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Measuring Industry Relatedness and Corporate Coherence

Author

Listed:
  • Giulio Bottazzi
  • Davide Pirino

Abstract

Since the seminal work of Teece et al. (1994) firm diversification has been found to be a non-random process. The hidden deterministic nature of the diversification patterns is usually detected comparing expected (under a null hypothesys) and actual values of some statistics. Nevertheless the standard approach presents two big drawbacks, leaving unanswered several issues. First, using the observed value of a statistics provides noisy and nonhomogeneous estimates and second, the expected values are computed in a specific and privileged null hypothesis that implies spurious random effects. We show that using Monte Carlo p-scores as measure of relatedness provides cleaner and homogeneous estimates. Using the NBER database on corporate patents we investigate the effect of assuming different null hypotheses, from the less unconstrained to the fully constrained, revealing that new features in firm diversification patterns can be catched if random artifacts are ruled out.

Suggested Citation

  • Giulio Bottazzi & Davide Pirino, 2010. "Measuring Industry Relatedness and Corporate Coherence," LEM Papers Series 2010/10, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  • Handle: RePEc:ssa:lemwps:2010/10
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    Citations

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    Cited by:

    1. Dosi, Giovanni & Mathew, Nanditha & Pugliese, Emanuele, 2019. "What a firm produces matters: diversi cation, coherence and performance of Indian manufacturing," MERIT Working Papers 2019-013, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    2. Tom Broekel & Matthias Brachert, 2015. "The structure and evolution of inter-sectoral technological complementarity in R&D in Germany from 1990 to 2011," Journal of Evolutionary Economics, Springer, vol. 25(4), pages 755-785, September.
    3. Arianna Martinelli & Önder Nomaler, 2014. "Measuring knowledge persistence: a genetic approach to patent citation networks," Journal of Evolutionary Economics, Springer, vol. 24(3), pages 623-652, July.
    4. Krafft Jackie & Quatraro Francesco & Colombelli Alessandra, 2011. "High Growth Firms and Technological Knowledge: Do gazelles follow exploration or exploitation strategies?," Department of Economics and Statistics Cognetti de Martiis LEI & BRICK - Laboratory of Economics of Innovation "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio 201114, University of Turin.
    5. Giovanni Dosi & Marco Grazzi & Daniele Moschella, 2017. "What do firms know? What do they produce? A new look at the relationship between patenting profiles and patterns of product diversification," Small Business Economics, Springer, vol. 48(2), pages 413-429, February.
    6. Evan Starr & Martin Ganco & Benjamin A. Campbell, 2018. "Strategic human capital management in the context of cross‐industry and within‐industry mobility frictions," Strategic Management Journal, Wiley Blackwell, vol. 39(8), pages 2226-2254, August.
    7. Jeff Alstott & Giorgio Triulzi & Bowen Yan & Jianxi Luo, 2017. "Mapping technology space by normalizing patent networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 443-479, January.
    8. Giovanni Dosi & Nanditha Mathew & Emanuele Pugliese, 2019. "What a firm produces matters: diversification, coherence and performance of Indian manufacturing firms," LEM Papers Series 2019/10, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    9. Emanuele Pugliese & Lorenzo Napolitano & Andrea Zaccaria & Luciano Pietronero, 2017. "Coherent diversification in corporate technological portfolios," Papers 1707.02188, arXiv.org.
    10. Andrea Tacchella & Andrea Zaccaria & Marco Miccheli & Luciano Pietronero, 2021. "Relatedness in the Era of Machine Learning," Papers 2103.06017, arXiv.org.

    More about this item

    Keywords

    corporate coherence; relatedness; null model analysis; patent data;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • D2 - Microeconomics - - Production and Organizations
    • L2 - Industrial Organization - - Firm Objectives, Organization, and Behavior

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