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A Quarterly Review
of Business and
Economic Conditions
Vol. 24, No. 4

Q&A with Bullard

St. Louis Fed President
Discusses New “Narrative”

Immigrants to U.S.
Where They Are From,
Where They Are Going

October 2016

THE FEDERAL RESERVE BANK OF ST. LOUIS
CENTRAL TO AMERICA’S ECONOMY®

Home Economics
The Changing Work Roles
of Wives and Husbands

C O N T E N T S

8

A Quarterly Review
of Business and
Economic Conditions

Changing Work Roles of Wives and Husbands

Vol. 24, No. 4

Q&A with Bullard

St. Louis Fed President
Discusses New “Narrative”

Immigrants to U.S.
Where They Are From,
Where They Are Going

October 2016

THE FEDERAL RESERVE BANK OF ST. LOUIS
CENTRAL TO AMERICA’S ECONOMY

®

By Limor Golan and Usa Kerdnunvong

The labor force participation rate for married men has dropped, while
the rate for married women has risen. Husbands may be working part
time or even staying out of the workforce, while wives—who have
become more educated—are more likely to work full time.
Home Economics
The Changing Work Roles
of Wives and Husbands

THE REGIONAL

ECONOMIST
OCTOBER 2016 | VOL. 24, NO. 4

3

4

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.

PRESIDENT’S MESSAGE

16

Labor Force Participation:
Demographics’ Role

In a Q&A, Our President
Discusses New Approach
President James Bullard explains
why the St. Louis Fed has adopted
a new approach to near-term projections for the U.S. macroeconomy and for the fed funds rate.

Director of Research
Christopher J. Waller
Chief of Staff to the President
Cletus C. Coughlin
Deputy Director of Research
David C. Wheelock
Director of Public Affairs
Karen Branding
Editor
Subhayu Bandyopadhyay
Managing Editor
Al Stamborski
Art Director
Joni Williams

Please direct your comments
to Subhayu Bandyopadhyay
at 314-444-7425 or by email at
subhayu.bandyopadhyay@stls.frb.org.

6

The Gender Wage Gap:
A Different Angle
By Limor Golan
and Andrés Hincapié
In this study, the gap is compared
from one generation to the next.
The changes in the wage gap are
linked to changes in labor supply
and to “statistical discrimination”—when women pay a price
because many other women are
less attached to the workforce
than men are.

You can also write to him at the
address below. Submission of a
letter to the editor gives us the right
to post it to our website and/or
publish it in The Regional Economist
unless the writer states otherwise.
We reserve the right to edit letters
for clarity and length.

14 Coming to America:
A Look at Our Immigrants
By Subhayu Bandyopadhyay
and Rodrigo Guerrero

DISTRICT OVERVIEW
20

METRO PROFILE

By Maria A. Arias and
Paulina Restrepo-Echavarria

Evansville, Ind., Shifts
from Cars to Services

Some believe the decline is due to
discouraged workers’ dropping
out of the labor force. A review
of national and District statistics,
however, suggests that demographic
changes—such as aging workers
and adults spending more years in
college—can explain this trend.

By Charles S. Gascon
and Andrew E. Spewak

18

E C O N O M Y AT A G L A N C E

19

N AT I O N A L O V E R V I E W

The education and health services
sector is the largest employer in
the metro area these days. Manufacturing, especially that related
to the auto industry, is still strong
but not what it once was. Another
challenge is the area’s slow population growth.

After Lackluster Start in
2016, Economy Improves
By Kevin L. Kliesen
There is a high probability that
real GDP growth in the third
quarter will be much stronger
than in the first half of the year.
Forecasters see this solid growth
carrying over to the fourth quarter, as well as to the first half of
next year.

23

RE ADER E XCHANGE

COVER IMAGE: © THINKSTOCK /STOCKBY TE

Single-copy subscriptions are free
but available only to those with
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www.stlouisfed.org/publications.

ONLINE EXTRA

You can also write to The Regional
Economist, Public Affairs Office,
Federal Reserve Bank of St. Louis,

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

P.O. Box 442, St. Louis, MO 63166-0442.

Different Races See a Different Impact of Education on Wealth
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 | October 2016

Where do most of our immigrants
come from? Which are the most
popular and least popular states for
settlement? These are not just trivia
contest questions—the answers
are important for those who make
policy and budget decisions on the
state and federal levels.

By William R. Emmons and Lowell R. Ricketts
Family wealth generally increases with education. But new research
shows that race and ethnicity can greatly affect the relative payoff.
There’s a gap—sometimes wide—between the wealth of Hispanics
and African-Americans and the wealth of whites and Asians at every
education level, from those with only a high school diploma to those
with an advanced degree.

P R E S I D E N T ’ S

M E S S A G E

Higher GDP Growth in the Long Run
Requires Higher Productivity Growth

R

eal gross domestic product (GDP)
growth in the U.S. has been relatively
slow since the recession ended in June 2009.
It has averaged about 2 percent over the
past seven years, compared with roughly
3 percent to 4 percent in the three previous
expansions. At this point, the slower growth
during the current recovery can no longer
be attributed to cyclical factors that resulted
from the recession—rather, it likely reflects
a trend.
A common topic of discussion among
observers of the U.S. economy is how to
return to a higher growth rate for the U.S.
economy. The pace of growth is important
because it has implications for the nation’s
standard of living. For instance, at an
annual growth rate of 1 percent, a country’s
standard of living would double roughly every
70 years; at 2 percent it would double
every 35 years; at 7 percent it would double
every 10 years.
While some might want to turn to monetary policy as the tool for increasing the
GDP growth trend, monetary policy cannot
permanently alter the long-run growth rate.
Leading theories say that monetary policy
can have only temporary effects on economic growth and that, ultimately, it would
have no effect on economic growth because
money is neutral in the medium term and
the long term. Monetary policy can only
pull some growth forward (e.g., when the
economy is in recession) in exchange for
less growth in the future. This process
allows for a smoother growth rate across
time—so-called “stabilization policy”—
but there would be no additional output
produced overall.
One of the most important drivers of
increased real GDP growth in the long

run is growth in productivity. In recent
years, average labor productivity growth
in the U.S. has been very slow. For the total
economy, it grew only 0.4 percent on average
from the second quarter of 2013 to the first
quarter of 2016, whereas it grew 2.3 percent
on average from the first quarter of 1995 to
the fourth quarter of 2005.

The U.S. experienced faster
productivity growth in the
not-too-distant past. If we
could return to the productivity growth rates experienced
in the late 1990s, the U.S.
economy would likely see
better outcomes overall.
What influences productivity over time?
The literature on the fundamentals of
economic growth tends to focus on three
factors. One is the pace of technological
development. Productivity improves as new
general purpose technologies are introduced
and diffuse through the whole economy.
Classic examples are the automobile and
electricity. The second factor is human
capital. The workforce receives better training and a higher level of knowledge over
time, both of which help make workers
more productive and improve growth over
the medium and long run. The third factor
is productive public capital. The idea is that
government would provide certain types of
public capital that would not otherwise be
provided by the private sector, such as roads,
bridges and airports. This type of public

capital can improve private-sector productivity and, therefore, may lead to faster
growth.
The U.S. experienced faster productivity growth in the not-too-distant past. If
we could return to the productivity growth
rates experienced in the late 1990s, the U.S.
economy would likely see better outcomes
overall. As a nation, we need to think about
what kinds of public policies are needed to
encourage higher productivity growth—
and, in turn, higher real GDP growth—over
the next five to 10 years. The above considerations suggest the following might help:
encouraging investment in new technologies, improving the diffusion of technology,
investing in human capital so that workers’
skillsets match what the economy needs,
and investing in public capital that has productive uses for the private sector. These are
all beyond the scope of monetary policy.

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

The Regional Economist | www.stlouisfed.org 3

Q&A with President Bullard
on New Approach to Projections
Federal Reserve Bank of St. Louis President James Bullard discussed the St. Louis Fed’s new
narrative regarding the outlook for the U.S. economy and monetary policy during an interview
with Jeremy Schwartz and Jeremy Siegel on “Behind the Markets” on Aug. 12. The content
originally aired on Business Radio Powered by The Wharton School, SiriusXM Channel 111.
The following excerpts are from the interview. They have been edited for clarity and length.
More information on this topic, including the entire interview, is available on President Bullard’s
webpage and in his Aug. 25 blog post. Links can be found in the endnotes.

Siegel: Why don’t you … spend a few
minutes setting out what you think is the
future for interest rates and why you think
this way.
Bullard: [The St. Louis Fed] put out … what

we called a new narrative on June 17. 1 …
The basic idea is that there’s an old narrative
that we were using really over the last five
years. We think it’s time to switch now to a
new narrative.
The old narrative had a long-run steady
state, which is very common in macroeconomics, and then the idea was that you’re
converging toward this steady state, so all
the variables [e.g., real GDP growth, unemployment, inflation] are going to go back to
their long-run values. And, you know, gaps
[between current values and goals] are getting to be zero, or we think they’re basically
zero as far as output gaps, and the distance
of inflation from target is not very large.
Therefore, you would get this idea that the
4 The Regional Economist | October 2016

policy rate [i.e., the federal funds rate target]
has to rise, and we certainly had that for
quite a while in our narrative. And so you
get this rising dot picture from the Fed.
[See the figure.]
In the June announcement, we abandoned
that narrative and we went to a new narrative, partly because we think parts of the old
narrative were not working and probably
were not going to work going forward. In the
new narrative, you get rid of this idea of a
long-run steady state and you go to the idea
of regimes instead. … And these regimes are
very persistent. Once you’re in one of these
regimes, what you want to do is make the
best monetary policy that you can based on
that regime.
Policy is regime-dependent, and it’s
unpredictable. You can switch out of these
regimes to something else, but it’s unpredictable when that will happen. Once you’re in
a regime, you just predict that you’re going
to stay there for the forecast horizon, which

is about two to two-and-a-half years for the
Fed. The current regime is characterized by
low growth, low productivity and especially
by very low real rates of return on government debt, what we’re calling r-dagger [r†].
We think this regime is going to persist,
so the policy rate can stay about flat over the
forecast horizon with just one increase to get
to the right level of the policy rate for this
regime. We’ve got the policy rate at only 63
basis points [0.63 percent] over the forecast
horizon. [The target range for the federal
funds rate has been at 0.25 to 0.50 percent
since December 2015.] …
Another important thing … is that the
cyclical dynamics in the economy, I think,
are pretty much over. You’ve got unemployment down basically at what the [Federal
Open Market] Committee thinks is the
natural rate of unemployment. … So, this is
a good time to think about a new narrative.
[The table and figure show the forecasts
based on the new narrative.]
Siegel: R[-dagger] is just, for clarification,
a short-term equilibrium real rate on topquality short-term instruments.
Bullard: Right. If you look at the ex-post

real rate of return on one-year U.S. Treasuries, so you take the Treasury yield and you
subtract off the Dallas Fed trimmed mean
inflation rate over the last three years, you’re
going to get about a minus 140 basis points.
We took that to heart as part of the regime.
It hasn’t changed much in the last three
years. We don’t see any reason for that to
really change over the forecast horizon of
two to two-and-a-half years.
We think we should just accept that as
an input to monetary policy for now and
then try to make monetary policy as best we
can, given that value. One way to justify the
63 basis point recommendation is to think
of a Taylor rule. … The Taylor rule would
produce a recommendation for the policy
rate. It’s a formula … that depends on gaps,
and we’re already saying let’s just take the
gaps to be about zero. [For example, there is
almost no gap between the current unemployment rate and the FOMC’s estimate of
the longer-run unemployment rate in the
Summary of Economic Projections, and
inflation as measured by the Dallas Fed
trimmed mean PCE inflation rate is close to
2 percent.]

be aware that, you know, there are certainly
other possibilities out there. … But for now,
we should make policy based on this regime.

