Urbanization and the distribution of people across the urban hierarchyin Sub-Saharan African countries

May 22, 2015

Vernon Henderson

Adam Storeygard, Uwe Deichmann, Thiemo Fetzer, Amar Shanghavi, Jamila Nigmatulina,…..

Part of a joint LSE-Oxford project with 2 components:1. Rural-urban & inter-urban allocation of resources within countries

2. Intra-urban allocation of resources, especially in primate citiesAll as driven by policy, for better or worse

• What drives the distribution of people across African cities?• “Extreme” primacy (fraction of urban residents in largest city)

• A lack of major secondary cities.

• This FACT Focus on urbanization via primate cities

• To what extent is this primacy driven by policies and institutions?

Key possible policy/institutional factors

1. Role of democratization [Fetzer and Shanghavi, 2015]• Representative democracy gives voice to hinterlands

• Related, democratization associated with fiscal decentralization

• Both may shift power from centre to the hinterlands

Do data suggest that there are relative economic or service improvements in hinterland cities with advent of democratization?

Other policies2. Role of natural resource rents [evolving work Henderson, Storeygard and

Nigmatulina]

• Main cities as “consumer cities”: Jedwab et al (2015) .

• How are resource rents spent? • General budget for nationwide services, vs. in the seats of power

• National versus regional capitals versus ordinary cities• Interaction with democratization

Do we see political cities growing relatively faster, with positive shocks to natural resource rents?

3. Role of inter-city transport links (Jedwab and Storeygard, evolving)

4. Trade agreements removing protection to local manufacturers • Role of secondary cities

5. Capital and export/import license allocations

African urbanization• Urbanization is proceeding slowly

• The annual growth of individual cities is not unusually high (3-4% a year, modestly above population growth rate).• In today’s more middle income developing countries at the height of rapid urbanization

growth rates were 5-6 % a year

• Have time to guide a better urbanization path?

• African urbanization is highly primate: • Explained by small country size and low level of urbanization?

• Normative: Is there excessive primacy? Hard to evaluate. The Congo’s, Kenya

• Regardless, there is a needed focus on primate cities:• The difficulty of managing cities of 4-5 million + with poor institutions and

government capacity • Cities as “collections of villages” with terrible intra-city transport

Benchmark: all developing countries

Data!• Data 1200 + cities above 20,000 in 2012 in 34 countries with usable

population census data in a cross-section.• Countries cover over 1/2 billion people.

• Differences over time: 21 - 24 countries ; first and last censuses 1984, 2014• Source: Citypopulation.de; is based on census reports. Also for Nigeria supplement with

Africapolis

• Cities and metro areas defined by census population of towns within the night lights footprint of the main city over the 2008-2012• Missing “towns” within the urban footprint. Understate population

• But generally larger than UN numbers for primate cities

• Where we can really define urban population, “true” growth rates very close to our growth rates

• Also have nights lights annual data 1992-2012: all countries

Population growth rates of cities: 1990-2012• Low growth “problem” for larger secondary cities

• Especially non-political• Issue of lack of indsutrialization?

Stats by city type: actual annualized growth rates between first and last census in sampleMinimum projected sizes 1990: 10,000. 2012: 16,823. 800 cities with 2 data points

Mean Median Max Min N

Primate 0.038 0.037 0.064 0.017 24

<100,000 political 0.035 0.031 0.167 0.0081 134

<100,000 non-political 0.032 0.032 0.095 -0.051 541

>100,000 political, not primate 0.028 0.027 0.10 -0.031 55

>100,000 non-political 0.019 0.022 0.122 -0.054 46

Transitions of cities by rank: relative size distribution (same cut-off: minimum to median size in projected 1990 and 2012 sizes)

cell 1, 2012 cell 2, 2012 cell 3, 2012 cell 4, 2012 cell 5, 2012 Exit Entry

Cell 1, 1990 0.439 0.413 0.0132 0 0 0.135 0.643

Cell 2, 1990 0.0651 0.678 0.211 0 0 0.046 0.299

Cell 3, 1990 0.0173 0.0983 0.659 0.173 0.00578 0.0462 0.0551

Cell 4, 1990 0 0.023 0.115 0.69 0.126 0.046 0.0029

Cell 5, 1990 0 0 0 0.159 0.841 0 0

Cell counts 65 345

Total cities in both periods: 800 cities over 10,000 in 1990. 24 countriesCell upper cut-offs [initial & final distribution]: -0.44 [35%, 33%]; 0.71 [30% & 37]; 1.65 [20%, 18]; 4.19 [10%, 9%]; 16.1 [5%, 4%]

1. Democratization[Fetzer and Shanghavi, 2015]

• Examine effect of sustained democratization in Africa over last 20 years

• Effect on city economic growth rates: Annual night lights outcomes (with country or city fixed effects, year fixed effects):• Regional capitals and other cities gain relative to national capital with

transition to democracy.• Ruling out election year effects, transition year effects, regional favouritism by leader,

etc.

