Xing Ni’s research while affiliated with Renmin University of China and other places

Penalties for corrupt acts are a vital component of integrity management systems within bureaucratic organizations, yet systematic inquiry into specific interventions is lacking. Drawing from the principal–supervisor–agent model, this article explores the criteria for imposing penalties on corrupt bureaucrats at the street level. We examine the impact of various factors on the severity of penalties for corruption, highlighting the influence of top-down anticorruption reform and local enforcement capacity. Utilizing text mining techniques, we build a comprehensive dataset containing 4025 cases of punishing corruption among grassroots officials in China from 2015 to 2023 and use multilevel analysis to explore these dynamics. Findings suggest that anticorruption reform directly led to an increase in the severity of penalties, but with substantial variation across practices. In particular, the analysis reveals a positive correlation between enforcement capacity and the severity of penalties. Moreover, while the relationship between harm and penalty severity represents a generalizable observation, significant disparities exist in the penalties imposed across various types of corruption. This study contributes to understanding the dynamics of multiple punitive measures and the factors leading to penalties imposed on corrupt street-level bureaucrats.