Forecast Based on the St. Louis Fed’s New Narrative
(As of June 2016)
Macroeconomic variable

Forecast over the next 2.5 years

Real GDP growth

Schwartz: Any closing thoughts?

2 percent

Unemployment

4.7 percent

Dallas Fed trimmed mean PCE inflation

2 percent

Policy rate (federal funds rate target)

0.63 percent

Bullard: I do think there’s some upside risk.

SOURCE: Federal Reserve Bank of St. Louis.
NOTE: GDP refers to gross domestic product, and PCE refers to the personal consumption expenditures price index. The 12-month Dallas Fed
trimmed mean PCE inflation rate is President Bullard’s preferred measure for assessing underlying inflation.

The Policy Rate Path
3.5
3.0

Percent

2.5
2.0

Summary of Economic Projections Median (June 2016)
St. Louis Fed Projection (June 2016)
Effective Federal Funds Rate

1.5
1.0
0.5
0.0
12/2013

12/2014

12/2015

12/2016

12/2017

12/2018

Longer
run

SOURCES: Federal Reserve Board, Federal Reserve Bank of St. Louis and FRED (Federal Reserve Economic Data).
NOTE: Each quarter, the Federal Open Market Committee (FOMC) releases a Summary of Economic Projections (SEP), which includes
the FOMC participants’ projections for key macroeconomic variables and the federal funds rate. The figure shows the median
projections for the policy rate from the June 2016 SEP.

Then you’ve got this r-dagger at minus
140 basis points, and then you’ve got an
inflation target in there of 2 percent. If you
… add the r-dagger to the inflation target,
you get a 60 basis point policy rate. That’s
really where the thinking behind the level
of the policy rate comes from.
Siegel: Could you kind of comment
on how you feel other members [of the
FOMC]—and I know you can’t speak for
them, certainly, but the general reaction
that you got?
Bullard: I can’t speak for other members.

You’ll have to talk to them. But one reason
we threw out this old narrative is it was getting very hard to work with it in this environment. You had to keep adjusting your
long-run steady state down to lower and
lower levels, and you had to keep stretching
out the length of time it was going to take to
actually get to that steady state. Now we’re
in a situation where the market expects us
to move only once this year. We only moved
once last year.

If you’re only moving once a year and
you’ve got 200 or 250 basis points to go [to
reach the steady state value of the policy
rate], it’s going to take a heck of a long time.
It’s going to take years and years to get there.
That’s way outside of normal business cycle
dynamics and what we would think about
in terms of macroeconomics. That got me
thinking that you can’t continue with this
same kind of concept. That’s why you have
to go to this regime concept, which breaks
down the idea of a steady state. It says that
you’re in a regime for now.
You could switch to a new regime in the
future. And if you switch to a new regime,
then you’re going to have to adjust policy
for that new regime. But, in the meantime,
there’s really no reason to expect that this
very low real rate on government paper is
going to go away. There’s really no reason
to think that the very low productivity that
we have right now is all of a sudden going to
snap back up to higher levels.
For those reasons, you should make
monetary policy for this regime and then

We’ve said 63 basis points over two to twoand-a-half years. But we know where the
other productivity growth regime is, and it’s
higher.2 We also know that there have been
times in the past where investors around the
world have not been so fond of government
paper as they are right now.
[For] both of those things, if they do
switch, they’re likely to switch in a way that
would lead to higher rates. So there’s some
upside risk if that would happen or start to
happen during the next two to two-anda-half years, and then we’d have to react
appropriately. But our idea is that that kind
of thing is unpredictable, and we’ll believe it
when we see it.

Schwartz: Upside risk is higher than the

downside risk probability?

Bullard: I think so. We think recession

probabilities are actually quite low right
now. You always live with recession risk, but
we just don’t see that as very likely over the
near term.

ENDNOTES
1 The June 17 statement and related public remarks

can be found on President Bullard’s webpage at
https://www.stlouisfed.org/from-the-president/
key-policy-papers. His St. Louis Fed On the
Economy blog post on Aug. 25 (“The St. Louis Fed’s
New Approach to Near-Term Projections”) can
be found at https://www.stlouisfed.org/on-theeconomy/2016/august/st-louis-fed-new-approachnear-term-projections.
2 For a discussion on what might improve productivity growth over time, see the President’s Message in
this issue of The Regional Economist.

The Regional Economist | www.stlouisfed.org 5

rd
a
C
e
Tim

LABOR MARKETS

Name

Breaking Down the
ut
Gender Wage n
Gap byO
Age
I
and
by
Hours
Worked
e
t
Da
By Limor Golan and Andrés Hincapié

T

he gender pay gap has declined substantially since the 1960s, a period of many
decades when women’s participation in the
labor market has risen and their working
hours have increased. An especially significant decline in the pay gap occurred in
the 1970s and the 1980s. The convergence
slowed down in the 1990s, and some gap still
remains.1
In this article, we examine the evolution of
the wage gap by cohorts. We also look at the
evolution over the life cycle to gain further
insight into the patterns and possible causes
of the gender wage gap. Using data from the
Panel Study of Income Dynamics (PSID),
we followed the evolution over the life cycle
for three cohorts: those born in 1941-1950,
those born in 1951-1960 and those born in
1961-1970.
The figure presents the evolution of the
gender pay gap over the life cycle for the first
two cohorts of white individuals. (An analysis
for all races is beyond the scope of this
article; there are other issues regarding labor
market pay gaps for nonwhites.) The red line
shows the median wage of females divided
by the median wage of males by age. Where
the line is sloping upward, the gender wage
gap is declining because the median female
wage is larger relative to the median male
wage; the opposite is true if the line is sloping
downward.
As can be seen from the two charts, the
gap increases with age, at least after the age
of 24, which is the age by which the majority
of individuals have completed their education. Thus, the gender gap when workers
are 24 is substantially smaller than the gap
when workers are in their mid-30s. This fact
is well-known,2 and one of the main reasons
for this pattern is that men and women make
6 The Regional Economist | October 2016

different choices over the life cycle. As they
get older, women are more likely than men
to work fewer hours outside the home and
have breaks in their labor force participation
(yielding less accumulated experience and
possibly fewer labor market skills) and are
less likely to hold highly compensated jobs
with promotion prospects.3
Life-Cycle Wage Gap

To further explore the role of labor market
experience, we plotted the evolution of the
gender pay gap for employees who work
full time continuously during their careers.
The blue dotted line in the charts shows the
gender pay gap within this subset. For each
age, we divided the median wage of females
who worked full time continuously up to that
age by the same for males.4 From the figure,
it is clear that the gender wage gap is smaller
for those who worked full time continuously
than for all workers in general. This is true
for all cohorts.
For those in the second cohort (born
1951-1960), the pay gap for those working
full time continuously is not only smaller but
decreases with age for the most part. This
latter fact is in contrast to what is seen in the
full sample.
We considered several possible explanations for this pattern. First, the composition of the sample changes. For example, if
skilled women (skill can be formal education
and training but also innate ability, which
is unobserved by the researchers) are more
likely to work full time continuously, then the
wage gap at a later age reflects the fact that
we are comparing the wages of less-skilled
women to those of men early on, while we
are comparing more-skilled women to men
at older ages. (The group of men working full

time continuously can be more stable because
both more-skilled and less-skilled men are
likely to work full time.) Second, while men
still work more hours than women, the gap in
hours declines in this group; so, the increase
in experience (and, therefore, labor market
skills) of women who work full time continuously is larger than that of men. Third,
the wage gap reflects discrimination, and
discrimination of women who continuously
work full time declines over time.
Regarding the first explanation, we calculated the share of college-and-above-educated
males and females among those who work full
time. If anything, after age 25, the education
of males continuously working full time is
increasing relative to that of females. However,
it is still possible that education is simply one
dimension of skill and that women in this
group are in fact increasingly skilled but their
skills are unobserved by the researcher.
Regarding the second explanation, the gap
of hours worked between males and females
who work continuously full time does not
decline substantially. Therefore, we ruled this
out, too.
Last, we turn to the third explanation:
Labor market experience and discrimination
are related.5 Specifically, firms often have
costs of hiring and training workers. When
they hire people for jobs with good promotion prospects and jobs that require training and long hours, they are likely to seek
individuals who are less likely to leave the
labor force or to reduce their hours substantially.6 While some women are more inclined
to participate in the labor market and work
full time, women in general are still more
likely to reduce hours or leave the labor force,
especially during childbearing years, relative
to what men are likely to do. This can lead

ENDNOTES

Median Wage Gap

1
2
3
4

COHORT 1: BORN 1941-1950

Female-to-Male Median Wage Ratio

1.00
0.95
0.90
0.85
0.80
0.75
0.70

All workers

0.65

Continuously working full time

0.60
0.55

20

24

28

32

36

40

Age

REFERENCES

COHORT 2: BORN 1951-1960

Female-to-Male Median Wage Ratio

1.00
0.95
0.90
0.85
0.80
0.75
0.70

All workers

0.65

Continuously working full time

0.60
0.55

20

24

28

Age

32

36

40

SOURCES: Panel Study of Income Dynamics and authors’ calculations.

to lower wages for equally qualified women.
Furthermore, since many factors affecting
labor supply are not known to employers
at the time of hiring, even women who are
likely to work long hours and are attached
to the labor market as much as men are
may earn lower wages because, on average,
women with the same qualifications as men
are less attached to the labor force than
men are.
This type of discrimination is often called
statistical discrimination because group
affiliation and group averages adversely affect
individuals in the group. Over time, employers can typically observe work experience,
whether individuals were working and
whether they were working full time or part
time. Therefore, employers can increasingly
identify workers who are less attached to the
labor market and, as a result, discrimination
of the type described above goes down with
age. Since this type of discrimination is more
likely to be directed at women, the wages of
women who work full time continuously may
grow relative to the wages of men due to a
decline in discrimination.

See Blau and Kahn.
See Erosa et al., as well as Gayle and Golan.
See Blau and Kahn for a survey.
We defined continuously working full time as those
who worked at least 49 weeks of each year and at
least 20 hours weekly. The patterns described in this
article hold for a higher number of hours a week, but
since we break the sample by gender and cohort, we
use 20 hours so that the sample is not too thin.
5 This explanation was proposed by Barron et al. and
was extended by Gayle and Golan.
6 In many cases, some full-time jobs have to be filled
if a worker decides to reduce hours and work part
time.
7 See Mulligan and Rubinstein.

