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A Quarterly Review
of Business and
Economic Conditions
Vol. 22, No. 1

Finding a Job

Disability Insurance

Nonparticipants Take
A Different Path

The Motives, Constraints
That Lead to Risky Work

January 2014

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

A Look at Japan’s
Slowdown and Its
Turnaround Plan

c o n t e n t s

4
The Regional

Economist
january 2014

VOL. 22, NO. 1

A Look at Japan’s Slowdown
and Its Turnaround Plan

A Quarterly Review
of Business and
Economic Conditions
Vol. 22, No. 1

CENTRAL TO AMERICA’S ECONOMY

For years, perhaps even decades, Japan’s economy has struggled with low
growth and low inflation. A year ago, new policies were put into place to turn
around the economy. Although there are similarities between Japan’s experience and that of other developed countries (including the U.S.), there are also
many differences.

P resident ’ s M essa g e

Around the World, Gender
Gaps in Labor Markets
Ebb and Flow

A Lesser-Known Dynamic
of Labor Markets
By Maria Canon, Marianna
Kudlyak and Marisa Reed

18 district overview
Engines of Growth Vary
in Four Largest Cities

By Silvio Contessi and Li Li

By Maria A. Arias
and Charles S. Gascon
In St. Louis, financial services
are proving to be a top driver
of growth. In Little Rock,
Louisville and Memphis, there
are other important growth
industries. A variety of metrics
can be used to gain insights into
what makes these local economies tick.

Senior Policy Adviser
Cletus C. Coughlin

Relatively little research has
been done on how nonparticipants in the labor market end
up with jobs. The study of this
transition can shed light on
employment trends, including
labor market changes in recessions and recoveries.

Deputy Director of Research
David C. Wheelock
Director of Public Affairs
Karen Branding

Although labor force participation rates for men and women
are converging in many
countries, the gender gap in
unemployment rates varies
significantly depending on the
country and the time period.

Editor
Subhayu Bandyopadhyay
Managing Editor
Al Stamborski
Art Director
Joni Williams

Please direct your comments

address below. Submission of a
letter to the editor gives us the right
to post it to our web site and/or
publish it in The Regional Economist
unless the writer states otherwise.
We reserve the right to edit letters
for clarity and length.
Single-copy subscriptions are free
but available only to those with
U.S. addresses. To subscribe, go to
www.stlouisfed.org/publications.

20 metro profile
Resiliency in Little Rock
Is No Longer a Given

16 econom y at a g lance

to Subhayu Bandyopadhyay

You can also write to him at the

The Motives, Constraints
That Lead to Risky Work

A Look at Japan’s
Slowdown and Its
Turnaround Plan

Director of Research
Christopher J. Waller

subhayu.bandyopadhyay@stls.frb.org.

Disability Insurance

Nonparticipants Take
A Different Path

By Juan M. Sánchez and Emircan Yurdagul

The Regional Economist is published
quarterly by the Research and Public Affairs
divisions of the Federal Reserve Bank
of St. Louis. It addresses the national, international and regional economic issues of
the day, particularly as they apply to states
in the Eighth Federal Reserve District. Views
expressed are not necessarily those of the
St. Louis Fed or of the Federal Reserve System.

at 314-444-7425 or by e-mail at

Finding a Job

January 2014

THE FEDERAL RESERVE BANK OF ST. LOUIS

Of Risky Occupations and
Social Security Disability
By Amanda M. Michaud
and David G. Wiczer
An examination of Social Security Disability Insurance looks
at the motives and constraints
that drive people to work in
risky occupations.

You can also write to The Regional
Economist, Public Affairs Office,
Federal Reserve Bank of St. Louis,
P.O. Box 442, St. Louis, MO 63166-0442.

national overview
Is the Economy
Spring-Loaded?

By Kevin L. Kliesen
This year could be the breakout year for the U.S. economy,
perhaps the best year since 2005
in terms of GDP growth. The
outcome depends critically on
the Fed’s ability to keep inflation
and inflation expectations stable.

By Charles S. Gascon
and Peter B. McCrory
Key service sectors—such as
health care and government—
as well as relatively stable house
prices helped Little Rock remain
resilient throughout the recession.
However, the future is less certain,
as the recovery has been slow.

23 reader e x c h an g e

COVER IMAGE: © ISTOCK

The Eighth Federal Reserve District includes

all of Arkansas, eastern Missouri, southern
Illinois and Indiana, western Kentucky and
Tennessee, and northern Mississippi. The
Eighth District offices are in Little Rock,
Louisville, Memphis and St. Louis.

2 The Regional Economist | January 2014

ONLINE EXTRA

The Rise and (Eventual) Fall
of the Fed Balance Sheet

Job-Search Methods
in Good, Bad Times

Read more at www.stlouisfed.
org/publications/re.

By Lowell R. Ricketts
and Christopher J. Waller

By James D. Eubanks
and David G. Wiczer

Quantitative easing has led to the largest
expansion of the Fed’s balance sheet since
World War II. While this, naturally, leads
to concern about inflation, the Fed has the
tools to unwind the balance sheet once the
economy builds steam.

Is one method of searching for a job better
than another? Do job-seekers change their
approach when a recession hits?

p r e s i d e n t ’ s

m e s s a g e

Some Perspectives on the
Notorious Summer of 2008

n late 2008, the U.S. economy was suffering in the aftermath of a financial panic
that was sparked by the collapse of Lehman
Brothers and American International Group
(AIG). The summer of 2008 has developed
a notorious reputation because it preceded
Lehman-AIG. In this column, I provide my
perspective on some features of the macroeconomic situation during that period.1
While many think that the financial crisis
began in 2008, in fact conventional dating
puts the beginning of the financial crisis
in August 2007. Therefore, the crisis had
been continuing for more than a year by the
time of Lehman-AIG, and the Fed had been
responding to the situation. In particular, the Federal Open Market Committee
(FOMC) had lowered the federal funds rate
target substantially between September 2007
and March 2008—from 5.25 percent to 2.25
percent. Because monetary policy operates
with a lag, a widely held expectation during
the first half of 2008 was that this aggressive
easing would help the economy considerably
throughout the rest of the year. This expectation turned out to be wrong, or at least naïve,
in the fall of 2008.
We now know that a recession started in
December 2007 and ended in June 2009.
During the summer of 2008, however, it
was not readily apparent that the U.S. was
actually in recession. According to initial
estimates, real U.S. gross domestic product
(GDP) growth was positive for the fourth
quarter of 2007 and the first and second
quarters of 2008.2 If one defines recession as
two consecutive quarters of declining GDP,
then the U.S. was not in recession based on
those figures. Also, in early July 2008, forecasts for the second half of the year were still
for modest growth. Therefore, as of August
2008 there was a good case to be made that
the U.S. economy would continue to muddle
through the financial crisis, as it had seemingly been doing for many months.
In reality, the economy contracted during the second half of 2008. Rather than
preventing the financial panic, the Fed’s substantial lowering of the policy rate may have

had a counterproductive effect by feeding
into another development during this period:
the global commodity price boom during
the second half of 2007 and the first half of
2008. The boom was especially pronounced
in oil prices. The lower interest rates may
have encouraged troubled financial firms
to borrow cheaply and attempt to profit in
commodities. This sort of “doubling down”
behavior is common during financial panics.
As of mid-June 2008, the price of crude oil
had nearly doubled in the span of about 10
months (whereas the year-over-year increase
was near zero as of August 2007). The commodity price shock slowed down auto sales
and other parts of the economy that are
sensitive to such prices. The slower economic
growth, in turn, worsened the financial crisis
and led to multiple financial firm failures
during the fall of 2008.
While the Bear Stearns event occurred in
March 2008, it had implications for events
during the second half of the year. Bear
Stearns was ranked 34th by revenue among
financial firms in the U.S. during 2007.
When JPMorgan Chase & Co. purchased the
failing firm with assistance from the Fed,
this suggested that the 33 financial firms that
were even larger than Bear Stearns had some
form of implicit insurance from the Fed. The
Fed, however, was not in a position to give
assistance to that many firms.
As of September 2008, investors had
already known for a year that Lehman Brothers was in deep trouble. As such, the Lehman
failure, while notable, was not particularly
surprising, and the U.S. economy could have
handled this single event. The fact that AIG,
which was one of only a handful of tripleA-rated firms in the U.S., was also in deep
trouble did come as a surprise. Moreover, the
financial problems of AIG, especially because
of its linkages with other firms as a provider
of insurance, spilled over and worsened
the financial situations of other firms. As
a result, the Lehman-AIG event brought
all financial firms under vastly increased
suspicion, driving the financial crisis from
mid-September 2008 onward.3

Following the Lehman-AIG event, the
FOMC changed the target policy rate to a
range of 0 to 0.25 percent in December 2008,
and the policy rate remains there more than
five years later. In my view, the debate at the
time of the decision did not take sufficient
account of the experience in Japan. The
Bank of Japan changed its policy rate to near
zero in the 1990s, and short-term rates are
still at zero today. The FOMC decision in
December 2008 may have unwittingly committed the U.S. to an extremely long period
of near-zero rates similar to the situation in
Japan, with unknown consequences for the
macroeconomy.4
The events of 2008 are likely to be studied for decades to come. The features of
the macroeconomic situation that I have
discussed here must be addressed in any
comprehensive accounting of what happened during that period.

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

ENDNOTEs
1 For more details, see my presentation on Nov. 21,

2013, “The Notorious Summer of 2008,” at http://
research.stlouisfed.org/econ/bullard/pdf/Bullard_
NWArkansas_2013November21_Final.pdf.
2 The current data instead show negative GDP growth
in the first quarter of 2008. To see data revisions
over time, visit the St. Louis Fed’s real-time database,
ALFRED (ArchivaL Federal Reserve Economic Data),
at http://alfred.stlouisfed.org/.
3 For more discussion on the largest financial firms
during this period, see my presentation on Nov. 18,
2009, “The First Phase of the U.S. Recovery,” at http://
research.stlouisfed.org/econ/bullard/Bullard
CommerceFinal.pdf.
4 See my 2010 Review article, “Seven Faces of ‘The
Peril,’ ” at http://research.stlouisfed.org/publications/
review/10/09/Bullard.pdf.

The Regional Economist | www.stlouisfed.org 3

J a p a n

4 The Regional Economist | January 2014

A Look at Japan’s
Slowdown and Its
Turnaround Plan

By Juan M. Sánchez and Emircan Yurdagul

he Japanese economy has been struggling with
low growth and low inflation for several years
(or even decades). These two symptoms are present
in several developed economies, including the U.S.1
In this article, we analyze the Japanese economic
experience, reviewing the headwinds and the recent
policies implemented. We show the main differences and similarities that Japan has with the U.S.
and also compare Japan’s performance with South
Korea’s. The case of South Korea is interesting
because the growth experience is similar to Japan’s
between 1970 and 1990, but South Korea didn’t suffer a slowdown in the years after that, as Japan did.

The Regional Economist | www.stlouisfed.org 5

FIGURE 1

OUTPUT PER WORKER (2005 THOUSAND $)

Output Comparison with U.S.
and South Korea
10

1990, the start of
Japan’s slowdown

8
6
4
2
0

1971

1981

1991

2001

2011

YEAR
Japan

U.S.

SOURCE: Penn World Table 8.0.

6 The Regional Economist | January 2014

South Korea

The economic history of Japan over
the past 40 years can be divided into two
subintervals: before and after 1990. In the
first period, gross domestic product (GDP)
grew at an annual rate of about 4.5 percent,
and the growth was persistent. This trend
stopped abruptly in the 1990s, after which
the economy grew at an annual rate of less
than 1 percent until 2011.
This break in the growth experience of
Japan can also be seen in terms of output
per worker. Figure 1 compares the patterns
in output per worker in Japan with those in
the U.S. and South Korea. For the period
between 1971 and 2011, the case of Japan is
clearly different from that of the U.S. and
South Korea. Until 1990, Japan was growing fast and catching up with the U.S. However, starting in 1990 the Japanese growth
rate slowed down and its gap with the U.S.
widened. During the same period, South
Korea sustained fast growth and narrowed
its gap with Japan and the U.S. In particular, between 1970 and 1990, Japan’s output
per worker grew at an annual rate of about
3.6 percent, whereas corresponding rates for
the U.S. and South Korea were 1.3 and 5.6
percent. From 1990 to 2011, Japan’s output
per worker rose at a rate less than 1 percent;
in comparison, the annual rate of growth in
the U.S. was 1.7 percent and in South Korea
was 3.8 percent.
What caused in Japan such a striking
change in the trend that was dominant for
at least two decades? It’s only logical to
think that the causes are connected to the
three main drivers of growth: capital, labor
and total factor productivity (TFP). Capital
captures the machinery and equipment
that are used by businesses in their operations. Labor captures workers’ input in
production operations and is measured as
the average hours worked by people engaged
in production as well as their skill level.
TFP measures the efficiency of a country in
producing output with given levels of capital
and labor. If Country A and Country B
have the same amount of capital and (quality-adjusted) labor, but Country A produces
more, then it must be that Country A has
higher TFP. With that framework in mind,
we can compute how much of the Japanese
growth (or lack thereof) was accounted for
by the changing patterns in capital, labor
and TFP.

