Abstract: The authenticity and quality of industrial statistical data directly affect 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 Law-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 an 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 a slightly poor. Furthermore, at the provincial-level, there is significant spatial heterogeneity in the quality of statistical data of 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 suspiciousness 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 impact 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.
 

Industrial statistics; Data quality; Comprehensive evaluation; Coal-related industry