On July 15th 2020 Ireland won its appeal against a European Commission ruling that it gave Apple illegal state aid in the form of tax advantages that gave it an unfair advantage over other EU countries. According to the Commission, Apple owes Ireland a hefty sum of €14.3 billion in uncollected taxes. Margrethe Vestager, the EU Commissioner for Competition Policy and ultimately the figurehead for the EU on this front, has been fighting what she determines are unfair tax breaks being applied by States like Ireland. Labelled as a “consumer champion” by some commentators, Vestager is determined to bring order to an ever growing digital economy.
Vestager is likely to bring the case to the EU’s highest court, the Court of Justice of the European Union with potentially significant implications for the future of Irish tax policies and the companies that have chosen to establish their EU headquarters on the island. Naturally there are conflicting opinions on the ruling. Some argue that Ireland is blatantly allowing international powerhouses funnel money through it’s Irish headquarters, akin to money laundering. Others argue without foreign direct investment, Ireland wouldn’t be in the strong position it finds itself in, Covid-19 crises and all. Quoting the EU directly:
“As a small open economy Ireland’s financial fortunes are largely dependent on international trade and influenced by global markets.”
https://ec.europa.eu/ireland/news/key-eu-policy-areas/economy_en
So, with this backdrop, I continued my exploration of Irish exports over the last 5 years and took a look into the commodities we’re sending overseas.
data preprocessing
In my last post I showed how easy it is to map data in Python by aggregating an Irish exports dataset published by the CSO. For this post I used the same dataset and followed a similar method of aggregating the data – except this time I condensed the commodities into groups. This was a critical part of the process because there are over 60 commodity classifications in the dataset and too many to graph at once.
Typically you’d have an SME on hand to confirm your groupings and naming conventions but in this case I’ll trust my gut – although upon reflection after looking at my results, the commodity group ‘Transport’ should have been something like ‘Automobiles’ but we’ll plough on anyway. By grouping the categories, I was able to reduce them down to 13 groups and 1 catch all group ‘None’. The catch all group is created as a back up if we miss any categories and for those that have no category at all so hopefully the total of this group is negligible. You’ll find an extract of how I did this below in the code snippet but as usual, you’ll find the full code at the bottom of this post.
# setting up a function that searches for the string in the category
# each category has a number with the description, so by searching for the number, I can assign it a new group
# for example, Live animals except fish etc. (00) is a category - so I search (00) & assign it agriculture
def commodity(x):
if '(00)' in x:
return 'Agriculture'
elif '(01)' in x:
return 'Food & Beverage'
elif '(02)' in x:
return 'Food & Beverage'
elif '(03)' in x:
return 'Food & Beverage'
elif '(04)' in x:
return 'Agriculture'
.......home of pharma & chemical exports
The next step in the process is to visualise the groupings. Since starting in Bank of Ireland I’ve been using Tableau as my main data visualisation tool & I have to say – I’m sold! It’s such a handy tool as long as the data is in the correct format (which it is, thanks to Python). So I continued the trend and used it to create the below visuals.
Line charts are useful tools for showing trends over time however when you have too many categories to visualise at once it’s sometimes difficult to see what story is.
Here we can see that there are 2 categories (pharma & chemical manufacturing) that are several times higher in value than all other categories and look to be strong areas of growth in Ireland. After that it’s quite difficult to see trends in other categories because the lines are overlapping. What we can say is that apart from machinery, things looks fairly steady suggesting minimal growth. For reference, the colour legend in the data prep section represents each commodity.
A way to visualise the composition of categories is to create bar charts. By allowing for a direct comparison from year to year we can see a clearer view of the difference between categories and years. By arranging the bars from highest to lowest, highest value being at the bottom and lowest on top, we can see how pharma and chemical manufacturing have a significant contribution to total exports. Whats difficult about this view is that we can’t really see the composition of the other categories – similarly to the line chart above, it looks steady but it’s hard to tell for sure. This is where stacked bar charts prove their worth!
stacks, stacks, stacks
A stacked bar chart is the same as a normal bar chart however the values are converted into percentage values of the total sum. Taking 2019 for instance, instead of showing the actual value of export for each commodity group, we can see the percentage value relative to all other commodities. This allows us to identify the areas of growth in the economy relative to all other areas.
For instance, while we see growth in the food & beverage sector in real value terms between 2015 & 2019 (€7.9B in 2015 to €10.B in 2019), in percentage worth to the economy, it remains steady because of the increases in other sectors. Taking a look into the agriculture sector we see a modest increase in real value terms (€3.5B in 2015 to €3.6B in 2019) but a decrease in percentage terms.
overall value
Heatmaps are a really useful tool if we’re concerned with a point in time proportional view of our data. In this instance, I totaled the value of each commodity group between the years 2015 & 2019 to see how much each contributes to the Irish economy. Again, we can see just how important pharma & chemical exports are to Ireland with general manufacturing and machinery being the next most important commodity groups.
the movers and shakers
The last visual I created shows the percentage change in each commodity group over the 5 years. As 2015 is the base year there is no value assigned. The value in 2016 represents the percentage change between 2015 & 2016, with 2017 being the change between 2016 & 2017 and so on. By representing the percentage change in colour format, it allows us to visualise areas of growth and decline. A darker brown/orange colour shows a higher negative change while a darker blue shows a higher positive change.
This view shows us that tobacco exports have rapidly declined while there has been 2 years of decline in transport related exports. The commodity seeing the biggest swings is machinery with a massive increase of 37% worth of exports in 2019. With companies like Kingspan & Combilift expanding in recent years I wonder how much these companies are contributing to that growth.
final thoughts
I was really surprised with just how much Ireland relies on high tech exports. As recent as the 1970’s Ireland’s main export was agriculture based products to the U.K. So, in this context, is it surprising that the Irish government has strongly opposed the anti-competition charges it has faced? And how long can it defend itself against the heavy hitters of the EU who claim that it’s cheating the system? Only time will tell.
Thanks for stopping by. If you’re interested in trying it for yourself, the code and data have been included below.
Paul Gleeson 