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

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.