View original document

The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.

August 2014 (July 17, 2014-August 14, 2014)

In This Issue:
Banking and Financial Markets

Monetary Policy

 Bitcoin versus the Dollar

 The Yield Curve and Predicted GDP Growth, July
2014

Households and Consumers
 Peer-to-Peer Lending Is Poised to Grow

Inflation and Prices
 Cleveland Fed Estimates of Inflation
Expectations, July 2014
 Recent Owners’ Equivalent Rent Inflation Is
Probably Not a Blip

Labor Markets, Unemployment, and Wages
 Job Search Before and After the Great
Recession
 State Unemployment Insurance Policy
Responses during the Great Recession

Regional Economics
 The Youngstown-Warren-Boardman Metropolitan
Statistical Area

Banking and Financial Markets

Bitcoin versus the Dollar
08.14.14
by Joseph G. Haubrich and Ashley Orr
You can’t hold a bitcoin in your hand, but you can
spend one. Bitcoins are digital representations of
value, a fiat currency based on cryptography—the
use of encryption to store and transfer value securely. Transactions using bitcoins are decentralized
in that they are validated and certified through a
network of users rather than one central administrative site.

Total Number of Bitcoin Transactions
Since March 2012
Transactions (thousands)
100

80

60

40

20
0
3/2012

3/2013

3/2014

Sources: Blockchain; Quandl.

Though bitcoin has attracted a lot of attention,
bitcoins are not widely accepted as a method
of payment at most retailers, so the transaction
volume associated with bitcoin is only a fraction
of that of other forms of payment. Since its inception, daily transaction volume has varied from days
with no transactions to over 100,000 transactions
on November 28, 2013. The median number of
transactions per day is 6,461, a tiny level of activity
compared to credit cards and US currency. In 2011,
for example, 20 billion credit card transactions were
processed, according to one report, while fewer
than 2 million Bitcoin transactions were confirmed
during the same time period.
The price of one bitcoin in terms of the US dollar has varied from five cents to over $1,000 since
its creation in 2009. As of July 2014, the price is
around $650 per bitcoin. Bitcoin trades simultaneously for different prices on different exchanges,
and the price is highly volatile.
This volatility is greater than that of the US dollar;
another way to put it is that bitcoin prices are subject to high rates of inflation and deflation, whereas
the Federal Reserve monitors the inflation rate in
the United States and can adjust monetary policy
to prevent hyperinflation or deflation. This allows
the holder of a US dollar to have confidence that
the value of his or her money will not be subject
to great losses, an assurance bitcoin holders do not
have.

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

2

Another way to note the changing value of bitcoin
is to look at what it will buy. The average monthly
price of a gallon of gasoline in US dollars since
2011 has varied $0.69. In bitcoin, it has varied
1.17326 bitcoin—$734.37 in terms of the current
exchange rate. One practical problem for merchants
posting prices in bitcoin is that they must quote
prices out to several decimal places, whereas prices
in most other currencies are rounded to two. So for
instance, if bitcoins were used to purchase a gallon
of unleaded gasoline in June 2014, the price would
have been 0.005994 bitcoin.

24-Hour Average Price of Bitcoin
Since 2013
US dollars/bitcoin average daily price

1000

While the supply of US dollars is adjusted by actions of the Federal Reserve in the market for bank
reserves, the supply of bitcoin increases as users of
the system, or “miners,” confirm transactions; this
will continue until the total supply reaches 21 million bitcoin.

800
600
400
200
0
1/2013

5/2013

9/2013

11/2013

3/2014

Note: The aggregated bitcoin price index is calculated as a weighted average
bitcoin price from multiple exchanges and given in terms of US dollars.
Source: Quandl.

Price of Gasoline Since April 2011,
US Dollars and Bitcoins
Price, bitcoins per gallon
1.4

Price, US dollars per gallon
4.50

1.2

4.00
3.50

1.0

3.00

0.8

2.50

0.6

2.00
1.50

0.4

1.00

0.2
0
4/2011

0.50
10/2012

4/2014

0.00
4/2011

10/2012

4/2014

Another difference between dollars and bitcoins
is the way they are produced. Bitcoins are created
when people validate transactions by solving a difficult math problem—a process known as bitcoin
“mining.” The economic cost of producing bitcoins, the rate of seigniorage, is tied to the rigor of
a mathematical problem, and each miner devotes
computational power to confirming transactions
and solving the problem. Once transactions are
confirmed, the miner who confirmed the transaction receives bitcoin as a reward, that is, compensation for his or her work. In comparison, for dollars,
the Federal Reserve determines the amount of
high-powered money that is produced (currency
plus bank reserves), which ultimately determines
the total number of dollars in the world. Even
ignoring bank accounts, there are a lot more dollars
around than bitcoins: The current supply of bitcoin
is nearly 13 million, whereas there are 34.5 billion
US currency notes in circulation; or nearly 2,700
bills for each bitcoin.

Sources: Bureau of Labor Statistics; Quandl.

In terms of value, the differences are also large. As
of January 2014, the amount of bitcoins in circulation valued in US dollars was around 9.3 billion;
by comparison the total value of all US currency is
nearly $1.2 trillion, or nearly 130 times the value of
all bitcoins (and we’re not counting bank accounts
in this either). Once the entire supply of 21 million
Federal Reserve Bank of Cleveland, Economic Trends | August 2014

3

bitcoins has been mined, their value (at the current
exchange rate) will be barely over 1 percent of the
value of US dollars (even assuming no growth in
US currency). So bitcoins, despite their high profile
and relatively high value, still make up only a small
portion of the value of US currency. And as a fraction of all payments in the world, it is even less.

Total Number of Bitcoins Mined
Bitcoins, millions
14
12
10
8

It’s perhaps too early to assess the future of bitcoin,
but in terms of number of transactions, total value,
and even price stability, it is not currently a major
competitor of the US dollar.

6
4
2
0
2009

2010

2011

2012

2013

2014

Note: All bitcoins mined prior to 2009 are included in data.
Sources: Blockchain; Quandl.

