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March 2013 (February 15, 2013-March 18, 2013)

In This Issue:
Banking and Financial Markets
 Has the Appetite for Risk Returned?

Labor Markets, Unemployment, and Wages
 Improvements in High School Graduation Rates

Growth and Production
 The Recession and Recovery from an Industry
Perspective

Monetary Policy
 Does Nonfarm Payroll Growth Improve the
Taylor Rule?
 Yield Curve and Predicted GDP Growth,
February 2013

Households and Consumers
 Educational Attainment and Demographic
Differences in Employment

Banking and Financial Markets

Has the Appetite for Risk Returned?
02.22.13
by Mahmoud Elamin and William Bednar
The year 2012 was a busy one for risky debt. The
total value of the various forms of risky debt that
were issued—corporate debt, asset-backed securities, collateralized debt obligations, and municipal
debt in particular—grew substantially over the
previous year, while yield spreads for these instruments decreased.
The drop in yields coupled with the increase in
issuance signals that funds suppliers are willing to
supply more funds at each yield. Fed policies might
be one of the factors behind this increase in willingness, as one goal of the Fed’s asset purchase policy is
to increase credit to private-sector investments such
as corporate debt. The increased issuance size coupled with decreasing yields made 2012 a borrower’s
market. Firms issued more debt to take advantage
of the lower yields, while investors handed more of
their funds to these firms, even though promised
yields were lower.
There are two risk categories of corporate debt,
and while both have grown, the riskier type has
bounced back even stronger. Investment-grade
corporate debt is the debt of companies that are
deemed safer, and it is rated by S&P as BBB- and
higher. Issuances of investment-grade corporate
debt were almost flat from 2010 to 2011, but they
increased 30 percent in 2012. On the other hand,
high-yield corporate debt, the riskier type, decreased 15 percent from 2010 to 2011 but experienced a huge surge of 47 percent in 2012.
As for yield spreads, the spread for investmentgrade debt over U.S. treasuries hovered close to
2 percent until July 2011, it peaked at about 3
percent in January 2012, and it declined to slightly
lower than 2 percent later in the year. High-yield
spreads were more volatile, fluctuating and bottoming out in the first half of 2011, surging by almost
60 percent in the second half of 2011 to about 8
percent, and experiencing a decline in 2012 to end
close to the lows of 2011 by year-end. It is parFederal Reserve Bank of Cleveland, Economic Trends | March 2013

ticularly striking to note that yields were actually
dropping in 2012, as issuances were increasing at
this high pace.

Corporate Debt Issuance: Investment Grade

Corporate Debt Issuance: High Yield
Billions

Billions

350

1200

300
1000
250
800

200

600

150

400

100

200

50
0

0
2010

2011

2010

2012

2011

2012

Source: SIFMA.
Source: SIFMA.

Corporate Debt Effective Yield Spread
Percent
9
8
7
High yield

6
5
4
3

Investment grade

Asset-backed securities (ABSs)—debt instruments
backed by auto loans, credit card debt, home equity
loans, and student loans—have been expanding
since 2010. In 2011 they increased 17 percent over
the previous year but they surged 58 percent in
2012. ABS yield spreads over treasuries declined
until they bottomed in first half of 2011. They
increased in the second half of 2011 but have been
declining in 2012. It is again noteworthy to mention the declining yields with surging issuances.

1
0
1/2010

7/2010

1/2011

7/2011

1/2012

7/2012

Sources: Bank of America Merrill Lynch; Haver Analytics.

Asset-Backed Security Issuance
Billions
250
200
150
100
50
0
2010

2011

2012

Collateralized debt obligations (CDOs) are the
notorious debt instruments that wreaked havoc
during the crisis. CDOs are similar to ABSs, but
they are usually backed by riskier debt. CDO issuances have been recovering at an increasing rate,
going up in 2011 by 260 percent over 2010, and
45 percent in 2012 over 2011. Although these rates
seem substantial, the level of CDO issuances is nowhere close to where it was before the last financial
crisis hit.
Municipal issuances declined strongly from 2010
to 2011, but grew about 28 percent from 2011 to
2012, a substantial increase. Issuances in 2012 are
still below the 2010 levels though. The effective
yield spread for municipal debt started low in the
beginning of 2010 and grew strongly in the second half of 2010, peaking at the end of the year.

Source: SIFMA.

Federal Reserve Bank of Cleveland, Economic Trends | March 2013

The spread was elevated during 2011, but then it
declined and remained at lower levels in 2012, dipping slightly towards the end of 2012.

ABS Effective Yield Spread
Percent
4.5
4.0

We see a clear uptick in issuances of risky loans
in 2012, concurrent with a drop in spreads over
treasury yields.

