Working Papers
Teams and Text: Collaborative innovation in the knowledge space
[latest version]
Job Market Paper 2024
This paper introduces a novel framework that integrates inventor teams and their patent texts. The model locates inventors, teams, and research fields within an innovation ecosystem, referred to as the knowledge space. I use this framework to measure the impact of a research field's development stage on team outcomes. I study how the quantity and type of prior work available shape a team’s ability to create new research fields and to direct existing ones toward specific targets. For targeted innovations, I examine automating production, mitigating climate change, and reducing cancer risks. The paper is the first to incorporate a Bayesian model of Natural Language Processing directly into a model of collaboration. The high-dimensional patent text data allows the derivation of an empirical measure for a team’s local knowledge field. I combine this with data on premature inventor deaths as a quasi-natural experiment. I find that teams produce more breakthrough innovations when they reduce their size and move into under-explored research areas. However, when targeting a specific objective, teams benefit from adding a new member and incorporating existing knowledge from established fields.
Keywords: Teams, Innovation, Patents, Latent Dirichlet Allocation, Topic Modelling
From Shares to Machines: How Common Ownership Drives Automation
With Dennis C. Hutschenreiter, Felix Noth, Stefano Manfredonia and Tommaso Santini
[IWH-Discussion Paper Series]
Submitted
Does increasing common ownership influence firms’ automation strategies? We develop
and empirically test a theory indicating that institutional investors’ common ownership
drives firms employing workers in the same local labor markets to boost automation-
related innovation. First, we present a model integrating task-based production and
common ownership, demonstrating that greater ownership overlap drives firms to inter-
nalize the impact of their automation decisions on the wage bills of their local market
competitors, thereby fostering more automation and reducing employment. Second, we
empirically validate the model’s predictions. By analyzing patent texts, the geographic
distribution of firms’ labor forces at the establishment level, and exogenous increases
in common ownership due to institutional investor mergers, we isolate the effects of
rising common ownership within and across labor markets. Our findings reveal that
firms experiencing a positive shock to common ownership with labor market rivals
exhibit increased automation, coupled with a decrease in employment. Conversely,
similar ownership shocks do not lead to heightened automation innovation if firms do
not share local labor markets.