Private-sector-led Investments

Private-sector investment data is easier to acquire compared to state-level information. Originally, I planned to seek both domestic investments and foreign investments but realized that there lacks a standardized parameter that can be used to measure domestic investments. On the other side, foreign private investments can be defined by cross-border M&A deals for the purpose of this project. As stated in the overview, data cleaning becomes the major issue in this section of the project.

Here is a quick review of the major issues, attempted solutions and the preliminary results: 

Major Issue 1: There are many “N.A.” listed in Seller Country and Seller Industry Sector, which posts an issue when I try to establish the connection between the seller and acquirer to see where companies in Japan and China most heavily invested in. 

Attempted solutions: I visualized the limited available data in Tableau to obtain a preliminary assessment of how the two countries’ companies are investing. 

Major Issue 2: There are duplicates of the country name will cause the double counting of some countries’ involvement in M&A. 

Attempted solutions: I separated the countries into two columns in Tableau and ignored the rest. The rationale behind this is such that “Japan” and “China” are usually the first two countries shown in the acquire country. Countries such as U.K. and Singapore are not within the scope of my analysis so it is fine to exclude them during data cleaning. 

Preliminary results are shown below: 

As is shown on the map, the maximum record for Japan is 86. Since the original M&A list has more than 1000 deals, it means that a lot of the seller countries are missing so when I tried to establish the connection between acquirer and seller, there are fewer viable data to use. 

For China, the result did not turn out well. From the map, we can observe a scattered investment pattern in the U.S., Japan, and Canada, but the number of records in each country is only 1 or 2, which does not make logical sense.This is caused by the fact that most of the targets China acquired did not have a country affiliation listed in the Bloomberg system, listed as "N.A.", therefore we see 72 deals in N.A. (which the map labeled in Africa by mistake). I have not yet found an alternative to remedy this imperfection in the dataset.