“I Paid A Bribe”—Lessons and Insights From Crowdsourced Corruption Reporting in India
研究利用印度“我付了贿赂”平台的用户举报数据，分析腐败类型、贿赂金额、地区差异等，展示实时举报数据在减少记忆偏差、精准定位腐败高发领域方面的价值，为反腐败政策提供参考。
ABSTRACT Preventing and reducing corruption has proven to be an enormous challenge. An important step in this process is to produce and use good metrics to identify where anti‐corruption resources would be most beneficial. Most measures of corruption, however, rely on surveys of perceptions or bribery incidence. Such surveys suffer reporting biases, not least of which is memory bias. Furthermore, they paint a picture of corruption in broad strokes, while reducing corruption requires targeted policies and interventions. User Incidence Reports are collected in real time, reducing memory bias, and potentially provide the details required to target corruption where it is most frequently reported. In this study, we examine the potential research and policy value of user incident reporting platforms like “I Paid A Bribe” (IPAB). IPAB provides rich data on corruption dynamics in India, including specific corruption types, bribe amounts, regional variations, and affected sectors. Using a combination of descriptive statistics and illustrative user reports, we demonstrate IPAB's benefits and identify remaining challenges.