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May 2010 (April 9, 2010 to May 13, 2010)

In This Issue:
Inflation and Prices

Banking and Financial Markets

 Prices are Falling, Prices are Falling!

 The Credit Crunch in Commercial Loan Syndication

Monetary Policy

International Markets and Foreign Exchange

 The Yield Curve, April 2010
 Recent Firming in the Federal Funds Market

 Global Imbalances
Regional Activity

 The Recovery So Far

 Homeowner and Rental Vacancy Trends in the Fourth
District

Households and Consumers

Labor Markets, Unemployment, and Wages

 Household Finances and a Sustainable Recovery

 Some Popular Locales Now Facing Gloomier Labor
Market

Growth and Production

Inflation and Prices

Prices are Falling, Prices are Falling!
04.29.10
by Brent Meyer

March Price Statistics
Percent change, last
1mo.a

3mo.a

6mo.a

12mo.

5yr.a

2009
average

Consumer Price Index
All items

0.8

0.9

1.7

2.3

2.4

2.8

Less food and energy

0.5

−0.2

0.6

1.1

2.0

1.8

Medianb

−0.2

0.0

0.4

0.6

2.4

1.2

16% trimmed meanb

0.3

0.6

0.9

1.0

2.3

1.3

a. Annualized.
b. Calculated by the Federal Reserve Bank of Cleveland.
Sources: U.S. Department of Labor, Bureau of Labor Statistics; and Federal Reserve
Bank of Cleveland.

CPI, Core CPI, and Trimmed-Mean
CPI Measures
12-month percent change
6.0
5.5
5.0
Median CPIa
4.5
4.0
CPI
3.5
3.0
2.5
2.0
1.5
Core CPI
1.0
0.5
16% trimmed-mean CPIa
0.0
-0.5
-1.0
-1.5
-2.0
-2.5
2000
2002
2004
2006
2008

2010

a. Calculated by the Federal Reserve Bank of Cleveland.
Sources: U.S. Department of Labor, Bureau of Labor Statistics, Federal Reserve
Bank of Cleveland.

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

Given the recent low readings on inflation, it
wouldn’t be too surprising to hear warnings of an
impending deflation. But is there cause for alarm?
A quick examination of the incoming data may
help to discern whether it’s time to panic.
The Consumer Price Index (CPI) ticked up a slight
0.8 percent (annualized rate) in March, largely driven by a spike in fresh fruits and vegetables prices
(up 72 percent at an annualized rate). Measures of
underlying inflation been relatively subdued lately.
The core CPI (excludes food and energy prices) was
virtually unchanged during the month. In fact, the
core CPI hasn’t moved much recently; the index
is actually down 0.2 percent over the past three
months and up only 0.6 percent over the past six
months. The measures of underlying inflation produced by the Federal Reserve Bank of Cleveland,
the median CPI and 16 percent trimmed-mean
CPI, have behaved similarly. The median CPI was
virtually unchanged in March, slipping down 0.2
percent after a 0.3 percent decline in February, and
has been flat over the past three months. The 16
percent trimmed-mean CPI increased 0.3 percent in March, somewhat lower than its 6-month
growth rate of 0.9 percent.
Over a longer time horizon (the past 12 months),
the headline CPI is up 2.3 percent, though this
largely reflects the path of energy prices. On the
other hand, measures of underlying inflation have
been slowing. The core CPI is up just 1.1 percent
over the period, and the 16 percent trimmed-mean
measure has edged up 1.0 percent. Perhaps more
striking is that the 12-month growth rate in the
median CPI has fallen from a recent high of 3.2
percent in September of 2008 to an all-time low
of 0.6 percent (the series goes back to 1968). The
longer-term trends in these inflation measures are
consistent with a marked disinflation (or a slowing
in the rate of price increases) but not a deflationary
episode yet.
2

CPI Component Price Change Distribution
Weighted frequency
60
March 2010
Past 3 months
2009 average

50
40
30
20
10
0

0 to 1
1 to 2
2 to 3
3 to 4
4 to 5
Annualized monthly percentage change

Source: Bureau of Labor Statistics.

CPI and Forecasts
Annualized quarterly percent change
8

Forecast

Actual

6
Top 10 forecast

4
2
0

Bottom 10 forecast

-2
-4
-6
-8
-10
3/06

3/07

3/08

3/09

3/10

3/11

Sources: Blue Chip’s Economic Indicators, April 2010; Bureau of Labor Statistics.

Share of CPI Components Decreasing

Perhaps there is something in the component pricechange distribution that would give cause for concern. The distribution does reveal a downward shift
in retail prices. In March, a majority (56 percent)
of the consumer market basket (by expenditure
weight) exhibited outright price decreases, compared to 46 percent over the prior three months,
and an average of just 20 percent in 2007. Outside
of this most recent episode, the last time the share
in that lower tail even broached 40 percent was in
2003, and that was only for one month (hitting 45
percent in April 2003). Taking a 6-month moving average (for a smoother trend), reveals that the
share of the overall index exhibiting falling prices
has risen steadily from a trough near 15 percent in
July 2008 to 46 percent in March. However, some
prices in the market basket are increasing, and
those increases are more than offsetting the price
declines in the aggregate (hence, the CPI rose 0.8
percent in March). Roughly 25 percent of the overall index increased at rates greater than 3.0 percent
in March, though this is about half as much as a
typical month in 2007.
While the latest readings on underlying inflation
are near zero, a significant deflationary episode
would likely require rampant and sustained price
decreases. Looking forward, it seems that professional forecasters haven’t put much weight on that
scenario. The Blue Chip Panel of Forecasters sees
inflation rates stabilizing near 2.0 percent by the
end of 2011. Even the most pessimistic bunch (the
bottom-ten average) does not see deflation on the
horizon. They expect retail prices to hover a little
under 1.0 percent over the forecast horizon.

Share of market basket

In summary, a brief look at retail prices shows that
the underlying inflation rate has slowed significantly over the past year or so, but a significant
deflation has yet to materialize. Moreover, inflation
expectations (from forecasters at least) seem relatively stable, which is important. If people expect
future prices to be lower than today’s prices, that
would likely influence their behavior today, and
deflation would become a self-fulfilling process. So
far, it doesn’t seem like that has happened.

