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Inspiration

During college, I had the chance to study in Argentina for a year and travel around Latin America. I remember being shocked by how much market share and local reputation Huawei had there compared to their North American competitors, which often came with absurd price tags due to trading terms and exorbitant import taxes. Although locals spoke fondly of Huawei, I felt suspicious of Chinese government’s intentions behind this global expansion of power. It was also around that time (2017) that my Ghanaian friends started telling me about fraudulent activities of Chinese companies in West Africa: shoddy projects that were supposed to help develop infrastructure fell apart faster than the time it took for them to be built; Chinese businessmen cleverly bypassing the government to trade directly with local chiefs – who often had more direct power, showering them with U.S. dollars for mining rights. It dawned on me that in regions like Africa and Latin America, where the price of Western products were out of reach for the common men, China has been quietly establishing a neo-colonial system of resource domination, influence, and power. This is the pretext under which I began this project.

Background

Ever since the dawn of the new millennium (2000), the rapid growth in Chinese economic power has driven the Chinese Communist Party (CCP) to invest in the global market, from Latin America to Eurasia to Africa. This trend has quickened its pace with the appointment of Jinping Xi as the President of China in 2013, who subsequently unrolled the Belt and Road Initiative (BRI), which comprises both a land and a maritime “Silk Road” that connects China with Eurasia and Africa. The BRI is commonly “associated with a very large program of investments in infrastructure development for ports, railways, and airports, as well as power plants and telecommunications networks” (“Belt and Road Initiative (BRI)”).

In this project, I will be examining global development projects made by the CCP from 2000 to 2017. I am interested in uncovering potential trends in the geographical distribution of countries being funded, the nature of the fund, and any debt conditions posed by the CCP in return. The time period between 2013 and 2017 (2013 was when the BRI rolled out) and the involvement of Huawei are of particular interest to me, as both have a huge impact on the global economy.

Data

Data Source: AidData’s Global Chinese Development Finance Dataset, Version 2.0 – AidData
Software:
– Excel: data cleaning
– Tableau: data visualization
– ArcGIS: data mapping
– Illustrator: poster making

I will be mainly using AidData’s Global Chinese Development Finance Dataset, an extremely comprehensive dataset that captures “13,427 Chinese government-financed projects worth $843 billion across 165 countries from every major world region”. The accompanying methodology provides guide on how to use the dataset, as well as a compiled geoJSON file that I will use for data mapping.

Methods

There are two components to this project:
– A series of charts presenting the findings on the geographical distribution, sector distribution, and debt conditions of Chinese foreign development projects from 2000 to 2017. These charts are grouped around three central questions (listed below);
– A mapping of the Chinese foreign development projects where an exact location has been provided (this data has already been compiled and is downloadable via a geoJSON file). It’s important to note that the range of projects mapped includes only a portion of the total number of projects in the dataset and is not meant to be a representation of larger trends. Instead, the data mapping in this section is meant for the user to explore: one can identify a certain country, then examine the projects completed in that country in detail.

Important modifications on the dataset:
– The original dataset splits the information between 3 tables: general, Huawei-funded, and military-related. In this project, all 3 tables are joined together in the hope of presenting a full picture of Chinese foreign development projects.
– Project funding amounts used in data analysis is the inflation-adjusted, constant U.S. dollar in 2017. This information was provided by AidData as a variable.

Question 1: Which geographic area (country, world region) received the most amount of money from China as project development funds?
To answer this question, I produced 3 charts:

A bar chart showing the most funded countries (countries that received more than $10 billion from China in total from 2000 to 2017). The countries are colored by their region.

An area chart of the total amount of funding China gave to each major world region across time. This is meant to capture the changes in funding given to each major world region.

Question 2: Which sector did the Chinese government fund the most?
To answer this question, I produced 2 charts:

A line chart of the total amount of funding China gave across time. Although this is not directly related to sectors, this provides background on the trend of Chinese involvement in foreign development projects. On this chart, 2 points of interest are noted: 2008 – when Beijing Olympics was held; and 2013 – when Jinping Xi became the President of China and rolled out the BRI initiative.

Question 3: What were the debt conditions of these projects like?
To answer this question, I produced 2 charts:

A side-by-side bar chart of the total amount of debt canceled for each world region and the total funding they received. A side-by-side chart was chosen after finding out that there are stark contrasts between the proportion of funding received by each continent and the amount of debt cancelled for them.

