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Commitment Versus Discretion
In Monetary Policy*
BY MICHAEL DOTSEY

W

hether policymakers should commit to a
certain course of action or have the flexibility
to approach each situation as it arises
continues to be a central question in the
design of monetary policy. A seminal article written by
two prominent economists in 1977 analyzed the benefits
of carrying out plans based on commitment rather than
discretion. Since then, others have joined the debate.
In this article, Mike Dotsey elaborates on the merits of
commitment versus discretion in setting monetary policy.

The debate over whether it is
better for a policymaker to commit
to a particular course of action or to
approach each situation with perfect
flexibility has been and continues to
be a central question in the design of
monetary policy. In 1977, economists
Finn Kydland and Edward Prescott
wrote the seminal article analyzing
the benefits of carrying out plans
based on commitment as opposed to
discretion. Since then, the benefits of
commitment have been analyzed in
many settings and in many economic
models. Indeed, in a 2007 speech to

Mike Dotsey is a
vice president and
senior economic
policy advisor in
the Philadelphia
Fed’s Research
Department.
This article is
available free of
charge at www.
philadelphiafed.
org/research-and-data/publications/businessreview/.
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the New York Association for Business
Economics, Philadelphia Fed President
Charles Plosser explained his views
on credibility and commitment in
monetary policymaking. This article
elaborates and expands on some of
these ideas.
To start with, let me first define
what we mean by commitment
versus discretion. Commitment is the
ability to deliver on past promises no
matter what the particular current
situation is. I should stress that, under
commitment, promised behavior
is generally contingent on future
events. Promises are not typically
blanket commitments to be fulfilled
irrespective of future situations. The
key aspect of commitment is that the
policymaker keeps his promise to act in
a certain way when a particular future

*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.

event comes to pass. The absence
of this ability is called discretion.
Under discretion, a policymaker is
allowed to change policy depending
on current circumstances and to
disregard any past promises. Because
the discretionary planner does not
make any binding commitments, it
would appear that discretion offers
more flexibility and it would seem to
be preferable to a policy whereby the
policymaker must honor past promises.
The idea that it is better for
a central bank to follow through
on policies promised in the past,
rather than being free to respond to
conditions as they evolve, is a subtle
and perhaps surprising one. Not
only are better long-run outcomes
achieved under commitment, but
monetary policy is also better able to
respond to shocks if the central bank
is constrained to honor past promises
concerning its future behavior. As
I’ll discuss below, lower inflation,
with no adverse effects to economic
activity, is obtained under a policy
of commitment, and such a policy
can achieve less volatility in both
inflation and output as well. Indeed,
the inability to commit often leads to
problems for policymakers.
Comparing policymaking under
discretion and under commitment
is an analysis of two polar cases. It
sidesteps the question of how a central
bank can act in a committed fashion
even if it desires to do so. Also, how
could a central bank convince the
public that it is operating in a manner
consistent with commitment when
the institutional setting places little
restriction on future policies? For
instance, the members of the policyBusiness Review Q4 2008 1

making boards change over time
as do the legislators that monitor
the behavior of monetary policy.
Commitment requires tying the hands
of future policymakers, and in reality,
we don’t even know who they will be.
Research analyzing ways that
policy can come close to the ideal
of full commitment has generally
proceeded along two lines. One is
institutional design. How does one
set up institutions that will improve
on discretionary outcomes? The
other is the role of reputation and the
credibility an institution can achieve
by behaving like a committed planner
over time. While of tremendous
interest, investigations into these areas
are beyond the scope of this article.
But we cannot hope to understand
these more advanced investigations
without first understanding the
different nature of policy under
commitment and under discretion.
Economists refer to the desire
to alter previously made plans
as the time-consistency problem
because, at each date, an individual
or policymaker finds it tempting to
deviate from what an earlier plan
dictated. The temptation to alter
strategies affects how others view your
proposed plan, and it is the interaction
between the public’s expectations
and the policymaker’s decisions that
leads to problems for a policymaker
who cannot commit. Economics has
many examples of the time-consistency
problem, and although I will primarily
focus on monetary policy, I will start
with a simpler setting that lays out the
basic issues in a fairly transparent way.
THE EXAMPLE OF THE
FLOOD PLAIN
Before we delve into monetary
policy, it will be helpful to look at the
difference between commitment and
discretion in a simpler setting. One
of the more famous examples used

2 Q4 2008 Business Review

by Kydland and Prescott to illustrate
the benefits of commitment over
discretion is that of the flood plain.
Recently, Robert King provided a
detailed description of this example,
which highlights the importance of
expectations and the role they play
in economic outcomes.1 The role of
expectations will also be a central
aspect in the analysis of monetary
policy.

