Abstract for: Using Systems Thinking to Unlock Collaboration Across the UK's Statistical System

Subnational economic and socioeconomic data are essential to policy design, evaluation, and accountability, yet the UK statistical system is highly fragmented across UK-wide, devolved, and local institutions. Existing debates tend to treat the principal tensions in this landscape (i.e., comparability versus local relevance, innovation versus core statistics, collaboration versus organisational autonomy) as discrete problems. This paper argues they are better understood as interacting feedback structures within a multi-level system. Using a qualitative system dynamics approach, we draw on 27 policy documents and 25 semi-structured interviews with 31 participants spanning UK, devolved, and local levels of government. Causal statements are coded from these materials, individual causal maps constructed, and then synthesised into integrated Causal Loop Diagrams capturing both horizontal collaboration across the four nations and vertical collaboration within each. The resulting structure is interpreted through system archetypes. Four recurrent dynamics are identified: a relative achievement structure that pulls the system between UK-wide coherence and locally relevant statistics; out-of-control dynamics in which short-term pivots toward available and core data risk widening longer-term gaps; systematic underachievement of collaboration among those least able to invest in it; and local coping responses that can erode public statistical infrastructure. These dynamics suggest that persistent failures of collaboration are not primarily the result of poor coordination or weak intent, but of structural feedback that reproduces the same problems despite repeated intervention. The paper contributes a systems framing of collaboration across the data value chain and provides a basis for co-designing interventions that address root causes rather than symptoms.