The Index Investor
December 2018

Key Takeaways

Based on high value information that we collected in November, we have increased our forecast probability of entering the Persistent Deflation Regime over the next 12 months to 50%. The probability of remaining in the current High Uncertainty Regime is 35%, while a transition to the High Inflation Regime is 5% and a return to the Normal Regime is 10%.

Compared to October, all five of our market stress indicators have risen, implying a continued increase in the level of underlying stress in financial markets during November 2018, and thus a further increase in the risk of substantial changes in asset class valuations, many of which we judge to be overvalued today.

In light of rising levels of uncertainty and conflict in the macro system, we have also reduced our estimate of the time remaining before several critical thresholds are reached.

Asset Class Valuation and Momentum Indicators (@30Nov18)

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 (@30Nov18)

Compared to October, all five of our market stress indicators have risen, implying a continued increase in the level of underlying stress in financial markets during November 2018, and thus a further increase in the risk of substantial changes in asset class valuations. BB rated bonds’ spread over the 10 Year US Treasury is still just 2.80%, which put it in just the 37th percentile since the series started in 1996. Such low credit spreads on speculative grade bonds are usually a sign of excessive credit growth, which underlies many other sources of market stress.

Market Stress Indicators: Forecast Discussion

We view financial markets as a complex adaptive system. The distribution of the changes generated by such a system follows a power law rather than the normal (Gaussian or "bell curve") distribution. The critical point is that large changes are much more common in complex adaptive systems than most people’s intuition leads them to believe.

While predicting the behavior of complex adaptive systems remains far more art than a science, various researchers have found that large changes in such systems are often preceded by subtle warning signs, as stress accumulates within them. While this research is not definitive, we believe that five warning signs are worth monitoring as potential indicators of growing stress within financial markets that could suddenly give rise to large changes in asset class valuations.

Our first indicator is the month-to-month autocorrelation of broad asset class returns (i.e., the relationship of this month’s returns to last month’s). A system under increasing stress loses resiliency, causing it to take longer to recover from perturbations; hence, autocorrelation increases as it approaches a critical transition (see, “Early Warning Signals for Critical Transitions” by Scheffer, et al).

The one-month autocorrelation of returns for the broad asset classes we monitor increased to .86 in October from (.62) in September. This indicates that financial markets are becoming more ordered and are potentially closer to a critical transition point (which would most likely be accompanied by sudden and substantial changes in asset class values) than they were last month.

The second market stress indicator we monitor is the Equity Market Related Economic Policy Uncertainty Index published by the Federal Reserve Bank of St. Louis (via its FRED economic database), which is based on research by Baker, Bloom, and Davis (see their paper, “Measuring Economic Policy Uncertainty”). The index is based on automated text analysis of leading newspapers and magazine publications, to identify the frequency with which words and phrases are used that indicate uncertainty.

In our evolutionary past, when uncertainty increased our probability of survival was enhanced by staying close to our group. We still have that instinct. Research has found that as uncertainty increases, we have an unconscious bias towards higher conformity of our own views with those of a larger group (i.e., reduction in cognitive diversity). Behaviorally, heightened uncertainty induces more “social copying” of others, likely due to both conformity bias and the rational belief that others may be acting on the basis of superior information. This increase in conformity and copying makes a social system more ordered as uncertainty increases, and also reduces its responsiveness to perturbations (i.e., increases autocorrelation) because of delays in the social copying process.

The key point is that increasing uncertainty induces more, not less order in social systems, and in so doing primes them for sudden non-linear change.

For the month of November, the average Economic Policy Uncertainty Index stood at the 83rd percentile of its values since the data series began in 1985 – to be clear, just 17% of values were higher over that period. This was a significant increase in average uncertainty since last month.

Our more fine-grained intra-month measure of uncertainty is the number of daily changes that are in the top and bottom 20% of the historical distribution. In November, it was in the 68th percentile -- 32% of rolling 30-day periods since 1985, has it been higher. Last month it was in the 82nd percentile.

To reiterate the point made above, high levels of uncertainty tend to cause people’s opinions to become more ordered, due to a higher tendency toward conformity and social copying. This primes a system for sudden, non-linear change.