and in areas not subject to flooding.
Thus, the best outcome is for people
to decide not to build houses in
areas subject to floods and for the
government to choose not to build
dams. If the government can commit
to never building a dam, this will be
the outcome. Everyone will believe
that the government will not build
a dam and no one wants a flooded
house. As a result, no one chooses to

Economists refer to the desire to alter
previously made plans as the time-consistency
problem because, at each date, an individual
or policymaker finds it tempting to deviate from
what an earlier plan dictated.
In this example, people make a
single decision: whether they wish to
live near the water. Unfortunately,
areas near the water are subject to
flooding. The government can prevent
flooding by building dams, but doing
so is expensive. The government also
has a single decision: whether to build
a dam. Furthermore, the government
wants its policies to conform to
individual preferences. It wants to do
what makes society as a whole better
off. There is no conflict between what
individuals think is best and what
the government thinks is best. The
problem is determining what the best
outcome will be, given that people
prefer living near the water and the
fact that building dams is costly. Of
course, the best outcome will depend
on how costly dams are relative to the
pleasures of living near the water.
The problem is interesting only if
we assume that, all things considered,
dams are prohibitively expensive,
and therefore, the best outcome is
for people to live away from water
1

See the article by Robert King.

build near the water. The individual’s
decision about where to build a house
is a relatively simple one and does not
depend on where other individuals
decide to build their houses. If you
want to avoid flooding, stay away from
the water.
Under discretion the government
cannot commit to not building a
dam. As King explains, this inability
complicates the problem considerably.
The government’s decision is now
based on how many people live near
the water. If a sufficient number
decide to live near the water, it is
now better to build a dam than to
subject many people to floods. Now,
an individual’s decision about where
to build is complicated. If he thinks
a lot of people will build houses near
the water, he should too because a
dam will be built, and he will have to
pay his share of the dam’s cost. If he
anticipates that only a few people may
build houses near the water, he should
not follow their example because he
will be subject to the risk of floods.
In either instance, if he anticipates
correctly, he either lives near the water

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protected by a dam or he lives in safety
away from the water. If incorrect, he
lives near the water and his house is
periodically flooded, or he pays for
a dam and lives in a less desirable
location.
If we focus on situations where
everyone behaves in a similar fashion,
there are two potential outcomes.
Everyone believes that no one else will
build near the water; no one does; and
no dam is built. That is the optimal
outcome and the one obtained under
commitment. If, however, everyone
believes that others will build near
the water, everyone does build near
the water, and a dam is built — a less
than desirable outcome. An important
thing to note is how complicated an
individual’s decision-making process
is. He must factor in not only what
he believes the government will do
but what everyone else will do as
well. It is precisely this feature of how
expectations affect an individual’s
decision that leads to the less desirable
results under discretion. I will return
to this aspect of behavior when I
discuss monetary policy.
THE LONG-RUN BENEFITS OF
COMMITMENT IN MONETARY
POLICY
Now let’s analyze the benefits
that commitment confers on average
inflation and average output. As
in the example just considered, a
key ingredient in the analysis is
the forward-looking behavior of
individuals. It is people’s ability to plan
ahead and anticipate the policymaker’s
actions that makes outcomes under
discretion sub-optimal.
In particular, we will analyze the
issue using a classical framework in
which prices and wages are perfectly
flexible. In such a setting, anticipated
changes to the money supply have no
effect on output. In this environment,
if firms believe the central bank is

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going to increase the money supply,
they respond by increasing prices. To
be concrete, consider the case where
individuals anticipate a doubling of
the money supply. In this case firms
respond by doubling their prices and
workers similarly respond by doubling
their wage demands. Workers would
like to be able to purchase the same
number of goods for a given number
of hours worked and firms are willing
to pay the higher wages because,
in the end, they are paying workers
the same amount in terms of goods
produced. Thus, a doubling of money
and a doubling of prices and wages
leaves everyone in the same position
as before. Therefore, anticipated
changes in money affect only prices,
and this is a long-run attribute of
every established model in monetary
economics.
However, unanticipated changes
in money do affect output. For
example, if the central bank adopts an
expansionary policy by unexpectedly
increasing the money stock, output
expands and inflation increases. Firms
and workers are both surprised by the
increase in money and initially do not
demand higher prices or higher wages.
The increased money stock, which is
held by the public, can now be used
to purchase more goods and aggregate
demand subsequently increases. As
firms and workers catch on to what has
happened, prices and wages increase,
resulting in inflation. Symmetrically,
unexpectedly tight monetary policy
lowers inflation and causes output to
contract.
Furthermore, there is a rate of
inflation that everyone prefers, which,
for the purposes of this article, need
not be specified. I will refer to this rate
as pi-star.2 Inflation above or below

