Abstract:China industrial enterprise database has become the preferred database to study China's micro enterprise activities. However, the lack of key indicators in the database seriously affects the update and use of the database. On the basis of referring to the main literature processing methods, this paper uses five interpolation methods to improve the database, including single imputation, MICE1、MICE2、MMICE1和MMICE2, so as to extend the Chinese industrial enterprise database to 2013, and evaluate the relative effectiveness of various interpolation methods by calculating the total enterprise productivity. The results show that:In the five interpolation methods, the single imputation method and MMICE1 are the two most effective interpolation methods, which can not only achieve the consistency of database features before and after interpolation, but also achieve the consistency of data structure features of total factor productivity. It is worth emphasizing that in terms of improving the database and calculating the total factor productivity, the former is a relatively economic method because of its simple process, while the latter is a relatively effective method because it can retain more sample information The research value of this paper is to provide basic research work for the use of Chinese industrial enterprise database.

Key words:chinese industrial enterprise database,single imputation,multivariate imputation by chained equations,mixed interpolation method,total factor productivity