1. Cities and Growth: What Do We Know? By Peter Gordon and Bumsoo Lee University of Southern California University of Illinois, Champaign-Urbana September 17, 2008

2. What Do We Know? 1.Why are there cities? –People have found ways to organize themselves so that agglomeration benefits dominate associated congestion costs. 2.Cities are “the engines of growth”. 3.Economic growth springs from entrepreneurial activity (e.g., discovery, J. Schumpeter). 4.“Optimal” urban scale discussions are not helpful; they suggest a static analysis.

3. 5.Prefacing a recent NBER symposium on agglomeration economies, (http://www.nber.org/books/glae08-1/),http://www.nber.org/books/glae08-1/ Ed Glaser writes: “… a central paradox of our time is that in cities, industrial agglomerations remain remarkably vital despite ever easier movement of goods and knowledge across space.”

4. Questions 1.How can cities be congenial hosts to entrepreneurship and discovery? 2.Can we understand the role of urban structure? 3.Which spatial arrangements internalize positive externalities while avoiding negative ones, in light of all other trade-offs? 4.Gordon and Moore (E and P(A), 1989) discussed land use optimizations that suggest an answer. Are there plausible tests?

5. Traffic and Transportation 1.Modern lifestyles generate massive volumes of non-work travel – during peak and off-peak hours. 2.Combining this with the absence of pricing, the continued growth of cities (metro areas) is remarkable. 3.Impending traffic “doomsday” is forever impending.

6. Distribution of daily person-trips by trip purpose and period of the week, 2001 * In billions

7. Distribution of daily person-trips by trip purpose and period of the week, 2001 * In percent

8. Growth of average daily person-trips per person by trip purpose and period of the week, 1990 to 2001 Work Non-Work All Family / School / Social / Personal Church Recreation

9. Industry Churning and Growth 1.Cities succeed and maintain their status in the urban ranking by “churning” industries (Duranton, 2006) as circumstances change. 2.Spatial form must change to make all of this possible. Test more than metro-area average densities. Y= year Z=sector e=employment c=MSA

10. * Results from GWR procedure. Employment shares by location type

11. Mean commute time by workplace location type vs. metro population size (drive alone mode) Y = -7.428 + 2.220 X Y = -58.734 + 6.065 X X Y = -18.063 + 2.933 X Y = -4.613 + 2.002 X MetroCBD SubcentersDispersed

12. Urban Spatial Structure and Metro Growth: Empirical Model 1 1.Glaeser’s (2003) supply-side urban growth model where N t and N t-1 denote population (employment) size in 2000 and 1990, respectively; X is a vector of metropolitan attributes; F is vector of spatial structure variables: dispersion and polycentricity

13. Estimation Results

14. Growth effects of spatial structure at different metro sizes 6.05%

15. Urban Structure and Growth: Model 2 Specification Locally Weighted Regression (LOESS) –Allows the coefficients of spatial structure variables to vary without restrictions –At each data point, a supply-side economic growth model is fit to a subsample of observations that are similar in Pop/Emp size (window size = 41 obs.) –Similar size observations get more weight

16. LOESS Results 1.LOESS results corroborate interaction term significance of OLS results. 2.Estimate of coefficient for dispersion is about zero near sample mean size pop (log pop =14, 1.25 million), positive for larger metros, negative for smaller metros. 3.Estimate of coefficient for polycentricity always near zero.

17. Model 2: Population Growth Model, Varying Coefficients of Spatial Structure

18. Model 2: Employment Growth Model, Varying Coefficients of Spatial Structure

19. Summary of Results 1.The growth effects of spatial structure (dispersion) were found to be contingent on metropolitan size. 2.When small, a metropolitan area with more clustered spatial form grows faster, realizing agglomeration economies in this way. But as a metro area becomes larger, more dispersion accommodates greater growth. 3.Just as a metro takes on higher-order economic functions to move up within an hierarchical urban system, it also (concurrently) restructures its spatial form in ways to mitigate congestion or other diseconomies of size for continued growth to be possible.

20. Conclusions 1.Growth can be accommodated. 2.Continued productivity, growth and prosperity require adaptable (open-ended) urban forms. Suggests importance of flexible land, labor and capital markets. Openness to spontaneous forms, learning feedback – and endogenous clustering (J. Jacobs) -- rather than approaches like “pro-cluster” policies. 3.None of the involved markets are (or can be) completely free or unhampered, but evidence of performance and spatial change is impressive.