Our third market stress indicator is the spread between the yield on AAA rated bonds and the 10-year US Treasury. We interpret this as a proxy for the level of investor concern about financial system liquidity. At the end of November 2018, this spread stood at 1.21%, (47th percentile), up slightly from last month’s 1.12% spread.

Our fourth market stress indicator is the yield spread between speculative BB rated bonds and the ten-year US Treasury. Throughout history, excessive credit growth has been a root cause of many financial crises. An indicator of such growth is falling credit spreads, particularly in the case of riskier borrowers. At the end of November, this spread increased from 2.53% to 2.80%, which still put it in only the 37thth percentile of spreads recorded since this data series began in 1996 – 63% the previous observations were higher. This is a dangerously low level this late in what is already an exceptionally long period without a serious economic downturn.

Our fifth market stress indicator is what we term the implicit “political risk premium” that is implicit in the price of gold. Our starting point for estimating this premium is the three different roles that gold plays. First, gold is a store of value in a world of fiat currencies. When the rate of money supply growth exceeds the growth of nominal GDP, gold’s price should increase to maintain its purchasing power. Between 2007 and 2017, the US money supply (M2) grew by about 86%, while nominal US GDP grew by 35%. The stock of gold grew by 18%, based on mine production over this period. We therefore infer that 33% of the increase in the price of gold represented the maximum potential gold price change that could be attributed to a desire to hedge inflation risk (86% less 35% less 18%).

Second, gold is a unit of account. We take this to mean that the annual change in GDP expressed in terms of physical gold (i.e., nominal GDP divided by the price of gold) should equal the change in real GDP calculated using the GDP price deflator to account for actual inflation over the period. A key challenge is the point at which to start this calculation.

We chose the price of gold in 1995/1996. In that period, the change in real global GDP measured using the IMF’s price deflator just about equaled the change in GDP measured in terms of physical gold. We interpret that coincidence as indicating that at that point in time, concerns about future inflation and political risk were minimal, and the change in the price of gold was mostly driven by its role as a unit of account. We calculated a subsequent series of gold prices that would produce the same change in “gold GDP” as the actual real GDP as calculated by the IMF. Between 2007 and 2017, “gold as a unit of account” warranted a 21% increase in its price.

Gold’s third role is as a hedge against inflation and what we term “political disaster” risk. We subtract the 21% estimated compensation for actual inflation from the 33% “gross” inflation risk hedge to derive an apparent 12% increase in the gold price that reflected the true risk premium to hedge against possible future inflation.

However, between 2007 and 2017 the price of gold actually increased by 81%. This implies that 48% of this (81% less 21% less 12%) represented a premium for some other type of uncertainty at the end of 2017. The interesting question is the nature of the uncertainty for which gold is believed by some investors to be a superior hedge than traditional ports in a storm like short-term US government securities, or similar securities issued by other developed countries.

The logical inference is that the uncertainty in question must reflect a situation in which short term US Treasuries would be a less effective hedge than gold. This could be a world of widespread hyperinflation, capital controls, and/or radical changes in nations’ governments (of course, this would also imply a preference for investing in gold coins rather than bullion, as while the latter may be a store of value, it is far less convenient as a means of paying for transactions).

To put this in further perspective, this gold price “disaster risk” premium sharply increased from 2008 to 2012, then declined before sharply increasing again after 2016. Arguably, a significant part of the former increase reflects concerns about the potential inflationary consequences of dramatic quantitative easing by central banks. But this is not likely to be the case after 2016.

Over the last month, the price of gold rose by 0.2%. Through November 2018, the price of gold has fallen by about 6% since the end of 2017, so, on a rough approximation, the political uncertainty premium now stands at about 42%, compared to 39% at the end of August 2018.

Macro Regime Forecast and Implications for Asset Class Values

Macro Regime Probabilities: Forecast Discussion

Summary: Why Did We Change Our Regime Probabilities?

These were the key pieces of new information we observed this month that caused us to increase the probability that over the next 12 months we will transition into the Persistent Deflation Regime, and decrease the probability of remaining in the High Uncertainty Regime. All of these pieces of new information are discussed in more detail in this month’s Evidence File (see below).

