The Dangerous Gamble of the Federal Reserve: Economic Divide Behind Robust Data, the Rich Get Richer, and the Poor Continue to Struggle

Author: ◢ J◎e McCann

Compiled by: Deep Tide TechFlow

(The following content was originally published in the macro section of our Asymmetric Market Update™️ in August.)

In our previous macro commentary, we focused on key topics related to potential market impacts, the global situation, and how to navigate these complex times.

We discussed the risks faced by small and medium-sized banks (in the month prior to the banking panic that surfaced and instilled fear in the markets) due to the uneven distribution of excess reserves, despite the large amount of reserves in the system.

We repeatedly mentioned mixed economic data and discussed the concept of the "duck economy": everything seems fine on the surface, but many things are happening beneath. The beauty lies in the eye of the observer. While headline economic data appears strong, a deeper analysis can weave any bullish or bearish narrative you prefer.

We also analyzed the "Magnificent Seven" in comparison to other stock markets. Similar to economic data, stock indices performed well; however, upon closer examination, it was found that the stocks of the Magnificent Seven performed exceptionally well, while other parts of the market performed mediocre or even declined.

In this issue of Asymmetric Macro, we will weave together all the concepts discussed previously into a coherent story, starting and ending with the theory of monetary policy.

Monetary Policy

For any dataset, you need to define the potential distribution before conducting meaningful analysis. To simplify the description, we will use three basic distributions. While none of them are perfect, the point will be clear. Headline economic data is used to describe the overall economy or average economy, which is conceptually reasonable because you cannot tailor economic policy for every individual (to give an extreme example). From many angles, this is "unfair" in reality and also impractical. Therefore, we use aggregate data to describe the state of the economy, determining the most appropriate monetary policy for that aggregate data. Let’s first understand the three types of distributions to describe the potential population.

Note: We are not writing a doctoral thesis. This discussion is not exhaustive and is not foolproof, as we have limited space. We have woven a story closely related to the current state of the world and economic policy. Therefore, rather than nitpicking trivial details, it is better to consider these concepts and their potential implications from a conceptual level.

Uniform Distribution

Figure: Uniform Distribution

As you can see, a uniform distribution means that each observation (in this case, the individual’s socioeconomic status) is the same. A uniform distribution would be the ideal of communism. A uniform distribution would also produce the best dataset for monetary policy analysis. If everyone is in the same position, there is no variance, so "average data" would perfectly represent everyone. Therefore, the monetary policy based on this data would be perfect (assuming economic theory is valid and strictly applied). We know this is not the case. The ideals of communism are often difficult to achieve.

Normal Distribution

Figure: Normal Distribution

In a normal distribution, the mean, median, and mode are the same. Exactly half of the observations (in this case, the individual’s socioeconomic status) lie to the right of the center, while the other half lie to the left. This distribution implies that socioeconomic density is highest near the mean, with the number of privileged or disadvantaged individuals gradually decreasing as one moves away from the mean. A dominant middle class and reasonable wealth distribution (like the more balanced situation in the U.S. in the not-so-distant past) allows even "average data" to be effective. Although it is not perfect, the density still clusters around the mean, so monetary policy based on this data is reasonable as it captures the state of the majority of the population (even though monetary policy is not relevant to the extremes of the population; in a normal distribution, that is a relatively small proportion).

Bimodal Distribution

Figure: Bimodal Distribution

A bimodal distribution refers to the presence of two modes. In other words, it is the result of combining two different distribution processes displayed in a single dataset.

This bimodal characteristic has recently appeared frequently in various aspects of our world. Let’s look at some relevant examples we mentioned earlier.

Uneven Distribution of Excess Bank Reserves

In the Asymmetric February 2023 release, we mentioned: "Despite the abundance of excess reserves in the system, they are not evenly distributed. These reserves are primarily concentrated in central banks (like JPM, etc.)."

Thus, despite the total amount of excess reserves being very ample, we experienced a banking crisis that forced the Federal Reserve to establish emergency funding facilities to support many banks lacking sufficient reserves. Before this facility was activated, several major banks had already collapsed. Why was this unexpected? Because the excess reserve data is surface data that does not take into account the actual distribution of these reserves. Many banks have no reserves, while some banks hold most of the reserves. This is a bimodal distribution. Aggregate data alone does not accurately reflect the true state of the banking industry. Therefore, the distribution here is crucial but has been overlooked.

