Urban Expansion: a global phenomenon with local causes? Stephen Sheppard Williams College Presentation for World Bank, April 30, 2007 Presentations and papers available at http://www.williams.edu/economics/urbangrowth/homepage.htm Urban Expansion Urban expansion taking place world wide Rich Evolving from transportation choices - car culture Failure of planning system? Poor Rural to urban migration Urban bias? Policy challenges Environmental impact from transportation Preservation of open space Pressure for housing and infrastructure provision Policy response Land use planning Public transport subsidies & private transport taxes Rural development Surprisingly few global studies of this global phenomenon Limited data availability 1
Analysis of urban expansion Some studies in developed economies Burchfield, et al. Earlier studies of urban sprawl More difficult and important situations in developing economies Limited ground based data Remotely sensed data are available Night illumination Landsat Higher resolution Collect and combine with other economic data Remote detection of economic outcomes Some differences are readily apparent Korea 1992 = blue 1998 = green 2003 = red Source: Chris Elvidge, NOAA 2
Remote detection of economic outcomes Data a global sample of cities Economic Decline can be detected Moldova Romania 1992 = blue 1998 = green 2003 = red Regions Population Size Class Income (annual per Class capita GNP) Source: Chris Elvidge, NOAA East Asia & the Pacific Europe 100,000 to 528,000 528,000 to 1,490,000 < $3,000 $3,000 - $5,200 Latin America & the Caribbean 1,490,000 and 4,180,000 $5,200 - $17,000 Northern Africa > 4,180,001 > $17,000 Other Developed Countries South & Central Asia Southeast Asia Sub-Saharan Africa Western Asia 3
Remote Sensing Measuring Urban Land Use 1986 The relative brightness in different portions of the spectrum identify different types of ground cover. Contrasting Approaches: 1. Open space within the urban area 60 50 2. Development at the urban periphery Satellite (Landsat TM) data measure for pixels that are 28.5 meters on each side reflectance in different frequency bands % Reflectance 40 30 20 10 0 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 Agricultural Soil Wavelength Bare Soil (micrometers) Aged Concrete Fresh Concrete Water Grass Dry Vegetation Sand Asphalt 3. Fragmented nature of development 4. Roadways in rural areas 2000 EarthSat Geocover Our Analysis 4
Accra Cairo 5
Cairo, detail Mexico City 6
Mumbai Urban density Mexico City 7
Land use density Mexico City Urban density - Cairo 8
Land use density - Cairo Hypotheses Urban economic theory implies several hypotheses that can be examined in the data 1. 2. 3. 4. 5. Hypothesis x > 0 L x > 0 y x < 0 t x < 0 r A x > 0 w Description 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 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 global demand (and hence world price) of the export good will increase urban extent and urban expansion. 9
Model estimation Basic model variables Data assembled from several sources Remotely sensed data National Censuses of Population WDI and other Economic Data Field research Estimates of the relation between total urban land use and economic variables Cross Section versus Difference Each city observed twice Sample of 120 cities Endogeneity Population, income, global connections and perhaps other variables are endogenously determined in part by urban land availability Requires an Instrumental Variables estimation strategy Variable Urban Land Use (km 2 ) Total Population Per Capita GDP (PPP 1995 $) Air Linkages Agricultural Rent ($/Hectare) Cost of fuel ($/liter) Rank Mean 400.6871 3,361,615 9,914 88.78808 1,641.608 0.581498 19 σ 533.7343 4,452,486 9,906 117.6716 3,140.596 0.328673 38 Min 8.91769 93,041 610 0 68.8372 0.02 1 Max 2328.87 27,200,000 35,354 659 19,442.1 1.56 196 10
OLS estimates Population Income Air linkages City Rank Agri Rent Fuel Cost East Asia Europe Latin America North Africa South Asia Southeast Asia SubSah Africa West Asia Constant F R-squared Parameter 0.7318 0.5241 0.0787 0.0290-0.2325-0.1183-8.5728 111.64 0.809 t 12.36 9.36 1.9 0.57-6.97-1.97-10.18 Parameter 0.7686 0.5145 0.0565 0.0330-0.2257-0.1517-0.0338-0.2809-0.3144-0.3940-0.5491-0.3557 0.1656 0.1465-8.8282 76.07 0.8411 t 11.6 7.97 1.25 0.68-5.92-2.4-0.15-1.96-1.78-1.37-2.46-1.55 0.8 0.66-8.36 Instrumental Variables Estimates Parameter t Parameter Population 0.4494 3.13 0.4419 Income 0.4624 5.08 0.5105 Air linkages 0.1897 2.06 0.1620 City Rank 0.1177 1.27 0.0544 Agricultural Rent -0.1730-4.13-0.1720 Fuel Cost -0.1294-1.94-0.0738 Constant -4.9194-2.6-4.9814 F 24.86 32.71 R-squared 0.7539 0.7575 t 3.66 6.78 2.1 0.77-4.81-1.22-3.16 11
Hypotheses Tested/Supported The data produce estimates that are consistent with our hypotheses 1. 2. 3. 4. 5. Hypothesis x > 0 L x > 0 y x < 0 t x < 0 r A x > 0 w Description Strongly confirmed doubling population increases urban land cover by 44 to 77 percent. Strongly Confirmed doubling national income increases urban land use by 46 to 52 percent Confirmed doubling fuel cost decreases urban land use by 8 to 15 percent Strongly confirmed doubling the value added per hectare in agriculture decreases urban land use by 17 to 23 percent Confirmed increased accessibility to global markets increases urban land use increasing the number of direct international flights increases urban land use by 6 to 16 percent, impact is clearly significant in IV estimates City rank is not statistically significant Policy Implications Use models to determine excess urban land use Use models to predicted required new urban land per year Using history between T1 and T2 is a guide, we can determine the required amount of land to make available for new urban development City Accra Cairo Mexico City Mumbai Addis Ababa Seoul Predicted 164.79 367.75 835.93 438.29 112.60 578.12 Actual 340.43 569.65 1058.53 450.60 119.03 706.12 Average Pct Excess 24.81% 26.98% 12.19% -8.84% -4.92% 10.93% Pop Growth T2 T1 0.026 0.016 0.016 0.020 0.028 0.005 Inc Growth T2 T1 0.018 0.019 0.017 0.035 0.007 0.050 Annual New Urban KM 2 6.91 9.02 15.95 11.37 1.90 18.03 12
Concluding observations Models work Theoretical Empirical Size (rank) might matter, but not as much as expected and perhaps not at all Plans should be made for urban expansion and infrastructure provision With global data we are developing a deeper understanding of the urban expansion that affects virtually every local area 13