(1) A new IMF report that concludes there is more slack in the world economy than was previously realized.

(2) A worsening of the US-China conflict that has the potential to severely disrupt world trade and supply chains, putting downward pressure on growth.

(3) A report by Bloomberg that 20% of China’s apartments are empty. Given the importance of real estate to China’s highly leveraged economy, this is a very negative indicator.

(4) Worsening disorder in Europe (continued confusion over Brexit and significant street demonstrations and rioting in France against president Macron’s proposed economic reforms), which along with the worsening of the US-China conflict has contributed to a rise in growth-depressing global uncertainty.

(5) Three major new reports and articles in the US, from highly respected sources (e.g., the American Enterprise Institute and the Brookings Institution) focused on how to respond to worsening inequality. Unfortunately, they did not leave us confident about either the completeness of their policy solutions, or, more importantly, that even if they were correct they could be effectively implemented in today’s political environment.


Forecast Methodology

The focus of our monthly macro forecast is twofold. First, the probability of a change in financial market regime that causes changes of 20% or more in asset class valuations over the next year. Second, contingent on such a change taking place, the probability of a subsequent transition to other regimes.

Our analysis focuses on four possible macro regimes: (1) Normal Times, where equity asset classes perform well; (2) a High Uncertainty regime that is usually short and transitory, where asset classes like short-term government bonds perform best; (3) High Inflation, where commercial property, real return bonds and other traditional hedges are favored; and (4) Persistent Deflation, which up to now has only been seen in Japan, and in which the relative performance of different asset classes remains most uncertain.

Our forecasting methodology is derived from our experience on the Good Judgment Project, as described in the book, “Superforecasting” by Gardner and Tetlock, as well as a range of other sources, from the intelligence community to systems dynamics and complex adaptive systems to statistics and political economy.

We start with base rate/reference case data about the historical probability of large changes in equity and bond valuations. We then analyze the current situation from both a quantitative and qualitative perspective. In the latter, we focus on the key endogenous drivers of macro regime change, including technological, economic, national security, social, and political trends and uncertainties. We also focus on three potential sources of exogenous shocks that could also produce a macro regime change, caused by environmental, disease, and cyber related events.

While most of our attention typically focuses on various flows (e.g., economic growth, change in the price level, sales, earnings, job creation, etc.), endogenously caused regime changes result when those flows push key stocks beyond a critical threshold or tipping point, often setting off non-linear reactions across multiple areas. As noted by Hyman Minsky and others, a classic example is the steady accumulation of outstanding debt until it reaches the point where it can no longer be serviced and triggers a crisis.

Base Rate Data

Since the end of World War Two, there have been fifteen months where a downturn in the US equity market began that eventually reduced asset class value by 20% of more. That is a hazard rate of about 1.75% per month. Put differently, in any given month there is a 98.25% probability that a 20%+ downturn won’t occur, or, in a given year, an 81% probability.

However, as the time without a 20%+ downturn extends, the compound probability that one will not occur shrinks. At the end of August 2018, it is more than nine years since the last equity market decline of 20% or more. The probability of that happening is only 15%.

To estimate the base rate for a 20% fall in bond prices (which historically has been caused by a sharp increase in inflation, as we saw in the late 1970s and early 1980s), we analyzed monthly historical AAA bond yields since 1919. For consistency, we used them to calculate the price of a ten-year zero coupon bond. We then calculated the probability of a price decline of 20% or more over three different holding periods: 12, 18, and 24 months. In any month, the annualized probability of a decline of 20% or more over the subsequent 12 months is 12%; over 18 months, 20%, and over 24 months, 25%.

The Current State of Quantitative Regime Predictors

Our quantitative 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.

As you can see in the following table, at the end of November 2018, this analysis indicates that, over the next 12 months, the balance of expectations was split between continuation of the High Uncertainty Regime and a transition to the Persistent Deflation Regime.

Qualitative 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.

