On a Line. Or Not.

Two weeks ago I was reading an article in the BBC that fact checked some of President Biden’s claims about the economy. Now I noted the other day in a post about axis lines and their use in graphics. Axis lines help ground the user in making comparisons between bars, lines, or whatever, and the minimum/maximum/intervals of the data set.

I was reading the article and first came upon this graphic. It’s nothing crazy and shows job growth in the aggregate for the first three months of a presidential administration. A pretty neat comparison in the combination of the data. I like.

Pay attention to what you see here. There will be a quiz.

I don’t like the lack of grid lines for the axis, however. But, okay, none to be found.

I keep reading the article. And then a couple of paragraphs later I come upon this graphic. It looks at the monthly figures and uses a benchmark line, the red dotted one, to break out those after January 2021 when Biden took office.

Spot the differences.

But do you notice anything?

The lines for the y-axis are back!

The article had a third graphic that also included axis lines.

I don’t have a lot to say about these graphics in particular, but the most important thing is to try and be consistent. I understand the need to experiment with styles as a brand evolves. Swap out the colours, change the styles of the lines, try a new typeface. (Except for the blue, we are seeing different colours and typefaces here, but that’s not what I want to write about.)

First, I don’t know if these are necessarily style experiments. I suspect not, but let’s be charitable for the sake of argument. I would refrain from experimenting within a single article. In other words, use the lines or don’t, but be consistent within the article.

For the record, I think they should use the lines.

Another point I want to make is with the third graphic. You’ll note that, like I said above, it does use axis lines. But that’s not what I want to mention.

At least we have lines.

Instead I want to look at the labelling on the axes. Let’s start with the y-axis, the percentage change in GDP on the previous quarter. The top of the chart we have 30%. As I’ve said before, you can see in the Trump administration, the bar for the initial Covid-19 rebound rises above the 30% line. It’s not excessive, I can buy it if you’re selling it.

But let’s go down below the 0-line. Just prior to the rebound we had the crash. Similarly, this extends just below the -30% line. But here we have a big space and then a heavy black line below that -30% line. It looks like the bottom line should be -40%, but scanning over to the left and there is no label. So what’s going on?

First, that heavy black line, why does it appear the same as the baseline or zero-growth line? The axis lines, by comparison, are thin and grey. You use a heavier, darker line to signify the breaking point or division between, in this case, positive and negative growth. Theoretically, you don’t need the two different colours for positive and negative growth, because the direction of the bar above/below that black line encodes that value. By making the bottom line the same style as the baseline, you conflate the meaning of the two lines, especially since there is no labelling for the bottom line to tell you what the line means.

Second, the heaviness of the line draws visual attention to it and away from the baseline, especially since the bottom line has the white space above it from the -30% line. Consider here the necessity of this line. For the 30% line that sets the maximum value of the y-axis, we have the blue bar rising above the line and the administration labels sit nicely above that line. There is no reason the x-axis labels could not exist in a similar fashion below the -30% line. If anything, this is an inconsistency within the one chart, let alone the one graphic.

Third, is it -40%? I contend the line isn’t necessary and that if the blue bar pokes above the 30% line, the orange bar should poke below the -30% line. But, if the designer wants to use a line below the -30% line, it should be labelled.

Finally, look at the x-axis. This is more of a minor quibble, but while we’re here…. Look at the intervals of the years. 2012, 2014, 2016, every two years. Good, make sense. 2018. 20…21? Suddenly we jump from every two years to a three-year interval. I understand it to a point, after all, who doesn’t want to forget 2020. But in all seriousness, the chart ends at 2021 and you cannot divide that evenly. So what is a designer to do? If this chart had less space on the x-axis and the years were more compressed in terms of their spacing, I probably wouldn’t bring this up.

However, we have space here. If we kept to a two-year interval system, I would introduce the labels as 2012, but then contract them with an apostrophe after that point. For example, 2014 becomes ’14. By doing that, you should be able to fit the two-year intervals in the space as well as the ending year of the data set.

