Dataset of papers on fast-track patent pre-examination reform and expanding the boundaries of corporate innovation

During the data processing, Python is used to extract the IPC classification number of enterprise patents, construct a historical patent pool, and calculate the innovation boundary indicators of enterprises based on whether patents enter new technology fields in subsequent years; At the same time, all continuous variables were truncated by 1%, resulting in 12981 annual observation records of enterprises. The row labels in this dataset correspond to the annual observations of each listed company, while the column labels include enterprise code, year, innovation boundary indicators, variables related to rapid patent pre-approval reform, as well as variables such as enterprise size, dual employment, return on assets, enterprise age, Tobin's Q value, proportion of fixed assets, shareholding ratio of the largest shareholder, financial leverage, and cash flow.

This dataset is suitable for examining the impact of rapid patent pre-approval reform on the innovation boundary of enterprises, as well as innovation risk resistance, information friction mitigation, heterogeneity, and dynamic effects.