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State and federal governments use governance platforms to achieve central policy goals through distributed action at the local level. For example, California’s 2014 Sustainable Groundwater Management Act (SGMA) mandates local policy actors to work together to create new groundwater management institutions and plans. We argue that governance platforms entail a principal-agent problem where local decisions may deviate from central goals. We apply this argument to SGMA implementation, where local plans may respond more to local political economic conditions rather than address the groundwater problems prioritized by the state. Using a Structured Topic Model (STM) to analyze the content of 117 basin management plans, we regress each plan’s focus on core management reform priorities on local socio-economic and social-ecological indicators expected to shape how different communities respond to state requirements. Our results suggest that the focus of local plans diverges from problem conditions on issues like environmental justice and drinking water quality. This highlights how principal-agent logics of divergent preferences and information asymmetry can affect the design and implementation of governance platforms. 

Abstract This paper demonstrates an automated workflow for extracting network data from policy documents. We use natural language processing tools, part‐of‐speech tagging, and syntactic dependency parsing, to represent relationships between real‐world entities based on how they are described in text. Using a corpus of regional groundwater management plans, we demonstrate unique graph motifs created through parsing syntactic relationships and how document‐level syntax can be aggregated to develop large‐scale graphs. This approach complements and extends existing methods in public management and governance research by (1) expanding the feasible geographic and temporal scope of data collection and (2) allowing for customized representations of governance systems to fit different research applications, particularly by creating graphs with many different node and edge types. We conclude by reflecting on the challenges, limitations, and future directions of automated, text‐based methods for governance research.