Analysis of the US Economy Post WW2 Expansion

Methodological Transparency Statement:

Though the methods I use for analysis suit my purposes, I don’t suggest them for other purposes. It may be clear to some; but to others that like these methods, it may not be. Methods of course cannot be “one size fits all” and that is the purpose of this transparency statement.

I use a confidence value as opposed to a margin of error; mainly because I think a margin of error is inappropriate for such complex systems. A 20% margin of error would essentially be an 80% confidence value; but the context just isn’t appropriate. With more discrete systems, the chances of covering ones bases is much higher. It’s much easier to account for the influential factors in the more discrete systems; thus the margin of error is more likely to be accurate. How I deal with the complexity of the system is of course statistical analysis; based upon observations of how the system has developed, but the complexity itself is reduced in influential factors, by this method.

The Orders of Logic:

This is the method I use for analysis. It begins with the most obvious influences; and increases in rigor to get to the root of the influences.

Primary:

These are considered the most obvious influential factors; because they have the largest tendency to produce substantial effects. They are often used as markers; by which more inquiry is directed.

Secondary:

These are the influences that are discovered when inquiring to the primary markers. Though seemingly less influential, in number, they can produce a primary influence. They are intended to be a pathway to the root of influence. This is where inquiry gets into the mechanics of the system.

Tertiary:

This is the attempt to get to the root of influence; based upon the central dogma of complex systems analysis. This is where analysis gets much more into the mechanics of the system; and normative influence as well.

The primary are the obvious influences. The tertiary are the root influences; and the secondary are the pathways between the two. Confidence in the methodology of course produces the confidence value; but it’s clear that specific influences are focused on, as opposed to an improbable account of all aspects of the system. This is the very reason that I think margin of error is inappropriate for such an analysis. There is likely more expectation of chaos and emergence as well; with such a complex system, so the opposing 20% value of course seems a bit high, but probably isn’t.

The End of the Expansion:

I contend that the end of the expansion occurred around 1970. The markers are the period of economic stagnation in the mid 70s and the increase of complexity in the late 70s. The expansion was a period of growth; so the context of it’s end is slowing growth. This would of course result in a perception of economic stagnation; by comparison to growth. The derivatives market was of course feeling the effects of lack of growth; in the lack of increases in value of derivatives. This resulted in the derivatives market creating new complexity; for the purpose of continuing increases in it’s value.

The inflation that occurred during the period of stagnation appeared to be from the mass re-balancing of books; to adjust to the slowing of growth. Once individual businesses re-balance, the effects are felt throughout the supply chains; which results in a mass re-balance. Prices go up for both products and services; because services require products. Products and services both are just part of overhead; so they correlate strongly with each other. An obvious influential factor like slowing growth should be expected to affect the entire economy. If there were a more serious influence to the inflation, it would be expected that it last longer. The fact that it didn’t suggests a system shock; and an abrupt, mass re-balance fits the bill. This would of course effect prices abruptly and spike an inflation rate.

Black Monday:

The properties boom in the early 1980s correlated with an exceptionally large growth spike. This was of course an obvious influence; but more inquiry was needed. It would seem obvious that the growth spike was associated with a large increase in leverage in properties; but the correlation still needs to be explained. The fact that the Black Monday crash was so severe is explained by such a high degree of leverage. One decade following the increase in complexity of the derivatives market seems a bit soon for such a severe bubble; without such a large degree of leverage. The market simply outgrew the products that it was leveraging itself on; and could not be sustained.

The .com Bubble:

In the 1990s, the economy was experiencing growth again; with the tech industry. Computers were becoming affordable, the internet was more accessible and was even being restructured for commerce. It was a good resource for trade; and day traders were using it to its’ potential. The internet was growing rapidly; but it was being over leveraged as well. The bubble may have come later; if e-commerce was more available at the turn of the century.

I contend that the US economy reached growth maximum with e-commerce; but there are always qualifiers. A new industry can of course result in new growth; but that is irrelevant in hindsight, though it may come to be tomorrow. This is of course Emergence; and weighs against my confidence value. The positive effects that e-commerce had on the US economy in the 2000s is however a declaration of how influential a new market can be.

9/11:

The destruction of the World Trade Center in 2001 probably made recovery from the .com Bubble take years longer. It’s easy to remember the human losses; but the connection between finance in the US and the rest of the world lost it’s central brick and mortar as well as the resident companies.

The Housing Bubble:

When an economy’s decline becomes more severe, the effects become easier to observe. They become more obvious. This is exacerbated by the fact that the housing market is the primary market that one gauges the stability of the economy with. The housing market was the financial foundation of the US economy; and an unstable housing market is a dire data point.

