2.3.2. Descriptive Statistics

Table 2 presents the descriptive statistics for the main variables. The average number of enterprise patents (tpatent) was 3.559, the average number of invention patents (ipatent) was 2.628, and the average number of utility model patents (upatent) was 3.017. It can be seen that new energy vehicle enterprises apply for more utility model patents. The maximum values of the invention patents, utility model patents, and total patents were 6.443, 6.724, and 7.407, respectively. The standard deviation was also relatively large, which indicates that there is a large gap in the level of technological innovation between different enterprises. The average quality of patents (width) was 0.219, the average quality of invention patents (iwidth) was 0.245, and quality of utility model patents (uwidth) was 0.18. It can be seen that the average quality of the utility model patents was significantly lower than that of the invention patents. The maximum patent quality, invention patent quality, and utility model patent quality were 0.723, 0.75, and 0.57, respectively. The maximum value of the government subsidies was 20.88, the minimum value was 12.68, and the standard deviation was 1.537. It can be seen that there were certain differences in the government subsidies received by enterprises.



In order to see the development of technological innovation in China's new energy vehicle industry in detail, Table 3 presents the annual mean value of the dependent variables. As shown in Table 3, from 2010 to 2019, the number of invention and utility model patents of the listed companies in the new energy vehicle industry maintained a steady upward trend. In 2010, the average annual number of patents of enterprises was 40.99, which nearly quadrupled to 159.32 in 2019, with an average annual growth rate of 16.28%. Overall, in 2010, the annual average patent quality of the listed companies in the new energy vehicle industry was 0.195, and the patent quality increased to 0.275 in 2019. The patent quality fluctuated from 2010 to 2015 and improved rapidly after 2015. In terms of the patent type, the quality of invention patents was significantly higher than that of the utility model patents.


**Table 3.** Descriptive statistics of the dependent variables by year (mean value).

## **3. Empirical Results and Analysis**

*3.1. Analysis of Benchmark Regression Results*

First, we discuss the impact of government subsidies on the number of new energy vehicle patents. It can be seen from columns (1)–(3) in Table 4 that the coefficients of government subsidies were significantly positive at the level of 1%, and the coefficients were 0.173, 0.147, and 0.162, respectively. The number of enterprise patent applications increased by 0.173%. The more subsidies the government gives to enterprises, the more invention patents and utility model patents the enterprises apply for.

**Table 4.** The regression results of government subsidies on the quantity and quality of technological innovation.


Note: Robust standard errors are given in parentheses. \* *p* < 0.1, \*\* *p* < 0.05, \*\*\* *p* < 0.01. CONTROLS includes capital structure (*lev*), profitability (*roa*), enterprise size (*size*), proportion of fixed assets (*ppe*), proportion of independent directors (*dir*), enterprise age (*age*), enterprise growth ability (*gov*), and human capital (*hc*).

Second, we discuss the impact of government subsidies on the NEV patent quality of new energy vehicles. As shown in columns (4)–(6) in Table 4, the coefficients of sub were relatively small and not significant, indicating that government subsidy had no significant impact on the quality of patents. The reason is that enterprises encouraged by industrial policies will significantly increase their patent applications in order to obtain more government subsidies. However, due to many uncertain risks in the process of early research and development, some enterprises prefer to carry out low-quality technological innovation with relatively short cycles and low investment than high-quality technological innovation to reduce the costs and risks [32–35].
