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The Perils of Fed’s Tight Monetary Policy: A Deeper Economic Divide
In our August edition of the Asymmetric Market Update™️, we delve into key topics related to potential market impacts, the global state, and strategies for navigating these complex times. You can subscribe for free here.
Previously, we discussed the vulnerabilities small and mid-sized banks face due to the uneven distribution of excess reserves, despite abundant reserves system-wide. This was before the banking panic that rocked markets last month.
Repeatedly, we’ve discussed mixed economic data and introduced the concept of a “duck economy”—seemingly smooth on the surface but paddling furiously underneath. Beauty lies in the eye of the beholder. While headline economic data may appear strong, delving deeper allows narratives of both bullish and bearish inclinations to be crafted.
We also compared the “Magnificent Seven Megacorps” with other stock markets. Like the economic data, stock indices performed well on the surface. However, a deeper look revealed that while the Magnificent Seven soared, other market segments faltered or even declined.
In this edition of Asymmetric Macro, we bring together all previously discussed concepts into a coherent narrative, starting with the theory of monetary policy itself.
Monetary Policy
For any dataset, understanding the underlying distribution is crucial before meaningful analysis can occur. For simplicity, we’ll discuss three basic distributions, although none are perfect. The main points, however, will be clear. Headline economic data describe the overall or average economy, a conceptually sound approach since tailoring economic policy for every individual is impractical (to put it mildly). From many perspectives, this is “unfair” and unworkable in reality. Therefore, we use aggregated data to describe the state of the economy and determine the most suitable monetary policy for that aggregate data. First, let’s understand the three types of distributions to describe potential populations.
Note: This is not a doctoral thesis. The discussion is not exhaustive nor foolproof, given our limited space. We’ve woven a story closely related to the current state of the world and economic policy. Rather than nitpick over minutiae, consider these concepts and their potential impacts at a conceptual level.
As you can see, a uniform distribution means each observation (in this case, individual socio-economic status) is the same. A uniform distribution would be the communist ideal. It would also produce the best dataset for analyzing monetary policy. If everyone is in the same position, there is no variance, and thus the “average data” perfectly represents everyone. Hence, the monetary policy based on this data would be perfect (assuming economic theory is valid and applied strictly by the book). We know this is not the case. Communist ideals are notoriously hard to realize.
In a normal distribution, the mean, median, and mode are the same. Exactly half of the observations (here, individual socio-economic statuses) lie to the right of the center, and the other half to the left. This distribution suggests that socio-economic density is highest near the mean, decreasing as one moves away from it. With a dominant middle class and a more equitable wealth distribution (as was more the case in the not-so-distant past in the USA compared to now), even “average data” could be somewhat effective. Although not perfect, the density is still centered around the mean, so monetary policy based on this data is reasonable as it captures the condition of the majority of the population (although it is irrelevant for the extremes at either end of the population; in a normal distribution, that’s a relatively small proportion).
A bimodal distribution means there are two modes. In other words, the outcomes of two different processes are combined into one dataset.
This bimodal feature has increasingly appeared in various aspects of our world. Let’s consider some relevant examples we’ve previously mentioned.
Uneven Distribution of Excess Bank Reserves
In Asymmetric’s February 2023 release, we mentioned, “Although there are plentiful excess reserves in the system, they are not evenly distributed. These reserves are primarily concentrated at the money center banks (like JPM).”
Thus, despite the vast quantity of excess reserves, we experienced a banking crisis that forced the Fed to set up emergency funding facilities to finance many banks lacking adequate reserves. Before this facility was activated, several major banks had already collapsed. Why did this catch everyone by surprise? Because the data on excess reserves was superficial and didn’t account for the actual distribution of these reserves. Many banks had no reserves, while a few had most of them. This is a bimodal distribution. Aggregate data alone did not accurately reflect the true condition of the banking sector. Hence, distribution is crucial here but was overlooked.
