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A Quarterly Review
of Business and
Economic Conditions
Vol. 21, No. 3
July 2013

Mortgage Defaults
Why Are Foreclosure
Rates So Much Lower
in Europe?

Metro Profile

Health Care, Food Service
Drive Economy
of Louisville, Ky.

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

.36 2.78 40.39

–9.33% 256.36 2.78 56.39

Financial Markets
An Engine for
Economic Growth
7.23 –2.35% 158.36 2.52
HOUSE
FOR SALE

5.23 –6.35% 248.3

c o n t e n t s

4

Financial Markets:
An Engine for Economic Growth

A Quarterly Review
of Business and
Economic Conditions
Vol. 21, No. 3
July 2013

Mortgage Defaults
Why Are Foreclosure
Rates So Much Lower
in Europe?

Metro Profile

Health Care, Food Service
Drive Economy
of Louisville, Ky.

The Federal reserve Bank oF sT. louis
CeNtral to ameriCa’s eCoNomy

®

By Yongseok Shin

Do developed financial markets lead to economic growth or
result from it? While some economists argue for the latter,
the author maintains that financial markets—despite their
shortcomings of late—are an essential ingredient for an
economy to grow in the long run.

–9.33% 256.36 2.78 56.39

5.23 –6.35% 248.36 3.52

Financial Markets
An Engine for
Economic Growth
–9.33% 256.36 2.78 40.39

7.23 –2.35% 158.36 2.52
HOUSE
FOR SALE

The Regional

3 president’s message

Economist
JULY 2013

|

VOL. 21, NO. 3

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.

Deputy Director of Research
David C. Wheelock
Director of Public Affairs
Karen Branding
Editor
Subhayu Bandyopadhyay
Managing Editor
Al Stamborski
Art Director
Joni Williams

driven feature in The Regional
Economist. Unlike many other
older cities, Louisville has
smoothly transitioned from the
industrial economy to the service
economy, thanks in no small
part to its strong health-care and
food-service industries.

10 The Racial Earnings Gap:
Changes since 1960

By Yang Liu
and Rajdeep Sengupta

Director of Research
Christopher J. Waller
Senior Policy Adviser
Cletus C. Coughlin

14 Mortgage Applicants
Turn to Credit Unions

By Maria Canon
and Elise Marifian
The earnings gap between
black and white men narrowed
between 1960 and 2000 for those
born in the South but widened
for those born in the North.
Since then, the gap has worsened
in both regions of the country.

An examination of mortgage
data for the Eighth District shows
that far fewer applications for
mortgages were made postcrisis
(annual average of 2010-2011)
than in 2004, before the crisis
began. Those who did seek
mortgages after the crisis turned
increasingly to credit unions, as
opposed to banks and thrifts.

21

Mapping the Big Service,
Manufacturing Industries
By Rubén Hernández-Murillo
and Elise Marifian
Urban areas still host most manufacturing jobs, despite the fact
that most manufacturing jobs lost
over the past decades were in these
areas. At the same time, urban
areas have become increasingly
more service-oriented, as service
industries thrive near large concentrations of people.

Please direct your comments
to Subhayu Bandyopadhyay

16	E c o n o m y a t a g l a n c e

at 314-444-7425 or by e-mail at
subhayu.bandyopadhyay@stls.frb.org.
You can also write to him at the
address below. Submission of a

12 Mortgage Defaults:
U.S. vs. Europe

letter to the editor gives us the right
to post it to our web site and/or
unless the writer states otherwise.
We reserve the right to edit letters

Single-copy subscriptions are free
but available only to those with
U.S. addresses. To subscribe, go to
You can also write to The Regional
Economist, Public Affairs Office,
Federal Reserve Bank of St. Louis,
P.O. Box 442, St. Louis, MO 63166-0442.

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 | July 2013

23

re ader e xchange

By Kevin L. Kliesen

for clarity and length.

www.stlouisfed.org/publications.

17 n a t i o n a l o v e r v i e w
Mixed Signals,
but Moving Forward

publish it in The Regional Economist

district overview

By Juan Carlos Hatchondo,
Leonardo Martinez
and Juan M. Sánchez
During the last global recession,
house prices fell in some European
countries almost as much as
in some U.S. states. However,
mortgage defaults occurred at a
much lower rate in Europe. The
authors say the difference might be
explained by two regulations that
apply in Europe but are used on
a limited or much less restrictive
basis in the U.S.

The housing market is improving,
fostering growth in related segments of the economy. However,
continued high unemployment
and lackluster growth in real earnings are causes for concern. Still,
GDP is growing, albeit slowly.
18 m e t r o p r o f i l e
Louisville Transitions
to Service Economy
By Charles S. Gascon
and Sean P. Grover
Louisville is the focus in the first
installment of this new data-

ONLINE EXTRA
Exploring the Link
between Drug Use
and Job Status
in the U.S.
By Alejandro Badel
and Brian Greaney

Does more unemployment
increase drug abuse? Does
drug abuse prolong joblessness? See what data from the
National Survey on Drug Use
and Health say about these
questions in this online-only
article. Read it at www.
stlouisfed.org/publications/re.

p r e s i d e n t ’ s

m e s s a g e

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

CPI vs. PCE Inflation:
Choosing a Standard Measure

T

wo different price indexes are popular
for measuring inflation: the consumer
price index (CPI) from the Bureau of Labor
Statistics and the personal consumption
expenditures price index (PCE) from the
Bureau of Economic Analysis. Each of these
is constructed for different groups of goods
and services, most notably a headline (or
overall) measure and a core (which excludes
food and energy prices) measure. Which
one gives us the actual rate of inflation that
consumers face?
On the headline vs. core issue, I prefer to
focus on headline inflation, measured as the
percentage change in the price index from a
year ago to smooth out the fluctuations in the
data. As I have discussed previously, headline measures attempt to reflect the prices
that households pay for a wide variety of
goods, not a subset of those goods.1 Headline
inflation is, therefore, designed to be the best
measure of inflation that we have.
Between the two headline indexes, the CPI
tends to show more inflation than the PCE.
From January 1995 to May 2013, the average
rate of inflation was 2.4 percent when measured by headline CPI and 2.0 percent when
measured by headline PCE. Hence, after
setting both indexes equal to 100 in 1995, the

CPI was more than 7 percent higher than the
PCE in May 2013.2 (See the chart.)
An accurate measure of inflation is important for both the U.S. federal government and
the Federal Reserve’s Federal Open Market
Committee (FOMC), but they focus on different measures. For example, the federal
government uses the CPI to make inflation
adjustments to certain kinds of benefits, such
as Social Security.3 In contrast, the FOMC
focuses on PCE inflation in its quarterly economic projections and also states its longerrun inflation goal in terms of headline PCE.
The FOMC focused on CPI inflation prior to
2000 but, after extensive analysis, changed
to PCE inflation for three main reasons: The
expenditure weights in the PCE can change
as people substitute away from some goods
and services toward others, the PCE includes
more comprehensive coverage of goods and
services, and historical PCE data can be
revised (more than for seasonal factors only).4
Given that the two indexes show different
inflation trends in the longer run, having a
single preferred measure that is used by both
the federal government and the FOMC might
be appropriate. What would it mean if it
were determined that headline PCE inflation
is the better measure of prices consumers face

INDEX (JANUARY 1995=100)

Headline CPI

150

Headline PCE

ENDNOTES
1

Comparing Price Indexes: CPI vs. PCE
160

(indicating, thus, that the CPI overstates the
true inflation rate)? Continuing to use the
CPI would imply over-adjusting for inflation
and, in effect, giving real increases in benefits
over time. In this scenario, benefits should be
adjusted for inflation using the PCE instead.
Conversely, if it were determined that headline CPI inflation is the better measure (and,
therefore, that the PCE understates the true
inflation rate), then the FOMC should target
CPI inflation rather than PCE inflation.
The FOMC carefully considered both
indexes when evaluating which metric to
target and concluded that PCE inflation is the
better measure. In my view, headline PCE
should become the standard and, therefore,
should be consistently used to estimate
and adjust for inflation. Although adopting a standard measure would likely not be
a simple matter, it would provide clarity to
the public about which one more accurately
reflects consumer price inflation.

2% annual inflation
2

140
130

3

120
110
100
90
1995

4

1997

1999

2001

2003

2005

2007

SOURCES: Data obtained from FRED® (Federal Reserve Economic Data) and author’s calculations.
FRED is a registered trademark of the Federal Reserve Bank of St. Louis.

2009

2011

2013

See “Measuring Inflation: The Core Is Rotten,”
Federal Reserve Bank of St. Louis Review, July/
August 2011, Vol. 93, No. 4, pp. 223-33. See http://
research.stlouisfed.org/publications/review/11/07/
bullard.pdf.
In 2002, the Bureau of Labor Statistics began
releasing a chain-weighted version of the CPI,
which behaves similarly to the PCE over long
periods of time.
CPI-W, the index for urban wage earners and
clerical workers, is used to adjust these benefits for
inflation, whereas CPI-U (headline) is shown in
the chart. The two show similar trends from 1995
to the present.
For more discussion, see the Monetary Policy
Report to the Congress, on Feb. 17, 2000, at
www.federalreserve.gov/boarddocs/hh/2000/
February/FullReport.pdf.
The Regional Economist | www.stlouisfed.org 3

f i n a n c i a l

s y s t e m

4

–9.33% 256.36 2.78 56.39

SOLD

Financial Markets

An Engine for Economic Growth
By Yongseok Shin

In the aftermath of the 2008 financial crisis, it is natural to
wonder about the roles that the highly developed financial sector
plays in our economy. Some might wonder whether this sector
causes more harm than it does good. In this article, I examine
data from countries with varying degrees of economic
development and argue that developed financial markets
are an essential ingredient of long-run economic growth.
4 The Regional Economist | July 2013

5.23 –6.35% 248.36

6 3.52 9.61 –2.86% 189.45 5.11 8.75 –4.01%

2

Before I begin, let me clarify two things.
First, it is not my contention that all
financial market activities have a positive impact on economic growth. To the
contrary, excesses and abuses in financial
markets can be detrimental to economic
growth in the long run. Second, developed
financial markets provide useful services
that do not directly contribute to economic
growth. For example, most insurance
policies are designed to enhance economic
welfare through better allocation of risk,
not through the promotion of economic
growth. More broadly, the purpose of this
article is not to list all the pros and cons of
financial market development. Rather, I
show the importance of financial markets to
economic growth. Knowing the important
contributions of well-functioning financial
markets will help us figure out (1) which
financial market activities to promote and
(2) where to direct our regulatory and
supervisory efforts.

The Schumpeterian Hypothesis

The nexus of finance and economic
growth was first emphasized by Joseph
Schumpeter in 1911. In Schumpeter’s
theory, widely known as the theory of “creative destruction,” innovation and entrepreneurship are the driving forces of economic
growth. He viewed finance as an essential
element of this process. Innovation and
entrepreneurship will thrive when the
economy can successfully mobilize productive savings, allocate resources efficiently,
reduce problems of information asymmetry
and improve risk management, all of which
are services provided by a developed financial sector.
The surest way to test such a hypothesis would be to perform a randomized,
controlled experiment, in which we would
improve financial markets in a randomly
chosen group of countries and shut down
financial markets in the others. Since it is
not possible (or desirable) to conduct such

experiments on national economies, economists have tried to infer the importance of
finance for economic growth from observations on countries with varying degrees of
financial and economic development.
The first attempts at empirical evaluations
of Schumpeter’s hypothesis came in the late
1960s and the early 1970s; these attempts
documented close relationships between
financial development and economic
development across countries.1 However,
critics refuted this evidence, rightly, since
correlation does not imply causation. Many
prominent economists argued that finance
simply follows economic development.2
More recently, researchers have responded
to this criticism. I highlight three different
approaches in this article.
Empirical Patterns across Countries

First, in a 1993 paper, Robert King and
Ross Levine addressed the correlation-notcausation issue by showing that countries
The Regional Economist | www.stlouisfed.org 5

FIGURE 1
Relationship between Financial Development and Economic Development
1.0
TOTAL FACTOR PRODUCTIVITY (TFP) RELATIVE TO THE U.S.

1.0

GDP/WORKER RELATIVE TO THE U.S.

0.8

0.6

0.4

0.2
Each dot is a country.
0

0

0.7

1.4

2.1

0.8

0.6

0.4

0.2
Each dot is a country.
0

2.8

0

0.7

EXTERNAL-FINANCE-TO-GDP RATIO

1.4

2.1

2.8

EXTERNAL-FINANCE-TO-GDP RATIO

SOURCE: Buera, Kaboski and Shin.

