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Philadelphia Fed Forecasting Surveys:
Their Value for Research*
BY DEAN CROUSHORE
T
he Federal Reserve Bank of Philadelphia has
conducted both the Survey of Professional
Forecasters and the Livingston Survey for
20 years. Both surveys of private-sector
forecasters provide researchers, central bankers, news
media, and the public with detailed forecasts of major
macroeconomic variables. The surveys have proved
helpful for people who are planning for the future, and
they have also provided useful input into the decisions of
policymakers at the Federal Reserve and elsewhere. In
this article, Dean Croushore provides an overview of the
surveys and discusses the ways in which researchers have
used the surveys.
The Federal Reserve Bank of
Philadelphia has conducted both
the Survey of Professional Forecasters and the Livingston Survey for 20
years. Both surveys of private-sector
forecasters provide researchers, central
bankers, news media, and the public
with detailed forecasts of major macroeconomic variables. The surveys have
been made available to the public at no
charge, reflecting the public education
Dean Croushore
is a professor of
economics at
the University of
Richmond and a
visiting scholar
in the Research
Department of
the Philadelphia
Fed. This
article is available free of charge at www.
philadelphiafed.org/research-and-data/
publications/.
www.philadelphiafed.org
mission of the Federal Reserve. The
surveys have proved helpful for people
who are planning for the future. They
have also provided useful input into
the decisions of policymakers at the
Federal Reserve and elsewhere. This
article will provide an overview of the
surveys and discuss the ways in which
researchers have used the surveys.
The Livingston Survey is the older
of the two Philadelphia Fed surveys.
It started when Joseph Livingston,
a Philadelphia newspaper reporter,
wanted to get a sense of what forecasters thought would happen to the
economy in the next year, and so he
began sending a survey to prominent
*The views expressed here are those of the
author and do not necessarily represent
the views of the Federal Reserve Bank of
Philadelphia or the Federal Reserve System.
economists around the country.1 He
continued to publish his survey every
six months, gathering and reporting on
the forecasts and tracking their evolution over time. His survey, which was
the only collection of private-sector
forecasts of macroeconomic variables
in the country at the time, gained a
national following. Economic researchers began using the survey extensively
in the early 1970s to test theories
about people’s expectations. By 1978,
Livingston was having trouble keeping up with all of the requests for the
data and turned the data over to the
Philadelphia Fed’s Research Department, which organized the data in a
computer database and made them
available to researchers on request.
Livingston still ran the survey, but the
Philadelphia Fed compiled the results
and maintained the database. Livingston provided the first report of the
survey’s results in his column in the
Philadelphia Inquirer. When Livingston died in 1989, the Fed took over
the administration of the survey and
carried on Livingston’s legacy. Since
the advent of the Internet, the Fed has
made all of the historical Livingston
data available on its website.2
1
Herb Taylor’s 1992 article describes the survey
and Livingston’s newspaper columns reporting
on the survey. For an in-depth discussion of the
setup of the survey and a description of early
research using it, see my 1997 article.
2
The Philadelphia Fed’s website (at: www.
philadelphiafed.org/research-and-data/realtime-center/livingston-survey/) contains background material about the Livingston Survey,
news releases from the survey going back to
1991, data files containing both forecasts of
individuals and means or medians across the
forecasters for each variable in each survey, and
an academic bibliography listing research papers
that have used the survey.
Business Review Q3 2010 1
The Survey of Professional
Forecasters began as the idea of
Victor Zarnowitz and others at the
American Statistical Association and
the National Bureau of Economic
Research. They began the ASA/
NBER Economic Outlook Survey in
1968 and successfully carried it out
for 22 years. The survey was similar
to the Livingston Survey in that it
asked private-sector forecasters for
their projections for the next year for
major macroeconomic variables. But
the ASA/NBER survey was conducted
more frequently than the Livingston
Survey (quarterly instead of semiannually), asked for quarterly forecasts
(instead of Livingston’s half-year
forecasts), and included some unique
questions about the probabilities of
different outcomes, instead of asking
just for the point forecasts (that is, the
most likely outcome) reported by the
Livingston Survey. In 1990, the ASA/
NBER turned the survey over to the
Philadelphia Fed, which rechristened it
the Survey of Professional Forecasters
(SPF).3
Why do people need forecasts?
When planning their personal
budgets, people need to know what
the forecast for inflation is; when
planning production, firms need to
forecast demand for their products;
when buying and selling financial
assets, investors need to forecast both
inflation and future interest rates;
3
For more on the setup of the Survey of
Professional Forecasters and its origins, see my
1993 article. The Philadelphia Fed’s website
(at www.philadelphiafed.org/research-anddata/real-time-center/survey-of-professionalforecasters/) contains background material
about the survey, news releases from the survey
going back to 1990, data files containing both
forecasts of individuals and means or medians
across the forecasters for each variable in
each survey, an academic bibliography listing
research papers that have used the survey, and
forecast error statistics that present data on the
accuracy of the survey forecasts.
2 Q3 2010 Business Review
and when setting policy, government
analysts need to know how the
economy is likely to fare in the future.
Forecasting surveys can help all of
these groups figure out the most likely
outcomes for the variables that most
concern them.
The Philadelphia Fed’s surveys
are not the only surveys of forecasters. A well-known U.S. survey is the
Blue Chip Economic Indicators, which
a European version of the Survey of
Professional Forecasters in 1999 after
consulting with the Philadelphia Fed
on its methods.
The table on page 3 lists the major
macroeconomic variables covered by
the surveys, for which the respondents
provide short-term forecasts (for the
next one to two years). In addition
to those variables, the surveys ask for
long-term forecasts — the SPF asks
in both the CPI and PCE price index for the
!"
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was started by Robert Eggert in 1976.
The Blue Chip concept was to publish
forecasts monthly (compared with the
quarterly SPF and the semi-annual
Livingston Survey) and to publish the
names of each forecaster along with
his or her forecast (forecasters for
both the SPF and the Livingston were
anonymous). In addition, the National
Association for Business Economics
(NABE) has produced a quarterly
survey of forecasters since the early
1960s, and the Wall Street Journal
also conducts a similar survey that is
reported in great detail on its website.
Direct measurement of consumers’
inflation expectations is gathered by
the monthly Reuters/University of
Michigan survey of consumers, which
asks a random sample of consumers for
their forecasts of inflation. For other
countries there have been a number of
surveys, most notably Consensus Forecasts, which gathers detailed forecasts
for all major developed countries in
the world and less detailed forecasts
for numerous other countries. Also,
the European Central Bank started
about forecasts for inflation in both
the CPI and PCE price index for the
next five years and the next 10 years,
while the Livingston Survey asks about
real GDP growth and CPI inflation
for the next 10 years. In addition, in
every survey, the SPF asks about the
probability of a decline in real GDP
in each of the next five quarters and
about the probability that real GDP,
the inflation rate in the GDP price
index, the CPI excluding food and
energy, and the personal consumption expenditures (PCE) price index
excluding food and energy will fall into
certain ranges. The latter questions are
designed to get an idea of the degree of
uncertainty that forecasters attach to
their forecasts. Each survey also asks
special questions from time to time on
a variety of topics of current interest.
Both the SPF and the Livingston
Survey provide anonymity for the
forecasters. The survey news release
lists the names of the forecasters, but
a reader cannot tell which forecaster
provided which forecast. The benefit
of anonymity is that the forecasters
www.philadelphiafed.org
TABLE
Variables Included in the Surveys
Both Surveys
nominal gross domestic product (GDP)
real (inflation-adjusted) GDP
unemployment rate
inflation (consumer price index, CPI)
industrial production
corporate profits after tax
business fixed investment
housing starts
interest rate on three-month Treasury bills
interest rate on 10-year Treasury notes
Livingston Survey
producer price index
S&P 500 stock prices
average weekly earnings
prime interest rate
retail trade sales
auto sales
Survey of Professional Forecasters
interest rate on AAA bonds
payroll employment
GDP price index
consumer price index excluding food and
energy prices
personal consumption expenditures price
index
personal consumption expenditures price
index excluding food and energy prices
consumption
residential fixed investment
federal government spending
state and local government spending
net exports
changes in private inventories
may be more likely to reveal their true
forecasts if they know that their name
will not be associated with a particular
forecast. If they think that their forecast is very different from that of other
forecasters, they would have no incentive to hide it. However, if they were
providing their forecasts in a nonanonymous survey (such as the Wall Street
Journal or the Blue Chip survey), they
might prefer to shade their forecasts
closer to the consensus, out of fear that
they will be seen as being out of the
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mainstream. Other forecasters might
be looking for attention and might intentionally make their forecasts stand
out from the crowd. The anonymity of
the SPF and Livingston avoids these
problems.4
4
In his study, Owen Lamont looked at a
nonanonymous survey, finding that forecasters
tended to distort their forecasts to manipulate
their reputations, while Tom Stark’s study
found no such evidence for the SPF, which is
anonymous.
The timing of the SPF and Livingston surveys differs, in part because
the SPF is conducted four times each
year, while the Livingston survey is
conducted just twice a year. More important, since the SPF focuses on the
national income accounts, the survey
forms are sent to participants immediately following the initial release of the
GDP data for the preceding quarter,
which occurs in late January, April,
July, and October each year. The
forecasters are given about 10 days to
Business Review Q3 2010 3
respond to the survey questions, and
they then e-mail their responses to the
Philadelphia Fed before the middle of
the following month (when key data
on consumer prices are released). The
Livingston Survey’s timing is based on
the release of the consumer price index
data in May and November, with the
forecasts due before the next month’s
release of the consumer price index.
EVALUATING THE SURVEY
FORECASTS
Given the uses to which people,
firms, and policymakers put the
surveys, it is important that the
forecasts be accurate. Of course, no
forecast is going to be on the mark
all the time. Economists have tested
the surveys extensively. Simple tests
examine the forecast errors over time
to see if they are zero, on average,
which would be the hallmark of
an unbiased forecast. Another test
is how precise the forecast is, that
is, how large the average error is.
More sophisticated tests look at the
correlation between forecast errors and
information available to forecasters
when they made their forecasts; if such
a correlation exists, the forecasters
in the survey are not using that
information efficiently.
A visual inspection of the data
sometimes suffices to see whether a
particular forecast has forecast errors
that are zero, on average. Figure 1
shows a scatter plot in which the
value of the inflation rate (based on
the GDP deflator over a one-year
period) is plotted on the vertical axis
and the forecasts from the Livingston
Survey for that year are plotted on
the horizontal axis. The 45-degree
line in the figure helps you gauge the
accuracy of the forecasts because if
the forecasts were perfect, every point
in the diagram would be on that line.
The fact that most of the points in
the graph are close to the 45-degree
4 Q3 2010 Business Review
line suggests that the forecasts are
fairly accurate. Formal statistical
tests confirm that the mean forecast
error in this series is not statistically
significantly different from zero.5
Despite the unbiasedness of the survey
forecasts over the entire period from
the early 1970s to the mid-2000s,
there are numerous periods in which
the survey forecasts appear to have
performed poorly. Figure 2 shows the
actual values of inflation (measured
using the GDP price index) over a oneyear period compared with the SPF
forecasts for the corresponding period.
The SPF forecasts for inflation
were clearly far from the mark in
the early and late 1970s, with very
large forecast errors. Perhaps these
forecast errors were understandable,
given the unprecedented increase in
the growth of the money supply that
occurred during that decade, which
caught forecasters by surprise. In the
early 1980s, the forecasts were wrong
in the opposite direction, as inflation
fell much more than the forecasters
thought it would. Similarly, in most
of the 1990s, the forecasters made a
string of forecast errors, with inflation
continually coming in lower than
the forecasters had projected. In that
period, productivity growth surged,
and it took some time before the
forecasters realized that the economy
was not overheating, but rather that
potential output was increasing more
rapidly than before, so inflation would
not be rising significantly.6 Thus, the
forecasters clearly go through periods
in which they make persistent forecast
errors.
In addition to periods in which
the forecasters make persistent forecast
5
See my 2010 paper.
6
These concepts are explored in more detail in
my 2010 paper.
errors, the forecasters in the surveys
may be inefficient in their use of other
information. Economists test this idea
by examining the relationship between
the survey’s forecast errors and data
that were known when the forecasters
made their forecasts. For example,
Laurence Ball and I found that output
forecast errors were associated with
changes in the real (inflation-adjusted)
federal funds rate (the interest rate
on short-term loans between banks,
which is the Federal Reserve’s main
policy instrument), which means that
the forecasters did not accurately
modify their forecasts in response to
a change in monetary policy. This
can be seen in Figure 3, which plots
the output forecast error from the
SPF (the actual rate of output growth
minus the forecasted rate of output
growth) against the lagged change
in the real federal funds rate. The
negative relationship between these
two variables implies that the output
forecasts from the SPF are not efficient
with respect to changes in monetary
policy.
A little-explored aspect of the SPF
is the probability distribution forecasts
it provides. Each forecaster is asked
to list the probability that real GDP
growth and inflation in the GDP price
index will fall into certain ranges. In
the most recent surveys, the forecasters
are asked to state the probability that
real GDP growth in the next year
will be 6 percent or more, 5.0 to 5.9
percent, 4.0 to 4.9 percent, 3.0 to
3.9 percent, 2.0 to 2.9 percent, 1.0
to 1.9 percent, 0.0 to 0.9 percent, -1.0
to -0.1 percent, -2.0 to -1.1 percent,
and -2.0 percent or less. The same
question is also asked for real GDP
growth in the following year. For the
percent change in the GDP price
index, the ranges are two percentage
points higher, so the top range is 8
percent or more, and so on.
www.philadelphiafed.org
FIGURE 1
Forecasts Versus Actuals: Livingston Survey
Actual (percent)
15
10
5
0
0
10
5
15
Forecast (percent)
FIGURE 2
Percent
14
12
Actual
10
8
6
Forecast
4
2
0
0
1971
1976
1981
1986
Percent
8
1991
Date
1996
2001
2006
Frank Diebold, Anthony Tay,
and Kenneth Wallis analyzed these
probability distribution forecasts from
the SPF using new methods. Their
goal was to test the accuracy of the
distribution forecasts, and for the most
part, they found that the forecasts
were reasonably accurate. However,
the forecasts failed to pass some tests:
(1) they placed too large a probability
on a large decline in inflation; and
(2) they made persistent inflation
forecast errors, though the forecasters
eventually adapted and the errors
disappeared. They also found that
when inflation was low, uncertainty
about inflation was also low.
