Public data openness and stock price crash risk: evidence from a quasi-natural experiment of government data platforms - Yongkang Lin; Linlin Zheng; Qiming Zhong (Data Science and Management)
Public data serves as a fundamental pillar in the advancement of the digital economy. Its importance for unlocking the value associated with information asymmetry has attracted substantial attention in both practice and theory. We leverage a quasi-natural experiment from China’s local public data openness platforms. Employing data for A-share listed firms from 2009 to 2021, we use a time-varying difference-in-differences model to systematically examine how public data openness affects corporate stock price crash risk. The results demonstrate that public data openness significantly reduces the accumulation of corporate stock price crash risk. This effect is primarily attributed to lower production of inappropriate information and enhanced information disclosure quality. Further analysis indicates that a supportive institutional environment amplifies the risk-reducing effect of public data openness. This effect is particularly pronounced in firms with strained government-market relationships, non-state ownership, and minimal agency conflicts. These insights highlight the potential that public data openness has for improving information efficiency and facilitating a transition toward digital governance.