The uneven distribution of reserves and the subsequent emergency funding facilities forced weaker banks to pay substantial interest costs to maintain their balance sheets and attract deposits. Meanwhile, strong banks (like JPM) earned significant interest income from their excess reserves. It is akin to "transferring wealth from the poor to the rich." Some may argue this is a punishment for mismanagement, which is not incorrect. But it still leaves you facing a bimodal distribution in the future. Given the dynamic changes, this situation is becoming increasingly bimodal.

Small Businesses vs. Giant Corporations

In the Asymmetric July 2024 update, we released the following chart:

Figure: The Magnificent Seven vs. Other 493 Companies, S&P 500 and Russell 2000

Observing the comparison between the Magnificent Seven and other stock markets (especially Russell) also shows some bimodal distribution. You will see a group of high-performing large companies; then there are small companies that are far less successful compared to these giants.

Some may argue this is the result of creative destruction in capitalism, which is not incorrect (we will ignore the impact of monopolistic/oligopolistic industries in this discussion). In any case, given the current dynamics, this still leaves you facing a bimodal distribution in the future, and this bimodal situation is still intensifying (or forming a series of monopolies under boundary conditions).

Some of these outcomes can be attributed to the scalability of technology. Once you dominate a field, you siphon off business potential and capital from competitors. Therefore, these large companies ultimately accumulate vast amounts of cash and achieve record profits. They buy back stock and earn substantial interest income from this cash. Meanwhile, small companies are burdened with heavier debts (and are not wealthy), having to pay significant interest to survive. It is akin to "transferring wealth from the poor to the rich."

Socioeconomic Distribution

We chose the chart below as a convenient example of bimodal distribution in socioeconomic status. This dataset has two distinct modes, representing the fragmentation of society. Is it useful to look at the average credit score here? Not at all. That is precisely the point. We are accustomed to looking at average data, but in a bimodal distribution, this may be minimally useful at best and could have very harmful and misleading effects on analysis at worst.

Figure: Socioeconomic Distribution of High Credit Scores

We could add more details around the distribution of personal savings, debt/credit service costs, etc., but we all know what it would show: a bimodal distribution. As the examples above illustrate, those paying high-interest costs are facing tremendous difficulties. Meanwhile, those with excess savings are enjoying the benefits of these high-interest rates. It is akin to "transferring wealth from the poor to the rich."

Figure: American Diners

As shown in the figure above, the wealthy are doing well.

Figure: Decline in Same-Store Sales at McDonald's

While those with less disposable income are faring poorly.

Putting It All Together

What do the three examples above have in common? Paying and receiving interest produces diametrically opposed results— the poor become poorer, and the rich become richer. This is 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 aggregate data. On average, everything seems fine and appears stable. However, one mode in this distribution is experiencing severe pain. High interest rates benefit the other mode. Thus, by maintaining high interest rates and waiting for the average data to weaken, the Federal Reserve is effectively oppressing the vulnerable rather than helping the strong. From this perspective, this approach seems quite distorted.

Why is the wealth gap continuing to widen? Because the way monetary policy is implemented exacerbates the wealth gap. This is not a treatise on the virtues of wealth redistribution, but in many major areas of our economic life, the wealth gap will continue to widen until we face some sort of collapse, debt relief, or other tail events.

Conclusion

In our view, the Federal Reserve should lower interest rates in July.

Employment has peaked and is clearly declining.

Inflation is at 2.5%, rapidly decreasing, and is expected to reach the 2% target by the end of the year.

However, the current real interest rate is 3%. In a steady-state, healthy economy, this number has historically been around 1%.

So what is the Federal Reserve doing?

They are focusing on aggregate data while ignoring the potential distribution.

This is where the strategic error occurs.

The wealthy and cash-rich are enjoying higher interest income (not to mention assets near historical highs). Meanwhile, those cash-poor are being crushed by interest expenses. Due to insensitivity to high interest rates, or even benefiting from them, the Federal Reserve is effectively waiting for the lower socioeconomic tier to deteriorate further to bring the average data down to target levels. Sorry, poor folks, you are suffering with little benefit.

If the Federal Reserve allows "tight monetary policy" to continue (as they say), they will face serious employment issues and hollowing out of small businesses. Once this happens, history shows it is difficult to reverse. They face the risk of a hard landing.

Everything seems normal until things suddenly go awry. Changes are often slow, then happen all at once.