Good Numbers, Bad Numbers

In politics, as in work and life generally, I’m a longtime fan of getting the right numbers, getting the numbers right, and understanding what they tell us and what they don’t. My recent essay, Water Bills, Fees, and Our Politics, applied that to local politics.

It also left me thinking that some illustrative, more general examples might be useful or at least fun. They’re not all from politics, let alone the current local election cycle.

Using Numbers Wrong

Let’s warm up with a softball.

The other day, I saw something like this floating around the Internet (I’m paraphrasing): “An orchestra consisting of 80 musicians can play a Beethoven symphony in 30 minutes. How many minutes would it take an orchestra of 120 musicians to play the same symphony?”


If you don’t think about the situation, you might just make a calculation and say 20 minutes. Your math would be right, but your answer would be wrong. A moment’s consideration will reveal the obvious: the number of minutes will be approximately the same, no matter what size the orchestra. It’s not like asking how many more Toyota Tundras you can make if you have three identical, adequately staffed and supplied Tundra factories instead of two.

Earlier this week, on a less lofty plane, Trump 2016 campaign manager Paul Manafort pled not guilty to a dozen new charges coming out of the special prosecutor’s investigation of alleged collusion between the campaign and the Russian government. That sounds pretty damning for the president, doesn’t it?

It’s definitely bad news for Manafort. But here’s a number some media outlets will highlight and others will bury. The number of these charges which are related to Russia or to the Trump campaign is zero. The twelve charges, which include money laundering and tax fraud, involve his past business dealings with Ukraine.

I’m not saying there won’t be any subsequent charges related to the campaign, just that a few of the headlines I saw yesterday put that number in its proper perspective, and most didn’t.

Numbers Without Context

We already picked on Beethoven, who can’t fight back — I hope — because he’s been dead 190 years. Now let’s pick on Microsoft, which is too big to fight back against the likes of me.

I have my Windows 10 lock and login screens set to show images Microsoft chooses, with some ongoing input from me as to whether I like a given image or am “not a fan.” Sometimes they add some text to identify the scene; I wish they’d do that more. Sometimes they use the text to motivate or inspire. I wish they’d do that less.

The other day, the text over my image said, “Only 6.7% of women graduate with STEM degrees.” STEM degrees – in Science, Technology, Engineering, and Math – are a big deal in an economy which thrives on discovering, inventing, and building things. I prefer STEAM, with A for Arts added, for the sake of producing fully-functioning, civilized humans, but that’s a separate discussion. STEM or STEAM — the importance of S, T, E, and M cannot be gainsaid.

(Ha! I’ve been wanting to use gainsaid in print for years. Thanks for the opportunity. Now, back to numbers.)


I admit, 6.7% sounds like a problem, and I think I’m supposed to be motivated to do something about it. But as dire and precise as it sounds, by just giving me the number, they haven’t told me what the problem is or how big it is — or whether it exists at all.

First, is that 6.7% of all women or 6.7% of women who receive college degrees? That would be nice to know.

Second, and more importantly, I gather they’re trying to highlight a gender-based disparity – but they haven’t identified one. What percentage of men who receive college degrees graduate in STEM? If it’s 39.6%, there’s clearly a gender disparity. If it’s 7.0% or 6.6%, we may have a shortage of STEM degrees overall, but it’s hard to pin it on gender. If it’s 1.3%, we have a gender-based disparity in the opposite direction. Without more information, we simply don’t know.

Finally, is there a shortage of STEM-degree-wielding men and women? By itself, our 6.7% doesn’t tell me that. I need to know something about the current and projected demand for such expertise in industry and education, before I can assess the situation responsibly.

Based on data from other sources, I believe there is a crucial discussion to be had. But by itself our 6.7% doesn’t tell us that, and it doesn’t tell us what that discussion should be. In truth, we need more data before we can even consider intelligently whether a gender-based disparity, if it exists, is actually a problem. (I’m not saying that more data will answer that question; it’s more of a moral or philosophical matter. But to reason well in those realms, it helps to know what the numbers tell us and what they don’t.)

If I’m to act emotionally, maybe 6.7% is enough. But if I’m to act rationally, I need to know more.

By comparison, an offhand remark in a friend’s recent e-mail tells me more on this subject than Microsoft’s solitary 6.7%. She’s just starting a computer science degree at a consequential university, where there are three women among the first-year students in the program and “about 85” men. Here, at least, we have some comparative data.

