Foreign input exposure of the EU auto industry and volume versus frequency of trade

Mots-clés:

Résumé:

What is the input exposure of the EU automotive industry to foreign countries, including China, over time? First, we build on the Czech case study of Hrubý and Šaroch (forthcoming) to calculate the foreign input exposure indicators across all EU Member States (MS) and observe the production dependence on intermediates sourced abroad. Then, to statistically calculate how much of the increasing value-added concentration over time is explained by frequency of trade and how much is given by increasing value-added across activities per se, we employ a recently published data on pass-through frequency of trade. Last, we qualitatively assess and summarise how foreign input exposure patterns develop across the EU MS.

We calculate all the foreign input exposure indicators for the EU MS with data from ICIO tables by the Organisation for Economic Co-operation and Development (OECD), closely following the taxonomy of Baldwin and Freeman (2022) and Baldwin et al. (2023), as used in Hrubý and Šaroch (forthcoming). Data from the 2023 release cover the period of 1995-2020 in current prices. Moreover, we check the results with the general findings based on the 2023 edition of the Trade in Value Added (TiVA) database and the trade statistics from the 2025 edition of the BACI database (Base pour l'Analyse du Commerce International). We employ (panel) local projection (Jordá, 2005) to calculate and assess the relation between the pass-through frequency of trade in inputs and its concentration (Inomata and Hanaka, 2024) and the value-added concentration of trade in inputs, given the exogenous shocks and changes to production and trade in the auto industry, controlling for observed and unobserved covariates.

In brief, we observe the increasingly important role of the Chinese supply chains in the EU automotive production over time, achieving the second largest supplier role behind Germany in almost all EU MS across a set of indicators. This change is driven by, among others, the rising global importance of the manufacturing sector in China, observed through the pass-through frequency of trade in inputs and by the value added generated thereafter. The pass-through trade frequency does not, however, explain the entire increase in value added. While we observe several key sectors for today’s and tomorrow’s auto industry, including electronics and electrical equipment, the shift in supply chains has been incremental and relatively straightforward for many years already. Recent trade data for 2023 and 2024 confirm the overall picture of the long-term analysis and highlight the sharp rise in trade with batteries. The results bear important conclusions regarding new industrial policy and its use in the recently very turbulent discussions on the future of the EU automotive industry.

References:

Baldwin, R., Freeman, R., & Theodorakopoulos, A. (2023). Hidden exposure: measuring US supply chain reliance (No. w31820). National Bureau of Economic Research.

Baldwin, R., & Freeman, R. (2022). Risks and global supply chains: What we know and what we need to know. Annual Review of Economics, 14(1), 153-180.

Hrubý, M., & Šaroch, S. (forthcoming). Revisiting foreign input exposure in the auto industry: Czechia meets China. International Journal of Automotive Technology and Management.

Inomata, S., & Hanaka, T. (2024). Measuring exposure to network concentration risk in global supply chains: Volume versus frequency. Structural Change and Economic Dynamics, 68, 177-193.

Jordà, Ò. (2005). Estimation and inference of impulse responses by local projections. American Economic Review, 95(1), 161-182.