Shaking up expectations for pension benefits

Ted Ballachine wrote me about his website Pension360 pointing me to a recent attempt at visualizing pension benefits in various retirement systems in the state of Illinois. The link to the blog post is here.

One of the things they did right is to start with an extended guide to reading the chart. This type of thing should be done more often. Here is the top part of this section.

It turns out that the reading guide is vital for this visualization! The reason is that they made some decisions that shake up our expectations.

For example, darker colors usually mean more but here they mean less.

Similarly, a person's service increases as you go down the vertical axis, not up.

I have recommended that they switch those since there doesn't seem to be a strong reason to change those conventions.

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This display facilitates comparing the structure of different retirement systems. For example, I have placed next to each other the images for the Illinois Teacher's Retirement System (blue), and the Chicago Teacher's Pension Fund (black).

 

It is immediately clear that the Chicago system is miserly. The light gray parts extend only to half of the width compared to the blue cells in the top chart. The fact that the annual payout grows somewhat linearly as the years of service increase makes sense.

What doesn't make sense to me, in the blue chart, is the extreme variance in the annual payout for the beneficiary with "average" tenure of about 35 years. If you look at all of the charts, there are several examples of retirement systems in which employees with similar tenure have payouts that differ by an order of magnitude. Can someone explain that?

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One consideration for those who make heatmaps using conditional formatting in Excel.

These charts code the count of people in the shades of colors. The reference population is the entire table. This is actually not the only way to code the data. This way of coding it prevents us from understanding the "sparsely populated" regions of the heatmap.

Look at any of the pension charts. Darkness reigns at the bottom of each one, in the rows for people with 50 or 60 years of service. This is because there are few such employees (relative to the total population). An alternative is to color code each row separately. Then you have surfaced the distribution of benefits within each tenure group. (The trade-off is the revised chart no longer tells the reader how service years are distributed.)

Excel's conditional formatting procedure is terrible. It does not remember how you code the colors. It is almost guaranteed that the next time you go back and look at your heatmap, you can't recall whether you did this row by row, column by column, or the entire table at once. And if you coded it cell by cell, my condolences.

But or because more information

Wall Street Journal uses this paired bar chart to show the favorable/unfavorable ratings of potential GOP candidates for the 2016 presidential elections. (link to original)

This chart form is fine. From this chart, we can easily see which candidates have the strongest favorable ratings. This is precisely how the candidates were sorted (green bars).

But this chart form has one weakness. It's trying to compress three dimensions into one. The dimension of distractors is harder to understand. The gray bars are not sorted, implying that the unfavorable ratings are not well correlated with favorable ratings. There is also a third category (unknowns) that is lurking.

A scatter plot would bring out the correlation between favorable and unfavorable more clearly. In the following version, I coded the unknowns in a green color. The lighter the color, the more unknowns.

Most candidates have somewhat more supporters than distractors detractors. Trump and Christie are clearly in trouble, with more distractors than supporters, and few unknowns (dark green). Fiorina, who just entered the race, is also weak though she could recover by winning over the substantial number of unknowns.

The scatter plot takes more effort to understand but, or because, it conveys more information.

Putting a final touch on Bloomberg's terrific chart of social movements

My friend Rhonda D. wins a prize for submitting a good chart. This is Bloomberg's take on the current Supreme Court case on gay marriage (link). Their designer places this movement in the context of prior social movements such as women's suffrage and inter-racial marriage.

Previously, I mentioned New York Times' coverage using "tile maps." While the Times places geography front and center, Bloomberg prefers to highlight the time scale. (In the bottom section of Bloomberg's presentation, they use tile maps as well.)

These are the little things I love about the graphic shown above:

• The very long time horizon really allows us to see our own lifetime as a small section of the history of the nation
• The gray upper envelope showing the size of the union is essential background data presented subtly
• The inclusion of "prohibition" representing a movement that failed (I wish they had included more examples of movements that do not succeed)
• The open circle and arrow indicators to differentiate between ongoing and settled issues

They should have let the movements finish by connecting the open circles to the upper envelope. Like this:

This makes the steepness of the lines jump out even more. In addition, it makes a distinction between the movements that succeeded and the movement that failed. (Prohibition was repealed in 1933. The line between 1920 and 1933 could be more granular if such data are available.)

 

Painting the full picture of the employment situation

It's very frustrating to read the mainstream articles about the recent unemployment report. For example, the New York Times said "U.S. Jobless Claims Hit 15-year Low." (link)

At this point, everyone should be aware of how employment statistics, in particular, the unemployment rate, is computed. Certainly, the editors at the Times have heard of U3 and U6 metrics, and the employment-population ratio. Any report that does not provide all of these metrics is a report that you can't trust.

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Here is a brief version of the story in a few charts that anyone can easily generate from the FRED site. (link)

If we only report the headline unemployment rate, the picture looks rosy.

The unemployment rate has been in steady decline since peaking in 2010. The current level hasn't been seen since mid-2008, and we may soon see levels reach levels prior to the recession, i.e. the level of the boom years!

Isn't that a surprise? That's what the mainstream media are reporting.

We are facing a far less rosy picture if we consider a different metric of unemployment.

It turns out the headline statistic uses a very liberal view of who's employed. This second chart is a more "common-sense" count of who's unemployed. Even though the first unemployment metric says we are almost back to pre-recession performance, the second metric says we are still about 2 percentage points above what it used to be in 2008. That is a much less happy picture.

There are two major distinctions between the metrics. If you have a part-time job for even one hour during the period when the government conducts its survey, you are considered "employed" on the first chart but not on the second. Besides, if you are too discouraged to even look for a job, you are not considered unemployed in the first chart but you are unemployed in the second.

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The most important chart, though, is the employment-population ratio. You might think that an unemployment rate of 5.5% means that 5.5% of the nation's population are unemployed. Not true. Perhaps it means 5.5% of the working-age population (excluding kids and elderly) are unemployed? Still not true.

As a result of a bipartisan effort, the base of that proportion is the number of people whom the government deems to be "wanting a job".

Before the latest recession, the proportion of people who "want a job" has been around 63% for a very long time. During the recession, this proportion plunged to below 59%. Currently, it has moved above 59% but this is about 4% below the mid-2008 level. An extra four percent of the population has decided that they "don't want a job", and they are not counted at all in the unemployment rate in the first chart above.

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This series of charts illustrate why looking at a single metric is dangerous. By the first metric, the job market is the same as in mid-2008. When we look at the other two metrics, we immediately see that it's the same but not really the same.

I have a whole chapter in Numbersense (link) on employment statistics. In the chapter, I mentioned John Crudele's columns at the New York Post. As usual, he is one who will peel back the onion. His take on the latest statistics is here. While his views can be a bit extreme, reading his take on these statistics is more beneficial to your health than those of the usual sources.