The Fed's risky gamble: Economic divide behind solid data, with the rich getting richer while the poor continue to struggle
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The Fed's risky gamble: Economic divide behind solid data, with the rich getting richer while the poor continue to struggle
If the Federal Reserve allows "tightening monetary policy" to continue, they will face severe employment issues and hollowing out of small businesses.
Navigating Web3 tides with focused insightsAuthor: ◢ J◎e McCann
Compiled by: 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 commentary, we focused on key topics related to potential market impacts, the global landscape, and how to navigate these complex times.
We discussed (one month before banking panic surfaced and spooked markets) the risks facing small and mid-sized banks due to uneven distribution of excess reserves, despite abundant system-wide reserves.
We repeatedly highlighted mixed economic data and introduced the concept of the "Duck Economy": everything appears fine on the surface, but many things are happening beneath. Beauty lies in the eye of the beholder. Despite strong headline economic data, a deeper analysis allows one to construct any bullish or bearish narrative they prefer.
We also analyzed the "Magnificent Seven" versus the rest of the stock market. Similar to economic data, stock indices look healthy; however, upon closer inspection, only the Magnificent Seven have performed exceptionally well, while the broader market has been flat or declining.
In this edition of Asymmetric Macro, we will weave all previously discussed concepts into a coherent story—starting and ending with monetary policy theory itself.
Monetary Policy
For any dataset, you need to define the underlying distribution before conducting meaningful analysis. For simplicity, we'll use three basic distributions. While none are perfect, the point will be clear. Headline economic data describes the aggregate or average economy, which is conceptually reasonable because you cannot tailor economic policy to every individual (take an extreme example). From many perspectives, this is practically "unfair" and unworkable. Therefore, we rely on aggregated data to describe the state of the economy and determine the most appropriate monetary policy for that aggregate. Let’s first understand three distribution types to describe potential populations.
Note: We are not writing a doctoral thesis. This discussion is neither complete nor foolproof, given space constraints. We are weaving a story closely tied to today’s world and the state of economic policy. So rather than nitpick trivial details, consider these concepts and their potential implications at a conceptual level.
Uniform Distribution
Figure: Uniform Distribution
As seen here, a uniform distribution means each observation (in this case, an individual's socioeconomic status) is identical. A uniform distribution would be the ideal for communism. It would also produce the best dataset for monetary policy analysis. If everyone were in the same position, there would be no variance, so "average data" would perfectly represent everyone. Thus, monetary policy based on such data would be perfect (assuming economic theory works and is applied strictly). We know this isn’t the case. Communism’s ideals 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 (individuals’ socioeconomic status) lie to the right of center, and half to the left. This implies that socioeconomic density is highest around the mean, tapering off as you move away, meaning fewer privileged or disadvantaged individuals exist further from the center. With a dominant middle class and relatively balanced wealth distribution (as the U.S. had more recently compared to now), even “average data” works reasonably well. Though imperfect, density clusters around the mean, so monetary policy based on such data is rational—it captures the condition of most of the population (though it matters less for the extremes; in a normal distribution, those are relatively small proportions).
Bimodal Distribution
Figure: Bimodal Distribution
A bimodal distribution has two modes. In other words, outcomes from two distinct processes are combined into one dataset.
This bimodal pattern has recently emerged frequently across various aspects of our world. Let’s examine some relevant examples we’ve previously mentioned.
Uneven Distribution of Bank Excess Reserves
In Asymmetric’s February 2023 release, we noted: “Despite abundant excess reserves in the system, they are not evenly distributed. These reserves are concentrated primarily in money center banks (like JPM).”
Therefore, despite sufficient total excess reserves, we experienced a banking crisis that forced the Fed to establish emergency funding facilities to support many banks lacking adequate reserves. Several major banks collapsed before this facility was activated. Why did this catch everyone off guard? Because the excess reserve data was surface-level, failing to account for actual distribution. Many banks had no reserves, while a few held most of them. This is bimodal distribution. Aggregate data alone failed to reflect the true state of the banking sector. Hence, distribution mattered—but was overlooked.