Bitcoin Value as a Fraction
of the Total Value of US Currency
0.009
0.008
0.007
0.006
0.005
0.004
0.003
0.002
0.001
0
2009

2010

2011

2012

2013

2014

Note: Number of bitcoins in circulation multiplied by the current exchange rate,
over total value of the US notes in circulation.
Sources: Board of Governors of the Federal Reserve System; Quandl.

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

4

Households and Consumers

Peer-to-Peer Lending Is Poised to Grow
08.14.14
by Yuliya Demyanyk and Daniel Kolliner
Peer-to-peer lending—a type of lending which
matches individual borrowers with investors—is a
recent innovation. But because it fills at least two
gaps left by traditional lending sources, the peer-topeer-lending market is likely to continue growing
for some time.

Peer-to-Peer Loan Originations Are Rising
and Standard Consumer-Finance Loans
Are Declining
Billions of dollars
150

0.8
0.7

120
0.6
Standard consumerfinance loans

90

0.5
0.4

60
Peer-to-peer
loans

30

0.3
0.2
0.1

0
1999

0
2001

2003

2005

2007

2009

2011

2013

Notes: Standard Consumer Finance Loans is the total amount of loans outstanding.
Peer-to-peer Loans is the total amount of loans originated. Shaded bars indicate
recessions.
Sources: Equifax; Federal Reserve Bank of New York’s Concsumer Credit Panel;
Lending Club.

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

Emerging first in the United Kingdom in 2005 and
arriving in the United States a year later, the peerto-peer market has been growing rapidly since its
inception, while traditional consumer bank loans
and credit-card lending have been declining. Since
the second quarter of 2007, the total amount of
money lent through bank-originated consumer-finance loans has been declining on average 2 percent per quarter and the total amount lent through
bank-originated credit cards has been declining on
average 0.7 percent per quarter. Meanwhile peer-topeer lending has been growing rapidly at an average
pace of 84 percent a quarter.
Peer-to-peer’s rapid growth may be attributable to
two of the benefits it provides. First, it can improve
access to credit for individuals who have short
credit histories. Second, it allows consumers to
consolidate credit card debt and lower their interest
rate more than they could by going through traditional lenders.
Peer-to-peer lenders use income, the type of employment, and even SAT scores in addition to
credit scores and histories to assess the creditworthiness of borrowers. As a result, peer-to-peer lending
could improve access to credit for consumers who,
for example, are denied a loan by a bank because
their credit histories are short, even if their credit
scores are sufficiently high. A significant number
of people fall into this category. According to data
from Equifax, one of the three largest US credit bureaus, 39.8 percent of people with credit histories
shorter than three years have credit scores higher

5

Average Peer-to-Peer Interest Rates Have
Been Lower than Credit Card Rates
since 2010:Q1
Percent
16
Credit card rate
14
Peer-to-peer rate

12

10

8
2007

2009

2011

2013

Note: Shaded bar indicates a recession.
Sources: Bankrate.com; Lending Club.

Average Top-Grade Peer-to-Peer Interest
Rates Have Always Been Lower than
Credit Card Rates
Percent
20
Peer-to-peer rate, grades C or D
18
Credit card rate

16
14
12

Peer-to-peer rate, grades A or B

10
8
2007

2009

2011

2013

Note: Shaded bar indicates a recession.
Sources: Bankrate.com; Lending Club.

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

than the subprime threshold, in other words, generally good enough to obtain a loan (Equifax, Federal
Reserve Bank of New York’s Consumer Credit
Panel).
Most peer-to-peer loans are used to consolidate
high-interest-rate credit card debt. Data provided
by Lending Club, a company that arranges peerto-peer loans, shows that 83.3 percent of peer-topeer loans are personal one-time loans, most of
which are put to use for this purpose. This may be
explained by the fact that interest rates on peerto-peer loans have been lower than those on credit
cards since 2010:Q1.
Not every peer-to-peer borrower manages to obtain
a better interest rate than a credit card rate. Peer-topeer loans are categorized by grades A to D, reflecting the probability of default. On average, around
50 percent of loans are awarded a grade of “A” or
“B.” These consumers are considered the least risky
borrowers, while borrowers with grades “C” or “D”
tend to be riskier. Borrowers with loans graded “A”
or “B” have consistently been getting better rates
through peer-to-peer lending compared to credit
cards. For borrowers with good scores, interest
rates have a strong negative correlation with the
credit card interest rates, meaning that when banks
increase their interest rates, peer-to-peer lenders
decrease theirs.
In comparison to bank-originated consumerfinance loans, peer-to-peer loans performed either
similarly or slightly better. On average, between
2010:Q2 and 2014:Q1, 3.2 percent of peer-to-peer
loans were past due compared to 3.7 percent of
standard consumer finance loans. Over this period,
peer-to-peer loans had a lower share of poorly performing loans in 10 of 16 quarters.
The peer-to-peer market is currently hundreds of
times smaller than the consumer finance and credit
card markets. However, the data suggest that the
peer-to-peer lending market will continue to grow.
One reason is that the supply of funds from investors for such lending has been increasing. Though
peer-to-peer lending started as individual investors

6

Peer-to-Peer Loans and Credit Cards
Perform Similarly
Percent not performing
5.0
Standard consumerfinance loans

4.5

Peer-to-peer
loans

4.0
3.5

lending to individual borrowers, institutional investors, such as community banks, have become involved over time. Another reason that peer-to-peer
lending is poised to grow further is that demand
for such loans has been increasing. Individuals who
either cannot get loans from traditional banks or
who wish to consolidate their credit card balances
at lower interest rates find peer-to-peer lending an
attractive alternative.

3.0
2.5
2.0
1.5
1.0
2010

2011

2012

2013

Note: Nonperforming loans from Equifax are defined as loans that are 30, 60,
90, or 120 days past due. Nonperforming loans from Lending Club are defined
as loans that are 16-30 or 30-120 days past due.
Sources: Equifax; Federal Reserve Bank of New York’s Concsumer Credit Panel;
Lending Club.

