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Urban Structure in a Climate of Terror
Stephen SheppardWilliams College
Guns and Butter – The Economic Causes and Consequences of Conflict
9-10 December 2005
Terrorism and urban structure
Why worry about urban structure?
Pace of urban expansion• Doubling of developing country urban
population in next 30 years• Enormous investment• Durable investment – distortion generates
costs over time
Impact on economic performance• Factor productivity• Distribution of non-market goods
Terrorism and urban structure Why worry about impact of terrorism? Policy concern regarding impact New technologies enhance impact
• General climate of terror• Affect large and anonymous population• Distribution of costs of terror
Test and distinguish between theories of urban structure in extreme conditions
Prospect for corrective public policy
Two perspectives• Empirical• Theoretical
Empirical evidence – analogy with war Cities appear to recover population after war
• Time of adjustment may still generate considerable costs
• Impact on urban structure remains unclear
Empirical evidence – city comparison
Find comparable cities with different exposure to terrorist incidents
Empirical Evidence – cross country model
Theoretical Perspectives
Three approaches to analysis:• New economic geography
• Harrigan and Martin (2002)
• Dynamic model• Rossi-Hansberg (2004)
• Traditional urban model Each models terrorism as a tax or distortion Different implications for public policy If data exist – potential for test to distinguish
Theory – new economic geography Based on Fujita, Krugman and Venables Increasing returns and monopolistic competition led to
agglomeration Terror attacks more likely in agglomerations Terrorism acts like a tax on production for firms in
agglomeration• No analytic solution – numerical simulation• Modest amounts of terrorism leave agglomeration unchanged• Higher levels destroy rationale for agglomeration and lead to
dispersion of production For many parameter values dispersal is an alternative stable
solution – end of terror does not restore agglomeration
Theory – dynamic model Agglomeration supported by production externality Identifies a steady-state allocation of land use and productive
capital Terrorism implies a risk of loss of structures (capital) at any
location where density exceeds a fixed level K0
With no adjustment costs – • Terrorist attack implies lower steady state capital at all locations• Capital density gradients have reduced range
Public policy • Subsidy to support agglomeration• If public sector has private knowledge about attack risk – can improve
efficiency
Theory – traditional urban model
Terrorism can be modeled as one of three distortions• Increased transportation costs• Reduced productivity of land in housing production• Reduced productivity of land in export good production
Impacts on density and maximum extent of urban area
Adapt the model of Brueckner (1987)
Modeling urban land use
Households:• L households • Income y • Preferences v(c,q)
• composite good c • housing q.
• Household located at x pays annual transportation costs t·x• The transportation costs increase in direct proportion to the expected
incidence of terrorism
In equilibrium, we must have:
for all locations x
max ,qv y t x q p x q u
Modeling urban land use Housing producers
• Production function H(N, l) to produce square meters of housing• N = capital input, l=land input
• Constant returns to scale and free entry determines an equilibrium land rent function r(x) and a capital-land ratio (building density) S(x)
• Land value and building density decline with distance • Combining the S(x) with housing demand q(x) provides a solution for the
population density D(x,t,y,u) as a function of distance t and utility level u The extent of urban land use is determined by the condition:
0
r x S xand
x x
Ar x r
Modeling urban land use
Equilibrium requires:
The model provides a solution for the extent of urban land use as a function of• Population• Income• Agricultural land value• Transportation cost
If we generalize to include an export sector, then urban land use will also depend on• MP of land in goods production• World price of the export good
0
2 , , ,x
x D x t y u dx L
• MP of land in housing production
• Land made available for housing
Hypotheses
0x
L
0x
y
0x
t
0A
x
r
0l
x
H
0x
0l
x
f
Comparative Static Result
Description of prediction and hypothesis
An increase in population will increase urban extent and urban expansion.
An increase in household income will increase urban extent and urban expansion.
An increase in transportation costs (terrorism) will reduce urban extent and limit urban expansion.
An increase in the opportunity cost of non-urban land will reduce urban extent and limit urban expansion.
An increase in the marginal productivity of land in housing production will increase urban extent and urban expansion. Increasing terrorism decreases urban extent.
An increase in the share of land available for housing development will increase urban extent and urban expansion.
An increase in marginal productivity of land in production of the export good will increase urban extent and urban expansion. Increasing terrorism decreases urban extent.
Data – a global sample of cities
Regions Population
Size Class Income (annual per
Classcapita GNP)
East Asia & the Pacific Europe Latin America & the Caribbean Northern Africa Other Developed Countries South & Central Asia Southeast Asia Sub-Saharan Africa Western Asia
100,000 to 528,000528,000 to 1,490,0001,490,000 and 4,180,000> 4,180,001
< $3,000 $3,000 - $5,200 $5,200 - $17,000 > $17,000
Data
Urban Pop. Cities Sample Population Sample Cities Cities in
Region in 2000 Population % N % Estimates
East Asia & the Pacific410,903,33
1 55057,194,97
9 13.9% 16 2.9% 8
Europe319,222,93
3 76445,147,98
9 14.1% 16 2.1% 15
Latin America & the Caribbean
288,937,443 547
70,402,342 24.4% 16 2.9% 10
Northern Africa53,744,935 125
22,517,636 41.9% 8 6.4% 8
Other Developed Countries367,040,75
6 53477,841,36
4 21.2% 16 3.0% 11
South & Central Asia332,207,36
1 64170,900,33
3 21.3% 16 2.5% 15
Southeast Asia110,279,41
2 26036,507,58
3 33.1% 12 4.6% 7
Sub-Saharan Africa145,840,98
5 33516,733,38
6 11.5% 12 3.6% 9
Western Asia92,142,320 187
18,360,012 19.9% 8 4.3% 7
Total2,120,319,
475 3,943415,605,6
24 19.6% 120 3.0% 90
The sample is representative of the global urban population in cities with population over 100,000
Stratified by region, city size and income level
Measuring urban land use
EarthSat Geocover Our Analysis
1986
2000
Contrasting Approaches:
1. Open space within the urban area
2. Development at the urban periphery
3. Fragmented nature of development
4. Roadways in “rural” areas
Change in urban land use: Cairo
Model estimation
Cross-country model• Total Urban Land Use• Urban area population• National GDP per capita• Terrorist incidents in preceding 10 years• Agricultural output per hectare arable land• Groundwater availability• Air linkages (city) and IP address share (country)• Environment type
Endogeneity? Additional variables?
