Story Telling in Economics

A Question I Once Raised During a Conference

Many years ago, when I was attending a session at an economics conference, I heard a presentation by a professor about the relationship between economic growth and technology change. In his presentation he purported to show a high correlation between the number of new patients (registered with the US Patent and Trademark Office) and economic growth. This enabled him to conclude that there was a causal relationship between technological change (as reflected by patent counts) and economic growth. This finding, by the way, is the kind that is very often hailed by organizations that offer research grants to economic professors and to other scientists. This is because findings serve as evidence for the “social benefits of R&D” which these organizations can, and often do, use to drum up political support for their organizations. It is also highly appealing to many people—admittedly, myself included—who love science and loving thinking about how beneficial scientific and technological advancement can be when it is properly and responsibly managed. So I realized that the paper being presented would be music to many people’s ears, and that it would help him receive praise, perhaps a publication, and perhaps even grant money, for his research. (Payson 2017, 3)

Given my own background on the topic … I had a question about his stated findings, which I politely asked during the question-and-answer session. In asking my question I mentioned that I was familiar with a well-known change in patent laws that occurred at the beginning of the time span that he was analyzing. As many who are familiar with patents know, the vast majority of patents that are issued have no real value and are not in fact used by the company that holds the patent. What generally occurs is that a company acquires a very valuable patent and also createes dozens of other patents that are “close” (in their subject matter) to that valuable one. The reason for their doing this is to protect their valuable patent so that no company can produce a similar patent that competes with theirs. The change in patent laws, which I just referred to, had made it easier for companies to acquire similar patents to ones that already existed, which essentially created a need for companies issuing important patents to “surround” their main patent by more of these other unused “protective patents.” (Payson 2017, 3)

So, in my question to the presenter, I asked whether it might simply be possible that the increase in registered patents that his study observed was attributable to that change in patent laws, which was apparently occurring at the same time that GDP was growing fairly well. GDP was growing at that time due to a general upturn in the economy in which employment was on the rise and inflaction had been brought under control. In other words, perhaps it was simpl a coincidental that both patent counts and real GDP were rising during the same period, but there was no causal relationship between the two. I asked him, essentially, if he thought that such a coincidence might be an alternative explanation for why patents and GDP were rising at the same time. (Payson 2017, 3-4)

The presenter’s reaction, especially in terms of his facial expression, reflected a typical response that I must have seen hundreds of times in my 35 years as an economist. Upon hearing my question he condescendingly smiled from ear-to-ear, while constraining himself from laughing, and he replied in an artificially diplomatic and sarcastic tone, “Oh I know all that [about the patent law change.] But … that’s not my story“—the story that he wanted to tell—and he was thoroughly amused that someone in the audience would be naïve enough to actually think about whether his findings were scientifically valid. Scientific validity of one’s findings is not only rarely discussed during paper presentations at economics conferences, but when it is, it is, more often than not, a source of amusement by the presenters of the papers and their audiences than an actual concern that might lead to improving people’s work. (Payson 2017, 4)

The Profession’s Genuine Arrogance toward Concerns about Scientific Integrity

(….) [M]any academic economists respond with smug, arrogant dismissial or laughter when the topic of scientific integrity or professional ethics is brought before them. It might be surprising to those who are less familiar with the profession that such arrogance and frivolity is as observable as much among some of the most prominent economics professors as among those who are not prominent. In the documentary Inside Job, one can observe this kind of arrogance directly among high-ranking professors as they were being interviewed. (Payson 2017, 4)

As another example, Deirdre McCloskey, a former member of the board of directors of the American Economic Association (AEA) (which consists only of highly ranked professors), has told of how she was there when the board broke into laughter when a letter was read aloud at one of their meetings. The letter was someone who was simply asking whether the AEA would consider adopting a code of ethics for economists. (Payson 2017, 4)

Many economics professors do not laugh or make arrogant statements, but express conceit in an entirely different way, such as feeling sorry for those who are even thinking about scientific integrity or professional ethics—thinking to themselves how pathetically stupid, naïve, or childishly innocent those people must be. There is, in fact a substantial literature on the more scholarly problem of arrogance in the academic economics profession. This literature was written entirely by “insiders”—highly prominent professors themselves, some even Nobel laureates. (Payson 2017, 4-5)

(….) In the absence of the commitment to contributing to useful knowledge, the behavior of the work of academic economists have been dominated by two other major forces: (1) the mathematical games that are played for the sake of getting published and acquiring grant money, and (2) cronyism within the profession, which, in combination with the mathematical game playing, has dominated the reward system and incentive system of the profession. (Payson 2017, 10)

