The CERF Blog

Today’s BLS jobs-report indicates the economy added 243 thousand jobs in January, which was about 90 thousand jobs above the consensus forecast of 155 thousand. Our forecast was 150,000. This gain was accompanied by a fall in the unemployment rate from 8.5 percent in December to 8.3 percent in January. The job-gains were pretty broad where the only sectors down were technology, financial, and government.

The jobs report was a good one in many respects, however, the long-term unemployment level remained at the same level as in January at 5.5 million persons. This is a very large number and it is scary for those who have been without a job for a long time as research shows that it becomes harder and harder for the long-term unemployed to find jobs.

This was a sizable job-gain for a labor market that has been relatively weak thus far in the recovery from the Great Recession. The natural question now is: is this gain sustainable? We have seen 5 months of job-gains in excess of 100 thousand jobs per month and 2 months over 200 thousand. Is this enough data to make a new and stronger trend of job growth?

Or, is this strength, ever-so-welcome, temporary in the face of too many over-riding fundamental economic weaknesses? I remind the reader that during spring 2011 we had three months of greater-than-200 thousand job growth months starting in February that was followed by anemic job-growth from May through August.

At this point, I suspect that the strength is temporary. This is due to a large number of factors. Europe is still not out of the woods, Asia is still looking weaker rather than stronger, U.S. real estate is still weak, household-sector wealth is still down, household-sector debt is still high, personal bankruptcies are still high, and banking is still weak.

In this case, I would not mind being wrong.

The US savings rate is really low and has been for some time.  For many years this was explained by the argument that people were getting wealthy through appreciation of their homes and stock portfolios, so the need for saving out of current income was low.  And sure enough, household net worth did expand nicely through the decade up to 2007 even with the low savings rate.  However, that story was shattered with the housing bust that began in 2007.  Yet, while savings rates have picked up a bit since 2007, they still are very low both historically and relative to other countries.

Many observers are content with low savings today.  Sure, they say, people have to eventually save more, but today there is a shortfall in aggregate demand so the last thing we need is expanded savings today.  This is short-sighted.  It would be desirable for savings rate to go up a lot, and the sooner the better. 

In order to preserve real consumption in retirement, what savings rate is required during the working years?  Well, the answer depends on several factors, the most important of which is the after-tax real return on your portfolio. Suppose your income at age 40 is $50,000, you expect your real income to be stable for the next 25 years until you retire and you would like to maintain real consumption stable through the remainder of your working years and retirement.  Further, your current net worth is zero and you have no retirement plan aside from social security.  In that case, if the real rate of return is 6% (which is a bit below the long-term real equity return) then the savings rate must be 9% to achieve a stable consumption path.  On the other hand, if the real rate of return is 2% (which is close to what many experts are currently projecting for balanced portfolios) then the savings rate must be 19%.

Although there are no doubt some households saving at this rate, it is not the norm.  Is this a huge problem for baby boomer retirement?  Well, maybe not.  The analysis above is very simple and neglects a number of important issues including positive initial wealth (aside from social security) and the potential for lower expenses after retirement.   For example, if the $50,000 wage earner mentioned above had initial net worth of $100,000, then the savings rate required to maintain real consumption drops to 12% assuming real returns of 2% per year, and drops to zero assuming real returns of 6% per year.

In its latest annual report on savings behavior, Vanguard¹ estimates that only 30% of households are saving adequately for retirement and that most households need to save between 12 and 15% of annual income in order to avoid a major reduction in their living standards in retirement.  This required savings rate is lower for lower income households, due to the fact that social security replaces a greater proportion of income for a lower income person.   Conversely, higher income people should be saving a greater portion of their income if they are going to maintain the same standard of living in retirement as during the working years.

While it might be expected that savings recommendations from a large mutual fund management company would be biased upward, the Vanguard estimates seem reasonable to me.   Again, the key is the long-term real rate of return on capital (i.e., the real after-tax investment return). 

