FEMALE AND MALE MIGRATION PATTERNS INTO THE URBAN SLUMS OF NAIROBI, : EVIDENCE OF FEMINISATION OF MIGRATION?

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FEMALE AND MALE MIGRATION PATTERNS INTO THE URBAN SLUMS OF NAIROBI, 1996-2006: EVIDENCE OF FEMINISATION OF

1 FEMALE AND MALE MIGRATION PATTERNS INTO THE URBAN SLUMS OF NAIROBI, : EVIDENCE OF FEMINISATION OF MIGRATION? Ligaya Batten PhD Student Centre for Population Studies London 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

2 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,

3 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

4 STUDY SETTING High Rural-Urban migration (esp. Nairobi) Over half urban population living in slums Rel. high education Informal Sector Poverty

5 STUDY SETTING (cont.) Source: APHRC 2002

6 STUDY SITE APHRC (African Population and Health Research Centre) Two urban slums Viwandani and Korogocho Population 60,000 Area 1km2 Employment Fertility Highly mobile population

8 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 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)

9 DATA Nairobi Urban Health Demographic Surveillance Site (NUHDSS) Nested surveys: Migration history Who? >= 12 years old sampled responses When? September April 2007 What is collected? 11 year migration history calendar (every month) Detailed cross-sectional questionnaire Birth histories and marital histories collected periodically

10 Timeline of Available Data NUHDSS Data N= 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 occurred Time period covered in retrospect

11 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.

12 METHODS Basic descriptive analysis Aim 1 Sequence Analysis Descriptive Analysis of Sequences Compare sub-groups Create typologies Logistic Regression Multinomial logistic regression Aim 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

13 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

14 RESULTS i. Descriptive Results ii. Migrant typologies iii. Feminization of migration?

15 DESCRIPTIVE RESULTS

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

17 Proportions of in-migrants

18 Origin of In-Migrants

19 Form (In-Migrants)

20 Motivations for In-Migration

21 Duration of stay Kaplan-Meier survival estimates Duration 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

22 AIM 1: CREATING MIGRANT TYPOLOGIES

23 LB-LSHTM2 Num ber of Sequences Migration History Indexplot for Whole Sample Within DSA Nairobi Slum Nairobi Non-Slum Other Urban Rural Outside Kenya Years

24 Slide 23 LB-LSHTM2 insert graphs comparing migrant types insert economic related graphs as well for IUSSP Ligaya, 08/09/2009

25 Migration History Indexplot for Males in Korogocho Migration History Indexplot for Females in Korogocho N u m b e r o f S e q u e n c e s Within DSA Nairobi Slum Nairobi Non-Slum Other Urban Rural Outside Kenya N u m b e r o f S e q u e n c e s Within DSA Nairobi Slum Nairobi Non-Slum Other Urban Rural Outside Kenya Years Years

26 Migration History Indexplot for Males in Viwandani Migration History Indexplot for Females in Viwandani N u m b e r o f S e q u e n c e s Within DSA Nairobi Slum Nairobi Non-Slum Other Urban Rural Outside Kenya N u m b e r o f S e q u e n c e s Within DSA Nairobi Slum Nairobi Non-Slum Other Urban Rural Outside Kenya Years Years

27 Descriptive Analysis of Sequences Sex Both Sites Korogocho Viwandani Mean length of stay (months) [Freq] Male [6561] [2703] [3858] Female [4926] [2420] [2506] Total [11487] [5123] [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]

28 Logistic Regression Independent Variables Odds Ratio (95% Conf. - Interval) Sex Male (ref.) Female 1.41** ( ) Study site Viwandani (ref.) Korogocho 0.28** ( ) Age group (at time of migration for migrants, 1996 for non-migrants) ** ( ) ** ( ) ** ( ) * ( ) (ref.) ** ( ) ** ( ) ** ( ) ** ( ) ** ( ) ** ( ) ** ( ) ** ( ) Highest education level reached No education (ref.) Primary 2.62** ( ) Secondary 2.32** ( ) Higher 3.32** ( ) ** p<0.001 * p=0.002

29 Index plots comparing migration typologies: Long term migrants Long Term Migrants - Male Long Term Migrants - Female 0 Within DSA 0 Within DSA Nairobi Slum Nairobi Slum N um ber of Sequences Nairobi Non-Slum Other Urban Rural Outside Kenya N um ber of Sequences Nairobi Non-Slum Other Urban Rural Outside Kenya Years Years

