Inflating Areas

One trend people have begun to follow lately is that of rising prices for consumer goods. If you have shopped recently for things, you may have noticed that you have been paying more than you were just a few weeks ago. We call this inflation. The Bureau of Labour Statistics (BLS) tracks this for a whole range of goods. We call the the consumer price index (CPI)

Prices can vary wildly for some goods, most notably food and energy. For those of my readers who drive, recall how quickly petrol/gasoline prices can change. Because of that volatility, the Bureau of Labour Statistics strips out food and energy prices and the inflation that excludes food and energy is what we call Core CPI.

Lately, we have been seeing an increase in prices and inflation is on the rise. To an extent, this is not surprising. The pandemic disrupted supply chains and wiped out supplies and stores of goods. But with many people working remotely, many now have pent up savings they want to spend. But with low supply and high demand, basic economics suggests rising prices. As supplies increase in the coming months, however, the rise in prices will begin to cool off. In other words, most economists are not yet concerned and expect this spike in inflation to be passing in nature. But not everyone agrees.

Last week, the Washington Post had an article examining the cause of inflation for a number of industries. To do so, it used some charts looking at prices over the past two years. This screenshot is from the used car section.

Going up…

I want to focus on the design of this graphic, though, not the content. The designers’ goal appears to be contrasting the inflation over the last year to that of the last two years. Easy peasy. Red represents one-year inflation and blue two-year.

Typically when you see a chart that look like this, an area or filled line chart, the coloured area reflects the total value of the thing being measured. You can also use the colour to make positive/negative values clearer. In this case, neither of those things are happening.

Because the blue, for example, starts at the beginning of the time series and at the bottom of the chart, it looks like an enormous amount of consistent blue growth. And when the line runs into May 2020, we begin to see what appears as a stacked area chart, with the blue area increasing at the expense of the red.

Another way of reading it could be that the 29.7% and 29.3% increases equal the shaded areas, but that’s also problematic. If the shaded area locked to the baseline like you’ll see in a moment, I could maybe see that working, but at this point it just leaves me confused.

Now you can use the area fill to make it clear when a line dips above or below the baseline, in this case 0%. And I took that approach when I reimagined the chart as seen below.

The earlier chart, reimagined

What we do here is we set the bottom of the area fill to the baseline. Consequently, where the chart is filled above 0 we have positive inflation, and where it falls below the 0 line we have negative inflation, or deflation.

We need to note here that the text in the original article talks about the monthly change in inflation, e.g. that used car prices have increased by 7.3% last month. That, however, is not what the chart looks at. Instead, the chart shows the change yearly, in other words, prices now vs last May. To an extent, the 29.7% increase is not terribly surprising given how terrible the recession was.

Ultimately, I don’t see the value in the filled blue and red areas of the chart because I am left more confused. Does the reader need to see how far back one year and two years are from May 2021? Don’t the date labels do that sufficiently well?

This is just a weird article that left me scratching my head at the graphics. But read the text, it’s super informative about the content. I just wish a bit more work went into the graphics. There are some nice illustrations beginning each section, but I kind of feel that more time was spent on the illustrations than the charts.

Credit for the piece goes to Abha Bhattarai and Alyssa Fowers.

Arrowheads

I don’t know if this is a trend, but I’ve now seen a few graphics appearing using arrows to show the direction or trend of the data. This graphic in an article by Bloomberg prompted me to talk about this piece.

I should add, after rereading my draft, that I’m not clear who made this graphic. I assume that it was the Bloomberg graphics team, because it appears in Bloomberg and all the data is presented to recreate the chart. But, it could also be a chart made by someone at Goldman Sachs that credits Bloomberg as a source and then someone at Bloomberg got hold of a copy. And a graphic made for a news/media outlet will typically be of a different quality or level of polish than one made perhaps by and for analysts. (Not that I think there should be said differences, as it does a disservice to internal users, but I digress from a digression.)

All the things going on in this chart.

The arrow here appears above the peak quarter, i.e. the second of 2021, for both the Goldman Sachs Economics forecast and the consensus forecast. But what does it really add? First, it adds “ink”, in this case pixels. Here, every pixel consumes our attention and there is a finite number of available pixels within the space of this graphic.