As mentioned above, the historical long-term real equity return is in excess of 6%.  But, it has been widely documented that the typical household has achieved a much lower real return on investment.  This is partly due to portfolio allocation into lower return asset classes like bonds and cash.  In addition, management fees and transaction costs reduce the net return.  Finally, poor timing and excessive trading further reduce the net return.  While it is feasible to constrain expenses and resist over-trading, evidence suggests that it is prudent to assume a modest real return, like around 2% per year.

To determine the most appropriate savings and consumption behavior for a particular household requires a lot more information than is being considered in my simple calculations, and probably in the Vanguard model as well.  You should consult with an advisor to obtain access to a sophisticated planning model.  Still, without doing the detailed calculations, I believe that most people will find that they are going to have to increase their savings rate a lot, or work longer than “normal retirement” or adjust to lower consumption in retirement. 

A Caveat

Many distinguished economists have published articles that contest my conclusion.  For example, economist Laurence Kotlikoff² argues that simple estimates of required savings rates are often over-stated due to methodological flaws in the calculations. In particular, he points out that optimal consumption smoothing is generally achieved by varying savings rates over time and that calculating the optimal savings rate requires the use of a complex mathematical model.  Kotlikoff argues that many households are saving too much and buying too much life insurance.  They are living like misers today so that they can live like kings at age 80.  In order to evaluate your own situation, you are encouraged to buy Kotlikoff’s online financial planning software.  This software (ESPlanner) is based on the latest economic theory and can be used to trace out the optimal savings rate path for you.

It is not surprising to me that an efficient algorithm would produce savings rates that are on average lower than those calculated according to financial planning rules of thumb.  However, I don’t believe most households are currently following savings profiles that are consistent with typical financial planning rules of thumb.  Instead, they are systematically under-saving relative to such rules.  Application of an efficient algorithm would likely reduce the degree of under-saving, but I don’t think it would change the sign.  Bottom line:  do the calculation but don’t be shocked if you find you are not saving enough.

¹“How America Saves 2011,” Vanguard, 2011.

²Laurence Kotlikoff, “Is Conventional Financial Planning Good for Your Financial Health?,” Economic Security Planning, 2006.

Louisiana Congressman Charles Boustany has introduced a bill banning withdrawals of welfare funds from ATMs at liquor stores, casinos, and strip clubs. Politicians do this sort of thing every now and then. It wasn’t that long ago that California’s legislature, in its own spasm of self-righteous Puritanism, was imposing its own, very similar, controls.
This is a mistake.

Welfare is provided to help maintain a minimum lifestyle for some of our very-low-income households. In the 21st Century America, a minimum lifestyle includes far more than basics to sustain life. It includes cell phones, computers, televisions, and even entertainment.

Apparently, Mr. Boustany objects to some welfare recipients’ choice of entertainment, or he objects to welfare recipients having any entertainment. The latter is ridiculous. People need occasional entertainment.

It is also ridiculous to attempt to control what type of entertainment welfare recipients enjoy. For one thing, the constraint is easily avoided. If you can’t use the ATM at the liquor store, you can always use the one at the gas station next door.

Even if the constraint wasn’t easily avoided, on what basis can we tell welfare recipients that they can spend three to four dollars for a coffee at Starbucks but not the same on a beer at the neighborhood bar? Why is Disneyland OK, but not the casino down the freeway? Where do we stop on this path?

The fact is, our country has a bunch of real problems for Congress to work one. They need to stop wasting time on stuff like this.

This morning’s much anticipated fourth quarter GDP release provides a preliminary estimate of real GDP growth of 2.8 percent. To be fair, perhaps the anticipation is experienced mostly by forecasting economists and financial market watchers. I am always particularly interested in fourth quarter as it closes out the year and in this case I forecasted an increase in growth of 2.2 percent, up from third quarter’s 1.8 percent growth.

The estimate is higher than my forecast by a fair amount actually, but in the grand scheme of forecasting, forecast errors, and the direction of change, I am reasonably happy. I had forecasted the increase in growth with trepidation because the economic fundamentals remain weak.

The fourth quarter data implies that the economy grew 1.7 percent in 2011, compared with 3.0 percent in 2010.

What were the drivers of the increase in fourth quarter growth? Consumption and Investment expenditures both rose, $50b and $80b respectively, trade was little changed, and government expenditures fell about $30b.

