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June 2008
(Covering May 9, 2008, to June 12, 2008)

In This Issue
Inﬂation and Prices
April Price Statistics
Money, Financial Markets, and Monetary Policy
The Yield Curve
International Markets
Exchange-Rate Pass-Through to Import Prices
Economic Activity and Labor Markets
Real GDP First-Quarter 2008 Preliminary Estimate
Labor Turnover and Employment in Diﬀerent U.S. Regions
The Employment Situation
Regional Activity
Fourth District Employment Conditions, March
Real Income Growth across Metropolitan Areas
Banking and Financial Markets
Business Loan Markets
FDIC Funds

Inﬂation and Prices

April Price Statistics
05.21.08
Michael F. Bryan and Brent Meyer

April Price Statistics
Percent change, last
1mo.a

3mo.a 6mo.a 12mo. 5yr.a

2007
avg.

Consumer Price Index
All items

2.5

2.3

4.5

3.9

3.2

4.2

Less food and
energy

1.3

1.2

2.2

2.3

2.2

2.4

Medianb

2.9

2.4

3.1

2.7

3.1

16% trimmed
meanb

2.7

2.5

3.0

2.8

2.5

2.8

All commodities

23.9

21.3

20.4

15.4

7.2

11.5

Nonpetroleum
imports

13.7

11.8

9.7

6.2

3.0

3.1

4.0

11.8

11.4

7.7

4.5

6.1

Import Price Index

Export Price Index
All commodities

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 Component Price Change Distributions
Weighted frequency
45
April 2008
40
March 2008
Average over the last 12 months
35
30
25
20
15
10
5
0
<0

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

Sources: U.S. Department of Labor, Bureau of Labor Statistics.

Federal Reserve Bank of Cleveland, Economic Trends | June 2008

The Consumer Price Index (CPI) rose at an annualized rate of 2.5 percent in April, following a 4.2
percent increase in March. The CPI was pushed up
by an 11.9 percent jump in food prices that was
tempered by a curious 20.8 percent fall in motor fuel prices and a 20.1 percent decrease in the
price of lodging away from home. Over the past
six months, the CPI has risen 4.5 percent (annualized rate). Consumer prices excluding food and
energy increased slightly in April, rising only 1.3
percent after an increase of 1.8 percent in March.
In contrast to the rather well-behaved headline
and core price indexes, the median and 16 percent
trimmed-mean CPI measures rose 2.9 percent and
2.7 percent, respectively.
Digging deeper into the components that comprise
the CPI, we can see that nearly 50 percent of the
market basket rose at rates exceeding 3.0 percent in
April, down slightly from 55 percent last month,
but in line with price changes over the past 12
months. Also, 28 percent of the CPI components
posted a decrease in price during the month, compared to 16 percent in March and an average of 20
percent over the past 12 months.
Longer-term trends in both headline and core CPI
have been falling since the ﬁrst of the year, but
remain elevated. The 12-month growth rate in the
CPI fell from 4.3 percent in January to 3.9 percent
in April, while the core CPI ticked down 0.2 percentage point to 2.3 percent. However, the longerterm trends in both of the trimmed-mean measures
have lingered near their January values.
Import prices continued to surge ahead in April,
rising 23.9 percent (annualized rate), following
an upwardly revised 40.9 percent jump in March.
Over the past three months, import prices are up
21.3 percent. Petroleum import prices are continuing to soar—up 66.9 percent in April and 187.9
percent in March—though the rise in import prices
2

appears to be relatively broad-based. Nonpetroleum
imports rose 13.7 percent, a month after posting their largest increase on record (13.9 percent).
Nonpetroleum import prices have shot up 6.2
percent over the past 12 months. Export prices increased 4.0 percent in April, following double-digit
monthly gains over the past three months, and are
up 7.7 percent over April of last year.

CPI, Core CPI, and Trimmed-Mean CPI
Measures
12-month percent change
4.75
4.50
4.25
4.00
3.75
CPI
3.50
Median CPI a
3.25
3.00
2.75
2.50
2.25
2.00
1.75
16% trimmed1.50
C ore C P I
mean CPI a
1.25
1.00
1998
2000
2002
2004
2006

Looking forward, the Blue Chip Consensus forecast
has consumer prices falling to 2.4 percent by the
end of 2009, although these projections were made
before the April price reports. Thirty of the ﬁfty
forecasters surveyed revised their 2008 inﬂation
forecasts upward in May from last month, as commodity and energy prices continued to rise.

2008

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

Import and Export Price Indexes

CPI and Forecasts

12-month percent change
18

Annualized quarterly percent change
7.0
6.0

14
10

Imports

3.0

2.0
1.0

Nonpetroleum imports

Exports

Bottom 10 forecast

0.0

-6

-1.0
-2.0

-10
1998

Top 10 forecast

Actual

4.0

-2

Forecast

5.0

2000

2002

2004

2006

2008

Source: Bureau of Labor Statistics.

-3.0
03/06

12/06

09/07

06/08

03/09

12/09

Sources: Blue Chip panel of economists, May 10, 2008.

Money, Financial Markets, and Monetary Policy

The Yield Curve
05.13.08
by Joseph G. Haubrich
Since last month, the yield curve has witnessed a
parallel upward shift, with both short-term and
long-term interest rates rising. One reason for
noting this is that 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 six recessions (as deﬁned by the NBER). Very ﬂat yield
Federal Reserve Bank of Cleveland, Economic Trends | June 2008

Yield Spread versus Real GDP Growth
Percent
12
10

R eal G DP growth
(year-to-year percent
change)

8
6
4
2
0

10-year minus three-month
yield spread

-2
-4
1953

1963

1973

1983

1993

2003

Note: Shaded bars represent recessions.
Sources: Bureau of Economic Activity; Federal Reserve Board.