FIGURE 2

2.5

2.5

2.0

2.0
MANUFACTURING-SERVICES RELATIVE TFP

MANUFACTURING-SERVICES RELATIVE PRICE

Relationship between Financial Development and Manufacturing-Services
Relative Productivity

1.5
1.0
0.5
0
–0.5

Industry-Level Evidence

1.5
1.0
0.5

IRL

0
CZE
HUN

–0.5

JPN

FIN
BEL
FRA
GBR
ITA
GER
AUS
ESP
SWE
AUT
DNK

USA
NLD

PRT

Each dot is a country.
–1.0

0

0.5

1.0

1.5

2.0

EXTERNAL-FINANCE-TO-GDP RATIO

2.5

–1.0

0

0.5

1.0

1.5

2.0

2.5

EXTERNAL-FINANCE-TO-GDP RATIO

SOURCE: Buera, Kaboski and Shin.
NOTE: In the right panel, the 18 countries are: Australia (AUS), Austria (AUT), Belgium (BEL), Czech Republic (CZE), Denmark (DNK), Finland (FIN), France
(FRA), Germany (GER), Hungary (HUN), Ireland (IRL), Italy (ITA), Japan (JPN), the Netherlands (NLD), Portugal (PRT), Spain (ESP), Sweden (SWE), the
United Kingdom (GBR) and the United States (USA).

6 The Regional Economist | July 2013

with higher levels of financial development
in 1960 experienced higher rates of economic
growth in the following three decades. King
and Levine measured a country’s financial
development in terms of the levels of credit
(e.g., bank loans and bonds issued) and stock
market capitalization, a metric that is still
widely used. Based on their findings, they
rejected the idea that finance merely follows
economic growth. But their results did not
prove—for at least two reasons—that finance
causes economic growth.
First, even though a country’s financial
development in 1960 is a predetermined
variable relative to the economic growth in
the next three decades, both financial and
economic development may still be mere
consequences of a common omitted factor. Second, because financial markets are
forward-looking, financial development in
1960 may be the consequence of anticipated
economic growth of the next few decades.
In this “reverse causality” view, financial
development may be a mere leading indicator of economic growth rather than a cause.

Researchers then tried to come up with
ways of testing Schumpeter’s hypothesis that
could surmount the above criticisms and
clearly determine causality. In an influential
paper in 1998, Raghuram Rajan and Luigi
Zingales worked with detailed firm-level data
that had not been used in the literature until
then to test Schumpeter’s hypothesis. Their
theory is that, if Schumpeter were correct,
industries that are more dependent on external financing would grow faster in countries
with more-developed financial markets.
Using a database of publicly traded firms in
the United States (Compustat), they ranked
industries in terms of “external dependence,”
which is a measure of how dependent an
industry is on external financing. Roughly
speaking, it is the fraction of a firm’s investment in a given year that is financed with
debt and equity, rather than the year’s cash
flow.3 There is a large variation in external
dependence across industries, with pharmaceuticals having the highest (1.49) and
tobacco the lowest (–0.45).4
Rajan and Zingales found that industries
that are more dependent on external financing grew faster than those industries that
are less dependent on external financing in

countries with developed financial markets,5
but it is the other way around in countries
with underdeveloped financial markets.
They concluded that their result is consistent
with the view of finance as a lubricant, just
as Schumpeter hypothesized.
While their test result is not a proof
of finance as a causal factor of economic
growth, many economists count it as the
most convincing evidence. The reason is
that it is much harder, albeit not impossible,
to come up with a plausible omitted-variable
argument or reverse-causality argument
on the relative performance of industries
across countries.
Building an Economic Laboratory:
A Model with Two Sectors

One weakness of the above empirical approaches is that the findings do not
shed much light on the exact mechanism
through which finance affects economic
growth. To answer this question, the third
and final approach that I discuss here takes
a different tack. Indeed, it turns the previous approaches on their head. It starts
by building an economic model whereby
financial markets do have an impact on the
long-run economic growth. The question
is not whether finance is a causal factor for
economic development (which is true by
assumption) but how big an impact financial
development has on economic development.
We can also determine the exact channels
through which finance affects economic
development.
For a representative and concrete example
of this modeling approach, I rely heavily
on a study that I conducted with Francisco
Buera and Joseph Kaboski in 2011, in which
we built a model with multiple industrial
sectors and with frictions in financial
markets that interfere with efficient allocation of resources. The modeling of multiple
industrial sectors was partly motivated by
the findings of Rajan and Zingales.
We started by establishing important
empirical facts on cross-country differences in economic development. First,
countries’ levels of financial development
are closely correlated with their levels of
economic development measured by output
per worker. Second, poor countries’ low
levels of output per worker are primarily
explained by their low levels of total factor

productivity (TFP). TFP measures the level
of the technology that combines capital
and labor to produce output. A country
with a high TFP produces more with a
given amount of capital and labor than a
country with a low TFP. Finally, the TFP
gap between rich and poor countries varies
systematically across industrial sectors of
the economy. For instance, less-developed
countries are particularly unproductive in
producing manufactured goods, including equipment and machinery. These facts

manufacturing operates at larger scales,
which translates into more dependence on
external financing.7
Sector-level TFP data are not available for
most countries. We take advantage of the
standard economic theory which implies
that the relative price between the output of
two sectors is the reciprocal of their relative
productivity. In the left panel of Figure 2,
we show the positive correlation between
a country’s relative price of manufactured
goods to services and its level of financial

For a representative and concrete example of this modeling
approach, I rely heavily on a study that I conducted with
Francisco Buera and Joseph Kaboski in 2011, in which we
built a model with multiple industrial sectors and with frictions
in financial markets that interfere with efficient allocation
of resources.
synthesize the findings of the two empirical
studies discussed above and shift the focus
onto an economy’s TFP rather than income
or output levels.
The left panel of Figure 1 shows the relationship between a country’s financial development, measured by the ratio of private
credit to gross domestic product (following
the metric of King and Levine), and its level
of economic development, measured by
output per worker.6 Each dot is a country,
and the fitted straight line shows the average
relationship between the two variables. The
output per worker is relative to the output
per U.S. worker. The figure confirms that
more-developed economies also have moredeveloped financial markets.
The right panel of Figure 1 shows the
relationship between a country’s financial
development and its level of aggregate TFP.
The figure is a reflection of the fact that
the difference across countries in terms of
economic development, measured in terms
of output per worker, is primarily explained
by the difference in their TFP levels.
To have a clear analysis, we consider
the simplest multisector economy: an
economy with two sectors—manufacturing
and services. We focus on the scale differences between manufacturing production and services production. On average,

development. This can be interpreted as
lower relative TFP of manufacturing to
services in countries that are less financially
developed.
In the right panel of Figure 2, we only
look at countries with sector-level TFP
data and show their relative manufacturingservices TFP against their level of financial
development. We verify that, for these
countries, the relative sector-level TFP
data are consistent with the sector-level
relative prices.
The primary goal of our 2011 study was
to present a rich quantitative framework
and analyze the role of financial frictions in
explaining the above empirical regularities
in economic development.
In our theory, a firm’s productivity
changes over time, generating the need to
reallocate capital from previously productive firms to currently productive ones.
Financial frictions hinder this reallocation
process by limiting the amount of credit
required for the expansion of newly productive firms. The degree of financial frictions is different across countries because
countries differ in terms of the effectiveness
with which credit contracts are enforced. In
countries with ineffective contract enforceability, creditors are likely to have trouble
recovering their loans. Knowing this, they
The Regional Economist | www.stlouisfed.org 7

FIGURE 3
Average Establishment Size of Industries
in the U.S. and Mexico
MEXICO VS. U.S. SCALES: DISAGGREGATED INDUSTRIES

LOG WORKERS PER ESTABLISHMENT IN MEXICO

8.0

* Manufacturing

*

AV Equipment
* Computers *Steel

*
*
* * **
** ** ** *
Admin. and Mgmt.
* **
******* * *
* ******* *
***
**
** **** ***** *
* ***** Transportation
**** ** *
*
****** * Dairy
Products
** * Clay Products
** Bakery and Tortilla
Retail

6.0
5.0
4.0
3.0
2.0
1.0
0

Auto

Services
Each data point is an industry.

7.0

Food and Accommodations

0

1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0
LOG WORKERS PER ESTABLISHMENT IN THE U.S.

SOURCE: Buera, Kaboski and Shin.

8 The Regional Economist | July 2013

will reduce the size of loans and demand
larger collateral.
We discovered that financial frictions
explain a substantial part of the above development regularities. Essentially, financial
frictions distort the allocation of capital
across firms and also their entry and exit
decisions, lowering aggregate and sectorlevel TFP. While the use of internal funds
or self-financing can alleviate the resulting
misallocation, it is inherently more difficult
to do so in sectors with larger scale and
larger financing needs. Thus, sectors with
larger scale (i.e., manufacturing) are affected
disproportionately more by financial frictions. This explains the empirical findings
of Rajan and Zingales.
The variation in financial development
across countries can explain a factor-oftwo difference in output per worker across
economies, which is equivalent to almost
80 percent of the difference in output per
worker between Mexico and the U.S. Consistent with the consensus view in the literature, the differences in output per worker in
our model are mostly accounted for by the
low TFP in economies with underdeveloped
financial markets.
In our model economy, the impact of
financial frictions is particularly large in the
large-scale, manufacturing sector. While
the sector-level TFP declines by less than
30 percent in services, it declines by more
than 50 percent in manufacturing, a result
broadly in line with the available sectorlevel productivity data shown in the right
panel of Figure 2. The differential impacts
of financial frictions on sector-level productivity are reflected on the higher relative
prices of manufactured goods to services in
financially underdeveloped economies.
Our analysis provides a clear decomposition of the main margins distorted by financial frictions. First, for a given set of firms
in operation, financial frictions distort the
allocation of capital among them (misallocation of capital). Second, for a given number of firms in operation, financial frictions
distort firms’ entry decisions, with productive-but-undercapitalized firms delaying
their entry and unproductive-but-cash-rich
firms remaining in business (misallocation
of entrepreneurial talent). Third, financial
frictions distort the number of firms operating in each sector. In our model economy,

whereas the misallocation of capital is
responsible for 90 percent of the effect of
financial frictions on the service-sector
TFP, it is the misallocation of entrepreneurial talent that accounts for more than
50 percent of the effect on the manufacturing-sector TFP.
The differential impacts of financial frictions across sectors in our model economy
produce an interesting testable implication
on the firm size distribution of each sector.
Financial frictions, together with the resulting higher relative price of manufactured
goods, lead to too few firms and too large
firms in manufacturing, and too many firms
and too small firms in services. To evaluate this implication, we perform a detailed
case study of Mexico and the U.S., and find
empirical support for it.
Figure 3 plots the average plant size in
Mexico (defined as the number of employees, vertical axis) against the average plant
size in the U.S. (horizontal axis) for 86
manufacturing industries and 12 service
industries. The overall average plant size is
substantially smaller in Mexico than in the
U.S., almost by a factor of three. However,
many industries (those lying above the
45-degree dashed line) have an average plant
that is larger in Mexico than in the U.S.
Indeed, the data have a slope (solid line)
that is significantly steeper than the
45-degree line. That means that the industries that are large scale in the U.S. have
an even larger scale in Mexico, while those
that are small scale in the U.S. have an even
smaller scale in Mexico. With the exception
of administration/management services,
those above the 45-degree line are manufacturing industries.
In summary, we developed a theory
linking financial development to output per
worker, aggregate TFP and sector-level relative productivity. Financial frictions distort
the allocation of capital and entrepreneurial
talent and have sizable adverse effects on
macroeconomic outcomes. Based on these
findings, we concluded that financial development, so long as it removes or alleviates
such frictions, promotes economic growth
in the long run.
Legal Origins and Financial Development

Empirical and theoretical analyses of
finance and economic development across

countries naturally raise the following
questions. Why are some countries more
financially developed than others? Why
don’t less developed countries adopt or
import more-advanced financial markets?
Recent research on this topic finds answers
in countries’ institutions, especially their
legal framework and rule of law.
For most countries, their overarching legal framework was either shaped
long before the emergence of the modern
finance-growth nexus or imposed on them
through colonial rule. Legal scholars have
categorized the laws that pertain to economic and financial contracts into four
traditions: (English) common law, French
civil law, German civil law and Scandinavian civil law. The scholars have found that
common-law countries generally have the
strongest, and French-civil-law countries
the weakest, legal protections for investors,
with German- and Scandinavian-civillaw countries in the middle. The strength
of investor protection explains, in turn, a
significant fraction of the differences in
financial development across countries. 8
This finding also explains why it may
be difficult for countries to improve their
financial markets, at least in the short term.
Financial markets are governed by rules that
are embedded into the institutional foundations of an economy, and such rules are
persistent and sluggish by nature. A reform
of financial markets, thus, likely presupposes an all-reaching, large-scale reform of
the whole economy.

deserve the government’s directed credit
initially, but the firm will become over time
either unproductive or sufficiently capitalized on its own. If the government cannot
wean such undeserving beneficiaries from
directed credit, the government’s efforts
only worsen the misallocation of capital in
the long run.
The studies reviewed in this article suggest that governments aiming for financial
development should focus on reforming
bureaucratic and judicial procedures of
the enforcement of economic contracts.
With transparent and effective contract
enforcement in place, financial development
will follow.

