Storytelling and Government Reputation amid Welfare Retrenchment: Experimental Evidence from Health Insurance Reform in China

• In Event: Communication, Narrative, and Framing in the Policy Process

Abstract

Welfare retrenchment policies, though often essential for ensuring fiscal sustainability, can undermine government legitimacy due to their divisive and redistributive nature. Thus, the role of political communication, especially in managing government reputation during such reforms, becomes pivotal. While storytelling strategies are increasingly recognized for facilitating government-citizen interaction, their specific impact on organizational reputation remains underexplored, particularly in the context of contentious welfare reforms. This study investigates the role of policy narratives in shaping government reputation and identifies the mechanisms underlying their effectiveness.

Building on the Narrative Policy Framework (NPF) and integrating insights from the Elaboration Likelihood Model (ELM), this study develops a theoretical model of policy narratives as a strategy for constructing government reputation. We offer a unique contribution by exploring the interaction between narrative strategies and government reputation through the lens of policy feedback and information processing. Using a survey experiment, we assess the causal effects of different narrative strategies on public perceptions of government reputation, focusing on reforming China’s personal medical insurance accounts—a highly debated example of welfare retrenchment. We manipulate three key narrative dimensions: source (official vs. self-media), framing (specific benefits vs. risky benefits), and expression format (text vs. image), leading to eight distinct experimental conditions.

The study hypothesizes that the central path is triggered by specific benefit framing, where personal relevance deepens cognitive engagement, and official media sources, which provide authority, encourage thoughtful evaluation. Text-based formats also prompt deeper analysis due to their logical structure. Conversely, the peripheral path is influenced by self-media sources, which enhance emotional appeal and relatability; risk benefit framing, which invokes uncertainty and emotional response; and image formats, simplifying complex information and focusing on visual cues. Narratives that pass through the central processing pathway can greatly affect government reputation because of the high level of involvement in this process, whereas the peripheral pathway makes it easier to achieve the policy goals of the narrative.

This study’s experimental design is based on data collected from 576 participants enrolled in China’s employee medical insurance scheme. Government reputation was measured using validated Likert scales, and statistical analyses, including ANOVA and moderated mediation analysis, were conducted to examine direct and indirect effects. This rigorous approach ensures robust causal inferences and addresses confounding variables, thereby strengthening the validity of the results.

Our findings indicate that well-crafted policy narratives significantly enhance government reputation, with policy support acting as a key mediating mechanism. Notably, image-based formats amplify the effectiveness of narratives, underlining the importance of delivery methods in shaping public perception. These results underscore the potential of strategic storytelling in mitigating reputational risks, fostering public trust, and promoting citizen engagement, particularly during contentious policy reforms.

By bridging narrative theory with causal inference methods, this study contributes to the field by extending the Narrative Policy Framework to include a detailed analysis of government reputation and communication strategies. We also provide practical insights for policymakers seeking to navigate the challenges of welfare retrenchment through strategic, evidence-based political communication.

Authors

• Pinquan Jiang, Renmin University of China
  Presenting Author

• Yuehui Yu, Renmin University of China
  Non-Presenting Co-Author