Overall, I have to say that this piece shocked me. The lack of attention to detail, the inconsistency, the clumsiness of the design and presentation. I would expect this from a lesser oganisation than the BBC, which for years had been doing solid, quality work.

The first chart is conceptually solid. If Biden spoke about job creation in the first three months of the administration vs. his predecessor, aggregate the data and show it that way. But the presentation throughout this piece does that story a disservice. I wish I knew what was going on.

Credit for the piece goes to the BBC graphics department.

Making America Save Again

For years, one issue with the American economy had been that we did not save enough. It’s understandable, as it’s hard to keep up with the image of the carefree American without profligate spending. But that’s also not great long-term. But thanks to Covid-19, we’ve now swung to the other side of the spectrum: Americans may be saving too much.

Saying that sounds callous to the devastation the pandemic has wrought upon large swathes of the economy. But it’s true in the aggregate as this New York Times piece explains. In particular, the authors highlight one example. Consider a corporate CEO who earned a $100,000 bonus for keeping the company he runs afloat during the recession. He adds $100k to the aggregate American income. But at a restaurant shuttered by the pandemic, owners lay off a hostess, a server, a bartender, and a dishwasher, each earning $25,000. Their collective lost income is $100,000 and so balances out that one CEO. And as CEOs are more able to work remotely than servers, it’s not hard to see how the upper-income earning cohorts of the economy have done well. In human-terms, four unemployed service industry people is terrible. But statistically, it’s a wash. Once we understand that, it makes the piece sensible.

It uses decomposition charts, basically stacked bar charts broken apart, to show what constitutes the two sides of the American household budget: earning and spending. I’ve taken a screenshot of the spending side of the ledger.

This is the aggregate, I’d be curious how this relates to you, my readers.

We see that starting from the baseline, the solid line, American households spent more money this year on durable goods. A dotted line then carries that adjusted baseline to the right for the next component of the ledger: nondurable goods. We spent more on those too, so the baseline moves up. The designers annotated the graphic, adding descriptions of what each bar represents in a casual, lighthearted tone. I’ve definitely been cooking for myself a lot more.

Here I wish we had some more traditional charting elements, e.g. axis lines and labels. Now this piece is published under the Upshot, a more conversational and less formal brand than the Times as a whole. That probably explains the casual annotations. But I think some basic axis labels, e.g. spending more vs. spending less, could add some context without the need for the annotations.

Where the piece might lose people is what happens after durable goods. Americans stopped spending on services, a decline of over half a trillion dollars. That’s a lot of money. And so the adjusted baseline shifts to well below where we started. Add on savings from things like interest rates (Jay Powell is the chair of the Federal Reserve, for whose Philadelphia bank I work in full disclosure) and Americans have spent more than half a trillion dollars less. And as the article explains, we’ve also saved an enormous amount, to the tune of $1 trillion. Add it together and you’ve got America saving $1.5 trillion in 2020.

That money has to go somewhere. And you can see where some of it went when you look at surging prices in GameStop. Longer term, when the pandemic begins to end, we are going to have a pent up demand from people who have had their lives on hold for a year or more. And if there is insufficient supply for whatever’s in demand, prices will rise and we could see a sharp jump in inflation. But that’s a post for another day.

Back to this graphic, as a statistical graphic, it works. But without axis labels and data definitions, barely so. However, I think it’s meant to be more casual and illustrative than data-driven. If I look at this piece through that lens, I do think it works.

Credit for the piece goes to Neil Irwin and Weiyi Cai.

The Covid Recession’s Continuing Impact on Youth

Earlier this week, some of the work work my team does was published. We produced a one-page summary of a far larger and more comprehensive (relative to the scope of the summary) survey of consumers during the Covid Recession. I will spare you the details of recreating existing templates from scratch and the design decisions that went into that bit—neither insignificant nor unsubstantial—and rather focus on the one graphic we designed.