2
Depending on one’s view of the structure of
the economy, the optimal rate could be slightly
negative, zero, or even perhaps slightly positive.

this rate is viewed as undesirable.
A second feature of the economy is
that the central bank and the public
desire output to be somewhat greater
than potential.3 The justification for
this assumption is that other features
of the economy, such as the lack of
perfect competition or the presence
of distortionary taxes, prevent the
economy from operating efficiently,
and to some extent, it is desirable for
the monetary authority to offset these
features.
Under these assumptions the
central bank can move output above
potential only if it surprises individuals
by inflating at a rate greater than pistar. Under commitment, the central
bank would inform the public that it
will keep inflation at pi-star. Knowing
that the central bank is capable of
honoring its promises, the public will
believe the central bank and expect
the inflation rate to be pi-star. With no
surprises, inflation will be pi-star, and
output will attain its potential level,
which is somewhat below its desired
level.
The question then is: Can a
policymaker who cannot commit to
achieving an inflation rate of pi-star do
better? Can that policymaker increase
output enough at the expense of some
surprise inflation to make everyone
better off? Perhaps surprisingly, the
answer is no. Suppose the public
initially thought that the central bank
would target inflation at the rate of
pi-star. Knowing this, the central bank
is now faced with the opportunity to
increase the level of output by creating
a bit of surprise inflation through
expansionary policy. In other words,

3
An economy’s potential output is the level that
would occur in the absence of any economic
distortions. Basically, it is the level that would
obtain if prices were free to vary, markets were
competitive, and there were no distortionary
taxes.

Business Review Q4 2008 3

a discretionary policymaker has an
incentive to deviate from the policy
that would occur under commitment.
A small increase in the inflation rate
would not be very costly, and the
benefit would entail more output.
Facing this tradeoff, the central bank
will generally find it desirable to
initiate additional inflation.
For concreteness, assume it is
desirable to generate a 1 percent
surprise increase in inflation. In this
case, it would be foolish for the public
to expect the inflation rate to be
pi-star. They should expect it to be
pi-star plus 1 percent. If the central
bank does not revise its strategy, the
outcome will be no surprise: Inflation
will be pi-star plus 1 percent, and
output will be at its potential level. At
this stage we can repeat the reasoning
in the previous paragraph. A further
attempt to surprise the public, with
say 0.5 percent additional inflation,
will generate increased output that, in
the end, may be worthwhile. If that is
the case, the public should anticipate
an inflation rate of pi-star plus 1.5
percent.
Again, all that occurs in the end
is more inflation and no additional
output. At some point additional
inflation will be too costly, and the
central bank will no longer try to fool
the public. The public will expect the
higher inflation rate, and output will
remain at potential. Forward-looking
individuals will not be fooled, but
under discretion, there is a temptation
to try to fool them. The result is
just more inflation. So, under both
commitment and discretion, output
remains at potential, but commitment
achieves this result with lower
inflation.
The example above makes clear
the long-run benefits of commitment
and of devising institutional
arrangements that prevent the
central bank from using discretionary

4 Q4 2008 Business Review

policy. Some economists have argued
that the gold standard was such an
arrangement or that currency boards
help achieve commitment. Others,
such as Kenneth Rogoff, have argued
for the benefits of appointing central
bankers who have a strong aversion

policymaker from reacting optimally to
economic shocks is actually mistaken.
The ability to keep promises allows
a central bank operating under a
policy of commitment to influence
expectations in a way that the
discretionary planner cannot. In a

Under both commitment and discretion, output
remains at potential, but commitment achieves
this result with lower inflation.
to inflation. Carl Walsh has suggested
contracts that penalize central bankers
if inflation deviates too much from its
target. Currently, there is a good deal
of interest in whether explicit forms
of inflation targeting help to achieve
the better outcomes associated with
commitment.4
THE RESPONSE TO SHOCKS
UNDER COMMITMENT AND
DISCRETION
The ability to respond to
economic shocks, such as oil-price
shocks or changes in productivity, so
as to limit their effects on economic
volatility is one of the supreme
challenges confronting central banks.
It is this aspect of monetary policy
that most often elicits arguments
touting the benefits of discretion. It is
argued by those in favor of discretion
that monetary policymakers must
be allowed a free hand to respond to
each situation as it arises and not be
constrained, for example, by promises
to keep inflation at some targeted rate.
Discretion is needed to adequately
guide the economy through turbulent
times.
However, the notion that
commitment unduly constrains the
4