Yield Spread versus One-Year-Lagged
Real GDP Growth
Percent
12
10

One-year-lagged real G DP growth
(year-to-year perc ent c hange)

curves preceded the previous two, and there have
been two notable false positives: an inversion in late
1966 and a very ﬂat curve in late 1998. More generally, though, a ﬂat curve indicates weak growth,
and conversely, a steep curve indicates strong
growth. One measure of slope, the spread between
10-year bonds and 3-month T-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 yield curve slope stayed the same, with both
long and short rates edging up. The spread remains
positive, with the 10-year rate moving up 31 basis
points to 3.85 percent, and the 3-month rate up
the same to 1.64 percent (both for the week ending
May 9). Standing at 221 basis points, the same as
April’s 221, the spread is above March’s 214 basis
points. Projecting forward using past values of the
spread and GDP growth suggests that real GDP
will grow at about a 3.0 percent rate over the next
year. This is on the high side of other forecasts.

8
6
4
2
0
10-year minus three-month
yield spread

-2
-4
1953

1963

1973

1983

1993

2003

Sources: Bureau of Economic Analysis; Federal Reserve Board.

Yield Spread versus Predicted GDP
Growth
Percent
6
5

R eal G DP growth
(year-to-year perc ent c hange)

P redic ted
G DP growth

3
2
1
0
10-year minus three-month
yield s pread

-1
-2
2002

2003

2004

2005

2006

2007

2008

Sources: Bureau of Economic Analysis; Federal Reserve Board.

Federal Reserve Bank of Cleveland, Economic Trends | June 2008

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
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 May stands
at 0.9 percent, just below April’s 1 percent, and
March’s 2.7 percent.
The probability of recession is below several recent
estimates, and perhaps seems strange the in the
midst of recent ﬁnancial concerns. But one aspect
of those concerns has been a ﬂight to quality, which
lowers Treasury yields. Also related is the reduction
of the federal funds target rate and the discount
rate by the Federal Reserve, which tends to steepen
the yield curve. Furthermore, the forecast is for
where the economy will be next May, not earlier in
the year.
On the other hand, a year ago, the yield curve
was predicting a 35 percent chance that the US
economy would be in a recession in May 2008, a
number that seemed unreasonably high at the time.
To compare the 0.9 percent chance of a recession to
4

some other probabilities and learn more about different techniques of predicting recessions, head on
over to the Econbrowser blog.

Probability of Recession Based on the
Yield Spread*
Percent
100
90
P robability of
rec es s ion

80
70

F orec as t

60
50
40
30
20
10
0
1960

1966

1972

1978

1984

1990

1996

2002

2008

*Estimated using probit model.
Note: Shaded bars indicate recessions.
Sources: Bureau of Economic Analysis; Federal Reserve Board; author’s calculations

Of course, it might not be advisable to take this
number quite so literally, for two reasons. 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?”

International Markets

Exchange-Rate Pass-Through to Import Prices
06.11.08
by Owen F. Humpage and Michael Shenk

Import Prices and the Value of the Dollar
12-month percent change
20
15
10

Nominal Broad Dollar Index

5
0
-5

Nonpetroleum import prices

-10
-15
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Source: Board of Governors of the Federal Reserve System; Bureau of Labor Statistics

Federal Reserve Bank of Cleveland, Economic Trends | June 2008

A dollar depreciation—like the broad-based, 26
percent one that we have experienced since February 2002—tends to raise the dollar price of all
goods and services imported into the United States.
Typically, however, less than the full amount of a
dollar depreciation gets passed through to the dollar prices of imports. Interestingly, the amount of
pass-through, both in the United States and other
industrial countries, seems to have declined along
with the level and volatility of worldwide inﬂation.
Most ﬁrms engaged in international trade do not
conform closely with the economists’ ideal of being perfectly competitive. Firms exporting to the
United States typically can mark up their homescurrency prices above their marginal costs and earn
signiﬁcant economic proﬁts. Such ﬁrms can—and
most likely will—react to a dollar depreciation by
cutting their proﬁt margins, at least temporarily, to
protect their U.S. market share. As a consequence,
5

U.S. consumers often will not see the full percentage
of a dollar depreciation reﬂected in dollar-denominated import prices.
How much of the exchange-rate depreciation eventually gets passed through to dollar import prices
depends on myriad industry-speciﬁc things that
inﬂuence the responsiveness of demand and production costs to price and output changes. In addition,
the size and expected duration of an exchange-rate
change, as well as its direction, seem important. As
one might then expect, estimates of pass-through at
the industry level show a great deal of variation.
Likewise, estimates of pass-through for the overall
economy show wide variation, so much so that we
ﬁnd it hard to specify their central tendency. Going
out of a limb—and it’s a slim one at that—passthrough in the Unites States seems to have been
less than 60 percent on average since the inception
of ﬂoating exchange rates in 1973. Moreover, passthrough in the United States seems low relative to
other industrialized countries.
While specifying a central tendency for pass-through
in the United States is diﬃcult, the evidence seems
to indicate more clearly that U.S. pass-through
has fallen by roughly one-half during the 1990s.
Researchers note a similar pattern in many other
industrialized countries.
In part, this might just reﬂect changes in the composition of U.S. imports, away from industries that
traditionally have had a high rate of pass-through
to industries that traditionally have had a low rate
of pass-through. In part, the declining rate of passthrough might reﬂect growing facilities for hedging
exposures to unanticipated exchange-rate changes.
Yet, two other explanations seem to loom large. One
is China’s growing inﬂuence in world markets. Because China pegged the renminbi to the dollar until
2005 and has since managed the renminbi’s movements, the dollar’s depreciation since 2002 has had
less of a negative eﬀect on China’s competitiveness
than it has had on many other nation’s trade positions. Foreign ﬁrms may cut their price mark-ups
more readily when they face Chinese competition.
The other—the dominant—explanation for a declinFederal Reserve Bank of Cleveland, Economic Trends | June 2008

ing rate of pass-through contends that in an environment of low and stable inﬂation, foreign ﬁrms
are more reluctant to pass through exchange-rate
changes into dollar prices. International trading
ﬁrms, despite having some pricing power, often face
a cost to changing prices, in large part because price
changes encourage customers to look elsewhere.
Such ﬁrms will only change prices when the gains
from doing so exceed the costs, so they will delay
until they ﬁnd that the exchange-rate change is
substantial and permanent. In a high-inﬂation environment, permanent depreciations are more likely
and the rising overall price level can quickly negate
relative pricing errors. Real GDP First-Quarter
2008 Preliminary Estimate

Economic Activity and Labor Markets

Real GDP First-Quarter 2008 Preliminary Estimate
06.03.08
by Brent Meyer

Revisions to Real GDP and Components
Annualized percent change
10
2008:Q1 advance
5
0
-5

2008:Q1 preliminary
Personal
consumption
Real GDP
Business
fixed
investment

Government
spending

Residential
investment
Exports

Imports

-10
-15
-20
-25
-30
Source: Bureau of Economic Analysis.

