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Strategic Interactions in Science and Technology Networks: Substitutes or Complements?

Michael Balzer, Adhen Benlahlou

TL;DR

This study develops a two-network model of peer effects in science and technology, estimating it with a rich cancer-focused dataset of coauthorship and coinventorship. Using an interrelated simultaneous equations network approach and endogeneity-robust IVs, it finds that productivity rises with network centrality in both domains and that scientific output contemporaneously boosts technological productivity, but the reverse effect is negligible. The results highlight asymmetric linkages from science to technology, consistent with a science-driven model of innovation, and offer guidance on policies that shape collaboration networks to enhance knowledge creation and downstream impact. Together, the theory and empirical strategy illuminate the micro-foundations of the science-technology co-evolution and quantify the returns to strategic collaboration.

Abstract

This paper develops a theory of scientific and technological peer effects to study how individuals' productivity responds to the behavior and network positions of their collaborators across both scientific and inventive activities. Building on a simultaneous equation network framework, the model predicts that productivity in each activity increases in a variation of the Katz-Bonacich centrality that captures within-activity and cross-activity strategic complementarities. To test these predictions, we assemble the universe of cancer-related publications and patents and construct coauthorship and coinventorship networks that jointly map the collaboration structure of researchers active in both spheres. Using an instrumental-variables approach based on predicted link formation from exogenous dyadic characteristics, and incorporating community fixed effects to address endogenous network formation, we show that both authors' and inventors' outputs rise with their network centrality, consistent with the theory. Moreover, scientific productivity significantly enhances technological productivity, while technological output does not exert a detectable reciprocal effect on scientific production, highlighting an asymmetric linkage aligned with a science-driven model of innovation. These findings provide the first empirical evidence on the joint dynamics of scientific and inventive peer effects, underscore the micro-foundations of the co-evolution of science and technology, and reveal how collaboration structures can be leveraged to design policies that enhance collective knowledge creation and downstream innovation.

Strategic Interactions in Science and Technology Networks: Substitutes or Complements?

TL;DR

This study develops a two-network model of peer effects in science and technology, estimating it with a rich cancer-focused dataset of coauthorship and coinventorship. Using an interrelated simultaneous equations network approach and endogeneity-robust IVs, it finds that productivity rises with network centrality in both domains and that scientific output contemporaneously boosts technological productivity, but the reverse effect is negligible. The results highlight asymmetric linkages from science to technology, consistent with a science-driven model of innovation, and offer guidance on policies that shape collaboration networks to enhance knowledge creation and downstream impact. Together, the theory and empirical strategy illuminate the micro-foundations of the science-technology co-evolution and quantify the returns to strategic collaboration.

Abstract

This paper develops a theory of scientific and technological peer effects to study how individuals' productivity responds to the behavior and network positions of their collaborators across both scientific and inventive activities. Building on a simultaneous equation network framework, the model predicts that productivity in each activity increases in a variation of the Katz-Bonacich centrality that captures within-activity and cross-activity strategic complementarities. To test these predictions, we assemble the universe of cancer-related publications and patents and construct coauthorship and coinventorship networks that jointly map the collaboration structure of researchers active in both spheres. Using an instrumental-variables approach based on predicted link formation from exogenous dyadic characteristics, and incorporating community fixed effects to address endogenous network formation, we show that both authors' and inventors' outputs rise with their network centrality, consistent with the theory. Moreover, scientific productivity significantly enhances technological productivity, while technological output does not exert a detectable reciprocal effect on scientific production, highlighting an asymmetric linkage aligned with a science-driven model of innovation. These findings provide the first empirical evidence on the joint dynamics of scientific and inventive peer effects, underscore the micro-foundations of the co-evolution of science and technology, and reveal how collaboration structures can be leveraged to design policies that enhance collective knowledge creation and downstream innovation.
Paper Structure (34 sections, 1 theorem, 35 equations, 2 figures, 10 tables)

This paper contains 34 sections, 1 theorem, 35 equations, 2 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Analytical framework
  3. Theory
  4. Game-theoretical model
  5. Network and notation
  6. Utility
  7. The peer effect game
  8. Empirical strategy
  9. The interrelated simultaneous equations network model
  10. Identification threats
  11. Estimation
  12. Data and network definition
  13. Data sources
  14. Variables definition
  15. Author productivity
  16. ...and 19 more sections

Key Result

Proposition 1

Suppose that Assumption ass:net holds. Then, for any $\bm{\alpha}^{S}$ and $\bm{\alpha}^{T}$, a unique Nash equilibrium exists given by

Figures (2)

  • Figure 1: Illustrative example of social networks of inventors (red) and researchers (blue) of six agents (nodes) with interrelation. Solid lines represent edges which indicate social connection. Interrelation of both social networks is represented by the dotted lines.
  • Figure 2: Example illustration of the MeSH tree structure for Neoplasms child nodes.

Theorems & Definitions (2)

  • Proposition 1: chen2018
  • Remark 1