2026-10-14T12:26:12Z https://www.scidb.cn/oai

10.57760/sciencedb.j00289.000df 2026-09-02T10:39:11Z

2026-09-02 How do technology finance policies drive the development of the digital economy: Effect testing and mechanism verification from the perspective of the innovation chain doi:10.57760/sciencedb.j00289.000df en This dataset consists of annual panel data from prefecture level cities, with a research sample time span from 2006 to 2023. It takes 154 prefecture level cities in China as the research objects, and the time resolution is annual. The original data is sourced from the "China Urban Statistical Yearbook," "China Information Yearbook," "China Labor Statistics Yearbook," and the China Industrial and Commercial Enterprise Registration Database. The data processing work was completed using Stata and Python software. During the data processing process, samples with a large number of missing data were removed, and linear interpolation was used to fill in the missing observations for a few individuals. Finally, a total of 2033 prefecture level city annual observation records were obtained. The row labels of this dataset correspond to annual observations of various prefecture level cities, while the column labels include city codes, years, indicators of urban digital economy development, and other city level related variables. The minimum value of the urban digital economy development index in the dataset is 0.000, the maximum value is 0.826, the mean is 0.06, and the standard deviation is 0.06, reflecting the uneven characteristics of urban digital economy development in China. The missing data mainly comes from changes in the statistical caliber of some cities and the lack of disclosure of relevant indicators in statistical yearbooks. Samples with many missing values are directly removed, and a small number of missing values are filled in using linear interpolation; There is a small amount of statistical bias in the original statistical data. By using the sample processing method mentioned above, data errors can be reduced. The dataset can be opened using Stata software and is suitable for econometric empirical analysis at the urban level.  technology; finance; digital economy Wang SiRan Lai Xiaomin Zhang Yan PUBLIC https://creativecommons.org/licenses/by-nc-sa/4.0/ dataset Science Data Bank