Replication Data for \"Interventions to Reduce Bureaucratic Discrimination: a Review of Behavioural Research\"

For most people, their most direct experience with “the state” is through everyday interactions with street-level bureaucrats. A fundamental principle of good bureaucratic practice is treating clients without any inappropriate difference. Still, conscious or unconscious bias can lead to discrimination in street-level bureaucracies. While acknowledging the problem, public policy and administration research offers few practical insights about what can be done to reduce bureaucratic discrimination. Adopting an interdisciplinary focus, this article reviews empirical behavioural research studies (N=32) on the effects of interventions to reduce discrimination by street-level bureaucrats. We find evidence of the effectiveness of five types of interventions: increasing passive/active representation, mechanisms to increase accountability, de-biasing training, direct engagement with clients, and policy change. However, causal mechanisms are often insufficiently explicated. Considering the role of scale, the type of relationship with clients, and the focus of the intervention on attitudes and/or behavior appear crucial factors influencing the effectiveness of interventions.

对多数民众而言，其对“国家”的最直观体验，源于与街头官僚（street-level bureaucrats）的日常互动。优良官僚治理实践的核心原则之一，是对服务对象一视同仁，杜绝任何不当差别对待。然而，有意识或无意识的偏见仍可能在街头官僚体系中引发歧视行为。尽管学界已意识到这一问题，但公共政策与行政学领域的现有研究，却极少能提供可切实减少官僚歧视的可行方案。本文采用跨学科研究视角，系统梳理了共计32项（N=32）探究街头官僚反歧视干预措施效果的实证行为学研究。研究证实五类干预措施具备有效性：提升被动/主动代表性、强化问责机制、去偏见培训、与服务对象直接互动，以及政策调整。但现有研究往往未充分阐释这些干预措施背后的因果作用机制。研究同时发现，干预规模、与服务对象的关系类型，以及干预聚焦于态度或行为的导向，均为影响干预效果的关键因素。