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A Quarterly Review
of Business and
Economic Conditions
Vol. 22, No. 2

Europe

Metro Profile

Debt Crisis Is Easing,
but Stability Is Not in Sight

After Stalling, Recovery
Resumes in St. Louis

April 2014

The Federal Reserve Bank of St. Louis
Central to America’s Economy®

The Ups and Downs
of Inflation and the
Role of Fed Credibility

c o n t e n t s

4
The Regional

Economist
april 2014

|

VOL. 22, NO. 2

The Ups and Downs of Inflation
By Diana A. Cooke and William T. Gavin

This look at interest rates and inflation in the U.S. over the past 50 years
helps to clarify ideas about the Fed’s monetary policy and its own credibility. The authors examine three periods corresponding to three different policies: when the Fed operated without credibility, when it was
earning credibility and when it was operating with credibility.

3
10

The Regional Economist is published
quarterly by the Research and Public Affairs
divisions 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.

P resident ’ s M essa g e

14

Is Today’s Low Inflation
due to a Liquidity Trap?

By Maria A. Arias and Yi Wen

Senior Policy Adviser
Cletus C. Coughlin

In contrast with many people’s
expectations, the Fed’s injection of $3.5 trillion into the
economy over the past five years
caused no significant inf lation
or increases in the price level.
There are many possible explanations in the mainstream. An
alternative is a liquidity trap.

Deputy Director of Research
David C. Wheelock
Director of Public Affairs
Karen Branding
Editor
Subhayu Bandyopadhyay
Managing Editor
Al Stamborski
Art Director
Joni Williams

16
to Subhayu Bandyopadhyay
at 314-444-7425 or by e-mail at

12

Credit Card Deleveraging
by Income and Age Groups

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.
Single-copy subscriptions are free
but available only to those with
U.S. addresses. To subscribe, go to
www.stlouisfed.org/publications.
You can also write to The Regional
Economist, Public Affairs Office,
Federal Reserve Bank of St. Louis,
P.O. Box 442, St. Louis, MO 63166-0442.

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.

By Juan M. Sánchez
The author shows that paying
down credit card debt in the
first few years after the financial
crisis was not just across older
households with higher income
but also by younger, middleclass households.

19

econom y at a g lance

20

district overview
Economic Mobility
in Eighth District Towns

2%

Director of Research
Christopher J. Waller

Please direct your comments

Debt Crisis in Europe:
Easing but Not Over

By Silvio Contessi and Li Li

By Alejandro Badel
and Julia Maues

In the wake of the financial
crisis, public debt in many European countries rose to levels not
seen since WWII. Although the
crisis started to abate in 2012, it
could heat up again. Most of the
ways used historically to pay off
such large debts don’t appear to
be viable options today.

Is intergenerational economic
mobility high or low in the
Eighth District? Are there areas
with extremely high or extremely
low mobility? These questions
are addressed using recent findings on the incomes of more
than 40 million people and their
parents between 1996 and 2012.

metro profile

22	national overview

After Stalling, Recovery
Resumes in St. Louis

A Cold Blanket
on the Economy

By Diana A. Cooke
and Charles S. Gascon

By Kevin L. Kliesen

After being in a stall pattern for
almost two years, the St. Louis
economy is once again looking up.
Hiring, particularly in the traditionally strong financial services
sector, is on the upswing. The
longtime struggles to maintain
population seem to be subsiding.
Even the retail sector is experiencing growth.

ONLINE EXTRA

Data on the economy that were
coming in early this year were
softer than expected. Given
that the recovery seemed to be
picking up steam in the second
half of last year, this slowdown
was unexpected. Was the cold
weather to blame?

23	reader e x c h an g e
Read more at www.stlouisfed.
org/publications/re.

Youth Unemployment: A Global Comparison
By James D. Eubanks and David G. Wiczer
Unemployment since the Great Recession has hit young people
much harder than others. In the U.S., the unemployment rate for
this group hovers around 14 percent. In some European countries,
the rate is three times higher.
cover image: © jonathan l arsen, istock, thinkstock

2 The Regional Economist | April 2014

p r e s i d e n t ’ s

m e s s a g e

The Rise and Fall
of Labor Force Participation in the U.S.

T

he labor force participation rate—
a measure of the number of people
actively involved in labor markets—has generally been a secondary concern in macroeconomics over the past several decades.
However, the sharp declines in the participation rate that followed the financial crisis
and recession of 2007-09 have put the topic
front and center. In this column, I will offer
my own perspectives on the issue.1
Labor market performance is at the heart
of the debate over how to characterize the
state of the U.S. economy. While unemployment hit 10 percent in the fall of 2009, it was
down to 6.7 percent this past February. The
unemployment rate has generally declined
faster than many forecasters anticipated.
In tandem with this rosy development,
however, labor force participation (LFP)
has declined substantially.
There are two main interpretations of
these data. The “bad omen” view interprets
the recent declines in LFP as suggestive of
a very weak labor market and discounts
the signal coming from recent faster-thanexpected declines in unemployment. The
“demographics” view interprets recent
declines in LFP as more benign and takes
the signal coming from recent faster-thanexpected declines in unemployment at face

value. Since the Federal Open Market Committee has explicitly tied monetary policy
choices to labor market performance, it is of
considerable importance which view is more
nearly correct.
Some background on the LFP data is in
order. Participation rose in the 1970s, 1980s
and 1990s; it peaked in 2000 and has been
in decline since. (See the chart.) Current
projections from the Bureau of Labor Statistics suggest that this decline will continue
over the coming decade. The rise in LFP is
often attributed in part to the maturing of
the baby boomers, as well as to the increase
in the number of women in the workforce.
The decline has often been attributed to the
aging of the labor force.
A satisfactory model has to account for
the rise and fall over many decades. A
demographically based model—which
assumes that certain demographic groups
have a certain propensity to participate in
market work—would seem to have a good
chance of success in explaining these data.
Based on some of the available literature on
this topic, my view is that carefully constructed empirical models of the trend in
the U.S. LFP rate indeed do a good job of
explaining the data.2 These models suggest
that the current participation rate is not far

Civilian Labor Force Participation Rate
68

from the predicted trend. This means, in
turn, that the cyclical component in LFP is
likely to be relatively small.
To the extent these models are correct,
then, the observed unemployment rate
remains as good an indicator of overall labor
market health as it has been historically. In
particular, the recent, relatively rapid declines
in unemployment can be understood as representing an improving labor market. This is
the judgment that should inform monetary
policy going forward.
The literature is not completely satisfactory, however. Simply saying that people
in certain demographic groups tend to
make the participation decision one way or
another does not do enough to analyze the
incentives of household labor supply decisions. The more we know about the details
of the household labor supply choices, the
better we can predict the impact of policy on
LFP. Furthermore, including more detailed
household decision-making in economic
models would allow us to better understand
what motivates or deters participation in
labor markets. I look forward to seeing
future research pushing in this direction.

67
66

(Percent)

65

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

64
63
62

ENDNOTEs
61

1 This column is based on my speech on Feb. 19, 2014,

60
59
58
1950

1960

1970

1980

1990

2000

Source: U.S. Department of Labor: Bureau of Labor Statistics

2010

and my article in the First Quarter 2014 issue of the
Federal Reserve Bank of St. Louis Review. Links to
both can be found at http://research.stlouisfed.org/
econ/bullard/the-rise-and-fall-of-labor-forceparticipation-in-the-u-s/.
2 For details on the literature, see my related speech
and Review article.

Shaded areas indicate U.S. recessions - 2014 research.stlouisfed.org

The Regional Economist | www.stlouisfed.org 3

4 The Regional Economist | April 2014

The Ups and Downs
of Inflation and the
Role of Fed Credibility
By Diana A. Cooke and William T. Gavin

“With inflation running below many central banks’ targets, we see rising
risks of deflation, which could prove disastrous for the recovery. If inflation
is the genie, then deflation is the ogre that must be fought decisively.”
—Christine Lagarde, managing director of the International Monetary Fund,
in a speech Jan. 15, 2014, to the National Press Club in Washington, D.C.

I

© jupiterimages, photos.com, thinkstock

n this speech, Christine Lagarde urged central banks in major
developed nations to stick with low interest policies in order
to fight off the threat of deflation. Expectations of deflation are
detrimental to recovering economies. If consumers know prices
will drop in the future, they will hold back spending in the
present, further depressing the economy. But what should
central banks do if the low interest rate policies are actually
causing inflation that is so low it raises the specter of deflation?

The Regional Economist | www.stlouisfed.org 5

FIGURE 1

2010

2006

2002

1998

1994

1990

1986

1982

1978

1974

1970

1966

1962

CPI
Core CPI

1958

16
14
12
10
8
6
4
2
0
–2
–4

1954

Percent

Inflation Trends

SOURCE: U.S. Bureau of Labor Statistics.
NOTE: The consumer price index (CPI) measures monthly changes in the prices paid for a representative basket of goods and services. The core CPI excludes
food and energy. The gray bars indicate recessions.

FIGURE 2
The Relationship between the Fed Funds Rate and 10-Year Treasury Bond Yield
25

Percent

20
15

Fed funds rate
10-year Treasury

10
5

2010

2006

2002

1998

1994

1990

1986

1982

1978

1974

1970

1966

1962

1958

1954

0

SOURCES: Federal Reserve Board and U.S. Treasury.
NOTE: The changing nature of the relationship between the fed funds rate (short-term rate) and the 10-year Treasury bond yield (long-term rate) is indicative of
three distinctly different eras associated with U.S. monetary policy: pre-1980, 1980-1986 and 1987-today. The gray bars indicate recessions.

There are two ways that a central bank
can cause low interest rates to be associated
with low inflation. The first is if a central
bank pursues a lower inflation target,
whether by design or indirectly; in this case,
people’s expectations of lower inflation may
lead to both lower interest rates and lower
inflation. The second way is by targeting the
key policymaking interest rate (such as the
federal funds rate in the U.S.) to a level that
is too low for too long to be consistent with
the central bank’s inflation target. The fed
funds rate, which is the overnight interest
rate at which a depository institution lends
funds at the Federal Reserve to another
institution, currently has a target of 0 to
0.25 percent. But the Fed’s inflation target
is 2 percent. Since the Fed set that inflation
target in January 2012, the inflation rate has
generally been below the target.
6 The Regional Economist | April 2014

Understanding the cause of unusually low
inflation is necessary to forming a policy
to fix it. The conventional wisdom, that
lower interest rates today will cause higher
inflation tomorrow, comes from historical
experience with a monetary policy that was
not credible. By credible monetary policy,
we mean that the public believes that the
central bank will do whatever is necessary
to achieve long-run price stability. When
a central bank is not credible, it is always
fighting inflation—as the Fed had to do in
the 1970s.
Earning credibility can be very costly.
The recessionary period from early 1980
through 1982 was associated with policies that were adopted to control inflation
and earn credibility. The benign period of
growth that began in the mid-1980s is often
attributed to the fact that monetary policy
had gained credibility.
In this article, we look at the history of
interest rates and inflation in the U.S. to
clarify ideas about monetary policy and
credibility. We examine three periods
corresponding to three distinctly different
policies associated with monetary policy:
1) operating without credibility, 2) earning
credibility and 3) operating with credibility.
After clarifying how credibility matters for
interest rates and inflation in these three
episodes, we turn to current events to discuss why low interest rates may now be putting downward pressure on inflation rates.
Pre-1980: No Credibility

During the 1970s, the U.S. experienced
a period of accelerating inflation that came
to be known as the Great Inflation.1 Figure 1
shows that inflation rose in fits and starts
from just under 2 percent in 1965 to 14.4 percent in June 1980. This period was often
characterized as an era of stop-go monetary
policy. When inflation rose, the Fed’s chief
monetary policymaking body, the Federal
Open Market Committee (FOMC), would
react by raising the fed funds rate high
enough to slow inflation. The relatively high
interest rate would lower aggregate spending, reduce the demand for labor and lead to
a recession. The FOMC would then switch
gears, lowering the fed funds rate sharply
to stimulate spending and job growth. The
stop-go nature of this policy before 1980
is evident in Figure 2, which shows the fed

funds rate (a short-term rate) and the yield
on 10-year Treasury bonds (a long-term
rate) from 1954 through 2013.
The relationship between the fed funds
rate and the 10-year Treasury rate during the period before 1980 displays three
distinct features. First, both interest rates
display rising trends and have roughly equal
average rates; the fed funds rate averaged
just 0.6 percent less than the 10-year rate.
Second, the fed funds rate was sometimes
as much as 2 percentage points higher
than the 10-year rate, which signaled a
poor long-term outlook. Third, periods of
relatively low interest rates were followed by
higher inflation and inflation expectations,
reflected in rising 10-year bond yields.
The lack of credibility also made setting
the fed funds rate above the 10-year rate
necessary in order to slow inflation expectations. When the FOMC raised interest rates
too slowly, inflation expectations would
rise to match the rise in interest rates, and
there was no dampening effect on either the
economy or inflation. The lack of credibility
meant that to succeed in lowering inflation,
the FOMC had to raise the fed funds rate
high enough to slow the economy. This led
to a belief that stabilizing inflation would
likely lead to high unemployment. A corollary to this idea was that low interest rates
would raise inflation and, at the same time,
lower the unemployment rate. What has
not been generally recognized is that these
dynamic relationships came to be part of
conventional wisdom in macroeconomics
when we were looking at data generated in a
period without credibility.
The lack of credibility caused inflation
to rise when interest rates were low. In the
stop-go policy, the FOMC adjusted interest
rates in response to both unemployment
and inflation. Gaining credibility would
require a period of prioritizing low inflation
over low unemployment. Only then would
long-run inflation expectations be set in a
way that did not fluctuate with short-term
interest rate policy.
1980-86: Earning Credibility