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

7

Inflation and Prices

Cleveland Fed Estimates of Inflation Expectations, July 2014
News Release: July 22, 2014
The latest estimate of 10-year expected inflation
is 1.89 percent, according to the Federal Reserve
Bank of Cleveland. In other words, the public currently expects the inflation rate to be less than 2
percent on average over the next decade.

Ten-Year Expected Inflation and
Real and Nominal Risk Premia
Percent
7
6

The Cleveland Fed’s estimate of inflation expectations is based on a model that combines information from a number of sources to address the
shortcomings of other, commonly used measures,
such as the “break-even” rate derived from Treasury
inflation protected securities (TIPS) or surveybased estimates. The Cleveland Fed model can
produce estimates for many time horizons, and it
isolates not only inflation expectations, but several
other interesting variables, such as the real interest
rate and the inflation risk premium.

5
4
Expected inflation
3
2
Inflation risk premium

1
0
1982

1986

1990

1994

1998

2002

2006

2010

2014

Source: Haubrich, Pennacchi, Ritchken (2012).

Real Interest Rate

Expected Inflation Yield Curve

Percent

Percent
2.5

12
10

July 2014
June 2014
July 2013

2.0

8
1.5

6
4

1.0

2
0.5

0
-2

0.0
1 2 3 4 5 6 7 8 9 10 12

-4
-6
1982

15

20

25

30

Horizon (years)

1986

1990

1994

1998

2002

2006

2010

2014
Source: Haubrich, Pennacchi, Ritchken (2012).

Source: Haubrich, Pennacchi, Ritchken (2012).

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

8

Inflation and Prices

Recent Owners’ Equivalent Rent Inflation Is Probably Not a Blip
08.11.14
by Amy Higgins and Randal Verbrugge
Recently, the overall rate of inflation has risen,
owing partly to inflation in Owners’ Equivalent
Rent (OER). But many wonder if the current rate
of OER inflation, which is now at levels not seen
since 2009, is simply a blip. We apply a forecasting
approach to estimate whether OER inflation will
continue to be elevated going forward, or whether
it will revert back to the lower levels that have been
more typical over the last several years. We find that
OER inflation is likely to remain elevated over the
next year.
OER is used in the US and in many other countries
to estimate inflation in homeowner housing costs.
At its core, OER captures the implicit rent that a
homeowner would have to pay if he or she were to
rent instead of own the same home (or equivalently,
the funds that the homeowner is sacrificing by living in the home instead of renting it to someone
else). The OER of a particular home is the rent that
the home would command under current market
conditions. In practice, statistical agencies estimate
OER inflation for homes in a particular part of a
city using inflation in the market rents of nearby
rental units. (For more details on how inflation is
estimated in the US, go to www.bls.gov.)
OER plays a prominent role in both the Consumer
Price Index (CPI) and the Personal Consumption Expenditures (PCE) price index because of
how heavily it is weighted when all the individual
components are aggregated into each index. In the
CPI, it accounts for roughly 25 percent of the total
index. In the PCE price index—the preferred inflation indicator of the Federal Open Market Committee—it accounts for approximately 12 percent.
In core inflation measures, OER accounts for an
even larger share. With such a large weight, the
OER component can affect the overall rate of inflation significantly.

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

9

As for what is causing OER to rise, a number of
factors have been proposed. Some financial writers have suggested that a shortage of rental housing is responsible, though not everyone agrees that
such a shortage exists. Proponents of the rentalhousing-shortage view point to historically low
ratios of completed privately-owned housing units
to population and a low ratio of private construction investment to GDP. However, if rental housing
were in short supply, one would expect to see historically low rental vacancy rates. Yet these rates are
not far from their levels in 1995, before the run-up
in housing prices. Still, it is possible that declining
vacancy rates could prompt some rent inflation. It
is also possible that some cities could be experiencing historically low vacancy rates, though this is not
true of the five cities we examine below.

Rental Vacancy Rates

Unemployment rates might also be expected to
affect rent inflation, and they have been dropping steadily. High unemployment rates might be
expected to dampen rent inflation, and declining
unemployment rates might be expected to feed it.

Rate
16
14
12
South

10

Midwest

8

Northeast
West

6
4
1994

1997

2000

2003

2006

2009

One might also expect rents to rise when house
prices rise, since higher home prices mean that real
estate is more costly. Housing prices seem to have
bottomed out in most regions of the country, and
in some cities they have rebounded fairly briskly.

2012

Source: US Census Bureau.

Finally, low interest rates obviously make it cheaper
to buy a home, and we would expect that low rates
would cause house prices to rise (since more buyers
can now afford a given home), and rents to rise less
than they would if interest rates were higher (since
some households would decide to buy rather than
rent). Low interest rates also reduce the costs that
landlords face, hence might be expected to reduce
market rents.
To forecast where OER inflation rates are headed
over the next year, we construct a forecasting model
that includes these four possible causes: vacancy
rates, unemployment rates, house price changes,
and interest rates. When we use data to estimate
the model, we can also test whether OER inflation
rates actually do respond to vacancy rates, unemployment rates, house prices, and interest rates.