ENDNOTES
1

2

3

4

Concluding Remarks

This article is not intended to be a wholesale defense of the financial sector. Rather,
my goal is to remind us of the essential
services that a developed financial sector
provides for technological innovation and
economic growth—mobilizing savings,
evaluating projects, managing risk, monitoring managers and facilitating transactions, just as Schumpeter envisioned.
We need to keep these essential services
in mind as we rethink our regulatory and
supervisory approaches in the wake of the
financial crisis.
Yongseok Shin is an economist at the Federal
Reserve Bank of St. Louis. For more on his
work, see http://research.stlouisfed.org/econ/shin/.

5

6

7

Policy Implications

Our analysis shows that, when the financial markets are not functioning properly,
there is room for a government to intervene
and improve upon the allocation of capital
across firms. Indeed, this is one of the most
cited justifications for industrial policy.
There are two important caveats. First, to
repeat the popular refrain against industrial
policy, governments cannot pick winners—
that is, it is not clear whether governments,
even with the best of intentions, can better
identify who deserves more capital than
can the market. Economic history shows
that the odds are not in governments’
favor. Second, it is hard to change policies
that favor particular groups once those
policies are instituted. A firm may well

8

The Schumpeterian hypothesis had been much
debated before then, but the relevant data required
for an empirical analysis were not available before
the late 1960s.
Joan Robinson argued, “By and large, it seems to
be the case that where enterprise leads, finance
follows.” See p. 86 of her book in the references.
A firm’s external dependence is defined as capital
expenditures (investment) minus cash flow from
operations, divided by capital expenditures. This
reveals what fraction of a firm’s investment is
financed with internal funds (cash flow) and
external funds. An industry’s external dependence is then defined as the median value of the
firm-level external dependence of all the firms
in that industry. Rajan and Zingales further
assume that an industry’s external dependence is
a technological feature of the industry and, hence,
the external dependence of an industry computed
from the U.S. data is common across all countries.
The external dependence in the data primarily
depends on two factors. First, industry-level
technologies are different in the lag between
investment and revenue generation. It is longer
in pharmaceuticals, in which it takes years of
research and development to produce marketable
new drugs. Tobacco firms, on the other hand,
have a stable revenue stream that can more than
pay for new investments. Second, in all industries,
young firms have higher external dependence than
mature firms, which can use the proceeds from
their past investment to pay for current investment. It turns out that most pharmaceutical firms
are young, and most tobacco firms are old.
Rajan and Zingales measured a country’s financial
development first in terms of the metric of King
and Levine and then in terms of the degree of disclosure prescribed by each country’s accounting
standards.
Gross domestic product (GDP) is computed in
international prices to account for the fact that the
same goods and services are often cheaper in poor
countries than in rich countries. Economists call
this procedure “purchasing-power parity” (or PPP)
adjustment. The data are for 1996 and come from
Penn World Tables Version 6.1.
In the U.S., the average number of employees for a
manufacturing establishment is 47, while it is 17 for
a service establishment. Across all the Organisation for Economic Co-operation and Development
member countries, the average manufacturing firm
hires 28 employees and the average service firm 8.
See La Porta et al.

REFERENCES
Buera, Francisco J.; Kaboski, Joseph P.; and Shin,
Yongseok. “Finance and Development: A Tale of
Two Sectors.” The American Economic Review,
2011, Vol. 101, No. 5, pp. 1,964-2,002.
King, Robert G.; and Levine, Ross. ‘‘Finance and Growth:
Schumpeter Might Be Right.’’ The Quarterly Journal of Economics, 1993, Vol. 108, No. 3, pp. 717-37.
La Porta, Rafael; Lopez-de-Silanes, Florencio;
Shleifer, Andrei; and Vishny, Robert W. “Law and
Finance.” Journal of Political Economy, 1998,
Vol. 106, No. 6, pp. 1,113-55.
Rajan, Raghuram G.; and Zingales, Luigi. ‘‘Financial
Dependence and Growth.’’ The American Economic Review, 1998, Vol. 88, No. 3, pp. 559-86.
Robinson, Joan. “The Generalisation of the General
Theory,” in The Rate of Interest and Other Essays.
London: Macmillan, 1952.

The Regional Economist | www.stlouisfed.org 9

l a b o r

i s s u e s

Changes in the Racial
Earnings Gap since 1960
By Maria Canon and Elise Marifian

I

ncome inequality between races has
been a widely used indicator of economic
prosperity and opportunity (or the lack
thereof) within the diverse population of the
U.S. The Civil Rights Act of 1964 prohibited
discrimination in public places, provided for
the integration of schools and other public
facilities, and made employment discrimination illegal, thus improving the quality of
education and providing more job opportunities for African-Americans. Nevertheless,
disparities remain. Labor economists have
investigated various sources of earnings
inequality in America since the act was
passed; some economists have considered
how the disparities in earnings change
within and across regions of the country.
Much of the research covers the 1960-2000
period; much less is known about racial
inequality in earnings over the years since.
Of particular interest might be the impact of
the Great Recession on such inequality.
This article aims to provide insight into the
recent trends in earnings inequality between
black men and white men. We replicated the
analysis in a 2006 study by Jacob Vigdor of
the 1960-2000 period using census data and
then examined disparities in annual earnings
since then, using yearly American Community Survey data from 2000 to 2011.
Figures 1 (1960-2000) and 2 (2000-2011)
present key results. The dotted line shows
the percentage differential in earnings for
Northern-born black males relative to Northern-born white males, holding constant other
variables.1 (For example, in 1960 Northernborn black males earned on average 40 percent
less than their Northern-born white counterparts.) The solid line plots the same comparison between Southern-born blacks and
whites. (Be aware that Northern-born and

10 The Regional Economist | July 2013

Southern-born does not necessarily mean
that the men continued to live in the North
or South, respectively.)
In Figure 1, we see that inequality declined
among both the Northern-born and
Southern-born from 1960 to 1970. Racial
earnings inequality among the Northernborn increased markedly from 1970 to 1990
and remained relatively stable from 1990
to 2000. On the other hand, among the
Southern-born, racial earnings inequality

Much of the research covers
the 1960-2000 period; much
less is known about racial
inequality in earnings over
the years since. Of particular
interest might be the impact
of the Great Recession on
such inequality.
declined only slightly from 1970 to 1980 and
increased slightly from 1980 to 2000. In 2000,
black-white earnings inequality among the
Northern-born was considerably greater than
the level in 1960, while inequality among the
Southern-born was reduced.
Figure 2 shows the results for 2000-2011.
The values for the Northern-born indicate
that the economic situation of blacks (as measured by annual earnings) declined considerably relative to that of whites; in other words,
earnings inequality continued to increase for
those born outside the South.
Similarly, the percent differential in
Southern-born blacks’ annual earnings relative to Southern-born whites’ worsened over

the 2000-2011 period. Those blacks born in
the South did not show evidence of converging faster with those blacks born in the North
during this decade. In addition, the increases
in slope magnitude from 2007 to 2010 indicate that during the Great Recession and in
the year following, racial earnings inequality
among the Southern-born increased even
more than in previous years. Lastly, it is
important to note that being a Southern-born
black male corresponds with a greater wage
differential relative to white counterparts
than does being a Northern-born black male.
For example, the results indicate that in 2011,
the annual earnings of Southern-born black
males were approximately 72 percent less
than those of Southern-born white males,
whereas Northern-born black males’ 2011
earnings were 61 percent less than those of
Northern-born white males.
What driving forces can explain
these trends?
Vigdor examined three hypotheses to
understand why it appears that the South
demonstrated more rapid progress than the
North in reducing the earnings gap between
blacks and whites from 1960 to 2000.2 While
each hypothesis seems to have had an effect
at some point throughout the 40-year period,
the results of his analysis suggest that much
of the “improvement” in the racial wage
gap in the South was merely a reflection of
changing regional demographics—what
he calls “selective migration”—and not of
actual improvement in relative earnings for
Southern-born blacks. The improvements
were the result of blacks and whites of differing abilities moving from South to North and
vice versa.
Vigdor’s results indicate that selective
migration accounted for 40 percent of the

FIGURE 1

BLACKS’ EARNINGS RELATIVE TO WHITES’ (%)

Relation among Race, Geography and
Earnings: 1960-2000
1960
0
–10
–20
–30

1970

1980

1990

2000

Black-White Gap among Northern-Born Men
Black-White Gap among Southern-Born Men

–40
–50
–60
–70

NOTES: The percentages on the vertical axis indicate, for example, that Northern-born black men made in 1960 about 40 percent less than Northern-born
white men. Samples are derived from the Integrated Public Use Microdata
Series (IPUMS) census data on white and black males age 21-60 born in the
48 contiguous states. Individuals with zero earnings are assumed to have
potential earnings below the median for their region/race cell and age.
Samples are weighted using IPUMS weights where appropriate.

FIGURE 2

BLACKS’ EARNINGS RELATIVE TO WHITES’ (%)

Relation among Race, Geography and
Earnings: 2000-2011
00 01 02 03 04 05 06 07 08 09 10 11
0
–10
–20
–30
–40
–50
–60
–70
–80

Black-White Gap among Northern-Born Men
Black-White Gap among Southern-Born Men

NOTES: The percentages on the vertical axis indicate, for example, that Northern-born black men made in 2000 about 28 percent less than Northern-born
white men. Being Northern-born or Southern-born doesn’t necessarily mean
that the men still live in the North or South, respectively. Samples are derived
from the Integrated Public Use Microdata Series (IPUMS) American Community
Survey data on white and black males age 21-60 born in the 48 contiguous
states. Individuals with zero earnings are assumed to have potential earnings
below the median for their region/race cell and age. Samples are weighted
using IPUMS weights where appropriate.

South’s relative improvement from 1960 to
2000 and all of the improvement from 1980
to 2000. More specifically, the black-white
wage gap improved among Southern residents but not for those born in the South.
Other studies have attempted to explain
the trends in earnings inequality between the
races through 2000. A 2010 paper by Dan
Black, Natalia Kolesnikova and Lowell Taylor
considered the average annual weeks worked
from 1970 to 2000 and found that this number declined for black men in each of the
14 cities examined, sometimes by as much as
25 percent, while the declines for white men

were relatively smaller. At the same time,
black men’s weekly hours of work remained
stable. Together, these two points suggest
that the earnings decline was likely related
to labor force attachment (manifested as
a drop in the average number of weeks
worked in a year) rather than to declines in
the number of hours that black men were
working. The authors found substantial
declines in the proportion of black men
employed, increases in the proportion of
black men unemployed and even larger
increases in the proportion of black men
not in the labor force. In other words,
black men’s labor force trends from 1970 to
2000 help explain why their average annual
weeks worked declined and why their total
annual earnings declined relative to those
of white men.
Yet can these studies by Vigdor and by
Black et al. explain the behavior in racial
earnings gaps over the 2000-2011 period?
While Vigdor argued that selective migration explained the South’s improvement
in racial earnings inequality relative to the
North’s, recent research by Greg Kaplan and
Sam Schulhofer-Wohl suggests that interstate
migration has been decreasing. In other
words, the selective migration story observed
in previous decades would no longer apply.
An alternative explanation for the
increased racial inequality could be that
African-American men were disproportionately hit by the Great Recession. Their
unemployment rate increased from 8.5
percent to 15.4 percent between December
2007 and December 2011. In comparison, the unemployment rate of white men
rose from 3.9 percent to 7.1 percent over
the same period.3 Given these unemployment rates, it is no surprise that for both
Southern-born and Northern-born blacks,
earnings declined relative to whites during
the Great Recession. Furthermore, the fact
that the labor force attachment for AfricanAmericans has decreased even more since
the Great Recession might help explain the
increase in the earnings gap since 2009.

ENDNOTES
1

2

3

Vigdor uses the term “North” to refer to Census
Bureau regions other than the South. Therefore,
the “North” in his study comprises the Northeast,
Midwest and West regions.
His second hypothesis is that changes in regional
labor markets, following the Civil Rights legislation and the manufacturing decline in the North,
improved Southern blacks’ economic prosperity.
His third hypothesis is that the South’s progress in
reducing the black-white earnings gap from 1960 to
2000 could be a consequence of greater educational
attainment among blacks, following desegregation
and the reduction of racial disparity in education.
The unemployment rate data are for black men age
20 and over, seasonally adjusted, and white men
age 20 and over, seasonally adjusted, from the U.S.
Bureau of Labor Statistics/Haver Analytics.