For a survey of inflation targeting and its
effects, see my 2006 Business Review article.

sense, this gives the policymaker who
can commit another tool to work with.
In fact, a policy under commitment
can achieve all of the outcomes of
a policy under discretion and can
also achieve outcomes unobtainable
under discretion. The committed
policymaker cannot do worse than the
discretionary planner.
It is precisely because a
policymaker who can commit has
the ability to follow through on
promised actions that he can influence
expectations in a desirable way. The
discretionary planner, because he
makes decisions period by period,
makes no promises and, as a result,
does not have a similar ability to
influence expectations. A planner
who can commit to future actions
in various situations can affect what
people expect will happen in these
situations, and these expectations
influence current behavior. By making
well-designed promises, policymakers
can influence expectations in ways
that elicit better economic outcomes.
However, along with these promises
comes the constraint to honor them.
Thus, actions today, which affect the
future, also affect future policy, and
this in turn implies that the history
of actions taken will affect current
policy. In this sense, the committed
policymaker is not free to base today’s

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policy only on current economic
conditions.
But having policy constrained
in this way should not be viewed as
a negative attribute of commitment.
These constraints, if designed
appropriately, can actually lead
to better outcomes through their
influence on expectations that allow
for better economic decisions. These
last points have been persuasively
illustrated by economists Richard
Clarida, Jordi Gali, and Mark Gertler
and by economist Michael Woodford.
To make these points more
concretely, I will use a simple
benchmark New Keynesian model
of the economy.5 That model has
two basic components. One is a
description of aggregate demand,
commonly referred to as an IS
curve, that essentially relates current
output demand to the level of the
real interest rate (the interest rate
adjusted for inflation) and to future
levels of output.6 Basically, high real
interest rates imply lower demand for
consumption and investment. A high
real interest rate implies a greater
return to saving and induces people
to consume less and save more. It also
means that firms must earn a higher
rate of return on each project in order
for those projects to be cost effective.
Thus, only relatively profitable projects
will be undertaken, and consequently,
there will be less investment.
Alternatively, greater future
economic activity implies both an
increase in current consumption
through a wealth effect and more
investment because future economic
prospects appear rosy. The important
5
For a more detailed description of the model
economy used in this section, see the article by
Richard Clarida, Jordi Gali, and Mark Gertler.
6
It is also common to describe the IS and
Phillips curves in terms of output relative to its
potential level, which is referred to as an output
gap.

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feature to note is that higher interest
rates reduce aggregate demand and
lower output.
The model’s other component is
a Phillips curve that relates current
inflation to future expected inflation
and to the level of output. This is the
supply part of the model. If future
inflation is expected to be high,
firms will want to raise prices more
aggressively today so that their prices
do not get too far out of line with the
behavior of prices in general. This
leads to greater inflation today. Thus,
higher expected future inflation leads
to higher inflation today. Also, when
the level of output is high, firms’ costs
of production rise, and as a result,
firms pass on some of these additional
costs to consumers. The result is
higher inflation. The economy will be
in equilibrium when the level of the
real interest rate and inflation implies
that output demand is equal to output
supply.
Importantly, in the model,
monetary policy can affect the level
of output. Underpinning this model
of the economy is the feature that
prices and wages are costly to adjust.
These costs may involve the resources
used in acquiring information, the
resources employed in figuring out
exactly what the correct price or wage
is, and the resources needed to change
prices. These costs imply that firms
and workers will not immediately and
fully react to changes in monetary
policy. As in our previous example, in
which unanticipated changes in policy
affected the economy, here anticipated
changes in policy affect the economy
as well. They do so because it takes
time for the price system to fully
respond to changes in policy. Thus,
the central bank can move output and
inflation around in response to an
economic shock.
The question I now address is:
Who does it better — a discretionary

policymaker or a committed
policymaker?
To answer this question,
I examine how both types of
policymakers and the economy
respond to an aggregate supply shock.
Figure 1 displays the model economic
responses to a 1 percent shock to the
inflation rate.7 Because the public does
not like inflation above target and the
central bank is trying to maximize
the public’s welfare, policy responds by
tightening: The central bank raises the
nominal interest rate (panel a). Note
that, under discretion, the interest
rate must be raised by approximately
50 basis points more than under
commitment. As a result, output
declines by more under discretionary
policy (panel b), but the effect of this
more aggressive tightening under
discretion has less of an effect on
inflation (panel c). Inflation moves
up more in response to the shock to
inflation and declines more slowly.
Policy under commitment experiences
a smaller rise in inflation and a more
rapid return of inflation to target, with
less loss of output. Policy also does
not need to be as aggressive because
inflation doesn’t rise as much.
How does the committed
policymaker achieve the best of both
worlds: less inflation as a result of
the shock and less loss of output
while at the same time acting less
aggressively? The answer is that
expectations of future inflation
affect current inflation. Under
commitment, individuals take into
account the policymaker’s promise
to bring inflation down and not
exploit the output gains arising from
inflation. As a result, expectations
of inflation do not increase as much
under commitment (panel d), implying

7

In these simulations the monetary authority
places only half as much weight on output
fluctuations as it does on inflation fluctuations.

