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A Quarterly Review
of Business and
Economic Conditions
Vol. 19, No. 3

Foreclosures

Hispanics

The Roles of Predatory Lending
and Household Overreaching

Population Growth
in District and in Nation

July 2011

The Federal Reserve Bank of St. Louis
C e n t r a l t o A m e r i c a’ s Ec o n o m y

®

Commodity
Price Gains
Speculation vs. Fundamentals

c o n t e n t s

4
The Regional

Economist
july 2011

|

VOL. 19, NO. 3

Commodity Price Gains
By Brett Fawley and Luciana Juvenal

Commodities of all sorts have risen in price over the past few
years. Some say that the prices reflect a bubble, driven by low
interest rates and excessive speculation. Others say the price gains
can be fully explained by supply and demand. Is either right?

3

president’s message

10

The Mismatch between
Job Openings, Job Seekers

The Regional Economist is published
quarterly by the Research and Public
Affairs departments of the Federal
Reserve Bank of St. Louis. It addresses
the national, international and regional
economic issues of the day, particularly
as they apply to states in the Eighth
Federal Reserve District. Views
expressed are not necessarily those
of the St. Louis Fed or of the Federal
Reserve System.
Please direct your comments to
Subhayu Bandyopadhyay at 314444-7425 or by e-mail at subhayu.
bandyopadhyay@stls.frb.org. You can
also write to him at the address below.
Submission of a letter to the editor
gives us the right to post it to our web
site and/or publish it in The Regional
Economist unless the writer states
otherwise. We reserve the right to edit
letters for clarity and length.

22

Examining the Growth
of Hispanic Population
By Rubén Hernández-Murillo
and Christopher J. Martinek
16

By Maria E. Canon
and Mingyu Chen

Senior Policy Adviser
Cletus C. Coughlin

Between 2000 and 2010, Hispanics
accounted for more than half of the
growth in total U.S. population.
In the Eighth District, the role of
Hispanics’ growth was much less
dramatic—except in rural areas.

A Closer Look
at House Price Indexes
By Bryan Noeth
and Rajdeep Sengupta
Tracking house prices is of
increasing importance to many
people. There are several prominent house price indexes for
the U.S. Knowing how they
differ can help people decide
which one to follow.

Today’s high unemployment rate
is often linked to a structural
imbalance—a mismatch between
the skills and location required
to fill vacant jobs and the skills
and geographical preferences of
the unemployed. But the evidence downplays the role of
this mismatch.

Director of Research
Christopher J. Waller

24

Deputy Director of Research
David C. Wheelock
Director of Public Affairs
Karen Branding
Editor
Subhayu Bandyopadhyay

18

Managing Editor
Al Stamborski

The Eighth Federal Reserve District

includes all of Arkansas, eastern
Missouri, southern Illinois and Indiana,
western Kentucky and Tennessee, and
northern Mississippi. The Eighth District offices are in Little Rock, Louisville,
Memphis and St. Louis.

12

This Southern Illinois city aims
to diversify beyond the state fair
for which it’s best known. One
approach has been to find uses
for the extensive fairgrounds for
the other 355 days of the year.

The Foreclosure Crisis
By William R. Emmons, Kathy
Fogel, Wayne Y. Lee, Liping Ma,
Deena Rorie and Timothy J. Yeager
At least early in the financial
crisis, the high rate of foreclosures
seemed to be due more to households’ overreaching than to predatory lending. A disproportionate
number of those being foreclosed
on were well-educated, well-off,
relatively young people.

21

n at i o n a l o v e r v i e w

Segregation Index
Shows Decline
By Alejandro Badel
and Christopher J. Martinek
The Index of Dissimilarity
suggests that segregation
declined for all four major
metropolitan areas in the Eighth
District between 1970 and 2000.
A breakdown of the index helps
to show how this happened.

communit y profile
Du Quoin, Ill.
By Susan C. Thomson

Art Director
Joni Williams
Single-copy subscriptions are free.
To subscribe, e-mail carol.a.musser
@stls.frb.org or sign up via www.
stlouisfed.org/publications. You can
also write to The Regional Economist,
Public Affairs Office, Federal Reserve
Bank of St. Louis, Box 442, St. Louis,
MO 63166.

district overview

26	Economy at a glance

27

reader exchange

Recovery Continues
By Kevin L. Kliesen
Although the pace of economic
activity has been inconsistent and
somewhat lackluster, the overall
economic environment is
expected to keep improving.
Cover photo © Keith Dannemiller/Corbis

2 The Regional Economist | July 2011

p r e s i d e n t ’ s

m e s s a g e

James Bullard, President and CEO
Federal Reserve Bank of St. Louis

The Effectiveness of QE2

T

he federal funds rate has been close to
zero since December 2008, when the
Federal Open Market Committee (FOMC)
voted to reduce the target to between
0 percent and 0.25 percent. With its policy
rate near the zero bound, the FOMC turned
to large-scale asset purchases (so-called
quantitative easing) as economic conditions
warranted further action. Quantitative easing was successful and showed that the Fed
can conduct effective policy even with the
fed funds rate near zero.
The FOMC’s first quantitative easing
program, which began in late 2008 and
ended in the first quarter of 2010, consisted
of purchases of agency debt, agency
mortgage-backed securities and longerterm Treasury securities. The program is
generally considered to have been successful in further easing monetary conditions.
Throughout the spring of 2010, however,
financial market stress in the U.S. increased
again, mostly in response to an intensification of the European sovereign debt crisis.
During the summer of 2010, the pace
of the U.S. economic recovery slowed. In
addition, inflation and expected inflation
were both quite low—some measures were
as low as they had been in 50 years. Inflation, while still positive, had been trending
downward (which is known as disinflation)
throughout the first half of 2010. As the
Japanese experience over the past 15 years
has shown, having mild deflation (i.e., declining prices) along with a near-zero policy rate
can lead to poor economic outcomes, and the
situation is difficult to escape.1, 2 Avoiding a
similar experience in the U.S. was one of the
primary motivations for a second round of
quantitative easing.
Fed Chairman Ben Bernanke gave a speech
in Jackson Hole, Wyo., on Aug. 27, 2010, in
which he first indicated that a second assetpurchase program may be needed. At the
Nov. 2-3 meeting, the FOMC made the decision to purchase Treasury securities at a pace

of about $75 billion per month through the
first half of 2011 for a total of $600 billion—
the program commonly known as QE2.3
The policy change was largely priced into
the markets ahead of the November FOMC
meeting, as financial markets are forwardlooking. The financial market effects of QE2
were entirely conventional. In particular,
real interest rates declined, expected inflation
increased, the dollar depreciated and equity
prices rose. The purchases of longer-term
Treasury securities essentially lowered the
risk-free real interest rate, which then caused
some investors to switch to riskier assets—
most notably U.S. equity markets, but also
emerging market equities and commodities
as an investment class—in search of higher
rates of return.
Following the November decision, many
people expected the program to have no
impact. Some even went so far as to say
that purchasing $7 trillion in longer-term
bonds was necessary. But based on the
fairly substantial financial market impact
of $600 billion in purchases, those views
have been dispelled.
While the effects on financial markets
occurred during the run-up to the November
decision, effects on the real economy (e.g.,
consumption and employment) are expected
to occur six to 18 months after the monetary
policy action, as is the case with conventional monetary policy. Determining exactly
which movements in real variables are due to
monetary policy and which ones are due to
other influences on the economy that occur
in the meantime can be difficult. Disentangling these effects is a standard problem in
monetary policy analysis. However, the real
effects of the asset-purchase program will
most likely be conventional, just as the financial market effects were.
As the experience with quantitative easing
has shown, monetary policy can be effective
even when nominal interest rates are at the
zero bound. QE2 was successful as a classic

easing of monetary policy in that the imprint
on the financial markets looked just like a
standard, aggressive monetary policy easing.4
Furthermore, the disinflationary trend of
2010 has apparently been reversed, and the
U.S. economy seems to have avoided the
Japanese-style outcome. Although a rulelike approach would have been preferable
from my point of view, rather than independent, isolated decisions with large amounts
of purchases, the impact of quantitative
easing on macroeconomic and financial
conditions showed that the Fed has plenty
of ammunition to carry out stabilization
policy even when the policy rate cannot be
lowered further.

endnotes
1

2

3

4

For more discussion, see Bullard, James. “Seven
Faces of ‘The Peril.’ ” Federal Reserve Bank of
St. Louis Review, September/October 2010, Vol. 92,
No. 5, pp. 339-52.
Also, see Hursey, Tim; and Wolman, Alexander L.
“Monetary Policy and Global Equilibria in a Production Economy.” Federal Reserve Bank of Richmond
Economic Quarterly, Fourth Quarter 2010, Vol. 96,
No. 4, pp. 317-37.
See Bullard, James. “QE2 in Five Easy Pieces.” Speech
at the High Profile Speaker Series, New York Society
of Security Analysts, New York City, Nov. 8, 2010.
Research at the St. Louis Fed suggests that quantitative easing programs in the U.S. can have international effects (e.g., a reduction in long-term foreign
bond yields), as well. See Neely, Christopher J. “The
Large-Scale Asset Purchases Had Large International Effects.” Federal Reserve Bank of St. Louis
Working Paper 2010-018C, January 2011.

The Regional Economist | www.stlouisfed.org 3

i nf l a t i o n

Commodity Price Gains:
Speculation vs. Fundamentals

By Brett Fawley and Luciana Juvenal

This spring, Wal-Mart CEO Bill Simon readied shoppers for
what he termed “serious” inflation: “We’re seeing cost increases
starting to come through at a pretty rapid rate.”

1

At the top of the list of cost-related pressures on prices of final
goods are gains in underlying commodity prices. Commodities—such as cotton, rubber, food, petroleum and metals—are
the raw materials from which all final goods begin. For many
businesses, commodities represent the second-largest driver
of variable cost, next to labor. Steep, sustained increases in the
cost of commodities materially affect the viability of businesses
and even industries; often, these price increases must be passed
through to consumers.
4 The Regional Economist | July 2011

photos © shut terstock

The heavy reliance of businesses on commodities is illustrated by the story of John
Anton, founder and owner of Anton Sport,
a wholesaler of athletic apparel in Tempe,
Ariz. Anton, who normally keeps on hand
30 boxes of cotton T-shirts as inventory,
was reported this February by The Wall
Street Journal to be sitting on 2,500 boxes
of cotton T-shirts, funded via a $300,000
loan.2 The impetus? A 90 percent increase
in the price of cotton over 2010.
Currently, commodity prices are making
headlines as much for the size of the price
increases as for the simultaneity of price
hikes across all types of commodities.
Figure 1 reveals that, prior to the global
recession, upward price trends took hold in
a variety of commodities. The financial crisis and ensuing recession induced an acute
decline from the 2008 peak in prices. But
beginning in 2009, the prices of all types
of commodities began to rise once again at
astronomical rates.
This synchronization of price movements
across a range of commodities has fostered,
in part, the assertion that the commodity
price boom is a bubble, driven primarily
by near-zero interest rates and excessive
speculation in commodity futures markets.
The counter argument is that market fundamentals—supply and demand for
the commodities themselves—can fully
explain the price gains. Ultimately, understanding the sources of the price gains is

essential for determining the proper policy
response, if any.
Arguments for Market Fundamentals

In the absence of “irrational exuberance,”
the price of any good or asset should be
driven by supply and demand. On both the
supply and demand side of commodities,
there is no shortage of shocks to explain, at
least in part, recent price gains.
Negative Supply Shocks

For crops and many other commodities,
annual production is largely at the discretion
of Mother Nature. With respect to agricultural commodities, a combination of bad
breaks from Mother Nature and stock-touse ratios at already historic lows seems to
explain much of the price increases.
Pre-existing stocks are a key source of
stability in commodity markets. When
stocks are low relative to use, the market is
less able to absorb pressures from supply
disruptions or unexpected demand; the
resulting pressure on prices is much stronger. A survey of commodities characterized
by rising prices uncovers many stock-to-use
ratios at historic lows.
In a report on the pre-recession spike
in food prices, the Food and Agriculture
Organization of the United Nations (FAO)
identified numerous reasons why stock
levels have been falling by an average rate
of 3.4 percent per year since the mid-1990s.3

Reasons included declines in the reserves
held by public institutions, development
of other less costly instruments of risk
management, increases in the number of
countries able to export, and improvements in information and transportation
technologies. Further, the FAO found
strong evidence that lower stock levels at
the beginning of the marketing season were
associated with higher prices throughout
the season, implying initial conditions in
“tight” markets matter. Compounding this
effect is further empirical evidence that the
price impact of low stocks becomes magnified when stocks reach critically low levels.
For all of these reasons, low stocks in
food and other crops mean that the weather
disruptions faced in 2010 were all that
much more significant. For example, the
47 percent increase in wheat prices in 2010
was largely attributable to drought in Russia
and China and to floods in Canada and
Australia. High cotton prices can be traced,
in part, to floods in China (the largest
producer) and Pakistan (the fourth-largest
producer).
In many cases, the high prices in one
market have spilled into other markets
because of the competition between crops
for the same land and growing resources.
Farmers are choosing to grow the crops
that are in shortest supply with the highest
prices, often introducing shortages in other
displaced crops.
The Regional Economist | www.stlouisfed.org 5

FIGURE 1

INDEX (JANUARY 2005=100)

Recent Commodity Price Growth
600
550
500
450
400
350
300
250
200
150
100

Rubber
Corn
Copper
Cotton
Oil

2006

2007

2008

2009

2010

2011

SOURCES: The Wall Street Journal and Bloomberg.
NOTE: The shaded area indicates the recession, as dated by
the National Bureau of Economic Research.

