07 Jul The Poverty of Nations
With great difficulty I have decided to compile a brief history of my study of economics and finance, my eventual disenchantment and finally my disdain for the field. At one time I had settled on not ever writing about it, leaving the matter something I could discuss verbally but with no interest in reviewing it definitively. At a friend’s request I changed my mind. While I maintain no desire for a more complete, formal refutation of the discipline I at least could detail my experience. I believe it could help to document what happened, and perhaps supplement my upcoming article on work. My hesitancy in revisiting the topic comes from a reticence I could compare to an intrusion. Economics inhabits a specific kind of liberalism and materialism to which I do not belong. A discussion about economics as a failed project has about as much relevancy for me as teasing out the distinctions between Sufi orders. Nevertheless I cannot deny it is a part of my own history, and so describing my history with it would provide a due service to myself and my friends. I also do not see many people who share my thoughts on the topic so perhaps it could acquaint the unfamiliar with the most pressing hang-ups.
A special thanks to Dr. William Briggs who is one of the few who has identified these specifics and from whom I borrow friendly terms like “The Deadly Sin of Reificiation.”
I embraced economics as a major while pursuing my four-year degree for two reasons. Professional ambition of course had a large part, finance itself being a lucrative field. I also had a sincere conviction: that the discipline’s models functioned as powerful instruments for bypassing real-world complexity. Formal systems often buckled under the weight of increasing complexity and modeling these problems could enhance decision making. Enamored with the apparent elegance of these models, especially when digitalized, I set my earnest goals on refining and grounding these models to work better than they currently did.
I took to studying economics not only in class but also at my retail job, on my lunch breaks, while driving, anywhere I identified leisure, for several years. Yale University posted entire course materials for free online, so I listened to lectures from leading professors like Bob Shiller and completed coursework for no other purpose than curing my ignorance. Economics and finance depended upon each other given our credit-based system, but finance had a sizable advantage over public policy: finance had real numbers, since it primarily dealt with prices; economics frequently had to create substitutes, or fabricate, its numbers. I knew economics as a discipline needed grounding, and I thought to quantify utility, to anchor the central and too-abstract concept in something measurable and materially evident. We already had the number for the supply side, since this was embodied in prices. The utility curve on the demand side needed an equally reactive force that could articulate use-value apart from the supply side. So long as the demand curve relied on the supply curve in supply-and-demand models for its derivation, it had no use. It had ironically no real utility.
My university program stipulated writing an undergraduate thesis, and my initial presentation to my advisor concerned quantifying utility. He graciously but firmly dismissed it for reasons that in retrospect are obvious. It was a sophomoric worry of mine to want that kind of concretion. Our curriculum had commitments to abstraction, and I was told in so many words to hang tight and eventually I would move past this superficial problem. I consequently assumed the problem lay with me.
Similar deficiencies seemed to originate from me as the program went on. Models failed to align with empirical data or make consistent predictions. I revised parameters for these models, which amounted to starting from scratch. I calibrated inputs by excluding outliers and adding rolling averages, or in other words, butchering data. Data doesn’t do anything wrong, it’s just data. I could not admit that the models could err, as I was told they could not. I blamed myself for these failures.
Other issues I could not however resolve by simply eroding my self-esteem. Take for instance models derived from historical data. Models to work at all must pretend to describe a formal system outside of history. They could process data and make predictions because they could receive history, or data, and output speculative data on what should become history. They could explain history and give it intelligibility. They must do this by disavowing their premises: models cannot acknowledge they originate from data and historical observations from the first, proven in the tongue-in-cheek finance maxim—“past results are not indicative of future performance.” The adage sounded less all the time like a serious qualifier and more like an incantation to protect us from liability.
Only in my final year did the crisis manifest fully. We ended at the origins: Adam Smith, but instead of his work The Wealth of Nations, what preceded it: the Scottish Enlightenment. We had open class discussions about the dependence of economic models on Newtonian time, the moral architecture behind economic thought, the speculative materialism it contained and the relationship of Smith to the philosophies of Francis Hutcheson and David Hume. It struck me as a disquieting genealogy. To be an economist, to practice economics, I had to grapple with its anthropology which viewed man as an appetitive mechanism and his society as a marketplace. Resources are scarce because resources are finite; consequently, since everything is finite for a materialist, a study of scarcity is a study of the cosmos. It studies material incentives, so it also explains intelligent desires. Economics in its self-conception was not a dismal science but queen of sciences, since it studies the chief characteristics of being.
I could not disentangle materialism from economics. Economic models only possess predictive capabilities because of their inputs, numbers. Numbers however are immaterial. Quantity cannot be materially diagnosed, only formally. Formal models, dealing with abstraction and symbol, presume to interpret what it has already dismissed. It lays claim to the mind of man because it operates within material boundaries, but the mind of man is not data either: it processes data, much like model. Between quantity and matter, I also sat pinched between form and substance. Economics needs to acknowledge a contradictory premise: models derive from material facts, to provide material facts meaning it does not itself possess. The model must be treated as more real than the reality it interprets, because it behaves like form, which is not real.
I had all this inchoately seeded in my mind, sprouting but not yet flowering into something I could articulate. I asked my advisor questions on these issues, which he likened to metaphysics and speculative theology. I unfortunately remember the phrase “if you want to write about ghouls and goblins” which silenced me for the remainder of my enrollment. I nonetheless entered a new awareness: the failure lay not in my understanding of the models, but in the models themselves. Models cannot say anything they were not told to say, because models are not intelligence. The Gini index mask critiques of countries with economic circumstance rather than political allegiances; the Pareto principle justifies centralized capital as being more productive, and thus blessed; GDP is a metric whose measure is frequently manipulated to establish a hegemonic status to a country. They all presume a vision of the human person and of the cosmos, philosophical commitments rendered in an intangible idiom, conceded all the time.
Consider then the implications of practicing the field. What academics must acknowledge, business must conceal. I would as a quantitative analyst need to show total confidence in models predicting the future, which I knew they could not do. I would need to hold others’ money as collateral to enrich myself on the basis of a lie. Most of my professors could not conceptualize a p-value well enough to explain what it actually represents, yet I would need to use it every day to ritually consecrate investments. At my final internship at a hedge fund (n.b. I interned there in lieu of a summer semester in Europe), I encountered the practical death to this intellectual arc. Forecasting was treated with as much superstition as haruspices reading data entrails. I could not in good conscience pursue work in the field I studied. I eventually migrated into the world of data engineering, my commitment to fact a harsh penalty for my career.
I had some delay in obtaining my degree on account of my undergraduate thesis. After over a year after obtaining the necessary credits, and roughly the same time invested in rudderless and disillusioned revision, I scraped by with a passing grade and the seething apathy of my advisor. He would not even leave his vehicle to sign it, having lost all patience for my antics. I wrote my thesis about the political economy of sewer and water infrastructure in my hometown. I entitled it “Expected Utilities.”