Effects of the USA-China Trade War

Mikolaj Wilk
Posted on Oct 21, 2019

Check out the R shiny application: mwilk.shinyapps.io/ustradedata/

Effects of the USA-China Trade War

 

Donald Trump's Twitter post from March 2, 2018:

“When a country (USA) is losing many billions of dollars on trade with virtually every country it does business with, trade wars are good, and easy to win. Example, when we are down $100 billion with a certain country and they get cute, don’t trade anymore-we win big. It’s easy!”

About a year and a half has passed since the Trump Administration first enacted tariffs and since that time, the U.S. trade policy has been an important issue not just for the United States but for the entire global economy. According to the Wall Street Journal, “Global economic growth has ebbed this year to its slowest pace since the 2009 recession, the IMF said. The main culprit for the malaise has been the trade war between the U.S. and China, which the fund estimates to have left a Switzerland-size hole in the global economy.”

The two countries were locked in this trade war with neither side offering to budge until  Friday, October 11th, when the U.S. and China reached what seemed to be the first phase of a trade deal. The U.S. agreed to forgo a scheduled increase in tariffs, while China agreed to purchase $40 to $50 billion worth of American. agricultural goods. However, the timeline for the agricultural purchases was not clearly outlined. So was this announcement really a first step towards a more comprehensive trade agreement? Or have we seen this movie before? 

These questions entail several others including the following: How have the trade policies of both the U.S. and China and other countries changed U.S. trade since 2017? Who have been the biggest winners and losers? What product categories have been the most affected? Finally, how substantial of an impact would China’s agreement to purchase agricultural products be and what is the future outlook if the current tariffs remain in effect? To attempt to answer these questions, I built an R Shiny application and investigated the monthly trade statistics from the U.S. Census Bureau, dating back to the beginning of 2017, when Donald Trump first took office. 

How has U.S. trade balance changed since 2017?

The data from the Census Bureau contains figures from 41 specific countries. The total trade balance between the U.S. and those selected countries was a deficit of about $764 billion in 2017 with an average of about $63.7 billion by month. Despite the addition of tariffs, the trade deficit widened in 2018 to about $842 billion with an average of about $70.2 billion monthly. In 2019, through the month of August, the trade deficit was about $548 billion with an average of about $68.6 billion, monthly.

The figures through the month of August are as follows:

2017: $497 billion, $62.1 billion average monthly

2018: $540 billion, $67.5 billion average monthly

2019: $548 billion, $68.6 billion average monthly

How have imports and exports changed since 2017?

Imports totaled about $2.15 trillion in 2017 ($178 billion monthly) and $2.32 trillion in 2018 ($193 billion monthly). In 2019 imports totaled $1.51 trillion through the month of August inclusive ($189 billion monthly).

The figures through the month of August are as follows:

$1.39 trillion in 2017, $174 billion monthly

$1.52 trillion in 2018, $190 billion monthly

$1.51 trillion in 2019, $189 billion monthly

Exports totaled about $1.37 trillion in 2017 ($114 billion monthly) and about $1.48 trillion in 2018 (123 billion monthly). In 2019, exports totaled about $966 billion through the month of August inclusive ($121 billion monthly).

Through the month of August:

$894 billion in 2017, $112 billion monthly

$982 billion in 2018, $123 billion monthly

$966 billion in 2019, $121 billion monthly

As shown in the data above, imports through the month of August compared to the previous years are only slightly down, while exports have decreased slightly more. For that reason the trade deficit increased as compared to previous years through the month of August.

Which countries had the biggest changes in trade with the U.S. from 2018 to 2019?

Top 5 Countries with the largest net negative change in trade balance through the month of August:

  • China: 30 billion
  • Saudi Arabia: 4.5 billion
  • Venezuela: 3.5 billion
  • Spain: 2.3 billion
  • Austria: 1.9 billion

GRAPH 1: USA-CHINA Trade Balance 2017-2019

As shown in the graph above, when tariffs first went into effect, our trade deficit with China actually increased from 2017 to 2018. However, from 2018 to 2019, our trade deficit with China has decreased by about 30 billion through the month of August 2018 to 2019. More specifically, the trade balance between the U.S. and China decreased in January and February 2019 but not below 2017 levels. Beginning in March and every month thereafter, our trade deficit with China has decreased even past 2017 levels. 

