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

Abstract

<p>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.<br></p>

本文研究大数据技术如何能够为所有政策利益相关者创建一个 “信息共享域”, 从而减少贫困治理项目中普遍存在的腐败 和信息不对称的困扰。信息共享域首先将分散的数据转化为具有明确政策目标的信息, 然后转变为可操作性强的知识。 这种转化提高了政府政策的精准性和政府部门间的协调性,从而增强了政府治理能力; 同时, 这种转化也有助于推进调查 和预防腐败, 从而提升政府公信力。Z省是中国最早通过建立大数据监督平台推进贫困治理的省份。透过对Z省政府官 员和居民的大量深度访谈以及参与式观察, 本文证实了上述研究观点。研究也阐明了数据—信息—知识的转化链在改善 政府治理中的重要性。