Junk Charts

Welcome

Junk Charts is made by Kaiser Fung, the Web’s first data visualization critic. I discuss what makes graphics work, and how to make them better. Think chartjunk + junk art.

Here is my first post from more than ten years ago. These are the posts with the most (clicks): 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. There are some things I say over and over again: the Trifecta Checkup, the self-sufficiency test, bumps charts, small multiples, the power of aggregation. I carefully tag all posts with topics so clicking on map shows all posts about maps. A list of topics is on the right column. Here is a call for more dataviz criticism.

Here is my other blog, about statistics and Big Data. Here is my twitter feed, aggregating both blogs. If you prefer RSS, there's one feed for Junk Charts, and one for Big Data Plainly Spoken.

I also write popular books about statistics applied to daily life. Here is Numbers Rule Your World, and Numbersense. Tom Peters tweeted: On my 13-hour Boston-Dubai flight, I re-read cover-2-cover Kaiser Fung's superb-useful-fun book Number Sense. Trust him, or trust me.

I am a business statistician and speaker/trainer for hire. This is about a few hours in my life. I built the data teams at Vimeo (still part of the family) and Sirius XM Radio. Click here to write me. Some citations in the media. Talks. A free course.

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Oct 2014: I'm teaching my data visualization workshop. This is a great way for you to learn how to make great graphics in a fun, immersive setting. See a fuller description here.

 

 

Men and women faced different experiences in the labor market

Last week, I showed how the aggregate statistics, unemployment rate, masked some unusual trends in the labor market in the U.S. Despite the unemployment rate in 2018 being equal, and even a little below, that in 2000, the peak of the last tech boom, there are now significantly more people "not in the labor force," and these people are not counted in the unemployment rate statistic.

The analysis focuses on two factors that are not visible in the unemployment rate aggregate: the proportion of people considered not in labor force, and the proportion of employees who have part-time positions. The analysis itself masks a difference across genders.

It turns out that men and women had very different experiences in the labor market.

For men, things have looked progressively worse with each recession and recovery since 1990. After each recovery, more men exit the labor force, and more men become part-timers. The Great Recession, however, hit men even worse than previous recessions, as seen below:

For women, it's a story of impressive gains in the 1990s, and a sad reversal since 2008.

P.S. See here for Part 1 of this series. In particular, the color scheme is explained there. Also, the entire collection can be viewed here. 

What to make of the historically low unemployment rate

One of the amazing economic stories of the moment is the unemployment rate, which at around 4% has returned to the level last reached during the peak of the tech boom in 2000. The story is much more complex than it seems.

I devoted a chapter of Numbersense (link) to explain how the government computes unemployment rates. The most important thing to realize is that an unemployment rate of 4 percent does NOT mean that four out of 100 people in the U.S. are unemployed, and 96 out of 100 are employed.

It doesn't even mean that four out of 100 people of working age are unemployed, and 96 out of 100 of working age are employed.

What it means is of the people that the government decides are "employable", 96 out of 100 are employed. Officially, this employability is known as "in labor force." There are many ways to be disqualified from the labor force; one example is if the government decides that the person is not looking for a job.

On the flip side, who the government counts as "employed" also matters! Part-timers are considered employed. They are counted just like a full-time employee in the unemployment metric. Part-time, according to the government, is one to 34 hours worked during the week the survey is administered.

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So two factors can affect the unemployment rate a lot - the proportion of the population considered "not in labor force" (thus not counted at all); and the proportion of those considered employed who are part-timers. (Those are two disjoint groups.)

The following chart then shows that despite the unemployment rate looking great, the U.S. labor market in 2018 looks nothing like what it looked like from 1990 to 2008.

Technical notes: all the data are seasonally adjusted by the Bureau of Labor Statistics. I used a spline to smooth the data first - the top chart shows the smoothed version of the unemployment rates. Smoothing removes month-to-month sharp edges from the second chart. The color scale is based on standardized values of the smoothed data.

 

P.S. See Part 2 of this series explores the different experiences of male and female workers. Also, the entire collection can be viewed here.

Happy New Year

In China, 2019 is the Year of the Pig. Half of the world's pigs live in China. This graphic is inspired by this BouncyMaps project, which generated the following cartogram.

 

The ebb and flow of an effective dataviz showing the rise and fall of GE

A WSJ chart caught my eye the other day – I spotted someone looking at it in a coffee shop, and immediately got a hold of a copy. The chart plots the ebb and flow of GE’s revenues from the 1980s to the present.

What grabbed my attention? The less-used chart form, and the appealing but not too gaudy color scheme.

The chart presents a highly digestible view of the structure of GE’s revenues. We learn about GE’s major divisions, as well as how certain segments split from or merged with others over time. Major acquisitions and divestitures are also depicted; if these events are the main focus, the designer should find ways to make these moments stand out more.

An interesting design decision concerns the sequence of the divisions. One possible order is by increasing or decreasing importance, typically indicated by proportional revenues. This is complicated by the changing nature of the business over the decades. So financial services went from nothing to the largest division by far to almost disappearing.

The sequencing need not be data-driven; it can be design-constrained. The merging and splitting of business units are conveyed via linking arrows. Longer arrows are unsightly, and meshes of arrows are confusing.

On this chart, the long arrow pointing from the orange to the gray around 2004 feels out of place. What if the financial services block is moved to the right of the consumer block? That will significantly shorten the long arrow. It won’t create other entanglements as the media block is completely disjoint and there are no other arrows tying financial services to another division.

 

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To improve readability, the bars are spaced out horizontally. The addition of whitespace distorts the proportionality. So, in 2001, the annotation states that financial services (orange) accounted for “about half of the revenues,” which is directly contradicted by the visual perception – readers find the orange bar to be clearly shorter than the total length of the other bars. This is a serious deficiency of the chart form but this chart conveys the "ebb and flow" very well.