The broad thrust of the summary is that while overall we are beginning to see some job recovery, that the recovery is uneven and that, in fact, those below the age of 36 are getting hit pretty hard (my words, not the authors). That while in some industries the young are recovering in good numbers, in other industries, industries with a larger share of the youth population, young people are still losing jobs. Then we broke those top line numbers out by industries in the below graphic captured by screenshot.

How different age groups in different industries are faring in the recession.

There are a couple of things from a design side to discuss. We had about two or three days from when we started the project to develop some ideas and then execute and produce the summary. And as I noted above, that also included quite a bit of time in emulating existing documents and building ourselves a new template should we need to do something similar in the future.

But for that graphic in particular, there’s one thing I wanted to highlight: the lack of values on the axis. The challenge here was that the data displayed is people not working. And when we compared this time period (Wave 3) to the earlier waves, we were looking for declines. And so if we going to say that 36+ are gaining construction jobs, that would be -2% value and the youth are about a -13% increase. If you are doing a bit of a double-take at a negative increase, so did the team. Ultimately, we used the data to generate the chart, but then opted for qualitative labelling on the axes. They simply point that in one direction, youth are either gaining or losing jobs, and the same for the 36+. To reinforce this idea, we also added some descriptors in the far corner of each quadrant that said whether the age groups were gaining or losing jobs.

Despite the unusual design decisions I took in the graphic, I’m really proud of this piece especially given its tight turnaround. It shows in almost real-time how fractured the recovery—is this a recovery?—is at this point.

Credit for the piece goes to the team on this, Tom Akana, Kate Gamble, Natalie Spingler, and myself.

Tariffs Are a Tax

This piece from the New York Times isn’t really even a graphic. It’s a factette, or small fact. The article is about how tariffs are raising the price of certain goods, in this case a bicycle. Tariffs do not add money to the US Treasury, they are instead an additional price paid by US consumers on goods—not services—originating from outside the US.

Thankfully I can't ride a bike
Thankfully I can’t ride a bike

Sometimes a big chart is not as impactful as one big number. And here, in the context of this story, a graphic showing trade flows between the US and Mexico may have been useful. But the real gut punch is showing how the tariffs on Mexico, for this one particular bike, could cost the US consumer an additional $90. A tariff is just another word for a tax paid by the American consumer.

Credit for the piece goes to the New York Times graphics department.

Living in the Dark

Earlier this month the Economist published an article that looked at a different way of measuring the economic output of North Korea. The state is so secretive that the publicly available data we all rely on for almost every country is not available. Nor would we necessarily believe their figures. So we have to rely on other measures to estimate the North Korean economy.

The article is about how luminosity, i.e. the lights on seen from space at night, can be used as a proxy for economic activity in the reclusive state.

No lights to guide me home
No lights to guide me home

The article is a fascinating read and uses a scatter plot to show the correlation between luminosity and GDP per capita then how that translates to North Korea, comparing it to older models.

Credit for the piece goes to the Economist graphics department.

Running Up the Debt

I was reading the paper this morning and stumbled across this graphic in a New York Times article that focused on the increasing importance of debt payments.

Those interest payment lines are headed in the wrong direction.
Those interest payment lines are headed in the wrong direction.

The story is incredibly important and goes to show why the tax cuts passed by the administration are fiscally reckless. But the graphic is really smart too. After all, it is designed to work in a single colour.

Credit for the piece goes to the New York Times graphics department.

The Cost of the US Tax Cut

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.

That's about half the page on those two charts.
That’s about half the page on those two charts.

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.

These two seem small by comparison…
These two seem small by comparison…

But I just loved that section page.

Credit for the piece goes to Matt Phillips.