When I work with authors or subject matter experts, I often find myself asking them “what’s the most important thing to communicate?” or something along those lines. If the person answers with a long laundry list, I remind them that if everything is important, nothing is important. If everything is set in bold, all caps text, what will look most important is the rare bit of text set in regular, lower-case letters.

In the above graphic, there are so many things screaming for my attention, it’s difficult to say which is the most important. First, I’m fairly certain that “US QoQ annualised GDP growth” could move to the graphic subhead or data definition. Allow the graphic’s data container to contain, well, data. Second, the data series labels can be moved outside the data container. The labels here have an inherent problem is that the Goldman Sachs Economics numbers are in blue, and that blue text has less visual weight than the black text of the Consensus label. Consequently, the Goldman Sachs Economics label recedes into the background and becomes lost, not what you want from your legend.

Third, I don’t believe the data labels here add anything to the chart. They function as sparkly distractions from the visual trend, which should be the most important aspect of a visual chart.

Finally, we get to the arrow, the impetus for this post. First, I should note that it is not clear what growth it shows. The fact the line is black makes me think it reflects the Consensus forecast whereas a blue line would represent the Goldman Sachs forecast. But it could also be the average of the two or even a more general “here’s the general shape”. The problem is that the shape matters. If you look at the slope of the actual forecasts, you see a sharp increase to the peak followed by a slower, more gradual taper. The arrow in the original graphic shows a decelerating curve that is shallower in the lead up to the peak and that is not what is forecast to happen.

Now we get to the issue I mentioned at the top, the extraneous labelling and data ink wasted. If we look at the chart as is, but remove the arrow, we see this.

Immediately to the right of the peak, we have have some blue data labels and then just a bit to the right of that, but sitting vertically above the label we have the bold blue text labelling the data series. But further to the upper right we have a dark and bold block of text that draws the eye away from the peak and into the corner. It draws the eye away from the very element of the shape the peak needs to be a peak, the trough in the wave. Consequently, it makes sense with the eye being drawn up and to the right that the designers threw an arrow in above the peak to show how, no, actually your eye needs to go down and to the right.

But what happens if we then strip out the data series labelling? Do we still need the arrow? Let’s take a look.

I would argue that no, we do not. And so let’s strip the arrow out of the picture and take a look.

Here the shape of the curve is clear, a sharp rise and then a gradual taper to the right. No arrow needed to show the contour. In other words, the additional labelling wastes our attention, which then forces us to add an arrow to see what we needed to see in the first place, but then further wasting our attention.

There are a number of other things I take issue with in this chart: the black outlines of the blue rectangles, the tick marks on the x-axis, the solid border of the container, the lack of axis lines. But the arrow points to this graphic’s central problem, a poorly thought out labelling structure.

So because the chart provides all the data, I took a quick stab at how I would chart it using my own styles. I gave myself a 3:2 ratio, less space than the original graphic had. This is where I landed. I would prefer the legend below the chart labelling, but it felt cramped in the space. And with so few data points along the x-axis, the chart doesn’t need a ton of horizontal space and so I repurposed some of it to create a vertical legend space.

I mixed typefaces only because my default does not have a proper small capitals and I wanted to use small capitals to reduce and balance out the weight of the exhibit label in the graphic title.

I could still tweak the spacing between the bars and perhaps the treatment of the years below the quarters could use some additional work, but the main point here is that the shape of the curve is clear. I need no arrow to tell the user that there is a peak and that after the peak the line goes down. The white space around the bars and the line does that for me.

Credit for the piece goes to either the Bloomberg graphics department or the Goldman Sachs graphics department. Not sure.

What Will the Next Recovery Look Like?

Earlier this morning, the Bureau of Economic Analysis released its US 2nd quarter GDP figures and the news…isn’t great. On an annualised basis, we saw -32.9% growth. That’s pretty bad. Like Great Depression level bad. I’ve posted on the social media how bad this current recession is and how nobody in the workforce today worked or didn’t through the Great Depression to really relate to the numbers we are seeing.