3.5
3.0
2.5

For further reading on the Fed’s policy moves, visit http://www.
clevelandfed.org/research/commentary/2013/2013-02.cfm.

2.0
1.5
1.0
0.5
0.0
1/2010

7/2010

1/2011

7/2011

1/2012

7/2012

Sources: Bank of America Merrill Lynch; Haver Analytics.

Global CDO Issuance

Municipal Security Issuance

Billions

Billions
500
450
400
350
300
250
200
150
100
50
0

50
45
40
35
30
25
20
15
10
5
0
2010

2011

2012

2010

2011

2012

Source: SIFMA.

Municipal Debt Effective Yield Spread
Percent
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1/2010

7/2010

1/2011

7/2011

1/2012

7/2012

Sources: Bank of America Merrill Lynch; Haver Analytics.

Federal Reserve Bank of Cleveland, Economic Trends | March 2013

Growth and Production

The Recession and Recovery from an Industry Perspective
03.08.2013
by Pedro Amaral and Sara Millington
Real GDP grew at an annualized rate of 0.1 percent in the fourth quarter of 2012, according to
the Bureau of Economic Analysis’s revised estimate.
Although this revision may confer the important psychological effect of keeping a streak of 14
consecutive quarters with positive growth alive (the
BEA’s first estimate indicated a 0.1 percent decrease
in real GDP), the reality is that the U.S. economy
stagnated in the last quarter of last year. This deceleration—growth in the third quarter of 2012 was a
robust 3.1 percent—primarily reflected decreases in
federal government spending, as military spending
fell at an annualized rate of 22 percent, and private
inventory investment.
If we compare the whole year of 2012 to 2011,
the picture is only slightly rosier. While growth
increased from 1.8 to 2.2 percent, this is very much
on par with the average growth rate for the recovery, but well below that of previous ones. It is important to note that the acceleration in growth we
experienced from 2011 to 2012 occurred even as
the contribution of personal consumption expenditures, the most important component of GDP,
actually diminished. Going forward, if we could
only combine the sort of contribution we had from
personal consumption expenditures in 2011 with
the one we had from private domestic investment
in 2012, maybe we could finally get a GDP growth
rate in 2013 that would match a more normal
recovery pace.

Output
Index (2007:Q4=100)
115
110

EHSA

105
100

FIRE

95
All private industries

Manufacturing

85
80

Construction
75
70
2008

2009

2010

2011

2012

Notes: Shaded bar indicates a recession. FIRE refers to finance, insurance, and
real estate, and EHSA refers to education, health care, and social assistance.
Source: Bureau of Economic Analysis.

Federal Reserve Bank of Cleveland, Economic Trends | March 2013

The overall growth rate of real GDP hides a fair
amount of heterogeneity across industries. While
the output of all U.S. domestic private industries
just recently surpassed its 2007:Q4 peak, some
industries remain well below that benchmark. Most
notably, construction remains extremely depressed
following the housing market collapse and has
yet to see meaningful signs of a recovery. Another
industry that still remains below the pre-recession
peak is manufacturing. This industry has actually
been staging a fairly speedy recovery, but it had a
5

deeper hole to climb out of, having been battered
more than the average during the recession.
On the other extreme there are industries that
seemingly breezed through the recession, like education, health care, and social assistance (EHSA).
This industry certainly benefited from the fact that
a lot of people who became unemployed decided
to go back to school and that medical expenditures
stay fairly constant even when incomes decline.
Curiously, an industry that came under a lot of
pressure during the recession, finance, insurance
and real estate (FIRE), has fared substantially better
than average and hardly experienced a decline during the whole recession episode.

Total Hours
Index (2007:Q4=100)
110
EHSA

105
100

FIRE

95
90

All private industries

85
Manufacturing

80
75

Construction

70
2008

2009

2010

2011

2012

Notes: Shaded bar indicates a recession. FIRE refers to finance, insurance, and real
estate, and EHSA refers to education, health care, and social assistance.
Sources: Bureau of Labor Statistics; Haver Analytics.

Productivity
Index (2007:Q4=100)
120
Construction

115
110
FIRE

105

EHSA

100

All private industries

95
Manufacturing
90
85
80
2008

2009

2010

2011

2012

While both EHSA and FIRE have increased their
production during the recovery, they have gone
about it in slightly different ways. To see this, it
helps to think of an industry’s output as depending on the total hours of work it uses in production
and how productive those hours are. In increasing
its output, EHSA relied more on the former than
on the latter. In contrast, FIRE was able to increase
its output while reducing its total hours, achieving
nearly 10 percent productivity gains.
Similarly, after being badly hit up until the recession’s trough in the second quarter of 2009,
manufacturing and construction have relied mostly
on productivity gains to recover. In the case of
manufacturing, productivity gains have helped the
industry increase its output, while in the case of
construction, they have helped to keep output constant in the face of a decline in total hours worked.
Total hours worked, in turn, are simply the product
of the number of employees and the average hours
each employee works: in economic jargon these are
referred to as the extensive and intensive margin,
respectively. In a typical recession, businesses make
more use of the extensive margin than the intensive
margin to adjust their labor input. That is, they let
employees go rather than reduce hours. From peak
to trough of the last recession, for example, businesses made only a 2 percent reduction in the average hours of their remaining employees. While by
adjusting the intensive margin, employers economize on the hourly wage, they save on a variety