Barron, John M.; Black, Dan A.; and Loewenstein,
Mark A. “Gender Differences in Training, Capital
and Wages.” Journal of Human Resources, Spring
1993, Vol. 28, No. 2, pp. 343-64.
Blau, Francine D.; and Kahn, Laurence M. “Gender
Differences in Pay.” Journal of Economic
Perspectives, Fall 2000, Vol. 14, No. 4, pp. 75-99.
Erosa, Andres; Fuster, Luisa; and Restuccia, Diego.
“A Quantitative Theory of the Gender Gap in
Wages.” European Economic Review, June 2016,
Vol. 85, pp. 165-87.
Gayle, George-Levi; and Golan, Limor. “Estimating
a Dynamic Adverse-Selection Model: Labor-Force
Experience and the Changing Gender Earnings Gap
1968–1997.” The Review of Economic Studies,
January 2012, Vol. 79, No. 1, pp. 227-67.
Mulligan, Casey B.; and Rubinstein, Yona. “Selection,
Investment and Women’s Relative Wages over
Time.” Quarterly Journal of Economics, 2008,
Vol. 123, No. 3, pp. 1,061-110.

Economists George-Levi Gayle and
Limor Golan found evidence for this type
of discrimination even after accounting for
differences in actual hours worked and for
unobserved changes in the composition of
ability of men and women who work continuously full time (as noted in the possible first
two explanations for the data above). Therefore, this type of discrimination accounts for
the changes in the gender gap.
Cohort Differences

The gender wage gap increases after the
age of 24 for the overall cohort (red lines).
The wage gap for young workers in the
first cohort is smaller, but it increases more
rapidly than the gap in the second cohort.
Although the overall gender pay gap was
larger in the first cohort, labor force participation of women was substantially lower
then. Therefore, for any age group there are
differences in the composition of women
who work. One possible reason for the faster
increase in the gender pay gap for the earlier
continued on Page 13
The Regional Economist | www.stlouisfed.org 7

8 The Regional Economist | October 2016

LABOR MARKETS

Home Economics
The Changing Work Roles
of Wives and Husbands
By Limor Golan and Usa Kerdnunvong

I

t is well-known that the labor force participation rate
for men and the hours worked by men have declined
over the past four decades. More men are reporting that
they either are not employed and not actively searching
for a job or are working part time; these two trends
are contributing to the decline in the average hours
worked by men in the past four or five decades.1
During this same time, women have increased their
representation in the labor market: The fraction of
women participating in the labor force has increased,
as has the number of hours women work outside the
home, with the majority of the increases driven by
growth in the labor supply of married women.

The Regional Economist | www.stlouisfed.org 9

FIGURE 1

Percent of Married Prime-Age Males

98

6

97

5

96

4

95
94

3

93

2

92
91

Labor Force Participation Rate (left)

90
89
1970

1

Part-time Labor Supply (right)
1975

1980

1985

1990

1995

2000

2005

2010

2015

SOURCES: Current Population Survey, Integrated Public Use Microdata Series (IPUMS) and authors’ calculations.

0

Percent of Husbands in Labor Force Working Part Time

Husbands, Age 25-54: Labor Force Participation and Part-Time Work

FIGURE 2

labor force participation rate of this group.
The trend is similar to that for men in general. In 1970, more than 97 percent of these
husbands participated in the labor market,
dropping below 93 percent in 2011 and
staying there. Meanwhile, the rate of parttime workers among prime-age husbands
increased substantially since the 1970s: Less
than 1.5 percent of the men worked part
time in 1970; this fraction has been about
4 percent or more since 2009. As for wives,
close to 26 percent of married women in the
labor force worked part time in 1970, but
only about 22 percent worked part time
after 2000.3

Husbands Who Work Part Time or Are Not Participating in the Labor

Household Labor Supply

Force, by Their Education

Several factors could contribute to the
decline in labor force participation and
hours worked by married prime-age men
and to the increase in labor supply of their
wives. The explanations include both
demand- and supply-side motives. One
explanation for the declines related to men
is that there is a drop in demand, especially
in the manufacturing sector; this decline in
demand is related to skill bias, technological changes and offshoring.4 The increase in
married female labor supply can be partly
explained by the increase in educational
attainment of women and the increase in
relative wages in high-skill occupations.5
In the context of household labor supply,
however, a decline in the gender pay gap can
cause an increase in the female labor supply
and a decrease in the male labor supply as
a response to the household’s joint decision
on labor and to the household’s overall
income. In other words, the higher income
generated by wives may reduce the incentive
of husbands to work many hours or to work
at all.6
Furthermore, increases in labor force
participation of married women and their
hours worked can also be due to an increase
in risk pooling in households, especially in
households in which women are married
to low-skilled men or to men working in
declining industries. With women’s strides
in education, they can provide insurance
within the household by working more
when men lose their jobs or when the
wages offered to men are low. This insurance motive may kick in even before the
husband loses his job. Wives may decide to

90
80
70
Percent

60
50
40
30
20
10
0
1970

1975

1980

1985

Part time; less than or equal to high school
Part time; some college
Part time; bachelor’s degree and higher

1990

1995

2000

2005

2010

2015

Nonparticipating; less than or equal to high school
Nonparticipating; some college
Nonparticipating; bachelor’s degree and higher

SOURCES: Current Population Survey, Integrated Public Use Microdata Series (IPUMS) and authors’ calculations.

The changes in the labor supply of men
and women may be related, especially if we
consider married men and women. Time
allocation—that is, how much a spouse
works, how much time each spends on
housework and child care, and how much
leisure each enjoys—is decided within
households. This context is needed to
analyze married individuals’ decisions to
withdraw from the labor market or to work
part time. Although many papers have documented the decline in the labor supply of
males,2 this article focuses on the changing
role of wives in providing economic support
for their families and changes in the labor
supply of prime-age (25-54) married males.
We focus on prime-age married males
because of this group’s traditional role of
being the breadwinners; these men are typically attached to the labor market and work
full time. Figure 1 shows changes in the
10 The Regional Economist | October 2016

Labor Supply and Education
Composition

The education composition of husbands
who either work part time or are nonparticipating has changed significantly over
time. As shown in Figure 2, in both groups,
the respective fraction of husbands with
high school education or less decreased,
and the fraction of husbands with at least
some college education increased since
1970. (During and after the Great Recession
of 2007-09, however, the fraction of males
who worked part time and had no more
than a high school diploma went up but
has since reverted to its decreasing trend.
As for better-educated husbands, there was
a relative decline in the fraction working
part time during the recession. The differences between the experiences of the two
groups during the recession can be due to
the differences in the demand for the skills
of educated and less-educated men; another
factor is that more-educated husbands are
more likely to have more-educated wives
with different labor market prospects.)
To further explore the changes in the
composition of households in which husbands do not participate in the labor force
or work part time, we turn to the education

FIGURE 3
Husbands Who Work Part Time or Are Not Participating in the Labor
Force, by Wife’s Education
90
80
70
Percent

60
50
40
30
20
10
0
1970

1975

1980

1985

1990

Husband works part time;
wife has no more than high school diploma
Part time; some college
Part time; bachelor’s degree and higher

1995

2000

2005

2010

2015

Nonparticipating; less than or equal to high school
Nonparticipating; some college
Nonparticipating; bachelor’s degree and higher

SOURCES: Current Population Survey, Integrated Public Use Microdata Series (IPUMS) and authors’ calculations.

FIGURE 4
Husbands Who Work Part Time or Are Not Participating in the Labor
Force, by Couple’s Education
90
80
70
60
Percent

work outside the home when there is just a
threat of unemployment or a decline in their
husbands’ earnings.
Another possible explanation for the
increase in married males’ working part
time has to do with the fact that finding
jobs can take time and effort. Working part
time allows the individual to spend more
time searching for a better-paying job or
investing in the acquisition and enhancement of skills, which often means going
back to school or even acquiring skills that
allow individuals to change occupation or
sector.7 Thus, in households in which wives
work full time, husbands might be able to be
choosier in accepting jobs—they can afford
to be less willing to take full-time jobs for
low pay or jobs that may not offer good
promotion prospects or other nonpecuniary qualities. These men may take part-time
jobs while searching for better jobs.
Next, we explore changes in characteristics of households in which prime-age men
were not participating in the labor force or
worked part time between 1970 and 2015.

50
40
30
20
10
0
1970

1975

1980

1985

1990

Part time; wife more educated
Part time; same education
Part time; wife less educated

1995

2000

2005

2010

2015

Nonparticipating; wife more educated
Nonparticipating; same education
Nonparticipating; wife less educated

SOURCES: Current Population Survey, Integrated Public Use Microdata Series (IPUMS) and authors’ calculations.

compositions of the wives. As Figure 3 demonstrates, the fraction of these husbands
who are married to women with high school
education or less declined significantly. The
fact that women in households in which
males work part time or do not participate
are more educated than in the past (and
given the decline in the gender earnings
gap) may imply that these women are more
likely to work, earn relatively more and
provide more economic support for their
families.
In addition to the education composition
of the men and women, the relative earning
potential of the spouses in the household
can be important to understanding how
the spouses allocate their time among jobs,
The Regional Economist | www.stlouisfed.org 11

in these households is higher than it used
to be. These patterns also suggest that these
women might have better labor market
prospects than men and have an important
role in providing economic support for their
families.

FIGURE 5
Husbands Who Work Part Time or Are Not Participating in the Labor
Force, by Wife’s Work Status
60
50

Percent

40

Relative Share of Wives’ Earnings

30
20
10
0
1970

1975

1980

1985

1990

Husband works part time; wife works full time
Husband works part time; wife works part time
Husband works part time; wife is nonparticipating

1995

2000

2005

2010

2015

Husband is nonparticipating; wife works full time
Husband is nonparticipating; wife works part time
Husband and wife are nonparticipating

SOURCES: Current Population Survey, Integrated Public Use Microdata Series (IPUMS) and authors’ calculations.

