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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. 

Abstract Research on political control over government bureaucracy has primarily focused on direct exercises of power such as appointments, funding, agency design, and procedural rules. In this analysis, we extend this literature to consider politicians who leverage their institutional standing to influence the decisions of local field officials over whom they have no explicit authority. Using the case of the US Forest Service (USFS), we investigate whether field-level decisions are associated with the political preferences of individual congressional representatives. Our sample encompasses 7,681 resource extraction actions initiated and analyzed by 107 USFS field offices between 2005 and 2018. Using hierarchical Bayesian regression, we show that under periods of economic growth and stability, field offices situated in the districts of congressional representatives who oppose environmental regulation initiate more extractive actions (timber harvest, oil and gas drilling, grazing) and conduct less rigorous environmental reviews than field offices in the districts of representatives who favor environmental regulation. By extending existing theories about interactions between politicians and bureaucrats to consider informal means of influence, this work speaks to (1) the role of local political interests in shaping agency-wide policy outcomes and (2) the importance of considering informal and implicit means of influence that operate in concert with explicit control mechanisms to shape bureaucratic behavior.