60.0
50.0
40.0

Month-to-month
6-month moving average

30.0
20.0
10.0
0.0
2000

2002

2004

2006

2008

2010

Sources: U.S. Department of Labor, Bureau of Labor Statistics, Federal Reserve Bank
of Cleveland.

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

Monetary Policy

The Yield Curve, April 2010
04.23.10
by Joseph G. Haubrich and Kent Cherny

Yield Curve Spread and Real GDP
Growth
Percent
11
9

GDP growth
(year-over-year change)

7
5
3
1
-1

Ten-year minus three-month
yield spread

-3
-5
1953

1963

1973

1983

1993

2003

Note: Shaded bars indicate recessions.
Source: Bureau of Economic Analysis, Federal Reserve Board.

Yield Spread and Lagged Real GDP Growth
Percent
11
One-year lag of GDP growth
(year-over-year change)

9
7
5
3
1

In the past two months, the yield curve has moved
up and gotten steeper, with long rates rising a bit
more than short rates. The difference between these
rates, the slope of the yield curve, has achieved
some notoriety as a simple forecaster of economic
growth. The rule of thumb is that an inverted
yield curve (short rates above long rates) indicates
a recession in about a year, and yield curve inversions have preceded each of the last seven recessions
(as defined by the NBER). In particular, the yield
curve inverted in August 2006, a bit more than a
year before the current recession started in December, 2007. There have been two notable false positives: an inversion in late 1966 and a very flat curve
in late 1998.
More generally, a flat curve indicates weak growth,
and conversely, a steep curve indicates strong
growth. One measure of slope, the spread between
10-year treasury bonds and 3-month treasury bills,
bears out this relation, particularly when real GDP
growth is lagged a year to line up growth with the
spread that predicts it.
The 3-month rate rose to 0.16 percent (for the
week ending April 16), up from February’s 0.10
percent. The 10-year rate increased to 3.85 percent,
up from February’s 3.74 percent. The slope, already
quite high, nudged up a bit to 369 basis points,
up from February’s 364 basis points. Projecting
forward using past values of the spread and GDP
growth suggests that real GDP will grow at about
a 1.17 percent rate over the next year, essentially
unchanged from February. Although the time
horizons do not match exactly, this comes in on the
more pessimistic side of other forecasts, although,
like them, it does show moderate growth for the
year.

-1
Ten-year minus three-month
yield spread

-3
-5
1953

1963

1973

1983

1993

2003

While such an approach predicts when growth is
above or below average, it does not do so well in
predicting the actual number, especially in the case
of recessions. Thus, it is sometimes preferable to

Sources: Bureau of Economic Analysis, Federal Reserve Board.

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

Yield Curve Predicted GDP Growth
Percent
5
4

GDP growth
(year-over-year change)

Predicted
GDP growth

3
2
1
0
Ten-year minus three-month
yield spread

-1
-2
-3
-4

-5
2002 2003 2004 2005 2006 2007 2008 2009 2010
Sources: Bureau of Economic Analysis, Federal Reserve Board, authors’
calculations.

Recession Probability from Yield Curve
Percent probability, as predicted by a probit model
100
90
80
70

Probability of recession

60
Forecast

50
40
30

Of course, it might not be advisable to take these
number quite so literally, for two reasons. (Not
even counting Paul Krugman’s concerns.) First,
this probability is itself subject to error, as is the
case with all statistical estimates. Second, other
researchers have postulated that the underlying
determinants of the yield spread today are materially different from the determinants that generated
yield spreads during prior decades. Differences
could arise from changes in international capital
flows and inflation expectations, for example. The
bottom line is that yield curves contain important
information for business cycle analysis, but, like
other indicators, should be interpreted with caution.
For more detail on these and other issues related to
using the yield curve to predict recessions, see the
Commentary “Does the Yield Curve Signal Recession?”
For more on other forecasts:
http://www.econbrowser.com/archives/2008/11/gdp_mean_estima.
html

20
10
0
1960

focus on using the yield curve to predict a discrete
event: whether or not the economy is in recession.
Looking at that relationship, the expected chance
of the economy being in a recession next April is
7.1 percent, just up from February’s estimate of 6.3
percent.

1966 1972 1978 1984

1990 1996 2002 2008

Note: Shaded bars indicate recessions.
Sources: Bureau of Economic Analysis, Federal Reserve Board, authors’
calculations.

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

For Paul Krugman’s column:
http://krugman.blogs.nytimes.com/2008/12/27/the-yield-curvewonkish/
“Does the Yield Curve Yield Signal Recession?,” by Joseph G.
Haubrich. 2006. Federal Reserve Bank of Cleveland, Economic
Commentary is available at:
http://www.clevelandfed.org/Research/Commentary/2006/0415.pdf

Monetary Policy

Recent Firming in the Federal Funds Market
04.30.2010
by John Carlson and John Lindner

Debt Outstanding and the Federal Funds Rate
Billions of dollars*
250

Percent
0.25
Federal funds rate
(right scale)

200

0.20

150

0.15

100

0.10

50
0
01/09

SFP securities outstanding
(left scale)

0.05
0.00

05/09

09/09

01/10

*Seasonally adjusted.
Source: Federal Reserve Board.

Starting at the end of February 2010, the effective
federal funds rate has seen a persistent firming in
daily average rates, where firming refers to a higher
rate, closer to the floor established by the interest
rate on reserves. This rise in the funds rate began
following the announcement by the Treasury to revive its Supplemental Financing Program (SFP) on
February 23. Specifically, the Treasury announced
that it would issue $200 billion in SFP debt and
deposit the funds in its account at the Fed. This
action removes reserves from the banking system
and could put upward pressure on the federal funds
rate. The SFP-related reserve draining is illustrative
of the kind of effect the Federal Reserve expects
from the implementation of its own newly designed
reserve-draining tools.
While it is tempting to attribute the firming in
observed rates to the revival of the SFP, it may
not be possible to separate the effects of the SFP
from other events that have occurred recently. For
example, the discount rate increased from 50 basis
points to 75 basis points on February 18, after
which markets may have altered their assumptions
about the future path of the federal funds rate and
started to trade funds at a higher rate.
Another factor adding to upward pressure on the
federal funds rate was an increase in new Treasury
debt issues around the April tax deadline. Because
the new issues become collateral for borrowers of
funds, they can generate so-called collateral effects. As firms vie for the funds in secured money
markets (repo markets, for example), an abundance
of longer-term collateral can be used to obtain
overnight funds. The issuance of a larger amount
of attractive collateral by the Treasury thus often
pushes up rates in secured money markets, as Treasury security holders bid the rates up. These rates
put pressure on unsecured (federal funds) markets,
which are close substitutes for repos.
Since the first settlement of the revived SFP auc-

