The Index Investor
July 2020

Our 12-month regime forecast probabilities have not changed over the past month. The probability of the Persistent Deflation Regime remains 65%; the High Uncertainty Regime 15%; the High Inflation Regime 15%, and the Normal Regime 5%.

This month we saw more evidence that equity investors have underestimated the underlying damage COVID19 has done to the demand side of the US economy (which will quickly become even more apparent if the US Congress fails to pass another stimulus bill to support middle and lower income households).

We have made no change to our 36-month regime forecasts. The probability the United States will be in the Persistent Deflation Regime remains 50%.

This forecast still rests on three assumptions. The first is that the increasing headwinds that were restraining aggregate demand growth before the devastating arrival of the COVID-19 pandemic either have not improved or are worsening. These include population aging, weak productivity growth, declining labor share of GDP, high levels of both inequality and debt, and the growing threat of job displacement as increasingly capable automation and artificial intelligence technologies are deployed.

The second assumption is that COVID19’s unprecedented demand and supply shocks have substantially worsen corporate and household debt servicing problems, and will lead to rising defaults, bankruptcies, and liquidations that will have a deflationary impact. We already see evidence of this in rising delinquencies and bankruptcy filings.

The third assumption is that the weak recovery of demand, accelerating debt problems, continued increase in tensions with China, and increasing domestic tensions in the United States between now and the November election will keep uncertainty higher than most people currently expect, which will further depress aggregate demand relative to supply and cause price declines in many sectors.

Our 36-month forecast probability for being in the High Inflation Regime remains at 35%.

Whether the High Inflation Regime comes to pass will depend on the interaction between COVID19’s shocks to demand and supply, along with even more important political factors.

We know that the demand shock has been swift and severe, and has triggered falls in the US Consumer Price Index. Current evidence leads to the conclusion that depressed demand may last longer than many expect. The longer it takes to develop and deploy an effective vaccine, more layoffs will occur, more bankruptcies will take place, and the long term impact of uncertainty will be stronger. All of these depress consumption, investment, and export demand. If supply remained constant, this would tend to decrease rather than increase prices.

However, COVID19 has also been a shock to aggregate supply; many past episodes of high inflation were caused by such shocks (e.g., oil supply cutbacks in 1973 and 1979). In June, the US Consumer Price Index reversed its recent three months of decline, and increased by 0.6%. This was primarily driven by a 5.1% increase in energy prices, which reflected the interaction of a modest increase in demand as quarantines were lifted with a much larger reduction in supply.

On balance, we continue to believe that the impact of demand destruction will outweigh the impact of supply reduction.

In our view, the most logical cause of a return to the High Inflation regime over the next 36 months would be a crisis of confidence in the US government and/or economy that causes a flight from the US dollar and a sharp rise in the price of imported goods (and very likely gold as well).

The most likely cause of such a crisis is an attempt by Donald Trump to disrupt the November election or his refusal to leave office if he id defeated. This past month has seen accumulating evidence that probability of such a crisis is increasing.

Another potential cause of such a crisis would be the outbreak of open conflict between the China and the United States (e.g., a Chinese invasion of Taiwan or rapid escalation of a kinetic encounter in the South China Sea) in which American forces were defeated and/or global supply chains were severely disrupted.

Finally, at the 36-month time horizon, our forecast probabilities for the High Uncertainty (10%) and Normal Regimes (5%) also remain unchanged. The former regime could occur if the Democrats win the White House and both houses of Congress in November, and struggle to revive the economy with good policy initiatives, but without an effective coronavirus vaccine while facing intensifying conflict with China.

Return to the Normal Regime would require the same political wins and policy initiatives, along with early deployment of a highly effective vaccine and a sharp reduction in US-China tensions (e.g., due to the removal of Xi Jinping from office).

In reviewing the current situation, we are again reminded that when uncertainty is high, people rely more heavily on social learning and copying what others are doing. Not only does this slow the diffusion of new information throughout social systems like economies and financial markets, but it also causes these systems to coalesce around a small number of increasingly fragile narratives. Under these conditions, rapid, non-linear changes are very likely to occur.

That certainly appears to be the case today in global financial markets.

