The Fed's Policy Gamble: Economic Divide Behind Stable Data

Original author: ◢ J◎e McCann

Original text translation: Deep Tide TechFlow

(The following content was originally published in the macro section of our August Asymmetric Market Update™️, which you can subscribe to for free here)

In our previous macro comments, we followed the key topics related to potential market impact, global situation, and how to deal with them in these complex times.

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

We have repeatedly mentioned mixed economic data and discussed the concept of ‘duck economy’: everything seems to be going smoothly on the surface, but in reality, there are many things happening behind the scenes. Beauty lies in the eyes of the observer. Despite the strong headline economic data, if you dig deeper, you can weave any bullish or bearish narrative you prefer.

We also analyzed the comparison between the “Magnificent Seven” and other stock markets. Similar to economic data, stock indices performed well; however, in-depth research revealed that the stocks of the Magnificent Seven performed extremely well, while other parts of the market performed mediocre or even declined.

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

monetary policy

For any data set, you need to define the potential distribution before doing meaningful analysis. To simplify the description, we’ll use three basic distributions. While none of them are perfect, the gist will be clear. Headline economic data is used to describe the overall economy or the average economy, which is conceptually sound because you can’t tailor economic policies to each individual (to take an extreme example). In many ways, this is “unfair” in reality and unenforceable. Therefore, we use aggregate data to describe the state of the economy and thus determine the most appropriate monetary policy for that aggregated data. Let us first understand the three types of distribution to describe the potential population.

Note: We are not writing a doctoral thesis. This discussion is not exhaustive, nor is it foolproof, because we are limited in space. We have woven a story closely related to the current world and economic policy status. Therefore, instead of nitpicking trivial details, it is better to consider these concepts and their potential impact from a conceptual level.

Uniform Distribution

Image: Uniform Distribution

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

Normal Distribution

Image: Normal Distribution

In a normal distribution, the mean, median, and mode are the same. Exactly half of the observed values (in this case, the socioeconomic status of the individual) are on the right side of the center, while the other half is on the left side of the center. This distribution means that socioeconomic density is highest around the mean, with the number of privileged or vulnerable individuals gradually decreasing as deviations from the mean increase. With a dominant middle class and a reasonable distribution of wealth (such as the United States was more balanced in the recent past than it is now), even “average data” can work. Although it’s not perfect, the density is still concentrated around the mean, so a monetary policy based on this data is justified because it captures the condition of the majority of the population (although monetary policy is not relevant for both ends of the population; In a normal distribution, that’s a relatively small proportion).

Bimodal Distribution

Figure: Bimodal Distribution

Bimodal distribution refers to the existence of two modes. In other words, the results of two different distribution processes are combined and presented in a set of data.

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

Uneven Distribution of Excess Bank Reserves

In the release of Asymmetric 2023 in February, we mentioned, “Despite the abundant excess reserves in the system, they are not evenly distributed. These reserves are mainly concentrated in currency center banks (such as JPM, etc.).”

Therefore, despite the total amount of excess reserves being very sufficient, we experienced a banking crisis that led the Fed to establish emergency funding facilities to assist many banks that lacked sufficient reserves. Prior to the activation of this facility, several major banks had already collapsed. Why was all of this surprising? Because the excess reserve data is surface-level data that does not take into account the actual distribution of these reserves. Many banks had no reserves, while some banks had most of the reserves. This is a bimodal distribution. Aggregating data alone does not accurately reflect the true situation of the banking industry. Therefore, the distribution here is crucial but overlooked.

The uneven distribution of reserves and subsequent emergency funding facilities have forced weak banks to pay a large amount of Interest to maintain their balance sheets and increase deposits. Strong banks (such as JPM) have earned substantial Interest income from their excess reserves. It’s like ‘transferring wealth from the poor to the rich.’ Some may think of this as a punishment for poor management, and they wouldn’t be wrong. But this still leaves you facing a bimodal distribution in the future. Given the dynamic changes, this situation is becoming increasingly bimodal.

