Large-scale shocks directly affect some firms and households and indirectly affect others through general equilibrium spillovers. In this paper, I describe how researchers can directly estimate spillovers using quasi-experimental or experimental variation. I then argue that spillover estimates suffer from distinct sources of mechanical bias that standard empirical tools cannot resolve. These biases are particularly relevant in finance and macroeconomics, where multiple spillover channels and nonlinear effects are common. I offer guidance on how to detect and overcome mechanical biases. An application and several examples highlight that the suggested methods are broadly relevant and can inform policy and multiplier calculations.

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1 April 2023

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Review of Financial Studies 36(4) , pp.1548-1584, 2023

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DOI: 10.1093/rfs/hhac057

https://academic.oup.com/rfs/article-abstract/36/4/1548/6671238?redirectedFrom=fulltext

This Journal article is published under the centre's Growth programme.