urbanization in tanzania phase 1: data assembly and preliminary analysis
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Urbanization in Tanzania Phase 1: Data assembly and preliminary analysis. Dr Hugh Wenban-Smith. Workshop objectives. Present IGC work on urbanisation in Tanzania Invite your views on directions for future work Seek collaboration with Tanzanian researchers - PowerPoint PPT PresentationTRANSCRIPT
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Urbanization in TanzaniaPhase 1: Data assembly and preliminary analysis
Dr Hugh Wenban-Smith
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Workshop objectives
• Present IGC work on urbanisation in Tanzania• Invite your views on directions for future work• Seek collaboration with Tanzanian researchers• Foster development of a community of urban
researchers here• Ultimately, help to inform policies for growth
linked to urbanisation
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Why research urbanisation?
• Popn of Dar up from 1/4m in 1965 to 4.4m in 2012 – nearly 20 times as big
• Other towns in Tanzania:– Arusha – xx times– Mbeya – xx times– Mwanza – xx times
• Engine of growth? (World Bank, 2009)• Big challenge to manage urban growth on this scale;
need to understand what’s driving urbanisation
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Census data: A great resource
• Tanzania censuses: 1967, 1978, 1988, 2002 and 2012
• Provide primary data of good quality (not “Poor Numbers”)
• Congratulations to NBS on a difficult job well done
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Urbanisation: Our approach
• Not enough to look just at growth of towns and cities• Urban areas are embedded in the wider economy
and form an urban system• Need to look at dynamics – e.g. effect of population
growth, conditions in rural areas, rural-urban migration and relations between large and small towns
• Regional differences help to identify causes
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Headline findings
• Total mainland popn up from 12m to 43.6m (3.6 times)
• Mainland urban popn up from 0.7m to 12.7m (18 times)
• Mainland rural popn up from 11.2m to 31m (3 times) - i.e. Big increase in pressure on land and other natural resources despite rapid urbanisation
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Regional analysis
• Going down to regional level reveals interesting differences
• Analytical tools:– Propensity for regional in-migration– Propensity for rural out-migration– Propensity for urban in-migration
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Propensity for regional in-migration
• P(rim)• Method– 1. Take base year regional population– 2. Add expected growth using national rate– 3. Subtract actual growth– 4. Divide by expected population– 5. Reverse sign (e.g. + instead of - ), convert to
percentage (x100)
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• Dar region 1978-2012– 1. 843,090– 2. + 1,681,124– 3. – 3,521,451– 4. /2,524,214 = - 0.729– P(rim) = 72.9
• Lindi region 1978-2012– 1. 527,624– 2. + 721,312– 3. - 337028– 4. /1,248,936 = 0.308– P(rim) = -30.8
P(rim): Two examples
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Region P(rim) 1978-2012
Dar es Salaam (DAR) 72.9
Rukwa/Katavi (RUK/KAT) 22.2
Arusha/Manyara (ARU/MAY) 20.6
Kigoma (KIG) 19.5
Kagera/Geita (KAG/GEI) 9.0
Tabora (TAB) 8.8
Mwanza/Geita/Simiyu (MWA/GEI/SIM)
6.0
Mbeya (MBE) -2.3
Shinyanga/Geita/Simiyu (SHI/GEI/SIM)
-3.6
Mara (MAR) -4.9
Ruvuma (RUV) -5.7
Morogoro (MOR) -7.4
Singida (SIN) -10.3
Pwani (PWA) -12.9
Dodoma (DOD) -13.2
Tanga (TAN) -17.9
Kilimanjaro (KIL) -23.6
Iringa/Njombe (IRI/NJO) -27.1
Mtwara (MTW) -29.2
Lindi (LIN) -30.8
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P(rom) and P(uim)
• Method similar to P(rim)• P(rom) = Percentage of expected rural popn
that migrates to own urban or other region• P(uim) = Urban in-migrants as a percentage of
expected urban popn
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P(rom) and P(uim)
-20.0
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Rank
PROM
PUIM
In P(rim) order (Dar on left; Lindi on right):
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More findings
• Regions with high out-migration also show high rural out-migration
• Regions with high urban in-migration do not follow this pattern
• Issues for future research– How do regions with high rural out-migration differ from
those with low?– How do regions with high urban in-migration differ from
those with low?– Does this change over time? Why?
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Migration vs In-city growth
• As urban popns grow, so does natural popn growth
• Is natural growth now more important than rural-urban migration? (Cf. Zambia)
• See Table 6 in working paper: in-migration still explains more than half urban growth but not for some regions (TAB, MAR, SIN, MTW and Lin)
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Some data problems
• Definition of ‘urban’ appears to vary between censuses
• When urban boundary expands, some of population increase not due to migration
• How to address these problems?– Investigate feasibility of a density based measure– Check boundary changes of regional capitals
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Future Research (1)
• Phase 1 work has assembled data and done some preliminary analysis
• Much more work needed if we are to understand the urbanisation process so as to identify policies needed to promote future growth, for rural as well as urban areas
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Future Research (2)
• Phase 2 of the IGC project will investigate:– How high rural out-migration regions differ from
others– How high urban in-migration regions differ from
others– How urbanisation in Tanzania relates to episodes
in post-Independence economic history (e.g. villagisation; SAP policies; Mining boom)
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Future Research (3)
• We realise that our project is only a start, at least providing usable data for other projects
• Plenty of room for other researchers, e.g. What are implications for urban governance, urban finance and urban infrastructure?
• Also what are implications for rural development and rural-urban interaction?
• Hope we have lighted a spark of interest• Look forward to a bushfire of comments, questions
and suggestions