Well there was a lot to poke and prod at in last night’s State of the Union. So over the next couple of days I will be looking at some of the data. I wanted to start with something I could look at over breakfast—unemployment rate data.
President Trump claimed unemployment rates are at the lowest rate in…I forget how many years he claimed. But in a while. And he is correct. But, as this chart shows, he entered office with unemployment rates very near those record lows. A few tenths of a percentage point lower and voila, all-time low. What the data shows is that the bulk of the fall in the unemployment rate actually came under the watch of the Obama administration. The rate peaked at the end of the Great Recession at 10% before falling all the way down to 4.8%, which is about the natural unemployment rate that is somewhere between 4.5% and 5%, what you would expect in a healthy economy.
Data is from the Bureau of Labour Statistics, chart is mine.
Earlier this month I wrote-up a piece from the Economist that looked at 2018 GDP growth globally. I admitted then—and still do now—that it was an oddly sentimental piece given the frequency with which I made graphics just like that in my designer days of youth and yore. Today, we have the redux, a piece from the New York Times. Again, nothing fancy here. As you will see, we are talking about a choropleth map and bar charts in small multiple format. But why am I highlighting it? Front page news.
I just like seeing this kind of simple, but effective data visualisation work on the front page of a leading newspaper.
I personally would have used a slightly different palette to give a bit more hint to the few negative growth countries in the world—here’s lookin’ at you, Venezuela—but overall it works. And the break points in the bin seem a bit arbitrary unless they were chosen to specifically highlight the called-out countries.
Then on the inside we get another small but effective graphic.
It doesn’t consume the whole page, but sits quietly but importantly at the top of the article.
There the small multiples show the year-on-year change—nothing fancy—for the world’s leading economies. A one-colour print, it works well. But, I particularly enjoy the bit with China. Look at how the extreme growth before the Great Recession is handled, just breaking out of the container. Because it isn’t important to read growth as 13.27% (or whatever it was), just that it was extremely high. You could almost say, off the charts.
Overall, it was just a fun read for a Sunday morning.
Credit for the piece goes to Karl Russell and the New York Times graphics department.
Today’s post is a very quick reaction to the news last night about President Trump calling Haiti, El Salvador, and African countries “shitholes” and trying to get rid of immigrants from those countries in favour of immigrants from places like Norway.
Norwegian contributions to American immigrants peaked well before the 21st century. At that time, Norway was poor and lesser developed. The data was hard to find, but on a GDP per capita level, Norway was one of the least developed countries in Western Europe. On a like dollar-for-dollar basis, El Salvador of 2008 is not too far from Norway 1850.
I wish I had more time to develop this graphic for this morning. Alas, it will have to do as is.
January is the month of forecasts and projections for the year to come. And the Economist is no different. Late last week it published a datagraphic showcasing the GDP growth forecasts of the Economist Intelligence Unit. I used to make this exact type of datagraphic a lot. And I mean a lot. But what I really enjoy is how successfully this piece integrates the map, the bar chart, and the tables to round out the story.
The easy thing to do is always the map, because people like maps. They can be big, and if the data set is robust, full of data and colour. But maps hide and obscure geographically small countries. And then you have to assume that people know all the countries in the world. Problem is, most people do not.
So the bar chart does a good job of showing each country as equals, a slim vertical bar. In such a small space, labelling every country is impossible, but the designers chose a select number of countries that might be of interest and called them out across the entire series.
Lastly, people always like to know who is #winning and who is a #loser. So the tables at the extreme ends of the chart showcast the top and last five.
I may have rearranged some of the elements, and dropped the heavy black rules between the bins on the legend, but overall I consider this piece a success.
Credit for the piece goes to the Economist Data Team.
I’ve worked on a few scatter plots of late and so this piece from the Economist grabbed my attention. It examines the correlation between unemployment rates and inflation rates. Broadly speaking, the theory has been that low unemployment rates lead to high inflation rates. But the United States has had low unemployment rates now for a few years, but inflation is around that ideal 2% realm. This theory is called the Phillips Curve.
The graphic does a nice job of showing three data series all in one plot. Normally, I would argue for splitting the chart into three smaller plots, a la the small multiples. But here, the data aligns just well enough that the overlapping is minimal. And smart colour choices mean that each data range appears clearly separate from the rest. A nice thoughtful addition is the annotations to the time period are set in the same colour as the dots themselves.
My only two quibbles: One, I would probably increase the height of the chart to better show the trend line. I find that for scatter plots, a more squarish profile works better than the long rectangle. Overall, though, a really well done chart. Second, I would consider adding a zero line to the x-axis to show 0% cyclical unemployment. But that might also not be terribly useful, because you can see how the curve should move regardless of that natural line.
Full disclosure: the Economist article cites a paper from the Philadelphia Fed Research Department, which employs me.
Credit for the piece goes to the Economist Data Team.
I know I have said it before, but I like the increasing number of graphics-led articles published by Politico. Many policy and politics stories are driven—or should be driven—by data. But, myself included, we cannot hit it out of the park at every plate appearance. And that is what we have from Politico today, actually last week.
The graphic focuses on the healthcare industry and its need for a larger labour force in coming years as the baby boomers continue to age and start to retire. If their own doctors retire along with them, who will be their new doctors?
But there are two components of the graphic on which I want to focus. The first is the projection of the number of registered nurses (RNs) in 2024 compared to a 2014 baseline.