Portfolio Allocation Implications of Our Forecast

We take two approaches to deriving the tactical asset allocation implications from our analyses. The first takes a systematic approach, and is based on relative asset class valuations. Our starting point is our “neutral” model portfolio, which is equally weighted across nine broad asset classes, and also includes a 10% allocation to alpha strategies (equity market neutral and global macro) that are designed to have a low correlation to returns on broad asset classes. Based on asset class valuations, we systematically vary the asset class weights (but not the active strategy weight), increasing from 10% to 15% when an asset class is likely undervalued, and 15% when it is very likely undervalued. In the case of overvaluations, we go to 5% and then into cash, if there are no undervalued asset classes with room for an increase. In effect, this replicates the systematic rebalancing strategy we used for 15 years in our previous model portfolios.Based on subscriber requests, this month we are re-introducing a feature from the previous version of The Index Investor: Tactical Asset Allocation Implications from our analyses.

The second tactical approach is based on our subjective view not only of current asset class valuations, but also of the implications of the broader macro trends and uncertainties that we analyze each month. Importantly, this subjective view reflects our primary goal of avoiding large downside losses, rather than seeking large upside gains.

Two final notes: First, with respect to US fixed income, we include credit products (investment grade and high yield) in the same asset class as government debt, and will shift into the former when their valuations become attractive. Second, we regard gold not as a separate asset class to be held long-term, but rather as a complement to cash, into which we shift in periods of substantial overvaluation across multiple asset classes.

More information about our investment beliefs, including our core philosophy, approach to asset allocation (including our model portfolios and their long-term track record), and views on various approaches to active and passive management can all be found here.

Here is our latest asset allocation view:

Implications of the Most Recent Three Month Asset Class Returns

Our quantitative forecast methodology focuses on the level and change in three-month returns, over the most recent and previous three-month periods, for those asset classes, which should perform best under different regimes (in this sense, our regimes can be regarded as macro factors). We assume that relatively higher returns are associated with more widely held investor belief in the probability that a given macro regime will develop in the future.

Note: The language we use to describe our estimated likelihood of asset class over or undervaluation is based on US Intelligence Community Directive 203 on Analytic Standards, which includes the following table:

Market Stress Indicators (@30Jun20)

As always, this month’s Evidence File contains important new indicators and surprises observed over the past month. Many of them indicate that a substantial increase in uncertainty very likely lies ahead that is not yet reflected either in dominant popular narratives or financial market prices. Key indicators include:

• The COVID pandemic is stimulating faster corporate adoption of automation technologies. At the same time, the reopening of schools in the United States has become politicized, with teachers unions apparently seeking to keep them closed (perhaps until the November election). This will almost certainly worsen economic conditions, and lead to more demand and supply destruction that will take longer than many expect to reverse. Student learning losses will almost certainly never be recouped, which will put further downward pressure on productivity and employment growth once demand revives. This scenario would also lead to more medium term social and political conflict, and higher levels of uncertainty.

• A genetic mutation seems to have increased the transmissibility of SARS-CoV-2 coronavirus. It is not yet clear whether it will lead to higher case fatality rates. The number of infections in the United States continued to increase in the past month, as mask use became increasingly politicized along partisan lines. Recent research has found differing genetic susceptibilities to the virus, whose lingering debilitating effects in some patients has become clear. Other research showed a wide variation in antibody responses in COVID patients, but possibly longer-lasting T-cell responses. The net impact on the degree and duration of immunity remains unclear.

• A new report from McKinsey concluded that, “The COVID-19 crisis has strongly affected Europe’s labor markets, and it may take years for employment to return to its pre-crisis levels.” With far weaker employment protections in the United States, recovery of the labor market will likely take longer.

• Delinquencies, bankruptcy filings, and other indicators of financial distress increased, in what already was an over-indebted economy before COVID arrived.

• New research from multiple parties concluded that, acting through a variety of channels (e.g., weaker financial and labor markets; higher individual uncertainty and risk aversion), the negative effects of the COVID pandemic will very likely last longer than many currently expect.

