Working Papers
Balzer, M. and Benlahlou, A., Strategic Interactions in Science and Technology Networks: Substitutes or Complements? Job Market Paper
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 sciencedriven 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.
Balzer, M. and Benlahlou, A., Gradient Boosting for Spatial Panel Models with Random and Fixed Effects (Submitted)
Abstract: Due to the increase in data availability in urban and regional studies, various spatial panel models have emerged to model spatial panel data, which exhibit spatial patterns and spatial dependencies between observations across time. Although estimation is usually based on maximum likelihood or generalized method of moments, these methods may fail to yield unique solutions if researchers are faced with high-dimensional settings. This article proposes a model-based gradient boosting algorithm, which enables estimation with interpretable results that is feasible in low- and high-dimensional settings. Due to its modular nature, the flexible model-based gradient boosting algorithm is suitable for a variety of spatial panel models, which can include random and fixed effects. The general framework also enables data-driven model and variable selection as well as implicit regularization where the bias-variance trade-off is controlled for, thereby enhancing accuracy of prediction on out-of-sample spatial panel data. Monte Carlo experiments concerned with the performance of estimation and variable selection confirm proper functionality in low- and high-dimensional settings while real-world applications including non-life insurance in Italian districts, rice production in Indonesian farms and life expectancy in German districts illustrate the potential application.
Benlahlou, A, Peer effects in Cancer research (Submitted)
Abstract: This paper develops a theory of scientific peer effects to study scientific productivity when authors care about the behavior of their coauthors. The theory predicts that an author’s output increases in their Katz- Bonacich centrality, a standard measure of centrality in networks. I test the model’s predictions using a detailed dataset of cancer researchers, showing that an author’s position in the coauthorship network (measured by Katz-Bonacich centrality) is a key determinant of their productivity. After controlling for observable characteristics and unobservable network-specific factors, my results demonstrate the importance of peer effects not only from superstar scientists but from a broader range of collaborators. These findings highlight the relevance of network externalities in scientific productivity and provide empirical evidence for the critical role of network structure in fostering knowledge creation. By controlling for prestige-driven effects, my results suggest that peer effects are driven by scientific complementarities rather than status. Additionally, the Nash-Katz-Bonacich linkage offers strong policy implications, particularly for designing optimal network structures to maximize collective knowledge production
Benlahlou, A, On the origin of scientific peer effects, strategic complementarity or conformity? (Submitted)
Abstract: Collaboration plays a critical role in scientific productivity, yet the mechanisms through which peer effects drive innovation remain underexplored. This paper investigates the microfoundations of scientific peer effects, focusing on how collaboration influences individual productivity. While existing literature emphasizes the importance of peer effects, it has not fully addressed the underlying processes. This work contributes by offering a clearer understanding of these mechanisms and providing insights for innovation policy, particularly in contexts where collaboration drives knowledge creation. The results show that peer effects arise from strategic complementarity, where collaborating with more productive coauthors enhances individual performance. These findings underscore the significant role of network externalities in shaping scientific outcomes and the structure of scientific collaboration.
Benlahlou, A , Peer Effects Among Inventors: Unpacking Their Origins in Cancer-Related Innovation (Submitted)
This paper develops a theory of peer effects to study inventors productivity when they care about the behavior of their collaborator. The theory predicts that an inventor’s output increases in their Katz-Bonacich centrality, a standard measure of centrality in networks. I test the model’s predictions using a dataset of cancer related innovation, showing that an inventor’s position in the coinventor network (measured by Katz- Bonacich centrality) is a key determinant of their productivity. After controlling for observable characteristics and unobservable network-specific factors, my results give an explanation of the mecanisms driving the peer effects among inventors, namely strategic complementarity and conformity. These findings highlight the relevance of network in shaping inventive productivity and provide empirical evidence for the critical role of network structure in fostering innovation at the inventor level. Additionally, the Nash-Katz-Bonacich linkage offers strong policy implications, particularly for designing optimal network structures to maximize collective knowledge production.
Publications
Balzer, M, and Benlahlou, A, 2025 Mitigating Consequences of Prestige in Citations of Publications , Scientometrics
Benlahlou, A., 2019., Team production game on bipartite networks. Economics Letters, 180, pp.94-98.
Selected Work In Progress
Balzer, M. and Benlahlou, A., Variable Selection in Spatial Autoregressive Networks with Endogenous Links: A Bayesian Approach.
Balzer, M. and Benlahlou, A., Funding Cancer Research Networks: A Question of Prestige or Scientific Impact?
Benlahlou, A. and Foerster, M., Risk Taking in Academia: Inferring Researchers’ Attitudes from Publication Data.
Benlahlou, A. and Ferrières, S., Stability in team production game on bipartite networks.
Benlahlou, A. and Ferrières, S., Efficient network in team production game on bipartite networks.