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

Debt Outstanding and the Federal Funds Rate
Percent
0.25

Trillions of dollars*
8.5
Federal funds
rate (right scale)

8.0

0.20

7.5
0.15
7.0
0.10

SFP securities
outstanding (left scale)

6.5

0.05

6.0
Public debt outstanding (left scale)
5.5
12/08
4/09
8/09

0.00
12/09

4/10

*Seasonally adjusted.
Sources: Federal Reserve Board; Treasury Department.

Supply Effects on the
Effective Federal Funds Rate
Percent
0.25

0.20

0.15

0.10
Decrease in
reserves begins
0.05
Reports of GSEs
reducing credit lines
0.00
1/1

1/16

1/31

2/15

Freddie Mac
delinquent loan
buyout
3/2
2010

3/17

4/1

4/16

Source: Federal Reserve Board.

tions on February 25, the funds rate has increased
steadily and persisted above levels that had not been
reached since before September 2009, when the
Treasury decided to reduce its SFP balance. Firm
conclusions are hard to draw from this observation,
though, because reserve balances have declined
as well and the flow effects of subsequent auction
settlements have been inconsistent in their influence on the funds rate.
Beginning on February 24, the balance of excess
reserves in the system has been in constant decline,
and thus funds available in the federal funds market
have been scarcer. However, not all declines in
reserves have corresponded to a rise in the federal
funds rate, suggesting that the flows of funds are
being counteracted by other market forces. On
March 4, government-sponsored enterprises (GSE)
began tightening their credit lines, effectively offering less cash in the funds market, which should
have had a persistent firming effect on the funds
rate. This firming, however, is dependent on the
demand for funds, which may be distorted by
calendar effects (like ends of quarters and tax day)
and collateral availability. These types of distortions were evident on March 16, when Freddie Mac
completed a delinquent loan buyout and removed
some of its lendable federal funds. In an action that
should have firmed the market, rates dropped 2
basis points, part of a continual tumble toward the
end of the first quarter.
The one sharp deviation in the upward rise in the
federal funds rate occurred at the end of the first
quarter, when the effective funds rate dropped to
0.09 percent. Such quarter-end deviations often occur as firms try to clean up their balance sheets.
What should be learned from these observations is
not that reserve draining may be futile and unpredictable, but that the market for federal funds is
complex. Movements and trends in the prevailing
rate often cannot be attributed to a single event,
and constant monitoring of the supply and demand
for federal funds will be essential in maximizing the
potential of the reserve draining tools available to
the Federal Reserve.

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

Growth and Production

The Recovery So Far
05.11.10
by Pedro Amaral

Contributions to Output Growth
2009:Q32010:Q1

2001:Q42002:Q2

1983:Q11983:Q3

1975:Q21975:Q4

Output

3.66

2.33

7.49

5.09

Consumption

1.89

2.23

4.13

3.53

Investment

2.19

−0.31

4.11

1.34

Net exports

−0.38

−0.65

−1.73

−0.38

Government expenditures

−0.03

1.03

0.95

0.57

Source: Bureau of Economic Analysis; authors’ calculations.

According to the Bureau of Economic Analysis’s
latest estimate, real GDP increased at a 3.2 percent
annualized rate in the first quarter of the year. This
increase was fueled by private consumption, which
increased 3.6 percent, the largest quarterly growth
in this category since the first quarter of 2007.
Private investment also increased at a healthy clip
(14.8 percent), albeit much lower than the previous quarter, when it increased 46.1 percent mainly
because of inventories. This is the third consecutive quarter in which real GDP registered positive
growth, an opportune time to start talking about
the recovery.
Although the NBER committee that sets the official recession dates has not, as of yet, announced an
ending date for the latest downturn, most economists would agree that it was sometime around the
end of the second quarter of 2009. That being the
case, the recovery period is now just a little over
three quarters old. Let’s take that point as a given
and assess how this recovery compares, so far, to
previous ones.
Centering our attention first on the relative
strength of the recovery, we note that real GDP
increased at an annualized rate of 3.6 percent over
the last three quarters. Looking over all the recessions since World War II, we see that this is faster
than the last two recessions, but slower than all the
others. Compared to this one, though, the last two
recessions were mild. In fact, this was the deepest of
all postwar recessions, so another way to compare
the strength of the recovery is to measure how far
the economy is from its “trend.”
The trend is computed by taking the peak GDP
prior to the recession and extrapolating what its
path would have been if growth had been the same
as the average growth throughout the post-WWII
period (roughly 0.8 percent per quarter). To compare deviations from the trend across recessions, we
index recessions by their peaks, as in the chart be-

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

low. By this yardstick, the recovery up to this point
seems feeble at most, but not the worst on record;
that would be the 2001 recession.

Recessions Relative to Trend Growth
Index (peak=100)
102
100
98

2000:Q4

96
1981:Q3

1973:Q4
2007:Q4

92
90
88
86
0

Quarters from peak
Source: Bureau of Economic Analysis; author’s calculations.

