The coincidence of sectoral slowdowns in the UK: Comparing transmission probabilities and economic linkages

This paper studies the transmission of sectoral shocks across broad macroeconomic sectors in the UK, using data from the Bank of England's Flow of Funds statistics. By combining two different approaches to quantify the spread of shocks, we are able to assess whether a greater statistical likelihood of shock transmission across sectors corresponds to greater actual economic linkages. A causal interpretation is possible via an epidemiological model estimated with Bayesian techniques using both network data on assets and liability connections between sectors as well as a panel data set on sectoral net worth growth. The combination of both approaches reveals a role as shock absorber of the Monetary Financial Institutions sector, and helps distinguish between more and less likely channels of shock transmission. The discrepancies between network data and the actual occurrences of spillovers highlight the contribution of the proposed methodology.  The approach can therefore be particularly valuable to policymakers in a systemic risk mitigation context.
This paper studies the transmission of sectoral shocks across broad macroeconomic sectors in the UK, using data from the Bank of England's Flow of Funds statistics. By combining two different approaches to quantify the spread of shocks, we are able to assess whether a greater statistical likelihood of shock transmission across sectors corresponds to greater actual economic linkages. A causal interpretation is possible via an epidemiological model estimated with Bayesian techniques using both network data on assets and liability connections between sectors as well as a panel data set on sectoral net worth growth. The combination of both approaches reveals a role as shock absorber of the Monetary Financial Institutions sector, and helps distinguish between more and less likely channels of shock transmission. The discrepancies between network data and the actual occurrences of spillovers highlight the contribution of the proposed methodology.  The approach can therefore be particularly valuable to policymakers in a systemic risk mitigation context.