Based on data from the China Industry Business Performance Database from 1998 to 2007 and the time of establishment of the administrative examination and approval center in each administrative division in China, this study attempts to empirically determine the effects of the reform of the administrative examination and approval system on the efficiency of resource allocation from the perspectives of the degree of enterprise-level productivity dispersion. The empirical results showed that the reform of the administrative examination and approval system significantly reduced the degree of productivity dispersion among enterprises in an industry, in addition to enhancing the efficiency of resource allocation in the industry. A further analysis of heterogeneity revealed that the reform of the administrative examination and approval system yielded greater enhancements of the efficiency of resource allocation in industries with lower entry and exit rates.
This paper aims to establish a driving style recognition method that is highly accurate, fast and generalizable, considering the lack of data types in driving style classification task and the low recognition accuracy of widely used unsupervised clustering algorithms and single convolutional neural network methods. First, we propose a method to collect the information on driver's operation time sequence in view of the imperfect driving data, and then extract the driver's style features through convolutional neural network. Then, for the collected temporal data, the Long Short Term Memory networks (LSTM) module is added to encode and transform the driving features, to achieve the driving style classification. The results show that the accuracy of driving style recognition reaches over 93%, while the speed is improved significantly.
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