What does "Right to work" actually do?

So, what's the argument over?

Arguments made in favor of right-to-work (RTW), at least for the consumption of the general public, all boil down to claiming a better employment atmosphere overall in a state. It increases overall employment, and it increases wages. Before I go on, I'm going to explain some of my personal biases. No law should ever be made without compelling need. A slight marginal improvement is usually not worth the burden put upon the citizenry by a new law, whatever that burden may be. Thus, the entire burden of proof is not that a law will not make things any worse, it is that a law must make things better. This is why I'm not explicitly testing anti-right-to-work claims. Anti-any-law automatically is favored as the "null hypothesis". Of course, some laws are trivial to justify. The damage done to people and society by practices such as child prostitution are so enormous, and the moral issue so clear-cut, that it is trivial to show an overriding social need for a law against such practices. When it comes to labor law, things can start to become less immediately clear-cut. What is RTW? I explain at the end if you don't already know.

Let's Talk Money

All State Differences Median Wages
Orange: Right-to-work state does better. Blue: Non-RTW state does better.

One conventional measurement that dominates the argument about right-to-work is wages. If you know me, though, you'll already know that I will not look at them in conventional ways. This is because most attempts to both support and attack RTW on a wage basis have been pretty much crap, and pretty much dishonest. Dishonest crap? Yes. The attempts to attack and defend RTW are based on "average" wages. That's just plain silly. I'll show how at the end of the article. Instead of average, I will use median.

Okay, so now that I've chosen median income as the basis of comparison (conveniently available from the Burea of Labor Statistics, how to compare? One way is to aggregate the two groups of states (RTW vs. non-RTW), subtract one aggregation from the other, et voila! But "simple" isn't always so simple. If there is a difference between the two, is that difference meaningful? There are several little statistical "tests" that could be used, but the tests make a lot of assumptions.

Our data covers all the possible bases (all 50 states for that year). Statistics may actually obscure information. Anyway, what aggregation should I use? Should I compare the average of the medians? That sounds pretty darn kooky, although it's easy to calculate. If average is a bad idea for individual states, what makes it a good idea for states as groups? Okay, how about the medians of the medians? Again, there could be problems with this. If nothing else, it's very coarse and clunky.

What can be done, then? There are only 50 states. Of these 24 are RTW, 26 are not. That's not much. That's only 624 pairwise comparisons of states. We have spreadsheets in the modern day. 624 subtractions are nothing! Okay, so I can do 624 subtractions of one state's median wage from another's, then what? Aggregate the subtractions and present column charts with error bars and all kinds of statistical gobbie-goo?

I could, but it would only hide more than reveal. After all, when I've got that few points of data (yes, 624 is few points in my world), why not just present them all and let the reader see directly? That's what I did. The figure to the right is a "histogram". It displays every single comparison, grouped in "income difference" brackets. Orange columns are where an RTW state had a higher median income than a non-RTW state. Blue columns are the other way around. If you mouse over, you'll see the limits of each bracket and the actual number of comparisons that fell into that bracket. Overall, an RTW state was better in 106 comparisons. A non-RTW state was better in 518 comparisons.

Is that "significant"? I hate "significant", and I do statistics for a living. "Significant" was just a shorthand that Professor Pearson came up with years ago as an arbitrary cut-off. Some moron could come along and note that less than 95% of the comparisons went against RTW. So? Does that mean the 5 out of 6 comparisons that went against RTW don't count? If you were told that, if you drank something, it had an 83% chance to make you sick and do nothing good for you, would you say "It's not a 95% chance, so it's not significant!" Of course you wouldn't. Statistics were invented to deal with situations where we do not have all the data points and are trying to make a conclusion. Here, we don't have to guess. We know that out of all possible combinations, RTW did more poorly in 518 out 624 comparisons. Those comparisons, by the way, were not of "typical" (middling) RTW states vs. "typical" (middling) non-RTW states. They included everything.

All State Differences Employment per Population
Orange: Right-to-work state does better. Blue: Non-RTW state does better.