Federal Reserve Bank of Cleveland, Economic Trends | June 2008

Real (inﬂation-adjusted) GDP increased at an
annualized rate of 0.9 percent in the ﬁrst quarter, according to the preliminary estimate from
the Bureau of Economic Analysis (BEA), up 0.3
percentage point from the advance estimate. The
magnitude of this revision is consistent with the
long-term trend for revisions from the advance
to preliminary release: From 1983 to 2004, the
average revision has been 0.2 percentage point
(the absolute average was 0.5 percentage point and
the standard deviation was 0.4 percentage point).
The latest revision was primarily due to upward
adjustments to nondurable consumer spending,
nonresidential ﬁxed investment, and net exports,
which were tempered by downward corrections to
private inventory investment and consumer spending on services. Nondurable consumer spending
was revised up from −1.3 percent to −0.3 percent
in the ﬁrst quarter, while 0.4 percentage point were
trimmed oﬀ consumer spending on services, negating any eﬀect on overall consumption. Nonresidential investment in structures was adjusted up from
−6.2 percent to 1.1 percent. While export growth
was revised down from 5.5 percent to 2.8 percent
in the ﬁrst quarter, import growth fell further, from
7

Final Sales of Real GDP
Percent change
8
One-quarter annualized
percent change

7
6
5
4
3
2

Four-quarter
percent change

1
0
-1

-2
1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Source: Bureau of Economic Analysis.

Final Sales of Real GDP
Percent change
8
7

One-quarter annualized
percent change

6
5
4
3
2
1

Four-quarter
percent change

0
-1
-2
1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Source: Bureau of Economic Analysis.

Real GDP Growth
Annualized quarterly percent change
6
Final estimate
Preliminary estimate
May Blue Chip forecast
5
4

Average GDP growth
(1978:Q1-2008:Q1)

3
2
1

of 2.5 percent to −2.6 percent.
An investigation into each component’s contribution to the percent change in real GDP yields some
interesting results. Most notably, a downward revision to private inventories—from an accumulation
of $20.1 billion to just $3.9 billion—subtracted
0.6 percentage point from growth. The advanced
estimate for the ﬁrst quarter had private inventories
adding 0.8 percentage point to growth, keeping
GDP out of the red. Changes in private inventories
can have muddling eﬀects on the interpretation of
GDP, which is why it may be useful to look at ﬁnal
sales of GDP.
The ﬁnal-sales-of-real-GDP statistic is basically real
GDP excluding inventories. It gives us a clearer
picture of demand by adding together consumer,
business, and government spending. It also may
provide an early warning sign of turning points in
the economy. The story is that if ﬁnal sales growth
is less than overall GDP growth (for some period
of time), inventories will begin to accumulate and
that will cause businesses to slow or halt production. This results in an impending slowdown or
recession. According to the advance estimate, ﬁnal
sales of real GDP for the ﬁrst quarter dipped below
zero for the ﬁrst time since the fourth quarter of
2005, falling 0.2 percent (at an annualized rate).
However, after the ﬁrst revision, ﬁnal sales grew 0.7
percent in the ﬁrst quarter, taking some wind out
of the recession argument (for the moment). On a
year-over-year basis, ﬁnal sales of real GDP ticked
down from 2.8 percent last quarter, to 2.7 percent
currently.
The Blue Chip consensus economic forecast is predicting that the economy will grow a shade above
zero next quarter, before snapping back in the third
quarter and rising to near trend growth by the end
of 2009. Of the 50 forecasters surveyed, nearly half
revised up their 2008 GDP forecast from the April
survey. On the other hand, 30 forecasters revised
their 2009 GDP outlook down.

0
Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
2008
2007
2006
2009
Sources: Blue Chip Economic Indicators, May 2008; Bureau of Economic Analysis.

Federal Reserve Bank of Cleveland, Economic Trends | June 2008

Economic Activity and Labor Markets

Labor Turnover and Employment in Diﬀerent U.S. Regions
06.03.08
by Murat Tasci and Beth Mowry

Job Openings and Turnover,
Regional Monthly Averages
(thousands)
Midwest
Hires

Northeast

South

West

U.S.

1,020

745

1,753

1,047

4,565

Separations

990

718

1,669

1,016

4,392

Job openings

731

617

1,362

821

3,530

Net hires

173

Note: The averages represent monthly JOLTS data since December
2000, when the series began.
Source: Bureau of Labor Statistics.

Job Openings and Turnover,
Regional Shares of U.S. Total
Midwest

Northeast

South

West

U.S.

.223

.163

.384

.229

1.00

Hires
Separations

.225

.163

.380

.231

1.00

Job openings

.207

.175

.386

.232

1.00

Net hires

.175

.161

.489

.176

1.00

Employment

.236

.190

.355

.219

1.00

Note: The averages represent monthly JOLTS data since December
2000, when the series began.
Source: Bureau of Labor Statistics.

Job Openings
Thousands
1,400

1,800

1,200

1,600

South
West

1,000

1,400
Midwest

800
600

1,200
1,000

Northeast
400
2001

2002

2003

2004

2005

2006

2007

800
2008

Notes: Seasonally-adjusted, 3-month moving averages. The shaded
bar represents a recession.
Source: Bureau of Labor Statistics.