Using the Politically Convenient Number

A key skill in doing the math and connecting the dots is knowing – or working until we find out – what the numbers don’t tell us. At the national level, for example, we might hope for our policy makers to understand what the official (U3) unemployment rate doesn’t tell us.

In calculating U3, the Bureau of Labor Statistics excludes individuals who don’t have a job but have stopped looking for one. They’re just as unemployed as anyone else, and their need for work is just as great, but they’ve given up — we hope temporarily.

Excluding them helps keep the unemployment rate lower, which is nice for politics and press releases. But for most policy-making purposes, officials should know to look at U5, which counts this discouraged demographic as unemployed, or U6, which also adds workers who are part-time for purely economic reasons – that is, the involuntarily underemployed. U5 is always higher than U3, and U6 is always higher than U5.

U6, U5, and U3 unemployment rates


Rock-and-a-Hard-Place Numbers

Certain numbers are convenient, if you’re on one side of a debate, because you can’t lose. But if you’re on the other side, you’re between the proverbial rock and a hard place, and you can’t win.

You may have read recently that the new Office of Management and Budget (OMB) Director Mick Mulvaney says that 94.8% of income tax revenue comes from the upper 20% of taxpayers, according to income. That’s up about ten percent since 2015. Meanwhile, the bottom 40% pay no income tax at all. The US government pays many of them money at tax time — and I don’t mean refunds of money they earned during the year and had withheld.

Here’s the problem with that — or its virtue, depending on which side you’re on. If you’re the right, and you want meaningful tax cuts, there’s no sensible or politically viable way to structure cuts so that they don’t reduce taxes for the people who pay 95% of income taxes. So anytime you try to cut taxes, you’re vulnerable to the well-traveled criticism that it’s at least partially a tax cut for the rich.

The Left is convinced that it’s immoral to do anything that benefits the rich. That’s why they’re the Left. There’s a philosophical debate to be had about that, but it doesn’t change the fact that “tax cuts for the rich” resonates exceptionally well with the mass of voters who don’t do the math or connect the dots — and with some who do, depending on their ideology.


Governments far and wide are beginning to wrestle with another rock-and-a-hard-place scenario: essentially, they borrowed many millions or billions of dollars from infrastructure, by not paying for anywhere near enough maintenance, and now those debts are coming due. In other words, infrastructure is failing. And infrastructure ain’t cheap.

But this one isn’t the numbers’ fault. It’s the governments’ fault — which means, ultimately, it’s the people’s fault. But the people are well nigh violent in their opposition to borrowing or raising taxes to repay this debt.

They reaped the short-term benefits for years, in lower taxes and less government debt. Now the bill comes due, and they don’t like the numbers.

That’s why we pay our elected officials, especially the local ones, the big bucks.

No, wait. We don’t. Maybe the numbers would be friendlier if we did?

Wide-eyed Number Wonder

I’ve noticed that some people see a large number and automatically assume that it’s too large. This is especially true of numbers having to do with money and government.

It follows that we should decrease that high number somehow, probably dramatically. We should do it without cutting programs or services — because everyone knows that every government is shot through with waste, fraud, and abuse. Just clean those up, and we can cut that $53 million city budget by at least $3 million without even trying very hard.

laptops and numbers

I’ve heard local candidates claim this — let’s suppose my numbers are invented merely for this argument — but they can never seem to identify $3 million of specific waste, fraud and abuse in a real budget. In the absence of such discoveries, they won’t tell us what programs should be cut, and how much, to achieve the economy they claim to seek.

Wide-eyed number wonder attracts voters who are more concerned than informed, but it does not lead to lower taxes in the long run. The people who lead us there in real life get the right numbers, get the numbers right, and know what the numbers tell us and don’t tell us.

Numbers Without Perspective

My alma mater, BYU, is having a historically bad season. Before Saturday’s victory against a 1-7 team, they too were 1-7. They may yet win another game or two, because their schedule was front-loaded with some of the best teams in the nation, but is back-loaded with teams they should be able to trounce. (Or at least beat, in the case of Fresno State.)

A good team beating a good team tells us something. A bad team losing to a bad team tells us something. A good team beating a bad team and a bad team losing to a good team tell us little.

Lavell Edwards Stadium at BYU

The first half of BYU’s schedule was so strong that it would have given us a good idea how good they were, if they had been good enough to defeat at least one good team.

The latter half of the schedule is so weak that, in a better season, winning out would have told us virtually nothing about how good they were. This season, it will clearly tell us how bad they are.

Long story short, to know what numbers mean, we need to put them in perspective.

