OUR PERSPECTIVE ON ISSUES AFFECTING GLOBAL FINANCIAL MARKETS

1Pg

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Pg12TEXTING TWENTY-FIRST CENTURY TRADE DATA: A SOCRATIC DIALOGUEHow the wonderful world of modern trade and production distorts statistical reality or what the iPhone tells us about the trade deficit.

5THE FALLACY OF PROBABILITY APPLIED TO SOCIETAL EVENTSWhy the probability of a eurozone breakup is not a roll of the dice. Proceed with caution when predicting societal events.

14

THE GREAT RESTRUCTURING AND THE CASE FOR US FIXED INCOMEThe US economy faces a Great Restructuring. There is no reason to think that the restructuring will be rapid nor is there reason to think it will be permanent. Invest accordingly.

GROSS DOMESTIC PRODUCT (GDP): WHAT’S IT GOOD FOR?The GDP also includes cigarettes, air pollution and broken windows repaired. So is it still a useful gauge of our well-being?

VIEWFIRST QUARTER 2012

When charged with the task of prediction, those

asked often respond with probability. Lately, questions

surrounding Europe abound. But notice, these questions

tend to ask: what is the probability of a euro zone

break-up? What is the probability that Greece defaults?

It would be nice to know. And indeed, many make a

living constructing elaborate, mathematically sound,

deliberately impressive models to engineer guesses

about the future. But the fact remains, even the most

ostentatious and comprehensive model cannot, “render

the future predictable,” and further, can give no sure

insight into the outcome of “human acts of choice.”1

Questions about predictions are, at their heart,

questions about probability. In fact, all predictions and

opinions regarding the future deal with probability,

even if the operative probabilistic assumptions are not

stated explicitly. To quote Ian Hacking: “By covering

opinion [our assumptions] with a veneer of objectivity,

we replace judgment by computation.”2 Especially when

dealing with financial markets, the ability to sustain

a “veneer of objectivity,” the ability to pretend as if

the future is always more predictable as a result of an

objective model, can mean success or failure.

Prediction and its danger: the Plight of the fortune tellers3

Prediction is a tough business, and no matter the

intelligence of the person or model used, we simply

cannot know the future with a final degree of certitude.

Predictions (especially if contrived by advanced models)

are valuable, but blind faith in their mechanics,

especially as they are often opaque to all but academics,

is not prudent. Indeed we need only survey the past few

years to see that even the most assiduous and capable

thinkers, working with equally formidable models, were

unable to predict with useful accuracy the trajectory of

the Great Recession.

For example, the recent release of the transcripts from the

2006 Federal Open Market Committee (FOMC) meetings

evidences how probability underpins prediction. Not

that the FOMC members were responsible, but as the

transcripts show, they were exceptionally oblivious to

the trouble ahead. Timothy Geithner, then the President

of the Federal Reserve Bank of New York, opined: “We

think the fundamentals of the expansion going forward

still look good.”4 As we all know now, the fundamentals

did not look good. Despite the density of intelligence at

the FOMC, it could not predict the defining economic

event of a generation.

What is more, Geithner’s statement veils underlying

probabilistic assumptions. First, he assumes that

the “fundamentals” to which he refers have a high

probability of impacting progress. This assumption leads

to a prediction about the future, namely that “[things]

going forward still look good.” In making a prediction

about the future, Geithner also assumes the data he

reviewed in the present to have a high probability of

holding constant (or relatively constant) over time.

Hence, even seemingly routine predictions depend

wholly on assumptions regarding probability, even if

these assumptions never meet the light of day.

At other times, though, predictions depend explicitly on

the mathematical calculation of probability. For example,

when economists build models to make predictions about

future growth, the models operate on a foundation

of probability theory.5 It follows that no matter how

ingenious, complicated, or impressive any economic

model, it fundamentally expresses probabilities.

1

BY COVERING OPINION [OUR ASSUMPTIONS] WITH A VENEER OF OBjECTIVITY, WE REPLACE jUDGMENT BY COMPUTATION

The Fallacy of ProbabilityApplied to Societal Events

A look at some of the literature surrounding President

Barack Obama’s 2009 stimulus package shows that not

only do the smartest models rest on mere probability,

but also that these probabilities can be quite wrong.

At its 2009 implementation, authors of the American

Recovery and Reinvestment Act, Christina Romer and

Jared Bernstein, projected 2012 unemployment with the

stimulus package to be under 7%.6 Running complicated,

comprehensive models, the authors came up with this

unemployment prediction.

With the unemployment rate at 8.3% today, it is easy

to look back with hindsight to declare these numbers

wrong. That is not the point. The point is rather that the

roughly $787 billion package was supposed to achieve

the projected unemployment; the money was deployed

under the assumption that these models were close to

accurate. And given that the accuracy of the models

depended on the probabilities that they estimated,

the efficacy of the stimulus plan relied explicitly on the

probabilistic assumptions in the model.

If this is the case, further exploration into probability

sheds light on the problems with, and danger in, placing

too much faith in modeled

predictions. What do we

even mean when we talk

about probability? A quick

review of the three principle

types of probability gives

more insight the meaning

of the oft-invoked word.

Kinds of Probability

Generally speaking there

are three basic perspectives

on probability: the

empirical (or frequentist),

the axiomatic, and the

subjective.

Empirical probability

answers questions like:

what is the probability

of two number six seeds

meeting in the Super Bowl?

To judge this, one would

consult empirical data,

evaluating historical records to state the likelihood of the

event. This kind of probability depends on the counting

of repeated trials. The heavy historical emphasis of

technical stock analysis, for example, involves empiricist

assumptions about probability—the thought being that

past prices and quantities traded help predict future

prices and quantities to be traded.