Federal Reserve Bank of Cleveland, Economic Trends | June 2008

The Bureau of Labor Statistics (BLS) provides labor
turnover and vacancy data for four broad census
regions (the Midwest, Northeast, South, and West)
and the entire nation as part of its Job Openings
and Labor Turnover Survey (JOLTS). According to
these data, which begin in December 2000, there
is signiﬁcant variation in the way labor turnover
behaves in the diﬀerent regions. In the Northeast,
for instance, employers hired an average of 745,000
workers a month, while in the South it was more
than 1.7 million. Similarly, the South has accounted for most of the job openings and separations
over the past eight years, whereas the Northeast
lagged behind other regions.
These ﬁgures should not be surprising given the
South’s greater employment numbers: Since 2001,
average total employment in the South has accounted for about 35.5 percent of U.S. nonfarm
payroll employment, compared to 23.6 percent for
the Midwest, 21.9 percent for the West, and 19
percent for the Northeast. But the curious behavior
emerges when we look at each region’s shareof various categories of labor turnover. The contribution
of the South is greater than its employment share
in virtually every category. Its share of hires, separations, and job openings are all about 38 percent of
the U.S. total. The West shows a similar pattern,
although much less pronounced. The Northeast,
on the other hand, seems to have contributed less
than its employment share in all measures of labor
turnover and job openings. The Midwest presents a
balanced picture in terms of hires and separations.
Its share of job openings, however, has been lagging
signiﬁcantly behind its employment share. As a result, the South’s employment share has grown over
this period (from 35 percent to 36 percent), more
rapidly than the West (from 21.5 percent to 22.2
percent), whereas that of Northeast and Midwest
have been shrinking somewhat (from 19.2 percent
to 18.5 percent and from 24 percent to 23 percent,
respectively).
9

Hires
Thousands

Thousands
2,200

1,700
1,500

2,000

South

1,300

1,800

West

1,100

1,600

Midwest

1,400

900

Time series data on turnover and job openings
reveal some interesting similarities and diﬀerences
across regions as well. During the last recession, for
example, all four regions experienced sharp declines
in job openings. Even though each region started
to recover later, only in the South and West had job
openings reached their pre-recession levels by 2007.
Interestingly, in the Midwest, job openings have
been virtually ﬂat for the past three years.

Northeast
700
500
2001

1,200

2002

2003

2004

2005

2006

2007

1,000
2008

Notes: Seasonally-adjusted, 3-month moving averages. The shaded bar
represents a recession.
Source: Bureau of Labor Statistics.

Separations
Thousands
1,500

Thousands
2,000
South
1,800

1,300
West

1,100

1,600

Midwest

900

1,400

Northeast
1,200

700
500
2001

2002

2003

2004

2005

2006

2007

1,000
2008

Note: Seasonally-adjusted, 3-month moving averages.
Source: Bureau of Labor Statistics.

Proportion of U.S. Job Openings
Region share of openings divided by region share of payrolls
1.2
South
1.1
West
1

Northeast

0.9
Midwest
0.8
0.7
2001

2002

2003

2004

2005

2006

2007

2008

Notes: Seasonally-adjusted data. The shaded bar represents a
recession.Proportions represent regional shares of openings divided
by regional shares of payrolls. Proportions greater than 1.0 imply that
a region has a disproportionately higher share of openings compared
to the United States as a whole.
Source: Bureau of Labor Statistics.

Federal Reserve Bank of Cleveland, Economic Trends | June 2008

Hiring activity also declined in all four regions during the last recession. Once again, although each
region had begun to recover by the end of 2003,
hiring remained below pre-recession levels in the
Midwest and Northeast. This picture, in conjunction with the separations data, implies that job reallocation has declined in the Northeast and Midwest
over time. While high levels of separations and hiring could be ineﬃcient if resources are being spent
unnecessarily to reallocate workers across diﬀerent
ﬁrms, regions, and states, they could also indicate
an active search by both workers and ﬁrms to ﬁnd
their best matches in the labor market.
Each region’s relative shares could give further
interesting details about regional labor markets. To
this end, we construct relative shares of labor turnover and job openings for all four regions, adjusting
each for the region’s employment share. This is
basically a ratio of two shares: the share of a region’s
job openings, hires, or separations over the share of
a region’s employment. So, for example, a ratio for
hires greater than 1 for a particular region means it
is hiring a higher number of workers than the U.S.
average. Several features of the data stand out when
interpreted in this way: First, the South leads U.S.
averages in all dimensions. Second, regional job
openings in the Midwest relative to the U.S. have
been the lowest among the four regions. Its job
openings rate has been around 20 percent below
the average for the past two years. Interestingly, the
Midwest looks like an average region when it comes
to hiring and separations. In particular, separations are not disproportionately higher than the US
average in the Midwest. Finally, the West stands
out as a region that has a separation rate at least 30
percent below average during our sample period.
Even though both the South and the West have the
highest average hiring for the United States, the
10

Proportion of U.S. Hires

West is distinguished by lower reallocation due to
lower separations than the South.

Region share of hires divided by region share of payrolls
1.2

Each region’s net employment creation (hires minus
separations) is signiﬁcantly positively correlated
with the U.S. total. This means that growth in total payrolls is associated with an increase in payrolls
in the diﬀerent regions. The correlation is highest
for the South, another sign that is consistent with
this region’s leading role in employment creation.
However, the correlations between the diﬀerent
regions are not very strong. Net employment
creation in the Midwest has the lowest correlation
with the other regions. This low correlation seems
to stem from the low correlation of separations
with other regions, not hires. This might indicate
a structural change that is aﬀecting only the Midwest, resulting in a regional labor market that does
not follow the rest of the country.

South
1.1
West
1

Midwest

0.9
Northeast
0.8
0.7
2001

2002

2003

2004

2005

2006

2007

2008

Notes: Seasonally-adjusted data. The shaded bar represents a recession.
Proportions represent regional shares of hires divided by regional shares
of payrolls. Proportions greater than 1.0 imply that a region has a disproportionately higher share of hires compared to the United States as a
whole.
Source: Bureau of Labor Statistics.

Proportion of U.S. Separations
Region share of separations divided by region share of payrolls
1.2
South
1.1
1.0

JOLTS Regions
The states of the Midwest region are: ND, SD, NE,
MN, KS, IA, MO, WI, IL, IN, MI, and OH.

Midwest

0.9

Northeast

0.8

The states of the Northeast region are: ME, NH,
MA, VT, RI, CT, NJ, NY, and PA.