• Negative COVID impacts are falling disproportionately on lower income groups. In the absence of more effective government action in polarized political systems, this will almost certainly increase inequality and worsen social and political conflicts.

• Last month, more evidence emerged to support the claim that the SARS-CoV-2 pandemic was triggered by an accidental release from a Chines lab that was either conducting coronavirus gain of function and/or bioweapons research. The implications for the new Cold War between China and the US are obvious and ominous.

• In an attempt to limit criticism of the Chinese Communist Party and Xi Jinping’s regime, China asserted that prohibitions on certain types of speech contained in the new Hong Kong security law applied globally.

• A growing number of observers are forecasting a crisis related either to the manner in which the US election in November is carried our, or Trump’s refusal to leave office if he is defeated.

Pre-Mortem Analysis

One of the most important forecasting disciplines is to ask yourself why your forecast could be wrong. Dr. Gary Klein’s research has shown that a very powerful and insightful way to do this is via a “pre-mortem analysis.” This method asks you to assume that it is a point in the future, and your forecast has been proven wrong (or your strategy or company has failed). You are then asked to look backward from this imagined point in the future, to explain why you failed, what you missed, and what you could have done differently to avoid your fate.

The pre-mortem method takes advantage of the fact that humans reason much more concretely and in more detail when explaining the past than they do when trying to forecast the future.

So let us assume that it is one year from now, and our current forecast has turned out to be wrong.

How did this happen? What developments did we fail to anticipate?

• A number of world leaders – Xi Jinping and Donald Trump, and to a lesser extent Vladimir Putin and Ali Khamenei are all facing sharply weakened economies and declining political popularity. History teaches us that this can lead to increased “foreign adventurism” to distract the public from worsening domestic conditions, as a nation rallies around its leader in a period of heightened external conflict. Should a “kinetic” conflict develop between China and the United States, or Iran and Israel, Saudi Arabia, and/ or the US, or between Russia and one or more European countries (e.g., due to a Russian incursion into the Baltics), it would generate a very sharp increase in uncertainty that would likely accelerate the already sharp COVID-19 economic slowdown. Given given high debt levels, this would very likely accelerate the arrival of the Persistent Deflation Regime.

• On the other hand, the removal from office (by one means or another) of Xi Jinping or Donald Trump * could * lead to a reduction in the dangerously growing conflict between the two nations, and increase cooperation in both the fight against COVID19 and the recovery of the global economy. This would somewhat reduce the probability of both the Persistent Deflation and High Inflation Regimes, and raise the relative probabilities of the High Uncertainty and Normal Times Regimes.

• A supply side shock of some type – beyond the disruption of global supply chains caused by COVID-19 -- could produce a sudden increase in inflation. The most likely scenario is a reduction in oil supplies due to a prolonged kinetic conflict between Iran and the US. An unlikely scenario could be major crop failures associated with the next solar cycle, which NASA forecasts will be the weakest in 200 years. McKinsey recently concluded that the probability of such a failure has increased due to changes in the environment, and now stands at about 10% over the next five years.

• Another route to the high inflation regime (repeatedly noted by Bridgewater’s Ray Dalio) would be a sudden loss of confidence in the US dollar relative to other currencies (driving up import prices), perhaps because of increasing deficit monetization and policy paralysis if a severe downturn continues without meaningful policy reforms to address critical structural challenges. However, a sharp rise in import prices due to a collapse in the USD exchange rate also requires relatively higher confidence in another currency, with the Euro being the most likely candidate. This currently seems unlikely, given both the Eurozone’s economic and political uncertainty, and the prospect of an intensifying conflict between the West and China. If confidence collapsed in all major currencies, the price of gold would rise, and price inflation would only occur in terms of gold.

Note: Combining Our Forecasts with Others From Other Sources and Extremizing the Result Should Increase Your Predictive Accuracy

Research has found that three steps can improve forecast accuracy. The first is seeking forecasts based on different forecasting methodologies, or prepared by forecasters with significantly different backgrounds (as a proxy for different mental models and information). The second is combining those forecasts (using a simple average if few are included, or the median if many are). The final step, which significantly improved the performance of the Good Judgment Project team in the IARPA forecasting tournament, is to “extremize” the average (mean) or median forecast by moving it closer to 0% or 100%.

