Nowadays, rarely a week goes by free from news about the so-called gender pay gap. There is even now a “wage-gap day“ on November 10. This is the day when women allegedly start to work for free for the rest of the year. The remaining 51 days are 14% of the year — a figure corresponding to the wage gap between women and men.
One can hardly find more mindless approach to this issue, however, as the idea behind the “working for free” narrative is an affront to any serious study of the of the wage-gap issue.
It is usually asserted that the gap is a product of discrimination and sexism. But any scientific approach to the issue requires that if we’re going to make such an assertion, but we must take into account other variables that affect wages, such as the self-sorting by employees, total work experience (including working time), and education. Once we correct for such variables the adjusted wage gap is generated, which is significantly smaller. It is closer to the 2-5% range, depending on the study. Without such adjustments one is obviously comparing apples and oranges. We could as well say that a truck driver earning 12-times less than a banker is working for free since February, almost for the whole year.
I do not want to deal here, however, with the obvious distinction between the statistical wage gap and the adjusted wage gap — even though the popular press constantly and passionately refuses to learn anything from the existing research. I would like to focus here on what is left after the adjustment: that small, but still positive wage gap of 2-5%.
Let us pause for a while and analyze how the adjustment is made. There are various widely used statistical tools to do it, but their general methodological core is the same. We classify workers according to some objective and easily recognized features such as education, working time, sector and so forth. Furthermore, by using econometric analysis, we try to see the statistical connection of each change (increase or decrease) of a particular objective variable resulting in changes of the wage level. After the filtering is done we can recognize how much education, working time, and experience can contribute to higher income. Yet that does not fully bring the wage gap difference to zero.
What is a proper approach to such residual? There are two approaches: an ideological one and a scientific one. A scientific one is plain and simple: my model is limited. It cannot explain the existing statistical wage gap with reference to some objective and easily measurable data.
The ideological approach is the more commonly used: I cannot explain the gap fully by my model, but I know the answer a priori without further proof! It must be discrimination and bias against women.
The scientific answer can easily be supplemented by a proper economic reflection actually related to the “socialist calculation debate.” As Austrian-friendly readers well know, the most important conclusion of that discussion was that central political owner has no objective means to properly recognize true productivity of the factors of production: machines, land, natural resources and workers too. In order to calculate one has to know the valuation of the factors, which in the market economy is discovered by the competitive process. That is the only way to arrive at a recognition of proper market value.
You probably imagine where it goes from here. The wage-gap debate depends depends on the idea that it is possible to discover some “objective value” of labor, which can then be inserted into an equation. Can a particular factor of production – i.e., a worker – be reduced to nothing more than adding up formal education, working time, sector, experience, etc.? If so, then we’ve solved the socialist calculation problem! Bureaucrats can then just calculate what each worker should be paid, and hand down those diktats to all employers. In other words, government “experts” can now calculate everyone’s wages ahead of time. Markets would no no longer be necessary.
In the real world, however, nothing has happened to change the relevance of Mises demonstration nearly a century ago that it is in fact impossible to calculate these values without the marketplace.
So what is the real value of labor when measured on a case-by-case basis? Fortunately there is an Austrian answer to this problem of the wage gap: some price discrepancies are reflections of immeasurable features which entrepreneurs discover in the market process. NBA players’ wages can hardly be fully reduced to such objective measures as: point-scoring, rebounding, height, speed, experience etc. There is simply more to their market value. There is no reason to assume that other labor markets should be any different. Some things just cannot be fully measured.