West

0.7
0.6
0.5
2001

2002

2003

2004

2005

2006

2007

The states of the South region are: TX, OK, AR,
LA, MS, AL, TN, KY, WV, VA, DE, MD, DC,
NC, SC, GA, and FL.

2008

Notes: Seasonally-adjusted data. The shaded bar represents a recession. Proportions represent regional shares of separations divided by regional shares of payrolls. Proportions greater than 1.0 imply that a region
has a disproportionately higher share of separations compared to the
United States as a whole.
Source: Bureau of Labor Statistics.

The states of the West region are: WA, OR, CA,
MT, ID, WY, NV, UT, CO, AZ, NM, HI, and AK.

Job Openings and Turnover, Regional Shares of
U.S. Total
U.S.
Midwest
Northeast
South

U.S.

Midwest

Northeast

West

.58

.77

.81

.75

.24

.29

.55

.47

.42

West

Note: The averages represent monthly JOLTS data since December 2000, when the
series began.
Source: Bureau of Labor Statistics.

Federal Reserve Bank of Cleveland, Economic Trends | June 2008

Economic Activity and Labor Markets

The Employment Situation
06.06.08
by Murat Tasci and Beth Mowry

Average Nonfarm Employment Change
Change, thousands of jobs
250

Revised
Previous estimate

200
150
100
50
0
–50
–100
–150
2005 2006 2007 2008
YTD

Q2 Q3
2007

Q1 Mar
2008

Apr

May

Nonfarm payrolls fell for the ﬁfth consecutive
month in May, coming in at a slightly smaller-thanexpected loss of 49,000. Along with the downward
revisions for March and April (a total of 15,000),
this ﬁgure brings the year-to-date monthly average
loss in payroll employment to 65,000. The last time
payrolls shrank for ﬁve consecutive months was in
mid-2003. The Bureau of Labor Statistics (BLS)
also reported today that the unemployment rate
shot up from 5.0 percent to 5.5 percent, its sharpest increase in 22 years.

Source: Bureau of Labor Statistics.

The job declines were broad-based, spreading
beyond the usual housing-related sectors that have
exhibited consistently poor performance in recent
months. The only major sectors to add jobs last
month were education and health services (54,000),
leisure and hospitality (12,000), and the government (17,000). The goods-producing sector lost a
total of 57,000 jobs, continuing along its 14-month
path of decline. Service-providing industries added
a very modest 8,000 jobs, much lower than April’s
addition of 72,000.
Within the goods-producing sector, manufacturing lost 34,000 jobs and construction lost 26,000.
Durable goods manufacturing as a whole shed
19,000 jobs, largely due to losses in wood products
(8,400) and computer and electronic products
(7,500). The most positive contribution came from
transportation equipment, which added 7,200 jobs,
largely because of jobs added in the motor vehicles
and parts subsector. The only small positives within
nondurable goods came from paper and paper
products (500) and chemicals (900).

Federal Reserve Bank of Cleveland, Economic Trends | June 2008

Labor Market Conditions
Average monthly change (thousands of employees, NAICS)
2005

2006

2007

2008 YTD

May 2008

Payroll employment

211

175

−65

Goods-producing

−38

−79

−57

Construction

−19

−42

−34

Heavy and civil engineering

−1

−6

−3

Residentiala

−2

−10

−28

−25.1

Nonresidentialb

−7

−5.2

−7

−14

−22

−41

−26

−4

−16

−30

−19

Service-providing

179

172

130

Retail trade

−30

−27.1

Financial activitiesc

−9

−4

−1

PBSd

−25

Manufacturing
Durable goods

Temporary help svcs.

−7

−23

−29.6

Education and health svcs.

Leisure and hospitality

Government

Local educational svcs.

14.1

5.1

5.5

Average for period (percent)
Civilian unemployment rate

5.1

4.6

a. Includes construction of residential buildings and residential specialty trade contractors.
b. Includes construction of nonresidential buildings and nonresidential specialty trade contractors.
c. Includes the finance, insurance, and real estate sector and the rental and leasing sector.
d. PBS is professional business services (professional, scientific, and technical services, management of companies and enterprises, administrative and support, and waste management and remediation services.
Source: Bureau of Labor Statistics.

The Unemployment Rate

Private Sector Employment Growth
Change, thousands of jobs: Three-month moving average
350
300
250
200
150
100
50
0
–50
–100
–150
–200
2003

2004

2005

2006

2007

Source: Bureau of Labor Statistics.

Federal Reserve Bank of Cleveland, Economic Trends | June 2008

2008

U.S. labor markets have not experienced an increase
in the unemployment rate of 0.5 percentage point
since February 1986. The decline in 1986 in fact
did not happen during a recession, but most such
sharp increases have been historically associated
with an overall economic downturn. The primary
reason behind the latest large uptick in the unemployment rate is labor force entry. The total number
of workers in the labor force increased in May by
577,000. An additional 285,000 workers lost their
jobs, which gave rise to an increase of more than
861,000 in the number of unemployed. However,
one needs to be cautious when interpreting these
monthly changes in household data, which are very
volatile. One interesting feature of the household
employment data in May was the unusually high
13

increase in teenage unemployment. The unemployment rate of workers aged 16 to 19 increased from
15.4 percent in April to 18.7 percent in May. This
increase of 3.3 percentage points has been the largest change observed since January 1948, when the
series begins. Hence, this latest unusual uptick in
the unemployment rate is partly due to an unusually high level of teenagers entering the labor force.

Regional Activity

Fourth District Employment Conditions, March
05.23.08
Tim Dunne and Kyle Fee

Unemployment Rates
Percent
8
7

Fourth District a

6
5
United States

3
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Notes: Seasonally adjusted using the Census Bureau’s X-11 procedure; Shaded bars
represent recessions; Some data reflect revised inputs, reestimation, and new
statewide controls. For more information, see http://www.bls.gov/lau/launews1.htm.
Source: U.S. Department of Labor, Bureau of Labor Statistics.