Overall, recent research on the
accuracy of the SPF and Livingston
forecasts has found that they are
reasonable, even if there are a few
areas in which they are imperfect.
However, as the literature using the
surveys for research evolved over time,
the accuracy of the forecasts was often
called into question.
USING THE SURVEYS
TO ANSWER RESEARCH
QUESTIONS
We now turn to a discussion
about the areas of research in which
researchers have used the SPF and
Livingston Survey. These include
investigating whether people have
rational expectations, studying how
people form their expectations,
conducting empirical studies of
macroeconomic theories, and
6
4
Forecast Error
2
0
-2
-4
1971
1976
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1981
1986
1991
Date
1996
2001
2006
7
This section discusses many of the major
research studies that have used the surveys.
For a more complete list of such studies, see
the bibliographies posted on the Philadelphia
Fed’s website at www.philadelphiafed.org/
research-and-data/real-time-center/survey-ofprofessional-forecasters/academic-bibliography.
cfm and www.philadelphiafed.org/researchand-data/real-time-center/livingston-survey/
academic-bibliography.cfm.
Business Review Q3 2010 5
FIGURE 3
Output Forecast Errors and Change in
Real Fed Funds Rate
Output Forecast Error
6
4
2
0
-2
-4
-6
-8
-8
-6
-4
-2
0
2
4
6
8
10
Change in Real Federal Funds Rate
answering questions about monetary
policy.7
Economists have written major
research papers using both the SPF
and Livingston surveys, beginning with
Stephen Turnovsky. Turnovsky tried
to show how forecasters formed their
expectations, and he developed an
early test of rationality of the forecasts
using the Livingston Survey. The
first paper to use the SPF (actually its
predecessor, the ASA/NBER survey)
was one by Vincent Su and Josephine
Su, which evaluated the accuracy of
the survey forecasts using only a few
years of data.
None of the earliest papers to use
the Livingston Survey are reliable,
however, because John Carlson
discovered a major flaw in the data
(which has subsequently been fixed).
Because the survey’s original purpose
was for journalism, Livingston did
not report the actual forecasts of the
6 Q3 2010 Business Review
forecasters in his newspaper column.
Instead, he modified the forecast
data if a data release occurred after
the forecasters had submitted their
forecasts but before his newspaper
column appeared and if the data
release would have changed the overall
nature of the forecasts.
Carlson gives the following example. Suppose the CPI was released
in September and October with a
value of 121.1 and the forecasters have
an average forecast for the following
June of 121.2. Then, if the November
data release (which came out after the
forecasters had answered the survey
but before the survey results were
reported) for the CPI is 121.1, the June
forecast is reasonable and Livingston
would not adjust the forecast. But
suppose the November CPI data were
released as 121.6. Then if Livingston
reported the November number and
the June forecast, it would appear that
the forecasters thought there would be
deflation, even though they were really
forecasting a small amount of inflation.
So, Livingston would instead report a
forecast of 121.7, which maintains the
0.1 increase in the CPI that the forecasters thought would happen. But this
means that the reported forecasts were
fictional and depended on Livingston’s
personal judgment. Carlson remedied
this situation by obtaining the true
forecast values from Livingston and
thus restoring the integrity of the data
set. Carlson showed that Livingston’s
adjustments made the forecasts look
better. Studies based on the incorrect data obtained somewhat different
results compared with results based on
the corrected data.
Rational Expectations. The
Philadelphia Fed’s surveys of
forecasters were initially used by
researchers in the early 1970s to
investigate the concept of rational
expectations, which asserts that
people do not make systematic errors
in forecasting. A number of early
papers had used the Livingston Survey
forecasts of inflation and rejected
the rational expectations hypothesis
because researchers found that the
survey forecasts were biased (with
a nonzero mean forecast error) and
inefficient (because the forecast errors
were correlated with data known when
the survey was taken).
But in a 1978 study, Donald
Mullineaux found a major flaw in the
statistical procedure previous studies
had used to test for and reject the
rationality of expected inflation in
the Livingston Survey.8 Mullineaux
then proposed a new test that is not
8
The flaw is that the test used in much previous
work (known as the Chow test) assumed identically and independently distributed (i.i.d.) errors
in a framework where that is unlikely to hold.
Mullineaux showed that the assumption can be
rejected.
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subject to the same statistical problem
and found that the properly specified
data are consistent with people having
rational expectations.
The early literature on rational
expectations often ran tests for
unbiasedness and inefficiency of the
survey forecasts. But those tests were
flawed in an important way because
they failed to account for the fact
that a forecast error in one survey
forecast carried over to other surveys
because the length of the forecast
horizon (eight or 14 months) was
longer than the interval between
surveys (six months). Thus, a sudden
rise in inflation in one period could
lead to forecast errors in two or three
consecutive surveys, a situation
that has come to be known as the
overlapping observations problem.
By failing to account for this
correlation in the forecast errors, the
researchers’ tests for unbiasedness and
inefficiency were overstating the case
against the surveys. Bryan Brown and
Shlomo Maital finally remedied this
situation, making a key methodological
contribution: recognizing the
overlapping-observations problem and
showing how to adjust the statistical
tests so that they gave the correct
inference. Brown and Maital then
tested the Livingston Survey data for
unbiasedness and efficiency. They
generally found no bias, unlike many
earlier researchers. But they did find
some evidence that the Livingston
Survey forecast errors were correlated
with changes in money growth.9
Another challenge to rational
expectations using the surveys came
from Eugene Fama and Michael
Gibbons. They created alternative
inflation forecasts based on nominal
and real interest rates, as well as
9
A related correlation is found in my paper with
Laurence Ball.
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changes in those rates. They showed
that the inflation forecasts based
on interest rates outperformed the
Livingston Survey forecasts of inflation
from 1977 to 1982.
Many other researchers became
convinced that forecasters did not
have rational expectations. One of
them, Douglas Pearce, did a simple
experiment to show how irrational
the survey forecasts were. Pearce then
constructed a forecast of inflation in
which the change in the inflation rate
from one period to the next depended
only on the unexpected change in the
inflation rate in the previous period
and ignored data on other variables
that a forecaster might use to forecast, including the money supply and
the strength of the economy. Pearce
correctly used only the data that the
participants in the Livingston Survey
had available to them at the time when
they made their forecasts (known as
real-time data; see the study that I did
with Tom Stark for more on this concept of real-time data analysis). Pearce
compared his simple model’s forecasts
with the forecasts from the Livingston
Survey and found that his model had
much better forecasts for inflation than
the survey. He also showed that the
rise in interest rates in the 1970s was
better explained by his simple model
than by the Livingston Survey.
If a very simple model can provide
better forecasts than the forecasters in
the survey, it would seem that the survey forecasts aren’t that valuable, and
professional forecasters are irrational
because they could have used Pearce’s
model and made better forecasts.10
10
Later research showed that Pearce’s results,
though powerful, weakened over time. If
you use the same method that Pearce used
and the additional data that we have today,
you would find that the survey now does
better than the simple model that Pearce
used. See my 2010 paper for an extensive
analysis of the use of Pearce’s method.
After many studies that found
fault with the forecasting surveys,
many economists began to believe that
either people did not have rational
expectations or that the surveys did
not represent people’s true forecasts,
or both. Michael Keane and David
Runkle sought to disprove both
hypotheses, arguing that much of the
literature on testing survey forecasts
for rationality suffered from three
flaws: (1) the use of the average
forecast across forecasters was wrong
because forecasters may have different
information; (2) other research
studies failed to adjust properly for
data revisions; and (3) other research
studies failed to account for the
correlation of forecast errors across
forecasters. Keane and Runkle avoided
these problems by using individual
forecasts on the GNP deflator, basing
their analysis on real-time data (the
first revision of the national income
data, which come out one month after
the initial release), and developing a
statistical method that accounts for
the correlation of forecast errors across
forecasters. They evaluated currentquarter inflation forecasts from the
SPF, finding that they were unbiased
and efficient.
Overall, the literature on rational
expectations has benefited tremendously from the existence of the SPF
and the Livingston Survey. Though
the results of tests for rationality have
been mixed over time, more recent
evaluations generally suggest that the
survey forecasts are fairly accurate
and pass most, though not all, tests for
rationality.
Expectations Formation. Research
on how people form expectations
has a slightly different goal than the
literature on testing rational expectations; it uses the surveys to investigate
what information forecasters use to
form their forecasts and the properties
of their forecasts.
Business Review Q3 2010 7
Alex Cukierman and Paul
Wachtel introduced the idea that
inflation expectations differ across
individuals because people have
different information at their disposal.
In this situation, an increase in
people’s uncertainty about inflation
leads to more variability in their
inflation expectations over time
than when inflation is more stable.
Cukierman and Wachtel used the
Livingston Survey forecasts on CPI
inflation to examine the differences
in inflation expectations across
forecasters. They found that the
variability of expected inflation across
forecasters is positively related to the
variability of the inflation rate and
the growth rate of the economy’s
output. Thus, volatility in the economy
translates into uncertainty in people’s
forecasts.
One branch of this literature is
devoted to finding variables that are
correlated with the survey forecasts,
thus revealing the data that forecasters
find important in forming their
forecasts. In a 1980 study, Donald
Mullineaux used the Livingston Survey
forecasts to examine how forecasters
form inflation expectations, using
real-time data on the money supply
(that is, the data known to forecasters
when they made their forecasts, rather
than revised data). He found that the
forecasters used money-growth data
in forming their forecasts, not just
lagged inflation data, so that inflation
forecasting models that are just
based on past inflation rates are not
efficient. Mullineaux found evidence
that the expectations-formation
process changed over time, perhaps
in response to changes in the way
monetary policy was conducted.
This is an important finding,
since it provides evidence that is
consistent with theoretical research by
Nobel Prize winner Robert Lucas, who
argued that when the Federal Reserve
8 Q3 2010 Business Review
changes the process by which it sets
monetary policy (a process that clearly
changed in the 1970s), equations such
as those describing the formation of
inflation expectations will undergo
significant changes. Mullineaux also
found evidence that the same model
determining inflation expectations
also determines actual inflation, so
that survey forecasts are rational.
One of the most important papers
in this literature is that of Victor Zarnowitz and Louis Lambros, who were
the first to combine and compare the
SPF point forecasts with the probability distribution forecasts.11 They considered two concepts: (1) consensus,
which is the degree to which the point
forecasts are similar across forecasters;
and (2) uncertainty, which is the degree to which an individual forecaster
thinks a certain outcome is likely and
is a measure of how much risk there is
to her or his point forecast. Zarnowitz
and Lambros found that consensus
across forecasters may be very different
from the uncertainty that each individual forecaster has about his or her
forecast. Previously, most researchers
had equated consensus and uncertainty, which had the effect of understating the true degree of uncertainty.
Zarnowitz and Lambros also found
that higher inflation rates were associated with greater uncertainty about
inflation and showed that increased
inflation uncertainty was associated
with lower real output growth.
Recently, numerous researchers
have begun focusing on how
households form their own inflation
11
The difference between a point forecast and a
probability distribution forecast can be illustrated by an example. The survey’s point forecast
for inflation in the next year could be 2.5 percent. The probability distribution forecast might
be a 25 percent chance that inflation will be 1.0
to 1.9 percent, a 50 percent chance that inflation will be 2.0 to 2.9 percent, and a 25 percent
chance that inflation will be 3.0 to 3.9 percent.
expectations. Gregory Mankiw,
Ricardo Reis, and Justin Wolfers noted
that professional forecasters disagree
with each other in their forecasts
of inflation, as do consumers. They
showed that the extent to which
forecasters disagree changes over
time. To explain these disagreements,
they developed a “sticky-information”
model. The basic idea of sticky
information is that collecting and
analyzing information involves
costs, so that people update their
expectations infrequently. They
then used the Michigan survey
of consumers, the SPF, and the
Livingston Survey to verify their
model. They found that their model
helps to explain the irrationality of
inflation expectations, including why
forecast errors are persistent and why
it takes some time before news is
incorporated into the forecasts.
A related paper is that of
Christopher Carroll, who developed
an interesting hypothesis: Households
may not have rational expectations,
but rather form their expectations by
reading professional forecasts, which
are rational. (See How Would You
Forecast?) Households’ expectations
may not be rational because they
only occasionally read the forecasts
of professional forecasters and don’t
always pay attention to them. To
test this view, Carroll examined
whether the forecasts in the Michigan
survey of consumers incorporate
information from the SPF, or vice
versa. By examining the relationship
between the actual inflation rate, the
Michigan consumer survey forecasts,
and the SPF forecasts of inflation, he
was able to show that the Michigan
forecast contains no additional
information that is not already in
the SPF, but the SPF does contain
additional information that is not in
the Michigan survey. He also found
evidence that SPF forecasts affected
www.philadelphiafed.org
How Would You Forecast?
I
f you were asked to forecast the economy,
how would you do it? You might say, “I
am not in the business of forecasting,
so I don’t know how I would construct
forecasts of the economy!” But it turns
out that most of us have some intuition
about how the economy is going to fare in the future. For
example, the Michigan survey of consumers asks people
who are not economists what they think the inflation
rate will be over the coming year, and the consumers
answer the question very well, in some periods forecasting
inflation better than the professional economists in the
Livingston Survey and the SPF.
One thing you could do is to look at recent values
and assume that the future will be just like today. Or
you might take a class at your local university and learn
techniques of time-series forecasting, which would be far
more sophisticated than assuming the future is like today
and would give you much better forecasts. But most of us
do not want to spend that much time to forecast for three
good reasons: (1) the costs of forecasting are high because
most of us do not know much about forecasting; (2) the
benefits of forecasting are low because our lives are not
strongly affected by being able to forecast better; and (3)
we can read the newspaper or surf the web and easily
learn about the forecasts of experts, so why should we
bother to make our own?