As for the composition of output, two things have
struck economists’ attention regarding the current
recovery. The first one is the behavior of inventories, which gave rise to the spectacular increase
in private investment last quarter, and which Ken
Beauchemin discussed in a Trends article last
month; the second and most recent surprise was
the strength of consumer expenditures in the first
quarter of 2010, as described in the opening paragraph. This puzzled economists because employment numbers remain weak, at best, and disposable
personal income did not grow at all in real terms
during that quarter.
The table below tries to put this in perspective by
looking at the three recovery quarters so far (not
just at the first quarter of 2010) and comparing it
to past recoveries. The first row presents the annualized output growth rates, while the subsequent rows
present the contribution of each component. What
is striking is just how weak consumption’s contribution has been compared to other recoveries. This
should help dispel the notion that consumption
increases are driving the recovery, but it still leaves
this recent spurt unexplained.
Inquiring a little bit deeper into personal consumption expenditures shows that the driver for the
recent increase was durable goods purchases, which
increased 11.3 percent. In contrast, nondurables
and services increased only 3.9 percent and 2 percent, respectively. This is not surprising, as durables
are households’ physical investments (things like
cars, TVs, and computer equipment), and as such
are much more volatile than the other consumption
components and thus tend to increase a lot faster
than output (or overall consumption for that matter) during recoveries.
Why did durables consumption increase so much
in the first quarter of 2010 when real personal disposable income was unchanged? The answer here is
threefold. First, after having peaked at 5.4 percent
in the second quarter of 2009, savings as a percentage of disposable income are down to 3.1 percent.
Second, but not unrelated, interest rates

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

are as low as they have ever been. The proliferation
of financing deals on big-ticket items (zero percent
financing anyone?) means households have very
little immediate out-of-pocket expenses. Finally, the
relative price of durables has been falling for three
quarters, making buying that big-screen TV look
like an increasingly better deal than splurging at the
movie theater.
For the Federal Reserve Bank of Cleveland’s Economic Trends
page “An Immoderate Inventory Cycle” visit
http://www.clevelandfed.org/research/trends/2010/0410/01gropro.
cfm

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

Households and Consumers

Household Finances and a Sustainable Recovery
04.21.10
by O. Emre Ergungor and Kent Cherny
Consumption accounts for roughly 70 percent of
the country’s gross domestic product. Consequently, a sustainable economic recovery depends on a
recovery in household consumption.

Personal Income and Consumption
12-month percentage change
10
8

PCE average
1990-2007

DPI average 1990-2007

To get a handle on prospects for near-term household consumption, we consider three indicators
likely to affect individuals’ propensity to consume—disposable income growth, existing debt
burdens, and overall household balance sheets—
within the context of historical averages taken prior
to the current recession (for income) and prior to
the housing boom (for debt and balance sheets). In
this manner, we can examine current household financial resources relative to periods when they were
not inflated by a housing-dependent economy or
undergoing sharp rebalancing in a deep recession.

6
4
2

Disposable
personal income

Personal consumption
expenditure

-2
-4
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Source: Bureau of Economic Analysis.

Household Borrowing
Percentage of nominal GDP
12
10
8

Net quarterly
household borrowing

6
4
2

Average 1990-2000

0
-2
-4
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

In thinking about household finances, the obvious
primary resource available for new consumption
is disposable personal income. Between 1990 to
2007, annual changes in personal income fluctuated within a range of roughly 2 percent to 8 percent,
with personal consumption expenditures almost
always tracking closely along. However, the recession and financial crisis in 2008 pushed both negative for the first time in over 20 years. Though they
have since turned positive again, both still remain
2-3 percent below their long-run growth averages.
Household consumption spending can also be
funded through debt. New individual borrowing
as a percentage of GDP has been negative in recent
quarters, though, meaning that on a net basis loans
are either being paid off (and not renewed) or are
defaulting, or a combination of the two. For a sense
of historical perspective, consider that the average borrowing level from 1990 to 2000—before
the loose loan underwriting environment of the
2000s set in—was about 4 percent of GDP. That
current levels are so far below this trend indicates
that a fundamental rebalancing of household debt
burdens is taking place. Personal savings rates show

Source: Bureau of Economic Analysis, Federal Reserve Board.

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

Consumer Debt Charge-Offs
Percentage of average loan balances
11
10
9
8
7

Pooled credit card
charge-offs

6
5
4
3
2
1

Bank mortgage
charge-offs

Bank non-mortgage
consumer loan charge-offs

0
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Source: Federal Reserve Board, Standard & Poor’s.

Household Debt Burden
Percentage of disposable personal income
14.5
14.0
13.5
13.0

Household debt
service ratio

12.5
12.0
11.5
11.0

Average 1990-2000

10.5
10.0
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Source: Federal Reserve Board.

Household Balance Sheets
Percent
22
21
20
19
18

Household
debt-to-assets ratio

17
16
15
14
13

Average 1990-2000
12
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Source: Federal Reserve Board.

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

that households are indeed saving more, explaining
part of the aggregate loan shrinkage.
Some of the contraction in household borrowing
can be explained by higher-than-average defaults
on mortgages, consumer loans, and credit cards.
Whether consumers are paying down existing debt
through savings or banks are writing bad loans off,
less aggregate debt in the financial system results in
either case.
The household debt service ratio, which measures
repayments as a percentage of income, has been
consistently falling since the third quarter of 2008.
As debt levels shrink, consumers are spending less
of their disposable income on repayments related
to mortgages and consumer loans. A good deal of
falling payments can also likely be traced to historically low interest rates, which lower debt service
requirements on new debt, refinanced debt, or debt
that carries floating interest rates. Still, the debt
service ratio needs to fall at least 1 percent more to
reach the average levels seen from 1990 to 2000.
Finally, households are unlikely to consume at high
levels if their debt liabilities are high relative to their
assets. Consumers borrowed money aggressively
from 2000 to 2008, pushing their debt-to-assets
ratio from below 13 percent in 2000 to a peak of
22 percent in 2008. Some of that leverage has disappeared as loans have been paid down or charged
off, but both the sheer amount of debt accumulated
in recent years and the declining value of many
household assets (namely homes) will continue putting downward pressure on consumer wealth—and
therefore the propensity to consume—for potentially years to come.
What does all of this bode for a recovery of consumption, the primary driver of the U.S. economy?
The data shown here point to a long road ahead for
a sustainable recovery. Consumers are paying down
loans or defaulting, and those looking for new
consumer loans are likely to find that banks are still
pulling back on lending, though individuals who
can secure a loan face historically low interest rates.
Given the hangover of outstanding debt and recent
memories of shrinking asset values, consumers may
not be motivated to ramp up their expenditures.
Rather, consumption will likely recover slowly as
12

households save more and await the return of an
improved labor market and the sustainable source
of funding—disposable income—that it typically
provides for consumption.
For more on personal savings rates:
http://www.clevelandfed.org/research/trends/2010/0410/01ecoact.
cfm
For more on credit cards:
http://www.clevelandfed.org/research/trends/2009/1009/01banfin.
cfm
For more on commercial bank lending:
http://www.clevelandfed.org/research/trends/2010/0410/01banfin.
cfm

