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Production Network Structure, Service Share, and Aggregate Volatility

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Abstract

This paper studies differences in production structures across countries and their implications for cross-country heterogeneity in GDP volatility. In particular, economies with more input-output connections—a denser network—are associated with less concentrated sales shares and lower volatility. The relationship between density and volatility is stronger in countries with a higher share of services in GDP (hereafter referred to as service share). To account for this evidence, I propose a generalized production network model in which denser economies display higher production complexity. If production is also specialized in industries that use labor and intermediates as substitute inputs, higher network density indeed lowers the concentration of sales shares and aggregate volatility. U.S. sectoral data suggest that the elasticity of substitution between labor and intermediates in service sectors is larger than one and larger than in non-service sectors. A calibrated model that then also matches each country’s production network can quantitatively generate observed cross-country empirical patterns. Furthermore, in contrast to previous work, the model predicts that: i) sectoral shocks play only a modest role in accounting for the observed business cycle dynamics, especially in dense and service-oriented economies; and ii) production diversification does not always lower volatility.

Suggested Citation

  • Jorge Miranda-Pinto, 2019. "Production Network Structure, Service Share, and Aggregate Volatility," Discussion Papers Series 607, School of Economics, University of Queensland, Australia.
  • Handle: RePEc:qld:uq2004:607
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    File URL: https://economics.uq.edu.au/files/46390/607.pdf
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    Cited by:

    1. Kevin Genna & Christian Ghiglino & Kazuo Nishimura & Alain Venditti, 2021. "Knowledge-Based Structural Change," Working Papers halshs-03164854, HAL.
    2. Gnangnon, Sèna Kimm, 2023. "The Quality of Aid for Trade Flows and Economic Complexity," EconStor Preprints 271538, ZBW - Leibniz Information Centre for Economics.
    3. Andrew T. Foerster & Andreas Hornstein & Pierre-Daniel G. Sarte & Mark W. Watson, 2022. "Aggregate Implications of Changing Sectoral Trends," Journal of Political Economy, University of Chicago Press, vol. 130(12), pages 3286-3333.
    4. Miranda-Pinto, Jorge, 2018. "A note on optimal sectoral policies in production networks," Economics Letters, Elsevier, vol. 172(C), pages 152-156.
    5. Gnangnon, Sèna Kimm, 2022. "Effect of the utilization of non-reciprocal trade preferences offered by the QUAD countries on beneficiary countries' economic complexity," Journal of the Japanese and International Economies, Elsevier, vol. 65(C).
    6. Sophie Osotimehin & Latchezar Popov, 2023. "Misallocation and Intersectoral linkages," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 51, pages 177-198, December.
    7. Thisse, Jacques-François & Behrens, Kristian & Kichko, Sergey, 2021. "Working from home: Too much of a good thing?," CEPR Discussion Papers 15669, C.E.P.R. Discussion Papers.
    8. Caraiani, Petre, 2019. "Oil shocks and production network structure: Evidence from the OECD," Energy Economics, Elsevier, vol. 84(C).
    9. Andrew T. Foerster & Eric LaRose & Pierre-Daniel G. Sarte, 2018. "Idiosyncratic Sectoral Growth, Balanced Growth, and Sectoral Linkages," Economic Quarterly, Federal Reserve Bank of Richmond, issue 2Q, pages 79-101.
    10. Jorge Miranda-Pinto & Eric R. Young, 2022. "Flexibility and Frictions in Multisector Models," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(3), pages 450-480, July.
    11. Miranda-Pinto, Jorge & Silva, Alvaro & Young, Eric R., 2023. "Business cycle asymmetry and input-output structure: The role of firm-to-firm networks," Journal of Monetary Economics, Elsevier, vol. 137(C), pages 1-20.
    12. Sèna Kimm Gnangnon, 2023. "Do unilateral trade preferences help reduce poverty in beneficiary countries?," International Journal of Economic Policy Studies, Springer, vol. 17(1), pages 249-288, February.
    13. Gnangnon, Sèna Kimm, 2023. "Effect of the duration of membership in the GATT/WTO on economic growth volatility," Structural Change and Economic Dynamics, Elsevier, vol. 65(C), pages 448-467.
    14. Gnangnon, Sèna Kimm, 2021. "Do Unilateral Trade Preferences Help Reduce Poverty in Beneficiary Countries?," EconStor Preprints 247346, ZBW - Leibniz Information Centre for Economics.
    15. Miranda-Pinto, Jorge & Young, Eric R., 2019. "Comparing dynamic multisector models," Economics Letters, Elsevier, vol. 181(C), pages 28-32.

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    More about this item

    Keywords

    input-output structure; volatility; service share; elasticity of substitution;
    All these keywords.

    JEL classification:

    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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