As our discussion in the text of Christopher Carroll’s
research suggests, most people do not spend much time
forecasting, but they do read about forecasts in the
media and on the Internet. As a result, the forecasts of
experts are distributed around the country gradually over
time. Thus, even though only a few economic experts
take the time to work out their own forecasts, their
views influence the forecasts of many citizens and thus
affect economic activity. A further reason to turn to a
survey like the Survey of Professional Forecasters or the
Livingston is that the surveys combine the efforts of a
number of forecasters who often look at the economy
from different perspectives. As a result, a forecast that
averages all of the projections (using the mean or the
median) is often superior to any individual forecast.*
*There is a substantial amount of research in the area of forecast combination, which shows that simple averages of many forecasts often perform
better than nearly all individual forecasts. See Alan Timmermann’s article for an overview.
later Michigan surveys but that the
Michigan survey did not affect later
SPF forecasts. This result suggests
that, over time, households come to
incorporate the SPF forecasts. Carroll’s
results are also supported by the fact
that when news coverage of inflation
is high, Michigan forecasts get closer
to SPF forecasts. Similar results occur
when Carroll uses the unemployment
rate in his empirical work, rather than
the inflation rate.
Empirical Macroeconomics.
One puzzle that survey forecasts
helped solve was the issue of why
real (inflation-adjusted) interest rates
declined so much in the 1970s. James
Wilcox used the Livingston Survey
forecasts of inflation in an attempt to
www.philadelphiafed.org
determine the main factors affecting
nominal and real interest rates. He
discovered that much of the decline
in real interest rates in the 1970s
(though not all) was due to increases
in expected inflation rates. He argued
that previous models failed to include
a supply-shock variable representing
the prices on inputs, such as oil prices.
Once he included such a variable and
used the Livingston Survey forecasts
to represent expected inflation in
calculating the real interest rate, his
model fit the data well. In a related
paper, Kajal Lahiri, Christie Tiegland,
and Mark Zaporowski found that
uncertainty about inflation (measured
using the probability variables in the
SPF) also affected real interest rates.
Their main result was that increased
uncertainty about inflation causes
the real interest rate to decline, with
investment spending declining more
than saving.
One of the most famous papers
that empirically tests macroeconomic
theory was that of Robert Hall, who
found evidence supporting economists’
major theory of consumption, which
is that income in a given year has
less impact on consumption spending
than households’ long-run average
income, a theory known as the lifecycle/permanent-income hypothesis.
Hall used the Livingston Survey to
calculate the expected inflation rate
and the expected return to the stock
market. He also found that changes in
Business Review Q3 2010 9
the real interest rate have little effect
on consumption spending, much less
than some economists had thought
before Hall’s research.
This discussion only touches
on some of the main studies in the
empirical macroeconomics literature
that have benefited from the
Philadelphia Fed’s surveys.
Monetary Policy. One of
the main mechanisms by which
monetary policy affects the economy
is by affecting people’s inflation
expectations. Researchers have
suggested that the Federal Reserve
bases monetary policy on inflation
and the degree to which output
in the economy is above or below
trend (known as the output gap).
The equation relating the federal
funds interest rate (which measures
monetary policy) to inflation and the
output gap is known as the Taylor rule,
named after John Taylor of Stanford
University, who developed the idea.
Most of the research done in this area
suggests that the Fed looks at past
inflation and the past output gap.
But Athanasios Orphanides used the
SPF to obtain forecasts of inflation
and output to use in the Taylor rule
and found that this produced better
estimates of what the Fed did than
using past data. Thus, the Fed appears
to follow a forward-looking Taylor rule
rather than a backward-looking rule.
How does the Fed respond to
changes in expected inflation? Sylvain
Leduc, Keith Sill, and Tom Stark
investigated this issue, using the
Livingston Survey as a source for the
economy’s expected inflation rate.
They found that before 1979, the Fed
responded to increases in expected
10 Q3 2010 Business Review
inflation by increasing the federal
funds interest rate. But because the
Fed did not increase the interest rate
by as much as expected inflation
increased, the real interest rate
declined. This more accommodative
monetary policy was followed by higher
inflation, and the authors concluded
that monetary policy contributed
to the rise in inflation in the 1970s.
However, after 1979, the Fed did the
opposite, tightening monetary policy
percentage-point rise in the long-term
expected inflation rate implies a 20
percent reduction in stock prices. Sean
Campbell and Frank Diebold showed
that the Livingston Survey could be
used to predict stock returns, with
stronger economic growth related to
lower stock returns, and vice versa.
The surveys have also been
used to investigate optimal methods
of forecasting. Andrew Ang, Geert
Bekaert, and Min Wei compared
( )
" * "
'
when expected inflation increased,
thus raising the real interest rate and
reducing future inflation.
Other Important Research
Results. One key question about the
data that are issued by government
statistical agencies is whether data
revisions are forecastable or not. Knut
Mork sought to answer that question
using the SPF survey as a measure of
information known at the time the
government releases its initial GDP
data. He found that GDP revisions
were correlated with the SPF forecast
of GDP, and thus the revisions were
forecastable, which means that the
government’s initial data releases are
not efficient and could be improved.
Some economists have also
used the Philadelphia Fed surveys to
investigate a hypothesis in financial
economics. Steven Sharpe related the
SPF forecasts of one-year inflation
rates and 10-year inflation rates to
stock returns, finding that a one-
inflation forecasts from the Livingston
Survey, SPF, and the Michigan survey
of consumers. They found that the
surveys forecast inflation better than
do a number of other forecasting
models that economists use. They also
found that the Michigan forecasts are
only slightly worse than the SPF and
Livingston forecasts but still do better
than the other forecasting methods.
SUMMARY
There can be little doubt that
the Philadelphia Fed’s surveys
of forecasters have played an
instrumental role in economic research
in the past 40 years. The surveys have
been used to test rational-expectations
theory, to analyze the formation of
inflation expectations, to conduct
empirical research in macroeconomics,
and to investigate the formation and
impact of monetary policy, and they
have been used in a variety of other
studies as well. BR
www.philadelphiafed.org
REFERENCES
Ang, Andrew, Geert Bekaert, and Min Wei.
“Do Macro Variables, Asset Markets, or
Surveys Forecast Inflation Better?” Journal
of Monetary Economics, 54 (May 2007), pp.
1163–1212.
Ball, Laurence, and Dean Croushore.
“Expectations and the Effects of Monetary
Policy,” Journal of Money, Credit, and Banking, 35 (August 2003), pp. 473-84.
Brown, Bryan W., and Shlomo Maital.
“What Do Economists Know? An Empirical Study of Experts’ Expectations,” Econometrica, 49 (March 1981), pp. 491–504.
Campbell, Sean D., and Francis X. Diebold.
“Stock Returns and Expected Business
Conditions: Half a Century of Direct
Evidence,” Journal of Business and Economic
Statistics, 27 (April 2009), pp. 266-78.
Carlson, John A. “A Study of Price
Forecasts,” Annals of Economic and Social
Measurement, 6 (Winter 1977), pp. 27-56.
Carroll, Christopher D. “Macroeconomic
Expectations of Households and Professional Forecasters,” Quarterly Journal of
Economics, 118 (February 2003), pp. 269-98.
Croushore, Dean. “Introducing: The Survey
of Professional Forecasters,” Federal Reserve
Bank of Philadelphia Business Review (November/December 1993).
Croushore, Dean. “The Livingston Survey:
Still Useful After All These Years,” Federal
Reserve Bank of Philadelphia Business
Review (March/April 1997).
Croushore, Dean. “An Evaluation of Inflation Forecasts from Surveys Using RealTime Data,” BE Journal of Macroeconomics,
10:1 (2010).
Croushore, Dean, and Tom Stark. “A
Funny Thing Happened on the Way to
the Data Bank: A Real-Time Data Set for
Macroeconomists,” Federal Reserve Bank
of Philadelphia Business Review (September/
October 2000), pp. 15-27.
Cukierman, Alex, and Paul Wachtel. “Differential Inflationary Expectations and the
Variability of the Rate of Inflation: Theory
and Evidence,” American Economic Review,
69 (September 1979), pp. 595-609.
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Diebold, Francis X., Anthony S. Tay, and
Kenneth F. Wallis. “Evaluating Density
Forecasts of Inflation: The Survey of Professional Forecasters,” in Robert F. Engle
and Halbert White, eds., Cointegration,
Causality, and Forecasting: A Festschrift in
Honor of Clive W.J. Granger. Oxford: Oxford
University Press, 1999, pp. 76–90.
Fama, Eugene R., and Michael R. Gibbons.
“A Comparison of Inflation Forecasts,”
Journal of Monetary Economics, 13 (1984),
pp. 327-48.
Hall, Robert E. “Intertemporal Substitution in Consumption,” Journal of Political
Economy, 96 (April 1988), pp. 339-57.
Keane, Michael P., and David E. Runkle.
“Testing the Rationality of Price Forecasts:
New Evidence from Panel Data,” American
Economic Review, 80 (1990), pp. 714-35.
Lahiri, Kajal, Christie Teigland, and Mark
Zaporowski. “Interest Rates and the Subjective Probability Distribution of Inflation
Forecasts,” Journal of Money, Credit, and
Banking, 20 (May 1988), pp. 233-48.
Lamont, Owen. “Macroeconomic Forecasts
and Microeconomic Forecasters,” Journal
of Economic Behavior and Organization, 48
(July 2002), pp. 265-80.
Leduc, Sylvain, Keith Sill, and Tom Stark.
“Self-Fulfilling Expectations and the
Inflation of the 1970s: Evidence from the
Livingston Survey,” Journal of Monetary Economics, 54 (March 2007), pp. 433-59.
Mankiw, N. Gregory, Ricardo Reis, and
Justin Wolfers. “Disagreement about Inflation Expectations,” NBER Macroeconomics
Annual 2003, pp. 209-48.
Mork, Knut Anton. “Ain’t Behavin’:
Forecast Errors and Measurement Errors in
Early GNP Estimates,” Journal of Business
and Economic Statistics, 5 (April 1987), pp.
165-75.
Mullineaux, Donald J. “On Testing for Rationality: Another Look at the Livingston
Price Expectations Data,” Journal of Political
Economy, 86 (April 1978), pp. 329-36.
Mullineaux, Donald J. “Inflation Expectations and Money Growth in the United
States,” American Economic Review, 70
(March 1980), pp. 149-61.
Orphanides, Athanasios. “Historical Monetary Policy Analysis and the Taylor Rule,”
Journal of Monetary Economics, 50 (July
2003), pp. 983-1022.
Pearce, Douglas K. “Comparing Survey and
Rational Measures of Expected Inflation:
Forecast Performance and Interest Rate Effects,” Journal of Money, Credit, and Banking,
11 (November 1979), pp. 447-56.
Sharpe, Steven A. “Reexamining Stock
Valuation and Inflation: The Implications
of Analysts’ Earnings Forecasts,” Review
of Economics and Statistics 84 (November
2002), pp. 632-48.
Stark, Tom. “Macroeconomic Forecasts and
Microeconomic Forecasters in the Survey of
Professional Forecasters,” Federal Reserve
Bank of Philadelphia Working Paper 97-10
(August 1997).
Su, Vincent, and Josephine Su. “An Evaluation of ASA/NBER Business Outlook
Survey Forecasts,” Explorations in Economic
Research, 2 (Fall 1975), pp. 588-618.
Taylor, Herb. “The Livingston Surveys: A
History of Hopes and Fears,” Federal Reserve Bank of Philadelphia Business Review
(January/February 1992).
Timmermann, Alan. “Forecast Combinations,” in G. Elliott, C.W.J. Granger, and A.
Timmermann, eds., Handbook of Economic
Forecasting. (Amsterdam: North Holland,
2006), pp. 135-96.
Turnovsky, Stephen J. “Empirical Evidence
on the Formation of Price Expectations,”
Journal of the American Statistical Association, 65 (December 1970), pp. 1441-59.
Wilcox, James A. “Why Real Interest Rates
Were So Low in the 1970s,” American Economic Review, 73 (March 1983), pp. 44-53.
Zarnowitz, Victor, and Louis A. Lambros.
“Consensus and Uncertainty in Economic
Prediction,” Journal of Political Economy, 95
(June 1987), pp. 591-621.
Business Review Q3 2010 11
CEO Pay and Corporate Governance*
BY ROCCO HUANG
O
ver the past few years, there has been strong
public outrage against current pay practices
for corporate CEOs. To deal with this issue,
the Dodd-Frank Wall Street Reform and
Consumer Protection Act signed into law by President
Obama on July 21, 2010 will allow shareholders to vote
on executive pay packages and federal regulators to
oversee executive compensation at financial firms. Are
there problems with CEO pay? According to a recent
survey, 98 percent of respondents from major financial
institutions “believe that compensation structures were a
factor underlying the crisis.” In this article, Rocco Huang
outlines what we know about how CEOs are paid, how
the pay is set, how CEO compensation affects CEOs’
incentives and actions and their firms’ performance, and
how government regulations affect CEO pay.
Recently, there has been strong
public outrage against current pay
practices for corporate CEOs, regardRocco Huang
is an assistant
professor of
finance at the Eli
Broad College
of Business,
Michigan State
University.
When he wrote
this article,
he was an economist in the Research
Department of the Philadelphia Fed. This
article is available free of charge at www.
philadelphiafed.org/research-and-data/
publications/.
12 Q3 2010 Business Review
ing both their high level relative to
that of ordinary workers and their
perceived insensitivity to poor performance. A search of the key words
“executive compensation” in the New
York Times returns 168 articles for the
first six months of 2009 and only 23
during the same months in 2008 —
a sevenfold increase. In June 2009,
President Obama appointed Kenneth
*The views expressed here are those of the
author and do not necessarily represent
the views of the Federal Reserve Bank of
Philadelphia or the Federal Reserve System.