Axiomatic probability deals with probability not as an

empirical matter, but as a matter of pure logic. A most

familiar example is that of rolling a fair die. With six

sides, the probability of a given side landing up is 1/6. The

chief problem with axiomatic probability is that is does

not work well when applied to human action. Arnold

Kling furnishes an instructive example: “Economist X has

one model of the economy. Economist Y has another

model of the economy. In X’s model, people believe in

X’s theory. In Y’s model, people believe in Y’s theory. It is

logically impossible for economist X and economist Y to

inhabit the same universe! Yet they do.” As economists

must make predictions about human actions, if two

maintain alternative axiomatic assumptions, then each

prediction will be unintelligible to the other.

The final perspective

on probability is

the subjective.

This is the realm

of speculation,

where analysts and

bloggers alike try to

divine such things

as the probability

of a euro area

breakup. Strictly

speaking, there

exists no historical

precedent off of

which we might

base an empiricist

analysis—the euro

area countries have

never before unified

in a monetary union.

What is more, there

exist no theoretical

frameworks that

might fix all

2

Unlike a game of chance (where all possible outcomes and their probabilities are known in advance), societal events feature unknown outcomes and defy mathematical probabilities.

outcomes as equally possible—axiomatic analysis cannot

work. So long as we acknowledge that all historical

instances are unique, and that immutable abstract

frameworks do not govern human decision, we remain

in the realm of subjective probability.

If this seems like it is the least rigorous, it may be

because it is the most honest, and the most familiar.

Casual references to probability are, generally speaking,

questions of subjective probability: is Tim Cook likely to

be a better CEO of Apple than Steve Jobs? The probability

that one executive outdoes the other cannot be known

objectively, no matter the sophistication of the model

used to guess.

The fact remains that when dealing with the sphere of

human action, the future is necessarily not present and

finally unknowable. Thus, with Ludwig von Mises, we

might smile and say, “Every man banks on good luck. He

counts on not being struck by lightning and not being

bitten by a viper. There is an element of gambling in

human life.”7

Probability in the World

Understanding the differences among these perspectives

on probability affords the following: first, an ability to

apply the proper perspective to the proper situation,

avoiding error; second, recognition of the differences

instills healthy humility in those who expect to predict

the future. As we walk about the world, recognizing the

operation of probabilistic assumption (both in our own

thinking and the thinking of others) keeps us away from

unfounded dogma, and returns us to nuance.

The failure to distinguish each type of probability from

another allows unfounded assumptions to pass as simple

fact. For example, economists often speak of an “output

gap,” which measures the difference between potential

GDP and actual GDP. A large output gap implies that

demand exceeds supply, where people would produce

more if they could. The measurement of the output gap

is an important one for real-world policy. It is thought

to indicate inflationary or deflationary risk. As precise

as these calculations are, based on complicated models

that factor in historical data to come up with potential

GDP, can we be so sure of their accuracy, of their use?

We cannot. Why? These models depend first and

foremost on an empirical (frequentist) assumption about

probability: that historical data provide trustworthy

information. If we believe that historical relationships

between, for instance, unemployment and GDP hold as

true today as ever, then we could accept the assumptions

underpinning the output gap. But, after the recent crisis,

two academics agreed that, “neither macroeconomic

theory nor existing empirical evidence suggests that

potential output is a smooth series,” nor yet that such

calculations should have impact on policy decisions.8

These scholars know that history does not always prove

itself repetitious.

Such candor is a luxury afforded to academics, not to

policy makers or money managers. To return to our

earlier example, in writing the policy brief for the

American Recovery and Reinvestment Act, Romer and

Bernstein had to present predictions to justify their policy

decisions; if the predictions are questionable, then it

follows that the policy should be suspect. Undercutting

your own policy by acknowledging the tenuous nature

of your evidence is not sound argumentation.

With economies world-wide floundering and looking

for answers, humility is as important as ever. No longer

can facades propped up only by sophisticated models

pass as comprehensive and trustworthy. As Raghuram G.

Rajan has written, “hidden...fault lines have developed

because in an integrated economy and integrated world,

what is best for the individual actor or institution is not

always best for the system.”9 Hence, financial/economic

3

IF WE BELIEVE THAT HISTORICAL RELATIONSHIPS BETWEEN, FOR INSTANCE, UNEMPLOYMENT AND GDP HOLD AS TRUE TODAY AS EVER, THEN WE COULD ACCEPT THE ASSUMPTIONS UNDERPINNING THE OUTPUT GAP

predictions that surface from “individual actors or

institutions” must be taken lightly.

Unlike academics (though many academics fall victim

to the same), financial institutions and policy makers

face decisive environmental constraints--namely that

predictions based on probability are often the only way

to justify action--and cannot readily admit confusion,

hesitation, or outright ignorance. However, no matter

who makes predictions, no matter how aware they are

of what they can and cannot know (i.e. the future),

the limits of probability are inescapable. The sphere

of future human action is, by definition, unknown

and assumptions otherwise can prove not simply

inconvenient, but ruinous.

SOURCES

1 Mises, Ludwig von. Human Action: A Treatise on Economics. New Haven: Yale UP, 1949.

2 Hacking, Ian. The Taming of Chance. Cambridge: Cambridge UP, 1990.

3 Rebonato, Riccardo. Plight of the Fortune Tellers: Why We Need to Manage Financial Risk Differently. Princeton: Princeton UP, 2007.

4 Federal Open Market Committee Meeting Minutes, December 2006.

5 Koop, Gary. “An Introduction to Probability for Econometrics.”

6 Romer, Cristina and Jared Bernstein. “The Job Impact of the American Recovery and Reinvestment Plan.” Council of Economic Advisors, 2009.