REFERENCES
Black, Dan A.; Kolesnikova, Natalia A.; and Taylor,
Lowell J. “The Economic Progress of African
Americans in Urban Areas: A Tale of 14 Cities.”
Federal Reserve Bank of St. Louis’ Review, September/October 2010, Vol. 92, No. 5, pp. 353-79.
Kaplan, Greg; and Schulhofer-Wohl, Sam. “Understanding the Long-Run Decline in Interstate
Migration.” Federal Reserve Bank of Minneapolis,
October 2012 (revised February 2013), Working
Paper 697.
Ruggles, Steven; Alexander, J. Trent; Genadek, Katie;
Goeken, Ronald; Schroeder, Matthew B.; and Sobek,
Matthew. Integrated Public Use Microdata Series
(IPUMS): Version 5.0 [Machine-readable database].
Minneapolis: University of Minnesota, 2010.
Vigdor, Jacob L. “The New Promised Land: BlackWhite Convergence in the American South, 19602000.” National Bureau of Economic Research,
March 2006, Working Paper 12143.

Maria Canon is an economist and Elise
Marifian is a research associate, both at the
Federal Reserve Bank of St. Louis. For more
on Canon’s work, see http://research.stlouisfed.
org/econ/canon/.
The Regional Economist | www.stlouisfed.org 11

h o u s i n g

i s s u e s

Europe May Provide
Lessons on Preventing
Mortgage Defaults
By Juan Carlos Hatchondo, Leonardo Martinez
and Juan M. Sánchez

I

t is well-known that house prices declined
sharply and mortgage defaults increased
abruptly from 2006 to 2010 in the U.S. In
Europe, where mortgage regulations are
significantly different, the behavior of house
prices and mortgage defaults displays somewhat different dynamics. Comparing the
experiences in these two regions sheds light
on the impact of alternative regulations.
Figures 1 and 2 show the evolution of
house prices and mortgage defaults in the
U.S. and Europe, respectively. To facilitate
the comparison, both series are normalized
to 100 in 2007. In the U.S., house prices
declined about 20 percent during this period,
and defaults increased by about 300 percent.1
In Europe, house prices declined much less,
slightly more than 5 percent, while mortgage
defaults increased little, about 26 percent
from trough to peak, 2007-2010.2 Given that
changes in prices and defaults are different, it
is hard to compare the experiences in Europe
and the U.S. directly. Here, this problem is
dealt with by comparing changes in defaults
for periods in which the changes in prices
were similar in the two regions.
The first panel of the table compares
changes in mortgage defaults in Europe
and the U.S. for periods when the change
in prices was similar. From 2008 to 2009,
prices declined almost 7 percent on average in Europe. As a response, default rates
increased, but only by 11 percent. In the U.S.,
from 2007 to 2008, house prices declined
on average by almost 8 percent. The corresponding increase in mortgage defaults was
much larger: more than 93 percent.
Why would there be such a difference in
the response of mortgage defaults to almostequal changes in house prices? A 2011 report
by the International Monetary Fund (IMF)
12 The Regional Economist | July 2013

points to two regulations used in Europe to
prevent mortgage defaults, one implemented
to a limited extent in only some states of the
U.S. and the other implemented on a much
less restrictive basis across the U.S.
The first regulation gives homeowners in
Europe more responsibilities after default
than most U.S. homeowners face. In Europe,
mortgages are recourse loans, meaning
that, after default, borrowers are responsible for the difference between the value of
the outstanding debt and the value of the
house. Consider this hypothetical case: If
Jaime bought a house in Spain for €500,000
in 2007 and defaulted in 2010 when he still
owed €450,000 but the house was worth only
€400,000 then, under recourse laws he is
responsible for €50,000.
This policy increases the cost of default,
which makes it less appealing to the homeowner. In most of the states in the U.S.,
mortgages are, in practice, nonrecourse.
Even when recourse is allowed, the deficiency judgment (the difference between the
loan and house value) could be discharged
in bankruptcy.
The second policy in Europe limits the
amount that households can borrow using
their house as collateral. Some European
countries have limits on loan-to-value (LTV)
ratios of 80, 85 or 90 percent. For example,
if the LTV limit is 80 percent, an owner of a
house worth €500,000 cannot borrow (using
the house as collateral) more than €400,000.
As a result of this policy, households have
more home equity. More equity means that
fewer mortgages end up underwater when
house prices drop. As a result, the default
rate is lower in Europe. In the U.S., LTV
policies are much less restrictive.
The impact of the recourse and LTV

policies is illustrated in the rest of the table.
The second panel of the table compares the
dynamics of house prices and defaults in states
with recourse laws to those in states without
recourse laws.3 We compare different periods
to evaluate the change in mortgage defaults
given similar changes in house prices. From
2007 to 2010, house prices declined by about
9 percent in recourse states, while the default
rate increased by about 217 percent. A very
similar change in prices—about 10 percent—
is observed for nonrecourse states between
2007 and 2008; for that group, defaults
rose about 186 percent, similar to what was
observed in recourse states. The lesson here
is that recourse as designed and implemented
in the U.S. has little effect on the default rate
on mortgages.4
As mentioned above, to understand why
recourse does not have as much effect on
default rates in the U.S. as it does in Europe,
one has to look at the interaction of recourse
laws in the U.S. with Chapter 7 bankruptcy.
In a 2009 paper, economists Wenli Li and
Michelle White estimated the probability of
bankruptcy for homeowners with mortgages and found that the probability of filing
bankruptcy was about 25 times greater if the
mortgage creditor had begun foreclosure
within the previous three months than if the
mortgage creditor had not done so.5
The third panel of the table illustrates that
recourse in Europe does play an important role in preventing defaults. The panel
compares a group of U.S. states with a group
of European countries; both groups have
recourse policies but no LTV policies.6 The
main difference between these two regions is
how recourse regulations are actually implemented, in particular, the fact that Chapter 7
bankruptcy restricts the role of recourse in

FIGURE 2
Prices vs. Defaults Europe
250
200

95
90

Prices

85
80

Defaults (right axis)

150
100

75

100
PRICES (2007=100)

PRICES (2007=100)

100

50

200

95
Prices

90

150

Defaults (right axis)

85

100

80
75

2005 2006 2007 2008 2009 2010 2011
YEAR

2005

SOURCE: Zillow Real Estate Research.

2007

2008
YEAR

2009

2010

2011

At the Federal Reserve Bank of St. Louis,
a life-cycle model in which households make
housing and financial decisions is being
built.7 The model reproduces many features
of U.S. mortgage and housing markets. That
artificial economy can be used to simulate
the effect of implementing limits on LTV and
recourse in the U.S. economy. Hopefully, the
results will shed light on the pros and cons of
implementing these policies.

Decline in prices
Increase in defaults
PANEL 2
Period
Decline in prices
Increase in defaults
PANEL 3
Period
Decline in prices
Increase in defaults
PANEL 4
Period

3

4

5

6

Juan Carlos Hatchondo is an assistant professor
at Indiana University and an economist at the
Federal Reserve Bank of Richmond. Leonardo
Martinez is an economist at the IMF Institute
for Capacity Development. Juan M. Sánchez is
an economist at the Federal Reserve Bank of
St. Louis. For more on Sánchez’s work, see
http://research.stlouisfed.org/econ/sanchez/.

7

For prices and defaults for the U.S., we used
data provided by Zillow Real Estate Research.
“Prices” are from the Zillow Home Value Index
for all homes, and “defaults” are foreclosures per
10,000 homes.
These data are an average of prices and defaults
for seven European countries with available data
from 2005 to 2010. Prices were obtained from the
International House Price Database provided by
the Globalization and Monetary Policy Institute of
the Federal Reserve Bank of Dallas. Defaults are
actually “arrears on mortgage or rent payment”
provided by Eurostat. A more comparable concept
in the U.S. is “mortgage delinquencies.” Growth
rates of mortgage delinquencies and foreclosures
in the U.S. were similar during this period.
States are grouped according to their recourse
policies, using the recourse classification from
the 2011 paper by Andra C. Ghent and Marianna
Kudlyak. The states without recourse policies
for which we also have price and default data are
Arizona, California, Minnesota, Oregon, Washington and Wisconsin. The states with recourse
policies that we used are Alabama, Arkansas, D.C.,
Maryland, Massachusetts and Missouri. These are
the recourse states that take the shortest time to
resolve a foreclosure.
See Clauretie. This view, however, is challenged by
Ghent and Kudlyak, using household-level data on
mortgage characteristics.
In a related 2011 paper, Kurt Mitman models
differences in bankruptcy and nonrecourse laws
across U.S. states.
The countries that are considered in Europe are
Denmark, France, Ireland, Italy, the Netherlands,
Spain and the United Kingdom. The data on
loan-to-value ratio limits are obtained from the
IMF report mentioned above. Countries with
maximum LTV on new loans smaller than
100 percent are considered as countries with LTV
limits. In our sample, only Denmark and Italy
belong to this group.
See Hatchondo, Martinez and Sánchez (2011).

REFERENCES

The Role of Recourse and LTV Limits in Preventing Mortgage Defaults
Period

2

50
2006

SOURCES: Prices from International House Price Database provided by the
Federal Reserve Bank of Dallas. Defaults from Eurostat.

the U.S. because a U.S. household can usually
discharge that obligation in bankruptcy.
Over roughly the same time period, house
prices in each group declined about the same
amount, but the increase in default rates was
very different: about 14 percent in Europe
and about 217 percent in the U.S. This
suggests that recourse, when designed and
implemented as in Europe, plays an important role in preventing defaults.
Limiting the amount of debt taken by
homeowners seems important, too. The last
panel of the table compares European countries with and without LTV limits. Over the
same period, each group experienced roughly
the same decline in house prices (about 10
percent). However, the default rate increased
only slightly in countries with an LTV limit,
while it increased by more than 14 percent in
countries without such a limit.

PANEL 1

1

250

105
DEFAULTS (2007=100)

105

ENDNOTES

DEFAULTS (2007=100)

FIGURE 1
Prices vs. Defaults U.S.

Europe

U.S.

2008-2009

2007-2008

6.8%

7.7%

11.0%

93.2%

U.S. recourse states

U.S. nonrecourse states

2007-2010

2007-2008

8.7%

10.1%

216.6%

186.2%

U.S. recourse states

Europe, non-LTV-limit countries

2007-2010

2007-2009

8.7%

10.2%

216.6%

14.4%

Europe, LTV-limit countries

Europe, non-LTV-limit countries

2007-2009

2007-2009

Decline in prices

8.6%

10.2%

Increase in defaults

3.5%

14.4%

Clauretie, Terrence. “The Impact of Interstate Foreclosure Cost Differences and the Value of Mortgages on Default Rates.” Journal of the American
Real Estate and Urban Economics Association,
September 1987, Vol. 15, No. 3, pp. 152-67.
Ghent, Andra C.; Kudlyak, Marianna. “Recourse and
Residential Mortgage Default: Theory and Evidence from U.S. States.” The Review of Financial
Studies, 2011, Vol. 24, No. 9, pp. 3,139-86.
Hatchondo, Juan Carlos; Martinez, Leonardo;
and Sánchez, Juan M. “Mortgage Defaults.”
Working Paper 2011-019, Federal Reserve Bank of
St. Louis, 2011. See http://research.stlouisfed.org/
wp/more/2011-019.
International Monetary Fund, “Global Financial
Stability Report.” April 2011.
Li, Wenli; White, Michelle J. “Mortgage Default,
Foreclosure and Bankruptcy.” Working Paper
15472, National Bureau of Economic Research,
2009.
Mitman, Kurt. “Macroeconomic Effects of Bankruptcy & Foreclosure Policies.” Working Paper
11-015, Penn Institute for Economic Research,
Department of Economics, University of Pennsylvania, 2011.

SOURCES: Zillow Real Estate Research, Federal Reserve Bank of Dallas, Eurostat, Ghent and Kudlyak,
and Global Financial Stability Report by the International Monetary Fund.
The Regional Economist | www.stlouisfed.org 13

housing in the eighth district

Mortgage Applicants
Turn to Credit Unions
after the Crisis
By Yang Liu and Rajdeep Sengupta

T

he origins of the recent financial crisis
have often been traced to the excesses
in the U.S. mortgage market. Most accounts
of the crisis tend to focus on a significant
decline in underwriting standards for
mortgages since 2000. After the crisis, the
pendulum appears to have swung in the
other direction. Anecdotal evidence suggests that borrowers are finding it difficult to
obtain housing loans. Some observers have
remarked that this difficulty may be one of
the causes of the slump in the U.S. market
for housing.
Using a data set of loan applications and
originations, we analyzed these trends for
the Federal Reserve’s Eighth District, based
in St. Louis.1 Our data came from the Home
Mortgage Disclosure Act (HMDA) files for
2004, 2009 and 2010.2 The HMDA data for
2004 were used as an indicator of the precrisis mortgage market conditions, whereas
HMDA data for 2009-2010 were used to
indicate postcrisis mortgage conditions. We
restricted our observations to first-lien, oneto four-family home mortgage loans.
As expected, the data show that the financial crisis adversely affected the demand for
mortgage loans in the District. Figure 1
displays a panel of scatter plots showing
pre- and postcrisis mortgage applications in
each county of the District. The horizontal axis of each plot measures the level of
2004 mortgage loan applications, while the
vertical axis measures the annual average of
2009-2010 mortgage loan applications. Each
dot in the chart represents one of the 339
counties. The plot also shows the 45-degree
line where the level of 2004 applications
equals the annual average of 2009-2010
applications. Simply put, a dot below the
45 degree line indicates that postcrisis
14 The Regional Economist | July 2013

applications for that county were fewer than
precrisis applications; a dot above indicates
the opposite.
For the District, there were 290,091 fewer
mortgage applications annually during
2009-2010 than in 2004 (a reduction of 33.3
percent). Figure 1A shows that 327 out of
the 339 counties in the District were located
below the 45-degree line—a widespread drop
in mortgage applications across the District.