Small Businesses vs. Giants

In the update of Asymmetric in July 2024, we released the following charts:

Figure: Magnificent Seven Giants and 493 Other Companies, S&P 500 and Russell 2000

Observing the magnificent seven giants and comparing them to other stock markets (especially Russell) also reveals a kind of bimodal distribution. You will see a group of large companies performing well; then there are the small companies that are far less successful compared to these giant companies.

Some may think of it as a result of creative destruction of capitalism, and they wouldn’t be wrong (we will ignore the impact of monopoly/Oligopoly in this discussion). In any case, given the current dynamic situation, 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 results can be attributed to the scalability of technology. Once you dominate a field, you will draw away business potential and capital from your competitors. As a result, these large companies eventually accumulate a large amount of cash, achieving record profits. They buy back stocks and generate substantial Interest income from this cash. In contrast, small companies carry heavier (and less affluent) debt burdens and have to pay a large amount of Interest to survive. It’s like ‘transferring wealth from the poor to the rich’.

Distribution of Social and Economic Factors

We have chosen the following chart as a convenient example of the bimodal distribution in the socio-economic state. This data set has two distinct modes, representing the fragmentation of society. Is it useful to look at the average credit score here? Not at all. That’s the point. We are accustomed to looking at average data, but in a bimodal distribution, this may have minimal utility at best and potentially harmful and misleading effects on analysis at worst.

Figure: Socio-economic distribution of high credit scores

We can add more details around the distribution of personal savings, debt/credit service fees, etc., but we all know what it will show: a bimodal distribution. As the above example shows, those who pay high Interest fees are facing huge challenges. And those who have excess savings are enjoying the benefits of these high interest rates. It’s like “transferring wealth from the poor to the rich”.

Image: American Diners

As shown in the above figure, the affluent have a good condition.

Image: McDonald’s same-store sales decline

Those with less disposable income are in a poor situation.

Combine all the content

What do the above three examples have in common? Payments and receiving Interest have resulted in completely opposite outcomes - the poor are getting poorer and the rich are getting richer. This is the crux of the problem. Wealth and assets are shifting from the weak to the strong.

Why is all this important? Monetary policy is based on aggregated data. On average, everything seems fine and stable. However, one mode in this distribution is experiencing severe pain. High interest rates benefit another mode. Therefore, by maintaining high interest rates and waiting for the average data to weaken, the Federal Reserve is actually further oppressing the weaker group instead of helping the stronger group. From this perspective, this approach seems very distorted.

Why does wealth inequality continue to widen? Because the implementation of monetary policy exacerbates wealth inequality. This is not a thesis on the virtue of wealth redistribution, but in many key areas of our economic lives, wealth inequality will continue to widen until we face some kind of collapse, debt relief, or other tail events.

Conclusion

In our view, the Fed should cut interest rates in July.

Employment has peaked and is clearly declining.

The inflation rate is 2.5%, which is rapidly decreasing and expected to reach the target of 2% by the end of the year.

However, the current actual Intrerest Rate is 3%. In a stable and healthy economy, historically this number is around 1%.

So what is the Federal Reserve doing?

They are collecting data without realizing the potential distribution.

This is the root cause of the strategy error.

People who are wealthy and have abundant cash enjoy higher Interest income (not to mention that their assets are approaching historical highs). While those who are cash-strapped suffer greatly from Interest expenditures. Due to insensitivity or even benefit from high interest rates, the Federal Reserve is actually waiting for further deterioration of the lower socioeconomic groups to bring the average data down to the target level. Sorry, poor people, you are suffering but getting almost no benefit.

If the Federal Reserve allows ‘tightening monetary policy’ to continue (as they say), they will face serious employment problems and the hollowing-out of small businesses. Once this happens, history has shown that it is difficult to reverse. They face the risk of a hard landing.

Everything seemed normal until things suddenly went awry. Changes are often slow, but then they happen in an instant.

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