With respect to nonagricultural commodities, the challenge of suppliers is less a result
of temporary negative shocks than it is a
result of rapidly expanding global demand.
Growing Demand

The convergence in income between developing and advanced countries represents a
significant driver of demand growth for commodities: Representative of the trend, more
than 90 percent of the increased demand for
agricultural commodities over recent years
has originated in developing countries.
For commodities such as metals, this
additional demand can take time to fully
accommodate. Figure 2 reveals the incredible pace at which demand for metals like
aluminum and copper has grown in the two
most populous emerging countries: China
and India. This huge demand growth is
a major contributor to the International
Copper Study Group’s findings that worldwide demand for refined copper exceeded
worldwide supply by 480,000 tons over the
first nine months of 2010.4 The mismatch
between supply and demand has unsurprisingly taken a large toll on inventories,
cutting them by more than half, from 1.1
million tons in 2001 to 412,000 tons by
September 2010.
Continued strong growth in emerging
countries, complemented by economic
recovery in the United States, Japan and
Europe, is expected to continue to put
upward pressure on prices of metals.
According to Bloomberg News, 13 of 14
industry analysts who were surveyed
expected a copper shortage this year.
While exploration and investment in
mining operations are under way, much
time and money will be required before
6 The Regional Economist | July 2011

new mines are operational. In the words
of U.S. Geological Survey specialist Daniel
Edelstein, “Mines aren’t just like factories,
where you just flip a switch.”
With respect to agricultural markets, the
FAO is correct to point out that increased
demand due to population and income
growth is largely predictable. Biofuels,
however, are cited as a new and persistent
shock to food demand.5 Figure 3 reveals an
unmistakable recent shift in the relationship
between oil prices and the price of popular
biofuel crops, such as corn (for ethanol) and
soy (for biodiesel). The enormous size of
energy markets compared with agricultural
markets means that energy-related demand
is capable of absorbing near-limitless
amounts of surplus crops, effectively placing
a floor below food prices. While great for
farmers, this is unwelcome news for the
impoverished and malnourished populations of the world. The effect of biofuels
is also not limited to crops used in their
production. Biofuel production represents
an alternative use of land, which affects all
agricultural products.
The outlook in oil markets, which drives
demand for biofuels, is not particularly
promising either. According to a recent
report from the International Monetary
Fund, oil demand in emerging markets is
quickly catching up to demand in advanced
countries after years of significantly lower
consumption rates by the former.6 Compounding this situation, production constraints in current exporting countries are
starting to bind, as oil fields have reached
maturity. One source of relief may come in
the form of shale oil, in which the United
States is rich. But extraction from shale will
not become sustainable until the price of oil
promises to stay above $80-105 a barrel.7
Overall, there is no doubt that fundamental shocks to supply and demand in commodities, both transitory and persistent,
can account for significant price pressures
in these markets. Some, however, remain
unconvinced that these fundamental shocks
are enough to explain the entirety of price
increases. Instead, they place some blame
on a bubble in commodity prices.
Arguments for a Bubble

An asset bubble is characterized by prices
detached from fundamentals, instead driven

The Role of Expansionary
U.S. Monetary Policy

The primary means by which expansionary monetary policy influences commodity prices is by decreasing the cost of
holding inventories. Anton, the apparel
wholesaler, provides a good example.
One component of the cost of holding
inventory is the prevailing interest rate.
Expanding inventory means borrowing money, as in the case of Anton, or
sacrificing the return that one could
earn from investing the money. Nearzero interest rates, as currently exist in
the United States, significantly decrease
the cost of holding inventory and, thus,
increase demand for commodities. In
this context, inventory buildups, such
as Anton’s, can be interpreted as symptomatic of overly loose monetary policy.
Broad declines in aggregate commodity
inventories, however, cast doubt on the
current importance of this effect.
The quotation of international commodity prices in dollars opens a second means for U.S. monetary policy in

FIGURE 2

Growth in Demand for Metals from China and India

PERCENT OF GLOBAL DEMAND

45
40
35

Aluminum

30

Copper

25
20
15
10
5
0

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

SOURCE: International Monetary Fund (2010).

FIGURE 3

Co-Movement between Oil and Corn Prices
PRICE OF CRUDE OIL ($/BARREL)

140
120
100
80

January 1982 – December 2004

60

January 2005 – June 2008

40

July 2008 – December 2008

20
0

January 2009 – May 2011

1

2

3

4

5

6

7

8

CORN PRICE ($/BUSHEL)

Co-Movement between Oil and Soy Prices
140
PRICE OF CRUDE OIL ($/BARREL)

by the anticipation of profiting from
higher prices tomorrow.
Commodity markets, however, do not
meet the usual theoretical criteria for
a bubble. Arguments for a speculative
bubble focus primarily on one marketplace for commodities: the futures
market. Commodity futures markets are
where both commercial and noncommercial traders can buy and sell standardized contracts for delivery of a specified
quantity of goods at a specified date in the
future. These contracts are short-term
instruments that have few constraints on
short-selling (betting on price decreases)
and that are easy to arbitrage (profit riskfree from mispricing). In contrast, theory
holds that bubbles are limited to markets
such as real estate, where the good in
question has a long lifespan, is hard to sell
before you own, and buying and selling is
costly in terms of time and money.
Still, some believe that a bubble is
forming in commodities due to either
expansionary U.S. monetary policy and/
or record flows of investment funds into
commodity futures. These possibilities
warrant careful consideration.

120
100
80
January 1982 – December 2004

60

January 2005 – June 2008

40

July 2008 – December 2008

20
0

January 2009 – May 2011

4

6

SOURCE: The Wall Street Journal.

8

10

12

14

16

SOY PRICE ($/BUSHEL)

photos © shut terstock

The Regional Economist | www.stlouisfed.org 7

particular to influence commodity prices.
When the dollar depreciates, goods priced
in dollars become more affordable to foreign
consumers, all else equal leading them to
increase consumption and bid up the prices
on these goods. This argument is countered,
however, by the observation that commodity prices rose significantly over recent years
regardless of the currency quoted in.
The rather recent argument that has been
put forth is that historically low U.S. interest rates have increased commodity prices
by driving investment funds into other
markets, including the financial markets of
emerging countries, to seek higher returns.
The evidence, however, is founded mostly

2006 to just under $200 billion by the end
of 2007. The proposed link between large
flows of capital into commodity markets
and increases in current prices appeals to
common sense: Speculative demand for
commodity-based assets increases demand
for the underlying commodity, increasing
its price. A second practically founded
rationale for why excessive speculation
must have played a role in rising commodity
prices is embodied by a U.S. Senate committee staff report in 2006: “The traditional
forces of supply and demand cannot fully
account for [energy price] increases.” 9
Despite these straightforward propositions, however, the true impact of specu-

To try to design policy around commodity prices would require
abrupt about-faces and would detract from a central bank’s
goal of bringing stability to markets.
on correlation and largely lacks a credible
transmission mechanism. Completing the
theory of how an inflow of capital to emerging markets inflates commodity prices
requires a link between the inflow of foreign
investment and a broad expansion in emerging market credit. Ultimately, the banking
systems of the developing countries receiving the influxes of capital must transmit the
funds into the general economy. But the
skepticism that developing countries like
Brazil, Thailand and Indonesia have shown
toward much of the capital inflows, labeling
the funds as “hot money” seeking shortterm returns, places uncertainty over the
extent that capital inflows are funding bidups in commodity prices among developing
countries.
The impact of increased speculation
in commodity futures markets, perhaps
exacerbated by low traditional investment
returns, has been an area of intense research
in recent years, however.
The Potential Costs
of Excessive Speculation

Just as well-documented as the large gains
in commodity prices prior to the recession
is the contemporaneous large influx of
capital into the commodity markets, namely
in long-only index funds. 8 According to
Barclay’s, index fund investment in commodities increased from $90 billion in early
8 The Regional Economist | July 2011

lative inflows on underlying commodity
prices remains debatable. A technical
report prepared for the Organisation for
Economic Co-operation and Development
(OECD) offers a useful examination of the
research done on both sides.10 In particular,
the authors pointed out both logical and factual inconsistencies within the argument for
a speculation-induced bubble in commodity prices. Logical inconsistencies include
a tenuous link between speculative inflows
and demand for the underlying commodity
and doubt over the extent that index fund
investors could artificially increase futures
and cash prices while only participating in
the futures market and not the spot market,
where commodities are sold for immediate
delivery. Factual inconsistencies are numerous. For example, inventories should have
risen between 2006 and 2008 according
to the bubble theory, but they actually fell.
Other reasons for discounting this theory
include:
• arbitraging index-fund buying is fairly
easy due to its predictable nature,
• commodity prices rose in markets with
and without index funds,
• speculation was not excessive after
accounting for hedging demand, and
• price impacts across markets were not
consistent for the same level of index
fund activity.
In addition to their own analysis, the

authors of the OECD report reviewed four
studies supporting a pre-recession commodity bubble and five studies discounting
a bubble. The authors concluded that “the
weight of the evidence at this point in time
clearly tilts in favor of the argument that
index funds did not cause a bubble in commodity futures prices.” Of the studies
supporting a bubble, they write, “These
studies are subject to a number of important
criticisms that limit the degree of confidence
one can place in their results.” Still, the
OECD report contains an important caveat
regarding the markets most often linked to a
speculative bubble: “The evidence is weaker
in the two energy markets studied because
of considerable uncertainty about the degree
to which the available data actually reflect
index trader positions in these markets.”
Sorting out the bubble arguments has
extremely important policy implications
going forward.
Are Policy Responses Required
in Commodity Markets?

The most important thing to remember
with respect to commodity markets is that
they are volatile. The traditional decision
of central banks to focus on core inflation,
which excludes food and energy, is easy to
understand in the context of recent movement in rubber markets.
During 2010, the price of rubber
increased by 114 percent. The run-up in
the price was largely attributed to bad
weather, low stocks and growing demand
from China’s automobile industry. Around
the end of 2010, many investors remained
bullish on rubber prices due to expectations of continuing strong demand. Indeed,
the real price of rubber reached a historic
peak in the middle of this February. Yet
only a month removed from that peak, the
price fell more than 30 percent in a matter of weeks, and the Thai government was
discussing price supports for rubber. The
price drop was due to uncertainty over
global demand, stemming first from unrest
in the Middle East and, subsequently, the
earthquake and resulting tsunami in Japan
and their uncertain effects on the demand
for rubber tires from Japanese carmakers
like Toyota, Honda and Nissan. This drop
was then followed by a 23 percent increase
in the price over the second half of March

as Thailand, the largest producer of rubber,
ultimately intervened to buy up domestic
rubber supplies and support prices, while
simultaneously telling farmers to restrict
supplies in an effort to bid prices back up.
Not only are large movements in commodity prices common, but they are often linked
to inherently unpredictable events. Just in
the past few months, cotton prices fell by 25
percent and oil had its largest one-day drop
in two years. To try to design policy around
commodity prices would require abrupt
about-faces and would detract from a central
bank’s goal of bringing stability to markets.
More pertinent questions with respect to
commodity markets are:
• Is strong regulation in futures markets
needed?
• Are large subsidies on biofuels good policy?
• Should U.S. monetary policy take into
consideration global economic conditions?
Some countries, like India, have already
begun to regulate commodity futures markets; other countries, including the United
States, have debated the issue. Both those
who believe in a speculative commodity
bubble and those who do not can agree that
properly functioning commodity futures
markets are integral to the real economy
because they allow those who do not wish to
hold the risk of future price movements to
sell that risk to willing parties. The OECD
report provides a reminder that index fund
investors are an important source of liquidity and of risk absorption for these markets.
Pushing such investors out of the market
could result in huge costs, which must be
weighed against the evidence that their
activity is hindering, and not enhancing,
the proper functioning of these markets.
With respect to biofuels, potential
negative effects, such as reversing a 30-year
downward trend in real food prices, are of
particular relevance because these markets
are currently highly dependent on government subsidies. Brazil’s ethanol from sugar
cane is the only biofuel whose production is
viable without government subsidies. In the
United States, subsidies on ethanol increase
the price that processors can afford to pay
for corn and break even (a function of oil
prices) by $63 per ton. This compares with
an average price of corn in 2005 (predating
heavy investments in biofuel) of $75 per ton
and a price of $163 per ton that processors

can already afford to pay and break even
given crude oil prices of $100 per barrel.
Government support of the industry is
motivated by benefits, such as energy independence and a reduction in the environmental impact, that accrue to society but
cannot be internalized by processors. But
recent life-cycle analysis of biofuels—an
analysis that takes into account the extra
land needed to grow crops and the production process—raises questions about the
environmental benefits. The question is
whether there may be less-costly and moreefficient ways to achieve the same policy
goals. The long-run success of biofuels is
likely to hinge on the development of second-generation fuels, which can make use
of more parts of the crop, as well as biofuels
based on highly efficient algae.
The final question regarding the consideration of global economic conditions in U.S.
monetary policy debate will require much
more convincing evidence before a firm
conclusion can be reached. If expansionary
U.S. monetary policy is transmitted globally to economies in danger of overheating,
which in turn bids up commodity prices
and, hence, increases price levels back at
home, then U.S. monetary policy should
care about output gaps around the world.
At the same time, the mere correlation of
commodity price increases with loose U.S.
monetary policy, without any convincing
empirical evidence or theoretical mechanisms for this avenue, is not enough to
determine that U.S. policy decisions should
factor in economic conditions from Latin
America to Europe, from Asia to Africa.
Ultimately, the greatest lesson from recent
trends in commodity prices may be the
reminder that economics is founded on the
assumption of a world with unlimited wants
and limited resources. A world with a growing population and ever-increasing income
parity implies a world with ever-increasing
competition for resources.

endnotes
1
2
3
4
5
6
7
8

9
10

See O’Donnell.
See Pleven and Wirz.
See Food and Agriculture Organization of the
United Nations (2009).
See Davis.
See Food and Agriculture Organization of the
United Nations (2008).
See International Monetary Fund (2011).
See Engemann and Owyang.
“Long-only” refers to the fact that these index
funds make only buy and sell decisions and do
not short futures contracts.
See Senate Report 109-65.
See Irwin and Sanders.

R eferences
Davis, Tony. “As Copper Price Booms, ‘Bust’
Is a Scary Thought.” Arizona Daily Star,
Jan. 2, 2011.
Engemann, Kristie M.; and Owyang, Michael T.
“Unconventional Oil Production: Stuck in a
Rock and a Hard Place.” The Federal Reserve
Bank of St. Louis’ The Regional Economist,
July 2010, Vol. 18, No. 3, pp. 14-15.
Food and Agriculture Organization of the United
Nations. The State of Food and Agriculture
2008. Rome: Food and Agriculture Organization of the United Nations, 2008.
Food and Agriculture Organization of the
United Nations. “The State of Agricultural
Commodity Markets: High Food Prices and
the Food Crisis—Experiences and Lessons
Learned.” 2009.
International Monetary Fund. “World Economic
Outlook, April 2011. Tensions from the
Two-Speed Recovery: Unemployment,
Commodities, and Capital Flows.”
International Monetary Fund. “Regional
Economic Outlook, October 2010. Asia and
Pacific: Consolidating the Recovery and
Building Sustainable Growth.”
Irwin, Scott H.; and Sanders, Dwight R. “The
Impact of Index and Swap Funds in Commodity Markets.” A technical report prepared for
the Organisation for Economic Co-operation
and Development, June 2010.
O’Donnell, Jayne. “Wal-Mart CEO Bill Simon
Expects Inflation.” USA Today, April 1, 2011.
Pleven, Liam; and Wirz, Matt. “Companies Stock
Up as Commodities Prices Rise.” The Wall
Street Journal, Feb. 3, 2011.
Senate Report 109-65, “The Role of Market
Speculation in Rising Oil and Gas Prices:
A Need to Put the Cop Back on the Beat.”
Staff report prepared by the Permanent
Subcommittee on Investigations of the
Committee on Homeland Security and
Governmental Affairs, U.S. Senate,
June 27, 2006.