FIGURE 6
Median Share of Wife’s Income

Percent of Household Income

70
60
50
40
30
All Married Couples

20

Husband Works Part Time

10
0
1970

Husband Doesn’t Participate in Labor Force
1975

1980

1985

1990

1995

2000

2005

2010

2015

SOURCES: Current Population Survey, Integrated Public Use Microdata Series (IPUMS) and authors’ calculations.
NOTE: Household income was calculated as the sum of the wife’s income and the husband’s income.

housework, child care and leisure. Figure 4
presents the change in composition of
part-time and nonparticipating husbands
by their education status relative to that of
their wives. Interestingly, among these two
groups of men, there is a clear decline in
those who are married to women with the
same education level or less and an increase
in the fraction of those who are married to
women who are more educated than they
are. The percentage of those married to
wives who are relatively more educated than
them increased from about 9 percent in 1970
to about 27 percent in 2015.
The patterns in both increasing education of women in households in which men
work part-time jobs and the fact that in
an increasing fraction of these households
women are more educated than the men
suggest that the earning potential of women
12 The Regional Economist | October 2016

Figure 5 describes the employment status
of women in households in which the
husbands work part time or are nonparticipating. Despite the short dip after the Great
Recession, the overall increasing trend of
the fraction of men who work part time and
are married to women who work full time is
clear. The patterns for wives of nonparticipating husbands are similar.
Next, we describe the role of the wife’s
income relative to the husband’s income and
how that relationship changed over time.
Figure 6 shows the median share of the
wife’s income in the total household income
for married households. 8 In the overall
sample, the share was close to 2 percent in
1970, which is consistent with traditional
families in which the men are the breadwinners. This share rose to about 30 percent by
the late 1990s and has fluctuated around
30 percent since then. The share of the wife’s
income in families with husbands who work
part time or are nonparticipating is always
higher than the share of the wife’s income
in all married families. However, in families
in which men work part time or do not
participate, the wife’s income in recent years
has been equal to or has exceeded that of the
husband.
Conclusion

Despite the findings that most of the
increase in nonparticipation of prime-age
males stems from relatively less-educated
males,9 the fraction of educated, prime-age
husbands who do not participate or who
work part time has increased over the past
few decades. At the same time, in these
households, the fraction of educated women
and the fraction of women who are more
educated than their husbands increased.
These data partly reflect decades of
demographic changes: rising college graduation rates in the overall population, with
women’s educational achievements surpassing men’s; a decline in the marriage rate,
especially for those with less education; and

changes in the marriage markets. Thus, the
composition of the households in which
males do not work or who work part time
has also changed. We found that in these
households the role of women in providing
income to the family is higher than it was in
the past. These changes are likely to affect
households’ labor supply and job-search
behavior (the intensity of search and what
kinds of jobs and pay people are willing
to accept).
In addition, the data show that changes
in labor supply during and right after the
Great Recession vary by the education of the
spouses: The fraction of males working part
time who had no more than a high school
education or who were married to women
with no more than a high school education
increased during the recession; meanwhile,
the fraction of better-educated males working part time and of males working part
time who were married to better-educated
females declined during the recession, suggesting differences in both labor market
opportunities and labor-search behavior for
more-educated families.
Although many papers suggest that the
role of the changes in labor demand is
important, the descriptive analysis cannot
be used to infer causal effect and to separate
demand and supply factors. However, it is
important to assess the role of the marriage
market and the role of both spouses in generating income and providing housework
in order to fully understand trends in labor
participation and hours worked and how
they interact with business cycles and labor
market conditions.
In particular, assessment is needed of
job-search behavior and the choice of sector
in which people want to work. Whether to
remain in a sector with high probability
of unemployment or to acquire new skills,
whether to work outside the home and, if
so, how many hours to work—all of these
decisions for husbands may depend on their
wives’ employment opportunities, as well as
their own employment opportunities.10
At the time this was written, Limor Golan was
an economist at the Federal Reserve Bank of
St. Louis and Usa Kerdnunvong was a senior
research associate at the Bank.

ENDNOTES
1

2
3

4
5
6
7
8

9

10

Nonparticipating individuals are those not
in the labor force, as they have not looked for
work in the past four weeks when surveyed,
even if they want a job.
See Doepke and Tertilt for a comprehensive
survey on labor supply behavior.
We use the Current Population Survey definition of part-time work: working fewer than
35 hours a week. The average number of hours
worked by prime-age males who work part
time also slightly declined, therefore reflecting
decline in labor supply.
See Acemoglu and Autor.
See Gayle and Golan.
See Jones et al.
See Guler et al.
Household income is the sum of the husband’s
and the wife’s income. Note that males who do
not work may still have positive income from
welfare payments, government programs (such
as unemployment compensation and veterans
benefits) and other nonlabor income (such as
income from investments or savings accounts).
See Council of Economic Advisers for discussion on the decline in prime-age male participation.
The spouse’s education and occupation can
affect choices of sector and skill acquisition.
Moreover, each individual’s labor market
prospects can affect both the decision to get
married and the choice of the spouse, given his
or her occupation.

REFERENCES
Acemoglu, Daron; and Autor, David H. “Skills,
Tasks and Technologies: Implications for
Employment and Earnings,” in Orley Ashenfelter and David E. Card, eds., Handbook of Labor
Economics, Vol. 4, 2011. Amsterdam: Elsevier.
Council of Economic Advisers. “The Long-term
Decline in Prime-Age Male Labor Force Participation.” June 2016, Report of the Executive
Office of the President of the United States.
Doepke, Matthias; and Tertilt, Michèle. “Families
in Macroeconomics.” Working Paper No. 22068.
National Bureau of Economic Research, March
2016.
Gayle, George-Levi; and Golan, Limor. “Estimating
a Dynamic Adverse-Selection Model: LabourForce Experience and the Changing Gender
Earnings Gap 1968–1997.” The Review of Economic Studies, 2011, Vol. 79, No. 1, pp. 227-67.
Guler, Bulent; Guvenen, Fatih; and Violante,
Giovanni L. “Joint-Search Theory: New
Opportunities and New Frictions.” Journal of
Monetary Economics, May 2012, Vol. 59, No. 4,
pp. 352-69.
Jones, Larry E.; Manuelli, Rodolfo E.; and McGrattan, Ellen R. “Why Are Married Women
Working So Much?” Federal Reserve Bank of
Minneapolis Research Department Staff Report
317, October 2014.

continued from Page 7
cohort is that there was a negative selection
of women into the labor market in earlier
cohorts.7 Married women were less likely
to work then, and those who worked were
women with lower skills. While skills can
be partly unobserved, taking a look at the
education composition of men and women
who worked in the first cohort suggests that
overall (after age 24) the fraction of working
men with at least a college degree was higher
than that of women, whereas the education
gap was much smaller in the second cohort.
Thus, some of the pay gap in the first cohort
could be due to the gap in education and
skills between the two sexes.
We investigated the changes in the education composition of men and women who
work full time continuously in each cohort.
For the group working full time continuously
in the first cohort, females were more educated than males up to age 28; however, the
wage gap is declining when males are more
educated than females. In the second cohort,
the education gap among those working full
time continuously declines (with females
being more educated than males in all age
groups). Thus, education composition does
not explain the evolution of the gender pay
gap differences in that group.
Conclusion

By comparing the differences in the
evolution of the gender pay gap not only by
age but by full-time/part-time status, we
demonstrated the importance of statistical
discrimination and its relationship to labor
force participation of women. As one would
expect, this type of discrimination plays a
smaller role for the third cohort (born 19611970) because women in this cohort are more
attached to the labor force than women in
the past.
At the time this was written, Limor Golan was
an economist at the Federal Reserve Bank of
St. Louis and Andrés Hincapié was a technical
research associate at the Bank.

The Regional Economist | www.stlouisfed.org 13

P O L I C Y

A N A LY S I S

Immigrants to the U.S.:
Where They Are Coming from,
and Where They Are Headed
By Subhayu Bandyopadhyay and Rodrigo Guerrero
© THINKSTOCK / MOODBOARD

I

mmigration into the U.S. is unevenly
distributed across its different states.
Although the share of the foreign-born population in the U.S. as a whole is 14.2 percent,
that of individual states ranges from a high
of 28.1 percent in California to a low of 1.9
percent in West Virginia.1 These differences
factor into policy debates. For example,
tax revenue that is collected from immigrants and taxpayer money that is spent on
immigrants affect states’ budgets. In this
article, we first discuss some factors that
can influence the level of immigration to a
state; then, we present some facts regarding
immigration levels in different states.

States with better job
opportunities, greater public
amenities, and more favorable
social or ethnic networks will
attract more immigrants.
State-Level Factors

Immigrant stock in a state is due to both
legal and unauthorized immigration. Legal
immigration is determined at the national
level by the federal government. However,
after being admitted into the U.S., an immigrant is free to choose the state of location.
In turn, this implies that states do not have
control over legal immigration, and their
respective legal immigrant shares are determined by their relative desirability in the
eyes of an immigrant. States with better job
opportunities, greater public amenities, and
more favorable social or ethnic networks
will attract more immigrants.
Unauthorized immigration, by its very
nature, is not directly a policy choice. How14 The Regional Economist | October 2016

ever, both state and federal policies can
influence states’ unauthorized immigrant
shares. For example, if there is greater
enforcement by the federal government at
the border, then unauthorized immigration
into the country is reduced; this, in turn,
will reduce inflows into the states. Similarly,
if one state is stricter than a neighboring
state in verifying the immigration status of
potential employees, unauthorized immigration to the first state may be discouraged, and the flow might be diverted to the
neighboring state.
We used data on foreign-born residents
of a state as a proxy for current and past
immigration flows. Admittedly, this measure is imperfect because it lumps together
naturalized citizens and foreign-born
individuals whose parents are both natives,
as well as legal and unauthorized immigrants. However, we used the data because
of its accessibility and reliability. Indeed,
if a state is more attractive to immigrants,
one would expect it to get a larger inflow of
immigrants, which should be reflected in a
correspondingly higher level of foreignborn residents.
States’ Shares

Figure 1 presents the share of foreignborn populations of different U.S. states.
The horizontal line, at 14.2 percent, represents the share of the U.S. population that
was born abroad. Only 14 states are above
this national average. This implies that
immigrants favor only a few states; alternatively, a few states are more hospitable than
others for immigrants.
The distribution of foreign-born shares
across states might also point toward an
accumulation effect: a higher share of

foreign-born may lead to a higher immigrant inflow. California and New York are
the top two destinations for the foreignborn, while Mississippi and West Virginia
have the lowest shares. Most states in the top
five destinations either have major urban
centers or are relatively close to the border.
In contrast, the lowest five tend to be more
sparsely populated or are interior states.
Although urban centers like New York City
or Los Angeles are likely to attract immigrants for a variety of reasons, including
ethnic networks on which fresh immigrants
can rely, distance from the border also plays
a role, especially for immigrants from a
neighboring poorer nation like Mexico.
Immigrants’ Home Countries

Figure 2 shows the top origin nations of
the immigrants. Mexico is the largest source
nation, providing about 4 percent of the U.S.
population, followed by India, China and
the Philippines. India contributes less than
a quarter of the share that Mexico contributes. This overwhelming weight of Mexico
stems from its proximity to the United
Sates. Canada is also a bordering nation,
but it is closer to the U.S. in terms of its
level of economic prosperity than is Mexico,
and, hence, the incentive for Canadians to
migrate to the U.S. is not comparable to that
for Mexicans.
States bordering Mexico (Arizona,
California, New Mexico and Texas) all have
Mexico as the leading source nation of
immigrants. Similarly, Florida shows Cuba
as the top source nation because of Florida’s
geography and history. On the other hand,
New Jersey has India as its largest source
nation. This suggests that distance between
source nations and potential destination

ENDNOTES

FIGURE 1
Share of Foreign-Born by State

1

Percent of State Population

30
25
2

20
15
Total foreign-born as a percentage of U.S. population

10
5
0

CA NY NJ FL NV HI TX MA MDDC AZ CT WA IL RI VA CO NM OR GA UY DE AK MNNC KS ID MI PA NE NH OK TN SC IA AR IN WI VT WY OHME LA MOND KY AL SD MT MSWV

SOURCE: Authors’ calculations from 2014 the American Community Survey, accessed via IPUMS-USA.
NOTE: The thick horizontal line, at 14.2 percent, represents the share of foreign-born population in total U.S. population.
All 50 states are listed, as is the District of Columbia.

