Data and Code for: Supply and Demand in Disaggregated Keynesian Economies with an Application to the Covid-19 Crisis

Principal Investigator(s): View help for Principal Investigator(s) David Baqaee, UCLA; Emmanuel Farhi

Name	File Type	Size	Last Modified
Replication code_ver2			12/21/2021 02:57:PM

Project Citation:

Baqaee, David, and Farhi, Emmanuel. Data and Code for: Supply and Demand in Disaggregated Keynesian Economies with an Application to the Covid-19 Crisis. Nashville, TN: American Economic Association [publisher], 2022. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2022-04-19. https://doi.org/10.3886/E152801V1

Project Description

Summary: We study supply and demand shocks in a disaggregated model with multiple sectors, multiple factors, input-output linkages, downward nominal wage rigidities, credit-constraints, and a zero lower bound. We use the model to understand how the Covid-19 crisis, an omnibus supply and demand shock, affects output, unemployment, and inflation, and leads to the coexistence of tight and slack labor markets. We show that negative sectoral supply shocks are stagflationary, whereas negative demand shocks are deflationary, even though both can cause Keynesian unemployment. Furthermore,complementarities in production amplify Keynesian spillovers from supply shocks but mitigate them for demand shocks. This means that complementarities reduce the effectiveness of aggregate demand stimulus. In a stylized quantitative model of the US, we find supply and demand shocks each explain about half the reduction in real GDP from February to May, 2020. Although there was as much as 6% Keynesian unemployment, this was concentrated incertain markets. Hence, aggregate demand stimulus is one quarter as effective as in a typical recession where all labor markets are slack.

Funding Sources: NSF ( 1947611)

Scope of Project

Subject Terms: Covid-19; unemployment; business cycles

JEL Classification:
E10 General Aggregative Models: General
E30 Prices, Business Fluctuations, and Cycles: General (includes Measurement and Data)

Geographic Coverage: USA

Time Period(s): 2/1/2020 – 5/1/2020

Data Type(s): aggregate data; census/enumeration data; program source code

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Published Versions

V1 [2022-04-19]

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