7 Mises, Ludwig von. Human Action: An Economic Treatise. New Haven: Yale UP, 1949.

8 Basu, Susanto and John G. Fernald. “What Do We Know (And Not Know) About Potential Output?” Federal Reserve Bank of St. Louis Review,Vol. 91, No. 4 (2009), pp. 187-213.

9 Rajan, Raghuram. Fault Lines: How Hidden Fractures Still Threaten the World Economy. Princeton: Princeton UP, 2010.

4

“We are suffering just now from a bad attack of economic

pessimism.”

– John Maynard Keynes, 1930

In the popular view, the United States economy faces

a prolonged period of stagnation. In many ways, basic

economic facts support this view. For example, the

broadest measure of US unemployment (which includes

workers who are marginally attached to the labor force

or who are working part-time for economic reasons), is

still 14.9% as of February 2012. Such a staggering number

shows significant weakness in the labor market.

Just 28% of Americans think the economy will be better a

year from now, according to the Pew Center, a testament

to the pervasive pessimism about the state of the

economy. General sentiment like this implies a difficult

road ahead for US consumers and the US economy.

Investors now wonder if the US is still home to great

investment opportunities.

However, is all of this trepidation justified? We don’t

think so. While recovery may indeed be slow, we believe

the US economy is in the midst of a Great Restructuring

not a Great Stagnation. To see why, investors must

understand a) the cause of the downturn—a severe

financial panic—and b) the nature of the economic

recovery—a coordination problem.

Viewed in this light, cautious optimism should prevail on

the state of the US economy, and the US fixed-income

markets are an attractive place for investors in 2012.

hoW did We get into this Mess? the financial PluMbing Matters

“Greed.” “Deregulation.” “Fannie Mae/Freddie Mac.”

“Irrational exuberance.” “Too-big-too-fail banks with a

license to take risk.”

These powerful and now oft-heard words and phrases

do much to vent popular frustration about the current

economic malaise. But they do not explain it. To this end,

we look to the plumbing of the financial system as the

lynch-pin of both crisis and recovery. Home plumbing, like

financial plumbing, only receives attention when things

are critical: if all is well, no one checks the pipes. Over

the last decade, this was the case with the US financial

plumbing. In good times few analysts delved into the

inner working of the financial system (the pipes) since it

smoothly performed its role as intermediary, connecting

savers with borrowers and fostering economic activity.

Now things are different. With the pipes clogged and the

house flooding, diagnostics work is in order.

First, recognize that the US financial system has evolved

over time. The most significant change has been the

evolution of the structure of the US financial system.

Where once banks ruled, now capital markets drive the

financial system. In the modern US financial system,

deposits include more than checking or savings accounts

at the local bank. Depositors include money market funds,

institutional investors engaged in repurchase agreements

and investors in short-term bond investments worldwide.

By late 2006, this “market-based” system accounted for

nearly $20 trillion in liabilities, compared to just $12

trillion for the traditional, bank-based system. At the

epicenter of this market-based system is the broker-dealer

7

The Great Restructuring and the Case for US Fixed Income

CAUTIOUS OPTIMISM SHOULD PREVAIL ON THE STATE OF THE US ECONOMY, AND THE US FIXED-INCOME MARkETS ARE AN ATTRACTIVE PLACE FOR INVESTORS IN 2012

nexus. By purchasing and financing securities holdings in

the overnight market (with repurchase agreements, or

“repos”), broker-dealers provide the liquidity lubricant

to the fixed-income markets (See Figure 1).

But, despite the new look of the financial system, the crisis

that erupted in 2007 was an old-fashioned “bank run.”

We’ve witnessed bank runs throughout history. Bank

runs occur when depositors question the collateral or

institution backing their deposits. In the more traditional

financial system, this consisted of deposits at the bank.

In the modern financial system, investors in the money

markets grew worried about their “deposits” and chose

the safety and comfort of FDIC-insured checking and

savings accounts as well as Treasury bonds. The result? A

flight from “risky” assets into “safe assets.”

At the onset of the most recent crisis, broker-dealer

balance sheets became severely constrained. No longer

able to hold risky securities and finance them in the

overnight markets due to lack of willing “depositors,”

dealers were forced to pare back their balance sheets and

also increased their holdings of safe assets. This sent the prices of securities tumbling across the board regardless of underlying fundamental valuation.

the fed is not “Printing Money”

How do the monetary and government authorities deal

with a crisis? The traditional banking system features

deposit insurance and access to the Federal Reserve’s

discount window built to stop runs and provide financial

stability. No such institutional architecture existed for

the non-traditional system. Instead, the Federal Reserve

rapidly evolved over the course of 2008-2009 into the

role of “dealer-of-last-resort.”1 Rather than “printing

money,” the Fed merely replaced the interbank market

that had evaporated as dealer balance sheets became

constrained. Through “Quantitative Easing (QE) 1”

and “QE2”, the Fed absorbed a significant number of

securities onto its balance sheet, acting more as a dealer than just as a lender of last resort.

shortage of “safe” assets: structural shift in Quantity and Quality of safe assets

While playing the role of dealer to the market-based

financial system, the Federal Reserve has not been able to

counteract the shortage of “safe assets” in the financial

system.2 As a result, asset supply struggles to keep up

with the global demand for store of value and collateral

by households, corporations, governments, insurance

companies, and banks on a global basis. The end result is

higher prices, or lower yields, on safe assets like Treasury

bonds, than would otherwise be the case.

For US markets, the supply of “safe” assets has remained

remarkably stable since 1952, at about 33% of all assets.