Variables used in analysis
Variable Mean σ Min Max
Urban Land Use (km2) in T1 245.787 361.236 8.918 1889.953
Urban Land Use (km2) in T2 328.384 436.680 15.786 2328.869
Total Population in T1 2290134 3109719 105468 14200000
Total Population in T2 2716493 3736139 141740 17300000
Per Capita GDP (PPP 1995 $) in T1 8459.487 8527.406 562.982 27328.930
Per Capita GDP (PPP 1995 $) in T2 9946.391 10094.010 626.035 32636.500
Terrorism Incidents in decade to T1 41.022 60.659 0 253.000
Terrorism Incidents in decade to T2 59.789 87.749 0 499.000
Agricultural output per hectare in T1 1686.840 3215.611 84.900 19442.110
Agricultural output per hectare in T2 1819.769 2903.535 68.837 14751.900
Air Linkages in T1 86.700 129.679 0 659.000
Air Linkages in T2 84.156 117.131 0 561.000
National share of IP addresses 0.054 0.158 0 0.594
Dichotomous variables in analysis
Variable Mean σ Min Max
Ground Water (1=shallow aquifer) 0.281518 0.451022 0 1
Temperate Humid Climate 0.077395 0.267979 0 1
Mediterranean Warm Climate 0.005109 0.071499 0 1
Mediterranean Cold Climate 0.017234 0.130515 0 1
Sampling Weight 0.011168 0.010542 0.000834 0.068174
Urban rank and the impacts of terror
Distribution of Cities by Rank
0
5
10
15
20
25
30
35
40
45
50
1 2 3 to 5 6 to 20 21 to 100 101 to 200
Rank
The impact of terrorism might be stronger in larger cities• Predicted in Rossi-Hansberg model• Implied in Harrigan-Martin model
Alternative terror measure:• Cities rank 1-5: incidents• Cities rank 6 up: 0
Estimates: terrorism and urban expansion
I II III IV V VI
Population 0.771 0.677 0.672 0.748 0.841 0.814
Income 0.541 0.520 0.544 0.662 0.702 0.642
Terror -0.108 -0.055 -0.078 -0.078 -0.088 -0.085
Agricultural Land -0.269 -0.274 -0.286 -0.290 -0.295
Air Linkages 0.106 0.114 0.109 0.072
IP Share 0.017 0.055 0.052
Groundwater 0.250 0.218
Temperate Humid -0.223 -0.296 -0.309 -0.317 -0.263 -0.224
Mediterr. Warm 0.971 1.040 0.944 0.763 0.794 0.757
Mediterr. Cold 0.735 0.729 0.724 0.811 0.900 0.932
F 97.84 84.19 95.89 85.45 92.81 93.96
R2 0.892 0.8868 0.882 0.8769 0.8703 0.824
Root MSE 0.44 0.45 0.46 0.47 0.48 0.55
N 176 176 176 176 176 180
Hypotheses consistent with data
0x
L
0x
y
0x
t
0A
x
r
0l
x
H
0x
0l
x
f
Comparative Static Result
Description of prediction and hypothesis
An increase in population will increase urban extent and urban expansion.
An increase in household income will increase urban extent and urban expansion.
An increase in transportation costs (terrorism) will reduce urban extent and limit urban expansion.
An increase in the opportunity cost of non-urban land will reduce urban extent and limit urban expansion.
An increase in the marginal productivity of land in housing production will increase urban extent and urban expansion. Increasing terrorism decreases urban extent.
An increase in the share of land available for housing development will increase urban extent and urban expansion.
An increase in marginal productivity of land in production of the export good will increase urban extent and urban expansion. Increasing terrorism decreases urban extent.
Concluding remarks
The models perform surprisingly well• Almost all parameter estimates significant at 10% level or
higher• All parameter estimates correct sign
Terrorism has an impact on urban structure• Reduces amount of land where capital is located• Consistent with both Rossi-Hansberg and simple urban
model• Estimated impact is robust to different specifications
Correctly signed but not significant in “differenced” model• Limited number of observations?• Explore alternatives when all data available
Future directions Endogeneity? Correlation between RHS variables
and model error• Potential problem with income and terror• Reduced problem by use of national variables• Problem with air linkages• Instruments:
• Data for neighboring cities• Physical conditions• Data being collected by field researchers
• Compare differenced and non-differenced models Other variables
• Regional and regime fixed effects?• Better measures of transport costs?• Infill versus peripheral development
• Test prediction of flatter density gradient• Distinguish between simple urban and dynamic urban model