Growth Accounting

Let’s look at the changes in total output,
capital and labor in the intervals 19701990, 1990-2007 and 2007-2011.2 Capital
is an estimate of the stock of accumulated
investments. Labor is the total labor force,
adjusted by the number of hours worked and
education. The growth rate of each factor is
adjusted, using a measure of its importance
in the aggregate economy, such that the sum
of the growth rates of capital, labor and TFP
is equal to the growth rate of output.
As a result of this exercise, we should
expect that, if TFP had no effect on the
growth rate of output, the growth rate of the
economy must be made up of the contribution of the growth rate in capital plus the
contribution of the growth rate in labor.
Needless to say, such equality does not hold
in general, giving economists an idea of
how important TFP is in accounting for the
growth experience of the economy. The top
panel of the table gives the results of this
exercise for Japan.3 The middle and bottom
panels show the results for the U.S. and
South Korea, respectively.
Total output grew rapidly in Japan from
1970 to 1990, on average 4.5 percent a year.
In the same interval, the output growth due
to capital accumulation was 2.4 percent a
year, accounting for more than 50 percent
of the output growth. On the other hand,
the contribution of labor growth was much
smaller, 0.73 percent, or about 20 percent
of the total growth in output. The remaining 30 percent of the total growth in total
output is attributed to the growth in TFP,
which grew at a yearly rate of 1.4 percent
during this period.
The middle row in the top panel of the
table shows the same exercise for the period
1990-2007. Looking at the factors’ growth,
the drop in the output growth is not surprising. The growth rates in capital, labor
and TFP were all smaller than in the earlier
period. However, the extent of output growth
that capital accounts for increased, suggesting
that this factor was not the primary explanation for the slowdown in growth. Strikingly,
the change in the labor input of production is
now slightly negative; this shows a potential
direction to look at in assessing the slowdown
in the Japanese economy.
Discontinued increase in labor force
participation, diminishing returns in

TABLE 1
Growth Accounting
Yearly Growth Rate
Total
output

Capital
stock

Labor
input

Total factor
productivity

1.37

Japan
1970-1990

4.45

2.35

0.73

1990-2007

1.24

0.85

–0.06

0.44

2007-2011

–0.79

0.13

–1.24

0.33

U.S.
1970-1990

3.18

0.98

1.45

0.75

1990-2007

2.95

0.87

0.90

1.18

2007-2011

0.15

0.32

–0.62

0.46

3.43

2.07

South Korea
1970-1990

8.93

1990-2007

5.60

2.72

0.99

1.89

2007-2011

3.07

1.42

0.15

1.51

SOURCE: Penn World Table 8.0.
NOTE: The human capital variable used to adjust labor is from the Penn World
Table and is a function of average years of schooling in a country.

higher education and decreasing hours all
may have contributed to the slowdown of
the Japanese economy during this period.
We can also see from the middle row that
TFP growth slowed down substantially,
too. There may be different explanations
for this observation. Perhaps, Japanese
corporations lost their edge in innovation,
or the institutions affecting the allocation of resources (e.g., government and the
financial sector) may be doing a worse job of
allocating the resources to the best producers. In fact, in their 2008 work, economists
Ricardo J. Caballero, Takeo Hoshi and Anil
K. Kashyap argued that the continued lending by the Japanese financial sector to the
otherwise insolvent, inefficient firms kept
the Japanese market congested, affected
the profitability of more-efficient firms and
prevented the economy from reaching the
optimal level of firm entry and exit.
Qualitatively, the changes from the 19902007 interval to the 2007-2011 interval are
in the same direction with the changes from
the 1970-1990 interval to the 1990-2007
interval. Capital, labor and TFP all have
growth rates lower than before, making the
output growth for 2007-2011 negative.
The U.S. Experience

One could argue that what happened in
Japan is natural for a rich, mature economy.

If that is the case, we should expect that the
U.S. would experience a similar slowdown—and it has, but only to some extent.
The experience of Japan may be useful to
understanding the slow recovery of the
U.S. after the financial crisis. To evaluate
that hypothesis, the same exercise that was
performed for Japan was undertaken for the
U.S., as well as for South Korea.
We found that the performance of labor
in Japan was a more-extreme version of
what happened in the U.S. and South
Korea. From the 1970-1990 interval to the
1990-2007 interval, growth in labor input
decreased, both in the U.S. and in South
Korea, though changes were milder than
in Japan. This suggests that economies
might grow less as they develop because the
growth of labor slows down.
In terms of the contribution of TFP,
changes in Japan from the 1970-1990 interval
to the 1990-2007 interval were more distinct
from the ones observed in the U.S. and South
Korea. For the U.S., TFP growth increased
between the two intervals and the contribution of TFP to output growth increased
much faster than in Japan. In South Korea,
the growth rate in the later interval was very
similar to the growth rate in the earlier one,
suggesting that TFP was not a cause for the
slowdown in output growth.
Why was the decline in the growth rate
of labor much more dramatic in Japan than
in the U.S. and South Korea? Why did TFP
growth slow down in Japan, in a fashion not
seen in the other two countries? An analysis
of the contemporaneous issues of Japan
might help to answer these questions.
Headwinds

Japan is facing headwinds that are arguably relevant, if not causes, for the slowdown
in its economy. The three challenges that
have received the most attention are the
aging population, low inflation and growing
public debt.
The aging of the population is strongly
connected to the stagnant labor input
illustrated in the table. Japan has the highest life expectancy among countries in the
Organization for Economic Cooperation
and Development—and Japan’s population
is aging rapidly. Since 1990, the ratio of the
population that is older than the working age (i.e., older than 64) to that of the
The Regional Economist | www.stlouisfed.org 7

FIGURE 2

INFLATION, CONSUMER PRICES (ANNUAL %)

Inflation Comparison
8

Japan
U.S.

South Korea

4
2
0
–2

1992

1997

2002

2007

2012

YEAR
SOURCE: World Bank.

FIGURE 3

TOTAL GOVERNMENT NET DEBT (% GDP)

Debt Comparison
150

Japan
U.S.
South Korea

100

2001

2003

2005

2007

2009

YEAR
SOURCE: International Monetary Fund.

2011

2013

working age (i.e., between 15 and 64) has
increased at an annual rate of about 4 percent. In 2012, this ratio reached an astonishing 39 percent. In comparison, the ratio
in the U.S. was 20 percent, and the ratio
in South Korea was 16 percent. The aging
population not only puts a dent in the labor
force, but it also affects the hours worked
by the working-age population, which must
spend time taking care of the elderly. If this
trend continues, the labor contribution to
the growth of output will continue to be
negative in the future.
The second potential problem is low
inflation (and deflation). Figure 2 shows
inflation in Japan, the U.S. and South Korea,
measured as the average annual percentage
change in the consumer price index for the
last three years. Notice that the fall in inflation coincides with the slowdown in the output documented above. Inflation in Japan
was about 3 percent in the beginning of the
1990s and fell to negative values by the end
of the decade; it has never really recovered.
The U.S. and South Korea also saw inflation fall until the early 2000s; however, the
decline was substantially worse in Japan.
The most prevalent argument against
deflation is that it induces households to hold
cash, dampening consumption. Another
argument is that deflation is the consequence
of strong demand for the Japanese currency.
This strong demand appreciates the Japanese exchange rate, and exporters lose their
competitive edge in the international market.
This may lead to less innovation, which in
turn would affect TFP growth.
Finally, Japan has a very high public debt
relative to GDP. Figure 3 shows the total
government net debt of Japan, the U.S. and
South Korea relative to GDP. 4 In Japan, the
ratio surpassed 140 percent by 2013 after an
annual growth rate of more than 6.4 percent
since 2001. These levels of debt together
with deflation put even more pressure on
the government as the amount to be repaid
grows even more in real terms.
“Abenomics”

In order to mitigate the ongoing low inflation, boost economic growth and reduce the
public debt, Japanese Prime Minister Shinzo
Abe launched a comprehensive package
of initiatives in 2012. The first initiative is
aimed at monetary easing, with the goal of
8 The Regional Economist | January 2014

increasing inflation to 2 percent. As part
of this effort, the Bank of Japan pledged to
increase the monetary base. In a speech last
October in New York, the governor of the
Bank of Japan, Haruhiko Kuroda, said that
the monetary base in Japan would double
in two years to the equivalent of $2.78 trillion—56 percent of nominal GDP. (For
the U.S., the corresponding rate is about
20 percent.)5
The second initiative involves fiscal
stimulus. The government is planning on
spending more money on the infrastructure of the economy not only to help future
economic growth but to create short-run
domestic demand for Japanese firms. Since
these policies will increase an already high
public debt, the government is starting,
among other things, to increase the consumption tax.
The final initiative of the so-called Abenomics pertains to structural reforms. The
plan includes the deregulation of several
industries. Measures will be taken to
increase the labor force participation rate
of the younger portion of the population.
Trade partnerships within the region will
be improved.
While fiscal stimulus and structural
reforms are likely to take several years to
produce an impact, we can already analyze
the effects of the first initiative, monetary
easing, by looking at the evolution of
nominal variables in Japan. Using monthly
data, we focused on three indicators. First,
we looked at the total value of shares of
publicly traded corporations in Japan. An
increase in this indicator for Japan on the
heels of the announcement of the new set
of policies would signal a positive response
in the market to Abenomics. That’s exactly
what happened, as shown in Figure 4. The
vertical line in this figure (and in Figures
5 and 6) corresponds with the December
2012 announcement of the prime minister’s
initiatives. Figure 4 shows that after late
2012, the value of shares increased by a large
percentage, with a slope much larger than in
the U.S. and South Korea. Such an increase
in the share prices can be attributed to
exchange rate depreciation, 6 or just to better
forecasts on profits.
Another way of measuring the impact of
Abe’s policies is to look at the exchange rate,
showing the value of one U.S. dollar in terms

Stock Market Value Comparison

Monthly Inflation Comparison

YEAR

–0.5

SOURCE: OECD’s Main Economic Indicators.
NOTES: Inflation rates are the averages of the last three observations. The
vertical rule marks the December 2012 announcement by the Japanese prime
minister of major initiatives to improve the economy.

NOTE: The vertical rule marks the December 2012 announcement by the
Japanese prime minister of major initiatives to improve the economy.

ENDNOTES
1

FIGURE 5
Exchange Rate Comparison
(against the U.S. Dollar)
130
Japan
South Korea

120

110

100

AUG. 2013

AUG. 2012

AUG. 2011

AUG. 2010

90
AUG. 2009

0.0

YEAR

SOURCE: Organization for Economic Cooperation and Development’s
Main Economic Indicators.

EXCHANGE RATE (TO $; AUG. 2011=100)

0.5

AUG. 2013

AUG. 2012

AUG. 2011

AUG. 2010

AUG. 2009

100

Japan
U.S.
South Korea

AUG. 2012

120

Output, capital, number of workers, average hours
and human capital variables are from Penn World
Table, version 8.0.7 Total factor productivity (TFP)
is calculated by dividing output by capital and
labor, weighting each factor by its share in output.
The age dependency ratio and yearly inflation
data are provided by the World Bank. Total share
prices and monthly consumer prices are from
the Organization for Economic Cooperation and
Development’s Main Economic Indicators, and
exchange rates are from the Board of Governors of
the Federal Reserve System, all three accessible via
FRED (Federal Reserve Economic Data), the main
economic database of the Federal Reserve Bank of
St. Louis. (See http://research.stlouisfed.org/fred2.)
The source for total government net debt data is the
International Monetary Fund, which is accessible
through EconomyWatch.com.

1.0

AUG. 2011

140

DATA NO T E

AUG. 2010

Japan
U.S.
South Korea

AUG. 2009

160

INFLATION, CONSUMER PRICES (MONTHLY %)

FIGURE 6

STOCK MARKET VALUE (AUG. 2011=100)

FIGURE 4

YEAR
SOURCE: Board of Governors of the Federal Reserve System.
NOTE: The vertical rule marks the December 2012 announcement by the
Japanese prime minister of major initiatives to improve the economy.

of the Japanese yen in recent years. A weaker
yen relative to the dollar after the introduction of the prime minister’s new policies
would raise the exchange rate from 2013 on.
Figure 5 shows the exchange rate for Japan
and compares it with South Korea’s exchange
rate with the dollar. The value of the yen
relative to the dollar decreased sharply in
the post-Abenomics period.

