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

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

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.

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Paper provided by Institute of Industrial Relations, UC Berkeley in its series Institute for Research on Labor and Employment, Working Paper Series with number qt80n3512q.

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Date of creation: 01 Jul 2009
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Handle: RePEc:cdl:indrel:qt80n3512q
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  1. Kim, E. Han & Morse, Adair & Zingales, Luigi, 2009. "Are elite universities losing their competitive edge?," Journal of Financial Economics, Elsevier, vol. 93(3), pages 353-381, September.
  2. J. M. C. Santos Silva & Silvana Tenreyro, 2006. "The Log of Gravity," The Review of Economics and Statistics, MIT Press, vol. 88(4), pages 641-658, November.
  3. Gourieroux Christian & Monfort Alain & Trognon A, 1982. "Pseudo maximum lilelihood methods : applications to poisson models," CEPREMAP Working Papers (Couverture Orange) 8203, CEPREMAP.
  4. Maryellen R. Kelley, 1994. "Productivity and Information Technology: The Elusive Connection," Management Science, INFORMS, vol. 40(11), pages 1406-1425, November.
  5. Rosenblat, Tanya & Mobius, Markus, 2010. "Getting Closer or Drifting Apart?," Staff General Research Papers 32113, Iowa State University, Department of Economics.
  6. Winkler Anne E. & Glanzel Wolfang & Levin Sharon & Stephan Paula, 2011. "The Diffusion of Information Technology and the Increased Propensity of Teams to Transcend Institutional and National Borders," Department of Economics and Statistics Cognetti de Martiis LEI & BRICK - Laboratory of Economics of Innovation "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio 201110, University of Turin.
  7. Adair Morse, 2006. "Are elite universities losing their competitive edge?," Proceedings, Federal Reserve Bank of Cleveland.
  8. Sanjeev Dewan & Kenneth L. Kraemer, 2000. "Information Technology and Productivity: Evidence from Country-Level Data," Management Science, INFORMS, vol. 46(4), pages 548-562, April.
  9. Ajay Agrawal & Avi Goldfarb, 2008. "Restructuring Research: Communication Costs and the Democratization of University Innovation," American Economic Review, American Economic Association, vol. 98(4), pages 1578-90, September.
  10. Anne Winkler & Sharon Levin & Paula Stephan, 2010. "The diffusion of IT in higher education: publishing productivity of academic life scientists," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 19(5), pages 481-503.
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