The story focuses on the future condition, but that colour is set to the lighter green thus drawing the reader’s eyes to the 2014 data point. Flipping those two colours would shift the focus of the chart to the 2024 timeframe, which would better match the text above.
Then we have the design decision to include a line chart for the growth rate, presumably total, for each category of RN from 2014 to 2024. The problem is that the chart itself does not sit on any baseline. While I do not care for the dual axis chart, that format at least keeps an axis legend on the right side of the chart. (You still have the problem of implying certain things based on what scale you choose to use relative to the first data series.) Here, because there is no chart lines associated with the growth data, I wonder if a table below the x-axis labels would be more efficient? Home health care, a very small category, will have the highest growth (a small change from a small base will beat the same small change or even slightly bigger changes from a far larger base) but the eye has the furthest to travel to reach the 61% number from the top of the bars or the labelling.
The other component I wanted to discuss is the scatter plot that compares the number of jobs to their average salary.
But this is a bubble chart, not a scatter plot, and so we have a third variable encoded in the size of the dot/bubble. The first thing I looked for was a scale for the size of the circles. What magnitude is the RN circle vs. the Personal Care Aides circle? There is none, but unfortunately that seems to be a common practice with bubble chart. But after failing to find that, I noticed that the circles decrease in size from right to left. That was when I looked to the legend and saw the y-axis in numbers of jobs and the x-axis in average salary. But then the circles are sized in proportion to the average salary of each profession to the other. In other words, the circles are basically re-plotting the x-axis. The physical therapist circle should be roughly twice as large, by area, than the vocational nurses. But we can also just see by the x-axis coordinates. The bubble chart-ness of the chart is unnecessary and the data could be told more clearly by stripping that away and making a straight-up scatter plot where all the circles are sized the same.
Credit for the piece goes to Christina Animashaun.
Yesterday the New York Times published a piece looking at the potential impacts of the proposed tax reforms on Americans. Big caveat, not a lot has been detailed about what the reforms entail. Instead, much remains vague. But using the bits that are clear, the Tax Policy Centre has explored some possible impacts and the Times has visualised them.
I like the opening graphic, though all are informative, that cycles through various proposals. It highlights which group benefits most from the proposals. The quick takeaway is that while all would moderately benefit, the rich do really well.
Yesterday Hurricane Ophelia hit Ireland and the United Kingdom. Yes, the two islands get hit with ferocious storms from time to time, but rarely do they enjoy the hurricanes like we do on the eastern seaboards of Canada, Mexico, and the United States.
Earlier this hurricane season the US had to deal with Harvey, Irma, and Maria. And in early October the Wall Street Journal published a piece that looked at the economic impact of the former two hurricanes as exhibited in economic data.
Overall the piece does a nice job explaining how hurricanes impact different sectors of the economy, well, differently. And wouldn’t you know it that leisure and hospitality is the hardest hit? But then they put together this stacked bar chart showing the impact of the hurricanes on both Florida/Georgia and Texas for Irma and Harvey, respectively.
The problem is that the stacked bar chart does not allow us to examine each hurricane as a specific data set. Because the Harvey data set is first, we have the common baseline and can compare the lengths of the magenta-ish bars. But what about the blue sets for Irma? How large is natural resources and mining compared to professional and business services? It is incredibly difficult to tell because neither bar starts at the same point. You must mentally move the bars to the same baseline and then hope your brain can accurately capture the length.
Instead, a split bar chart with each sector having two bars would have been preferable. Or, barring that, two plots under the same title. Then you could even sort the data sets and make it even easier to see which sectors were more important in the impacted areas.
Stacked bar charts work when you are trying to show total magnitude and the breakdowns are incidental to the point. But as soon as the comparison of the breakdowns becomes important, it’s time to make another chart.
Last week I covered a lot of Red Sox data. And your feedback has been fantastic. I think you can look forward to more visualisation of sportsball data. But since this is not a sports blog, let us dive back into some other topics. Like today’s piece on economic growth.
It comes from the Economist and explores the development history of national economies relative to that of the United States. The point of the chart was to illustrate what the researchers determined was the middle income trap, a space in which countries develop and become semi-rich, but then can never quite escape.
The Economist makes the point that the definition of middle income matters. The range is enormous and one statistic says that it could take 48 years to graduate at a healthy rate of economic growth. I wonder is this bit, however, could also have been charted. The show don’t tell mantra works well here for setting up the problem, but a chart or two showing that exact range could have supplemented the text and perhaps made it more digestible.
Credit for the piece goes to the Economist Data Team.
Initially I was not going to post this work, if only because other things came up and I do have to prioritise what I post on my site. It had nothing to do with the work’s quality, which I think is actually quite good. What am I talking about? Well today’s piece is from a Pro Public article about the impact of immigration on economic growth. And it turns out the two are linked. Why? Well, the overly simplistic explanation is that we will need immigrants to pick up the slack in the labour force that will otherwise begin shrinking in years to come.
But why take my word for it when you can take charts’ word for it. The piece does a great job of showing how changes in immigration numbers can help grow or shrink economic growth. And if you recall, President Trump has promised growth rates of 4%. But, and this is why I decided to post this, yesterday it was announced that Trump will support legislation intending to halve immigration to the United States over the next ten years. As my screenshot captured, a reduction in immigration will actually lead to lower economic growth and put us further away from the 4% rate.