Description:

George Nikolakoudis (Princeton): Incomplete Information in Production Networks 

Abstract: I develop a model of production networks in which firms choose inputs under incomplete information about sectoral productivity and aggregate demand disturbances. As in the rational expectations framework of Lucas (1972), firms use input prices as endogenous signals to infer broader economic conditions. Theoretically, I show that the effect of sectoral productivity shocks on aggregate output depends on the interaction between the economy's input-output structure and firm-level uncertainty. Specifically, shocks to upstream sectors have a larger impact on aggregate output during periods of high productivity uncertainty, while shocks to downstream sectors have a larger effect on output during periods of high demand uncertainty. When calibrated to historical U.S. data, the model generates a state-dependent measure of sectoral importance that diverges significantly from traditional metrics, particularly in economic downturns. On aggregate, the interaction between uncertainty and the U.S. economy's network structure dampened the impact of productivity shocks on aggregate output by 50% during Covid (a period of high measured demand uncertainty) relative to the Dot-Com bubble (a period of high measured productivity uncertainty) or non-crisis periods.