Junk Charts

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 differenet 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.

 

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.

 

The merry-go-round of investment bankers

Here is the start of my blog post about the chart I teased the other day:

 

Today's post deals with the following chart, which appeared recently at Business Insider (hat tip: my sister).

It's immediately obvious that this chart requires a heroic effort to decipher. The question shown in the chart title "How many senior investment bankers left their firms?" is the easiest to answer, as the designer places the number of exits in the central circle of each plot relating to a top-tier investment bank (aka "featured bank"). Note that the visual design plays no role in delivering the message, as readers just scan the data from those circles.

Anyone persistent enough to explore the rest of the chart will eventually discover these features...

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The entire post including an alternative view of the dataset is a guest blog at the JMP Blog here. This is a situation in which plotting everything will make an unreadable chart, and the designer has to think hard about what s/he is really trying to accomplish.

Webinar Wednesday

I'm delivering a quick-fire Webinar this Wednesday on how to make impactful data graphics for communication and persuasion. Registration is free, at this link.

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In the meantime, I'm preparing a guest lecture for the Data Visualization class at Yeshiva University Sims School of Management. The goal of the lecture is to emphasize the importance of incorporating analytics into the data visualization process.

Here is the lesson plan:

1. Introduce the Trifecta checkup (link) which is the general framework for effective data visualizations
2. Provide examples of Type D data visualizations, i.e. graphics that have good production values but fail due to issues with the data or the analysis
3. Hands-on demo of an end-to-end data visualization process
4. Lessons from the demo including the iterative nature of analytics and visualization; and sketching
5. Overview of basic statistics concepts useful to visual designers

 

The French takes back cinema but can you see it?

I like independent cinema, and here are three French films that come to mind as I write this post: Delicatessen, The Class (Entre les murs), and 8 Women (8 femmes). 

The French people are taking back cinema. Even though they purchased more tickets to U.S. movies than French movies, the gap has been narrowing in the last two decades. How do I know? It's the subject of this infographic: 

How do I know? That's not easy to say, given how complicated this infographic is. Here is a zoomed-in view of the top of the chart:

 

You've got the slice of orange, which doubles as the imagery of a film roll. The chart uses five legend items to explain the two layers of data. The solid donut chart presents the mix of ticket sales by country of origin, comparing U.S. movies, French movies, and "others". Then, there are two thin arcs showing the mix of movies by country of origin. 

The donut chart has an usual feature. Typically, the data are coded in the angles at the donut's center. Here, the data are coded twice: once at the center, and again in the width of the ring. This is a self-defeating feature because it draws even more attention to the area of the donut slices except that the areas are highly distorted. If the ratios of the areas are accurate when all three pieces have the same width, then varying those widths causes the ratios to shift from the correct ones!

The best thing about this chart is found in the little blue star, which adds context to the statistics. The 61% number is unusually high, which demands an explanation. The designer tells us it's due to the popularity of The Lion King.

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The one donut is for the year 1994. The infographic actually shows an entire time series from 1994 to 2014.

The design is most unusual. The years 1994, 1999, 2004, 2009, 2014 receive special attention. The in-between years are split into two pairs, shrunk, and placed alternately to the right and left of the highlighted years. So your eyes are asked to zig-zag down the page in order to understand the trend. 

To see the change of U.S. movie ticket sales over time, you have to estimate the sizes of the red-orange donut slices from one pie chart to another. 

Here is an alternative visual design that brings out the two messages in this data: that French movie-goers are increasingly preferring French movies, and that U.S. movies no longer account for the majority of ticket sales.

A long-term linear trend exists for both U.S. and French ticket sales. The "outlier" values are highlighted and explained by the blockbuster that drove them.

 

P.S.

1. You can register for the free seminar in Lyon here. To register for live streaming, go here.
2. Thanks Carla Paquet at JMP for help translating from French.

Crazy rich Asians inspire some rich graphics

On the occasion of the hit movie Crazy Rich Asians, the New York Times did a very nice report on Asian immigration in the U.S.

The first two graphics will be of great interest to those who have attended my free dataviz seminar (coming to Lyon, France in October, by the way. Register here.), as it deals with a related issue.

The first chart shows an income gap widening between 1970 and 2016.

This uses a two-lines design in a small-multiples setting. The distance between the two lines is labeled the "income gap". The clear story here is that the income gap is widening over time across the board, but especially rapidly among Asians, and then followed by whites.

The second graphic is a bumps chart (slopegraph) that compares the endpoints of 1970 and 2016, but using an "income ratio" metric, that is to say, the ratio of the 90th-percentile income to the 10th-percentile income.

Asians are still a key story on this chart, as income inequality has ballooned from 6.1 to 10.7. That is where the similarity ends.

