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Economic Trends
February 2008
(Covering January 10, 2008, to February 14, 2008)

In This Issue
The Economy in Perspective
This old house...
Inﬂation and Prices
December Price Statistics
Money, Financial Markets, and Monetary Policy
Another Move, but with Less Surprise
What is the Yield Curve Telling Us?
International Markets
Chinese Inﬂation and the Renminbi
Economic Activity and Labor Markets
Real GDP Fourth-Quarter 2007 Advance Estimate
The Pass-through of Oil Prices to Gasoline Prices
The Employment Situation
Manufacturing Employment
Housing Markets
Regional Activity
The Ups and Downs in Regional Employment Statistics
The Erie Metropolitan Statistical Area
Banking and Financial Markets
Fourth District Community Banks

The Economy in Perspective

This old house…
1.25.08
by Mark S. Sniderman
There once were some bankers from Gaﬀ
Whose products were layered with math.
With assets worth billions
Now stated in millions,
Those chaps were too clever by half!

in the direction of sturdier consumer protection, such as
the Federal Reserve’s proposed revisions to its Truth in
Lending regulation (adopted under the Home Ownership and Equity Protection Act), higher standards for
state banking supervisors, who license mortgage brokers,
and stronger ﬁnancial literacy programs.

The incentives story reminds us that human nature
is susceptible to the lure of the fast buck, such as the
chance to earn excessive returns from mortgage-backed
securities or buying a house with no money down. In
recent years, mortgage lures became so powerful that inHouses have foundations and support elements, plumbvestors happily ﬁlled the entire structure—from wholeing and electrical, heating and cooling systems, insulation, sale investment bankers to retail mortgage brokers—with
and, of course, décor. So too, the mortgage ﬁnance indus- cash, all fees and commissions paid up front. And many
try is made up of a set of components such as property
borrowers, it is said, tried to live beyond their means
appraisers, and loan brokers, originators, servicers; and
either by borrowing heavily to acquire a home or mainholders. And just as houses cannot be built without the
taining their living standards by cashing out equity built
consent of local oﬃcials who determine zoning and build- up in better times. Not having to put much equity into
ing codes, the mortgage ﬁnance industry operates under
the deal, and having low monthly payments, created
the jurisdiction of various federal and state regulators.
strong incentives for home buyers hoping to live the
American Dream.
Back in the day, mortgage holders were most likely the
originating banks and thrift institutions (as they were
So how can we build a stronger structure for ﬁnancing
fondly called), but the residents of that staid “buy and
mortgages? Several ideas are being advanced, including
hold” bungalow have been displaced by occupants of
more borrower equity in the deals; more disclosure to
glamorous “originate and sell” mansions. These ocborrowers about the terms and conditions of the loan;
cupants include independent brokers selling loans on
better education for borrowers before they shop for
behalf of mortgage banks, which themselves raise funds
loans; greater investor liability for any illegal, unfair, or
in capital markets instead of relying on insured deposits. abusive practices committed earlier in the ownership
And now the family of mortgage holders include not
chain.
only the familiar secondary market entities Fannie Mae,
Lawmakers and regulators are ﬁnding some holes in the
Freddie Mac, Ginnie Mae, and FHA/VA, but also and
mortgage ﬁnance industry that merit repair, but they
importantly, global investors who hold claims to porshould realize that the industry participants—brokers,
tions of mortgage pools that have been aggregated by
originators, investment bankers, rating agencies, and
investment banks, layered with private insurance, and
consumers—are also likely to change their behavior in
graded by private rating agencies.
response to the market forces unleashed by the current
Explanations of the mortgage debacle range from lendﬁasco. There is every reason to believe that the rehabbed
ers’ greed and borrowers’ naiveté on the one hand, to all industry will be sturdier than the one it replaces and able
actors in the drama merely responding to the incentives
to protect everyone it serves from losing the roofs over
in front of them. The greed-cum-naiveté story leads us
their heads.
With the U.S. mortgage ﬁnance industry in a serious
state of disrepair, now is the time to draw up the blueprints, acquire some new tools, roll up our sleeves, and
get to work building a sounder structure.

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

Inﬂation and Prices

December Price Statistics
02.15.08
by Michael F. Bryan and Brent Meyer

December Price Statistics
Percent change, last
1mo.a

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

2006
avg.

Consumer Price Index
All items

3.4

5.6

3.3

4.1

3.0

2.6

Less food and
energy

2.9

2.7

2.6

2.4

2.1

2.6

Medianb

3.2

3.3

2.9

2.5

3.1

16% trimmed
meanb

3.3

3.5

2.8

2.4

2.7

Finished goods

−0.7

13.3

7.0

6.8

4.3

1.6

Less food and
energy

2.2

2.0

2.1

1.8

2.1

Producer Price Index

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
4.75
4.50
4.25
4.00
CPI
a
3.75
Median CPI
3.50
3.25
3.00
2.75
2.50
2.25
2.00
1.75
1.50
a
1.25
16% trimmed-mean C P I
1.00
1995
1997
1999
2001

2005

The 12-month growth rate in the CPI ticked down
to 4.1 percent in December from 4.4 in the previous month, but it is still substantially higher than
August’s 2.0 percent reading.
Almost 57 percent of the components of the CPI
advanced at rates exceeding 3 percent in December,
compared to a little less than 50 percent for 2007
on average. Another fact attesting to some upward
price pressure from the component-price-change
distribution is that only 14 percent of the index’s
components declined during the month, while the
average over 2007 was 24 percent.

C ore C P I
2003

The Consumer Price Index (CPI) rose at an annualized rate of 10.0 percThe Consumer Price Index
(CPI) rose at an annualized rate of 3.4 percent in
December, down from November’s 10.0 percent
increase, but still slightly elevated compared to its
long-term (5-year) trend. Measured over the last
three months, retail prices have outpaced their
6-month, 12-month, and 5-year averages. The
CPI advanced 4.3 percent in the fourth quarter,
compared with 1.9 percent in the third. The CPI
excluding food and energy (core CPI) increased
2.9 percent during the month, outpacing all of its
longer-term trends. Both the median and 16 percent trimmed-mean CPI measures increased more
than 3.0 percent in December, rising 3.2 and 3.3
percent, respectively.

2007

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.

An example of the recent price pressures reﬂected
in the component price change distribution is the
recent trend in medical care prices. Over the past
six months, medical care prices have risen at an
annualized average of 5.6 percent, compared to
an average monthly increase of 4.2 percent over
the past 10 years. This has pushed the longer-term
growth rate from 4.0 percent to 5.1 percent during
the last six months.
Some argue that commodity prices are a leading
indicator of inﬂation, as they measure material
input costs for producers, and increases in them

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

CPI Component Price Change Distributions
Weighted frequency
45
December

2007 average
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.

CPI: Medical Care
12-month percent change
12.0
11.0
10.0
One-month annualized
9.0
perc ent c hange
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007

may eventually be passed on to retailers. The commodity spot price index, constructed by the Commodity Research Bureau, has risen 47 percent from
August 2005, but the increases have been moderating lately. On a year-over-year basis, the series has
fallen from 24.6 percent in June to 16.9 percent in
December, with the raw materials index following a
similar trend.
The 12-month growth rate in core goods decelerated throughout most of 2007 and is currently at
0.1 percent. The longer-term trend in core service
prices has fallen slightly, from 3.8 percent at the
beginning of the year to 3.3 percent in December.
Looking forward, professional forecasters see inﬂation moderating through the ﬁrst half of 2008 and
trending slightly above 2.0 percent throughout
2009, but these projections were made before the
release of December’s CPI report. The Blue Chip
panel’s inﬂation forecast has grown more pessimistic over the past two months, and this latest CPI
report may continue that trend.

