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Regional Economic Divide And The Role Of Technological Spillovers In Italy. Evidence From Microdata

Author

Listed:
  • Francesco Aiello

  • Paola Cardamone

    (Dipartimento di Economia e Statistica, Università della Calabria)

Abstract

This paper assesses the impact of R&D efforts on production in the North and Centre-South of Italy by using a panel of 1203 manufacturing firms over the period 1998-2003. The estimations are based on a nonlinear translog production function augmented by a measure of R&D spillovers. This measure combines the geographical distance between firms, the technological similarity within each pair of firms and the technical efficiency of each firm. The estimation method takes into account the endogeneity of regressors and the potential sample selection issue regarding firms’ decision to invest in R&D. Results show that the external stock of technology exerts a higher impact in the Centre-South of Italy. Finally, it emerges that R&D capital and R&D spillovers are substitutes for Northern firms and complements for Centre-Southern firms.

Suggested Citation

  • Francesco Aiello & Paola Cardamone, 2010. "Regional Economic Divide And The Role Of Technological Spillovers In Italy. Evidence From Microdata," Working Papers 201010, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    • Handle: RePEc:clb:wpaper:201010
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    File URL: http://www.ecostat.unical.it/RePEc/WorkingPapers/WP10_2010.pdf
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    Cited by:

    1. Anna M. Ferragina & Giulia Nunziante, 2018. "Are Italian firms performances influenced by innovation of domestic and foreign firms nearby in space and sectors?," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 45(3), pages 335-360, September.
    2. Paola Cardamone, 2017. "A Spatial Analysis of the R&D-Productivity Nexus at Firm Level," Growth and Change, Wiley Blackwell, vol. 48(3), pages 313-335, September.
    3. Prakash, Navendu & Singh, Shveta & Sharma, Seema, 2021. "Technological diffusion, banking efficiency and Solow's paradox: A frontier-based parametric and non-parametric analysis," Structural Change and Economic Dynamics, Elsevier, vol. 58(C), pages 534-551.
    4. Aiello, Francesco & Castiglione, Concetta, 2014. "Being efficient to stay strong in a weak economy. The case of calabrian manufacturing firms," MPRA Paper 54366, University Library of Munich, Germany.
    5. Cardamone, Paola, 2014. "R&D, spatial proximity and productivity at firm level: evidence from Italy," MPRA Paper 57149, University Library of Munich, Germany.
    6. Qureshi, Mahvash Saeed & Tsangarides, Charalambos G., 2012. "Hard or Soft Pegs? Choice of Exchange Rate Regime and Trade in Africa," World Development, Elsevier, vol. 40(4), pages 667-680.
    7. Ascani, Andrea & Balland, Pierre-Alexandre & Morrison, Andrea, 2020. "Heterogeneous foreign direct investment and local innovation in Italian Provinces," Structural Change and Economic Dynamics, Elsevier, vol. 53(C), pages 388-401.
    8. Giulio Cainelli & Andrea Fracasso & Giuseppe Vittucci Marzetti, 2015. "Spatial agglomeration and productivity in Italy: A panel smooth transition regression approach," Papers in Regional Science, Wiley Blackwell, vol. 94, pages 39-67, November.
    9. Succurro, Marianna, 2014. "Intangible assets finance: a complementary or substitution effect between external and internal channels? Evidence from the Italian divide," MPRA Paper 57247, University Library of Munich, Germany.

    More about this item

    Keywords

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    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • L29 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Other
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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