Significant New Information in Observed in November 2018

In our methodology, new information is valuable insofar as it provides an updated indicator of how close we are to a critical threshold, or it is surprising and causes us to question the structure of our overall system model (e.g., the existence of another critical threshold we should monitor, or the range of possible outcomes for a key uncertainty).

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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 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. We believe that the system is currently in its most uncertain state, characterized by high degrees of underlying disorder and social conflict.

Our current forecast question is this: what is the probability we will either remain in the High Uncertainty Regime or transition to another regime over the next twelve months?

We currently estimate there is a 35% chance of remaining in the High Uncertainty Regime over the next 12 months, which will see the commencement of what promises to be a tumultuous US presidential campaign, and perhaps resolution of the Brexit saga (or at least the “end of the beginning”, and possibly the “beginning of the end”).

We also conclude that over the next 12 months, the probability of returning to the Normal Regime is slight, at 10%, as is the probability of entering the High Inflation regime over the next 12 months, at only 5%.

We estimate that the probability of entering the Persistent Deflation Regime over the next 12 months is now 50%. With the waning in the US of the stimulative effect of the Trump tax cuts, increasing uncertainty will have a more strongly negative impact on aggregate demand. This will be magnified by the substantial amount of debt that has been taken on by many companies, which more of them will likely struggle to service, which in turn will put more pressure on their cost structures, and potentially lead to increased unemployment, which will accelerate the downward spiral.

While we expect the High Uncertainty Regime will produce declines of 20% or more in equity asset classes, it is unlikely that any other asset class will experience a gain of 20% or more. We estimate there is a roughly even chance that gold could be the exception, with that increase heavily tied to continued global confidence in the US government and economy. While the apparent inflation and political uncertainty premia in the gold price today are high relative to the last 25 years, they are still below the peak reached in 2012, and it is possible that herding in the face of increasing uncertainty could produce 20% price gains.

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 the high uncertainty regime has given way to a return to the Normal Regime, rather than to the Persistent Deflation regime.

How did this happen? What didn’t we anticipate happening?

(1) In previous issues, we have conjectured that perhaps because of an intensifying domestic debt crisis (and its own fear of Japanese-style deflation), or a belief that it had not yet achieved sufficient advantages to pursue more intense conflict with the United States, China could reach a new trade agreement with the US and EU to support continued economic growth. This would reverse (at least in the short-term) the growing tension in the US/China relationship, providing a strong confidence boost to the world economy and financial markets. In light of Novembers events (which we cover in this issue), we now regard the probability of this pre-mortem scenario as quite low.

(2) However we should not yet dismiss Donald Trump’s replacement by Mike Pence as a viable pre-mortem scenario that could cause our forecasts to be wrong. That said, upending our forecast would still require a Pence administration to make significant progress on a number of critical policy fronts, including relations with China, the productivity of the US healthcare and education systems, increasing real median household income, and reducing inequality.

Note: Combining this Forecast with Others and Extremizing the Result Should Increase 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: The Fed Calls Time on the United States’ Hidden Debt Crisis: Public Sector Pensions

Normally, the Federal Reserve’s quarterly “Z.1” release of the National Accounts of the United States is not a cause of much excitement, let alone angst. But this year's 3rd Quarter release different, because the Fed finally added its very considerable weight to a critical argument over the true size of America’s unfunded public sector defined benefit plan liabilities.

As one who has been involved with the public pension issue for almost twenty years, I cannot overemphasize the importance of what the Fed has done, even if its consequences remain uncertain.

To be sure, recent years have seen a rising chorus of concern over the potential understatement of US public sector pension liabilities, by, among others, Robert Novy-Marx and Joshua Raugh, Jeffrey Brown and David Wilcox, Andrew Biggs, Steve Malanga, David Crane, Chad Aldeman, the Pew Charitable Trusts, and the credit rating agencies. Yet America’s growing public sector pension crisis has mostly remained in the “grey swan” category, or a recognized and growing risk, with a potentially substantial negative impact, whose consequences are believed to be so far in the future that they have yet to trigger or force significant action in the present.

So what did the Fed do? Technically, it began to report unfunded public sector pension liabilities, as calculated by the US Bureau of Economic Analysis using a new methodology that makes two important changes from the previous one.