Did inflation increase? Figure 6 shows
the monthly inflation pattern, measured as
the average percentage increase in consumer prices for the last three months.
Although the changes are very small, notice
that monthly inflation started increasing
after December 2012 and kept increasing
even as the U.S. and South Korea experienced decreasing inflation.
In the short run, Abenomics is showing
certain success with changing the course
of nominal variables. To what extent the
new policies will help the Japanese economy
overcome more-structural and longer-term
issues—such as the shrinking labor force
and low growth of productivity—remains
to be seen.
Japan’s long-lasting issues with low inflation and low growth, and its recent attempts
to overcome them, certainly provide an
invaluable experiment for the U.S. economy.
However, this article shows that during the
past 20 years these two economies have
had very different demographic trends that
affected economic growth. Hence, the Japanese experience should be approached with
caution for guiding U.S. policy.
Juan M. Sánchez is an economist and Emircan
Yurdagul is a technical research associate, both
at the Federal Reserve Bank of St. Louis. For
more on Sánchez’s work, see http://research.
stlouisfed.org/econ/sanchez.

5
6

For instance, in his 2010 paper, James Bullard,
president of the Federal Reserve Bank of
St. Louis, considered Japan’s experiences as a
potential scenario for the U.S.
The reason for studying 2007-2011 separately is to
isolate the potential effects of the financial crisis,
which started in 2007.
See Hayashi and Prescott, and Kobayashi for
similar exercises.
To get the net debt, debt instruments such as
monetary gold and SDRs (special drawing rights),
currency and deposits, debt securities, loans,
insurance, pensions, standardized guarantee
schemes, and other accounts receivables are
subtracted from the gross amount.
See Kuroda.
For instance, firms that make transactions mostly
in U.S. dollars may see their (yen-denominated)
share prices increase even if the profits (in terms
of the U.S. dollars) are not expected to change.
For the Penn World Table, see Feenstra, Inklaar
and Timmer.

REFERENCES
Bullard, James. “Seven Faces of the Peril.” Federal
Reserve Bank of St. Louis Review, September/
October 2010, Vol. 92, No. 5, pp. 339-52. See
http://research.stlouisfed.org/publications/
review/10/09/Bullard.pdf.
Caballero, Ricardo J.; Hoshi, Takeo; and Kashyap,
Anil K. “Zombie Lending and Depressed Restructuring in Japan.” American Economic Review,
December 2008, Vol. 98, No. 5, pp. 1,943-77.
Hayashi, Fumio; and Prescott, Edward C. “The 1990s
in Japan: A Lost Decade.” Review of Economic
Dynamics, January 2002, Vol. 5, No. 1, pp. 206-35.
Feenstra, Robert C.; Inklaar, Robert; and Timmer,
Marcel P. “The Next Generation of the Penn
World Table,” Working Paper No. 19255, National
Bureau of Economic Research, 2013. See
www.ggdc.net/pwt.
Kobayashi, Keiichiro. “Payment Uncertainty, the
Division of Labor, and Productivity Declines in
Great Depressions.” Review of Economic Dynamics, October 2006, Vol. 9, No. 4, pp. 715-41.
Kuroda, Haruhiko. “Overcoming Deflation—the
Bank of Japan’s Challenge.” Speech at the Council
on Foreign Relations, New York, N.Y., Oct. 10,
2013. See www.bis.org/review/r131016c.pdf.

The Regional Economist | www.stlouisfed.org 9

w o r k

Around the World,
Gender Gaps
Ebb and Flow
By Silvio Contessi and Li Li
© gett y images

abor market dynamics are different for men
and women. In the United States during
the 2007-09 recession, men took a particularly
hard hit and experienced a stronger recovery
from the trough—two phenomena sometimes
labeled “man-cession” and “he-covery.”
Although these differences appeared unusual
during the crisis, recent research suggests that
these patterns were by no means unique to
the Great Recession but were similar to the
labor market dynamics for men and women
observed over the past 30 years.
But what about in other countries? This article
compares these phenomena in more-recent years
across advanced economies in the Organization for Economic Cooperation and Development (OECD) with a focus on the Group
of Seven (G-7) countries: Canada, France,
Germany, Italy, Japan, the U.K. and the U.S.
Labor Force Participation Rates

The labor force participation rate is defined
as the ratio of the labor force to the workingage population.1 As of 2011, the last year
for which we have comparable data for all
countries, the participation rates for men and
women were 70.1 percent and 57.5 percent in
the U.S. and 69.5 percent and 50.9 percent in
the OECD.2 The U.S. labor participation rate
for women steadily increased after World War
II but started to flatten out in the early 1990s;
the rate for men constantly declined. In the
OECD, for which data are available only since
1990, the trends were perhaps less marked
but similar, in the sense that they showed a
convergence between the two genders.
Naturally, there were differences across
countries even within this relatively homogeneous group. Figure 1 compares the
evolution of the gender gap in labor force
participation—the difference of labor force
10 The Regional Economist | January 2014

participation rates for men and for women—
in the U.S., OECD countries as a group and
individual G-7 countries from 1991 to 2011.
Two facts stand out: 1) in the long run,
female labor participation increased in all
countries; 2) while these countries shared a
similar trend, there were considerable differences. The U.K., the U.S., France and Canada
had relatively smaller gender gaps, which
became smaller over time. Germany, Italy
and the U.K. showed the largest improvements
in the gap, while Japan’s was relatively static.
These diverse changes depended both on
initial conditions (some countries had small
gender gaps at the beginning of this period)
and on labor market incentives, human capital
accumulation and cultural attitudes.
Unemployment Rates

What about unemployment rates? Here,
we considered the difference between the
unemployment rate for male and female
workers since 2007 and its relationship with
labor force participation.
In the U.S., both genders experienced
severe labor market adjustments, with a
contraction of total labor participation and a
sharp increase in unemployment rates. The
contraction of total labor participation is due
mostly to the fact that the male participation rate dropped by 1.1 percent, while the
female participation rate remained stable,
two facts consistent with long-term trends.
The larger number of jobs lost by men in
2008-09 quickly caused the male unemployment rate to peak at 11.2 percent in October
2009, a stark increase of 6.4 percentage points
relative to November 2007, while the female
unemployment rate increased less, from 4.6
percent to 8.7 percent (a difference of 4.1 percentage points) over the same period. Finally,

the recovery brought faster job growth for
men than for women.
The initial widening followed by a narrowing of the gender unemployment gap is
not unique to the recent recession but a more
general feature of the labor market in the U.S.
in recession times.
What happened in other countries during
the same period? OECD and G-7 countries
showed similar labor market adjustments.
Figure 2 shows the unemployment rate differences between men and women by country.3
In all countries, the men’s unemployment
rate increased greater than the women’s,
which is reflected by the upward trend in
the unemployment gap during the recession.
In other words, men were impacted more
severely during the recession than women.
Afterward, some countries rebounded while
others maintained their relatively large gaps,
particularly the countries that had a slow
recovery, if any.
Why are changes in the unemployment
rate different for men and women during
recessions? The roles played by men and
women in the labor force help to explain
these facts. Theories of brain-based technological change suggest that men and women
are not perfect substitutes in all occupations. Although men are endowed with the
same brain abilities (used for mental labor)
as women are, men have the advantage in
brawn abilities (used for physical labor).
When technological change is biased in favor
of brain-intensive activity—as it arguably has
been over the past 50 years—and labor market institutions favor entry of women into the
labor force, there tend to be more women in
brain-intensive occupations and industries
in which women can specialize according to
their comparative advantage. Although this

FIGURE 1
Gender Gap in Labor Force Participation,
1991-2011
1991

2001

2011

25
20
15

Canada

France

U.S.

U.K.

Germany

OECD

Japan

10
Italy

PERCENTAGE POINT DIFFERENCE

SOURCE: World Bank.
NOTE: The gap in labor force participation rates between men and women has
been shrinking, in general. In Italy, for example, the participation rate was
more than 30 percentage points higher for men than for women in 1991; by
2011, that gap had shrunk by about 10 percentage points.

FIGURE 2
Gender Gap in Unemployment Rate
by Country
2

Canada
U.K.

U.S.
OECD

Germany
Japan

Italy
France

1
0
–1

2013:Q1

2012:Q2

2011:Q3

2010:Q1

2009:Q2

2007:Q1

–4

2008:Q3

–3

2010:Q4

–2

2007:Q4

PERCENTAGE POINT DIFFERENCE

SOURCE: Organization for Economic Cooperation and Development (OECD).
NOTE: The data points correspond to the difference in men’s unemployment
rate and women’s unemployment rate in each country (men’s minus women’s).
Any line above the 0 line indicates that men had a higher unemployment rate;
below 0 indicates that women had a higher unemployment rate. For example,
in Canada in 2009:Q2, the men’s unemployment rate was 2.7 percentage
points higher than the women’s. In Italy in 2007:Q4, the women’s rate was
more than 3 percentage points higher than the men’s. The gray bar denotes
the latest recession.

bias did contribute to increased female labor
participation, it also sustained a large heterogeneity in female-to-male worker ratios
across occupations and sectors, as different
sectors mix various occupations differently.
In the U.S., the male labor force was hit
harder during the recent recession because
more jobs were lost in occupations and sectors
that traditionally employ more men and are
cyclically sensitive, particularly manufacturing and construction.4 Women, on the other
hand, tend to occupy a large share of employment in industries that are largely resistant to

downturns, industries such as education and
health care. This explains a large part of the
difference between the unemployment rates
of men and women in the U.S. since 2007 and
also in other countries. Within the G-7, the
countries that had the smallest gender participation gaps also experienced larger unemployment increases for men than for women
because the two genders are more likely to
work in industries in which they can exploit
their comparative advantage.
English-speaking countries (the U.K., the
U.S. and Canada) and, to a lesser extent,
France and Germany, experienced a simultaneous increase in unemployment that
affected men disproportionately. But after
the peak of the crisis, these differences were
at least partly reduced. In France, the unemployment rate has been consistently larger
for women, though there was some cyclical
variation consistent with what was happening in the other countries. In Italy and Japan,
we did not observe the inverted U-shaped
curve of the unemployment rate gender gap
during recessions, perhaps because in these
countries the relatively low participation rate
of women did not allow them to specialize in
relatively acyclical industries (such as health
care and education) as much as women did in
other countries.
Why is this cross-country evidence important? Some of the cross-country differences
in unemployment rates are explained by differences in women’s unemployment rate, and
this is affected by labor force participation.
Therefore, policies that affect female labor
participation (such as maternity leave regulation or the marginal taxation of second earners) affect the way women select into certain
occupations and sectors, which in turn affects
the unemployment rates of the two genders.
Although these policies tend to reflect societal
and cultural preferences, in several countries
there may be room for changes. More generally, economic theory and recent evidence
suggest that allowing specialization according
to comparative advantage by gender may bring
quantitatively important welfare gains, as it
has in the U.S. since the 1960s.

ENDNOTES
1

2
3
4

Although the working-age population is considered to be 16 and older in the U.S., 15 and older is
used in many other countries and is used by the
OECD. Therefore, 15 was used as the cutoff for all
countries’ data in this article so that like comparisons could be made.
The OECD is made up of 34 countries.
The unemployment rate for men minus the unemployment rate for women.
Data from the Bureau of Labor Statistics (BLS)
show that in 2007 the female labor share in the
manufacturing, transportation and utilities,
mining, and construction sectors was about 30
percent, 24.5 percent, 13.7 percent and 9.4 percent,
respectively. Female labor shares in other sectors
are above 40 percent. The total employment of
these four sectors accounts for about one-third
of total nonfarm employment, and the drop in
employment in these four sectors during the
recent recession was significant: 14.7 percent, 6.8
percent, 7.4 percent and 19.8 percent, respectively.

REFERENCES
Albanesi, Stefania; and Şahin, Ayşegül. “The Gender
Unemployment Gap.” Federal Reserve Bank of
New York Staff Reports No. 613, April 2013.
Hoynes, Hilary; Miller, Douglas L.; and Schaller,
Jessamyn. “Who Suffers during Recessions?”
Journal of Economic Perspectives, Summer 2012,
Vol. 26, No. 3, pp. 27-48.
Contessi, Silvio; and Li, Li. “From ‘Man-Cession’
to ‘He-Covery’: Same Old, Same Old.” Federal
Reserve Bank of St. Louis Economic Synopses,
2013, No. 2.

