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Technology Adoption in Input-Output Networks

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  • Xingtong Han
  • Lei Xu

Abstract

We study how input-output networks affect the speed of technology adoption. In particular, we model the decision to adopt the programming language Python 3 by software packages. Python 3 provides advanced features but is not backward compatible with Python 2, which implies it comes with adoption costs. Moreover, packages are dependent on other packages, meaning one package’s adoption decision is affected by the adoption decisions of other packages because many packages are linked to each other. We build a dynamic model of technology adoption that incorporates an input-output network and estimate it using a complete dataset of Python packages. We are among the first to link the literature of dynamic discrete choice models to network analysis. We also contribute to the literature on technology adoption by showing the adverse effects that input-output networks can have on how technology is adopted in a dynamic setting. We show that a package’s adoption decision is significantly affected by the adoption decisions of its dependency packages. We conduct counterfactual analyses of cost subsidies that target a community level and show that network structure is crucial to determining an optimal policy of cost subsidy.

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  • Xingtong Han & Lei Xu, 2019. "Technology Adoption in Input-Output Networks," Staff Working Papers 19-51, Bank of Canada.
    • Handle: RePEc:bca:bocawp:19-51
    DOI: 10.34989/swp-2019-51
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    1. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
    2. Joseph Farrell & Garth Saloner, 1985. "Standardization, Compatibility, and Innovation," RAND Journal of Economics, The RAND Corporation, vol. 16(1), pages 70-83, Spring.
    3. Igal Hendel & Aviv Nevo, 2006. "Sales and consumer inventory," RAND Journal of Economics, RAND Corporation, vol. 37(3), pages 543-561, September.
    4. Olivier De Groote & Frank Verboven, 2019. "Subsidies and Time Discounting in New Technology Adoption: Evidence from Solar Photovoltaic Systems," American Economic Review, American Economic Association, vol. 109(6), pages 2137-2172, June.
    5. David Atkin & Azam Chaudhry & Shamyla Chaudry & Amit K. Khandelwal & Eric Verhoogen, 2017. "Organizational Barriers to Technology Adoption: Evidence from Soccer-Ball Producers in Pakistan," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(3), pages 1101-1164.
    6. Gautam Gowrisankaran & Joanna Stavins, 2004. "Network Externalities and Technology Adoption: Lessons from Electronic Payments," RAND Journal of Economics, The RAND Corporation, vol. 35(2), pages 260-276, Summer.
    7. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
    8. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
    9. John Rust & Christopher Phelan, 1997. "How Social Security and Medicare Affect Retirement Behavior in a World of Incomplete Markets," Econometrica, Econometric Society, vol. 65(4), pages 781-832, July.
    10. Christian Belzil & Jörgen Hansen, 2002. "Unobserved Ability and the Return to Schooling," Econometrica, Econometric Society, vol. 70(5), pages 2075-2091, September.
    11. Garth Saloner & Andrea Shepard, 1995. "Adoption of Technologies with Network Effects: An Empirical Examination of the Adoption of Teller Machines," RAND Journal of Economics, The RAND Corporation, vol. 26(3), pages 479-501, Autumn.
    12. Thierry Magnac & David Thesmar, 2002. "Identifying Dynamic Discrete Decision Processes," Econometrica, Econometric Society, vol. 70(2), pages 801-816, March.
    13. Hall, Bronwyn H. & Khan, Beethika, 2003. "Adoption of New Technology," Department of Economics, Working Paper Series qt3wg4p528, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    14. Igal Hendel & Aviv Nevo, 2006. "Sales and Consumer Inventory," RAND Journal of Economics, The RAND Corporation, vol. 37(3), pages 543-561, Autumn.
    15. Igal Hendel & Aviv Nevo, 2006. "Sales and consumer inventory," RAND Journal of Economics, RAND Corporation, vol. 37(3), pages 543-561, September.
    16. Katz, Michael L & Shapiro, Carl, 1985. "Network Externalities, Competition, and Compatibility," American Economic Review, American Economic Association, vol. 75(3), pages 424-440, June.
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    1. Xintong Han & Pu Zhao, 2019. "Pay for Content or Pay for Marketing? An Empirical Study on Content Pricing," Working Papers 19-03, NET Institute.

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    Keywords

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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • L23 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Organization of Production
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • O14 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Industrialization; Manufacturing and Service Industries; Choice of Technology
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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