Although further research is
needed … some anecdotal
evidence may explain this
rapid growth in the popularity
of credit unions.
The drop was greater for new purchases
(Figure 1B) when compared with refinances (Figure 1C). Annual applications
for purchases fell by 47.3 percent (139,707
applications) after the crisis; 319 counties
experienced a decline in purchase applications. In contrast, applications for refinances
fell by 25.3 percent (138,634 applications);
307 counties experienced a decline in
refinance applications. Clearly, the drop
in numbers was roughly the same for both
purchases and refinances, but purchases
constituted a smaller proportion of applications near the peak of the boom in 2004.
Interestingly, HMDA data also allowed
us to sort the applications by the agency
that supervises each lending institution to
which the application is made. Since different agencies supervise different types of
lending institutions, we could use this variable to examine the differences in pre- and

postcrisis applications by lending institutions. We sorted loan data by three different
types of financial institutions: banks and
thrifts, credit unions and “HUD-supervised
mortgagees.” This last category denotes
loans made by institutions that are not
supervised by any of the major agencies.3
Banks and thrifts in the District experienced a moderate decrease in annual mortgage applications of 14 percent (or 71,738
applications) after the crisis (Figure 1D).
Consumers filed fewer mortgage application
loans to banks and thrifts in 252 counties.
HUD-supervised mortgagees suffered the
largest loss in mortgage loan applications
on an annual basis (Figure 1F). They
received 229,219 fewer loan applications,
or a decline of 65.5 percent. In all but one
of the District’s counties, consumers filed
fewer mortgage loan applications to HUDsupervised mortgagees. It is important
to point out that the reduction of 229,219
applications in this sector accounted for
79 percent of the annual loan application
decline in the District.
In contrast, credit unions enjoyed a
surprising boom in home mortgage applications (Figure 1E). On an annual basis,
mortgage applications rose by 10,813—an
increase of 122 percent. Of the 275 counties
in the District that recorded loan applications filed with credit unions, 222 counties recorded an increase in applications.
Furthermore, annual applications increased
by more than 100 percent in 123 District
counties.
Although further research is needed
to account for this rapid and anomalous
increase, some anecdotal evidence may
explain this rapid growth in the popularity of credit unions. First, there has been

ENDNOTES

FIGURE 1

POSTCRISIS MORTGAGE LOAN APPLICATIONS

Mortgage Loan Applications in the Eighth District Pre- and Postcrisis
A. All Loans

96,000

B. Purchase

30,000

66,000

64,000

20,000

44,000

32,000

10,000

22,000

0

0

0

0

32,000 64,000

96,000

D. Banks and Thrifts

52,000
39,000

0

10,000

20,000 30,000

E. Credit Unions

2,700

13,000

900

14,000

0

0

13,000 26,000 39,000 52,000

3

0

22,000

44,000 66,000

F. HUD-Supervised Mortgagees

28,000

0

2

42,000

1,800

26,000

1

C. Refinance

0

900

1,800

4
5

0

2,700

0

14,000

28,000 42,000

6
7

PRECRISIS MORTGAGE LOAN APPLICATIONS
COUNTIES IN THE EIGHTH DISTRICT

In Figure 1, the horizontal axis of each panel shows the level of 2004 mortgage loan applications, while the vertical axis
shows the annual average of 2009-2010 mortgage loan applications. Each dot in the chart represents one of the 339 counties in the District. The plot also shows the 45-degree line where the level of 2004 applications equals the annual average
of 2009-2010 applications. Simply put, a dot below the 45-degree line indicates that postcrisis applications for that county
were fewer than precrisis applications; a dot above indicates the opposite.

8

9

Figure 2 displays a panel of scatter plots showing pre- and postcrisis mortgage origination vis-à-vis applications for each
county of the District. The horizontal axis shows the number of applications in the county, while the vertical axis measures
the number of originations. The dots in red show the 2004 levels for each county, while the blue dots show the annual average
for 2009-2010 in the same counties. The red and blue lines are the corresponding lines of fit for each period. A higher line
indicates a higher origination rate for a given level of applications.10
10

FIGURE 2
Mortgage Loan Applications vs. Originations in the Eighth District
A. All Loans

MORTGAGE LOAN ORIGINATIONS

60,000

C. Refinance

36,000

40,000

14,000

24,000

20,000

7,000

12,000

0

0

0

32,000 64,000

96,000

0

D. Banks and Thrifts

10,000

20,000 30,000

0

26,000

1,400

16,000

13,000

700

8,000

18,000

36,000

54,000

0

22,000

44,000 66,000

F. HUD-Supervised Mortgagees

2,100

0

0

E. Credit Unions

39,000

0

R eferences

B. Purchase

21,000

The Eighth Federal Reserve District includes all
of Arkansas and portions of Illinois, Indiana,
Kentucky, Mississippi, Missouri and Tennessee.
In what follows, we use the annual average for the
2009-2010 HMDA data. However, the choice of
years for pre- and postcrisis indicators is ad hoc.
The major supervisory agencies include the Federal Reserve System, Federal Deposit Insurance
Corp., Office of the Comptroller of the Currency,
Office of Thrift Supervision and National Credit
Union Administration.
See Prevost.
Credit unions are nonprofit depository institutions that are democratically controlled by their
members. Membership in a credit union is usually
limited by law and is organized around a common
bond or “field of membership.”
See Morrison.
The term “origination” here implies the actual disbursement of funds upon approval of the mortgage
application. All originations require approval of
the mortgage application. However, not all
approved applications lead to originations since
the borrower can still reject the terms of the loan.
A “line of fit” (shown in Figure 2) is a line that is
drawn through the data on a scatter plot to
describe the trend of the data. This is different
from the 45-degree line in Figure 1.
A word of caution is in order here: While the plots
include confidence intervals for the lines of fit,
stricter criteria may not reveal statistically significant differences between the lines of fit in some
of the plots. Nevertheless, this remains a simple
and useful way to distinguish between pre- and
postcrisis origination rates.
The majority of the dots and crosses overlap in the
lower left of each figure.

Prevost, Lisa. “The Credit Union Alternative,”
The New York Times, Dec. 13, 2012. See www.
nytimes.com/2012/12/16/realestate/mortgagescredit-unions-grow-in-popularity.html?_r=0.
Morrison, David. “Two Credit Unions Offering
No Money Down Mortgages,” Credit Union
Times, March 14, 2013. See www.cutimes.
com/2013/03/14/two-credit-unions-offeringno-money-down-mortgages.

24,000

0

900

1,800

2,700

0

0

14,000

28,000 42,000

MORTGAGE LOAN APPLICATIONS
2004 EIGHTH DISTRICT COUNTIES
2009-10 EIGHTH DISTRICT COUNTIES

2004 TREND LINE
2009-10 TREND LINE

record growth in the membership of credit
unions—much of this has been attributed to
consumer disillusionment with big banks.4
Moreover, a large share of the growth in
mortgage business is concentrated among
the largest credit unions—which typically
have lower limits on membership.5 Second,
at least two of these large credit unions have

95% CONFIDENCE INTERVAL

reportedly been offering members mortgages without requiring any down payment
or mortgage insurance.6
To find out how loan-approval patterns
in 2009-2010 differed from those in 2004,
we examined the mortgage loan origination
rate during the two periods. Figure 2
displays a panel of scatter plots showing
The Regional Economist | www.stlouisfed.org 15

e c o n o my

a

g l a n c e

Eleven more charts are available on the web version of this issue. Among the areas they cover are agriculture, commercial
banking, housing permits, income and jobs. Much of the data are specific to the Eighth District. To see these charts, go to
www.stlouisfed.org/economyataglance.
REAL GDP GROWTH

CONSUMER PRICE INDEX (CPI)

8
PERCENT CHANGE FROM A YEAR EARLIER

6

6
4
PERCENT

2
0
–2
–4
–6
–8
–10

Q1
’08

’09

’10

’11

’12

’13

3

0

–3

May

’08

’10

’09

’11

’12

’13

RATES ON FEDERAL FUNDS FUTURES ON SELECTED DATES

0.16

2.5

0.15

2.0

0.14

1.5

0.13

1.0

5-Year

0.12
0.11

0.5

10-Year

0.0

0.10

20-Year

–0.5
’09

’10

July 5

’11

’12

0.09

’13

June 13

July 13

01/30/13

05/01/13

03/20/13

06/19/13

Aug. 13 Sept. 13 Oct. 13 Nov. 13
CONTRACT MONTHS

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

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

5

11
10

10-Year Treasury
Fed Funds Target

4

9
PERCENT

8
7

3
2

6

4

1-Year Treasury

1

5

June

June

’08

’09

’10

’11

’12

0

’13

’08

’09

’10

’11

’12

’13

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

90

FA R M I N G C A S H R E C E I P T S

240

Exports

BILLIONS OF DOLLARS

BILLIONS OF DOLLARS

60

Imports

45
30

0

Crops

220

75

15

Trade Balance

’08

’09

’10

’11

’12

NOTE: Data are aggregated over the past 12 months.
16 The Regional Economist | July 2013

All Items, Less Food and Energy

3.0

PERCENT

PERCENT

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

U.S. AGRICULTURAL TRADE

Rajdeep Sengupta is an economist formerly
with the Federal Reserve Bank of St. Louis.
Yang Liu is a senior research associate at
the Bank.

CPI–All Items

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

PERCENT

pre- and postcrisis mortgage origination
vis-à-vis applications for each county of the
District. The horizontal axis of each plot
shows the number of applications in the
county, while the vertical axis measures the
number of originations.7 The dots in red
show the 2004 levels for each county, while
the blue dots show the annual average for
2009-2010 in the same counties.
We plotted the corresponding “line of fit”
for each period. 8 A higher line indicates a
higher origination rate for a given level of
applications.9 At first glance, therefore, it
is surprising that the postcrisis line of fit in
almost all plots of Figure 2 appears higher
than the precrisis trend lines. A possible
explanation of this feature of the data is
that although there are fewer applications
postcrisis, their quality is significantly better. This may be partly due to the fact that
real-estate salesmen are only willing to do
business with preapproved buyers.
Figure 2 reveals two important patterns.
First, the differences in origination rates for
refinances (Figure 2C) appear to be greater
than those for purchases (Figure 2B). Refinancing after a sharp decline in home prices
can be tricky because existing homeowners
would likely have to cover for the shortfall in
home equity if they wanted to take advantage
of lower mortgage rates. While this reduces
the set of applicants, it can also ensure an
improvement in the applicant pool, thereby
resulting in higher origination rates.
Second, among all lending institutions,
only credit unions’ loan origination rates
show a marginal decline (Figure 2E), primarily due to smaller origination growth relative
to a larger increase in applications. In light
of the anecdotal evidence given above, a possible explanation is that a significant increase
in annual mortgage applications made credit
unions more selective.

a t

Livestock

200
180
160
140
120

May

’13

100
’08

March

’09

’10

’11

’12

NOTE: Data are aggregated over the past 12 months.

’13

n a t i o n a l

o v e r v i e w
FOMC June 2013 Economic Projections for 2013-2015
10
8
7.8
PERCENT

Mixed Signals,
but Moving Forward

6

2

D

0

Housing’s Strength Spreads

Breaking down the GDP data indicates
that housing continues to be a source of
strength. Through the first five months of
2013, new and existing home sales, as well as
housing permits, posted double-digit annualized growth rates compared with the same
period in 2012. Moreover, house prices rose
sharply, boosting the confidence of home
builders. By contrast, commercial construction exhibited much less vigor.
Brisk gains on the housing front are
beginning to boost other segments of the
economy. For example, the housing boom
appears to be triggering an upswing in
household spending. Through the first
four months of 2013, sales of household
furnishings and durable equipment like
appliances increased at about a 3.5 percent
annual rate—much stronger than the
1.9 percent growth in total personal
consumption expenditures. Elsewhere,
automotive manufacturers have boosted
production of light trucks, which are used
extensively in the construction industry.
The rebound in consumer spending,
at first glance, is perhaps not too surprising, given other key developments. First,

7.3

6.7

2014
2015

6.0

4

By Kevin L. Kliesen
espite pockets of strength, the U.S.
economy continues to struggle to build
consistent momentum. Real GDP growth
rebounded in the first quarter of 2013 after
ending 2012 on a relatively weak note. Real
GDP grew at a 0.4 percent annual rate in
the fourth quarter but then sped up to a
modest 1.8 percent annual rate in the first
quarter. The momentum swing in the first
quarter, though, was not expected to carry
into the second quarter. According to the
May Survey of Professional Forecasters, real
GDP growth was expected to slow to about
1.75 percent in the second quarter before
rebounding to an average of about 2.5 percent over the second half of this year.

