Urban Structure in a Climate of Terror

Stephen SheppardWilliams College

Guns and Butter – The Economic Causes and Consequences of Conflict9-10 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

0xL

0xy

0xt

0A

xr

0l

xH

0x

0l

xf

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 Pacific 410,903,331 550

57,194,979 13.9% 16 2.9% 8

Europe 319,222,933 764

45,147,989 14.1% 16 2.1% 15

Latin America & the Caribbean

288,937,443 547

70,402,342 24.4% 16 2.9% 10

Northern Africa 53,744,935 12522,517,63

6 41.9% 8 6.4% 8

Other Developed Countries 367,040,756 534

77,841,364 21.2% 16 3.0% 11

South & Central Asia 332,207,361 641

70,900,333 21.3% 16 2.5% 15

Southeast Asia 110,279,412 260

36,507,583 33.1% 12 4.6% 7

Sub-Saharan Africa 145,840,985 335

16,733,386 11.5% 12 3.6% 9

Western Asia 92,142,320 18718,360,01

2 19.9% 8 4.3% 7

Total 2,120,319,475 3,943

415,605,624 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 MaxGround Water (1=shallow

aquifer) 0.281518 0.451022 0 1Temperate Humid Climate 0.077395 0.267979 0 1Mediterranean Warm Climate 0.005109 0.071499 0 1Mediterranean Cold Climate 0.017234 0.130515 0 1Sampling Weight 0.011168 0.010542 0.000834 0.068174

Urban rank and the impacts of terror

Distribution of Cities by Rank

05

101520253035404550

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 -0.085Agricultural 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

0xL

0xy

0xt

0A

xr

0l

xH

0x

0l

xf

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