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

Commodity Spot Prices (indexed)

Commodity Spot Prices (percent change)

Index (1967=100)
450

12-month percent change
40
35
30
25
20
15
A ll c ommodities
10
5
0
-5
-10
-15
-20
-25
1995
1997
1999

400

350
A ll c ommodities
300

250

200
1995

1997

1999

2001

2003

2005

2007

Source: Commodity Research Bureau.

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

R aw indus trial materials

2001

2003

2005

2007

Source: Commodity Research Bureau.

Core CPI Goods and Core CPI Services

CPI and Forecasts

12-month percent change
8.0
One-month annualized
7.0
perc ent c hange
6.0
C ore s ervic es
5.0
4.0
3.0
2.0
1.0
0.0
-1.0
-2.0
-3.0
C ore goods
One-month annualized
-4.0
perc ent c hange
-5.0
-6.0
1995
1997
1999
2001
2003
2005
2007

Annualized quarterly percent change
7.0
Forecast
6.0
Actual
5.0
Top 10 forecast
4.0

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

Sources: Blue Chip panel of economists, January 10, 2007.

3.0
2.0
1.0

Bottom 10 forecast

0.0
-1.0
-2.0
-3.0
3/06

12/06

9/07

6/08

3/09

12/09

Money, Financial Markets, and Monetary Policy

Another Move, but with Less Surprise
01.31.08
by John Carlson and Sarah Wakeﬁeld

January Meeting Outcomes
Implied probability
1.0
0.9
0.8

New home sales
Durable goods
FOMC meeting

0.7
3.75%

4.00%

0.6
0.5

3.00%

3.50%

0.4

4.25%

3.25%

0.3
2.75%

0.2

2.50%

0.1

0.0
12/11 12/16 12/21 12/26 12/31 01/05 01/10 01/15 01/20 01/25

One-Month LIBOR Spread
Percent
1.20
1.00
0.80
0.60
0.40
0.20
0.00
-0.20
12/06

At its scheduled meeting yesterday, the Federal
Open Market Committee (FOMC) lowered its
target for the federal funds rate 50 basis points to 3
percent. In the post-meeting statement the FOMC
noted that, “Financial markets remain under considerable stress, and credit has tightened further for
some businesses and households. Moreover, recent
information indicates a deepening of information
of the housing contraction as well as some softening in labor markets.”

03/07

06/07

09/07

01/08

Note: Daily observations. LIBOR spread is the one-month LIBOR rate minus the
one-month OIS Rate.
Sources: Bloomberg Financial Services and Financial Times.

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

In its assessment of risks the FOMC indicated that
the “policy action, combined with those taken earlier, should help to promote moderate growth over
time and mitigate the risks to economic activity.
However, downside risks to growth remain.”
Yesterday’s decision followed just nine days after
the January 21 decision to lower the target 75
basis points to 3.5 percent. That move, taken at
an unscheduled meeting, surprised participants in
fed funds futures and options markets. Until that
decision, traders had not seriously entertained the
prospect that the fed funds rate would be as low as
3 percent after yesterday’s meeting. After the new
target was announced, market participants began to
place some probability that the outcome could go
as low as 2.5 percent.
4

Over the past week, however, the market gained
conﬁdence that the FOMC would choose 3 percent
as its new target.

Three-Month LIBOR Spread
Percent
1.20
1.00

Equity markets greeted the FOMC decision by
swinging wildly. Initially equity prices reacted
favorably, jumping almost two percentage points.
The excitement was short-lived, however, as prices
fell sharply near the end of trading, ending the day
down about one-half percentage point.

0.80
0.60
0.40
0.20
0.00
12/06

03/07

06/07

09/07

01/08

Note: Daily observations. LIBOR spread is the three-month LIBOR rate minus
the three-month OIS Rate.
Sources: Bloomberg Financial Services and Financial Times.

Although credit terms have tightened further for
some businesses and households, concerns about
liquidity have lessened substantially. The spread
between the term borrowing rate in the London
interbank market (LIBOR) and the cash market
rate (OIS), is a closely watched indicator of liquidity conditions. Spreads for both one-month and
three-month borrowings have declined well oﬀ
recent peaks, although they remain above more
normal levels.

Money, Financial Markets, and Monetary Policy

What Is the Yield Curve Telling Us?
01.30.08
by Joseph G. Haubrich and Katie Corcoran

Yield Spread and Real GDP Growth*
Percent
12
10

R eal G DP
P
growth
(year-to-year
perc ent change)

8
6
4
2
0

10-year minus 3-month
yield s pread

-2
-4
1953

1963

1973

1983

1993

2003

*Shaded bars represent recessions.
Sources: Bureau of Economic Analysis; Federal Reserve Board.

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

Since last month, both long-term and short term
interest rates have decreased, with short rates dipping more, leading to a steeper yield curve. 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 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.

Yield Spread and Lagged Real GDP
Growth
Percent
12
One-year-lagged real G DP growth
(year-year perc ent c hange)

10
8
6
4
2
0

10-year minus 3-year
yield s pread

-2
-4
1953

1963

1973

1983

1993

2003

Sources: Bureau of Economic Analysis; Federal Reserve Board.

Yield Spread and Predicted GDP Growth
Percent
6
R eal G DP growth
(year-to-year perc ent c hange)

5
4

P redic ted
G DP growth

3
2
1
0
10-year minus 3-month yield s pread
-1
-2
2002

2003

2004

2005

2006

2007

2008

Sources: Bureau of Economic Analysis; Federal Reserve Board.

Probability of Recession Based on the
Yield Spread*
Percent
100
90
80

P robability of
R ec es s ion

70
60

F orec as t

50
40
30
20
10
0
1960 1966 1972 1978 1984 1990 1996 2002 2008
*Estimated using probit model.
Sources: Burea of Economic Analysis; Federal Reserve Board; and
author’s calculations.

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

The yield curve has continued getting steeper,
although both short and long rates have falled
recently. The spread remains positive, with the 10year rate at 3.58 percent, while the 3-month rate
jumped down to 2.31 percent (both for the week
ending January 25). Standing at 127 basis points,
the spread is above December’s 120 basis points
and November’s 82 basis points. Projecting forward
using past values of the spread and GDP growth
suggests that real GDP will grow at about a 2.6
percent rate over the next year. This is broadly in
the range of other forecasts.
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 January
stands at 4.8 percent, down a bit from December’s
5 percent and November’s 9 percent.
The probability of recession is below several recent
estimates, and perhaps seems strange in the midst
of recent ﬁnancial concerns, but one aspect of those
concerns has been a ﬂight to quality, which lowers
Treasury yields, and a reduction in both 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 January, not earlier in the year.
The 4.8 percent given by our approach is close to
the 9.5 percent calculated by James Hamilton over
at Econbrowser (though we are calculating diﬀerent events: Our number gives a probability that
the economy will be in recession a year from now;
Econbrowser looks at the probability that the quarter the second quarter of 2007 was in a recession.)
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
6

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,
they 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

Chinese Inﬂation and the Renminbi
02.07.08
By Owen F. Humpage and Michael Shenk
China is increasingly worried about its inﬂation
rate, which topped 6.5 percent on a year-over-year
basis in December. One thing that the People’s Bank
of China might do to garner more control over
inﬂation is to allow its exchange rate more ﬂexibility.

Inflation Rates
12-month percent change
30
25
20
15
C hina
10
5

U.S .

0
-5
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Sources: International Monetary Fund, International Financial Statistics and
Bureau of Labor Statistics.