2012 (Actual)
2013

1.7

2.5

3.3

3.3

REAL GDP

1.6
UNEMPLOYMENT RATE

1.7 1.8
1.0
PCE INFLATION

SOURCE: Board of Governors of the Federal Reserve System.
NOTES: Projections are the midpoints of the central tendencies. The actual and projected unemployment rates are
for the fourth quarter. The growth of gross domestic product (GDP) and personal consumption expenditures (PCE)
is the percentage change from the fourth quarter of the previous year to the fourth quarter of the indicated year.

consumer confidence and household wealth
rose sharply over the first half of 2013. Second, gains in private-sector jobs averaged a
little more than 200,000 per month over the
first six months of 2013.
However, other factors were working
in the opposite direction. These include
the payroll tax increase in January, higher
gasoline prices over the first half of the year,
tepid growth of real average hourly earnings
over the past few years and the relatively
high levels of long-term unemployment.
These factors may help explain some of the
unexpected softness in total consumption
spending that occurred in April and May.
Despite healthy profit margins and a relatively low cost of capital, real business fixed
investment increased at just a 0.4 percent
annual rate in the first quarter and was up
only 3.7 percent from four quarters earlier.
Similar to April’s weak consumption data,
production of business equipment fell by
0.5 percent in April. However, there are
signs that business investment is picking up,
as new orders to manufacturers for capital
goods increased strongly in April and May.
Thus, consistent with most forecasts, the
data point to modest growth in the second
quarter. Moreover, with abundant levels of
cash on corporate balance sheets, it appeared
that many firms still harbored a considerable
amount of uncertainty about the near-term
outlook. Financial market conditions,
though, remain healthy, according to the
St. Louis Fed’s Financial Stress Index.
Few Worries on the Inflation Front

Reflecting a notable slowing in food
price gains and sizable drop in consumer
energy costs, headline inflation has been

exceptionally modest thus far in 2013.
Through the first five months of the year, the
consumer price index (the headline version,
which factors in food and energy) increased
at only a 0.7 percent annual rate—about
1 percentage point slower than for the same
five-month period in 2012. Core inflation
(excluding food and energy) also slowed
relative to last year, but by not as much as
the headline inflation rate. Over the first
five months of 2013, the core consumer
price index (CPI) advanced at a 1.8 percent
annual rate, 0.5 percentage points slower
than last year’s gain over the same period.
Blue Chip forecasters don’t expect these
exceptionally low levels of inflation to persist:
The headline CPI is projected to increase at
about a 2 percent annual rate over the second
half of this year. But, signals from the bond
market suggest that longer-term inflation
concerns appear relatively muted. In early
June, yields on inflation-sensitive 30-year
Treasury securities remained well below their
peak of 4.9 percent (in early April 2010) during this business expansion.
On balance, stable inflation expectations
and a lessening of some of the uncertainties
and headwinds that have hampered hiring
and business investment the past year or more
should lead to faster growth and low inflation
going forward. Indeed, this is the takeaway
from the latest economic projections of the Federal Open Market Committee. (See chart.)
Kevin L. Kliesen is an economist at the Federal
Reserve Bank of St. Louis. Lowell R. Ricketts, a
senior research associate at the Bank, provided
research assistance. For more on Kliesen’s work,
see http://research.stlouisfed.org/econ/kliesen/.
The Regional Economist | www.stlouisfed.org 17

m e t r o

p r o f i l e

Welcome to the Metro Profile. This new feature
replaces the long-running Community Profile. In
each issue, we will focus on one of the more than
20 metropolitan statistical areas in the District,
using data to explain what drives the economy
of that MSA. For the most part, the data come
from FRED® (Federal Reserve Economic Data),
the main economic database of the St. Louis Fed.
Some of the information in these articles is also
anecdotal, provided by business contacts for use
(anonymously) in our economic reports, such as
the Beige Book and Burgundy Books.
Let us know what you think about Metro Profile.
Submit your comments in a letter to the editor
at www.stlouisfed.org/publications/re/letter.cfm.
You can also mail a note to the editor, Subhayu
Bandyopadhyay, at the Federal Reserve Bank of
St. Louis, Box 442, St. Louis, MO 63166-0442.
FRED® is a registered trademark of the Federal Reserve Bank of St. Louis.

Louisville Successfully Transitions from Industrial to Service Economy
By Charles S. Gascon and Sean P. Grover

The Louisville-Jefferson County, Ky.-Ind., metropolitan statistical area (known informally as the Louisville MSA) is
the largest MSA in Kentucky and the third-largest MSA in the Federal Reserve’s Eighth District. The Louisville MSA
has a population of 1,251,351 and a labor force of 643,271. The per capita personal income was $39,037 in 2011 (the most
recent year for which data are available), about 6.1 percent less than that for the U.S.
See the accompanying figures for perspective on the data cited in this article; the table and charts also include
additional data that help tell the story about Louisville’s economy.

I

n the postrevolutionary U.S., Louisville was an important western outpost.
Situated at the Falls of the Ohio, Louisville
became a key port for the western frontier.
Similar to its inland-port contemporaries,
such as Cincinnati and St. Louis, Louisville
had an industrial river economy in the
beginning; growth was driven by heavy
manufacturing, shipping and trade. Louisville also gained attention for its bourbon
whiskey, Louisville Slugger baseball bats and
Kentucky Derby, cultural hallmarks that live
strong today.
Postwar Louisville saw a movement away
from heavy manufacturing and away from
river trade, as production processes and labor
needs changed across the country. Coupled
with deurbanization and population loss,
Louisville’s economic transition was typical
of that of industrial cities. Atypical was
Louisville’s ease of adapting to a modern
postindustrial service economy.
18 The Regional Economist | July 2013

As it stands today, the city is a particularly
strong hub for the health-care and foodservice industries. Logistics and distribution,
as well as recently expanding manufacturing,
are other industries of note.
This economic transition also helped
dampen urban population loss experienced
in similar cities and even helped garner
healthy population growth in recent years.
Over the past 10 years (2002-2012), Louisville’s population increased by 9.7 percent,
noticeably faster than Kentucky’s growth
of 7.1 percent and just above the national
rate of growth, 9.1 percent. Within the
metro area, Jefferson County, Ky., holds a
substantial majority of Louisville’s population: about 60 percent of the total. In the
past decade, there has been some shift in the
population: Jefferson County grew 7.3 percent, while Clark County, Ind., the secondlargest county in the MSA, grew at almost
twice the rate: 14.3 percent.

Much of the MSA’s growth over the period
can be attributed to Kentucky-based counties
surrounding the city. Spencer, Shelby and
Oldham counties grew the fastest, with rates
of 31.7, 25.5 and 24.7 percent, respectively,
between 2002 and 2012. These three counties largely outpaced population growth in
Kentucky and the nation. As a result of this
growth, Spencer, Shelby and Oldham counties gained about 1.7 percent of Louisville’s
population; today, about 10 percent of the
population is located in these three counties.
Economic Drivers

Humana, a managed-health-care company
on the Fortune 100 list, has headquarters in
downtown Louisville. By revenue, Humana
is the largest publicly traded company based
in town. With 11,000 local employees, it is
the second-largest Louisville company by
local employee count.
Six of Louisville’s 10 largest employers

One other economic driver of note is the
air-freight arm of United Parcel Service,
UPS Airlines. Although its parent company
calls Atlanta home, UPS Airlines is based in
Louisville. Because of this presence, UPS is
the largest local employer, with a July 2012
local employee count of 20,117, nearly twice
the amount of the runner-up, Humana.
This employment presence has driven the
growth in the region’s transportation sector,
which employs about 3 percent of the MSA’s
workers. Between January 2003 and January 2013, payroll employment in the transportation sector increased by 1,600 jobs, or
6.4 percent of total employment growth over
this period.
Louisville’s location at a nexus of transportation systems has made it a trade and
distribution hub throughout its history. As
such, the Logistics and Distribution Institute at the University of Louisville keeps the
national LoDI Index, which gauges the health
of logistics and distribution activity. The
most recent reading showed 51, indicating
a healthy amount of logistics and distribution activity (greater than 50 indicates good
health), which is generally viewed as a positive sign for the economy.

FIGURE 1

FIGURE 2

Logistics and Distribution Index

Nonfarm Payroll Employment

54
53
52
51
50
49
48
47
A reading greater than 50 indicates good health.
46
45
44
05
06
07
08
09 10 11 12
YEAR

13

NOTE: Data are from the University of Louisville and are easily accessible in the
St. Louis Fed’s economic database, FRED, using this series ID: LODINIM066N.
Gray shading in all figures indicates recession period.

MSA Snapshot
Louisville-Jefferson County, Ky.-Ind. (MSA)
Population
Labor Force
Unemployment Rate
Personal Income (per capita)		

Manufacturing
Professional and Business
Government
Education and Health Services
Trade, Transportation and Utilities
0

5

10
15
PERCENT OF TOTAL NONFARM

07

08

25

LARGEST LOCAL EMPLOYERS
1. United Parcel Service Inc.
2. Humana Inc.
3. Norton Healthcare Inc.
4. Ford Motor Co. Kentucky Truck Plant
5. KentuckyOne Health Inc.

Population Growth by County 2002-12

INDIANA
Washington
Clark
Floyd
Harrison

OHIO
Scott
Trimble
Henry
Oldham
Jefferson

Louisville
U.S.

06

20

Shelby

In the postrecession years, Louisville’s
nonfarm payroll employment growth has
typically been on track with the nation’s.
However, Louisville has seen increases in the
last year that have outpaced those of the U.S.
As of March 2013, year-over-year growth in
nonfarm employment doubled the national
rate of 1.5 percent. This 3 percent growth
translates to an increase of 18,800 jobs over

4
3
2
1
0
–1
–2
–3
–4
–5
–6
05

1,251,351
643,271
7.8%
$39,037

largest sectors by Employment

Current Conditions

YEAR-OVER-YEAR GROWTH

INDEX

operate in the health-care industry; they
range from insurance companies to hospitals. Norton Healthcare and KentuckyOne Health are two other companies of
note, with 9,658 and 5,898 local employees,
respectively, as of July 2012. Helped by the
strong research atmosphere stemming from
the University of Louisville and the region’s
early advances in heart transplants, Louisville’s health-care industry has consistently
driven economic growth.
Education and health-services payroll
employment, which comprises about
14 percent of total nonfarm employment,
has seen largely positive growth over the
past decade. Between January 2003 and
January 2013, employment in education and
health services increased by 14,300 (5,500
in the narrower health-services industry),
while total payroll employment in Louisville
increased by about 26,000 jobs.
Several international restaurant brands
also call Louisville home. The most notable
is Yum Brands, owner of KFC, Pizza Hut,
Taco Bell and WingStreet, making it the
largest fast-food restaurant company in
the world. By revenue, Yum Brands is the
second-largest publicly traded company
headquartered in Louisville and employed
1,558 workers locally, as of July 2012. Papa
John’s pizza has also become an international brand and is a top local employer, as
is the restaurant chain Texas Roadhouse.
In the food industry, however, employment
in the corporate headquarters of these restaurant companies falls under professional
and business services employment, which
comprises about 12 percent of Louisville’s
total nonfarm employment.

09
YEAR

10

11

12

13

NOTE: Data are from the U.S. Bureau of Labor Statistics and are easily accessible in the St. Louis Fed’s economic database, FRED, using these series IDs:
Louisville (LOINA) and US (PAYEMS).

Spencer
Nelson
Meade
Bullitt
less than 5%
5% to 10%

KENTUCKY
10% to 15%
15% to 20%

over 20%

NOTES: Population and employment are from the U.S.
Census Bureau and U.S. Bureau of Labor Statistics and
are easily accessible in the St. Louis Fed’s economic
database, FRED. For the first two panels and map, see
these FRED series (IDs are in parentheses): Population
(LOIPOP); Labor Force (LOILF); Unemployment Rate
(LOIUR); Personal Income (LOIPCPI); Manufacturing
(LOIMFG); Professional and Business (LOIPBSV); Government (LOIGOVT); Education and Health (LOIEDUH);
and Trade, Transportation and Utilities (LOITRAD). Data
for the employers panel are as of July 2012 and come
from the Louisville Business Journal Book of Lists.

For your convenience, key data that pertain
to the Eighth District have been aggregated
on a special web page at https://research.
stlouisfed.org/regecon/.
To see all that FRED offers, go to
http://research.stlouisfed.org/fred2/.

The Regional Economist | www.stlouisfed.org 19

FIGURE 4

Unemployment Rate

Manufacturing Employment

12

20

10

15

YEAR-OVER-YEAR GROWTH

PERCENT

FIGURE 3

8
6
4
2

Louisville

0
05

06

10
5
0
–5
–10
–15

U.S.

