Poster #20 - Credit-seeking or Blame Avoidance：How Central Inspections Shape Local Innovation Dynamics in China

• In Event: Saturday Poster Luncheon

Abstract

While local government policy innovation is pivotal to national development, existing studies predominantly focus on policy diffusion—whether a policy is adopted—rather than examining the content and direction of innovation. This gap is critical, as incremental yet adaptive policy adjustments often constitute the primary source of transformative governance. Drawing on principal-agent theory and the political logic of central-local interactions, this study investigates how China’s central inspection system—a hierarchical oversight mechanism—shapes local governments’ policy innovation behaviors. By integrating the dual logics of blame avoidance and credit seeking, we theorize how inspections alter local incentives for structural innovation across policy domains and temporal phases.

Employing a mixed-methods analysis of three million municipal policy texts (2010–2023) from the PKULaw database, this study measures policy innovation salience through NLP-driven topic modeling and keyword extraction, while operationalizing inspection exposure via a staggered difference-in-differences (DID) design that tracks cities across four inspection phases: pre-inspection, active, rectification, and post-inspection. Preliminary findings reveal a dual dynamic in local governance innovation: during central inspections, economic policies exhibit risk-averse suppression due to blame avoidance logic, whereas environmental and social policies remain resilient as their outcomes carry lower political risks. Temporal analysis uncovers a “spotlight effect” – innovation surges during active inspections, particularly in cities with younger leadership cohorts and intense peer competition where inspections create credit-claiming opportunities, followed by post-inspection declines as central attention wanes. Notably, cities with historical penalties from central authorities demonstrate persistent innovation suppression, suggesting hierarchical memory entrenches long-term risk aversion. Moderator analyses further indicate that leadership age structures and prior reprimands systematically shape these patterns, revealing how political accountability cycles and organizational memory interact to constrain or enable policy experimentation across governance domains.

This study advances policy innovation literature by shifting focus from adoption to content-driven innovation, revealing how hierarchical controls paradoxically both constrain and catalyze localized experimentation. Practically, it offers insights for designing inspection systems that balance accountability with incentives for resilient policy solutions. By integrating temporal and domain-specific analyses, we provide a nuanced lens to understand central-local collaboration in authoritarian governance.

Author

• YIQI TANG, Shanghai University of Finance and Economics
  Presenting Author