The Public Impact Fundamentals and paradigms in public administration

The Public Impact Fundamentals and paradigms in public administration

As we work alongside governments to improve their public impact we occasionally get asked how the Public Impact Fundamentals relate to some of the established paradigms of public administration, such as New Public Management, or to some of the newer methods used by governments, such as design thinking, behavioural insights or randomised control trials. We think of the Public Impact Fundamentals as a tool that goes further than these differing approaches rather than as yet another competing theory in public policy, but let us explain…

In the history of public administration, we can identify at least three broad paradigms: First, a pre-Weber era, where administrative policies were driven by personal relationships, such as loyalty to a king rather than by legality or loyalty to the state. Second, a Weberian era, which was based on the notion of ideal-type bureaucracy where officials operated under a unified and clearly defined control and disciplinary system. Finally, and most recently, we saw the rise of New Public Management (NPM).

While most observers agree that NPM is well past its prime, no other paradigm has so far managed to achieve a similar degree of intellectual dominance. Developed during the 1980s as part of an effort to make the public service more “business-like”, NPM sought to improve efficiency in the public sector by using management models drawn from the private sector. It incorporated a reliance on market mechanisms, outsourcing, professional human resource practices and some elements of digital technology.

Reforms under the NPM regime often focused on improving “customer service” using decentralised service delivery models under which local agencies could get more freedom in how they delivered services. Citizens would have, for example, the right to choose between a range of services as well as the right to opt out of a service delivery entirely.

Unfortunately, since its heyday, NPM has largely failed to deliver on its ambitious promises of better results and higher productivity in government. As a result, today, the paradigm is well on its way out. With no visible replacement on the horizon, it leaves open the question: What comes next? What will public administration theory and practice look like in the coming decades?

The Public Impact Fundamentals may provide a clue. Unlike NPM, or any other paradigm that came before, the Fundamentals do not focus on the “how”; they do not attempt to dictate what the structure underpinning a bureaucracy should be or how policy problems ought to be solved. Instead, the Fundamentals focus on the characteristics of what effective policymaking looks like.

In recent years, there has been a shift away from a prescriptive public administration regime to one which is focused less on adhering to one particular method and more on finding the best way to achieve some desired outcome. Instead of relying on one dominant paradigm governments now, quite rightly, tend to follow a pluralistic strategy of “letting a thousand flowers bloom”. Behavioural insights, artificial intelligence and machine learning, crowdsourcing, design thinking and a whole host of other methods vie for policymaker’s attention.

This shift gave us a variety of methods, each contributing to achieving better outcomes. For one, design thinking may be very well suited to define clear objectives, to test the feasibility of a given policy or to increase stakeholder engagement. Randomized control trials, on the other hand, are a particularly good tool to increase our confidence about the evidence in favour of a policy.

What, however, has got lost in the transition is a common language to understand policymaking and governance. We believe that the Public Impact Fundamentals can provide an overarching organising principle that illuminates how these different methods contribute to achieving impact, while also helping to explore the relative strengths and limits of different methods.

 

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