Luciana Juvenal is an economist and Brett
Fawley is a senior research associate, both
at the Federal Reserve Bank of St. Louis. See
http://research.stlouisfed.org/econ/juvenal/
for more on Juvenal’s work.

The Regional Economist | www.stlouisfed.org 9

u n e m p l o y m e n t

The Mismatch between
Job Openings and Job Seekers
photos © shut terstock

By Maria E. Canon and Mingyu Chen

T

he 2007-09 recession had a severe impact
on the U.S. labor market. During the
recession, more than 89 million employees
lost their jobs, while fewer than 82 million
were hired.1 The unemployment rate spiked
to a 27-year high of 10.1 percent in October
2009. Since then, the labor market has experienced a slow recovery; the unemployment
rate still stood at 9.1 percent in May.
In the 2010 annual report of the Federal
Reserve Bank of St. Louis, David Andolfatto
and Marcela Williams suggested that search
“frictions” might explain why the unemployment rate remained high even while job

different contexts, such as industries, occupations and educational levels. Geographic
characteristics can be measured at different
levels, such as metropolitan statistical areas
(MSAs), states and, at an even larger level,
census regions. Economists have recently
paid close attention to mismatch and have
investigated whether it is causing the currently high unemployment rate in the U.S.
Some evidence suggests that mismatch
might have increased since the recession
started. The figure shows the average
monthly share of vacant jobs and share of
employment lost by industry from December

Mismatch can be interpreted as a poor match between the
skills and location required to fill vacant jobs and the skills
and geographic preferences of unemployed workers.
openings appeared to have increased during
the recent recovery. One type of friction
that they mentioned relates to employeremployee pairings: Each job and worker has
idiosyncratic characteristics that make some
job-worker pairings more productive than
others. As employers and workers usually
cannot anticipate where the best pairing is
located, they must expend time and resources
to search out the best matches.
Mismatch can be interpreted as a poor
match between the skills and location
required to fill vacant jobs and the skills
and geographic preferences of unemployed
workers. The idea, also known as structural
imbalance, was first identified by a group
of European economists in the 1970s, when
they were struggling to understand the
consistently high unemployment rate in
some European countries.2
In general, skills can be represented in
10 The Regional Economist | July 2011

2007 to February 2011.3 Most new positions
have been created in some sectors, while most
job loss has been concentrated in others.
Since these new jobs usually require different
skills than what unemployed workers from
different sectors have, firms and unemployed
workers may take longer to find their best
matches. For example, over 50 percent of
the jobs lost between December 2007 and
February 2011 were in manufacturing and
construction, while more than 90 percent
of new positions opened in other industries.
The education and health sector has experienced steady employment growth since the
recession started; 20 percent of all job openings have occurred in this sector.
In the rest of the article, we review the
role of two types of mismatch (skill and
geographic) in explaining the increase in
unemployment that occurred during and
after the 2007-09 recession.

Skill Mismatch

Economists Ayşegül Şahin, Joseph Song,
Giorgio Topa and Giovanni Violante recently
derived mismatch indexes from an economic
model.4 In their framework, the aggregate
labor market is comprised of many small
labor markets, categorized by skill levels
or working locations (e.g., industries and
MSAs). Şahin and others define mismatch as
the distance between the observed allocation
of unemployed workers across sectors and
the “optimal” allocation. The optimal allocation of unemployed workers is the allocation
that, given the distribution of vacancies in
the economy, would occur if there were free
movement of workers across labor markets.
The authors’ indexes allow them to quantify
not only the level of mismatch but also the
proportion of the increase in unemployment
that can be attributed to mismatch.
Using five industries as divisions of the
aggregate labor market, Şahin and her
co-authors found that the fraction of unemployed workers misallocated increased by
10 percentage points during the 2007-09
recession; the fraction then dropped but
remained at a level higher than its prerecession level. But this increase in mismatch can
explain only between 0.4 and 0.7 percentage
points of the total increase of five percentage points in the unemployment rate from
the beginning of 2007 to the middle of 2009.
Therefore, although skill mismatch increased
during the recession and influenced unemployment to some degree, it is not the main source
of the increase in the unemployment rate.
Geographic Mismatch

The 2007-09 recession was accompanied
by a steep decline in housing prices. Some
economists and commentators have argued

E ndnotes

Share of Job Vacancies and Lost Employment by Industries

1

PERCENT

M o n t h ly Av e r a g e f r o m D e c e m b e r 2 0 0 7 t o F e b r u a r y 2 0 1 1

30
25
20
15
10
5
0
–5
–10
–15
–20

Vacancy Share
Employment Lost Share
2
3

Construction Manufacturing

Professional
Trade,
Transportation and Business
Services
and Utilities

Education
and Health
Services

Leisure and
Hospitality

Government

Other

SOURCES: Job Openings and Labor Turnover Survey and the Current Population Survey.
NOTE: A negative share of lost employment in the education and health services sector means it gained employment during the examined period;
that growth was about 18 percent of all the total employment lost.

Vacancy share of an industry is the number of openings in that industry over the total number of job openings in
the U.S. Lost employment share of an industry is the number of jobs lost in that industry over the total number of
jobs lost in the U.S.

that the housing crisis may slow down geographic mobility of job applicants. Economists Fernando Ferreira, Joseph Gyourko
and Joseph Tracy concluded from past
research that negative equity significantly
reduced the mobility of homeowners. Unemployed workers who owe more than what
their home is worth are less likely to apply for
and accept positions that are in places that
would require them to sell their homes.
If this is the case, then a geographic mismatch is likely to occur and lead to prolonged
high unemployment rates. Economist Sam
Schulhofer-Wohl, however, points out that
Ferreira and his co-authors systematically
dropped from their data some observations
of homeowners with negative equity who
move; this resulted in a misleading conclusion. Schulhofer-Wohl found that negative
equity does not reduce mobility of homeowners, a finding that is consistent with what is
suggested by the empirical results from Şahin
and others. Şahin and her co-authors found
that geographic mismatch, measured at
census region level, was very low throughout
the recession and has had no impact on the
recent dynamics of U.S. unemployment.

not find evidence of it being the principal
source. The newly developed measure of
mismatch indicates a rise in skill mismatch
(across industries) but only associates it with
a minor increase in the unemployment rate.
The geographic mismatch (across census
regions) does not have a significant effect
on the labor market.
One potential alternative explanation for
the persistently high unemployment rate
is the extended hiring time. Although job
vacancies have been rising, the increased
number of unemployed workers makes those
openings more competitive. Anecdotal
evidence suggests that, since the last recession started, companies have had a difficult
time deciding who the “best” candidates
are; therefore, the hiring time is extended.
According to an article in The Wall Street
Journal, a survey conducted recently by the
Corporate Executive Board indicated that
positions that typically took two months to
fill before the recession are sometimes taking
four times longer to fill.5 Even with qualified
applicants on hand, recruiters might be holding out for better candidates.

Conclusion

Maria E. Canon is an economist and Mingyu
Chen is a research analyst, both at the Federal
Reserve Bank of St. Louis. See http://research.
stlouisfed.org/econ/canon/ for more on Canon’s
work.

Although mismatch has recently raised
a lot of attention among economists as a
potential explanation for the increase in
unemployment, the existing literature does

4
5

Data are from the Job Openings and Labor Turnover Survey. Job loss is measured by the number
of employees separated from payroll, and number
of hires is measured by the additions of personnel
to payroll.
See Padoa-Schioppa for a collection of papers on
findings of mismatch in the 1970s.
December 2007 is the starting date of the 2007-09
recession. Vacancy share of an industry is the
number of openings in that industry over the total
number of job openings in the U.S. Lost employment share of an industry is the number of jobs
lost in that industry over the total number of jobs
lost in the U.S.
Their definition of mismatch builds on the findings of Jackman and Roper.
See Light.

R eferences
Andolfatto, David; and Williams, Marcela. “Many
Moving Parts: A Look inside the U.S. Labor Market.” Annual Report 2010, Federal Reserve Bank
of St. Louis, April 2011. See www.stlouisfed.org/
publications/ar/
Ferreira, Fernando; Gyourko, Joseph; and Tracy,
Joseph. “Housing Busts and Household Mobility.”
Journal of Urban Economics, July 2010, Vol. 68,
No. 1, pp. 34-45.
Jackman, Richard; and Roper, Stephen. “Structural
Unemployment.” Oxford Bulletin of Economics
and Statistics, Vol. 49, No. 1, pp. 9-36.
Light, Joe. “Corporate News: Jobs Open, but Hiring
Remains Slow—Recruiters Say They Have Trouble
Finding Candidates for Skilled Positions, and
Managers Hold Out for Better Prospects.”
The Wall Street Journal, March 7, 2011.
Padoa-Schioppa, Fiorella. Mismatch and Labour
Mobility. Cambridge: Cambridge University
Press, 1991.
Şahin, Ayşegül; Song, Joseph; Topa, Giorgio; and
Violante, Giovanni L. “Measuring Mismatch
in the U.S. Labor Market.” Manuscript, revised
March 2011. See www.newyorkfed.org/research/
economists/sahin/USmismatch.pdf
Schulhofer-Wohl, Sam. “Negative Equity Does Not
Reduce Homeowners’ Mobility.” Federal Reserve
Bank of Minneapolis Working Paper 682, revised
December 2010.

The Regional Economist | www.stlouisfed.org 11

h o u s i n g

The Foreclosure Crisis in 2008:
Predatory Lending
or Household Overreaching?
© Roy Scot t/get t y images

By William R. Emmons, Kathy Fogel, Wayne Y. Lee, Liping Ma, Deena Rorie and Timothy J. Yeager

W

atching southern Florida home prices
spiral out of reach, Mr. Briar decided
to take the plunge in 2004 and buy his first
home. The mortgage broker he worked
with encouraged him to enter into a 2/28
contract, in which the interest rate is fixed
for the first two years and then resets to a
higher floating rate. Mr. Briar bought the
home, and the mortgage broker transferred
the loan to Wall Street, where it was packaged and securitized into a collateralized
debt obligation (CDO). Mr. Briar struggled
to pay his mortgage even during the first

vastly different. If predatory lending was the
primary culprit, strong consumer protection
laws like those in the Dodd-Frank law might
be sufficient to avoid a future foreclosure
crisis; that’s because such laws would prevent
Wall Street banks from making high-risk
loans that borrowers could not possibly
afford. If household overreaching was the
primary culprit, preventing another foreclosure crisis is a much more complex policy
challenge. A return to high appreciation in
home prices could again set off dynamics
in which even borrowers with decent credit

Certainly, both predatory lending and household overreaching
occurred during the subprime housing bubble. But it is important to identify the primary reason for the foreclosure crisis
because the policy implications are vastly different.
two years. Meanwhile, Florida home prices
plunged, and, eventually, Mr. Briar permanently defaulted on his loan. The servicing
bank foreclosed nine months later.
Although Mr. Briar is a fictitious person,
this story has played out for millions of
households over the past few years. Did Mr.
Briar overreach by taking on too much housing debt, or was he duped by Wall Street?
The answer is difficult to ascertain because
it ultimately depends on the intentions of
the borrower and the lender. After the fact,
a lender would hardly admit to deceiving a
borrower, and the borrower would be more
than willing to place at least some of the
blame for the foreclosure on the lender.
Certainly, both predatory lending and
household overreaching occurred during the
subprime housing bubble. But it is important
to identify the primary reason for the foreclosure crisis because the policy implications are
12 The Regional Economist | July 2011

would overreach and end up in homes they
ultimately couldn’t afford. The only comprehensive solution might be to prevent the
formation of asset price bubbles, a solution
that would require policymakers, such as the
central bank, to recognize and deflate such
bubbles when they occur.
To distinguish between the predatory
lending and overreaching hypotheses, we
tapped two nationwide data sources to
analyze the characteristics of households in
foreclosure. Because private motivations
were unobservable, we argue that households
with low income and education levels should
be the most vulnerable to predatory lending practices because such borrowers, all
else equal, are more likely to have a poorer
understanding of the contract terms at the
time of origination. In contrast, households
most susceptible to overreaching are those
that have high economic aspirations relative

to their current income and net worth; these
households could already have relatively high
incomes and be well-educated.
Profiles of Foreclosed Households

