The Networks and Governance Lab (NGL)
The Networks and Governance Lab (NGL) works to discover how humans and organizations interact to address policy problems at a local and global scale. Blending theories and frameworks from political science, public administration, economics, and public policy with inferential methods from network science, the NGL seeks to model and understand how network structure, composition, and processes shape our collective capacity to solve public problems. Research in the lab spans the study of informal human networks within public organizations and local public service delivery networks to the formation of global city networks to confront transnational challenges.
The lab is not only a research unit, but also has the objective of supporting the learning needs of the students in the department’s doctoral program as well as visiting scholars and students from other universities. Professors working with the NGL support the development of affiliated students and visiting scholars through active involvement in research presentations, coding sessions, dissertation dialogues, and workshops on data collection and analysis. Meetings of the full Lab typically occur every two weeks and often include outside scholars working on related research.
Alejandra Medina is a doctoral candidate in the Department of Public Administration at the University of Illinois at Chicago.
Abstract: This article provides a systematic review of the network formation literature in the public sector. In particular, we code and categorize the theoretical mechanisms used in empirical network research to motivate collaboration and tie formation. Based on a review of the 107 articles on network formation found in 40 journals of public administration and policy from 1998 to 2019, we identify 15 distinct theoretical categories. For each category, we describe the theory, highlight its use in the literature, and identify limitations and concerns with current applications. Overall, we find that most studies rely on a similar set of general theories of network formation. More importantly, we find that most theoretical mechanisms are not well specified, and empirical tests are often unable to directly assess the specific underlying mechanism. The results of our review highlight the need for our field to embrace experimental designs, develop panel network datasets, and engage in more network-level research.