Feinberg as the special master for
executive compensation (known in
the media as the “pay czar”) to oversee
the compensation of top executives at
companies that have received federal
bailout assistance. To deal with this
issue, the Dodd-Frank Wall Street
Reform and Consumer Protection Act
signed into law by President Obama
on July 21, 2010 will allow shareholders
to vote on executive pay packages and
federal regulators to oversee executive
compensation at financial firms.
Are there problems with CEO
pay? According to a recent survey by
the Institute of International Finance
(IIF), 98 percent of respondents from
major financial institutions “believe
that compensation structures were a
factor underlying the crisis.” By analyzing executive compensation data,
financial economists have improved
their understanding of CEO pay. We
know a lot about how CEOs are paid,
how the pay is set, how CEO compensation affects CEOs’ incentives and
actions and firm performance, and
how government regulations affect
CEO pay.
HOW ARE CEOs PAID?
The structure of CEO pay is more
complicated than just a base salary
plus bonus. Their pay packages are not
just bigger; they are also very different from those of ordinary workers.
We need to understand the special
structure of CEOs’ packages before we
can say anything about whether they
are paid too much. Unless otherwise
stated, we will focus on CEO pay practices in the United States because they
have been much better researched.
Cash bonuses account for, on avwww.philadelphiafed.org
erage, half of a CEO’s total compensation. We may think that a CEO is paid
a bonus for better performance. But
the evidence suggests otherwise. Kevin
Murphy has combed through the
research of his fellow economists and
finds no evidence of a significant relationship between the size of a CEO’s
cash bonus and the firm’s performance,
measured as return on equity or stock
returns. And indeed, Yaniv Grinstein
and Paul Hribar find that CEOs were
as likely to receive bonuses for making
acquisitions that negatively affected
shareholder wealth (as measured by
negative stock returns upon announcement of the acquisition news) as well
as for acquisitions that increased shareholder wealth.1 Thus, CEOs receive
a bonus for an acquisition whether or
not shareholders believe the acquisition increases their wealth.2
If cash bonuses are not much
different from base salaries except that
they are paid at the end of the year
instead of every month, what motivates
CEOs to work harder and to make better decisions for the company? Equitybased compensation such as stock
options plays an important role.
An option is a contract that
gives the owner the right, but not the
obligation, to buy or sell an underlying
asset at a specific price on or before
a certain date. CEOs are typically
awarded options allowing them to
purchase company stock on a future
date but at the stock price prevailing
at the time the option is granted, thus
allowing CEOs to benefit from future
1
The change in stock price after the announcement reflects how shareholders believe the
acquisition is affecting their financial interests.
The stock price declines if shareholders believe
that the announcement is bad news.
2
Note that it is certainly possible that the
CEOs may be right and the shareholders may be
wrong about the acquisition’s eventual benefits
and costs.
www.philadelphiafed.org
stock-price appreciation if the company
performs well. Naturally, stock options
reward CEOs for better performance
if better performance leads to higher
future stock prices. Indeed, Brian Hall
and Jeffrey Liebman’s study finds that,
unlike cash bonuses, stock options
make CEO pay very sensitive to company performance.
For example, in their sample, Hall
and Liebman find that, for a moderate
change in firm performance (moving
from a median stock-price performance
pensions. CEOs stay in their positions
for six years, on average. According to
Lucian Bebchuk and Robert Jackson,
the executives’ pension plans had a
median actuarial value of $15 million.
The ratio of the executives’ pension
value to the executives’ total compensation during their service as CEO had
a median value of 34 percent. That
is, more than a quarter of the money
the CEO receives from the company
comes after his or her retirement.
They also find that, in contrast to
for CEOs is their pensions.
to a 70th percentile performance), a
CEO’s compensation increases more
than 50 percent, which represents an
increase in CEO wealth of about $1.8
million. Most of the increase comes
from appreciation in the value of stock
options. That’s a large reward for improving a company’s performance from
middle of the pack to better than 70
percent of its peers.
Lucian Bebchuk and Yaniv
Grinstein show that the level of CEO
compensation in the United States had
been increasing in the decade before
the stock market downturn in 2001,
and the lion’s share of the increases resulted from equity-based compensation
such as stock options (see the figure).3
Finally, another important source
of compensation for CEOs is their
3
Taxation and accounting considerations partly
contributed to the popularity of equity-based
compensation. Section 162(m) of the Internal
Revenue Code, enacted in 1993, places a $1
million limit on the deductibility (against
corporate profits) of non-performance-related
executive compensation, giving rise to a tax
advantage for equity-based compensation.
Furthermore, until 2006 when it became
mandatory, firms were not required to count
stock option grants to executives as expenses on
their income statement, allowing them to report
higher earnings.
stock options, CEOs’ pensions do not
depend much on their performance
on the job. Poorly performing CEOs
do not get less pension money after
retirement. After a CEO retires, he is
seldom in the media spotlight, and few
people bother to look into the paycheck of a retired CEO. Bebchuk and
Jackson’s research, however, illustrates
that the omission of pension plan values by researchers and the media leads
to significant overestimation of the
extent to which executive pay is linked
to performance.
Finally, the composition of CEO
compensation is somewhat different in
commercial banks. (See What’s Different About Compensation for Commercial
Bank CEOs?)
Compensation Practices Outside the U.S. There are relatively few
academic studies on CEO pay practices
outside the United States. The several
studies that have been conducted suggest that, in other countries, CEO pay
is much lower and stock options play a
much less important role. A study by
Martin J. Conyon, John E. Core, and
Wayne R. Guay compares the largest 250 British companies with about
1,200 U.S. firms of similar size and
Business Review Q3 2010 13
FIGURE
Percent
600
Increase in equity-based compensation - all firms
500
400
300
200
100
0
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2002
2003
Percent
160
Increase in cash-based compensation - all firms
140
120
100
80
60
40
20
0
1993
1994
1995
1996
1997
1998
1999
2000
2001
Source: Bebchuk, Lucian A., and Grinstein, Yaniv. “The Growth of Executive Pay,” Oxford Review
of Economic Policy, 21 (2005), pp. 283-303.
finds that, in 1997, the median pay of
a U.S. CEO was more than twice the
median pay of a British CEO. But the
gap is shrinking: In 2003 the median
pay of a U.S. CEO was only 30 percent
more. However, the personal fortune
of U.S. CEOs is tied much more closely
to company stock-price movements.
In 2003, their equity incentives (the
sensitivity of the value of their stock
and options holdings to changes in
stock prices) were about 4.6 times
greater than those of UK CEOs. After
adjusting for what is reasonably needed
14 Q3 2010 Business Review
to compensate U.S. CEOs for bearing
the higher risk of equity-based compensation, the researchers find that the
risk-adjusted pay for the U.S. CEOs is
not consistently higher than that for
UK CEOs.
CEOs SET THEIR OWN PAY
WHEN THEY CAN
CEO Compensation Is High
When the Board Is Weak. Your
bosses set your pay, but who sets the
pay for the CEO? The board of directors (in the U.S. representing the inter-
ests of shareholders, and in some other
countries, other stakeholders as well)
supervises a CEO and sets his pay. It
has become more common for the full
board to delegate a compensation committee to set a CEO’s pay. Normally,
the human resources department
makes an initial recommendation.
Then the compensation committee
reviews the recommendation and, if
necessary, revises it, sometimes with
input from compensation consulting
firms such as Towers Perrin. Finally,
the full board of directors votes on the
CEO pay proposal.
Strictly speaking, there is no such
thing as “CEOs without bosses,” unless
the CEO happens to be the majority
owner of the company, a rarity among
large corporations. Let’s rephrase the
question: Who sets the pay for the
CEOs who are effectively their own
bosses because they have more power
than the board of directors?
A CEO has financial incentives
to persuade the board of directors and
influence the pay-setting procedure
in a direction that enriches him. The
outcome of the bargaining depends on
the CEO’s relative influence vis-à-vis
the board of directors’. Below we present some evidence that in firms where
the CEOs are more powerful, they are
paid more. (For alternative views on
how higher CEO pay can better serve
the interests of shareholders, see Maybe
It’s Really Worth Paying Top Dollar for
Managers.)
A study by John Core, Robert
Holthausen, and David Larcker identifies the following corporate board
arrangements as potential causes of a
weaker board vis-à-vis the CEO. First,
having a large number of directors on
the board can make the board weaker
because it’s harder for a large board
to coordinate and override the CEO’s
wishes. Second, more of the outside
directors — that is, the directors who
are not current or past employees of
www.philadelphiafed.org
What’s Different About Compensation
for Commercial Bank CEOs?
T
he structure of CEO compensation at commercial banks
differs from that in other industries. According to a study
by Kose John and Yiming Qian, based on a sample of 120
commercial banks from 1992 to 2000, CEOs’ pay-performance
sensitivity is lower in banking firms than in manufacturing
firms. In particular, the sensitivity is lower in more highly leveraged banks.
Elijah Brewer, William Hunter, and William Jackson document that
equity-based compensation becomes more important after the Riegle-Neal Act
of 1994. They find that, after deregulation, the equity-based component of
bank CEO compensation increases significantly, on average, for the industry.
Riskier banks have significantly higher levels of equity-based compensation, as
do banks with more investment opportunities.
After deregulation, the opportunity to acquire other banks opened
up. Stronger incentives for CEOs may have become more important after
deregulation. For example, Liu Yang, Haluk Unal, and Kristina Minnick
find that higher pay-performance sensitivity leads to more value-enhancing
acquisitions. Among those banks that acquired another bank, highersensitivity banks experienced significantly better announcement returns than
lower-sensitivity banks. Announcement returns are stock returns calculated
in a three-day window around the announcement of acquisitions. The
positive market reaction can be rationalized by better long-term performance.
Following acquisitions, banks with high pay-performance sensitivity experience
greater improvement in their operating performance as measured by the return
on assets.
the firm — have been appointed by
the CEO.4 Third, more of the “outside
directors” are borderline insiders; that
is, the director or his employer receives
payments from the company in excess
of his board pay. Examples include a
board member who is a partner in a
law firm that provides services to the
company or one who is a supplier that
sells products to the company. Fourth,
more of the outside directors are busy;
that is, the director serves on three or
more other boards. Finally, the CEO is
4
In a more recent paper, Jeff Coles, Naveen
Daniel, and Lalitha Naveen call them “cooption board members.”
www.philadelphiafed.org
the chairman of the board and makes
himself literally the boss of his supposed bosses.
Core, Holthausen, and Larcker
then show that companies with such
boards give their CEOs higher pay.
However, it is important to note that
a correlation between CEO pay and
certain firm characteristics, such
as board size, does not necessarily
equal causality and results should be
interpreted with some caution. For
example, certain types of complex
businesses may require a higher quality
CEO (and hence one who earns higher
pay), a larger board to provide a broad
array of advice, more insider execu-
tives on the board to supply operational information, more outsiders with
connections in the industry through
board memberships, and a CEO who
is also the chairman in order to reduce
coordination problems. These correlations do not necessarily prove that, for
example, having a larger board results
in higher CEO pay.
Pay for Performance or for Good
Luck? A study by Marianne Bertrand
and Sendhil Mullainathan finds that
CEOs are rewarded for good luck.
First, let me explain what I mean
by good luck. For a petroleum company with large oil reserves, profits
increase with oil prices, but the CEO
should take no credit for this windfall.
For a company that exports goods to
foreign countries, when the U.S. dollar
gets cheaper, profits go up. Again, the
CEO does nothing to make this happen. These are examples of good luck
and have no relationship to the CEO’s
efforts.
Ideally, CEO pay should not be
tied to luck, that is, factors that affect
firm performance that are beyond
the CEO’s control. The effect of
good luck should be filtered out when
setting CEO pay. However, using
several measures of luck, Bertrand
and Mullainathan find that CEO pay
in fact responds as much to a dollar
earned through luck as to a dollar
earned through CEO effort. For every
one-percentage-point rise in accounting returns due to changes in oil prices
or the exchange rate, they find that
CEO pay increases by about 2 percent,
roughly the same as the response to
accounting returns not due to those
lucky factors.
Many firms have large shareholders who have a strong incentive to
watch over the CEO and who also
have the ability to have their voice
heard. They are the motivated bosses.
They pay their CEOs less for luck.
Bertrand and Mullainathan find that
Business Review Q3 2010 15
for each additional large shareholder
(defined as a shareholder, other than
the CEO, who owns blocks of at least 5
percent of the firm’s common shares),
the pay-for-luck effect declines by 10
percent. For each additional large
shareholder who also has a seat on
the board of directors, the pay-for-luck
effect declines by 33 percent. And in a
firm without any large shareholders, a
CEO who has spent nine years in the
position has about a 35 percent greater
pay-for-luck effect than one who is just
starting at the firm. The overall results
suggest that CEOs without bosses seem
to set their own pay and they set it to
their own advantage.
No one can have good luck all the
time. Are CEOs punished for bad luck?
Gerald Garvey and Todd Milbourn
show that they aren’t. They find that
luck affects pay less when the luck is
bad than when it is good. Their study
finds that the average executive loses
25 to 45 percent less pay from bad luck
than is gained from good luck.
Backdating: How to Make Your
Own Luck. CEOs also seem to be able
to influence the timing of stock option
awards in their favor. In a recent study,
Lucian Bebchuk, Yaniv Grinstein, and
Urs Peyer posit that the practice of
option backdating is more likely when
the CEO is more powerful than the
board of directors, which is supposed
to monitor and discipline him. A
backdated option is one in which the
grant date of the option is chosen
after the date has already passed. It
is like buying a lottery ticket after
seeing the wining number. The three
researchers identify options granted at
the lowest price of the month, which
they call “lucky options.” Choosing
a date when the stock price is low is
a direct transfer from stockholders
to the executive who exercises the
option, since he looks back and sets
the exercise price of the option at the
lowest possible price.
