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The Impact of Information Technology on Scientists’ Productivity, Quality and Collaboration Patterns

Author

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  • Ding, Waverly W.
  • Levin, Sharon G.
  • Stephan, Paula E.
  • Winkler, Ann E.

Abstract

This study advances the prior literature concerning the impact of information technology on productivity in academe in two important ways. First, it utilizes a dataset that combines information on the diffusion of two noteworthy and early innovations in IT -- BITNET and the Domain Name System (DNS) -- with career history data on research-active life scientists. This research design allows for proper identification of the availability of access to IT as well as a means to directly identify causal effects. Second, the fine-grained nature of the data set allows for an investigation of three publishing outcomes: counts, quality, and co-authorship. Our analysis of a random sample of 3,771 research-active life scientists from 430 U.S. institutions over a 25-year period supports the hypothesis of a differential return to IT across subgroups of the scientific labor force. Women scientists, early-to-mid-career scientists, and those employed by mid-to-lower-tier institutions benefit from access to IT in terms of overall research output and an increase in the number of new co-authors they work with. Early-career scientists and those in top-tier institutions gain in terms of research quality when IT becomes available at their campuses.

Suggested Citation

  • Ding, Waverly W. & Levin, Sharon G. & Stephan, Paula E. & Winkler, Ann E., 2009. "The Impact of Information Technology on Scientists’ Productivity, Quality and Collaboration Patterns," Institute for Research on Labor and Employment, Working Paper Series qt80n3512q, Institute of Industrial Relations, UC Berkeley.
  • Handle: RePEc:cdl:indrel:qt80n3512q
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    References listed on IDEAS

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    1. Adams, James D. & Black, Grant C. & Clemmons, J. Roger & Stephan, Paula E., 2005. "Scientific teams and institutional collaborations: Evidence from U.S. universities, 1981-1999," Research Policy, Elsevier, vol. 34(3), pages 259-285, April.
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    15. Daniel S. Hamermesh & Sharon M. Oster, 2002. "Tools or Toys? The Impact of High Technology on Scholarly Productivity," Economic Inquiry, Western Economic Association International, vol. 40(4), pages 539-555, October.
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    Cited by:

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    3. Malgorzata Wachowska, 2014. "Excessive Accumulation Of Knowledge As A Challenge To Science Policy," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 9(3), pages 29-40, September.
    4. Fernanda Leite Lopez de Leon & Ben McQuillin, 2020. "The Role of Conferences on the Pathway to Academic Impact Evidence from a Natural Experiment," Journal of Human Resources, University of Wisconsin Press, vol. 55(1), pages 164-193.
    5. Barham, Bradford L. & Foltz, Jeremy D. & Prager, Daniel L., 2014. "Making time for science," Research Policy, Elsevier, vol. 43(1), pages 21-31.
    6. Pierre Azoulay & Jeffrey L. Furman & Joshua L. Krieger & Fiona E. Murray, 2012. "Retractions," NBER Working Papers 18499, National Bureau of Economic Research, Inc.
    7. Jason Chan & Anindya Ghose & Robert Seamans, 2013. "The Internet and Hate Crime: Offline Spillovers from Online Access," Working Papers 13-02, NET Institute.
    8. Venturini, Francesco, 2015. "The modern drivers of productivity," Research Policy, Elsevier, vol. 44(2), pages 357-369.

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    More about this item

    Keywords

    JEL O33; J44; J16;
    All these keywords.

    JEL classification:

    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J44 - Labor and Demographic Economics - - Particular Labor Markets - - - Professional Labor Markets and Occupations
    • 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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