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Chinese supply chain shocks

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  • Khalil, Makram
  • Weber, Marc-Daniel

Abstract

In structural vector autoregressive models of US and euro area manufacturing, we use sign restrictions to identify shocks that alter the frictions to Chinese supply chain trade. We find a quantitatively significant role of such shocks for the decline of US manufacturing output at the height of the Sino-American trade tensions in 2019. At the beginning of the Covid-19 pandemic in early 2020, the results point towards large spillovers from the shutdown in China to manufacturing in the US and the euro area. Moreover, during the recovery in 2020 and 2021, positive Chinese supply chain shocks related to the shift of preferences towards goods with a large China valued-added content played a role. Interestingly, the impact of China-specific trade shocks is not limited to manufacturing sectors that are highly exposed to China. Furthermore, negative Chinese supply chain shocks cause upward price pressure across the whole manufacturing industry.

Suggested Citation

  • Khalil, Makram & Weber, Marc-Daniel, 2021. "Chinese supply chain shocks," MPRA Paper 110356, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:110356
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    1. Jean-Noël Barrot & Julien Sauvagnat, 2016. "Input Specificity and the Propagation of Idiosyncratic Shocks in Production Networks," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(3), pages 1543-1592.
    2. Atsushi Inoue & Lutz Kilian, 2020. "The Role of the Prior in Estimating VAR Models with Sign Restrictions," Working Papers 2030, Federal Reserve Bank of Dallas.
    3. Baumeister, Christiane & Hamilton, James D., 2020. "Drawing conclusions from structural vector autoregressions identified on the basis of sign restrictions," Journal of International Money and Finance, Elsevier, vol. 109(C).
    4. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
    5. Raphael Lafrogne-Joussier & Julien Martin & Isabelle Mejean, 2023. "Supply Shocks in Supply Chains: Evidence from the Early Lockdown in China," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 71(1), pages 170-215, March.
    6. Khalil, Makram & Strobel, Felix, 2024. "US trade policy and the US dollar," Journal of International Economics, Elsevier, vol. 151(C).
    7. Christoph E. Boehm & Aaron Flaaen & Nitya Pandalai-Nayar, 2019. "Input Linkages and the Transmission of Shocks: Firm-Level Evidence from the 2011 Tōhoku Earthquake," The Review of Economics and Statistics, MIT Press, vol. 101(1), pages 60-75, March.
    8. 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.
    9. Juan F. Rubio-Ramírez & Daniel F. Waggoner & Tao Zha, 2010. "Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(2), pages 665-696.
    10. Jonas E. Arias & Juan F. Rubio‐Ramírez & Daniel F. Waggoner, 2018. "Inference Based on Structural Vector Autoregressions Identified With Sign and Zero Restrictions: Theory and Applications," Econometrica, Econometric Society, vol. 86(2), pages 685-720, March.
    11. Jesse LaBelle & Fernando Leibovici & Ana Maria Santacreu, 2021. "Global Value Chains and U.S. Economic Activity During COVID-19," Review, Federal Reserve Bank of St. Louis, vol. 103(3), pages 271-288, May.
    12. Vasco M Carvalho & Makoto Nirei & Yukiko U Saito & Alireza Tahbaz-Salehi, 0. "Supply Chain Disruptions: Evidence from the Great East Japan Earthquake," The Quarterly Journal of Economics, Oxford University Press, vol. 136(2), pages 1255-1321.
    13. repec:zbw:bofrdp:2018_014 is not listed on IDEAS
    14. Sebastian Heise, 2020. "How Did China’s COVID-19 Shutdown Affect U.S. Supply Chains?," Liberty Street Economics 20200512, Federal Reserve Bank of New York.
    15. Baumeister, Christiane & Hamilton, James, 2018. "Inference in Structural Vector Autoregressions When the Identifying Assumptions are Not Fully Believed: Re-evaluating the Role," CEPR Discussion Papers 12911, C.E.P.R. Discussion Papers.
    16. Meier, Matthias & Pinto, Eugenio, 2024. "COVID-19 Supply Chain Disruptions," European Economic Review, Elsevier, vol. 162(C).
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    Cited by:

    1. Puch González, Luis Antonio & Ruiz, Jesús, 2024. "The asymmetry puzzle: the supply chain disruptions news shocks effects on oil prices and inflation," UC3M Working papers. Economics 43758, Universidad Carlos III de Madrid. Departamento de Economía.

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    More about this item

    Keywords

    Cross-border supply-chain disruptions; China; trade tensions; Covid-19 recession; US and euro area manufacturing.;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • F41 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Open Economy Macroeconomics
    • F62 - International Economics - - Economic Impacts of Globalization - - - Macroeconomic Impacts

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