FIGURE 2
Where Immigrants to the U.S.
Are Coming from
4.0
Percent of U.S. Population

3.5
3.0
2.5
2.0
1.5
1.0

Korea
Dominican
Republic

Cuba

Germany

Vietnam

El Salvador

Philippines

China

India

0

Mexico

0.5

SOURCE: Authors’ calculations from the 2014 American
Community Survey, accessed via IPUMS-USA.
NOTE: The figures for China include individuals born in Hong
Kong and Macau, and the figures for Korea include individuals
born in North Korea and South Korea.

states might be an important factor for
countries that are relatively close to the U.S.
(e.g., Mexico or Cuba), but not as much for
distant countries like India. New Jersey may
be drawing people of Indian origin due to
economic and social opportunities.
Why One State over Another

A definitive answer is beyond the scope
of this article. We offer some correlations—
imprecise as they are—of state foreign-born
stocks with potential state-level factors that
may affect immigration.
The table lists these correlations for
Mexico as the source nation and all the U.S.
states as potential hosts. Using Mexico as
the sole source nation keeps our analysis

simple and tractable. Mexico is a reasonable
benchmark, given its overwhelming weight
as a source of the foreign-born population
in the U.S.
Distance between a state and Mexico
City is negatively correlated with the state’s
Mexican-born share.2 So, proximity matters. On the other hand, per capita income
of a state does not seem to be very indicative of where an immigrant locates. More
important is its total income (i.e., gross state
product, or GSP) and its total population.
Perhaps this is because a sparsely populated
state may have high per capita income but
may not offer a potential immigrant the
same opportunities of life that may be available in a larger and more urban state, where
more publicly provided goods like public
transportation in urban areas or accessible
public education may turn out to be immigrant magnets.
Policy Coordination Is Key

Clearly, immigrants are spread out quite
unevenly across different U.S. states and
come from many nations. This dispersion presents both challenges to the states
and opportunities for them. Accordingly,
sensible immigration policy for the nation
critically depends on the coordination and
cooperation between the federal and state
governments.
Subhayu Bandyopadhyay is an economist and
Rodrigo Guerrero is a research associate, both
at the Federal Reserve Bank of St. Louis. For
more on Bandyopadhyay’s work, see https://
research.stlouisfed.org/econ/bandyopadhyay.

All the figures presented in this article are authors’
calculations based on the 2014 American Community Survey (ACS), conducted by the Census Bureau
and made available via IPUMS-USA. (IPUMS
stands for Integrated Public Use Microdata Series.)
We estimated the distance between each state and
Mexico City using the great-circle distance formula
and assuming the Earth is a sphere with a radius of
6,371 kilometers. A state’s latitude and longitude
data correspond to an internal point that is at or
near the state’s geographic center, as calculated by
the Census Bureau.

REFERENCE
IPUMS-USA, University of Minnesota.
See www.ipums.org.

State Variables That May Influence
Where Immigrants Move
A Case Study Using Mexico
Mexico-Born Share
of State Population
Distance from Mexico City
Population
Real gross state product (GSP)

–0.35
0.52
0.52

Real GSP per capita

–0.05

Unemployment rate

0.16

SOURCES: Authors’ calculations from the 2000-2014 American
Community Survey, the Bureau of Economic Analysis, and the
Bureau of Labor Statistics.
NOTE: Correlation is a measure of the linear relationship between
two variables and takes on a value between –1 and 1. A positive
value indicates that the two variables tend to move together,
while a negative value indicates that they tend to move in
opposite directions. The further away the value is from 0, the
stronger the relationship, with +/–1 representing a perfect
correlation, meaning if there is a change in one variable, the
other is changed in a fixed proportion. We used Mexico as the
source country for this table because it is the leading source
nation for U.S. immigration, providing more than one fourth of
the foreign-born stock that is in the U.S. This table provides a
simple benchmark.
The Regional Economist | www.stlouisfed.org 15

D I S T R I C T

O V E R V I E W

Demographics Help Explain the Fall
in the Labor Force Participation Rate

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 Paulina Restrepo-Echavarria

L

abor market performance is at center
stage in monetary policy discussions.
As such, measures of employment growth
and the unemployment rate are constantly
being scrutinized. Recently, however, the
measure of labor force participation (LFP)
has increasingly drawn attention; research
studies during the past several years have
focused on LFP in an attempt to explain the
slow and jobless recovery since the end of
the Great Recession.
The LFP rate measures the share of the
population that actively participates in the
labor market—the total number of people
employed and unemployed as a share of the
working-age population.1 As economists

The share of prime-working-

TABLE 1
Labor Force Participation Rates (percent)
U.S.

Arkansas

Illinois

Indiana

Kentucky

Mississippi

Missouri

Tennessee

61.8

58.2

62.8

63.8

60.0

58.6

61.7

60.6

1995

66.4

64.8

68.3

70.1

62.4

62.6

70.5

66.6

2015

62.6

58.0

65.0

63.8

56.6

56.4

65.7

59.1

1976

SOURCE: Bureau of Labor Statistics.

What does the nation’s labor force look
like? How does the St. Louis Fed’s district compare? In this article, we explore
demographic changes; we describe how the
composition of the labor force has changed
nationwide and in the District’s states over
the past 30 years and how these changes tie
into the LFP rate.3

age people (those between

Participation Trends

25 and 54 years old) now

The national LFP rate is hump-shaped:
It hovered between 58 and 60 percent until
the early 1970s, increased at a relatively fast
pace for two decades (surpassing 66 percent
by the end of the 1980s) and continued to
rise until it reached its peak of 67.3 percent
in the year 2000. Then, the participation
rate remained fairly steady, declining only
slightly, until 2009, when the pace of decline
accelerated.
State-level data show that the seven states
in the District exhibited the same rising
and falling hump-shaped pattern since
1976 (when the data first became available),
peaking sometime between 1995 and 2000.
Among them, Mississippi has usually had
the lowest participation rates, followed by
Kentucky, Arkansas and Tennessee, all with
rates lower than the national average. On
the other hand, Missouri, Illinois and Indiana have had the three highest participation

makes up about 64 percent
of the labor force, both in the
nation and in the District.
have tried to explain the national economy’s
slow and long recovery by decomposing
the factors that affect the labor market, two
general views have emerged. James Bullard,
the economist who is the president and
CEO of the Federal Reserve Bank of
St. Louis, describes these two views as
the “bad omen” view, which says that the
declines in the LFP rate are due to people
leaving the labor force because of the poor
state of the economy, and the “demographics” view, which states that the changes in
the rate are a reflection of changes in the
demographics of the labor force.2
16 The Regional Economist | October 2016

rates in the District, at or above the national
average for most of the period. (See Table 1.)
In the Labor Force or Not?

To better understand the changes in LFP,
we used data from the Current Population
Survey’s Annual Socioeconomic Supplement to decompose the labor force and
the nonparticipants by three demographic
characteristics: sex, age and educational
attainment during the last 30 years.4 Table 2
summarizes the results for 2015.
As with the participation rate, the general
demographic composition of those in the
labor force and those not in the labor force
in the District’s states highly resembles
the national average, particularly when it
comes to breakdowns by sex and age. There
are marked differences with some states,
however, when it comes to educational
attainment.
Breakdown by Sex

The changes over the years portray the
well-documented national trends of increasing participation of women in the labor
force between the early 1970s and its peak
during the early 2000s and of the longerterm decline in male participation.
In 2015, the labor force was 53 percent
male and 47 percent female, while 40

TABLE 2

By Age
By Sex
By Age
By Educational
Attainment

Not in Labor Force

U.S.

Arkansas

Illinois

Indiana

Kentucky

Mississippi

Missouri

Tennessee

Male

53.1

50.8

52.3

52.3

52.1

52.3

51.8

53.0

Female

46.9

49.2

46.7

47.7

47.9

47.7

48.2

47.0

16 to 24

13.2

14.0

12.7

13.3

16.0

14.8

13.6

14.0

25 to 54

64.4

65.5

64.7

63.1

64.5

64.4

63.4

61.9

55 to 64

16.7

16.0

16.4

17.7

14.0

16.9

16.9

17.7

Over 65

5.8

4.6

6.2

5.9

5.4

3.9

6.1

6.4

Less than High School
By Educational
Attainment

Labor Force

By Sex

Demographics of Those in and Not in the Labor Force in 2015 (percent)

9.6

11.1

8.1

9.5

7.1

10.2

7.8

9.9

HS Diploma

27.0

35.7

25.5

33.6

31.5

32.7

29.0

29.7

Some College

28.9

28.2

28.3

26.6

29.7

33.4

30.7

28.4

College Graduate

22.1

18.0

25.3

19.3

19.6

14.3

20.9

19.1

Grad School and More

12.3

6.9

12.7

10.9

12.1

9.3

11.5

12.8

Male

40.2

43.1

40.3

40.8

41.8

41.8

40.3

39.3

Female

59.8

56.9

59.7

59.2

58.2

58.2

59.7

60.7

16 to 24

19.3

15.3

19.5

19.4

13.8

17.9

17.7

15.6

25 to 54

25.9

25.6

23.8

23.6

29.6

28.9

20.1

28.1

55 to 64

15.3

17.1

15.2

15.6

16.7

19.9

15.2

15.0

Over 65

39.5

42.0

41.5

41.4

39.9

33.3

47.0

41.2

Less than High School

24.7

23.7

23.8

24.9

30.9

29.5

20.1

28.0

HS Diploma

31.3

37.3

30.6

42.7

34.1

32.6

31.1

34.2

Some College

24.8

24.2

24.0

20.5

21.8

24.2

27.0

22.2

College Graduate

12.5

11.1

13.5

7.5

8.3

8.5

13.9

10.3

6.7

3.6

8.2

4.3

4.9

5.1

7.9

5.3

Grad School and More

SOURCES: Current Population Survey’s Annual Socioeconomic Supplement and authors’ calculations.

percent of nonparticipants were male and
60 percent were female. The breakdown by
gender is a lot more even than it was in 1976,
when 59 percent of the labor force was male
and only 28 percent of nonparticipants were
male. Between 1985 and 2015, the rise of
women in the labor force ranged from
1 percentage point in Missouri and Tennessee to 5 percentage points in Indiana
and Kentucky, compared with a 2 percentage point increase nationwide. In contrast,
changes in the share of nonparticipant
males ranged from 3 percentage points
higher in Tennessee to 12 percentage points
higher in Kentucky, compared with 7 percentage points higher nationwide.
Breakdown by Age

The share of prime-working-age people
in the nation’s labor force (those between
25 and 54 years old) peaked in 1995, at 72
percent, and now makes up about 64 percent
of the labor force both in the nation and in

the District. Nationwide, the share of those
between 16 and 24 years old was 13 percent
in 2015, its lowest point in the postwar era,
and the share of those 55 years old and older
was 23 percent, its highest point. Trends
are very similar across the District, though
Kentucky has a slightly younger labor force,
with 16 percent of people between 16 and 24
years old and 19 percent who are 55 or older.
Breakdown by Education

Nationally and in the District, educational attainment has increased substantially. On average, the labor force in the
District has a lower educational attainment
than the national average; however, there
are some important differences to highlight.
In Arkansas, the share of those in the labor
force with less than a high school diploma
and the share of those with a high school
diploma but no college are the highest, at
11 percent and 36 percent, respectively. Similarly, Mississippi and Arkansas have a lower

share of college graduates in their labor
force, with 24 percent and 25 percent having
a bachelor’s degree or higher. In contrast,
Illinois has the labor force with the highest
educational attainment, with 38 percent of
the labor force having a bachelor’s degree or
higher. As for those not in the labor force, in
both Missouri and Illinois there is an aboveaverage share of people with a bachelor’s
degree or higher (22 percent in both states,
compared with 19 percent nationwide).
Making Ends Meet