Many CEOs seem to have more
luck than ordinary people. The three
researchers find that during the
period 1996-2005, 12 percent of firms
provided one or more lucky grants
due to opportunistic timing. It is not
surprising that “CEOs without bosses”
are more likely to get lucky. Bebchuk,
Grinstein, and Peyer find that lucky
grants were more likely when the
Maybe It’s Really Worth Paying Top Dollar for Managers
T
here is plenty of evidence suggesting
that CEOs influence their own pay for
their own financial interest. However,
many questions remain unanswered.
For example, it is difficult to explain
why compensation has increased so much in the late
20th century compared with earlier periods, solely on the
basis of weak corporate governance. Actually, greater
pressure from institutional investors should have reduced
the power of CEOs in the past two decades. The current
corporate governance environment is not perfect, but it is
reasonable to say that it was even worse in the early 20th
century.
Xavier Gabaix and Augustin Landier developed a
theoretical model that attempts to explain why CEO pay
has risen so rapidly. They find that a very small dispersion
in CEO talent can justify large pay differences. They
show that the six-fold increase in U.S. CEO pay between
1980 and 2003 can be fully attributed to the six-fold
increase in market capitalization of large companies
during that period. Alex Edmans, a professor of finance
at the Wharton School, has argued that “being slightly
better can have a huge effect on firm value. It’s really
worth paying top dollar for the most talented managers.”*
For example, at a $20 billion company, a half-percent
improvement in results would translate into $100 million,
which is a huge sum of money relative to an average
CEO’s annual pay.
Edmans and Gabaix’s review paper is a good starting
point to read more about the emerging literature that uses
optimal contracting theories to explain many seemingly
inefficient CEO pay arrangements as efficient outcomes,
for example, the recent rapid increase in pay, the low level
of incentives and their negative correlation with firm size,
pay-for-luck, the widespread use of options (as opposed to
stock), severance pay and debt-like compensation such as
pensions, and the insensitivity of incentives to risk.
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16 Q3 2010 Business Review
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company did not have a majority of
independent directors on the board
or the CEO had longer tenure. Both
factors are associated with increased
influence of the CEO on pay-setting
and board decision-making.
However, the size of the gains
from this practice is economically
small. David Aboody and Ron Kasznik
find that the practice increases the
CEO’s option award value by a mean
of $46,700 (the median is $18,500),
representing only 2.5 percent of
reported total CEO compensation.
The puzzle remains: Why do wealthy
CEOs backdate options? Christopher
Armstrong, an accounting professor at
the Wharton School, has speculated
that “maybe they underestimated the
probability of getting caught, or they
thought everyone else was doing it and
they were entitled.”5 At this point, we
can only speculate on the real reasons.
CEO COMPENSATION
STRUCTURE AFFECTS
CORPORATE POLICY
All of the evidence I’ve discussed
so far concerns the division of the
firm’s profits between shareholders and
CEOs. But there is also evidence that
CEO compensation affects corporate decision-making, including the
riskiness of the firm’s operating and
financial decisions and the firm’s accounting policy
Compensation and Firm Risk.
Many studies have shown that compensation does affect CEOs’ incentives and actions, and the investment,
financial, and accounting policies they
adopt. The general finding is that option-like compensation arrangements
are associated with more risk-taking in
the companies these CEOs run. Op-
5
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www.wharton.universia.net/index.cfm?fa=print
Article&ID=1662&language=english
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tions increase in value when the firm’s
stock price becomes more volatile.
The CEO gains when the stock price
is very high, but the option is simply
not exercised when the price is low.
Thus, everything else equal, the holder
of the option prefers firm policies that
increase stock price volatility.
Jeffrey Coles, Naveen Daniel, and
Lalitha Naveen, for example, confirm
that CEO compensation arrangements
affect investment policy, debt policy,
and firm risk. In firms where a large
fraction of CEO pay is in options,
CEOs adopt riskier policies. These
policy choices include relatively more
investment in research and development, more industry focus (that is, less
diversified activities), and higher financial leverage (that is, more debt). Chief
financial officers’ (CFOs) compensation arrangements matter, too. Sudheer
Chava and Amiyatosh Purnanandam
show that in firms in which the CFO
has greater incentive to increase risk
because of stock options, firms use
more short-term debt, which may create more volatile firm performance.
It is important to note that riskier
policies can be both good and bad for
the shareholders. In some cases, without the stock options, a senior executive may be too risk averse (because
he may be afraid of losing his job) and
may fail to maximize shareholders’
interests. But shareholders need to
recognize that the mix of executive
compensation can affect corporate
policies and set executive pay according to the risk profile they desire.
Compensation and Dodgy Accounting. Changing long-term policies
may not have as direct and as fast an
impact as changing short-term earnings numbers in financial reports. On
average, stock prices respond positively
to unexpectedly better earnings numbers and negatively to unexpectedly
worse ones, so CEOs have an incentive to manipulate reported earnings.
Economists have shown that equitybased compensation is related to “earnings management”: activities that may
raise short-term earnings in financial
reports.
Daniel Bergstresser and Thomas
Philippon find that the use of accrual
accounting to manipulate reported
earnings is more pronounced at firms
where the CEO’s compensation is
more closely tied to the value of his
stock and option holdings. Accrual
accounting allows a firm to recognize
revenues and costs at the time of sale
rather than when payment is received
or at the time of purchase rather than
when payment is made. This gives
accountants some discretion in timing
revenues and costs opportunistically,
for example, increasing short-term
reported earnings by booking revenue
earlier.6
Bergstresser and Philippon identify
such “discretionary” accruals and
show that they are more likely to be
observed when CEOs’ compensation
is more closely tied to the value of
stock and option holdings. They also
find that CEOs do benefit financially
through such manipulations. During
years of high accruals (that is, revenues
and reported earnings are increased
by accrual accounting), CEOs exercise
6
Consider the following simple example. It is
December, and the fiscal year for a boating
company ends on December 31. A new client
has just reserved 12 fishing trips for the next
12 months. In principle, revenues should be
matched with corresponding expenses and
booked as each trip is actually taken. However,
the accountant using accrual accounting can
take a more aggressive approach and book
the revenue now, in December, by arguing
that the company is already incurring some
costs in preparing for those trips. As a result,
the boating company sharply increases its
revenue for the fiscal year, but it also records
an equally large accounts receivable number
on its balance sheet because payments for the
trips have not been received (but are expected)
from the client. The accounting choice makes
the reported income look better for the current
fiscal year, but it reduces future income.
Business Review Q3 2010 17
an unusually large number of options.
In addition, CEOs and other insiders
sell large quantities of shares. The
selling of shares by insiders is often
interpreted as evidence that they
expect the stock price to fall in
the future, as would happen if they
expected future reported revenues to
be low due to discretionary accruals.
Some CEOs push the envelope
even further. Natasha Burns and Simi
Kedia find that CEO stock options are
related to incidences of accounting
misreporting. A firm has misreported if
it later restates its financial statements
because the original financial
statements were not in accordance
with generally accepted accounting
principles (GAAP). They identify 215
misreporting firms among the S&P
1500 firms (excluding financial firms).
These are likely to be a small subset of
total misreporting firms because many
others may go uncaught.
Burns and Kedia find that a
firm with a CEO whose stock option
portfolio value is more sensitive to
stock price is more likely to misreport.
They also find that the sensitivity
of other compensation components
(equity, restricted stock, etc.) does
not matter. This is a sensible result,
because, relative to other components
of compensation, stock options are
associated with stronger incentives
to misreport. Through stock options,
CEOs can benefit from higher shortterm accounting performance (and
higher stock price) but relatively
limited downside risk, for example,
the risk of getting caught and having
to restate earnings downward. For
instance, if a CEO owns out-of-money
stock options with the strike price
of $25 but the current stock price is
$20, an increase in stock price to $26
will greatly increase the value of his
options, but a decline in the stock
price to $15 will not make him much
worse off.
18 Q3 2010 Business Review
GOVERNMENT REGULATIONS
CAN AFFECT COMPENSATION
CEO compensation contracts
are private agreements between the
shareholders and the CEOs. Nevertheless, government regulations can
affect executive compensation through
empowering shareholders, to whom the
CEO is ultimately answerable.
In response to the corporate
scandals in 2001-02, by 2003 the major
U.S. stock exchanges had revised their
listing standards and imposed new
requirements for directors’ and committees’ independence, requirements
intended to enhance board oversight.7
The rules require that all firms have a
majority of independent directors and
that the compensation, nominating,
and audit committees shall consist of
independent directors.
Although firms were not required to comply with the rules until
2004, Vidhi Chhaochharia and Yaniv
Grinstein find that many firms already
adhered to the rules even before the
rules became mandatory. However, in
2000 about 12 percent of firms in their
sample did not comply with any of the
requirements regarding independent
directors. Chhaochharia and Grinstein
find a significant decrease in CEO
compensation when those firms finally
appointed a majority of independent
directors to their boards and removed
all insiders from their compensation,
nominating, and audit committees.
They also note that the significant
decrease in compensation is due to a
decrease in the option-based portion
of the compensation. The cash portion
of compensation shows no significant
drop. Their results suggest that board
structure is a significant determinant
of the size and structure of CEO compensation. Note that the rules do not
See Securities and Exchange Commission
Release No. 34-48745.
7
dictate directly how much the CEOs
should be paid but, instead, influence
it through making the board of directors more accountable to shareholders.
The United Kingdom experimented with another law that has been
incorporated into the Dodd-Frank
Wall Street Reform and Consumer
Protection Act in the U.S. In the UK,
nonbinding advisory votes by shareholders on executive compensation
packages have been required for all
listed firms since 2002; that is, shareholders have a direct say in executive
pay. Mary Ellen Carter and Valentina
Zamora find that shareholders express
their anger through voting. Their
analysis indicates that shareholders
disapprove of higher salaries, weak payfor-performance sensitivity in bonus
pay, and greater potential dilution in
stock-based compensation, particularly
stock-option pay.
Shareholders’ disapproval does not
have a binding power on the company,
and disapproval rates rarely exceed 50
percent. However, the board of directors does listen and react. When shareholder disapproval is stronger, boards
respond and subsequently decrease
grants of stock-option compensation to
CEOs, without increasing base salary
or the pay-for-performance sensitivity
of bonus pay accordingly.
WHAT DO WE KNOW AND
NOT KNOW?
What can we take away from
economists’ collective knowledge
about CEO pay? First, there seems
to be a disconnect between CEO
compensation and CEO performance,
but the problems are concentrated
in firms where the board of directors
is weak and large shareholders are
not present. Second, stock options
are an important component of
CEO compensation, and they seem
to correlate with more risk-taking.
Third, government policies can indeed
www.philadelphiafed.org
influence how CEOs are paid by
empowering the shareholders and the
board of directors.
Many questions, however, remain
unanswered. Bengt Holmstrom, a
scholar of compensation and incentives and himself a board member of
a large family company with a billion
dollars in revenue, believes that most
existing theories do not explain the
following two puzzles. First, if the
strong influence of CEOs on their
own pay-setting process explains why
they are paid so much, why has CEO
compensation increased so much in
the late 20th century, exactly when
pressure from large institutional investors arguably should have reduced the
influence of CEOs vis-à-vis the shareholders? 8 Second, why do executive
pay patterns in closely held companies
such as family firms (where CEOs are
closely monitored by well-motivated
owners) resemble those in publicly held
companies? These and other questions
pose a challenge for researchers seeking to explain the causes and effects of
executive compensation practices. BR
8
Institutional investors held only about 10
percent of U.S. equities in 1953 but over 70
percent by the end of 2006.
REFERENCES
Bebchuk, Lucian A., and Jesse M. Fried.
“Pay Without Performance: Overview of
the Issues,” Journal of Applied Corporate
Finance, 17:4 (2005), pp. 8-22.
Carter, Mary Ellen, and Valentina Zamora.
“Shareholder Remuneration Votes and
CEO Compensation Design,” Working
Paper (February 2009).
Bebchuk, Lucian A., and Yaniv Grinstein.
“The Growth of Executive Pay,” Oxford
Review of Economic Policy, 21 (2005),
pp. 283-303.
Chava, Sudheer, and Amiyatosh K.
Purnanandam. “CEOs vs. CFOs:
Incentives and Corporate Policies,” Journal
of Financial Economics, 97:2 (August 2010),
pp. 263-78.
Bebchuk, Lucian A., Yaniv Grinstein, and
Urs Peyer. “Lucky CEOs,” NBER Working
Paper.
Bebchuk, Lucian A., and Robert Jackson,
Jr. “Putting Executive Pensions on the
Radar Screen,” Journal of Corporation Law
(2005).
Bergstresser, Daniel B., and Thomas
Philippon. “CEO Incentives and Earnings
Management: Evidence from the 1990s,”
Journal of Financial Economics, 80 (2006)
pp. 511-29.
Bertrand, Marianne, and Sendhil Mullainathan. “Are CEOs Rewarded for Luck? The
Ones Without Principles Are,” Quarterly
Journal of Economics, 116:3 (2001),
pp. 901-32.
Chhaochharia, Vidhi, and Yaniv Grinstein.
“CEO Compensation and Board
Structure,” Journal of Finance, 64:1(2009),
pp.231-61.
Coles, Jeffrey L., Naveen D. Daniel and
Lalitha Naveen. “Managerial Incentives
and Risk-Taking,” Journal of Financial Economics, 79 (2006), pp. 431-68.
Conyon, Martin J., John E. Core, and
Wayne R. Guay. “Are US CEOs Paid More
Than UK CEOs? Inferences from RiskAdjusted Pay,” Working Paper (2009)
Core, John E., Robert W. Holthausen, and
David F. Larcker. “Corporate Governance,
Chief Executive Officer Compensation,
and Firm Performance,” Journal of Financial
Economics, 51:3 (1999), pp. 371-406.
Brewer, Elijah, William C. Hunter, and
William E. Jackson. “Deregulation and the
Relationship Between Bank CEO Compensation and Risk Taking,” Federal Reserve
Bank of Chicago Working Paper 2003-32
(November 2003).
Edmans, Alex, and Xavier Gabaix. “Is
CEO Pay Really Inefficient? A Survey
of New Optimal Contracting Theories,”
European Financial Management, 15:3 (June
2009), pp.486-96.
