Mapping supply chains with micro data: the case of the Italian automotive industry

This paper develops a firm-level input–output framework to measure participation in production networks using transaction-level data and applies it to the automotive industry. Using a novel dataset of firm-to-firm transactions, we construct a firm-specific measure of supply chain exposure based on the Ghosh inverse, which is a sufficient statistic for the upstream propagation of demand shocks within a standard production network model. We use this framework to trace both direct and indirect sales linkages across firms. We find that around 8,000 Italian firms are significantly exposed to the EU automotive industry, with indirect linkages accounting for more than half of the supply chain. Exposure is highly heterogeneous across firms, sectors, and regions, and simulated demand shocks generate sizeable upstream propagation effects: a 1 per cent decline in final demand for cars would reduce value added among final producers by 63 million euros, with an additional 93 million propagating upstream through the network. The framework is scalable and can be extended to other downstream industries with clearly identifiable final producers, providing a flexible tool for analysing production networks and the upstream transmission of demand shocks.