The demographic composition of those
in the District’s labor force and those not in
the labor force is very similar to the nation’s.
The share of women in the labor force has
increased, while the share of men not in the
labor force has also increased. The labor force
has generally aged, while the share of those
over 55 years old not in the labor force has
also increased. Furthermore, a larger share
of the working-age population is reaching
The Regional Economist | www.stlouisfed.org 17

E C O N O M Y

A

G L A N C E

REAL GDP GROWTH

CONSUMER PRICE INDEX (CPI)

6

PERCENT

2

0

–2

Q2
’11

’12

’13

’14

’15

PERCENT CHANGE FROM A YEAR EARLIER

4

4

’16

CPI–All Items
All Items, Less Food and Energy

2

0

–2

August

’11

’12

’13

’14

’15

’16

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

3.00

RATES ON FEDERAL FUNDS FUTURES ON SELECTED DATES

0.7

5-Year

2.75

10-Year

2.50

0.6

2.00

Sept. 30

1.75

PERCENT

20-Year

2.25
PERCENT

higher levels of education, with the share of
the labor force having at least a bachelor’s
degree continuing to increase steadily.
Piecing together these characteristics of
the working-age population, we can help
explain the declining labor force participation rate. The clearest trend is the overall
aging of the working-age population, largely
because the baby boomers started retiring
in the early 2000s. Similarly, more education implies spending more years in school,
giving people later starts to their working
careers. Also putting downward pressure on
the LFP rate is the increased participation
of women in the labor force, since families
have to decide how to balance their time
accordingly. That is, with more mothers
working full time, fathers may decide to stay
at home or work only part time to help care
for children and do any work that is needed
at home.

A T

1.50

03/16/16

6/15/16

4/27/16

7/27/16

9/21/16

0.5

0.4

1.25

Paulina Restrepo-Echavarria is an economist
and Maria Arias is a senior research associate,
both at the Federal Reserve Bank of St. Louis.
For more on the former’s work, see https://
research.stlouisfed.org/econ/restrepo-echavarria.

1.00

’12

’13

’14

’15

0.3

’16

1st-Expiring
Contract

NOTE: Weekly data.

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

3-Month

6-Month

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

10

4
10-Year Treasury

9

3

4

REFERENCE
Bullard, James. “The Rise and Fall of Labor Force
Participation Rates in the United States.” Federal
Reserve Bank of St. Louis Review, First Quarter
2014, Vol. 96, No. 1, pp. 1-12.

PERCENT

7
6
5

2
Fed Funds Target

1
1-Year Treasury

4
3

September

’11

’12

’13

’14

’15

0

’16

’11

’12

’13

’14

’15

September

’16

NOTE: On Dec. 16, 2015, the FOMC set a target range for the
federal funds rate of 0.25 to 0.5 percent. The observations
plotted since then are the midpoint of the range (0.375 percent).

U.S. AGRICULTURAL TRADE
90

AVERAGE LAND VALUES ACROSS THE EIGHTH DISTRICT

6

Exports

YEAR-OVER-YEAR PERCENT CHANGE

2

The standard definition for working-age population is the civilian noninstitutional population
above the age of 16. Note that to be considered
unemployed, the person must be available to work
and have been actively looking for a job in the
previous month. If a person is neither employed
nor unemployed, that person is not in the labor
force, also called nonparticipant.
See Bullard.
The state-level data we use are statewide averages.
However, the only state that is entirely in the
Eighth District is Arkansas. The other states are
Illinois, Indiana, Kentucky, Mississippi, Missouri
and Tennessee.
Note that adding the share of those in the labor
force to the share of those not in the labor force
equals the total working-age population; so,
1 minus the labor force participation rate gives
us the share of those not in the labor force.

PERCENT

1

3

8

75
BILLIONS OF DOLLARS

ENDNOTES

12-Month

CONTRACT SETTLEMENT MONTH

Imports

60
45
30
15

Trade Balance

0
’11

’12

’13

’14

’15

NOTE: Data are aggregated over the past 12 months.

August

’16

4
2
0
–2
–4
–6
–8

Quality Farmland
Ranchland or Pastureland

2015:Q2 2015:Q3 2015:Q4 2016:Q1 2016:Q2
SOURCE: Agricultural Finance Monitor.

On the web version of this issue, 11 more charts are available, with much of those charts’ data specific to the Eighth District.
Among the areas they cover are agriculture, commercial banking, housing permits, income and jobs. To see those charts, go to
www.stlouisfed.org/economyataglance.
18 The Regional Economist | October 2016

O V E R V I E W

After a Start
That Was Lackluster,
Economy Improves
By Kevin L. Kliesen

U

.S. economic conditions have improved
since our last report in July. Paced by
healthy job growth, solid increases in household consumption expenditures, further gains
in the housing and commercial real estate
sectors, and continued low inflation, there is a
high probability that real gross domestic product (GDP) growth in the third quarter will be
much stronger than its average over the first
half of this year. Indeed, professional forecasters see solid growth, low inflation and healthy
labor markets carrying into the fourth quarter
of 2016 and the first half of 2017. But, as with
any forward-looking view of the economy,
one must attempt to determine—as much as
possible—whether the evolving data suggest
either stronger or weaker outcomes than the
forecast consensus.

What We Know—or Think We Know

Over the first half of the year, real GDP
increased at a tepid 1 percent annual rate,
and the all-items (headline) personal consumption expenditures price index (PCEPI)
rate rose at a 0.3 percent annual rate. These
increases were substantially smaller than
forecasters were expecting in late 2015.
Weaker-than-expected real GDP growth
over the first half of the year largely reflected
unexpected weakness in real business and
residential fixed investment and a sizable
inventory correction, while lower inflation
reflected declines in food prices and the
lagged effects of the sharp decline in crude
oil prices from June 2015 to February 2016.
Although third-quarter data are incomplete at the time of this writing, the available
evidence suggests a high probability that
real GDP growth in the third quarter will be
appreciably stronger than over the first half
of the year. Part of this acceleration reflects
the likely end of the inventory correction
that has been underway for more than a year.
Briefly, the demand for goods was weaker
than expected, so firms slowed production
and used available inventory stocks to help
meet existing demand. The net effect was a
decline in inventory investment and weaker

St. Louis Fed’s Economic News Index
4.0
Percent Changes at Annual Rates

N A T I O N A L

3.5

3.5

3.5

3.6

3.6

3.6

3.3

3.0

2.9

2.9

3.0

9/23/16

9/30/16

10/7/16

2.5
2.0
1.5
1.0
0.5
0.0

8/12/16
ENI (Q3)

8/19/16

8/26/16

9/2/16

9/9/16

9/16/16

RGDP (Q2)

SOURCES: Federal Reserve Bank of St. Louis, Bureau of Economic Analysis.
NOTE: The Economic News Index (ENI) predicts real growth in gross domestic product (RGDP). The chart shows predictions made on
various dates in August and October for growth in Q3 (July-September). Actual real GDP growth in Q2 is also shown.

real GDP growth. Over the first half of 2016,
real private inventory investment subtracted
0.8 percentage points from real GDP growth.
As the U.S. economy transitioned to the
second half of the year, the economic landscape importantly suggested that consumer
spending would remain vibrant. This vibrancy
reflects many factors, including the likelihood
of continued healthy job gains, faster growth
of nominal wages and salaries, and an expectation of weaker gasoline prices that helps to
boost consumer purchasing power.
Of course, there are risks to any forecast,
and the unexpected weakness in August
retail sales was worrisome. With sentiment
among homebuilders and potential homebuyers remaining elevated, it is likely that
real residential fixed investment will also
strengthen over the near term. Indeed, housing starts and new-home sales in 2016 are on
pace to be the strongest since 2007. Similarly,
business fixed investment should rebound.
However, surveys of business executives
suggest that the boost to capital spending
will be modest because a large percentage of
firms remain reluctant to expand in the face
of higher-than-average levels of uncertainty
about the health of the global economy and
the near-term direction of economic policy.
The St. Louis Fed has developed a new tool
that uses these and other monthly data flows
to forecast the growth of real GDP during
the current quarter for which the official
estimate is not yet available. This new tool
is termed the Economic News Index (ENI).1
As of early October, the ENI predicts that
real GDP growth will be about 3 percent in
the third quarter. But even with this surge in
growth, real GDP over the past year will have
increased by only 1.5 percent. Unless labor

productivity begins to accelerate, real GDP
growth is likely to remain about 2 percent
for the foreseeable future, which is consistent
with the St. Louis Fed’s new characterization of the economic outlook and the latest
economic projections of the Federal Open
Market Committee.2
Inflation, Where Art Thou Inflation?

Inflation pressures rebounded in August
after easing in July. Still, with the PCEPI up
by only 1.0 percent over the past year, inflation is likely to be only modestly higher than
last year’s 0.6 percent increase. This outcome reflects three key factors. First, despite
higher-than-average inventories of crude oil
and gasoline, oil prices have drifted higher
and could boost inflation pressures over the
second half of the year. Second, food prices
have declined thus far in 2016 and show few
signs of rebounding. Finally, inflation expectations remain low and stable. The St. Louis
Fed’s inflation prediction model indicates a
52 percent probability that inflation will be
between zero and 1.5 percent over the next
12 months.
Kevin L. Kliesen is an economist at the Federal
Reserve Bank of St. Louis. Brian Levine, a
research associate at the Bank, provided
research assistance. See http://research.stlouisfed.
org/econ/kliesen for more on Kliesen’s work.
ENDNOTES
1

2

For more information on the ENI, see an article in
the April 2016 Regional Economist at www.stlouisfed.org/publications/regional-economist/april-2016/
tracking-the-us-economy-with-nowcasts.
See www.stlouisfed.org/~/media/Files/PDFs/
Bullard/papers/Regime-Switching-Forecasts17June2016.pdf.

The Regional Economist | www.stlouisfed.org 19

M E T R O

P R O F I L E

Evansville, Ind., Adapts
As Manufacturing,
Population Growth Slide
By Charles S. Gascon and Andrew E. Spewak

Situated along the Ohio River in southwestern Indiana, Evansville
emerged as a major manufacturing hub in the 20th century.
Today, the region’s economic footprint has evolved as it strives to
be connected to surrounding areas and around the globe.

E

vansville serves as the headquarters for
seven publicly traded companies and is
accessible via the river, two interstates, four
freight rail lines and a regional airport. Over
the past five years, the economy has shifted
into a recovery from the Great Recession.
The unemployment rate reached as low as
3.9 percent in August 2015, Evansville’s best
mark since 2001 and well below the national
average that month.

and median house value, at $120,000, are
consistent with the Indiana state averages.
Rent is 5 percent cheaper than it is statewide,
contributing to a relatively low cost of living.
The workforce is about as educated as the rest
of the state, as 23 percent of the population
over 25 has a bachelor’s degree. Although
that number is only about half a percentage
point lower than the Indiana average, it is 6
percentage points below the national average.

