1. Introduction
China’s Industrial Policy for the Automobile Industry, introduced in 1994, identified the automobile industry as a pillar industry and established the status of the automobile industry in the national economy. China’s auto industry has achieved rapid development by exchanging market for technology [1]. As of 2021, China’s auto production was the first in the world for 13 consecutive years (Data source: International Organization of Motor Vehicle Manufacturers
With the increase in industrialization in China, China is facing serious resource and environmental problems. China is the world’s largest importer of crude oil; China’s net imports amounted to 540 million tons (Data source: Annual Review of Energy Statistics in China, 2021) of crude oil in 2020. China’s external dependence on oil is as high as 72.68%, far above the international oil security alert line. The import of crude oil not only requires a great deal of foreign exchange, but also brings negative impacts on national energy security [6]. Vehicle emissions are a direct source of urban pollution and urban traffic activities can increase the level of air pollution, seriously damage public health, and hinder economic and social progress [7]. Therefore, the issues of sustainable development have become the focus of public attention. Sustainable development refers to harmonious development between humans and nature. Sustainable development necessitates meeting the needs of the present without jeopardizing the ability of future generations to meet their own needs. This inevitably requires human beings to change their production and consumption patterns in order to rationally exploit natural resources and fossil fuels, so as to reduce the negative impact on the environment. The advent of the automobile not only changed the means of human production, but also changed the pattern of human consumption [8,9]. Therefore, the automobile industry is an important field of sustainable development. The Chinese government has proposed to achieve peak carbon by 2030 and carbon neutrality by 2060. New energy vehicles can effectively reduce vehicle exhaust pollution, lower oil consumption, and reduce carbon emissions, which can help in achieving the carbon peak and carbon neutrality. At present, not only China but many countries around the world have begun to devote efforts to the field of new energy vehicles. For example, Germany, France, and the United Kingdom plan to ban the sale of conventional fuel cars in 2030, 2040, and 2040, respectively. China’s Hainan province, the country’s first pilot city, aims to ban the sale of conventional fuel vehicles by 2030. Some major automobile producers have also announced plans to ban the sale of conventional fuel cars. For example, Volkswagen plans to electrize all its models by 2030, Audi plans to stop the production of conventional fuel cars by 2033, Honda plans to completely stop selling conventional fuel cars by 2040, and Chinese auto company BYD has stopped selling fuel cars since March 2022 [10,11]. Therefore, whether from the national level or the enterprise level, there will be fierce competition in the field of new energy vehicles. In 2021, China sold 3.54 million (Data source: China Association of Automobile Manufacturers) new energy vehicles, 1.5 times more than the same period last year, and it has sold more than one million vehicles for four consecutive years. China’s new energy vehicle market shows an explosive growth trend (Figure 1). Globally, China’s new energy vehicle sales have ranked first in the world for seven consecutive years, becoming an important driving force for the electrification transformation of the global industry. China is a forerunner in the field of traditional fuel vehicles, but has the first-mover advantage and scale advantage in the new energy vehicle industry, which creates opportunities for China’s new energy vehicles to overtake its competition on the corner [12,13].
Corner overtaking was originally a racing term for the use of turning to overtake at a corner. Now, it is usually used in political, economic, and other fields. It generally refers to the leapfrog development of emerging countries or emerging economies through some unconventional means in a particular stage of social development, which is a path by which latecomer countries can catch up with developed countries [14,15]. In the period of industrial transformation, through technical support, industrial policy, financial policy, institutional innovation, and other beneficial measures, a country can form an endogenous development advantage to achieve leapfrog development [16,17]. The corner is the window of opportunity for emerging countries or new industries to catch up [18,19]. China’s auto industry is a late-developing industry; compared to the auto industry powerhouse countries such as the United States, Germany, and Japan, the Chinese auto industry’s shortcomings are obvious, and the core competitiveness is insufficient. In the particular period of transformation of the auto industry to new energy, China already has a first-mover advantage and scale advantage, and China’s new energy vehicle market scale is huge. China has established a complete industrial chain of new energy vehicles. In the automobile motor, the battery and electric control system have achieved complete independent intellectual property rights. China has certain competitive advantages in the new energy vehicle sector, unlike in the traditional fuel vehicle sector, which faces bottlenecks in key technologies. In the decline of traditional fuel vehicles and the rapid development of new energy vehicles, the window of opportunity for the curve has appeared.
China’s new energy vehicle subsidy policy can reduce enterprise costs, so, under the incentive of the policy, manufacturers accelerate the transformation from traditional fuel vehicles to new energy vehicles. However, with the development of new energy vehicles, the financial pressure is gradually increasing. Subsidies for new energy vehicles reached 22.204 billion yuan (Data source: Ministry of Industry and Information Technology of the People’s Republic of China) in 2021. New energy automobile manufacturers rely too much on government subsidies, their market competitiveness is low, and there is even a phenomenon of subsidy fraud among new energy vehicles. Therefore, in 2015, the Ministry of Finance and other four departments issued a notice that the subsidy would gradually decline from 2017 [20,21]. In order to promote the healthy development of China’s new energy vehicle industry, the Parallel Management Measures for Average Fuel Consumption of Passenger Vehicle Enterprises and New Energy Vehicle Credits (referred to as dual-credit policy) was introduced in September 2017 as an alternative to the subsidy policy [22]. China’s dual-credit policy is based on the U.S. zero emissions policy and the EU emissions trading system, and it consists of average passenger vehicle fuel consumption points (CAFC points) and new energy vehicle points (NEV points) [23]. The dual-credit policy is a market-oriented industrial policy that stipulates that manufacturers of new energy vehicles will receive NEV points, which can be traded freely, while the production of fuel vehicles that do not meet the fuel consumption requirements will receive negative CAFC points, which can be offset either through the transfer of positive CAFC points by affiliated companies or through the purchase of NEV points. The negative CAFC points can be offset either through the transfer of positive CAFC points by related enterprises or through the purchase of NEV points. Therefore, under the role of market regulation, automakers will choose to increase the production of new energy vehicles, which will not only generate revenue from the sale of vehicles but also profit from the trading of NEV points, which creates a huge incentive for automakers.
In the background of the declining new energy vehicle subsidy policy, we consider whether the dual-credit points policy with market-based and mandatory dual innovation can promote the healthy and orderly development of the new energy automobile industry and help China’s new energy automobile industry to achieve curve overtaking. Therefore, this paper uses the dual-credit policy introduced in 2017 as a quasi-natural experiment, takes listed new energy vehicle companies in the Shanghai and Shenzhen stock markets as research objects, and uses the difference-in-difference model to estimate the policy effects of the dual-credit policy since its implementation to investigate whether market-based industrial policy innovation can help China’s auto industry, which is in a late-developing position and in a period of industrial transformation, to achieve corner overtaking.