Investment expenditures are driven by a four main components, business structures, equipment and software, residential, and inventory investment. All of these components are volatile, but one of them, inventory expenditures, is super volatile. Sure enough, about $55b of the $80b investment expenditure increase was due to inventory investment. I hope that the shelf-stocking was not overdone for if it was, there would be a slowdown in inventory investment this quarter.

Another interesting movement within Investment was residential, up at an annualized growth rate of about 11 percent. While residential investment in states like Nevada, California, Florida remain at historic lows, it is booming in states like North Dakota, Oklahoma, and Texas. We can thank the middle part of the country for this source of growth.

The $30b pullback in government expenditure breaks down to a $20b decline in Federal and a $10b decline in State/local expenditures. The Federal change was due to a defense spending contraction, as non-defense expenditures rose slightly.

Inflation, as measured by the GDP deflator, fell dramatically from 2.6 percent in third quarter to 0.4 percent in fourth quarter. Subdued inflation in a time of relatively high unemployment is a good thing, as it helps those unemployment or partially-unemployed households manage expenses.

The BEA measure of the personal savings rate fell from 3.9 percent in third quarter to 3.7 percent in fourth quarter. This worries me, as household debt levels are still high. I have argued this before and will do it again: consumption in an era of high household debt does not help the economy. What is needed is savings and investment. Future growth depends on it.

Last September, I proposed (“The Warren Buffett Tax”) that the most effective way to extract a lot of tax revenues from Warren Buffett would be to impose a wealth tax.  My tongue-in-cheek proposal was this:  Tally up your assets and liabilities, calculate the difference (net worth) and then pay a tax of 20% on net worth above a threshold of $20 million.  I called this the “20 after 20” plan.  Based on official estimates of household wealth, the proportion of households that would be affected by this tax is very small, only about 100,000 households or .1% of the population.  But this sub-population has approximately $9 trillion or 15% of entire household net worth.  If it could be collected, then this tax would generate huge tax proceeds, completely wiping out the federal deficit this year.  My calculation was this:  tax base of $7 trillion ($9 trillion total net worth less the threshold amount of $20 million for each of the 100,000 households that would be affected) times tax rate of 20% yields $1.4 trillion in revenues.  No pain on 99.9% of households and the budget is balanced!  That is, it is balanced for this year.  In future years even the theoretical proceeds of this tax would decline.

In a Wall Street Journal editorial¹ on Monday, January 8, Stanford Professor Ronald McKinnon proposes a similar idea.  His plan is to place a tax rate of 3% on all wealth in excess of $2 million (the “3 after 2” plan).  The McKinnon plan would affect more households (approximately 2 million, or 2%) than my plan, but the tax rate would be a lot lower.  Potential revenues from the McKinnon plan are smaller than the 20 after 20 plan, but still are pretty substantial.  The top 2% of households have about $30 trillion in net worth.  After netting the threshold of $2 million per household, this leaves a tax base of $26 trillion, 3% of which is $780 billion.

The rationale for the professor’s plan is that Republican plans to flatten the income tax structure will always be vulnerable to the charge of “tax cuts for the rich.”  By combining a flatter income tax structure with the addition of a wealth tax, this charge would be muted.

Even though I am pleased that such a leading economist as Professor McKinnon has followed in my footsteps, I would like to make one point clear:  I was only kidding!

While it is true that a wealth tax would probably find a positive reception from the Occupy Wall Street crowd, it is really not such a good idea. There are huge problems in measurement, compliance and incentives.

Measurement Issues

What is wealth and how can we measure it?  Wealth is the value of assets minus the value of liabilities.  It is easy to measure the market value of some assets, like publicly traded securities or insured bank deposits.  But it is not so easy for other assets, like non-traded securities, non-public companies, real estate and art.  And then there is the problem of coverage.  Presumably retirement funds should be included, including the value of pension fund accounts and 401K plans.  If a wealthy person funds a charitable foundation, should that be included in his or her net wealth?  If not, that is a gaping loophole.  For example, since Warren Buffett has pledged the bulk of his wealth to the Bill and Melinda Gates Foundation, if charitable foundations are not subject to tax that even the Buffett Tax won’t hit Mr. Buffett.