Although manufacturing is

Key Sectors

key to the MSA’s history and
today’s economy, it has not
been the area’s principal
driver of job growth over the
past decade.
The Evansville metropolitan statistical area
(MSA) consists of Vanderburgh, Posey and
Warrick counties in Indiana and Henderson
County in Kentucky. The MSA is emblematic
of many Midwestern metro areas. With a
population topping 315,000, the Evansville
MSA ranks 158th of 382 MSAs in the country.
Both median household income, at $48,000,
20 The Regional Economist | October 2016

The sector that employs the largest number of people these days is education and
health services. Although manufacturing
has slipped to No. 2, the region’s workforce
is still thought of as more blue-collar than
white-collar.
The manufacturing sector employs 23,000
people, or 15 percent of the total workforce,
a percentage that is nearly twice the national
average. However, that share has diminished
since 2000, when it was 20 percent.
Automotive manufacturing lays the
foundation for the sector, as five firms with
at least 500 employees each are involved with
auto manufacturing. These include Skanska,
a construction firm that provides support
for auto manufacturers in the region. Gibson

PHOTO PROVIDED BY TOYOTA MOTOR MANUFACTURING, INDIANA

Many people who live in the Evansville metro area
commute to Gibson County to work at the Toyota
auto plant there.

County, to the north, is home to Toyota
Motor Manufacturing and Toyota Boshoku,
which employ almost 6,000 people combined. Though Gibson County is not within
the MSA, about 4,000 Evansville residents
commute daily to work there.
Plastics manufacturing is also noteworthy,
employing one-fifth of area manufacturing
workers. Berry Plastics is headquartered in
Evansville and employs 2,700 people locally.
Although manufacturing is key to the
MSA’s history and today’s economy, it has
not been the area’s principal driver of job
growth over the past decade. The education
and health services sector provides 18 percent
of Evansville’s jobs, a slightly greater portion
than nationally. About three-quarters of
these 26,500 workers come from the health
services side. Evansville’s two largest
employers, Deaconess Health System and
St. Mary’s Health System, together account for
approximately 9,000 workers. Each operates
at multiple locations throughout the MSA
and in nearby rural communities, showing
the region’s widespread connectivity.
Even though manufacturing, not education and health services, has historically been
the top employer, it has dwindled over time.
Whirlpool Corp., once the largest employer

FIGURE 1
Employment Shares in Evansville’s Largest Sectors
Education and Health Services

22

Manufacturing

20
18
16
14

to be expected, given that demographics drive
the sector’s growth, making it less susceptible
to economic downturns. Evansville’s aging
population has led to an increase in demand
for health care.
Recovery and Outlook

In spite of a prolonged decline, the economy
has made modest gains since 2011. For starters, the manufacturing sector has added
approximately 800 jobs since reaching its
historic trough. Additionally, real-wage growth
has turned upward for manufacturing and
the region as a whole. From 2011 to 2015,
real wages increased by 0.7 percent and 0.6
percent annually for manufacturing and the
overall MSA, respectively. Largely due to the
stock market’s recovery in 2009, real personal
income per capita began to rebound earlier
than wages, rising by over 1 percent annually
for the region from 2009 to 2014.
One of the most difficult challenges facing
the region is expanding its population. Since
2000, the population has grown a meager 6.5
percent, less than half of the 14 percent growth
nationally in the same time frame. One key
driver of population growth elsewhere is the
inward migration of households—from within
the country but also from overseas. Only 2.2
percent of the Evansville area’s population
is foreign-born, compared with 14.2 percent
nationally. Since new immigrants tend to
move to areas in which foreign-born residents
already have a significant presence,2 it may be
difficult for Evansville to bring in more immigrants in order to drive expansion. Absent
immigration, population growth must depend
on local residents’ having more children. But
15 percent of the MSA’s population is at least

MSA Snapshot
Evansville, Ind.
Population.............................................................................................315,693
Population Growth (2010-2015).............................................1.2%
Percentage with Bachelor’s Degree or Higher............... 23%
Percentage with a HS Diploma or Higher........................... 90%
Per Capita Personal Income..................................................$40,816
Median Household Income.....................................................$47,988
Unemployment Rate (May 2016)...............................................4.3%
Gross Metropolitan Product....................................... $14.9 billion
SOURCES: U.S. Census Bureau, Bureau of Economic Analysis/Haver
Analytics, Bureau of Labor Statistics/Haver Analytics

Largest Employers

Deaconess Health System.............................................................5,600
St. Mary’s Health System................................................................3,529
Berry Plastics.............................................................................................2,699
Skanska...........................................................................................................2,460
T.J. Maxx Distribution Center........................................................1,500

Industry Breakdown by Employment
Other Services
Transport, Warehousing
and Utilities
Wholesale Trade

Financial Activities
4%
4%

4%

Information 1%
18%

Education
and Health
Services

6%

Construction

15%

10%

Leisure and
Hospitality

Manufacturing

10%

Government

12%

11%

Professional and
Business Services

Retail Trade

INDIANA
Clay

Richland Lawrence
ILLINOIS

Wayne
Dubois

Hamilton
Spencer
Saline

Pike

Gibson

Posey

Warrick
Vanderburgh
Evansville
Henderson

Union
Pope

Daviess

Hardin

Lawrence

Orange

INDIANA

White

Gallatin

Knox

Martin

in Evansville, best exemplifies the sector’s
long-term trend. The company employed
almost 10,000 manufacturing workers in its
prime during the 1970s before shrinking. It
ultimately had just 1,200 employees in 2010
before the company left the region entirely.
While manufacturing’s decline began
decades ago, it was still the top sector in
2000 by a wide margin, employing 32,000.
But from 2000 until 2011, the downturn
worsened considerably. Close to 11,000
manufacturing jobs were lost on net, leaving
Evansville at a 30-year low in 2011 for manufacturing employment. Inflation-adjusted
(real) manufacturing wages declined during
the Great Recession (2007-09) by 0.1 percent
annually and continued to decrease for a
couple more years afterward.
Though the overall economy did not
experience long-term decline on the same
scale as the manufacturing sector, the recession’s impact on the rest of the MSA was
significant. From 2007 to 2009, Evansville’s
employment declined nearly 2.5 percent
annually. As a whole, the economy lost more
than 7,000 jobs on net. Plus, real wages for
the entire MSA continually declined during
a five-year period that began with the start
of the recession in 2007. Because wages on
average comprise 50 percent of real personal
income per capita in the U.S.,1 that measure
of income dropped 1.2 percent annually during the recession, compared with a decrease
of 0.2 percent nationally in that period.
Helping to make up for some of the losses,
education and health services expanded
rapidly, adding 5,000 jobs on net from 2000
to 2011, including 1,300 during the recession.
The surge amid tough business conditions is

The health services and education sector has replaced
manufacturing as the top-employing sector in the
Evansville MSA. In health services alone, about 20,000
people work, including those at the St. Mary’s Medical
Center (above).

Wabash

SOURCES: Bureau of Labor Statistics/Haver Analytics

PHOTO BY ST. MARY’S HEALTH SYSTEM

Edwards

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

10

1991

12
1990

Percent of Total Employment

24

KENTUCKY

Webster

Daviess

McLean

Crittenden
Hopkins

The Regional Economist | www.stlouisfed.org 21

Crawford
Perry

A park in downtown Evansville provides easy access to the
Ohio River. Miles of walking and biking paths line the edge
of the river as it wraps around the city.

PHOTO BY JIM WINNERMAN

FIGURE 2
Change in Evansville Manufacturing Employment (2000-2011)
0
–2,000
–4,000
–6,000
–8,000
–10,000
–12,000

Transportation
Equipment

Paper
Manufacturing

Machinery

Primary Metal

Electrical
Equipment and
Appliances

Other
Manufacturing

Total

SOURCE: Bureau of Labor Statistics.

FIGURE 3
Unemployment Rate
12
10
Nation

Percent

8

Evansville

6
4
2
0
1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

Charles Gascon is a regional economist and
Andrew Spewak is a research associate at the
Federal Reserve Bank of St. Louis. For more on
Gascon’s work, see http://research.stlouisfed.
org/econ/gascon.

SOURCES: Bureau of Labor Statistics/Haver Analytics.
NOTE: The shaded areas indicate U.S. recessions.

FIGURE 4

FIGURE 5

Population

Real Personal Income Per Capita

170
150
140

Evansville

45,000

Indiana

40,000

2015 Dollars

130
120

Nation

ENDNOTES

Evansville

1
2

35,000
30,000

3

110

SOURCES: Bureau of Labor Statistics/Haver Analytics.
22 The Regional Economist | October 2016

SOURCES: U.S. Census Bureau/Haver Analytics.

2014

2010

2006

2002

1998

1994

1990

1986

2015

2010

2005

2000

1995

1990

1985

1980

1975

1970

20,000

1982

90

1978

100

1974

25,000
1970

Index: 1970=100

50,000

Nation

160

65 years old, over a percentage point higher
than the national average. An older population implies that fewer people are having
children and raising families, slowing down
population growth. Economists also have
found that having a higher percentage of
elderly residents in an area slows down
economic growth.3 Consequently, efforts to
attract more immigrants and millennials to
the region are underway.
In order to encourage economic growth,
several development projects are in the
works. A health science research center and
a new local orthopedic and neuroscience
facility will fuel the continued growth of
the education and health services sector.
Additionally, there is a renewed emphasis on
revitalizing downtown Evansville, especially
with regard to bringing in tourists. Major
investments include a new hotel and adjacent
convention center, as well as renovations to
the local casino. Finally, while 85 percent of
the population currently lives on the Indiana
side of the river, an ongoing project to build a
bridge between Indiana and Kentucky aims
to further unite the MSA’s four counties.

Supplements to wages and salaries make up an
additional 20 percent of personal income.
Bandyopadhyay, Subhayu; and Guerrero,
Rodrigo. “Immigration Patterns in the District
Differ in Some Ways from the Nation’s.” The
Regional Economist, April 2016, pp. 18-19.
Maestas, Nicole; Mullen, Kathleen J.; and Powell,
David. “The Effect of Population Aging on
Economic Growth, the Labor Force and Productivity.” National Bureau of Economic Research
Working Paper Series, No. 22452, July 2016.

AR SE KA D
A N
S TG E
E R E CE OX NC OHMA I N
ASK AN ECONOMIST
Michael W. McCracken has been an economist at the Federal Reserve Bank of St. Louis
since 2008. An econometrician, he focuses
his research on forecasting and, in particular,
evaluating the accuracy of forecasts from
different models. When he isn’t working, he
enjoys hiking, spending time with his family
and following University of Kansas basketball.
For more on his research, see https://research.
stlouisfed.org/econ/mccracken.
McCracken and his family at Disney’s
Epcot park in Florida.