of fixed costs by firing an extra person. In the last
recession, this tendency was mostly noticeable in
FIRE, where average hours never fell.

Total Number of Employees
Index (2007:Q4=100)
120
110

EHSA

100

All private industries

FIRE
90
80

Manufacturing

Construction

60
2008

2009

2010

2011

2012

Average Hours
Index (2007:Q4=100)
103
102
101

A word of caution in interpreting these cross-industry differences: adjustments to labor input do not
occur in a vacuum. They are ultimately a function
of technological change and consumer preferences
and depend (and in turn help determine) product
and factor prices for each industry. Finally, they
also depend on labor market conditions that are
industry-specific. As an example, industries with
higher unionization rates, everything else being the
same, will tend to see relatively smaller decreases in
the extensive margin, as firing costs are relatively
higher.
The four industries we have highlighted here cover
only 50 percent of total private production. But
they serve to illustrate the different ways that U.S.
industries adjusted their production and labor usage during the last recession.

FIRE

EHSA

100

Construction

All private industries

98
97
Manufacturing

96
95
94
93
2008

2009

2010

2011

2012

Federal Reserve Bank of Cleveland, Economic Trends | March 2013

Households and Consumers

Educational Attainment and Demographic Differences in Employment
03.18.13
by Dionissi Aliprantis and Nelson Oliver
It is well-known that employment outcomes such
as unemployment rates and employment-to-population ratios vary markedly across demographic
groups. Differences in unemployment rates are
especially pronounced across age and racial groups.
For example, in January 2013 the unemployment
rate for African Americans was approximately
double that of whites.

Unemployment Rate in January 2013 (percent, seasonally adjusted)
All
7.9

Gender

Age

Race

Ethnicity

Male

Female

16-24

25-34

35-44

45-54

55+

White

African American

Asian

Hispanic

8.0

7.8

16.8

7.7

6.5

6.0

7.0

13.8

6.3

9.7

Source: Bureau of Labor Statistics.

It is also well-known that employment outcomes
depend significantly on educational attainment,
and that levels of educational attainment vary
across race and ethnicity. For example, in 2012,
35 percent of Hispanics had not completed high
school, compared with 8 percent of whites. (Note
that Hispanic represents an ethnic category, so that
both African–American and white racial categories
include some Hispanics, while the Hispanic group
contains individuals who identify as neither African
American nor white.)

Educational Attainment, 2012
Percent of population 25 and older
40
White

Black

Asian

Hispanic

35
30
25
20
15
10
5
0
No high school High school
diploma
graduate

Some college

Bachelor’s
degree

Advanced
degree

Source: U.S. Census Bureau.

Federal Reserve Bank of Cleveland, Economic Trends | March 2013

We examine recent data on employment-to-population ratios and find that although educational
attainment explains much about labor market
outcomes by race and ethnicity, it does not explain
everything. We look at the percent differences in
these ratios for three groups relative to whites,
compared at the same level of educational attainment. (A value of zero means the ratios are identical, positive values mean a group has a higher ratio
than whites, and negative values the opposite.)
We find that differences in labor market outcomes
across race and ethnic groups remain even at similar
levels of educational attainment. Whites and Asians
have very similar employment ratios at all levels
of educational attainment, Hispanics have much
8

higher employment ratios of African Americans
has declined at the top and low ends of educational
attainment.

Employment Levels
for High School Dropouts
Percent difference in employment-to-population ratios
0.5
0.4

Hispanic-White

0.3
0.2
0.1

Asian-White

0
-0.1

African-American-White

-0.2
-0.3
-0.4
2000

2002

2004

2006

2008

2010

2012

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

For those with some college, Hispanics also have
a higher overall employment-to-population ratio
than whites. (Some college includes associates
degrees, technical or professional accreditation, one
or more college courses, etc.) African Americans
with some college tended to be employed more on
average over the period leading up to the recent
recession.