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

Banking and Financial Markets

The Credit Crunch in Commercial Loan Syndication
04.21.10
by Jian Cai and Kent Cherny
Commercial lending by banks has fallen to
double-digit, negative growth rates, both on- and
off-balance-sheet. The current financial crisis has
also impacted the market size and composition of
syndicated loans, which are a unique type of commercial loans.
A syndicated loan is a credit facility arranged for a
commercial borrower, by a group (“syndicate”) of
banks. Member banks within the syndicate share
the lending commitments, risk exposure, fees and
interest revenues. Through syndication, banks are
able to comply with regulatory limits on risk concentration (such as a minimum capital-asset ratio
and a maximum size of a single loan relative to a
bank’s equity capital) and avoid excessive exposure
to individual companies. Meanwhile, participating in syndicated loans gives banks opportunities
to diversify their loan portfolios and maintain or
develop relationships with greater numbers of corporate borrowers.

Syndicated Loan Origination
Billions of dollars
3,000
2,500

Outside the U.S.

2,000
1,500

United States

1,000
500
0
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Providing many benefits to both lending institutions and borrowing firms, the syndicated loan
market has experienced tremendous growth since
the early 1990s and is now one of the most significant sources of corporate finance. According to loan
origination data from Thomson Reuters DealScan,
the amount of newly originated syndicated loans
in the U.S. market increased 7 times over between
1992 and 2007, from $242 billion to nearly $2 trillion. The increase outside the U.S. was even more
dramatic, growing 42 times in volume over the
same period. However, syndicated loans were down
sharply in 2009, with only about $1.3 trillion
originated worldwide that year, from a peak of $4.5
trillion just two years prior. This 70 percent decline
shows that this market was every bit as susceptible
to the reassessment of risk as other credit markets
following the financial crisis.
Commercial loan syndication worldwide is concentrated primarily in North America (nearly 95

Source: Thomson Reuters DealScan.

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

percent of it in the U.S.) and Europe, with both
markets having recently experienced the same
boom and bust. The Asian market also fell to less
than 40 percent of its 2007 peak, though this market utilizes syndicated loans to a far lesser extent
than either the U.S. or Europe.

Syndicated Loan Origination by Region
Billions of dollars

As U.S. volumes have fallen, banks have also been
charging significantly higher interest rates in the
syndicated market. Risk spreads—measured over
LIBOR, the cost of funds for banks borrowing
among themselves—were on average flat at 236237 basis points during 2005-2007, before jumping nearly 20 percent to 282 basis points in 2008
and then almost another 40 percent in 2009, to
391 basis points. Because these are spreads over
LIBOR, the increase can be attributed to a distinct
reassessment of risk within the syndicated loan
market itself, not simply to an increase in arranging
banks’ cost of funds.

2,250
2,000
1,750
1,500

North America

1,250
1,000

Europe

750
500
Asia

250

Latin America

0
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Source: Thomson Reuters DealScan.

Syndicated Loan Rates
Spread over LIBOR, in basis points
450
400
350
300

Syndicated loan
spread over LIBOR

250
200
150
100
50
0
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

The kinds of companies that receive syndicated
loans have actually been fairly constant over the
past five years, although absolute levels of credit
extended have dropped off. Typically, about half of
syndicated loans flow to public companies that are
rated by the major rating agencies. These tend to be
larger firms with a lot of publicly available information. A significant share (30-40 percent) also goes
to privately held companies. A small share of loan
syndication credit goes to unrated, public companies. Following the crisis, credit extension contracted more sharply for public, rated companies than
for private companies.
Interest rates rose as syndicated loan originations
fell. Historically, the spreads on these loans have
been correlated to the amount of monitoring a
lender would have to do to limit its credit exposure.
Private companies generally are subject to the fewest requirements for providing standardized financial documentation, and so are generally charged
the highest interest rate. Publicly owned companies
must meet a minimum of documentation requirements, and therefore are easier to analyze and
monitor (which means a lower interest rate).
Interestingly, those companies that produce voluminous amounts of required financial statements

Source: Thomson Reuters DealScan.

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

Syndicated Loans by Borrower Type
Billions
2,000
1,800
1,600

Public company, unrated
Private company
Public company, rated

1,400
1,200
1,000
800
600
400
200
0
1992 1994 1996 1998 2000 2002 2004 2006 2008
Source: Thomson Reuters DealScan.

Syndicated Loan Rates by Borrower Type
Spread over LIBOR, in basis points
450
400
350

Private company

300

Public company, unrated

250

and carry an outside credit risk grade—public, rated companies—saw the biggest spike in their interest rates following the crisis, even exceeding private
and public, unrated companies in 2009. Although
all companies saw their perceived riskiness reassessed upward in 2008 and 2009, the marked leap
in interest rates for public, rated companies may
have been due to concern about the ratings themselves, which came under closer scrutiny following
the torrent of mortgage security downgrades. If official ratings were called into question, their contribution to lowering interest rates would have to have
been partially or completely withdrawn.
In addition to restricting credit through higher
interest rates and lower volumes, syndicates are also
offering credit primarily to larger borrowers than
in the past. Since loan volumes are falling and the
value of real annual sales by syndicated loan borrowers is rising quickly, the average company size
per loan must be rising. Larger, more established
companies may give lenders more confidence about
repayments, and their business models and financial conditions are likely to be better known and
understood than those of smaller firms.

200
150

Public company, rated

100
50
0
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Source: Thomson Reuters DealScan.

Syndicated Loan Borrower Size
Real annual sales, in billions of 1982-84 dollars
4,000
3,500
3,000
2,500
2,000

Borrower sales

1,500
1,000
500
0
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
Source: Thomson Reuters DealScan.