Silvio Contessi is an economist and Li Li is a
senior research associate, both at the Federal
Reserve Bank of St. Louis. For more on
Contessi’s work, see http://research.stlouisfed.
org/econ/contessi.
The Regional Economist | www.stlouisfed.org 11

d i s a b i l i ty i n s u r a n c e

Understanding the
Motives and Constraints
That Lead People
to Risky Occupations
By Amanda M. Michaud and David G. Wiczer
© corbis

ome occupations take a heavier toll
on workers’ bodies than others. For
example, a production-line worker’s back
endures considerably more stress than that of
an office worker in an ergonomic chair. Such
differences in activities at work over a career
culminate in striking differences in disability
outcomes for older Americans. A group of
occupations representing about one-third of
the labor force has twice the risk of disability
that others have. People in these occupations
are demographically different from the rest
of the population. They also earn less and
save less than other people do. These differences should not be overlooked in discussing
the merits of Social Security Disability Insurance (SSDI), a public insurance program
that is designed to provide income to those
unable to work.
With 8.9 million people receiving SSDI
payments1 in October 2013, there justifiably
have been concern and discussion about the
program’s size, almost 6 percent of the size
of the labor force. Many economists have
discussed reasons for the program’s size and
recent expansion2—the number receiving
benefits grew by more than 50 percent in
the past 10 years—but few have studied the
connection between the type of work one
performs and the risk one faces of a physically limiting disability. This is an important
aspect that should probably be part of any
discussion about changing the disability
insurance program. It’s too late for old
people on disability to change their career
choice, but any reform of the disability policy
may affect young people still choosing an
occupation. Policymakers also need to be
aware of the incentives—intended or not—in
the program, both as it stands now and as it
might be restructured in the future.

12 The Regional Economist | January 2014

Receipt of disability insurance depends
both on health and vocational factors. To
measure the connection between occupation and health, we looked at the limitations
to Activities of Daily Living (ADL), such as
dressing and walking across a room. The
data are from the University of Michigan
Health and Retirement Study,3 which surveys
about 15,000 people over the age of 50 about
their health, income, savings and personal
characteristics. Workers’ jobs are categorized
into 17 occupations, and these survey respondents also report their primary occupation
over their lifetime.4

table 1

Disability across Occupations

NOTE: The percentages refer to those with an Activities of Daily Living (ADL)
limitation, such as trouble in dressing or walking across the room. The risky
occupations have roughly twice the probability of disability before the age
of 65.

Table 1 shows a sample of occupations and
their disability risk. To construct these estimates, we grouped workers by their primary
lifetime occupation, then computed the fraction who reported some difficulty with one of
the ADLs during their working life before 65.
Occupations’ disability rates were disparate
and bimodal; a large group had very low
rates, while those in another large group were
more than twice as likely to have experienced
some disability. The picture looked quite
similar when we assigned each occupation
a score based on how many and how severe
were the disabilities, rather than just tallying
any incidence.
What are these “high-risk” occupations,
representing about one-third of the labor
force? In the top tail, with rates 175 percent
or more of the median, were the heavily
physical occupations, as expected. The largest group was machine operators. Those who
work with industrial machines and those
who work with transportation equipment,
such as truck drivers, were about equally
at risk and comprised 42 percent of the

A Sample of Risky and Safe Occupations
Occupation

Percent with an ADL Limitation

Construction and Extraction

10.9

Machine Operators

10.7

Farming, Forestry, Fishing

10.6

Transport Operators

9.9

Administration

5.9

Sales

5.8

Management

4.3

Professionals

3.6

SOURCES: University of Michigan Health and Retirement Study and authors’
calculations.

population in high-risk occupations. Workers in construction, extraction and agriculture accounted for an additional 22 percent.
Workers from these occupations were,
understandably, much more likely to apply
for and receive SSDI. In our sample, they
accounted for about 46 percent of the
recipients of SSDI, despite being only about
33 percent of the population. To put this
another way, 21 percent of workers in the
riskier occupations received benefits from
SSDI, whereas only 12 percent from the rest
of the occupations did.5
Different Demographics

Workers in the riskier occupations also
differed in demographic characteristics from
those in other occupations. By analyzing these tendencies, we might gain some
insights as to why some people choose riskier
occupations and some choose safer ones.
Table 2 outlines some crucial differences.

For one, those in riskier occupations were
less-educated than those in safer occupations.
The former were half as likely to have a high
school diploma and less than half as likely to
have any college experience. Yet, workers in
riskier occupations were paid relatively well.
Though the average earnings were lower
among this group, that was partly an effect of
educational differences. When we controlled
for their education and other demographics,6 they made just about the same as their
counterparts and, compared with workers
with similar education and demographic
characteristics, workers in risky occupations
made $5,000 more a year.
The relatively high pay in riskier occupations is consistent with the classical theory of
“compensating differentials.” 7 By this theory,
wages should be higher than otherwise
expected as compensation for the potential
of physical harm. Assuming some additional
risk of disability might be one way for lesseducated workers to increase their salaries.
Those in riskier occupations also had lower
savings than those in safer occupations. This
observation holds when we controlled for
earnings and demographics via a regression,
excluded housing and pension wealth or used
table 2
Characteristics of Those Who Work
in Risky and Safe Jobs
Risky

Safe

Male

60%

43%

No High School

47%

23%

Some College

18%

48%

$25,000

$32,000

Earnings
Residual Earnings

$36,516

$38,346

Total Household Wealth

$122,000

$169,000

Liquid Household Wealth

$11,000

$25,000

Ratio of Household
Wealth to Earnings

1.23

1.40

Ratio of Household
Wealth to Residual
Earnings

0.84

1.44

SOURCES: University of Michigan Health and Retirement Study and authors’
calculations.
NOTE: To obtain residual earnings, we used a regression to adjust earnings for
educational and demographic differences between safer and riskier occupations. Total household wealth is the total value of all assets owned by the
household. Liquid household wealth excludes illiquid assets such as housing
and pensions but includes liquid assets such as cash, savings and stocks.
The ratio of household wealth to earnings is the ratio of household assets to
raw income. Household wealth to residual earnings is the ratio of household
assets to adjusted income. A higher ratio indicates that a larger fraction of
income is saved. Wealth and earnings variables are medians.

the wealth-to-earnings ratio instead of raw
wealth. From the perspective of a simple
theory of precautionary savings, this was
puzzling: If workers in certain occupations
faced a much higher risk of disability, with its
corresponding loss of income and increased
expenses, we would expect them to save a
larger fraction of their income. Economists
sometimes explain differences in saving
behavior by differences in time preferences:
If some people put a relatively higher value
on their current welfare, they will save less of
their income than those with more interest
in future rewards. Interestingly, this same
difference in preferences might explain why
some people take on riskier jobs, in which
they trade higher pay today for potentially
greater problems later in life. If these differences exist, the compensating differential
could actually be lower than otherwise
because a person who chooses a risky occupation is less concerned with future injury
and, hence, demands less compensation.
Understanding the motives and constraints that push some people into riskier
occupations is quite important for the design
and assessment of the SSDI program. People’s underlying differences may be enough to
allow them to efficiently choose their occupations. On the other hand, SSDI transfers
money to riskier occupations, and this may
alter people’s calculus when they decide. To
what extent does disability insurance encourage people to work in riskier occupations,
and is that desirable? Machine operators
incur considerable bodily risk, but the
products of their work are vital. Although
the rolls of those receiving disability benefits
have been rising quickly, we do not have a
good benchmark for what should be their
optimal size, nor do we know the effects of
the availability of disability insurance on
individuals in the job market.

ENDNOTES
1 Data on coverage come from the Social Security

2
3

4
5

Administration. See www.ssa.gov/OACT/STATS/
dibStat.html.
See, for example, Autor and Duggan; Golosov and
Tsyvinski.
We used the extract with contributions from the
RAND Center for the Study of Aging, available
at http://hrsonline.isr.umich.edu/modules/meta/
rand/index.html.
Respondents are asked about their longest-held
occupation over their lifetime.
These rates of receiving SSDI in our sample are a
bit high. Autor and Duggan, using administrative
Social Security data, calculate that 10.9 percent of
men and 8.3 percent of women between the ages
of 55 and 64 are enrolled in SSDI. However, rather
than a single-year cross section, we looked at
whether an individual ever receives benefits after
the age of 50, which should increase the figure
somewhat.
To control for this variation, we took residuals
from a regression on education level, a quadratic
in work life, gender and self-employment. We
regressed separately for respondents and their
spouses for each wave of data.
See Rosen.

R eference s
Autor, David H.; and Duggan, Mark G. “The Growth
in the Social Security Disability Rolls: A Fiscal
Crisis Unfolding.” Journal of Economic Perspectives, Summer 2006, Vol. 20, No. 3, pp. 71-96.
Golosov, Mikhail; and Tsyvinski, Aleh. “Designing
Optimal Disability Insurance: A Case for Asset
Testing.” Journal of Political Economy, April 2006,
Vol. 114, No. 2, pp. 257-79.
Rosen, Sherwin. “The Theory of Equalizing Differences,” in Ashenfelter, Orley; and Layard, Richard;
eds., Handbook of Labor Economics. Amsterdam:
North-Holland Publishing Co., 1986, pp. 641-92.

Amanda M. Michaud is an assistant professor
of economics at Indiana University in Bloomington. David G. Wiczer is an economist at the
Federal Reserve Bank of St. Louis. For more on
his work, see http://research.stlouisfed.org/econ/
wiczer.

The Regional Economist | www.stlouisfed.org 13

l a b o r

m a r k e t s

Not Everyone Who
Joins the Ranks
of the Employed
Was “Unemployed”
By Maria Canon, Marianna Kudlyak and Marisa Reed
© iStock

he labor market is comprised of employed
and unemployed workers. The former
have jobs. The latter do not but are able
to work and are actively seeking jobs. In
contrast, labor market nonparticipants are
neither working nor searching for jobs. Transitions into and out of labor force nonparticipation have been noted in recent studies to
aid understanding of labor market dynamics.1
In particular, the flows between nonparticipation and unemployment have attracted
attention in explaining the dynamics of
unemployment during the 2007-09 recession and its aftermath. However, the flows
between nonparticipation and employment
have received considerably less attention.
The number of workers transitioning
from nonparticipation to employment is
substantial—almost 3.7 million each month
on average between 2003 and 2013, according to the Bureau of Labor Statistics.2 Given
this magnitude, this flow’s contribution to
understanding labor market dynamics is
nontrivial.
For this article, we studied the behavior of
nonparticipation-to-employment (N-E) flows
from January 2003 to August 2013. We first
compared aggregate flows from nonparticipation to employment (N-E) with the flows
from unemployment to employment (U-E).
Importantly, we found that the former was,
on average, higher than the latter by a factor
of 1.6, that is, N-E flows were on average 60
percent higher than U-E flows.
We then examined the ratio of these flows
by occupation and industry. We found that
there existed substantial heterogeneity by
occupation and industry. For example, workers in services, management and professional
occupations were more likely to come from
nonparticipation than from unemployment;

14 The Regional Economist | January 2014

conversely, the unemployed were more likely
to end up with jobs in physically demanding
occupations, such as construction, than were
the nonparticipants.
Analysis

The gross flow from N-E is the number of
individuals who are not in the labor force in
one month and are employed in the following month. These people are bypassing the
unemployment status. Consequently, nonparticipating workers who become employed
are not receiving unemployment benefits
or actively searching for jobs in the month
preceding the start of their jobs.
For this analysis, we used Current Population Survey (CPS) data. We matched individuals in two consecutive months. To calculate
the N-E gross flow, we counted employed
workers in the current month who were out
of the labor force in the previous month. We
did the same for the U-E gross flow, which
counts currently employed workers who were
unemployed in the previous month.
We found that the ratio of N-E to U-E
aggregate flows declined during the 2007-09
recession.3 (See Figure 1.) The figure also
shows that the ratio did not fall below 1,
indicating that newly employed workers are
more likely to come from nonparticipation
than from unemployment even in a slack
labor market. (Conversely, a reading below 1
would indicate that newly employed workers
are more likely to come from the ranks of the
unemployed than from the ranks of labor
force nonparticipants.)
Next, we analyzed the ratio of N-E flows to
U-E flows by occupation and industry. (See
Figures 2 and 3.) The differences were substantial. Although there was less pronounced
cyclicality within each occupation and

industry, the ratio of N-E to U-E consistently
fell between the end of 2007 and mid-2009
across all sectors.
The ratios within the major occupations
formed two distinct patterns. N-E flows were
larger in services, management and professional occupations. For example, the average
ratio of professional and related occupations
was 2.32. This ratio indicates that more than
twice as many employed workers in these
occupations came from nonparticipation
than from unemployment. Physically intensive occupations, such as construction workers and miners, showed the opposite pattern.
Workers in construction had an average ratio
of 0.68 and theirs was the only occupation to
have an average lower than 1, indicating that
new construction workers were more likely
to come from unemployment than from nonparticipation. This suggests that recent job
experience may be more important for these
types of jobs.
Heterogeneity also existed across industries. For example, manufacturing had
an average monthly ratio of 1.19, while
the educational and health services sector
had an average monthly ratio of 2.51. The
industries with higher ratios had more recent
hires from nonparticipation, which could be
partly driven by the hiring of recent graduates. Compared with the occupation ratios,
the industry ratios of N-E to U-E were more
volatile. This volatility shows that there were
more differences between their reactions to
the same economic conditions. For example,
hiring in mining changed more dramatically
than in professional and business services,
which had a more constant ratio of N-E to
U-E flows. This difference suggests that
industries had different levels of sensitivity
to changes in the economy.