2. Literature Review and Research Hypotheses
2.1. Literature Review
New energy vehicles are a strategic emerging industry in China, with obvious externalities. The support and guidance of industrial policy is crucial to the development of the new energy industry and is a common choice around the world, so the industrial policy of the new energy vehicle industry has become the focus of research. On the one hand, the research focuses on the impact of new energy vehicle industrial policies on market players, such as the performance of manufacturers, financing, behavioral strategies, and the purchase intentions of new energy vehicle consumers. Liu et al. used three types of subsidy policies for new energy vehicles in China and used the event study method to assess the effectiveness of subsidy-based policies on the financing of new energy vehicle listed companies, and they found that there was significant heterogeneity in policy effects, with tax incentives and government procurement having a significant positive impact, while the impact of financial subsidies was not significant and depended largely on the specific policy content [24]. Yu et al. divided the financial subsidies into ex ante financial subsidies and ex post financial subsidies, and investigated the impact of the two different types of subsidies on the financial performance of new energy vehicle enterprises through a panel regression model [20]. Wang et al. concluded that fiscal subsidies are an indispensable policy tool for the development of the new energy vehicle industry, but the impact on new energy vehicle firms is controversial. Using data from listed new energy vehicle companies in China from 2009 to 2018, the impact of fiscal subsidies on the financial performance of firms in different stages of the new energy vehicle industry chain was studied, and the impact of fiscal subsidies on the financial performance of upstream firms was greater than the impact on midstream and downstream enterprises [25]. Li et al. studied the optimal channel strategy for automakers to obtain new energy vehicle points under the dual-credit policy. When the technology level of new energy vehicles is low, automakers will choose the procurement point strategy, while, when the cost of procurement is high, automakers tend to choose the natural strategy; thus, under the influence of the dual-credit policy, automakers will change their behavioral strategy [26]. Yu et al. used a network evolutionary game model under the consideration of an asymmetric competition and cooperation structure to reveal the diffusion pattern and trend of new energy vehicles among manufacturers under the influence of the dual-credit policy, and the cooperation strategy can effectively promote the diffusion and penetration of new energy vehicles [27]. Wang et al. analyzed the data affecting consumers’ willingness to purchase new energy vehicles based on a survey of 22 Chinese provinces, and found that subsidy policies and incentives can significantly increase consumers’ willingness to purchase [28]. Zhao et al. used a three-stage evolutionary game model to simulate the diffusion process of new energy vehicles in four authority networks, and the findings suggest that government subsidy policies play a facilitating role in the diffusion process of new energy vehicles [29]. Li et al. discussed the impact of industrial policies on consumers’ willingness to purchase new energy vehicles based on consumer sample survey data; the improvement of new energy vehicle infrastructure by production policies, and the economic benefits of purchasing policies for consumers, have a positive impact on the significance of consumer purchases [30]. On the other hand, the research focuses on the impact of new energy vehicle industry policies on the new energy vehicle industry, such as industrial development, innovation incentives, etc. Yao et al. argue that incentive-based industrial policies are indispensable in the development of the new energy vehicle industry, that the new energy vehicle industry is changing from policy-driven to market-driven, and that the implementation or cancellation of policies should avoid one-size-fits-all approaches and design more precise and personalized policies to guide the healthy development of the new energy vehicle industry [31]. Yu et al. argue that the rise of China’s new energy vehicle industry cannot be achieved without the support of industrial policies, which are jointly guided by the government and the market in terms of technology, with market-oriented forward-looking policies implemented in the process of market cyclical adjustment [32]. Jiang et al. argue that government subsidies should be reduced until they are withdrawn altogether; this is because they found in their study that government subsidies have a crowding-out effect on the R&D investment intensity of new energy vehicle enterprises and that the marginal utility of government subsidies decreases [33]. Qin, Shufeng and Xiong, Yongqing divided China’s new energy vehicle industry policies into subsidized and non-subsidized policies based on the mode of action of industrial policies. The study showed that the incentive of subsidized policies for innovation in new energy enterprises rose and then fell, and innovation was more inclined towards low-quality innovation, while the incentive of non-subsidized policies gradually increased, and innovation was more inclined towards high-quality innovation [34]. Dong, Feng and Zheng, Lu argue that the dual-credit policy, as a market-incentive environmental regulation, is important to promote new energy vehicles and the upgrading of the automobile industry in the context of subsidy withdrawal, and they tested the impact of the dual-credit policy on the TFP of new energy vehicle enterprises via the propensity score matching multiple difference method (PSM-DID), which proved that the market-incentive environmental regulation may trigger a positive productivity effect and validate the Porter hypothesis [35].
Through the review of the literature, it is found that China’s new energy automobile industry policies have contributed greatly to the development of the new energy automobile industry. China’s new energy vehicles already have a window of opportunity for corner overtaking. There is a consensus in academia and industry that subsidized industrial policies are not sustainable. In the context of subsidy decline, empirical studies related to whether the market-oriented dual-credit policy can help China’s new energy vehicles to achieve corner overtaking are lacking. The marginal contribution of this paper lies in the following aspects. First, it uses empirical methods to test the theory of curve overtaking. This paper takes China’s new energy automobile industry as the research object, and uses the difference-in-difference model (DID) to empirically test the theory of curve overtaking. Second, it tests the effect of the dual-credit policy and identifies its mechanism. This has certain reference value for other industries in China to realize curve overtaking.
2.2. Research Hypotheses
The dual-credit policy has a mandatory nature, and the policy stipulates that the minimum ratio of new energy points in 2019 and 2020 must meet 10% and 12% (Parallel Management Measures for Average Fuel Consumption of Passenger Vehicle Enterprises and New Energy Vehicle Credits). This requires car companies to increase their investment in new energy vehicles, purchase production lines, conduct research and development, and produce new energy vehicles, which will allow China’s new energy vehicle industry to form a first-mover advantage, and the initial investment and research and development are ultimately reflected in the performance of listed new energy vehicle companies [23,36].
The dual-credit policy helps China’s new energy vehicle industry to form a first-mover advantage and improve the performance of listed new energy vehicle companies, thus realizing the corner overtaking of China’s auto industry.
Published in 1994, the Automotive Industry Industrial Policy provides joint ventures between China and foreign companies, the Chinese side having no less than 50% of the shares, and requires a certain percentage of domestic production of vehicle parts. In this institutional arrangement, there are many advantages, and these advantages have helped China to quickly establish a modernized automotive industry system. First of all, to attract a large amount of foreign capital, domestic enterprises can only invest part of the funds and foreign auto brands jointly finance the construction of plants; the use of part of the funds can serve to quickly expand production. Secondly, China can introduce the production technology and equipment process of the joint venture brand and quickly improve the quality of the car, but also obtain the endorsement of foreign car brands to achieve rapid development in the domestic market. However, in the context of environmental protection and the ban on the sale of fuel cars, the development of the automotive industry has entered a period of sudden change recently; the most important aspect for the development of the automotive industry is no longer the capital, the traditional inner accumulator technology, and brand premium, but, more importantly, the ability to quickly exploit the momentum of automotive development and accurately grasp the direction of the development of the automotive industry. In this regard, the joint venture car brands, due to path dependence, are unable to change direction, and the impact of the low voice of the domestic representatives of joint venture car manufacturers means that they are not able to quickly seize the development opportunities [37,38]. Therefore, this paper argues that the heterogeneous property rights of automobile enterprises may lead to the different effects of the dual-credit policy on different types of enterprises.