In late 1979, the U.S. dollar was in crisis
and European central bankers called on Fed
Chairman Paul Volcker to find a way to end
this period of high and rising inflation.2 On
Oct. 6, 1979, the FOMC announced that it

was adopting a new procedure for monetary
policy. Policymakers switched from targeting a narrow range for the fed funds rate to
targeting a narrow range for bank reserves.
Money demand—and, therefore, bank
reserve demand—is highly volatile in the
short run. By targeting the interest rate, the
Fed allows money demand fluctuations to
be absorbed by accommodating fluctuations
in money supply. On a month-to-month
basis before Oct. 6, 1979, the FOMC was
setting the interest rate while the market
was setting the quantity of reserves. As part
of its new policy to end inflation, the FOMC
announced that it would no longer set the
interest rate, but rather would set the supply
of reserves consistent with a target path
for the money supply. This meant that the
market would set the interest rate. Highly
volatile money demand then created highly
volatile interest rates.
The effect on interest rates of switching
from an interest rate target to a target for
bank reserves shows up in Figure 2 as a
dramatic increase in both their level and
volatility. Between January 1979 and
December 1982, the standard deviation of
monthly changes in the fed funds rate was
1.92 percentage points, while pre-Volcker,
the monthly standard deviation was just
0.4 percentage points. In January 1981, the
fed funds rate peaked at just over 20 percent
on a weekly average basis. (Figure 2 shows
monthly averages that dampen this weekly
variation.)
After the recession ended in 1982, the
FOMC was still worried about building credibility and once again raised interest rates in
response to rising inflation. Figure 2 shows
that the fed funds rate rose from 8.6 percent
in May 1983 to 11.6 percent in August of
1984. This tightening occurred during a
major banking crisis, which saw Continental
Illinois National Bank and Trust Co., at one
time the seventh-largest bank in the U.S. as
measured by deposits, go into bankruptcy.
During the crisis, the unemployment rate
never fell below 7.2 percent. But the tighter
policy was aimed at preserving the progress
made on lowering inflation.
Keeping the policy rate high, despite high
unemployment rates, convinced the public
that the Fed would do whatever was necessary to maintain low inflation. The policy
worked: Inflation fell sharply to a low of

1.1 percent in December 1986. The Fed
gained credibility for its inflation expectations, although not without causing a severe
recession and double-digit unemployment
rates. What’s most worrisome about the loss
of credibility—at least in the eyes of those
who lived and worked through this period—
is the high cost of regaining it.
1987-2007: Operating with Credibility

Alan Greenspan became Fed chairman in
June 1987. Soon after—on Oct. 19, 1987—
the stock market crashed. The Fed flooded
the market with about $600 million in
excess reserves (which were withdrawn after
a few weeks).3 The economy weathered the
crisis, and the Fed continued to raise the fed
funds rate target to just under 10 percent in
reaction to an inflation scare that was associated with the rise in the 10-year rate.4 This
uptick was only temporary, however, and
marked the highest peak in inflation and
interest rates from then until the present.
This period of low inflation and credible
monetary policy was accompanied by dramatic changes in the relationship between
the fed funds rate and the yield on 10-year
Treasury bonds. Notice the contrast from
the earlier period, as evident in both Figures
1 and 2. As inflation stabilizes at about 2 or
3 percent, interest rates continue to trend
lower. Also, the fed funds rate is never
much higher than the 10-year rate. Since
January 1987, the fed funds rate has been,
on average, 1.6 percentage points below the
10-year Treasury rate.
Perhaps the most surprising result
occurred after Sept. 2, 1992. This was when
the FOMC decided to set the fed funds rate
target at 3 percent, a rate approximately
equal to the perceived trend in inflation.
The rate was held at this level for 16 months.
It was felt that such a low interest rate for
so long would cause higher inflation and,
in October 1993, the 10-year rate began to
rise from a low of 5.3 percent to a peak just
under 8 percent in November 1994. But
the FOMC did not have to raise the federal
funds rate above the 10-year rate to end
this brief inflation scare. The FOMC began
to raise the fed funds rate target in February 1994. It was raised rather sharply to
6 percent in early 1995, but, by then, the
10-year rate had already begun to retreat.
On a 12-month moving average basis, the
The Regional Economist | www.stlouisfed.org 7

consumer price index (CPI) inflation rate
peaked at 2.9 percent in August 1994.
2008-13: The Financial Crisis
and Unexpectedly Low Inflation

During the Great Inflation,
when the Fed did not have
credibility, it was difficult
for the Fed to stop the rise
of inflation. Now, when
credibility is deeply rooted,
it seems just as difficult to
stop inflation from falling.

The Greenspan chairmanship ended in
2006. President George W. Bush appointed
Ben Bernanke to replace Greenspan on Feb. 1,
2006. The Great Moderation of the Greenspan
era began to fade almost immediately.
The housing boom began to cause serious
financial distress in the summer of 2007
and eventually led to an all-out crisis with
the bankruptcy of Lehman Brothers Inc.
on Sept. 15, 2008. The FOMC flooded the
market with bank reserves to prevent a
worldwide collapse of financial markets.
The flood of excess reserves drove the fed
funds rate to 0; the FOMC followed on Dec.
16, 2008, by setting a target range for the fed
funds rate at 0 to 0.25 percent.
This range has been held there for five
years, and FOMC members expect it will
stay there until sometime in 2015. As has
been the case since Greenspan’s experience
with a low fed funds rate in 1992, the exceptionally low fed funds rate of today has not
led to higher inflation. Indeed, the opposite
has occurred, as inflation and inflation forecasts continue to track below the 2 percent
target of the FOMC.
During the Great Inflation, when the Fed
did not have credibility, it was difficult for
the Fed to stop the rise of inflation. Now,
when credibility is deeply rooted, it seems
just as difficult to stop inflation from falling.
Long-run inflation expectations appear to
have stabilized at about 2 percent in this
period; so, it seems highly unlikely that
expectations about the Fed’s target are the
cause of low interest rates and below-target
inflation today. To understand why low
interest may be causing low inflation, we
turn to the Fisher equation.
The Fisher Equation

Irving Fisher (1867-1947) is one of
America’s greatest monetary economists.
An important reason for his fame is the
Fisher equation, which links the nominal
interest rate to the real interest rate through
inflation expectations:
nominal interest rate =
real interest rate + expected inflation rate
8 The Regional Economist | April 2014

The Fisher equation is an accounting
identity. The equation also helps us to think
about how the Fed’s interest rate policy
may influence inflation. Nominal interest
rates are the interest rates that people pay
to borrow or that they earn on their savings
accounts or bond holdings. The fed funds
rate is an example of a nominal interest
rate—it is the reported rate at which depository institutions (such as banks) lend funds
on deposit at the Fed to other banks that also
have accounts at the Fed. This rate is not
adjusted for inflation. The real interest rate,
on the other hand, is the rate of return that
is earned after adjusting for inflation. When
borrowers and lenders agree on the nominal
interest rate, they do not know what inflation
rates will be in the future. Instead, they set
the interest rate based on their expectations
of inflation. For example, suppose your price
for lending $100 is a 3 percent increase in
real purchasing power. Because you expect
inflation to rise by 2 percent over the year,
you and the borrower agree upon a nominal
interest rate of 5 percent. If the actual rate of
inflation was 3 percent, then the real interest
rate would be only 2 percent.
After the fact, it’s simple to calculate what
the real rate of return of the loan was. Since
the FOMC set the fed funds target at 0 to
0.25 percent, the Fed has paid 0.25 percent
on bank deposits held as reserves; so, no
bank with an account at the Fed has an
incentive to lend funds at less than this rate.
Since December 2008, inflation in the CPI
has averaged 1.6 percent. This means the
average real return on bank deposits at the
Fed has been –1.35 percent.
Before the fact, the real interest rate is not
as easy to measure. Except for the indexed
bonds issued by the U.S. government, we do
not have direct measures of the real interest rate.5 Gross domestic product (GDP)
growth adjusted for inflation, however, is a
good indicator of real interest rate trends.
When the economy is doing well, there is a
higher return to a given amount of capital
and labor; thus, real interest rates are higher.
Between 2010:Q4 and 2013:Q4, year-overyear change in GDP averaged 2.21 percent.
This positive growth is in stark contrast to
the decline of 0.03 percent between 2007:Q4
and 2010:Q4. Since real output is increasing, real interest rates must be on the rise,
as well. If the real interest rate is moving

up and the nominal interest rate is being
pegged near 0 by the Fed, then the Fisher
equation predicts that there will be downward pressure on inflation.
Monetary policy has been much in the
news because the 2007-09 recession was
exceptionally deep, monetary policy was
exceptionally easy and, yet, the recovery has
been unusually tepid. Why has the Fed been
keeping the policy rate low? Because the
common belief is that low nominal rates will
stimulate spending and push the economy
toward recovery. However, the consistently
low inflation forecasts across all major countries are disconcerting and make us suspect
the Fisher equation is making itself felt in
the data more than predicted. As major
economies are recovering, we would expect
real returns to rise. Therefore, according to
the Fisher equation, with the fed funds rate
near 0, the inflation rate would have to be
negative. The low rates set by the Fed could
actually be contributing to low inflation and
low inflation expectations.
Looking Forward

There is a great deal of uncertainty about
future monetary policy because the outlook
for interest rates, inflation and real economic growth is inconsistent with the Fisher
equation. The low interest rate outlook is
inconsistent with 2 percent inflation expectations and a normal recovery. A normal
recovery will lead to rising real interest rates
and should make the nominal interest rate
higher than the 2 percent coming from the
inflation objective. The uncertainty arises
because there are dramatically different
ways that the inconsistency can be resolved.
Consider three alternative scenarios:
1. The Fed loses credibility, and we return
to 1970s-style inflation. This is the
concern of some FOMC members who
have dissented on a regular basis. In this
scenario, real interest rates continue to
be low, but inflation expectations and the
10-year rate begin to rise rapidly. The
Fed is forced to raise the fed funds rate
as inflation accelerates. This seems an
unlikely outcome, at least in the next
year or two.
2. The Fed maintains credibility, and people
expect 2 percent inflation to continue
indefinitely. The Fed is successful in

engineering a recovery with a gradual
rise in interest rates. Interest rates rise
enough to prevent a loss of credibility,
but not so much as to cause another
recession. This is the outcome that is
considered most likely by private and
government economic forecasters.
3. The Fed decides to keep rates exceptionally low until the economic data clearly
demonstrate that the economy is at full
employment. The problem with this scenario is that neither the Fed nor privatesector economists are able to predict
turning points. The economy is likely to
be well beyond ordinary measures of full
employment before the data reveal that
the threshold has been met. The Fisher
equation suggests that keeping nominal
rates low while the economy recovers
will put downward pressure on inflation. In this scenario, interest rates and
inflation stay well below normal for a
long time. This outcome is more likely if
forward guidance sets a lower threshold
on the inflation target.
The reason it is so hard to predict which
of these scenarios might play out is that
the result depends so much on what people
think will happen. Inflation expectations
are the key. A surge in inflation expectations
leads to the first scenario above. Expectations anchored at 2 percent will support the
second scenario. Expectations of falling
inflation or even of deflation are likely to
lead to the third outcome, which is a concern
because it looks so much like the Japanese
economy from 1995 to the present.

ENDNOTES
1

2
3
4
5

See Nelson, who explains why, during this period,
many economists and policymakers did not feel
that it was important for the Fed to focus sharply
on price stability.
See Lindsey, Orphanides and Rasche for a description of events and policy actions taken at this time.
See Neely for a description of the Fed’s reactions to
crises in financial markets.
See Goodfriend for a description of inflation
scares and the Fed’s response to them.
See Fleming and Krishnan for a description of
Treasury inflation-protected securities (TIPS).

REFERENCES
Fleming, Michael J.; and Krishnan, Neel. “The
Microstructure of the TIPS Market.” Federal
Reserve Bank of New York Economic Policy
Review, March 2012, Vol. 18, No. 1, pp. 27-45.
Goodfriend, Marvin. “Interest Rate Policy and the
Inflation Scare Problem: 1979-1992.” Federal
Reserve Bank of Richmond’s Economic Quarterly,
Winter 1993, Vol. 79, No. 1, pp. 1-23.
Lindsey, David E.; Orphanides, Athanasios; and
Rasche, Robert H. “The Reform of October 1979:
How It Happened and Why.” Federal Reserve
Bank of St. Louis’ Review, November/December
2013, Vol. 95, No. 6, pp. 487-542.
Neely, Christopher J. “The Federal Reserve Responds
to Crises: September 11th Was Not the First.”
Federal Reserve Bank of St. Louis’ Review, March/
April 2004, Vol. 86, No. 2, pp. 27-42.
Nelson, Edward. “The Great Inflation of the Seventies: What Really Happened?” Federal Reserve
Bank of St. Louis, Working Paper 2004-001.