However, the main supplier of safe financial debt evolved

over the same period from the traditional commercial

8

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010-100

0

100

200

300

400

500

600

700

800U

S D

olla

rs B

illio

ns

SOURCE: Federal Reserve

"Safe Assets" (Treasuries + Deposits) on Dealer Balance Sheets

"Risk Assets" (Corps, Munis, Agencies) on Dealer Balance Sheets

US BROKER-DEALERS ARE KEY PART OF THE FINANCIAL PLUMBING SYSTEM

"Safe Assets"(Treasuries + Deposits) onDealer BalanceSheets

"Risk Assets"(Corps, Munis,Agencies) on Dealer Balance Sheets

fig. 1

banks to the market-based system.3 In the 1950s, bank

deposits comprised 80% of the total share of safe assets.

This share persisted into the 1960s and late 1970s. Then,

with the advent of the money market mutual fund, broker-

dealer commercial paper and repurchase agreements,

securitized debt and other asset-backed securities, the

share of safe assets changed dramatically. By 2007, the

share of safe assets made up of insured bank deposits

tumbled to just 27%. As the panic progressed, more and

more categories of safe assets became increasingly less

safe, causing a shortage.

All told, nearly $8 trillion in “safe assets” disappeared

between 2007 and 2011. The implication: with an

overwhelming demand for safe assets, a shortage means

lower bond yields than otherwise would be the case.

the Puzzle: More Micro, less Macro

The run on the financial system and the scramble for safe

assets scattered the pieces of the macro economy far and

wide. This is because the modern credit system (described

above) was at the heart of the “coordination system” of

economic activity. The Great Restructuring is the process

of putting those pieces back together. It is a coordination

process which, first and foremost, takes time.

Mainstream economists treat an economic downturn as a

cyclical, consumer-led demand slump. As such, they worry

almost exclusively about “demand.” In turn, by tinkering

with the levers of monetary policy (through interest

rates) and fiscal policy (through stimulus spending) they

believe that the economy, like a simple organism, can be

poked and prodded back on the right path (equilibrium).

For us the economy is far more complex, resembling

something more like an intricate jig-saw puzzle than a

lab rat. The pieces of the jig-saw puzzle include labor,

capital, and entrepreneurs—all dispersed throughout

the economy. A recession occurs because the economy

gears itself toward building the wrong set of goods and

services. In the case of the US, the wrong set of goods

consisted primarily of residential housing. Recovery

invariably means moving these pieces around until we

find a better fit.

9

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

800

1,000

1,200

1,400

1,600

1,800

2,000

USD

Bill

ions

129,000

130,000

131,000

132,000

133,000

134,000

135,000

136,000

137,000

138,000

Thousands of Employees

SOURCES: Bureau of Economic Analysis, Bureau of Labor Statistics

US Corporate Profits Before Taxes (Left)

US Total Nonfarm Payroll (Right)

Recession Periods - United States

STRUCTURAL SHIFT FAVORS CORPORATIONS OVER EMPLOYEES

Corporate profits reach all-time highs…

…but total employment is still well below pre-crisis levels…

FOR US THE ECONOMY IS FAR MORE COMPLEX, RESEMBLING SOMETHING MORE LIkE AN INTRICATE jIG-SAW PUzzLE THAN A LAB RAT

“Skills mismatch” is an indicative labor-market

phenomenon, and one that indicates a coordination

problem. In the words of the president of the Federal

Reserve Bank of Minneapolis, Narvana Kocherlakota,

“Firms have jobs, but can’t find appropriate workers.

The workers want to work, but can’t find appropriate

jobs. There are many possible sources of mismatch—

geography, skills, demography—and they are probably

all at work....The Fed does not have a means to transform

construction workers into manufacturing workers. In

this example, firms have jobs, but can’t find appropriate

workers.”4

Research has shown, quite to Kocherlakota’s point,

that few workers will return to their previous jobs after

recession. According to Pew Research Center analysis

of the Bureau of Labor Statistics data, almost half of

workers expect their layoffs to be “permanent.”5 Try

as they might, policymakers will likely be unable to re-

ignite “demand” for residential real estate, and in turn

the demand for construction labor, at least in the near

term. Instead, a Great Restructuring becomes necessary.

But the entire process occurs in a decentralized manner.

Where do jobs come from? New industries emerge,

powering economic growth.

In fact, on a microeconomic scale, the Great Restructuring-

-and, indeed, all economic change--is characterized by a

creation of new firms to follow the destruction of old

or unprofitable firms. Countries that stand athwart

this restructuring process and attempt to preserve

the old productive arrangements achieve stagnation,

not prosperity. Recent examples include Japan, which

refused to liquidate zombie firms to help restructure

the economy, and Western Europe, which imposed labor

regulations that stifled necessary restructuring.6

The US economy today thrives with less than 10% of the

labor force employed in agriculture or manufacturing

sectors. Far more job openings read: “web programmer”,

“graphic designer”, “data analyst” or “social media

market specialist”. These are jobs from new industry.

Meanwhile, existing firms have redoubled their efforts

to innovate and enhance productivity in order to survive

and prosper. How can we see this? Real gross domestic

product (GDP), or total US economic output, exceeded

its pre-recession peak by the end of 2011. But, US payroll

employment remains well below its pre-recession peak

(See Figure 2 on page 4). While this is often called a

“jobless recovery,” it could be labeled an “efficiency

recovery,” too. The US economy produces more output

with far fewer labor inputs than before the recession.

Cost cutting and labor saving innovation have no doubt

boosted productivity.

10

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

500

1,000

1,500

2,000

Basi

s Po

ints

Merrill Lynch High Yield Master Index, Option Adjusted Spread (OAS)

Trendline: Average

HIGH YIELD BOND SPREADS ARE STILL NEAR THEIR HISTORICAL AVERAGE

Historical Average Spread 6.00%

SOURCE: Merrill Lynch

hoW do investors navigate the great restructuring?