The data used in our analysis of foreclosed
households came from two sources. RealtyTrac compiles nationwide data on homes
in foreclosure. Acxiom compiles data on
millions of U.S. households each quarter and
segments households based on economic,
demographic and consumption patterns. To
obtain a profile of foreclosed households, we
combined these two large datasets by household for the third quarter of 2008. The dataset contains more than 40 million records
and more than 200,000 foreclosures.
Figure 1 presents key statistics from our
dataset on households in foreclosure alongside households not in foreclosure. Defaulted
homes were more expensive, on average. The
median market value of homes in foreclosure
was $242,400 versus $199,129 for homes not
in foreclosure. As expected, the median
loan-to-value ratio was much higher on
defaulted properties, at 96 percent, which was
more than 30 percentage points higher than
on nondefaulted properties. Homes in foreclosure also were slightly newer and smaller
in terms of square footage.
Household characteristics, shown in the
bottom panel, reveal that households in
foreclosure had slightly fewer members and
were significantly younger. The median
head-of-household age for a foreclosed
household was 44, eight years younger than
the median for households not in foreclosure.
Heads of households in foreclosed properties
were less likely to be married and more likely
to be single. They had lower incomes and
much shorter length of residence. Although

mean years of education were similar at just
over 14, households in foreclosure had a
median 12 years of education compared with
a median of 16 years for households not in
foreclosure.
Because we were interested in identifying
the characteristics of households that were
responsible for a disproportionate number
of foreclosures, we looked beyond the simple
averages described above. PersonicX Life
Stage Segmentation is an Acxiom classification scheme that divides households into
21 life stages based on marital status, number
of children in the household, employment
status and other socio-economic characteristics.1 A number and letter correspond to the
name of each group listed in Figure 2. The
number corresponds to the age of the group,
with lower numbers representing younger
demographics; the letter approximates the
group’s cultural generation. Groups ending
in B represent the Baby Boomers, while X
and Y represent Generation X and Generation Y. M represents the Mature generation,
mostly those in their 50s and 60s, and S represents Seniors, most of whom are retired.
To see which of the 21 PersonicX groups
contributed the most disproportionately to
the foreclosure crisis, we calculated the share
of total foreclosures represented by each
group and the share of all households represented by each group. We subtracted the
household share from the foreclosure share to
derive the “excess foreclosure shares” of each
group. Group 07X, for example, accounted
for 5.52 percent of all households but 11.3
percent of all foreclosures. The excess share
of foreclosures is the difference of these two
ratios, or 5.78 percentage points. Figure 2
plots the 11 PersonicX Groups with the highest excess foreclosure shares.
Figure 2 shows that excess foreclosures
came primarily from younger, relatively affluent households, a finding more consistent
with the overreaching hypothesis. In particular, the group with the largest number of
excess foreclosures was 07X, Cash & Careers.
This Generation X group was the most
prosperous of the generation of adults born
in the mid-1960s and early 1970s. Out of the
first 10 PersonicX groups with excess foreclosures, Cash & Careers members ranked
first in average household income ($59,500),
net worth and years of education (14.8). The
second most-overrepresented group in terms

Figure 1

U.S. Property and Household Characteristics by Foreclosure Status
Not in Foreclosure

In Foreclosure

Mean

Median

Mean

Median

Home Market Value

$278,115

$199,129

$290,653

$242,400

Home Purchase Amount

Property Characteristics
$198,598

$140,000

$253,650

$199,950

Loan to Value

64.6%

65.0%

90.7%

96.0%

Year Home Built

1969

1974

1972

1978

Home Size (square feet)

2,376

1,907

1,554

1,526

Household Characteristics
Household Size

3.3

3.0

2.9

2.0

Annual Income

$55,700

$51,500

$51,241

$48,800

Years of Education

14.8

16.0

14.1

12.0

Age

53.1

52.0

45.1

44.0

Length of Residence

9.1

9.0

5.3

4.0

1.4

1.0

1.5

1.0

Number of Children
Married

70.7%

56.2%

Single

25.7%

36.9%

SOURCES: Acxiom, RealtyTrac and authors’ own calculations.

Figure 2

Excess Foreclosure Percentages by PersonicX Group for U.S. Households
5.78 percentage points

07X Cash & Careers
02Y Taking Hold

3.66

03X Transition Blues

2.97

09B Boomer Singles

2.40

05X Gen X Parents

1.78
1.70

08X Jumbo Families
1.43

01Y Beginnings
06X Mixed Singles

1.24

10B Mixed Boomers

1.19
1.18

04X Gen X Singles
14B Our Turn

0.05

SOURCES: Acxiom, RealtyTrac and authors’ own calculations.

of excess foreclosures was 02Y, Taking Hold.
These were Generation Y households with an
average age of 27.8 years, second-highest average income ($55,500), third-highest net worth
and fifth-highest education level (14.1 years).
These two groups’ characteristics were consistent with our expectations of households that
are most likely to overreach.
The two groups in Figure 2 that were most
likely to be victims of predatory lending were
Group 01Y, Beginnings and Group 06X, Mixed
Singles because these groups ranked ninth
or 10th in income, net worth and education.
Yet these groups ranked seventh and eighth,

The figure shows the groups of people with the
highest excess foreclosure rates. The classifications come from Acxiom’s PersonicX Life Stage
Segmentation. In the names of the groups, the
lower numbers represent younger people. The
letters after the numbers stand for: B=Baby
Boomers, X=Generation X and Y=Generation Y.
For example, 07X Cash & Careers accounted for
5.52 percent of all households but 11.3 percent
of all foreclosures, meaning its excess share of
foreclosures was 5.78 percentage points.

The Regional Economist | www.stlouisfed.org 13

Figure 3

Annualized Foreclosure Rates, 2008:Q3

North Dakota

Washington

Montana
Minnesota

Idaho

Wyoming

Michigan

New York

New
Hampshire

Oregon

Vermont

Maine
Wisconsin

South Dakota

Massachusetts

Iowa
Nebraska

Rhode Island

Pennsylvania
Illinois
Nevada

Utah

Ohio

Indiana

Connecticut
New Jersey

Colorado
Kansas

Delaware
Maryland

West
Virginia

Missouri
Kentucky

Virginia

California
Oklahoma
Arizona

New Mexico

Tennessee

Washington, D.C.

North
Carolina

Arkansas

South
Carolina

Mississippi

Alabama

Georgia

Texas
Louisiana

Alaska

Hawaii
Florida

9.63 to 11.00

8.25 to 9.62

6.88 to 8.24

5.50 to 6.87

4.13 to 5.49

2.75 to 4.12

1.38 to 2.74

0.00 to 1.37

No data available

SOURCE: RealtyTrac

The foreclosure percentages for each
state were calculated by taking the
annualized number of households that
were in foreclosure during the third
quarter of 2008 and dividing them
by the total number of households
in that state.

14 The Regional Economist | July 2011

respectively, in share of excess foreclosures,
and jointly, they accounted for just 2.67
percentage points of excess foreclosures
relative to 9.44 percentage points for groups
07X and 02Y.
Rather than rely solely on Acxiom’s
groupings, we also separated all the households into quadrants based on income and
education to identify the most leveraged
households in each quadrant based on their
loan-to-income ratio. We conjectured that
the most over-leveraged households in
the low-income, low-education (bottom)
quadrant were more likely to be victims of
predatory lending, while the most overleveraged households in the high-income,
high-education (top) quadrant were more
likely to have overreached. Our tests showed
that the most-leveraged households in the
top quadrant were statistically more likely to
enter foreclosure than the other households
in the same quadrant. This pattern was not
true, however, for households in the bottom quadrant. Once again, overreaching

appeared to be the more important explanation of mortgage foreclosure.
Geographic Patterns of Foreclosures

In addition to household profiles, our
hypotheses also have differing implications
for the geographic distribution of foreclosures. The predatory lending hypothesis
predicts that the geographic distribution of
foreclosures will reflect the spatial distribution of low-income and low-educated households because bankers (or their brokers) will
seek out households most easily deceived,
regardless of the household’s location. In
contrast, the overreaching hypothesis
predicts that bubble dynamics will be the
important factor explaining the foreclosures.
This hypothesis implies that foreclosure
rates will spike in specific “hot spots” where
households and speculators bid up prices in
an effort to buy more-expensive homes before
these homes become unaffordable.
We identified real estate hot spots using
data from the Federal Housing Finance

Agency House Price Index between 2000 and
2007. The areas with the most significant
home appreciation are Florida and the states
in the Southwest and in the Northeast.
Figure 3 is a map of foreclosure rates
by state for the third quarter of 2008. The
overreaching hypothesis suggests that there
should be a strong correlation between the
states with the greatest price increases and
the states with the highest foreclosure rates.
Indeed, the concentration of foreclosures in
the Southwest and in Florida is consistent
with overreaching as a more important
explanation than predatory lending for
the foreclosure crisis. The main outliers in
Figure 3 are the Great Lakes states, such as
Michigan, Illinois, Indiana and Ohio, all of
which experienced moderate home-price
appreciation but relatively high foreclosure
rates. Foreclosures in these states are more
likely driven by a weak economy rather than
by housing price bubbles.
To more firmly support this visual evidence, we ranked all of the 50 states by home
price appreciation (between 2000 and 2007)
and foreclosure rates (in 2008) to evaluate
their statistical correlation. The overreaching
hypothesis suggests that these two characteristics should be positively correlated.
Indeed, for all the states, the correlation is
0.23—positive as the overreaching hypothesis
suggests, though not statistically different
from zero. When we exclude the Great Lakes
states, however, the rank correlation rises to
0.43 and is statistically significant. Again,
the evidence is more consistent with the overreaching hypothesis than with the predatory
lending hypothesis.
Policy Response to Asset Bubbles

By combining household foreclosure data
from RealtyTrac with household data from
Acxiom, we were able to create a profile of
households in foreclosure during the early
stages of the financial crisis. We found that
many foreclosed households were young with
relatively high income and education levels.
Moreover, geographic foreclosure patterns
were consistent with bubble dynamics as
illustrated by the positive correlation between
home-price appreciation and subsequent
foreclosure rates. The weight of the evidence
supports the overreaching hypothesis.
Consequently, strong predatory lending
restrictions, while desirable, would likely

be insufficient to avoid a future foreclosure
crisis should another housing bubble emerge.
In our view, the ultimate underlying cause
of the foreclosure crisis was the emergence of
a significant housing price bubble and its subsequent collapse. Unfortunately, preventing
asset price bubbles is a much more complex
policy problem to address than protecting
consumers from predatory lending.
The late economist Hyman Minsky argued
that capitalist economies go through leverage cycles, in which credit access becomes
progressively easier as an economy grows
strongly. The success of lenders and firms in
the good years, combined with appreciating
capital assets, reduces the perception of risk
and encourages increasingly riskier financing. Financial innovation exacerbates the
leverage cycle as financial firms devise new
ways to extend credit. Eventually, asset prices
peak and then begin to decline, financial
instability emerges and latent systemic risk is
unleashed in a financial crisis.
This leverage cycle, which Minsky called
the financial instability hypothesis, may be
inherent to the capitalist system. Minsky’s
thesis might portray the subprime financial
crisis quite well, but it also would suggest
that future crises can result from asset
bubbles in other sectors of the economy,
not just housing.
If capitalist economies are subject to periodic asset price bubbles, Minsky suggested
that policymakers take steps to eliminate
bubbles that threaten to become systemically
important. This, of course, requires the ability to 1) recognize an asset bubble, 2) classify
the bubble as a systemic risk to the economy
and 3) curb the formation of the bubble either
through monetary policy actions or through
more-targeted interventions, such as higher
bank capital requirements or more stringent
mortgage underwriting criteria.

endnote
1

A list of the 21 PersonicX life stages and their
descriptions is available from the Acxiom web
site at www.acxiom.com/products_and_
services/Consumer%20Insight%20Products/
segmentation/Pages/index.html

R eferences
Minsky, Hyman P. Stabilizing An Unstable
Economy. New York: McGraw Hill, 2008.
Originally published by Yale University Press,
New Haven, Conn., 1986.

William R. Emmons is an economist at the
Federal Reserve Bank of St. Louis. See
stlouisfed.org/emmonsvitae for more on his
work. Kathy Fogel, Wayne Y. Lee, Liping Ma,
Deena Rorie and Timothy J. Yeager are at the
Sam M. Walton College of Business at the University of Arkansas. See http://waltoncollege.
uark.edu/finn/PredatoryLendingOverreaching.
pdf for the complete research paper.

The Regional Economist | www.stlouisfed.org 15

h o u s i n g

A Closer Look
at House Price Indexes
By Bryan Noeth and Rajdeep Sengupta

C

entral to economic events of recent
times were the rapid increases in house
prices after 1995 and the ensuing downturn
in those prices around 2006-07. Naturally,
the importance of accurate measurement of
house price trends can hardly be overemphasized. Several prominent house price
indexes have been developed for the United
States. These include the National Association of Realtors (NAR) median index, the
Census Bureau median index, the S&P/
Case-Shiller national index, the CoreLogic
index and the Federal Housing Finance
Agency (FHFA) index.1 Each differs in

© spark studio/get t y images

conditions. Dealing with houses that differ
in “hedonic” characteristics—such as the
square footage, number of bedrooms and
distance from city center—can be tricky.
To deal with these issues, economists
have adopted a “repeat sales” methodology,
which measures price changes of the same
house between a previous and current sale.2
Examples of repeat sales indexes include the
Case-Shiller, CoreLogic and FHFA indexes.
This method allows economists to control for
home characteristics—the previous sale price
being considered an appropriate surrogate
for the hedonic information. An obvious

It is not always a fact that home price indexes move in tandem.
It is not difficult to record instances where changes in home
prices differ in both direction and magnitude.
methodology, in its emphasis on the various
segments of the housing market or both. To
the casual observer, the difference in price
changes recorded on each of the indexes
can be perplexing. Therefore, knowing how
the indexes differ from one another can be
instructive as to which index to follow.
Housing price indexes are calculated by
tracking home prices in a given region over a
period of time. Ideally, one would track the
price of a random sample of houses. However, this method has operational problems
because, at any particular point in time, not
all houses are for sale; additionally, there may
be variations in the type of houses sold. If
one merely tracked the price of homes sold
over time (e.g., as is found in median house
price indexes, such as the NAR and the
Census indexes), observed changes could be
due to changes in the composition of homes
sold as opposed to changes due to market
16 The Regional Economist | July 2011

limitation is the omission of sales of new
homes. Additionally, to maintain consistency, repeat sales indexes often drop houses
that have undergone major improvements or
deterioration. Consequently, this method’s
calculations require a large number of repeat
sales, which can be problematic for nonmetropolitan areas and also during downturns.
Finally, it has been shown that repeat sales
with larger time gaps in between transactions
have greater variance, leading some indexes
to adjust their weight downward.
Two median price indexes are noteworthy: the NAR index and the Census Bureau
median index. The former dates to 1968. The
data come from surveys of sales of existing
single-family homes from NAR affiliates.
The national median is calculated by valueweighting the median within each of the four
census regions in the country by the number
of single-family homes in each region.