William T. Gavin is an economist and Diana
A. Cooke is a research analyst, both at the
Federal Reserve Bank of St. Louis. For more on
Gavin’s work, see http://research.stlouisfed.org/
econ/gavin/.

The Regional Economist | www.stlouisfed.org 9

F O M C

The Liquidity Trap:
An Alternative
Explanation for
Today’s Low Inflation

2%

By Maria A. Arias and Yi Wen

F

rom January 2009 to December 2013,
the Federal Reserve’s balance sheet grew
by approximately $3.5 trillion due to the
large-scale asset purchase (LSAP) policies
implemented to aid the ailing economy after
the Great Recession. These unconventional
monetary policies, also known as quantitative easing (QE), increased credit availability in the private lending markets and put
downward pressure on real interest rates.
During normal times, for each 1 percent
increase in the growth of money, inflation increases by 0.54 percent, based on a

reduced to a range between 0 and 25 basis
points. The media and some Fed officials
expressed concern about inflation becoming rampant because of the large amount
of money that was being injected into the
economy. But those fears have not materialized. On the contrary, it wasn’t long
before policymakers’ anxiety focused on the
possibility of falling into a Japanese-style
deflation.3 (See figure.)
Several reasons have been provided for
the persistently low inflation. For example,
Fed Chair Janet Yellen said in 2009 when

Investors hoard the increased money instead of spending it
because the opportunity cost of holding cash—the forgone earnings from interest—is zero when the nominal interest rate is zero.
linear regression of the inflation rate on
money growth for the precrisis period.1
Money supply (M0) increased 40.29 percent
between December 2008 and December
2013, or about 8 percent per year on average.
Under this pace of annual money growth,
we would have seen inflation of 4.3 percent
per year, or a price level increase of at least
40 percent in 2013 compared with the price
level in 2008.2 But this did not happen.
Thus, in contrast with many people’s
expectations, the injection of $3.5 trillion
into the economy has not caused any significant inflation or increases in the price
level. Why?
Inflation Expectations

From their first implementation, LSAPs
were declared by the Fed’s Federal Open
Market Committee (FOMC) to be a new
policy tool to boost the economy after the
target federal funds rate had already been
10 The Regional Economist | April 2014

she was still president of the Federal Reserve
Bank of San Francisco that inflation would
not take hold during a recession because
of little pressure for prices and wages to
increase given that resources through the
economy were underused.4 Others say the
unusually low inflation stems from the
weakening of the money multiplier, as banks
continue to hold excess reserves instead
of extending more credit through loans.5
Still others point to the FOMC’s increased
communications and forward guidance in
anchoring future inflation expectations, as
well as to the knowledge that the LSAPs will
eventually be reversed.6
There also exists an alternative explanation
for the generally unanticipated disinflation or
low inflation levels—the liquidity trap.7
Excess Liquidity

Conventionally, the expansion of the
money supply will generate inflation as more

money is chasing after the same amount of
goods available. During a liquidity trap,
however, increases in money supply are
fully absorbed by excess demand for money
(liquidity); investors hoard the increased
money instead of spending it because the
opportunity cost of holding cash—the
forgone earnings from interest—is zero when
the nominal interest rate is zero. Even worse,
if the increased money supply is through
LSAPs on long-term debts (as is the case
under QE), investors are prompted to further
shift their portfolio holdings from interestbearing assets to cash.
On one hand, if the increase in money
demand is proportional to the increase in
money supply, inflation remains stable. On
the other hand, if money demand increases
more than proportionally to the change in
money supply due to the downward pressure LSAPs exert on the interest rate, the
price level must fall to absorb the difference
between the supply and demand of money.
That is, the increase in aggregate demand
for real money balances then has to be
accommodated by an overall decrease in
the price level for any given money supply
in the goods market. Therefore, the lower
the interest rate through LSAPs, the lower
the price level (due to the disproportionately
higher money demand). The Fed’s policy to
pay positive interest rates on reserves can
only reinforce the problem by making cash
more attractive as a store of value. 8
Economist Yi Wen (the co-author of this
article) showed last year that large-scale
asset purchases by the Fed at the current
pace could reduce the real interest rate by
2 percentage points, but would have an
insignificant effect on aggregate employment and fixed capital investment, would

reduce the aggregate price level significantly,
and would put severe downward pressure on
the inflation rate—thanks to firms’ portfolio adjustments between cash and financial
assets in a liquidity trap.9
Risks of Declining Inflation

Not only high inflation, but low inflation
can be bad for the economy. Low inflation
makes cash more attractive to investors
as a store of value, everything else equal.
This makes the liquidity trap easier to occur
and gives the Fed less room to reduce the
real interest rate as desired during a recession. Furthermore, quantitative easing
through LSAPs can reinforce the liquidity
trap by further reducing the long-term
interest rate. In other words, more monetary injections during a liquidity trap can
only reinforce the liquidity trap by keeping
the inflation rate low (or the real return to
money high).
Therefore, the correct monetary policy
during a liquidity trap is not to further
increase money supply or reduce the interest
rate but to raise inflation expectations by
raising the nominal interest rate. If LSAP
policies are reversed and the money supply decreases as the Fed sells assets in the
marketplace, the nominal interest rate will
increase and investors will be more likely to
shift their portfolios away from cash toward
interest-bearing assets. If demand for
money decreases more than proportional to
the decrease in money supply due to upward
pressure on the interest rate, inflation will
increase. In other words, only when financial assets become more attractive than cash
can the aggregate price level increase.
Of course, this type of policy-reinforced
liquidity trap would take place only if the

economy is in a deep recession in the first
place. If the economy is not in a recession,
monetary injections should lead to more
inflation instead of less inflation because a
lower interest rate generally reduces people’s
incentive to save and increases their incentive to spend.
The irony is that expansionary monetary policy is often called for only when
the economy is in a recession. This policy
dilemma makes economics a dismal science.
One way to escape from it is to use expansionary fiscal policy (as suggested by the
economist John Maynard Keynes). However, with the already high level of government debt across industrial countries, it
takes courage and vision to implement bold
expansionary fiscal policies.10
Inflation Expectations and LSAPs

Inflation started declining in early 2012
and was significantly below FOMC members’ forecasts in 2013. Since the beginning
of this year, the committee has slowed the
pace of LSAPs as broad economic activity
has improved, but the target federal funds
rate will remain near the zero lower bound
for a longer period. Inflation is expected
to continue being stable and move toward
the 2 percent target rate of the FOMC as the
economy improves, but it will not increase
much until the demand for money decreases
and the effects of the liquidity trap wane.
Yi Wen is an economist and Maria A. Arias is a
research associate, both at the Federal Reserve
Bank of St. Louis. For more on Wen’s work, see
http://research.stlouisfed.org/econ/wen/.

Headline Inflation

Percent Change from Year Ago

6
FOMC inflation target of 2 percent

4.5
3
1.5
0

Personal consumption expenditures price index
Consumer price index

–1.5
–3
2008

Expected inflation five years forward

2009

2010

2011

2012

SOURCES: Bureau of Economic Analysis, Bureau of Labor Statistics, Federal Reserve Board and Haver Analytics.

2013

ENDNOTES
1

2

3
4
5
6

7
8

9
10

“Normal times” refers to the postwar period prior
to the Great Recession (1960-2007). The effect of
changes in the money supply (M0) on headline consumer price index (CPI) inflation during this time
frame was calculated using a linear regression model.
The implications are similar if we use the total
monetary base (M0 + bank reserves) instead of
M0. During normal times, inflation increases
0.26 percent for every 1 percent increase in money
base growth. So, since the money base grew
123 percent during the five-year period from
December 2008 to December 2013, inflation would
have been 6.3 percent per year on average.
See Bullard.
See Yellen.
See Fawley and Wen on the decline of the money
multiplier and monetary aggregates.
As Andolfatto and Li note when describing the effect
of QE in Japan during the 2000s, “even large changes
in the monetary base are not likely to have any
inflationary consequences if people generally believe
the program will be reversed at some future date.”
For related discussion on this alternative, see
Haltom and Krugman.
Ricketts and Waller describe the Fed’s policy tools
to avoid runaway inflation, including paying a
positive interest rate on excess reserves.
See Wen.
See Wen and Wu for an empirical study of the
powerful effects of fiscal policies in China that
helped China to escape the Great Recession after
the financial crisis in 2007-08.

REFERENCES
Andolfatto, David; and Li, Li. “Quantitative Easing
in Japan: Past and Present.” Federal Reserve Bank
of St. Louis Economic Synopses, 2014, No. 1,
Jan. 10, 2014. See http://research.stlouisfed.org/
publications/es/article/10024.
Bullard, James. “Seven Faces of ‘The Peril.’ ” Federal
Reserve Bank of St. Louis Review, September/
October 2010, Vol. 92, No. 5, pp. 339-52.
Fawley, Brett; and Wen, Yi. “Low Inflation in a World
of Securitization.” Federal Reserve Bank of
St. Louis Economic Synopses, 2013, No. 15, May 31,
2013. See http://research.stlouisfed.org/
publications/es/article/9801.
Haltom, Renee. “Liquidity Trap.” Federal Reserve
Bank of Richmond Region Focus, First Quarter
2012, p. 10. See www.richmondfed.org/
publications/research/region_focus/2012/q1/
pdf/jargon_alert.pdf.
Krugman, Paul. “Monetary Policy in a Liquidity Trap.”
The New York Times, Opinion Pages, April 11, 2013.
Blog post. See http://nyti.ms/19ONWJZ.
Ricketts, Lowell R.; and Waller, Christopher J.
“The Rise and (Eventual) Fall in the Fed’s Balance
Sheet.” Federal Reserve Bank of St. Louis
The Regional Economist, January 2014, Vol. 22,
No. 1. See www.stlouisfed.org/publications/re/
articles/?id=2464.
Wen, Yi. “Evaluating Unconventional Monetary
Policies—Why Aren’t They More Effective?”
Working Paper 2013-028B, Federal Reserve Bank
of St. Louis, 2013. See http://research.stlouisfed.
org/wp/2013/2013-028.pdf.
Wen, Yi; and Wu, Jing. “Withstanding Great
Recession like China.” Federal Reserve Bank of
St. Louis Working Paper 2014-007A.
Yellen, Janet. “A View of the Economic Crisis and
the Federal Reserve’s Response.” Presentation
to The Commonwealth Club of California in
San Francisco on June 30, 2009.
The Regional Economist | www.stlouisfed.org 11

household balance sheets

Paying Down Credit Card
Debt: A Breakdown
by Income and Age
By Juan M. Sánchez
© istock

12 The Regional Economist | April 2014

Percent of Disposable Personal Income

5.5
5.0
4.5
Jan. 80
Jan. 82
Jan. 84
Jan. 86
Jan. 88
Jan. 90
Jan. 92
Jan. 94
Jan. 96
Jan. 98
Jan. 00
Jan. 02
Jan. 04
Jan. 06
Jan. 08
Jan. 10
Jan. 12
Jan. 14

4.0

SOURCE: Federal Reserve Board.
NOTE: Income in this case refers to disposable personal income. Consumer
debt includes outstanding credit extended to individuals for household, family
and other personal expenditures, excluding loans secured by real estate. Data
are seasonally adjusted.

FIGURE 2
Decomposition of Consumer Debt
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0

Jan. 13

Jan. 12

Jan. 11

Jan. 10

Jan. 09

Auto Loans
Credit Cards
Student Loans
Jan. 08

less credit, but households also could have
had a reduced willingness to borrow. This
article documents how the deleveraging process regarding credit card debt varies across
households with different backgrounds. It
also decomposes changes across the variations in the share of people in debt (called
“the extensive margin”) and changes in the
amounts of debt held by borrowers (“intensive margin”).
Figure 1 shows that, measured as a percentage of disposable personal income,
scheduled consumer debt payments decreased
from 6 percent at the beginning of the recession to about 5 percent in mid-2010. Since
then, the rate has remained about 5 percent.
Consumer debt includes outstanding
credit extended to individuals for household,
family and other personal expenditures,
excluding loans secured by real estate.

6.0

Jan. 07

2010, from $3,538 to $2,791.