First, liabilities are now calculated on a projected basis, rather than simply those that have been accrued to date by current and retired employees. In a world in which salaries and benefit promises continue to grow, and longevity increase, this methodology presents a more accurate estimate of the size of a pension plan’s liabilities.

Second, these future liabilities are discounted to their present value using the yield on AAA rated corporate bonds (about 4.10% at the end of November). This is significantly lower than the discount rate used by most state and local pension plan sponsors.

The Government Accounting Standards Board (GASB) allows these plans to (a) use accrued by not projected liabilities, and (b) discount them at a rate equal to the expected long-term investment return on the assets they hold. Use of this discount rate has been a source of increasing controversy in recent years.

Under the ERISA law, which applies to private but not public sector defined benefit pension plans, the former must discount their future liabilities using the yield on “High Quality Corporte Bonds” (which are further defined as those rated AAA, AA, A by Standard and Poor’s, or the equivalent by Moody’s, Fitch, and other rating agencies). The logic for using this rate is that the risk of default on a private company’s defined benefit pension obligations is equivalent to the risk of default on its bonds. Note that this logic is completely separate from the investment return the plan earns on its assets.

ERISA does not apply to public sector pension plans. Doing so, however, would make the essence of the grey swan issue crystal clear. According to the National Association of State Retirement Plan Administrators, the median public sector defined benefit pension plan currently uses a discount rate of 7.375%. To put this in perspective, at the end of November, the average yield on Single B rated corporate bonds was 7.48%. Using ERISA’s logic, which accords with financial economics theory, public plans’ use of this high discount rate implies that their beneficiaries are exposed to significant default risk (or, put differently, the risk of having their retirement benefits cut, as frequently happens when bankrupt private sector pension plans are transferred to the Pension Benefit Guarantee Corporation).

But this does not align with what public sector plan beneficiaries are told, or the way courts have behaved in the case of recent municipal bankruptcies, where pension obligations have been treated as senior to other forms of debt (including general obligation bonds, which heretofore were thought to be the most senior of all unsecured municipal debt issues). Seven states go so far as to protect public sector pension benefits in their constitutions.

If this is in fact the case, then it makes no sense to discount public sector pension plan liabilities at an unrealistically high 7.375% rate. Rather, they should be discounted at a rate that, at minimum, reflects the true probability of default by the plans’ municipal sponsors. For example, the average S&P credit rating on state government general obligation bonds is AA. At the end of November, the taxable equivalent yield on AA rated municipal bonds is 5.00% (using a 35% marginal tax rate). Yet as we have seen, courts have found that pension obligations are senior to general obligation bonds; hence, the Fed’s use of the lower AAA corporate bond yield is logical. In fact, if one were to claim that no plan default was possible, then the appropriate discount rate would be that on US Treasuries (e.g., the current 3.14% yield on 30 year Treasury bonds).

The initial result of the BEA and Fed’s changes was a very substantial increase in the present value of state and local defined benefits pension fund liabilities to approximately $8 trillion dollars, and a substantial increase in the funding shortfall (estimated liabilities less the current value of pension plan assets) to $4.2 trillion. This is significantly greater than the total value of state and local governments’ outstanding debt, which the Fed places at $3.1 trillion.

To put this into perspective, compare the total liabilities of state and local governments ($7.3 trillion) with others shown in the Fed’s 2018/third quarter report

• Federal Government Debt = $17.8 trillion

• Residential Mortgage Debt = $10.3 trillion

• Non-Financial Corporate Debt = $9.6 trillion

• Student Loan Debt = $1.6 trillion

While the initial impact of the Fed’s was shock, the longer term effects will likely be much more severe.

The first impact may come in disclosure statements made in collection with state and municipal bond issues. Now that the Fed has put a stake in the ground about their true size, continuing to use the GASB methodology to calculate unfunded public pension fund liabilities will almost certainly increase the risk of future litigation, against issuers, bond underwriters, and disclosure counsel. Once disclosed, however, the size of unfunded pension liabilities may end up reducing some issuers’ access to municipal bond markets.