Notice how whites now appears at the bottom of the list while blacks shows up as the second "worse" in terms of income inequality. Even though the underlying data are the same, what can be seen in the Bumps chart is hidden in the two-lines design!

In short, the reason is that the scale of the two-lines design is such that the small numbers are squashed. The bottom 10 percent did see an increase in income over time but because those increases pale in comparison to the large incomes, they do not show up.

What else do not show up in the two-lines design? Notice that in 1970, the income ratio for blacks was 9.1, way above other racial groups.

Kudos to the NYT team to realize that the two-lines design provides an incomplete, potentially misleading picture.

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The third chart in the series is a marvellous scatter plot (with one small snafu, which I'd get t0).

What are all the things one can learn from this chart?

• There is, as expected, a strong correlation between having college degrees and earning higher salaries.
• The Asian immigrant population is diverse, from the perspectives of both education attainment and median household income.
• The largest source countries are China, India and the Philippines, followed by Korea and Vietnam.
• The Indian immigrants are on average professionals with college degrees and high salaries, and form an outlier group among the subgroups.

Through careful design decisions, those points are clearly conveyed.

Here's the snafu. The designer forgot to say which year is being depicted. I suspect it is 2016.

Dating the data is very important here because of the following excerpt from the article:

Asian immigrants make up a less monolithic group than they once did. In 1970, Asian immigrants came mostly from East Asia, but South Asian immigrants are fueling the growth that makes Asian-Americans the fastest-expanding group in the country.

This means that a key driver of the rapid increase in income inequality among Asian-Americans is the shift in composition of the ethnicities. More and more South Asian (most of whom are Indians) arrivals push up the education attainment and household income of the average Asian-American. Not only are Indians becoming more numerous, but they are also richer.

An alternative design is to show two bubbles per ethnicity (one for 1970, one for 2016). To reduce clutter, the smaller ethnicites can be aggregated into Other or South Asian Other. This chart may help explain the driver behind the jump in income inequality.

 

 

 

 

 

Education deserts: places without schools still serve pies and story time

I very much enjoyed reading The Chronicle's article on "education deserts" in the U.S., defined as places where there are no public colleges within reach of potential students.

In particular, the data visualization deployed to illustrate the story is superb. For example, this map shows 1,500 colleges and their "catchment areas" defined as places within 60 minutes' drive.

It does a great job walking through the logic of the analysis (even if the logic may not totally convince - more below). The areas not within reach of these 1,500 colleges are labeled "deserts". They then take Census data and look at the adult population in those deserts:

This leads to an analysis of the racial composition of the people living in these "deserts". We now arrive at the only chart in the sequence that disappoints. It is a pair of pie charts:

 The color scheme makes it hard to pair up the pie slices. The focus of the chart should be on the over or under representation of races in education deserts relative to the U.S. average. The challenge of this dataset is the coexistence of one large number, and many small numbers.

Here is one solution:

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The Chronicle made a commendable effort to describe this social issue. But the analysis has a lot of built-in assumptions. Readers should look at the following list and see if you agree with the assumptions:

• Only public colleges are considered. This restriction requires the assumption that the private colleges pretty much serve the same areas as public colleges.
• Only non-competitive colleges are included. Precisely, the acceptance rate must be higher than 30 percent. The underlying assumption is that the "local students" won't be interested in selective colleges. It's not clear how the 30 percent threshold was decided.
• Colleges that are more than 60 minutes' driving distance away are considered unreachable. So the assumption is that "local students" are unwilling to drive more than 60 minutes to attend college. This raises a couple other questions: are we only looking at commuter colleges with no dormitories? Is the 60 minutes driving distance based on actual roads and traffic speeds, or some kind of simple model with stylized geometries and fixed speeds?
• The demographic analysis is based on all adults living in the Census "blocks" that are not within 60 minutes' drive of one of those colleges. But if we are calling them "education deserts" focusing on the availability of colleges, why consider all adults, and not just adults in the college age group? One further hidden assumption here is that the lack of colleges in those regions has not caused young generations to move to areas closer to colleges. I think a map of the age distribution in the "education deserts" will be quite telling.
• Not surprisingly, the areas classified as "education deserts" lag the rest of the nation on several key socio-economic metrics, like median income, and proportion living under the poverty line. This means those same areas could be labeled income deserts, or job deserts.

At the end of the piece, the author creates a "story time" moment. Story time is when you are served a bunch of data or analyses, and then when you are about to doze off, the analyst calls story time, and starts making conclusions that stray from the data just served!