6.5

Jan. 06

24 percent between 2007 and

7.0

Jan. 05

credit card debt decreased

Consumer Debt Service Payments

Jan. 04

Consumer Finances, mean

FIGURE 1

Jan. 03

According to the Survey of

Figure 2 shows the evolution of the main
components: credit card debt, auto loans
and student loans. The balances on auto
loans, represented by the blue dashed line,
decreased during 2008-2009 but recovered
quickly, starting early in 2010. Student
loans, represented by the green dashed line,
increased continuously from 2003 until 2013.
Credit card debt, represented by the red solid
line, is the component that shows the clearest
negative trend since the financial crisis. As a
consequence, the rest of the article is focused
on credit card debt.
The Federal Reserve’s Survey of Consumer
Finances (SCF) asks households about their
outstanding obligations. The survey asks not
just about the outstanding credit card debt
but also about income and age of the head of
households. In what follows, the changes in
credit card debt are broken down by income
and age. The focus will be on the years 2007
and 2010 because those years are the closest
to the period of deleveraging in credit card
debt for which SCF data are available. The
definition of credit card debt is the amount
of debt outstanding after the last payment;
so, it does not contain the debt of those who
pay the full amount every month.
Notice that changes in the mean level of
debt of a group may be because the share of
households with debt in that group changed
or because the mean debt of those in debt
changed. In economics, these changes are
referred to as extensive and intensive, respectively. According to the SCF, mean credit
card debt decreased 24 percent between
2007 and 2010, from $3,538 to $2,791.2 Of
this percentage drop, one-third (8 percent)
was due to the intensive margin and twothirds (16 percent) was due to the extensive
margin. This decomposition is used below

Total Balance ($, Trillions)

U

.S. households started a deleveraging
process as soon as the Great Recession began, which was in late 2007. They
continued along this path until mid-2010.
Among the different types of consumer debt
(auto loans, credit card, student loans), this
trend of paying down debt was particularly
striking for credit card debt. Research on the
reasons behind this trend is ongoing.1 The
increased risk during the crisis could have
motivated financial institutions to extend

SOURCE: Federal Reserve Bank of New York, Quarterly Report on Household and
Credit, November 2013.

to characterize the borrowing behavior of
different income and age groups.
Table 1 shows credit card debt for the years
2007 and 2010 by income groups. Each
group contains 25 percent of the households.

The first group, the poorest, had mean credit
card debt of $1,013 in 2007. In the same
year, the richest group had mean credit card
debt of $6,103.
Not only is the dollar amount of debt for
each group different in both 2007 and 2010,
but the percentage change for each group
between those two years is different. The
middle groups, sometimes referred to as the
middle class, are responsible for most of the
deleveraging. While the first and fourth
quartiles decreased debt by about 14 percent,
the second and third income quartiles
decreased credit card debt by 28 and 38 percent, respectively.
Except for the third income quartile,
changes in debt are mainly due to the fact
that fewer households are in debt. For
instance, the richest households in debt
decreased their mean debt by only 0.4 percent; so, most of the 14.6 percent decrease is
due to the fact that fewer households in this
group are in debt.
Table 2 shows credit card debt levels and
changes between 2007 and 2010 for different
age groups. There is a hump-shape profile
of debt over the life cycle. For instance, in
2007 households in the age groups 18 to
37 and 63 to 95 had about half of the mean
credit card debt of those in the age groups
38 to 49 and 50 to 62.
The changes in borrowing are very
heterogeneous across different age groups.
Households with a head of household ages
38 to 49 decreased borrowing by only
13 percent. In contrast, households headed
by someone 18 to 37 decreased credit card
debt by 28 percent, and households headed
by someone older than 62 decreased credit
card debt by 33 percent.
The relative importance of the intensive
and extensive margins for households deleveraging varies across households of different
ages, too. The extensive margin is more
important for young households, for whom
it accounts for more than 80 percent of the
change. In contrast, for the oldest households, it is the intensive margin that accounts
for more than 80 percent of the variation.
The data analyzed here reveal that
although the deleveraging of U.S. households after the financial crisis was present
across all types of households, it was actually
more important for households with middle
income that are younger than 38 or older

than 62. In addition, the analysis shows that,
except for some groups, most of the change
in outstanding credit card debt is accounted
for by changes in the share of households in
debt—the extensive margin—and not by the
amounts borrowed by those in debt—the
intensive margin.
The findings in this article suggest that several factors may be behind the deleveraging.
The worsening of labor market conditions,
in particular the higher risk of unemployment, may account for some of the changes,
especially those of young households with
lower income. However, this factor is
unlikely to account for the deleveraging by
richer households headed by those older than
62. This seems to indicate that other factors,
like shocks that increase the desire by older/
richer households to save, may be necessary
to understand the deleveraging.

ENDNOTES
1 See Athreya et al.
2 Notice that this change does not take inflation into

account. Thus, in real terms, the decline in borrowing was even larger.

R eference s
Athreya, Kartik; Sánchez, Juan M.; Tam, Xuan S.;
and Young, Eric R. “Labor Market Upheaval,
Default Regulations, and Consumer Debt.” Working Paper No. 2014-002A, Federal Reserve Bank of
St. Louis, January 2014. See http://research.
stlouisfed.org/wp/2014/2014-002.pdf.

Juan M. Sánchez is an economist at the Federal
Reserve Bank of St. Louis. For more on his work,
see http://research.stlouisfed.org/econ/sanchez/.
Research assistance was provided by Emircan
Yurdagul, a technical research associate at
the Bank.

table 1
Credit Card Balances by Income Groups
Income Quartile

2007

2010

Change

Dollars

Dollars

Percent

Intensive

Margin Adjusted
Extensive

1st, poorest

1,013

889

–13.1

–3.6%

–9.5%

2nd

2,407

1,828

–27.5

–8.5%

–19.0%

3rd

4,732

3,232

–38.1

–20.7%

–17.4%

4th, richest

6,103

5,276

–14.6

–0.4%

–14.2%

Overall

3,538

2,791

–23.7

–8.1%

–15.6%

SOURCE: Survey of Consumer Finances (SCF).
NOTE: In this case, “intensive” refers to the mean debt of those in debt, and “extensive” refers to the share of households with debt.

table 2
Credit Card Balances by Age Groups
Age of Head
of Household
18-37

2007

2010

Change

Margin Adjusted

Dollars

Dollars

Percent

Intensive

Extensive

2,744

2,077

–27.8

–4.7%

–23.1%

38-49

4,525

3,973

–13.0

–2.7%

–10.4%

50-62

4,695

3,580

–27.1

–9.3%

–17.8%

63-95

2,231

1,609

–32.7

–27.6%

–5.1%

Overall

3,538

2,791

–23.7

–8.1%

–15.6%

SOURCE: SCF.
The Regional Economist | www.stlouisfed.org 13

o v e r s e a s

Debt Crisis in Europe
Is Easing, but Stability
Remains a Long Way Off
By Silvio Contessi and Li Li
© shut terstock

everal historical examples show that
financial crises generate large increases in
private and public debt that take many years
and sometimes drastic measures to be worked
out. The recent global financial crisis was
no different. In the wake of the crisis, which
began in 2007, the public debt of the affected
countries increased to levels not seen since the
years after World War II. Also rising was the
perceived risk of default on this debt.
The initial worries lay with four peripheral
countries of the European Union (Greece,
Ireland, Portugal and Spain, sometimes
referred to by the acronym of GIPS or PIGS)
but soon extended to Italy (thus becoming
GIIPS or PIIGS) in the summer of 2011 and
later to Cyprus, Slovenia and even France. As
a consequence, financial markets and investors demanded higher yields to keep buying
the debt issued by this group of countries;
some countries, such as Portugal and Ireland,
stopped issuing debt almost entirely and
turned to borrowing from the European
Union (EU) and the International Monetary
Fund (IMF).1
Thanks to intervention by the European
Central Bank (ECB), to fiscal packages in
various countries and to the restructuring of
the Greek debt, the yields of many of these
countries’ government debt started trending
down in 2012, causing a softening of the debt
crisis. That softening has continued to date
but may heat up again in the near future.
In this article, we explain how the concepts of government debts and deficit are
relevant in the Economic and Monetary
Union (EMU) in Europe and how they
evolved after the beginning of the financial
crisis in a group of countries. Finally, we
briefly discuss possible paths that countries
can follow to adjust from the debt overhang.
14 The Regional Economist | April 2014

EU and EMU

The process of European integration led
to the creation of the EU when the Treaty
of Maastricht came into force in 1993. The
EU is an unusual political and economic
partnership that resembles a confederation; it currently comprises 28 countries.
Countries can join if they meet the so-called
Copenhagen criteria. In 1999, a subset of 11
EU countries formed the EMU, also known
as the euro zone or euro area. The EMU
adopted a common currency, the euro, and
its members relinquished monetary policy
to the ECB, based in Frankfurt.
In order to access the EMU, countries
must comply with a series of criteria,
including two regarding fiscal positions.
The Treaty of Maastricht requires that a
member government’s annual budget deficit
not exceed 3 percent of its gross domestic
product (GDP) and that the gross government debt to GDP not exceed 60 percent of
the country’s GDP. In exceptional circumstances, countries are allowed to exceed
these limits temporarily, but such deviations
are monitored under the EU’s Stability and
Growth Pact. As of this year, 18 countries
belong to the EMU.
What Happened after
the Financial Crisis?

The figure illustrates the ratios of debt and
deficit to GDP for the GIIPS (and for the U.S.
and the Group of 7 for comparison purposes)
at four points in time: 2007, 2009, 2011 and
2013. (Only projections are available at this
time for 2013.) The changes in the two ratios
are more marked than what one would see in
plain vanilla recessions that are not associated with financial crises.

The Evolution of Debt and Deficit Ratios
200
180
160
Debt-to-GDP Ratio

S

140
120
100
80
60
40
20
–4

0
U.S.
Greece

4
8
12
Deficit-to-GDP Ratio
Italy
Ireland

Portugal
Spain

16

20
G-7

SOURCE: International Monetary Fund, World Economic Outlook, October 2013.
NOTE: The ratios are plotted four times on each country’s line; each symbol
along the line represents a year, starting on the bottom with 2007 and then
moving along the line to 2009, 2011 and 2013. (Data for 2013 are projected.)
For example, in Ireland in 2009, the deficit-to-GDP ratio was about 13 percent
and the debt-to-GDP ratio was about 62 percent. The G-7 is composed of Canada, France, Germany, Italy, Japan, the United Kingdom and the United States.

During a recession, governments increase
spending while tax revenue falls due to the
contraction of GDP. The combination of
these two forces increases deficits, which
can potentially quickly raise the debt-toGDP ratios.
This effect can be seen very clearly in the
figure, not only for the GIIPS countries but
for the U.S. and the Group of 7. From 2007
until 2009 (roughly, the recession period for
most of these countries), both the deficit and
debt ratios rose. As the recession ended, the
deficit ratios started to decline because tax
revenue grew and primary deficits (excluding
interest) contracted. But the debt ratios kept
rising, in part because primary balances are

still negative and in part because the burden
of interest is now larger.
The most dramatic jump in debt- and
deficit-to-GDP ratios in our sample of countries is certainly Ireland. In addition to the
cyclical factors affecting these ratios, Ireland
witnessed the failure and subsequent bailout
by the government of the country’s large
banks. The deficit-to-GDP ratio jumped to
about 13.8 percent in 2009, before retreating
to about 13.1 percent in 2011 and then to an
estimated 7.6 percent in 2013. While Ireland
entered the financial crisis with an overall
surplus and small debt-to-GDP ratio, it faced
a debt-to-GDP ratio of more than 123 percent
in 2013, clearly beyond the limit set in the
Treaty of Maastricht.
How do these debt increases compare with
historical experiences? Economists Carmen
M. Reinhart and Kenneth S. Rogoff, wellknown for their 2009 book “This Time Is
Different: Eight Centuries of Financial Folly,”
looked at a large sample of crises before 2007.
They found that real public debt increased by
86.3 percent on average within three years of
the crisis.2 Between 2007 and 2010, the U.S.
initially had a relatively large debt-to-GDP
ratio that increased by about 48 percent,
while for the G-7 this increase was about
38 percent and for the GIIPS the increase
averaged 86 percent. Between 2007 and 2012,
these percentages were about 60 percent,
50 percent and 132 percent.
How Can Debt Overhangs
Be Worked Out?

The monetary stance in many countries
has kept interest rates at favorably low levels
for the past few years and will perhaps do so
for the near future. Thus, interest payments
on debt are at a moderate level, particularly
on new debt issued by each country. But
how will these large debt-to-GDP ratios be
worked out?
There are five ways in which large government debts, or debt overhangs, have been
worked out historically: 1) Inflation surprises, i.e., realized inflation rates higher
than those expected by consumers and firms
(and therefore not built into existing contracts); high inflation rates can help reduce
the real burden of repaying the principal
of the outstanding debt; 2) GDP growth,
which reduces the debt-to-GDP ratio (if
it’s larger than the growth rate of the debt

outstanding) and increases tax revenue; 3)
debt restructuring, which consists of partial
or total default on outstanding debt; 4) fiscal
consolidation, through a combination of
higher taxes and lower spending, sometimes
referred to as fiscal-adjustment austerity;
and 5) financial repression, such as directed
lending to governments by captive domestic audiences (for example, pension funds),
explicit or implicit limits on interest rates,
regulation of international capital movements, and similar measures.3
Recent data show that inflation and growth
measures do not bode well for European
countries. Inflation is trending downward,
below the 2 percent target set by the ECB
for the year-over-year harmonized index of
consumer prices. The growth rate of GDP
is projected to be very modest in the near
future. In January 2014, the IMF forecast
meager real GDP growth rates of 1 percent
for the euro area as a whole.
Debt restructuring was experimented with
in Greece in 2012. The Greek government
and private holders of Greek government
bonds struck an agreement in which private
creditors accepted a haircut of 53.5 percent
on the face value of Greek government bonds
and could choose to swap their high-rate
bonds with short maturity for low-rate bonds
with long maturity. Although debt restructuring is generally shunned by European
governments, more debt restructuring could
occur in the coming years.
European countries are currently proceeding with a mix of fiscal austerity and
financial repression, both of which lead to a
very slow adjustment of debt-to-GDP ratios.
While such ratios keep rising in Europe in
the aftermath of the crisis, some countries
are making slow progress in regaining their
national debt sustainability. For example,
Ireland and Portugal returned in 2013 to
issuing treasury bonds and borrowing
directly from financial markets.
Whichever route is taken by each government, the road to sounder fiscal stability will
probably be long and difficult.

