Following the Flow of FDI

Posted on Jul 31, 2018


Policymakers around the globe look to foreign direct investment as a leading indicator of a nation's economic strength.  FDI can be used as an engine for growth in developing / transitioning countries and can also signal a mature economy with diversified, global holdings.  In addition, tracking FDI provides insight into popular tax planning strategies shared by investors around the world.  The application that I've built allows a user to visualize these trends and flag them for further research.  As an example, the below chart displays the density of the nearly $400B (USD) in outflows from the United States in 2016.




The dataset used to produce the application comes from the Organization for Economic Co-operation and Development (OECD) and contains self-reported bilateral FDI flows for the 36 member countries between 2005 and 2016.  The OECD defines a foreign direct investor as an individual, an incorporated or unincorporated public or private enterprise, or a government.  An FDI relationship is formed when a foreign direct investor acquires 10% or more of the voting power in a business enterprise in another country.  Common examples include M&A, building new facilities, reinvesting profits, and loans.

While there are several nations in the Top 20 of GDP not included in the OECD (China, India, Brazil, Russia, Indonesia,  and Saudi Arabia), the OECD does account for roughly 60% of global GDP.

Case Studies:

As an example, the most blatant anomaly that a user is likely to identify is the Netherlands.  As a nation ranking 17th in GDP, it is curious to see the Dutch ranked 2nd overall in total inflows for the dataset at ~$234B.  Further research would lead the user to identify Netherlands as a tax-friendly environment for investors (no local income tax, no tax on interest or royalties).


As a second example, a user is able to identify the Philippines as the recipient of nearly $3.2B in FDI inflows in 2016, with Japan as the leading contributor.  Further research reveals that the Philippines has invested heavily in infrastructure, wooing previously skittish investors as a result.  The user will see that FDI inflows to the Philippines in the dataset has increased from roughly $30M USD in 2009 to well over $3B USD in 2016.

Future Work:

The next logical step to take with this application would be to seek out a more inclusive dataset.  There are ample opportunities for data storytelling with FDI amongst nations that are not currently OECD members (see China's "Belt and Road Initiative").

A more detailed analysis also has potential to yield interesting results.  For example, a user may derive value from obtaining more detail on which sector inflows and outflows are heading into or leading out from.

A refreshed analysis in 5-10 years time also has the potential to demonstrate what effect shifts in U.S. economic policy under a new administration have on U.S. foreign direct investment.

To view the application yourself, simply click on the following link:

The author can be reached at [email protected]

About Author

Thomas Deegan

Graduate Student in Computer Science at The University of Chicago
View all posts by Thomas Deegan >

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 Data Analysis 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