Over the last decade, China has managed the
renminbi-dollar exchange rate closely. Between
1998 and July 2005, the People’s Bank pegged the
renminbi at 8.28 per U.S. dollar. In mid 2005, the
People’s Bank loosened its reigns on the exchange
rate and has since allowed the renminbi to appreciate 12½ percent relative to the dollar. Given China’s
trade surplus with the United States, many observers
think that a larger renminbi appreciation is in order.
Many believe that China manages the renminbi–dollar exchange rate to encourage a large trade surplus
with the United States and to attract strong inward
direct foreign investments. There is, however, another element to the story. China limits the ability of
its residents to reinvest the dollars that they acquire
through trade and inward investments outside of the
country. Instead, they must exchange the lion’s share
of these dollars—and other foreign currencies—for
renminbi with the People’s Bank. This strategy has
contributed to China’s acquisition of a huge portfolio of foreign exchange. Economists guess that nearly
70 percent of this portfolio is held in liquid U.S.
dollar assets, like U.S. Treasury securities.
When Chinese residents fork over the funds to the
People’s Bank, they receive renminbi in exchange,
and the renminbi monetary base—a narrow mea-

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

sure of money—expands. For many years, this was
not a problem. China’s economy grew quickly, and
the expanding monetary base accommodated that
growth. If anything, money growth often seemed
too slow. Between 1998 and 2003, prices in China
frequently fell, suggesting that money growth was
not keeping pace with the economic expansion. By
2003, however, China’s reserve accumulation started
to accelerate, and inﬂation began warming up.

China’s Foreign Exchange Reserves
Trillions of U.S. dollars
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2

In 2003, the People’s Bank started to oﬀset—or
sterilize—the expansionary eﬀects of its oﬃcial reserve accumulation on its monetary base by selling
renminbi bonds to the banking system. The bond
sales drained away part of the renminbis created
when the People’s Bank bought dollars. Since then,
the People’s Bank has sterilized nearly one-half of
the eﬀects of its reserve accumulation on the monetary base. This suggests that the banking system
is holding a lot of low-yielding sterilization bonds,
which, in such a vibrant growing economy, must
have a signiﬁcant opportunity cost.

0.0
1990 1992 1994 1996 1998 2000 2002 2004 2006
Sources: International Monetary Fund, International Financial Statistics.

Sterilization of Reserve Flows
Trillions of renminbis
4.0
Four-quarter change in monetary base
Four-quarter change in foreign exchange reserves
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Q1
Q3
2003

Q1
Q3
2004

Q1
Q3
2005

Q1
Q3
2006

Q1
Q3
2007

Sources: International Monetary Fund, International Financial Statistics.

But the People’s Bank has probably made money
from the deal over the past few years, since the yield
on U.S. Treasury securities has exceeded the interest rate on short-term Chinese securities. Since last
summer, however, those proﬁts may have disappeared, as inﬂation in China has pushed rates on
the Bank’s short-term instruments up and turmoil
in ﬁnancial markets has pushed yields on U.S. Treasury securities lower.
The People’s Bank has taken other measures to
reduce inﬂationary pressures in China. Since the
beginning of 2007, it has raised reserve requirement
11 times, reaching a new high. In addition, the
central bank has hiked oﬃcial (and administered)
lending and deposit rates. Observers widely anticipate further moves to tighten monetary policy and
lower the inﬂation rate.

Renminbi Dollar Exchange Rate
Renminbi per U.S. dollar
9.0
8.5
8.0
R eal
7.5
7.0
6.5

Nominal

Dollar appreciation
Dollar depreciation

6.0
5.5
5.0
1992

1994

1996

1998

2000

2002

2004

2006

2008

Sources: Sources: International Monetary Fund, International Financial Statistics and
Bureau of Labor Statistics.

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

Ironically, China’s tight management of the renminbi–dollar exchange rate seems to be eroding its
competitive position, albeit ever so slightly thus far.
Exchange rates are not the only thing that matters
for a country’s competitive position. Inﬂation in
China relative to inﬂation in the United States also
aﬀects the relative price of goods. Over the past year,
the rate of inﬂation in China has exceeded the rate
8

of inﬂation in the United States. The real renminbi–
dollar exchange rate combines all three of these
variables—the conventional exchange rate, inﬂation
in China, and inﬂation in the United States—into
a convenient metric. Since its peak in August 2006,
the dollar has depreciated 9 percent against the
renminbi in real terms, compared to 7½ percent in
conventional exchange-rate terms. To be sure, this
diﬀerential is not a big deal, but it does bolster our
point. China might get better control over inﬂation
by adopting more exchange-rate ﬂexibility.
Ironically, China’s tight management of the renminbi–dollar exchange rate seems to be eroding its
competitive position, albeit ever so slightly thus far.
Exchange rates are not the only thing that matters
for a country’s competitive position. Inﬂation in
China relative to inﬂation in the United States also
aﬀects the relative price of goods. Over the past year,
the rate of inﬂation in China has exceeded the rate
of inﬂation in the United States. The real renminbi–
dollar exchange rate combines all three of these
variables—the conventional exchange rate, inﬂation
in China, and inﬂation in the United States—into
a convenient metric. Since its peak in August 2006,
the dollar has depreciated 9 percent against the
renminbi in real terms, compared to 7½ percent in
conventional exchange-rate terms. To be sure, this
diﬀerential is not a big deal, but it does bolster our
point. China might get better control over inﬂation
by adopting more exchange-rate ﬂexibility.

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

Economic Activity

Real GDP Fourth-Quarter 2007 Advance Estimate
02.05.07
By Brent Meyer

Real GDP and Components 2007:
Fourth-Quarter Advance Estimate
Annualized percent change, last:
Quarterly change
(billions of 2000$)

Quarter

Four quarters

Real GDP

18.5

0.6

2.5

Personal consumption

40.5

2.0

2.5

12.8

4.2

4.8

Durables

11.2

1.9

1.7

Services

Nondurables

18.7

1.6

2.5

Business ﬁxed investment

25.4

7.5

7.4

Equipment

9.9

3.7

Structures

11.6

15.8

16.0

Residential investment

-30.6

-23.9

-18.3

Government spending

13.1

2.6

2.5

National defense

-0.8

-0.6

1.4

Net exports

12.1

—

Exports

13.8

3.9

7.7

Imports

1.6

0.3

1.4

-34.0

—

Change in business
inventories

Source: Bureau of Labor Statistics.

Contribution to Percent Change in
Real GDP
Percentage points
4

Last four quarters
2007:IIIQ
2007:IVQ

3
2
1
0

Business
fixed
investment

Change in
inventories

Residential
investment
Personal
consumption

-1

Government
spending

Exports
Imports

-2
Source: Bureau of Economic Analysis.

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

Real GDP grew at an annualized rate of 0.6 percent (weaker than expected) in the fourth quarter
of 2007, according to the advance release by the
Bureau of Economic Analysis. This marked deceleration from the third quarter’s growth of 4.9
percent primarily reﬂects a slowdown in private
investment, personal consumption, exports, and
federal government expenditures. Gross private
domestic investment decreased 10.2 percent in the
fourth quarter, as residential investment continued
to lose ground, falling 23.9 percent in the fourth
quarter after having fallen 20.5 percent in the third.
Business inventories fell $34.0 billion during the
quarter, after adding $24.8 billion last quarter. Exports decelerated from an increase of 19.1 percent
in the third quarter to a gain of 3.9 percent in the
fourth. Imports and federal government consumption were left virtually unchanged from a quarter
ago, both series rising only 0.3 percent. Personal
consumption rose 2.0 percent in the fourth quarter,
compared to 2.8 percent in the third.
Personal consumption contributed 1.4 percentage
points to the percent change in real GDP, which
is slightly oﬀ its pace over the past four quarters, when it contributed 2.0 percentage points
to growth. The housing correction continued to
dampen GDP growth in the fourth quarter, taking
away 1.2 percentage points, after having reduced it
a similar 1.1 percentage points last quarter. Inventories more than reversed last quarter’s 0.9 percentage point addition, deducting 1.3 percentage points
from growth.
Real private inventories fell $3.4 billion at a (seasonally adjusted annualized) rate in the fourth
quarter, their ﬁrst decrease since the second quarter
of 2003. Since the beginning of 2004, inventory
growth has been trending down and has averaged
$27.1 billion since coming out of the last recession,
compared to a $43.0 billion quarterly average during the last business cycle.
10

Real Change in Private Inventories
Billions (Chained 2000$, SAAR)
140
120
100
80
60
40
20
0
-20
-40
-60
-80
-100
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006

Looking forward, the Blue Chip panel of economists expect below-trend real GDP growth of 2.2
percent in 2008. Recent data releases have been
somewhat weak, especially on the housing side,
hinting that ﬁrst-quarter growth will be slow.
Indeed, the Blue Chip panel expects ﬁrst-quarter
growth to be 1.3 percent, before steadily rising
closer to trend growth by 2009.