Compliance Issues

Even if the measurement issues could be adequately addressed, the compliance cost of implementing the tax could be enormous.  Some means of tracking or validating the values of tens of millions of homes and small businesses would have to be developed and maintained. 

Incentive Issues

There is debate about the incentive effects of a wealth tax.  One argument is that a wealth tax would encourage rich people to move out of low yielding assets like cash and take greater investment risk.  Another is that such a tax would encourage capital flight out of the U.S. as entrepreneurs moved their businesses to more tax favored countries. 

International Experience

Many countries currently have wealth taxes including France, Switzerland, Norway, The Netherlands and India.  The rates are generally about 1% on net worth above a modest threshold.  Lower rates lessen the disincentive effects, but not the measurement costs.  One study in the Netherlands concluded that the administrative cost relative to revenue raised was five times higher for the wealth tax than the income tax.  Perhaps this is one reason why many countries have eliminated the wealth tax.  The ranks of countries that formerly had a wealth tax and have subsequently eliminated it include Austria, Denmark, Finland, Germany, Iceland, Sweden and Spain.

A wealth tax is another tax on capital and likely would impair capital formation.  Capital formation is a key driver of improved productivity and economic growth.  This is where growth in real wages comes from.  If you really want the rich to pay more tax, a better direction is to create a highly progressive consumption tax where a threshold amount of consumption is not taxed at all (say, $30,000) but huge consumption expenditures are taxed at very high rates, like 100% or more.    

¹Ronald McKinnon, “The Conservative Case for a Wealth Tax,” The Wall Street Journal, January 9, 2012.

In his book Econoclasts¹, historian Brian Domitrovic produces what he believes is the first historically rigorous account of the development of supply-side economics (that is, one that relies on primary sources).  He reviews contributions to supply-side theory by Nobel Prize winning economists Robert Mundell and Robert Lucas, as well as significant contributions from outstanding economists that have not yet won a Nobel, like Martin Feldstein, Art Laffer and John Rutledge.

As early as the 1950s, Mundell proposed an economic policy program of tight monetary policy to stabilize the value of the currency and low tax rates to stimulate economic activity.  During the late 1960s and early 1970s, economic policy in the U.S. moved in the opposite direction.  Easy money allowed rising inflation that combined with a highly progressive and non-indexed tax rate structure to produce high and rising tax rates on income, savings and capital formation. 

Martin Feldstein documented negative effects on capital formation of high and progressive tax rates, especially in a period of high inflation.  Art Laffer sketched on a napkin the famous curve that shows tax rates can be so high that increases in tax rates reduce revenues, because the tax base falls.  Conversely, lowering tax rates from very high levels can increase revenues, if the increase in the tax base more than offsets the lower tax rate.  Even if that is not the case, the key insight is that the tax base, economic activity, will be greater under lower marginal tax rates.

These and other arguments created a strong demand for lower marginal tax rates.  Although resisted by President Jimmy Carter, capital gains taxes were lowered in 1978.  But, it is often argued that these ideas fully arrived in Washington with the election of Ronald Reagan in 1980 and arrival of the supply-side team in January 1981.  But this team did not consist only of supply-siders; also included were monetarists, government spending hawks and regulatory experts.

The Players (partial list)

Office of Management and the Budget (OMB)

       Director:  David Stockman (Government spending hawk)

       Chief Economist:  Larry Kudlow (Monetarist and Supply-Sider)

Treasury

       Secretary:  Don Regan (Wall Street CEO)

       Under Secretary for Monetary Affairs:  Beryl Sprinkel (Monetarist)

       Assistant Secretary for Tax Policy:  Norman Ture (Supply-Sider)

       Assistant Secretary for Economic Policy:  Paul Craig Roberts (Supply-Sider)

Council of Economic Advisors (CEA)

       Chairman:  Murray Weidenbaum (De-regulation expert)

Consultants and Advisors

       Art Laffer (Supply-Sider)

       Allan Meltzer (Monetarist)

