The value of adopting a systems approach to the productivity puzzle

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Is policy to blame for lagging productivity in the UK?
Since the 2007 financial crisis, productivity growth in the UK has been persistently weak confounding policy makers and prompting a flurry of research around what has become known as the productivity puzzle. There are many theories about the roots of this poor performance but one constant is the perception that public policy ought to be able to intervene to improve outcomes. While most acknowledge that there is likely no silver bullet there is emerging consensus that the evolution of productivity policy in the UK has not (yet) yielded predictable or positive impacts on productivity performance. We argue that the siloed nature of productivity policy may be hindering the development of an effective productivity programme and that adopting a systems approach to policy may provide new insights into the productivity puzzle.

It’s complicated: systems and productivity policy
We observe that even when a productivity policy in the UK was explicitly defined it was conceptualised as a bundle of separate policy areas rather than as a coherent policy programme. Even though there have been efforts to join-up policies and work cross-departmentally the policies affecting productivity have largely been enacted in disconnected silos.

The policy model depicted in Fig 1 (from the Industrial Strategy) illustrates this kind of thinking, which assumes that policies created in silos will aggregate to produce positive outcomes. Yet problems rarely conform to these assumptions. Policy spaces are messy, complex, and, sometimes even chaotic. There can be relationships between phenomena that appear stable but are subject to sudden shifts, or relationships that seem like they should function a certain way but do not. In fact, the ‘working whole’ is not reducible and cannot be described by the attributes of its parts alone.

Systems approaches focus on mapping and explaining systems or processes characterised by complexity – in other words systems that lack order and stability and universal laws. Fig 2 reimagines, in a vastly simplified form, the productivity landscape as seen through a systems lens. The core tenets of systems thinking are that outcomes are driven by intersections and interdependencies between elements within a system and that these relationships are (a) imperfectly recognised in the state of the art of research and policy and that (b) these relationships are not easily knowable.

What can systems approaches offer policy?
Here we highlight two of several high-level implications of adopting a systems approach to productivity policy.

First, bundling disparate policies is not enough. Rather, policies should be conceptualised to uncover, understand, and act on the interdependencies inherent in the system. This is not an easy task but will certainly remain elusive as long as policy silos dominate. Here there is an opportunity to build bridges between policymakers and researchers to better map and model the policy landscape.

Secondly, systems approaches encourage the adoption of experimental policy practices. These are interventions designed to engage with core stakeholders in targeted programmes and are explicitly designed to process feedback to better understand policy effects and uncover missing links. These policies emphasise experimental policy designs that incorporate rigorous and collaborative evaluation processes as well as the potential for frequent course corrections in response to findings. While experimental processes have been adopted in some areas of policy at present they tend to be small-scale and disconnected projects.

The Productivity Insights Network aims to contribute to the development of more effective policy by adopting a systems lens to mapping the systems and subsystems that affect productivity. The project’s mandate specifically aims to uncover intersections and interdependencies that have been overlooked or underplayed in an effort to better understand the dimensions of the productivity puzzle.

Dr Jen Nelles, City University of New York
Professor Tim Vorley, University of Sheffield

Read the full report here.