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Measuring Shortages since 1900

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Abstract

This paper introduces a monthly shortage index spanning 1900 to the present, constructed from 25 million newspaper articles. The index captures shortages across industry, labor, food, and energy, and spikes during economic crises and wars. We validate the index and show that it provides information beyond traditional macroeconomic indicators. Using predictive regressions, we find that shortages are associated with persistently high inflation and lower economic activity. A structural VAR model reveals that, compared to a traditional supply shock, surprise movements in shortages produce less inflation relative to their GDP impact, suggesting that shortages are associated with constraints on price adjustment that limit inflation but magnify the decline in real activity. We also show that post-pandemic shortages and inflation were primarily driven by supply forces, with demand factors playing a less important role.

Suggested Citation

  • Dario Caldara & Matteo Iacoviello & David Yu, 2025. "Measuring Shortages since 1900," International Finance Discussion Papers 1407, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgif:1407
    DOI: 10.17016/IFDP.2025.1407
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    References listed on IDEAS

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    1. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    2. Ascari, Guido & Bonam, Dennis & Smadu, Andra, 2024. "Global supply chain pressures, inflation, and implications for monetary policy," Journal of International Money and Finance, Elsevier, vol. 142(C).
    3. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    4. Baumeister, Christiane & Hamilton, James D., 2018. "Inference in structural vector autoregressions when the identifying assumptions are not fully believed: Re-evaluating the role of monetary policy in economic fluctuations," Journal of Monetary Economics, Elsevier, vol. 100(C), pages 48-65.
    5. Pablo Burriel & Iván Kataryniuk & Carlos Moreno Pérez & Francesca Viani, 2024. "A New Supply Bottlenecks Index Based on Newspaper Data," International Journal of Central Banking, International Journal of Central Banking, vol. 20(2), pages 17-67, April.
    6. Ernest Liu & Vladimir Smirnyagin & Aleh Tsyvinski, 2024. "Supply Chain Disruptions and Supplier Capital in U.S. Firms," Cowles Foundation Discussion Papers 2402, Cowles Foundation for Research in Economics, Yale University.
    7. Lin Chen & Stephanie Houle, 2023. "Turning Words into Numbers: Measuring News Media Coverage of Shortages," Discussion Papers 2023-8, Bank of Canada.
    8. David Baqaee & Emmanuel Farhi, 2022. "Supply and Demand in Disaggregated Keynesian Economies with an Application to the COVID-19 Crisis," American Economic Review, American Economic Association, vol. 112(5), pages 1397-1436, May.
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    Cited by:

    1. Caldara, Dario & Conlisk, Sarah & Iacoviello, Matteo & Penn, Maddie, 2026. "Do geopolitical risks raise or lower inflation?," Journal of International Economics, Elsevier, vol. 159(C).
    2. Fourné, Friederike & Zarges, Lara, 2026. "Identifying macroeconomic shocks using firm-level data: Material shortages in the German manufacturing sector," Journal of Economic Dynamics and Control, Elsevier, vol. 187(C).
    3. Mori, Lorenzo, 2026. "Fiscal shocks and the surge of inflation," European Economic Review, Elsevier, vol. 187(C).
    4. Hernández-Román, Luis G., 2026. "Monetary policy trade-offs amid global supply chain disruptions," Journal of Monetary Economics, Elsevier, vol. 160(C).
    5. Caporin, Massimiliano & Gupta, Rangan & Subramaniam, Sowmya & Torrent, Hudson S., 2026. "Supply Constraints and Conditional Distribution Predictability of Inflation and its Volatility: A Nonparametric Mixed-Frequency Causality-in-Quantiles Approach," Research in Economics, Elsevier, vol. 80(2).

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • N10 - Economic History - - Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations - - - General, International, or Comparative

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