There are considerable diﬀerences in unemployment rates across counties in the Fourth District.
Of the 169 counties that make up the Fourth
District, 24 had an unemployment rate below the
national average in March, and 145 had a higher
unemployment rate than the national average.
Rural Appalachian counties continue to experience
higher levels of unemployment than others in the
district.

County Unemployment Rates
U.S. Unemployment rate = 5.1%

3.9%
5.1%
6.1%
7.1%
8.1%

The district’s unemployment rate jumped 0.4
percent to 5.7 percent for the month of March.
The increase in the unemployment rate can be
attributed to increases in the number of people
unemployed (6.6 percent) and the labor force (0.1
percent), along with a decrease in the number of
people employed (–0.3 percent). The district’s unemployment rate was higher than the national rate
in March (by 0.6 percent), as it has been since early
2004. Since this time last year, both the Fourth
District and the national unemployment rates have
increased by 0.7 percentage point.

5.0%
6.0%
7.0%
8.0%
10.3%

Note: Data are seasonally adjusted using the Census Bureau’s X-11 procedure.
Source: U.S. Department of Labor, Bureau of Labor Statistics.

The distribution of unemployment rates among
Fourth District counties ranges from 3.9 percent to
10.3 percent, with a median county unemployment
rate of 6.1 percent. Pennsylvania counties tend to
populate the middle to lower half of the distribution, with roughly two-thirds of Kentucky’s Fourth
District counties in the upper half of the distribution.
The distribution of monthly changes in unemployment rates shows that the median county’s
unemployment rate increased 0.37 percentage

Federal Reserve Bank of Cleveland, Economic Trends | June 2008

point from February to March. The county-level
changes indicate that 69 percent of Ohio counties
experienced an increase in unemployment rates that
exceeded 0.4 percentage point. Alternatively, Pennsylvania counties averaged no change in unemployment rates, with 11 out of the 19 Fourth District
Pennsylvania counties actually showing declines in
unemployment rates.

County Unemployment Rates
Percent
11.0
10.0
9.0
8.0

Ohio
Kentucky
Pennsylvania
West Virginia

Median unemployment rate = 6.1%

7.0
6.0
5.0
4.0
3.0

County

Note: Data are seasonally adjusted using the Census Bureau’s X-11 procedure.
Source: U.S. Department of Labor, Bureau of Labor Statistics.

Change in County Unemployment Rates:
February 2008 to March 2008
Percentage points
2.4
Ohio
2.2
Kentucky
2.0
Pennsylvania
1.8
West Virginia
1.6
1.4
1.2
Median change in unemployment rate = 0.37 percentage point
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
County
Note: Data are seasonally adjusted using the Census Bureau’s X-11 procedure.
Source: U.S. Department of Labor, Bureau of Labor Statistics.

Regional Activity

Real Income Growth across Metropolitan Areas
CPI Growth, 2000–2006
Average annual percent change

06.06.08
by Timothy Dunne and Kyle Fee

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

1. Milwaukee-Racine, WI
2. Portland-Salem, OR-UH
3. Atlanta, GA
4. Cleveland-Akron, OH
5. Denver-Boulder-Greeley, CO
6. Kansas City, MO-KS
7. Chicago-Gary-Kenosha, IL-IN-WI
8. Cincinnati-Hamilton, OH
9. Minneapolis-St. Paul
10. Dallas-Fort Worth, TX
11. Detroit-Ann Arbor-Flint, MI
12. Seattle-Tacoma-Bremerton
13. San Francisco-Oakl-San Jose

14. St. Louis, MO-IL
15. Pittsburgh, PA
16. USAAVG
17. Houston-Galveston-Brazoria, TX
18. Anchorage, AK
19. Honolulu, HI
20. Washington-Baltimore, DC-MD-VA-WV
21. Phila-Wilmington-Atl City
22. Tampa-St. Pete-Clearwater, FL
23. NY-No. NJ-LI, NY-NY-CT-PA
24. Boston-Brockton-Nashua
25. Miami-Fort Lauderdale, FL
26. Los Angeles-Riverside-Orange
27. San Diego, CA

Source: Bureau of Labor Statistics.

Federal Reserve Bank of Cleveland, Economic Trends | June 2008

A standard measure of regional economic performance is per capita income growth. Typically,
analysts try to remove the eﬀects of inﬂation on the
raw data for income growth by converting nominal
per capita income for an area into real or constantdollar income. The Consumer Price Index (CPI)
is often used to make such adjustments. The chart
below shows the real per capita income growth rates
for a number of metropolitan areas from 2000 to
2006. (Note that the Bureau of Economic Analysis
releases an adjusted income growth series, but it
uses the PCE price index to deﬂate the raw data.
We use the CPI here because of some comparisons
we make below. Using the CPI instead of the PCE
doesn’t aﬀect the rankings of cities with respect to
their growth, although it does aﬀect the magnitude
of the real growth rates.)
15

Real per Capita Income Growth, 2000-2006
Percent
15
Data deflated with national CPI
10

-5

-10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

1. Atlanta, GA
2. Detroit-Ann Arbor-Flint, MI
3. Portland-Salem, OR-UH
4. Denver-Boulder-Greeley, CO
5. Dallas-Forth Worth, TX
6. Cleveland-Akron, OH
7. Kansas City, MO-KS
8. Chicago-Gary-Kenosha, IL-IN-WI
9. San Francisco-Oakl-San Jose
10. Minneapolis-St. Paul
11. Cincinnati-Hamilton, OH
12. St. Louis, MO-IL
13.Milwaukee-Racine, WI

14. Seattle-Tacoma-Bremerton
15. Boston-Brockton-Nashua
16. Tampa-St. Pete-Clearwater, FL
17. NY-No.NJ-LI, NY-NY-CT-PA
18. Pittsburgh, PA
19. Los Angeles-Riverside-Orange
20. Phila-Wilmington-Atl City
21. Houston-Galveston-Brazoria, TX
22. Anchorage, AK
23. Washington-Baltimore, DC-MD-VA-WV
24. Honolulu, HI
25. Miami-Fort Lauderdale, FL
26. San Diego, CA

Sources: Bureau of Labor Statistics; Bureau of Economic Analysis.