07

08

09 10
YEAR

11

12

13

The Ohio River Bridges Project commenced
in June 2013 with the construction of a sixlane cable-stayed downtown bridge and the
overhaul of the existing Kennedy Bridge.
With an estimated cost of $2.6 billion, this
undertaking represents about 1 percent of
the metro area’s annual output, as measured
by gross metropolitan product.1 It appears
the project will benefit the local economy
through construction and skilled labor, as
well as improved transportation.

–20
05

Louisville
U.S.

06

07

08

09
10
YEAR

11

12

13

NOTE: Data are from the U.S. Bureau of Labor Statistics and are easily accessible in the St. Louis Fed’s economic database, FRED, using these series IDs:
Louisville (LOIUR) and US (UNRATE).

NOTE: Data are from the U.S. Bureau of Labor Statistics and are easily accessible in the St. Louis Fed’s economic database, FRED, using these series IDs:
Louisville (LOIMFG) and US (MANEMP).

a 12-month period, or almost three-quarters
of the increase experienced over the past 10
years. These gains have helped to reduce the
unemployment rate over the past year to a
level consistent with the national rate.
The apparent stall in the decline of unemployment in recent months, even as employment growth is strong, is likely attributable
to growth in Louisville’s labor force. This is a
good reflection of better labor conditions, as
more area workers who have been out of the
labor force re-enter and seek employment.
Since March 2012, Louisville has added
almost 19,000 jobs to nonfarm payrolls, with
almost 15,000 people entering the labor force.
During that time, the unemployment rate fell
from 8.5 percent to 7.8 percent.
Manufacturing employment, representing
about 12 percent of total nonfarm employment in Louisville, has been a particularly strong growth driver. Generally on
trend with the U.S. as a whole since 2005,

Louisville’s manufacturing employment
began largely outpacing U.S. growth over
the past year. Manufacturing employment
increased by about 9 percent (6,300 jobs),
contributing one-third of the new jobs in
Louisville over the past year. These increases
in growth are attributable to increased
production from auto manufacturers, such
as Ford and Toyota. Ford’s truck plant is
the fourth-largest local employer, with 8,696
employees. GE Appliances has also picked
up employment lately as it added a product
line; it now has 5,000 local employees.
This strong growth from manufacturing
employment and the consistency from Louisville’s traditionally strong sectors like health
care have contributed to an overall positive
current outlook for Louisville’s economy.

The Federal Reserve Bank of St. Louis is
looking for local business leaders in the
Eighth District to join the Bank’s panel of
contacts. Leaders are surveyed between
four and eight times per year to gather
information about the economy in their
area; this information is distilled and
passed on to our president and others who
participate on the Federal Open Market
Committee, our nation’s chief monetary
policymaking body. All information is
compiled in a manner to preserve anonymity. To see a sample survey, go to https://
research.stlouisfed.org/beigebooksurvey.

20 The Regional Economist | July 2013

What’s around the Bend?

Recently, U.S. cities have come under
criticism for their decaying and dilapidated
infrastructure, specifically in bridge maintenance. This problem is particularly bad in
older industrial cities, where tax revenue has
been hurt by suburbanization and dwindling urban economies. Bucking the trend,
Kentucky and Indiana have started on the
Ohio River Bridges Project, which involves
repairing a number of bridges and building two new ones over the Ohio River. One
of the new bridges will connect downtown
Louisville with sister city Clarksville, Ind.
The second bridge will complete an interstate loop outside of the city center; this is a
smaller undertaking.
Since 2003, politicians in both states have
pushed for a cost-effective solution to the
region’s transportation and safety problems.

Louisville-Southern Indiana Ohio River Bridges Project

An artist’s rendering of the downtown bridge.

“The Ohio River Bridges Project will have
a positive impact on construction employment growth and will generate significant
economic benefits over the next three-plus
years.”
–Louisville area commercial real estate contact

2

Assuming no major shifts, Louisville can
expect its stable-growth sectors of health
care and logistics to provide consistency
in employment trends moving forward.
Coupled with recent vigor in heavy manufacturing, such as for autos, and the large undertaking of the Ohio River Bridges Project, area
employment conditions should continue to
improve to postrecession bests. Should these
trends continue, Louisville will continue to
experience solid economic activity in this
postrecession time.
Charles S. Gascon is a regional economist, and
Sean P. Grover is a research associate, both at
the Federal Reserve Bank of St. Louis.
ENDNOTES
1

2

This estimate is based off the most recent data on
gross metropolitan product for Louisville, assuming the project takes about four years to complete.
In order to assess the regional economy, the Federal
Reserve Bank of St. Louis collects anecdotal information from a panel of business contacts multiple times
a year. This is an excerpt from results of the survey
taken between May 1 and May 15, 2013. For more
information, see https://research.stlouisfed.org/
regecon/.

d i s t r i c t

o v e r v i e w

Urban Areas Host the Largest
Manufacturing and Service
Employers
By Rubén Hernández-Murillo and Elise Marifian

O

ver the past few decades, manufacturing employment as a share of total
employment has declined across the U.S.,
with most of the manufacturing jobs lost
in metropolitan areas. At the same time,
cities have become increasingly more
service-oriented.1 Despite this general trend,
metropolitan areas—and, in particular, large
metropolitan areas—still contain the great
majority of manufacturing jobs.
Similar to those across the U.S., urban areas
in the Eighth District host the largest employers in manufacturing; urban areas also host
the largest service employers. While service
industries naturally thrive near large concentrations of people, manufacturing industries
also gain from locating in urban areas, where
they are near suppliers and firms in similar or
related industries, including firms in related
financial, legal and educational services. Cities also provide manufacturing firms potential
workers of varying skill levels. Understanding
the existing location patterns of both manufacturing and service industries is important because firms’ location choices are in
response to not only geographic advantages
but also to public policies aimed at promoting employment growth or at developing
targeted industries in certain areas.2
This article describes the geographic distribution of the largest (by employment) manufacturing and service industries in the 339
counties in the Eighth District. The best data
for analyzing the distribution of industries
and establishments across counties come
from the County Business Patterns (CBP)
statistics of the U.S. Census Bureau. The data
are the latest available—as of March 2011.3
The analysis reveals interesting patterns.
First, we found that in the Eighth District,
both the largest manufacturing and the

largest service industries were related to the
food industry. Other important manufacturing industries were related to the auto industry, while other important service industries
were related to the health-care industry. We
also found that manufacturing employment was concentrated in a small number of
industries, whereas service employment was
spread across a larger number of industries.
In addition, the average manufacturing
establishment employed about three times
as many people as did the average service
establishment. Finally, except for a handful
of counties in smaller urban areas—such
as Tupelo, Miss.; Jasper, Ind.; and Paducah,
Ky.—the largest concentrations of manufacturing and service employment and establishments occurred in or around the largest
metro areas of the District.
The Largest Manufacturing
and Service Industries

The largest three-digit manufacturing
industry in terms of employment was food
manufacturing (NAICS 311), with 109,212
employees and 1,065 establishments.4 Other
top three-digit manufacturing industries
included transportation equipment (NAICS
336), with 84,152 employees and 646 establishments; fabricated metal products (NAICS
332), with 73,381 employees and 2,434
establishments; machinery manufacturing
(NAICS 333), with 64,065 employees and
1,117 establishments; and plastics and rubber
products (NAICS 326), with 61,424 employees and 716 establishments.
Among the service industries, the largest
three-digit industry in terms of employment was also food-related: Food services
and drinking places (NAICS 722) employed
454,361 people in 24,248 establishments across

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.

the District.5 Other top service industries
included: hospitals (NAICS 622), with 302,804
employees and 454 establishments; administrative and support services (NAICS 561), with
294,588 employees and 13,533 establishments;
ambulatory health care (NAICS 621), with
260,645 employees and 24,736 establishments;
and professional, scientific and technical
services (NAICS 541), with 223,153 employees
and 27,291 establishments.
Across the Eighth District, manufacturing employment was less diversified when
compared with service employment. The 10
largest three-digit manufacturing industries
in the District employed almost 80 percent of
the total District manufacturing employment
and made up about 74 percent of all manufacturing establishments in the District.
In contrast, the top 10 largest three-digit
service industries in the District employed
only about 56 percent of total District service
employment and represented only about
46 percent of all service establishments in
the District. Manufacturing industries also
employed more people per establishment on
average, compared with services. Considering only the top 10 manufacturing industries,
District counties were home, on average, to
1,687 manufacturing jobs in 31 establishments, or about 55 people per establishment.
In contrast, District counties, on average,
were host to 6,828 people in about 374 establishments in the largest service industries, or
about 18 people per establishment.
The Geographic Distribution
of Employment

The maps present the distribution of
employment and establishments across
District counties for the 10 largest three-digit
manufacturing and service industries in
The Regional Economist | www.stlouisfed.org 21

Legend

ENDNOTES
Metropolitan Statistical Areas

Service Employment

1
2
3

109 – 14,409
14,410 – 56,139
56,140 – 105,447
105,448 – 278,503

Service Establishments
17 – 477

4

478 – 1,818
1,819 – 5,317
5,318 – 12,701

Legend
Metropolitan Statistical Areas

Manufacturing Employment
0 – 1,626
1,627 – 4,852

5

4,853 – 15,455
15,456 – 28,712

Manufacturing Establishments
0 – 27
28 – 97
98 – 227
228 – 652
SOURCES: CBP, NAICS and geographic data from the U.S. Census Bureau.

terms of employment. Perhaps not surprising, the highest concentrations of manufacturing and service employment occurred in
or around large urban areas in the District,
mostly in metropolitan areas but also in
some micropolitan areas.6
The highest per-county levels of manufacturing employment, in excess of about 5,000
people, occurred in counties near Fayetteville, Fort Smith and Little Rock, Ark.;
St. Louis and Springfield, Mo.; Tupelo, Miss.;
Memphis and Jackson, Tenn.; Evansville
and Jasper, Ind.; and Louisville and Bowling
Green, Ky. These areas also contained the
largest number of establishments, usually
exceeding the District average of 31 establishments per county. Only St. Louis County,
Mo., and Jefferson County, Ky., employed
more than 20,000 people in the top 10 manufacturing industries.
The largest concentrations of service
employment, exceeding 50,000 people,
occurred near Fayetteville, Little Rock,
22 The Regional Economist | July 2013

Memphis, St. Louis, Springfield and Louisville. Similar to the manufacturing scenario,
counties in these areas also contained the largest number of service establishments, often
exceeding 2,500 establishments. In the District, only five counties employed more than
100,000 people in the top 10 service industries:
St. Louis City and St. Louis County, Mo.;
Pulaski County, Ark.; Jefferson County,
Ky.; and Shelby County, Tenn. Among the
largest nonmetropolitan service-employing
counties were Adams County, Williamson
County and Jackson County in Illinois;
St. Francois County in Missouri; Lee County
in Mississippi; and McCracken County in
Kentucky, with all exceeding 10,000 employees in the top 10 service industries.
Rubén Hernández-Murillo is an economist
and Elise Marifian is a research associate, both
at the Federal Reserve Bank of St. Louis. For
more on Hernández-Murillo’s work, see http://
research.stlouisfed.org/econ/hernandez/.

6

See Friedhoff et al.
See Helper et al.
Although establishment data are always provided,
county-level industry employment data are often
suppressed to prevent identity disclosure. In
the case of data suppression, employment data
were imputed using establishment counts by size
class. For additional information on the use of the
County Business Patterns data set and a previous
analysis using these data, see Hernández-Murillo
and Marifian.
According to the North American Industry Classification System (NAICS), industries are classified
with increasing degree of detail using classifications with two to six digits. For example, manufacturing (NAICS 31) is the broadest category,
and following with finer level of detail, we have
food manufacturing (NAICS 311), bakeries and
tortilla manufacturing (NAICS 3118), bread and
bakery product manufacturing (NAICS 31181),
and finally, frozen cakes, pies and other pastries
manufacturing (NAICS 311813). The 2011 County
Business Patterns use 2007 NAICS codes. (See
www.census.gov/econ/cbp/download/index.htm.)
Additional information on NAICS codes can be
found at www.census.gov/cgi-bin/sssd/naics/
naicsrch?chart=2007.
We define the service sector as the sum of industries with NAICS codes greater than or equal to
420 and less than 920.
A metro area contains a core urban area of 50,000
or more people, while a micro area contains an
urban core of at least 10,000 but fewer than 50,000
people. For more information, see www.census.
gov/econ/cbp/index.html, footnote 4.