Source: Bureau of Economic Analysis.

Real GDP Growth
Annualized quarterly percent change
6
Forecast period
5
4

Average
1981-2007

Final estimate
Advance estimate
Blue Chip forecast

Blue Chip top ten and
bottom ten average

3
2
1
0
IVQ IQ
2006

IIQ IIIQ IVQ IQ
2007

IIQ IIIQ IVQ IQ
2008

IIQ IIIQ IVQ
2009

Source: Blue Chip Economic Indicators, January 2008; Bureau of Economic Analysis.

Economic Activity

The Pass-through of Oil Prices to Gasoline Prices
02.06.07
By Andrea Pescatori and Beth Mowry

Gas Expenditure as a Portion of Total
Consumption Expenditure
Percent
6

1
1978

1983

1988

1993

1998

2003

Source: Bureau of Economic Analysis.

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

Changes in the price of gasoline, particularly in the
last few years, have been closely watched by consumers. Gasoline expenditure is a substantial part of
the average household’s total consumption expenditure, ranging from 2 percent to 5 percent since
the late 1970s. Moreover, the share of household
expenditure that must be devoted to gasoline is
aﬀected by changes in the relative price of gasoline,
tending to rise when gas prices spike, because it is
hard to adjust the quantity of gasoline consumed,
especially in the short run. Economists say that the
demand for gasoline has a low price-elasticity of
substitution. In other words, changes in gasoline
prices have a strong impact on the consumption of
other goods and services as well as of gasoline.
11

The single most important factor aﬀecting the price
of gasoline is the price of crude oil, which accounts
for roughly half of the price of a gallon of gas at
the pump. About 45 percent of the oil reﬁned
in the world today winds up as gasoline, which
makes it the primary product of the downstream
oil industry. The remainder of each barrel of oil
yields byproducts like jet fuel, kerosene, heating oil,
and diesel. After the cost of oil, a substantial share
of the pump price comes from federal, state, and
sometimes local taxes, reﬁning margins (that is, the
costs and return of reﬁners), the retailer’s markup,
and distribution and marketing margins.

Gasoline and Crude Oil Prices
Price, cents/gal
350
300

Retail gas price
Oil price

250
200
150
100
50
0
1978

1983

1988

1993

1998

2003

a. All types of gasoline, U.S. city average retail price, including taxes.
b. West Texas Intermediate monthly spot crude oil prices.
Source: Energy Information Administration and The Wall Street Journal.

The ratio between the price of a gallon of averagegrade gasoline at the pump and the price of crude
oil per gallon—which we refer to as the gas-oil
ratio—trended up from the late 1980s until 1999
but has been trending down recently.

Gas and Oil Price Growth
Month-to-month rate
25

Most of the change in the ratio over time has been
caused by the eﬀect of taxes. An excise tax is a given
tax per unit, which means that when the gasoline
price goes up the tax rate falls and vice versa. Notice that the highest tax rate, 60.7 percent, occurred
when oil prices were at their record low of $12.01
per barrel in February 1999.

Gasoline
Crude oil

15
5
-5
-15
-25
1978

1983

1988

1993

1998

2003

Note: Shaded bar represents 1999.
Source: Department of Energy and Energy Information Administration.

Gas/Oil Price Ratio
Price ratio
4.0

Percent
80

3.0

2.0

1.0

0.0
1986

Gas/oil ratio
Gas/oil net of tax ratio

20
Gas tax rate
0

1991

1996

2001

2006

a. Gas/oil price ratio is U.S. city average retail gasoline price divided by WTI
crude spot price.
b. Gas/oil net of tax ratio is gas price excluding tax divided by oil price.
c. Tax rate is gas price with tax divided by gas price without tax.
Source: Energy Information Administration and The Wall Street Journal.

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

However, taxes cannot account for the entire path
of the gas-oil ratio and, in particular, for the recent
downward trend. This trend might be explained by
a compression of the margins in the downstream
oil industry when oil prices are high. In particular,
reﬁners—who contribute 10 percent–30 percent to
the gasoline price—have been shrinking their margins on gasoline products, reﬂecting both a higher
oil–gasoline transformation rate (thanks to technological progress) and lower returns on gasoline.
Lower returns could be due to higher competition
in the industry or to the fact that companies have
been trying to absorb some of the recent upward
trend in oil prices rather than pass it entirely on
to consumers. By doing so they hope to prevent a
substantial change in the future demand for gasoline (its long-run price elasticity is higher than the
short-run elasticity because consumers have more
time to adjust to any price change). However, this
tactic will be sustainable only if the trend in the
price of crude oil reverses.
12

A glance at ﬁgure 3 above will conﬁrm that the
price of gas and the price of crude are highly correlated. But to determine how much of an oil price
increase is passed on to the gasoline consumer,
we need to look at the “oil-price pass-through,”
which refers to the eﬀect of changes in oil prices on
changes in gas prices.
When we calculate the eﬀect of contemporaneous and past changes in oil prices and gas taxes on
the retail price of gasoline from 1986 to today, we
ﬁnd that, on average, less than half of an oil price
change is passed to consumers. The time that it
takes is relatively short; it passes through within
the same month of the oil-price increase or in the
month month after; on the other hand, changes in
excise taxes do not appear to have a signiﬁcant effect on the price of gasoline.
A casual observation of gas price changes over
time suggests a remarkable change in the volatility
of gasoline price after 1998 (the sample variance
triples after 1999). Because the two periods diﬀer
so markedly, we redo the calculation of the passthrough, this time splitting the sample into two
subsamples, one pre-1999 and one post-1998. In
the earlier subsample, the pass-through is much
lower and slower, amounting to about 30 percent
over the course of two months. Furthermore,
changes in taxes have a signiﬁcant eﬀect before
1999. In the more recent period, there has been a
dramatic increase in the pass-through. After 1998,
about 72 percent of a change in the price of oil
passes through to gasoline consumers within a
month’s time. If one looks at the pass-through before the eﬀects of taxes are added to the calculation,
the pass-through amounts to about 96 percent!
What the results from splitting the samples suggest is that the higher volatility of the gas price
series could be attributed to a higher pass-through
from oil prices. In fact, even if oil prices have not
shown any particular increase in their volatility, the
transmission of crude price ﬂuctuations to gasoline
prices has changed. The downstream oil industry
is no longer smoothing ﬂuctuations in the price
of crude for U.S. households and it is no longer
guaranteeing relatively stable gasoline prices. This
could be due to more compressed margins within
Federal Reserve Bank of Cleveland, Economic Trends | February 2008

the industry (a hypothesis not really supported by
the data) or by limits in reﬁneries’ capacity (capacity utilization has been averaging above 90 percent
in the past 10 years).
Finally, we recalculate the pass-through to see if
positive and negative changes in oil prices aﬀect
gas prices the same way, and whether their passthroughs have changed over time. We observe that
in the pre-1999 sample, a decline in the price of oil
had no eﬀect on gas prices within one month, while
in more recent years, a price decline has a strong
pass-through of almost 50 percent within the same
amount of time. In fact, after 1998, 95 percent of a
decline in the price of oil passes through to gasoline
prices within a month—this ﬁgure is about 100
percent after taking taxes into account. However,
we also ﬁnd that after 1998, the pass-through of
oil price changes is often erased at the pump after
about ﬁve months. This is true especially for oil
price declines, which show a strong reversion effect at about ﬁve months. Oil price increases also
show a somewhat weaker reversion eﬀect. In other
words, an initial reduction in gasoline prices due to
a reduction in the price of crude oil will not last for
long, unless the oil price reduction is sustained.