The second impact will come in state and municipal pension plan annual reports, where the same issue will arise. Disclosure of substantially larger unfunded liabilities will inevitably lead to political concerns as to how they should be addressed.

The third impact will come at budget time, specifically in the calculation of sponsors’ “Annual Required Contribution” (ARC) to their public pension plans. If the Fed’s higher estimate of the unfunded liability is used, then ARCs will be significantly higher. In the absence of benefit cuts, this leaves state and local politicians with a no-win political choice between large cuts in other spending programs, or large tax increases. When some politicians inevitably try to make it, the case for the latter will likely be weakened by the presentation of data showing how few public sector employees actually stay on long enough to receive full benefits. Moreover, because of long vesting periods for new employees, many public sector pension plans serve to transfer potential benefits from employees who leave relatively early, to those who hang on until they qualify for full benefits. “Shining the light of day” on these aspects of public sector pension plan operations does not seem likely to boost political support for them.

Which is not to say that such support will not be offered by (usually Democratic) politicians who are heavily dependent on public sector unions for their reelection. Thus the fourth impact is likely to be heightened political conflict in many states, perhaps accompanied by calls for a federal bailout of state and municipal pension plans, which would convert state conflicts into a national one, in which taxpayers across the nation would be asked to bailout public pensions in the states which have been most irresponsible in their management. These include (with the Fed’s funding ratios in parentheses):

• Illinois (25%)

• New Jersey (30%)

• Kentucky (32%)

• Massachusetts (33%)

• Connecticut (34%)

• South Carolina (36%)

• Pennsylvania (37%)

Moreover, this inevitable national debate over state and local pension bailouts will come at the very time that the federal budget itself will likely be under severe pressure from a combination of structural forces (e.g., an ageing population, increased conflict with China, higher interest payments on growing debt, etc.), and cyclical ones (e.g., automatic stabilizer payments, like social safety net benefits, during an extended economic downturn).

This brings us to the final impact, which was well described by Rob Arnott and Lisa Meulbroek in their Wall Street Journal article, “The Stealth Pension Mortgage on Your House” (5Aug18). The essence of their argument is that when it comes to raising taxes to fund public sector pensions, the least mobile tax base is real estate, as taxpayers can find various ways (including moving) to shift earnings and purchases to other states, and thus reduce local income and sales tax revenue. In fact, as others have argued, in some locations a substantial portion of recent tax increases is already being used to fund higher pension contributions (e.g., see, “Pensions Make Illinois Property Taxes Among the Nation’s Most Painful”, by Divounguy, Hill, and Tabor).

Moreover, the size of this “stealth pension mortgage” is non-trivial. As they authors note, “on average nationwide, unfunded state and local pension burdens represent 20% of real estate values. This often rivals or exceeds owners’ equity in their homes…If real estate prices eventually adjust to reflect unfunded pension obligations, many homeowners’ equity could be at risk.”

Last but certainly not least is the predictable response by some public pension advocates when confronted with the above arguments. “There’s not need to worry. High investment returns will solve the problem.” In fact, as AEI’s Andrew Biggs recently pointed out, because of reductions in the future rate of inflation they expect, in real terms the future returns public pension funds expect to earn are at an all time high (“Public Sector Pensions Assume Record High Investment Returns” published by AEI).

The key question is where they expect these returns to come from. Real interest rates are at near-record lows. Most equity markets are substantially overvalued. And if, as we currently forecast, the global economy is poised to enter the Persistent Deflation Regime, high real equity returns are unlikely.

To be sure, many public pension funds have, since the 2008 financial crisis, shifted a substantial portion of their portfolios into private equity, hedge funds, and other strategies in the hope of earning higher returns. Unfortunately, there is abundant evidence that in most cases these hopes are not being realized, even before the high expenses associated with these allocations are taken into account.

In sum, higher investment returns will almost certainly not solve the dire public pension funding situation the Fed has now brought into painful and unavoidable focus. The grey swan has finally arrived.

If you have any questions about anything we have written in this issue, please don’t hesitate to get in touch, at contact@indexinvestor.com