Forecasts for binary events (e.g., the probability an event will or will not happen within a given time frame) are most useful to decision makers when they are closer to 0% or 100% than the uninformative “coin toss” 50%. As described by Baron et al in “Two Reasons to Make Aggregated Probability Forecasts More Extreme”, forecasters will often shrink their probability estimates towards 50% to take into account their subjective belief about the extent of potentially useful information that they are missing.

When you average multiple forecasters’ estimates, you are including more information, which should increase forecast confidence and push the mean estimate closer to 0% or 100%. However, this doesn’t happen when you use simple averaging. For this reason, forecast accuracy is increased when you employ a structured “extremizing” technique to move the mean estimate closer to 0% or 100%.

You can download an extremizing model from our website to use when combining the forecasts you use in your decision process.

The extremizing factors in our model are those that the Good Judgment Project found maximized the accuracy of combined forecasts. Note that the extremizing factor is lower when average forecaster expertise is higher. This is based on the assumption that a group of expert forecasters will incorporate more of the full amount of potentially useful information than will novice forecasters.

Feature Article: Can the US Successfully Meet the Threat Posed by Worsening Inequality?

Inequality has always been a difficult subject for people to discuss. Yet for eons humanity has struggled with it, and the emotions and behaviors it triggers.

In 1975, Arthur Okun captured this struggle in what has become a timeless book, Equality and Efficiency: The Big Tradeoff. As he summarized, “Institutions in a capitalist democracy prod us to get ahead of our neighbors economically after telling us to stay in line socially. This double standard professes and pursues an egalitarian political and social system while simultaneously generating gaping disparities in economic well-being.”

A lot has changed since 1975. Okun wrote during a period when there was increasing concern with growing economic stagnation. The elections of Margaret Thatcher (1979) and Ronald Reagan (1980) marked the beginning of the neo-liberal response to this with its greater policy focus on markets and economic efficiency. Forty years later, there is abundant evidence that we have once again overshot, and now face a pressing need to renew our focus on equality.

All around us, we see today the negative effects and dangerous threats that emerge when inequality passes a critical threshold.

Economically, excessive inequality is one of the forces that have weakened aggregated demand. For example, in 2018 the top 10% of households accounted for 35% of total income, but just 23% of consumption spending. The next highest 10% received 18% of income, and accounted for 16% of spending. Unsurprisingly, analysts have pointed to the sharp drop in consumption spending by the top 10% after COVID arrived as an important cause of the current economic contraction.

Socially, the negative effects of rising inequality have also been recognized for a very long time. In his 1899 book, The Theory of the Leisure Class, the economist Thorsten Veblen warned of the cancerous impact of what he called “conspicuous consumption” by economic elites, and the feelings of envy and resentment it stirs up among the masses. In recent years, researchers have developed a deeper understanding of the psychology and neuroscience behind this phenomenon.

For example, in “Interdependent Utilities: How Social Ranking Affects Choice Behavior”, Bault et al find a critical difference between the way people approach decisions when the results will be private versus when they will be observed by others. In the private condition, people are risk averse (i.e., possible losses have a higher weight than possible gains), but in the public condition this reverses, and people become risk seeking, to avoid the envy that comes from watching others’ relative outperformance. The difference is that in the public case, the payoff is involves both money and relative social status.

In a recent paper, Claudius Gros shows how “the repercussions of envy are amplified when societies become increasingly competitive” (“Self-Induced Class Stratification In Competitive Societies Of Agents: Nash Stability In The Presence Of Envy”. See also, “Investigating the Health Consequences for White Americans Who Believe White
Americans Are Wealthy”, by Cooley et al).

Many have argued that this phenomenon has driven the accumulation of debt by the middle class, as they have tried to keep up with the increasingly affluent Joneses. This process has been supercharged in recent years, as the Joneses, egged on by aspirational (envy-based) marketing, have engaged in something resembling competitive conspicuous consumption, which social and other media have made painfully visible to everyone else in the world.

