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What Can Stockouts Tell Us About Inflation? Evidence from Online Micro Data

In: NBER International Seminar on Macroeconomics 2022

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  • Alberto Cavallo
  • Oleksiy Kryvtsov

Abstract

We use a detailed micro dataset on product availability and stockouts to construct a direct high-frequency measure of consumer product shortages during the 2020–2022 pandemic. We document a widespread multi-fold rise in stockouts in nearly all sectors early in the pandemic. Over time, the composition evolved from temporary to more permanently discontinued products, concentrated in fewer sectors. We show that unexpected shocks to stockout levels have significant inflationary effects within three months. These effects are larger and more persistent for imported goods and import-intensive sectors. We develop a model of inventories in a sector facing both demand and cost disturbances, and use the observed joint dynamics of stockouts and prices to show that these effects can be associated with elevated costs of replenishing inventories and higher exposure to trade.
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Suggested Citation

  • Alberto Cavallo & Oleksiy Kryvtsov, 2022. "What Can Stockouts Tell Us About Inflation? Evidence from Online Micro Data," NBER Chapters, in: NBER International Seminar on Macroeconomics 2022, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:14899
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    1. Oleksiy Kryvtsov & Virgiliu Midrigan, 2010. "Inventories and Real Rigidities in New Keynesian Business Cycle Models," NBER Chapters, in: Sticky Prices and Inflation Dynamics (NBER-TCER-CEPR), pages 259-281, National Bureau of Economic Research, Inc.
    2. Victor Aguirregabiria, 1999. "The Dynamics of Markups and Inventories in Retailing Firms," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 66(2), pages 275-308.
    3. Pawel Krolikowski & Kristoph Naggert, 2021. "Semiconductor Shortages and Vehicle Production and Prices," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2021(17), pages 1-7, July.
    4. John C. Driscoll & Aart C. Kraay, 1998. "Consistent Covariance Matrix Estimation With Spatially Dependent Panel Data," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 549-560, November.
    5. James A. Kahn & Mark Bils, 2000. "What Inventory Behavior Tells Us about Business Cycles," American Economic Review, American Economic Association, vol. 90(3), pages 458-481, June.
    6. Alberto Cavallo & Eduardo Cavallo & Roberto Rigobon, 2014. "Prices and Supply Disruptions during Natural Disasters," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(S2), pages 449-471, November.
    7. Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-529, October.
    8. Andrew B. Abel, 1985. "Inventories, Stock-Outs, and Production Smoothing," NBER Working Papers 1563, National Bureau of Economic Research, Inc.
    9. Hassan, Tarek & Hollander, Stephan & van Lent, Laurence & Schwedeler, Markus & Tahoun, Ahmed, 2020. "Firm-Level Exposure to Epidemic Diseases: Covid-19, SARS, and H1N1," CEPR Discussion Papers 14573, C.E.P.R. Discussion Papers.
    10. Cavallo, Alberto, 2013. "Online and official price indexes: Measuring Argentina's inflation," Journal of Monetary Economics, Elsevier, vol. 60(2), pages 152-165.
    11. Bils, Mark, 2016. "Deducing markups from stockout behavior," Research in Economics, Elsevier, vol. 70(2), pages 320-331.
    12. Alberto Cavallo & Roberto Rigobon, 2016. "The Billion Prices Project: Using Online Prices for Measurement and Research," Journal of Economic Perspectives, American Economic Association, vol. 30(2), pages 151-178, Spring.
    13. Kanika Mahajan & Shekhar Tomar, 2021. "COVID‐19 and Supply Chain Disruption: Evidence from Food Markets in India†," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(1), pages 35-52, January.
    14. Andrew B. Abel, 1985. "Inventories, Stock-Outs and Production Smoothing," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 52(2), pages 283-293.
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    Citations

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    Cited by:

    1. Youngna Choi, 2022. "Economic Stimulus and Financial Instability: Recent Case of the U.S. Household," JRFM, MDPI, vol. 15(6), pages 1-25, June.
    2. Hugh Montag & Daniel Villar, 2022. "Price-Setting During the Covid Era," Economic Working Papers 547, Bureau of Labor Statistics.
    3. Brent Meyer & Nicholas B. Parker & Xuguang Sheng, 2021. "Unit Cost Expectations and Uncertainty: Firms' Perspectives on Inflation," FRB Atlanta Working Paper 2021-12a, Federal Reserve Bank of Atlanta.
    4. Richard K. Crump & Stefano Eusepi & Marc Giannoni & Ayşegül Şahin, 2022. "The Unemployment-Inflation Trade-off Revisited: The Phillips Curve in COVID Times," NBER Working Papers 29785, National Bureau of Economic Research, Inc.
    5. Patrick W. McLaughlin & Alexander Stevens & Shawn Arita & Xiao Dong, 2023. "Stocking up and stocking out: Food retail stock‐outs, consumer demand, and prices during the COVID‐19 pandemic in 2020," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 45(3), pages 1618-1633, September.
    6. Rachel Soloveichik, 2022. "Theoretical Inflation for Unavailable Products," BEA Working Papers 0193, Bureau of Economic Analysis.
    7. Galina Hale & John Leer & Fernanda Nechio, 2022. "Inflationary Effects of Fiscal Support to Households and Firms," Working Paper Series 2023-02, Federal Reserve Bank of San Francisco.
    8. Melissa Maas & Gumataw Kifle Abebe & Christopher M. Hartt & Emmanuel K. Yiridoe, 2022. "Consumer Perceptions about the Value of Short Food Supply Chains during COVID-19: Atlantic Canada Perspective," Sustainability, MDPI, vol. 14(13), pages 1-20, July.
    9. Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023. "Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany," Discussion Papers 34/2023, Deutsche Bundesbank.
    10. Uzma Khan & Alexander DePaoli, 2024. "Brand loyalty in the face of stockouts," Journal of the Academy of Marketing Science, Springer, vol. 52(1), pages 44-74, January.
    11. Higo, Masahiro & Shiratsuka, Shigenori, 2023. "Consumer price measurement under the first wave of the COVID-19 spread in Japan: Scanner data evidence for retailers in Tokyo," Japan and the World Economy, Elsevier, vol. 65(C).
    12. Sonan Memon, 2022. "Inflation in Pakistan: High-Frequency Estimation and Forecasting," PIDE-Working Papers 2022:12, Pakistan Institute of Development Economics.

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    More about this item

    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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