IDEAS home Printed from https://ideas.repec.org/p/crd/wpaper/19001.html
   My bibliography  Save this paper

Technology Adoption in Input-Output Networks

Author

Abstract

This paper investigates the role of network structure in technology adoption. In particular, we study how the network of individual agents can slow down the speed of adoption. We study this issue in the context of the Python programming language by modeling the decisions to adopt Python version 3 by software packages. Python 3 provides advanced features but is not backward compatible with Python 2, which implies adoption costs. Moreover, packages form an input-output network through dependency on other packages in order to avoid writing duplicate code, and they face additional adoption costs from dependencies without Python 3 support. We build a dynamic model of technology adoption that incorporates the input-output network. With a complete dataset of package characteristics for historical releases and user downloads, we draw the input-output network and develop a new estimation method based on the dependency relationship. Estimation results show the average cost of one incompatible dependency is one-third the fixed cost of updating a package’s code. Simulations show the input-output network contributes to 1.5 years of adoption inertia. We conduct counterfactual policies of promotion in subcommunities and find significant heterogeneous effects on the adoption rates due to differences in network structure. Length: 43 pages

Suggested Citation

  • Xintong Han & Lei Xu, 2019. "Technology Adoption in Input-Output Networks," Working Papers 19001, Concordia University, Department of Economics.
  • Handle: RePEc:crd:wpaper:19001
    as

    Download full text from publisher

    File URL: http://leixu.org/Xu_JMP.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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, Oxford University Press, vol. 132(3), pages 1101-1164.
    3. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
    4. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xingtong Han & Lei Xu, 2019. "Technology Adoption in Input-Output Networks," Staff Working Papers 19-51, Bank of Canada.
    2. Xintong Han & Lei Xu, 2018. "Technology Adoption in a Hierarchical Network," Working Papers 18-05, NET Institute.
    3. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Invited Paper ---Learning Models: An Assessment of Progress, Challenges, and New Developments," Marketing Science, INFORMS, vol. 32(6), pages 913-938, November.
    4. A. Ronald Gallant & Han Hong & Ahmed Khwaja, 2018. "The Dynamic Spillovers of Entry: An Application to the Generic Drug Industry," Management Science, INFORMS, vol. 64(3), pages 1189-1211, March.
    5. Hugo Reis, 2020. "Girls' Schooling Choices And Home Production: Evidence From Pakistan," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(2), pages 783-819, May.
    6. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
    7. Eggleston, Jonathan, 2016. "An efficient decomposition of the expectation of the maximum for the multivariate normal and related distributions," Journal of Econometrics, Elsevier, vol. 195(1), pages 120-133.
    8. Denis Fougère & Julien Pouget, 2003. "Les déterminants économiques de l'entrée dans la fonction publique," Économie et Statistique, Programme National Persée, vol. 369(1), pages 15-48.
    9. Mutuc, Maria Erlinda M. & Rejesus, Roderick M. & Pan, Suwen & Yorobe, Jose M., 2012. "Impact Assessment of Bt Corn Adoption in the Philippines," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 44(1), pages 117-135, February.
    10. Khorunzhina, Natalia, 2013. "Structural estimation of stock market participation costs," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2928-2942.
    11. Tobias Salz & Emanuel Vespa, 2020. "Estimating dynamic games of oligopolistic competition: an experimental investigation," RAND Journal of Economics, RAND Corporation, vol. 51(2), pages 447-469, June.
    12. Burlig, Fiona & Preonas, Louis & Woerman, Matt, 2020. "Panel data and experimental design," Journal of Development Economics, Elsevier, vol. 144(C).
    13. Fernandez-Cornejo, Jorge & Wechsler, Seth James, 2012. "Fifteen Years Later: Examining the Adoption of Bt Corn Varieties by U.S. Farmers," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124257, Agricultural and Applied Economics Association.
    14. Bruneel-Zupanc, Christophe Alain, 2021. "Discrete-Continuous Dynamic Choice Models: Identification and Conditional Choice Probability Estimation," TSE Working Papers 21-1185, Toulouse School of Economics (TSE).
    15. García-Pérez, J. Ignacio & Jiménez-Martín, Sergi & Sánchez-Martín, Alfonso R., 2013. "Retirement incentives, individual heterogeneity and labor transitions of employed and unemployed workers," Labour Economics, Elsevier, vol. 20(C), pages 106-120.
    16. Carsten Helm & Mathias Mier, 2020. "Steering the Energy Transition in a World of Intermittent Electricity Supply: Optimal Subsidies and Taxes for Renewables Storage," ifo Working Paper Series 330, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    17. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    18. Christian Bayer & Falko Juessen, 2012. "On the Dynamics of Interstate Migration: Migration Costs and Self-Selection," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 15(3), pages 377-401, July.
    19. Thierry Kamionka & Guy Lacroix, 2018. "Homeownership, Labour Market Transitions and Earnings," Cahiers de recherche 1819, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    20. Lassassi, Moundir & Hammouda, Nacer-Eddine, 2009. "Déterminants de la participation au marché du travail et choix occupationnel: une analyse microéconométrique appliquée au cas de l'Algérie [Microeconometric analysis of determinants of occupational," MPRA Paper 31189, University Library of Munich, Germany.

    More about this item

    Keywords

    dynamic adoption; network dependency; structural estimation;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:crd:wpaper:19001. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/deconca.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Economics Department (email available below). General contact details of provider: https://edirc.repec.org/data/deconca.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.