Let's Talk Jobs

Enough on income (for now). What about claims on employment? Here is where it gets more murky. First, we have the crap input problem. Most analyses of RTW use the "unemployment rate". Funny thing about the "unemployment rate". When a Democrat is president and the "unemployment rate" goes down, Republicans say that it's a crappy metric. When a Republican is president and the "unemployment rate" goes down, Democrats say that it's a crappy metric. If the value of a metric depends entirely upon who is in office and what party is commenting, it's a flat-out crappy metric!

There is a number I will use. Numbers of people employed (for 2013, from the BLS) divided by total population (estimated for 2013, US Census) is a good reflection of not only those who are working, but how much the fruits of their labor ends up having to be distributed among "mouths to feed". In 218 comparisons, RTW states came out better. In 406 comparisons, non-RTW states came out better. It's closer, but I'm not comfortable saying this is a wash. After all, roughly 2/3 of the time, RTW lost out again. So, even though it's closer, RTW lags behind non-RTW in simply providing employment to the population at large.

All State Differences Median Wages Adjusted by Employment, Population, and Cost of Living
Orange: Right-to-work state does better. Blue: Non-RTW state does better.

Let's Talk Money and Jobs

The tale is not told, though. After all, what if RTW states (like Texas) happen to be very populous states and non-RTW states (like Alaska) are sparsely populated? Then, even though on a pure state-by-state basis, RTW might not do well, in terms of overall prosperity of human beings, it might shine! But how to measure that? If we are thinking primarily of ordinary people—and we should, since the arguments about RTW always get down to whether it helps ordinary people, we can start with median income, again. If everyone were making the median income, the median income would not change. Then multiply the median income by the number of employed people in a state to get an aggregate income estimate. We also have to take into account that a state may have a lot more people to support on top of those who are working. Divide the aggregate income by the state's total population. This "population-adjusted income" can give us an idea of how well each state does vs. another in terms of the comfort of its mass of people.

Let us not forget cost of living, since higher wages can be passed on to consumers by the businesses paying them. What does that give us? You've probably already been looking over the last graph. As you can see, RTW still does not do as well as non-RTW in terms of income, adjusted by employment, population, and cost of living. In 202 comparisons, an RTW state did better than a non-RTW state, but in 422 comparisons, a non-RTW state did better than an RTW state.

What does this tell us? Look at the charts and tell yourself what it tells you. The argument tested is "Right to work improves the lot of the worker". While this might be sometimes true, it is false in two-thirds of the comparisons. This is enough to severely call into question the argument that RTW is of benefit to the ordinary worker. What it tells me, personally, is that government interference in the free market is a bad idea even if that interference is supported by large businesses. If government is doing its job in terms of police and night-watchman duties, unions are not able to actually force employers to accept any terms at all, except by using the exact same tactics that companies can use to "force" restrictive clauses into contracts--aka "playing hard-ball", and businessmen do it through purely legal means all the time. However, when government decides to meddle, and RTW is meddling by government in what should be a purely business-to-business (yes, Virginia, unions are businesses) interaction, things don't work as well. Socialistic meddling in favor of employers is no less stupid than socialistic meddling in favor of workers.

What is the take-home? Given the data at hand, a compelling argument in favor of enacting or maintaining RTW cannot be made except perhaps in a few extreme circumstances. By and large, RTW is not a policy that produces enough benefit to be worthy of being kept as law. Government meddling is never anything better than a necessary evil, and if it is not actually necessary, then it is merely evil.

What is "right to work"?

Several states in the USA have laws that are called "right to work" by their proponents. In a "right-to-work" (RTW) state, a union and employer are prohibited from entering into an agreement to "govern the extent to which an established union can require employees' membership, payment of union dues, or fees as a condition of employment, either before or after hiring". This has a lot of political fol-de-rol associated with it, nearly all of it hypocrisy. More "pro-business" groups support such law, since it restricts those nasty-wasty scary pants-wet-inducing unions. Those groups come out in favor of banning "non-compete" or "conflict of interest" clauses in employment contracts. On the other hand, "pro-labor" groups (which always oppose "right-to-work") also oppose non-compete and conflict of interest clauses. If exclusivity in any business agreement—labor contracts are just a sales contract, after all—must be prohibited, then it must be prohibited in all business agreements. But I'm trying to apply logic to politics, which only serves to make the stupid angry.

