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Factor Demand Linkages, Technology Shocks, and the Business Cycle

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  • Sean Holly

    (University of Cambridge and Centre for International Macroeconomics and Finance)

  • Ivan Petrella

    (Birkbeck College, University of London)

Abstract

This paper argues that factor demand linkages can be important for the transmission of both sectoral and aggregate shocks. We show this using a panel of highly disaggregated manufacturing sectors together with sectoral structural VARs. When sectoral interactions are explicitly accounted for, a contemporaneous technology shock to all manufacturing sectors implies a positive response in both output and hours at the aggregate level. Otherwise there is a negative correlation, as in much of the existing literature. Furthermore, we find that technology shocks are important drivers of the business cycle. © 2012 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Suggested Citation

  • Sean Holly & Ivan Petrella, 2012. "Factor Demand Linkages, Technology Shocks, and the Business Cycle," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 948-963, November.
  • Handle: RePEc:tpr:restat:v:94:y:2012:i:4:p:948-963
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    Cited by:

    1. Vasco M. Carvalho, 2014. "From Micro to Macro via Production Networks," Journal of Economic Perspectives, American Economic Association, vol. 28(4), pages 23-48, Fall.
    2. Saldías, Martín, 2013. "A market-based approach to sector risk determinants and transmission in the euro area," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4534-4555.
    3. Natalia Bailey & Sean Holly & M. Hashem Pesaran, 2016. "A Two‐Stage Approach to Spatio‐Temporal Analysis with Strong and Weak Cross‐Sectional Dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 249-280, January.
    4. Christian Matthes & Felipe Schwartzman, 2019. "The Demand Origins of Business Cycles," 2019 Meeting Papers 1122, Society for Economic Dynamics.
    5. Swapnil Singh & Roel Beetsma, 2018. "Optimal Monetary Policy Under Sectoral Interconnections," De Economist, Springer, vol. 166(3), pages 309-336, September.
    6. Alexander Chudik & M. Hashem Pesaran, 2016. "Theory And Practice Of Gvar Modelling," Journal of Economic Surveys, Wiley Blackwell, vol. 30(1), pages 165-197, February.
    7. Dissou, Yazid & Karnizova, Lilia, 2016. "Emissions cap or emissions tax? A multi-sector business cycle analysis," Journal of Environmental Economics and Management, Elsevier, vol. 79(C), pages 169-188.
    8. Cantore, Cristiano & Ferroni, Filippo & León-Ledesma, Miguel A., 2017. "The dynamics of hours worked and technology," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 67-82.
    9. Yukiko Saito & Makoto Nirei & Vasco Carvalho, 2014. "Supply Chain Disruptions: Evidence from Great East Japan Earthquake," 2014 Meeting Papers 595, Society for Economic Dynamics.
    10. Ivan Petrella & Raffaele Rossi & Emiliano Santoro, 2019. "Monetary Policy with Sectoral Trade‐Offs," Scandinavian Journal of Economics, Wiley Blackwell, vol. 121(1), pages 55-88, January.
    11. Peng, Ling & Hong, Yongmiao, 2013. "Productivity spillovers among linked sectors," China Economic Review, Elsevier, vol. 25(C), pages 44-61.
    12. Yongsung Chang & Sunoong Hwang, 2015. "Asymmetric Phase Shifts in U.S. Industrial Production Cycles," The Review of Economics and Statistics, MIT Press, vol. 97(1), pages 116-133, March.
    13. Christian Matthes & Felipe Schwartzman, 2019. "What Do Sectoral Dynamics Tell Us About the Origins of Business Cycles?," Working Paper 19-9, Federal Reserve Bank of Richmond, revised 29 Mar 2019.
    14. Cristiano Cantore & Miguel León-Ledesma & Peter McAdam & Alpo Willman, 2014. "Shocking Stuff: Technology, Hours, And Factor Substitution," Journal of the European Economic Association, European Economic Association, vol. 12(1), pages 108-128, February.
    15. Bams, Dennis & Pisa, Magdalena & Wolff, Christian C, 2015. "Ripple effects from industry defaults," CEPR Discussion Papers 10891, C.E.P.R. Discussion Papers.
    16. Molnarova, Zuzana & Molnárová, Zuzana & Reiter, Michael, 2016. "Business Cycles and the Propagation of Shocks in the Input-Output Network," Annual Conference 2016 (Augsburg): Demographic Change 145804, Verein für Socialpolitik / German Economic Association.
    17. Filip Rozsypal, 2015. "Schumpeterian business cycles," 2015 Meeting Papers 320, Society for Economic Dynamics.
    18. Julián Ramajo & José Manuel Cordero & Miguel Ángel Márquez, 2017. "European regional efficiency and geographical externalities: a spatial nonparametric frontier analysis," Journal of Geographical Systems, Springer, vol. 19(4), pages 319-348, October.
    19. Vasco M. Carvalho, 2014. "From Micro to Macro via Production Networks," Working Papers 793, Barcelona Graduate School of Economics.
    20. Zuzana Molnarova, 2020. "Industry evidence and the vanishing cyclicality of labor productivity," Vienna Economics Papers 2001, University of Vienna, Department of Economics.
    21. Tokui, Joji & Kawasaki, Kazuyasu & Miyagawa, Tsutomu, 2017. "The economic impact of supply chain disruptions from the Great East-Japan earthquake," Japan and the World Economy, Elsevier, vol. 41(C), pages 59-70.
    22. Eiji Goto, 2020. "Industry Impacts of Unconventional Monetary Policy," 2020 Papers pgo873, Job Market Papers.

    More about this item

    Keywords

    factor demand linkages; technology shocks; business cycles; manufacturing;

    JEL classification:

    • E20 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - General (includes Measurement and Data)
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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