All of this suggests that an extended period of high

unemployment does not necessarily mean sour returns

for US corporations or economic stagnation—quite

the opposite, in fact. For investors, the result is higher

corporate profits. Corporate profits have rebounded

since the recession, up 40% compared to pre-recession

levels—bringing them to record highs (See also Figure 2

on page 4).

Investors must remember that wider and even volatile

credit spreads do not necessarily reflect underlying

economic fundamentals. They do tell us a lot about

the clogged nature of the financial plumbing. The

dealer system described earlier—which is the nexus of

fixed income market liquidity—remains balance sheet

constrained. Where this has a negative impact on short

term liquidity, it presents an opportunity for long-

term investors. Even in this environment of improving

fundamentals, credit spreads on both investment

grade and high yield bonds remain at or beyond long-

term historical wides (See Figures 3 and 4). This offers

a significant pick up yield given the structural shortage

of safe assets that could keep Treasury yields low for a

significant period of time.

In particular, the high yield asset class continues to

mature and provide ample investment opportunities.

The market has grown from $780 billion at the end of

2007 to $1.4 trillion in market value today. Over 1,500

distinct issuers ranging from Ford Motor Company to

Chesapeake Energy and HCA (the largest U.S. hospital

operator) comprise the market. The reasons to choose

the high yield marketplace as an investment are many.

First, the high yield asset class provides good

diversification: in terms of total return, the asset class has

a low correlation with many other asset classes, especially

US Treasuries. As a result, as US Treasury yields remain

low or gradually rise, high yield should outperform. If

you are worried about higher interest rates harming your

fixed-income investments, this is a point to consider.

Second, the return profile is attractive. Compared to

equities, high yield has a higher Sharpe ratio and lower

volatility and has achieved solid absolute and relative

returns over 1, 3, 5 and 10-year periods.

Third, strong corporate fundamentals shine in a Great

Restructuring. The average high yield company over

the course of 2009 through 2011 has been reporting

stronger margins and cash flows and earnings. These

solid fundamentals are expected to continue into 2012 as

both the US and global economies stabilize further and

trend growth resumes.

Fourth, a refinancing wave swept over the market

in 2009-2011. Relatively low interest rates since 2009

resulted in over $185 billion of global high yield issuance

in 2009, over $300 billion in 2010, and over $275 billion

in 2011. Refinancing of existing debt accounted for 58%

of new high-yield issuance in 2011, according to Barclays

Capital. In short, robust issuance indicates that companies

are refinancing debt at more favorable interest rates to

reduce interest costs and debt coverage ratios. This is a

positive sign. It means that net, new corporate issuance is

flat to negative. That is, the market is not being flooded

with excessive net, new supply.

Fifth, despite all the doom and gloom of “double dip

recession” rhetoric emanating from the US, high yield

defaults have been on the decline! From a pre-crisis

low of 1.6% in 2006, the high yield default rate peaked

around 13% range post-crisis. Since then, it has plunged

below 2%, according to both Moody’s and S&P, leaving

the current default rate well below the 25-year historic

average of 4.5% even if defaults rise in 2012.

These statistics portend good things because the high

yield market does well (credit spreads compress) in

environments of improving corporate health and default

11

INVESTORS MUST REMEMBER THAT WIDER AND EVEN VOLATILE CREDIT SPREADS DO NOT NECESSARILY REFLECT UNDERLYING ECONOMIC FUNDAMENTALS. THEY DO TELL US A LOT ABOUT THE CLOGGED NATURE OF THE FINANCIAL PLUMBING

statistics. Finally, while market fundamentals have

improved, yield levels still remain very attractive. As of

this writing, yield levels in the 6.75% range will continue

to attract inflows from both institutional and retail

investors, enhancing market liquidity.

the doWnturn is not a PerManent one

The bottom line: credit spreads can remain wide

and volatile given the broken nature of the global

financial system. A slow, structural recovery means US

corporations earn profits as the unemployment rate only

slowly returns to pre-recession levels. Treasury yields

remain disappointingly low as the supply of “safe assets”

contracts. Investors searching for income can still look to

the US fixed income markets as an opportunity.

Keynes finished his essay in 1930 by stating: “I believe

that this [economic pessimism] is a wildly mistaken

interpretation of what is happening to us. We are

suffering, not from the rheumatics of old age, but from

the growing-pains of over-rapid changes, from the

painfulness of readjustment between one economic

period and another.”7

Today in the United States there is no reason to think the

Great Restructuring will be rapid nor is there reason to

think this slow growth will be permanent. With a proper

understanding of the macroeconomic backdrop—a post-

financial crisis Great Restructuring—investors with a

longer-term perspective can yet make a strong case for

US fixed-income.

Invest accordingly.

SOURCES

1 Perry Mehrling. “The New Lombard Street: How The Fed Became the Dealer of Last Resort.”

2 Richard J. Caballero, “On the Macroeconomics of Asset Shortages,” Working Paper, MIT and NBER, November 6, 2006.

3 Gary Gorton, Stefan Lewellen and Andrew Metrick, “The Safe-Asset Share,” AER Papers & Proceedings, 2012.

4 “Inside the FOMC,” Speech by Narayana Kocherlakota, President, Federal Reserve Bank of Minneapolis, Marquette, Michigan, August 17, 2010.

5 “Five Long-Term Unemployment Questions,” The Pew Charitable Trusts, February 1, 2012.

6 Ricardo J. Caballero. Specificity and the Macroeconomics of Restructuring. Cambridge:: The MIT Press, 2007.

7 Economic Possibilities of Our Grandchildren,” John Maynard Keynes, Essays in Persuasion, New York: W.W.Norton & Co., 1963, pp. 358-373.