The Census Bureau median index differs
from the NAR index mainly in that the former covers new homes as opposed to existing structures. Consequently, the Census
median index is typically higher than the
NAR index (see Figure 1) since, historically,
new homes have been higher-priced than
existing homes. In terms of both indexes,
prices have clearly fallen since their peaks
in 2006-07. However, the gap between the
two has widened recently, largely due to
the steeper decline in the NAR index. One
possible reason: Existing homes have seen
an increase in foreclosures and short sales,
placing downward pressure on the NAR
index. Distressed sales are less of a concern
in the market for new homes, and the Census median index has not fallen as sharply.
Indexes of repeat sales are more commonly cited than median indexes. The
FHFA index is published quarterly by the
FHFA and goes back to 1975. The FHFA
also publishes several other indexes, including regional, state, metropolitan, purchase
only, average and median price indexes on a
monthly and quarterly basis.
Standard & Poor’s publishes the CaseShiller proprietary family of indexes, which
includes quarterly national, monthly
10- and 20-composite metropolitan area,
and individual metropolitan series.
The final index is the monthly CoreLogic
index, a proprietary index published by
CoreLogic and dating to 1976. Additionally,
CoreLogic publishes a variety of indexes
based on property locations, price tiers, property types, loan types and distress levels.
Among the three major repeat sales
indexes, the FHFA index is significantly
different from the other two. FHFA collects
data from conforming mortgages only (i.e.,

those securitized by Fannie Mae or Freddie Mac).3 The Case-Shiller and CoreLogic
indexes, however, include all available arm’slength transactions on single-family homes,
including sales financed with nonconforming mortgages—such as jumbo, Alt-A and
subprime. As a result, these indexes include
sales of higher-priced homes (those financed
with jumbo mortgages) and transactions
with more-volatile sales prices (those
financed by Alt-A or subprime mortgages).
Moreover, unlike the FHFA index, the CaseShiller and CoreLogic indexes value-weight
transactions so that higher-valued homes
have greater effect on the index.4 A final
distinction is that the FHFA index includes
refinances, whereas the Case-Shiller and
CoreLogic indexes do not.5
While the Case-Shiller and CoreLogic
indexes are similar, they are different on
two counts. In addition to value-weighting, the Case-Shiller series employs an
Figure 1

Median Price Indexes
300

$ THOUSANDS

250
200
150
100

Census Bureau median house price index

50
2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2000

2001

NAR median house price index

0

SOURCES: National Association of Realtors and the Census
Bureau/Haver Analytics

Figure 2

Repeat Sales Indexes
500
200

400

150
CoreLogic

100
S&P/Case-Shiller

50

300
200
100

FHFA (right axis)

2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011

0

0

INDEX SET AT 100 FOR THE YEAR 1980

INDEX SET AT 100 FOR THE YEAR 2000

250

interval-weighting procedure that places
greater weight on repeat sales with shorter
intervals. Such a weighting scheme is not
adopted by CoreLogic. Also, CoreLogic has
larger coverage because it includes mortgage
data in place of public records in states with
nondisclosure laws. This helps it obtain a
broader coverage by including some states
with nondisclosure laws that are omitted in
the Case-Shiller index.
Figure 2 shows various repeat sales
indexes.6 Notably, the FHFA index is flatter than the other two indexes. First, the
CoreLogic and Case-Shiller indexes place
more weight on higher-valued homes; so,
if higher-priced homes have larger appreciations and, subsequently, larger depreciations, then these indexes will likely see
larger swings. Second, the FHFA index is
less volatile because it does not include nonconforming loans. Combined, these factors
can help explain why the FHFA index is
flatter than the other two series.
Not surprisingly, the CoreLogic and
Case-Shiller indexes tend to move together
because of their similar computation and
included loan types. However, the CoreLogic index tends to be slightly higher than
the Case-Shiller national index. This is possibly due to the smaller weight on lengthier
intervals between sales in the Case-Shiller
index. Stated differently, the statistical procedure used in the Case-Shiller index likely
mitigates the influence of sales pairs with
extreme price changes.
It is not always a fact that home price
indexes move in tandem. It is not difficult
to record instances where changes in home
prices differ in both direction and magnitude. This is true, for example, of the FHFA
and Case-Shiller indexes for the second
quarter of 2010. The differences in methodology and composition determine the
behavior of each index at different points in
time. Knowledge of individual index calculation aids in understanding the observed
disparities among the indexes.

endnotes
1
2

3
4
5
6

The FHFA house price index was formerly
titled the OFHEO index.
This methodology was developed by Bailey,
Muth and Nourse and was later modified by
Karl Case and Robert Shiller (1987, 1989).
See http://en.wikipedia.org/wiki/
Non-conforming_mortgage
See Aubuchon and Wheelock.
The FHFA also publishes a purchase-only
index that excludes refinances.
Note that the Case-Shiller index is quarterly,
whereas the CoreLogic is monthly.

R eferences
Aubuchon, Craig P.; and Wheelock, David C.
“How Much Have U.S. House Prices Fallen?”
Federal Reserve Bank of St. Louis National
Economic Trends, August 2008.
Bailey Martin J.; Muth, Richard F.; and Nourse,
Hugh O. “A Regression Method for Real
Estate Price Index Construction.” Journal
of the American Statistical Association,
December 1963, Vol. 58, No. 304, pp. 933-42.
Case, Karl E.; and Shiller, Robert J. “Prices of
Single-Family Homes Since 1970: New
Indexes for Four Cities.” New England
Economic Review, September/October 1987,
pp. 45-56.
Case, Karl E.; and Shiller, Robert J. “The
Efficiency of the Market for Single-Family
Homes.” American Economic Review,
March 1989, Vol. 79, No. 1, pp. 125-37.

Rajdeep Sengupta is an economist and Bryan
Noeth is a research associate, both at the
Federal Reserve Bank of St. Louis. See http://
research.stlouisfed.org/econ/sengupta/ for more
on Sengupta’s work.

SOURCES: CoreLogic, Standard & Poor’s and Federal Housing
Financing Agency/Haver Analytics
The Regional Economist | www.stlouisfed.org 17

c o m m u n i t y

p r o f i l e

Du Quoin Strives
To Diversify
Beyond State Fair
By Susan C. Thomson

L

photo by susan c. thomson

Du Quoin/Perry County, Ill.
by the numbers
Population for City/County.......................6,109/22,350 *
Labor Force.....................................................NA/9,554 **
Unemployment Rate........................... NA/10.2 percent **
Per Capita Personal Income.......................NA/$24,290 ***
* U.S. Census Bureau, 2010 census
** BLS/HAVER, April 2011, seasonally adjusted
*** BEA/HAVER, 2009

largest Employers
General Cable Corp. ................................................ 215 †
Heartland Baking LLC .............................................. 200 † †
Marshall Browning Hospital..................................... 190 † °
Du Quoin Community Unit School District No. 300..... 187 †
Wal-Mart................................................................... 110 † †
† Self-reported
† † Reference USAGOV, Infogroup Inc.
° 150 full-time equivalents

18 The Regional Economist | July 2011

amppost banners around town bear the old-time
image of a horse-drawn, two-wheeled cart with
a seated, mustached driver. Thus does Du Quoin,
Ill., hitch its own wagon to its namesake event, the
Du Quoin State Fair, long famed for harness racing.
The fair is the social, recreational and economic
event of the year, unfolding over 10 days in late summer and peaking on Labor Day. It’s a somethingfor-everyone affair, with auto and motorcycle races,
horse shows, livestock and farm equipment exhibits,
carnival rides, musical acts and food. Of course,
there’s also harness racing, though the schedule was
reduced to three days from five last year, reflecting
the sport’s waning popularity, says the fair’s manager, John Rednour Jr.
Attractions are spread out across the fair’s 1,435
acres and its 77 buildings. The centerpiece is a
7,700-seat grandstand that looks out on a stage
and the one-mile, lighted, circular racing track.
An estimated 350 temporary jobs make the fair
briefly the city’s largest employer. Upward of
300,000 people attend.
Local hotels are booked solid weeks in advance,
with the overflow spilling out for miles around, says
Stacy Hirsch, executive director of the Chamber of
Commerce in Du Quoin (pronounced du-COIN).
The fair boosts business for restaurants, gas stations and stores, adds Judy Smid, president of the
Du Quoin Tourism Commission and proprietor of
a downtown gift shop. Certified public accountant
Harold Emling says the merchants tell him they get
about a month’s worth of their revenue from the fair.
Typical of state fairs, the Du Quoin event
celebrates agriculture, historically a foundation
of Southern Illinois’ economy, along with coal.
Agriculture remains the Du Quoin area’s economic
base, says Jeffrey Ashauer, the city’s economic

photo by Terry Jones

development consultant. Prospering from
recent high prices for their corn, wheat
and soybeans, farmers “come to town, buy
new vehicles, put their money in the bank
and go to Wal-Mart,” he says.
Coal, meanwhile, has taken its lumps
since the Clean Air Act dried up markets for the state’s high-sulfur product.
Most of the Du Quoin area’s coal mines
were shut down 20 or more years ago,
and the resulting job losses account for
Perry County’s chronically above-average
unemployment rate since, says Daniel
Fulk, president of Du Quoin State Bank.
The Great Recession? “Du Quoin has
been in a statistical recession for the last
30 years,” he says. “We’re used to it.”
Through it all, the community has proved
persevering and resilient, qualities that
position it to “survive very nicely in the
future,” he adds.
Rex Duncan, one of the four members
of the city’s elected governing commission, also makes an upbeat case for the
city, based on its brick-and-mortar assets.
Showing a visitor around, he points out
an Amtrak station with daily passenger
service to Chicago and New Orleans, an
$18 million high school under construction and a “basically new” hospital.
Marshall Browning Hospital dates to
1922, but it has been almost completely
rebuilt over the past decade at a cost of
nearly $10 million. Emergency room,
radiology department, pharmacy, labs,
surgical suites, offices, single-bed patient
rooms—everything has been upgraded to

state-of-the-art. A physicians’ building
and a 22-unit independent-living center
have been added to the 19-acre campus
on the edge of town.
The hospital has an annual payroll of
$6.5 million and, between its 25 beds
and extensive out-patient services, is able
to meet 75 percent of the community’s
health-care needs, says the chief executive,
William Huff.
“We don’t have a lot of industry here,”
observes Emling, while acknowledging
that two of the city’s other leading
employers happen to be manufacturers.
General Cable Corp. has been a fixture
in town under various names and owners
since 1965, making insulated cable, especially for electric utilities. The plant has
thrived not only on good relations between
management and the Teamsters-led labor
force but also on the resulting flexibility to quickly change product lines, says
human resources manager Kathy Hanks.
Although 20 production jobs were cut
during the recession, all have since been
added back.
Heartland Baking, a commercial cookie
maker that started up in 2006 in a bakery
shuttered by its previous owner, is one
of only two tenants in the city’s 90-acre,
20-year-old industrial park. The other
is MPP, an electroplating company that
moved to the park from Kansas City
in 2000.
Besides state tax credits linked to the
number of jobs created, Heartland and
MPP each got a low-interest $400,000 loan

photo provided by John Croessman, Du Quoin Evening Call

The Du Quoin State Fair has long been
associated with harness racing. Despite
the reputation, the races were held last
year only on three days instead of the
usual five because of declining popularity.
As at any major county or state fair,
the exhibition of livestock remains a
centerpiece of the action.

The Regional Economist | www.stlouisfed.org 19

photo provided by General cable corp.

photo by susan c. thomson

Wendell Killian at work at General Cable Corp.,
the largest employer in Du Quoin. The plant has
thrived in town for more than 40 years, thanks
to good labor-management relations and the
resulting flexibility to quickly change product
lines, says the human resources manager.
The Medicine Shoppe pharmacy is one
of several downtown businesses that have
taken advantage of $5,000 grants for updating
their facades. The money became available
through a tax-increment financing (TIF) district.

20 The Regional Economist | July 2011

funded by the federal block grant program,
with principal and interest repayable to the
city. The city has used the income to make
civic improvements and other low-interest
business loans.
Ashauer says the park has proved a tough
sell to out-of-town prospects for three reasons: U.S. manufacturing has been moving
offshore, Illinois is perceived as businessunfriendly and Du Quoin isn’t located on
an interstate.
That “tough sell” has become a bit less
tough now that the city has decided to outfit
the park with solar panels. The installation,
financed with $405,000 in federal stimulus
money plus $135,000 in city development
funds, is expected to be finished later this
year. The panels are expected to shave
electric bills for park tenants by 10 percent.
Given that promise, word of the park is now
spreading “like wildfire,” Ashauer says.
As a further inducement, the city in 2009
classified the park and some nearby land
as a tax-increment financing (TIF) district,
where any new or increased city real estate
taxes will be automatically reinvested.
This was the second of Du Quoin’s two
TIF zones. The original, set up two years
earlier, covers the dozen square blocks of
the city’s Victorian-era downtown.
As the decades passed, Wal-Mart opened
outside of downtown and retailers gravitated to malls. The city’s core was showing
signs of wear and neglect. “Your downtown
is like your home; if you don’t keep it up, it
falls apart,” says Mayor John Rednour Sr.,
father of the fair manager.
As one remedy, the city has set aside
$100,000 from the downtown TIF and
made it available in grants of up to $5,000
to downtown owners for updating their
buildings’ facades. Projects began this past
spring. In the interest of creating synergies
between downtown and the fair, TIF grants
are also available to downtown businesses
that make improvements designed to attract
fairgoers. One of these went to a restaurant
that added outdoor seating.
The fair has been a source of pride, fun
and dollars to its hometown since its birth
as a private enterprise in 1923. It has gone
on uninterrupted, even through its 1986
takeover by the Illinois Department of Agriculture, which also runs the larger state fair
every August in Springfield.

Independent audits, available from the
state for the years through 2009, show the
Du Quoin fair losing money annually for
the previous decade on revenue averaging
a little more than $1 million and expenses
ranging from $1.5 million to more than
$2 million.
But it’s unfair to judge the fair on those
numbers alone, officials say. For instance,
while the audit shows a deficit of $863,288
for the 2000 fair, the festivities spun off
more than $8 million in economic benefit
to Perry County, according to an analysis
by the University of Illinois and the Federal
Reserve Bank of Chicago. A similar multiplier effect still applies, Rednour Jr. says.
Many fair events are free, and there is no
general admission charge, just a parking fee.
While striving to keep the fair “affordable
for people to come with their kids and show
them a good time,” Rednour Jr. says he’s
also nudging it toward break-even by cutting some expenses and raising some fees.
Separately, fair managers several years ago
began a drive to lure paying events in the
year’s remaining 50½ weeks. With their 1,200
electrically equipped campsites, the grounds
have proved popular for RV rallies lasting a
week or more. Other nonfair money-makers
have included bull riding, rodeos, monster
truck shows, charity events, weddings, picnics,
flea markets, demo derbies, horse shows, auto
races, motorcycle races and, of course, harness races. Annual revenue from off-season
business has grown to between $650,000
and $700,000, officials say.
These extra events also boost the local
economy, says Thomas Jennings, director
of the Illinois Department of Agriculture.
As for the fair proper, it’s “a good deal for
the community,” he says. “The state supports all of the communities in Illinois.
The fair is our opportunity to support
Southern Illinois.”
But for how long and how much? Until
this year, the fair’s future was never in
doubt, Emling says. With the state of Illinois facing a deficit of more than $9 billion
for fiscal 2012, all department budgets face
cuts. And the fair, like every other expense,
will “have to work its way through the legislature,” Jennings says.

