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Control over agency budgets is a critical tool of political influence in regulatory decision making, yet the causal mechanism of budgetary control is unclear. Do budgetary manipulations influence agencies by imposing resource constraints or by transmitting powerful signals to the agency? I advance and test a stochastic process model of adaptive signal processing by a hierarchical agency to address this question. The principal findings of the paper are two. First, presidents and congressional committees achieve budgetary control over agencies not by manipulating aggregate resource constraints but by transmitting powerful signals through budget shifts. Second, bureaucratic hierarchy increases the agency's response time in processing budgetary signals, limiting the efficacy of the budget as a device of political control. I also show that the magnitude of agency response to budgetary signals increased for executive-branch agencies after 1970 due to executive oversight reforms. I conclude by discussing the limits of budgetary manipulations as a device of political control and the response of elected authorities to adaptive signal processing by agencies.

Control over agency budgets is a critical tool of political influence in regulatory decision making, yet the causal mechanism of budgetary control is unclear. Do budgetary manipulations influence agencies by imposing resource constraints or by transmitting powerful signals to the agency? I advance and test a stochastic process model of adaptive signal processing by a hierarchical agency to address this question. The principal findings of the paper are two. First, presidents and congressional committees achieve budgetary control over agencies not by manipulating aggregate resource constraints but by transmitting powerful signals through budget shifts. Second, bureaucratic hierarchy increases the agency's response time in processing budgetary signals, limiting the efficacy of the budget as a device of political control. I also show that the magnitude of agency response to budgetary signals increased for executive-branch agencies after 1970 due to executive oversight reforms. I conclude by discussing the limits of budgetary manipulations as a device of political control and the response of elected authorities to adaptive signal processing by agencies. (American Political Science Review / FUB)

This article examines the application of organizational reputation to public administration. Organizational reputation is defined as a set of beliefs about an organization's capacities, intentions, history, and mission that are embedded in a network of multiple audiences. The authors assert that the way in which organizational reputations are formed and subsequently cultivated is fundamental to understanding the role of public administration in a democracy. A review of the basic assumptions and empirical work on organizational reputation in the public sector identifies a series of stylized facts that extends our understanding of the functioning of public agencies. In particular, the authors examine the relationship between organizational reputation and bureaucratic autonomy.

How do political actors learn about their environment when the "data" provided by political processes are characterized by rare events and highly discontinuous variation? In such learning environments, what can theory predict about how learning actors will take costly actions that are difficult to reverse (e.g., eliminating programs, approving a risky new product, revising a security policy, firing or recalling an appointed or elected official)? We develop a formal model for this problem and apply it to the termination of bureaucratic agencies. The conventional wisdom that "the older a bureau is, the less likely it is to die" (Downs 1967,Inside Bureaucracy) persists but has never been properly tested. This paper offers a learning-based stochastic optimization model of agency termination that offers two counterintuitive predictions. First, politicians terminate agencies only after learning about them, so the hazard of agencies should be nonmonotonic, contradicting Downs's prediction. Second, if terminating agencies is costly, agencies are least likely to be terminated when politicians are fiscally constrained or when the deficit is high. We assess the model by developing a battery of tests for the shape of the hazard function and estimate these and other duration models using data on U.S. federal government agencies created between 1946 and 1997. Results show that the hazard rate of agency termination is strongly nonmonotonic and that agencies are less likely to be terminated under high deficits and divided government. For the first 50 years of the agency duration distribution, the modal termination hazard occurs at five years after agencies are enabled. Methodologically, our approach ties the functional form of a hazard model tightly to theory and presents an applied "agenda" for testing the shape of an empirical hazard function. With extensions, our model and empirical framework are applicable to a range of political phenomena.

We present a model of learning and policy choice across governments. Governments choose policies with known ideological positions but initially unknown valence benefits, possibly learning about those benefits between the model's two periods. There are two variants of the model; in one, governments only learn from their own experiences, whereas in the other they learn from one another's experiments. Based on similarities between these two versions, we illustrate that much accepted scholarly evidence of policy diffusion could simply have arisen through independent actions by governments that only learn from their own experiences. However, differences between the game-theoretic and decision-theoretic models point the way to future empirical tests that discern learning-based policy diffusion from independent policy adoptions.

Why and how do groups share information in politics? Most studies of information exchange in politics focus on individual-level attributes and implicitly assume that communication between any two policy actors is independent of the larger communication network in which they are embedded. We develop a theory stating that the decision of any lobbyist to inform another lobbyist is heavily conditioned upon their mutual relationships to third par-ties. We analyze over 40,000 dyadic relationships among lobbyists, government agencies, and congressional staff using sociometric data gathered in the 1970s health and energy policy domains. The results cohere with recent findings that lobbyists disproportionately inform those with similar preferences and show in addition that political communication is transitive: holding constant the degree of preference similarity, a lobbyist is more likely to communicate with another lobbyist if their relationship is brokered by a third party.