Economic Activity

The Employment Situation
02.01.08
By Murat Tasci and Beth Mowry

Average Nonfarm Employment Change
Change, thousands of jobs
250

Revised
Previous estimate

200
150
100
50
0
-50
2004 2005 2006 2007

III
2007

Nov Dec Jan

Source: Bureau of Labor Statistics.

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

Nonfarm payroll employment declined by 17,000
in January to 138,102. This indicates the ﬁrst decline in nonfarm employment since August 2003.
The total unemployment rate declined to 4.9 percent from the previous month’s 5 percent, mostly
due to a 42,000 decline in the civilian labor force.
The Bureau of Labor Statistics (BLS) also revised
its payroll employment numbers for the last two
months. The November payroll employment gain
was revised downward, from 115,000 to 60,000,
whereas the December payroll employment change
was revised upward, from 18,000 to 82,000. Overall, monthly payroll employment rose by 94,000 on
average in the last quarter of 2007 and by 95,000
for the whole year.
14

Labor Market Conditions
Average monthly change
(thousands of employees, NAICS)
2004

2005

2006

2007 YTD

Jan 2008

Payroll employment

176

212

Goods-producing

175

−17

−37

−51

Construction

−19

−27

Heavy and civil
engineering

−1

−8

Residentiala

−2

−10

−28

Nonresidentialb

−1

−7

−14

−22

−28

Manufacturing
Durable goods

−4

−15

−12

Nondurable
goods

−9

−8

−10

−7

−16

Service-providing

148

179

172

132

Retail trade

−8

−2

−11

−7

−9

Leisure and hospitality

Government

−18

Local educational svcs.

−4

Financial

activitiesc

PBSd
Temporary help
svcs.
Education and
health svcs.

Average for period (percent)
Civilian unemployment
rate

5.5

5.1

4.6

4.9

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.

Private Sector Employment Growth
Change, thousands of jobs: 3-month moving average
350
300
250

Large contributors to January’s job loss were construction (-27,000 jobs), manufacturing (-28,000),
and government (-18,000). Among these sectors,
construction and manufacturing have been declining throughout the past year, falling by 19,000 and
22,000 per month on average, respectively. Perhaps
the main reason behind the decline in January’s
report was the weak service sector. Even though
nonfarm payroll employment in services increased
by 132,000 per month on average last year, it increased by only 34,000 in January, 2008, mostly led
by a 47,000 gain in education and health services.
The three-month moving average of private sector employment growth shows a deﬁnite declining
trend over the past year, and even more broadly
over the past two years. Currently, the three-month
moving average of private sector employment
growth stands at 42,000, the lowest value since
September 2003.
January’s diﬀusion index slipped to 46.2, indicating
that more industries cut back payrolls than added
to them. Once again, this index value is the lowest
it’s been since August 2003.
These numbers all point to a weak labor market
in January, with many sectors worsening from the
previous month. However, as we always caution,
monthly data are volatile and subject to revision.
Payroll gains in December and January are subject
to revision in the next report. The BLS also revises
annual payroll numbers once a year, reﬂecting
changes in seasonal adjustment factors and updates
to the industrial classiﬁcation system. The revision
released with January’s employment report also
aﬀected the past several years. As a result of this revision, the average monthly change in nonfarm payroll employment declined from 111,000 to 95,000
for 2007, and from 189,000 to 175,000 for 2006,
and virtually did not change for 2004 and 2005.

200
150
100
50
0
-50
-100
-150
-200
2002

2003

2004

2005

2006

2007

2008

Source: Bureau of Labor Statistics

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

Labor Market Conditions and Revisions
Average monthly change (thousands of employees, NAICS)
Nov
current

Revision
to Nov

Dec
current

Revision
to Dec

Jan
2008

Payroll employment

−55

−17

Goods-producing

−52

−7

−61

−51

Construction

−57

−20

−45

−27

Heavy and civil engineering

−0.5

−4.9

−1

−8

Residentiala

−52.4

−23

−32.3

−4

−28

Nonresidentialb

−4.3

−8.1

−3

−20

−28

−19

−12

Manufacturing
Durable goods
Nondurable goods

−5

Financial

Education and health svcs.

−12

−7

−1

−2

−30

−11

−8

Temporary help svcs.

−16

PBSd

134

−23

activitiesc

−1

−48

Retail trade

112

Service-providing

−20

−7

−9

Leisure and hospitality

−11

Government

−12

−3

−18

−5

−3

−4

Local educational svcs.

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.

Economic Activity and Labor

Manufacturing Employment
01.29.08
by Yoonsoo Lee and Beth Mowry

Payrolls In Manufacturing and
Nonmanufacturing
Thousands of workers
22,000

140,000
Manufacturing

20,000

120,000

18,000

100,000

16,000

80,000

14,000

60,000
Nonmanufacturing

12,000
10,000
8,000
1940

40,000
20,000
0

1950

1960

1970

1980

1990

2000

Manufacturing in the United States has been on the
decline since the early 1980s, shedding more than
5½ million jobs over the past three decades. The
13.9 million workers employed in manufacturing
today are just a shadow of the peak of the 19.5 million employed in the sector back in 1979. Manufacturing employment also seems not to be recovering
after recessions. It used to follow the same pattern
as nonmanufacturing employment over the business
cycle, contracting during recessions and rebounding
and growing during recoveries. During the last two
recoveries, however, manufacturing gains appear to
have softened or disappeared altogether.

Note: Shaded bars indicate recession.
Source: Bureau of Labor Statistics.

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

During the most recent recession in 2001, overall
nonfarm employment growth stalled but eventually
resumed its upward trend. Manufacturing employment, on the other hand, never recovered from its
fall. An index of employment since the 2001 prerecession peak shows that manufacturing employment is now only 85 percent of what it was at the
peak. Nonfarm employment excluding manufacturing, in the meantime, has increased 8 percent.

Nonfarm Employment Change since
March 2001
Thousands of workers
110
Nonmanufacturing
105
100
95
Manufacturing

90
85
80
2001

2002

2003

2004

2005

2006

2007

Source: Bureau of Labor Statistics.

Nonfarm Employment Change Before and
After 2001 Recession
Monthly change, thousands of workers
600
500
Nonmanufacturing

400

Manufacturing and nonmanufacturing employment both took a big dive during the 2001 recession. Although the pace of expansion for both had
started to soften in advance of the recession’s onset,
the nonmanufacturing sector continued to add
jobs up until it started. In contrast, manufacturing
started losing jobs during the summer of 2000, well
before the recession began. Nonmanufacturing payrolls experienced a rebound afterward and worked
back into expanding territory. The monthly decline
in manufacturing payroll numbers, by comparison,
has merely become less pronounced.

300
200
100
0
Manufacturing

-100
-200
-300
1997

1999

2001

2003

2005

2007

Note: Seasonally-adjusted; Shaded bar indicates recession.
Source: Bureau of Labor Statistics.

Major Manufacturing Sectors, 2007
Fabricated metal products
11.3%
Machinery
8.8%
Other
35.8%

Plastics and rubber
products
Chemicals
5.6%
6.3%

Computer and
electronic products
9.3%
Transportation equipment
12.1%
Food manufacturing
10.8%

Note: Employment shares as of December 2007.
Source: Bureau of Labor Statistics.