The heterogeneity of car manufacturers’ property rights may have different feedback effects on the dual-credit policy.
According to the Annual Report on the Implementation of Parallel Management of Average Fuel Consumption of Passenger Vehicle Enterprises and New Energy Vehicle Points (2021), the cumulative trading of new energy points and fuel consumption points exceeded 4 million points, and the trading amount reached 4.3 billion yuan, of which the unit price of new energy points being traded exceeded 1204 yuan per point, in 2020. It can be seen that the dual-credit policy has realized that new energy vehicles are not only commodities but also financial products, helping enterprises to broaden their sources of income, and there are even cases wherein the profit from trading points exceeds the profit from selling cars [39,40].
The dual-credit policy changes the performance of automobile manufacturers by adjusting their expectations.
Subsidized industrial policies, on the one hand, create a huge financial burden, and, on the other hand, they cause the incentives to be distorted. The over-reliance of new energy vehicle enterprises on government fiscal subsidies, and even the phenomenon of fraudulent subsidies, is not conducive to new energy vehicle enterprises improving their market competitiveness and the development of China’s new energy vehicle industry [41]. During the gestation period of new energy vehicle industry development, the subsidy-based industrial policy reduced the cost of new energy vehicle enterprises and expanded the scale of the new energy vehicle industry. In the formative years of the industry, it is necessary to reduce the use of government resource allocation tools such as subsidy-based policies, reduce government intervention, consider the role of the market, and promote the healthy and orderly development of the new energy vehicle industry under the conditions of full market competition [42].
The dual-credit policy changes the performance of automobile manufacturers by affecting the level of market competition.
3. Study Design
3.1. Sample Selection and Data Sources
In this paper, given the introduction of Order No. 44 of the Ministry of Industry and Information Technology of the People’s Republic of China and five other departments, the Parallel Management Measures for Average Fuel Consumption of Passenger Vehicle Enterprises and New Energy Vehicle Credits (hereinafter referred to as the dual-credit policy), in September 2017, is used as a quasi-natural experiment. A difference-in-difference model (DID) is constructed to test the causal relationship between the performance of listed new energy vehicle companies and the dual-credit policy reform, using 2017 as the implementation year of the policy. The Decision on Accelerating the Cultivation and Development of Strategic Emerging Industries, issued by the State Council in 2010, listed the new energy automobile industry as a strategic emerging industry in China, so the period 2010–2021 was chosen as the study interval for the sample.
Zotye Auto and Hanma Technology faced the risk of delisting due to financial anomalies, and were specially treated by the Shanghai and Shenzhen Stock exchanges (marked as ST). In order to avoid the influence of the financial anomalies of individual companies on the empirical results, the above two companies were excluded. Xiaokang Stock, BAIC Blue Valley, and CIMC Automobile had been listed for a short time and did not meet the research time range, so they were also excluded. In 2020, FAW Jiefang Automobile changed its name from FAW Sedan to FAW Jiefang, and its main business focus changed from passenger cars to commercial vehicles. In order to avoid its influence on the empirical results, this sample was also removed. There were 17 listed companies that met the final requirements. According to the dual-credit policy, a natural experiment was carefully designed, in which 10 companies mainly engaged in passenger cars were identified as the experimental group and 7 other commercial vehicle companies as the control group; the specific samples are shown in Table 1. The data used for the study were obtained from the annual reports of listed companies, the WIND database, and the CSMAR database, as well as the China Automotive Market Yearbook and China Automotive Industry Yearbook. The data and models were processed using Stata17.
3.2. Model and Variable Definitions
In order to explore the impact of the dual-credit policy on the performance of listed new energy vehicle companies, the following model was constructed:
CPit = β0 + β1DIDit + β2Controlit + μi + λi + εit(1)
DIDit = Timeit × Treatedit(2)
CPit denotes firm performance and is expressed in terms of Tobin’s Q. Timeit is a time dummy variable; the dual-credit policy was introduced in 2017, so 2017 was the year in which the policy started to exert an impact. Specifically, 2017–2021 will be taken as 1, and other years will be taken as 0. The term Treatedit is a dummy variable that indicates whether the listed new energy vehicle companies are affected by the dual-credit policy, and the affected companies are assigned a value of 1; otherwise, we use 0. DIDit is the policy variable, which is the interaction term between the time dummy—i.e., time—and the treatment group dummy—i.e., treated. It is used to estimate the effect of the dual-credit policy, and its coefficient β is the main variable of interest. Controitl is a set of time-invariant firm characteristic variables, such as firm age (Age), firm size (Size), capital structure (Lev), profitability (Roe), government subsidies (Subsidies), and tax preference (Tax preference). These variables are used as control variables and are defined with reference to Table 2. μi and λi control for individual firm effects and time effects, respectively. The subscript i represents the enterprise, and the subscript t represents time. εit represents the random disturbance term.
3.3. Descriptive Statistics
Table 3 reports the descriptive statistics of the main variables. For the explanatory variable of corporate performance (CP), the mean value is 1.372, minimum value 0.747, maximum value 5.548, and variance 0.465, which indicate that the performance of Chinese listed new energy vehicle companies is relatively good and the fluctuation in corporate performance is relatively flat. The mean value of enterprise age is 2.946, the maximum value is 3.611, and the minimum value is 2.197. The mean value of enterprise size is 14.78, the maximum value is 18.34, and the minimum value is 11.28. Both in terms of enterprise age and enterprise size, the size of Chinese new energy vehicle listed companies is relatively similar. This is consistent with the industry characteristics of oligopoly in the auto industry, which is also consistent with the historical status of the development of China’s new energy vehicle industry. In terms of capital structure, the average value of the gearing ratio is 0.625, which indicates that the asset structure is in a relatively reasonable range. However, from the point of view of profitability, the average value of Roe is 0.0634, the maximum value is 0.638, and the minimum value is −1.658, which indicates that the overall profitability of Chinese listed new energy vehicle companies is low, the range of extreme values is large, and the gap in the profitability of different companies in the same industry is large.
