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

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.

💡 Research Summary

The paper develops a simultaneous‑equation network model to capture how an individual’s scientific (paper) and technological (patent) productivity depend on both personal attributes and the positions of collaborators in two overlapping networks. Each productivity equation includes (i) an idiosyncratic component, (ii) a within‑activity peer effect proportional to the sum of collaborators’ outputs in the same network, and (iii) a cross‑activity peer effect that links scientific output to technological output and vice‑versa. The strength of these effects is summarized by a modified Katz‑Bonacich centrality, (I‑βA)⁻¹·1, where β captures the decay weight for indirect connections and differs for within‑activity (β_S, β_T) and cross‑activity (β_cross) links.

Identification is challenging because the adjacency matrices are endogenous: more productive scholars are more likely to form links, and the error terms may be correlated with network formation. The authors address this by (a) estimating link formation probabilities for every dyad using a logistic regression on exogenous dyadic characteristics (same institution, geographic proximity, demographic similarity, etc.), (b) constructing predicted adjacency matrices from these probabilities, and (c) using the predicted matrices as instrumental variables in a two‑stage least‑squares (2SLS) estimation of the simultaneous equations. Community (field‑year) fixed effects are added to control for unobserved common shocks.

The empirical setting focuses on cancer research, a domain where scientific discovery and technological application are tightly intertwined. The authors compile the full set of cancer‑related publications (from PubMed) and patents (USPTO/EPO) from 1990 to 2020, match authors to inventors, and build yearly co‑authorship and co‑inventorship networks. Panel data on individual publication counts and patent counts serve as the dependent variables.

Key findings: (1) Both scientific and technological productivities increase with the individual’s Katz‑Bonacich centrality in the respective network; a one‑percent increase in centrality raises publication output by about 0.12 % and patent output by about 0.09 %. (2) The cross‑activity coefficient from science to technology (θ) is positive and statistically significant (≈0.07), indicating that higher scientific output boosts later technological output. By contrast, the reverse coefficient (φ) from technology to science is small (≈0.02) and not statistically different from zero, suggesting an asymmetric relationship where science drives technology but not vice‑versa. (3) The estimated cross‑activity decay parameter β_cross is positive (0.05‑0.08), confirming that indirect, multi‑step connections across the two networks still matter for productivity.

Robustness checks include adding network density and clustering controls, employing dynamic panel GMM with five‑year moving averages, and sub‑sample analyses by cancer sub‑field (e.g., oncology, immunology). Results remain stable across specifications. Limitations are acknowledged: the exogeneity of dyadic covariates is assumed but not proven, the analysis treats publications and patents as pure counts without quality weighting, and the focus on cancer may limit external validity.

Policy implications are clear: enhancing the centrality of researchers—through collaborative grants, joint‑venture incentives, or platform‑based university‑industry programs—can raise both scientific and technological outputs simultaneously. Strengthening cross‑network bridges (e.g., facilitating scientists’ participation in patenting activities) is especially effective because scientific knowledge appears to be the primary engine of downstream innovation.

In sum, the study provides the first micro‑level empirical evidence of simultaneous peer effects in science and technology, validates a theoretical model linking network centrality to productivity, and highlights the asymmetric but potent role of scientific activity in fueling technological advancement. Future work could extend the framework to other domains and explore richer measures of knowledge quality and diffusion.