R eferences
Friedhoff, Alec; Wial, Howard; and Wolman, Harold.
“The Consequences of Metropolitan Manufacturing Decline: Testing Conventional Wisdom.” 2010.
Washington: Brookings Institution.
Helper, Susan; Krueger, Timothy; and Wial, Howard.
“Locating American Manufacturing: Trends in the
Geography of Production.” 2012. Washington:
Brookings Institution.
Hernández-Murillo, Rubén; and Marifian, Elise A.
“Manufacturing and Construction Decline in the
Ranks of Top 10 Employers.” Federal Reserve Bank
of St. Louis The Regional Economist, October 2012,
Vol. 20, No. 4, pp. 14-16. See www.stlouisfed.org/
publications/re/articles/?id=2286.
U.S. Census Bureau. County Business Patterns: 2011.
U.S. Department of Commerce, April 2013. See
www.census.gov/econ/cbp/download/index.htm.

READER

E X CHANGE

ASK AN ECONOMIST
Michael Owyang is an economist
at the St. Louis Fed. His research
interests are time series econometrics, forecasting and regional
analysis. He likes pepperoni on his
pizza and drinks too much coffee.
For more on Owyang’s work, see
http://research.stlouisfed.org/
econ/owyang/.

Q: What is potential output, and how is
it measured?
A. When discussing the performance of the U.S. economy,
people sometimes cite the output gap, which is the difference
between actual and potential output. But what is potential output? A common misperception is that it is the maximum output
the economy could produce if everyone were employed and all
capital were used. Economists define potential output as what

Letter to the editor
We received comments from several readers regarding a statement
appearing in “Banks and Credit Unions: Competition Not Going Away”
(April 2013 issue of The Regional Economist). The article states that credit
unions and Subchapter S corporations are “similarly exempt” from federal
income taxes. We asked Julie L. Stackhouse, senior vice president of the
St. Louis Fed’s Banking Supervision and Regulation division, to clarify the
tax treatment of Subchapter S corporations. Her comments are below:
A Subchapter S corporation is a corporation that has between one and
100 shareholders and that passes through net income or losses to shareholders in accordance with Internal Revenue Code, Chapter 1, Subchapter S.
Subchapter S election is subject to criteria beyond restrictions on number
of shareholders, including limitations on the class of permissible stock
(only one class is allowed) and on who may be an eligible shareholder.
There is no guarantee of dividends from the Subchapter S corporation to
its shareholders for purposes of paying tax liability.
Because of these limitations, most commercial banks are organized as
typical C corporations. Earnings of a C corporation are first taxed at the
corporate level and then again at the shareholder level when dividends
are paid on those earnings.
Credit unions, in contrast, do not pay taxes at the corporate level,

can be produced if the economy were operating at maximum

nor do they have an outstanding tax liability that is passed through to

sustainable employment, where unemployment is at its natural

their members.

rate. Therefore, actual output can be either above or below
1

potential output.
Unlike actual GDP, we cannot observe potential GDP and must

In summary, Subchapter S corporations avoid the double taxation
experienced by C corporations and their shareholders. However, these
advantages do not amount to an exemption from federal taxation.

estimate it. As a result, different economists can have different
views of potential output. One way to construct potential GDP

The St. Louis Fed Financial Stress Index

is by fitting a trend line through actual GDP. Looking at a short

The St. Louis Fed Financial Stress Index (STLFSI) measures the degree of

sample period, however, may lead to an inaccurate estimate of

financial stress in U.S. markets; values below zero suggest below-average

potential. For instance, starting in 2000 would lead to a trend

financial market stress, and values above indicate the opposite. To see the

line that is defined by the expansion period and is relatively
steep. If, on the other hand, output rose above potential during

latest weekly reading, as well as to find out how the index is constructed,
see www.stlouisfed.org/newsroom/financial-stress-index/.

the expansion period, then the trend line would be slightly flatter.
The latter case implies that output would have been above
potential during the boom period and perhaps not quite so far
below potential during the recession.
Many people believe that the previous decade had a housing
bubble, with construction much higher than in normal times.
If that is correct, the notion that the economy was producing
output above potential prior to the recession does not seem that
far-fetched. In that case, actual output today may not be as far
below potential as a lot of people think.
ENDNOTE
1	See

Okun, Arthur M. “Potential GNP: Its Measurement and Significance,” Cowles Foundation Paper 190, reprinted from the 1962 Proceedings of the Business and Economic
Statistics Section of the American Statistical Association. See http://cowles.econ.yale.
edu/P/cp/p01b/p0190.pdf.

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

The Regional Economist | www.stlouisfed.org 23

F e d e r a l r e s e r v e B a n k o F s t. l o u i s

Annual Report 2012

n e xt

i s s u e

Does Okun’s Law Still Hold?
GDP and Unemployment Data: 1948-2013
2.0
1948:Q2–1960:Q4
1.5
1961:Q1–2007:Q4
1.0
2008:Q1–2013:Q1
0.5

–4.0

–3.0

–2.0

–1.0

0.0
0.0

1.0

2.0

3.0

4.0

After the Fall
Rebuilding Family Balance SheetS,
Rebuilding the economy

5.0

0.5
–1.0
–1.5

M

otivated by the desire to determine the potential
productive capacity of the economy, Arthur Okun

empirically examined the relationship between the
unemployment rate and the output growth. The relationship that he identified—that a one-percentage-point
decrease in the real GNP growth rate was associated with
a 0.3-percentage-point increase in the unemployment
rate—has since been identified as Okun’s Law.
Since this discovery in the 1960s, many policymakers,

media and macroeconomic textbooks have cited this

Learn about the Importance of Strong Household
Balance Sheets in Just Eight Minutes
This short video summarizes the key points made in the essay from
the new annual report of the Federal Reserve Bank of St. Louis. The
video explains the importance of strong household balance sheets not
only for individual families but for the overall economy. The goals
of the Fed’s new Center for Household Financial Stability are also
explained. To watch the video, go to www.stlouisfed.org/ar.

figure as a rule-of-thumb way of transforming changes
in output growth to changes in labor market outcomes
and vice versa.
The lead article in October’s issue of The Regional
Economist will look into modern empirical work assessing
Okun’s Law and whether that unemployment and output
relationship holds at a regional level.

printed on recycled paper using 10% postconsumer waste

economy

at

a

The Regional

glance

Economist

JULY 2013

REAL GDP GROWTH

PERCENT CHANGE FROM A YEAR EARLIER

6

6
4
2
PERCENT

VOL. 21, NO. 3

CONSUMER PRICE INDEX

8

0
–2
–4
–6
–8
–10

|

Q1
’08

’09

’10

’11

’12

’13

CPI–All Items
All Items, Less Food and Energy

3

0

–3

May

’08

’10

’09

’11

’12

’13

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

RATES ON FEDERAL FUNDS FUTURES ON SELECTED DATES

3.0

0.16

2.5

0.15

2.0

0.14

1.5

0.13

PERCENT

PERCENT

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

1.0

5-Year

0.5

0.11

10-Year

0.0

0.10

20-Year

–0.5
’09

’10

0.12

July 5

’11

’12

0.09

’13

June 13

July 13

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

03/20/13

06/19/13

Aug. 13 Sept. 13 Oct. 13 Nov. 13

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

11

5

10

10-Year Treasury
Fed Funds Target

4

9
8

PERCENT

PERCENT

05/01/13

CONTRACT MONTHS

NOTE: Weekly data.

7

3
2

6

1-Year Treasury

1

5
4

01/30/13

June

June

’08

’09

’10

’11

’12

0

’13

’08

’09

’10

’11

’12

’13

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

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

90

FA R M I N G C A S H R E C E I P T S

240

Exports

BILLIONS OF DOLLARS

BILLIONS OF DOLLARS

60

Imports

45
30
15
0

Crops

220

75

Trade Balance

’08

’09

’10

’11

’12

NOTE: Data are aggregated over the past 12 months.

Livestock

200
180
160
140
120

May

’13

100
’08

March

’09

’10

’11

’12

NOTE: Data are aggregated over the past 12 months.

’13

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

225
Crops

175

Livestock

125

75

April

98

99

00

01

02

03

04

05

06

07

08

09

10

11

12

13

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

All

$100 million­$300 million

Less than
$300 million

$300 million$1 billion

Less than
$1 billion

$1 billion$15 billion

Less than
$15 billion

More than
$15 billion

Return on Average Assets*

1.12

0.87

0.85

0.90

0.88

0.98

0.94

1.16

Net Interest Margin*

3.21

3.72

3.71

3.71

3.71

3.80

3.76

3.07

Nonperforming Loan Ratio

3.49

2.25

2.23

2.37

2.31

2.55

2.44

3.81

Loan Loss Reserve Ratio

2.10

1.84

1.84

1.82

1.83

1.85

1.84

2.17

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

NET INTEREST MARGIN*

1.01
1.05
1.26

1.01
1.00
1.11

Illinois

1.03
1.05

Indiana
2.00

0.79
0.77
0.96
0.92
0.87
0.81

.30

.60

First Quarter 2013

3.73

Mississippi

3.61
3.91

Missouri

3.67
3.78

1.50

1.80

2.10

PERCENT

3.30

4.21

2.70

2.50

1.26

Indiana

1.44
1.74
1.79

3.00

1.77
1.87

Mississippi

1.94

Missouri

2.78

3.50

1.67

Tennessee

3.27

4.00

4.50

PERCENT

First Quarter 2012

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

2.17

1.60
1.63

Kentucky

2.63

1.88

First Quarter 2013

1.98

Illinois

1.97

1.97

Arkansas

1.79

2.00

1.80

Eighth District

2.38
2.46

1.00 1.50

First Quarter 2012

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

3.18

.50

0.0 0.4 0.8 1.2 1.6 2.0 2.4 2.8 3.2 3.6 4.0 4.4 4.8
First Quarter 2013

2.90
2.80

4.49

3.37
3.52

First Quarter 2012

2.31

.00

3.91
4.13

Kentucky

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

1.38

3.46
3.65

Tennessee
1.20

.90

4.07
4.16

Arkansas

0.99

.00

3.71
3.93

Eighth District

.00

.40

.80

First Quarter 2013

1.20

1.60

2.11

2.04

2.00

First Quarter 2012

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

2.40

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

Eighth
District †

Arkansas

1.2%

0.4%

Illinois

Indiana

Kentucky

Mississippi

Missouri

Tennessee

0.7%

1.9%

Total Nonagricultural

1.6%

Natural Resources/Mining

2.6

–8.8

–12.4

1.7

–1.5

–18.2

–1.5

0.0

NA

Construction

2.7

–2.5

–6.0

–4.5

–3.2

–0.6

0.1

0.9

NA

Manufacturing

1.0

1.7

–0.3

0.4

3.0

4.8

0.3

–0.4

3.0

Trade/Transportation/Utilities

1.7

1.6

3.7

0.9

2.8

1.9

0.3

1.0

1.9

Information

0.7

–1.9

–2.1

–0.4

–2.0

–5.1

0.0

–3.1

–2.1

Financial Activities

1.4

1.8

0.8

2.1

0.4

3.7

2.3

1.9

1.2

Professional & Business Services

3.1

2.5

–0.8

2.0

2.4

1.7

10.9

0.8

5.0

Educational & Health Services

1.9

1.8

1.8

2.3

1.9

1.1

1.0

1.0

2.1

Leisure & Hospitality

2.7

2.3

0.1

1.1

3.3

3.2

1.8

2.8

3.8

Other Services

1.1

0.2

–2.8

1.2

–0.4

–3.3

–0.4

1.0

1.7

–0.3

–0.5

–0.4

–0.1

–0.9

–0.2

0.1

–0.6

–1.4

Government

1.0%

1.6%

1.3%

1.4%

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

exports

U nemployment R ates

year-over-year percent change

I/2013
United States

IV/2012

I/2012

4.5

United States

7.7%

7.8%

8.3%

Arkansas

7.2

7.2

7.3

Illinois

9.3

8.7

8.9

Indiana

8.7

8.4

8.3

Kentucky

7.9

8.0

8.3

Mississippi

9.4

9.0

9.2

Missouri

6.7

6.6

7.1

Tennessee

7.8

7.7

8.1

15.9
37.1

Arkansas

7.8
5.4

Illinois
Indiana
Kentucky

12.3
10.1

3.9

7.8

Mississippi
–1.7

Missouri

–10

32.2

9.5
3.8

Tennessee
PERCENT

29.7

6.8

0
2012

15.6

10

20

30

40

2011

H ousing permits / first quarter

REAL PERSONAL INCOME* / first QUARTER

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

year-over-year percent change

22.5
–18.6

United States

31.7

Arkansas

–1.5
5.3

Illinois

35.6
13.0
12.5
40.0
11.7

–7.9

8.2

0

2013

2012

6

0.5

–0.8

0.5

Kentucky

12 18 24 30 36 42 48

All data are seasonally adjusted unless otherwise noted.

1.0
1.0

0.4

Missouri
41.5

1.3

0.7

Mississippi
24.8
23.1

–24 –18 –12 –6

1.5

–0.4

Indiana

–8.1

1.5

0.5

1.6

0.4
1.2
1.1

Tennessee
PERCENT

–1.00

–0.50

2013

0.00

0.50

1.00

1.50

2012

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

2.00