When does open government shut? Predicting government responses to citizen information requests

• 成果类型: Article
• 署名作者: Bagozzi, Benjamin E.; Berliner, Daniel; Almquist, Zack W.
• 署名单位: University of Delaware; University of London; London School Economics & Political Science; University of Washington; University of Washington Seattle
• 刊物名称: REGULATION & GOVERNANCE
• ISSN/ISSBN: 1748-5983
• DOI: 10.1111/rego.12282
• 发表日期: 2021
• 页码: 280-297
• 关键词: bureaucratic responsiveness; field experiment; TRANSPARENCY; COMPETITION; CHALLENGES; freedom; police; REFORM

摘要

Methods for the analysis of big data on citizen-government interactions are necessary for theoretical assessments of bureaucratic responsiveness. Such big data methods also stand to benefit practitioners' abilities to monitor and improve these emerging transparency mechanisms. We consider supervised latent Dirichlet allocation (sLDA) as a potential method for these purposes. To this end, we use sLDA to examine the Mexican government's (non)responsiveness to all public information requests filed with the federal Mexican government during the 2003-2015 period, and to identify the request topics most associated with (non)responsiveness. Substantively, our comparisons of the topics that are most highly predictive of responsiveness and nonresponsivess indicate that political sensitivity plays a large and important role in shaping official behavior in this arena. We thus conclude that sLDA provides unique advantages for, and insights into, the analysis of (i) textual records of citizen-government interactions and (ii) bureaucratic (non)responsiveness to these interactions.