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The GSCPI: A New Barometer of Global Supply Chain Pressures

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Abstract

We propose a novel indicator to capture pressures that arise at the global supply chain level, the Global Supply Chain Pressure Index (GSCPI). The GSCPI provides a new monitoring tool to gauge global supply chain conditions. We assess the index’s capacity to explain inflation outcomes, using the local projection method. Our analysis shows that recent inflationary pressures are closely related to the behavior of the GSCPI, especially at the level of producer price inflation in the United States and the euro area.

Suggested Citation

  • Gianluca Benigno & Julian di Giovanni & Jan J. J. Groen & Adam I. Noble, 2022. "The GSCPI: A New Barometer of Global Supply Chain Pressures," Staff Reports 1017, Federal Reserve Bank of New York.
  • Handle: RePEc:fip:fednsr:94243
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    References listed on IDEAS

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    1. Regis Barnichon & Christian Brownlees, 2019. "Impulse Response Estimation by Smooth Local Projections," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 522-530, July.
    2. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    3. Jan J. J. Groen & Kevin McNeil & Menno Middeldorp, 2013. "A New Approach for Identifying Demand and Supply Shocks in the Oil Market," Liberty Street Economics 20130325, Federal Reserve Bank of New York.
    4. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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    More about this item

    Keywords

    global supply chain; inflation; transportation costs;
    All these keywords.

    JEL classification:

    • F40 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - General
    • F10 - International Economics - - Trade - - - General
    • F20 - International Economics - - International Factor Movements and International Business - - - General

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