ENDNOTES
1 For Portugal, the bailout loan was split among the

European Financial Stability Mechanism (EFSM),
the European Financial Stability Facility (EFSF)
and the IMF. For Ireland, the bailout was from
EFSM, EFSF, IMF, the National Pension Reserve
Fund and bilateral loans from the United Kingdom, Denmark and Sweden.
2 See Reinhart and Rogoff (2009).
3 See Reinhart and Rogoff (2013).

R eference s
Contessi, Silvio. “An Application of Conventional
Sovereign Debt Sustainability Analysis to the
Current Debt Crises.” Federal Reserve Bank of
St. Louis Review, May/June 2012, Vol. 94, No. 3,
pp. 197-220.
Reinhart, Carmen M.; and Rogoff, Kenneth S. “The
Aftermath of Financial Crises.” National Bureau
of Economic Research Working Paper Series,
No. 14656, 2009.
Reinhart, Carmen M.; and Rogoff, Kenneth S.
“Financial and Sovereign Debt Crises: Some Lessons Learned and Those Forgotten.” International
Monetary Fund Working Paper, No. WP/13/266,
2013.

Silvio Contessi is an economist and Li Li is a
senior research associate, both at the Federal
Reserve Bank of St. Louis. For more on Contessi’s work, see http://research.stlouisfed.org/
econ/contessi.
The Regional Economist | www.stlouisfed.org 15

m e t r o

p r o f i l e

After Stalling,
Recovery Resumes
in St. Louis
By Diana A. Cooke and Charles S. Gascon
© Rudy Bal asko, istock, thinkstock

The St. Louis metropolitan statistical area (MSA) spans the Mississippi River, taking in parts of Missouri on
the west and Illinois on the east. It is the largest MSA in Missouri and in the Federal Reserve’s Eighth District.
In 2012, the St. Louis MSA had a population of 2,795,794 and a labor force of 1,403,773. Per capita income was
$44,625, roughly 2 percent above the national average. Gross metropolitan product (GMP) was $116.5 billion
in 2012, equivalent to just over 50 percent of the gross state product (GSP) in Missouri.

F

rench fur traders Pierre Laclede and
Auguste Chouteau founded St. Louis
250 years ago, in February 1764. Their small
trading village would soon blossom into a
thriving metropolis. Following the Lewis
and Clark exploration, entrepreneurs made
fortunes in St. Louis, selling supplies to
adventurers traveling west. Later in the
19th century, St. Louis became a major
industrial center and home to the largest

Strong health care and
financial services sectors
have been key in helping the
local economy through the
Great Recession of 2007-09
and the recovery.
brewery in the U.S. The high volume of
trade attracted many banks and financial
firms. Financial services remain an important growth sector to this day. In the past
several decades, St. Louis has emerged as a
leader in the health care industry, too.
The MSA has struggled to retain population: During the 1970s and early 1980s, it
16 The Regional Economist | April 2014

lost an average of roughly 3,000 people per
year. Since then, it has grown slower than
the national average, although the population has increased every year since 1982.
Between 2002 and 2012, the population
increased by 95,673.
Population growth in Missouri and
Illinois counties was roughly proportional
to the size of their population; counties in
Missouri accounted for 76 percent of the
growth between 2002 and 2012, while those
in Illinois accounted for 24 percent. The
greatest percentage change in population
occurred in three Missouri counties—
St. Charles, Lincoln and Warren—all
located in the northwestern portion of the
MSA. At the other extreme, four counties
lost population: St. Louis city (which is in
Missouri and is considered legally a county
unto itself), St. Louis County (Missouri),
and Macoupin and Bond counties (Illinois).
Economic Drivers

The financial services sector has long been
a major driver of the economy in the MSA.
About 90,000 people (or 6.7 percent of the
area’s workers) are employed by financial
services firms, much higher than the national
average of 5.7 percent. Although the sector

© FUSE, thinkstock

The St. Louis MSA has about 14 percent more workers in the
health care sector, compared with national averages.

employs a relatively small share of workers,
financial activities accounted for almost 20
percent of St. Louis’ GMP in 2012.
St. Louis’ health care sector employs just
under 200,000 workers (about 15 percent of
total employment), of which about 70,000
are employed by the region’s hospitals.
Three health care firms in the metro area
—BJC HealthCare, SSM Health Care and
Mercy—rank among the top 10 largest
employers and, as of June 2013, collectively
employed 47,883 workers. BJC is the largest
employer in the area.
Hospital employment has been a bright spot
for St. Louis. Over the past decade, regional
hospital employment grew 13.9 percentage

points faster than did such employment
in the nation overall. Relative to national
averages, the St. Louis MSA has about
14 percent more workers in the health care
sector and 60 percent more hospital workers.1 In 2012, output from the health care
sector accounted for about 9 percent of
St. Louis’ GMP.2
Strong health care and financial services
sectors have been key in helping the local
economy through the Great Recession
of 2007-09 and the recovery. During the
recession, the metro area lost about 90,000
jobs; the health care sector added about
11,000 jobs. The local financial services
sector lost about 1.5 percent of its workforce
(1,200 jobs), which was only a quarter of the
national rate.
The importance of these sectors for job
growth after the recession has been even
more pronounced. Combined, these two
sectors have added almost 20,000 jobs, more
than twice as many jobs as the rest of the
local economy. Moreover, these sectors
added jobs faster than the national rate,
with financial services adding jobs five
times as fast as the financial services sector
did nationwide.
A Stalled Recovery

Changing Trends

Despite the temporary standstill, the
current economic outlook in the MSA is
somewhat encouraging: 2013 employment
growth showed positive momentum, the
financial services sector continued to add
jobs faster than the national rate, and the

MSA Snapshot
St. Louis, Mo.
Population
2,795,794
Labor Force
1,403,773
Unemployment Rate
6.9%
Personal Income (per capita)		
$44,625
Gross Metropolitan Product		$116.5 billion

largest sectors by Employment
EDUCATION AND HEALTH SERVICES
professional and business services
GOVERNMENT
RETAIL TRADE
LEISURE AND HOSPITALITY
0

2

4

6

8

10

12

16

14

18

20

PERCENT OF NONFARM EMPLOYMENT

largest local employers
1. BJC HealthCare
2. Boeing Defense, Space & Security
3. Washington University in St. Louis
4. Scott Air Force Base
5. SSM Health Care
st. louis msa population growth
(PERCENT) by County 2002-2012
Macoupin
ILLINOIS
MISSOURI

Calhoun

Lincoln

Warren

Bond

Jersey
Madison

St. Charles

St. Clair

Franklin

Monroe
Jefferson

–10 to 0

5 to 10

0 to 5

10 to 20

Clinton
St. Louis City
St. Louis County

M
is

sis
sip

20 to 30

pi
ver
Ri

After the Great Recession ended in 2009,
St. Louis firms began to hire workers at a
pace that generally mimicked the national
trend; the local economy added almost
20,000 jobs in the first 18 months of the
recovery, and employment growth was positive in most industries. In the spring of 2011,
however, the recovery in the MSA stalled.
In the year that followed, the metro area lost
about 4,000 jobs, while the national economy
continued to add jobs at its previous pace.
The job losses in the St. Louis area were not
evenly distributed across industries or across
the counties. Rather, the majority of the jobs
were in sectors particularly sensitive to local
conditions: construction, retail trade, and
professional and business services. Geographically, the Illinois portion of the MSA
appeared to have been most affected.
Continuing struggles in the local housing
market are one potential factor of the stalled
recovery in the MSA. Although housing
prices did not fall locally as much as they
did nationally, the recovery in the St. Louis
area lagged the nation’s gains. Year over

year, housing prices in St. Louis continued
to decline through the end of 2012, while
prices nationally turned back up six months
earlier. Moreover, the local construction
industry lost almost 7,000 jobs between the
spring of 2011 and 2012. This decline is in
stark contrast with the national trend, where
construction employment increased by
almost 3 percent.
The stalled recovery is also evident in
the local services sector, specifically professional and business services and retail, which
together employ a quarter of the region’s
workforce. Professional and business services include accounting, law, waste management and security services, as well as other
businesses typically driven by local demand.
Between the spring of 2011 and 2012, the
industry shed over 1,000 jobs, or 0.6 percent
of its workforce. During the same period,
the retail sector—which includes local
grocers, small retailers and big-box stores—
eliminated 1,400 jobs, almost 1 percent of its
workforce. Nationally, the professional and
business services sector and the retail sector
increased their payrolls by 3.5 percent and
1.2 percent, respectively.
Across the region, the stalled recovery is
most evident in Illinois. Labor department
data indicate that the pace of hiring in Illinois was faster than in both Missouri and the
nation before the spring of 2011. Between the
spring of 2011 and 2012, firms on the Illinois
side of the MSA reduced employment by 600
workers a month, resulting in an employment drop of 3.5 percent. In the Missouri
counties of the MSA, employers continued
to add about 350 workers per month. Since
the spring of 2012, employment growth in
Missouri outpaced growth in Illinois. The
diverging trend continued in 2013. In the
Illinois counties, employment declined by
over 2,500 jobs in the first half of the year. In
the Missouri counties, employment increased
by more than 5,000 jobs. Both sides of the
MSA have total employment levels about 4
percent below their prerecession peaks.

NOTES: Population, employment, personal income
per capita and gross metropolitan product data are
from the Census Bureau, Bureau of Labor Statistics
and Bureau of Economic Analysis. These MSA-level
data series are easily accessible in the St. Louis
Fed’s economic database, FRED (Federal Reserve
Economic Data), which can be accessed at http://
research.stlouisfed.org/fred2. For the panels and
maps, see these FRED series (IDs in parentheses): population (STLPOP); labor force (STLLF);
unemployment rate (STLUR); personal income
(STLPCPI); leisure and hospitality (STLLEIH); retail
trade (SMU29411804200000001SA); government
(STLGOVT); professional and business services
(STLPBSV); and education and health (STLEDUH).

The Regional Economist | www.stlouisfed.org 17

FIGURE 3

Hospital Employment

Nonfarm Payroll Employment
102

110

100

94
U.S.

92

2011

2010

2009

St. Louis
2008

90

2012

2010

2008

2004

2002

2000

1998

1996

1994

1990

60

1992

The financial services sector continues to be a major
economic driver in the St. Louis MSA. Financial activities
accounted for almost 20 percent of St. Louis’ GMP in 2010.

SOURCE: Bureau of Labor Statistics

SOURCE: Bureau of Labor Statistics

NOTE: Data are easily accessible in the St. Louis Fed’s
economic database, FRED, using these series IDs: St. Louis
(SMU29411806562200001SA) and U.S. (CES6562200001).

NOTE: Data are easily accessible in the St. Louis Fed’s economic
database, FRED, using these series IDs: St. Louis (STLNA) and
U.S. (PAYEMS).

FIGURE 2

FIGURE 4

Professional Business Services, Logging,
Mining, Retail, Construction Employment

St. Louis Metro Employment, by State
102

96
94

2013

Ill. Portion

2012

Mo. Portion

2011

2008

88

U.S.

2010

92
90

2013

2012

2011

2010

2009

2008

2007

2006

2005

U.S.
St. Louis

98

2009

December 2007=100

100

2004

102
100
98
96
94
92
90
88
86
84
82

2003

December 2007=100
18 The Regional Economist | April 2014

U.S.
St. Louis

96

2007

80

98

2013

90

2012

100

70

© FUSE, thinkstock

December 2007=100

120

2006

December 2007=100

FIGURE 1

SOURCE: Bureau of Labor Statistics.

source: Bureau of Labor Statistics

NOTE: Data are easily accessible in the St. Louis Fed’s economic
database, FRED, using these series IDs: St. Louis Professional
and Business Services (STLPBSV), St. Louis Construction, Mining,
and Logging (STLNRMN), St. Louis Retail Trade (SMU29411804
200000001SA), U.S. Professional and Business Services (USPBS),
U.S. Construction (USCONS), U.S. Mining and Logging (USMINE),
and U.S. Retail Trade (USTRADE).

NOTE: Data from the BLS’ Quarterly Census of Employment and
Wages cover 98 percent of all nonfarm payroll jobs. Illinois data
were adjusted to account for a spike in January 2011 employment
due to reclassification of workers.

professional and business services sector
displayed strong growth. Moreover, some
long-term population trends started to
reverse. On the other hand, employment
growth in the health care sector was relatively flat in 2013, and policy changes pose
new challenges to that industry
The retail sector showed signs of improvement: Two new outlet malls opened in the
far western suburb of Chesterfield last summer, and one has already begun planning an
expansion. Promising projects are on the
horizon for the city of St. Louis: Ikea and
Whole Foods have finalized plans to open
new stores in the fall of 2015, for example.