12

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

100

200

300

400

500

600

700

800

Basi

s Po

ints

BofA Merrill Lynch U.S. Corporates BBB - Option Adusted Spread (OAS) Trendline: Average

INVESTMENT GRADE CORPORATE BOND SPREADS ARE ABOVE HISTORICAL AVERAGE

HistoricalAverageSpread2.13%

SOURCE: Merrill Lynch

TEXTING TWENTY-FIRST CENTURY TRADE DATA: A SOCRATIC DIALOGUE

SOURCES: U.S Department of Homeland Security. What Every Member of the Trade Community Should Know About: Customs Value. Washington: Government Printing Office, 2006.Dedrick, J., Kraemer, K.L., Linden, G. (2011) “Capturing Value in Global Networks: Apple’s iPad and iPhone.”U.S Department of Homeland Security. What Every Member of the Trade Community Should Know About: U.S. Rules of Origin. Washington: Government Printing Office, 2004.

What’s going on? How is this calculated?

With all those imports that must be a nightmare.

Ok, but even if we really are accounting properly for the things that come into our ports, how do we know the value of our imports? After all, the trade data is listed in $.

Transaction Value?

But what about the country of origin—how do we know all this stuff comes from China?

What do you mean? How could we possibly measure trade with China if we didn’t know that it was actually from China?

Importers work with US customs, classifying and valuing their shipments before they arrive at US ports.

Well, it is true that in the trade data, since the late 1980s, we have consistently imported more from China than we have exported to China.

It isn’t so bad. Since 1988, the Harmonized Commodity Description and Coding System has made compiling and reviewing international shipping data a regular breeze.

US Customs depend on individual importers to calculate and report the value of their imports. They derive the $ amount of their goods according to transaction value.

Yep. It is a term developed by Customs that is the total payment, excluding transportation and insurance costs, that the buyer importing a good makes to the seller who is exporting the good.

This is where it gets complicated.

Well, US Customs does their best to determine the country of origin, according to two categories: a) wholly obtained or b) substantial transformation.

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

-250

SOURCE: Census BureauNumbers for 2012 are estimates.

USD

Billi

ons

-200

-150

-100

-50

0

US TRADE DEFICIT WITH CH

INA

O no! Bureaucratic jargon.

Hey did you see this? China is on the rise, and the US is way down in the latest trade data!

TEXTING TWENTY-FIRST CENTURY TRADE DATA: A SOCRATIC DIALOGUE

That makes sense. But what about my iPhone? Is it really “Made in China.”

CHIN

A

CHIN

A

TRADITIONALAPPROACH

VALUE-ADDED APPROACH TO CALCULATE TRADE DATA

JAPA

N

GER

MAN

Y

US

KORE

A

REST

OF

WO

RLD

US

TRA

DE

DEF

ICIT

100% 34

%

13%

17%

100%

6%

4%

27%

SOURCE: Xing, Yuqing (2010). “How the iPhone Widens the United States Trade Deficit with the People’s Republic of China.”

% VALUE ADDED TO IPHONE

Here’s how it works: an imported good that is wholly obtained is “wholly the growth, produce, or manufacture of a particular country.” For example, an imported avocado that was grown in Mexico would be classified as a Mexican import as the product imported was grown only in that country.

Yep. In fact, if you look at it, the distribution of value-added should look more like this:

I know: Chinese workers add a mere $6.50 to the entire US$179 manufacturing cost of an iPhone. But because China is deemed to “substantially transform” the various components of the device (i.e. assemble the phone), China is the country of origin and hence receives all of the transaction value for iPhone exports in the trade data.

Rightly so. After all, the intricacies of global trade data resist simple representation. It’s best to keep that in mind.

GOOD POINT. The iPhone falls into the other category when determining the country of origin, “substantial transformation.” So long as it is deemed that the product has undergone substantial transformation in a given country that is the country of origin.

Wait, the iPhone isn’t really “Made in China”? You’re telling me that a number of countries worldwide simply ship the component parts to be assembled in China–yet China receives all the credit for the iPhone in the trade data?

WOW...So if we calculated trade data according to which countries added value, our trade deficit with China would be very different

The trade data is misleading. Surely the iPhone isn’t the only product comprised of parts from all over the world that is assembled in China. I wonder and worry at the accuracy of the rest of the reported numbers.

WILL DO!

But China receives all the credit for the iPhone in the trade data?

So what does this have to do with my iPhone?

Think about it. The iPhone has parts that come from all over the world. Countries in Asia, Europe, and North America contribute components for the phone. In fact, China adds very little by way of parts to the iPhone. THEY JUST ASSEMBLE IT.

15

“Too much and for too long, we seem to have surrendered personal excellence and community values in the mere accumulation of material things. Our Gross National Product, now, is over $800 billion dollars a year, but that Gross National Product - if we judge the United States of America by that - that Gross National Product counts air pollution and cigarette advertising, and ambulances to clear our highways of carnage. Yet the gross national product does not allow for the health of our children, the quality of their education or the joy of their play. It does not include the beauty of our poetry or the strength of our marriages, the intelligence of our public debate or the integrity of our public officials...It measures everything in short, except that which makes life worthwhile. And it can tell us everything about America except why we are proud that we are Americans.”

– Robert Fitzgerald Kennedy

1968 Campaign Trail1

A glance in the rear-view mirror suggests tangible

economic progress over the last 60 years. If we look

only at labor productivity in the United States since

Robert F. Kennedy gave his speech, growth has been

astounding. But labor productivity (the consequence

of divison of labor) is only one of many metrics which

measure economic output. It begs the question, how

do we best measure economic progress and growth

that spans decades? And what are the shortcomings

and consequences of such growth? Professors and

economists use myriad metrics to calculate growth and

determine the magnitude of expansion or contraction

within a specific country or entire region.