Employment Levels
for High School Graduates
Percent difference in employment-to-population ratios
0.5
0.4
0.3

Hispanic-White

0.2

Asian-White

0.1
0

African-American-White

-0.1
-0.2
-0.3
-0.4
2000

2002

2004

2006

2008

2010

2012

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

Employment Levels for Those
with Some College Education
Percent difference in employment-to-population ratios
0.5
0.4
0.3
0.2

Hispanic-White

0.1
0
-0.1

Asian-White

African-American-White

-0.2
-0.3
-0.4
2000

2002

2004

2006

2008

2010

2012

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

Federal Reserve Bank of Cleveland, Economic Trends | March 2013

Between 2000 and 2007, Hispanics and African
Americans with bachelor’s degrees (BAs) were more
likely to be employed than whites. The Great Recession has had an uneven effect on these racial patterns. We can see a clear shift down, for instance, in
the employment of African–American BA holders
relative to white BA holders since the Great Recession began. While these figures have been annualized, it appears that African Americans and white
BA holders are now employed at more similar rates
than before the recession.

The relative employment of African Americans with
high school diplomas to whites with high school
diplomas seems to have undergone a sustained
decline since 2000, and this decline appears to only
have been accelerated by the Great Recession. In
2000, the employment-to-population ratio of African American high school graduates was 6 percent
higher than for whites, and by 2011 this figure had
fallen to 4 percent less than whites.
Meanwhile, African American high school dropouts
were 16 percent less likely to be employed than
white high school dropouts in 2000, and this ratio
has been further declining since then. The long-run
decline appears to have only been accelerated by
the recent recession. Hispanic high school dropouts
have also undergone a long-run decline in their
employment ratio relative to whites since 2000.
This decline, however, does not appear to have been
strongly influenced by either the 2001 recession
or the Great Recession. Though the employmentto-population ratios of Asians and whites are very
similar in each educational category, the difference
between the two is greatest for high school dropouts.
9

Employment Levels for Those
with Bachelor’s Degree or Higher
Percent difference in employment-to-population ratios
0.5
0.4
0.3
0.2
0.1

African-American-White

Hispanic-White

0
-0.1

Asian-White

-0.2
-0.3
-0.4
2000

2002

2004

2006

2008

2010

2012

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

Federal Reserve Bank of Cleveland, Economic Trends | March 2013

If educational attainment alone determined employment, then whites and Hispanics would have
very different overall employment ratios. However,
despite having very different shares of individuals
in each educational category, whites and Hispanics
have very similar overall employment-to-population
ratios (59.4 percent and 59.5 percent, respectively,
in 2012). Factors other than educational attainment are affecting white and Hispanic employment
outcomes within each educational category, leading
to the similarity in overall outcomes.
In the case of African-Americans, their employment-to-population ratio has declined relative to
the other groups. What is particularly surprising
is that this relative decline occurred at both ends
of the educational spectrum. The difference in
outcomes could reflect differences in labor markets—weaker performance in inner-city labor markets—or perhaps differences in the demographic
composition within the educational groupings or
differences in the workforce experiences within the
groups. In short, in order to more fully understand
the drivers of changes in the employment-topopulation ratios, one would need to examine the
underlying data that would allow one to control
for differences in location and within-group demographics.

Monetary Policy

Does Nonfarm Payroll Growth Improve the Taylor Rule?
02.25.13
by Charles T. Carlstrom, Saeed Zaman, and
Samuel B. Chapman
There has been a lot of interest in financial circles
in finding a guidepost or rule of thumb that reflects
how monetary policymakers decide how to set
interest rates. Given that the federal funds rate—
the short-term interest rate set by the Federal Open
Market Committee (FOMC)—has been at zero for
a while, such a rule may not seem useful today. But
presumably it will be once the rate is above zero,
and it is interesting to see what the rule suggests
about when the rate will increase. Some versions
of the rule predict an earlier increase than the
FOMC’s current projections, and we explain why
this would be so.

Estimated Taylor Rule: Unemployment-Gap
and Partial-Adjustment Version
Percent
10
9
8
7

Effective federal funds rate

6
5
4
3
2

Unemployment-gap Taylor rule
(Average absolute residual: 0.34)

1
0
1987

1990

1993

1996

1999

2002

2005

Note: Shaded bars indicate recessions.
Source: Bureau of Labor Statistics, Board of Governors of the Federal System,
authors’ calculations.

