Technological congruence and Smart Specialisation: evidence from European regions

ABSTRACT

This paper links the Smart Specialisation Strategy (S3) with technological congruence, which stems from the coherence of directed technological change with the structural conditions of local factor markets. We argue that complementary to regional branching, technological congruence is a crucial dimension of S3, and that it has powerful effects on total factor productivity (TFP). The spatial econometrics evidence for European regions over the period 1980–2011 shows that technological congruence has significant effects on TFP, even if potentially mitigated by some market limits that make policy intervention relevant. Therefore, some S3 policy implications are highlighted.

KEYWORDS:

• output elasticity
• knowledge spillovers
• pecuniary externalities
• agglomeration effect
• Research and Innovation Strategies for Smart Specialisation (RIS3)

JEL:

• O11
• O30
• R11
• R38

ACKNOWLEDGEMENTS

The authors acknowledge Laura Abrardi and the support of the research project PRIN 20177J2LS9. We also thank all the participants at the 2nd TEM conference for their comments on an earlier draft of this paper, and are grateful for the prize awarded by the conference.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1 Intuitively, from (10), both lower cost and higher output elasticity increase the optimal amount of the production factor. However, the output elasticity is also affected by its relative factor cost. Indeed, deriving (1) for α, we observe that the factor costs affect productivity:

dY/dα = Yln(α/1 – α) + ln(w/r).

Therefore, as suggested by the induced technological change literature, for firms it is efficient to increase the cheaper factor’s output elasticity, and then the factor cost impacts its optimal amount twice.

2 Appendix A in the supplemental data online shows the geographical coverage of the database, the definitions of the variables, the main descriptive statistics, the Spearman correlation coefficients of the variables and the S3’s regional adoptions until 2016.

Additional information

Funding

This work was supported by the Università degli Studi di Torino [grant number PRIN 20177J2LS9].