4. Results
4.1. Baseline Regression Results
In order to eliminate the influence of the individual effect and time effect of manufacturers on the regression results, this paper adopts the individual time two-way fixed effect model to study the influence of the dual-credit policy on the performance of listed new energy vehicle companies. The explanatory variable is the policy variable (DID), and the explained variable is the performance of listed companies of new energy vehicles (CP). In addition, in order to explore the dynamic effect of the dual-credit policy impact on the performance of listed new energy vehicle companies, the interaction terms DID2018, DID2019, DID2020, and DID2021 are introduced. The specific approach is to take 1 as the time dummy variable in 2018, 2019, 2020, and 2021, and 0 as the time dummy variable in other years, and cross-multiply with the experimental group to form a new policy variable.
Table 4 reports the baseline regression results and dynamic effects of the dual-credit policy variable and the explained variable. Model (1) and Model (2) are the regression results of policy variables and the performance of listed new energy vehicle companies. The time trend item was added to Model (3) and Model (4) to report the dynamic effects of policy variables. The results show that the cross-multiplication terms between the policy variable and the time dummy variable are significantly positive regardless of whether the control variable is added.
(1) The dual-credit policy significantly improves the performance of listed new energy vehicle companies.
The results of Model (1) show that the coefficient of the policy variable (DID) without the inclusion of control variables is 0.325 and is significant at the 1% level. As shown by Model (2), the coefficient of the policy variable (DID) remains significant at the 1% level after the introduction of the control variables, with little change in the coefficient. This shows that the dual-credit policy has significantly improved the performance of listed new energy vehicle companies. Firm size, subsidies, and tax preference are significantly related to firm performance, but other control variables do not show significance. Hypothesis 1 is verified.
(2) The incentive effect of the dual-credit policy becomes stronger as the policy is implemented, but the marginal utility of the policy shows a trend of increasing and then decreasing.
From Models (3) and (4), it is clear that the dynamic effects of policies show significant variability. In terms of regression coefficients, the coefficients are positive and increasing from year to year. In terms of significance, the policy effects are not significant in 2018 and 2019 and only start to be significant after 2020, and their significance and coefficients do not change significantly after adding the control variables. On the one hand, this shows that the dual-credit policy has increased the performance of listed companies in new energy vehicles year by year. On the other hand, it shows that after the policy is introduced, new energy vehicle companies need some time to adjust to it. In terms of marginal utility, the marginal effect of the policy tends to increase first and then decrease, and the marginal utility of the policy is greatest in 2020. There may be several reasons for this shift in policy margins. First, the policy is mandatory. The policy requires automakers to reach a certain percentage of new energy credit, such as 10% in 2019 and 12% in 2020. Therefore, in the initial period of policy promulgation, incentives for new energy vehicle manufacturers are gradually enhanced. Second, once the new energy credits of automobile manufacturers meet the policy requirements, the incentive effect of the policy will be weakened.
4.2. Robustness Tests
4.2.1. Parallel Trend Test
In the baseline regression, the causal effect of the dual-credit policy on the performance of listed companies in new energy vehicles is verified, but the difference-in-difference model (DID) requires the experimental and control groups to meet the assumption of a parallel trend before the policy shock, so it is necessary to test whether the model meets the parallel trend. In this paper, 2017 is taken as the year of policy implementation, i.e., Current; the year before the policy is implemented is recorded as Before1, the year before the policy is implemented is recorded as After1, and so on. Figure 2 shows the results of the parallel trend test, where the regression results are close to zero and insignificant before the policy is implemented, and, after the policy is implemented, the regression results show a gradually increasing trend and are significant, which satisfies the hypothesis of the same trend before the policy is implemented.
4.2.2. Placebo Test
To exclude the effect of omitted variables or chance factors on the empirical results, a placebo test is performed on the baseline regression. The idea of the placebo test in this paper is to replace the original explanatory variables using the variables associated with the explanatory variables, and to re-run the replaced model for empirical analysis as a means to determine whether the original model is robust. Tobin’s Q reflects the performance of listed companies and the capital market’s expectations of the corporate market. Capital market expectations rely on the market operations of listed new energy vehicle companies to make judgments, so market performance was chosen as the explanatory variable for the placebo test. The main business focus of the listed new energy vehicle companies is the production and sales of new energy vehicles, so new energy vehicle sales are an important indicator of market performance, and therefore, new energy sales are used as a proxy variable for market performance and denoted by sales. In order to reduce the effect of the volume scale, the volume is logarithmicized.
Models (1)–(4) are the regression results of policy variables on the market performance (Sales) of listed new energy vehicle companies, where Models (1) and (2) are the baseline regression results, and Models (3) and (4) are the regression results of dynamic effects (Table 5). First, for market performance, the regression results show that the effects of whether or not to include the control variable policy are positive and significant at the 1% level. Secondly, in terms of dynamic effects, the impact of the policy increases year by year. In summary, the effects of policy variables on replacement variables are consistent with the empirical results in Section 4.1.
4.3. Heterogeneity Analysis
From the above analysis, it can be seen that the dual-credit policy has improved the performance of listed companies of new energy vehicles, but we should note that the impact may be different for companies with different ownership systems. In order to build a modern automobile industry system and to protect the national automobile industry, China is following a development path of exchanging market for technology. On the one hand, foreign automobile companies are introduced to establish joint venture brands with domestic automobile companies to absorb foreign capital and introduce technology, and, on the other hand, they seek to protect the national automobile industry in order to restrict foreign automobile companies. Therefore, due to the unique nature of the ownership system of China’s auto industry, in terms of traditional fuel cars, joint ventures with foreign enterprises, with joint venture brands of car companies, are more technologically advanced, with more capital; compared to domestic car brands, these two aspects of the disadvantages are obvious. However, as the date of the ban on the sale of fuel cars in various countries approaches, new energy vehicles are becoming the future direction of the auto industry; instead of joint ventures with foreign car companies, it will be path-dependent, with joint venture car brands in corporate control, which will also be subject to the constraints of foreign car companies, but also with the timely adjustment of the direction of development compared to domestic cars. Therefore, it is necessary to analyze the heterogeneity exhibited by firms with different property rights systems with respect to policy implementation. In order to conduct a more heterogeneous analysis, and with reference to the availability of data, this paper defines listed new energy vehicle companies without joint venture brands as domestic automobile companies and listed new energy vehicle companies with joint venture brands as joint venture automobile companies, and we use group regression to empirically demonstrate the differences and similarities of the dual-credit policy on the performance of enterprises under different ownership systems.
4.3.1. Listed Companies of Domestic Automobiles
Table 6 reports the regression results and dynamic effects for domestic automotive listed companies. From Model (1) and Model (2), it is clear that the policy effect is significantly positive at the 1% level with or without the addition of control variables. Compared with the regression results of the full sample, the coefficients of the policy effects of domestic automotive listed companies are all higher than those of the full sample. From Models (3) and (4), the effect of the policy varies across years, with smaller regression coefficients in 2018 and 2019 and more pronounced policy effects in 2020 and 2021. Both in terms of the baseline regression results and dynamic effects, the results of domestic car companies are largely similar to those of the full sample.