Implications

figure 1

First, our findings imply that the transitions from nonparticipation to jobs are
important in understanding the bigger
question of how nonemployed workers find
jobs. In particular, the findings put the
spotlight on the question of whether there
is a conceptual difference between the two
nonemployment statuses—unemployment
and nonparticipation—in the CPS data.
In the labor literature, there is currently
no widely accepted definition of the role of
nonparticipation in labor market dynamics.
For example, a study by economists Olivier
Blanchard and Peter Diamond and one by
David Andolfatto and Paul Gomme do not
distinguish between unemployment and
nonparticipation as separate labor market
states in their models. They studied the gross
flow as calculated by nonemployment-toemployment, where nonemployment is the
sum of nonparticipating and unemployed
workers. If nonemployment flows told the
entire story of individuals joining employment, we would expect to see a constant ratio
of N-E to U-E flows, rather than one that
changes with the business cycle.
Because the ratio of N-E flows to U-E flows
changes, it is likely that nonparticipation and
unemployment describe different populations
of nonemployed individuals who react differently to labor market conditions. Another
study documented different subgroups
coming from nonparticipating workers.4 The
authors suggested the existence of a “waiting”
group, whose members are more likely than
the rest of the nonparticipants to take a job
if wages and conditions are satisfactory. The
people in the waiting group are, thus, similar
to unemployed workers, though the former do
not actively search for work. This difference
between nonparticipating workers suggests
a high variability of N-E flows in response to
business conditions, which is consistent with
our findings of a procyclical (moving in the
same direction as the economy) pattern in the
N-E to U-E ratio in the CPS data.
Second, our findings uncovered heterogeneity by occupation and industry; this
difference creates challenges for studies of
mismatch between vacancies and job seekers
in the economy. When these studies define
job seekers, they typically consider only
unemployed workers.5 If the ratio of N-E
transitions relative to U-E transitions were

Hires from Nonparticipation Relative to Hires from Unemployment
2

RATIO

1.75
1.5

1.25
1
2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

SOURCE: Current Population Survey (CPS).
NOTE: The figure shows the ratio of N-E (nonparticipation to employment) to U-E (unemployment to employment). The data are annual averages of monthly
series constructed from matched month-to-month CPS data, January 2003-August 2013. To calculate the N-E gross flow, we counted employed workers in the
current month who were out of the labor force in the previous month. We did the same for the U-E gross flow, which counts currently employed workers who were
unemployed in the previous month. The gray bar corresponds to a recession period from the peak to the trough in the business cycle.

figure 2
Hires from Nonparticipation Relative to Hires from Unemployment, by Major Occupation
4

RATIO

3
2
1
0

2003

2004

2005

2006

Construction and extraction
Management, business and professional
Production

2007

2008

2009

2010

Sales and related
Transportation and moving materials
Installation, maintenance and repair

2011

2012

2013

Office and admin support
Professional and related
Service

SOURCE: Current Population Survey.
NOTE: See note from Figure 1. The lines display hires from nonparticipation (N-E) divided by hires from unemployment (U-E) within selected occupations.
All major occupations are included except armed forces and farming, fishing, and forestry.

Figure 3
Hires from Nonparticipation Relative to Hires from Unemployment, by Major Industry
5

RATIO

4
3
2
1
0

2003

2004

2005

2006

Agriculture, forestry, fishing, hunting
Construction
Wholesale and retail trade

2007

2008

2009

Professional and business services
Leisure and hospitality
Mining

2010

2011

2012

2013

Manufacturing
Financial activities
Educational and health services

SOURCE: Current Population Survey.
NOTE: See note from Figure 1. The lines display hires from nonparticipation (N-E) divided by hires from unemployment (U-E) within selected major industries.

the same across all sectors, then omitting
the job seekers within the nonparticipating
population would not substantially affect the
calculation of mismatch indexes. However,
since the ratios differ by sector, the difference
between indexes using all job seekers and

those using only unemployment might be
significant.
Consequently, understanding the transitions into jobs from unemployment and from
continued on Page 16
The Regional Economist | www.stlouisfed.org 15

e c o n o my

a t

g l a n c e

Eleven more charts are available on the web version of this issue. Among the areas they cover are agriculture, commercial
banking, housing permits, income and jobs. Much of the data are specific to the Eighth District. To see these charts, go to
www.stlouisfed.org/economyataglance.
R E A L G D P G R OW T H

CONSUMER PRICE INDEX (CPI)

8
4
PERCENT

2
0
–2
–4
–6
–8
–10

Q3
’08

’09

’10

’11

’12

PERCENT CHANGE FROM A YEAR EARLIER

’13

All Items, Less Food and Energy

Maria Canon is an economist at the Federal
Reserve Bank of St. Louis. Marianna Kudlyak
is an economist and Marisa Reed is a research
associate, both at the Federal Reserve Bank
of Richmond. For more on Canon’s work, see
http://research.stlouisfed.org/econ/canon.

–3

December

’08

’09

’10

’11

’12

’13

RATES ON FEDERAL FUNDS FUTURES ON SELECTED DATES

3.0

0.13

2.5

3
4

0.11

1.5

PERCENT

0.12

2.0

5-Year

’10

’11

Jan. 10
’12

’13

0.07

’14

REFERENCES
09/18/13
10/30/13

12/18/13
01/13/14

Jan. 14 Feb. 14 March 14 April 14 May 14 June 14

NOTE: Weekly data.

CONTRACT MONTHS

C I V I L I A N U N E M P L O Y M E N T R AT E

I N T E R E S T R AT E S
5

11
10

10-Year Treasury
Fed Funds Target

PERCENT

9
8
7

3
2

1-Year Treasury

December

’08

’09

’10

’11

’12

’13

’08

’09

’10

’11

’12

’13

NOTE: On Dec. 16, 2008, the FOMC set a target range for
the federal funds rate of 0 to 0.25 percent. The observations
plotted since then are the midpoint of the range (0.125 percent).

U.S. AGRICULTURAL TRADE

AVERAGE LAND VALUES ACROSS THE EIGHTH DISTRICT

6,000

Exports

5,000

DOLLARS PER ACRE

BILLIONS OF DOLLARS

75
Imports

45
30
15
0

Trade Balance

’08

’09

’10

’11

’12

NOTE: Data are aggregated over the past 12 months.
16 The Regional Economist | January 2014

Quality Farmland

Ranchland
or Pastureland

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

November

’13

See, for example, Diamond, as well as Kudlyak and
Schwartzman. See also references to recent works on
the developments in labor force participation in Canon,
Debbaut and Kudlyak.
Data available at www.bls.gov/webapps/legacy/
cpsflowstab.htm.
For additional analysis, see Canon, Kudlyak and Reed.
See Jones and Riddell.
See Canon, Chen and Marifian.

0.10

0.08

20-Year

–0.5

0.09

10-Year

0.0

ENDNOTES
1

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

0.5

nonparticipation and the differences across
sectors will help reveal trends in employment
and will help explain how the labor market
changes in recessions and recoveries.

CPI–All Items

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

1.0

continued from Page 15

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

Andolfatto, David; and Gomme, Paul. “Unemployment
and Economic Welfare.” Federal Reserve Bank of
Cleveland Economic Review, 1998:Q3, pp. 25-34. See
www.clevelandfed.org/research/review/1998/
98-q3-andolfatto.pdf.
Blanchard, Olivier J.; and Diamond, Peter. “The Cyclical
Behavior of the Gross Flows of U.S. Workers.” Brookings Papers on Economic Activity, 1990, Vol. 21, No. 2,
pp. 85-155. See www.brookings.edu/~/media/Projects/
BPEA/1990%202/1990b_bpea_blanchard_diamond_
hall_murphy.PDF.
Canon, Maria; Chen, Mingyu; and Marifian, Elise A.
“Labor Mismatch in the Great Recession: A Review of
Indexes Using Recent U.S. Data.” Federal Reserve Bank
of St. Louis Review, May/June 2013, Vol. 95, No. 3,
pp. 237-72. See http://research.stlouisfed.org/
publications/review/13/03/237-272Canon.pdf.
Canon, Maria; Debbaut, Peter; and Kudlyak, Marianna.
“A Closer Look at the Decline in the Labor Force
Participation Rate.” Federal Reserve Bank of St. Louis
The Regional Economist, October 2013, pp. 10-11. See
www.stlouisfed.org/publications/pub_assets/pdf/
re/2013/d/labor_force.pdf.
Canon, Maria; Kudlyak, Marianna; and Reed, Marisa.
“Finding Jobs from Unemployment versus from Out of
Labor Force.” 2013, unpublished manuscript.
Diamond, Peter. “Cyclical Unemployment, Structural
Unemployment.” IMF Economic Review, August 2013,
Vol. 61, No. 3, pp. 410-55. See www.palgrave-journals.
com/imfer/journal/v61/n3/pdf/imfer201313a.pdf.
Jones, Stephen R.G.; and Riddell, W. Craig. “The Measurement of Unemployment: An Empirical Approach.”
Econometrica, January 1999, Vol. 67, No. 1, pp. 147-62.
Kudlyak, Marianna; and Schwartzman, Felipe. “Accounting for Unemployment in the Great Recession:
Nonparticipation Matters.” Federal Reserve Bank of
Richmond Working Paper Series, No. 12-04, June 2012.
See www.richmondfed.org/publications/research/
working_papers/2012/pdf/wp12-04.pdf.

national overview

A Timeline of the FOMC’s Economic Projections for 2014
8

By Kevin L. Kliesen

014 could be a watershed year for the
U.S. economy. If the headwinds that
have plagued the economy the past few years
finally begin to wane, as many forecasters and
financial market participants expect, then
the economy could grow somewhere close
to 3 percent. If so, real GDP growth in 2014
would be the best since 2005—and it would
also likely generate continued improvement
in labor market conditions. This outcome,
though, depends crucially on the Fed’s ability
to keep inflation and inflation expectations
stable at a time when the growth of the monetary base was on pace to increase by between
40 and 50 percent in 2013.
A Look Back at 2013

A year ago, the consensus of Blue Chip
forecasters was that U.S. real gross domestic
product (GDP) would increase by 2.2 percent
in 2013, that headline consumer price index
(CPI) inflation would be 1.9 percent and
that the unemployment rate would average 7.5 percent during the fourth quarter of
2013.1 Although fourth-quarter GDP data
will not be published until late January 2014,
some data in December has surprised to the
upside. For example, the unemployment rate
dropped below 7 percent in December, and
some forecasters have raised their estimate of
real GDP growth in the fourth quarter above
3 percent. However, forecasters did not foresee the sharp slowing in CPI inflation, which
may end up about 1.25 percent in 2013.
After several years of forecasts that were
generally too optimistic, the economy’s actual
performance was pretty close to expectations.
Still, the economy faced several headwinds last
year that have imparted a drag on growth.
Key Headwinds in 2013

Despite an extremely accommodative
monetary policy and robust gains in housing
construction and home sales, the economy
struggled to build consistent momentum in
2013. Although the relatively weak growth
of real GDP reflected many factors, three
stood out. First, the pace of real personal

6
PERCENT

A Spring-Loaded
Economy?

6.7

6.6

6.5

Projection made in June 2013
Projection made in September 2013
Projection made in December 2013

4
2

3.3

3.0

3.0
1.7

REAL GDP

UNEMPLOYMENT RATE

1.6

1.5

PCE INFLATION

NOTE: Projections are the midpoints of the central tendencies. The projections for real GDP and inflation are for the
percentage change from the fourth quarter of 2013 to the fourth quarter of 2014. Inflation is measured by the personal
consumption expenditures chain-price index. The projection for the unemployment rate in 2014 is for the average of the
monthly rates in the fourth quarter of 2014.

consumption expenditures (PCE) steadily
downshifted throughout the year. Although
consumer outlays on durable goods like autos
and household furnishings have been strong,
expenditures on nondurables and services—
together comprising nearly 90 percent of
household expenditures—have been especially
weak. This outcome is perhaps more puzzling
considering the huge increase in household
wealth during this business expansion. The
slowdown in consumer spending in 2013
could have partly reflected the payroll tax
increase in January 2013, which helped to
reduce real after-tax income.
A second key reason for the economy’s
weaker-than-expected performance in 2013
was the exceedingly weak growth of real
nonresidential (business) fixed investment.
Through the first three quarters of 2013, real
business fixed investment (BFI) in equipment and structures had only increased at
a 1.5 percent annual rate. That puts it on
track to be the weakest since 1986, excluding recession years. This development is
all the more confusing given the backdrop
of healthy profit margins and relatively low
levels of financial market stress. Anecdotal
evidence regularly reported in the minutes
of the Federal Open Market Committee
(FOMC) meetings suggests that many firms
have been reluctant to commit to large capital outlays in the face of higher-than-usual
amounts of uncertainty about economic
policy or the growth of the economy.
A third reason for the relatively weak
growth in real GDP has been the retrenchment in real federal government expenditures;
those cutbacks reduced real GDP growth by
an average of 0.3 percentage points per quarter
for the first three quarters of 2013.