Business Review Q4 2008 5

FIGURE 1
Economic Responses Under Commitment and Discretion
(a) interest rates

1.4

(b) output
-0.1

1.0

discretion

discretion
-0.3
percent

percent

commitment
0.6

0.2

-0.2

commitment

-0.5

-0.7

0

2

4

6

8

10

12

14

-0.9

16

0

2

4

6

quarters

(c) inflation

1.4

8

10

12

14

16

quarters

(d) expected inflation

0.8

discretion

discretion
0.6

commitment

1.0

commitment

0.4
percent

percent

0.6

0.2

0.2
0.0
-0.2

-0.6

-0.2

0

2

4

6

8

10

12

14

16

-0.4

0

2

4

quarters

that firms do not raise their current
prices as aggressively as they would
in an environment characterized by
discretion. The stability of inflation
expectations under commitment
implies that policy does not have
to be as aggressive in order to bring
down inflation, and as a result, output
does not have to decline by as much.
Contrary to intuition, the constraint
of having to abide by past promises
actually allows the committed

8

In this simple model, the committed and
discretionary policymakers achieve the same
outcomes in response to a shock to aggregate
demand. However, this is not generally true in
more sophisticated models.

6 Q4 2008 Business Review

6

8

10

12

14

16

quarters

policymaker to achieve superior
economic outcomes in response to
economic disturbances.8
Commitment’s superiority to
discretion can be further characterized
by investigating what kind of inflation
and output tradeoffs confront the
economy under the two different types
of policy. In this model of the economy,
decreasing the variability of inflation
can be achieved only by allowing
output to be more variable. If, in order
to combat inflation or disinflation,
the policymaker responds more
aggressively to inflationary shocks,
output will end up varying more
because of the more aggressive policy
response. Therefore, the more the
policymaker tries to limit the volatility

of inflation, the greater the volatility
of output will be. Symmetrically, the
policymaker can lower the volatility
of output only by accepting more
volatility in inflation. Thus, the
policymaker will have a whole menu
of attainable combinations of output
variability and inflation variability to
choose from. The particular choice
will depend on the public’s preferences.
Figure 2 graphs the choices
available to each type of policymaker.
Because people dislike volatility in
both output and inflation, points that
lie closer to the origin are preferred. It
is obvious that under commitment the
economy can achieve better outcomes
than under discretion because the
curve depicting the tradeoff under

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FIGURE 2
Output and Inflation Tradeoffs
standard deviation of output gap
3.6

discretion
commitment

3.2
2.8
2.4
2.0
1.6
1.2
0.8
0.4
0.0
0.0

0.4

0.8

1.2

1.6

2.0

2.4

standard deviation of inflation

commitment lies everywhere below
the curve under discretion. This
means that for any given level of
variability in inflation, the committed
policymaker can obtain less variability
in output than the discretionary
planner. Similarly, for any degree of
volatility in output, the committed
planner can generate less volatility
in inflation. Thus, not only will the
economy achieve a lower average
rate of inflation under commitment,
it will also experience less volatile
inflation. This depiction along with
the discussion in the previous section
highlights the observation made
earlier: Under commitment, policy
can achieve outcomes that cannot be
achieved under discretion.
AN EXAMPLE:
OIL-PRICE SHOCKS
There are many examples of the
benefits of commitment — or, in U.S.

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monetary policy, at least examples in
which the Federal Reserve has had
sufficient credibility that the public
believed that monetary policy would
behave in a manner that approximates
commitment. I will contrast two
episodes, both involving oil-price
shocks.
Although I cannot give definitive
proof for the following argument, one
can view the differential economic
impact of oil-price shocks in the
1970s and 2000s through the lens of
commitment.9 In one instance, the
Fed lacked credibility for maintaining
low inflation and in the other the Fed
had that credibility. The theoretical
material covered suggests that the