Federal Reserve Bank of Cleveland, Economic Trends | March 2013

2008

There are many variations of this so-called “Taylor
rule” out there, but one variation that is commonly
used and is consistent with the FOMC’s dual mandate of price stability and maximum employment is
one that has the Federal Reserve responding positively to increases in core inflation above its target
and negatively to increases in unemployment above
the long-run normal level of unemployment. This
version of the rule is usually expanded to include
the previous quarter’s federal funds rate to reflect
the likelihood that the FOMC adjusts the fed funds
rate gradually toward its desired target.
When this version of the rule is estimated with data
from 1987-2008, it seems at first glance to do a
decent job of tracking the actual federal funds rate.
But looks can be deceiving. It misses the actual
funds rate by an average of 34 basis points. That is
not much better than a simple, naïve rule that assumes today’s funds rate is merely yesterday’s funds
rate—the average absolute miss for this simple rule
and the same data is 37 basis points. In the end, the
common version of the Taylor rule basically just
predicts that the funds rate will be where it was last
quarter. This implies there can be significant misses
with it.

Zooming in over the time period 1992-2000, we
can see clearly an example of such a miss. In mid1994, for example, the miss was quite large—about
91 basis points.

Estimated Taylor Rule (Unemployment-Gap
and Partial-Adjustment Version),
Zoom to 1992–2000
Percent

In the end, the common version of the Taylor rule
basically just predicts that the funds rate will be
where it was last quarter. That is, the Taylor rule is
always a step behind the actual funds rate. We can
modify the rule to account for this fact by including a leading indicator of the unemployment rate.
One decent candidate is nonfarm payroll growth

7.0
6.5

Effective federal funds rate

6.0
5.5
5.0
4.5

Unemployment-gap Taylor rule

4.0
3.5

Miss: 0.91 percentage points

3.0
2.5
2.0
1992

1993

1994

1995

1996

1997

1998

1999

2000

Note: Shaded bar indicates a recession.
Source: Bureau of Labor Statistics, Board of Governors of the Federal System,
authors’ calculations.

Unemployment Rate
and Nonfarm Payroll Growth
Percent, year-over-year

Percent

9
Nonfarm payroll
growth

-1

4
Unemployment rate

-2
-3
1987

3
2
1990

1993

1996

1999

2002

2005

2008

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

Comparison of Estimated Taylor Rules
Percent
10

Effective federal funds rate
Unemployment-gap Taylor rule
(average absolute residual: 0.34)
Unemployment-gap and employment
Taylor rule (average absolute residual: 0.22)

9
8
7

To say nonfarm payroll growth is a leading indicator is to say nonfarm payroll employment typically
rises before the unemployment rate drops. In fact,
increases in payroll growth appear to lead to a fall
in unemployment about two quarters later. For
example, the unemployment rate reached its nadir
in 2006:Q4, three quarters after the peak in payroll
growth (2006:Q2).
When nonfarm payroll growth is added to the
Taylor rule above, the new rule does a much better
job of capturing the movements of the actual fed
funds rate. The predicted path of the rate is still a
little behind the actual funds rate, but it is much
better than it was with just the unemployment
gap. In fact, the modified rule enhances the “fit”
substantially. Before, the average absolute miss was
34 basis points; now it is 22 basis points. This is a
sizable improvement, given that it eliminates more
than one-third of the original miss.
It is worth noting that there are time periods, such
as 2007-2008 (going into the financial crisis),
when both rules display large misses. Such periods
illustrate that Fed does not mechanically follow
any rule. Instead, especially when unusual developments are taking place or are anticipated, it will
deviate from its usual behavior.

6
5
4
3
2
1
0
1987

1990

1993

1996

1999

2002

2005

2008

Note: Shaded bars indicate recessions.
Source: Bureau of Labor Statistics, Board of Governors of the Federal System,
authors’ calculations.

Federal Reserve Bank of Cleveland, Economic Trends | March 2013

For comparison purposes, the chart below zooms
into the same 1992-2000 period discussed earlier.
Focusing on the 1994-1996 time period, it is quite
obvious that the Taylor rule with payroll growth
and the unemployment gap does substantially better than the rule with only the unemployment gap.
In fact, the problem of the Taylor rule being a step
behind the actual funds rate is largely eliminated. A
12

Comparison of Estimated Taylor Rules,
Zoom to 1992–2000
Percent
7.0
6.5
6.0
5.5
5.0
4.5
4.0
Effective federal funds rate
Unemployment-gap Taylor rule
Unemployment-gap and
employment Taylor rule

3.5
3.0
2.5
2.0
1992

1993

1994

1995

1996

1997

1998

1999

2000

Source: Bureau of Labor Statistics, Board of Governors of the Federal System,
authors’ calculations.