4.3.2. Jointly Listed Automotive Companies
Table 7 shows that the dual-credit policy also has a positive effect on the joint venture companies, but it is not as significant as for the domestic companies. The regression coefficient with the addition of control variables is 0.187, which is significant at the 10% level. In terms of dynamic effects, although the dual-credit policy has a positive effect on the performance of JVs in general, it is only significant after 2020 and the coefficient is much smaller than that of the overall sample. However, the effect is significant only after 2020, and the coefficient is much smaller than the regression coefficient of the overall sample.
4.3.3. Comparative Analysis of Domestic Automobile Companies and Joint Venture Automobile Companies
One of the purposes of this paper is to determine whether China’s dual-credit policy can help the new energy vehicle industry to achieve corner overtaking by comparing the heterogeneity of property rights. Combining the empirical results in Table 6 and Table 7, it is found that the dual-credit policy has a more obvious effect on the performance of domestic listed companies, and the coefficient of 0.566 for domestic listed companies is 0.379 higher than that of 0.187 for joint ventures, which is a huge difference. In terms of a dynamic effect, regardless of whether we consider the listed domestic cars or the listed joint venture cars, the effect of the dual-credit policy becomes more and more obvious as time passes, but the domestic cars remain superior, which verifies Hypothesis 2. This paper suggests that this may be due to the following reasons. First, with the negative moderating effect of firm size, the coefficient of firm size in the control variables is negative and significant. Chinese joint venture auto companies are generally larger in size, while domestic auto companies are smaller in size. It is easier for domestic auto companies to complete the transition to the new energy vehicle industry, and they can quickly launch new energy vehicle products to meet the market demand and quickly capture the market. Large companies have higher transition costs and a slower market transition, so the effect of the dual-credit policy on domestic companies is better and more significant. Second, the joint venture’s decision-making is dependent on the joint venture company’s influence, not according to the changes in the automotive market, to make timely adjustments. Although the joint venture car companies are jointly funded by Chinese and foreign entities, corporate profits are focused mainly on the joint venture car brand, while the Chinese side fails to optimize the engine, transmission, and other key technologies, so the decision regarding the major transformation of the enterprise by foreign influence is relatively large. In summary, the dual-credit policy can significantly promote the performance of China’s listed companies in new energy vehicles, while the impact on domestic cars is higher than that on joint venture cars, and there is an opportunity to achieve the corner overtaking of domestic cars.
4.3.4. Parallel Trend Test
Because the overall sample passed the parallel trend test and robustness test, the heterogeneity analysis only needed to be verified by the parallel trend test. The results of the tests are shown in Figure 3 and Figure 4, which show that the performance of domestic automotive companies passed the parallel trend test and had a significant impact on the performance of domestic automotive companies shortly after the implementation of the dual-credit policy, with a continuous upward trend over time. However, the results are not significant for listed companies with joint ventures in automobiles. This indicates that the effect of the dual-credit policy is significantly different between domestic and joint venture listed companies, with the incentive effect on domestic listed companies being better than that on joint venture cars.
5. Mechanism Testing
The above empirical results indicate that the dual-credit policy significantly improves the market competitiveness of listed companies in new energy vehicles, and the impact on domestic listed companies is better than that on joint venture listed companies. Thus, through which channels does the dual-credit policy have an impact on the performance of listed new energy vehicle companies? Will heterogeneous companies react differently? In light of the above, this paper examines the mechanism by which the dual-credit policy affects the performance of listed companies in new energy vehicles from two aspects: corporate market expectations and market competition.
5.1. Enterprise Market Expectation Mechanism
The so-called dual-credit refers to the average fuel consumption points (CAFC points) and new energy vehicle points (NEV points). The offset requirements of the points indicate that negative CAFC points can be offset by positive CAFC points or by NEV points, while negative NEV points can only be offset by positive NEV points, and NEV points can be traded freely, while CAFC points can only be transferred to each other in affiliated companies. Therefore, the dual-credit policy forces enterprises to reduce vehicle energy consumption to obtain positive CAFC points on the one hand, and encourages enterprises to produce high-quality new energy vehicles accepted by the market to obtain positive NEV points on the other. Therefore, the implementation of the dual-credit policy can adjust enterprises’ expectations of the new energy vehicle market and accelerate their transformation to new energy vehicles. The production and sales of new energy vehicles can not only qualify for the production of traditional fuel vehicles by obtaining positive NEV points to offset negative CAFC points, but can also obtain profits by trading positive NEV points, which creates a huge incentive for automakers to transition to new energy vehicles.
This paper constructs a model to test whether the dual-credit policy can improve the performance of listed companies in new energy vehicles by adjusting their market expectation mechanism:
CPit = β0 + β1DIDit + β2DIDit × Expectationit + β3Expectationlit + β4Controlit + μi + λi + εit(3)
Enterprise market expectation (Expectationit) measures the enterprise’s expectation of the new energy vehicle market, expressed as the proportion of enterprise new energy vehicle sales to total vehicle sales. The implementation of the dual-credit policy will affect the market expectations of enterprises in the new energy vehicle industry, thus accelerating the transition to new energy vehicles. The strength of enterprises’ expectations for the new energy vehicle market will be directly expressed in the production and sales of new energy vehicles. If enterprises have good expectations for the market of the new energy vehicle industry, they will increase the production of new energy vehicles, so the proportion of new energy vehicle sales to total vehicle sales can be used to measure the enterprises’ expectations for the new energy vehicle market.
Table 8 reports the results of testing the mechanism of adjusting firms’ market expectations by the dual-credit policy. It is found that the regression results of the full sample have a positive moderating effect on the market expectations of enterprises, which can strengthen the promotion effect of the double points policy and verify Hypothesis 3. Based on the heterogeneity of property rights, comparing columns (3), (4) and (5), (6), we find that the policy effect and the interaction term of domestic car companies are significantly positive, while the moderating effect of joint venture car companies is not significant. This indicates that the dual-credit policy can adjust the market expectation of enterprises to influence the performance of new energy vehicle companies, and the market expectation of enterprises is better than that of joint venture vehicles. The new energy vehicle transformation of China-made enterprises results in more independent and autonomous decision-making ability than joint ventures, and domestic cars, in the face of changes in the automotive market, can be more independent and are faster to adjust market expectations.
5.2. Market Competition Mechanism
The implementation of the dual-credit policy marks a shift from a subsidy-based policy to a market-based policy for China’s new energy vehicle industry. The trading and offsetting of points means that car companies have to compete not only in the auto product market but also in the points trading market, causing new energy vehicles to participate in the market competition in a broader and deeper area. In order to verify whether the dual-credit policy can help enterprises to improve, the following model was constructed:
CPit = β0 + β1DIDit + β2DIDit × Marketit + β3Marketit + β4Controlit + μi + λi + εit(4)
Market competitiveness Marketit = Xit/Xt,Xit denotes the new energy vehicle sales of firm i in year t, andXt indicates the total new energy vehicle sales.
