Policy Research and the Limits of Statistical Utilitarianism

I should begin by affirming that I have no objection, in principle, to the use of or the appeal to statistical information, to assessing the empirical impacts of a given policy choice. [1] Such appeals are an important aspect of public policy research and of the advocacy of libertarian principles. Statistics, in and of themselves, are not the problem; rather, it is the application of statistics to which we must direct our careful attention, asking about the illuminative limits of a given data set: How much can we safely — indeed, scientifically — extrapolate from these figures? What hasn’t been accounted for here that might affect the way we think about the public policy implications? We need to be asking these questions in light of the fact that all apparently scientific methods and practices are culturally and ideologically contingent, based on the preexisting views that we take into our analyses. From cultural, educational, and other experiences, we have developed biases about benchmarks such as efficiency and utility, biases that shape our understanding of the contours of good policy.

As an example of misguided attempts to reduce complex systems to something “legible,” “simplified,” and therefore controllable, James C. Scott describes the “scientific forestry” efforts of 18th and 19th century Germany, instituted to be statistically optimal, to maximize timber yields. Expert bureaucrats increasingly thought that they could plan aspects of social and economic life using metrics that they mistook for legitimate science. As Henry E. Lowood writes, “In the second half of the 18th century, few occupational groups rivaled government officials in their attention to numbers.” [2] Ostensibly science- and data-driven, the German forests suffered from a host of problems, though these were not immediately apparent. In fact, for almost a century, it appeared that the forests had been a success. Only much later did the unintended consequences begin to manifest, did the hubris of the modernist’s shallow understanding of science and the capabilities of statistics reveal itself. As is so often the case, the libertarian will be reminded of Bastiat’s words: “In the economic sphere an act, a habit, an institution, a law produces not only one effect, but a series of effects. Of these effects, the first alone is immediate; it appears simultaneously with its cause; it is seen. The other effects emerge only subsequently; they are not seen; we are fortunate if we foresee them.” The Prussian effort at planning and managing forests for maximum productivity failed for the same reasons that planned economies fail. Economies, like forests, are complicated, composite phenomena, subsuming millions of smaller components, too complex to be doctored or controlled. [3]

By definition, we can’t quantify the unseen, the dormant or hypothetical potentialities that a chosen course of action has perhaps quite unintentionally forestalled. We can’t measure in statistics the dolors (to borrow the utilitarian terminology) that haven’t yet manifested themselves. Nor can we measure the foregone hedons, the positive outcomes that we’re missing out on because of a past choice. The Washington Examiner’s Timothy P. Carney often makes this point in his reporting on the Ex-Im Bank; its greatest harms may be those we will never actually see and thus cannot gauge, the opportunities that the bank has crowded out to the detriment of its competitors. So what is the solution? Far from removing statistical methods from public policy research and analysis, we must instead be mindful of the limits of those methods, leaving plenty of room for philosophical and analytical approaches that duly account for aspects of social and economic experience that are either very difficult or currently impossible to measure in practice. We find an analogous critique in recent rebukes of “mathiness” in the economics scholarship. Rendered partially blind by the “tunnel vision” Scott describes, policy wonks often make recommendations that fail to fully and properly account for the inherent messiness of conditions in reality. And, as in the economics profession, the internal politics of the policy research community are among the key drivers of this trend. In real life, utility calculations simply aren’t as easy as our statistics and charts would have us to believe. In point of fact, the various philosophical questions presented by what’s called the “trolley problem” show how difficult it is in practice to make utility calculations where human lives are implicated — as they always are in public policy debates. Expounding on this point using the example of employment figures, Scott writes, “Those who gather and interpret such aggregate data understand that there is a certain fictional and arbitrary quality to their categories and that they hide a wealth of problematic variation. Once set, however, these thin categories operate unavoidably as if all similarly classified cases were in fact homogeneous and uniform.” Scott’s insight holds for several areas of public policy inquiry, not only the question of employment, but, taking that example alone, how many libertarians accept the government’s employment numbers as accurate? How many of us think that the government’s GDP numbers are an accurate measure of economic conditions or overall wealth in the United States?