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

Finally, we ask where the funds from syndicated
loans are being put to use in the economy. Over
at least the past five years, the majority of loan
proceeds have been used for “corporate” purposes,
which means for working capital and general
business operations. In the late 1990s and early
2000s, a lot of these loans were used for refinancing arrangements, such as restructuring a firm’s
balance sheet (for example, by adding a new source
of debt or buying back shares) and rolling over or
paying down other debt. This practice has declined
in past years with the continued development of
the corporate bond market—including the highyield (“junk”) market—which serves as a financing
alternative to commercial loan syndication. Historically, syndicated loans were also used as a form of
credit enhancement, standing behind commercial
paper and other loans as a source of payment if the
borrower defaulted. Loans for that purpose have
all but disappeared in the last two years. Acquisition loans—used to finance the takeover or merger
of another firm by the borrower—experienced
booms first during the late-90s Internet bubble
16

Syndicated Loan Purposes
Billions
2,000
1,800
1,600
1,400

Backup line
Acquisitions
Refinancing
Corporate purposes

1,200
1,000
800
600
400
200
0
1992 1994 1996 1998 2000 2002 2004 2006 2008
Source: Thomson Reuters DealScan.

and again from 2004 to 2008 at the height of the
credit bubble. They have since been reined in, and
today most syndicated loans (over 70 percent) are
going to companies planning to use the money for
general business purposes.
In summary, banks use syndicated loans to limit
their credit exposure to a single firm or market while still meeting their clients’ borrowing
needs. This loan category—like every other credit
market—swelled in the mid-2000s, but the credit
crunch and its attendant risk environment led to
a contraction in this type of credit. The contraction has come in the form of lower volumes, higher
interest rates, and an emphasis on lending only to
large firms. Meanwhile, borrowers in the syndicated
loan market have moved away from more aggressive
or speculative ventures like mergers and acquisitions, and are now using loan funds primarily to
operate and develop existing businesses.
For more on commercial bank lending:
http://www.clevelandfed.org/research/trends/2010/0410/01banfin.
cfm

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

International Markets

Global Imbalances
04.23.10
by Owen F. Humpage and Caroline Herrell

Global Imbalances: Current Account
Balance in Percent of World GDP
Percent
3

China & emerging market economies
Germany & Japan
Oil exporters
U.S.
Other

2
1
0
-1
-2
-3

1996

1998

2000

2002

2004

2006

2008

Source: International Monetary Fund.

Real Business Fixed Investment
Percent of real GDP
12
10
8
6
4
2
0
1945

1955
1950

1965
1960

1975
1970

1985
1980

1995
1990

2005

We have never quite understood the pejorative connotation associated with “global imbalances.” Every
day people around the world choose how much of
their income to spend or save and what types of
goods—domestic or foreign—to buy. Some also
select the kinds of things, and how much of them,
to produce. People make these choices solely with
the intent of improving their own lives, and by and
large they seem pretty successful at it. Currentaccount deficits or surpluses merely aggregate these
individual choices. How can this be a bad thing?
Some believe that current-account deficits and
surpluses have simply grown too large, but what exactly do they mean? Since the early 1980s, nations
have generally loosened restrictions on cross-border
financial flows, allowing savers to seek out higher,
safer returns abroad. While this benefits both savers
and investors, it naturally produces bigger, more
persistent current-account surpluses and deficits.
An increase in real business fixed investment,
financed in part with foreign funds, has accompanied the persistent U.S. current-account deficits
since the early 1980s with few, if any, adverse consequences. Size alone cannot matter.
Others worry about the sustainability of large current-account deficits. To be sure, a current-account
deficit cannot rise indefinitely relative to a nation’s
GDP, a proxy for its ability to service and eventually to pay down the associated debts. At some
point, international investors will balk at holding
these debts, possibly resulting in a sharp depreciation and a wrenching increase in interest rates.
Emerging-market and developing countries, which
must finance their current-account deficits by issuing claims denominated in foreign currencies, do
sometimes encounter adjustment problems, but
developed countries, which finance their deficits in
their own currencies, so far have weathered currentaccount reversals without serious consequences.

2000

Source: Bureau of Economic Analysis.

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

Maybe current-account imbalances are only a
problem when governments meddle with the
aforementioned individual choices. China, for
example, interferes with the adjustment of its real
exchange rate by limiting private financial flows
and by sterilizing the impact of its reserve accumulation on its monetary base. This helps prop up
their current-account surplus relative to the United
States. When markets cannot function freely, the
outcomes can be unwelcome. In that case, cursing
current-account surpluses and deficits seems a little
like blaming the sneeze instead of the cold.
For more on sustainability:
http://www.clevelandfed.org/research/trends/2007/0107/01intmar.
cfm
For more on China’s exchange-rate policies:
http://www.clevelandfed.org/research/trends/2009/1209/01intmar.
cfm

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

Regional Activity

Homeowner and Rental Vacancy Trends in the Fourth District
04.14.10
by Stephan Whitaker and Rob Pitingolo
The recent housing boom drew high levels of
investment into construction and created excess
supply. When demand faltered, prices and sales
volumes dropped, leading to increased housing
vacancy nationwide. This is a concern to bankers
because a vacant home is often associated with a
loan in delinquency. Rising vacancy rates cause falling values for any asset associated with residential
real estate.
Financial professionals, academic researchers, and
the national media have focused attention on the
housing “crisis,” with elevated vacancy a key part of
the discussion. Now that we are over two years into
the crisis, it seems like a good time to return to the
data on vacancy and answer some critical questions.
Is vacancy still a problem? Is it rising? Are the attention to vacancy and efforts to lower it still justified?
In particular, what are the trends in the Fourth
District’s housing markets?

Vacancy Rates, Cincinnati-Middletown MSA
25
20

Rental vacancy

Homeowner vacancy

8 per. moving average
(rental vacancy)

8 per. Mov. Avg.
(homeowner vacancy)

15
10
5
0
1996
1998
2000
2002
2004
2006
2008
1997
1999
2001
2003
2005
2007
2009
Note: Metropolitan Statistical Areas (MSAs) are defined by the U.S. Office of
Management and Budget.
Source: U.S. Census Bureau.

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

The vacancy data presented here are released quarterly by the Census Bureau. They are estimates of
the percentage of all rental units and all nonrental
units (called homeowner units in the data) in a
metro area or state that are vacant at the time of the
survey. The quarterly observations are quite volatile,
so we have added an eight-quarter moving average
to reveal the long-term trends.
Two of the large metro areas in the Fourth District,
Cincinnati and Pittsburgh, show relatively little
change in their rental vacancy from the mid-1990s
through last year. Their homeowner vacancy, however, has increased. In Cincinnati, homeowner vacancy was around 2 percent at the beginning of the
2000s and has risen to around 4 percent. In both
MSAs, the rise seems to have ended in the last year.
A two percentage point increase in vacancy may not
sound like a reason for concern. However, framed
differently, this is a 100 percent increase in vacancy,
involving tens of thousands of housing units.