Here’s a quick political example. (If you want to know more of this one, see my more detailed discussing of city fees, water bills, politics, and candidates.)

Let us suppose that my water/sewer/garbage bill from my city is about $50 per month. However, because of pressure on limited supplies (caused by growth), and because previous administrations dug a deep hole for their successors, it’s about to go up. The current administration has a pair of options which will solve the problems. One is to build a water treatment plant, so that scarce culinary water can be reused. That will raise my water bill to $130 per month. The other is to build a pressurized irrigation system for watering our lawns, so we can use our abundance of irrigation water for that, not our drinking water. That will raise my water bill to $115 per month. Suppose city officials choose the latter — and just for fun, suppose the city’s voters overwhelmingly approve borrowing $48 million to build the new system.

If my only perspective is that my water bill increased from $50 to $115, then the pressurized irrigation system looks like folly to me.

If I understand that $50 was not among my choices for the future — that my bill could not stay at $50, but had to increase, either to $115 or $130 — then choosing the cheaper option looks more like wise frugality and less like profligate folly.

Useless Ranges

The small, illegal sign by the road says I could make “up to $55k per month” investing someone else’s money in real estate or the stock market.

I assume that means, at best, that one person made that much once, for at least one month. I also assume that most people would make far less — or the advertiser could afford better signs and would give me better numbers.

buyer in car lot

It’s the end of the model year, and for one make last year’s cars are “up to 30% below MSRP.” Again, I assume that you’d give me better numbers if you had them. in other words, I tend to assume the worst. One car might be 30% below MSRP. The others could be 0.03% below MSRP, for all I know. And that’s when I believe your numbers.

Irrelevant Comparisons

Did you know that the interest portion of my monthly home mortgage payment is more than I pay each month for laundry soap, Netflix, library fines, and gourmet root beers combined? Obviously, I have far too much debt. It’s out of control.

My heading is a dead giveaway, but let me put that in other terms. Did you know that my city pays more in monthly debt service than it does for a month’s worth of police protection?

These are not useful comparisons.

On the other hand, if my city’s per-capita debt load — or interest payments — were twice as high as those of other Utah cities which are similar in size, stage of development, and economic activity, then we’d have a comparison that could tell us something. If my city paid significantly higher or lower wages to police officers, that would have some impact too.

Instead, we have someone trying to push political buttons with numbers which mean nothing.

Poll Numbers

Make a poll so you can report on it. It happens all the time.

Then make the poll say what you want to report (deliberately or accidentally), then believe your own poll numbers. It’s a common mistake. (You could be Hillary Clinton if . . .)

Election Day 2016 found me in highly dubious political company. The only people I knew who were openly predicting a Donald Trump victory over Hillary Clinton were leftist propagandist Michael Moore and obscure blogger Yours Truly. I didn’t want either candidate to win, but that’s beside the point.

The polls, which were legion, predicted a solid Clinton victory and perhaps even a landslide in the Electoral College — the only place where the results would matter. But even if I hadn’t suspected a lot of pollsters of skewing their samples away from Trump, and even if I hadn’t suspected the Big Media Acronyms of cherry-picking poll results to reinforce their fondest desires, I’d have been profoundly suspicious of the polls for two reasons unique to that presidential race.

writing on papers

First, they seemed to be counting on African-American turnout to be almost as high as it was in 2008 and 2012 for President Obama. That seemed to me like a dangerous assumption, if not bizarre.

Second, because it almost always makes them more accurate, polls usually draw their samples from past voters in similar elections and measure the responses of likely voters. I thought there was ample reason to expect last November’s results to turn these very sensible practices on their heads. I expected a lot of new voters — or voters who hadn’t voted in a long time — to turn out and vote for Donald Trump. These are exactly the kinds of voters the polls are sensibly designed to exclude.

It was not a sensible year — and both of these things happened. And Michael Moore and I were both right. A year later, I can’t say that either of us views the results with unabashed glee, but that’s not my point. My point is . . .

The only poll that matters is the election itself. All other polls are at best interesting, and at worst filled with flaws that we’ll have to wait for the election to reveal.

Parting Thoughts

It’s been fun, at least for me. Thanks for reading.

And please, before you mark your ballot, this and every year, take some time to consider how well candidates do the math and connect the dots. This means that to some degree you must do the math and connect the dots. If we could fill our boards and councils and legislatures full of such people, the exact opposite of Al Gore’s Y2K election rhetoric might be true: Everything that should be up might really be up. And everything that should be down might really be down.