Behavioral Public Administration

Behavioral public administration is an interdisciplinary research field that studies public administration topics by connecting insights from public administration with psychology and, more broadly, the behavioral sciences. Behavioral public administration scholars study important public problems such as discrimination, corruption, and burnout. Various public administration scholars—including Herbert Simon—have stressed the importance of connecting psychology and public administration. Yet until the early 2010s, public administration did not work systematically on this connection. This has changed profoundly with the development of various overview articles, dedicated special issues in general public administration journals, and development of new journals. Behavioral public administration has several uses. First, behavioral public administration tests and extends theories and concepts from psychology in political-administrative settings. Examples include tests of prospect theory and the choice overload hypothesis in public-administrative settings. Second, it tests and extends the micro-foundations of public administration theories and concepts, such as concerning co-production and isomorphism. Third, behavioral public administration scholars develop new theories and concepts. This has probably been less widespread than the previous two uses, but is nonetheless already apparent in, for instance, concepts such as public service motivation, policy alienation, and administrative burden. Fourth, behavioral public administration can help in tackling practical public problems. Insights from behavioral public administration have been used to increase diversity within public organizations and reduce burnout. The field of behavioral public administration can develop further. The field could move beyond one-shot single studies and aim to build cumulative knowledge. This can be done via large-scale collaborations and replications. In addition, it is also beneficial if behavioral public administration scholars broaden their methodological toolkit to answer different kinds of research questions. It should not only focus on causal inference questions but also on questions concerning description of societal problems (e.g. via representative surveys) or concerning prediction (e.g. by using machine learning).