The Outlook for 2014

FOMC participants see in the coming year
faster growth of real GDP, further declines in
the unemployment rate and continued modest
inflation. (See chart.) This outcome seems
reasonable given the following developments.
First, real after-tax wages and salaries have
started to increase from year-earlier levels.
Further gains, bolstered by continued solid
employment growth and stable gasoline prices,
will help boost consumer spending, which
appears to have been strong in the fourth
quarter. Second, state and local finances
have improved, and their expenditures are on
pace to increase in 2013 for the first time in
four years. Third, commercial and industrial
construction activity is beginning to pick up,
and the housing recovery shows few signs
of faltering. Fourth, an improving global
economy, continued healthy profit margins
and waning levels of uncertainty should begin
to boost business capital spending. Finally,
continued confidence in the Fed’s ability to
manage its exit from unconventional policies will help to keep financial markets stable
and, more importantly, inflation and inflation
expectations in check. This outcome will be
a further boost to business and household
confidence.
Kevin L. Kliesen is an economist at the Federal
Reserve Bank of St. Louis. Lowell R. Ricketts, a
senior research associate at the Bank, provided
research assistance. See http://research.stlouisfed.org/econ/kliesen/ for more on Kliesen’s work.
ENDNOTE
1 The forecasts for real GDP growth and CPI inflation

are for the period from the fourth quarter of 2012 to
the fourth quarter of 2013.

The Regional Economist | www.stlouisfed.org 17

d i s t r i c t

o v e r v i e w

Engines of Growth
Vary in Four Largest Cities
The Eighth Federal Reserve District
is composed of four zones, each of
which is centered around one of
the four main cities: Little Rock,
Louisville, Memphis and St. Louis.

By Maria A. Arias and Charles S. Gascon

ince the recession officially ended in June
2009, the U.S. economy has experienced
steady growth in jobs at a pace of about 1.5
percent per year. However, the recovery has
not been uniform across sectors of the economy or across regions. Take, for example,
the four major metropolitan statistical areas
(MSAs) in the Eighth District: St. Louis;
Little Rock, Ark.; Louisville, Ky.; and
Memphis, Tenn. Employment growth in the
Louisville MSA has been the fastest, with
the manufacturing sector contributing the
most jobs. Growth in the three other MSAs
has been slightly below the national rate.
Which sectors are driving the recoveries
in these four MSAs? An examination of
common performance metrics helps to
identify them.
Two Important Metrics

One of the most popular metrics used
by economists to identify key industries
within a region is location quotients (LQs)
for each sector. An LQ is a way to measure
how concentrated an MSA’s employment is
within a sector relative to the nation’s. It is
calculated by dividing the share of employment in a given sector within a region by
the sector’s share of national employment
over a given period.1 If an LQ has a value
of 1, the regional and national shares are
the same; values less than 1 indicate the
region employs relatively fewer workers;
values greater than 1 indicate the region
employs relatively more workers than
the nation does. For example, the LQ for
Memphis’ transportation and utilities sector
is 3.2, indicating that Memphis employs
3.2 times as many workers in this sector
than the national average. In this case,
10.6 percent of Memphis’ workers are
18 The Regional Economist | January 2014

employed in the transportation and utilities
sector, compared with the national average
of 3.3 percent.
A second metric is the difference between
an industry’s employment growth rate
regionally and its growth rate nationally.
Just as we compare overall growth of a
region to a national benchmark, comparing the regional growth of industries to a
national benchmark can help identify the
sectors generating local growth or leading a
national trend. For example, in the St. Louis
MSA, employment growth in the financial
activities sector has increased by about
9.6 percent since the recession ended;
nationally, employment in this sector has
increased by 1.5 percent, for a relative
growth rate 8.1 percentage points above
the national average. Relatively stronger
employment growth may be an indication
that: (1) factors specific to the region are
generating growth in this sector; (2) major
employers are hiring and/or relocating
workers to the region; or (3) firms belonging
to that sector are expanding in the region.
Combining these two metrics is one way to
identify the sectors that have been important
to a region’s growth. The figure plots the
industry LQs for each metro area on the horizontal axis and the relative growth rate for
the industry on the vertical axis. One way to
interpret the figure is to cluster the industries
based on their quadrant in the graph.
Industries in the upper-left quadrant
employ relatively fewer workers regionally
compared with the nation, but the growth
rates of these industries have been faster
than their national averages. These sectors
may be considered “emerging” industries
for the region. In Memphis, the education
and health services sector is one of these

industries; the sector has an LQ of 0.9 and a
growth rate that is 3 percentage points higher
than the national rate.
Industries in the bottom-left quadrant
employ relatively fewer workers regionally
and are growing at slower rates than the
corresponding industries at the national
level; these may be considered “noncompetitive” sectors.
The industries in the bottom-right quadrant of the graph employ a relatively larger
share of workers but are growing slower than
the national average. These industries may
have significant importance to the region.
For example, in Memphis, the transportation
sector stands out among the rest, with an LQ
of more than 3 and a growth rate just below
the national rate.
The upper-right quadrant is the most likely
place for a region’s important growth industries to be located. These sectors employ a
relatively larger share of workers than the
national average, and their employment
growth rates exceed the national rates. In
St. Louis, the financial activities sector stands
out in the graph. The sector employs about
6.6 percent of the region’s workers, versus
5.8 percent nationally (with an LQ of 1.1), and
the relative growth rate was more than 8 percentage points higher than the national rate.
The education and health services sector is
also in the upper-right quadrant, with an LQ
of 1.2 and a growth rate that is 1.7 percentage
points higher than the national average.
In Louisville, the manufacturing and
wholesale trade sectors are in the upperright quadrant. Louisville’s manufacturing
sector has grown 13.1 percentage points
faster than the national rate, which is five
times as fast (16 percent locally versus
2.9 percent nationally).

Employment Shares and Job Growth by Industry Relative to the U.S.
ST. LOUIS
15

0
–5

–10

–15

–20

0.5

1.5

–20

Metrics beyond Employment

MEMPHIS
15

5
RELATIVE GROWTH

0
–5

–10

–15

–20
0

LITTLE ROCK

RELATIVE GROWTH

While most regional analysis tends to
focus on employment metrics—in part due
to their availability, long history and timely
release—many other metrics may be used.
For example, since 2007 the Bureau of Economic Analysis has reported gross metropolitan product (GMP) and has disaggregated
the data by sector. These data reinforce some
of the trends noted above: Between 2009 and
2012, financial activities in St. Louis were
the largest reported contributor to GMP
growth (0.64 percentage points of the total
3.62 percent growth). Over the same period
in Memphis, the transportation and utilities
sector was the largest contributor to growth
(1.38 percentage points of the total 3.78 percent growth).
The trade in goods (imports and exports)
for each metro area is another useful metric
for identifying important sectors. The data
are collected by the Census Bureau and are
organized and repackaged by the Brookings Institution.2 Regional trade data show
the flows of products internationally and
domestically. If a region is a net exporter of
a good, the region is thought to be producing
more of a product than it needs for local consumption. On the other hand, a region may
be a net importer of products that are used
as inputs into a production process. Of the
four major MSAs in the District, Memphis
was the only net exporter of goods in 2010,
with a trade surplus of $29.3 billion, driven
by exports of chemicals and plastics ($32 billion). The MSA with the largest trade deficit
was Louisville, with a net balance of

LOUISVILLE

RELATIVE GROWTH

Little Rock, as the state capital, employs
a relatively larger share of state government
workers, with an LQ of 2.5 and the relative
growth rate of 0.7 percentage points. Unlike
in the other MSAs, all three levels of government employment in Little Rock (federal,
state and local) have relative growth rates
above zero.
Another metric, the standard deviation of
each region’s LQs, is used to determine the
relative level of specialization. By this metric,
the St. Louis MSA may be considered the
most diversified across sectors: Its largest LQ
is 1.2 and its smallest is 0.3, with a standard
deviation of 0.2. Memphis may be the most
specialized of the four MSAs, with LQs ranging from 3.2 to 0.5.

–20

2
LQ

Retail Trade

Federal Government

Education and Health

Manufacturing

Wholesale Trade

State Government

Prof. and Business Services

Financial Activities

Transport and Utilities

Local Government

Leisure and Hospitality

Nat. Res., Mining and Const.

4
Other Services

★

Information

SOURCES: Bureau of Labor Statistics and authors’ calculations.
NOTE: The figure plots the location quotients (LQs) and relative growth rates for each two-digit North American Industry Classification System (NAICS) industry
within the metro area calculated using data between 2009:Q3 (the start of the recovery) and 2013:Q3. An LQ of 1 means the regional and national shares are the
same; values less than 1 indicate the region employs relatively fewer workers, and values higher than 1 indicate the opposite. “Relative growth” measures the
difference between local growth and national growth in percentage points, with 0 marking the national average.

$24.4 billion in imports, $10.5 billion of which
were imports of chemicals and plastics.
Future investigation into regional trade
flows data may provide additional insights
into the sectors that are driving growth in the
District’s largest metro areas.
Charles S. Gascon is a regional economist and
Maria A. Arias is a research analyst, both at the
Federal Reserve Bank of St. Louis.

ENDNOTES
1 All of the calculations in this article use data from

2009:Q3 through 2013:Q3 unless otherwise noted.

2 See Tomer et al.

REFERENCE
Tomer, Adie; Puentes, Robert; and Kane, Joseph.
“Metro-to-Metro: Global and Domestic Goods
Trade in Metropolitan America,” Global Cities Initiative: A Joint Project of the Brookings Institution
and JPMorgan Chase, October 2013.

The Regional Economist | www.stlouisfed.org 19

m e t r o

p r o f i l e

Long-Resilient
Little Rock Faces
Uncertain Pace
of Recovery
By Charles S. Gascon and Peter B. McCrory
© ocean/corbis

In 1722, French explorer Jean-Baptiste Bénard de La Harpe identified a rock jutting out along the bank of the
Arkansas River as la petite roche, or “the little rock.” It signified the geographic transition from the alluvial plains
formed by the Mississippi River to the east and the Ouachita Mountain foothills to the west. Over the past century,
Little Rock has transitioned from an economy that produced lumber and cottonseed to one that predominantly
provides services—the lion’s share of which is in health, education and state government.

he Little Rock-North Little RockConway metropolitan statistical area
(henceforth, Little Rock) is the largest metro
area in Arkansas, with an estimated population of 717,666. All counties in this area
experienced growth in population between
2002 and 2012, aside from Perry County,
which declined by a marginal 0.2 percent. In
aggregate, the Little Rock metropolitan statistical area (MSA) grew in population by nearly
15 percent, faster than Arkansas and the nation
(9.0 percent and 9.3 percent, respectively).
Pulaski County—home to almost half of the
area’s population, including the city of Little
Rock—grew by 6.8 percent. The bulk of the
MSA’s growth came from the outlying area;
all counties, except Perry in the northwestern
corner of the MSA, grew faster than Pulaski.
This population expansion was accompanied by similar, though less uniform, trends
in personal income growth. In real terms,
personal income per capita increased in all
counties in the metro area. Pulaski County,
which has the highest per capita income, saw
its income grow in real terms by 9.1 percent
over the past decade, a rate that was outpaced
by Faulkner (11.2 percent), Perry (16.5 percent), Lonoke (9.5 percent) and Saline
20 The Regional Economist | January 2014

(24.8 percent) counties. (Per capita income
growth in the nation grew 11.4 percent over
the same period.) Thus, incomes for most of
the counties in the metro area are converging.
Resiliency during the Recession

