Dataset on Government Data Openness and Cross-Regional Collaborative Innovation in China

It is mainly used for robustness testing, exclusion testing, model setting, and variable measurement methods that were not presented in the main body of the report due to space limitations. Specifically, it includes five parts: firstly, using the Lewbel instrumental variable method based on heteroscedasticity identification to test the potential endogeneity issues of government data openness policies; Secondly, introduce the frequency and amount of corporate mergers and acquisitions to examine whether mergers and acquisitions constitute a competitive explanation for cross regional cooperative innovation growth; Thirdly, using the spatial Durbin model to examine the spatial spillover effects of government data openness and collaborative innovation activities in neighboring cities; Fourthly, based on the gravity model, analyze the impact of government data openness on the cooperative resistance caused by geographical distance, and explain the calculation method of geographical distance between cities; Fifth, construct a cross regional collaborative innovation network and explain the measurement methods for network indicators such as degree centrality, intermediary centrality, proximity centrality, eigenvector centrality, as well as degree distribution Gini coefficient and intensity distribution Gini coefficient.

The above materials are used to supplement the identification strategy, robustness analysis, and collaborative innovation network measurement process of the paper, providing further support for the main empirical conclusions of the paper.