Urban Structure in a Climate of Terror

1. Urban Structure in a Climate of Terror Stephen Sheppard Williams College Guns and Butter – The Economic Causes and Consequences of Conflict 9-10 December 2005

2. 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

3. 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

4. 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

5. Empirical evidence – city comparison • Find comparable cities with different exposure to terrorist incidents

6. Empirical Evidence – cross country model

7. 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

8. 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

9. 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

10. 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)

11. 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

12. 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:

13. 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 • MP of land in housing production • Land made available for housing

14. Hypotheses

15. Data – a global sample of cities

16. Data The sample is representative of the global urban population in cities with population over 100,000 Stratified by region, city size and income level

17. Measuring urban land use 1986 • Contrasting Approaches: • Open space within the urban area • Development at the urban periphery • Fragmented nature of development • Roadways in “rural” areas 2000 EarthSat Geocover Our Analysis

18. Change in urban land use: Cairo

19. 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?

20. Variables used in analysis

21. Dichotomous variables in analysis

22. Urban rank and the impacts of terror • 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

23. Estimates: terrorism and urban expansion

24. Hypotheses consistent with data

25. 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

26. 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