Recoveries in construction and other services
sectors resulted in about 7,000 new jobs in
2013, up from 2,000 new jobs in 2012.
Growth of the health care industry
showed signs of slowing in 2013. Over the
past five years, the local health care sector
added an average of 3,500 jobs per year.
In 2013, the sector lost over 2,000 jobs.
Nonetheless, anecdotal information suggests
the long-term outlook remains somewhat
promising, with projects such as BJC’s
$1 billion campus renewal contributing to
the turnaround of the construction industry.
Many construction and contracting firms
expect a growing portion of their revenue

e c o n o my

a

g l a n c e

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 are specific to the Eighth District. To see these charts, go to
www.stlouisfed.org/economyataglance.
R E AL G D P G R O W T H

CONSUMER PRICE INDEX (CPI)

8
4
PERCENT

2
0
–2
–4
–6
–8
–10

Q4
’08

’09

’10

’11

’12

’13

All Items, Less Food and Energy

3

0

–3

February

’09

’10

’11

’12

’13

’14

RATES ON FEDERAL FUNDS FUTURES ON SELECTED DATES

3.0

0.14

2.5

0.13

2.0

0.12

1.5

0.11

1.0

PERCENT

PERCENT

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

5-Year

0.5

0.08

20-Year

–0.5

’10

’11

0.10
0.09

10-Year

0.0

March 14
’12

’13

0.07

’14

10/30/13
12/18/13

01/29/14
03/20/14

March ’14 April ’14 May ’14 June ’14 July ’14 Aug. ’14

NOTE: Weekly data.

CONTRACT MONTHS

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

4

11

10-Year Treasury
Fed Funds Target

10
3

9
PERCENT

8
7
6

2
1-Year Treasury

1

5
4
’09

February

February

’10

’11

’12

’13

0

’14

’09

’10

’11

’12

’13

’14

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 AL T R A D E

90

ENDNOTES

AVERAGE LAND VALUES ACROSS THE EIGHTH DISTRICT

7,000

Exports

6,000

75
Imports

60

DOLLARS PER ACRE

BILLIONS OF DOLLARS

employed in the health care sector and 3.5 percent
in hospitals. Of St. Louis’ workforce, 14.8 percent
is employed in the health care sector and
5.5 percent in hospitals. Hospital employment
is included in health care figures.
2 Health care sector output is not available for the
MSA, only “health and education” is. Missouri
sector-level GSP was used to decompose the data.
3 St. Louis city (–0.12%), St. Louis County (+0.11%).

CPI–All Items

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

Charles S. Gascon is a regional economist and
Diana A. Cooke is a research analyst, both at
the Federal Reserve Bank of St. Louis.

1 About 13 percent of the nation’s workforce is

PERCENT CHANGE FROM A YEAR EARLIER

6

6

PERCENT

to come from these health care construction projects. Nationally, the outlook for
the health care industry is also strong: Data
from the Bureau of Labor Statistics project
the health care industry to generate the largest job growth over the next 10 years.
Another notable shift over the past few
years has been the relative convergence of
population growth rates: The rates of the
MSA’s counties are converging, and the
population growth of the MSA itself is converging with that of the nation.
Over the past decade, the Missouri counties of St. Charles, Warren and Lincoln led
the region’s population growth, averaging upward of 3 percent growth per year
during the early 2000s. At the same time,
the city of St. Louis and St. Louis County
reported population losses, with declines
of 1.2 percent and 0.2 percent, respectively.
Over the past few years, these growth rates
have converged, with the outlying counties’
growth slowing to 1 percent or less per year.
Population growth in St. Louis city and
county have moved in the opposite direction, both reporting virtually no change in
population during 2012.3
The region’s population growth has also
converged with the national rate. In the
1970s and ’80s, the St. Louis metro population grew roughly 1 percent slower than the
national rate. In recent years, the difference
in population growth has declined to about
0.5 percent. Albeit below the national rate
as a whole, the Missouri counties in the
MSA have converged with national trends,
while the Illinois counties still have about a
1 percent gap between the 2012 population
growth rates and national averages.

a t

45
30
15

Trade Balance

0
’09

’10

’11

’12

’13

NOTE: Data are aggregated over the past 12 months.

Quality Farmland

Ranchland or Pastureland

5,000
4,000
3,000
2,000
1,000

January

’14

0

2012:Q4 2013:Q1 2013:Q2 2013:Q3 2013:Q4
SOURCE: Agricultural Finance Monitor.
The Regional Economist | www.stlouisfed.org 19

d i s t r i ct

o v e r v i e w

Measured Economic Mobility
in the District
Is Below the U.S. Average
By Alejandro Badel and Julia Maues

I

s intergenerational economic mobility high or low in the Eighth District?
Are there areas with extremely high or
extremely low mobility? In this District
Overview, we provide answers to these
questions, using results from a 2014 study by
economists Raj Chetty, Nathaniel Hendren,
Patrick Kline and Emmanuel Saez (CHKS
hereafter).
The CHKS study has attracted a great deal
of interest, in large part because it measures
mobility using a comprehensive data set that
contains the incomes of more than 40 million people and their parents between 1996
and 2012. The data set is constructed from
anonymized federal tax returns.
The measures of intergenerational economic mobility in CHKS are computed by
taking the group of people who were born
in 1980-82 and comparing the income of
their parents in 1996-2000 (when they were
between 14 and 20 years old) with their own
family income in 2011-12 (when they were
between 29 and 32 years old).
Each of the mobility measures in CHKS is
calculated for each group of people growing up in the same “town” (regardless of
whether they moved afterward). CHKS
used the Census Bureau’s commuting zones
as the geographical definition of a “town.”
Each commuting zone consists of several
adjacent counties that are chosen according
to observed commuting patterns. A person
is assigned to a particular commuting
zone if his or her family was living there in
1980-82.
While the CHKS study presents several
indicators of intergenerational economic
mobility, we focus on a particular one: the
probability of moving up in one generation.
CHKS obtains this indicator by considering,
20 The Regional Economist | April 2014

for each commuting zone, the group of 14to 20-year-olds whose family income was
in the bottom 20 percent of the national
income distribution in 1996-2000. The indicator is the fraction of that group that, as
grown-ups (i.e., by ages 29-32), had a family
income in the top 20 percent of the national
income distribution.
Let’s now look at economic mobility in
the Eighth District. To do so, we look at the
mobility indicator in all of the commuting
zones that contain at least one county
belonging to the Eighth District.
Best and Worst in the District

The Eighth District is composed of
339 counties in all or parts of seven states:
Arkansas, Illinois, Indiana, Kentucky,
Mississippi, Missouri and Tennessee. These
counties are covered by 81 commuting
zones.
Averaging the mobility indicator across
these counties, we calculate that the probability of moving from the bottom 20 percent of the income distribution to the top
20 percent of the income distribution in
one generation was 6.4 percent in the
Eighth District.1
This probability is comparable to that
faced by those growing up in Tampa, Fla.
(6 percent), Baltimore (6.4 percent), and
Chicago (6.49 percent). However, it is
much lower than the probability of moving
up for those growing up in Salt Lake City
(10.8 percent), and San Jose, Calif. (12.9
percent). The probability of moving up for
those growing up in the Eighth District was
also 1.7 percentage points lower than the
national average (8.1 percent).2
Panel A in the table presents the probability of moving up for people growing up in

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.

the 10 largest commuting zones (as measured by population in 2000) that contain
at least one county of the Eighth District.
Those growing up in Memphis had the lowest probability of moving up (2.8 percent),
followed by St. Louis and Cincinnati (both
at 5.1 percent). The highest probability was
measured for Fayetteville, Ark. (9.2 percent),
followed by Edwardsville, Ill. (8.7 percent).
The differences in chances of moving up
are striking: The probability of moving up
was 1.8 times larger for those who grew up
in St. Louis than for those who grew up in
Memphis, while it was 1.8 times larger for
those who grew up in Fayetteville than for
those who grew up in St. Louis.
The second column of Panel A presents
the ranking of each commuting zone (in
terms of probability of moving up) among
all the commuting zones in the nation.
This column shows that for the 10 largest
commuting zones that contain at least one
county of the Eighth District, the probability of moving up is pretty much in the
bottom half of the national distribution.
Panel B displays the four commuting zones
in the District where people had the greatest
chances to jump up the income ladder, as well
as the four zones where people had the worst
chances of making this leap. The probability
of moving up in one generation ranges from
2.2 percent for those growing up in Greenville, Miss., to 11.7 percent for those growing
up in Olney, Ill. The bottom four commuting
zones all rank in the bottom 1 percent of the
national distribution. At the other extreme,
there are no areas of the District with mobility in the top 1 percent of the national distribution. The highest-ranked commuting zone
in the District ranks at the 73rd percentile of
the national distribution.

Probability of Moving Up in One
Generation, by Commuting Zone
A. 10 Largest Commuting Zones in the Eighth District
Probability of
moving up
(%)

Percentile
in national ranking
(with 0 being lowest)

Fayetteville, Ark.

9.2

52.1

Edwardsville, Ill.

8.7

47.9

Evansville, Ind.

7.9

38.4

Springfield, Mo.

7.1

31.3

LexingtonFayette, Ky.

5.4

15.2

Little Rock, Ark.

5.4

15.0

Louisville, Ky.

5.2

13.6

Cincinnati

5.1

12.3

St. Louis

5.1

11.9

Memphis, Tenn.

2.8

1.0

B. Top and Bottom Four in the Eighth District
Probability of
moving up
(%)

Percentile
in national ranking
(with 0 being lowest)

Top 4
Olney, Ill.

11.7

73.7

Kirksville, Mo.

11.3

70.9

Harrisburg, Ill.

11.2

69.7

Vincennes, Ind.

11.0

68.0

Memphis, Tenn.

2.8

1.0

Clarksdale, Miss.

2.7

0.7

Yazoo City, Miss.

2.5

0.5

Greenville, Miss.

2.2

0.1

Bottom 4

C. Top and Bottom Four in the Nation
Probability
of moving up
(%)

Percentile
in national ranking
(with 0 being lowest)

Bowman, N.D.

47.0

100.0

Lemmon, N.D.

35.7

99.9

Williston, N.D.

33.8

99.7

Carrington, N.D.

33.3

99.6

Top 4

Panel C displays the probability of moving up and the percentile in the national distribution for the top four and bottom four
commuting zones in the nation. Two commuting zones in the Eighth District rank
in the nation’s bottom four: Yazoo City,
Miss., and Greenville, Miss. Not shown in
this panel is the Memphis commuting zone,
which ranks 722 among 729 commuting
zones in the CHKS report.
Comparing the top four commuting
zones in Panel B with those in Panel C
shows that the District does not have areas
with extremely high income mobility. Such
mobility in the nation’s top commuting
zone is more than four times higher than in
the District’s top commuting zone. On the
other hand, the District contains areas with
extremely low income mobility. Why? In
the next District Overview, in the July issue
of The Regional Economist, we will provide a
quick introduction to the factors that may be
part of an explanation for these differences in
income mobility. However, we leave a more
complete investigation of the forces behind
these patterns to future research on the
economy of the Eighth District.
In summary, the probability of moving up
for people born in the Eighth District taken
as a whole is only somewhat lower than the
national average. However, the District
contains pockets where the probability of
moving up is extremely low, and it contains
no areas with remarkably high income
mobility.

ENDNOTES
1 This figure is obtained by assigning to each county

the probability of moving up in its commuting
zone and then taking a weighted average (with
the weights being equal to the counties’ population in 2012) across counties. This allows an exact
estimate of the probability of moving up in the
Eighth District.
2 This figure is obtained as a weighted average across
all commuting zones with weights equal to the
population of each commuting zone in 2000. An
identical result would be obtained using a countyby-county weighting strategy as we did for the
Eighth District, but is not necessary here.

R eference
Chetty, Raj; Hendren, Nathaniel; Kline, Patrick;
and Saez, Emmanuel. “Where Is the Land of
Opportunity? The Geography of Intergenerational
Mobility in the United States.” National Bureau
of Economic Research, January 2014, Working
Paper 19843.

Alejandro Badel is an economist and Julia
Maues is the economic content manager in
Public Affairs, both at the Federal Reserve Bank
of St. Louis. For more on Badel’s work, see
http://research.stlouisfed.org/econ/badel.

Bottom 4
Yazoo City, Miss.

2.5

0.5

Mission, S.D.

2.4

0.4

Eufaula, Ga.

2.3

0.3

Greenville, Miss.