Prior to the recession, the unemployment
rate in Little Rock tracked the national average; since then, the metro area’s economy
has proved to be more resilient than the
nation’s. From peak to trough, the U.S.
shed 6.3 percent of its payroll employment,
whereas Little Rock lost 4.7 percent. As for
the unemployment rate, Little Rock’s rose at
a slower pace than the nation’s and peaked
at 7.1 percent. The unemployment rate stood
at 6.8 percent in November in Little Rock.
Some of the differences in the unemployment
rate can be accounted for by changes in labor
force participation. Immediately prior to
the recession, the participation rate in Little
Rock mirrored the national rate. During the
course of the recession, labor force participation in Little Rock is estimated to have
declined faster than—and remained below—
the national participation rate.1
Little Rock’s resilience during and
throughout the recession can be attributed

to the confluence of three factors: (1) the
metro area was less exposed to the housing
crisis; (2) a substantial portion of employees
work for state and local governments; and
(3) the health and education services sector
continued to grow along a prerecession trend.
It is worthwhile to note that both state government and health and education services
experienced sustained year-over-year growth
throughout the recession and currently
account for about one out of every four jobs
in Little Rock.
A key feature of the recession was the steep
decline of housing prices across the nation.
In Little Rock, house prices rose before the
financial crisis at a slower pace than they did
nationwide and declined temperately during
the ensuing crisis. Little Rock, and Arkansas more generally, was less exposed to the
cyclical volatility and risk inherent in the
prerecession real estate buildup. U.S. house
prices rose by nearly 50 percent between
2002 and 2007; in Little Rock, they rose by
about 30 percent. As house prices collapsed,
the nation’s house price index dropped
considerably, bottoming out in the second
quarter of 2011 after declining by about
20 percent. The house price index in Little

Rock declined by only 3 percent from its
prerecession maximum.
The Arkansas state government is the
largest single employer in the Little Rock
area, employing 9.4 percent of the area’s
workers and contributing, along with
local government, about 13 percent to the
metro area’s gross output. The rest of the
Little Rock economy declined significantly
throughout the recession even as the state
and local government employment grew
year over year.
Across the country, growth in state tax
revenue experienced an earlier, deeper
decline during the recession and rebounded
at a later point than did Arkansas tax
revenue. In Arkansas, the muted decline
in revenue and the relatively fast rebound
helped to insulate the Little Rock economy
because large portions of the workforce were
employed by the state.
Health and education services expanded
along prerecession trends throughout
and beyond the recession. In 2007, just
before the economic downturn, this sector
employed 13.6 percent of the workforce
though it only contributed 8.2 percent to
regional production. While the economy
was officially in recession, this sector added
more than 1,500 jobs even as the rest of
Little Rock shed just over 14,000 jobs. The
net downward effect on employment was
dampened by the well-established health
and education services sector in the region.

Current Conditions

Since January 2012, employment in state
and local governments has steadily declined
by an average of 0.65 percent year over year,
possibly reflecting the end of federal stimulus
money as well as the lagged effect of lower
state tax revenue in recent years.2 In recent
months, the shutdown of the federal government revealed how reliant Arkansas state
employees are on federal funding. During
the shutdown, Gov. Mike Beebe suspended
all state programs that depend upon federal
funding, directly affecting 673 state employees already on furlough.3 Although political
brinkmanship concerning the federal budget
subsided at the end of the year, the Little
Rock economy remains exposed to such
budgetary crises in Washington.
Of the 55 largest hospitals and medical
centers in Arkansas, 14 are located in the
Little Rock MSA.4 About one-third of all
jobs in the health and education services
20 to 50
sector across the state are in Little Rock.
10 to 20
to 10
Although this industry has seen large50 payto 5
-5 to 0
roll growth in recent years, much likeNothe
data
state and local governments, the health-care
sector still faces significant economic and
regulatory challenges.
With the implementation of the Affordable
Care Act, Little Rock has found itself on the
national stage: Late in September, the federal
government approved a plan to allow Medicaid funding to be used to purchase private
insurance in Arkansas—the first state to

MSA Snapshot
Little Rock-North Little Rock-Conway, Ark.
Population
Labor Force
Unemployment Rate
Personal Income (per capita)
largest sectors by Employment
LEISURE AND HOSPITALITY
STATE GOVERNMENT
Retail Trade
professional and business services
health and education services
0

6
10
2002
-82012

Figure 2

Unemployment Rate: U.S., Arkansas,
Little Rock

Federal Housing Finance Agency House
Price Index: U.S., Arkansas, Little Rock

Faulkner
Lonoke

Perry

Saline
Pulaski
Grant
No data2002
–5 to 0

0 to 5
- 2012

GROWTH IN POPULATION by County 2002-2012

ARKANSAS
Faulkner
Lonoke

Arkansas

Little Rock

U.S.

Little Rock

Arkansas

2013:Q1

2012:Q1

U.S.

2011:Q1

2010:Q1

2009:Q1

Little Rock 62.1%

2008:Q1

100

2007:Q1

U.S. 63%
Arkansas 58%

0 to 5
5 to 10

10 to 20
20 to 50

110

2006:Q1

120

2005:Q1

Labor Force Participation,
November 2013:

No data
–5 to 0

2004:Q1

0
05

Saline

Grant

130

Little Rock 66%

10 to 20
20 to 50

5 to 10

Pulaski

2003:Q1

ARKANSAS

140

U.S. 66%
Arkansas 62.8%

2002:Q1

Labor Force Participation,
December 2007:

GROWTH IN PER CAPITA INCOME
by County 2002-2012

150

2002:Q1=100

PERCENT, SEASONALLY ADJUSTED

PERCENT OF TOTAL NONFARM

Perry

Figure 1

717,666
341,098
6.8%
$41,662

SOURCE: Bureau of Labor Statistics.

SOURCE: Federal Housing Finance Agency.

NOTE: The shaded area indicates a U.S. recession. Data are easily accessible
in the St. Louis Fed’s economic database, FRED, using these series IDs: Little
Rock (LRSUR), Arkansas (ARUR) and U.S. (UNRATE).

NOTE: The shaded area indicates a U.S. recession. Data are easily accessible
in the St. Louis Fed’s economic database, FRED, using these series IDs: Little
Rock (ATNHPIUS20780Q), Arkansas (ARSTHPI) and U.S. (USSTHPI).

NOTES: Population, employment and personal income per capita
data are from the Census Bureau, Bureau of Labor Statistics and
Bureau of Economic Analysis. These MSA-level data series are easily
accessible in the St. Louis Fed’s economic database, FRED (Federal
Reserve Economic Data). For the panels and maps, see these FRED
series (IDs in parentheses): population (LRSPOP); labor force (LRSLF);
unemployment rate (LRSUR); personal income (LRSPCPI); leisure and
hospitality (LRSLEIH); professional and business (LRSPBSV); and
education and health (LRSEDUH). State government and retail trade
employment data are contained within the following aggregate data
series, which are also available on FRED: government (LRSGOVT) and
trade, transportation and utilities (LRSTRAD).

The Regional Economist | www.stlouisfed.org 21

Figure 3

285

75
Private Employment (Left Axis)

280

72.5

275

270

67.5

265

2013

2012

2011

2010

2009

2008

2007

Government Employment (Right Axis)

2006

260

2005

THOUSANDS OF JOBS, SEASONALLY ADJUSTED

Private versus Government Payroll
Employment in Little Rock

62.5
© istock

SOURCE: Bureau of Labor Statistics.
NOTE: The shaded area indicates a U.S. recession. Data are easily accessible
in the St. Louis Fed’s economic database, FRED, using these series IDs: Government Employment in Little Rock MSA (LRSGOVT), and Private Employment
can be calculated as Nonfarm Payroll (LRSNA) less Government Employment
(LRSGOVT).

Figure 4

355
Prerecession Trend

350
345
340
335
Postrecession Trend

330

2013

2012

2011

2010

2009

2008

2007

Little Rock

2006

325

2005

THOUSANDS OF JOBS, SEASONALLY ADJUSTED

Employment in Little Rock

SOURCE: Bureau of Labor Statistics.
NOTE: The shaded area indicates a U.S. recession. The black horizontal line
is the prerecession seasonally adjusted peak of 349,600 nonfarm jobs. The
prerecession trend line is estimated from data from January 2005 through
January 2008 (slope: 541 jobs per month); the postrecession trend line is
estimated with data from January 2010 through December 2012 (slope:
212 jobs per month). Data are easily accessible in the St. Louis Fed’s economic database, FRED, using the following series ID: Little Rock (LRSNA).

win such approval. The fiscal impact of this
approach is unclear at this point; nonetheless, this sector may benefit from the uptick
in demand for services and the ability to pay
from the newly insured population. As of
late October, more than 66,000 Arkansans
statewide have applied for health insurance
under this Medicaid expansion plan.5
Little Rock’s ability to weather the recession better than the nation and the state was,
in large part, dependent upon consistent
employment growth at all levels of government, mostly state government. However, this
is the only sector that has fared worse since
22 The Regional Economist | January 2014

Health care is a major driver of the economy in the Little
Rock area. Fourteen of the state’s 55 largest hospitals and
medical centers are in the Little Rock MSA. About one-third
of all jobs in the health and education services sector across
the state are in Little Rock.

the rebound in employment began in early
2010 when compared with its performance
during the recession. This trend reversal in
Little Rock follows patterns seen across the
nation for government employment.
Though faring relatively well during
the recession, employment continued to
decline in Little Rock for eight months
beyond June 2009—when the nation officially emerged from the recession. Nearly
all industries in Little Rock were affected
by this period of enduring contraction. In
early 2010, employment in Little Rock hit
its trough and began to recover, though at a
slower pace when compared with the nation
and with the metro area’s prerecession
growth patterns.
Between 2010 and 2012, Little Rock added
approximately 3,500 jobs per year—far
less than the approximately 6,000 jobs its
economy was adding per year prior to the
recession. These total figures do not fully
capture sector-level dynamics. Despite
bolstering the Little Rock economy during
the recession, the government sector experienced flat-line growth during this nascent
stage of the recovery. Countervailing this
slowdown was growth in the private sector,
which added jobs at nearly the same rate as
before the recession. Relative to the nation,
however, most industries outside of the government sector grew at a slower rate.
Since 2013, payroll employment growth
has picked up. For the first 11 months of
2013, Little Rock’s payroll employment was
up by an annualized 2.0 percent, at pace

with the nation. In aggregate, Little Rock
outstripped its prerecession pace of job creation and was on pace to add roughly 7,000
jobs by the end of 2013—driven primarily
by growth in professional and business services, retail trade, and health and education
services. Professional and business services
jobs alone accounted for half of all jobs created in 2013; another 30 percent of the new
jobs were in health and education services.
Total employment grew at such a pace that
the Little Rock economy breached the prerecession peak in late 2013; as of November,
employment exceeded that peak by 500 jobs.
So, the signals on the economy in Little
Rock continue to be mixed. Government
payroll employment not only continued
to decline in 2013, but it shed jobs at a
quickening pace. On the other hand, broad
improvements in the real estate sector have
led to the creation of construction jobs at
a rate not seen since before the recession.
Overall, it is still unclear whether the uptick
in growth in the MSA is yet another intimation of the region’s economic resiliency
observed during the recession or simply a
transitory divergence along a slower expansionary trend.
Charles S. Gascon is a regional economist and
Peter B. McCrory is a research analyst, both at
the Federal Reserve Bank of St. Louis.

E ndnote s
1 Authors’ calculations of labor force participation

rates in Little Rock.

2 By Sept. 30, 2010, 70 percent of the stimulus money

had been doled out; by Sept. 15, 2011, nearly 85
percent of the stimulus package had been paid out
and the large majority of the remaining funds were
already obligated for use in upcoming projects. See
“Memorandum for the Heads of Executive Departments and Agencies,” at www.whitehouse.gov/sites/
default/files/omb/memoranda/2011/m11-34.pdf.
3 Demillo, Andrew. Associated Press, Oct. 9, 2013,
“Mike Beebe: No More State Money for Federal
Programs.” Arkansas Business. See www.arkansasbusiness.com/article/95113/mike-beebe-no-morestate-money-for-federal-programs.
4 Arkansas Book of Lists 2013: The Ultimate Guide to
Who’s Who in Arkansas Business, Vol. 29, No. 53,
Dec. 31, 2012, to Jan. 6, 2013. Most hospitals and
medical centers reported as of the end of the 2012
fiscal year. Reported values were compared to the
average nonfarm payroll in 2011. Jobs reported by
hospitals are full-time employees.
5 Associated Press, Oct. 24, 2013, “Arkansas Signs
62K People for State Health Insurance.”

READER

E X CHANGE

ASK AN ECONOMIST

get to know fred

Carlos Garriga has been an economist in the Research
division of the Federal Reserve Bank of St. Louis since
2007. His main areas of interest are macroeconomics,
public finance and financial economics. Garriga has
studied the effects of mortgage innovations in the housing
boom and the role of the housing market in the financial
crisis. In his free time, he enjoys spending time with
his family and any outdoor activity. See http://research.
stlouisfed.org/econ/garriga for more on his work.