The uneven distribution of reserves and subsequent emergency funding facilities led to vulnerable banks having to pay substantial interest expenses to maintain their balance sheets and increase deposits. Meanwhile, strong banks (like JPM) earned significant interest income from their excess reserves. It’s like “transferring wealth from the poor to the rich.” Some might argue this is the penalty for poor management, which is not incorrect. But still, you’re faced with a bimodal distribution moving forward. Considering the dynamics, this situation is becoming increasingly bimodal.
Small Businesses vs. Giant Corporations
Observing the contrast between the Magnificent Seven and other stock markets (especially the Russell), you also see a sort of bimodal distribution. You’ll notice a group of highly successful large companies; then there are the small companies, which are far less successful compared to these giants.
Some might argue this is the result of capitalist creative destruction, which is not incorrect (we’ll ignore the impact of monopolies/oligopolies in this discussion). Nonetheless, given the current dynamics, you’re still facing a bimodal distribution, and this bimodal situation is worsening (or forming monopolies at the extremes).
Some of these results can be attributed to the scalability of technology. Once you dominate in an area, you drain commercial potential and capital from your competitors. Thus, these large companies end up amassing significant cash and record profits. They buy back stock and earn substantial interest income from this cash. Small companies, meanwhile, carry more significant debt burdens (and are not affluent), having to pay substantial interest to stay afloat. It’s like “transferring wealth from the poor to the rich.”
Socio-Economic Distribution
We’ve chosen the below chart as a convenient example of a bimodal distribution in socio-economic status. This dataset has two different modes, representing societal fragmentation. Is it useful to look at average credit scores here? Not at all. That’s exactly the point. We’re accustomed to looking at average data, but in a bimodal distribution, it could be at best useless and at worst very harmful and misleading to analysis.
We could add more detail around the distribution of personal savings, debt/credit service costs, etc., but we all know what it would show: a bimodal distribution. As shown in the examples above, those paying high interest costs are in great distress. Those with excess savings are enjoying the benefits of these high rates. It’s like “transferring wealth from the poor to the rich.”
Bringing It All Together
What do the three examples above have in common? Paying and receiving interest results in diametrically opposed outcomes—the poor get poorer, and the rich get richer. That’s the crux of the issue. Wealth and assets are being transferred from the weak to the strong.
Why does this matter? Monetary policy is based on aggregated data. On average, everything looks fine and seems stable. Yet, one mode of this distribution is experiencing severe pain. High rates benefit another mode. Thus, by maintaining high rates and waiting for the average data to weaken, the Fed is actually oppressing the vulnerable rather than helping the powerful. This approach seems very twisted from that perspective.
Why does the wealth gap continue to widen? Because the way monetary policy is implemented exacerbates the wealth gap. This isn’t a paper on the virtues of wealth redistribution, but in many key areas of our economic life, the wealth gap will continue to widen until we face some sort of collapse, debt forgiveness, or other tail event.
Conclusion
In our view, the Fed should have cut rates in July.
Employment has peaked and is clearly declining.
Inflation is at 2.5% and falling rapidly, expected to reach the 2% target by year’s end.
Yet, the real interest rate is currently 3%. In a steady, healthy economy, this number has historically been about 1%.
So what is the Fed doing?
They are focusing on aggregated data and ignoring the underlying distribution.
That’s where the strategic error occurs.
Wealthy and cash-rich individuals enjoy higher interest income (not to mention assets near historic highs). Cash-poor individuals suffer due to their interest expenses. Indifferent to, or even benefiting from, high rates, the Federal Reserve is essentially waiting for the lower socio-economic tiers to deteriorate further to bring the average data down to target levels. Sorry, poor folks, you’re suffering with little to no benefit.
If the Fed allows “tight monetary policy” to continue (that’s their term), they will face severe employment issues and the hollowing out of small businesses. Once that happens, history shows it’s hard to reverse. They are at risk of a hard landing.
Everything seems normal until it suddenly isn’t. Changes are often slow to unfold and then happen all at once.
Disclaimer: The projections and information presented here are for educational purposes only and should not be considered financial advice. CoinGrab.Asia assumes no responsibility for any losses resulting from the use of this data. Readers are encouraged to perform their own research and proceed cautiously before engaging in any related activities.