Comparison of Lift-off Dates Predicted
by Payroll-Growth Taylor Rule and FOMC
1.0
0.9
Payroll-growth
Taylor rule
(Lift-off 2014:Q3)

0.7
0.6
0.5
0.4
0.3
0.2
0.1

FOMC median projection
for federal funds rate
(Lift-off 2015:Q2)

0.0
2013

2014

Given that this new Taylor rule does a better job
of estimating past values of the federal funds rate,
the next step naturally is to use it to project the
future path of the federal funds rate. To produce
the future federal funds rate path, we use the most
recent economic projections of FOMC participants
(December 2012), which are reported in the Survey
of Economic Projections (SEP). Specifically, we use
their projections for the core personal consumption expenditures price index (for inflation), the
unemployment rate, and the long-run normal level
of unemployment. Since the SEP does not include
a forecast for nonfarm payroll growth, we use the
forecast for it from Macroeconomic Advisors (MA),
a private forecasting firm.
We compare the federal funds rate path and the
liftoff dates implied by our modified Taylor rule
with median fed funds rate projections from the
December 2012 SEP. We define liftoff as the date at
which the projected fed funds rate exceeds 50 basis
points.
According to the fed funds rate path estimated
with our modified Taylor rule, the first fed funds
rate increase would occur in the third quarter of
2014, about four quarters earlier than the median
fed funds rate projection from the December 2012
SEP.

Percent

0.8

review of FOMC minutes around this time period shows clearly that the Committee was closely
monitoring labor markets, and FOMC participants
explicitly mentioned nonfarm payroll employment
and the unemployment rate as their rationale for
policy moves. Furthermore, the strong gains posted
in payroll employment throughout this period
coincided with fed funds rate increases.

SEP estimate of
unemployment
rate=6.5%
(2015:Q3)
2015

Note: Since the long-run federal funds rate may have changed over the period
since the Taylor rule was estimated, we instead use the median projection of the
long-run federal funds rate from the Summary of Economic Projections,
December 2012.
Sources: Bureau of Labor Statistics; Bureau of Economic Analysis; Board of
Governors of the Federal System, Summary of Economic Projections, December
2012; authors’ calculations.

Federal Reserve Bank of Cleveland, Economic Trends | March 2013

The quite different exit dates at first glance seem
surprising. But they could be explained two different ways. For one, some other Taylor-type policy
rules imply a later liftoff than does our adjusted
policy rule. With these rules, liftoff is closer to the
date suggested by the FOMC’s December projections. Alternatively, one way the Committee can
stimulate the economy today is by promising that
the funds rate will stay at zero longer than it would
typically, where “typically” would be the Taylor
13

rule projection. By promising to keep rates low
longer, they can lower long-term interest rates and
stimulate the economy. The recent FOMC meeting statement (January 2013) reaffirms this point:
“To support continued progress toward maximum
employment and price stability, the Committee expects that a highly accommodative stance of monetary policy will remain appropriate for a considerable time after the asset purchase program ends and
the economic recovery strengthens.” Furthermore,
according to a recent speech by FOMC Governor
Janet Yellen, keeping interest rates lower than the
prescriptions of the well-known Taylor rule or its
variants would be optimal in terms of economic
outcomes.
For further reading on the Taylor rule, read the Economic Commentary “The Taylor Rule: A Guidepost for Monetary Policy?” at
http://www.clevelandfed.org/research/commentary/2003/0703.pdf.
For the complete text of FOMC Governor Janet Yellen’s speech,
visit http://www.federalreserve.gov/newsevents/speech/yellen20121113a.htm.

Federal Reserve Bank of Cleveland, Economic Trends | March 2013

Monetary Policy

Yield Curve and Predicted GDP Growth, February 2013
Covering January 19, 2012–February 22, 2013
by Joseph G. Haubrich and Patricia Waiwood

Highlights

Overview of the Latest Yield Curve Figures
February

January

December

Three-month Treasury bill rate (percent)

0.13

0.08

0.07

Ten-year Treasury bond rate (percent)

2.00

1.87

1.69

Yield curve slope (basis points)

187

179

162

Prediction for GDP growth (percent)

0.4

0.6

Probability of recession in one year (percent)

6.4

7.1

8.6

Sources: Board of Governors of the Federal Reserve System; authors’ calculations.

Yield Curve Predicted GDP Growth
Percent
GDP growth
(year-over-year change)

4
2
0
-2

Predicted
GDP growth

Ten-year minus three-month
yield spread

-4
-6
2002

2004

2006

2008

2010

2012

Sources: Bureau of Economic Analysis, Federal Reserve Board, authors’
calculations.