Moreover, as Jack Goldstone and Peter Turchin have shown (in their development of Structural Demographic Theory), increasing inequality within elites is another source of social conflict that has worsened in recent years (see, “The Wealthy and Privileged Can Revolt Too”, by Noah Smith; “Our Suicidal Elites” by Joel Kotkin, “The Discrete Terror of the American Bourgeoisie” by Ed Luce, and for a French view, “Jupiter Falls to Earth”, by Christophe Guilluy).

Political shocks like Brexit and the election of Donald Trump have their roots in the economic struggles, social resentments, and class anger that have built up as inequality has gradually worsened since the 1970s.

In sum, there is no doubt that inequality has already had increasingly negative effects. That is why we included it as one of two critical medium term economic uncertainties in our May 2019 feature article on multipath reasoning about the future (“Multipath Analysis: A Systematic Process for Reducing the Dimensionality of Global Macro Forecasting Challenges”).

However, before discussing what can be done to reverse the worsening effects we see today, we first have to dig into the causes of inequality, some of which may surprise you.

Typically, conversations (and arguments) about the causes of inequality divide into two categories: Those rooted in individual decisions, and those rooted in the nature of “the system” however it is defined. But before delving into both of those, let’s start the cause that is almost never discussed: the impact of randomness (or, if you will, luck).

Let’s assume a world of perfect initial equality, where 50 people each start out with the same endowments of talent, resources, and opportunity. For ease in modeling, we’ll proxy this by (1) everyone starting with an equal endowment of $1,000, and (2) everyone facing an equal chance of their capital earning an annual return of between (20%) and 20%, with all values between those extremes equally likely. Each individual’s annual return is determined by a unique random number generator.

We ran this simulation over 20 years, fifteen times. What do you think we found?

At the end of 20 years, the median multiple (over our fifteen simulations) of highest of the 50 individuals’ resources compared to the lowest was 11x. Across the fifteen scenarios, the highest multiple value was 21x and the lowest was 6x.

What about shares of the total ending resources for all 50 individuals? The top 20% (i.e., the top 10/50) share had a median value of 38%. Across the fifteen scenarios, the highest was 43% and the lowest was 34%.

What about the top 10%? Their median share of total resources was 22%. The highest was 26% and the lowest 20%.

Looking at the top 20%, the median multiple between the top 10%’s ending share and the next 10%’s ending share was 1.4x, with a high of 1.6x and a low of 1.3x. Even within the top 20%, randomness generates inequality.

Finally, after 20 years, what percent of the 50 individuals ended up with less than their initial $1,000 endowment? The median was 60%, with a high of 66% and a low of 50%. This was caused by strings of negative returns from which they never recovered.

In this surprising result, we see a familiar investment lesson: Losses have a larger negative impact on long-term results than gains. If you start with $1,000, make 20% the first year ($1,200), then lose 20% the second year ($960), you have to earn 25% in year three just to get back to $1,200. (Unfortunately, the power of envy causes too many investors to spend far less time on managing risks than finding ways to show higher short-term returns than their peers).

This simple model highlights a critical point: Randomness (luck) has a much larger impact on the emergence of inequality than most of us realize. In fact, because of the cognitive dissonance it creates, we subconsciously refuse to acknowledge, much less admit it.

Now let’s move on to the more familiar roles of personal decisions and the nature of “the system.”

In a January 2020 report, Pew Research found that 60% of Republicans believed that different life choices contributed a great deal to inequality. Only 27% of Democrats agreed (overall, 42% agreed). In contrast, 62% of Republicans agreed that major changes in the system were needed to reduce inequality, as did 88% of Democrats (81% overall). See: “Most Americans Say There is Too Much Economic Inequality in the U.S.”

Perhaps the best-known example of the impact of personal decisions on inequality is Isabel Sawhill and Ron Haskin’s “Success Sequence” – “at least finish high school, get a full-time job and wait until age 21 to get married and have children…Only 2.4 percent of Americans who follow the success sequence live below the poverty line, while over 70 percent enjoy at least middle-class incomes, defined as 300 percent of the poverty line or more. For Americans who—for a host of reasons—don’t follow the sequence, the picture is reversed” (see their paper, “Work and Marriage: The Way to End Poverty and Welfare”. Also, “The Millennial Success Sequence: Marriage, Kids, And The ‘Success Sequence’ Among Young Adults”, by Wang and Wilcox).