30 Index plots comparing migration typologies: Recent migrants Recent Migrants - Male Recent Migrants - Female 0 Within DSA 0 Within DSA Nairobi Slum Nairobi Slum Nairobi Non-Slum Nairobi Non-Slum N u m ber of Sequences Other Urban Rural Outside Kenya N u m ber of Sequences Other Urban Rural Outside Kenya Years Years

31 Index plots comparing migration typologies: Serial migrants Serial Migrants - Male Serial Migrants - Female 0 Within DSA 0 Within DSA Nairobi Slum Nairobi Slum N u m ber of Sequences Nairobi Non-Slum Other Urban Rural Outside Kenya N u m ber of Sequences Nairobi Non-Slum Other Urban Rural Outside Kenya Years Years

32 Index plots comparing migration typologies: Circular migrants Circular Migrants - Male Circular Migrants - Female 0 Within DSA 0 Within DSA Nairobi Slum Nairobi Slum N u m b e r o f S e q u e n c e s Nairobi Non-Slum Other Urban Rural Outside Kenya N u m b e r o f S e q u e n c e s Nairobi Non-Slum Other Urban Rural Outside Kenya Years Years

33 Index plots comparing migration typologies: Rural (to slum) migrants Rural Migrants - Male Rural Migrants - Female 0 Within DSA 0 Within DSA Rural Rural N u m ber of Sequences N u m ber of Sequences Years Years

34 Index plots comparing migration typologies: Urban (to slum) migrants Urban Migrants - Male Urban Migrants - Female 0 Within DSA 0 Within DSA Nairobi Slum Nairobi Slum N um ber of Sequences Nairobi Non-Slum Other Urban Rural N um ber of Sequences Nairobi Non-Slum Other Urban Rural Years Years

35 Multinomial Logistic Regression Recent Migrant Serial Migrant Circular Migrant Independant Variables RRR RRR RRR Sex Male (ref.) Ref. Ref. Ref. Female + ns ns Study site Viwandani (ref.) Ref. Ref. Ref. Korogocho ns Age group (ref.) Ref. Ref. Ref ns ns ns ns ns ns ns ns ns ns ns ns ns Ns ns Ns ns Ethnic Group Kikuyu (ref.) Ref. Ref. Ref. Luhya Luo Kamba ns +++ ns Kisii ++ ns ++ Other ns ns ns

36 Multinomial Logistic Regression (cont.) Recent Migrant Serial Migrant Circular Migrant Independant Variables RRR RRR RRR Highest education level reached No education (ref.) Ref. Ref. Ref. Higher education level - ns ns Ever Married Status Never Married (ref.) Ref. Ref. Ref. Ever Married Socio-economic status (1-10) Poorest [1] (ref.) Ref. Ref. Ref. Less poor - - Ns Relationship to Household Head Household Head (ref.) Ref. Ref. Ref. Spouse +++ ns ns Child ++ ns +++ Other relative ++ ns ns Unrelated Economic reason for moving to the DSA? No (ref.) Ref. Ref. Ref. Yes ns Associational migrant? No (ref.) Ref. Ref. Ref. Yes

37 AIM 2: IS THERE A TREND OF FEMINIZATION OF MIGRATION?

38 Numbers of male and female migrants, and sex ratios,

39 Odds ratios comparing female migration compared to male migration, by cohort of migration Year Group Odds Ratio Confidence Interval [ ] [ ] [ ]

40 Numbers of male and female autonomous migrants, and sex ratios,

41 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 [ ] Male Associational 1.14 [ ] Female Autonomous 1.07 [ ] Female Associational 1.10 [ ]

42 Numbers of male and female economic migrants, and sex ratios,

43 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 [ ] Male Economic 1.04 [ ] Female Non-economic 1.09 [ ] Female Economic 1.07 [ ]

44 CONCLUSIONS AND DISCUSSION

45 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

46 Conclusions (ii) Trend of feminisation of migration found: Decrease in the sex ratio of migration into the study site from Decrease in the sex ratio of autonomous migration into the study site from Decrease in the sex ratio of economic migration into the study site from

47 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, socioeconomic status Definition of economic and autonomous migration open to interpretation

48 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

49 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.

50 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!

Abstract: *I would like to acknowledge the research support of the Economic and Social Research Council (UK).

Abstract: *I would like to acknowledge the research support of the Economic and Social Research Council (UK). FEMALE AND MALE MIGRATION PATTERNS INTO THE URBAN SLUMS OF NAIROBI, 1996 - Ligaya Batten 1, Angela Baschieri 1, Eliya Zulu 2 This paper is a working draft prepared for presentation at the 29 meeting of