My Love/Hate Relationship With The Dismal Science

I’m a bit late weighing in on the Michael D “economics is rubbish” debate but I thought I’d lend my own perspective nonetheless as someone who has just come through the academic system. I completed my BA in economics 22 years ago, on 9/11. Sadly, my graduation day will obviously be remembered for the wrong reasons.

I’ll officially complete my Masters in Applied Economics next month, on June 7th. With my final dissertation pretty much finished and written, I’m enjoying a bit of downtime now outside of my usual work commitments and reflecting on what has been a gruelling two and a half years of study.
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While it’s quite uncommon, I’m glad I had over 20 years of a gap in-between doing my BA and my MSc because I got to see whether or not the syllabus had evolved during that time. Surprisingly, I found that it hasn’t evolved much at all. Is that a bad thing? Not necessarily. The laws of physics haven’t changed either. E still equals MC squared. We are still standing on the shoulders of giants like Einstein. In economics, the law of supply and demand goes back to the early 1600’s when it was first discussed by medieval philosopher, Thomas Aquinas. Adam Smith then developed it further in “The Wealth of Nations” before Alfred Marshall invented the first supply/demand diagrams in 1890. These diagrams form the foundation of economic theory and are still included in all economics textbooks around the world today.
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The enormous response by our governments during the pandemic is a reminder too that Keynesianism is still alive and well almost 100 years on. In America especially, this in turn has invoked a critical response from the ‘Austrians’ who, like Milton Friedman in the 1960’s, warned of the inflationary dangers from increasing the money supply too much. Many of these old theories clearly remain relevant today and I count myself fortunate to be studying and applying my knowledge of economics at another unique time in history.

This brings me back to Michael D’s criticism of economics where he blamed the profession for not moving with the times and ignoring climate change. In both my undergrad and post grad programmes, the topic of pollution and negative externalities associated with economic development were extensively covered in microeconomics. Questions asked in these units included “how should we tax carbon?” and “is there a socially optimum level of tax that benefits society and the climate?” So, going back as far as the late 1990’s in my case, climate change has always been covered in economics. Michael D has, thus, got this one wrong.

What is Michael D proposing either way? Should we raise carbon taxes at all costs? Sacrifice economic growth and penalise businesses for using fossil fuels? What are the alternatives?  Some politicians have even suggested raising taxes on air travel. With fewer options available for long distance travelling, higher taxes would have little effect on demand and would be of no benefit to the climate. Consumers would bear the brunt of higher prices in the meantime.

Should we tax diesel cars out of existence, too? Again, what are the alternatives? The infrastructure to support EV demand is still too underdeveloped. How many charging points can you count on your two hands around Dublin City? Rural Ireland? Climate change is a real issue but we need to tackle it sensibly. This starts at government level where subsidies are provided to help increase the supply of alternatives, rather than taxing the things that we still need. Funding these transitions comes through economic growth and higher tax revenues. Michael D should know these simple facts.

I do have one major bone to pick with the economics profession, however. Naturally before enrolling in the masters programme I assumed that the assignments would be tougher than what I experienced at undergrad level. I was still shocked, however, at the outsized emphasis placed on mathematics in the syllabus, most of which is pure baloney if I’m being totally honest. I think most economists would agree. The maths doesn’t provide any more insight into why, for instance, monopolies charge higher prices or why technological innovation improves productivity. These same concepts that are taught in undergrad courses get overcomplicated by arduous, theoretical equations at masters level. Masters students are made suffer through pages of high level calculus and linear algebra in almost every topic throughout the syllabus that bear no extra relevance to the concepts they’ve already studied. Outside of academia and research, these equations have virtually no practical use.

In one assignment during a finance module, we discussed the issue of bank runs. Given that one of the papers we were studying had just won the Nobel Prize, The Diamond and Dybvig model of banking, this was surely a unit that I would enjoy. How wrong I was. Instead of discussing bank runs in plain English, we were bombarded with complex sets of equations to describe the simple fact that banks borrow short and lend long. To my bewilderment, our assignments nonetheless involved calculating first and second order derivatives, solving optimisation problems, plotting utility functions, setting budget constraints and eventually arriving at something as indigestible and inconsequential as the following:

Let me repeat, these equations have no meaning in the real world. They are not used by bank managers or investment firms. They are merely theoretical models to explain simple facts. Painful to say the least.
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Another bug bearer of mine was Game Theory, the mathematical application to decision making. If the previous module wasn’t bad enough, this subject is by far the most bizarre, abstract and without question the most fruitless topic in economics. I challenge anyone to justify the relevance of game theory in any practical sense or explain why it is even included in the syllabus in the first place. While the famed Nash equilibrium, a simple concept that supposes a ‘player’ cant improve his outcome by deviating from his existing strategy, is somewhat interesting, do we need complicated mathematical formulas to spell out the obvious? Rival firms, for instance, don’t need game theory to predict what their competitors will do if they lower their prices. A person bidding at an auction doesn’t need game theory to gain a strategic advantage over other bidders. Below is a sneak peak of a typical ‘game tree’. Mad stuff.
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​Aside from satisfying academic curiosity, the majority of the maths found at post graduate level have virtually no use outside of a university setting. I was really surprised by that. I had no idea just how intangible and theoretical most of these models would be.
I’m beginning to fully appreciate now why economics is sometimes referred to as the ‘dismal science.’ What’s the point in all this maths when there’s no place for it in the real world? Even the more practical topics like statistics and econometrics have their limitations.

Let me give you my own first hand example of this. The general goal of most economic research is to find relationships between certain variables, X and Y for instance. Let’s assume X is interest rates and Y is house prices. If interest rates are rising while house prices are falling, we might assume that higher interest rates cause lower house prices. But how can we be sure that other factors aren’t affecting house prices, too? What about the unemployment rate, GDP, or incomes levels? As economists often say, correlation doesn’t imply causation. Other tests are often needed to further prove a point. And still then, they may remain open to interpretation.

One of my final research assignments was a study that involved investigating the causes of inflation in Ireland between 2000-2022. In my model I included the money supply, the unemployment rate, the velocity of money (how fast money moves around the economy in a given time), short term interest rates, government spending and private consumption as my variables. By and large none of my variables seemed to be affecting inflation. The correlations were essentially zero. Understandably, inflation during most of the 22-year period remained fairly stable so, over the long term, correlations between the variables would have been quite small. But what about the years following the pandemic when inflation rose to 9%? Did my model fail to observe subtle changes within the 22-year period? That was the first alarm bell.

Slightly concerned, I explained the scenario to my professor who confirmed my suspicions. Indeed, short term variances in large datasets can sometimes be ignored. Interesting.

Determined to dig deeper, I decided to focus on just the 3 year period between 2020-2022.

I was quickly able to show that as velocity and inflation were rising during the pandemic, the money supply was falling. I found this really exciting as it contradicted standard theory. I also knew from studying QE in Japan that QE, while pushing up the money supply, was very unlikely to impact consumer prices. As we know, QE money stays largely within the financial systems in banks and government bonds because that’s where central banks park their cash. I also suspected that the money supply would shrink as soon as banks stopped lending and the demand for credit fell during the pandemic. This was indeed what my data was now showing. It was a classic liquidity trap.

While velocity slumped during the early parts of the pandemic, the economy experienced 2 velocity shocks thereafter when the economy re-opened partially in mid 2021 and then again in 2022 when the economy had fully re-opened. According to my results, velocity, but not the money supply as many people would have predicted, played a much bigger role in determining inflation in Ireland during the early parts of that period. I present graphs below from Stata to illustrate.

​Fig 1: M2 money supply in Ireland (2020-2022)

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​Fig 2: The velocity of money in Ireland (2020-2022)

​I was also able to show that although government spending soared, nominal incomes remained fairly stable. This again was consistent with what I already knew from managing my own business during the pandemic. And while the government wage subsidies were a great help to workers and firms, they also had to be paid back to the state! I concluded, therefore, that inflation in Ireland had less to do with fiscal or monetary policy and more to do with atypical consumer behaviour and supply bottlenecks, both of which were very specific to the pandemic.

But here’s the catch. While the data clearly guided me to my conclusion, a large part of my conclusion was also driven by my own interpretations and what I believed to be just common sense. Suppose someone else with a different viewpoint argued that the money supply was already very high prior to the pandemic? The lagged effects of monetary policy or bank lending in this case may have caused prices to rise either way. Which viewpoint would be correct?

The answer essentially is inconclusive because of the very complex nature of economic variables and the room for endless interpretation. Political persuasion is another factor. Admittedly, I lean more towards Keynes than Friedman. Had I taken a more Friedman-like approach I might have concluded outright that the rise in the money supply between 2018 – 2020 was enough in itself to cause inflation. If not totally impartial, a researcher will almost always find what he or she wants in the data which can help drive their agenda. Sad but true.
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Ultimately policymakers will base their decisions on a mix of reliable data with some element of common sense and plausible interpretation. And that’s what I love about economics. Unlike physics or geometry, economics is not an exact science and often requires independent and creative thinking to strengthen one’s hypothesis. Economics in this sense is like philosophy. It invites debate and intellectual discourse. It is at times as much to do about opinion, politics and culture as it is about hard data and numbers. Perhaps that’s not so dismal after all.