State aid, R&D, and the digital content of trade

ABSTRACT

We test for the role of state aid as a driver of digital competitiveness at the industry level, focusing on the digital factor content of trade in a sample of European countries. Results show that state aid can increase digital competitiveness, particularly in R&D-intensive industries and in relation to the export of (digital) capital-intensive goods and services. Interestingly, aggregate state aid appears to be more effective than specific R&D funds in explaining the performance of country-industries in foreign markets, highlighting the importance of prior adequate structural conditions for the latter effect to materialize.

KEYWORDS:

• Factor content of trade
• ICT capital
• state aid
• sectoral R&D capability

JEL CLASSIFICATION:

• F14
• O30

Acknowledgements

We are grateful to Roman Stöllinger for providing data and statistical assistance, and to two anonymous referees for their valuable suggestions and comments in improving the early version of this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ For further information on the sample composition, see Appendix, Table A1.

² For a detailed description of the numerical variables, see Appendix, Table A2.

³ Specifically, labour statistics comes from the European Labour Force Survey (LFS) and the Survey on Italian Occupations (ICP) for digital tasks, whereas capital stocks are retrieved from the EU KLEMS and Eurostat.

⁴ Specifically, intermediate linkages quantify the amount of direct and indirect intermediate goods required from industry A to obtain one unit of industry B output, while Input-Output tables allow for quantification of bilateral trade flows between A and B.

⁵ These principles include the prohibition to act in favour of any distortion of market competition and trade within the EU by favouring certain companies or goods (following TFEU, Article 107).

⁶ According to the EU legislation (2006), R&D and innovation state aid includes funding for personnel, equipment, patenting and other costs related to R&D projects, which might be relevant boosters of both innovation capital and employment.

⁷ R&D stock information is provided by Eurostat. Details on the conversion procedure to constant terms is available in the Appendix, Table A2.

⁸ For a recent discussion of the use and interpretation of the IHS transformation in applied economics, see Bellemare and Wichman (2020).

⁹ As an additional robustness check, we estimate the specification using the Poisson Pseudo Maximum Likelihood (PPML) estimator. Results support the findings of the IHS specifications and are available upon request.

¹⁰ The interpretation of the underlying elasticities is left as an exercise in the Appendix (Table A4), as it is in the main interest of this analysis to assess first the influence and significance of each regressor separately, and then to look at the presence of interaction effects on the different FC exports, rather than considering directly the overall impact of the variable (i.e. main and interaction effects together).

¹¹ Specifically, we take the 2004 enlargement towards the Eastern bloc (i.e. Bulgaria, Czechia, Estonia, Hungary, Latvia, Lithuania, Romania, Slovakia, Slovenia) as a reference for the group of new EU members and replicate the analysis. Results are presented in the Appendix (Table A3).