Short and Long Term

One week ago today, President Trump touted soaring stock prices as an indicator of a roaring economy. In truth, stock market prices are not that. They are driven by fundamentals, such as GDP growth, wage increases, and inflation. Furthermore stock prices can be fickle and volatile. Whereas a recession does not begin overnight, the factors build over a period of time, a stock market correction can happen in a single day.

So one week hence, the stock market has seen fully one-third of its gains over the past year wiped out. That is over $1 trillion gone from market funds, 401ks, college saving funds, &c. But again, not to freak people out, these things can and do happen. But because they can and do happen, presidents do not often go touting the stock market as it can come back and bite them.

This morning’s paper therefore had a pleasant graphic to accompany a story about the recent declines. And it was on the front page.

The front page
The front page

Like with the choropleth story I covered a little over a week ago, the graphic in today’s paper was not revolutionary nor earth shattering. It was two line charts as one graphic. What was neat, however, was how it supported two different articles.

One graphic, two articles
One graphic, two articles

But when I looked closer I found what was really neat: context.

Notice the little arrow…
Notice the little arrow…

The chart does a great job of showing that context of adding nearly $8 trillion in value over the course of the administration. But then that sharp decline at the right-side of the chart is blown out into its own detail to show how all was steady until Friday’s economic news was released. I think perhaps the only drawback is how tiny and fragile that arrow feels. I wonder if something a little bolder would better draw the eye or connect the dots between the two charts. Maybe even moving the “… and the last week” line above the chart line would work.

Anyway, I was just curious to see how the charts were depicted on the web. And then lo and behold I was treated to two graphics on the home page. The other is for an article about flood risks to chemical plants, not part of this post. But the focus of our post on the stock market was the same as in print. But here is the homepage with two different graphics, always a treat for a designer like myself.

The New York Times homepage this morning
The New York Times homepage this morning

Credit for the piece goes to the New York Times graphics department.

An Ailing Graphic on the Healthcare Labour Force

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.

We need more. Just more.
We need more. Just more.

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.

Bursting these bubbles…
Bursting these bubbles…

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.

Get Ready Folks

Well have we got an interesting week this week. Friday begins Trump Time. So hold onto your Twitter accounts, folks. But before we get there, I wanted to do a short week of some data-driven graphics that take a look at the state of things.

Instead of what I had intended for today, let us take a look at a new post from the Wall Street Journal that examines GDP, inflation, industrial production, and the unemployment rate in advanced economies. At its most basic level, the graphics show how many of the 39 advanced economies have a value within a one-percentage point range. The size of the dots indicates how many countries fall within the bin.

A look at advanced economies' GDPs
A look at advanced economies’ GDPs

What keeps getting me, however, is the colour. Nowhere does the piece explain what the colour represents. Does it represent anything? I think it might only be used to show the ranges in the values, not the number of countries sharing said values. And if that is the case, it is a poor design decision.

My eye goes to the colour first before it goes to the dot density let alone the size of the dots. Like a Magic Eye, when I stare at the piece long enough, I begin to see the overall trend for each metric. But blink and the colours reassert their visual dominance.

I wonder what would happen if the graphic settled on a single colour? My instinct says that the patterns would become far clearer, because colour change would no longer be a visual pattern needing interpretation—even though it needs no interpretation from a data standpoint. By limiting the number of visual patterns, the piece would make the data stand out more clearly and make for clearer communication.

If an editor screams something like “It needz more colourz!!1!”, I would reserve four separate colours and then use one and only one for each of the four metrics.

That all said, what the piece does really well is explain segments of the data. In the above screenshot, you can clearly see and get the overall GDP story. But then from there you read down and get explanations or callouts of the overall to provide more context and information. The designer greys out the remainder of the dots and allows the colour to emphasise those countries in focus. A lightly transparent overlay allows for the background dots to remain faintly visible while the text can clearly be read.

All in all, I am not sure where I fall on this particular piece. It does some things well, others not so much. But either way, the piece does paint an interesting portrait of populism’s potential causes.

Credit for the piece goes to Andrew Van Dam.