Data and Code for: Large Shocks Travel Fast

Principal Investigator(s):  View help for Principal Investigator(s) Alberto Cavallo, Harvard University. Harvard Business School. Division of Research; Francesco Lippi, Luiss University and EIEF; Ken Miyahara, University of Chicago. Social Sciences Division

Version:  View help for Version V1

Name File Type Size Last Modified
  REPLICATION_PACKAGE 09/24/2024 11:38:AM

Project Citation: 

Cavallo, Alberto, Lippi, Francesco, and Miyahara, Ken. Data and Code for: Large Shocks Travel Fast. Nashville, TN: American Economic Association [publisher], 2024. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-10-23. https://doi.org/10.3886/E198826V1

Project Description

Summary:  View help for Summary
We document a sizeable increase in the frequency of price adjustments following the large energy shocks of 2022. We use a tractable New Keynesian model, calibrated to the pre-shock data, to interpret such a pattern. The calibration highlights the state-dependence of firms' decisions: prices are adjusted rapidly when markups are misaligned. In the model, a large cost shock triggers a swift increase in the frequency of price adjustments, causing a rapid pass-through from costs to prices. Time-dependent models, as the Calvo model, miss this frequency response, failing to capture the sudden inflation surge after a large shock.

Scope of Project

Subject Terms:  View help for Subject Terms Inflation; Prices
JEL Classification:  View help for JEL Classification
      E50 Monetary Policy, Central Banking, and the Supply of Money and Credit: General
Geographic Coverage:  View help for Geographic Coverage France, Germany, Italy, Netherlands, Spain, United Kingdom, United States of America
Time Period(s):  View help for Time Period(s) 1/1/2019 – 7/22/2023
Collection Date(s):  View help for Collection Date(s) 1/1/2019 – 7/22/2023
Universe:  View help for Universe
Price changes of large retailers' online stores in France, Germany, Italy, Netherlands, Spain, United Kingdom and the United States of America during 2019 and mid 2023
Data Type(s):  View help for Data Type(s) observational data

Methodology

Data Source:  View help for Data Source
PriceStats (2023). PriceStats Microdata. PriceStats, https://www.pricestats.com (accessed August 2023).
Unit(s) of Observation:  View help for Unit(s) of Observation product id, price change, date, country

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Published Versions

V1 [2024-10-23]

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