       John Rutledge (Portfolio shift theorist and Supply-Sider)

Reagan’s economic forecast was pulled together by Larry Kudlow at OMB.  He hired a California economic forecasting outfit, the Claremont Economics Institute (CEI) under Chairman John Rutledge, to prepare the forecast.  During the fall of 1980, CEI’s private forecast featured the Reagan Scenario as the most likely case.  The Reagan Scenario included a sharp drop in inflation, a recession in 1982 due to tight monetary policy, passage of the Kemp Roth tax cuts and an economic boom starting in 1983.  Consistent with falling inflation, nominal interest rates were projected to fall over the forecast horizon (1981-1985).

Rosy Scenario

The official economic forecast of the Reagan Administration (released with the President’s proposed budget for fiscal 1982) had some of the flavor of CEI’s Reagan Scenario, but there were substantial changes due to input from economists at the CEA and Treasury. That is, the final forecast was the product of a committee.  The biggest change was to eliminate the projected recession for 1982 on the grounds that the tax cut would immediately induce a spike in investment and savings that would offset the drag from tight money.  CEI’s forecast of negative growth in 1982 was replaced by plus 5 percent!  The second major change was that the drop in inflation forecasted by CEI was moderated.  CEA Chairman Murray Weidenbaum is quoted in Econoclasts “Nobody is going to predict two percent inflation on my watch.  We’d be the laughingstock of the world.”

Well, in the event, inflation did come down much as the private CEI forecast called for.  By 1985, CPI inflation had fallen to 3% from 12% in 1980.  As people look back on Reaganomics today, they often focus on the budget deficit, which did increase dramatically during the early 1980s.  This was a third area of dispute in the forecast committee.  The CEI forecast called for a large deficit in 1982 due to the projected recession and the effects of lower tax rates.  Over time, as the economy recovered and government spending growth was constrained, the federal deficit was projected to decline.  The official forecast, however, did not show a large budget deficit, mostly due to the first change mentioned above – elimination of the CEI projected recession.

Yet, the CEI Reagan Scenario is pretty much what happened.  The deficit spiked in 1982 due to the sharp recession that began in the fourth quarter of 1981.  The recession ended in late 1982 and a very strong economy recovery began.  Growth averaged over 4.5% for the last six years of the Reagan presidency.  Strong growth did lower the deficit as a share of GDP, but lack of success in lowering federal government expenditures prevented the projected surplus from coming to pass.  Interestingly, Domitrovic blames the failure to curtail non-defense spending in part on the government view that inflation would fall more slowly than it did.  Caps on increases in government spending were left at 6-8% when they should have been lowered to 3-4%. 

The official economic forecast released by the Administration was labeled “Rosy Scenario” because the combination of falling inflation and a boom in real growth was taken by many to be excessively optimistic.  It is hard to know how the private CEI Reagan Scenario forecast would have been taken, with its even sharper (and yet more accurate) projected decline in inflation. 

Personal Experience

My very minor role in this history is that I was the econometrician (keeper of the forecasting model) at CEI back then, much like Dan Hamilton is the senior econometrician at CERF today.  In contrast to what you might have heard, there really was an econometric model at CEI, and it resided on my APPLE 2e computer.  While it is a little embarrassing to have to go back thirty years to find a forecast that was any good, the CEI Reagan Scenario turned out to be pretty much spot on.

Today, after a 25 year career as an executive in the financial services industry, I am now once again associated with an economic forecasting outfit, the Center for Economic Research and Forecasting (CERF) at CLU.  Over the past few years, the CERF forecast has been consistently more negative on economic growth than the consensus, and has turned out to be more accurate than the consensus.  While my contribution to this accuracy has been zero, it is nice to be associated with a group with a great track record. 

Optimal Policy Mix

If you listen to supply-side economists today, they seem to be arguing that the optimal policy mix is the same today as they felt it was in 1980; namely, tight money to stabilize the value of the dollar and lower tax rates to stimulate the economy.  Yet, economic conditions are quite different today.  Inflation and interest rates are much lower and tax rates are lower as well. 

