FEMALE AND MALE MIGRATION PATTERNS INTO THE URBAN … · 2009. 11. 30. · Ligaya Batten PhD Student Centre for Population Studies London School of Hygiene and Tropical Medicine

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

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

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

STUDY SETTING

• High Rural-Urban migration (esp. Nairobi)

• Over half urban population living in slums

• Rel. high education• Informal Sector• Poverty

STUDY SETTING (cont.)

Source: APHRC 2002

STUDY SITE

APHRC (African Population and Health Research Centre)Two urban slums –Viwandani and KorogochoPopulation ≈60,000Area ≈ 1km2EmploymentFertilityHighly mobile population

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)

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

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

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.

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

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

RESULTS

i. Descriptive Results

ii. Migrant typologies

iii. Feminization of migration?

DESCRIPTIVE RESULTS

Age and Gender Structure of Viwandani & Korogocho in Dec 2006, by in-migrant status

Viwandani Korogocho

Proportions of in-migrants

Origin of In-Migrants

Form (In-Migrants)

Motivations for In-Migration

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

AIM 1:CREATING MIGRANT

TYPOLOGIES

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

LB-LSHTM2 insert graphs comparing migrant types

insert economic related graphs as well for IUSSP Ligaya, 08/09/2009

0

1000

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3000

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mbe

r of

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ces

0 2 4 6 8 10 11Years


Migration History Indexplot for Males in Korogocho0

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mbe

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ces

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Migration History Indexplot for Females in Korogocho

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Migration History Indexplot for Males in Viwandani0

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0 2 4 6 8 10 11Years


Migration History Indexplot for Females in Viwandani

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]

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

Index plots comparing migration typologies: Long term migrants

0

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1400

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mbe

r of

Seq

uen

ces

0 1 2 3 4 5 6 7 8 9 10 11Years


Long Term Migrants - Male0

200

400

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1000N

um

ber

of S

equ

ence

s

0 1 2 3 4 5 6 7 8 9 10 11Years


Long Term Migrants - Female

Index plots comparing migration typologies: Recent migrants

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0 1 2 3 4 5 6 7 8 9 10 11Years


Recent Migrants - Male0

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Recent Migrants - Female

Index plots comparing migration typologies: Serial migrants

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700

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0 1 2 3 4 5 6 7 8 9 10 11Years


Serial Migrants - Male0

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0 1 2 3 4 5 6 7 8 9 10 11Years


Serial Migrants - Female

Index plots comparing migration typologies: Circular migrants

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75

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125

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175

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0 1 2 3 4 5 6 7 8 9 10 11Years


Circular Migrants - Male0

25

50

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100

125N

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ber

of S

equ

ence

s

0 1 2 3 4 5 6 7 8 9 10 11Years


Circular Migrants - Female

Index plots comparing migration typologies: Rural (to slum) migrants

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mbe

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Within DSARural

Rural Migrants - Male0

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0 1 2 3 4 5 6 7 8 9 10 11Years

Within DSARural

Rural Migrants - Female

Index plots comparing migration typologies: Urban (to slum) migrants

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600

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1400

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mbe

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ces

0 1 2 3 4 5 6 7 8 9 10 11Years

Within DSANairobi SlumNairobi Non-SlumOther UrbanRural

Urban Migrants - Male0

200

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1000

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mbe

r of

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uen

ces

0 1 2 3 4 5 6 7 8 9 10 11Years

Within DSANairobi SlumNairobi Non-SlumOther UrbanRural

Urban Migrants - Female

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

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 +++ +++ +++

AIM 2:IS THERE A TREND OF

FEMINIZATION OF MIGRATION?

Numbers of male and female migrants, and sex ratios, 1996-2005

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]

Numbers of male and female autonomousmigrants, and sex ratios, 1996-2005

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]

Numbers of male and female economicmigrants, and sex ratios, 1996-2005

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]

CONCLUSIONS AND DISCUSSION

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

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

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

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

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.

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!

Documents

FEMALE AND MALE MIGRATION PATTERNS INTO THE URBAN … · 2009. 11. 30. · Ligaya Batten PhD Student Centre for Population Studies London School of Hygiene and Tropical Medicine