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Structural propagation in a production network with restoring substitution elasticities


  • Nakano, Satoshi
  • Nishimura, Kazuhiko


We model an economy-wide production network by cascading binary compounding functions, based on the sequential processing nature of the production activities. As we observe a hierarchy among the intermediate processes spanning the empirical input–output transactions, we utilize a stylized sequence of processes for modeling the intra-sectoral production activities. Under the productivity growth that we measure jointly with the state-restoring elasticity parameters for each sectoral activity, the network of production completely replicates the records of multi-sectoral general equilibrium prices and shares for all factor inputs observed in two temporally distant states. Thereupon, we study propagation of a small exogenous productivity shock onto the structure of production networks by way of hierarchical clustering.

Suggested Citation

  • Nakano, Satoshi & Nishimura, Kazuhiko, 2018. "Structural propagation in a production network with restoring substitution elasticities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 986-999.
  • Handle: RePEc:eee:phsmap:v:512:y:2018:i:c:p:986-999
    DOI: 10.1016/j.physa.2018.08.110

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    References listed on IDEAS

    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. Daron Acemoglu & Vasco M. Carvalho & Asuman Ozdaglar & Alireza Tahbaz‐Salehi, 2012. "The Network Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 80(5), pages 1977-2016, September.
    3. Martha G. Alatriste Contreras & Giorgio Fagiolo, 2014. "Propagation of economic shocks in input-output networks: A cross-country analysis," Post-Print hal-01474258, HAL.
    4. Rutherford, Thomas F, 1999. "Applied General Equilibrium Modeling with MPSGE as a GAMS Subsystem: An Overview of the Modeling Framework and Syntax," Computational Economics, Springer;Society for Computational Economics, vol. 14(1-2), pages 1-46, October.
    5. repec:eee:phsmap:v:488:y:2017:i:c:p:187-204 is not listed on IDEAS
    6. Diewert, W. E., 1976. "Exact and superlative index numbers," Journal of Econometrics, Elsevier, vol. 4(2), pages 115-145, May.
    7. Vasco M. Carvalho, 2014. "From Micro to Macro via Production Networks," Working Papers 793, Barcelona Graduate School of Economics.
    8. McNerney, James & Fath, Brian D. & Silverberg, Gerald, 2013. "Network structure of inter-industry flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6427-6441.
    9. Ramcharran, Harri, 2001. "Productivity, returns to scale and the elasticity of factor substitution in the USA apparel industry," International Journal of Production Economics, Elsevier, vol. 73(3), pages 285-291, October.
    10. Ceberio, Josu & Mendiburu, Alexander & Lozano, Jose A., 2015. "The linear ordering problem revisited," European Journal of Operational Research, Elsevier, vol. 241(3), pages 686-696.
    11. repec:eee:phsmap:v:492:y:2018:i:c:p:1168-1180 is not listed on IDEAS
    12. de Santa Helena, E.L. & Nascimento, C.M. & Gerhardt, G.J.L., 2015. "Alternative way to characterize a q-Gaussian distribution by a robust heavy tail measurement," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 435(C), pages 44-50.
    13. Hu, Fei & Zhao, Shangmei & Bing, Tao & Chang, Yiming, 2017. "Hierarchy in industrial structure: The cases of China and the USA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 871-882.
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