A bar chart of the average length of delayed debt payment that China allows, displayed by continent. A shorter length is an indication of harsher terms and conditions; and a longer length reflects more leniency.

Then, I exported the project locations for map plotting.

The geoJSON file was duplicated 3 times to produce 3 map layers, and each layer was filtered to reveal only 1 project type (general development, Huawei-funded, and military-related). Due to the fact that there are 3285 general development projects (note that this is only a small portion of the 13,427 projects captured in the dataset, which was used for the first part of analysis), and only 21 Huawei-funded projects and 19 military-related projects, different styling was used for each layer.
– General development projects were represented by a small red dot (or a polygon where geographical borders of the project location was recorded), with no effects applied;
– Huawei-funded projects were represented by a larger neon blue dot. A glow effect was also added to make these projects stand out.
– Military-related projects were represented in a similar fashion as Huawei-funded projects. They can be seen as a large, glowing neon green dot.

You can view the map here. Note that the map can only be viewed under “Map Viewer”, not “Map Viewer Classic”.

Lastly, to improve the presentation of my analysis results, I compiled key features of my findings into a poster designed on Illustrator.

Results

I had thought a Latin American or an African country would receive the most amount of funding from China, but in reality it was Russia – the only European country that made it to the chart –which makes sense but still came as a surprise. Additionally, in the top countries that received the most amount of funding (more than $10 billion) from China, most are Asian (8), followed by African (5), Latin American (4), Middle East (1), and European (1). There is a significant gap between the amount the top 3 countries (Russia, followed by Venezuela and Angola) received. When comparing the total funding China gave to each major world region, certain trends corresponded with the findings above as well: Asia continues to be where China funds the most, followed by Africa and America; however, the European share grew significantly, which is the result of Russia as an outlier (it contributed $125 billion alone to Europe’s total share of $150.78 billion) and does not mean China gave much money to the rest of Europe.

When analyzing the data through time, other interesting findings are revealed: Latin American only started receiving a significant portion of funding after 2007; Europe (possibly Russia) received the largest chunk of funds compared to other regions in 2009 and 2013, which corresponds to the time of the 2 pivotal events in recent Chinese political and cultural history: the Beijing Olympics was held in 2008, and Jinping Xi became President and the BRI was rolled out in 2013. Overall, the amount of money China spent on projects abroad grew dramatically from 2000 to 2017, with the highest at $237.30 billion (2015), more than 20 times than Chinese funding in 2000 ($10.91 billion).

Sectorally, leaving aside multisector projects, the “Industry, Mining, Construction” sector was the most funded, followed by “Energy” and “Transport & Storage”. Although “Social Infrastructure & Services” is also one of the most funded sectors, its share is only a little more than 1/10th of the mining sector. From this, we can see that although China does invest money in other regions’ infrastructure, its main ambition is still in their natural resources – far from the “humanitarian” outreach goals stated in official BRI documents. The fact that “Transport & Storage” (possibly of Chinese goods abroad) also speaks of the China’s ambition to dominate in the global market.

When it comes to debt conditions, apart from Oceania, Middle East received the least amount of funding than all other major world regions, but receives the most lenient debt conditions. 8% of their debt was forgiven (compared to the next highest: Africa at 1.36%) and they have the longest average grace period at 8 years (followed by Africa at around 6.5 years). Considering only a portion of the captured projects contains debt condition data, this could either be a fluke resulting from insufficient data, or a real reflection of China’s leniency towards the Middle East.

Reflection

I had originally intended to make the final presentation in a dashboard, but only later did I realize all the data needed to be cleaned and compilated thoroughly (or be joined) first for this to be possible. I made my Tableau visualizations by duplicating the dataset multiple times and filtering each a different way to produce a different graph, which made backtracking time-consuming and ruled out the dashboard option for presentation. This is something I will keep in mind during my next project.
Some other directions for the project to develop in the future include deep-diving into potential reasons that explain the phenomena revealed by the analysis above. For example, why is the Middle East receiving preferential treatment? Another direction to take this project further would be to single out certain representative projects (or projects that involve a large sum of funds) and do a multimedia “case study” on them, including images or videos.

References

Belt and Road Initiative (BRI). European Bank for Reconstruction and Development. Retrieved on November 9, 2022, from https://www.ebrd.com/what-we-do/belt-and-road/overview.html.