effects of the oil shocks on economic
activity and inflation could be
different under these two different
settings. In actuality, they were quite
different.
The two episodes are the oilprice shock of the late 1970s and a
more recent oil-price shock in the
early 2000s.10 By the time the oilprice shock of 1979 hit, more than
doubling oil prices over the course of
the year, inflation had already reached
9 percent. These historically high
inflation rates were caused by overly
easy monetary policy. It is fair to say
the Federal Reserve had, by the time
of the oil shock, lost credibility for
maintaining low inflation. The rise in
oil prices further ignited inflationary
pressures, and without credibility for
maintaining low inflation, the Fed
was put in the situation of ratifying
the higher expected inflation or trying
to contain inflation with a large
subsequent loss of output. It chose the
first option, and by the first quarter of
1980, inflation had increased to more
than 15 percent.
In contrast, from the end of
2003 to the end of 2005 oil prices
more than doubled, yet inflation
remained contained without any
significant adverse effect on output.
The main difference between these
two episodes is the credibility that
the Federal Reserve now enjoys for
maintaining low and stable inflation.
This credibility is portrayed by
the stability of various measures of
inflation expectations over this period.
For example, the 10-year expected

10

9
Recent evidence outlined in the article by
Sylvain Leduc, Keith Sill, and Tom Stark is
consistent with the interpretation of events
described here.

There are many other documented episodes.
Some are discussed in President Plosser’s
speech, and the history of inflation scares
is documented in the article by Marvin
Goodfriend. Also, for a more detailed analysis
of appropriate monetary policy in the face of
shocks to oil prices, see the article by Sylvain
Leduc and Keith Sill.

Business Review Q4 2008 7

inflation rate in the Philadelphia Fed’s
Survey of Professional Forecasters
hardly moves over this period, and
expected inflation as represented by
the difference between the yield on
10-year nominal and indexed Treasury
bonds is quite stable. Therefore, as
in Figure 1, the more recent oilprice shock had very little impact on
inflation expectations, and as a result,
there has been no need for exceedingly
aggressive policy. In turn, there has
been very little impact on output. The
current FOMC is committed to low
and stable inflation and is perceived
in that light. Acting as a committed
policymaker has its benefits both in
theory and in practice.

SUMMARY
This article has explored the
benefits of policy under commitment
versus discretion. In particular, it
has discussed the added benefits
policymakers derive from fulfilling past
promises. Rather than constraining
policy, adhering to honoring policy
promises enables monetary policy
to attain outcomes that cannot be
attained by a policy arrived at anew at
each point in time. Committed policy
generates lower long-run inflation
without any adverse effects on
economic activity and ameliorates the
effects of economic disturbances.
In practice, achieving and maintaining the credibility that allows a

central bank to follow policies consistent with the assumption of full commitment is not easy or straightforward.
The credibility the Fed has achieved is
due, in no small part, to the leadership
of the two previous Fed Chairmen,
Paul Volcker and Alan Greenspan.
The current Chairman, Ben Bernanke,
is maintaining their example of commitment to low and stable inflation.
The benefits of following a committed plan are now so entrenched in
policy-making circles that most central
banks aggressively strive to maintain
their credibility. The loss of credibility
presents grave problems for monetary
policymakers, problems that have been
highlighted in this article. BR

King, Robert G. “Discretionary Policy and
Multiple Equilibria,” Federal Reserve Bank
of Richmond Economic Quarterly, 92:1
(Winter 2006), pp. 1-5.

Plosser, Charles I. “Credibility and
Commitment,” speech delivered to
the New York Association of Business
Economists, New York, March 6, 2007.

Kydland, Finn E., and Edward C. Prescott.
“Rules Rather Than Discretion: The
Inconsistency of Optimal Plans,” Journal of
Political Economy, 85 (1977), pp. 473-91.

Rogoff, Kenneth. “The Optimal Degree of
Commitment to an Intermediate Monetary
Target,” Quarterly Journal of Economics,
100:4 (1985), pp. 1169-89.

Leduc, Sylvain, and Keith Sill. “A
Quantitative Analysis of Oil-Price Shocks,
Systematic Monetary Policy, and Economic
Downturns,” Journal of Monetary
Economics, 51 (2004).

Walsh, Carl. “Monetary Policy Design:
Institutional Developments from a
Contractual Perspective,” International
Finance, 3:3 (November 2000), pp. 375-89.

REFERENCES

Clarida, Richard, Jordi Gali, and Mark
Gertler. “The Science of Monetary Policy:
A New Keynesian Perspective,” Journal of
Economic Literature, 37 (December 1999),
pp. 1661-1707.
Dotsey, Michael. “A Review of Inflation
Targeting in Developed Countries,”
Federal Reserve Bank of Philadelphia
Business Review (Third Quarter 2006), pp.
10-20.
Goodfriend, Marvin S. “Interest Rate
Policy and the Inflation Scare Problem:
1979-1992,” Federal Reserve Bank of
Richmond Economic Quarterly, 79/1
(Winter 1993), pp. 1-23.

