Green technological diversification and regional recombinant capabilities: the role of technological novelty and academic inventors

ABSTRACT

We study the entry of regions in new green technological specializations, specifically investigating the role of recombinant novelty and academic inventors and the interplay between the two. We conduct an empirical analysis on a panel of Italian NUTS-3 regions observed from 1999 to 2009. The results show that both recombinant novelty and the presence of academic inventors are positively associated with new entries in green technological specializations, and that their interaction provides a compensatory mechanism in regions lacking adequate novel combinatorial capabilities. The findings of this study are relevant for policymakers involved in the elaboration of successful regional specialization strategies in green technological domains.

KEYWORDS:

• green specialization
• recombinant novelty
• academic inventors

JEL:

• O33
• R11

ACKNOWLEDGEMENTS

This article is based on a working paper included in the Collegio Carlo Alberto Notebooks titled ‘Regional Differences in the Generation of Green Technologies: The Role of Local Recombinant Capabilities and Academic Inventors’, no. 617 (December 2020) (ISSN 2279-9362). The authors gratefully acknowledge the comments of two anonymous referees and of the editor.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1. Recombinant reuse refers to the refinement and improvement of existing combinations; recombinant creation refers to the introduction of novel and unexplored combinations (Carnabuci & Operti, 2013).

2. We assume that the average academic inventor is endowed with higher education attainment than the average inventor in industry, on the grounds of the argument that entering academia requires holding a PhD, while this is not necessary for most occupations, including innovation-related ones. This is particularly true in the Italian context where the PhD rate of the working population is relatively low in comparison with other advanced economies, but holding a PhD is compulsory to obtain a tenured academic position.

3. In the data section we document that patents involving academic inventors scores, on average, higher in terms of a number of indicators – including patent scope and generality – compared with patents not involving academics. See section 3.2.2 and Appendix A in the supplemental data online.

4. Green CPC four-digit classes are under the subsection Y02 of the CPC classification. For the complete CPC scheme, see https://www.uspto.gov/web/patents/classification/cpc/html/cpc.html/.

5. We also take three- and five-year windows to check the robustness of the dependent variable (see section 4.2). We thank an anonymous reviewer for suggesting implementing this robustness check.

6. We assign each novel patent to all the regions where its inventors reside. To avoid multiple counting in the case of inventors on the same patent residing in different regions, we assign to regions only the corresponding share of novel patents. Therefore, the measure of recombinant novelty is based on a fractional count.

7. See section 4.2 for a set of robustness checks that concerns the variable ACAD.

8. Appendix A in the supplemental data online provides a detailed description of these results.

9. RTA GREEN and RTA GREEN^2 refer to the lagged count of RTAs in GTs that do not consider new RTAs forming the dependent variable. Therefore, RTA GREEN does not overlap with the dependent variable.

10. This is an alternative to the Box–Cox transformations, defined as follows:

$y=\log \left[y_i+{(y_i^2+1)}^{{\displaystyle \frac{1}{2}}}\right]$.

The inverse sine can be interpreted as a standard logarithmic variable (except for very small values of y) but it is defined at zero (Johnson, 1949; Burbidge et al., 1988; MacKinnon & Magee, 1990).

11. The variance inflation factor for all models is below the rule of thumb of 10 for all variables, except for the quadratic term RTA GREEN^2, as expected.

12. The coefficient of the control variable R&D PC is never significant, except for a model where all the other control variables are excluded (the results are available from the authors upon request). This is most likely because R&D PC broadly measures the local effort in R&D, without specifically identifying R&D effort for environmental innovation, for which data are not available.

13. We also implemented a spatial Durbin auto-regressive model to investigate spatial effects. In particular, we intend to check whether academic inventors and recombinant novelty from other places (e.g., neighbouring regions) influence the local entry in new green specializations. Besides confirming the main findings of this work, the model does not reveal any tangible spatial effect as the coefficients of the spatially lagged regressors of interest are not statistically significant. The results are available from the authors upon request.

Additional information

Funding

The authors gratefully acknowledge funding from the Italian Ministry of University and Research under the Scientific Research Program of National Relevance (PRIN) 2017 project ‘Innovation for Global Challenges in a Connected World: The Role of Local Resources and Socioeconomic Conditions’ [grant number 20177J2LS9]; and support from the University of Turin and Collegio Carlo Alberto local research funds.