Sawhill and Haskin’s research was based on US evidence. Other researchers have arrived at similar conclusions using evidence from other countries. For example, using Canadian data, Christopher Sarlo found that the Success Sequence factors had a strong impact on life outcomes, along with two other decisions: avoiding both addiction and a criminal record (“The Causes of Poverty”).

In terms of our simple model, good decisions can affect both starting resource endowments as well as the distributions of possible annual returns on those endowments that different individuals face.

However, there is also plenty of evidence that system factors (broadly construed) have evolved since Sawhill and Haskins published their paper in 2003, and now have a stronger impact on both individuals and aggregate inequality. In our model, these system factors affect both starting resource endowments and the distributions of possible annual returns facing different individuals.

Consider the Success Sequence. The work of many researchers has made clear that the quality of education varies widely across American school districts, as well as across schools and classrooms within them. And unlike many other countries, the United States severely limits’ parents’ ability to choose better schools for their children.

The quality of university and technical education programs also varies widely, and over the last 50 years the price of the former has sharply risen in real terms.

Moreover, access to these programs is not a level playing field. For example, in “Legacy and Athlete Preferences at Harvard”, Arcidiacono et al “examine the preferences Harvard gives for recruited athletes, legacies, those on the dean’s interest list [i.e., applicants related to large donors], and children of faculty and staff (ALDC). Among white admits, over 43% are in these categories. Among admits who are African American, Asian American, and Hispanic, the share is less than 16% each.” Their model of admissions “shows that roughly three quarters of white ALDC admits would have been rejected if they had been treated as typical white applicants.”

Rising inequality is an emergent result of multiple interacting causes. For example, a poor education system creates an increasingly severe shortage of workers with the skills required to use leading edge technologies as the transition to an economy driven by brains rather than brawn accelerates. Companies that can attract these workers strengthen their competitive advantage over other companies, which enables them to pay higher wages, limiting their competitors’ ability to do so. Research has shown that this is yet another driver of increasing inequality.

This is but one aspect of the challenges associated with getting a good full-time job in today’s economy. A combination of increased use of automation technologies and moving production and supply chains to other countries has eliminated many middle income jobs in the United States, leaving a mix of high income opportunities (that require high quality education and training), and much less secure and poorly paid service and other gig economy jobs. Many of those jobs do not provide health care benefits, whose real cost has also dramatically increased over the past 50 years.

Also, in recent years three fundamental aspects of the US economic system have changed in a way that worsens inequality. First, the rate of labor productivity growth has significantly declined. Second, in many industries, the traditional link between increasing productivity and rising real wages has been broken. Third, labor’s overall share of GDP has shrunk in recent years, for a variety of reasons (see, “Declining Worker Power and American Economic Performance” by Stansbury and Summers, “Labour Share in G20 Economies” by the International Labour Organization, and “A New Look at the Declining Labor Share of Income in the United States” by the McKinsey Global Institute).

These structural changes have been so strong in their impact that they have weakened the link between education and middle class incomes (see, “Education and the Dynamics of Middle Class Status” by Hardy and Marcotte, and “Is College Still Worth It? It’s Complicated” by Emmons et al from the Federal Reserve Bank of St. Louis).

Looking at Sarlo’s two additional Success Sequence factors (avoiding addiction and a criminal record), America’s systems for addressing mental health and addiction issues vary widely in quality, and there is abundant evidence that the criminal justice system treats some groups more harshly than others.

In sum, while making good individual decisions can certainly help to reduce rates of poverty and the extent of inequality, system factors have increasingly reduced their potential positive impact on inequality.

So what should be done to reduce inequality, and how likely is that to happen?

In the immediate term, the sharp income reductions caused by COVID that have disproportionately affected people with lower incomes can be offset with government aid, including direct cash payments, support for food banks, and expansions of eligibility for government provided or subsidized health care (i.e., Medicate and health insurance exchanges). Housing is an equally critical challenge with fewer obvious solutions. While current bans on evictions can be extended, this has knock-on negative effects on landlords’ ability to service their debts. An expedited bankruptcy process is the likely solution.