But that’s all today. The sun will come out tomorrow. (And scorch the Earth as climate change renders certain parts of the globe uninhabitable to mankind. But we’ll get to those posts in later weeks.) And when it does come out, eventually, what will the recovery look like? I’ve seen a few mentions recently in the media of a V-shaped recovery. What is this mysterious V-shape?

A long time ago, in a galaxy far away. Or during the last recession in Chicago, I worked with some really smart people in some of my professional projects and we covered the exact same question. There are a couple key “shapes” to an economic recovery. And when we say recovery, we mean just to return to pre-recession peak levels of growth. Anything above that is an expansion. That’s what we want to get back to.

What kind of shape will the recovery take?
Who knew typographers loved economics?

The V-shape we hear a lot about is a sharp recovery after the economy bottoms out (the trough). Broadly speaking, if a recession has to last two consecutive quarters (it doesn’t, but that’s a pretty common definition so let’s stick with it), then in a V-shape, we are talking about a recovery one or two quarters later.

Similar to the V is the W-shape, where things start to improve rapidly, but some kind of shock to the economic system and things go back negative once again before finally picking up quickly. It’s not hard to imagine something going horribly wrong with the Covid-19 pandemic to be just that external shock that could push the economy back down again.

Similar still is the U-shape. Here, after hitting rock bottom, growth isn’t quite as quick to pick up as we linger in the depths of the valley of recession. But after a bit of time, we again see a rapid recovery to pre-recession levels of growth.

These are all pretty short term recoveries, the W being a little bit longer because two sharp downturns. But they are nothing compared to what’s also possible.

First we have the L-shape. Here, after hitting bottom, things start to recover quickly. But that recovery is slow and takes a long time. Growth remains slower than average, creeping up to average, and then still takes its time to reach pre-recession levels. Is something like this possible? Well, if vaccines fail and if some countries still can’t get their act together (cough, US, cough), the willingness of consumers to go out, eat, drink, buy things, travel, and generally make merry could be suppressed for a long time. So it’s certainly not out of the question.

And then lastly we have the UUUU-shape. Though you could probably add or subtract a U or two. This features more drawn out stays at the bottom of the valley with quick and sharp upticks in growth. But those growths, never reaching pre-recession levels, also collapse quickly back into declines, though also never really reaching the same depths as earlier. Essentially, the recovery faces multiple setbacks knocking the economy back down as it sputters to life. As with the L-shape, it’s also not hard to imagine a world where a country hasn’t managed to contain its outbreak struggling to get back on its feet.

What do you think? Are we at rock bottom? Did I miss a recovery type?

Credit for the graphic is mine.

Urban Heat Islands

Yesterday was the first day of 32º+C (90º+F) in Philadelphia in October in 78 years. Gross. But it made me remember this piece last month from NPR that looked at the correlation between extreme urban heat islands and areas of urban poverty. In addition to the narrative—well worth the read—the piece makes use of choropleths for various US cities to explore said relationship.

My neighbourhood's not bad, but thankfully I live next to a park.
My neighbourhood’s not bad, but thankfully I live next to a park.

As graphics go, these are effective. I don’t love the pure gradient from minimum to maximum, however, my bigger point is about the use of the choropleth compared to perhaps a scatter plot. In these graphics that are trying to show a correlation between impoverished districts and extreme heat, I wonder if a more technical scatterplot showing correlation would be effective.

Another approach could be to map the actual strength of the correlation. What if the designers had created a metric or value to capture the average relationship between income and heat. In that case, each neighbourhood could be mapped as how far above or below that value they are. Because here, the user is forced to mentally transpose the one map atop the other, which is not easy.

For those of you from Chicago, that city is rated as weak or no correlation to the moderately correlated Philadelphia.

I lived near the lake for eight years, and that does a great deal for mitigating temperature extremes.
I lived near the lake for eight years, and that does a great deal for mitigating temperature extremes.

Granted, that kind of scatterplot probably requires more explanation, and the user cannot quickly find their local neighbourhood, but the graphics could show the correlation more clearly that way.

Finally, it goes almost without saying that I do not love the red/green colour palette. I would have preferred a more colour-blind friendly red/blue or green/purple. Ultimately though, a clearer top label would obviate the need for any colour differentiation at all. The same colour could be used for each metric since they never directly interact.

