Measuring Industry Relatedness and Corporate Coherence
AbstractSince 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.
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Bibliographic InfoPaper provided by Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy in its series LEM Papers Series with number 2010/10.
Date of creation: 15 Aug 2010
Date of revision:
corporate coherence; relatedness; null model analysis; patent data;
Find related papers by 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-07-10 (All new papers)
- NEP-BEC-2010-07-10 (Business Economics)
- NEP-ECM-2010-07-10 (Econometrics)
- NEP-IPR-2010-07-10 (Intellectual Property Rights)
- NEP-TID-2010-07-10 (Technology & Industrial Dynamics)
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