Are the official national data credible? Empirical evidence from statistics quality evaluation of China's coal and its downstream industries

The authenticity and quality of industrial statistical data directly affects all types of systematic research based on it. Considering the limitations of extant data quality evaluation literature on research objects and evaluation methods, we constructed a new data quality comprehensive inspection and evaluation model based on Benford's Law (BL) and the technique for order of preference by similarity to ideal solution (TOPSIS), selected coal-related industries as the research object, and conducted an empirical test along the research path of “Industry→Province→Indicator”. The results showed that, at industry level, the quality of statistical data for China's coal-related industries from 2001 to 2016 was generally poor. Among the eight sample industries selected, the data quality for five industries (including coal, electricity, and steel) was assessed as poor or slightly poor. Furthermore, at the provincial level, there is significant spatial heterogeneity in the quality of statistical data for various industries affected by factors such as economic structure, marketization level, and industrial diversity. Compared with other types of statistical indicators, industry financial indicators are more prone to data quality problems at the indicator level, and the suspicious indicators of different industries show certain common characteristics and some industry differences. To improve the quality of industrial statistical data and reduce the possible adverse impacts of data quality problems, based on the research findings, we propose targeted countermeasures and suggestions on how to prevent data fraud and effectively identify and rationally use suspicious data. • A novel model for evaluating the quality of industrial statistical data is proposed. • The quality of statistical data for China's coal and its downstream industries is generally poor. • There is significant spatial heterogeneity in the level of statistical data quality between provinces. • The suspiciousness indicators not only show certain industry common features, but some industry differences.