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Financial Frictions in Production Networks

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  • Saki Bigio

    (Columbia Business School)

Abstract

We show that the organization of production among firms in an economy has important implications for the impact of financial frictions. We set up a model in which firms use output of other firms as inputs for their own production. We allow for arbitrary network structures such that aggregate production functions are constant. Therefore, in the absence of frictions these structures are allocatively equivalent. We then provide several examples which illustrate that when firms face liquidity constraints, different input-output structures require vastly di§erent amounts of aggregate liquidity in order to implement identical allocations. This implies that the input-output structure of the economy is an important determinant of its response to a financial shock. Our main result is that financial constraints have a stronger impact on aggregate output when firms are engaged in a larger amount of transactions among themselves. Finally, we calibrate the model to match the input-output matrix of the U.S. economy and use this to explore the extent to which these interrelationships can explain the drop in output during the latest recession.

Suggested Citation

  • Saki Bigio, 2013. "Financial Frictions in Production Networks," 2013 Meeting Papers 121, Society for Economic Dynamics.
  • Handle: RePEc:red:sed013:121
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    References listed on IDEAS

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    1. Andrew T. Foerster & Pierre-Daniel G. Sarte & Mark W. Watson, 2011. "Sectoral versus Aggregate Shocks: A Structural Factor Analysis of Industrial Production," Journal of Political Economy, University of Chicago Press, vol. 119(1), pages 1-38.
    2. Enghin Atalay, 2017. "How Important Are Sectoral Shocks?," American Economic Journal: Macroeconomics, American Economic Association, vol. 9(4), pages 254-280, October.
    3. 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.
    4. Saki Bigio, 2015. "Endogenous Liquidity and the Business Cycle," American Economic Review, American Economic Association, vol. 105(6), pages 1883-1927, June.
    5. Loukas Karabarbounis, 2014. "The Labor Wedge: MRS vs. MPN," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 17(2), pages 206-223, April.
    6. Shaowen Luo, 2020. "Propagation of Financial Shocks in an Input-Output Economy with Trade and Financial Linkages of Firms," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 36, pages 246-269, April.
    7. Zetlin-Jones, Ariel & Shourideh, Ali, 2017. "External financing and the role of financial frictions over the business cycle: Measurement and theory," Journal of Monetary Economics, Elsevier, vol. 92(C), pages 1-15.
    8. Ezra Oberfield, 2011. "Business networks, production chains and productivity: A theory of input-output architecture," Working Paper Series WP-2011-12, Federal Reserve Bank of Chicago.
    9. David Rezza Baqaee, 2018. "Cascading Failures in Production Networks," Econometrica, Econometric Society, vol. 86(5), pages 1819-1838, September.
    10. Dupor, Bill, 1999. "Aggregation and irrelevance in multi-sector models," Journal of Monetary Economics, Elsevier, vol. 43(2), pages 391-409, April.
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    More about this item

    JEL classification:

    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System

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