From big data to higher bureaucratic capacity: Poverty alleviation in China

Jiangnan ZHU, Hanyu XIAO, Bin WU

Research output: Contribution to journalArticlespeer-review

3 Citations (Scopus)

Abstract

This study explores how big data technologies can create an “information commons” shared by all policy stakeholders to alleviate the corruption and information asymmetries long endemic to poverty alleviation programs. We argue that the information commons can transform discrete data first into information with clear policy purposes and then into actionable knowledge. This process increases bureaucratic competence by improving policy accuracy and the efficiency of bureaucratic coordination and augments bureaucratic reliability by facilitating the investigation and prevention of corruption. We substantiate our propositions through extensive field interviews with officials and citizens in a Chinese province that is using China's first monitoring platform powered by big data technology to implement anti-poverty policies. Our study illustrates the importance of data–information–knowledge chains in improving governance. Copyright © 2022 This article is protected by copyright. All rights reserved.
Original languageEnglish
JournalPublic Administration
Early online date14 Dec 2022
DOIs
Publication statusE-pub ahead of print - 14 Dec 2022

Citation

Zhu, J., Xiao, H., & Wu, B. (2022). From big data to higher bureaucratic capacity: Poverty alleviation in China. Public Administration. Advance online publication. doi: 10.1111/padm.12907

Keywords

  • Big data
  • Information commons and actionable knowledge
  • Bureaucratic capacity
  • Corruption
  • Poverty alleviation

Fingerprint

Dive into the research topics of 'From big data to higher bureaucratic capacity: Poverty alleviation in China'. Together they form a unique fingerprint.