1. FEMALE AND MALE MIGRATION PATTERNS INTO THE URBAN SLUMS OF NAIROBI, 1996 - 2006: EVIDENCE OF FEMINISATION OF MIGRATION? Ligaya Batten PhD Student Centre for Population Studies London School of Hygiene and Tropical Medicine

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

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 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)

9. 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

10. Timeline of Available Data 199619971998199920002001200220032004200520062007 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 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 ViwandaniKorogocho

17. Proportions of in-migrants

18. Origin of In-Migrants

19. Form (In-Migrants)

20. Motivations for In-Migration

21. Duration of stay

22. Aim 1: Creating Migrant Typologies

26. Descriptive Analysis of Sequences SexBoth SitesKorogochoViwandani Mean length of stay (months) [Freq] Male97.35 [6561]111.09 [2703]87.72 [3858] Female93.14 [4926]108.14 [2420]78.67 [2506] Total95.55 [11487]109.70 [5123]84.15 [6364] Mean number of places lived [Freq] Male1.63 [6561]1.37 [2703]1.82 [3858] Female1.65 [4926]1.40 [2420]1.90 [2506] Total1.64 [11487]1.38 [5123]1.85 [6364] Mean number of residence episodes [Freq] Male1.67 [6561]1.39 [2703]1.86 [3858] Female1.69 [4926]1.43 [2420]1.95 [2506] Total1.68 [11487]1.41 [5123]1.90 [6364]

27. Logistic Regression Independent VariablesOdds Ratio(95% Conf. - Interval) Sex Male (ref.)1.00- Female1.41**(1.27 – 1.58) Study site Viwandani (ref.)1.00- Korogocho0.28**(0.25 – 0.31) Age group (at time of migration for migrants, 1996 for non-migrants) 0-40.01**(0.01 – 0.02) 5-90.06**(0.05 – 0.07) 10-140.17**(0.14 – 0.21) 15-190.77*(0.66 – 0.91) 20-24 (ref.)1.00- 25-290.56**(0.47 – 0.67) 30-340.32**(0.27 – 0.40) 35-390.19**(0.15 – 0.25) 40-440.19**(0.14 – 0.26) 45-490.17**(0.11 – 0.26) 50-540.16**(0.10 – 0.27) 55-590.19**(0.09 – 0.38) 60+0.14**(0.07 – 0.28) Highest education level reached No education (ref.)1.00- Primary2.62**(1.94 – 3.54) Secondary2.32**(1.70 – 3.16) Higher3.32**(1.70 – 6.48) ** p<0.001* p=0.002

28. Index plots comparing migration typologies: Long term migrants

29. Index plots comparing migration typologies: Recent migrants

30. Index plots comparing migration typologies: Serial migrants

31. Index plots comparing migration typologies: Circular migrants

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

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

34. Multinomial Logistic Regression Recent MigrantSerial MigrantCircular Migrant Independant VariablesRRR Sex Male (ref.)Ref. Female+ns Study site Viwandani (ref.)Ref. Korogocho----ns Age group 15-19--- -- 20-24 (ref.)Ref. 25-29ns+++ 30-34++ns+++ 35-39ns +++ 40-44+++ns 45-49ns 50-54ns 55-59nsNs++ 60+nsNsns Ethnic Group Kikuyu (ref.)Ref. Luhya+++ ++ Luo++++++ Kambans+++ns Kisii++ns++ Otherns

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

36. Aim 2: Is there a trend of feminization of migration?

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

38. Odds ratios comparing female migration compared to male migration, by cohort of migration Year GroupOdds RatioConfidence Interval 1996-990.85[0.79 – 0.93] 2000-021.06[0.97 – 1.15] 2003-051.21[1.11 – 1.31]

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

40. Odds ratios for a one year increase, comparing autonomous and association migrants, by sex. SexFormOdds Ratio[95% Conf. Interval] MaleAutonomous0.98[0.97 – 0.99] MaleAssociational1.14[1.12 – 1.16] FemaleAutonomous1.07[1.04 – 1.09] FemaleAssociational1.10[1.08 – 1.11]

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

42. Odds ratios for a one year increase, comparing economic and non- economic migrants, by sex. SexReasonOdds Ratio[95% Conf. Interval] MaleNon-economic1.03[1.01 – 1.05] MaleEconomic1.04[1.02 – 1.05] FemaleNon-economic1.09[1.07 – 1.10] FemaleEconomic1.07[1.04 – 1.10]

43. Conclusions and discussion

44. 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

45. 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

46. 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

47. 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

48. 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.

49. Acknowledgements SupervisorAngela Baschieri (LSHTM) AdvisorsEliya Zulu (APHRC) Jane Falkingham (Soton) John Cleland (LSHTM) DataAfrican Population and Health Research Center (APHRC) FundingEconomic & Social Research Council (ESRC). Thank you for listening!