Smart Specialisation Strategies (S3) represent the European Union’s primary instrument for place-based industrial policy. While widely adopted across regions, systematic evidence on whether S3 effectively steers technological change remains limited. We provide the first EU-wide technology-level causal evaluation of S3 by combining a harmonised database of regional priorities with patent data for nearly 400 European regions mapped into 25 technological domains. Using a Double Machine Learning framework, we estimate the effect that adopting a specific S3 priority produces to targeted technological areas. Results show that S3 priorities significantly redirect inventive activity, increasing the likelihood of technological entry up to 10% points depending on the policy dimension and reduces it from other dimensions. Effects are consistent across programming periods and robust to alternative specifications. Our findings support evaluating industrial policy in terms of directionality rather than aggregate innovation output and offer a quantitative framework to compare policy instruments in terms of their steering capacity

The Impact of Smart Specialisation Strategies on Steering Innovation: A Technology-Level Causal Evaluation of EU S3 Policy

Alessio Bumbea
Writing – Original Draft Preparation
;
Andrea Mazzitelli
Writing – Review & Editing
;
2026-01-01

Abstract

Smart Specialisation Strategies (S3) represent the European Union’s primary instrument for place-based industrial policy. While widely adopted across regions, systematic evidence on whether S3 effectively steers technological change remains limited. We provide the first EU-wide technology-level causal evaluation of S3 by combining a harmonised database of regional priorities with patent data for nearly 400 European regions mapped into 25 technological domains. Using a Double Machine Learning framework, we estimate the effect that adopting a specific S3 priority produces to targeted technological areas. Results show that S3 priorities significantly redirect inventive activity, increasing the likelihood of technological entry up to 10% points depending on the policy dimension and reduces it from other dimensions. Effects are consistent across programming periods and robust to alternative specifications. Our findings support evaluating industrial policy in terms of directionality rather than aggregate innovation output and offer a quantitative framework to compare policy instruments in terms of their steering capacity
2026
978-3-032-30877-1
Smart Specialisation, Industrial Policy, Innovation Directionality, Double Machine Learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12606/48945
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