• Corresponding results on school completion (DHS) • Regional capitals and other cities gain relative to national capital.

Smoothed indicator vs actual score: Burundi (Bai, 1997)

Raw data: average luminosity

2. Growth and natural resource incomes

• Effect on city growth

• Annualized city population growth between census intervals• 21 countries : 15 have 3 censuses; 6 have 2. Therefore 36 episodes. 876 cities.

• City growth as a function of place in hierarchy:• National capital, regional (15% of cities) capital, ordinary as interacted with:

• Shocks to resource rents:

• Annualized growth in price index: (smoothed) log of real commodity prices weighted by historical (77-83) share of that commodity export in country’s GDP .

• 14 Mining-oil-gas commodities (petro, gold, natural gas, aluminum, copper, nickel, zinc, timber (logs), etc.)

• Crops for export (cotton, sisal, wheat, cocoa, tea, etc.)

Price shocks

1983

1977

1983

1977

Price index =

ln Price index ln ; : country; , : time; : international commodity

1/ 7

; : length of lag; : length of smo

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Priceshock = ( ) / Ljs js L

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Graph of price indices(also share of natural resource rents in GDP)

Annualized city growth rate

1 2 3 4

Resource price shock 0.056(0.033)

0.053(0.040)

Shock*national capital 0.063(0.062)

0.102(0.065)

0.540**(0.157)

0.647***(0.210)

Shock * regional capital

0.175**(.077)

0.181**(0.071)

0.201**(0.084)

0.210**(0.079)

Controls: Base set plus later period FE, national & regional cap

YES Episode FE’s“Country-year”

YES plus lagged dependent variable

Episode FE’s plus lagged dependent variable

N 1607 1607 1607 1607

** 0.05; *** 0.01

Sniff tests: • Cross-section. Strong effects of share of resource rents in GDP on national and regional capital sizes• Here look at growth episodes [s.d. of price shock is 0.036]

• No effects for price indices for crops (food & non-food)• No interaction with average polity-2 score over the interval, except for regional capitals having enhanced rent effects if polity higher.

Work to be done……..

Second investigation: Urbanization of countries and districts

• Has urbanization been an “escape valve” for climate deterioration in a region (Sub-Saharan Africa) with poor climate and a low level of economic development, over the last 50 years? [Henderson Storeygard and Deichmann (2015)]• Growth of share urban in each district 1960-2010 (369 districts in 29 countries) in response to

climate change

• District variation between census years within countries

• Also use night lights data for 1992-2008

• Find climate deterioration has no affect on urbanization in districts where cities have no industrial base (75% of districts). Cities just serve farmers

• Promotes urbanization when there is an industrial (traded good) sector to draw people out of farming.

District level urbanization and climate

• Urbanization as an escape valve for adverse productivity shocks to agriculture• Climate change and variation in an arid region which has had drying out over

last 50 years

• Low irrigation and low use of fertilizers.

Figure 1 . Historical levels of moisture

Moisture/wetness is precipitation divided by potential evapotranspiration (PET) from UNEP (1992). PET is calculated using the Thornthwaite (1948) method (Willmott et al, 1985)

Figure 3. Year by year declining moisture in African sample

Main idea and result

• Think of districts as small open economies. Two types• Type 1: Only export is primary (rural) products. Towns produce non-traded

goods to service farmers (75% of our districts)• Climate has essentially no effect of urban share. Cities tied to farm outcomes - move in

tandem

• Towns also produce manufactures which are traded goods• Competing sector to agriculture

• If climate improves, draws people out of city to farms.• Urban share & total city income (night lights) drop

• Challenge: Lack of economic censuses to detail manufacturing• Unlike India (Colmer, 2015 who looks at implied seasonal move from farm to

manufacturing)

• Use 1965 Oxford atlas economic census for Africa (thanks: Moradi)

• Urban share growth rate (annual.) = [ -1.02***+ 0.125*** (8-# modern industries)]* moisture growth rate• At maximum industry, a 1 s.d. increase in moisture growth rate leads to a reduction

in urban share growth rate of ½ of its sample mean (more in arid districts)

Regression results:• Growth in urban share related to moisture growth,controls, and episode (“country-year”) fixed effects• Plots change in urban share growth in response to moisture growth in the district

Other Aspects• Missing element due to lack of data:

• Does climate change induce industrialization?• No data across a significant sample of countries

• In many countries little manucfaturing beyond traditional manufactures

• Additional (shorter term fluctuations)• Night lights of cities: annual variation in response to annul changes in rainfall

in city’s immediate hinterland (1158 cities)• Same pattern of results: More rain: city lights decline in areas with manufacturing

• Our results: Not masking conflict effects