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Shared knowledge and the coagglomeration of occupations

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Abstract

This paper provides an empirical analysis of the extent to which people in different occupations locate near one another, or coagglomerate. We construct pairwise Ellison-Glaeser coagglomeration indices for U.S. occupations and use these measures to investigate the factors influencing the geographic concentration of occupations. The analysis is conducted separately at the metropolitan area and state levels of geography. Empirical results reveal that occupations with similar knowledge requirements tend to coagglomerate and that the importance of this shared knowledge is larger in metropolitan areas than in states. These findings are robust to instrumental variables estimation that relies on an instrument set characterizing the means by which people typically acquire knowledge. An extension to the main analysis finds that, when we focus on metropolitan areas, the largest effects on coagglomeration are due to shared knowledge about the subjects of engineering and technology, arts and humanities, manufacturing and production, and mathematics and science.

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  • Jaison R. Abel & Todd M. Gabe, 2013. "Shared knowledge and the coagglomeration of occupations," Staff Reports 612, Federal Reserve Bank of New York.
  • Handle: RePEc:fip:fednsr:612
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    1. The “Parabox”: Glass Skyscrapers and the City
      by Jason Barr in Skynomics Blog on 2018-07-25 12:52:44

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    1. Kristian Behrens, 2016. "Agglomeration and clusters: Tools and insights from coagglomeration patterns," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 49(4), pages 1293-1339, November.
    2. Megha Mukim, 2015. "Coagglomeration of formal and informal industry: evidence from India," Journal of Economic Geography, Oxford University Press, vol. 15(2), pages 329-351.
    3. Lukas Kuld & Sara Mitchell & Christiane Hellmanzik, 2021. "Manhattan Transfer: Productivity effects of agglomeration in American authorship," Trinity Economics Papers tep0821, Trinity College Dublin, Department of Economics.
    4. Behrens, Kristian & Guillain, Rachel, 2017. "The determinants of coagglomeration: Evidence from functional employment patterns," CEPR Discussion Papers 11884, C.E.P.R. Discussion Papers.
    5. Stefan Leknes, 2017. "Churning in thick labor markets. Evidence of heterogeneous responses along the skill and experience gradients," Discussion Papers 866, Statistics Norway, Research Department.

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    More about this item

    Keywords

    coagglomeration; geographic concentration; labor market pooling; knowledge spillovers; occupations;
    All these keywords.

    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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