Federal Reserve Bank of Cleveland, Economic Trends | March 2013

Over the past month, the yield curve has moved
up, getting somewhat steeper in the process, as long
rates moved more than short rates. The threemonth Treasury bill rose to 0.13 percent (for the
week ending February22), up from January’s 0.08
percent and nearly double December’s 0.07 percent. The ten-year rate moved up to 2.00 percent,
a rate not seen since last April, and was above January’s 1.87 percent and December’s 1.69 percent.
The slope increased to 187 basis points, up from
January’s 179 basis points and December’s 162
basis points.
The steeper slope was not enough to have an
appreciable change in projected future growth,
however. Projecting forward using past values of
the spread and GDP growth suggests that real GDP
will grow at about a 0.4 percent rate over the next
year, down a bit from January and December. The
strong influence of the recent recession is still leading towards relatively low growth rates. Although
the time horizons do not match exactly, the forecast comes in on the more pessimistic side of other
predictions, but like them, it does show moderate
growth for the year.
The slope change had a bit more impact on the
probability of a recession. Using the yield curve
to predict whether or not the economy will be in
recession in the future, we estimate that the expected chance of the economy being in a recession
next February is 6.4 percent, down from January’s
7.1 percent, and below December’s value of 8.6
percent. So although our approach is somewhat
pessimistic as regards the level of growth over the
next year, it is quite optimistic about the recovery
continuing.

Recession Probability from Yield Curve
Percent probability, as predicted by a probit model
100
90
80
70
60

Forecast

50
40
30
20
10
0
1960

1966 1972

1978 1984

1990 1996

2002 2008 2014

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

Yield Curve Spread and Real GDP
Growth
Percent
10
8
GDP growth
(year-over-year change)

6
4
2

-4

The slope of the yield curve—the difference between the yields on short- and long-term maturity
bonds—has achieved some notoriety as a simple
forecaster of economic growth. The rule of thumb
is that an inverted yield curve (short rates above
long rates) indicates a recession in about a year, and
yield curve inversions have preceded each of the last
seven recessions (as defined by the NBER). One of
the recessions predicted by the yield curve was the
most recent one. The yield curve inverted in August
2006, a bit more than a year before the current
recession started in December 2007. There have
been two notable false positives: an inversion in late
1966 and a very flat curve in late 1998.
More generally, a flat curve indicates weak growth,
and conversely, a steep curve indicates strong
growth. One measure of slope, the spread between
ten-year Treasury bonds and three-month Treasury
bills, bears out this relation, particularly when real
GDP growth is lagged a year to line up growth with
the spread that predicts it.
Predicting GDP Growth
We use past values of the yield spread and GDP
growth to project what real GDP will be in the future. We typically calculate and post the prediction
for real GDP growth one year forward.
Predicting the Probability of Recession

0
-2

The Yield Curve as a Predictor of Economic
Growth

Ten-year
minus
three-month
yield spread

-6
1953 1959 1965 1971 1977 1983 1989 1995 2001 2007
Note: Shaded bars indicate recessions.
Source: Bureau of Economic Analysis, Federal Reserve Board.

While we can use the yield curve to predict whether
future GDP growth will be above or below average, it does not do so well in predicting an actual
number, especially in the case of recessions. Alternatively, we can employ features of the yield curve
to predict whether or not the economy will be in a
recession at a given point in the future. Typically,
we calculate and post the probability of recession
one year forward.
Of course, it might not be advisable to take these
numbers 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 materi

Federal Reserve Bank of Cleveland, Economic Trends | March 2013

Yield Spread and
Lagged Real GDP Growth
Percent
10
8
6

One-year lag of GDP growth
(year-over-year change)

4
2
0
-2
-4

Ten-year minus
three-month
yield spread

ally 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?” Our friends at the Federal Reserve
Bank of New York also maintain a website with
much useful information on the topic, including
their own estimate of recession probabilities.

-6
1953 1959 1965 1971 1977 1983 1989 1995 2001 2007

Sources: Bureau of Economic Analysis, Federal Reserve Board.

For more on the yield curve, read the Economic Commentary “Does
the Yield Curve Signal Recession?” at http://www.clevelandfed.org/
Research/Commentary/2006/0415.pdf.
For more on the Federal Reserve Bank of New York’s estimate fo
recession, visit http://www.newyorkfed.org/research/capital_markets/ycfaq.html.

Federal Reserve Bank of Cleveland, Economic Trends | March 2013

Labor Markets, Unemployment, and Wages

Improvements in High School Graduation Rates
03.01.13
by Jonathan James
In January the Department of Education reported
more positive news on one of the key indicators of
the health of public high schools. During the 20092010 academic year (the most recent year for which
national figures are computed), the estimated average freshman graduation rate (AFGR) reached a
40-year high of 78.2 percent. This is up 2.7 points
from 75.5 percent during 2008-2009. While this
is welcome news, the big picture remains that the
dropout situation in many public high schools
persists at epidemic levels, leaving plenty of room
for future progress.

Average Freshman Graduation Rate
Percent
80
79
78
77
76
75
74
73
72
71
70
1970

1975

1980

1985

1990

1995

2000

2005

2010

Importantly, the recent progress is part of a decadelong trend in improving graduation rates. The
trend is due in part to the No Child Left Behind
Act, passed in the early 2000s, which began forcing
states to better measure and improve their graduation rates. These efforts, along with others, have
resulted in substantial progress, taking the AFGR
from nearly all-time lows in the late 1990s to nearly
all-time highs in the current release.

