Analyzing the Effectiveness of Networks for Addressing Public Problems: Evidence from a Longitudinal Study

Abstract

AbstractWhile scholars and practitioners frequently laud the potential of networks to address complex policy problems, empirical evidence of the effectiveness of networks is scarce. This study examines how changes in network structure (centralization and transitivity), network composition (sector diversity and geographic range), and tie properties (stability and strength) influence community‐level outcomes. Relying on a statutory requirement in the state of Iowa requiring local governments to file all instances of intergovernmental and intersectoral collaboration, we measure collaboration networks in 81 counties over 17 years in the areas of crime and economic development. Using fixed effects models, we examine how changes in the structure and composition of these county‐level networks affect substantive policy outcomes. Our findings indicate that network properties matter, but that the specific properties may be context dependent. We find network centralization and stability are stronger predictors of crime while network composition is more strongly associated with economic development.Evidence for Practice The performance of intergovernmental collaborations is rarely studied, either in terms of the outcomes of individual agreements or from the networks resulting from multiple agreements. The properties (structure, composition, stability) of the networks that emerge from the actions of local government officials to address critical public problems are generally not visible to the public, state policy makers, or even the local officials themselves. Our research suggests that properties of these networks of agreements affect their impact on improving public problems, suggesting opportunities for policy makers to encourage more effective structures. Our finding suggests that county networks effectively designed to improve economic development would differ in meaningful ways from one effectively designed to reduce crime. Our findings indicate that network centralization and stability are stronger predictors of crime rates while network composition is more strongly associated with indicators of economic development. Stable networks, as measured by average amount of time the agreements are in place, may reduce violent crime, property crime, and unemployment.