Table 9 reports the results of the test of the market competition mechanism. From the test results of the full sample, the interaction terms of both policy variables are positive and significant at certain levels, indicating that the market competition mechanism has a positive moderating effect on the policy effect, which verifies Hypothesis 4. Looking at domestic cars and joint ventures, the regression results for domestic cars are very similar to those for the full sample, and the market mechanism has the same moderating effect on domestic cars, but the results for joint ventures are not significant.
In summary, for the full sample, the dual-credit policy improves the performance of listed new energy vehicle companies by adjusting their market expectations and strengthening the market competition mechanism. There are some differences in the mechanisms of action based on the consideration of heterogeneity. In terms of adjusting the market expectations of enterprises, dual credit can significantly affect the market expectations of enterprises to improve the performance of listed new energy vehicle companies, but the impact on joint venture vehicles is not significant. In terms of the market competition mechanism, the findings are similar to those of the firm market expectations mechanism, which is not significant for JVs. Therefore, the heterogeneity of the property rights structure has led to a difference in the mechanism of the dual-credit policy, and it is the difference in the mechanism of action that provides China’s new energy vehicle industry with the opportunity to achieve corner overtaking.
6. Conclusions and Recommendations
6.1. Conclusions
This paper investigates the impact of the dual-credit policy on the performance of listed new energy vehicle companies by using a quasi-natural experiment constructed by the dual-credit policy in China’s Shanghai and Shenzhen stock markets. According to the heterogeneity of property rights, a comparison model between domestic new energy vehicles and joint venture new energy vehicles is carefully constructed, and the comparative analysis of regression results is used to determine whether domestic new energy vehicles achieve corner overtaking in the joint venture of new energy vehicles. Finally, the mechanism of the dual-credit policy is tested.
The study finds that (1) the dual-credit policy significantly improves the performance of listed new energy vehicle companies, but the marginal utility of the policy decreases; (2) the impact of the dual-credit policy on the performance of domestic listed new energy companies is better than that of joint venture listed new energy companies; (3) the dual-credit policy improves the competitiveness of listed new energy vehicle companies mainly through the market expectation and market competition mechanism; (4) there is a large difference in the mechanism of the dual-credit policy for domestic and joint venture new energy vehicles, with domestic new energy vehicles improving their performance through corporate market expectations and market competition mechanisms, while neither mechanism is significant for joint venture new energy vehicles. Possible reasons for this are as follows. First, the adjustment of the market expectations of the joint venture automobile company is influenced by foreign capital, and it is not able to make timely judgmental adjustments according to market conditions. Secondly, joint ventures are highly competitive in the field of traditional automobiles, and joint ventures are reluctant to reorient their industries. Third, joint venture car companies are relatively large in size and difficult to transform. Fourth, the path dependence of the joint venture car company must be considered.
6.2. Recommendations
6.2.1. Build a Dual Market Mechanism Based on the Trading of New Energy Vehicle Products and Regulated by the Double Credit Virtual Trading Market
The mandatory dual-credit policy enforces the transformation of traditional cars at the early stage of the development of the new energy vehicle industry, which is conducive to the formation of a first-mover advantage in the new energy vehicle industry in China, but we also face a situation involving the diminishing marginal utility of the policy. The tradability of points involves the change in the new energy vehicle policy from the mandatory to the active participation of vehicle enterprises, which improves the motivation of vehicle enterprises to develop new energy vehicles. Therefore, it is necessary to speed up the development of the points trading system, establish a points trading market, and regulate points trading. We should build a dual market mechanism based on the trading of new energy vehicle products and regulated by the virtual trading market of double points. This will promote the healthy and orderly development of China’s new energy vehicle market by incorporating consumers, manufacturers, and the government into a unified market mechanism.
6.2.2. Strengthening the Protection of the Domestic Automobile Industry
The dual-credit policy has different mechanisms for domestic cars and joint venture cars. In the traditional fuel car industry, domestic cars have many disadvantages in relation to joint venture cars, resulting in domestic cars’ competitiveness and corporate performance displaying a large gap with those of joint venture cars. In the field of new energy vehicles, domestic cars already have a first-mover advantage. BYD, the leading domestic new energy vehicle company, has a reputation in the field of new energy vehicles, with blade batteries, DMI hybrid systems, and other core technologies, but there are still many domestic new energy vehicles that are not recognized by the market and consumers. This is not a small challenge for our national automobile industry. Therefore, in order to achieve a transition in China’s auto industry regarding new energy vehicles and facilitate the development of the domestic auto industry, the government should devise corresponding support policies for domestic vehicles, strengthen support for the domestic auto industry, and protect the domestic new energy vehicle industry and production system.
6.2.3. The Government Coordinates Scientific and Technological Resources to Provide Technical Support for the Development of New Energy Vehicles
The development direction of new energy vehicles is undetermined, and various countries are testing different new energy vehicle technologies, such as pure electric vehicles, oil–electric hybrid vehicles, fuel cell vehicles, hydrogen energy vehicles, etc. Each technology requires significant investment in R&D and support from related disciplines, so policies should coordinate the efforts of all sides of society to provide technical support for breakthroughs in new energy vehicle technology.
6.2.4. Introduction of Wholly Foreign-Owned Enterprises
Under the business model of Sino–foreign joint venture, China’s automobile industry relies on foreign advanced technology and attracted foreign capital, which has helped China to realize the rapid development of the automobile industry, gradually establish a modern automobile industry system, and quickly become the leading country in automobile production and sales. The restriction on the foreign share ratio protects China’s national auto industry and improves the localization rate of auto parts, but in terms of core technology, always firmly in the hands of foreign companies, until now, there has been no major change. Therefore, under the condition that our auto industry has a certain foundation, we can explore the relaxation of foreign equity ratio restrictions in the auto industry and the introduction of wholly foreign-owned enterprises to increase market competition, forcing domestic car companies to innovate and enhance their core competitiveness.
6.2.5. Incorporating a Wider Range of Vehicle Types into the Dual-Credit Policy System
At present, the dual-credit policy is implemented for new energy passenger cars, while buses, trucks, special-purpose vehicles, and other commercial vehicles are not included in the double points policy system. To force commercial vehicle enterprises to innovate, it is necessary to coordinate the development of the entire new energy industry, form an industry-wide first-mover advantage, achieve new energy core technology breakthroughs, and master the core competitiveness of the new energy vehicle industry.
