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How Do Agglomeration Externalities and Workforce Skills Drive Innovation? Empirical Evidence from Italy

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  • Rosalia Castellano

    (University of Naples “Parthenope”)

  • Gaetano Musella

    (University of Naples “Parthenope”)

  • Gennaro Punzo

    (University of Naples “Parthenope”)

Abstract

Based on four research hypotheses, this paper investigates whether and how the propensity for innovation of a territory depends on (i) agglomeration externalities (specialisation vs. diversification); (ii) the interaction between skills complementarity (overlapped, unlinked, connected skills) and agglomeration externalities; (iii) inter-regional workers’ mobility; (iv) workers’ mobility in both intra- and inter-regional flows. Although these factors have been explored from a one-by-one perspective, there is little evidence of their joint actions on a location’s propensity for innovation. To propose new insights into how these factors work together, we perform the Spatial Durbin Model (SDM) using data on Italian provinces from official sources. The SDMs are estimated globally on all the Italian provinces and separately on the two macro-areas of northern and southern provinces to compare the effects of intra- and inter-regional workers’ mobility on innovation. The results can be summarised as follows: (i) specialisation plays a more decisive role in fostering innovation than diversification; (ii) the interaction between skills complementarity and specialisation has a strong impact on innovation activities; (iii) the contribution to the innovation of workers’ mobility with overlapped skills is greater when the mobility occurs between provinces of the same macro-area; (iv) geographical proximity improves the territory’s ability to absorb the related skills regardless of its productive structure. The provided evidence may help policymakers with the appropriate information to foster innovation.

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  • Rosalia Castellano & Gaetano Musella & Gennaro Punzo, 2024. "How Do Agglomeration Externalities and Workforce Skills Drive Innovation? Empirical Evidence from Italy," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(2), pages 6737-6760, June.
  • Handle: RePEc:spr:jknowl:v:15:y:2024:i:2:d:10.1007_s13132-023-01405-7
    DOI: 10.1007/s13132-023-01405-7
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    More about this item

    Keywords

    Innovation propensity; Patents; Agglomeration externalities; Workers’ skills; Spatial Durbin Model;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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