Bivariate choropleths
Showing both absolute and relative values on the same chart 2

Showing both absolute and relative values on the same chart 1

Visual Capitalist has a helpful overview on the "uninsured" deposits problem that has become the talking point of the recent banking crisis. Here is a snippet of the chart that you can see in full at this link:


This is in infographics style. It's a bar chart that shows the top X banks. Even though the headline says "by uninsured deposits", the sort order is really based on the proportion of deposits that are uninsured, i.e. residing in accounts that exceed $250K.  They used a red color to highlight the two failed banks, both of which have at least 90% of deposits uninsured.

The right column provides further context: the total amounts of deposits, presented both as a list of numbers as well as a column of bubbles. As readers know, bubbles are not self-sufficient, and if the list of numbers were removed, the bubbles lost most of their power of communication. Big, small, but how much smaller?

There are little nuggets of text in various corners that provide other information.

Overall, this is a pretty good one as far as infographics go.


I'd prefer to elevate information about the Too Big to Fail banks (which are hiding in plain sight). Addressing this surfaces the usual battle between relative and absolute values. While the smaller banks have some of the highest concentrations of uninsured deposits, each TBTF bank has multiples of the absolute dollars of uninsured deposits as the smaller banks.

Here is a revised version:


The banks are still ordered in the same way by the proportions of uninsured value. The data being plotted are not the proportions but the actual deposit amounts. Thus, the three TBTF banks (Citibank, Chase and Bank of America) stand out of the crowd. Aside from Citibank, the other two have relatively moderate proportions of uninsured assets but the sizes of the red bars for any of these three dominate those of the smaller banks.

Notice that I added the gray segments, which portray the amount of deposits that are FDIC protected. I did this not just to show the relative sizes of the banks. Having the other part of the deposits allow readers to answer additional questions, such as which banks have the most insured deposits? They also visually present the relative proportions.


The most amazing part of this dataset is the amount of uninsured money. I'm trying to think who these account holders are. It would seem like a very small collection of people and/or businesses would be holding these accounts. If they are mostly businesses, is FDIC insurance designed to protect business deposits? If they are mostly personal accounts, then surely only very wealthy individuals hold most of these accounts.

In the above chart, I'm assuming that deposits and assets are referring to the same thing. This may not be the correct interpretation. Deposits may be only a portion of the assets. It would be strange though that the analysts only have the proportions but not the actual deposit amounts at these banks. Nevertheless, until proven otherwise, you should see my revision as a sketch - what you can do if you have both the total deposits and the proportions uninsured.


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Michael Wexler

As always, a fun read. I’ll point out that your emphasis on the amount of $ is not actually the important part. The original chart focused on the right aspect, that a large percent of their assets were uninsured which is a reflection of “safety” of the bank, perceptually. Note the $209B in asset for SVB is large but not even top 5 on the chart by total assets. Yet look at the outsize impact the bank had… which every gov regulator missed, by assuming smaller asset size meant less danger to the system (“systemic impact”). Other than Citi (always a bit of a mess), the large banks have 50%ish uninsured, but this has no one really worried. In addition, that 50% of their large base is a huge amount of money as you mention… interesting, but not as risky as the concentration of over-250K money per account at the smaller banks.

Also, consider how you would calc this %age, and the unmentioned step function. Each person (acct, entity, whatever) has up to 250K into the insured bucket, then every dollar over it goes into the uninsured. So, if every account has $500K, we’d get a 50% uninsured. If every account was 250K and one account was a zillion, we’d also get to 50% uninsured… but most folks come out fine and one wealthy person loses lots; this may appear as a safer/better outcome, even though both entities have the same "score". A useful 3rd dim would be number of accounts: that gives you a sense of concentration (though I leave it to you to show a clever way to display it!). Citi, for example, has biased towards wealthy urbanites and closed branches, so I suspect median USD per account is probably higher than we’d expect, esp. over $250K. But Chase and BoA bank the US, so they have a mix of lower and upper account sizes, and their %ages reflect this.

Your chart does present well, as always… but I think it buries the lede. It hides the at-risk %age in tiny bars, and the sort order becomes “ordinal” since it’s harder to see the step change from row to row.

Thanks for all these charting ideas!

PS: as an idea, perhaps you can go behind the scenes, show us how you pull the data out of the charts, and how you create your new ones (R or python? Favorite library? Do you edit afterwards in a paint program?)

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