Models for the spreading of shocks in production networks are one of the key tools to assess the economic and consequently societal effects caused by large crises events, such as natural disasters, or the COVID-19 pandemic. These models simulate how the initial shocks – stemming from such crises scenarios – spread from the directly affected parts of the economic network to the overall system. The economic networks underlying these models are usually sector level Input-Output tables, that are strongly aggregated representations of highly complex firm level supply networks.
However, a quantitative assessment of individual companies’ impact on the networks’ overall production is hitherto non-existent. We construct the firm-level production network of an entire country and present a novel approach for computing the economic systemic risk (ESR) of all firms within the network. We demonstrate that 0.035% of companies have extraordinarily high ESR, impacting about 23% of the national economic production should any of them default.
If firms within given industry sectors are linked very heterogeneously to firms from other sectors, the aggregated sector level network will lead to potentially false shock spreading dynamics for crises assessment models. In fact initial shocks – triggering the shock spreading – that would produce vastly different cascades on the firm level network, will be indistinguishable on the sector level network.
We show that firms within given industry sectors exhibit vastly heterogeneous interlinking in the production network. By using our firm-level shock propagation model tailored for firm-level production networks, we show that crises scenarios with the same initial shock size with respect to the affected output of each sector, but affecting different companies within the sectors, lead to substantially different shock cascades.
Most results are based on Diem, C., Borsos, A., Reisch, T., Kertész, J., & Thurner, S. (2021). Quantifying firm-level economic systemic risk from nation-wide supply networks. Available at SSRN 3826514.