Vacancy Rates, Pittsburgh MSA
25
20

Rental vacancy

Homeowner vacancy

8 per. moving average
(rental vacancy)

8 per. moving average
(homeowner vacancy)

Vacancy Rates, Cleveland-Elyria-Mentor MSA
25
20

Rental vacancy
8 per. moving average
(rental vacancy)

Homeowner vacancy
8 per. moving average
(homeowner vacancy)

Vacancy Rates, Columbus MSA
25

Rental vacancy

8 per. moving average
(rental vacancy)
Homeowner vacancy

8 per. moving average
(homeowner vacancy)

10
5
0
1996
1998
2000
2002
2004
2006
2008
1997
1999
2001
2003
2005
2007
2009
Note: Metropolitan Statistical Areas (MSAs) are defined by the U.S. Office of
Management and Budget.
Source: U.S. Census Bureau.

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

A change that large could depress rents and home
prices.
The other two large MSAs, Cleveland and Columbus, have experienced substantial movements in
the measures, particularly in rental vacancy. In the
Cleveland area, rental vacancy rose from around
10 percent in the late 1990s to over 15 percent in
2006. In the last three years, that figure has come
back down to around 13 percent. In Columbus,
the rental vacancy rate started lower, at around
7 percent in 1999, and rose above 15 percent by
2005. Since then, Columbus’s rental vacancy has
declined back below 10 percent and appears to be
on a downward trajectory. The homeowner vacancy
trends in Cleveland and Columbus follow the same
pattern as those in Cincinnati and Pittsburgh. They
have drifted upward by 1-2 percentage points since
the year 2000 but have recently flattened or declined.
In one of the Fourth District’s smaller MSAs, Akron, the pattern seen in Cleveland and Columbus is
repeated with a bit of a twist. Akron’s homeowner
vacancy rose between 2000 and 2008 and then declined. Its rental vacancy rate also started off low in
1999, just above 5 percent, and climbed above 15
percent. Rental vacancy there turned down in 2005
and dipped below 10 percent by 2007. The twist is
in the last two years of the data. The rental vacancy
has turned up again, with the moving average suggesting a 4 or 5 percentage point increase.
The data series for Dayton and Toledo are only
available for the last five years. Since 2005, it appears that rental vacancy in Dayton rose, while
homeowner vacancy fell and then rose. In Toledo,
both rental and homeowner vacancy might be
higher in 2009 than in 2005, but the difference is
not large enough to be significant.
While aggregating across an entire state does not
represent a housing market, it could be of interest
for an investor who holds mortgages or properties
across the state. In rental vacancies, all four states
experienced rises in rental vacancy between the late
1990s and mid-2000s. The upturn ended earliest
in West Virginia, perhaps in 2003. Pennsylvania
had a decline in rental vacancy until 2003 and then
an increase until 2005. The rental vacancies have
21

drifted down for Pennsylvania, Kentucky, and West
Virginia since 2005. Ohio’s rise in rental vacancy
was more or less steady for an entire decade before
turning down in 2006. Since then, Ohio’s rental
vacancy has dropped quickly to meet Kentucky and
West Virginia at 11 percent.

Akron, OH
25

Rental vacancy

8 per. moving average
(rental vacancy)
Homeowner vacancy

8 per. Mov. Avg.
(homeowner vacancy)

In the homeowner vacancy rates, West Virginia and
Pennsylvania have converged at around 2 percent.
Kentucky and Ohio both began lower in this series,
rose between 1998 and 2008, and then turned
down in the last two years.

10
5
0
1996
1998
2000
2002
2004
2006
2008
1997
1999
2001
2003
2005
2007
2009

In a troubled housing market during a weak economic period, the bad news is that homeowner
vacancy in the Fourth District is higher than it
was before the housing boom and bust. The good
news is that homeowner vacancy is level or falling
in most regions of our district. Rental vacancy, too,
is down in five of the seven Fourth District MSAs
in the data and two of the District’s four states.
Overall this suggests that our housing stock and
prices are partway through an adjustment to the
new economic conditions. Our continued attention
will be warranted until vacancy returns to historical
norms, or the market dictates that the new, higher
levels of vacancy are the norm for our region.

Vacancy Rates, Dayton MSA
25

Rental vacancy

8 per. moving average
(rental vacancy)
Homeowner vacancy

8 per. moving average
(homeowner vacancy)

Vacancy Rates, Toledo MSA

Vacancy Rates, Fourth District

14.0

Rental vacancy

Homeowner vacancy

8 per. moving average
(rental vacancy)

8 per. moving average
(homeowner vacancy)

12.0
10.0

8 per. moving average (Ohio: rental)
8 per. moving average (Pennsylvania: rental)
8 per. moving average
(Kentucky: rental)
8 per. moving average
(West Virginia: rental)

8.0
10
6.0
5
4.0
0
1996
1998
2000
2002
2004
2006
2008
1997
1999
2001
2003
2005
2007
2009
Note: Metropolitan Statistical Areas (MSAs) are defined by the U.S. Office of
Management and Budget.
Source: U.S. Census Bureau.

8 per. moving
8 per. moving
8 per. moving
8 per. moving

average
average
average
average

(Ohio: homeowner)
(Pennsylvania: homeowner)
(Kentucky: homeowner)
(West Virginia: homeowner)

2.0
0.0
1996
1998
2000
2002
2004
2006
2008
1997
1999
2001
2003
2005
2007
2009
Source: U.S. Census Bureau.

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

Labor Markets, Unemployment, and Wages

Some Popular Locales Now Facing Gloomier Labor Market
05.14.10
by Daniel Hartley

National Employment Figures
Ratio
64

Rate
10

Employment-to-population ratio

60
Unemployment rate
58
2006:M1

2007:M1

2008:M1

2009:M1

4
2010:M1

Source: Bureau of Labor Statistics.