Source: National Center for Education Statistics.

Average Freshman Graduation Rate
Percent
100
95

91.4 91.8

2007-2008
2008-2009
2009-2010

93.5

90
85

83
81 82

80
75

71.4

70
65

Breaking down these trends by race and ethnicity
shows that while all groups saw improvements on
average, the greatest gains were attributed to groups
with historically low on-time graduation rates.
The AFGR for Hispanic students was up nearly 8
percentage points from two years earlier, and the
estimated graduation rate for black students was up
nearly 5 percentage points over the same period.
White students experienced the smallest gains, with
a 2 percent increase in the last two years.

69.1
64.2 64.8

66.1

65.9
63.5

63.5
61.5

60
55
50
American Indian/
Asian/
Alaska Native Pacific Islander

Hispanic

Black

Source: National Center for Education Statistics.

Federal Reserve Bank of Cleveland, Economic Trends | March 2013

White

An important question that remains is whether we
can expect these trends in the graduation rate to
continue. Part of the answer to this question will
depend on the effect of future changes in how the
graduation rate is measured. A major challenge in
the past has been that each state used a different
method to measure high school graduation rates.
This made comparing graduation rates across states,
as well as constructing a national rate, very difficult.
18

As states continued to construct their own graduation rates, in 2001 the Department of Education
began using the AFGR as a benchmark measure of
the high school graduation rate. It was considered
the most reliable estimate given the available data
reported by each individual state, and it could also
be computed all the way back to the late 1960s.

Comparison of AFGR 2009-2010
to Preliminary ACGR 2010-2011
Difference in measured graduation rates
15
Fourth District
10
National average

5
0
-5
-10
-15

MN AK VT NJ GA UT WV MI OH PA NH IA US FL RI VA NC SD LA DE NV TN AR CT MS
OR CO NM WI MO ND CA KS WA DC WY MT AL MA MD NY ME IL NE AZ HI SC TX IN

Note: Excludes Idaho, Kentucky, and Oklahoma who have not yet reported.
Source: National Center for Education Statistics.

Ohio Department of Education
Graduation Rate Estimates
Percent
88

Ohio
(New adjusted cohort
graduation rate)

86
84

Ohio
(Old adjusted cohort graduation rate)

82
NCES AFGR
80
78.2

78
76
2003

2004

2005

2006

2007

2008

2009

2010

Sources: National Center for Education Statistics; Ohio Board of Education.

Federal Reserve Bank of Cleveland, Economic Trends | March 2013

2011

However, beginning in the 2010-2011 academic
year, all state education agencies will now be required to report graduation rates based on a more
rigorous and uniform standard. The measurement
is defined as the adjusted cohort graduation rate
(ACGR), and it is designed to be a more accurate
estimate of the on-time graduation rate than the
AFGR. The goal of the ACGR is to fully track a cohort of ninth graders who are entering high school
for the first time, adding and subtracting dropouts
and transfers, and calculating the fraction earning a
regular diploma after four years.
The Department of Education released preliminary
data for the 2010-2011 academic year at the state
level using this new measure. A comparison of the
two measures for 2009-2010 illuminates two facts.
First, on a national scale, the previous measure (the
AFGR) is a fairly accurate estimate of the more
refined measure. This is because on average, the
AFGR is an overestimate of the graduation rate in
some states and an underestimate in others, and
these misestimates tend to offset each other. As a
result, we would expect future national estimates
under the new standard to be similar to current
estimates and hopefully similar to current trends.
The second point however is that while the AFGR
may be reliable on a national level, it may not
provide a good estimate for any given state. Consequently, under the more rigorous standard,
compared to methods previously used, many states
may experience large changes in their estimated
graduation rates . One example is Ohio. Prior to
2010 the state reported an estimate of the graduation rate based on its own adjusted cohort formula.
Between 2002 and 2009 this number was around
85 percent. However, in the 2010-2011 academic
year, under the more accurate, uniform standard,
the estimated on-time graduation rate is actually
lower—78.2 percent.
19

Looking forward, an improved measure of the graduation rate will not only provide us with a more
accurate picture of the dropout problem, it will also
reveal the areas that are in most need of improvement. With such information, in conjunction with
the trending improvements in graduation rates,
we are hopefully positioned to continue to make
substantial progress on one of the major challenges
facing the education system.

Federal Reserve Bank of Cleveland, Economic Trends | March 2013

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Federal Reserve Bank of Cleveland, Economic Trends | March 2013

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