The impact of open government data on entrepreneurial activity: a quasi-experiment with machine learning

Open government data (OGD) is a philosophy advocating for the public release of government-held data to citizens and stakeholders. While numerous countries are increasingly adopting this approach, empirical evidence regarding its impact on entrepreneurial activity remains limited. Our study utilizes a difference-in-differences (DID) approach to rigorously assess the causal effect of OGD on entrepreneurial activity and its underlying mechanisms. Additionally, we employ a machine learning technique, Causal Forest, to delve into the pivotal city characteristics that shape OGD's impact and to assess how these features moderate its effects. Our findings reveal a significantly positive effect of OGD on entrepreneurial activity, primarily driven by its capacity to enhance government efficiency, foster venture capital, and promote technical innovation. Furthermore, we identify sci-tech investment and informatization level as critical city characteristics that positively moderate the impact of OGD.

Automated summary

This study examines the impact of open government data (OGD) on entrepreneurial activity and its underlying mechanisms using a difference-in-differences (DID) approach and the machine learning technique, Causal Forest. The findings indicate a significantly positive effect of OGD on entrepreneurial activity, driven by improved government efficiency, increased venture capital, and enhanced technical innovation. Additionally, sci-tech investment and informatization level are identified as critical city characteristics that moderate the impact of OGD.