By Muyuan Chen, Jingyi Jiang, Bin Ji
COVID-19 epidemic is a human tragedy and is showing a huge impact on the global economy. The pandemic has affected countries all over the world including not only local economy but also international trade. China, as one of the first countries suffered from the epidemic, is also not immune. And with the stay-at-home orders issued in mid-March, France is also in a state to fight the epidemic. This paper is to study the impact of the epidemic on trade between these two countries
In response to a question on the impact of COVID-19 on China’s economy on Febuary11, the Foreign Ministry said that the fundamentals and long-term positive trend of China’s economy will not change because of the epidemic, and Chinese government has the ability to minimize the impact of the epidemic on China’s economy. However, we cannot deny that the epidemic has had some negative effects on trade all over the world. Only after analyzing accurate data can we get the exact answer.
Due to the different peak times of the epidemic, the study focused on the peak of the epidemic in China from February to March and the peak in France from April to May. By studying more than 40 kinds of import and export commodities by applying OLS (ordinary least squares), we can see the growing trend of trade between China and France, as well as the challenges brought by COVID-19.
Data And Empirical Strategy
The data used in the paper covers the import and export between China and France. And by collecting that of Feb.-Mar. 2019, Feb.-Mar. 2020, Apr.-May. 2019, and Apr.-May. 2020 from Trendeconomy, a database with international trade data of different countries, a comparison between trade with and without COVID-19 can be revealed.
From the database, we get 21 types of goods for trades, including food, clothing, machinery manufacturing and so on. Data 1 shows the export (Feb.-Mar. 2019,2020) from France to China with 80 samples. Data 2 shows the import (Apr.-May. 2019, 2020) of France from China with 84 samples. Data 3 shows the export (Apr.-May. 2019,2020) from France to China with 80 samples. Data 4 shows the import (Feb.-May 2019, 2020) of France to China with 84 samples.
In exports, we miss the data of one kind of goods (art, collectibles and antiques). But it will not have a big impact to the overall trend since we already have enough samples to represent the whole import and export volume between two countries.
In order to get an accurate intervention effect evaluation, we choose the general regression model to test the impact brought by COVID-19 on trade between China and France.
We use value (total trade per commodity) as the measure of foreign trade, and one dummy variables as explanatory variables: year (whether an COVID-19situtation occurred). We divided the data into experimental group and control group.
Model 1 (impact of COVID-19 to the export from France to China from Feb. to Mar. in 2019 and 2020)
Model 2 (impact of COVID-19 to import of France from China from Apr. to May. In 2019 and 2020)
Model 3 (impact of COVID-19 to export from France to China from Apr. to May. In 2019 and 2020)
Model 4 (impact of COVID-19 to import of France from China from Feb. to Mar. in 2019 and 2020)
After analyzing the data above, we get four models. According to Model1 and Model 4 with the data of trade from Feb. to Mar., which is the peak time of epidemic in China, the trade volume including both import and export between two countries declined. By Model 2 and Model 3 with the data of trade volume from Apr. to May., which is the peak time of epidemic in France, the import increased but export decreased.
However, we cannot trust the differences with 95% confidence interval. After processing the data with Stata, we find that the results are not significant according to the p value of year (model 1:0.463, model 2: 0.912, model 3: 0.248, and model 4: 0.866), and R-squared that are too close to 0, indicating that variables in the model are not enough to explain the change of value. Such result can be explained by the incompleteness of the regression model since there are no control variables. With more control variables like unit prices and market volatility, we can get a more convincing model.
Results and interpretation
Although the data are not significant, we can still conclude that the epidemic has not had a significant impact on China-France trade. In order to get a more convincing answer, we decide to do some other models and still use value (total trade per commodity) as the measure of foreign trade, and one dummy variables as explanatory variables: year (whether an COVID-19situtation occurred) in regression model.
Due to the wide variety of goods, we suspect that the trade in different types of goods interferes with the final result, so we extract the data of textile and garment industry from China to France and agricultural products from France to China for dealing with.
As usual, the traditional peak season of the textile and garment industry comes after the Spring Festival, which will lead to the increase of textile goods from all over the world including France. However, because of the outbreak in Apr. and May. in Europe, the volume of imports is likely to be affected at the peak of industrial trade if we believe in the great impact done by COVID-19 in trades between two countries.
We extract data of textiles from France to China (Apr.-Mar., 2019 and 2020) to do both regression model and t-test.
Model 5 (regression model)
Model 6 (t-test)
From the model, we see that the p value, which is 0.28, is close to the p value of model 3 (impact of COVID-19 to export from France to China from Apr. to May. In 2019 and 2020). Moreover, the hypothesis in t-test assuming that there is a decrease in trade volume of textiles gets p value of 0.86, which cannot demonstrate the significance of data. Consequently, the result is still insignificant for textiles and garment industry.
In addition, since France is the largest agricultural country in Europe and the world’s second largest exporter of agricultural products and food and China imports a lot of French pork and dairy products every year, we decided to use this representative kind of commodity to get a more convincing result of the impact of epidemic on trades. We extract the data of agricultural products and food from France to China and use regression model to see if these data can give significant result.
Model 7 (regression model)
After analyzing the data, unfortunately, the result is not significant. The p value of regression model is 0.15, which cannot indicate that the result can be trusted in 95% confidence interval.
As a result, our analysis of individual commodities doesn’t have a significant answer.
In a nutshell, though the conclusions of the models are not significant, a slightly decrease in trades can be seen. Such result may be caused by the few control variables in the models or may be caused by real negative effect of epidemic. But as the epidemic gets better in many parts of the world, especially in China, trade between China and France will be as prosperous as before.