Ageing in Sub-Saharan Africa: Tracing the elderly in population censuses - The example of Tanzania

1. Ageing in Sub-Saharan Africa: Tracing the elderly in population censuses - The example of Tanzania Doris Schmied Chair for Urban and Rural Geography University of Bayreuth, Germany

2. Elderly in Sub-Saharan Africa (SSA) • Rapidly growing numbers of elderly in Africa • Rapidly changing life situations and roles of elderly • Information on elderly very limited • Mainly empirical research in anthropology, sociology and health studies • Some practical development work • Scarce statistical information on elderly – except censuses

3. Census data in Sub-Saharan Africa "Understandably, population censuses in statistically underdeveloped countries are the principal sources of information on a wide range of areas which are of vital importance to development planning." Yet "Utilization of the census information has been found to be minimal.“ (Tanzania 1988 Population Census, The Analytical Report)

4. Census data in Tanzania 4 censuses after independence 1967, 1978, 1988, 2002 all de facto censuses, all carried out in August

5. General problems with censuses in Sub-Saharan Africa • Logistic problems • including all households • including all members of households • Enumeration staff • training • honesty • Incorrect or misleading answers of interviewees because • they find liaison with the enumerator unsatisfactory • they are unaware of the significance/importance of their information • they interpret terms used differently (multi-lingual situation) • they cannot or do not wish to part with the correct information

6. Problems of tracing the elderly in the censuses • Problems with age in general • Age is not an unambiguous concept • Birthdays and years are not importantin Africa • Problems with the definition of "the elderly" • Most traditional African/Tanzanian societies are "gerontocratic" (although rapidly changing) • Old age = senior position in society (Kisuahili mzee) • Differences between sexes: • old men: loss of physical abilities, but apogee of economic/social power • old women: women after menopause, loss of child-bearing ability, status based on number of children (sons) or traditional knowledge, power over daughters-in-law • Cultural diversity: e.g. age-set societies (horizontal bonding through rites of passage) • 65+ : "past working life" - a European concept transferred to SSA

7. Difficulties with data on age in the censuses • Data on age may seem or is distorted because: • Age data is collected directly and indirectly • Interviewed people may be unaware of their own age • Household heads may not know the age of the members of their household • Age stated is influenced by intentions of the interviewed • Age stated reflects digital preferences: tendency to rounding/heaping • Age stated is influenced by enumeration procedure: cards used to facilitate the identification of age predispose answers • Major events influence age distribution

8. Digital preference, Tanzania

9. Digital preference, Tunduru District

10. Digital preference: Dodoma Rural, Dodoma Region Influence of major crisis? 1921, 1947, 1954famine 1991 Cholera outbreak (57 people die in Ndogorowe Village alone) 1974, 1986, 1998 serious food shortages

11. Age in the Tanzanian census Can data on age be used at all? What about data on "elderly"?

12. Age ratios, Tanzania 1967, 1978 and 1988 censuses

13. Sex ratios, Tanzania 1967, 1978 and 1988 censuses

14. Can data on age be used at all? • What about data on "elderly"? • Data on age can be used because • age distortion in old age is hardly more pronounced than at younger age • age distortions have followed a similar pattern in all censuses: hardly any changes over time • broad age groups level out distortions to a great extent • Important to keep data weaknesses in mind! • data on elderly women are more distorted than on elderly men • small size of old age groups means that data tends to exaggerate tendencies

15. The 2002 Population and Housing Census Information largely available on the net Includes detailed data on age New: district data - District Profiles New: expanded questionnaire (socio-economic data)

16. Testing the 2002 Tanzania Population and Housing Survey • Proportion of elderly • Sex ratios of elderly • Ageing in the city and the countryside • Marital status of elderly • Disability among elderly

17. Proportion of elderly (65+ years) by district, Tanzania Mainland, 2002

18. Sex ratios of all elderly (65+ years) by district, Tanzania Mainland, 2002

19. Sex ratios of elderly (65 - 69 years) by district, Tanzania Mainland, 2002

20. Sex ratios of elderly (70 - 74 years) by district, Tanzania Mainland, 2002

21. Sex ratios of elderly (75 - 79 years) by district, Tanzania Mainland, 2002

22. Sex ratios of elderly (80+ years) by district, Tanzania Mainland, 2002

23. 65-69 70-74 75-79 80+

24. Ageing in the city - Dar es Salaam, 2002

25. Ageing in the countryside - Tunduru District, 2002

26. Marital status of elderly Ngorongoro District Tunduru District

27. Disability among elderly Ngorongoro District Tunduru District

28. Results Recent census data from Sub-Saharan Africa can and should be used to gain more information on elderly (e.g. IPUMS census data on Kenya and South Africa) Researchers have to be aware of the considerable shortcomings BUT Census information on elderly is more valuable than guestimates of international organizations Census information allows regional differentiation Weaknesses of data do not prevent the formulation of working hypotheses Census information on elderly forms an important basis for empirical research