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

Economic indicators have received increased attention in recent months, as economists try to
determine the extent to which housing troubles
may have spilled over into the broader economy.
Employment reports indicate a deﬁnite softening
in the labor market. Nonmanufacturing jobs are
still being added, but at a slowing pace. December’s
recent employment report, for example, showed a
small gain of 18,000 nonfarm payrolls with a loss
of 31,000 in manufacturing. Manufacturing numbers have been on a long-term decline and have not
experienced even a modest gain since June 2006.
While the manufacturing sector is losing employees, it is not losing them at such a dramatic rate as
was observed before or during the 2001 recession.
However, direct comparisons between 2001 and recent months requires caution because employment
ﬁgures are subject to monthly and annual revisions.
The pie chart above includes some of the largest
manufacturing sectors. Transportation equipment,
fabricated metal products, and food manufacturing account for about a third of manufacturing
employment. Of all the subsectors within manufacturing, not a single one has added payrolls over
the past decade. However, some sectors have borne
17

Employment Change in Major
Manufacturing Sectors
Thousands of workers
1997
2007

Plastics and rubber products
Chemicals
Food manufacturing

Transportation equipment
Computer and electronic products
Machinery
Fabricated metal products

400

800

1,200

1,600

2,000

2,400

Source: Bureau of Labor Statistics, Haver Analytics.

a larger brunt of the industry’s decline than others. While fabricated metal products and the food
manufacturing sectors have held onto employees
relatively well, losing just 7.5 percent and 3.9
percent of their payrolls since 1997, most areas did
not fare so well. Computer and electronic products
lost nearly 30 percent of their employees, and the
printing and related activities sector lost nearly 25
percent. Apparel manufacturing took the worst hit
of all categories and now has only 31 percent of the
employees it had a decade ago. However, this sector
is smaller than the others mentioned and accounted
for only 4 percent of all manufacturing payrolls
even before the drop.

Economic Activity and Labor

Housing Markets
01.18.07
by Michael Shenk

Existing Single-Family Home Sales
Millions of units
6.5

Thousands of dollars
240

6.0

220

5.5

200
180

5.0
Median sales price

4.5

160

4.0

140

3.5

120

3.0
1995

100
1997

1999

2001

2003

2005

2007

Note: Shaded bar indicates recession.
Source: National Association of Realtors.

Existing Single-Family Homes for Sale
Months of supply
11

Millions of units
4.4
4

3.6

3.2

2.8

2.4

1.6

1.2
1990

3
1994

1998

2002

2006

Note: Shaded bars indicate recession.
Source: National Association of Realtors.

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

Existing single-family home sales were largely
unchanged for the second consecutive month, as
they increased a mere 0.7 percent in November. For
the past three months, existing single-family home
sales have hovered around the 4.4 million mark,
perhaps providing some evidence of stabilization
in the housing market. Sales of existing singlefamily homes have basically been free-falling for
26 months since peaking in September 2005 at 6.3
million units.
If the market is going to stabilize, the inventory of
existing single-family homes for sale will play a critical role. Currently, the inventory level in terms of
actual homes on the market, while below its peak,
has not yet shown any deﬁnitive signs of turning a
corner and beginning to descend. Relative to the
current sales pace, the supply of homes remains
very much elevated at just under 10 months.
The market for new single-family homes, though
smaller than the market for existing homes, is
considered by some to be more of a leading indicator of housing activity because of when sales are
recorded. Existing home sales are recorded at the
time of closing, but new home sales are recorded
when contracts are signed, a step much earlier in
18

New Single-Family Home Sales
Millions of units
1.4

Thousands of dollars
280

1.3

260

1.2

240

1.1

220

1.0

200

the home-buying process. Thus the fact that new
single-family home sales fell 9.0 percent in November after three months of relative stability may give
some optimists cause for concern.

0.9

180
Median sales price

0.8

160

0.7

140

0.6

120

0.5
1995

100
1997

1999

2001

2003

2005

Inventories of new homes for sale remained elevated in November. The actual number of homes on
the market declined over the month, as it has done
fairly regularly since peaking in July 2006. However, the level of inventory measured relative to the
current sales pace has continued to increase during
this period, as sales continued their rapid decline.

2007

Note: Shaded bar indicates recession.
Source: Census Bureau.

New Single-Family Homes for Sale
Months of supply

Thousands of units

600

550

500

450

400

350

300

250
1990

3
1994

1998

2002

2006

Note: Shaded bars indicate recession.
Source: Census Bureau.

Regional Activity

The Ups and Downs in Regional Employment Statistics
01.31.08
By Tim Dunne, Guhan Venkatu and Kyle Fee

Unemployment Rates
Percent
8
7
United States

Fourth District

6
5
4
December increase in
U.S. unemployment rate
3
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
a. Seasonally adjusted using the Census Bureau’s X-11 procedure.
Note: 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 and Bureau of Labor Statistics.

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

Our standard monthly employment report typically provides various employment statistics for
the Fourth District and its major metropolitan
areas. This month we take a diﬀerent tack, as the
District’s November employment numbers varied
widely from our expectations. The Fourth District’s
unemployment rate dropped sharply in November,
falling to 5.2 percent from 5.7 percent in the previous month. Meanwhile, the national unemployment rate held relatively steady until December,
when it saw a substantial increase of 0.3 percent.
However, we are cautious about interpreting the
large drop in the District’s unemployment rate as a
sign of an improving labor market.
19

For one thing, the drop in the district’s unemployment rate is more likely the result of an atypical calendar and its eﬀect on the way the data are reported
than on something in the labor market.

Unemployment Rates in Three
Fourth District States
Percent
8
7

Kentucky
Ohio

United States

6
5
4

Pennsylvania

3
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Note: 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.

First, Thanksgiving fell very early this year and may
have caused retailers to move their seasonal hiring up
further in the month than in recent years. Second, in
response to the early Thanksgiving holiday, the Labor
Department and Census Bureau moved the “reference week” for the Current Population Survey—a
key input into the estimation of state and county unemployment rates—to the week of November 4–10.
The reference week is the week in a month during
which individuals are asked about their employment status; normally, this is the week that contains
the 12th of the month. The change in the reference
week this month may have inﬂuenced November’s
state-level statistics. Finally, the change of the reference week, combined with the early Thanksgiving,
may have introduced more noise into the seasonaladjustment process that is applied to remove from
the data any seasonally-induced swings in the labor
force, employment, and unemployment series.
A look at state-level unemployment rates supports
this idea. November’s declines were completely
reversed in December in Fourth District states.
Ohio’s unemployment rate went from 5.6 percent
to 6.0 percent, Pennsylvania’s from 4.2 percent to
4.7 percent, and Kentucky’s from 5.0 percent to
5.7 percent.
We can’t construct December’s unemployment
rate for the Fourth District because data are not
available yet for individual counties. However, we
believe the trends in the state-level data will hold
for the Fourth District as a whole.

Ohio Employment Data
Month over month percent change
1.2
1
0.8
0.6
0.4
0.2

Current Data
End of 2006 Data

-0
-0.2
-0.4
-0.6
1976 1980 1984 1988 1992 1996 2000 2004 2008
Source: U.S. Department of Labor, Bureau of Labor Statistics.