What is FRED? Short for Federal Reserve Economic Data, FRED is an online database consisting
of more than 156,000 economic data time
series from 61 national, international, public and
private sources. FRED, created and maintained by
the Research division at the St. Louis Fed, goes far
beyond simply providing data. FRED combines
data with a mix of tools to help the user understand, interact with, display and disseminate the
data. In essence, FRED helps users tell their data
stories. See more at http://research.stlouisfed.
org/fred2.

Garriga hiking in Utah.

Q: What were some of the lasting effects caused by the recent
housing crisis?
A: There are changes in regard to how people view the purchase of a home. In the past, people had this idea that you should try to buy a house as soon as possible. People had this idea
that the price of a house could only go up. Today, people don’t want to rush such an important
decision, perhaps because of the fear of a decline in prices. Young households, in particular, are
more reluctant to get into housing. In general, homeownership might not be a value for young
people in the long run; if so, its reputation as a safe investment may be dramatically changing.
Indeed, the rate of homeownership in the U.S. fell in 2013 to a level not seen since the 1990s.
(See top chart.)
Another important effect is that the contribution of the construction sector to the rest of the
economy is being reduced. This is more likely a short-term or medium-term effect. Construction is not employing as many people as in the past 10 years (see bottom chart), and that has
a broader impact on the economy than many people realize. People in the construction sector
buy a lot of resources from other sectors. When construction is down, other sectors suffer, and
the effects can be quite sizable and enduring.
Homeownership Rate for the United States (RHORUSQ156N)
Source: U.S. Department of Commerce: Census Bureau

70
69

In February, the Federal Reserve Bank of St. Louis
will begin accepting nominations for its 20142015 Student Board of Directors. Students must
be nominated by one of their teachers. During
their year on the board, the high school students
will meet bimonthly at the St. Louis Fed; they
will discuss issues related to economics and
personal finance, listen to speakers on topics
ranging from career planning to leadership
development, and compete for two summer
internships. After Feb. 1, teachers who wish to
nominate students should visit www.stlouisfed.
org/education_resources/student-board/.
Nominees must be seniors at St. Louis-area high
schools during the 2014-2015 academic year.

67
(Percent)

More than 20 video clips from the Nov. 18 symposium at the St. Louis Fed on student loan debt are
now available for viewing on our web site. “Generation Debt: The Promise, Perils and Future
of Student Loans” was a sold-out event.
Among the speakers was Rohit Chopra, who
oversees student loans on behalf of the Consumer Financial Protection Bureau. Others were
national higher education expert Sandy Baum of
George Washington University; William Elliott of
the University of Kansas; Jen Mishory of Young
Invincibles; Gary Ransdell, president of Western
Kentucky University; Caroline Ratcliffe of the
Urban Institute; and leading researchers from
the Federal Reserve System.
To watch the videos, go to www.stlouisfed.
org/household-financial-stability/multimedia/
video.cfm.
Student Board of Directors

66
65
64
63
62
1965

1970

1975

1980

1985
1990
1995
Shaded areas indicate U.S. recessions.
2014 research.stlouisfed.org

2000

2005

2010

2015

All Employees: Construction (USCONS)
Source: U.S. Department of Labor: Bureau of Labor Statistics

8,000
7,000
(Thousands of Persons)

Watch Videos from conference
ABOUT student loans and debt

6,000
5,000
4,000
3,000
2,000
1,000
0
1930

1940

1950

1960
1970
1980
Shaded areas indicate U.S. recessions.
2014 research.stlouisfed.org

1990

2000

2010

2020

We welcome letters to the editor, as well as
questions for “Ask an Economist.” You can submit
them online at www.stlouisfed.org/re/letter or
mail them to Subhayu Bandyopadhyay, editor,
The Regional Economist, Federal Reserve Bank of
St. Louis, P.O. Box 442, St. Louis, MO 63166-0442.
To read letters to the editor, see www.stlouisfed.
org/publications/re/letters/index.cfm.

The Regional Economist | www.stlouisfed.org 23

n e xt

i s s u e

Interest Rates
and Inflation
over the Past 60 Years
In the April issue of The Regional
Economist, read about inflation,
interest rates and monetary policy
in the U.S. over the past six decades.
The history will show how the
dynamics of interest rates and
inflation have changed with changes
in the Federal Reserve’s objectives,
implementation strategies and
credibility of monetary policy.

Read about Some of the Memorable Leaders,
as well as the Milestones, in Fed History

o begin its observance
of the Federal Reserve’s
centennial, the St. Louis Fed
has published a special issue
of the Review, its research
journal. This collection
of previously published
articles reflects significant
historical themes and perspectives—some related to
the Fed System and others
focused on the legacy of the
St. Louis Fed. The articles
Customers of the St. Louis
include Milton Friedman’s
Fed line up in the mid-1920s
1976 reminiscence of Homer
at the tellers’ windows in the
Jones, an influential research
lobby to conduct financial
director of the St. Louis Fed.
transactions, such as redeeming U.S. savings bonds.
Another article focuses on
former St. Louis Fed President Darryl Francis,
a leading critic of U.S. monetary policy in the ’60s and ’70s. Other topics include governmentsponsored enterprises, the monetary policy reform of 1979 and “Seven Faces of ‘The Peril,’ ”
a 2010 paper by current St. Louis Fed President James Bullard.
To read this issue of the Review online, go to http://research.stlouisfed.org/publications/
review/. For more on the Fed centennial, check our web site periodically throughout the year:
http://fraser.stlouisfed.org/centennial/.
TM

printed on recycled paper using 10% postconsumer waste

economy

The Regional

glance

Economist

january 2014

REAL GDP GROWTH

VOL. 22, NO. 1

CONSUMER PRICE INDEX

8
6
4
PERCENT

2
0
–2
–4
–6
–8
–10

Q3
’08

’09

’10

’11

’12

’13

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

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

RATES ON FEDERAL FUNDS FUTURES ON SELECTED DATES

0.15
0.14

PERCENT

0.13
0.12
0.11
0.10
0.09
0.08

07/31/13
09/18/13

Dec. 13

10/30/13
12/18/13

Jan. 14 Feb. 14 March 14 April. 14 May 14
CONTRACT MONTHS

C I V I L I A N U N E M P L O Y M E N T R AT E

I N T E R E S T R AT E S

11
10

PERCENT

9
8
7
6
5
4

November

’08

’09

’10

’11

’12

’13

U . S . A G R I C U LT U R A L T R A D E

AVERAGE LAND VALUES ACROSS THE EIGHTH DISTRICT
6,000

Exports

5,000

DOLLARS PER ACRE

BILLIONS OF DOLLARS

75
Imports

45
30
15
0

Trade Balance

’08

’09

’10

’11

’12

NOTE: Data are aggregated over the past 12 months.

Quality Farmland

Ranchland
or Pastureland

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

October

’13

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

U.S. CROP AND LIVESTOCK PRICES / INDEX 1990-92=100
275

225
Crops
Livestock

175

125

November

’98

’99

’00

’01

’02

’03

’04

’05

’06

’07

’08

’09

’10

’11

’12

’13

YEAR

commercial bank performance ratios
U . S . B an k s by A sset S i z e / third Q U A R T E R 2 0 1 3

All

$100 million$300 million

Less than
$300 million

$300 million$1 billion

Less than
$1 billion

$1 billion$15 billion

Less than
$15 billion

More than
$15 billion

Return on Average Assets*

1.05

0.91

0.89

0.96

0.93

1.08

1.01

1.06

Net Interest Margin*

3.20

3.78

3.77

3.92

3.85

3.04

Nonperforming Loan Ratio

2.89

1.93

1.91

1.96

1.94

2.07

2.01

3.16

Loan Loss Reserve Ratio

1.87

1.74

1.71

1.72

1.69

1.71

1.92

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

NET INTEREST MARGIN*

0.94
0.92
1.25

1.13
0.98
1.06

0.92

1.24

0.82
0.80
0.98
0.97

.00

.20

0.44

.40

Third Quarter 2013

3.90
4.08

Kentucky

3.83
4.07

Mississippi

3.65
3.89

Missouri

3.65
3.83
3.42
3.54

Tennessee
.80

.60

1.00

1.20

1.40

PERCENT

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

Third Quarter 2012

Third Quarter 2013

3.06

1.37
1.51

1.87

Arkansas

2.66

2.11

1.15
1.28

Indiana

1.64

Kentucky

2.34

1.58

Mississippi

2.25

2.59
2.81

.30 .60 .90 1.20 1.50 1.80 2.10 2.40 2.70 3.00 3.30

Third Quarter 2012

NOTE: Data include only that portion of the state within Eighth District boundaries.
SOURCE: FFIEC Reports of Condition and Income for all Insured U.S. Commercial Banks
* Annualized data

1.77

1.62
1.72

Tennessee
PERCENT

1.81

1.88
2.01

Missouri

2.21

2.13

1.52
1.59

Illinois
1.94

1.57

1.70
1.86

Eighth District

2.48
2.31

1.55

Third Quarter 2012

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

1.95

Third Quarter 2013

3.47
3.64

Indiana

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

.00

4.11
4.19

Arkansas
Illinois

1.09
1.09

0.25

3.74
3.89

Eighth District

.00

.40

.80

Third Quarter 2013

1.20

1.60

2.00

Third Quarter 2012

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

2.40

regional economic indicators
nonfarm employment growth / third Q U A R T E R 2 0 1 3
year-over-year percent changE
United
States

Eighth
District †

Arkansas

1.3%

1.0%

0.9%

1.6%

Illinois

Indiana

Kentucky

Mississippi

Missouri

Tennessee

1.4%

1.5%

Total Nonagricultural

1.7%

Natural Resources/Mining

3.0

–2.2

0.0

2.6

3.3

–8.5

0.0

–3.2

Construction

2.9

0.6

–1.5

–0.1

–8.2

1.0

14.3

7.3

Manufacturing

0.1

–0.1

–0.6

–1.5

0.4

–0.9

–0.6

0.8

2.0

Trade/Transportation/Utilities

1.9

2.1

3.8

1.3

4.6

1.9

0.7

1.7

Information

0.2

–2.1

0.2

–0.7

–0.5

–7.7

1.4

–4.4

–1.6

Financial Activities

1.4

1.8

1.1

1.7

2.4

2.2

1.0

2.4

1.4

Professional & Business Services

3.6

2.7

1.7

3.0

2.1

2.2

9.7

0.7

2.8

Educational & Health Services

1.8

1.3

2.9

1.6

–0.1

0.4

–0.4

1.9

1.7

Leisure & Hospitality

3.0

–0.7

0.8

3.9

6.1

2.6

4.0

4.7

Other Services

0.8

–0.2

–3.8

1.9

–0.9

–4.2

–1.1

0.2

–0.3

–0.2

–0.3

–0.8

2.9

–0.2

0.4

–1.4

–1.3

Government

0.9%

1.9%

† Eighth District growth rates are calculated from the sums of the seven states. For Natural Resources/Mining and Construction categories, the data exclude
Tennessee (for which data on these individual sectors are no longer available).

District real gross state product by industry-2013

U nemployment R ates

Information 3.8%

Financial Activities

III/2013

II/2013

III/2012

United States

7.3%

7.6%

8.0%

Arkansas

7.4

7.3

Illinois

9.2

8.9

Indiana

8.1

8.4

Kentucky

8.4

8.1

8.3

Construction 3.4%
Natural Resources
and Mining 1.3%

Mississippi

8.5

9.1

9.3

Missouri

7.1

6.8

7.0

Tennessee

8.5

8.3

8.1

Trade
Transportation
Utilities

Professional and
Business Services

18.8%

11.2%
Manufacturing

16.6%

Education and
Health Services
Leisure and
Hospitality 3.8%

8.8%
11.1%
Government

Other Services 2.4%

United States $13,431 Billion
District Total $ 1,639 Billion
Chained 2005 Dollars

H ousing permits / third quarter

REAL PERSONAL INCOME* / third QUARTER

year-over-year percent change in year-to-date levels

year-over-year percent change

21.1
–10.0

31.7

18.0
18.6
22.1

–3.3

15.4

17.7

2013

2.5
2.3
2.3

1.4
1.1

2.9
1.9

0.7

3.4

Mississippi
24.7

1.5

Kentucky

Missouri

15.7

–15 –10 –5

Arkansas

Indiana
36.9

7.7

1.5

Illinois
27.8

6.6

United States

43.4

5 10 15 20 25 30 35 40 45 50
2012

All data are seasonally adjusted unless otherwise noted.

2.1
2.8

0.1
1.9

Tennessee
PERCENT

2.9

0.00 0.40 0.80 1.20 1.60 2.00 2.40 2.80 3.20 3.60
2013

2012

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

Full text of Regional Economist : A Quarterly Review of Business and Economic Conditions : January 2014, Vol. 22, No. 1

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