
Seq
uen
ces
0 1 2 3 4 5 6 7 8 9 10 11Years
Within DSANairobi SlumNairobi Non-SlumOther UrbanRuralOutside Kenya
Recent Migrants - Male0
250
500
750
1000
Nu
mbe
r of
Seq
uen
ces
0 1 2 3 4 5 6 7 8 9 10 11Years
Within DSANairobi SlumNairobi Non-SlumOther UrbanRuralOutside Kenya
Recent Migrants - Female
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Index plots comparing migration typologies: Serial migrants
0
100
200
300
400
500
600
700
Nu
mbe
r of
Seq
uen
ces
0 1 2 3 4 5 6 7 8 9 10 11Years
Within DSANairobi SlumNairobi Non-SlumOther UrbanRuralOutside Kenya
Serial Migrants - Male0
100
200
300
400
500
Nu
mbe
r of
Seq
uen
ces
0 1 2 3 4 5 6 7 8 9 10 11Years
Within DSANairobi SlumNairobi Non-SlumOther UrbanRuralOutside Kenya
Serial Migrants - Female
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Index plots comparing migration typologies: Circular migrants
0
25
50
75
100
125
150
175
Nu
mbe
r of
Seq
uen
ces
0 1 2 3 4 5 6 7 8 9 10 11Years
Within DSANairobi SlumNairobi Non-SlumOther UrbanRuralOutside Kenya
Circular Migrants - Male0
25
50
75
100
125N
um
ber
of S
equ
ence
s
0 1 2 3 4 5 6 7 8 9 10 11Years
Within DSANairobi SlumNairobi Non-SlumOther UrbanRuralOutside Kenya
Circular Migrants - Female
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Index plots comparing migration typologies: Rural (to slum) migrants
0
300
600
900
1200
1500
1800
Nu
mbe
r of
Seq
uen
ces
0 1 2 3 4 5 6 7 8 9 10 11Years
Within DSARural
Rural Migrants - Male0
300
600
900
1200
1500
1800
Nu
mbe
r of
Seq
uen
ces
0 1 2 3 4 5 6 7 8 9 10 11Years
Within DSARural
Rural Migrants - Female
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Index plots comparing migration typologies: Urban (to slum) migrants
0
200
400
600
800
1000
1200
1400
Nu
mbe
r of
Seq
uen
ces
0 1 2 3 4 5 6 7 8 9 10 11Years
Within DSANairobi SlumNairobi Non-SlumOther UrbanRural
Urban Migrants - Male0
200
400
600
800
1000
Nu
mbe
r of
Seq
uen
ces
0 1 2 3 4 5 6 7 8 9 10 11Years
Within DSANairobi SlumNairobi Non-SlumOther UrbanRural
Urban Migrants - Female
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Multinomial Logistic RegressionRecent Migrant Serial Migrant Circular Migrant
Independant Variables RRR RRR RRRSex Male (ref.) Ref. Ref. Ref.Female + ns nsStudy site Viwandani (ref.) Ref. Ref. Ref.Korogocho - --- nsAge group 15-19 --- --- --20-24 (ref.) Ref. Ref. Ref.25-29 ns +++ +++30-34 ++ ns +++35-39 ns ns +++40-44 +++ ns ns45-49 ns ns ns50-54 ns ns ns55-59 ns Ns ++60+ ns Ns nsEthnic Group Kikuyu (ref.) Ref. Ref. Ref.Luhya +++ +++ ++Luo ++ +++ +Kamba ns +++ nsKisii ++ ns ++Other ns ns ns
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Multinomial Logistic Regression (cont.)
Recent Migrant Serial Migrant Circular MigrantIndependant Variables RRR RRR RRRHighest education level reached No education (ref.) Ref. Ref. Ref.Higher education level - ns nsEver Married Status Never Married (ref.) Ref. Ref. Ref.Ever Married --- --- ---Socio-economic status (1-10) Poorest [1] (ref.) Ref. Ref. Ref.Less poor - - NsRelationship to Household Head Household Head (ref.) Ref. Ref. Ref.Spouse +++ ns nsChild ++ ns +++Other relative ++ ns nsUnrelated --- --- ---Economic reason for moving to the DSA? No (ref.) Ref. Ref. Ref.Yes ns --- ---Associational migrant? No (ref.) Ref. Ref. Ref.Yes +++ +++ +++
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AIM 2:IS THERE A TREND OF
FEMINIZATION OF MIGRATION?
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Numbers of male and female migrants, and sex ratios, 1996-2005
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Odds ratios comparing female migration compared to male migration, by cohort of
migration
Year Group Odds Ratio Confidence Interval
1996-99 0.85 [0.79 – 0.93]
2000-02 1.06 [0.97 – 1.15]
2003-05 1.21 [1.11 – 1.31]
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Numbers of male and female autonomousmigrants, and sex ratios, 1996-2005
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Odds ratios for a one year increase, comparing autonomous and association migrants, by sex.
Sex Form Odds Ratio [95% Conf. Interval]
Male Autonomous 0.98 [0.97 – 0.99]
Male Associational 1.14 [1.12 – 1.16]
Female Autonomous 1.07 [1.04 – 1.09]
Female Associational 1.10 [1.08 – 1.11]
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Numbers of male and female economicmigrants, and sex ratios, 1996-2005
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Odds ratios for a one year increase, comparing economic and non- economic
migrants, by sex.
Sex Reason Odds Ratio [95% Conf. Interval]
Male Non-economic 1.03 [1.01 – 1.05]
Male Economic 1.04 [1.02 – 1.05]
Female Non-economic 1.09 [1.07 – 1.10]
Female Economic 1.07 [1.04 – 1.10]
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CONCLUSIONS AND DISCUSSION
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Conclusions (i)• Female migrants more mobile than male
• Strong differences between study sites
• Migrant types:• Females – recent migrants
• Korogocho – serial migrants
• Economic migrants – serial and circular migrants
• Associational migrants – recent, serial and circular migrants
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Conclusions (ii)• Trend of feminisation of migration found:
• Decrease in the sex ratio of migration into the study site from 1996 - 2006
• Decrease in the sex ratio of autonomous migration into the study site from 1996 - 2006
• Decrease in the sex ratio of economic migration into the study site from 1996 - 2006
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Limitations• Under-sampling of migrants in the
migration history survey
• Recall bias
• Time varying data lacking for certain important characteristics• E.g. Marital status, education level, socio-
economic status
• Definition of economic and autonomous migration open to interpretation
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Implications
• Feminisation of migration may have both social and demographic consequences:• Change in women’s roles, increase in women’s
empowerment• May lead to a number of positive consequences –
gender equality in the labour market, improvements in child health and education
• Urban “modernised” lifestyles - potential for fertility decline and therefore reduction in future population growth
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Planned Future Work
• Use cluster analysis to group sequences according to characteristics other than the place of origin, such as motivation, ethnicity, education level, and perhaps other demographic characteristics
• Use migration typologies as explanatory variables for exploring the following:• Employment
• Identify which migrant types have the best chances of employment in the study site, by sex (controlling for employment status in the place of origin).
• Establish the extent to which unemployment increases the likelihood of out-migration from the study site.
• Fertility• Describe the trends in family building patterns of migrants on
non-migrants over the last eleven years.
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Acknowledgements
• Supervisor Angela Baschieri (LSHTM)• Advisors Eliya Zulu (APHRC)
Jane Falkingham (Soton)John Cleland (LSHTM)
• Data African Population and Health Research Center (APHRC)
• Funding Economic & Social Research Council (ESRC).
• Thank you for listening!