A refined experimentalist governance approach to incremental policy change: The case of process-tracing China’s central government infrastructure PPP policies between 1988 and 2017

How public policies change incrementally over time remains understudied. This paper contributes to the studies of incremental policy change by integrating the theories of policy layering and learning into a theoretical framework of experimental governance (EG). Using a mixed research method, we apply this framework to process-tracing the changing trajectory of China’s central government infrastructure public-private partnership (PPP) policies from 1988 to 2017 by looking at evolving policy goals, policy measures, and policy co-issuing networks. Results suggest that China’s central government infrastructure PPP policy change follows a refined EG approach in which policies change incrementally in a layering pattern, primarily driven by learning. Findings provide a new account of incremental policy change.