Real per Capita Income Growth, Deflated
with National CPI and Regional CPI
15
National deflator
Regional deflator

-5

-10

In our adjusted series, real income growth was
highest in San Diego, Miami-Fort Lauderdale,
and Honolulu and lowest in Atlanta, Detroit-Ann
Arbor-Flint, and Portland-Salem. With respect to
Fourth District metropolitan areas, Cleveland-Akron experienced very modest growth of 1.1 percent
over the period, ranking it sixth-lowest among the
26 metropolitan areas. Cincinnati had a growth
rate of 2.7 percent, placing it in the middle of the
distribution, and Pittsburgh’s growth rate was 7.6
percent, the highest in the Fourth District.
This standard approach to measuring real income
growth assumes that the changes in price levels
experienced in metropolitan areas are similar to the
change in price levels at the national level. No adjustments are made for diﬀerences in inﬂation rates
across metropolitan areas. But inﬂation rates are
not necessarily identical in diﬀerent regions. For the
areas in the ﬁgure above, the Bureau of Labor Statistics (BLS) produces region-speciﬁc CPI’s, which
provide estimates of how price levels have changed
over time within a particular region. Comparing
changes in regional price indexes across regions
shows which region has experienced a more rapid
change in prices—not which region has a higher
price level or higher living costs. The distribution
of metropolitan average CPI growth rates indicates
considerable variation in inﬂation across regions.
Metropolitan areas on the low end, like Milwaukee,
Portland, and Atlanta, had average annual inﬂation
rates of 2.1 to 2.3 percent, while metropolitan areas
on the higher end, such as Miami, Los Angeles, and
San Diego, had rates of 3.8 to 4.1 percent.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.

Anchorage, AK
Atlanta, GA
Boston-Brockton-Nashua
Chicago-Gary-Kenosha, IL-IN-WI
Cincinnati-Hamilton, OH
Cleveland-Akron, OH
Dallas-Fort Worth, TX
Denver-Boulder-Greeley, CO
Detroit-Ann Arbor-Flint ,MI
Honolulu, HI
Houston-Galveston-Brazoria, TX
Kansas City, MO-KS
Los Angeles-Riverside-Orange

14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.

Miami-Fort Lauderdale ,FL
Milwaukee-Racine ,WI
Minneapolis-St Paul
NY-No. NJ-LI,NY-NY-CT-PA
Philadelphia-Wilmington-Atlantic City
Pittsburgh, PA
Portland-Salem, OR-UH
San Diego, CA
San Francisco-Oakland-San Jose
Seattle-Tacoma-Bremerton
St Louis, MO-IL
Tampa-St. Pete-Clearwater, FL
Washington-Baltimore, DC-MD-VA-WV

Federal Reserve Bank of Cleveland, Economic Trends | June 2008

These diﬀerences in regional CPI growth rates aﬀect
the calculation of real per capita income growth, as
well. The ﬁgure below shows real per capita income
growth of diﬀerent metropolitan areas calculated
with both the national and regional price indexes.
Comparing the two measures of real income
growth across areas, we note that the series deﬂated
by the regional CPI shows less overall variation
across regions than the series that uses the national
CPI. Real income growth rates using the national
CPI range from −7.0 to 11.5 percent over the period from 2000 to 2006. Using the regional CPIs as
deﬂators, the range falls to −4.2 to 9.9 percent.
16

Real per Capita Income Growth, 2000–2006
Percent
15
Data deflated with region-specific CPI
10

-5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

1. Atlanta, GA
2. Detroit-Ann Arbor-Flint, MI
3. Boston-Brockton-Nashua
4. Portland-Salem, OR-UH
5. Dallas-Fort Worth, TX
6. Denver-Boulder-Greeley, CO
7. San Francisco-Oakl-San Jose
8. NY-No. NJ-LI, NY-NY-CT-PA
9. Los Angeles-Riverside-Orange
10. Tampa-St. Pete-Clearwater, FL
11. St. Louis, MO-IL
12. Cleveland-Akron, OH
13. Minneapolis-St. Paul

14. Kansas City, MO-KS
15. Chicago-Gary-Kenosha, IL-IN-WI
16. Seattle-Tacoma-Bremerton
17. San Diego, CA
18. Cincinnati-Hamilton, OH
19. Phila-Wilmington-Atl City
20. Washington-Baltimore, DC-MD-VA-WV
21. Miami-Fort Lauderdale, FL
22. Milwaukee-Racine, WI
23. Pittsburgh, PA
24. Houston-Galveston-Brazoria, TX
25. Anchorage, AK
26. Honolulu, HI

Sources: Bureau of Labor Statistics; Bureau of Economic Analysis

Moreover, there are signiﬁcant diﬀerences in real
per capita income across cities, depending on which
deﬂator is used. For example, when we use the
national CPI to adjust for changes in prices over
time, New York has a real income growth rate of
6.4 percent compared to Cleveland’s 1.1 percent for
the 2000–2006 period. When we use the regional
CPIs, Cleveland’s real income grows at 4.0 percent,
while New York’s grows at 3.0 percent. A key diﬀerence between Cleveland and New York in changes
in underlying prices is that New York experienced
much higher growth in the housing component of
its regional CPI than did Cleveland.
Comparing the relative rankings of Fourth District
cities in income growth across the two series using
the diﬀerent CPIs, we see that Fourth District cities
move up in the rankings when the regional CPI
is used to adjust for growth in prices. With the
regional CPI, Cleveland is near the center of the
distribution in income growth, with Cincinnati
and Pittsburgh well above the median metropolitan
area. This move up the rankings for Cleveland and
Cincinnati is particularly noticeable. These cities
had modest nominal income growth compared to
other cities but experienced below-average regional
inﬂation, which resulted in relatively stronger real
per capita income growth.