2.2

0.1

SOURCE: See www.equality-of-opportunity.org/.
NOTE: Some of the cities that are listed as being in the District (such as
Cincinnati) are not actually within the borders of the District; however, at least
one county in their commuting zone (as defined by the Census Bureau) is part
of the District.
Commuting zones carry the name of the main town covered by the commuting zone. Since some commuting zones may cross state borders, the state
assigned to the commuting zone may not correspond to the state where the
main town is located.
The Regional Economist | www.stlouisfed.org 21

national overview

The FOMC’s March 2014 Economic Projections for 2014-15
8

By Kevin L. Kliesen

T

he U.S. economy exhibited considerable
strength over the second half of 2013.
After increasing at a 1.8 percent annual rate
over the first half, the advanced estimate
showed that real gross domestic product
(GDP) increased at a brisk 3.7 percent
annual rate over the second half. In
response, nonfarm payrolls rose by an
average of 204,500 per month from June
to November, and the unemployment rate
dropped from 7.5 percent to 7 percent.
Meanwhile, inflation and inflation expectations remained relatively low and stable and
below the 2-percent long-run inflation target
of the Federal Open Market Committee
(FOMC). Given the spate of good news over
the second half of 2013, most private-sector
forecasters and FOMC policymakers began
to raise their expectations for the economy’s
performance in 2014.
Well, the U.S. economy’s sprint toward
a gold medal in 2014 suddenly looks rather
shaky. First, a significant percentage of
the data measuring economic activity in
December 2013 and January 2014 has been
unexpectedly soft. Foremost among them,
nonfarm payrolls saw an average gain
of only about 106,000 in December and
January—about half as much as market
expectations. Next, many of the major
housing reports were markedly weaker than
expected. Although construction spending inched up in January, housing starts
and permits plunged that month, and sales
of existing homes in January were at their
lowest level since July 2012. Retailers and
manufacturers also experienced significant
weakness in January: Retail sales posted
their largest decline since June 2012, while
output at manufacturers registered its largest percentage decline since May 2009.
Weaker-than-expected data flows resulted
in a marked downward revision to real GDP
22 The Regional Economist | April 2014

6
PERCENT

Weather Throws
a Cold Blanket
on the U.S. Economy

2013 (Actual)
2014

7.0
6.2

5.8

2015

4
2
0

2.5

2.9

3.1
1.0

REAL GDP GROWTH

UNEMPLOYMENT RATE

1.6

1.8

PCE INFLATION

NOTE: Projections are the mid-points of the central tendencies. The projections for real GDP growth and inflation are the percentage change from
the fourth quarter of the previous year to the fourth quarter of the indicated year. Inflation is measured by the personal consumption expenditures
chain-price index. The projection for the unemployment rate is the average for the fourth quarter of the year indicated.

growth in the fourth quarter of 2013, from
an annual rate of 3.2 percent to a 2.6 percent
rate. Less momentum heading into 2014,
compounded by some softer data in January and February, has spurred professional
forecasters to mark down their estimates for
growth of real GDP in the first quarter of
2014. The February 2014 Survey of Professional Forecasters now projects that real
GDP will increase at a 2 percent annual rate
in the first quarter, 0.5 percentage points
less than three months earlier.
What’s going on out there?! Is the U.S.
economic expansion in the early stages of
its demise, or is this merely a lull related to
the harsh winter weather that gripped a significant portion of the nation in December,
January and early February?
Weather or ... Not?

At this point, the evidence suggests that
weather considerations may be responsible
for much of the emerging weakness in the
first quarter. This tentative conclusion is
based on the following factors. First, many
of the economic data releases—for example,
those issued by the government, the Federal
Reserve and private organizations—have
specifically mentioned that adverse weather
affected the statistics reported in the release.
In particular, the Fed’s Beige Book noted
that severe weather contributed to weakerthan-expected economic conditions in
many areas in January and early February.
Compounding this problem is that many
key monthly data series, such as retail sales
and factory orders, tend to be highly volatile
from month to month. Second, other key
data do not indicate a looming demise of

the business expansion. Important in this
regard are the continued low levels of weekly
initial claims for state unemployment insurance benefits. Initial claims data tend to be
very sensitive to the state of the economy,
particularly near peaks and troughs of the
business cycle. The larger-than-expected
rebound in payroll employment and
manufacturing production in February
was heartening in this regard, providing
further evidence of the temporary nature
of the first-quarter lull in activity. Third,
financial markets—which are also sensitive
to changes in economic data and expectations of future growth—show few signs of
stress, and stock prices continue to increase.
Fourth, the FOMC and the majority of professional forecasters continue to expect that
the economy will perform solidly this year:
real GDP growth of about 3 percent, further
declines in the unemployment rate and an
inflation rate modestly less than 2 percent.
A point of caution is in order, though:
It is often extremely difficult to gauge the
underlying strength of the economy even in
the best of times; so, we’ll just have to wait
and see if the emerging slowdown in the first
quarter was a weather-related short-lived
economic disturbance, a worrisome return
to the pattern of slower-than-normal growth
seen during this expansion or something
worse.
Kevin L. Kliesen is an economist at the Federal
Reserve Bank of St. Louis. Lowell R. Ricketts,
a senior research associate at the Bank,
provided research assistance. See http://
research.stlouisfed.org/econ/kliesen/ for more
on Kliesen’s work.

READER

E X C HANGE
Register Now for Symposium
On Balance Sheets
of Younger Americans

ASK AN ECONOMIST
Kevin L. Kliesen is a business economist and Research
officer at the Federal Reserve Bank of St. Louis. His main
areas of interest are monetary and fiscal policy and
macroeconomic forecasting. Kliesen co-developed the
St. Louis Fed’s Financial Stress Index with a former colleague. He initially went to college to be a farmer, but then
fell in love with economics. Every spring and summer, he
returns to his roots to try to make his plants and flowers
thrive in the St. Louis summer. For more on Kliesen’s work,
see http://research.stlouisfed.org/econ/kliesen.

Kliesen in front of the Banca d’Italia while vacationing in Rome.

Q: As the city of St. Louis turns 250, how well is it positioned in
terms of employment growth? What are some of the opportunities out there for the area?
A: St. Louis is a service-based economy, much like the U.S. is as a whole. In the St. Louis metro
area, the sectors with the largest growth rates are the education and health care sector, the professional and business services sector, and the financial activities sector.
Over the longer term, St. Louis is fairly well-positioned in terms of employment growth, given
the area’s concentration in health care, technology and financial services. The Bureau of Labor
Statistics recently published employment projections for the next 10 years. The top two occupations when it comes to job growth are health-care related. Due to the aging of the baby boomers,
there is more demand for health-care services. Also, as people get older and accumulate wealth,
demand for financial services typically increases.
Employment in manufacturing has been relatively flat recently. Many people believe that
manufacturing has large spillovers to the local economy. However, recent research by Enrico
Moretti, an economist at the University of California at Berkeley, shows that jobs in the innovation
sector—such as computer companies and biotechnology firms—have a much higher multiplier
effect.1 Using data on 9 million workers in 320 U.S. metropolitan areas, Moretti found that the multiplier effect for the innovation sector is about three times as large as that of extractive industries
and traditional manufacturing. In other words, the most important effect of high-tech companies
on local economies is outside high tech. Additionally, because innovation jobs are typically higher
paying, the service jobs that are created as spillovers pay more, too. St. Louis would benefit from
more growth in this area. In this vein, recent entrepreneurial efforts in St. Louis at the so-called
T-Rex new-firm incubator are promising.2

April Is National Financial
Literacy Month
The Federal Reserve Bank of St. Louis offers a
myriad of free resources to help those who want
to learn about personal finance and basic economics—or for those who want to teach others
about these important life skills.
Our menu includes videos and podcasts, short
essays and exercises. There are tried-and-true
tools, such as flash cards and glossaries, as well
as up-to-date technologies, such as online chats
and mobile apps. Many of the resources are available in Spanish, too.
For teachers—from kindergarten through
college—there are lesson plans, webinars,
audioconferences and in-person workshops. The
award-winning classroom materials are used in
every state, as well as in many other countries.
To get started teaching yourself or to teach
others, go to www.stlouisfed.org/education_
resources.
Catch Up on Dialogue with the Fed

ENDNOTES

Since 2011, the Federal Reserve Bank of St. Louis
has held a dozen public discussions in its series
titled Dialogue with the Fed: Beyond Today’s
Financial Headlines. To see video highlights of
these presentations by our economists and other
experts, go to www.stlouisfed.org/dialoguewith-the-fed. Check back soon for highlights
from the March 31 Dialogue about bitcoin and
other virtual currencies.

1 Moretti, Enrico. The New Geography of Jobs. New York: Houghton Mifflin Harcourt, 2012.
2 See http://downtowntrex.com.

Education and Health Services Employment in St. Louis, MO-IL (MSA)
Professional and Business Services Employment in St. Louis, MO-IL (MSA)
Employees on Nonfarm Payrolls in St. Louis, MO-IL (MSA)
Financial Activities Employment in St. Louis, MO-IL (MSA)

112.5
110.0
Index (June 1, 2009=100)

Registration is open until May 2 for a research
symposium May 8-9 at the Federal Reserve Bank
of St. Louis. New and cutting-edge research from
leading academics nationwide on a wide range of
topics will be presented at the event, titled “The
Balance Sheets of Younger Americans: Is the
American Dream at Risk?” These topics include:
student loans, economic mobility, homeownership, savings and balance-sheet portfolio
allocation, and child development accounts and
parental expectations.
The cost to attend is $100 ($20 for students).
For more information, see www.stlouisfed.org/
americandream.

107.5
105.0

We welcome letters to the editor, as well as
questions for “Ask an Economist.” You can submit

102.5

them online at www.stlouisfed.org/re/letter or
mail them to Subhayu Bandyopadhyay, editor,

100.0

The Regional Economist, Federal Reserve Bank of
97.5

Jan. 2010

July 2010

Jan. 2011

July 2011

Jan. 2012

July 2012

Jan. 2013

July 2013

St. Louis, P.O. Box 442, St. Louis, MO 63166-0442.

The Regional Economist | www.stlouisfed.org 23

p o s t - G r e a t

R e c e s s i o n

Youth Unemployment
Notably High in Southern Europe
By James D. Eubanks and David G. Wiczer

n a recession, the youth unemployment
rate often rises more sharply and recovers
more slowly than the average unemployment
rate. The recent recessions in Europe and
North America were especially damaging
to workers between the ages of 15 and 25.
Unemployment among this group rose in
nearly all countries, but some countries
experienced a more severe spike than others.
The differences in the severity of youth
unemployment from country to country are
the result of differing long-run trends, initial
conditions and labor market structures.
The unemployment rate is the number of
unemployed individuals divided by the total
labor force. Movements in the unemployment rate can result from changes in either
or both statistics. If an individual without
a job is not actively searching for employment, he or she is neither unemployed nor
in the labor force. Because those younger
than 25 frequently move into and out of the
labor force, often in response to economic
pressures, youth unemployment figures may
understate the impact of a recession: Young
people dropping out of the labor force during
bad times will dampen the increase in the
youth unemployment rate.1
The youth labor force participation rate
did fall somewhat in both the United States
and in Europe during the Great Recession,
although youth participation had been falling in both areas for some time. In the U.S.,
it fell from 66 percent in January 2000 to
59 percent at the start of the Great Recession
(December 2007) and then on to 55 percent
in March 2013. Corresponding figures in
the EU were 65 percent, 62 percent and
59 percent.
In the United States, the youth unemployment rate rose from 11.5 percent in early
The Regional Economist | Online Only Article

Unemployment in Southern Europe for Those Younger than 25 and for Those Older
70
60
Unemployment Rate

I

50

Portugal, 25-74

Portugal, Under 25

Italy, 25-74

Italy, Under 25

Greece, 25-74 Years

Greece, Under 25

Spain, 25-74 Years

Spain, Under 25

1995

2000

40
30
20
10
0
1990

Years

2005

2010

SOURCE: Statistical Office of the European Communities/Haver Analytics.

2008 to a high of 19 percent in late 2009 and
then began a steady decline.2 Canada had a
similar experience, with a peak of 15.8 percent in late 2009. In contrast, many nations
in Europe continue to face crisis levels of
youth unemployment. Most had higher
youth unemployment rates to begin with.
Italy, for instance, began 2008 with a youth
unemployment rate of 20 percent. By the end
of 2013, that rate had more than doubled, to
42 percent. Spain’s experience has been even
more extreme, with youth unemployment
rising from 21 percent to 55 percent over the
same period. Other nations in Europe, such
as Germany and Sweden, have had much
milder experiences.
Europe

Nowhere in Europe is the youth unemployment situation more dire than in Greece,
Italy, Portugal and Spain. While these
Southern European countries have very high
unemployment rates for the general population, their youth unemployment rates are
unprecedentedly severe.
In the figure, we can see the rapid and continued rise in youth unemployment rates in

these four countries. The spike in Greece has
been the most pronounced, increasing from
22 percent at the start of 2008 to 36 percent
at the end of 2010 and continuing to rise
through 2013 to nearly 60 percent. As the
unemployment rate for prime-aged workers
in Greece neared levels seen in the American
Great Depression, the youth unemployment
rate in Greece exceeded twice that rate.
Spain has had a similarly severe jump in
youth unemployment. While youth unemployment is relatively less extreme in Italy, it
is even more disproportionate: At the end of
2013, the youth unemployment rate there was
nearly four times greater than the non-youth
rate. Portugal’s youth unemployment rate
was lower than the other three in late 2013,
but the rate is notable for its longer-run trend:
Youth unemployment in Spain, Greece and
Italy was stable or falling in the first half of
the decade, but in Portugal youth unemployment has been trending upward since 2000.
These countries are all marked by “rigid”
labor markets, which contribute to their
high unemployment rates. In a rigid labor
market, employers are reluctant to hire a
relatively risky young worker because of high

hiring costs or difficulty in firing. Rigidity
can result from such labor market features
as high rates of unionization or universal
statutory severance payments. For instance,
35 percent of workers in Italy are unionized,
as are 25 percent in Greece, 20 percent in
Portugal and 15 percent in Spain, whereas
only 11 percent are in the United States and
17 percent are in the 34-member Organization for Economic Cooperation and Development as a whole.3 Regarding severance,
Spain and Greece required payment of 52
and 24 weeks of salary in 2008 after 20 years
of employment, while Portugal required 87
weeks.4 In contrast, the U.S. has no universal statutory severance requirement for any
tenure of employment.
The World Bank provides one way to quantify the rigidity of a labor market through its
“rigidity of employment index,” with higher
scores indicating a more rigid market.5 In
2008, the average for the developed countries in the OECD was 26. Portugal, Italy,
Greece and Spain were all considerably more
rigid, with index values of 43, 38, 47 and 49,
respectively.
The economies of these four Southern
European countries also feature “two-tier”
labor markets, which can increase the likelihood that young workers will lose their jobs
during bad times. In a two-tier labor market,
some jobs have relatively flexible terms of
employment, short contracts and low adjustment costs. Spain is most-often cited for its
two-tier system, although Italy now has a
similar environment after a spate of reforms
in 2003.6
The U.S.