8 Q4 2008 Business Review

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, 54:2 (March 2007), pp. 433-59.

Woodford, Michael M. “Optimal Monetary
Policy Inertia,” manuscript (May 1999).

www.philadelphiafed.org

The Mismeasured Personal Saving Rate
Is Still Useful:
Using Real-Time Data to Improve Forecasting*

P

BY LEONARD NAKAMURA

eople make decisions based on information.
Often, with hindsight, they could have made
better choices. Economics faces a similar
problem: Economic data, when first released,
are often inaccurate and may subsequently be revised. In
this article, Leonard Nakamura uses the U.S. personal
saving rate — a statistic that has often been initially
low, then substantially revised upward — to discuss how
modern economic statistical techniques can improve
forecasting.
People make decisions based on
information. A quarterback scanning
receivers or a corporate executive concluding a merger must usually make
decisions with inadequate information.
With hindsight, they often could have
made a better choice. A similar problem holds true for economics: Initial
economic statistics are often inaccurate and may be subsequently revised
as better data become available. One
consequence of this process of initial
data releases that are later revised is
that economists now realize that the
quality of economic forecasts needs to

be judged against the data available
at the time. A second consequence is
that when economists make forecasts,
they should be aware that the statistics
will be revised and incorporate this
information into their forecasts.
The U.S. personal saving rate,
which has been averaging less than 1
percent of after-tax personal income
for the past three years, has often been
initially low and then substantially
revised upward. I will take this statistic
as an example and discuss how modern
economic statistical techniques can
improve forecasting, by taking into
account the difficulties of measuring
saving in the short run.

Leonard
Nakamura is
an assistant vice
president and
economist in
the Philadelphia
Fed’s Research
Department. This
article is available
free of charge at
www.philadelphiafed.org/research-and-data/
publications/business-review/.

USING THE SAVING RATE AS A
FORECASTING TOOL
Households often make decisions
about how much to spend or how

www.philadelphiafed.org

*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.

much to save based not just on their
current income but also on their
expectations of future income. To
the extent that households base such
decisions on expected future income,
economists may draw inferences
from that behavior and use them to
make forecasts about households’
income. For example, households may
save more when they expect their
future income to decline, such as in
retirement, and they may save less
when they expect their future income
to rise. If so, an economist might be
able to infer that households expect to
retire — and therefore suffer a fall in
income — from their saving behavior.
However, understanding the
economic behavior in question is only
part of the difficulty of forecasting. In
practice, economic forecasting suffers
from the problem that at the moment
a forecast is made, current data on the
economy may be imperfect. So the
forecaster must try to estimate what
will happen tomorrow, not knowing
fully what is happening today. As time
passes, the data will be improved,
but that fact is cold comfort to the
forecaster. In the case of data on saving, the personal saving rate is often
not measured well initially, making
forecasting more difficult.
Averaging across all households
in the U.S. economy, we expect
household saving to be positive. After
all, positive saving supports a rising
stock of capital that will make workers
more productive. From 1946 to 1992,
the personal saving rate was generally stable (Figure 1). If the personal
saving rate has been generally stable
over time, then whenever the personal
saving rate is low, it should tend to rise
Business Review Q4 2008 9

10 Q4 2008 Business Review

that changes in the saving rate can
be used for forecasting movements in
income.1
First, we need to go back in time
to recover the pattern of past reports
of income, consumption, and saving,
before and after revision. In doing so,
we must remember that many initial
economic reports rely on data based on
surveys that are incomplete and that
may contain errors, and the surveys
may only imperfectly capture the
economic activity they are supposed to
target. Over time, more complete data
become available as does additional
information that helps place each survey in context, making possible a more
accurate view of economic activity.
As a consequence, economic reports
may be revised and may become more
accurate. As we shall see, the data on

atically undercounted U.S. investment
in developing new products, which
has resulted in uncounted income and
saving. In this situation, is it possible
to still use the saving rate to make
forecasts?
In our working paper, Tom Stark
and I point out that, in the past, initial
reports of low saving rates have repeatedly been revised upward. That is,
there is a historical tendency to initially undermeasure the personal saving
rate. One reason may be that income
is harder to count, and thus easier to
underestimate, than is spending, but
over time we solve the underestimation problem. So the current situation,
in which we are likely undercounting
income and saving, is similar to past
episodes.
If the personal saving rate is typically understated when first reported, a
low level of personal saving may not be
very useful for forecasting. Nevertheless, this article will present evidence

1

Here, and elsewhere in the article, income
means real income, that is, income adjusted for
inflation.

