Research Question

US and China are two major markets for Electric Vehicles (EV). Two markets combined sold over 60% of world’s EV in 2017. Despite the high market share, car makers are facing totally different types of competition in those two markets. In US, EV market is dominated by Tesla along while in China, there are significantly more brands competing with each other.

It is really interesting that why china is able to have a good number of EV makers while the market is still growing. One factors that need to be considered here is local protectionism. This kind of policy is not like nation-wide subsidize policies from which all the EV makers can be benefited. Local governments and state-owned enterprises have great incentives to shift their EV purchases toward local brands in order to gain job creations and tax revenue. Although purchases from government agencies are small numbers, they are important for the local EV makers to survive when the EV market is at early stage, supporting infrastructures are still waiting to be built and private purchase is extremely low.

I would like to examine the market protectionism in China’s EV market, what patterns does it exhibits, how it evolves over time, how local markets are affected by it and how it affects EV technology adoption in China. For this analysis, I would like to examine is there disproportional local brand purchase in each province, are those orders coming from personal buyers or government agencies and how the EV market evolve over time.

Data Source

NEV data 2015-2017.xlsx

The data I have in hand is the insurance filing data from Innovation Center of Energy and Transportation (iCET). The data contains the location and specification information of each newly insurance car in China from 2015 to 2017. The data is not publicly disclosed so we cannot list the source for this data set.

For 2016, there is no city data in it. Most of the non-numeric data are in Chinese except some car models. In order to present in a chart, translation is required. There is also a portion of the data that is missing ownership and OEM. Overall, the dataset is a good representation of what is happening in the Chinese EV market from 2015 to 2017 since insurance is required for all new vehicles.

I split the original data into three files according to years and those three datasets are loaded here for analysis.

OEM_localtion.xlsx

This dataset is created by Innovation Center of Energy and Transportation (iCET). It includes all OEM locations in China. This is the original data that have not been manipulated at all.

passenger vehicle sales data 2015-2016.xlsx

2017 passenger vehicle sales.xlsx

Those two datasets are provided by Innovation Center of Energy and Transportation (iCET). It includes total auto sale in China from 2015 to 2016. The data is original data that has not been touched by others. Since we have some NA in the NEV sale data, there will be some bias if we combine those two dataset together. However, only a few rumber of EV sales are affected by this NA issue and giving the size of Chinese auto market, we can ignore the small bias here.

Results

In this analysis, we choose 15 provinces out of 31. The reason for this is that not all provinces have local EV makers in 2015 and 2016. In order to analysis local purchase over time, I limited the scope to provinces that have EV makers since 2015. I also distinguished the province into two groups: Top 5 Provinces and Other Provinces. Top 5 provinces include Beijing, Shanghai, Zhejiang, Guangdong and Hunan.

As shown in the previous chart, those 5 provinces in total has more EV sales than other 10 provinces’ combined in 2015 and 2016, and only slightly less in 2017. They represent provinces with relatively big EV market at the beginning stage.

For those two groups, I break down order types into two categories, personal purchases and state purchases. This is determined according to the ownership of the car. I then plot the share of local brand of EV sales based on the province group and order type and the following chart presents the result.