Yet, only one has endured the test of time.

Notwithstanding R.F. Kennedy’s words, for the last 80

years Gross Domestic Product (GDP) has been the most

prominent measure of economic growth. But if Kennedy

was correct regarding the shortcomings of GDP, then

why has this measure remained so popular among policy-

makers? And perhaps there are alternative measures

that better reflect not only output, but overall progress

and well-being of both the individuals within a country

and the nation as a whole.

a history of gdP

For centuries economists debated the cause of

fluctuations in business activity (the “economy”). Some

argued that a slump in the demand for goods led to the

idleness in factory production while others identified

production of the wrong mix of goods as the operative

force leading to a decline in business activity.

Through the late 18th and most of the 19th centuries,

economists such as, “[Jean Baptise] Say, James Mill

and [David] Ricardo, following Adam Smith, opposed

the view that a general lack of demand was the prime

threat to prosperity, arguing that the main obstacle is

inability or unwillingness to produce.”3 These thinkers

argued that large-scale economic evaluation must take

as an invariable point of departure, considerations of

aggregate supply (or the ability “to produce”).

Once in the 20th century, however, the prevailing winds

of thought shifted. In the early 1930s, John Maynard

Keynes was reading the work of Thomas Malthus, a

British economist working in the early 1800s. As a result

of his reading of Malthus, Keynes moved stridently

away from considerations of aggregate supply, and

reoriented (indeed, an orientation that persists to this

day) economic thinking toward aggregate demand: “it

was because Keynes was reading...[Malthus] in late 1932

that the General Theory was written as it was, focusing

on effective demand as the central issue in the theory of

unemployment and depressions.”4

As theoretically impressive as Keynes’ turn to aggregate

demand was, he proposed no way of measuring his

key indicator. However, in a 1937 report presented to

congress, Simon Kuznets developed the concept of GDP

as a means of measuring aggregate demand: GDP was

effectively a national accounting system. Hence, GDP

denoted (and denotes still) the total value of goods and

services produced within a country over some unit of

time. The initial goal of GDP was to make it easier for

GDP: What’s It Good For?

16

policy-makers to manage a national economy through

crises and war, without consideration of utility.

As a result, Kuznets designed GDP to capture all

economic production by individuals, companies, and

the government in a single measure, increasing during

economic expansion and declining during contraction.

The computational simplicity of GDP and the premise

that spending patterns alone accurately measured

growth made it attractive to most economists.

Although the measure was an immediate economic

success, the pioneer of GDP warned of its shortcomings

and cautioned against its use as a measure of welfare.

Specifically, Kuznets stated, “Distinctions must be kept in

mind between quantity and quality of growth, between

costs and returns, and between the short and long run.

Goals for more growth should specify more growth of

what and for what.”5 Ironically, Kuznets resigned from

working with the U.S. Commerce Department in the

1940s over their refusal to include unpaid housework as

a component of GDP, one of several omissions prevalent

in the measure.

In 1959, Stanford economist Moses Abramovitz became

one of the first to publicly question how accurately

GDP reflects a society’s overall well-being. Abramovitz

warned, “We must be highly skeptical of the view that

long-term changes in the rate of growth of welfare

can be gauged even roughly from changes in the rate

of growth of output.”6 But such criticisms failed to win

much support as academics and policy makers continued

to rely on GDP data.

For example, Arthur Okun, former staff economist for

John F. Kennedy, theorized that for every three-point

increase in GDP, unemployment would fall by one

percentage point and vice versa: this is now known as

Okun’s law. The message became clear to policy-makers,

continue to increase the size of the economy and the

welfare of your nation will reap the benefits.

MaKing sense of gdP

The world has changed a great deal in the decades

since Kennedy’s speech, as innovation reshaped the

economic landscape. Financial markets have become

global, growing dramatically in both size and scope.

Revolutionary strides in technology, most notably cell

phones and computers, have made global communication

possible, and knowledge a democratic commodity.

But how does this effect our day to day? How does GDP

growth play out on a small scale? Take for example

mowing the lawn. For anyone who wants their lawn

mowed, the options are roughly three: mow yourself,

pay a family member, pay a professional. While the

differences between the three may seem trivial, the

choice of who mows your lawn matters in terms of GDP.

If you mow your lawn, you pay for your equipment, but

not for labor. If you pay your child to mow your lawn, you

pay for your equipment, pay for labor, but presumably

save because the child is not a professional. Finally, if you

pay a professional to mow your lawn, you pay only for

labor (and, one hopes, for quality).

How does this breakdown factor into GDP? Take the last

option. Paying a professional to mow your lawn, divides

labor more efficiently: you need not spend the time

to mow it yourself, nor must you supervise in the case

of a child mowing. Division of labor relates directly to

productivity, which relates to employment, which relates

to increased consumer spending (the primary driving

force of GDP).

