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Matching and learning in cities: urban density and the rate of invention

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  • Gerald Carlino
  • Satyajit Chatterjee
  • Robert Hunt

Abstract

This paper examines the role local labor markets play in the production of innovations. The authors appeal to a labor market matching model (á la Berliant, Reed, and Wang 2004) to argue that in dense urban areas, workers are more selective in their matches and are therefore more productive. They find that, all else equal, patent intensity (patents per capita) is 20 percent higher in a metropolitan area with an employment density (jobs per square mile) twice that of another metropolitan area. Since local employment density doubles nearly four times across their sample, the implied gains in inventive output are substantial. In addition, the authors find evidence of an optimal employment density, i.e., one that maximizes patent intensity, of about 2,150 jobs per square mile–roughly the level of Baltimore or Philadelphia. They also find that, all else equal, a city with a more competitive market structure, or one that is not too large (a population less than 1 million) will have a higher patent intensity. These findings confirm the widely held view that the nation's densest locations play an important role in creating the flow of ideas that generate innovation and growth. ; Superseded by Working Paper 06-14

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Bibliographic Info

Paper provided by Federal Reserve Bank of Philadelphia in its series Working Papers with number 04-16.

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Date of creation: 2005
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Handle: RePEc:fip:fedpwp:04-16

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Keywords: Patents ; Urban economics ; Employment (Economic theory);

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  1. Helsley, Robert W. & Strange, William C., 2004. "Knowledge barter in cities," Journal of Urban Economics, Elsevier, vol. 56(2), pages 327-345, September.
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  3. Henderson, Vernon & Kuncoro, Ari & Turner, Matt, 1995. "Industrial Development in Cities," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 103(5), pages 1067-90, October.
  4. Marcus Berliant & Robert R. Reed III & Ping Wang, 2004. "Knowledge Exchange, Matching, and Agglomeration," Urban/Regional, EconWPA 0405007, EconWPA, revised 03 Dec 2004.
  5. Zvi Griliches, 1998. "Patent Statistics as Economic Indicators: A Survey," NBER Chapters, in: R&D and Productivity: The Econometric Evidence, pages 287-343 National Bureau of Economic Research, Inc.
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  14. Duranton, Gilles & Puga, Diego, 2004. "Micro-foundations of urban agglomeration economies," Handbook of Regional and Urban Economics, Elsevier, in: J. V. Henderson & J. F. Thisse (ed.), Handbook of Regional and Urban Economics, edition 1, volume 4, chapter 48, pages 2063-2117 Elsevier.
  15. Rosenthal, Stuart S. & Strange, William C., 2004. "Evidence on the nature and sources of agglomeration economies," Handbook of Regional and Urban Economics, Elsevier, in: J. V. Henderson & J. F. Thisse (ed.), Handbook of Regional and Urban Economics, edition 1, volume 4, chapter 49, pages 2119-2171 Elsevier.
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  17. Jaffe, A.B. & Trajtenberg, M., 1992. "Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations," Papers, Tel Aviv 14-92, Tel Aviv.
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  21. repec:fth:harver:1473 is not listed on IDEAS
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Citations

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Cited by:
  1. Dani Shefer & Haim Aviram, 2005. "Incorporating Agglomeration Economies In Cost-Benefit Analysis Of Transport Projects," ERSA conference papers ersa05p133, European Regional Science Association.
  2. Sherrill Shaffer & Robert N. Collender, 2008. "Rural Economic Performance And Federal Credit Programs," CAMA Working Papers 2008-26, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  3. Bettencourt, Luis M.A. & Lobo, Jose & Strumsky, Deborah, 2007. "Invention in the city: Increasing returns to patenting as a scaling function of metropolitan size," Research Policy, Elsevier, Elsevier, vol. 36(1), pages 107-120, February.

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