Q: What is “big data,” and how does FRED-MD
contribute to it?
A: Statistical analysis has evolved. In the past, it was focused on
one variable measured across people or one variable measured
across time. But with the advent of superfast computers, researchers
and analysts can jointly model a large number of variables, each with
a large number of observations across time. That is “big data.”
Although being able to use big data has benefits, such as improving
the accuracy of forecasts, collecting the data can be extremely timeconsuming. To that end, my co-author, Serena Ng of Columbia University,
and I (along with tremendous assistance from staff at the St. Louis Fed’s
data desk) created FRED-MD, a monthly database of over 130 macroeconomic time series that cover categories such as output and income,
the labor market and prices. The data series are similar to the ones used
by James Stock of Harvard and Mark Watson of Princeton, who created
a macroeconomic data set that has become the benchmark for a lot
of what people do in economics when they are working with big data.
With Stock and Watson’s choice of data as a guide, we used series that
are available in FRED (Federal Reserve Economic Data), the St. Louis
Fed’s main economic database. Now, rather than having thousands of
economists separately put together their own data set, they can simply
download a spreadsheet from our website.1
FRED-MD has several advantages. For one, using series from FRED al-

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To see other education-oriented resources that we offer, go to the Econ
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videos, podcasts, courses, infographics and more for multiple audiences.
SEE HOW COMMUNITY BANKERS FEEL
ABOUT KEY ISSUES IN INDUSTRY
More than 500 community bankers from around the country took a
survey earlier this year about key industry issues, including compliance
costs, small-business lending, financial technology, and mergers and
acquisitions. The results of the survey were released at the fourth annual
Community Banking in the 21st Century Research and Policy Conference,
held at the Federal Reserve Bank of St. Louis at the end of September.
The survey’s results can be seen at www.communitybanking.org.
There, you will also find the research papers that were presented at the
conference, as well as a series of short videos that show how community
bankers and state regulators have given their communities a boost.
The conference is sponsored every year by the Federal Reserve
System and the Conference of State Bank Supervisors.
CREDIT RATINGS VARY WIDELY ACROSS LOW- AND
MODERATE-INCOME NEIGHBORHOODS
In the latest issue of Bridges, our community development newsletter,
read about the disparities in credit ratings across low- and moderate-

lows us to update our data set relatively quickly each month. In addition,

income (LMI) neighborhoods around the country. Those areas with better

anyone can access the latest file as well as previous vintages, which

credit ratings tend to have a higher percentage of white occupants and

allows for easier replication of empirical work and for easier comparison

are usually located in the East, West and Upper Midwest. Those areas

between methods used in different lines of research. In other words,

with poorer credit ratings tend to have a higher percentage of black

results won’t differ simply because the researchers used two different

residents and tend to be located in the South. The disparity is important

data sets. Another advantage of FRED-MD is that it saves users from

not just to the residents but to the banks that are required to provide fair

having to incorporate revisions and changes to the data themselves.

and impartial access to credit in underserved areas.

Those are handled by the experts at the data desk.
Our main goal in providing this core data set was to make it easier for
those who do empirical analysis of big data. Instead of spending time
collecting the data, they can focus on the bigger questions that they are
trying to answer.

This article is based on consumer credit data for LMI areas in more
than 200 metro areas around the country. For the first time, these data
are available to the public. (See link in article.)
To read this and all the other articles in this issue of Bridges, see
www.stlouisfed.org/publications/bridges/summer-2016. Bridges is a
quarterly newsletter that aims to inform bankers, community develop-

1

For more information on FRED-MD and FRED-QD, which is a database of quarterly
observations, see https://research.stlouisfed.org/econ/mccracken/fred-databases.

ment organizations, representatives of state and local government
agencies, and others, about current issues and initiatives in community
and economic development.
The Regional Economist | www.stlouisfed.org 23

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ECONOMY

AT

A

THE REGIONAL

GLANCE

ECONOMIST

OCTOBER 2016

REAL GDP GROWTH

4

2

0
Q2
’11

’12

’13

’14

’15

PERCENT CHANGE FROM A YEAR EARLIER

4
PERCENT

VOL. 24, NO. 4

CONSUMER PRICE INDEX

6

–2

|

CPI–All Items
All Items, Less Food and Energy

2

0

August

–2

’12

’11

’16

’13

’14

’15

’16

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
3.00

0.7

5-Year

2.75

10-Year

2.50

0.6

2.00

Sept. 30

1.75

PERCENT

20-Year

2.25
PERCENT

RATES ON FEDERAL FUNDS FUTURES ON SELECTED DATES

1.50

03/16/16

6/15/16

4/27/16

7/27/16

9/21/16

0.5

0.4

1.25
1.00

’12

’13

’14

’15

0.3

’16

1st-Expiring
Contract

NOTE: Weekly data.

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

3-Month

6-Month

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

10

4

9

10-Year Treasury

3

7

PERCENT

PERCENT

8

6
5

2
Fed Funds Target

1
1-Year Treasury

4
3

12-Month

CONTRACT SETTLEMENT MONTH

September

’11

’12

’13

’14

’15

0

’16

’11

’12

’13

’14

’15

September

’16

NOTE: On Dec. 16, 2015, the FOMC set a target range for the
federal funds rate of 0.25 to 0.5 percent. The observations
plotted since then are the midpoint of the range (0.375 percent).

U . S . A G R I C U LT U R A L T R A D E
90

AVERAGE LAND VALUES ACROSS THE EIGHTH DISTRICT
6

BILLIONS OF DOLLARS

75
Imports

60
45
30
15
0

Trade Balance

’11

’12

’13

’14

’15

NOTE: Data are aggregated over the past 12 months.

August

’16

YEAR-OVER-YEAR PERCENT CHANGE

Exports

4
2
0
–2
–4
–6
–8

Quality Farmland
Ranchland or Pastureland

2015:Q2 2015:Q3 2015:Q4 2016:Q1 2016:Q2
SOURCE: Agricultural Finance Monitor.

U.S. CROP AND LIVESTOCK PRICES
140

INDEX 1990-92=100

120

Crops
Livestock

100
80
60
40

August

’01

’02

’03

’04

’05

’06

’07

’08

’09

’10

’11

’12

’13

’14

’15

’16

COMMERCIAL BANK PERFORMANCE RATIOS
U.S. BANKS BY ASSET SIZE / SECOND QUARTER 2016
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*

0.99

1.05

1.03

1.08

1.06

1.08

1.07

0.97

Net Interest Margin*

3.01

3.82

3.81

3.80

3.81

3.78

3.79

2.84

Nonperforming Loan Ratio

1.49

1.12

1.15

1.00

1.06

1.04

1.05

1.61

Loan Loss Reserve Ratio

1.33

1.42

1.43

1.36

1.39

1.21

1.28

1.34

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

NET INTEREST MARGIN*
0.99

1.10
1.28
1.20

1.13
1.13
0.96
1.02

.20

.40

.60

Second Quarter 2016

.80

Indiana

3.70
3.84

Kentucky

3.78
3.75

Mississippi

3.85
3.72

1.04
1.03

Missouri

1.04

Tennessee

1.00

1.20

3.53
3.58

Illinois

0.98
0.96

.00

4.08
4.16

Arkansas

1.02
1.05

0.35

3.69
3.72

Eighth District

1.40

PERCENT

3.45
3.57
3.29
3.28

0.0 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50

Second Quarter 2015

Second Quarter 2016

N O N P E R F O R M I N G L O A N R AT I O
0.97

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

1.16

1.06

1.22

1.04

0.82

.25

.50

Second Quarter 2016

.75

Arkansas

1.20

0.74

Indiana

1.25

1.18
1.07

1.50

Second Quarter 2015

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

1.75

PERCENT

1.31

1.29
1.35

1.13

Tennessee

1.38

1.38

0.90

Missouri

0.95

1.34

1.21
1.26

Kentucky

1.02

1.00

1.18

Mississippi

1.10

.00

1.71

1.08

0.84

Eighth District

Illinois

1.20
1.34

0.82

Second Quarter 2015

.00

.25

.50

.75

Second Quarter 2016

1.00

1.54

1.26

1.25

1.50

Second Quarter 2015

For additional banking and regional data, visit our website at:
https://fred.stlouisfed.org.

1.75

REGIONAL ECONOMIC INDICATORS
N O N FA R M E M P L O Y M E N T G R O W T H / S E C O N D Q U A RT E R 2 0 1 6
YEAR-OVER-YEAR PERCENT CHANGE
United
States

Total Nonagricultural

1.7%

Natural Resources/Mining

Eighth
District †

Arkansas

1.3%

2.0%

Illinois

Indiana

0.7%

Kentucky

1.2%

Mississippi

1.5%

Missouri

Tennessee

0.8%

2.5%

0.8%

–16.5

–12.4

–19.3

–4.2

–10.5

–18.9

–11.0

–0.8

–2.2

3.6

1.8

1.1

1.3

3.9

–0.2

0.3

2.4

NA

–0.3

0.1

–0.4

–1.6

–0.2

1.5

0.9

–1.0

3.4

Trade/Transportation/Utilities

1.7

1.8

2.7

0.6

3.3

3.3

1.7

–0.1

2.8

Information

0.9

–2.7

5.3

–3.5

–6.1

–4.6

–1.2

–3.6

1.0

Financial Activities

2.1

1.1

0.5

–0.3

1.2

3.2

–3.1

3.4

2.4

Professional & Business Services

2.8

1.6

4.6

1.5

–2.4

2.6

–2.1

3.9

2.5

Educational & Health Services

3.0

2.6

3.8

1.5

4.4

3.7

1.9

0.9

4.3

Leisure & Hospitality

2.7

2.3

3.1

3.1

2.0

1.3

3.4

1.4

1.6

Other Services

1.2

0.6

2.1

0.6

–0.1

–1.2

–0.6

0.4

2.6

Government

0.4

–0.3

0.3

–0.2

–0.6

–1.1

0.9

–0.8

0.2

Construction
Manufacturing

† Eighth District growth rates are calculated from the sums of the seven states. For the Construction category, data on Tennessee are no longer available.

U N E M P L O Y M E N T R AT E S
II/2016

EIGHTH DISTRICT REAL ADJUSTED GROSS CASINO REVENUE
I/2016

800
750

II/2015

4.9%

4.9%

5.4%

Arkansas

3.9

4.2

5.4

Illinois

6.4

6.4

5.9

Indiana

5.0

4.8

4.8

Kentucky

5.2

5.7

5.3

500
450
400

Mississippi

5.9

6.5

6.4

350
300

Missouri

4.4

4.2

5.1

Tennessee

4.2

4.9

5.8

MILLIONS OF DOLLARS

United States

Mississippi

Indiana

Illinois

700
650

Missouri

600
550

2008:Q1 2009:Q1 2010:Q1 2011:Q1 2012:Q1 2013:Q1 2014:Q1 2015:Q1 2016:Q1

* NOTES: Adjusted gross revenue = Total wagers minus player winnings.
Native American casino revenue is not included. In 2003 dollars.
SOURCE: State gaming commissions.

HOUSING PERMITS / SECOND QUARTER

REAL PERSONAL INCOME / SECOND QUARTER

YEAR-OVER-YEAR PERCENT CHANGE IN YEAR-TO-DATE LEVELS

YEAR-OVER-YEAR PERCENT CHANGE

–0.9

16.9
25.0

11.9
7.4

–3.8

–8.0
8.9

24.0

–6.5
18.4

2016

2.2

1.8

29.4

10 15 20 25 30 35 40 45

2015

All data are seasonally adjusted unless otherwise noted.

3.0

2.4
3.9
1.4

Kentucky

4.1
2.2

1.3
2.3

Missouri

3.0
2.9

Tennessee
PERCENT

4.4

2.4

Mississippi
30.0

5

Arkansas

Indiana
39.1

–15 –10 –5 –0

2.2

Illinois

7.8

–4.9

United States

5.2

0

1
2016

2

3

4

5

2015

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

6