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

A ﬁnal word of caution on comparing monthly
labor statistics. The 2007 data have not yet been
revised and are not equivalent to those of earlier
years. Revised data are updated and adjusted for
seasonal factors, but they are also markedly less
volatile, because the revision process smoothes
out natural month-to-month ﬂuctuations. To
see this, look at the graph below, which shows
the monthly percent changes in employment for
Kentucky, Ohio, and Pennsylvania from 1988 to
2007. The data for 2007, which are unrevised,
20

show considerably more variation than the data for
previous years. Note though that diﬀerent degrees
of smoothing emerge across states after revision.
Ohio’s post-revision data are relatively smooth,
while Pennsylvania’s retain signiﬁcant month-tomonth ﬂuctuations. However, these diﬀerences are
not evidence necessarily that Ohio’s economy has
lower month-to-month employment ﬂuctuations
than Pennsylvania’s. Rather, they are more likely the
result of diﬀerences in the way states modify their
data during the revision process.
The next chart illustrates the degree of smoothing that can occur in these data series. The chart
displays the month-to-month percentage change
in the employment data both before and after the
last annual revision for Ohio. Note that the data
for 2006 prior to the revision (the red line) show
high volatility but following the revision the series
is smoothed for 2006 (the blue line). Revised 2007
employment data will be released at the end of February and will undoubtedly show much less monthto-month volatility.

Regional Activity

The Erie Metropolitan Statistical Area
01.22.08
By Tim Dunne and Kyle Fee

Location Quotients for Erie MSA and the U.S., 2006 The Erie metropolitan statistical area (MSA) is
located in the northwest corner of Pennsylvania on
Natural resources, mining, construction
Lake Erie. Home to 279,811 people, Erie, a Great
Manufacturing
Trade, transportation, utilities
Lakes city, has an employment history of heavy
Information
industry and manufacturing. In 2006, Erie was
Financial activities
still heavily invested in manufacturing industries,
Professional and business services
having about an 80 percent higher proportion of
Education and health services
Leisure and hospitality
its workforce in manufacturing than the nation as
Other services
a whole. Meanwhile, Erie’s service industry workGovernment
force was proportionately higher in health services
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8
industries relative to the nation and lower in inNote: A location quotient is the simple ratio of a given industry’s employment
formation, ﬁnancial, and professional business and
share in one region to the industry’s employment share in the nation. A location
quotient of one indicates that the industry accounts for the same share of
services industries.
employment in the region and the nation.
Sources: U.S. Department of Labor, Bureau of Labor Statistics.

Looking at the components of annual employment growth in the Erie MSA, the strongest driver
of employment growth from year to year has been
the service sector industries of education, health,
leisure, government and other services. Not surprisFederal Reserve Bank of Cleveland, Economic Trends | February 2008

ingly, manufacturing employment is the biggest
drag on Erie’s employment growth.

Components of Employment Growth,
Erie MSA
Percent change
4
Natural resources, mining &
3
2

Erie’s most recent employment growth has come
from growth in tourism-related industries. Erie’s
total nonfarm employment growth from October
2006 to October 2007 is 0.7 percent, while employment in the leisure and hospitality industries has
jumped 6.6 percent over the same period. On the
down side, goods-producing industries lost employment at a rate substantially above the national rate.

Retail and wholesale trade

construction
Financial, information and
business services
Education, health, leisure, government
and other services

Manufacturing
Transportation, warehousing
and utilities

U.S.

1
0
-1
-2
-3
2001

2002

2003

2004

2005

Since the last business cycle peak in March 2001,
Erie lost 0.9 percent of its total nonfarm employment, compared to Pennsylvania’s gain of 1.6 percent and the nation’s gain of 4.4 percent. From its
lowest employment levels in July of 2003, Erie has
expanded its employment 4.5 percent. Over that
same period, Pennsylvania’s employment grew 3.7
percent and the nation’s grew 6.5 percent.

2006

Note: The white bars represent total annual growth for the Erie MSA. The red line is
U.S. growth.
Sources: U.S. Department of Labor and Bureau of Labor Statistics.

Payroll Employment Growth,
October 2006 - October 2007
U.S.

Compared to other cities on Lake Erie, Erie actually has performed reasonably well. While employment is still below the city’s 2001 level (similar
to the decline experienced by its neighbor to the
north, Buﬀalo, New York), the Erie labor market
has been stronger than Cleveland’s or Toledo’s.

Erie MSA

Total nonfarm
Goods-producing
Manufacturing
Natural resources, mining & construction
Service-providing
Trade, transportation, and utilities
Information
Financial activities
Professional and business services
Education and health services
Leisure and hospitality
Other services
Government
-3 -2 -1 0 1 2 3 4 5 6
Year-over-year percent change
Sources: U.S. Department of Labor, Bureau of Labor Statistics.

Payroll Employment since March 2001
Index, March 2001 = 100
106
U.S.

104

Pennsylvania

102
100
98

Erie MSA

96
94
2001

2002

2003

2004

2005

2006

2007

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

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

Disaggregating employment into manufacturing
and nonmanufacturing components, we see that
the Erie metropolitan area underperformed relative to the U.S. average in both sectors. Since the
last business cycle peak in March 2001, Erie lost
25.5 percent of its manufacturing jobs, while the
nation lost 17.5 percent. This manufacturing drag
on Erie’s economy is particularly important because
Erie has a much higher share of manufacturing
than the United States as a whole. Alternatively,
Erie’s nonmanufacturing employment growth has
tracked the national trend pretty closely over the
past six years.
Like Buﬀalo and Cleveland, Erie’s manufacturing
employment has suﬀered a steep decline, though
the time-series patterns for Buﬀalo and Cleveland
diﬀer. Erie’s steepest drop occurred in the 2001–
2003 period, but since mid-2003 it has stabilized
somewhat, while in Buﬀalo and Cleveland it has
continued to contract. Toledo’s decline has mirrored that of the United States, though recently,
22

Toledo is showing some relative weakness.

Payroll Employment since March 2001
Index, March 2001 = 100
106

Where Erie looks quite diﬀerent from the other
cities along Lake Erie is in the growth of nonmanufacturing employment. Erie has consistently added
nonmanufacturing jobs at a faster rate than the other
Lake Erie cities. Erie’s 6.8 percent nonmanufacturing
growth exceeds Buﬀalo’s 2.7 percent gain, Toledo’s
0.6 percent loss, and Cleveland’s 1.5 percent loss.

U.S.

104
102
100

Erie

98
Buffalo
96

Toledo

94
92
2001

The relatively slow growth of Erie’s labor market
is also reﬂected in the metro area’s statistics on per
capita personal income. Over the last six years, Erie’s
nominal growth in per capita income has been
substantially lower than Pennsylvania’s or the United
States’. Nominal per capita income grew in Erie at
17.9 percent, while Pennsylvania and the United
States had similar rates of 23.5 percent and 22.7
percent, respectively. Moreover, Erie has substantially
lower per capita income. In 2006, the Erie metro
area’s per capita income was only 79 percent that of
Pennsylvania’s and the United States’.

Cleveland
2002

2003

2004

2005

2006

2007

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

Payroll Employment since March 2001
Index, March 2001 = 100
110
105
100

Erie Nonmanufacturing
U.S. Nonmanufacturing
U.S. Manufacturing
Erie Manufacturing

95
90

Payroll Employment since March 2001,
Nonmanufacturing
Index, March 2001 = 100
110

85
80

108

U.S.

75
70
2001

106
Erie
2003

2005

2007

104

Buffalo

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

102
Toledo

100
98

Cleveland

96
2001

Payroll Employment since March 2001,
Manufacturing
Index, March 2001 = 100

2003

2005

2007

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

Per Capita Income Growth since 2000
Percent
30

105
100

$36,689 in 2006

$36,629 in 2006

Pennsylvania

U.S.

95
20

$28,941 in 2006

90
U.S.

85
Buffalo

80
75
70
2001

Toledo

Cleveland
5

Erie

0
2003

2005

2007

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

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

Erie
Source: Bureau of Economic Analysis.