Banking and Financial Markets

Business Loan Markets
Domestic Banks Reporting
Tighter Credit Standards

05.27.08
by Joseph G. Haubrich and Saeed Zaman

Net percent
60

The Federal Reserve Board’s April 2008 survey of
senior loan oﬃcers (covering the months of January through March 2008) found signiﬁcant tightening of standards for commercial and industrial
loans since the last survey. About 55 percent of
the domestic banks and 60 percent of the foreign
banks surveyed reported having tightened standards for commercial and industrial loans to large
and medium-sized ﬁrms. The remaining fraction
of those surveyed reported little change in lending
standards. The reasons cited for tightening included
a more-uncertain economic outlook, reduced tolerance for risk, decreased liquidity in the secondary
market for these loans, and worsening of industry-

Medium and large firms

40
30
20
10

Small firms

0
-10
-20
-30
2000

2001

2002

2003

2004

2005

2006

2007

2008

Source: Senior Loan Officer Opinion Survey on Bank Lending Practices,
Board of Governors of the Federal Reserve System, April 2008.

Federal Reserve Bank of Cleveland, Economic Trends | June 2008

speciﬁc problems. A large fraction of domestic and
foreign banks increased the cost of credit lines and
premiums charged on loans to riskier borrowers.
A substantial majority of the domestic and foreign
banks surveyed raised their lending spreads (loan
rates over the cost of funds).

Domestic Banks Reporting Stronger
Demand
Net percent
45
30

Small firms

15
0
-15
Medium and large firms

-30
-45
-60
-75
2000

2001

2002

2003

2004

2005

2006

2007

2008

Source: Senior Loan Officer Opinion Survey on Bank Lending Practices,
Board of Governors of the Federal Reserve System, April 2008.

Quarterly Change in Commercial and
Industrial Loans
Billions of dollars
100
90
80
70
60
50
40
30
20
10
0
-10
-20
-30
-40
2001
2002

2003

2004

2005

2006

2007

Source: Federal Deposit Insurance Corporation, Quarterly Banking Profile,
Fourth Quarter 2007.

Utilization Rate of Commercial and
Industrial Loan Commitments

Demand for commercial and industrial loans
continued to weaken over the period surveyed,
although by less than in the previous survey period. About 20 percent of the domestic banks and
25 percent of the foreign banks surveyed reported
weaker demand. Those who reported weaker
demand cited decreased investment in inventories, plants and equipment as a reason, as well as a
decrease in customers’ need to ﬁnance mergers and
acquisitions. Those who reported stronger demand
said that it was caused by customers who were shifting their borrowing to the banks from other banks
or ﬁnancial ﬁrms with less attractive borrowing
terms.
After recording the biggest-ever quarterly increase
of $90 billion in third quarter of 2007, bank and
thrift holdings of business loans went up moderately by $51 billion in the fourth quarter of 2007.
This increase marks the ﬁfteenth consecutive quarterly increase in these holdings. The sharp reversal
in the trend of quarterly declines in commercial
and industrial loan balances on the books of FDICinsured institutions prior to the second quarter of
2004 is still going strong.
The utilization rate of business loan commitments
(draw downs on prearranged credit lines extended
by banks to commercial and industrial borrowers)
jumped up to 37.83 percent of total commitments.
The higher demand by borrowers may point to the
diﬃculty in obtaining credit from the capital markets due to the recent ﬁnancial turmoil.

Percent of loan commitments
41
40
39
38
37
36
35
34
2001

2002

2003

2004

2005

2006

2007

Source: Federal Deposit Insurance Corporation, Quarterly Banking Profile,
Fourth Quarter 2007.

Federal Reserve Bank of Cleveland, Economic Trends | June 2008

Banking and Financial Markets

FDIC Funds
05.22.08
by Joseph G. Haubrich and Saeed Zaman

FDIC-Insured Deposits
Billions of dollars
4,400

In 2007, deposits insured by the FDIC insurance fund grew at a 3.4 percent annual rate. As of
December 31, 2007, the FDIC has insured $4.3
trillion of member deposits. Growth in reserves
outstripped insured deposits. As a result, the insurance fund’s reserve-to-deposit ratio increased 1
basis point, from 1.21 percent at year end 2006 to
1.22 percent in 2007. The reserve-to-deposit ratio
remained in the mandated target range of 1.15–1.5
percent.

4,000
3,600
3,200
2,800
2,400
2,000
1,600
1995

1997

1999

2001

2003

2005

2007

Source: Federal Deposit Insurance Corporation, Quarterly Banking Profile,
Fourth Quarter 2007.

tions. After a record-breaking trend of 10 consecutive quarters without any bank failure, three institutions failed in 2007, with assets totaling $2.3
billion. The rarity of thrift institution failures over
the past seven years contrasts vividly with the widespread solvency problems that plagued the industry
throughout the 1980s.

Fund Reserve Ratio
Percent of insured deposits
2.00
1.75

Targets
1.50
1.25
1.00
0.75
0.50
0.25
0.00
1995

1997

1999

2001

2003

2005

Bank failures since 1995 have been miniscule in
terms of numbers and total assets of failed institu

2007

Source: Federal Deposit Insurance Corporation, Quarterly Banking Profile,
Fourth Quarter 2007.

Federal Reserve Bank of Cleveland, Economic Trends | June 2008

At the end of 2007, the total number of problem
institutions (those with substandard examination
ratings) rose to 76, an increase of 26 institutions
from the end of 2006. Moreover, the increase in the
number of problem institutions led to an increase
in the amount of total assets held by problem
institutions, which ballooned to $22 billion from
$8.3 billion over the same period. The jump in the
number of problem institutions and the high value
of those institutions’ assets —combined with the
ongoing ﬁnancial mess—suggest that the Deposit
Insurance Fund’s losses might go up in the near
future.

Problem Institutions

Failed Institutions
Number of institutions
11

Total assets, billions of dollars
5.0

4.5

4.0

3.5

Number of institutions
180

Total assets, billions of dollars
45

160

140

120

100

3.0

6
2.5

2.0

1.5

3
2

1.0

0.5

0.0

0
1995

1998

2001

2004

2007

Source: Federal Deposit Insurance Corporation, Quarterly Banking Profile,
Fourth Quarter 2007.

0
1995

1997

1999

2001

2003

2005

2007

Source: Federal Deposit Insurance Corporation, Quarterly Banking Profile,
Fourth Quarter 2007

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Federal Reserve Bank of Cleveland, Economic Trends | June 2008

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