Although unemployment in the U.S. is
far below the levels in Southern Europe, the
Great Recession still brought the highest
unemployment rates the U.S. has seen since
1981. This episode has been particularly
severe not only because unemployment has
remained high for so long, but also because
young and less-educated workers have been
hit so hard. Whereas the overall unemployment rate has fallen almost to its historic
average, youth unemployment remains
elevated at 14.2 percent, although the rate has
been falling more quickly since the beginning of 2013.
Several factors may explain the steep rise
and slow recovery in youth unemployment
The Regional Economist | Online Only Article

in the U.S. When the recovery began, the
large pool of unemployed, experienced
workers may have been more attractive
to employers, crowding out the younger
applicants. Workers younger than 25 also
have less education than older workers, and
less-educated workers have higher unemployment rates. Educated workers offer
more general skills, which make them more
valuable to a wider range of employers.
In addition, young workers require more
training, which makes them less attractive
to hire. When per-worker profit margins
are low, such as in a recession, the cost of
training only makes those margins tighter.
This is also consistent with the education
gap: More-educated workers provide more
general skills; so, the value of these workers
to an employer comes relatively less from
the specific training they get on the job.
Before employers were sure of the recovery,
they may have been reluctant to hire young
workers who require more training.

ENDNOTES

Conclusion

Gregg, Paul; and Tominey, Emma. “The Wage Scar
from Male Youth Unemployment.” Labour Economics, February 2004, Vol. 12, No. 4, pp. 487-509.
Oreopoulos, Philip; von Wachter, Till; and Heisz,
Andrew. “The Short- and Long-term Career
Effects of Graduating in a Recession.” American
Economic Journal: Applied Economics, January
2012, Vol. 4, No. 1, pp. 1-29.

Bad labor markets have repeatedly been
shown to have long-lasting effects on youth
in many different countries.7 The postponed
plans and stalled careers of millions of young
workers are a national concern and should
spur reflection on the institutions, policies
and labor market structures that contribute
to such different experiences across countries.
David G. Wiczer is an economist and James
D. Eubanks is a research associate, both at the
Federal Reserve Bank of St. Louis. For more
on Wiczer’s work, see http://research.stlouisfed.
org/econ/wiczer/.

1

2

3
4

5
6
7

For example, consider a country with a youth
labor force of 100, out of which 90 are employed
and the remaining 10 are actively looking for
work. Following the definition above, the youth
unemployment rate is 10 percent. If one of the
unemployed workers drops out of the labor force,
the unemployment rate drops to 9.09 percent.
All unemployment statistics are based on harmonized quarterly averages taken from the Statistical
Office of the European Communities, Statistics
Canada and the Bureau of Labor Statistics.
These figures are averages (rounded) over the
period 2008-12 from the OECD Stat Extracts.
Severance pay figures are furnished by the
International Finance Corp. and the World Bank.
See www.doingbusiness.org/data/exploretopics/
employing-workers.
World Bank World Development Indicators series
IC.LGL.EMPL.XQ.
See The Economist, www.economist.com/node/
21547828, for a description of the system.
Many of these studies show immediate and
persistent negative wage effects, as well as negative
impacts on graduating and being unemployed in a
recession. Others also show that worker’s mobility
is hampered, which can be a cause of this limited
wage growth. See, e.g., Gregg and Tominey or
Oreopoulos et al.

REFERENCES

Change Service Requested

n e xt

Educators: Register Now
for Global Economic Forum
The St. Louis Fed will host a free conference June 30 and July 1 for teachers
about globalization and its impact on
the U.S. economy. Economists with
expertise on China and India will be

i s s u e

Challenges and Opportunities
for the U.S. in Latin America

I

n the July issue of The Regional Economist,
read about the key issues facing U.S. business

interests in their dealings with Latin American
economies these days. The article will focus in
particular on matters related to trade, immigration and investment. There is no “one size fits
all” approach to doing business in Latin America,

among the speakers. Lesson plans

given the wide-ranging level of development

will be provided, and there will be a

among the countries south of the Rio Grande.

videoconference on the second day
with teachers who are gathering in
five other Reserve bank cities across
the country at the same time. Registration deadline is June 16. Go to
http://www.stlouisfed.org/newsroom/
events/?id=552.

printed on recycled paper using 10% postconsumer waste

REAL GDP GROWTH

CONSUMER PRICE INDEX

8
4
PERCENT

2
0
–2
–4
–6
–8
–10

Q4
’08

’09

’10

’11

’12

PERCENT CHANGE FROM A YEAR EARLIER

6

6

’13

CPI–All Items
All Items, Less Food and Energy

3

0

–3

February

’09

’10

’11

’12

’13

’14

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

RATES ON FEDERAL FUNDS FUTURES ON SELECTED DATES

3.0

0.14

2.5

0.13

2.0

0.12

1.5

0.11

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

1.0

5-Year

0.5

0.09

10-Year

0.0

0.08

20-Year

–0.5

’10

’11

0.10

March 14
’12

’13

0.07

’14

10/30/13
12/18/13

March ’14 April ’14 May ’14 June ’14 July ’14 Aug. ’14
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

4

10-Year Treasury
Fed Funds Target

10
3

9
8

PERCENT

PERCENT

01/29/14
03/20/14

7
6

2
1-Year Treasury

1

5
4
’09

February

February

’10

’11

’12

’13

0

’14

’09

’10

’11

’12

’13

’14

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

90

AVERAGE LAND VALUES ACROSS THE EIGHTH DISTRICT
7,000

Exports

6,000
Imports

60

DOLLARS PER ACRE

BILLIONS OF DOLLARS

75

45
30
15

Trade Balance

0
’09

’10

’11

’12

’13

NOTE: Data are aggregated over the past 12 months.

Quality Farmland

Ranchland or Pastureland

5,000
4,000
3,000
2,000
1,000

January

’14

0

2012:Q4 2013:Q1 2013:Q2 2013:Q3 2013:Q4
SOURCE: Agricultural Finance Monitor.

U.S. CROP AND LIVESTOCK PRICES / INDEX 1990-92=100
140
120

Crops
Livestock

100
80
60

February

40
’99

’00

’01

’02

’03

’04

’05

’06

’07

’08

’09

’10

’11

’12

’13

’14

YEAR

COMMERCIAL BANK PERFORMANCE RATIOS
U.S. BANKS BY ASSET SIZE / FOURTH QUARTER 2013
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*

1.06

0.89

0.87

0.96

0.92

1.09

1.01

1.07

Net Interest Margin*

3.21

3.80

3.79

3.80

3.79

3.91

3.85

3.05

Nonperforming Loan Ratio

2.68

1.71

1.70

1.75

1.73

1.88

1.82

2.94

Loan Loss Reserve Ratio

1.76

1.67

1.67

1.62

1.64

1.60

1.62

1.80

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

NET INTEREST MARGIN*
1.03

0.92

1.23
1.17

.40

1.24

1.00

1.20

1.40

PERCENT

Fourth Quarter 2013

2.71

1.20
1.40
2.60
1.98

1.42

2.24

2.08
1.91

.90 1.20 1.50 1.80 2.10 2.40 2.70 3.00

1.11

1.27

Kentucky

1.56

Mississippi

1.55

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

1.81
1.75
1.79
1.94

1.60
1.68

Tennessee

Fourth Quarter 2012

2.00

1.43
1.56

Indiana

PERCENT

1.80

1.77

Arkansas

Missouri
2.41
2.60

Fourth Quarter 2013

1.63

Illinois
1.78

1.32

Fourth Quarter 2012

Eighth District

2.25
2.08

.60

0.0 0.4 0.8 1.2 1.6 2.0 2.4 2.8 3.2 3.6 4.0 4.4

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

1.77

.30

3.44
3.55

Fourth Quarter 2012

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

.00

3.66
3.83

Missouri
Tennessee

.80

Fourth Quarter 2013

3.70
3.87

Mississippi

0.61

.60

3.82
4.02

Kentucky

1.24

0.91

.20

3.94
4.19

Indiana

0.84
0.77

.00

3.47
3.62

Illinois

1.09
1.12

0.40

4.23
4.19

Arkansas

0.96
1.04

0.91

3.77
3.90

Eighth District

.00

.30

.60

Fourth Quarter 2013

.90

1.20

1.50

1.80

Fourth Quarter 2012

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

2.10

REGIONAL ECONOMIC INDICATORS
N O N FA R M E M P L O Y M E N T G R O W T H / F O U RT H Q U A RT E R 2 0 1 3
YEAR-OVER-YEAR PERCENT CHANGE
United
States

Eighth
District †

Arkansas

Illinois

Indiana

Kentucky

Mississippi

Missouri

Tennessee

1.4%

1.3%

Total Nonagricultural

1.8%

1.2%

1.0%

1.1%

1.8%

Natural Resources/Mining

4.2

0.1

2.0

3.2

5.3

–4.2

1.5

–4.8

NA

Construction

3.2

1.6

3.8

–0.3

–5.9

–0.5

17.7

6.6

NA

Manufacturing

0.7

0.1

–0.2

–1.3

0.6

–1.3

0.2

1.5

2.1

Trade/Transportation/Utilities

1.9

1.4

2.2

1.2

3.6

–0.7

0.8

1.3

1.1

Information

0.3

–1.4

–1.4

0.5

–0.4

–6.5

0.5

–3.4

–1.6

Financial Activities

1.1

1.7

3.0

0.7

2.3

0.5

1.7

3.5

2.1

Professional & Business Services

3.7

3.1

3.0

3.4

3.6

4.8

5.3

1.5

2.2

Educational & Health Services

1.7

1.3

1.9

1.2

0.5

0.4

0.6

2.3

1.7

Leisure & Hospitality

3.5

2.4

0.2

1.6

3.3

2.2

2.1

2.0

4.6

Other Services

0.5

0.7

–5.1

3.1

0.8

–3.7

–1.3

1.8

–0.5

–0.2

–0.2

–0.5

–0.5

2.3

–0.2

–0.4

–1.2

–1.1

Government

0.2%

1.7%

† 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 are no longer available).

EIGHTH DISTRICT PAYROLL EMPLOYMENT BY INDUSTRY-2013

U N E M P L O Y M E N T R AT E S
IV/2013

III/2013

IV/2012

United States

7.0%

7.2%

7.8%

Arkansas

7.5

7.7

7.5

Illinois

9.0

9.2

Indiana

6.9

Kentucky

Financial Activities
Information 1.6%

Professional and
Business Services

5.4%

Educational and
Health Services

12.6%

9.0

Trade,
Transportation
and Utilities

20.2%

7.5

8.1

Manufacturing

11.7%

8.1

8.4

8.2

Mississippi

8.0

8.6

9.1

Missouri

6.1

6.6

6.8

Tennessee

7.9

8.3

8.1

14.8%
Leisure and
Hospitality

9.9%
15.9%

Construction 3.7%

Other Services 4.0%

Natural Resources
and Mining 0.3%

Government

HOUSING PERMITS / FOURTH QUARTER

REAL PERSONAL INCOME* / FOURTH QUARTER

YEAR-OVER-YEAR PERCENT CHANGE IN YEAR-TO-DATE LEVELS

YEAR-OVER-YEAR PERCENT CHANGE

19.7
–8.8

Arkansas

16.2
12.2
12.5

Illinois
30.6

10.6
6.7
6.0

2013

0

14.6

41.9

5 10 15 20 25 30 35 40 45 50
2012

All data are seasonally adjusted unless otherwise noted.

3.4
0.3

2.0

0.6
3.7
0.2

Missouri

2.8

0.1

Tennessee
PERCENT

4.2
0.6

Mississippi
38.2

–15 –10 –5

3.5

–0.1

Kentucky

11.6

4.0

–0.3

Indiana

26.4

16.8

0.4

United States

33.5

4.1

–1.00–0.50 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50
2013

2012

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