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

10

And then, as long as the relationships that have prevailed in the past continue to hold in the future, we
can use current data to give us an idea about future
rent developments.
In our forecasting model, we also include two other
variables which might help forecast OER inflation.
The first is previous OER inflation. The second is
the price/rent ratio, a measure of the “gap” between
housing prices and rents. Like the price/earnings
ratio associated with stocks, housing assets are
sometimes evaluated through the lens of the price/
rent ratio. Over long horizons, this ratio should
be stable—although the ratio should also depend
upon the real interest rate, with a low real interest
rate causing the ratio to rise. When the price/rent
ratio is high, we would expect adjustment: either
house prices should fall, or rents should rise, or
both.
The specific data we look at for the first four factors
are the regional vacancy rate, the local unemployment rate, the local rate of year-over-year house
price appreciation, and the real mortgage interest
rate (i.e., the average 30-year fixed mortgage rate,
adjusted for inflation by subtracting the expected
inflation rate as reported in the Survey of Professional Forecasters). We examine the relationship of
these variables plus the price/rent ratio to year-overyear OER inflation.
We look at the four Census regions (Northeast,
Midwest, South, and West) and five cities: Cleveland, Los Angeles, Miami, New York, and Philadelphia. These cities were chosen because they are
among the handful of cities for which we have
monthly OER data, and because each Census
region is represented by at least one city. We use
quarterly data from 1990:1-2014:2 to gauge the
strength of the relationships, and we then use our
model as a forecasting model to forecast OER
inflation in each region over the next year (2014:32015:2). (For an in-depth investigation of OER
inflation versus rent inflation, see “Explaining the
Rent–OER Divergence, 1999–2007”.)
The estimation method used is a vector autoregression, estimated using Bayesian methods. This methodology often has excellent forecasting properties.
Federal Reserve Bank of Cleveland, Economic Trends | August 2014

11

OER Year-over-Year Inflation Rates
Percent
4.0
Forecast
3.5
South

3.0

West
2.5
Northeast
Midwest

2.0
1.5
1.0
2012

2013

2014

2015

Source: Bureau of Labor Statistics; authors’ calculations.

OER Year-over-Year Inflation Rates
Percent
5.0
Forecast

4.5
4.0
3.5

2.5

Los Angeles
Miami
Cleveland
New York City

2.0

Philadelphia

3.0

1.5
1.0
0.5
0.0
2012

2013

2014

2015

Source: Bureau of Labor Statistics; authors’ calculations.

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

Our results are surprising. OER inflation does not
appear to be influenced by vacancy rates, unemployment rates, the real interest rate, or our gap
measure. Of the variables investigated, only lagged
house price appreciation appears to have a statistically significant relationship to OER inflation
(previous OER inflation is also statistically significant). In one sense, this is a conundrum, because it
suggests that we “cannot explain” OER inflation using the “usual suspects.” High vacancy rates do not
appear to slow OER inflation down appreciably;
neither do high unemployment rates, low interest rates, or a low price/rent ratio. The only usual
suspect which appears to feed into OER inflation is
lagged house price appreciation—and even then, it
appears to be statistically significant in only about
half of the cases investigated. The unemployment
rate appeared to be statistically significant at the 10
percent level in two of the Census regions.
OER inflation has a considerable “momentum”
component; that is, high OER inflation tends to be
followed by high OER inflation. It is this momentum that dominates the OER forecasts below.
Our forecasting models suggest that, barring large
unforeseen shocks, OER inflation is likely to slow
somewhat in the Northeast, rise to about 3 percent
in the South, remain at about 2.9 percent in the
West, and rebound to about 2 percent in the Midwest. However, there is considerable uncertainty
surrounding these forecasts.
In our model, lagged house price appreciation and
recent OER inflation are the most important predictors of future OER inflation. Other commonly
suggested influences of OER inflation—vacancy
rates, unemployment rates, the price/rent gap, and
interest rates—are generally not useful predictors.

12

Labor Markets, Unemployment, and Wages

Job Search Before and After the Great Recession
08.12.14
by Dionissi Aliprantis, Anne Chen,
and Christopher Vecchio
Since the onset of the Great Recession, unemployment rates have been high and job-finding rates
have been low. These persistent trends raise concerns that unemployed workers may have become
discouraged by poor job prospects. To begin understanding the job searching behavior of the unemployed, we examine data from the American Time
Use Survey (ATUS) and find that a greater proportion of the unemployed are spending time searching for a job after the Great Recession than before.
We also find important differences in job search
time by educational attainment, age, and gender—
including decreases in search time for some groups.

Time Spent on Job Search
(Conditional on Searching)
Density
.004

.003
2008−2012
.002
2003−2007
.001

0
0

100

200

300

400

500

600

700

800 900 1000

Mean job search time, minutes per day
Notes: Survey weights were used to compute the kernel density.
Sources: American Time Use Survey, Bureau of Labor Statistics; authors’ calculations.

To compare the amount of time the unemployed
spend on their job search before and after the recession, we analyzed data from the ATUS, which asks
respondents how much time they spent on various
activities the previous day. Activities classified as job
searching include sending out resumes, conducting
interviews, commuting, asking for information,
and looking for information on the internet or in
the newspaper. We compared ATUS data on job
searching before and after the Great Recession,
combining the years 2003 to 2007 for the prerecession period and the years 2008 to 2012 for the
post-recession period.
As we would expect, the proportion of unemployed
individuals who spent some time on an average day
searching for a job increased from 20 percent to 24
percent after the recession. However, and perhaps
surprisingly, among those unemployed who did
search, the average time spent on job search looked
very similar in the five years on either side of the
Great Recession.
The proportion of unemployed persons spending
time job searching varied dramatically by level of
educational attainment over the past decade. Between 2008 and 2012, for example, 17 percent of
those unemployed who were high school dropouts

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

13

spent some of their day searching for a job, while
for those with high school diplomas or associate’s
degrees the figure is 23 percent, and for those holding at least a bachelor’s degree it is 35 percent.

Job Search Time across Educational
Attainment Levels
Mean job search time, minutes per day
80

2003−2007
2008−2012

60

40

20

0
Less than
high school

Some college or
associate’s degree

Bachelor’s degree
or higher

Sources: American Time Use Survey, Bureau of Labor Statistics; authors’
calculations.