Susan C. Thomson is a freelance writer.

n a t i o n a l

o v e r v i e w

Recovery Continues despite
New Risks, Old Problems
By Kevin L. Kliesen

T

he macroeconomic environment
continues to improve, although the
pace of economic activity has been bumpy
and somewhat lackluster. In particular, the
unexpected slowing in real GDP growth
during the first quarter (1.8 percent from the
fourth quarter’s 3.1 percent) occurred against
the backdrop of healthy increases in privatesector employment and a modest decline in
the unemployment rate.
As policymakers, businesses and consumers grapple with the lingering effects
of the financial crisis and recession, some
additional risks have emerged. Chief among
these are sharply higher energy prices and
the uncertainties stemming from developments in Europe, the Middle East and Japan.
Still, most forecasters continue to believe
that the economy will shake off the firstquarter doldrums of unexpectedly high
inflation and subpar output growth and
will soon transition to lower inflation rates
and a stronger pace of economic activity.1
Help Wanted

Among the most notable developments
of late has been the sharp rebound in
monthly private-sector payroll employment. Although the pace of hiring slowed
in May, private employment increased by
182,000 jobs per month over the first five
months of 2011. Average monthly gains in
total nonfarm payrolls were a bit smaller
because state and local governments
reduced employment to help correct their
fiscal imbalances. However, the economy’s
growth has not been brisk enough to bring
about dramatic reductions in the unemployment rate, which remained quite elevated in
May (9.1 percent). Professional forecasters
generally expect total nonfarm job gains to
average about 190,000 per month through
the first half of 2012, with the unemployment rate slowly falling to about 8.25 percent by June 2012.

The Return of Oil
at $100 per Barrel

Perhaps surprisingly, the rise in oil prices
and the resulting surge in average gasoline
prices to near $4 per gallon nationally have
not yet derailed consumer spending or
impinged on planned capital expenditures by
businesses. The previous surge in oil prices,
in 2007-2008, helped push the economy into
recession, but today’s dynamics are much
better: Equity prices are rising, real interest
rates are lower, real household incomes are
strengthening, and housing construction and
household wealth are no longer plunging
at a rapid rate. In addition, the rebound in
global growth has benefited many firms,
especially manufacturers. This development, in conjunction with a weaker dollar,
has kept U.S. exports expanding at a rapid
clip. Relatively strong business expenditures
on equipment and software are a key signal
that firms expect solid economic conditions
going forward.
The construction sector remains the fly
in the ointment, as housing starts and new
home sales continue to linger near record
lows, and office and commercial construction
languishes. Moreover, house prices continue
to drift lower because of the large number of
unsold houses on the market and high foreclosure rates—although the latter have been
trending lower. The growth of federal government outlays has also weakened because
of the waning federal stimulus program and
pressures to reduce the extraordinarily large
budget deficit.
The rise in oil prices and some of the
lingering uncertainties spawned by events
overseas have not shaken the confidence of
financial markets either. Equity prices have
risen sharply since late August 2010, and the
St. Louis financial stress index has returned
to its prefinancial-crisis levels. Improving
economic and financial market conditions
have begun to increase the demand for bank

loans by businesses, and consumer credit
has started to rise modestly.
Inflation Increases

Sharply higher energy prices, as well as
rising food prices, have pushed headline
inflation rates to levels last seen during the
2007-08 oil price shock. Over the past year,
the CPI rose by 3.4 percent. A key worry
associated with an oil shock (or higher food
prices) is the impact that “pass-through”
effects may have on prices of nonfood and
nonenergy goods and services. If long-term
inflation expectations are viewed as low and
stable and if monetary policy is viewed as
credibly committed to long-term price stability, then these pass-through effects tend
to be modest and temporary.
Accordingly, most economists and Federal
Reserve policymakers view the sharp rise in
inflation as a temporary deviation from a low
and stable inflation environment. As long as
this expectation persists, the unemployment
rate remains high and the pace of growth
uneven, most forecasters and financial market participants believe that the Federal Open
Market Committee will maintain its existing
federal funds rate target of 0 to 0.25 percentage points for the remainder of 2011—and
maybe into the first half of 2012.
Kevin L. Kliesen is an economist at the Federal
Reserve Bank of St. Louis. See http://research.
stlouisfed.org/econ/kliesen/ for more on his work.

endnote
1

References in this article to inflation are to “headline
inflation,” which factors in food and energy prices.

The Regional Economist | www.stlouisfed.org 21

d i s t r i c t

o v e r v i e w

Hispanics Play Different Role
in District’s Growth than in Nation’s

The Eighth Federal Reserve District is composed of four zones,
each of which is centered around one of the four main cities:
Little Rock, Louisville, Memphis and St. Louis.

By Rubén Hernández-Murillo and Christopher J. Martinek

T

he U.S. Census Bureau recently released
the 2010 redistricting data for the nation.
These data are the first to provide local-level
information on population, race/ethnicity,
age and housing unit counts from the
2010 census. Aside from helping define

percent of the nation’s population.1 The
demographic trends in the Eighth Federal
Reserve District in terms of population
growth by racial and ethnic categories were
quite different from the national trends.2
The table provides a snapshot of population

congressional district boundaries, the data
reveal interesting trends over the past
decade across various demographic groups.
One trend that has received a lot of attention is the dramatic growth of the Hispanic
population, which in 2010 represented 16.3

Detailed Data on 2010 Census

United States and Eighth
District Comparison

2000
Population

2010
Population

Change
since 2000

Percentage
Change

Hispanic
Contribution
to Growth

United States

Non-Hispanic
White Alone
Contribution
to Growth

Non-Hispanic
Black Alone
Contribution
to Growth

Non-Hispanic
Asian Alone
Contribution
to Growth

Non-Hispanic
Other Single Race
Contribution
to Growth

Non-Hispanic
Multiple Race
Contribution
to Growth

281,421,906

308,745,538

27,323,632

9.7%

5.4%

0.8%

1.3%

1.5%

0.2%

0.5%

Rural

48,040,217

50,130,733

2,090,516

4.4

2.4

0.8

0.2

0.2

0.2

0.5

Urban

233,381,689

258,614,805

25,233,116

10.8

6.0

0.8

1.6

1.8

0.2

0.5

13,720,816

14,569,665

848,849

6.2

2.0

1.7

1.3

0.5

0.1

0.6

Rural

5,603,261

5,690,716

87,455

1.6

1.2

0.0

–0.3

0.2

0.0

0.4

Urban

8,117,555

8,878,949

761,394

9.4

2.5

2.9

2.4

0.8

0.1

0.7

Fayetteville-SpringdaleRogers, Ark.-Mo.

347,045

463,204

116,159

33.5%

11.6%

15.9%

1.3%

2.0%

1.6%

1.1%

Bowling Green, Ky.

104,166

125,953

21,787

20.9

2.6

13.2

2.3

1.8

0.2

0.8

Columbia, Mo.

145,666

172,786

27,120

18.6

1.7

11.9

2.3

1.5

0.1

1.2

Springfield, Mo.

368,374

436,712

68,338

18.6

1.5

14.6

0.8

0.6

0.1

1.0

Little RockN. Little Rock-Conway, Ark.

610,518

699,757

89,239

14.6

3.4

5.3

4.3

0.7

0.1

0.7

Jonesboro, Ark.

107,762

121,026

13,264

12.3

2.5

2.8

5.7

0.6

0.1

0.7

Elizabethtown, Ky.

107,547

119,736

12,189

11.3

2.2

6.4

0.8

0.4

0.2

1.3

1,161,975

1,283,566

121,591

10.5

2.7

4.0

2.2

0.7

0.1

0.8

Eighth District Counties

Metro Area Population Growth

Louisville-Jefferson
County, Ky.-Ind.
Fort Smith, Ark.-Okla.
Memphis, Tenn.-Miss.-Ark.
Hot Springs, Ark.

273,170

298,592

25,422

9.3

4.4

2.0

0.4

0.6

1.1

0.8

1,205,204

1,316,100

110,896

9.2

3.1

–1.4

6.3

0.7

0.1

0.4

88,068

96,024

7,956

9.0

2.7

4.3

0.8

0.3

0.1

0.9

Jackson, Tenn.

107,377

115,425

8,048

7.5

1.8

–0.8

5.5

0.3

0.1

0.6

Jefferson City, Mo.

140,052

149,807

9,755

7.0

1.0

4.4

0.8

0.3

0.0

0.5

Texarkana, Texas-Ark.

129,749

136,027

6,278

4.8

1.9

–0.2

2.1

0.3

0.1

0.6

Evansville, Ind.-Ky.

342,815

358,676

15,861

4.6

1.1

1.2

0.8

0.4

0.1

0.9

Owensboro, Ky.

109,875

114,752

4,877

4.4

1.6

1.2

0.6

0.3

0.1

0.7

2,721,491

2,837,592

116,101

4.3

1.2

0.3

1.4

0.8

0.0

0.6

107,341

100,258

–7,083

–6.6

0.6

–7.7

0.2

0.1

0.0

0.2

St. Louis, Mo.-Ill.
Pine Bluff, Ark.
SOURCE: U.S. Census Bureau.

22 The Regional Economist | July 2011

growth by race and Hispanic origin in the
U.S. and the Eighth District. The top panel
summarizes differences in rural and urban
areas, while the bottom panel illustrates
population trends across metropolitan areas
in the Eighth District.
Overall Population Growth

Between 2000 and 2010, the nation’s population grew by 9.7 percent to 308,745,538.
About 56 percent of the growth in U.S. total
population was accounted for by individuals
who identified themselves as Hispanic or
Latino (5.4 out of 9.7 percent). In the Eighth
District, total population between 2000 and
2010 increased by 6.2 percent to 14,569,665.
Hispanics represented 3.6 percent of the District’s total population. Although the contribution to growth of the Hispanic population was the largest among all groups, it
accounted for only about a third of total
population growth (2.0 out of 6.2 percent).
Almost 50 percent of the total growth in
the Eighth District was accounted for by the
combined growth of non-Hispanic individuals who identified themselves as nonHispanic white alone or non-Hispanic black
alone (1.7 and 1.3, respectively, out of 6.2
percent). Growth in the non-Hispanic Asian
population was the second largest contributor to national population growth, representing about 15 percent of overall growth (1.5
out of 9.7 percent), but in the Eighth District,
the population growth of non-Hispanic
Asians accounted for only about 8 percent of
overall growth (0.5 out of 6.2 percent).
Rural and Urban Growth

Although Hispanics’ contribution to overall growth was less dramatic in the Eighth
District than in the nation as a whole, breaking up total population across urban and
rural counties reveals that Hispanic population growth was a more important contributor to rural population growth in the Eighth
District than in the nation. This distinction
is important because the Eighth District is
more rural than the nation as a whole.
The 2010 census indicates that 39.1 percent
of the District’s population lives in rural
counties, while only about 17 percent of the
nation’s population lives in rural counties.3
The growth in rural population of the nation
was 4.4 percent, while the growth in urban
population was 10.8 percent. The population

in rural counties of the Eighth District grew
by 1.6 percent, while population in urban
counties grew by 9.4 percent.4
In terms of contributions to growth,
Hispanic population growth accounted for
about 55 percent of the nation’s population
growth for both rural and urban counties
(2.4 of 4.4 percent in rural counties and 6 of
10.8 percent in urban counties). In contrast,
Hispanic population growth accounted for
75 percent of relatively modest rural population growth in the Eighth District (1.2 of 1.6
percent) and slightly more than 25 percent of
urban population growth (2.5 of 9.4 percent).
MSA Population Growth

Across the Eighth District’s metropolitan
statistical areas (MSAs), with the exception
of Pine Bluff, Ark., population increased in
every metropolitan area from 2000 to 2010.
Fayetteville-Springdale-Rogers, Ark.-Mo.,
led the District MSAs with a 33.5 percent
population growth. The largest contributions to growth in this location came from
the Hispanic population, with about 34 percent of overall growth (11.6 of 33.5 percent)
and from non-Hispanic white individuals,
with about 47 percent of overall growth
(15.9 of 33.5 percent).
Population growth in most of the District
MSAs was driven predominantly by growth
in the non-Hispanic white population. The
exceptions were Memphis, Tenn.-Miss.-Ark.;
Texarkana, Texas-Ark.; Jackson, Tenn.;
and most notably, Pine Bluff, Ark., where
decreases in the non-Hispanic white population subtracted from overall growth. In
contrast, growth in the St. Louis, Mo.-Ill.,
and Jonesboro, Ark., areas can be predominantly attributed to growth in the nonHispanic black population. Growth in the
non-Hispanic Asian population also made up
a significant proportion of total population
growth in the St. Louis MSA. Fort Smith,
Ark.-Okla., and Owensboro, Ky., more
closely resembled the national trend of Hispanic population growth accounting for the
largest share of total population growth.
Rubén Hernández-Murillo is an economist and
Christopher J. Martinek is a research associate,
both at the Federal Reserve Bank of St. Louis. See
http://research.stlouisfed.org/econ/hernandez/
for more on Hernández-Murillo’s work.

