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Machine learning (ML) methods have gained a great deal of popularity in recent years among public administration scholars and practitioners. These techniques open the door to the analysis of text, image and other types of data that allow us to test foundational theories of public administration and to develop new theories. Despite the excitement surrounding ML methods, clarity regarding their proper use and potential pitfalls is lacking. This article attempts to fill this gap in the literature through providing an ML “guide to practice” for public administration scholars and practitioners. Here, we take a foundational view of ML and describe how these methods can enrich public administration research and practice through their ability develop new measures, tap into new sources of data and conduct statistical inference and causal inference in a principled manner. We then turn our attention to the pitfalls of using these methods such as unvalidated measures and lack of interpretability. Finally, we demonstrate how ML techniques can help us learn about organizational reputation in federal agencies through an illustrated example using tweets from 13 executive federal agencies. All R code, analyses, and data described in this article can be found in the Supplementary Appendix. [ABSTRACT FROM AUTHOR]

We assess the turnover intentions of federal employees using Hirschman's theory of exit, voice, and loyalty. Specifically, we follow other studies that have tested the effects of loyalty and voice on the likelihood a person states their intention to leave. However, using large-scale survey evidence from the federal workforce, we are able to assess the impact of loyalty, voice, and other factors (including assessments of pay) on the likelihood that a respondent will retire, leave for another federal agency, or leave for another sector. Our statistical analysis provides evidence that perceptions about voice and loyalty limit exit. Yet, the effects of voice, loyalty, and pay vary with exit option. [ABSTRACT FROM AUTHOR]

Agencies must simultaneously weigh technical information along with the political and distributive consequences of their decisions. In a study of the US Environmental Protection Agency’s (EPA’s) implementation of federal pesticide regulations, I show that the EPA accounts for both case and constituency factors. I assess the effects of both uncertainty and information in agency decisions. I offer the use of a multilevel model with three types of random effects to capture specific case factors (the crop, the pest, and the pesticide) to account for unobservable heterogeneity in outcomes. The models show that the impacts of uncertainty and information depend on the decision environment. [ABSTRACT FROM PUBLISHER]

We assess the usefulness of a theory of organizations, the Resource-Based View, for the study of public agencies. We examine the differential impacts of an array of organizational resources (administrative, human, financial, physical, political, and reputation resources) on a core measure of federal agency effectiveness. Our analysis shows that certain types of resources have positive impacts on agency effectiveness, such as administrative (number of members in top governing structure), personnel (the level of professionalization of its employees), financial (spending authority from offsethng collections), and political (presidential attention and the agency's public reputation), although certain other resources have negative impacts. Our study showE that strategic knowledge about resources can enhance understanding of agency performance. [ABSTRACT FROM AUTHOR]

Recent studies contend that public health agencies in the United States are in a state of turmoil because their organizational form causes them to take on conflicting mandates. One key reason is thought to be a trade-off in design: between creating stand-alone agencies for health and environment and creating agencies that combine the two. We use data on the organizational form of state health and environmental agencies to assess the choice of organizational form as a problem of institutional design from the perspective of path-dependency theory. Our results suggest that initial social conditions help explain the choice of combined or independent agencies. We believe that this case provides useful information about the roots of organizational design for resolving competing demands and provides insight into concerns about the delivery of health and environmental protection in the states. [ABSTRACT FROM AUTHOR]

How governments design institutions for the revelation of information depends on how the costs and benefits are distributed across affected groups. In this study examine the incidence of different kinds of constituencies and how governments give citizens access to information that affects them. A number of important studies have sought to understand the effect of such rules on the revelation of private information and policy outcomes, but we know little about the sources of those rules. Do the rules coincide with constituencies that benefit from their existence? Are they absent where strong constituencies can avoid bearing the costs of the rules? Using data on community "right-to-know" protections regarding environmental hazards, I compare the incidence of the benefits and costs of these design choices in the context of rules that reveal information and charge the cost of information revelation to the regulated community. The models show that the incidence of right-to-know protections depends on the presence or absence of constituencies that would enjoy their benefits or bear their costs. However, organizational costs limit the ability of affected constituencies to obtain institutional designs that reflect their interests. [ABSTRACT FROM AUTHOR]

We assess Hirschman's theory of exit, voice, and loyalty in the context of voluntary exit from organizations in the public workforce. Specifically, we test the effects of loyalty and voice on the likelihood a person states their intention to leave. We assess these relationships using data from the Federal Human Capital Survey. Our statistical analysis provides evidence that perceptions about voice and loyalty limit exit at all levels of the organizational hierarchy. Yet, dissatisfaction with pay is also a substantial cause of intention to leave--and this effect is greatest for executive-level employees. We also show evidence for "motivation crowding" when pay-based motivation is emphasized. [ABSTRACT FROM AUTHOR]

Pure incentive schemes rely on the agent's self-interest, rather than more coercive control, to motivate subordinates. Yet most organizations, and in particular public agencies, rely very little on pure incentive contracts and instead use coercive mechanisms of monitoring and sanctioning that many theorists find objectionable. We use principal-agency theory to investigate the problem. Principal-agency theory has tacitly assumed throughout that it is in the principal's interests to find a set of incentives that induce efficient levels of effort from the agent. We show that this is not necessarily the case. We identify a problem we denote as "the principal's moral hazard constraint" in which bonuses large enough to produce the efficient incentive effect are prohibitively expensive for the principal. Potential solutions to this problem-involving penalization or joint ownership-are unavailable in the public sphere. This means that for a large class of control problems in agencies, the principal's self-interest will result in the inefficient use of monitoring and oversight rather than outcome-contingent incentives. Although monitoring is often thought of as resulting from the agent's moral hazard, it can just as reasonably be seen as resulting from the principal's moral hazard. [ABSTRACT FROM AUTHOR]

Focuses on the effects of structural choices on democratic responsiveness of public agencies in the United States. Importance of bureaucratic discretion in government administration; Accounts of U.S. Attorneys data on prosecution of regulatory crimes; the rule of law constraining bureaucratic decision.

An introduction is presented in which the editor discusses reports within the issue on topics including links betweeb theory and analysis, workshop on the proper use of panel analysis to deal with the data of public administration, and analysis of public administration using Bayesian statistics.