While a high tax rate structure (relative to the rest of world) likely is one of the impediments to growth today, it seems to me that bigger problems are falling home prices, high debt levels and rising regulatory burdens.

The major aim of supply side policy is to establish conditions to increase the long-term rate of economic growth.  Today, that means establishing a credible plan for corralling the growth of government expenditures, rationalizing regulations and simplifying the tax code.   Also, it wouldn’t be a bad idea to liberalize legal immigration. 

¹Brian Domitrovic, Econoclasts, ISI Books, 2009.

In a terrific book published last year,  Antti Ilmanen¹ has investigated the drivers of investment returns from a number of angles including asset class (that is, equities, fixed income, commodities), investment strategy (value, growth, momentum, leverage, carry), and risk driver (exposure to economic growth, inflation, liquidity).  The basic idea is that you obtain strong investment returns either by taking exposure to sources of systematic risk or by taking advantage of mistakes of other investors.   He documents a variety of investment strategies that have earned significant positive risk premiums (return above the risk free return) over the recent past.  These strategies include the following

1.  Holding long equity positions

2.  Emphasizing value stocks

3.  Emphasizing small capitalization stocks

4.  Momentum (that is, riding the trend)

5.  Selling volatility (selling options)

6.  Carry (buying high yielding assets and shorting low yielding assets)

7.  Supplying liquidity to the market by holding illiquid assets

Why did these strategies work well in the past?

A key issue is whether the strategies reflect reward for taking on systematic risk or not.  If they do, then there is reason to suspect that future returns will be strong.  Unless, that is, the asset class or investment strategy becomes overcrowded. 

Here we have another potential application of Goodhart’s Law.  As indicated in previous blogs, Goodhart’s Law (named after economist Charles Goodhart) says that once an observed empirical relationship begins to be relied upon, it will no longer be reliable.   As investors learn about strategies that have worked well in the past, it is likely that some of them will attempt to copy the strategy and by doing so weaken the conditions that supported high historical returns. 

For example, equities have done very well in the past 100 years, with annual (arithmetic) returns of approximately 11%.  There are several partial explanations for this.  First, equities were really cheap 100 years ago.  Dividend yields were 5% (greater than corporate bond yields) compared to 2% today.  Price/earnings ratios were significantly lower than today.  The U.S. economy performed very well over this period, and we did not lose a major war nor incur a huge inflation.  Based on initial conditions alone, it is likely that equity returns over the next few decades will lag behind the historical record. 

Still, a fundamental principle of finance theory is that the overall stock market carries systematic risk and therefore the average stock investor will earn a risk premium in reward for taking on this risk.  For many years, financial theory maintained that the market return was the only source of systematic risk.  However, recently evidence has accumulated that support the idea of multiple risk factors.  In particular, small stocks and value stocks (as indicated, for example, by low ratios of price per share to book value per share) do better than large stocks or growth stocks.  One interpretation for this is that both small size and low price indicate substantial exposure to an economic downturn, and that the higher returns on these strategies reflect this greater exposure.  Similarly, the strategies of selling volatility (selling options), carry and holding illiquid assets each have a similar flavor – the investor earns insurance premiums for taking on a significant risk.

The one strategy that Ilmanen highlights that is not like selling insurance is momentum trading.  The momentum trader buys when the price is rising and sells when it is falling.  This is more like being long options than short options.  The rationale for the success of this strategy, according to Ilmanen, is that it takes advantage of cognitive errors of other investors, like the error of holding on too long to losing investments.

Will these strategies continue to work in the future?

Ilmanen describes three methods by which an investor can assess future returns.  First, extrapolate historical returns.  Second, utilize a model that assesses fair value.  Buy when the asset is cheap, sell when it is dear.  Finally, identify forward looking indicators of value, like price earnings multiples or yield curve slope.   Each of these methods may fall victim to Goodhart’s Law.

To see this, consider the case of hedge funds.  Several of the strategies listed above are deployed by hedge funds.  In recent years hedge funds have been able to report strong uncorrelated returns (positive “alpha”) using strategies like option selling, trend following and carry.  Yet, as the success of these strategies has become well known, more investors are attempting to achieve excess returns in the same ways.  This is likely to render the strategies less effective.  For example, option selling is a winning strategy so long as the volatility implied by option prices (“implied vol”) is greater than the actual volatility of the price of the underlying instrument.  But as more and more investors sell options, this is less likely to be true.