[T]o examine the validity of the claim that these are highly useful branches of knowledge [e.g., economics], let us ask what their contribution to mankind’s welfare is supposed to be. To judge by the cues from training courses and textbooks, the practical usefulness … consists of helping people to find their niche in society, to adapt themselves to it painlessly, and to dwell therein contentedly and in harmony with their companions. (Andreski 1973, 26, in Social Sciences as Sorcery)

Andreski 1973, 26, in Social Sciences as Sorcery

Literature-Only Discourse and the Pretense of Scientific Merit

Regardless of all the various arguments made against most theoretical economics, “defenders of the faith” will continue to espouse the party line. That is, they will say that, regardless of the bad and unproductive habits of theoretical economics, good things—namely, genuine and extremely valuable discoveries in economic theory—do fall out of the chaos. They will continue to argue that these valuable discoveries, even though they may be rare, ultimately justify the chaos and the inefficiencies of the system. To get past this convenient, blind faith, I will argue that it is possible for us to identify what characteristics of most top-ranked, theoretical literature actually do prevent it from contributing to valuable knowledge. In this way, we may be able to filter it out from this point on, without removing any of the top-ranked literature that is truly valuable. (Payson 2017, 51)

Defining the Filter

Let us consider a subset of all published papers in economics that meet all of the following three criteria. If it meets any one of the criteria, the paper may still be considered as an acceptable contribution to useful knowledge. (Payson 2017, 51)

Criterion 1: The paper uses a model that has no “real application.” Along these lines, if the paper presents a model for the purpose of being persuasive on a particular policy position, but presents no real evidence in support of that position (and is only a model that essentially “rediscovers its assumptions”) then it would still meet this criterion of having no real application. (Payson 2017, 51)

Criterion 2: The paper relies on assumptions or data that cannot be verified, or the situation exists in which alternative assumptions or data can be reasonably found that would yield entirely different, conflicting results (as in the McCloskey’s A-Prime, C-Prime Theorem). (Payson 2017, 51-52)

Criterion 3: The methodology of the paper would only be understood, valued, and genuinely studied by a very small group of other economists with advanced expertise in that highly specific topic. (Payson 2017, 52)

Let us call a paper that meets all of these criteria a “literature-only paper”—its purpose is only for the career advancement of the author and for the production of literature to be read and actually understood by a very small audience. Similarly, let us call the work done by economists to produce literature-only papers “literature-only work” or “literature-only discourse.” To be clear, this chapter does not discuss top-ranked literature in general—only literature-only papers that meet all (every one) of the above-mentioned criteria. (Payson 2017, 52)

(….) The only thing that truly constitutes “scientific merit”—indeed, the only thing that really matters in science—is an honest and successful effort to learn how the world actually works—not an effort to create impressive systems of mathematical equations that only very smart and very educated people can proudly decipher. Many graduate students in economics, especially those with little interest or experience in natural science, are ignorant of this. They then go on to become economics professors where they remain ignorant, and pass on their ignorance to their graduate students, the cycle repeats with each generation. (Payson 2017, 52)

In response to this accusation, many theoretical economists will argue that, from looking at the work itself, we have no basis for distinguishing between valid, scientific economic theory, and invalid, unscientific economic theory. Nevertheless, I would like to propose a very simple test could enable us to make this distinction: We look at the assumptions made in the analysis, and ask, “Can an alternative set of equally defensible assumptions be made that will lead to very different conclusions?” If the answer is “Yes,” then conclusions of the research in question have no degree of certainty—implying that the research has not contributed to our understanding of how the real world works. If those conclusions are then used to provide a false understanding of how the real world works, then this is simply a deception, which may be harmful in various respects. (Payson 2017, 52-53)

Let us call economic theory that falls under this category “unscientific economic theory” to bring home the point that science plays no role in justifying the existence of such self-serving conceptual games…. So why has the problem not been solved? The answer is that this solution or anything like it, cannot be heard by unscientific theoretical economists—it falls on deaf ears. (Payson 2017, 53)

Selling New Terminology and Supposedly New Concepts

(….) In many cases new terminology is offered in literature-only discourse as the basis for a new theoretical model that appears to capture an important concept. In general, the important concept is already known and understood under different names. Nevertheless, when a prominent theoretical economist presents a new term that they promote as a “new concept,” and at the same time present a very elaborate and sophisticated model to supposedly “explain” the concept in mathematical terms, it may appear, especially to naïve observers, that their research has truly discovered something important. Many may have trouble distinguishing in their own minds the value of the new terminology from the value of the arbitrary assumptions that were used to create a sophisticated model to explain it. (Payson 2017, 60)

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