Indeed, Joseph Schumpeter once wrote of Adam Smith’s

thinking on the division of labor: “nobody, either before

or after Adam Smith, ever thought of putting such a

burden upon division of labor. With [Smith] it is practically

the only factor in economic progress.”2 Therefore,

taking Adam Smith as the guide to our example, hiring

THE COMPUTATIONAL SIMPLICITY OF GDP AND THE PREMISE THAT SPENDING PATTERNS ALONE ACCURATELY MEASURED GROWTH MADE IT ATTRACTIVE TO MOST ECONOMISTS

17

the professional to mow your lawn contributes with

significance to economic growth. Rather than paying

your self (via your opportunity cost), or paying your child,

paying the professional encourages increased division of

labor, enhances productivity and grows GDP. In brief, it

means as a society we are wealthier.

shortcoMings of gdP

GDP measures economic growth by adding Consumption

+ Investment + Government Spending + (Exports-

Imports). In most developed countries, consumption is

the largest component of GDP. But does all consumption

result in productive growth, or simply an additional dollar

spent? In other words, how can we be sure “whether

the final goods and services that were produced during

a particular period of time are a reflection of real

wealth expansion, [not simply] a reflection of capital

consumption.”7

For example, US construction and home-building were

booming during the early to mid-2000s, with capital

pouring into the sector. GDP was reported in positive

territory from 2002 to 2007 with residential investment’s

share of total output, reaching a high of 5.1% in Q1

2006. Though new homes were built at an unsustainable

or even undesirable pace, according to GDP growth, all

was well.

It took less than two years for the real estate market to

collapse, with national home prices declining by 30%.

To this day, a flood of homes built during the boom

remain vacant. In retrospect, the excessive build-up of

housing was predicated on spending capital, rather than

sustainable and healthy growth. Perhaps all consumption

is not created equal.

Even in normal times there are several ways that,

when evaluated alone, GDP provides incomplete or

misleading data. GDP reporting omits business cut-backs

in intangible assets such as research and development,

product design, and employee training. These cutbacks

enable corporations to reduce costs and temporarily

increase profits. Many economists argue the omission

of intangible assets such as research and development

grossly overstates the GDP measure and does not truly

reflect the prospect for long-term sustainable growth.

GDP excludes underground economic activity such as

illegal trade, gambling, drug trafficking, and other

black-market activities. GDP also excludes economic

activity where money is not involved, such as bartering.

Not only does GDP fail to capture important economic

contributors to growth, GDP ignores the potential

social costs of negative by-products of growth, such as

pollution, toxic waste, and traffic. GDP does not include

items that significantly impact social welfare such as

crime reduction, peaceful international relations, and

reduced drug and alcohol abuse. GDP does not include

the benefit of leisure items such as paid vacation and

holidays. And perhaps most important to some critics,

GDP fails to measure the happiness of a nation. Hence

we see that GDP, as RFK suggested, misses much when it

comes to measuring national well-being.

alternatives and the future of gdP

It is remarkable that the GDP measure has withstood

the test of time, enduring largely unchanged since

its creation over 80 years ago. In fact, in 1999, the US

Department of Commerce declared GDP to be one of the

great inventions of the 20th century (this is not a joke).

DID YOU kNOW: GDP VS. GNP the united states did not formally adopt gdP until 1991, but instead used gross national Product (gnP) as its measure for economic growth. similar to gdP, gnP was also created during the 1930s, by a u.K. economist named richard stone.

gnP is quite similar to gdP, but differs slightly in composition. both gdP and gnP measure the final goods and services produced by domestically-owned means of production. however, gdP defines its scope within the borders of a country, while gnP defines its scope according to ownership of production, regardless of location. thus, gnP should better encapsulate multi-national operations and recognition of investment income earned abroad.

18

However, as the world evolves, so should the methods we

use to measure progress and growth. GDP was originally

created to measure national output and aggregate

economic growth, not social and economic welfare.

Indeed, we cannot, as RFK does, lose sight of one simple

fact. No matter the problems with GDP, what it leaves

out and what it misses in capturing general happiness, no

discussions of the adequacy of GDP nor concerns about

the happiness of a nation would be possible without an

already high standard of living.

Our obsession with the “happiness” of our children,

our students, and our nation, while certainly important,

depend on our historically high standard of living. Brad

DeLong reminds us that, “those of us living in the United

States today have a level of productivity--a material

standard of living-- somewhere between 14 and 25 times

that of our counterparts back in the late nineteenth

century.”8

We now understand that even if GDP misses much,

the fact that our current discussions about human

prosperity rarely have anything to do with basic survival

is a testament to the progress we have already made.

Only because we no longer have to worry about simply

feeding ourselves and finding shelter, do we have the

luxury of choice: only with the supreme luxury of choice

do considerations of happiness obscure concerns for

more primitive needs.

Given its ease of calculation and historical consistency,

GDP remains relevant. Despite the plethora of suggested

alternatives to GDP, none has yet proven an appropriate

substitute. For now, GDP remains the bellwether

economic measure of output and growth within a

nation, playing a critical role in the formation of both

fiscal and monetary policy.

SOURCES

1 Remarks of Robert F. Kennedy at the University of Kansas, March 18, 1968. John F. Kennedy Presidential Library and Museum Archive.

2 Rosenberg, Nathan. “Adam Smith on the Division of Labour: Two Views or One?” Economica, Vol. 32, No. 126, (1965), pp. 127-139.

3 Baumol, William J. (1999). “Retrospectives: Say’s Law.” The Journal of Economic Perspectives, Vol. 13, No. 1 (1999), pp.195, 204.

4 Kates, Steven. “The Malthusian Origins of the General Theory or How Keynes Came to Write a Book About Say’s Law and Effective Demand.”History of Economics Review, Vol. 21 (1994), pp. 10-20.

5 Kuznets, Simon. “How To Judge Quality”. The New Republic, October 20, 1962.

6 Abramovitz, Moses. “The Welfare Interpretation of Secular Trends in National Income and Product” in The Allocation of Economic Resources, ed. Moses Abramovitz. Stanford: Stanford UP, 1959.

7 Shostak, Frank. “What is Up With GDP?” Ludwig von Mises Institute Online, 2001.

8 DeLong, Bradley (2001). “The Facts of Economic Growth.”

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