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Input Diffusion and the Evolution of Production Networks

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  • Vasco M. Carvalho
  • Nico Voigtländer

Abstract

The adoption and diffusion of inputs in the production network is at the heart of technological progress. What determines which inputs are initially considered and eventually adopted by innovators? We examine the evolution of input linkages from a network perspective, starting from a stylized model of network formation. Producers direct their search for new inputs along vertical linkages, screening the network neighborhood of existing suppliers to identify potentially useful inputs. A subset of these is then adopted, following a tradeoff between the benefits from input variety and the costs of customizing new inputs. Guided by this framework, we document a novel stylized fact at both the sector and the firm level: producers are more likely to adopt inputs that are already used – directly or indirectly – by their current suppliers. In particular, using disaggregated input-output data, we show that initial network proximity of a sector in 1967 significantly increases the likelihood of adoption throughout the subsequent four decades. A one-standard deviation decrease in network distance is associated with an increase in the adoption probability by one third to one half. Similarly, U.S. firms are significantly more likely to develop new input linkages among their suppliers’ network neighborhood. Our results imply that the existing production network plays a crucial role in the diffusion of inputs and the evolution of technology.

Suggested Citation

  • Vasco M. Carvalho & Nico Voigtländer, 2015. "Input Diffusion and the Evolution of Production Networks," Cambridge Working Papers in Economics 1550, Faculty of Economics, University of Cambridge.
  • Handle: RePEc:cam:camdae:1550
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    Cited by:

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    2. Mandel, Antoine & Veetil, Vipin P., 2021. "Monetary dynamics in a network economy," Journal of Economic Dynamics and Control, Elsevier, vol. 125(C).
    3. Gualdi, Stanislao & Mandel, Antoine, 2016. "On the emergence of scale-free production networks," Journal of Economic Dynamics and Control, Elsevier, vol. 73(C), pages 61-77.
    4. Emmanuel Dhyne & Ayumu Ken Kikkawa & Magne Mogstad & Felix Tintelnot, 2021. "Trade and Domestic Production Networks," Review of Economic Studies, Oxford University Press, vol. 88(2), pages 643-668.
    5. Nikolay Chernyshev, 2018. "From Productivity Shifts to Economic Growth: Intersectoral Linkage as an Amplifying Factor," CDMA Working Paper Series 201801, Centre for Dynamic Macroeconomic Analysis.
    6. Stanislao Gualdi & Antoine Mandel, 2019. "Endogenous growth in production networks," Journal of Evolutionary Economics, Springer, vol. 29(1), pages 91-117, March.
    7. Muscillo, Alessio & Pin, Paolo & Razzolini, Tiziano & Serti, Francesco, 2018. "Does "Network Closure" Beef up Import Premium?," IZA Discussion Papers 12036, Institute of Labor Economics (IZA).
    8. Mandel, Antoine & Taghawi-Nejad, Davoud & Veetil, Vipin P., 2019. "The price effects of monetary shocks in a network economy," Journal of Economic Behavior & Organization, Elsevier, vol. 164(C), pages 300-316.
    9. Längle, Katharina & Xu, Ankai & Tian, Ruijie, 2021. "Assessing the supply chain effect of natural disasters: Evidence from Chinese manufacturers," WTO Staff Working Papers ERSD-2021-13, World Trade Organization (WTO), Economic Research and Statistics Division.
    10. Yoshiyuki ARATA & Philipp MUNDT, 2019. "Topology and Formation of Production Input Interlinkages: Evidence from Japanese microdata," Discussion papers 19027, Research Institute of Economy, Trade and Industry (RIETI).
    11. Matthew Delventhal, 2019. "The Globe as a Network: Geography and the Origins of the World Income Distribution," 2019 Meeting Papers 840, Society for Economic Dynamics.
    12. Kerstin Hötte, 2021. "Skill transferability and the stability of transition pathways- A learning-based explanation for patterns of diffusion," Journal of Evolutionary Economics, Springer, vol. 31(3), pages 959-993, July.
    13. Kerstin Hotte, 2021. "Demand-pull and technology-push: What drives the direction of technological change? -- An empirical network-based approach," Papers 2104.04813, arXiv.org, revised Dec 2021.
    14. Hötte, Kerstin, 2020. "How to accelerate green technology diffusion? Directed technological change in the presence of coevolving absorptive capacity," Energy Economics, Elsevier, vol. 85(C).
    15. Harald Fadinger & Christian Ghiglino & Mariya Teteryatnikova, 2015. "Income Differences and Input-Output Structure," Vienna Economics Papers vie1510, University of Vienna, Department of Economics.
    16. Charles D. Brummitt & Kenan Huremović & Paolo Pin & Matthew H. Bonds & Fernando Vega-Redondo, 2017. "Contagious disruptions and complexity traps in economic development," Nature Human Behaviour, Nature, vol. 1(9), pages 665-672, September.
    17. Cantner, Uwe & Vannuccini, Simone, 2021. "Pervasive technologies and industrial linkages: Modeling acquired purposes," Structural Change and Economic Dynamics, Elsevier, vol. 56(C), pages 386-399.
    18. Harald Fadinger & Christian Ghiglino & Mariya Teteryatnikova, 2015. "Income Differences and Input-Output Structure," Vienna Economics Papers vie1510, University of Vienna, Department of Economics.

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    More about this item

    Keywords

    Input adoption; directed network search; dynamics of production networks;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • D57 - Microeconomics - - General Equilibrium and Disequilibrium - - - Input-Output Tables and Analysis
    • L23 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Organization of Production

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