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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 ( 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 ; Superseded by Working Paper 06-14
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Suggested Citation

  • Gerald Carlino & Satyajit Chatterjee & Robert Hunt, 2005. "Matching and Learning in Cities: Urban Density and the Rate of Invention," Levine's Bibliography 784828000000000160, UCLA Department of Economics.
  • Handle: RePEc:cla:levrem:784828000000000160
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    Cited by:

    1. Sherrill Shaffer & Robert N. Collender, 2009. "Federal Credit Programs and Local Economic Performance," Economic Development Quarterly, , vol. 23(1), pages 28-43, February.
    2. Mauro L. Ghinamo, 2012. "Explaining The Variation In The Empirical Estimates Of Academic Knowledge Spillovers," Journal of Regional Science, Wiley Blackwell, vol. 52(4), pages 606-634, October.
    3. 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.
    4. Daniel Shefer & Haim Aviram, 2005. "Incorporating agglomeration economies in transport cost‐benefit analysis: The case of the proposed light‐rail transit in the Tel‐Aviv metropolitan area," Papers in Regional Science, Wiley Blackwell, vol. 84(3), pages 487-508, August.
    5. Dani Shefer & Haim Aviram, 2005. "Incorporating Agglomeration Economies In Cost-Benefit Analysis Of Transport Projects," ERSA conference papers ersa05p133, European Regional Science Association.
    6. Thor Berger & Carl Benedikt Frey, 2017. "Industrial renewal in the 21st century: evidence from US cities," Regional Studies, Taylor & Francis Journals, vol. 51(3), pages 404-413, March.
    7. 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, vol. 36(1), pages 107-120, February.
    8. Qiu, Jun & Lyu, Ping & Tian, Min, 2024. "Do talent housing policies foster regional innovation? An analysis based on labor force heterogeneity," Economic Analysis and Policy, Elsevier, vol. 83(C), pages 150-164.

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