ENDNOTES
1

2

3

4

The census collects race and Hispanic origin
information in accordance with the U.S. Office of Management and Budget’s (OMB) 1997
Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity,
which prescribe that race and Hispanic origin
be considered distinct concepts necessitating
the separate questions.
For the purposes of this article, we compare
Hispanics with individuals who reported
non-Hispanic origin and only one race (white,
black or Asian) to form mutually exclusive
categories.
Urban counties, here, are defined as those
making up part of a census-designated
metropolitan statistical area.
Some counties of MSAs listed in the lower
portion of the table are located outside of the
District and are not included in the figures
presented in the upper portion. For example,
in the Fort Smith, Ark.-Okla., MSA, Sequoyah
County, Okla., is located outside of the
District. Similarly, some counties located in
MSAs considered outside the District and not
included in the lower portion of the table are
included in the tabulation for the upper portion of the table, for example, Greene County,
Ind., in the Bloomington, Ind., MSA.

census C hanges
Unlike previous censuses, the 2010 census
did not include a “long form” questionnaire.
Previously, the long form was given to roughly
one in six households to gather information on
such things as educational attainment, income,
housing costs and other socio-economic characteristics of the population. (The long form
continues to be administered every year as part
of the American Community Survey.)
One of the reasons for eliminating the long
form was to improve return rates. The mail
participation rate for the 2010 census was 74 percent of occupied households, the same rate that
was achieved for the 2000 census short form.
However, when the elimination of the long form
is factored in, a larger portion of questionnaires
was returned in 2010.
The Census Bureau makes an attempt to follow up with households that do not respond by
mail; the bureau will call, visit the household or
contact neighbors and building managers. As a
last resort, the bureau will impute counts using
statistical models that reflect the characteristics
of the neighborhood. By the time all the methods of filling in missing forms are exhausted,
the bureau determines the proportion of records
that provide usable information. Last year, this
proportion was 99.62 percent, slightly higher
than the 2000 proportion of 99.43 percent.
In addition to the response rates, the bureau
considers several other measures of accuracy
of the data-collection process. One of the most
important post-census process indicators is
the Census Coverage Measurement survey, a
quality-check survey of 300,000 households.
Results from this survey will be matched to
census responses to estimate overcounts and
undercounts by geography, ethnicity, race, gender and age. The bureau will publish the results
next year but will not revise existing population
count estimates.

The Regional Economist | www.stlouisfed.org 23

R a c e

Black/White Segregation
in the Eighth District:
A Look at the Dynamics
© Paul Burns/get t y images

By Alejandro Badel and Christopher J. Martinek

B

ased on a popular index, racial segregation decreased in the Eighth District’s
four major metropolitan areas between 1970
and 2000. This decline was not particular
to the Eighth District; for example, a similar
decline occurred in Chicago.
To help explain what happened, we created a simple way to decompose the decline
in the index; by doing so, we found that
the decline can be explained by opposing
forces that are the same in all metro areas.
The force that lowered the index of segregation was an increase in racial integration in
historically highly black and highly white
communities. The forces that partly offset
this decrease were the suburbanization of the
white population into new, highly white communities and, to a lesser extent, the increased
segregation in communities that experienced
“tipping” from highly white in 1970 to highly
black in 2000.

The Basics of Our Study

Racial segregation exists in a city to the
extent that people of different races do
not share the same areas.1 Different types
of areas can be analyzed, such as blocks,
neighborhoods or counties. For this article,
we documented the extent and evolution
of black/white segregation across census
tracts of the Eighth District between 1970
and 2000.2 Although 1970 is a good starting
point (since it was the first decennial census
year after the Civil Rights Act of 1964), we
focused on the 1970-2000 period mainly
because there exist adequate data for it.
The data we used come from the Neighborhood Change Database (NCDB).3 This
dataset is built by transforming the original Census Bureau data in such a way that
tract borders do not vary between 1970 and
24 The Regional Economist | July 2011

2000.4 Using it, we could observe segregation changes within fixed plots of land. (Data
from the 2010 census are not yet available
in the NCDB format.) We used the Index
of Dissimilarity (IOD), a popular measure
of segregation among sociologists and
economists, because it has a straightforward
interpretation.
The Index of Dissimilarity

The IOD varies from zero to 100 percent.
An IOD of 90 implies that at least 90 percent
of one of the two groups (in this case, either
black or white) would need to move to a
different neighborhood to make all neighborhoods end up with the same racial mix.
Consider a dessert party in which two
buckets of vanilla ice cream and one bucket
of chocolate ice cream are to be served.
To serve all guests with the same vanillachocolate mix, each guest would need to
be served two scoops of vanilla with each
scoop of chocolate. If each bucket contains
100 scoops, all one ends up doing is serving
1 percent of the total amount of vanilla ice
cream together with each 1 percent of the
total chocolate ice cream. The IOD captures
how far the party is from the homogeneous
distribution by comparing the percentages
of the total chocolate and vanilla ice cream
served onto each plate. For example, a plate
that contains 5.7 percent of the chocolate
ice cream and 1.3 percent of the vanilla ice
cream contributes (5.7% – 1.3%) to the IOD
(i.e., 4.4 percentage points). Adding up the
contributions from all plates with excess
chocolate gives the total index. (The calculation is identical if we consider plates with
excess vanilla instead.) When the percentages are equal on all plates, the index is zero.
When no plate contains both flavors, the

index is 100 percent—full segregation.
For a concrete example, consider St. Louis
in 1970. In that year, the population of
St. Louis was 2,071,043. Of those, 375,090
persons were black and 1,688,491 were white.5
St. Louis as a whole was 18.2 percent black.
The left panel of the diagram summarizes
segregation in St. Louis by joining all tracts
that were more than 18.2 percent black into
what we call the “highly black” (HB) area and
by joining all tracts that were less than 18.2
percent black into what we call the “highly
white” (HW) area. The diagram shows that
94.2 percent of the black persons in St. Louis
lived in HB tracts while only 10.6 percent
of the white persons lived in those tracts.
(Recall that these two percentages would have
needed to be equal for the neighborhood to
have been exactly 18.2 percent black.) One
hypothetical way for the HB area to become
fully integrated would be to reduce its percentage of blacks in that area to 10.6 percent,
which would be equal to the percentage of
whites in that area. To achieve this reduction,
the equivalent of 83.6 percent of all black people in St. Louis would have needed to move
out of the HB area. If this amount of black
people would have moved into the HW area,
the percentage of all black persons living in
the HW area would have risen from 5.8 percent in the diagram to 89.4 percent—exactly
equaling the percentage of all white persons
living in the HW area. Therefore, this movement would have sufficed to achieve perfect
integration in HW and also in HB areas.
In summary, 83.6 percent of all black
persons in St. Louis would have needed to
change neighborhoods in 1970 in order to
make all areas fully integrated. This percentage was the IOD for St. Louis in 1970. This
exercise could be repeated with the white

E ndnotes

Diagram of a Segregated City and Its Change over Time
Initial Situation in 1970

1

change between 1970 and 2000

Highly Black Tracts (St. Louis)
Total Population: 531,772
Racial Mix: 66.4% black
Percent of Black Population: 94.2%
Percent of White Population: 10.6%

4

1
3

2

2

Highly White Tracts (St. Louis)

2*

Total Population: 1,531,809
Racial Mix: 1.4% black
Percent of Black Population: 5.8%
Percent of White Population: 89.4%

4*

3*

1*
3

NOTE: We report some statistics for St. Louis in the “Initial Situation in 1970” panel, but a similar partition can be done for any MSA. We do not report statistics
directly on the “Change between 1970 and 2000” panel. Statistics for each of this panel’s numbered areas are reported in the table.

4
5

population moving out of HW areas, and the
resulting IOD would be unchanged.

the diagram. The diagram shows how cities
change between two points in time—say 1970
and 2000. In 1970, the city is represented
by solid lines, and area types 1, 2 and 3 are
HB, while 1*, 2* and 3* are HW, just like in
the left panel of the diagram. In 2000, the
city is represented by dotted lines. Each area
represents neighborhoods that experienced
different kinds of changes between 1970 and
2000. We can name each kind of change
using popular terminology:
White Resegregation: Tracts that stay HW,
represented by area 1.
Black Resegregation: Tracts that stay HB,
represented by area 1*.
Tipping Black to White: Tracts that switched

IOD’s Change over Time

In 1970, the IOD in District metropolitan statistical areas (MSAs) was very high,
ranging from 73.3 percent in Little Rock to
83.6 percent in St. Louis, while it was slightly
above 90 percent in Chicago. The IOD fell for
all MSAs in our table between 1970 and 2000.
The largest declines happened in Louisville
(20 percentage points) and Little Rock (15
percentage points), while Chicago, St. Louis
and Memphis observed milder declines
(approximately 12 percentage points).
To get some notion as to why the IOD fell
in all of our MSAs, consider the right panel of

6

The U.S. pattern of racial residential segregation has been studied by economists since
the mid-20th century, following the seminal
works of Gunnar Myrdal and, later, Thomas
Schelling. Sociologists have also made important contributions to the measurement and
theory of racial segregation. For an overview
of segregation measurement, see www.census.
gov/hhes/www/housing/resseg/app_b.html
Census tracts are small units of land delineated
by the Census Bureau. These units subdivide a
county and usually contain between 2,500 and
8,000 people.
Tract level data come from the Neighborhood
Change Database (NCDB) by Geolytics Inc.
The database contains tract-level population
counts from the 1970, 1980, 1990 and 2000
U.S. decennial censuses.
The Census Bureau redefines tract boundaries
for each decennial census.
In this population count, we only consider
black and white population. We also consider
tracts with population density of fewer than
100 people per square kilometer as empty and
normalize their population to zero.
Note that an empty tract contains zero percent
of each of the populations, so that it contributes 0 percent to the Index of Dissimilarity.
The change in segregation in these areas is
the new level of segregation (zero) minus the
old level.

R eferences
Schelling, Thomas C. “Dynamic Models of Segregation.” Journal of Mathematical Sociology,
May 1971, Vol. 1, No. 2, pp. 143-86.
Myrdal, Gunnar. “An American Dilemma: the
Negro Problem and Modern Democracy.”
New York: Harper & Brothers, 1944.
Massey, Douglas; and Denton, Nancy. “The
Dimensions of Racial Residential Segregation,”
Social Forces, December 1988, Vol. 67, No. 2,
pp. 281-315.

continued on Page 26

Index of Dissimilarity in 1970 and 2000, Eighth District and Chicago (Percent)
Little Rock

Louisville

Memphis

St. Louis

Chicago

Index of Dissimilarity 1970

73.33

81.42

82.31

83.58

90.17

Index of Dissimilarity 2000

58.29

60.77

70.23

71.95

77.74

–15.05

–20.66

–12.07

–11.64

–12.43

–15.1

–18.2

Change 1970 to 2000

Decomposition: Contribution to Change by Each Type of Tract (Percentage Points)
Black Resegregation (1)
Tipping B to W (2)

–15.0

–14.3

–22.4

0.0

–0.1

0.1

0.0

0.0

–2.6

0.0

–0.3

–0.1

–0.1

Black Suburbanization (4)

0.0

0.1

0.7

0.1

0.3

White Resegregation (1*)

–12.7

–12.0

–6.3

–4.3

–5.7

2.8

0.2

0.6

1.1

5.3

White Depopulation (3*)

–0.9

–1.1

0.0

–0.2

0.0

White Suburbanization (4*)

13.3

6.5

15.4

6.8

6.0

–15.05

–20.66

–12.07

–11.64

–12.43

Black Depopulation (3)

Tipping W to B (2*)

TOTAL

NOTES: Each line of the decomposition represents an area of the right panel of the diagram. Negative numbers represent a decrease in segregation. Not all
columns add up exactly because of rounding.
The Regional Economist | www.stlouisfed.org 25

e c o n o m y

C ONSUMER PRI C E IN D E X

8

PERCENT

4
2
0
–2
–4
–6
–8

Q1
06

07

08

09

10

PERCENT CHANGE FROM A YEAR EARLIER

6

6

11

3

0
CPI–All Items
All Items Less Food and Energy

–3

May

06

08

07

09

10

11

NOTE: Each bar is a one-quarter growth rate (annualized);
the red line is the 10-year growth rate.

3.0
2.5
2.0
1.5
1.0
0.5
0.0
–0.5
–1.0
–1.5
–2.0
–2.5

RATES ON FEDERAL FUNDS FUTURES ON SELECTED DATES

0.22
1/26/11
3/15/11

4/27/11
6/16/11

0.19
PERCENT

PERCENT

IN F LATION - IN D E X E D TREASURY YIEL D SPREA D S

5-Year

0.16

0.13

10-Year
20-Year
June 10, 2011

07

08

09

10

0.10

11

June 11 July 11 Aug. 11 Sept. 11 Oct. 11 Nov. 11
CONTRACT MONTHS

NOTE: Weekly data.

C IVILIAN UNEMPLOYMENT RATE

INTEREST RATES

11

6

10

5

9

4
PERCENT

8
7

10-Year Treasury
Fed Funds Target

1

5
4

3
2

6

06

07

08

09

10

May

1-Year Treasury

May

0

11

06

07

08

09

10

11

NOTE: On Dec. 16, 2008, the FOMC set a target range for
the federal funds rate of 0 to 0.25 percent. The observations
plotted since then are the midpoint of the range (0.125 percent).

U . S . AGRI C ULTURAL TRA D E

F ARMING C ASH RE C EIPTS

80

190
170

60

Exports

40
Imports

20

0

150
130
110

Trade Balance

06

07

08

09

April

10

NOTE: Data are aggregated over the past 12 months.
26 The Regional Economist | July 2011

g l a n c e

BILLIONS OF DOLLARS

Alejandro Badel is an economist and Christopher J. Martinek is a research associate, both
at the Federal Reserve Bank of St. Louis. See
http://research.stlouisfed.org/econ/badel/ for
more on Badel’s work.

REAL G D P GRO W TH

PERCENT

from HB to HW, represented by area 2.
Tipping White to Black: Tracts that switched
from HW to HB, represented by area 2*.
Depopulation: Tracts that became vacant,
represented by areas 3 and 3*.
Suburbanization: Tracts that were empty
in 1970 but became populated by 2000, represented by areas 4 and 4*.
For any city, each area described by the
right panel of the diagram contributes to
the change in the IOD over time. This
contribution depends on the size of the area
and on the change in segregation within
the area. Therefore, we can decompose
time changes of the IOD by calculating
the portion that accrues to each area. The
table presents this decomposition, and we
describe its contents below.
Both White Resegregation and Black
Resegregation had large negative effects on
the IOD. This means that although many
tracts stayed HB or HW between 1970 and
2000, these types of tracts became more mixed.
Tipping White to Black appreciably helped
to increase the IOD in Chicago (5.3 percentage points) and Little Rock (2.8 percentage
points). This implies that the tipping tracts
became at least as segregated after becoming
HB as they were when HW. Tipping Black to
White did not have a large effect on the index
in any MSA.
Depopulation of HB tracts reduced the
IOD in Little Rock by 2.6 percentage points,
while the effect in other MSAs was below one
percentage point. This means that the tracts
that were HB in 1970 and were empty or very
sparsely populated by 2000 were highly segregated in 1970.6 In contrast, Depopulation
of HW tracts did not appreciably change the
IOD. Suburbanization into new HB tracts
did not impact the index appreciably, except
in Memphis, where it increased the index by
0.7 percentage points. In contrast, Suburbanization into HW tracts had a large positive
effect on the index in all MSAs, with the largest effects in Little Rock and Memphis.

a

Eleven more charts are available on the web version of this issue. Among the areas they cover are agriculture, commercial
banking, housing permits, income and jobs. Much of the data is specific to the Eighth District. To see these charts, go to
stlouisfed.org/economyataglance

BILLIONS OF DOLLARS

continued from Page 25

a t

11

90

06

Crops

Livestock

07

08

February

09

10

NOTE: Data are aggregated over the past 12 months.