In particular, then, libertarian policy wonks ought to take care to temper empirical and statistical analyses with the insights of, for example, Ludwig von Mises’s praxeological method and methodological individualism more generally, a practice that can help us avoid some of the problems with attempts at a purely utilitarian/mathematical calculus. As an example of what can go wrong when we don’t hold true to our individualist philosophy, when we fall into the many traps of wonkishness, let’s look at the following tweet from the Adam Smith Institute’s Sam Bowman. I don’t mean to pick on Mr. Bowman, of course; he is an eminently smart, capable, and qualified public policy expert who frequently makes great contributions to the advancement of free market ideas. Notwithstanding his talents, I think Mr. Bowman is wrong in the following claim, and I think it illustrates some of the defects of wonkishness that I’ve been discussing: “This is why I like tax credits, basic incomes, negative income taxes. I don’t think we can magic people richer, we have to give them money.” This statement followed a discussion of the undeniable fact that some people won’t and can’t earn enough to survive, to cover their basic living costs. But herein lies the problem with the kind of utilitarian wonkishness I’ve been worried about: Without fully taking into account the individualism that defines libertarianism and distinguishes it from other political philosophies, we are led to believe that policies such as the negative income tax and the basic income are practical equivalents of tax credits. It is furthermore taken for granted that if we observe some social or economic problem, the answer must always be to turn to the state, attempting to legislate it away. But the whole point of libertarianism, I thought, is that it treats the first two policy choices as examples of redistributing stolen wealth, the final example as a praiseworthy limitation on how much money the government will steal in the first place. Rather than examining the ways in which the state artificially raises living costs, or exploring the ways in which private charity might address the issue identified, the wonk simply sees that, as things are, the marginal productivity of many individuals falls short of the amount of money they will need to survive, and jumps directly to the conclusion that therefore the state should “give them money.” In the libertarian view, though, simply giving some people other people’s money is trying to “magic people richer.” The whole libertarian philosophical structure is founded, at least in part, on the contention that not only does this kind of program not work as a practical matter (itself an economic and utilitarian argument), but that it is also wrong (unjust or inequitable). The wonkish utilitarian sees societal wealth in the abstract, as a kind of open pool to which we apply public policy, tweaking and twisting to get the whole social machine in working order — like the scientific forests, to obtain the best yield. But the libertarian, as a libertarian, doesn’t think that the state ought to be doing any of these things in the first place. It just doesn’t follow from the fact that some people aren’t productive enough to survive without help that the state is the only way to furnish the necessary help.

To be clear — because I don’t think that I was in my previous shot at this subject — I am an enthusiastic fan of the work product created by libertarian think tanks, which indeed is the reason that I think it’s important to humbly offer a respectful critique every now and then. I’ve been attached, in one way or another, to several libertarian/free market think tanks and groups, and I will continue to be involved. To my mind, libertarianism is strongest when it’s at its most consistent, hence the importance of calling upon the libertarian policy community to honestly appraise the particular phenomenon of wonkishness that I’m considering here. Education, as I argued in my previous post on this subject, is the single most important thing we libertarians can do to advance the cause of a free society. As Lucy Parsons wrote, “[A] long period of education must precede any great fundamental change in society, hence [we] do not believe in vote-begging or political campaigns, but rather in the development of self-thinking individuals.” A belief in libertarian education and advocacy drives my associations within the libertarian community and movement. We must safeguard an interdisciplinary approach that prosecutes the case for freedom in several different but mutually reinforcing ways, avoiding reflex appeals to stats, which more often than not obscure at least as much truth as they reveal. Libertarian policy wonks should try not to be “technicians” or “technical advisors” in the employ of political authority, merely “advising the State on how to be more efficient in going about its evil work.” [4] Avoiding the dangers of myopia presented by positivism, we should continue the work of developing a rigorous methodology of individualism, engaging in the work of political economy rather than trying to understand either politics or economics (or any other discipline) in a vacuum.

[1] As I argued in a previous article, “[S]tudies and statistics, situated within a proper context, are capable of revealing or throwing into relief general principles and truths. Too often, however, there is flagrant oversight with respect to that more general and universal context, disposing economists to make far too much of the empirical data on hand. Building predictions based on these piecemeal, incomplete data — representing at best snapshots or fragments of the unbounded totality of economic relationships — is a parlous venture . . . .”

[2] See also, Jean-Guy Prévost’s A Total Science: Statistics in Liberal and Fascist Italy.

[3] As Hayek writes in “The Use of Knowledge in Society,” “One reason why economists are increasingly apt to forget about the constant small changes which make up the whole economic picture is probably their growing preoccupation with statistical aggregates, which show a very much greater stability than the movements of the detail.”

[4] Murray Rothbard, Economic Controversies.

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