It’s easy to lie with statistics. This is a well-worn truth (or at least something that is generally believed to be true). One popular way to do this is to compare two different things. Ideally, you try to arrange to compare two things that look similar, even if they really aren’t.
Take, for example, the recent public unions debates in Wisconsin. Without judging the merits of unions (such judgments are not our purpose in this blog), some of the statistics being shot around were highly suspect. For example, some people were quoting data that said that teachers are paid more (on the average) than the average person in the country. This is probably true, but it’s not a very useful comparison because the populations in question don’t match very well. Teachers are almost certainly more educated than the average employed individual in the US, for a start. Expecting them to make no more than someone working a job that requires only a high-school diploma is unrealistic. (This isn’t to denigrate those jobs by any means; they’re very important and I’m thankful that people do them. But the reality is that they get paid less than jobs that require more training, at least under our current system.) Other factors one needs to consider are things like time in the current profession. (Are teachers more or less likely to have more experience than the average worker? I honestly don’t know, but it surely matters for comparisons of pay.) In order to make a valid comparison, you need to control for as many of these factors as you can. In general, you can’t control for every single factor, but to just blithely compare two very different populations and attempt to infer conclusions is reckless at best. At worst, it’s intentionally misleading.
Incidentally, attempts to control for these factors suggests that teachers make around 5% less than private sector workers with comparable backgrounds. You’re welcome to question the source of the study; it doesn’t appear to be as neutral as I’d like to see when making a policy decision. And you’re also welcome to feel that 5% isn’t enough difference to worry about. But this suggests that the teacher are not as over-paid as commentators have claimed, once you compare them to a more equivalent sample.
Another example I heard recently was on The Daily Show. Rand Paul was on and he claimed that the richest 1% of Americans pay 30% of our total income taxes and therefore are doing more than their fair share. Seems pretty striking, doesn’t it? One percent of us paying 30%? Wait a minute, though: if they’re the richest 1%, they should surely be paying more than the average since they, by definition, are richer than average. Even with a flat tax rate (rather than the progressive tax rate we nominally have), that’s to be expected. So the question is, how much do the richest 1% own? The answer apparently is around 35% of the wealth in this country. Oh, dear. That means that they’re underpaying their taxes relative to you and I. (I’ll assume that you’re in the poorest 99%, like I am.) In fact, that means that our actual, effective tax rate is regressive: the rich appear to be paying lower rates than the poor. (There are a number of reasons to think this is true just based on various loopholes in the tax code, of course. For one thing, capital gains are taxed at a lower rate than their income would require otherwise.)
This is not meant to judge the tax rates or who should pay how much. That debate ultimately requires judgments that go beyond simple facts into philosophy and ethics. But to get to that point, we need data that tells us what’s really going on. Tossing out misleading facts may help you win the argument, but it doesn’t really help craft a truly informed policy. Part of telling the truth means comparing apples to apples. Only then can people judge your case for themselves, and that should be the goal.