As with any model in any science, we need to ask the questions: “how well does this model reflect real-world observations? Are its assumptions likely to hold and are they key to the model?” Negative answers to these questions do not necessarily imply the model should be scrapped. All models, after all, are simplifications. Any model that could handle every possible variation would be unwieldly and thus not provide much insight. Further, as Harold Demsetz warned us, just because the real world differs from some theoretical outcome it does not mean that alternatives are necessarily better, especially when one situation is viewed through the lens of reality and the other through the lens of theory.
In the previous post, I discussed some of the political realities surrounding economic justifications for trade restrictions, primarily using the optimal tariff model as an example. Are these objections enough to recommend against using policy to try to influence patterns of trade, or am I simply making the Nirvana Fallacy?
It is certainly true that markets can fail (broadly defined as failed to achieve some optimal level or distribution) and these failures can be corrected through judicious government actions. However, these actions can cause more harm than they actually solve. Indeed, for something like an optimal tariff, even if done with the best of intentions, it can backfire and result in a much worse scenario without much effort. Further, these justifications can be misused or hijacked to give intellectual cover for essentially selfish goals.
However, the biggest issue with trade policy is mistakes can become systematic and entrenched. Aside from the reasons discussed above, most political systems, and democracies/republics in particular, are designed to be slow-moving. This means if a policy is determined to be detrimental, it may take a while to repeal or alter even if we assume no self-interest lobbying or other barriers preventing the legislation from being changed.
National trade policy also necessarily must be general. As such, it is likely to be geared toward the average person or firm. The policy may be too restrictive for some and too broad for others; it may lead to a rather substantial misallocation in resources. Consider the following example: Imagine a room with 10 people inside, five of whom are six feet tall and five of whom are five feet tall. The average height of the room would be five feet, six inches. If there is a policy to build a door in the room so people may come and go, how would the policy be structured? Maybe it is structured so that the door must be at least six feet tall, thus everyone can easily use it, but that’d mean less wall space for windows and pictures and other things, and for half the people it’d be too tall a door. Likewise, they could order the door be at least five feet, six inches (the average height), but then the tall people would find the door inconvenient to use while the short people would have no problem. The necessity of general rules is part of the reason why government action should be limited to negative rules (eg, do not steal) rather than positive rules (eg eat five servings of veggies a day).
We also need to consider the knowledge problem. The actual level of knowledge necessary to accomplish these optimal policies is both dispersed and not even necessarily consciously known to the people holding the knowledge. Acquiring both the necessarily knowledge and acquiring it in a timely fashion are impossible. What’s more, what statistical information we can gleen has some major caveats attached. Economic data is collected primarily though the use of surveys to a sample of individuals and firms and then extrapolated to the aggregate level. However, as with any survey, these surveys are subject to the same caveats, assumptions, and error terms as anything else; they may not truly represent the real economic activity they are trying to measure. Further, as additional statistical techniques are run on them, the error terms must get larger and larger, not to mention the potential for error from sampling issues, violations of various assumptions, and the like.
With all these potential for errors, which are not limited to just the political realm, we are faced with the question “how to contain damage from failures?” As mentioned above, government rules quickly become systematic and entrenched. This means that an error in government policy could quickly affect the entire society. If, for example, government bets that the Next Big Thing is going to be autonomous cars and pours subsidies into their development, and then people decide they don’t want autonomous cars for whatever reason, the American taxpayers are held holding the bag. If the various government-supported firms go under, the taxpayers will not be reimbursed, nor have anything to show for the spending of funds that could have gone toward education, health care, infrastructure, or other uses. The error would be felt (to varying degrees) nationwide. Conversely, if a private firm makes the same bet and are subsequently proven wrong, the ones who feel the loss most acutely are the firm itself: the owners, the shareholders, and those who did business with them. The losses would be generally confined to those individuals and firms. Because of these potential losses, these private firms are more likely to be careful with their spending and their projects than would government-sponsored entities.
It is from the expectation of failure and not success that I argue for the presumption of liberty in policy. With managed trade policy, while there is the potential for upside, there is a rather substantial risk of a large downside, too. Should that downside occur (a very probable event in my opinion), it’s effects would be systemic and hard to remove. Conversely, if private individuals and firms make errors, they would be the primary recipient of the downsides. Thus, I argue that free markets are more robust to error than government is. This is not an argument from Nirvana, but rather from Hell.
A Force for Good 