Story time starts with the following sentence: "What would it take to make sure that distance doesn’t prevent students from obtaining a college degree? "

The analysis provided has nowhere shown that distance has prevented students from obtaining a college degree. We haven't seen anything that says that people living in the "education deserts" have fewer college degrees. We don't know that distance is the reason why people in those areas don't go to college (if true) - what about poverty? We don't know if 60 minutes is the hurdle that causes people not to go to college (if true).We know the number of adults living in those neighborhoods but not the number of potential students.

The data only showed two things: 1) which areas of the country are not within 60 minutes' driving of the subset of public colleges under consideration, 2) the number of adults living in those Census blocks.

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So we have a case where the analysis is incomplete but the visualization of the analysis is superb. So in our Trifecta analysis, this chart poses a nice question and has nice graphics but the use of data can be improved. (Type QV)

 

 

 

Graphical advice for conference presenters - demo

Yesterday, I pulled this graphic from a journal paper, and said one should not copy and paste this into an oral presentation.

So I went ahead and did some cosmetic surgery on this chart.

I don't know anything about the underlying science. I'm just interpreting what I see on the chart. It seems like the key message is that the Flowering condition is different from the other three. There are no statistical differences between the three boxplots in the first three panels but there is a big difference between the red-green and the purple in the last panel. Further, this difference can be traced to the red-green boxplots exhibiting negative correlation under the Flowering condition - while the purple boxplot is the same under all four conditions.

I would also have chosen different colors, e.g. make red-green two shades of gray to indicate that these two things can be treated as the same under this chart. Doing this would obviate the need to introduce the orange color.

Further, I think it might be interesting to see the plots split differently: try having the red-green boxplots side by side in one panel, and the purple boxplots in another panel.

If the presentation software has animation, the presenter can show the different text blocks and related materials one at a time. That also aids comprehension.

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Note that the plot is designed for an oral presentation in which you have a minute or two to get the message across. It's debatable as to whether journal editors should accept this style for publications. I actually think such a style would improve reading comprehension but I surmise some of you will disagree.

Is the chart answering your question? Excavating the excremental growth map

San Franciscans are fed up with excremental growth. Understandably.

Here is how the Economist sees it - geographically speaking.

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In the Trifecta Checkup analysis, one of the questions to ask is "What does the visual say?" and with respect to the question being asked.

The question is how much has the problem of human waste in SF grew from 2011 to 2017.

What does the visual say?

The number of complaints about human waste has increased from 2011 to 2014 to 2017.

The areas where there are complaints about human waste expanded.

The worst areas are around downtown, and that has not changed during this period of time.

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Now, what does the visual not say?

Let's make a list:

• How many complaints are there in total in any year?
• How many complaints are there in each neighborhood in any year?
• What's the growth rate in number of complaints, absolute or relative?
• What proportion of complaints are found in the worst neighborhoods?
• What proportion of the area is covered by the green dots on each map?
• What's the growth in terms of proportion of areas covered by the green dots?
• Does the density of green dots reflect density of human waste or density of human beings?
• Does no green dot indicate no complaints or below the threshold of the color scale?

There's more:

• Is the growth in complaints a result of more reporting or more human waste?
• Is each complainant unique? Or do some people complain multiple times?
• Does each piece of human waste lead to one and only one complaint? In other words, what is the relationship between the count of complaints and the count of human waste?
• Is it easy to distinguish between human waste and animal waste?

And more:

• Are all complaints about human waste valid? Does anyone verify complaints?
• Are the plotted locations describing where the human waste is or where the complaint was made?
• Can all complaints be treated identically as a count of one?
• What is the per-capita rate of complaints?

In other words, the set of maps provides almost all no information about the excrement problem in San Francisco.

After you finish working, go back and ask what the visual is saying about the question you're trying to address!

 

As a reference, I found this map of the population density in San Francisco (link):

 

Common charting issues related to connecting lines, labels, sequencing

The following chart about "ranges and trends for digital marketing salaries" has some problems that appear in a great number of charts.

The head tilt required to read the job titles.

The order of the job titles is baffling. It's neither alphabetical nor by salary.

The visual form suggests that we could see trends in salaries reading left-right, but the only information about trends is the year on year salary change, printed on top of the chart.

Some readers will violently object to the connecting lines between job titles, which are discrete categories. In this case, I also agree. I am a fan of so-called profile charts in which we do connect discrete categories with connecting lines - but those charts work because we are comparing the "profiles" of one group versus another group. Here, there is only one group.

The N=3,567 is weird. It doesn't say anything about the reliability of the estimate for say Chief Marketing Officer.

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A dot plot can be used for this dataset. Like this:

The range of salaries is not a great metric as the endpoints could be outliers.

Also, the variability of salaries is affected by two factors: the variability between companies, and sampling variability (which depends on the sample size for each job title). A wide range here could mean that different companies pay different salaries for the same job title, or that very few survey responders held that job title.