Burns, Natasha, and Simi Kedia. “The
Impact of Performance-Based Compensation on Misreporting,” Journal of Financial
Economics, 79:1 (2006), pp. 35-67.
Gabaix, Xavier, and Augustin Landier.
“Why Has CEO Pay Increased So Much?”
Quarterly Journal of Economics, 123:1 (February 2008), pp. 49-100.
www.philadelphiafed.org
Garvey, Gerald T., and Todd T. Milbourn.
“Asymmetric Benchmarking in Compensation: Executives Are Rewarded for Good
Luck but Not Penalized for Bad,” Journal
of Financial Economics, 82:1 (2006), pp.
197-225.
Grinstein, Y., and P. Hribar. “CEO
Compensation and Incentives: Evidence
from M&A Bonuses,” Journal of Financial
Economics, 71:1 (2004), pp. 119-43.
Hall, Brian J., and Jeffrey B. Liebman.
“Are CEOs Really Paid Like Bureaucrats?”
Quarterly Journal of Economics, 113:3
(1998), pp. 653-91.
Holmstrom, Bengt. “Pay Without Performance and the Managerial Power Hypothesis: A Comment,” Journal of Corporation
Law, 30 (2005), pp. 703-15.
John, Kose, and Yiming Qian. “Incentive
Features in CEO Compensation in the
Banking Industry,” Economic Policy Review,
9:1 (April 2003).
Murphy, Kevin J. “Executive Compensation,” in Orley Ashenfelter and David
Card, eds., Handbook of Labor Economics.
New York: Elsevier, 1999.
Yang, Liu, Haluk Unal, and Kristina Minnick. “Pay for Performance? CEO Compensation and Acquirer Returns in BHCs,”
Working Paper.
Business Review Q3 2010 19
BY WENLI LI AND FANG YANG
H
omeownership is an integral part of the
American culture. Over the past 70 years,
the U.S. government has devoted significant
public resources to encouraging and
promoting homeownership. The recent financial crisis
has prompted the government to spend even more on
preserving homeownership, despite the fact that the
financial crisis itself was led by the meltdown of the
U.S. housing market. Now, an increasing number of
academicians and media reporters are questioning the
previously unquestionable: Has the American dream
turned into an American obsession? In this article, Wenli
Li and Fang Yang analyze the economic benefits and costs
associated with owning one’s residence. They re-examine
a variety of rationales that have been put forward in
support of homeownership and examine the evidence for
an economic cost associated with homeownership.
live in housing units they own has
risen from around 40 percent before
World War II to close to 70 percent
today. The financial crisis that
started in 2008 has prompted the
government to spend even more on
preserving homeownership, despite
the fact that the financial crisis itself
was led by the meltdown of the U.S.
housing market. In light of these
developments, an increasing number
of academicians and media reporters
are now questioning the previously
unquestionable: Has the American
dream turned into an American
obsession?1
In this article, we analyze the
economic benefits and costs associated
with owning one’s residence. We
re-examine a variety of rationales
that have been put forward in support
of homeownership, namely, housing
as a means of saving and a means
1
The strength of the nation lies in the
homes of its people. — Abraham Lincoln
A nation of homeowners is
unconquerable. — Franklin D. Roosevelt
Wenli Li is an
economic advisor
and economist in
the Philadelphia
Fed’s Research
Department.
This article is
available free of
charge at www.
philadelphiafed.
org/research-and-data/publications/.
20 Q3 2010 Business Review
Homeownership, like baseball
and hotdogs, is an integral part of
the American culture. Over the past
70 years, the U.S. government has
devoted significant public resources
to encouraging and promoting
homeownership. (See Housing Policies
That Promote Homeownership for a
summary of the various programs.)
The percentage of households that
*The views expressed here are those of the
authors and do not necessarily represent
the views of the Federal Reserve Bank of
Philadelphia or the Federal Reserve System.
For media reports, see, among many others,
“Shelter, or Burden?” (The Economist, April
16, 2009); “Building Castles of Sand” (The
Economist, June 18, 2009); National Public Radio reporter Kai Ryssdal’s interview with 2006
Nobel Prize winner Edmund Phelps (March
26, 2009); columnist Robert Samuelson’s “The
Homeownership Obsession” (Washington Post,
July 30, 2008); and the 2008 Nobel Prize winner
Paul Krugman’s column in the New York Times
(“Home Not-So-Sweet Home,” June 23, 2008).
Fang Yang is
an assistant
professor,
University at
Albany, State
University of New
York.
www.philadelphiafed.org
Housing Policies That Promote Homeownership
A
large variety
of government
programs have
served over the
years to increase
homeownership
in the United States. Most of these
policies work by reducing the cost of
homeownership or by increasing the
flow of capital to the housing market.
The oldest and perhaps most
powerful of these policy tools lies in
the federal income tax code formed
in 1913. Homeowners can deduct
interest on mortgages of up to $1
million on their taxes; they can also
deduct local property taxes. Profits
(capital gains) from house sales are
also shielded from taxation for up to
$250,000 ($500,000 for a married
couple filing jointly) if the owner
used the property as a primary
residence for two of the five years
before the date of sale.
Finally, as Satyajit Chatterjee
explained in his 1996 Business Review
article, if we lease our housing unit
to another household, our rental
income as a landlord would be taxed.
However, if we own the house we live
in, we are effectively paying ourselves
rent, and the associated rental
income is not taxed, according to the
current tax law. In 2008, according
to the Office of Management and
Budget, these tax breaks are both
about $145 billion. Note that this
calculation does not count the
possible taxation of rental income in
an owner-occupied unit.
The government also funnels
cheap credit into government
housing agencies, including the
Federal Home Loan Banks and
Fannie Mae and Freddie Mac.a These
agencies borrow at preferential rates
and were long perceived as backed
by the U.S. Treasury. In July 2008,
right before the Federal Housing
Finance Agency (FHFA) was formed,
Fannie Mae and Freddie Mac held
or guaranteed $5.2 trillion worth of
mortgages, two-fifths of the national
total.b
The Federal Housing
Administration (FHA) insures
mortgages for low- and moderateincome families that require only a 3
percent down payment. Created by
the National Housing Act of 1934,
the FHA insures private mortgage
lenders against borrower default on
residential real estate loans. These
are the borrowers who typically have
no credit history, a history of credit
problems, or not enough cash to
cover the down payment and closing
costs and who almost certainly
wouldn’t qualify for a conventional
home mortgage. The FHA has
quadrupled its insurance guarantees
on mortgages in just the last three
years. Currently, the FHA insures
$560 billion of mortgages.
a
According to its website, the FHFA was “formed by a legislative merger of the Office of
Federal Housing Enterprise Oversight (OFHEO), the Federal Housing Finance Board (FHFB),
and the U.S. Department of Housing and Urban Development (HUD) government-sponsored
enterprise mission team. The FHFA regulates Fannie Mae, Freddie Mac, and the 12 Federal
Home Loan Banks.”
b
www.philadelphiafed.org
of investment. We argue that both
rationales are no longer valid. We also
examine the evidence for an economic
cost associated with homeownership,
that is, the reduced mobility rate.
In a nutshell, while owning one’s
own residence carries economic
benefits for many households, it is
not for everyone, at least not on
economic grounds. As the quotes
from Lincoln and Roosevelt suggest,
not all arguments for supporting
homeownership are economic in
nature. We do not explore in detail
some of the noneconomic arguments
that have been offered as reasons
to subsidize homeownership. These
noneconomic benefits are typically
termed social benefits. (See The Social
Benefits of Homeownership.)
HOMEOWNERSHIP
AND SAVING
The main economic argument for
homeownership is that it is the most
important way in which the majority
of families accumulate wealth, since
houses give households a means of
saving as they pay off their mortgages
and increase their home equity.
This mechanism effectively forces
households to save more than they
otherwise would. While there have
been some historical merits to this
argument,2 the changing economic
environment has rendered it flawed.
2
The study by Donald Haurin, Patric Hendershott, and Susan Wachter explores the wealth
accumulation and housing choices of young
households and confirms the joint nature
of the decision of house tenure and wealth
accumulation. On the one hand, homeownership is an important component of total
wealth. On the other hand, households need a
minimum amount of wealth to purchase their
first house. Other authors, including Louise
Schneier and Gary Engelhardt, have analyzed
savings in response to differentiating housing
prices. Although results in some studies are
contradictory, in general, young households
in more expensive areas tend to save more.
Business Review Q3 2010 21
Why Don’t People Save
Enough? The idea of using housing
as a commitment to save rests on the
observation that people lack selfcontrol. The typical real-life examples
of this behavioral problem include
people postponing their decision to
go on a diet, to exercise, or to quit
smoking. In the case of economic
decisions, numerous surveys have
found that households often report
that they ought to be saving at a higher
rate than they are actually doing
now. Therefore, it is not surprising
that households will not achieve their
desired level of “targeted” saving,
since short-run preferences for instant
gratification undermine their efforts to
implement long-run plans that require
patience.3
Economists have formalized this
lack of self-control using the idea of
hyperbolic discounting. A household
with hyperbolic preferences would
say the following: “Next Christmas,
I will buy modest gifts and use the
savings for my retirement. But this
Christmas, I’ll splurge.” Of course,
when next Christmas comes around,
the household splurges again! In effect,
the household is really two households:
a patient household when it thinks
about its long-term preferences and
an impatient household whenever
it actually confronts an immediate
choice.4 These preferences induce what
economists call a dynamic inconsistency.
A direct implication of the
hyperbolic discounting model is
that households with these types of
preferences will try to pre-commit
themselves to a scheme that will be
3
Richard Thaler’s article was one of the first
to point out several “anomalies” in households’
saving behavior.
4
The article by George-Marios Angeletos,
David Laibson, Andrea Repetto, Jeremy Tobacman, and Stephen Weinberg provides a good
review of this literature.
22 Q3 2010 Business Review
costly to break. In our earlier examples,
that amounts to going on a for-fee diet
plan, buying a health club membership,
or buying cigarettes by the pack
instead of by the carton because
having a carton of cigarettes at hand
increases the temptation to smoke
more, even though buying cigarettes
by the carton costs less.5 In the case
of savings decisions, households will
hold their wealth in an illiquid form,
such as housing, since such assets are
5
Not all attempts to pre-commit are successful,
as Stefano DellaVigna and Ulrike Malmendier
show in their study of individuals who take out
expensive long-term gym memberships, but
seldom go to the gym.
costly to liquidate and thus relatively
better protected from splurges on
consumption.
Does Owning a House Help
Households Save More? The
effectiveness of using one’s house as a
means of forced savings has weakened
substantially in recent years. For the
majority of households, housing is
indeed the most important asset in
their portfolio. With the exception
of the stock market boom in the late
1990s, housing as a share of total
household assets has been trending up
for the past four decades (Figure 1).
Unfortunately, households are not
necessarily accumulating more wealth
by buying up more housing assets.
T
here is no hard and fast distinction between economic
and social benefits. In this article, we call the benefits of
homeownership that accrue to the individual household
“economic.” But homeowners may also confer benefits
on their neighbors and communities, or on the nation; we
term these benefits “social.” The basic argument for the social benefits of
homeownership is that homeownership improves homeowners’ incentives in
a number of ways. Because of transaction costs, homeowners are less likely
to move and hence remain more embedded in their communities for a longer
time. This may promote civic involvement. Homeowners are also residual
claimants of their property: When it comes time to sell, they reap the profits
and suffer the losses. Thus, they tend to maintain their properties and are
better neighbors than renters.
According to Edward Coulson’s Business Review, the empirical evidence
for the social benefits of homeownership includes the following. First,
owner-occupants maintain their dwellings to a greater extent than renters
(or landlords) maintain theirs: More money is spent on maintaining owneroccupied housing than is spent on maintaining rental property; homeowners
spend more time gardening than renters; and rental property depreciates
faster than owner-occupied property. Second, homeowners’ children are
more successful, measured by such factors as lower teenage pregnancy rates
and higher educational attainment, than kids from non-owner-occupied
dwellings. Third, homeowners socialize more with their neighbors.*
*
However, in a recent study, Grace Wong Bucchianeri finds little evidence that homeowners
are happier by any of the following measures: life satisfaction, overall mood, overall feeling, and
general moment-to-moment emotions.
www.philadelphiafed.org
FIGURE 1
!
Total Assets
Percent
35
30
25
20
15
1960
1964
1968
1972
1976
1980
1984
1988
1992
1996
2000
2004
2008
year
Data source: Federal Reserve Board, Flow of Funds (annual); last point plotted: 2008
Thanks to financial developments over
the past several decades, more and
more households with limited means
are able to borrow, and those who
are borrowing are also increasingly
borrowing more. During the housing
boom years, it was not uncommon for
many households to purchase their
houses with less than 20 percent down
or even a zero down payment. For example, combo loans have been used to
reduce the down payment requirement
while avoiding mortgage insurance.
The “80-20” combo loan program corresponds to the traditional loan-to-value ratio of 80 percent, using a second
loan for the 20 percent down payment.
The “80-15-5” program requires a 5
percent down payment provided by
the homebuyer with the remaining 15
percent coming from a second loan.
www.philadelphiafed.org
There are many other new mortgage
products, such as interest-only mortgage contracts, that allow households
to pay only the interest part of the payment for a number of years. The result
is that households don’t accumulate
any home equity during those years.
Even after households have accumulated some home equity, because
of the declining cost of mortgage
refinancing or home equity lines of
credit, many households are now so
easily able to tap their home equity to
pay pressing bills that they simply do
not accumulate wealth.6 A popular
6
We have seen a continued decline in average
points and fees on conventional loans closed
— from 2.5 percent of the average loan amount
in 1983 to around 1 percent at the end of 1995
and 0.5 percent in 2004. (See Wenli Li’s 2005
Business Review articles for more details.)
phrase used to describe this phenomenon during the housing boom years
was “treating the house as an ATM.”