The Primary (obvious) Influences:

1. The enormous amount of leverage

2. The added complexity with the new insurance contracts

3. Increases in fraud via predatory lending

4. The difference between the cost of living and increased value of the housing market

The Secondary Influences:

1. Growth of the economy was being replaced with an inflated foundational market

2. Some awareness of the problem was resulting in increases in insurances

3. Nefarious contracts were being touted as affordable housing

4. For the first time in many decades, millions of people could not pay their mortgages

The Tertiary (root) Influences:

1. The economy was in decline

Though notional market value was increasing, so was the national debt. This is one of the ways that the debt vs GDP metric is flawed. The notional value is not a real value of the means of production. When both notional value and GDP increase, this is more of a red flag when debt is on the rise as well; especially when much of it is due to market bailouts. These data points are inconsistent. Notional value is a large part of GDP; because it’s based on profit margins. Profit margins are not necessarily based on increases in sales. They are affected by both price increases and cut costs; and they are not at all affected by bailouts. The increase in debt itself was a much more obvious influence; because it was the clear point of impact. It was the most telling data point; because many of the affects on GDP were diverted to it.

2. There were measures to profit from the decline

Insurance contracts are a form of checks and balances; though in a crisis they compound the negative effects. Though a lot of money changes hands, it’s not associated with a product or service; and during a crisis, it’s a very expensive, added consequence. Insurance contracts punished those who lost track of the markets; but it also resulted in loss of established complexity. They also became an added layer of complexity as derivatives of derivatives; that were not only not correlated to the market itself, inflating the market itself, but also increasing the losses at least 10 fold. The entirety of the market of derivatives of derivatives was completely one sided. Only the insurance contracts had a possibility of paying out; and at that level of leverage, it devastated the entire world economy.

3. Fraud obscured the decline

Both the ratings and notional value of the mortgage bonds were completely inappropriate; considering the individual mortgages that were packaged with in them. The vast majority of mortgages in the vast majority of bonds were at high risk for default; and they were packaged and sold as low risk. Those who packaged and rated the bonds were aware of it.

4. The population had become too poor on average to support the housing market

If the population of the US were as able to afford their mortgages as in previous decades, the housing bubble would not have happened. The bad data from debt to GDP ratio, to the rise of the housing market, to the ratings and notional value of bonds distracted from the underlying issue of a population that was becoming poorer; with respect to the cost of living.

The Pandemic:

The Pandemic of course had many negative effects on the supply chains. Populations spending so much time at home may have had a positive effect on e-commerce, which also eased some of the negative effects on sales; but it also had a negative effect on the balance between commercial and residential markets. Demand for products for commercial markets plummeted; demand for products for residential markets skyrocketed. The system shocks were countless; and businesses that were on the brink from the existing decline were lost. There was a larger degree of loss of established complexity during the Pandemic than would be expected under more favorable conditions… like the inconsistent, easily accessible data suggested.

Persistent Inflation:

The affects of inflation are increases in the cost of living and when money is printed, domestic devaluation. The root of inflation is negative effects on average income and increases in prices. The current, persistent inflation appears to be latent. The bubbles themselves have been manipulating not only information, but also the apparent standard of living. After almost two decades of decline, the data is only now beginning to show the negative effects on the cost of living; even though it has been clear in the particulars, for some time.

The likely reason that the inflation is to be persistent is that the root of it cannot be affected by the measures that are chosen. Increases in interest rates are likely to be passed down to the consumer; thus only exacerbating the problem. This would seem likely to result in runaway inflation.

Aggregation of Wealth:

Following the Post WW2 Expansion, the slowing of growth resulted in increases in complexity of the derivatives market; that not only hid the slowing of growth in the GDP and the increased notional value of the markets, it also aggregated the wealth away from the middle and working classes. The statistics concerning the particulars were of course inconsistent with common data being presented. The data points that were more commonly focused on in Politics and Finance, and too often in Economics, were not accurately representing the health of the economy. While the economy is growing, the derivatives market distributes wealth to more individuals; and while it’s in decline it aggregates it away from more individuals.

Loss of Small (non corporate) Businesses:

The derivatives market increases the competitiveness of the businesses that it invests in. Over the past few decades many small, privately owned businesses went out of business; because they couldn’t compete with the price points of their corporate competitors. Of course this is also influenced by the decreasing buying power of the population. The lower price points has a positive effect on the cost of living; thus adding another order of logic to the analysis, that helps to properly assess the state of the economy.

Dividends and Market Saturation:

Dividends being a product of increased quarterly earnings, require the increase in quarterly profits. Profits however are not necessarily a product of increased sales. Profit margins in saturated markets should be expected to be stagnant; however that is rarely the case. Profit margins, under the condition of saturated markets tend to be a product of both cutting costs and increasing prices. Cutting costs flattens wages; as they are a portion of overhead, and increasing prices increases the cost of living more directly.

The data in the particulars show both flattening of wages and steady increase of prices. This is a clear indication of market saturation; but it isn’t enough. There are other indications; such as employment of management companies (that streamline businesses to cut costs), mergers (that prop up weaker investments), and indications of decreased funding for essential overhead, such as security and safety measures (which have results that can be found in increases in other statistics). These are all very common in the US. Due to it’s apparent degree, there appears to be a very concerning number of saturated markets in the US.

Risk Assessment:

There appears to be a risk of Economic Depression; that could be as high as 85% or more, this year. There is no clear way to address the inflation increases; outside of redistribution of wealth, or the emergence of a new, large, lucrative market. If the inflation runs away, the US economy is very likely to collapse; having a similar effect on the global economy.