Last Thursday, the US entered its longest bull market in history. And the New York Times covered the story on the front page, which makes this another episode of covering graphics when they land on the Times’ front page. Of course, last week was a big news week away from the economy and so it is no surprise that the above-the-fold coverage was on the scandals besetting the president and those of his team who have pleaded guilty or been convicted of crimes by juries.
But you will note that below the fold is that nice little graphic. Here we see it in more detail.
What I like about the graphic is how it uses the blue fill to draw attention to the bull markets but then also labels how long each was. Those keen on the story will note there is a debate whether a particular 19.9% drop qualifies for the 20% drop usually used to benchmark the beginning and ending of a bull market. That is why there is that second label with the black arrows on the graphic.
It also uses the negative space created by the shape of the graphic to contain its title, text, and caption information.
Even the Washington Post admits there sort of is no such thing, because standards vary across the world. But broadly speaking, you have enough for the essentials and then a little extra to spend discretionarily. The concept really allows us to instead benchmark global progress in development. Regardless, yesterday the Post published a calculator that allows you to compare household income across the world to that global middle class.
The catch, however, is that income is priced in US dollars, which is the currency of very few countries. But thankfully, the Post gives the methodology behind the calculator at the end of the piece so you can understand that and the other little quirks, like rural vs. urban China.
From a design standpoint, there is not much to quibble with. I probably would not have opted for red vs. green to showcase global middle and global lower-than-middle class. But the concept certainly works.
Credit for the piece goes to Leslie Shapiro and Heather Long.
We have been looking at tariffs a little bit this week, but unfortunately one of the side effects of tariffs is job losses. And of course when it comes to people losing jobs, not all countries in the developed world handle them the same. Last month the Washington Post published an article examining how those countries compare in a number of related metrics such as unemployment compensation, notice for termination, and income inequality.
It uses a series of bar charts to show the dataset and reveal how the United States fares poorly compared to its peers. The chart above looks at the earning needed for termination from employment and the differences are stark. The outlined bar chart shows longer tenured employees and the full bars as coloured. Of course this makes it look like a stacked bar chart or filled bar chart. Instead I wonder if a dot plot would be clearer. It would eliminate the confusion in determining what if any share of the empty bar is held by the full bar.
The chart for unemployment insurance versus assistance is a bit better. Here the bar represents insurance and the lines assistance. I like how the lines continue off beyond the margins to indicate an unlimited timeframe for assistance. However, for those countries where assistance is short-lived, the bars versus lines again begin to look like an instance of a share of a total, which they are not.
The weather in Philly the past week has been just gross. It reminds of Florida in that it has been hot, steamy, storms and downpours pop up out of nowhere then disappear, and just, generally, gross. I do not understand how people live in Florida year round. Anyway, that got me thinking about this piece from a month ago in the New York Times. It looked at the impact of climate change and living conditions in South Asia. Why is South Asia important? Well, it is home to nearly a billion people, a large number of whom are poor and demanding resources, and oh yeah, has a few countries that have fought several wars against each other and are armed with nuclear weapons. South Asia is important.
The map from the piece—it also features a nice set of small multiples of rising temperatures in six countries—shows starkly how moderate emissions and the high projection of emissions will impact the region. Spoiler: not well. It notes how cities like Karachi, for example, will be impacted as hotter temperatures mean lower labour productivity means worse public health means lower standard of living. And it doesn’t take a rocket scientist to see how things like demand for water in desert or arid areas could spark a conflict between Pakistan and India. Although, to be very clear, the article does not go there.
As to the design of the graphic, I wonder about the use of white for no impact and grey for no data. Should they have been reversed? As it is, the use of white for no impact makes the regions of impact, most notably central India, stand out all the more clearly. But it then also highlights the regions of no data.
Credit for the piece goes to Somini Sengupta and Nadja Popovich.
Today is a great World Cup day. The two teams for which I am rooting are playing—thankfully not yet against each other. Later this afternoon England takes on Colombia. But this morning Sweden will play Switzerland. (Neutrality is no longer an option.) And in the spirit of Sweden, I figured I would return to my winter trip to Stockholm and dig out a graphic. This one seemed particularly relevant.
It may be difficult to read, because it is in Swedish along with being large, but it shows medieval trade routes connecting Sweden to Europe. For example, Stockholm received cloth from East Anglia in modern-day England and from Bruges in Flanders, beer from modern-day Germany, and wine from modern day France and Spain.
Even in the Medieval period, international trade was vital to the economies of the emerging European cities and states.
Credit for the piece goes to the Medieval Museum design department.