Economists have estimated that households’ marginal propensity to consume
out of increased housing wealth ranges
from 3 to 4 cents on a dollar to over 10
cents, comparable to or even exceeding
the marginal propensity to consume
out of increases in financial wealth.7 In
other words, for every dollar of houseprice appreciation, homeowners take
out 3, 4, or even 10 cents of their home
equity for other consumption purposes,
such as making home improvements,
buying new cars or appliances, or even
taking vacations.8 Owning a house
is no more a means of forced savings
than putting money into stock mutual
funds is. Back in 1997, David Laibson
pointed out that financial innovation
may have reduced households’ savings
rate by providing too much “liquidity,”
weakening forced savings in previously
illiquid assets.
Indeed, economic data show that
the mortgage leverage ratio has been
consistently rising since the mid 1980s.
Home equity as a share of households’
net worth has not changed much and
even declined from the mid 1980s to
the late 1990s and during the current
crisis (Figure 2). The increase in the
mortgage leverage ratio — the ratio
of the amount of the mortgage to the
value of the house — is prevalent
among homeowners of all ages.9 The
cash-out mortgage refinancing rate
7
See the article by Wenli Li and Rui Yao.
8
In some instances, homeowners use cashed-out
funds for home improvements, which potentially raise the value of the house and thus can
be viewed as wealth building. We do not have
updated statistics on the extent of such activity,
but early studies by the Federal Reserve Board
indicate that about 40 percent of homeowners
who took out cash claimed to have used part of
their cashed-out funds for home improvements
during refinancing in 1998 and early 1999.
9
See Wenli Li’s 2005 Business Review article.
Business Review Q3 2010 23
FIGURE 2
# $% & '
Percent
60
50
Mortgage leverage
40
30
House equity/net worth
20
10
0
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
year
Data source: Federal Reserve Board, Flow of Funds (annual); last point plotted: 2008
FIGURE 3
( # )*
)* + ,
Percent
50
45
40
35
30
25
20
— the share of mortgage refinancings
(number of loans) in which borrowers
took out larger loans than they owed
in relation to total mortgage refinancings — also trended up from as early as
1991 until 2006 (Figure 3).
Second Homes and Investment
Properties. Not all housing combines
consumption and investment decisions; vacation homes and investment
properties have become increasingly
important. According to Home Mortgage Disclosure Act data (HMDA),
after a drop from the early 1990s to
the late 1990s, the percent of mortgage
loan applications for non-owner-occupied dwellings started to increase in
1999 and reached a peak of 13 percent
in 2006 (Figure 4) that exceeded its
previous peak in 1993. More recent
data from LPS Analytics indicate a
similar pattern. Starting from January
2005, the share of second homes and
investment properties in all mortgages
has been consistently increasing, flattening out in 2007, while the share of
loans for primary residences has been
declining (Figure 5).10 In 2009, about 8
percent of total mortgages in the LPS
database are for second homes and
investment properties. The increasing share in investment properties is
especially noticeable.
While combining a consumption
good and an investment good tends
to increase saving (at some cost, e.g.,
illiquidity, lack of diversification), vacation homes, compared with primary
residences, generate much less consumption value to owners, on average,
especially for working families.11 In
most cases, investment properties have
15
10
5
0
1991
1993
1995
1997
1999
2001
2003
2005
year
Data source: Federal Housing Finance Agency (annual); last point plotted: 2008
24 Q3 2010 Business Review
2007
10
Notice the discrepancy between the charts
derived from HMDA data and those derived
from LPS data. This discrepancy arises because
the HMDA chart is based on all mortgage
applications, while the LPS chart is based on
approved loans.
11
A working individual typically starts with two
weeks of vacation time annually.
www.philadelphiafed.org
no consumption value to their owners.
Furthermore, owners often expect
to flip investment properties fairly
quickly. This makes the purchase of investment properties more of a short- to
medium-term investment strategy, similar to buying stocks. Therefore, buying
second and investment homes is more
susceptible to fluctuations in income
and house prices than buying primary
residences. In other words, owners are
more likely to be constrained or have
more incentives to walk away from
their investment properties in times of
difficulty, and this further weakens the
argument that second and investment
homes force households to save. Not
surprisingly, during the current crisis,
the foreclosure rates of investment
properties have risen at a much faster
rate than that of loans for primary
residences. Even for second homes,
foreclosure rates have also exceeded
those for primary homes in recent
months (Figure 6).
Nonetheless, second homes or
vacation homes enjoy tax benefits
similar to those for primary homes,
provided that households stay in their
second homes at least 14 days a year or
that for at least 10 percent of the time
the property is rented out. Investment
property owners can deduct their
operating losses, repair expenses, and
depreciation from their income taxes.
Taken together, all of the government
programs to subsidize housing also
increase investment in second homes
and flipping (investment properties).
FIGURE 4
- . ( (
-
Percent
14
13
12
11
10
9
8
7
6
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Data source: Home Mortgage Disclosure Act (HMDA) data (annual); last point plotted: 2008
FIGURE 5
-'/
0 %
Percent
Percent
100
6
5.5
www.philadelphiafed.org
98
invest - left axis
96
5
94
4.5
92
4
primary - right axis
90
88
3.5
second - left axis
86
3
84
2.5
82
80
2
20
0
20 501
05
20 03
0
20 505
0
20 50
0 7
20 509
0
20 511
0
20 60
0 1
20 603
0
20 60
06 5
20 07
0
20 609
0
20 61
0 1
20 70
07 1
20 0
0 3
20 705
07
20 07
0
20 709
0
20 71
0 1
20 80
0 1
20 80
0 3
20 805
08
20 07
0
20 809
0
20 811
0
20 901
09
20 03
0
20 90
0 5
20 907
0
20 90
0 9
20 91
1 1
20 00
10 1
20 03
10
05
HOMEOWNERSHIP AND
INVESTMENT
Another argument for homeownership often heard is that housing is a
relatively safe asset that pays off in the
long run. This argument turns out to
be a myth as well.
The Returns to Investing in
Housing. Similar to returns to individual stocks, the return and volatility
/
Data source: LPS Applied Analytics, Inc. (monthly); last point plotted: July 2009
Business Review Q3 2010 25
FIGURE 6
Mortgage Foreclosure Rates
Percent
8
7
invest
6
second
5
4
3
2
1
primary
0
20
0
20 501
05
20 03
0
20 505
0
20 50
0 7
20 509
0
20 511
0
20 60
0 1
20 603
0
20 60
06 5
20 07
0
20 609
0
20 61
0 1
20 70
0 1
20 70
0 3
20 705
07
20 07
0
20 709
0
20 71
0 1
20 80
08 1
20 0
0 3
20 805
08
20 07
0
20 809
0
20 811
0
20 901
09
20 03
0
20 90
0 5
20 907
0
20 90
0 9
20 91
1 1
20 00
10 1
20 03
10
05
of investing in housing vary across
time and depend importantly on market conditions in particular locations.
Over the past three decades, in the
aggregate, house prices have indeed
fluctuated much less than the prices
of stocks. Housing overall has also
fared better in crises than other assets.
Even during this crisis, the S&P/CaseShiller home price index (Composite
10)12 adjusted by the consumer price
index (shelter) indicates that house
prices as of the second quarter of 2009
have fallen to only a tad below their
2004 levels (Figure 7).
But for most people, the volatility of their local housing market is
more relevant than the volatility of
the national market. And volatility in
individual housing markets, like that
of individual company stocks, can be
a lot larger. For example, the standard
deviation of real annual house price
changes between 1975 and 2008 was
3.4 for the nation, 1.5 percent or less in
Cleveland, Indianapolis, and Louisville, but 11.6 percent in Boston, 9.9
percent in Honolulu, and 9.7 percent
in San Jose. This high volatility in
local housing markets implies that, like
owning individual stocks, households
can lose big as well as win big when
buying and selling houses. And the
opportunities for diversification are
fewer in housing markets than in stock
markets. While someone can buy individual stocks or an overall stock index
such as the S&P 500 market index
offered by most mutual fund companies, the market for trading such price
indexes for housing at the national
and local level remains very thin. (We
will talk about this again in the next
section.)
Data source: LPS Applied Analytics, Inc. (monthly); last point plotted: July 2009
FIGURE 7
) ) ) 8 0 9
+: '
9,
Percent
40
FHFA House Price Index
Case-Shiller House Price Index
S&P 500
Dow Jones Industrial Average
30
20
10
0
-10
-20
-30
1975
12
The 10 cities are Boston, Chicago, Denver,
Las Vegas, Los Angeles, Miami, New York, San
Diego, San Francisco, and Washington, D.C.
26 Q3 2010 Business Review
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
year
Data source: Federal Housing Finance Agency; S&P; Dow Jones (annual); last point plotted: 2008
www.philadelphiafed.org
Comparing the rate of return on
housing with that of other assets such
as stocks is a tricky business. Ignoring
leverage and tax concerns, it is not
obvious that owning housing as an
asset pays off in the long run. We construct Sharpe ratios for the 10 cities
included in the Case-Shiller house
price index and the nation. A Sharpe
ratio is a measure of an asset’s reward
per unit of risk and helps us compare
risk-adjusted returns across assets. We
find that between 1976 and 2008, of
the 10 cities, Denver, Chicago, Los
Angeles, and Las Vegas all have much
lower Sharpe ratios than the S&P 500
stock index. In other words, in riskadjusted terms, the return to housing
in these areas is lower than the return
to holding stocks. The Sharpe ratios
for Miami and Washington, D.C. are
also a tad below that of the S&P 500.
Although the Sharpe ratio for the
overall house price index is somewhat
higher, as we argued earlier, it is not
clear that households have access to
this market.
Some Complications in Calculating the Returns to Housing. Of
course, this calculation is incomplete
because leverage can magnify even
modest returns. Given that houses
are usually bought with big loans (as
a matter of fact, a house is the only
asset a family with limited means
can buy with a big loan), they can
bring in returns much higher than
the house-price appreciation rate.
Here is an example. Suppose a family
bought a house for $200,000 with a
$40,000 down payment (equity). In
one year, the house’s price appreciated 2 percent. The rate of return for
the family for that year was actually a
whopping 10 percent (= ($200,000 *
2 percent)/$40,000). But leverage also
increases risk. In that sense, buying
houses with a large mortgage loan is
similar to buying stocks on margin. It
is great in a favorable (bull) market,
www.philadelphiafed.org
but it works against the owner in an
unfavorable (bear) market. Let’s say
that the $200,000 house a family purchased with a $160,000 mortgage falls
in value to $150,000. The outstanding
debt of $160,000 exceeds the value of
the property. Because the family owes
more than it owns, it has negative net
worth. Leverage is therefore a doubleedged sword.
There are also other complications
in calculating the effective rates of return on housing because of additional
costs associated with owning one’s own
residence and the various government subsidies. Homeowners must pay
taxes on their properties in addition to
maintenance fees. Effective property
tax rates range anywhere from 0.17
percent to 2.77 percent of the house
value, according to the National Association of Home Builders, and maintenance fees are typically 1 to 2 percent
of the house value. Mortgage interest
payments and property taxes, however,
are deductible from federal income
taxes. Assuming an annual depreciation rate of 2.5 percent, a property tax
rate of 1.5 percent, a mortgage interest
rate of 7 percent, and a marginal income tax rate of 25 percent for a typical taxpayer, the adjusted real rate of
return on housing actually falls below
zero (1.3-2.5-1.5+0.25(7+1.5))=-0.575
percent! Remember that 1.3 percent
is the real rate of return of the national house-price index between 1975
and 2009.13 Meantime, under the 25
percent marginal income tax rate for
a typical taxpayer, the rate of return
on stocks during the same period falls
only to 4.5*(1-0.25)=3.375 percent.
It is worth reiterating that the effective rate of return we just calculated
13
Note that we didn’t take out the mortgage
interest from the rate of return on the grounds
that a stock bought on margin would have
required paying interest on the borrowed funds
as well.
is for an average homeowner. For many
moderate- to low-income homeowners,
the effective rate of return from investing in housing may be smaller. The
reason is as follows. Lower-income
homeowners benefit less from deductions of property tax and mortgage
interest payments because of the progressive nature of the federal income
tax and the fact that property tax is
calculated solely on the value of the
property. To claim the mortgage interest deduction, taxpayers must itemize
when filing federal tax returns, rather
than taking the standard deduction.
Because of the progressive nature of
the federal income tax, the value of
itemized deductions rises as income
rises. Those facing the highest marginal tax rates — high-income taxpayers — receive a much more powerful
tax benefit from tax deductions than
low-income taxpayers receive. As a
result, low-income taxpayers are less
likely to itemize, placing the benefits of
the home mortgage interest deduction
out of reach. In addition, high-income
earners tend to have more valuable
houses. In general, the greater the
house value, the greater the interest
payment on the associated mortgage.
The table on page 28 illustrates the
regressive nature of the deduction
for home mortgage interest. Those in
lower-income groups claim few deductions, while those earning over $75,000
in adjusted gross income claim the vast
majority.
Housing as a Hedge Against
Other Assets. Although investing in
housing may not be as attractive an
investment strategy as conventional
wisdom claims, owning one’s own
residence can be used as a hedge
against ownership of other assets.
Standard portfolio theory predicts
that owning one’s house, especially
the build-up of home equity, helps
diversify risks households face that are
not positively correlated with house-
Business Review Q3 2010 27
TABLE
Upper-Income Taxpayers, 2003
Percentage of Returns
Claiming Mortgage
Interest Deduction
Percentage of All
Tax Returns in
Income Group
Average Mortgage
Interest Deduction
per Return
4.0%
37.8%
$278
$20,000 - $29,999
13.1%
14.1%
$910
$30,000 - $39,999
24.2%
10.7%
$1,674
$40,000 - $49,999
35.2%
8.0%
$2,462
$50,000 - $74,999
50.9%
13.3%
$4,068
$75,000 - $99,999
69.0%
7.3%
$6,210
$100,000 - $199,999
78.9%
6.8%
$8,928
$200,000 and over
75.7%
1.9%
$14,374
Adjusted Gross Income
Under $20,000
Source: Internal Revenue Service, Tax Foundation calculations.
price movement. For instance, between
January 1998 and December 2007, the
correlation coefficients of the S&P/
Case-Shiller house price index with
the Lehman aggregate bond index
and the S&P 500 stock index are,
respectively, -0.056, and -0.086. This
means that when financial assets fall in
value, house prices typically rise, and
vice versa. Thus, housing potentially
can be used to hedge against shocks
to investment in stocks, at least during
the period in question.14
14
Given the low correlation coefficients, we do
not wish to emphasize the potential benefits
of homeownership as a hedging instrument,
especially since only 40 percent of households
participate in the stock market, while nearly
two-thirds of Americans own their primary
residences.