Average Job Search Time: Unemployed
Males with At Least a Bachelor’s Degree
Mean job search time, minutes per day
150
125

2003−2007
2008−2012

100
75
50
25
0
20−30

30−40

40−50

Although time spent by the unemployed on job
searching increased across all educational attainment levels after the Great Recession, the increase
was largest at the extremes. For unemployed high
school graduates and those with an associate’s
degree, the average time spent searching increased
from 32 minutes to 37 minutes a day. However,
for unemployed high school dropouts the average
search time increased from 17 minutes to 28 minutes, and for those with at least a bachelor’s degree
it increased by almost 50 percent from 46 minutes
to 67.
For nearly all age categories, unemployed males
with at least a bachelor’s degree spent much more
time searching for a job after the recession than
before it. For males between 20 and 30 the average
search time more than tripled, and for males between 30 and 40, and 40 and 50 the average search
time increased by 65 and 76 percent, respectively.
For males over 50, average job search time actually
decreased slightly over this period.
Recent changes in the job search time of unemployed females with at least a bachelor’s degree were
different from males. For example, in contrast to
males, job search time did not increase uniformly
for females between 20 and 50. In fact, for most
females in this age range, average job-search time
actually decreased. So while the average job-search
time of females 20 to 30 years of age with at least a
bachelor’s degree was higher than for males of the
same age and education before the Great Recession,
this pattern had reversed in the period of 2008 to
2012. Finally, and perhaps most interestingly, the
job-search behavior of unemployed females over 50
with at least a bachelor’s degree changed dramatically before and after the Great Recession.

50−65

Sources: American Time Use Survey, Bureau of Labor Statistics; authors’
calculations.

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

While our findings do not rule out the existence of
discouraged workers, we found that total job search
time has increased in recent years. Our broader
finding is that the job search patterns of the unemployed have changed in the aftermath of the Great
14

Average Job Search Time: Unemployed
Females with At Least a Bachelor’s Degree
Mean job search time, minutes per day
150

2003−2007
2008−2012

125

Recession, with important differences by educational attainment, age, and gender, including decreases
in search time for some groups. Understanding
these differences could help us to understand not
only recent changes in the labor market, but also in
educational attainment, household formation, and
other important processes driving our economy.

100
75
50
25
0

20−30

30−40

40−50

50−65

Sources: American Time Use Survey, Bureau of Labor Statistics; authors’
calculations.

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

15

Labor Markets, Unemployment, and Wages

State Unemployment Insurance Policy Responses
during the Great Recession
08.12.14
by Pedro Amaral, Jessica Ice, and Brad Kaplita
During the Great Recession, state unemployment
insurance systems faced unequal burdens depending on how well their accounts within the federal
Unemployment Trust Fund (UTF) were funded
and how severely they were hit by the recession.
This article describes the different ways that states
responded to the effects of the recession on their
unemployment insurance systems because of these
factors.
Financing Unemployment Insurance
The US unemployment insurance (UI) system is
jointly funded by federal and state payroll taxes.
The federal share is financed by a federal tax,
charged to employers, of 6.0 percent on the first
$7,000 of wages of each employee. (However, the
effective tax rate is often 0.6 percent because of
a 5.4 percent rebate that most employers get for
paying on time). Revenues from the federal tax are
used to cover state and federal administrative costs,
for loans to states with insolvent UTF accounts,
and for the federal share of unemployment benefits.
The states’ share of UI claims is financed through a
state tax, which is also levied on employers’ payrolls, but both the statutory rate and the taxable
wage base (the amount of an individual’s income
on which a tax is levied) varies from state to state.
When states have state tax revenues left over after
paying their UI claims, they can supplement their
UTF accounts. If they are unable to raise enough
revenue to pay their UI claims, they can dip into
their UTF accounts and obtain loans from the
federal government. However, if a state is unable to
repay its loans and its UTF account becomes insolvent for two consecutive Januaries, it will suffer a
federal tax “credit reduction.”1 This means that the
federal tax rebate that employers receive for paying
their taxes on time will be reduced (usually by 0.3
percent per year, cumulatively, until the state is able

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

16

to repay its loan). These federal tax credit reductions place a higher tax burden on employers, who
may react by reducing payrolls, in turn decreasing the tax base for state tax revenues and placing
more stress on the state’s unemployment agencies’
finances.
Insolvency during the Great Recession

States by Solvency Status
Federal tax credit
reductions

Insolvent without tax
credit reductions

Never insolvent

Arizona

Alabama

Alaska
District of Columbia

Arkansas

Colorado

California

Hawaii

Iowa

Connecticut

Idaho

Louisiana

Delaware

Kansas

Maine

Florida

Massachusetts

Mississippi

Georgia

Maryland

Montana

Illinois

New Hampshire

North Dakota

Indiana

South Dakota

Nebraska

Kentucky

Tennessee

New Mexico

Michigan

Texas

Oklahoma

Minnesota

Oregon

Missouri

Utah

Nevada

Washington

New Jersey

West Virginia

New York

Wyoming

North Carolina
Ohio
Pennsylvania
Rhode Island

In the aftermath of the Great Recession, states were
faced with higher-than-normal unemployment
compensation costs. During this period, the federal government was funding the large majority of
unemployment compensation payouts in the form
of the extended benefits programs, which offered a
maximum of 73 weeks of benefits. However, federal
benefits could not be paid out until an individual
had exhausted the fully-state-funded unemployment insurance benefits, usually lasting 26 weeks.
As claims increased with the onset of the recession
in 2008-2009, federal tax credit reductions began
posing a problem for a large number of states in
2011, as by that time they had had insolvent accounts for two years.
To look at how states in different fiscal situations
behaved, we start by dividing them into three distinct groups: states that were never insolvent, states
that were insolvent but managed to pay back their
federal loans and did not incur the federal tax credit
reduction, and states that became insolvent and
ended up incurring a federal tax credit reduction.
Unemployment Insurance Policy Responses

South Carolina
Vermont
Virginia
Wisconsin
Sources: Department of Labor; Bivens, Smith, and Wilson (2014).

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

Unemployment rates were substantially higher in
states that suffered federal tax credit reductions,
while they were very similar in solvent states and
states that were insolvent but managed to escape
the federal tax credit reduction. Holding all things
equal, states with higher unemployment rates
need to spend more on unemployment claims and
therefore will suffer more fiscal pressure. At the
same time there may also be a feedback mechanism
at work, creating a potentially vicious cycle: federal
tax credit reductions increase the employers’ effective tax rates, which may lead to less hiring and
higher unemployment.