Why Average Can Be Silly

Here's a very simple example: Suppose you have a conference room with five graduate students and one well-established full professor who has discovered some lucrative inventions and gets patent royalties. The graduate students get $24,000 per year for their work as teachers and laboratory assistants. The faculty member gets $120,000 a year for his salary and those patents he has a piece of. The "average" annual pay for the room is $40,000 per year. Some political idiot can them come along and say that $40,000 a year is "typical" for that room. It's obvious that it isn't. There is a big difference between $24,000 and $40,000.

What number would give us a better idea of the "typical" income? That would be the "median", the "number in the middle". Half of everyone in the group makes less than that, half of everyone makes more than that. In our example, the median would be $24,000. Remember, we're not talking about the full range, but if you have to pull out one number that is most likely to represent a given individual in that room.

So, for income, I will use state medians instead of state "averages". If you bother to look up the matter, you will find out that state medians in the USA are always lower than state "averages". This is because of how top-heavy the US income distribution is. This is also a good indication that "average" is a particularly stupid way to express "typical" income in the USA. If this were a non-issue, we would not see an invariant relationship between "average" and median.

Before I forget, here is the data I used to generate the histograms, nicely summarized.

More Gallup Misrepresentation. This time, obesity.

Yet another Gallup survey is making the rounds, lately. This one is about obesity by state. This time, instead of quintiles, the accompanying map is, as you can see, split into arbitrary cut-offs of obesity. If you want, you can go over there and look at their map or look at the first map below. It represents the cut-offs in approximately same colors. (If you mouseover the map, you'll get more information by state.)

Obesity by State, Arbitrary Categories (darker is more)

"Squashed" Range (darker is more)

Full Range (darker is more)

This map is an excellent example of how data presentation choices can be fraudulent without being fraudulent, how to lie without lying. Honest people use quintiles, quartiles, percentiles, and other such non-parametric numbers to represent either data that has a long, uneven, and strung-out range (like achievement test scores), or to group a different set of data to show how it is distributed (like wealth per quintile). It just so happens that you can look a the obesity percentages for yourself. Notice that the data is not the strung-out and scattered. In fact, it is very densely-packed. It also is not linked to some other unevenly-distributed data.

The obesity rates are actually very densely packed. From 0% obese to 100% obese, the lowest state's rate is 19%. The highest is 35.2%. Look at that first map, again. Is a difference of about 16 percentage points worth that much a visual difference?

How else to represent the difference so people can get an idea of reality instead of a visual lie that technically represents the actual numbers? The second, or "squashed scale" map does that. The "worst color" (dark gray) is matched to rate of 35.2%. The "best color" is matched to 19%. The range between is then evenly filled in among the three color points. Look different? It does. Yes, there is some rough correspondence between the misleading map that comes from Gallup and the (somewhat) more truthful map I created

But I'm not finished. You see a third map. This is a map where the "worst color" corresponds to 100% obesity in the population (which is the worst possible condition) and the "best color" corresponds to no obesity. Thus, changes in color correspond to linear differences along the full possible range. Having a hard time telling the states apart? That is because the differences among them in this index really are quite small--total range is 16.2 percentage points out of 100. This map shows you what that looks like.

So, why does Gallup do this, and why do people so stupidly and eagerly swallow such dishonest representation of data? First, explaining Gallup. I don't work there, so this is speculation, but Gallup makes its money off turmoil. Anything they publish that will stir the pot will inspire more surveys that they can sell. Likewise, presenting things in extreme (and dishonest) ways ensures that there will be more arguments, leading to more survey commissions, leading to more dishonest data presentation, leading to more arguments. It's a lucrative circle for Gallup.

But why do people so eagerly devour such steaming dog turds of quasi-information? First, they're simple. People like very stark, very simple things to natter on about with each other. People do not like complex and shaded descriptions. They want things to be very neatly pigeonholed, and this comforts them. In addition, people with agendas want things presented as rigidly and extremely as possible to the public, all the better to sound the panic alarm and drum the masses into obedience. Finally, we are taught that only rigid and extreme answers can be "true". We are indoctrinated to be mental weaklings, to always see the world as "good" and "evil" with nothing in between. We are taught that someone who is able to see gradual differences is a "fence-sitter" or "spineless". We are told that only extremism is good--although it's only actually extremism when it's someone you don't like doing it.