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.