The uneven reserve distribution and subsequent emergency funding led weaker banks to pay substantial interest expenses to maintain their balance sheets and attract deposits. Meanwhile, stronger banks (like JPM) earned significant interest income from their excess reserves. It’s like “transferring wealth from the poor to the rich.” One might argue this is punishment for poor management, which isn’t wrong. But this still leaves you with a bimodal structure going forward. Given ongoing dynamics, the situation is becoming increasingly bimodal.
Small Businesses vs. Giant Corporations
In Asymmetric’s July 2024 update, we shared the following chart:
Figure: The Magnificent Seven vs. Other 493 Companies, S&P 500 and Russell 2000
Comparing the Magnificent Seven with the rest of the stock market (especially Russell), we again see a bimodal pattern. There’s one group of large companies performing exceptionally well, and another group of smaller firms lagging far behind in comparison.
One could argue this is capitalism’s creative destruction at work—which isn’t incorrect (we’ll set aside monopolistic/oligopolistic industry effects for this discussion). Regardless, given current dynamics, this still leaves us with a growing bimodal divide—one that continues to widen (or under boundary conditions, evolve toward a series of monopolies).
Some of these outcomes stem from technology’s scalability. Once dominant in a field, a company drains business potential and capital from competitors. As a result, large firms accumulate vast cash reserves and record profits. They buy back shares and earn substantial interest income from their cash holdings. Smaller firms, meanwhile, carry heavier debt burdens (not being cash-rich) and must pay high interest just to survive. Again, it’s like “transferring wealth from the poor to the rich.”
Socioeconomic Distribution
We selected the chart below as a convenient example of bimodality in socioeconomic conditions. This dataset has two distinct modes, reflecting societal fragmentation. Is average credit score useful here? Not at all. That’s precisely the point. We’re accustomed to looking at averages, but in a bimodal distribution, averages are at best useless and at worst dangerously misleading.
Figure: High Credit Score Socioeconomic Distribution
We could add more layers—distribution of personal savings, debt/credit servicing costs—but we all know what they’d show: bimodal distribution. As shown above, those paying high interest face severe hardship, while those with excess savings benefit handsomely from high rates. Again, it’s “transferring wealth from the poor to the rich.”
Figure: U.S. Diners
As shown above, affluent groups are doing well.
Figure: McDonald’s Same-Store Sales Decline
Meanwhile, those with less disposable income are struggling.
Putting It All Together
What do the three examples above have in common? Interest payments and receipts yield starkly opposite outcomes—the poor get poorer, the rich get richer. That’s the crux. Wealth and assets are transferring from the weak to the strong.
Why does this matter? Monetary policy is based on aggregated data. On average, everything looks stable and healthy. Yet one mode within this distribution is suffering severely, while high interest benefits the other. By maintaining high rates and waiting for average data to weaken, the Fed is effectively imposing greater pressure on the vulnerable, not helping the strong. Viewed this way, the policy appears deeply distorted.
Why does wealth inequality keep widening? Because the way monetary policy is implemented exacerbates it. This isn’t an essay about the virtues of wealth redistribution, but across many core areas of our economic life, wealth gaps will continue expanding until we face some kind of collapse, debt relief, or other tail event.
Conclusion
In our view, the Fed should have cut rates in July.
Employment has peaked and is clearly rolling over.
Inflation stands at 2.5%, falling rapidly, and is expected to hit the 2% target by year-end.
Yet real interest rates are currently 3%. In a steady-state, healthy economy, this number historically hovers around 1%.
So what is the Fed doing?
They’re focusing on aggregate data and ignoring the underlying distribution.
This is where strategic error occurs.
The wealthy and cash-rich enjoy higher interest income (not to mention asset prices near all-time highs). Those cash-poor suffer heavily from interest expenses. Due to low sensitivity—or even benefit—from high rates, the Federal Reserve is effectively waiting for lower socioeconomic groups to deteriorate further, so average data can fall to target levels. Sorry, poor folks—you’re bearing the pain with little gain.
If the Fed allows “tight monetary policy” to persist (their term), they risk severe employment issues and hollowing out of small businesses. Once this happens, history shows it’s hard to reverse. They’re risking a hard landing.
Everything seems fine—until it suddenly isn’t. Change is often slow, then happens in an instant.
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