17

Unemployment Rate
Percent
11.0
10.0
9.0
8.0

Insolvent with
federal tax credit
reduction
Insolvent without
federal tax credit
reduction
Never insolvent

7.0
6.0
5.0
4.0
2008

2009

2010

2011

2012

2013

2014

Source: US Department of Labor.

Statutory Taxable Wage Base
Dollars (median)
30,000
25,000

Never insolvent

20,000
Insolvent with
federal tax credit
reduction
Insolvent without
federal tax credit
reduction

15,000
10,000
5,000
0
2008

2009

2010

2011

2012

2013

2014

Although on average states that did not receive the
federal tax credit reduction faced lower unemployment rates, some states faced insolvency while
others did not. A striking difference between the
solvent and insolvent groups is the size of their
statutory taxable wage base (the dollar amount of
wages that taxes are levied on in each state). The
statutory taxable wage base for solvent states was
almost double that of insolvent states (regardless of
whether they received a federal tax credit reduction
or not). Moreover, since employment levels also
tended to be lower for insolvent states, the total
taxable wage base (the statutory wage base times
the number of employed people) was also lower
there. Thus, the data indicate that solvency depends
not only on a state’s level of unemployment and
the volume of claims it must pay, but also on how
prudently it uses its fiscal instruments. In fact, one
could make a strong argument that states with
broad tax bases are precisely those that did not end
up in insolvency.
The insolvent states eventually increased their
statutory taxable wage base the most as a response
to their fiscal woes. This policy response is unsurprising for two reasons: first, insolvent states began
with a much lower taxable wage base and had much
more room to grow it, and second, solvent states
probably did not see any need to make increases,
given their sounder fiscal situation.

Source: US Department of Labor.

Growth in Statutory Taxable Wage Base
Median, index 2008=100
Insolvent with
federal tax credit
reduction
Insolvent without
federal tax credit
reduction

140
135
130
125
120

Never insolvent

115
110
105
100
95
2008

2009

2010

2011

2012

2013

2014

Source: US Department of Labor.

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

States that went through insolvency also increased
their maximum state payroll tax rate by more than
both solvent states and states with federal credit
reductions. Increasing the state tax perhaps contributed to the ability of some states to escape federal
credit reductions by allowing them to repay federal
loans while others did not.
While the fiscal consolidation effort for states that
became insolvent but escaped federal tax credit
reductions was done largely by increasing revenues,
states that suffered federal tax credit reductions
attempted to balance their trust fund budgets
through reductions in spending. States with federal
tax credit reductions kept the maximum dollar
amount of weekly UI benefits the unemployed
could receive constant and reduced the maximum
number of weeks that an individual could receive
18

state UI benefits. Note that this strategy does not
necessarily mean they were able to cut overall
spending, as unemployment rates, and therefore
claims, were higher in these states.

State Maximum Statutory Unemployment
Tax Rates
Index 2008=100
130
125

110

Insolvent without
federal tax credit
reduction
Insolvent with
federal tax credit
reduction

105

Never insolvent

120
115

100
95
2008

2009

2010

2011

2012

2013

2014

Source: US Department of Labor.

Statutory Maximum Weekly Dollar Benefits
Median, index 2008=100
130
125
120
Never insolvent
115

Insolvent without
federal tax credit
reduction

110

To be sure, the way states have managed the funding of their UI programs in the past is as important
a determinant of the current fiscal situation of
those programs as the unemployment burden the
state faces. Moreover, unemployment at the state
level is also partly determined by past and current
fiscal policies, so we are not claiming any causality, but merely pointing to some important, and
suggestive, correlations. Based on this evidence,
states that escaped a federal penalty were faced with
a less severe unemployment burden and they also
undertook some policy measures to increase their
revenues by increasing their taxable wage base and
state maximum tax. States that suffered federal tax
credit reductions also experienced more adverse labor market outcomes and took additional measures
to reduce deficits through attempts to reduce their
spending on unemployment insurance programs.

105
Insolvent with
federal tax credit
reduction

100
95
2008

2009

2010

2011

2012

2013

1. These credit reductions are officially titled Federal Unemployment Tax Act or “FUTA” credit reductions.
The authors thank Lockhart Taylor at the North Carolina Department of Commerce for his contributions to this article.

2014

Source: US Department of Labor.

State Maximum Weeks of Unemployment
Insurance Benefits
Weeks
28.0
27.5
Insolvent without
federal tax credit
reduction
Never insolvent

27.0
26.5
26.0
25.5
25.0
24.5

Insolvent with
federal tax credit
reduction

24.0
23.5
23.0
2008

2009

2010

2011

2012

2013

2014

Source: US Department of Labor.

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

19

Monetary Policy

Yield Curve and Predicted GDP Growth, July 2014
Covering June 24, 2014–July 25, 2014
by Joseph G. Haubrich and Sara Millington
Overview of the Latest Yield Curve Figures

Highlights
July

June

May

Three-month Treasury bill rate (percent)

0.03

0.03

0.03

Ten-year Treasury bond rate (percent)

2.49

2.63

2.54

Yield curve slope (basis points)

246

260

251

Prediction for GDP growth (percent)

1.5

1.4

1.4

Probability of recession in one year (percent)

2.46

1.99

2.31

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

Yield Curve Predicted GDP Growth
Percent
Predicted
GDP growth

4
2
0
-2

Ten-year minus three-month
yield spread

GDP growth
(year-over-year
change)