In the short-term, the challenges to reducing inequality seem even more politically daunting.

Improvements to the education and workforce training systems (or, more broadly, the human capital system) are obviously critical to increasing productivity, but they have repeatedly been met with fierce resistance (e.g., by K-12 teachers unions, colleges and universities, and the large number of competing non-profits focused on worker retraining). While there are proposals to break this logjam (e.g., moving to a national system of stackable certified competencies, similar to the European National Qualifications Framework, and linking participation in a federal jobs program to completing worker retraining), the weight of historical evidence suggests that no matter how critical it is, progress in this area will very likely remain glacial, absent much more aggressive and sustained demands for change by the business community.

Improvements to the US healthcare and social care systems, including processes and institutions focused on mental health and addiction treatment, will also very likely face strong resistance, in addition to squeezed post-COVID budgets. Again, the weight of historical evidence suggests progress will be slow here too, despite polling data showing that reducing health care costs is a critical issue to voters across the political spectrum.

On the other hand, programs that have already been shown to be effective at boosting incomes, like the Earned Income Tax Credit, could be expanded more easily, potentially to include federal wage subsidies. These are likely to be more effective and meet less resistance than attempts to increase the minimum wage, which will put further stress on many businesses that are already barely holding on in the post-COVID economy (e.g., “People versus Machines: The Impact of Minimum Wages on Automatable Jobs” by Lordan and Neumark).

While broad changes to the US education and healthcare systems seem very unlikely at this point, narrower solutions may garner more support and still have a significant impact, particularly integrated approaches to support lower income children early in their lives. As Currie and Rossin-Slater find in their paper “Early-life Origins of Lifecycle Well-being: Research and Policy Implications”, “there is a robust and economically meaningful relationship between early-life conditions and well-being throughout the lifecycle, as measured by adult health, educational attainment, labor market attachment, and other indicators of socio-economic status.”

“However, there is some variation in the degree to which current policies in the U.S. are effective in improving early-life conditions. Among existing programs, some of the most effective are the Special Supplemental Program for Women, Infants, and Children (WIC), home visiting with nurse practitioners, and high-quality, center-based early childhood care and education. In contrast, the evidence on other policies such as prenatal care and family leave is more mixed and limited.”

Changes to the tax system are also critical to reducing inequality. They could include not only higher marginal rates on top incomes, but also and end to provisions like interest deduction (which promotes use of debt in capital structures), taxation of private equity funds carried interest as capital gains rather than income, and imposition of a wealth tax. Two other ideas may have an even greater impact – a carbon tax, with a portion of the revenue rebated to low income taxpayers or used to provide equity to start-up businesses, and a progressive consumption tax that discourages destructive conspicuous consumption.

Structural changes that increase the bargaining power of labor relative to capital could also contribute to the reduction of inequality. The reshoring of supply chains to make them more resilient (and in response to worsening US-China relations) will help. So too would changes that strengthen collective bargaining processes in the private sector. Placing restrictions on companies repurchasing their own equity, or making such buybacks contingent on wage increases could also help. Most difficult of all would be a switch in the US immigration system to one like Canada’s, that gives preference to talented applicants who can make the largest contribution to increasing national productivity growth, in contrast to low skilled immigrants who put downward pressure on low income workers’ wages (an argument which in years past the Democratic Party strongly supported).

After reading this list, it is clear that we do not lack for testable policy ideas for limiting the worsening inequality that has emerged from the interaction of bad decisions, bad luck, bad systems, and now the COVID-19 pandemic.

Moreover, we have at hand a growing number of analyses of how we arrived at today’s predicament, and how we might get out of it (e.g., “Capitalism and the Future of Democracy” by Isabel Sawhill, and “The Economics of Belonging”, by Martin Sandbu).

More darkly, in the growing influence of populist authoritarians on both the left and the right, we are also witnessing the accumulation of painful – and scary -- evidence of the likely consequences if worsening inequality is not reversed.

