The Importance of Theory: Trade, Jobs, and Wages

Theory is vital to understanding the world. It both helps us make sense of the world around us and make predictions. Theory is like a pair of glasses: a good pair helps you see better. A bad pair makes things worse. And no pair is just useless.

One of the major problems these days is that the trade policy of the Trump Administration is guided by horrifically bad theory (when guided by theory at all). Even worse, they do not even seem to grasp easy-to-verify facts and figures (e.g., Trump’s claim that the US trade deficit with China is $1 trillion. It’s less than $300 billion. Personally, I’d have relatively more faith in central planners if they could get even the basics right).

This post aims to be a corrective lens on some of the nonsense. Of course, to the True Believers, none of this will matter; this post isn’t for them. This post is for those who want to understand trade theory.

International trade does not differ from domestic trade. Whether one is trading with Bret in Boston, Brad in Baton Rouge, or Bobby in Berlin, the same principles hold. Specifically, people think on the margin when making buying and selling decisions. They will buy a good when the marginal cost of making the good exceeds the marginal benefit of making the good. In other words, they will buy when it is cheaper to buy and make when it is cheaper to make.Likewise, they will sell when the marginal cost of making the good is less than the marginal benefit of making the good. Or, they will sell when they can get more for selling the good than it costs to make it.

Ricardo’s simple model, in which comparative advantage determines the patterns of trade, has been shown time and time again to hold, both at the individual level and at the international level. Technical elaborations have come about (e.g., the Hecksher-Ohlin ‘Standard Trade Model,’ Stolper-Samuelson and Factor Price Equalization, the Specific Factors Model, the Product Life Cycle Theory, etc), but we will stick with the simple model, as these elaborations do not change the underlying logic.

Since people are thinking and acting on the margin, we should expect international trade patterns to reflect these margins. In other words, a nation should generally import what it is relatively bad at making (i.e., low productivity goods/services) and export what it is relatively good at making (i.e., high productivity goods/services). Furthermore, since wages are tied to productivity, we should expect the wages in import-competing industries to be relatively low and the wages in export-competing industries to be relatively high. Indeed, that is what we see.

Writing for the Peterson Institute for International Economics, J. Bradford Jensen and Lori G. Keltzer show that the overwhelming majority of jobs “at risk” to trade are in sectors with low productivity and low wages. Conversely, the sectors with the highest productivity and wages are exporters (see figures 4 and 7). These data are a little old (the report is from 2008), but I have been working on updating the figures. The pattern isn’t changing; just the numbers.

Since trade patterns follow a certain logic and are not random, we should not expect job losses from trade to be random, either. Protectionists like to argue implicitly that trade job losses are either random (and thus they cite average wages) or that trade job losses are non-random, but for some reason, firms look to offshore their most productive, least costly business (thus focusing solely on high productivity industries and wages). But job losses would be in low-wage areas, and job gains would be in high-wage areas.

Consequently, any jobs “saved” by tariffs will also be those low-productivity jobs at the expense of high-productivity jobs. Take textiles, for example. Textile manufacturing in the US faces staunch competition from abroad. According to the BLS, textile manufacturing workers earn an average of $17.78/hr. That’s just 54.4% of the national average ($32.66). Conversely, oil & petroleum extraction workers—one of our major exports—earn an average of $28.39/hr. (Note: these data exclude managers and other supervisory workers. These are just non-supervisory.) Tariffs may “save” some low-productivity jobs, but at the cost of higher-productivity jobs. (To head off any objections, it is true that the two industries chosen are the polar opposites of the scale, but the point remains.)

Now, of course, as trade expands, some textile workers may get laid off. What are their alternatives? Are they doomed to live on the public dime the rest of their lives? After all, their skills are no longer needed in the US economy (what economists call “structural unemployment”). The answer is: probably not. Even relatively low-productivity service jobs pay approximately the same as these low-end manufacturing jobs. Food prep workers earn an average of $17.32/hr. Retail sales workers: $17.05/hr. Are these declines in wages for the textile manufacturing worker? Sure. But they are pretty comparable wages. If the worker wasn’t on welfare with textiles, he likely wouldn’t be working as a retail or food prep worker either. And all this assumes the worker takes no steps to re-train. If they do acquire skills for which there is more demand, they can increase their wages.

Life happens on the margins. Thus, adjustments to trade will happen on the margins as well. Good theory helps us see the potential impacts of tariffs along various margins (and to dismiss what impacts are unlikely to occur).

Let me end with a personal story from grad school:

I was taking Robin Hanson’s Law & Econ grad class (ECON 841). For part of the grade, we had to present an original paper. I had come up with what I thought was a very clever model. The math worked out, and it was quite pretty. I presented the paper, and Dr. Hanson said just one thing: “This is interesting, but where’s the economics?’ With that one simple question, he blew up my model. I couldn’t answer. The mathematics were precise. The model was logically perfect. But it explained nothing. There was no theory. It amounted to little more than “what if?” I learned two important lessons that day: 1) if I never wanted to be embarrassed like that again, I’d have to make sure theory is included, and 2) give someone enough assumptions, and they can prove anything they want.

Good theory prevents bad vision. Bad theory, no matter how mathematically precise, no matter how many fancy Greek letters you throw in, will mislead.