Overall this is a strong piece and speaks to an important topic. But the graphics could be a wee bit more effective with just a few tweaks.

Credit for the piece goes to Meg Anderson and Sean McMinn.

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.

Trump-won Counties Are Winning

Yesterday we looked at how China and the European Union are planning their tariff/trade war retaliation to target Trump voters. Today let’s take a look at how those voters are doing as this article from Bloom does.

Lots of green, but some noticeably red counties in Florida.
Lots of green, but some noticeably red counties in Florida.

The article is not terribly complicated. We have four choropleth maps at the county level. Two of the maps isolate Trump-won counties and the other two are Clinton-won. For each candidate we have a GDP growth and an employment growth map.

In the Trump-won maps, the Clinton-won counties are white, and vice versa. Naturally, because the Democratic vote is greatest in the large cities, which, especially on the East Coast, are in tiny counties, you see a lot less colour in the Clinton maps.

Not a whole lot to see here…
Not a whole lot to see here…

Design wise, I should point out the obvious that green-to-red maps are not usually ideal. But the designers did a nice job of tweaking these specific colours so that when tested, these burnt oranges and green-blues do provide contrast.

Here they appear more of a yellow to grey
Here they appear more of a yellow to grey

But I am really curious to see this data plotted out in a scatter plot. Of course the big counties in the desert southwest are noticeable. But what about Philadelphia County? Cook County? Kings County? A scatter plot would make them equally tiny dots. Well, hopefully not tiny. But then when you compare GDP growth and employment growth and benchmark them against the US average, we might see some interesting patterns emerge that are otherwise masked behind the hugeness of western counties.

But lastly. And always. Where. Are .Alaska. And. Hawaii? (Of course the hugeness problem is of a different scale in Hawaii. Their county equivalents are larger than states combined.)

Credit for the piece goes to the Bloomberg graphics department.

The Uneven Rebound of Manufacturing

Admittedly, I only read today’s piece because of the photograph on the Washington Post’s homepage. It featured a giant banner saying Lordstown (Ohio) was the home of the (Chevy) Cruze. Every single time I drove between Philadelphia and Chicago I would see that sign. It was also near the halfway point, so whichever way I was headed I only had about six or so hours to go.

But the article itself is about the trials of people working in the area where that plant is located, near Youngstown, Ohio. GM, who owns Chevy, is shutting down the plant as it moves away from the manufacture of cars and focuses on trucks and SUVs. The story is about the people, but it did have this nice little map.

Quite a few locales in the Northeast haven't rebounded
Quite a few locales in the Northeast haven’t rebounded

It does a nice job of showing that while manufacturing has, in fact, rebounded since the Great Recession in 2009, that rebound has been uneven. There are some areas of the country, like Youngstown, that have seen manufacturing continue to disappear.

Credit for the piece goes to Kate Rabinowitz.

The World Grows On and On

I mentioned this this time last year, but I used to make a lot of datagraphics about GDP growth. The format here has not changed and so there is nothing new to look at there. But, the content is still interesting. And the accompanying Economist article makes the point that high growth rates are not always what they seem. After all, Syria’s high growth rate is because its base is so small.

The 2019 GDP growth forecasts
The 2019 GDP growth forecasts

Credit for the piece goes to the Economist Data Team.

Millennials Are the Worst

Happy Friday, everybody. We made it to week’s end. But wouldn’t you know it? Millennials are still terrible. Admittedly this piece is over a year old, but I could not remember ever seeing it before.

If you do not recall, last year there was a debate about the spending habits of millennials and why they are not out there buying homes and properties. The point was that we waste our money on experiences like expensive coffees and, most specifically, avocado toast. So amidst all this, the BBC decided to look at how many pieces of avocado toast would be needed to purchase an apartment in 10 global cities. Neither Philadelphia nor Chicago were on that list, but New York is.

Note they even have local prices for avocado toast to make the index more accurate.
Note they even have local prices for avocado toast to make the index more accurate.

Ultimately, I have never had avocado toast. But it sounds pretty good. But I find it a stretch to think the reason I do not own a home is because I am trying to eat 12,135 slices of avocado toast.

Credit for the piece goes to Piero Zagami.