Yet in our current political climate – even after COVID’s shocking arrival – it will very likely be difficult, if not impossible, to marshal enough political support to enact and effectively implement the reforms needed to do this.

If there is a reason to be hopeful, it rather perversely lies with Xi Jinping. It may well be that only the growing danger and demands of a new Cold War will finally force us to reverse the downward path we are on today.

High Value Information Observed In June 2020

In our model of the complex global macro system, change drivers are arrayed across a roughly chronological process (albeit one with many feedback loops), in which technological and environmental changes precede changes in the economy and national security, which in turn lead to changes in society and politics, all of which produce the effects we observe in investor behavior and financial market valuations and returns.


In our methodology, we classify new information as significant and highly valuable if either it (1) is an “indicator”, which reduces our uncertainty about the value of a parameter in our mental model for making sense of the dynamic macro system, or (2) it is a “surprise” which increases our uncertainty about either the range of potential values for a parameter or the structure of our model. With respect to indicators, the higher our priori probability is for a regime, the more we look for indicators that it will not occur, and the lower our prior probability for a regime, the more we look for indicators that it will occur. Put differently, we tend to look for high value indicators that disconfirm our prior views.

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System Tipping Points/Critical Threshold Analysis

Like Professors Andrew Lo, Doyne Farmer and others, we regard financial markets as a complex adaptive system (CAS), that exist as part of a larger macro system comprised of other CAS between which there are multiple feedback loops. These other systems include those that produce technology innovations, and economic, environmental, national security (including cyber), social, demographic, and political outcomes.

We also find that these systems tend to operate and generate effects in a rough chronological sequence, albeit with many feedback loops between them. The following chart highlights that the changes we observe in different areas at any point in time are actually part of a much more complex evolutionary process.

While most media coverage of these systems focused on flows (e.g., the size of the government deficit), rapid non-linear change in complex adaptive systems is often caused by a key stock (e.g., the amount of outstanding government debt) exceeding a critical threshold.

The next table highlights the key macro system stocks that we monitor.

In the next section, we will discuss information received over the past month that is related to these stocks, and which we believe is significant to our assessment of the probabilities that a critical threshold will be reached and a regime change will occur. We will conclude with our estimate, at the end of this month, of how close the macro system is to these critical thresholds, and the implications for financial market regime change probabilities.

How Close is the Macro System to One or More Critical Thresholds?

As we have noted, the macro drivers of financial market regime changes typically follow a rough chronological sequence, from technology to economic, security, social, and political causes and effects. Yet there are many feedbacks loops between them, creating complex root causes for many of the critical thresholds we have identified.

Understanding the time dynamics in this complex system is critical to avoiding substantial downside investment risk.

We use the UK Met Office Warning Model to communicate our assessment of these time dynamics. We estimate the time remaining before a critical macro system threshold is reached that could trigger a regime change, which is usually accompanied by substantial changes in asset class valuations.

The model uses three increasingly serious levels of warning, from “Be Aware” (condition yellow), to “Be Prepared” (condition orange), to “Take Action” (condition red).

For our purposes, we denote as “Be Aware” (yellow) critical thresholds that we assess to be three or more years away. We estimate that “Be Prepared” (orange) thresholds could be reached within 1 to 3 years. “Take Action” thresholds are very likely to be reached within one year.

Given their nature, we also note that in our three “wildcard” areas (Environment and Energy related; Disease and Human Caused Bioevents; and Cyber and Electromagnetic Events), our forecasts have higher levels of uncertainty.

The following charts summarize our current estimate of the time remaining before different critical thresholds will be reached.

Conclusion

At the highest level, we believe the complex adaptive global macro system can be in one of four states, based on its degree of order versus disorder, and degree of social cooperation versus conflict. A very coarse-grained reading of history suggests that these states evolve in a predictable cycle, from ordered/cooperative, to disordered/cooperative, to disordered/conflicted, to ordered/conflicted.

We believe that the system is currently in its most uncertain state, characterized by high degrees of underlying disorder and social conflict, both domestically and internationally. Beyond some point, intensifying conflict eventually increases the degree of order in the system. That appears to be happening now, via the increasing conflict between China, Russia, and Iran and the United States and other Western nations.