MSA Labor Force Index
Index level
115
110
105
100
95
2006:M1

2007:M1

2008:M1

Detroitc
Los Angelesc
Clevelandb
Pittsburghb
New Orleansd

2009:M1

2010:M1

San Bernardinoa
Chicagoa
New Yorka
Oklahoma Cityd

a. MSAs with the largest population as of the 2000 Census.
b. MSAs in the fourth Federal Reserve District.
c. MSAs with the highest unemployment rates in March 2010.
d. MSAs with the lowest unemployment rates in March 2010.
Source: Bureau of Labor Statistics.

While the national employment numbers give the
most up to date reading of the employment situation in the nation, they mask a lot of variation in
employment conditions at the local labor market
level. This variation has increased dramatically during the recent recession.
The national employment numbers released on
May 7, 2010 reveal that while the unemployment
rate increased from 9.7 percent in March to 9.9
percent in April, the employment to population
ratio increased from 58.6 percent in March to 58.8
percent as well. While the employment to population ratio and the unemployment rate usually move
in opposite directions, they can both increase if the
number of people employed increases but the size
of the labor force increases at a faster rate.
Simply looking at the national average of the
unemployment rate masks the fact that there is a
large amount of variation in unemployment rates
at the Metropolitan Statistical Area (MSA) level.
MSAs are the typical geographical entity that
defines a local labor market. As of March (the most
recently available month of data at the MSA level),
the unemployment rates in MSAs with at least one
million people ranged from a low of 6.1 percent in
the Oklahoma City MSA to a high of 15.5 percent
in the Detroit-Warren-Livonia MSA. A sample of
other MSAs—those with the highest and lowest
unemployment rates in March of 2010, those with
the largest population as of the 2000 Census, and
two in the fourth Federal Reserve District—gives
an idea of the variation since January 2006.
The variation in unemployment rates across MSAs
increased markedly beginning around January
2009. This increase is reflected in the entire set of
46 MSAs with a 2000 population above one million. While the mean unemployment rate of that
set of MSAs changed from 4.6 percent to 10.0
percent from March 2006 to March 2010, the
standard deviation also increased from 0.9 percent

MSA Unemployment Rate
Percentage points
20
15
10
5
0
2006:M1

2007:M1

2008:M1

Detroit
Los Angeles
Cleveland
Pittsburgh
New Orleans
Source: Bureau of Labor Statistics.

2009:M1

2010:M1

San Bernardino
Chicago
New York
Oklahoma City

to 2.2 percent during that time period. In all MSAs
with a population greater than one million, the
unemployment rate increased from March 2006 to
March 2010. Remarkably, the largest increase (10.2
percent in Riverside-San Bernardino-Ontario) was
almost an order of magnitude larger than the smallest increase (1.3 percent in New Orleans- MetairieKenner). MSAs in the Fourth District (Pittsburgh,
Cleveland, Columbus, and Cincinnati) saw below
average increases in the unemployment rate during this period. Thus despite having higher than
average unemployment rates before the most recent
recession, Fourth District MSAs with populations
greater than one million currently have unemployment rates that are average or slightly below average.
While this may sound like a relative improvement for the region, it is important to consider
whether the improvement in Fourth District MSAs
unemployment rates relative to other MSAs was
achieved through a relative decrease in the number
of unemployed people or a relative decrease in the
size of the labor force. In fact, the size of the labor
force in the Cincinnati, Columbus, and Pittsburgh
MSAs grew slightly from January 2006 to March
2010. The size of the labor force in the Cleveland
MSA fell, but only slightly. The main reason why
the unemployment rate of Fourth District MSAs
improved relative to other MSAs over this period
is that the number of unemployed people grew at a
slower rate in Fourth District MSAs than in many
other MSAs.
Across MSAs, the relative contribution of each of
these factors to the unemployment rate has varied
quite a bit. On one extreme is the Riverside-San
Bernardino-Ontario MSA, where the labor force
grew by about 15 percent from January 2006 to
March 2010. Almost all of this growth occurred
between January 2006 and mid-2008. Around
mid-2008 the unemployment rate began rising
quickly in the MSA, implying that the number of
unemployed people began rising. The other extreme
case is the Detroit-Warren-Livonia MSA, where the
labor force shrank by about 2.5 percent from January 2006 to March 2010. Thus some fraction of the
increase in the unemployment rate in the DetroitWarren-Livonia MSA came from the fall in the size

MSA Number Unemployed Index
Index level
300
250
200

of the labor force. The Cleveland-Elyria-Mentor
MSA also saw a declining labor force from January
2006 to March 2010, but only by about 1 percent.
Finally, the other MSAs plotted all saw about a 2
percent to 4 percent increase in the size of their
labor force over this period.

150
100
50
2006:M1

2007:M1

2008:M1

Detroit
Los Angeles
Cleveland
Pittsburgh
New Orleans

2009:M1

2010:M1

San Bernardino
Chicago
New York
Oklahoma City

Source: Bureau of Labor Statistics.

Meanwhile, the number of unemployed people tripled in the Riverside-San Bernardino-Ontario MSA
from January 2006 through March 2010 and grew
by about 2.5 times in the Los Angeles-Long BeachSanta Ana MSA. In the Chicago-Naperville-Joliet,
Detroit-Warren-Livonia, and New York-Northern
New Jersey-Long Island MSAs , the number of
unemployed roughly doubled from January 2006
to March 2010. However, in the Fourth District
MSAs of Cleveland-Elyria-Mentor and Pittsburgh
the number of unemployed grew by about 75 percent over this period.
One possible explanation for patterns observed
above is that warmer climate MSAs that experienced large housing booms may have attracted
population during the boom and are now saddled
with much greater unemployment in the wake of
the bust. However, MSAs in the Fourth District
where house price increases were more modest,
have moved from being relatively high unemployment rate MSAs to being average unemployment
rate MSAs in the wake of the recession, due to
slower relative growth of the number of unemployed people in these MSAs. Of course, this is
only good news for Fourth District MSAs relative
to other MSAs as the national unemployment rate
has just about doubled from 4.7 percent to 9.9
percent during this period. Being an average MSA
in March 2010 means having substantially higher
unemployment than being a high unemployment
MSA in January 2006.

Federal Reserve Bank of Cleveland, Economic Trends | May 2010

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Federal Reserve Bank of Cleveland, Economic Trends | May 2010

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