-4
-6
2002

2004

2006

2008

2010

2012

2014

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

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

Since last month, the yield curve reversed it course,
pivoting back downward around the short end. The
three-month (constant maturity) Treasury bill rate
stayed fixed at 0.03 percent (for the week ending
July 25), even with June and May’s levels of 0.03
percent. The ten-year rate (also constant maturity)
decreased to 2.49, down from June’s 2.63 percent
and 5 basis points below May’s level of 2.54 percent. The pivot dropped the slope to 246 basis
points, below June’s 260 basis points and May’s 251
basis points. By recent standards, the yield curve
remains steep, as the mean slope has been 193 basis
points (median of 218) since 2000.
Despite the flatter slope, predicted future growth
increased, albeit by a small amount. Projecting
forward using past values of the spread and GDP
growth suggests that real GDP will grow at about
a 1.5 percentage rate over the next year, just up
from the 1.4 percent forecasts in May and June.
The influence of the past recession continues to
push towards relatively low growth rates. Although
the time horizons do not match exactly, the forecast is slightly more pessimistic than some other
predictions, but like them, it does show moderate
growth for the year..
The flatter slope did slightly increase the probability of a recession, though only slightly. Using the
yield curve to predict whether or not the economy
will be in a recession in the future, we estimate
that the expected chance of the economy being
in a recession next July at 2.46 percent, up from
June’s reading of 1.99 percent and still above May’s
probability 2.31 percent. So although our approach
is somewhat pessimistic with regard to the level
of growth over the next year, it is quite optimistic
about the recovery continuing.

20

The Yield Curve as a Predictor of Economic
Growth

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

Forecast

50
40
30
20
10
0
1960 1966 1972 1978 1984 1990 1996 2002 2008

2014

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

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

6
4
2
0
-2

10-year minus
three-month yield spread

-4
-6
1953

1965

1977

1989

2001

2013

Note: Shaded bars indicate recessions.
Source: Bureau of Economic Analysis, Board of Governors of the Federal Reserve
System.

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

The slope of the yield curve—the difference between the yields on short- and long-term maturity
bonds—has achieved some notoriety as a simple
forecaster of economic growth. The rule of thumb
is that an inverted yield curve (short rates above
long rates) indicates a recession in about a year,
and yield curve inversions have preceeded each of
the last seven recessions (as defined by the NBER).
One of the recessions predicted by the yield curve
was the most recent one. The yield curve inverted
in August 2006, a bit more than a year before the
current recession started in December 2007. There
have been two notable false positives: an inversion
in late 1966 and a very flat curve in late 1998.
We use past values of the yield spread and GDP
growth to project what real GDP will be in the future. We typically calculate and post the prediction
for real GDP growth one year forward.
Predicting the Probability of Recession
While we can use the yield curve to predict whether
future GDP growth will be above or below average, it does not do so well in predicting an actual
number, especially in the case of recessions. Alternatively, we can employ features of the yield curve
to predict whether or not the economy will be in a
recession at a given point in the future. Typically,
we calculate and post the probability of recession
one year forward.
Of course, it might not be advisable to take these
numbers quite so literally, for two reasons. First,
this probability is itself subject to error, as is the
case with all statistical estimates. Second, other
researchers have postulated that the underlying
determinants of the yield spread today are materially different from the determinants that generated
yield spreads during prior decades. Differences
could arise from changes in international capital
flows and inflation expectations, for example. The
bottom line is that yield curves contain important
information for business cycle analysis, but, like
other indicators, should be interpreted with caution. For more detail on these and other issues related to using the yield curve to predict recessions,
21

see the Commentary “Does the Yield Curve Signal
Recession?” Our friends at the Federal Reserve
Bank of New York also maintain a website with
much useful information on the topic, including
their own estimate of recession probabilities.

Yield Spread and Lagged Real GDP
Growth
Percent
10
8

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

6
4
2
0

Ten-year minus
three-month yield spread

-2
-4
-6
1953

1965

1977

1989

2001

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

2013

22

Regional Economics

The Youngstown-Warren-Boardman Metropolitan Statistical Area
08.05.14
by Kyle Fee and Ashley Orr

Employment Recovery in Different
Business Cycles
Percent change
6

1990 (US)
1990 (Youngstown)
2001 (US)

4
2

2007 (US)

0
-2

2007 (Youngstown)

-4

2001 (Youngstown)

-6

Youngstown, Ohio, is at the center of a larger
metropolitan statistical area (MSA) that includes
the cities of Youngstown, Warren, and Boardman,
Ohio, and the counties of Trumbull and Mahoning in Ohio and Mercer in Pennsylvania. The area,
often referred to as the Mahoning Valley and once
known as the Steel Valley, was home to a flourishing steel industry from the mid- 1800s to the
1970s. Back in the 1920s, the valley’s steel production ranked second in nation, but today most of
the steel mills have been closed down, sold, and
scrapped. Now producing little steel, the area has
experienced a vast population decline and has yet to
regain employment lost from previous recessions.

-8
-10
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80
Months following the peak
Sources: Bureau of Labor Statistics/Haver Analytics.

Location Quotients in 2013 and Two Recessions
Government
Other services

Ohio 2013

Leisure and hospitality

Youngstown 2013

Educational and health services

Youngstown 2001
recession

Professional and business services

Youngstown 1990
recession

Financial activities
Information
Trade, transportation, and utilities
Manufacturing
Logging, mining, and constructtion

0

0.5

1.0

1.5

Note: The location quotient (LQ) is used to measure the degree to which an industry is concentrated in a region
relative to a reference economy. An LQ greater than 1 indicates that a region (in this case, the Youngstown
MSA) has a higher ratio of employment in a given industry in comparison to the reference economy (in this
case, the United States).
Sources: Bureau of Labor Statistics/Haver Analytics.

Recent employment statistics for the Youngstown
MSA are startling. Employment typically falls
around recessions but picks back up once the
recovery begins. The Youngstown MSA followed
this pattern after the 1990 recession, but in the
two subsequent recessions, employment did not
bounce back. Since the business cycle peak in 2001,
employment has fallen almost continuously. At
the time the 2007 recession hit 81 months later,
employment was nearly 6 percent lower than it was
in 2001. During the 2007 recession, employment
continued to decline until November 2009, where
23 months into this business cycle, the Youngstown
MSA’s job market had lost an aggregate 14 percent
since its peak in 2001. As of May of 2014, the
MSA’s employment has expanded but still remains
nearly 10 percent below where it was at the peak in
2001 and 4 percent below where it was at the start
of the 2007 recession. By comparison, employment
levels in the nation as a whole recovered after all
three recessions.
Even with the near absence of the steel industry,
manufacturing still accounts for a significant share
of employment in the Youngstown MSA. The
share of employment in manufacturing has been
higher in the Youngstown MSA than in both Ohio
and the nation as a whole since 1990, even as the

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

23

Population and Employment
Index, 2000= 100
120
115
Population,
US

110
105

Population,
Ohio

100
95

Population,
Youngstown

90
85
2000

Employment,
Youngstown
2002

2004

2006

2008

2010

2012

Sources: Census Bureau/Haver Analytics.