Banking and Financial Institutions

Fourth District Community Banks
01.22.08
by Joe Haubrich and Saeed Zaman

Asset Quality
Percent
1.2

Net charge-offs, excluding JP Morgan Chase

1.1
1.0
0.9

a
Problem assets ,
excluding JP Morgan Chase

0.8
0.7
0.6
0.5
a
Problem assets ,
including JP Morgan Chase

0.4
0.3
0.2
1994

Net charge-offs, including JP Morgan Chase
1996

1998

2000

2002

2004

2006

Notes: Data are through 2007:IIIQ only. Data for 2007 are annualized.
a. Problem assets are shown as a percent of total assets, net charge-offs as a
percent of total loans.
Source: Authors’ calculation from Federal Financial Institutions Examination Council,
Quarterly Banking Reports of Condition and Income, third quarter 2007.

Coverage Ratio
Percent
30
27

Including JP Morgan Chase

24
21

Excluding JP Morgan Chase
15
12

1996

1998

2000

2002

Fourth District banks held $12.45 in equity capital
and loan loss reserves for every dollar of problem
loans, which is above the recent coverage ratio low
of 10.75 at the end of 2002, but well below the
record high of 24.97 at the end of 2004.
Equity capital as a percent of Fourth District banks’
assets (the leverage ratio) rose to 9.73 percent (from
9.34 percent at the end of 2006).

9
1994

Overall, ﬁnancial indicators point to some weakening of Fourth District banks’ balance sheets. Asset quality, as measured by net charge-oﬀs (losses
realized on loans and leases currently in default
minus recoveries on previously charged-oﬀ loans
and leases) deteriorated in the third quarter of
2007. Net charge-oﬀs increased to 0.37 percent of
total loans (from 0.34 percent at the end of 2006).
Problem assets (nonperforming loans and repossessed real estate) as a share of total assets rose to
0.95 percent, from 0.72 percent at the end of 2006.
The increase in problem assets may translate into
higher charge-oﬀs in the future if borrowers cannot
catch up with their late payments. At the national
level, the picture is similar; both asset quality ratios
have deteriorated. Net charge-oﬀs and nonperforming loans rose to 0.43 percent of loans (up from
0.33 percent at the end of 2006) and 0.56 percent
of assets (up from 0.45 percent at the end of 2006).

2004

2006

Notes: Data are through 2007:IIIQ only. Data for 2007 are annualized. Efficiency is
operating expenses as a percent of net interest income plus non-interest income.
Source: Authors’ calculation from Federal Financial Institutions Examination Council,
Quarterly Banking Reports of Condition and Income, third quarter 2007.

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

The percent of unproﬁtable institutions in the
Fourth District rose to 8.66 percent for the third
quarter of 2007 (from 6.36 percent at the end of
2006). Unproﬁtable banks’ asset size also rose, as
the share of District banks’ assets accounted for by
unproﬁtable banks increased from 0.23 percent to
0.45 percent. Industrywide, the percent of unprofitable institutions rose from 7.7 percent to 9.67
percent at the end of the third quarter of 2007. The
asset size of unproﬁtable banks also went up from
0.59 percent at the end of 2006 to 1.93 percent at
the end of the third quarter of 2007. So, the industrywide increase in the number of unproﬁtable
24

banks was restricted not only to smaller ﬁnancial
institutions but broad based.

Core Capital (Leverage) Ratio
Percent
11
Capital Ratio, excluding JP Morgan Chase
Capital Ratio, including JP Morgan Chase
10
9
8
7
6
5
4
1994

1996

1998

2000

2002

2004

2006

Unprofitable Institutions
Percent
9
8
7

Assets in unprofitable institutions
excluding JP Morgan

6
5
4

Unprofitable institutions
excluding JP Morgan

Unprofitable institutions
including JP Morgan

Net income posted by FDIC-insured commercial
banks headquartered in the Fourth Federal Reserve
District for the ﬁrst three quarters of 2007 was
$7.6 billion —$10.14 billion on an annual basis.
(JP Morgan Chase, chartered in Columbus, is not
included in this discussion because its assets are
mostly outside the District and its size—roughly
$1 trillion—dwarfs other District institutions.) The
U.S. banking industry as a whole posted earnings
of $107.09 billion for the same period—$142.78
billion on an annual basis.
Fourth District banks’ net interest margins (core
proﬁtability computed as interest income minus
interest expenses divided by average earning assets) fell to 2.96 percent of total income at the end
of the third quarter of 2007, but it is still higher
than the U.S. average of 2.87 percent. Non-interest
income relative to total income slipped for both
Fourth District banks and the national average,
to 28.51 percent for District banks, and to 28.42
percent for the nation.

3
2
1

Assets in unprofitable
institutions including JP
Morgan

0
-1
1994

1996

1998

2000

2002

2004

2006

Fourth District banks’ eﬃciency (operating expenses as a percent of total income) continued to
worsen in the third quarter of 2007, deteriorating
to 56.69 percent from the 52.64 percent record set
in 2002. (Lower numbers correspond to greater efﬁciency.) Banks outside the Fourth District also deteriorated, as the national average climbed to 55.05
percent, from 54.64 percent at the end of 2006.
At the end of the third quarter of 2007, District
banks posted a 1.20 percent return on assets (down
from 1.41 percent at the end of 2006) and a 12.37
percent return on equity (down from 15.05 percent
at the end of 2006). The District’s decline resonated
the with the downward trend nationwide: Return
on assets nationwide was down to 1.01 percent
(from 1.14 percent at the end of 2006), and return
on equity was down to 10.92 percent (from 12.23
percent at the end of 2006).

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

Annual Net Income

Income Ratios

Billions of dollars
22
Excluding JP Morgan Chase
20
Including JP Morgan Chase
18

Percent
5.00

Percent
44

4.75
4.50
4.25

Net interest margin excluding
JP Morgan Chase

42
Non-interest income/income
including JP Morgan Chase 40
38
36

4.00

3.75

3.50

3.25

3.00

2.75

2.50

2.00
1995

0
1995

1997

1999

2001

2003

2005

2007

Note: Data are through 2007:IIIQ only. Data for 2007 are annualized.
Source: Authors’ calculation from Federal Financial Institutions Quarterly Examination
Council, Banking Reports of Condition and Income, third quarter 2007.

Net interest margin including
JP Morgan Chase

2.25

Non-interest income/income
excluding JP Morgan Chase

22
20

1997

1999

2001

2003

2005

2007

Note: Data are through 2007:IIIQ only. Data for 2007 are annualized.
Source: Authors’ calculation from Federal Financial Institutions Examination Council,
Quarterly Banking Reports of Condition and Income, third quarter 2007.

Efficiency Ratio

Earnings

Percent
70

Percent
1.7

Excluding JP Morgan Chase

1.4

1.6
1.5

Including JP Morgan Chase

Percent
20
Return on equity excluding JP Morgan
18

1.3

1.2

1.1

1.0

0.9

0.8

50
1994

1996

1998

2000

2002

2004

2006

0.7
1995

Return on assets
excluding JP Morgan

10
8

Return on equity
including JP Morgan

6
4

Return on assets
including JP Morgan

2
0

1997

1999

2001

2003

2005

2007

Note: Data are through 2007:IIIQ only. Data for 2007 ammualized.
Source: Authors’ calculation from Federal Financial Institutions Examination Council,
Quarterly Banking Reports of Condition and Income, third quarter 2007.

Economic Trends is published by the Research Department of the Federal Reserve Bank of Cleveland.
Views stated in Economic Trends are those of individuals in the Research Department and not necessarily those of the Federal Reserve Bank of Cleveland or of the Board of Governors of the Federal Reserve System. Materials may be reprinted
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ISSN 0748-2922

Federal Reserve Bank of Cleveland, Economic Trends | February 2008

Full text of Economic Trends (Federal Reserve Bank of Cleveland) : Economic Trends - February 2008

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