Theories of nonprofit density have assumed a variety of dispositions toward the state, including opposition, suspicion, indifference, and mutual dependence. In this article, we conduct the first large-scale simultaneous empirical test of the two most prominent nonprofit theories: government failure theory and interdependence theory. The former characterizes nonprofit activities as substitute or oppositional to state programs, accounting for the limitations and failures of government-provided services and more reflective of the heterogeneity of demand for services. The latter emphasizes the more complementary and collaborative nature of nonprofit activities, focusing on the overlapping agendas of nonprofits and the state and the mutual dependency that arises from partnership. The theories are difficult to test empirically because both predict the same relationship between state capacity and the size of the nonprofit sector, albeit for theoretically distinct reasons. A true joint test requires the separation of government support from private support for nonprofits. Using a newly constructed panel dataset in which we separate out nonprofit revenue sources normally agglomerated in the Internal Revenue Service 990 data, we examine the empirical merits of both theories to answer the question of whether human service nonprofit organizations thrive when government fails or when government collaborates. Our findings suggest that government funding has a more favorable effect on nonprofit density than private donations. The findings raise several policy and management implications that need evaluation.

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Network-focused research in public administration has expanded rapidly over the past two decades. This rapid growth has created come confusion about terminology and approaches to research in the field. We organize the network literature in public administration using compact citation networks to identify coherent subdomains focused on (1) policy formation, (2) governance and (3) policy implementation. We trace how these domains differ in their approach to defining the role of networks, relationships and actors and to what extent the articles apply formal network analysis techniques. Based on a subsequent content analysis of the sample articles, we identify promising research awnues focused on the wider adoption of methods derived from social network analysis and the conditions under which networks actually deliver improved results.

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Public administration research has documented a shift in the locus of discretion away from street-level bureaucrats to “systems-level bureaucracies” as a result of new information communication technologies that automate bureaucratic processes, and thus shape access to resources and decisions around enforcement and punishment. Advances in artificial intelligence (AI) are accelerating these trends, potentially altering discretion in public management in exciting and in challenging ways. We introduce the concept of “artificial discretion” as a theoretical framework to help public managers consider the impact of AI as they face decisions about whether and how to implement it. We operationalize discretion as the execution of tasks that require nontrivial decisions. Using Salamon’s tools of governance framework, we compare artificial discretion to human discretion as task specificity and environmental complexity vary. We evaluate artificial discretion with the criteria of effectiveness, efficiency, equity, manageability, and political feasibility. Our analysis suggests three principal ways that artificial discretion can improve administrative discretion at the task level: (1) increasing scalability, (2) decreasing cost, and (3) improving quality. At the same time, artificial discretion raises serious concerns with respect to equity, manageability, and political feasibility.