It would be useful to come up with indicators that would predict which historical rule is likely to work.  For example, by monitoring the spread between actual and implied option volatility investors can assess the prospects for option buying or selling strategies.  Of course, these “second level” indicators could become subject to the Law as well.  This is why it is not easy running a successful hedge fund.   The ability to consistently earn excess returns depends on having proprietary information or insights that are not, by definition, widely available.  Over time, Goodhart’s Law makes outperformance increasingly difficult.

Asset Allocation for Mom and Pop

The individual investor has to answer a few important questions.  First, what is his or her degree of risk aversion?   The answer to this question should determine his or her exposure to risky assets.   Second, what is the investor’s time horizon?  This will determine the selection of the risk free asset.  For those investors with long-term horizons, the relevant risk free asset is a long duration bond (ideally, a long duration bond that is indexed to inflation), not cash.  Third, does the investor have specialized information that would be useful in determining when any of the generic strategies highlighted by Ilmanen are likely to perform well.  Or, given his or her individual situation, is there a greater than average willingness to take on specific risks?  Absent affirmative answers to either of the last two questions, the individual investor should probably stick with passive index or exchange traded funds that attempt to replicate broad asset classes.

¹Antti Ilmanen, Expected Returns, Wiley, 2011.

In a previous article (December 10, Goodhart’s Law and Monetary Policy) we discussed Goodhart’s Law which says that once an observed empirical relationship begins to be relied upon, it will no longer work. In that article we applied the law to monetary policy.

Goodhart’s Law also applies to bank capital regulations.  The Basel Committee on Bank Supervision is an international body that recommends standards for minimum bank capital levels.  According to Basel I, bank assets are weighted according to their perceived riskiness, with cash and government bonds (sovereign debt) receiving a zero risk weight and business loans 100% risk weight.  By overweighting their balance sheets with low risk weight assets, banks can operate with very low equity capital.  For example, Deutche Bank AG (stock symbol DBK) has approximately $3 trillion in assets and equity capital of just over 2 percent of assets.  Yet, DBK’s regulatory capital ratio is reported (September 30, 2011)¹ at 13.8% because risk weighted assets are just 15 percent of total assets.  For the sake of DBK shareholders, one hopes that DBK’s low risk weighted asset calculation is not driven by large investments in sovereign debt of fellow EU countries like Greece, Italy, Portugal and Spain.

Closer to home, another unfortunate selection of risk weight was 20% for highly rated (AAA) mortgage backed securities.  Assuming an 8% Tier 1 capital requirement, this means that Tier 1 capital (mostly equity) must be just 1.6% (=.20*8%) of AAA mortgage securities.  This created an enormous demand for AAA tranches and spawned an explosion in financial innovation.  The basic objective was to produce as many AAA securities out of pools of non-agency loans (securities created from agency loans, those guaranteed by an agency of the U.S. government, were automatically AAA).  The method to accomplish this was subordination and credit tranching.  All losses in the pool of loans were directed to the owners of the equity or “first loss” tranche until that piece was wiped out.  Then, the losses were directed to the “mezzanine” tranches until they were wiped out.  Only then were credit losses applied to the “senior bonds.”  Rating agency models determined for each pool of loans the degree of subordination required so that the senior bonds would be awarded AAA ratings.  Typically, 80% or more of the principal value of a non-agency security would be awarded the AAA rating.  Investors viewed AAA rated bonds as essentially risk free, and there was huge demand for those bonds during the housing boom. 

Paradoxically, the effect of the huge demand for AAA bonds drove underwriting standards lower.  This would not have been disastrous had the ratings agency models taken account of changing underwriting standards and raised subordination levels accordingly.  But they did not.  Perhaps the problem was Goodhart’s Law.  Rising home prices during the boom reduced foreclosure rates and loan losses, even as standards were weakening.  If credit models are trained on historical data, they would tend to progressively generate more benign forecasts during the boom, even as the risk was rising.  As it turned out, the rating agency models underestimated the probability of a severe and widespread drop in housing prices.