11

R e a d e r

e x c h a n g e

ask AN economist

Letters to the Editor

Yi Wen is an economist and assistant vice
president in the Research division at the Federal
Reserve Bank of St. Louis. He joined the St. Louis
Fed in 2005 after teaching at Cornell University for
six years as an assistant professor. His research
field is in macroeconomics with a focus primarily
on the business cycle. His hobbies include walking, swimming and playing badminton. To read
more on his work, see http://research.stlouisfed.
org/econ/wen/

This is in response to “A Closer Look: Assistance Programs in the Wake

Yi Wen in Leshan, China.

Q. Why does the U.S. have such a large trade deficit
with China?
Prices of consumer goods in the United States have been remarkably
low and stable for decades. One of the most important reasons for this,

of the Crisis” in the January 2011 issue of The Regional Economist.
Dear Editor:
Thank you for this excellent article on the “great recession.” It cuts
through quite a bit of mythology and lays out the facts in a clear and
coherent way. The graphics and use of the Blinder and Zandi simulations
provide a reasonable picture of the but-for world without intervention.
Personally, I think that without U.S. assistance programs in place, the
off-shore reverberations would have been far more reaching than the
simulations suggest. Additionally, aggressive assistance in Europe and
Asia was probably as valuable as the U.S. programs in helping to stave
off global disasters that go beyond what the simulation can predict.
Somehow in some way, the global political machinery gave way to
common sense at a time that it absolutely had to.
Kyle Stiegert, professor of agricultural economics at the University
of Wisconsin in Madison

besides sound monetary policies conducted by the Fed, is international
trade with developing countries, such as China.
Chinese workers need to use 16 million T-shirts to exchange for one

This is in response to “Are Small Businesses the Biggest Producers of
Jobs?” in the April 2011 issue. This letter has been edited for space
reasons. To read it in its entirety, see www.stlouisfed.org/publications/

Boeing 737-800 airplane from us (at about $5 per T-shirt). More than

re/letters/index.cfm

Each year, China sells goods to us at very low prices. For example,

that, they even lend goods to us by keeping our paper money for a
long time.
The result is a huge trade deficit with China: For every dollar Americans spend on Chinese goods, Chinese spend 30 or fewer cents on
American goods. China currently holds a total of $3 trillion in foreign
reserves, mostly in U.S. dollars or U.S. government bonds. This means
that U.S. consumers have been enjoying huge quantities of low-cost
goods by borrowing cheaply from China at negative real interest rates.
The question is why Chinese people are willing to lend goods to us
when they are still struggling with very low per capita income and con-

Dear Editor:
The article is directed at making the very salient point that we should look
at net job creation, not gross, when assessing the dynamics of labor
demand by small businesses. Unfortunately, the article presents an incomplete picture of the U.S. labor market that leaves the reader with the impression that firms with 500+ employees are the main drivers of employment.
Using 1992 as a baseline, it is clear why the authors can say that nearly
40 percent of jobs created have been at the largest firms. I would argue,
however, that the heady years of the 1990s (a period that included an
expansion of technology and free-trade agreements that we have not

sumption levels. One answer from economic theory is that they have a

seen since) do not provide a reasonable baseline from which to derive

strong need to save for a rainy day. At their current stage of economic

long-term labor market expectations.

development, Chinese workers do not have a well-developed financial

Indeed, the more recent decade provides a marked contrast. When we

market and social safety net, both of which would reduce their need

begin this analysis using the year 2000 as our baseline, a different picture

to save and would allow them to borrow when needed. Hence, even

emerges—one where small firms not only create more jobs, but where they

though their general economy is growing very fast, the rising uncertainty

create jobs that are more robust to economic downturns. It is intriguing to

for each individual in both spending needs (such as the rising costs in

note the trend in the early 2000s (and today), when smaller firms are

health care, education and housing) and income prospects (such as

increasing employment, while the largest firms continue to hemorrhage jobs.

unemployment risk) induces them to save excessively to provide the

It should not be assumed that the distribution of employment in an

self-insurance that is not available to them from the market. Therefore,

advanced economy will naturally be biased toward employment at large firms.

for every dollar a Chinese worker makes in trading with the U.S., he or

This is a consequence of policy, and I fear that the article by Mr. Kliesen and

she feels the need to save at least a quarter. The remaining part of the

Ms. Maués could be interpreted as a reason to continue the same policies

dollar is not even spent entirely on U.S. goods because Chinese workers

that have resulted in this labor force distortion. Last year, the German

(firms) also need dollars to buy raw materials from other countries to
produce consumption goods, as China is a resource-poor country. This
implies that the total imports of China from us will be substantially less
than its total exports to us, leading to the U.S.-China trade imbalance.

minister of finance, Wolfgang Schäuble, noted that, “The United States lived
on borrowed money for too long, inflating its financial sector unnecessarily
and neglecting its small and mid-sized industrial companies” (emphasis added).
Andrew Smale, master’s student in applied economics, University of
Minnesota in Minneapolis-St. Paul

Submit your question in a letter to the editor. (See Page 2.)
One question will be answered by the appropriate economist in each issue.

To read past issues of The Regional Economist, see www.stlouisfed.org/publications/re
The Regional Economist | www.stlouisfed.org 27

rts
Many Moving Pa
et
Labo r Mark
e the U.S.
A look InsId

l
A n n u A

t
R e p o R

The essay in the St. Louis Fed’s annual report focuses on the labor market, shining light on trends that aren’t
often thought about by the general public; a sidebar shows how U.S. workers fared during the Great Recession
compared with workers in other industrialized countries. Also included in the report are financial statements,
messages from key leaders and a “getting to know you” section with our boards of directors. Read the report
at www.stlouisfed.org/publications/ar/

2 0 1 0

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economy at a

The Regional

glance

Economist

july 2011

REAL GDP GROWTH

4
2
0
–2
–4
–6

Q1
06

07

08

09

10

PERCENT CHANGE FROM A YEAR EARLIER

6

6

PERCENT

VOL. 19, NO. 3

CONSUMER PRICE INDEX

8

–8

|

11

3

0
CPI–All Items
All Items Less Food and Energy

–3

May

06

08

07

09

10

11

NOTE: Each bar is a one-quarter growth rate (annualized);
the red line is the 10-year growth rate.

3.0
2.5
2.0
1.5
1.0
0.5
0.0
–0.5
–1.0
–1.5
–2.0
–2.5

RATES ON FEDERAL FUNDS FUTURES ON SELECTED DATES

0.22
1/26/11
3/15/11

4/27/11
6/16/11

0.19
PERCENT

PERCENT

I N F L AT I O N - I N D E X E D T R E A S U RY Y I E L D S P R E A D S

5-Year

0.16

0.13

10-Year
20-Year
June 10, 2011

07

08

09

10

0.10

11

June 11 July 11 Aug. 11 Sept. 11 Oct. 11 Nov. 11
CONTRACT MONTHS

NOTE: Weekly data.

C I V I L I A N U N E M P L O Y M E N T R AT E

I N T E R E S T R AT E S

11

6

10

5
4

8

PERCENT

PERCENT

9

7

10-Year Treasury

2

6

Fed Funds Target

1

5
4

3

06

07

08

09

10

May

1-Year Treasury

May

0

11

06

07

08

09

10

11

NOTE: On Dec. 16, 2008, the FOMC set a target range for
the federal funds rate of 0 to 0.25 percent. The observations
plotted since then are the midpoint of the range (0.125 percent).

U . S . A G R I C U LT U R A L T R A D E

FA R M I N G C A S H R E C E I P T S

190
170

60

BILLIONS OF DOLLARS

BILLIONS OF DOLLARS

80

Exports

40
Imports

20

130
110

Trade Balance

0

150

06

07

08

09

April

10

NOTE: Data are aggregated over the past 12 months.

11

90

06

Crops

Livestock

07

08

February

09

10

NOTE: Data are aggregated over the past 12 months.

11

U.S. CROP AND LIVESTOCK PRICES / INDEX 1990-92=100
215
195
175
155

Crops

Livestock

135
115
95
75

May

96

97

98

99

00

01

02

03

04

05

06

07

08

09

10

11

commercial bank performance ratios
U . S . B an k s by A sset S i z e / first Q U A R T E R 2 0 1 1

All

$100 million­$300 million

Less than
$300 million

$300 million$1 billion

Less than
$1 billion

$1 billion$15 billion

Less than
$15 billion

More than
$15 billion

Return on Average Assets*

0.86

0.57

0.59

0.57

0.58

0.68

0.63

0.92

Net Interest Margin*

3.57

3.91

3.91

3.81

3.86

3.90

3.88

3.48

Nonperforming Loan Ratio

4.85

3.25

3.11

3.86

3.50

4.16

3.85

5.18

Loan Loss Reserve Ratio

3.19

1.96

1.94

2.09

2.01

2.44

2.24

3.51

R E T U R N O N AV E R A G E A S S E T S *
0.51

–0.88

NET INTEREST MARGIN*

0.81

Arkansas

0.99
0.96

Illinois

3.66
3.60

Indiana

3.61
3.48

1.42

1.73

0.40
0.30

0.10

–1.00

–.50

.00

0.71

Missouri

0.73

Tennessee

1.00

1.50

2.00

PERCENT

3.70
3.84
3.43

3.03

0.0

1.0

3.0

4.0

5.0

First Quarter 2010

L O A N L O S S R E S E RV E R AT I O

3.45
3.40

Eighth District

3.45

Arkansas

2.20
2.21
1.86

4.00

4.45

2.14

1.64
1.36

Illinois

2.85
2.59

Indiana

2.38
2.18

1.68
1.46

Kentucky
3.42
3.56

2.0

First Quarter 2011

2.09
1.94

2.46

3.81

3.55
3.44

First Quarter 2010

N O N P E R F O R M I N G L O A N R AT I O

1.82
1.87

Mississippi

2.20
2.21

Missouri

3.95
4.54

4.99

.00 .50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 5.50
First Quarter 2011

4.63
4.50

Kentucky
Mississippi

.50

First Quarter 2011

4.21
3.97

1.01
0.88

–0.53

0.36

3.91
3.72

Eighth District

3.05

Tennessee
PERCENT

First Quarter 2010

NOTE: Data include only that portion of the state within Eighth District boundaries.
SOURCE: FFIEC Reports of Condition and Income for all Insured U.S. Commercial Banks
* Annualized data

.00

.70

1.40

First Quarter 2011

2.10

2.80

3.74

3.50

First Quarter 2010

For additional banking and regional data, visit our web site at:
www.research.stlouis.org/fred/data/regional.html.

4.20

regional economic indicators
nonfarm employment growth / first Q U A R T E R 2 0 1 1
year-over-year percent changE
United
States

Total Nonagricultural

Eighth
District †

Arkansas

1.1%

1.7%

1.3%

1.0%

1.8%

0.9%

Illinois

Indiana

Kentucky

Mississippi

Missouri

Tennessee

1.2%

0.1%

1.1%

Natural Resources/Mining

10.8

4.8

5.0

7.1

2.7

4.8

5.7

0.0

NA

Construction

–1.1

–0.7

–3.7

0.0

3.2

–7.2

4.6

–3.1

NA

Manufacturing

1.7

1.3

–0.9

1.5

2.9

2.7

–1.8

1.9

–0.3

Trade/Transportation/Utilities

0.9

0.8

1.8

1.4

0.4

0.3

1.1

–0.1

1.0

Information

–1.6

–2.8

2.2

–3.6

–2.9

–1.2

1.6

–4.9

–1.8

Financial Activities

–0.7

–0.5

2.5

–1.8

0.3

–1.0

–1.6

1.6

–0.6

Professional & Business Services

2.8

3.7

5.8

3.3

5.3

6.0

11.3

0.1

3.0

Educational & Health Services

2.1

2.3

1.8

2.8

2.2

1.7

2.8

1.4

2.8

Leisure & Hospitality

1.3

1.6

4.8

2.0

–1.0

6.0

1.4

0.3

0.8

Other Services

2.0

1.1

5.4

1.5

–1.4

3.9

0.8

0.7

–0.7

–1.3

–0.7

0.2

–0.7

–2.0

1.0

–1.9

–1.2

–0.1

Government

† Eighth District growth rates are calculated from the sums of the seven states. For Natural Resources/Mining and Construction categories, the data exclude
Tennessee (for which data on these individual sectors is no longer available).

U nemployment R ates

exports
year-over-year percent change

I/2011
United States

IV/2010

8.9%

Arkansas

9.6%

7.8

Illinois

9.6

10.3

Mississippi

10.2

Missouri

9.3

Tennessee

9.6

9.5

9.4

35.9

–25.9

Tennessee

10.3

PERCENT

31.1

–13.6

Missouri

9.6

9.7

–7.8

Mississippi

10.9

25.3

–13.6

Kentucky

10.9

10.2

19.9

–22.3

Indiana

10.7

10.2

–2.1
–8.9

Illinois

11.1

20.9

–18.7

Arkansas

8.0

9.4

8.8

Kentucky

United States

9.7%

7.9

8.9

Indiana

I/2010

26.7

–11.9

–40 –30 –20 –10
2010
2009

0

10

20

30

40

H ousing permits / first quarter

REAL PERSONAL INCOME* / first QUARTER

year-over-year percent change in year-to-date levels

year-over-year percent change

–12.6

United States

22.3
17.6

–10.0
–15.8

Illinois

35.3

–12.8

4.7

–34.9

2011

0

10

3.1

20

30

2010

All data are seasonally adjusted unless otherwise noted.

40

50

–0.5
2.5

–1.3

Tennessee

40.6

60

PERCENT

3.1

0.8

Missouri

47.1

3.1

0.1

Mississippi

–10.0

–50 –40 –30 –20 –10

3.5

–1.5

Kentucky

17.0

–12.4

3.2

–1.0

Indiana

37.3

–14.8

3.1

–0.4

Arkansas

50

3.0

0.5

–2

–1
2011

0

1

2

3

2010

*NOTE: Real personal income is personal income divided by the PCE
chained price index.

4