28 Q3 2010 Business Review
One question naturally arises:
Is owning one’s residence the most
efficient way to make a portfolio
investment in housing? Remember,
owning a home subjects a household’s
wealth to shocks to local housing
markets, which are much more volatile
than the housing market as a whole.
In principle and ideally, one should be
able to take advantage of movements
in house prices without having to own
one’s residence. Furthermore, one
should even be able to hedge against
house-price movements in the local
market by owning shares of other
housing markets. While such markets
exist, they are as yet not feasible for
most households.
Housing derivatives first appeared
in 2006 as futures contracts (S&P/
Case-Shiller house-price index
futures and options) on the Chicago
Mercantile Exchange. However, in
the euphoria of the housing boom of
the past decade, they attracted little
attention from builders and developers.
Investors prefer to make bearish bets
via more customized instruments.
In June 2009, Karl Case and Robert
Shiller, the namesakes of the CaseShiller house-price index, launched
a product called MacroShares to
open up the market in order to retain
investors. MacroShares are securities
that reflect the value of the S&P/CaseShiller house-price indexes in 10 large
urban centers. The securities are issued
in pairs: one for investors who wish to
bet on the upward movement of house
prices, and one for those who think
www.philadelphiafed.org
prices will fall. Unlike actual houses,
MacroShares are traded on public
exchanges and are therefore liquid.
Trading in MacroShares has been
light so far, but there are hopes that
investors will participate in this market
more after their experience during the
current crisis.15
HOMEOWNERSHIP
AND MOBILITY
Owning one’s home may also have
important implications for households’
mobility. A mobile society is important
for an efficient labor market. If households cannot move to gain access to
better jobs in alternative labor markets,
the quality of the match in the labor
market will suffer. People will be stuck
in jobs they hate and for which they
are not suited, and employers will have
less-productive employees. Furthermore, when local economies decline,
unemployed homeowners may find it
difficult to search for new jobs. Ten
years ago, British economist Andrew
Oswald argued that homeownership was positively correlated with
unemployment: that is, the higher a
country's rate of homeownership, the
higher its long-term unemployment
rate. This claim is still controversial,
but economists have begun to explore
the connections between mobility and
homeownership more rigorously.
Homeowners may be reluctant
to move for several reasons. First, in
15
Another potential way to diversify housing
risk is through the purchase of securitized real
estate, or equity real estate investment trusts
(EREITs). However, EREITs, especially those on
residential housing, remain a very small share of
the aggregate real estate investment available.
As pointed out in Elul (2008), the most
important limit on hedging housing price risk
through the use of an aggregate index is that
the majority of movements in individual house
prices are due to idiosyncratic factors, rather
than resulting from aggregate volatility.
www.philadelphiafed.org
addition to a range of social concerns
such as schools, friends, and families,
homeowners may be reluctant to move
because of the added financial burden.
Selling and buying a house incurs
substantial transaction costs (typically
6 to 8 percent of the house value).
Having negative home equity also requires households to put up additional
cash beyond standard closing costs to
be able to move. Of course, households
can also walk away from their houses
by defaulting or filing for bankruptcy.16
But such actions have a derogatory
impact on their ability to borrow in the
future.
Second, even when households are
not financially constrained and have
the funds to sell the house and move,
they may still be reluctant to move if
doing so means selling their house at
a loss. Economists have termed this
reluctance “an aversion to loss.” Using
data from downtown Boston in the
1990s, David Genesove and Chris
Mayer find that condominium owners
are averse to realizing losses. Those
owners that have higher loan-to-value
ratios (and, thus, are more likely to
experience a nominal loss and have
to pay the bank) tended to set higher
asking prices and were much less likely
to sell than other sellers, after controlling for other observables, including
owner type (resident owner or investor), estimated price index at the time
of entry, estimated value at last sale,
and so forth.17
The United States is generally a
mobile society. Around 12 percent of
16
See Wenli Li’s 2009 Business Review article.
17
Despite all the controls, it is still highly likely
that leverage ratios proxy for other important
household financial characteristics such as
income and liquid wealth. Thus, readers should
take this argument with a grain of salt.
American homeowners typically move
in any two-year period, yet families
with negative equity are around half as
likely to relocate. Those facing higher
mortgage rates are 25 percent less
likely to move, according to a recent
study by Fernando Ferreira, Joseph
Gyourko, and Joseph Tracy that used
data from the American Housing
Survey from 1985 to 2005.
Lower mobility by definition can
be observed only over time, so it will
take a few years to know how the
impact of negative equity will play out
in this cycle.
.
CONCLUSION
Our review of the economic
benefits and costs of homeownership
suggests that the economic case for
subsidizing homeownership has, at the
minimum, been oversold. And we
have not addressed the offsetting costs.
Indeed, economists have found that
government subsidies incur a cost to
the general economy. For example, in
his article, Martin Gervais studied the
welfare consequences of the preferential tax treatment of housing capital
and found that the current tax structure crowds out business capital and
leads to a loss in consumption of over
1 percent. Separately, Karsten Jeske
and Dirk Krueger have studied the role
of implicit guarantees for governmentsponsored enterprises and found that
they reduce aggregate welfare, as
measured by changes in consumption,
by 0.32 percent.
The net dollar value of owning one’s home remains a question
for economists and policymakers to
consider. One thing that is certain is
that homeownership is not for everyone, and thus, based on the economic
benefits, the case for trying to achieve
a nation of homeowners needs to be
rethought. BR
Business Review Q3 2010 29
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Andrea Repetto, Jeremy Tobacman, and
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(2001), pp. 47-68.
Brady, Peter J., Glenn B. Canner, and Dean
M. Maki. “The Effects of Recent Mortgage
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the Social Benefits of Homeownership,”
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DellaVigna, Stefano, and Ulrike
Malmendier. “Paying Not to Go to the
Gym,” American Economic Review, 96:3
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Elul, Ronel. “Collateral, Credit History,
and the Financial Decelerator,” Journal
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Ferreira, Fernando, Joseph Gyourko,
and Joseph Tracy. “Housing Busts and
Household Mobility,” Journal of Urban
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Mayer. “Equity and Time to Sale in the
Real Estate Market,” American Economic
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Genesove, David, and Christopher Mayer.
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Quarterly Journal of Economics, 116:4
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Gervais, Martin. “Housing Taxation
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Spiegel. “The Policy Implications
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www.philadelphiafed.org
RESEARCH RAP
Abstracts of
research papers
produced by the
economists at
the Philadelphia
Fed
You can find more Research Rap abstracts on our website at: www.philadelphiafed.org/research-and-data/
publications/research-rap/. Or view our working papers at: www.philadelphiafed.org/research-and-data/
publications/.
BANKRUPTCY REFORM’S ROLE IN
THE MORTGAGE CRISIS
This paper argues that the U.S. bankruptcy reform of 2005 played an important
role in the mortgage crisis and the current
recession. When debtors file for bankruptcy,
credit card debt and other types of debt are
discharged — thus loosening debtors’ budget
constraints. Homeowners in financial distress
can therefore use bankruptcy to avoid losing
their homes, since filing allows them to shift
funds from paying other debts to paying their
mortgages. But a major reform of U.S. bankruptcy law in 2005 raised the cost of filing and
reduced the amount of debt that is discharged.
The authors argue that an unintended consequence of the reform was to cause mortgage
default rates to rise. Using a large data set of
individual mortgages, they estimate a hazard
model to test whether the 2005 bankruptcy
reform caused mortgage default rates to rise.
Their major result is that prime and subprime
mortgage default rates rose by 14 percent and
16 percent, respectively, after bankruptcy
reform. The authors also use difference-in-difference to examine the effects of three provisions of bankruptcy reform that particularly
harmed homeowners with high incomes and/
or high assets and find that the default rates of
affected homeowners rose even more. Overall,
they calculate that bankruptcy reform caused
the number of mortgage defaults to increase
by around 200,000 per year even before the
start of the financial crisis, suggesting that the
reform increased the severity of the crisis when
it came.
Working Paper 10-16, “Did Bankruptcy
Reform Cause Mortgage Default Rates to Rise?”
www.philadelphiafed.org
Wenli Li, Federal Reserve Bank of Philadelphia;
Michelle J. White, University of California-San
Diego; and Ning Zhu, University of CaliforniaDavis
BASEL II AND CAPITAL TO SUPPORT
MORTGAGE PORTFOLIOS
The recent mortgage crisis has resulted
in several bank failures as the number of
mortgage defaults increased. The current
Basel I capital framework does not require
banks to hold sufficient amounts of capital
to support their mortgage lending activities.
The new Basel II capital rules are intended
to correct this problem. However, Basel
II models could become too complex and
too costly to implement, often resulting in
a trade-off between complexity and model
accuracy. In addition, the variation of the
model, particularly how mortgage portfolios
are segmented, could have a significant impact
on the default and loss estimated and thus
could affect the amount of capital that banks
are required to hold. This paper finds that the
calculated Basel II capital varies considerably
across the default prediction model and
segmentation schemes, thus providing banks
with an incentive to choose an approach
that results in the least required capital for
them. The authors also find that a more
granular segmentation model produces smaller
required capital, regardless of the economic
environment. In addition, while borrowers'
credit risk factors are consistently superior,
economic factors have also played a role in
mortgage default during the financial crisis.
Working Paper 10-17, “Can Banks
Circumvent Minimum Capital Requirements?
Business Review Q3 2010 31
The Case of Mortgage Portfolios under Basel II,” Christopher
Henderson, Federal Reserve Bank of Philadelphia, and Julapa
Jagtiani, Federal Reserve Bank of Philadelphia
THE ROLE OF INVENTORIES
IN THE ECONOMIC CRISIS
This paper examines the role of inventories in the
decline of production, trade, and expenditures in the U.S.
in the economic crisis of late 2008 and 2009. Empirically,
the authors show that international trade declined more
drastically than trade-weighted production or absorption
and there was a sizeable inventory adjustment. This
is most clearly evident for autos, the industry with the
largest drop in trade. However, relative to the magnitude
of the U.S. downturn, these movements in trade are
quite typical. The authors develop a two-country general
equilibrium model with endogenous inventory holdings in
response to frictions in domestic and foreign transactions
costs. With more severe frictions on international
transactions, in a downturn, the calibrated model shows
a larger decline in output and an even larger decline in
international trade, relative to a more standard model
without inventories. The magnitudes of production, trade,
and inventory responses are quantitatively similar to those
observed in the current and previous U.S. recessions.
Working Paper 10-18, “The Great Trade Collapse of
2008-09: An Inventory Adjustment?” George Alessandria,
Federal Reserve Bank of Philadelphia; Joseph P. Kaboski,
Notre Dame University; and Virgiliu Midrigan, New York
University
BAYESIAN FORECASTING
USING A DEMOCRATIC PRIOR
This paper proposes Bayesian forecasting in a vector
autoregression using a democratic prior. This prior is
chosen to match the predictions of survey respondents.
In particular, the unconditional mean for each series in
the vector autoregression is centered around long-horizon
survey forecasts. Heavy shrinkage toward the democratic
prior is found to give good real-time predictions of a range
of macroeconomic variables, as these survey projections
are good at quickly capturing end-point shifts.
Working Paper 10-19, “Evaluating Real-Time VAR
Forecasts with an Informative Democratic Prior,” Jonathan
H. Wright, Johns Hopkins University, and Visiting Scholar,
Federal Reserve Bank of Philadelphia
32 Q3 2010 Business Review
SHORT-TERM NOMINAL INTEREST RATE AND
INFLATION EXPECTATIONS
The author shows that the short-term nominal
interest rate can anchor private-sector expectations into
low inflation, more precisely, into the best equilibrium
reputation can sustain. He introduces nominal asset
markets in an infinite horizon version of the BarroGordon model. The author then analyzes the subset
of sustainable policies compatible with any given asset
price system at date t=0. While there are usually many
sustainable inflation paths associated with a given set
of asset prices, the best sustainable inflation path is
implemented if and only if the short-term nominal bond is
priced at a certain discount rate. His results suggest that
policy frameworks must also be evaluated on their ability
to coordinate expectations.
Working Paper 10-20, “Sustainable Monetary Policy and
Inflation Expectations,” Roc Armenter, Federal Reserve Bank
of Philadelphia
ASSESSING THE PERFORMANCE OF CREDIT
RATING SYSTEMS
In this paper, the authors use credit rating data from
two Swedish banks to elicit evidence on banks’ loan
monitoring ability. They test the banks’ ability to forecast
credit bureau ratings, and vice versa, and show that bank
ratings are able to predict future credit bureau ratings.
This is evidence that bank credit ratings, consistent with
theory, contain valuable private information. However,
the authors also find that public ratings have an ability to
predict future bank ratings, implying that internal bank
ratings do not fully or efficiently incorporate all publicly
available information. This suggests that risk analyses by
banks or regulators should be based on both internal bank
ratings and public ratings. They also document that the
credit bureau ratings add information to the bank ratings
in predicting bankruptcy and loan default. The methods
the authors use represent a new basket of straightforward
techniques that enable both financial institutions and
regulators to assess the performance of credit rating
systems.
Working Paper 10-21, “Credit Ratings and Bank
Monitoring Ability,” Leonard I. Nakamura, Federal Reserve
Bank of Philadelphia, and Kasper Roszbach, Sveriges
Riksbank, University of Gronigen, and Visiting Scholar,
Federal Reserve Bank of Philadelphia
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