Thus, capital regulations that were carefully designed to accurately measure and control risk taking wound up being used to accumulate ultra-risky positions which have failed or are in the process of failing.  This is Goodhart’s Law morphed into Murphy’s Law.

What are solutions to the challenges suggested by Goodhart’s Law?

One answer is simply to be careful about relying on historical relationships.  Not only can they stop working at any time, the likelihood of failure increases as they become more popular.  So, maybe the answer is to only rely on historical relationships that are not generally known.  This means you will have to keep coming up with new ones. 

Another solution is to take great care in developing metrics and setting objectives.  If you can focus on the real objective instead of a proxy, then your reliance on the assumed relationship between target and proxy is lessened. 

A better method of assigning capital in accordance with risk levels is to explicitly measure expected and unexpected losses.  And the best method for doing this is stress testing.  More on this in another blog.

¹Simon Johnson, “Deutsche Bank Could Transfer Contagion,” Bloomberg News, 2011.

California has now had three consecutive months of job gains, and the State’s unemployment rate has been declining, albeit slowly.  That’s an improvement, but it’s not time to break out the bubbly.

For one thing, those job gains have been pretty darn small, and they haven’t been enough to drive down the unemployment rate.  Outmigration and a shrinking labor force are the reasons that California’s unemployment rate is declining.

We’ve also seen this picture before.  Almost a year ago, we commenced five months of increasing jobs, and stronger growth than we are currently seeing.  Then, the growth stopped.  We saw three out of five months with declining jobs.

This is what data looks like when an economy is bouncing along the bottom.  Long sustained periods of positive data, or negative data for that matter, just don’t happen.  We get some good data, sometimes very good.  Then some bad data comes along.  The key is not to let the good data get you too excited, nor do you want to let the bad data depress you too much.

Absent a new recession, caused by, say, the collapse of the Eurozone or an oil supply interruption, California is in for a long slow recovery.

This reality is reflected in demographic data.  Just last week, the Los Angeles Times had an article with the headline “Proportion of California’s transplant population reaches 100-year low.”  People go where the opportunity is, and unfortunately, it isn’t in California these days.

Forecasting is always difficult. It is even more difficult when the data keep changing. This year, we’ve been plagued by very large adjustments to GDP data. Most have been downward adjustments, but a few have been upward adjustments.

Productivity has been the source of most of the changes. Jobs data get revised too, but we haven’t seen revisions near the size as we’ve seen for GDP, and GDP growth is the sum of employment growth and productivity growth.

Recently, the initial estimate for 2011’s third-quarter GDP growth was revised downward from a 2.5 percent annual growth rate to only a 2.0 percent annual growth rate.

Still, even a 2.0 percent growth rate represents a nice pickup from the extraordinarily weak first two quarters. Unfortunately, much of that improvement came in the form of productivity growth rather than job growth.

It confirms our judgment last summer, when we expected the Country to avoid the second dip so many forecasters expected after the August data revisions to the first two quarters’ GDP data.

That doesn’t mean we’re out of the woods yet. The probability of one of both of two very serious events that we’ve been warning about for months seems to be increasing daily.

A significant interruption in oil supply from the Middle East would have catastrophic impacts on Western economies. The probability of such an interruption is becoming alarmingly high, in our estimation.  A week or so ago, there were headlines that a natural gas line in Egypt was sabotaged, the Kuwaiti government has collapsed, and Syrian atrocities are continuing, perhaps increasing. The likelihood of an oil-supply interruption is high, and the economic impacts of an interruption are very serious. Economic recession will affect all developed economies.

The other risk is a financial crisis associated with the breakup of the Eurozone. While the markets are giddy today with the prospect of yet more Eurozone bailouts, the bailouts are only bandages.

Fundamentally, the Eurozone is a contradiction that cannot be sustained. Some countries will have to leave it. When they do, there will be losses. Financial institutions and governments will face stresses not seen since September 2008. The resulting recession will be serious and widespread.