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

  • Gerald Carlino
  • Satyajit Chatterjee
  • Robert Hunt

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 ; Superseded by Working Paper 06-14

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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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  1. Glaeser, Edward Ludwig & Kallal, Hedi D. & Scheinkman, Jose A. & Shleifer, Andrei, 1992. "Growth in Cities," Scholarly Articles 3451309, Harvard University Department of Economics.
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  3. Duranton, Gilles & Puga, Diego, 2004. "Micro-foundations of urban agglomeration economies," Handbook of Regional and Urban Economics, in: J. V. Henderson & J. F. Thisse (ed.), Handbook of Regional and Urban Economics, edition 1, volume 4, chapter 48, pages 2063-2117 Elsevier.
  4. Marcus Berliant & Robert R. Reed III & Ping Wang, 2000. "Knowledge Exchange, Matching, and Agglomeration," Econometric Society World Congress 2000 Contributed Papers 0261, Econometric Society.
  5. Feldman, Maryann P. & Audretsch, David B., 1999. "Innovation in cities:: Science-based diversity, specialization and localized competition," European Economic Review, Elsevier, vol. 43(2), pages 409-429, February.
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  9. Eberts, Randall W. & McMillen, Daniel P., 1999. "Agglomeration economies and urban public infrastructure," Handbook of Regional and Urban Economics, in: P. C. Cheshire & E. S. Mills (ed.), Handbook of Regional and Urban Economics, edition 1, volume 3, chapter 38, pages 1455-1495 Elsevier.
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  12. repec:fiu:wpaper:0401 is not listed on IDEAS
  13. Edward L. Glaeser & Matthew E. Kahn, 2001. "Decentralized Employment and the Transformation of the American City," NBER Working Papers 8117, National Bureau of Economic Research, Inc.
  14. Adam B. Jaffe & Manuel Trajtenberg & Rebecca Henderson, 1992. "Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations," NBER Working Papers 3993, National Bureau of Economic Research, Inc.
  15. repec:fth:harver:1473 is not listed on IDEAS
  16. James E. Rauch, 1991. "Productivity Gains From Geographic Concentration of human Capital: Evidence From the Cities," NBER Working Papers 3905, National Bureau of Economic Research, Inc.
  17. Anselin, Luc & Hudak, Sheri, 1992. "Spatial econometrics in practice : A review of software options," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 509-536, September.
  18. 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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  21. Lucas, Robert Jr., 1988. "On the mechanics of economic development," Journal of Monetary Economics, Elsevier, vol. 22(1), pages 3-42, July.
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