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

1. FEMALE AND MALE MIGRATION PATTERNS INTO THE URBAN SLUMS OF NAIROBI, 1996 - 2006: EVIDENCEOF 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

11. Aims • Define migrant typologies and assess differences between female and male migrant types. • 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 • Descriptive Results • Migrant typologies • 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

22. Aim 1:Creating Migrant Typologies

26. Descriptive Analysis of Sequences

27. Logistic Regression

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

35. Multinomial Logistic Regression (cont.)

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

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

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

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

40. Conclusions and discussion

41. 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

42. 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

43. 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

44. 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

45. 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.

46. 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!