Following up on yesterday’s post about the facts on tariffs, today we look at an article from Politico that polled voters on their feelings about trade and trade policy. Now the poll dates from the beginning of June and unfortunately a lot of things have changed since then. But, the data overwhelmingly supports the conclusion that voters, at that time at least, do not support placing tariffs on goods coming into the US.
Let’s take a look at another component of the article, however, a chart exploring the infamous trade deficit. First of all, trade deficits do not work like how the president says they do—but we will come back to that in another post. In short, trade deficits are neither good nor bad. They are just one way of describing one facet of a trade relationship between two countries.
This piece looks at the trade balance between the United States and China.
Now, from the topical standpoint, it does a really nice job of showcasing how our imports have surged above our experts. From a topical standpoint, however, we do not know if this is a total trade deficit or just in goods, like the president prefers to talk about, or in goods and services, the latter of which accounts for way more than half of the US economy.
From a design perspective, I have a few thoughts and the first is labelling. The chart does label the endpoints of the data set, 1985 and 2017. But aside from a grey bar representing the Financial Crisis, there are few other markers to indicate the year. In smaller charts, I often do this myself, because space. But here there is enough space for at least a few intervening years to be labelled.
Secondly, the white outline of the red line. I have talked before of a trend to showcase a line over other lines with that thin stroke. But this is the first time I can recall the effect being used over an area filled with colour. Is it necessary? Because the area is light and the line dark and bright, probably not.
Then the outline appears on the text in the graphic, in particular the labels of imports, exports, and the trade deficit label. The labels for the imports and exports likely are necessary because of that light grey used for the text. But, as with the line for the trade deficit, its label likely provides sufficient contrast the thin white outline isn’t necessary.
Unless you avoid the news, we all heard a lot about tariffs this weekend. So this morning, instead of going with some other things I found, I decided I wanted to look and see just what the data is on tariffs. Turns out Trump is wrong on the data about tariffs. In short, in 2016 the US had a slightly higher average tariff for all products at 1.61%. The EU was at 1.6%. And the Canadians? They charged an outrageous 0.8%.
The data comes from the World Bank.
And over breakfast, I did not really have the time to clean this graphic up, so it shows the whole world. Though it goes to show you, the western countries against which Trump raged this weekend generally have low tariffs, some lower than what the US.
I know I’ve looked at the Times a few times this week, but before we get too far into the next week, I did want to show what they printed on Saturday.
It is not too often we get treated to data on the front page or even the section pages. But last Saturday we got just that in the Business Section. Two very large and prominent charts looked at federal government borrowing and the federal deficit. Both are set to grow in the future, largely due to the recently enacted tax cuts.
The great thing about the graphic is just how in-the-face it puts the data. Do two charts with 14 data points (28 total) need to occupy half the page? No. But there is something about the brashness of the piece that I just love.
And then it continues and the rest of the article points, at more normal sizes, to treasury bill yields and car loan rates. The inside is what you would expect and does it well in single colour.
Today’s piece isn’t strictly about data visualisation. Instead it’s a nice article from the BBC that explores the nascent industry of undersea mining. What caught my interest was the story of Soviet submarine K-129, which sank mysteriously in the middle of the Pacific. But that isn’t even half the story, so if you are interested go and read the article for that bit.
But that sinking may have created the beginning of the undersea mining industry. And so as I read on, I found a nice mixture of text, photography, and graphics explaining processes and such. This screenshot is a comparison of the size of an undersea mining zone compared to a land-based copper mine.
Some of the graphics could use some polish and finesse, but I do appreciate the effort that goes into creating pieces like this. You will note that four different people had to work together to get the piece online. But if this is perhaps the future of BBC content, this is a great start.
Credit for the piece goes to David Shukman, Ben Milne, Zoe Barthlomew, and Finlo Rohrer.
Off of yesterday’s piece looking at the potential slowdown in British economic growth post-Brexit, I wanted to look at a piece from the Economist exploring the state of the UK’s current trade deals.
I understand what is going on, with the size of the bubbles relating to British exports and the colour to the depth of the free trade deal, i.e. how complex, thorough, and wide-ranging. But the grouping by quadrant?
With trade, geographical proximity is a factor. Things that come from farther cost more because fuel, labour time, &c. One of the advantages the UK currently has is the presence of a massive market on its doorstep with which it already has tariff- and customs-less trade—the European Union.
Consequently, could the graphic somehow incorporate the element of distance? The problem would be how to account for routes, modes of transport, time—how long does a lorry have to queue at the border, for example. Alas, I do not have a great answer.
Regardless of my concepts, this piece does show how the most valuable trade partners already enjoy the deepest and largest trade deals, all through the European Union. And so the UK will need to work to replicate those deals with all of these various countries.
Credit for the piece goes the Economist Data Team.