6.2.6. Exploring Different New Energy Technology Points Assignment Methods
The dual-credit policy plays the role of a baton, while the specific direction of the development of new energy vehicles is not yet clear. In order to encourage new energy vehicles in different directions, we can dynamically adjust the points according to the stage of development of new energy vehicles and the needs of society. At present, the main basis of China’s new energy vehicle market is pure electric vehicles, and in 2020, pure electric vehicles accounted for more than 80% (China Automotive Development Annual Report 2021) of the total number of new energy vehicles. Pure electric vehicles are constrained by the power battery, and there is no revolutionary breakthrough in battery technology, coupled with the high cost of post-battery maintenance and the environmental hazards of improper maintenance, so this paper considers pure electric vehicles to be a transitional stage product among new energy vehicles. Is it worthwhile to explore the possibility of lowering the points for pure electric vehicles and increasing the points assigned to other types of new energy vehicles to encourage other energy types of new energy vehicles? With this idea, the points can be dynamically adjusted according to the stage of development of new energy vehicles and the needs of society.
Methodology, Y.L.; resources, Y.L. and L.Z.; data curation and analysis, Y.L., L.Z. and J.L.; writing—original draft, Y.L., L.Z., J.L. and X.Q.; writing—review and editing, Y.L., L.Z. and J.L.; final checks, Y.L., L.Z., J.L. and X.Q.; supervision, L.Z. and Y.L. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
Not applicable.
We would like to extend particular thanks to the editor and the anonymous reviewers for their valuable comments that helped in greatly improving the quality of this paper.
The authors declare no conflict of interest.
Footnotes
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Enlarge this image.Figure 4. Parallel trend examination of joint venture automotive listed companies.
Research samples.
| Experimental Group of Car Companies (Stock Code) | Control Group Car Companies (Stock Code) |
|---|---|
| Jianghua Automobile (600418) |
Jinlong Motor (600686) |
Variable definitions.
| Type | Indicator | Specific Indicator | Definition |
|---|---|---|---|
| Explained variables | Corporate Performance | CP | Tobin’s Q |
| Explanatory variables | Policy Variables | DID | Time × Treated |
| Control variables | Firm Age | Age | Take logarithm of business registration time for enterprises |
| Enterprise Size | Size | Logarithmic value of total assets | |
| Capital Structure | Lev | Balance sheet ratio | |
| Profitability | Roe | Corporate Return on Net Assets | |
| Government Subsidies | Subsidies | Government grants/Operating income | |
| Tax Preference | Tax preference | (Various taxes and fees paid by enterprises—Tax refunds)/Business income |
Descriptive statistics of main variables.
| VARIABLE | N | Mean | Sd | Min | Max |
|---|---|---|---|---|---|
| CP | 204 | 1.372 | 0.465 | 0.747 | 5.548 |
| Age | 204 | 2.946 | 0.248 | 2.197 | 3.611 |
| Size | 204 | 14.75 | 1.364 | 11.28 | 18.34 |
| Lev | 204 | 0.625 | 0.147 | 0.301 | 0.975 |
| Roe | 204 | 0.0634 | 0.235 | −1.658 | 0.638 |
| Subsidies | 204 | 0.0113 | 0.0157 | 0 | 0.161 |
| Tax preference | 204 | 0.0297 | 0.0341 | −0.0522 | 0.115 |
Baseline regression results and dynamic effects.
| VARIABLE | CP | |||
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| DID | 0.325 *** | 0.370 *** | ||
| (0.104) | (0.112) | |||
| DID2018 | 0.222 | 0.247 * | ||
| (0.138) | (0.134) | |||
| DID2019 | 0.184 | 0.210 | ||
| (0.134) | (0.135) | |||
| DID2020 | 0.493 *** | 0.653 *** | ||
| (0.157) | (0.196) | |||
| DID2021 | 0.543 *** | 0.666 *** | ||
| (0.195) | (0.188) | |||
| Age | 0.227 | 0.118 | ||
| (1.272) | (1.285) | |||
| Size | −0.283 ** | −0.342 *** | ||
| (0.123) | (0.121) | |||
| Lev | −0.022 | −0.009 | ||
| (0.505) | (0.512) | |||
| Roe | −0.033 | −0.108 | ||
| (0.168) | (0.147) | |||
| Subsidies | −2.778 ** | −1.805 | ||
| (1.400) | (1.268) | |||
| Tax preference | 3.766 ** | 4.845 ** | ||
| (1.879) | (1.932) | |||
| _cons | 2.004 *** | 4.934 | 1.995 *** | 5.906 |
| (0.220) | (4.769) | (0.217) | (4.737) | |
| Enterprise fixed | YES | YES | YES | YES |
| Year fixed | YES | YES | YES | YES |
| N | 204 | 204 | 204 | 204 |
| R2 | 0.499 | 0.543 | 0.512 | 0.566 |
Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.
Regression results and dynamic effects of replacement variables.
| VARIABLE | Sales | |||
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| DID | 4.790 *** | 4.680 *** | ||
| (0.419) | (0.417) | |||
| DID2018 | 4.486 *** | 4.482 *** | ||
| (0.573) | (0.586) | |||
| DID2019 | 4.186 *** | 4.054 *** | ||
| (0.606) | (0.641) | |||
| DID2020 | 4.284 *** | 3.944 *** | ||
| (0.709) | (0.687) | |||
| DID2021 | 4.961 *** | 4.846 *** | ||
| (0.730) | (0.709) | |||
| Age | −4.664 | −4.325 | ||
| (4.794) | (5.091) | |||
| Size | 0.542 | 0.364 | ||
| (0.425) | (0.448) | |||
| Lev | 3.449 ** | 3.329 * | ||
| (1.650) | (1.726) | |||
| Roe | 0.365 | 0.159 | ||
| (0.388) | (0.402) | |||
| Subsidies | −1.630 | 1.662 | ||
| (6.902) | (7.709) | |||
| Tax preference | −13.058 | −12.642 | ||
| (9.598) | (10.404) | |||
| _cons | 3.949 *** | 6.782 | 3.653 *** | 7.879 |
| (0.486) | (13.502) | (0.568) | (13.978) | |
| Enterprise fixed | YES | YES | YES | YES |
| Year fixed | YES | YES | YES | YES |
| N | 204 | 204 | 204 | 204 |
| R2 | 0.829 | 0.838 | 0.811 | 0.819 |
Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.