MSAs with the Greatest Net Migration
from Youngstown (number of people)
Akron MSA: 852
Portage County: 640
Youngstown MSA

Pittsburgh MSA: 1,028
Allegheny County: 604
Columbus MSA: 852
Franklin County: 710
Outflow MSAs
Counties of greatest
migration within MSA
Dayton MSA: 728
Montgomery County: 378

Source: American Community Survey, 2007-2011 net migration data.

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

manufacturing sector has declined considerably
over that time. Since 1990, manufacturing’s share
of total employment has declined from 24 percent
to 13 percent in the MSA, while in the nation it
has declined from 16 percent to 9 percent. The
Youngstown MSA’s employment shares also exceed
Ohio’s and the nation’s in the trade, transportation,
and utilities sector, other services sector, and the
educational and health services sector.
Overall, the employment shares of most sectors in
the Youngstown MSA relative to the nation have
been fairly stable across business cycles. However,
since 2001 the share of employment in the professional and business services sector has increased in
Youngstown relative to the nation. This trend is encouraging, as the sector has accounted for roughly
30 percent of job growth nationally since 2010.
Mirroring the continuous declines in overall employment is a similar decline in Youngstown’s population. Since 2000, the two series have both fallen
significantly. However, employment has declined
much more and at a quicker pace over the period.
Recently, employment has started to increase, while
population continues to decline.
Much of the population decline over the last several
decades has been fueled by major migrations out
of the MSA. In general, though, when people
decide to leave Youngstown, they often stay fairly
close to home. County-to-county migration data
from the American Community Survey for 20072011 shows that 35 percent of residents left the
Youngstown MSA for the South, 33 percent went
to the Midwest, 22 went elsewhere in the Northeast, and 11 percent went to the West. However,
the greatest outflows to cities were to Pittsburgh
(1028 movers), Columbus (852), Akron (852),
and Dayton (728). The most popular destinations
farther afield were Salt Lake County, Utah (186
movers), and Sarasota County, Florida (167).
Per capita personal income is much lower in
Youngstown than in the surrounding MSAs, Ohio,
and the nation as a whole. On average over the
last 20 years, annual per capita personal income in
Youngstown has been $5,927 less than the national

24

average. However, since 2009 income growth in
Youngstown’s MSA has outpaced the nation’s (13
percent versus 11 percent).

Per Capita Personal Income
US
40,000

Ohio

35,000

Youngstown

30,000

25,000
20,000
15,000
1990

1993

1996

1999

2002

2005

2008

2011

Sources: Bureau of Economic Analysis/Haver Analytics.

Selected Demographics, 2012
Youngstown MSA

Ohio

US

558,206

11,544,225

313,914,040

White

85.9%

82.7%

73.9%

Black

11.0%

12.2%

12.6%

Other

3.1%

5.1%

13.5%

Under 18

21.1%

23.0%

23.5%

18 to 64

60.3%

62.2%

62.8%

65 and older

18.6%

14.8%

13.7%

18.80%

25.3%

29.1%

43.5

39.3

37.4

Total population
Percent by race

Percent by age

Percent with a bachelor’s
degree or high (25 and older)
Median age

Sources: The American FactFinder, US Census Bureau.

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

Youngstown’s MSA has an older and less educated
population than the nation as a whole. The proportion of residents over 65 is nearly 5 percentage
points higher in the MSA than in the nation (19
percent versus 14 percent), and whereas nationally
23 percent of those in this age bracket hold a bachelor’s degree or higher, only 13 percent of those in
the Youngstown MSA do. Worrisome is that this 10
percent differential in educational attainment occurs in younger cohorts as well. Only 23 percent of
the Youngstown MSA residents aged 25 to 34 hold
a bachelor’s degree or higher, while the national average is 32 percent. The averages for the population
as a whole are 19 percent with a bachelor’s degree
or higher in the Youngstown MSA versus nearly 30
percent for the nation.
Overall, statistics on the Youngstown MSA’s
economy indicate that although the Mahoning
Valley may be down, it’s definitely not out. There
are a number of obstacles to overcome: the region’s
industries are undergoing restructuring, employment in the Youngstown MSA has yet to recover
fully from the recent recessions, the population
is declining, and educational attainment is lower
than the national average. However, on the upside,
per capita income growth is on par with and even
slightly better than the nation, and shares of employment are rising in the professional and business
services sector, one of the sectors cited for leading
the national recovery.
Additionally, ongoing growth of the shale industry
is likely to provide a boost to the regional economy.
While the full effect remains to be seen, we expect
the industry to provide more jobs and contribute to
the region’s share of employment and GDP, given
the early indicators. With nearly 140 producing
wells in neighboring Carroll County, the supply
needs of the drilling sector are just beginning to
affect the MSA. For example, a $1 billion steel pipe
plant recently opened in Youngstown, creating 350
jobs.

25

Economic Trends is published by the Research Department of the Federal Reserve Bank of Cleveland.
Views stated in Economic Trends are those of individuals in the Research Department and not necessarily those of the Federal Reserve Bank of Cleveland or of the Board of Governors of the Federal Reserve System. Materials may be reprinted
provided that the source is credited.
If you’d like to subscribe to a free e-mail service that tells you when Trends is updated, please send an empty email message to econpubs-on@mail-list.com. No commands in either the subject header or message body are required.
ISSN 0748-2922

Federal Reserve Bank of Cleveland, Economic Trends | August 2014

26