Top 5 Countries with the largest net positive change in trade balance through the month of August:

  • Mexico: 15.7 billion
  • Taiwan: 5.4 billion
  • Switzerland: 4.7 billion
  • France: 3.6 billion
  • South Korea: 3.1 billion

GRAPH 2: USA-MEXICO Trade Balance 2017-2019

In every month in 2019 the trade deficit is larger than in 2017 or 2018.  As shown in the data and graph above, our trade deficit with Mexico has increased; Consequently, Mexico has been one of the "winners" of new trade policies.  

What product categories have been most affected?

Through the month of August top 5 categories with the largest change in imports (Increase)

  • Road vehicles: 10 billion
  • Medicinal & pharmaceutical products: 9.2 billion
  • Total Chemical & Related Products: 7.4 billion
  • Total miscellaneous manufactured articles: 7.2 billion
  • Total Machinery and Transport Equipment: 4.6 billion

Through the month of August, top 5 categories with the largest change in imports (Decrease)

  • Petroleum products and preparations: 19.9 billion
  • Office machines: 7.9 billion
  • Telecommunications equipment: 6.5 billion
  • Iron and steel: 3.9 billion
  • Nonferrous metals: 3.2 billion

Through the month of August, top 5 categories with the largest change in exports (Increase)

  • Total Mineral Fuels & Lubricants: 9.1 billion
  • Petroleum products & preparations: 9 billion
  • Medicinal and pharmaceutical products: 4.9 billion
  • Total: Chemicals and Related Products: 3.5 billion
  • Gas, natural and manufactured: 1 billion

Through the month of August top 5 categories with the largest change in exports (Decrease)

  • Total Machinery and transport equipment: 6.3 billion
  • Total Crude Materials Except Fuels: 3.7 billion
  • Gold, nonmonetary: 3.1 billion
  • Specialized industrial machinery: 3 billion
  • Food and live animals: 2.6 billion

So what is the outlook if even the current tariffs remain in effect?

In 2019 we have only begun to feel the effects of the tariffs as shown above in graph #1. Between the first 8 months of 2018 and 2019, imports from China have decreased about $43.3 billion, while exports to China have also decreased about $13.3 billion. Therefore, China's increasing agricultural purchases alone will likely not be enough to offset the effects of the tariffs that are still in place if no further agreement is made. The situation could even worsen if the scheduled December tariffs are enacted. Certainly, this trade war does not appear to have been "good", or "easy to win," and since there is still no end in sight, we may only have seen the initial effects thus far. 

About Author

Mikolaj Wilk

Mikolaj Wilk

Data Scientist, Developer, Scholar and Scoutmaster with a background in Physics, determined to unlock business value and solve problems with analytical and data-driven methods.
View all posts by Mikolaj Wilk >

Leave a Comment

No comments found.

View Posts by Categories


Our Recent Popular Posts


View Posts by Tags

#python #trainwithnycdsa 2019 airbnb Alex Baransky alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus API Application artist aws beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep Bundles California Cancer Research capstone Career Career Day citibike clustering Coding Course Demo Course Report D3.js data Data Analyst data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization Deep Learning Demo Day Discount dplyr employer networking feature engineering Finance Financial Data Science Flask gbm Get Hired ggplot2 googleVis Hadoop higgs boson Hiring hiring partner events Hiring Partners Industry Experts Instructor Blog Instructor Interview Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter lasso regression Lead Data Scienctist Lead Data Scientist leaflet linear regression Logistic Regression machine learning Maps matplotlib Medical Research Meet the team meetup Networking neural network Neural networks New Courses nlp NYC NYC Data Science nyc data science academy NYC Open Data NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time Portfolio Development prediction Prework Programming PwC python python machine learning python scrapy python web scraping python webscraping Python Workshop R R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn Selenium sentiment analysis Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau team TensorFlow Testimonial tf-idf Top Data Science Bootcamp twitter visualization web scraping Weekend Course What to expect word cloud word2vec XGBoost yelp