Regression results and dynamic effects of domestic automobile companies.
| VARIABLE | CP | |||
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| DID | 0.496 *** | 0.566 *** | ||
| (0.116) | (0.157) | |||
| DID2018 | 0.286 * | 0.353 ** | ||
| (0.147) | (0.175) | |||
| DID2019 | 0.248 * | 0.235 | ||
| (0.137) | (0.160) | |||
| DID2020 | 0.745 *** | 0.941 *** | ||
| (0.188) | (0.262) | |||
| DID2021 | 0.946 *** | 1.103 *** | ||
| (0.199) | (0.200) | |||
| Age | −0.032 | −0.170 | ||
| (2.348) | (2.146) | |||
| Size | −0.266 ** | −0.339 *** | ||
| (0.116) | (0.103) | |||
| Lev | 0.206 | 0.389 | ||
| (0.563) | (0.569) | |||
| Roe | −0.033 | −0.130 | ||
| (0.184) | (0.168) | |||
| Subsidies | −3.226 ** | −2.419 * | ||
| (1.613) | (1.396) | |||
| Tax preference | 3.829 | 5.289 * | ||
| (2.838) | (2.735) | |||
| _cons | 2.083 *** | 5.275 | 2.074 *** | 6.363 |
| (0.284) | (6.918) | (0.284) | (6.403) | |
| Enterprise fixed | YES | YES | YES | YES |
| Year fixed | YES | YES | YES | YES |
| N | 144 | 144 | 144 | 144 |
| R2 | 0.542 | 0.576 | 0.578 | 0.627 |
Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.
Regression results and dynamic effects of joint venture automobile companies.
| CP | ||||
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| DID | 0.153 | 0.187 * | ||
| (0.111) | (0.104) | |||
| DID2018 | 0.159 | 0.118 | ||
| (0.159) | (0.120) | |||
| DID2019 | 0.120 | 0.180 | ||
| (0.152) | (0.152) | |||
| DID2020 | 0.242 | 0.452 ** | ||
| (0.163) | (0.204) | |||
| DID2021 | 0.141 | 0.313 * | ||
| (0.162) | (0.170) | |||
| Age | −2.534 | −2.597 | ||
| (1.584) | (1.632) | |||
| Size | −0.478 *** | −0.517 *** | ||
| (0.153) | (0.164) | |||
| Lev | −0.046 | −0.097 | ||
| (0.681) | (0.693) | |||
| Roe | −0.108 | −0.144 | ||
| (0.165) | (0.150) | |||
| Subsidies | −2.192 * | −1.597 | ||
| (1.267) | (1.259) | |||
| Tax preference | 6.659 *** | 7.476 *** | ||
| (2.342) | (2.514) | |||
| _cons | 2.120 *** | 14.571 ** | 2.117 *** | 15.237 ** |
| (0.285) | (6.251) | (0.286) | (6.503) | |
| Enterprise fixed | YES | YES | YES | YES |
| Year fixed | YES | YES | YES | YES |
| N | 144 | 144 | 144 | 144 |
| R2 | 0.527 | 0.629 | 0.528 | 0.639 |
Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.
Enterprise market expectation mechanism test.
| Full Sample | Domestic Automobile Company | Joint Venture Automobile Company | ||||
|---|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | (6) | |
| DID | 0.079 | 0.164 | 0.261 ** | 0.333 * | 0.041 | 0.237 * |
| (0.094) | (0.114) | (0.131) | (0.200) | (0.117) | (0.124) | |
| DID_Expectation | 1.390 *** | 1.218 *** | 1.091 *** | 0.987 *** | 0.418 | −0.731 |
| (0.398) | (0.313) | (0.408) | (0.332) | (0.914) | (0.841) | |
| Expectation | −0.785 ** | −0.470 * | −0.774 ** | −0.484 | −0.746 ** | 0.062 |
| (0.371) | (0.273) | (0.364) | (0.315) | (0.376) | (0.230) | |
| Age | 0.367 | 0.365 | −2.522 | |||
| (1.272) | (2.618) | (1.611) | ||||
| Size | −0.219 * | −0.228 ** | −0.497 *** | |||
| (0.119) | (0.106) | (0.169) | ||||
| Lev | 0.213 | 0.397 | −0.050 | |||
| (0.511) | (0.599) | (0.693) | ||||
| Roe | −0.016 | −0.010 | −0.109 | |||
| (0.172) | (0.185) | (0.167) | ||||
| Subsidies | −2.375 * | −2.676 * | −2.316 * | |||
| (1.285) | (1.528) | (1.339) | ||||
| Tax preference | 4.766 ** | 4.529 | 6.430 *** | |||
| (1.885) | (2.744) | (2.406) | ||||
| _cons | 2.115 *** | 3.645 | 2.178 *** | 3.682 | 2.215 *** | 14.776 ** |
| (0.255) | (4.787) | (0.312) | (7.555) | (0.315) | (6.490) | |
| Enterprise fixed | YES | YES | YES | YES | YES | YES |
| Year fixed | YES | YES | YES | YES | YES | YES |
| N | 204 | 204 | 144 | 144 | 144 | 144 |
| R2 | 0.533 | 0.567 | 0.565 | 0.589 | 0.545 | 0.629 |
Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.
Market competition mechanism test.
| Full Sample | Domestic Automobile Company | Joint Venture Automobile Company | ||||
|---|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | (6) | |
| DID | 0.307 *** | 0.260 ** | 0.410 *** | 0.372 ** | 0.172 | 0.276 |
| (0.114) | (0.131) | (0.124) | (0.167) | (0.136) | (0.177) | |
| DID_Market | 0.987 | 2.557 *** | 1.446 | 2.946 ** | 1.046 | −0.703 |
| (0.785) | (0.909) | (0.991) | (1.225) | (1.057) | (1.751) | |
| Market | −0.604 | −0.457 | 0.503 | 0.571 | −0.962 | −0.789 |
| (0.472) | (0.585) | (0.534) | (0.529) | (0.847) | (0.878) | |
| Age | 1.149 | 1.033 | −3.392 | |||
| (1.530) | (2.363) | (2.538) | ||||
| Size | −0.352 *** | −0.363 *** | −0.470 *** | |||
| (0.115) | (0.117) | (0.150) | ||||
| Lev | −0.018 | 0.233 | −0.010 | |||
| (0.501) | (0.565) | (0.646) | ||||
| Roe | −0.037 | −0.041 | −0.111 | |||
| (0.166) | (0.186) | (0.162) | ||||
| Subsidies | −2.887 ** | −3.311 * | −1.980 | |||
| (1.403) | (1.685) | (1.236) | ||||
| Tax preference | 3.322 * | 2.979 | 7.002 *** | |||
| (1.962) | (2.828) | (2.572) | ||||
| _cons | 2.015 *** | 3.430 | 2.075 *** | 3.722 | 2.137 *** | 16.659 * |
| (0.223) | (5.257) | (0.288) | (6.916) | (0.291) | (8.590) | |
| Enterprise fixed | YES | YES | YES | YES | YES | YES |
| Year fixed | YES | YES | YES | YES | YES | YES |
| N | 204 | 204 | 144 | 144 | 144 | 144 |
| R2 | 0.501 | 0.553 | 0.547 | 0.588 | 0.529 | 0.631 |
Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.
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; Qiao, Xinpei 2 1 Xinjiang Research Center for High-Quality Macroeconomic Development, Xinjiang University, Urumqi 830002, China; School of Economics and Management, Xinjiang University, Urumqi 830002, China
2 Department of East-Asia Studies Graduate School, PaiChai University, Daejeon 35337, Republic of Korea