FIGURE 1
(Reasonably) Stable Personal Saving Rate,
1946 to 1992
Percent
12.0

10.0

Personal Saving Rate

8.0

6.0

4.0

2.0

1992

1990

1988

1986

1984

1982

1980

1978

1976

1974

1972

1970

1968

1966

1964

1962

1960

1958

1956

1954

1952

1950

1948

0.0
1946

back to its average rate, and vice versa,
a process called mean reversion. Since
personal saving is defined as after-tax
personal income minus personal outlays, a low saving rate seemingly must
have one of two implications: Either
consumption is expected to fall, or
income is expected to rise. The oftenexpressed view that if saving is low,
consumers are overspending and must
soon cut back is a tempting one. But
as this article will show, the evidence
strongly favors the view that if saving
is low, it’s more likely the case that
income is expected to rise.
The saving rate may change for
other reasons, as well. For example,
households save not only for retirement
but also for a rainy day. This “precautionary saving” stems from households’
concern that they may suffer a loss
of income due to layoffs or ill health.
In recent years, financial innovations
and the moderation of the severity and
frequency of U.S. recessions in recent
years may have reduced households’
fears of the consequences of income
loss. To the extent that changes such
as these influence the saving rate, the
saving rate will not accurately predict
changes in income.
Recently, the measured U.S.
saving rate has been very low: under 1 percent of after-tax income in
2005, 2006, and 2007. This stands in
contrast to a saving rate of 8.5 percent
over the 46 years from 1946 to 1992.
Does the fact that the U.S. is experiencing a low saving rate imply swiftly
rising income?
It turns out that the current low
level of the personal saving rate may
well be due to mismeasurement. As
I argued in a 2001 Business Review
article, personal saving is hard to
measure and may be understated,
particularly over the past 20 years or
so, as a result of changes in the way the
economy behaves and is measured. In
particular, we appear to have system-

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personal saving are particularly vulnerable to changing relationships between
initial data and economic activity
as a whole. Yet recognizing that the
data on saving are imperfect does not
exempt us from doing our best to see
what information we can extract from
the imperfect series.
The inaccuracy of initial economic reports matters because forecasters,
and the decision-makers who rely on
their forecasts, do not have the luxury
of waiting for more accurate measures.
They must use the reports available
to make their decisions. Economists
cannot avoid being concerned about
saving and consumption because
consumption constitutes a large
proportion of demand for output:
Personal consumption expenditures
have averaged about two-thirds of
gross domestic product (GDP) over the
last quarter century. To use these data
as well as we can, we turn to economic
theory, on the one hand, and empirical
analysis on the other.
CONSUMPTION THEORY
AND FORECASTING
The modern theory of consumption dates back to the 1950s and the
work of Milton Friedman. Friedman
showed, in the work for which he
received the Nobel Prize in 1976, that
when our income falls temporarily, we
— consumers — are unlikely to reduce
our consumption as much as income
falls. This argument is called the permanent income hypothesis.
The fundamental argument is that
we generally prefer not to consume
a lot one year and a little the next;
we prefer more equal consumption
over the two years. Economists say
that consumers prefer a smooth path
of consumption rather than one that
bounces up and down. In particular,
suppose we know that in one year we
will have a lot of income and in the
following year much less. The prefer-

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ence for smooth consumption means
that we will consume about the same
each year. So we will save much of our
income the first year in order to spend
it in the next.2
John Campbell, building on sophisticated theories of Friedman’s permanent income hypothesis developed
by Robert Hall and Marjorie Flavin,

question: How much forethought does
the average consumer have? If a large
number of households are like the
grasshopper, an alternative view of the
historically low personal saving rate
that the U.S. (and other countries)
currently suffers from is that we must
inevitably experience a decline in consumption and a recession (Figure 2).

The permanent income theory says that if
consumption will be kept the same when
income goes up, consumers should expect
their future incomes to rise and all of the rise in
income to go into saving.
has argued that if personal saving fell
for consumers as a whole, this would
likely forecast an expected increase in
income.
The underlying logic can be seen
as follows. Suppose consumers raise
their consumption while their income
remains the same, so their saving falls.
Saving can return to normal in one of
two ways. Consumers could be intending to reduce their consumption in
the future. But that would involve an
uneven, rather than a smooth, path for
consumption. More likely, consumers
have raised their consumption because
they expect their incomes to rise in the
future.
The Greek fable of the Ant and
the Grasshopper, in which a grasshopper who sings all summer starves in the
winter, while an ant who saves during
the summer is well provided for, serves
as a reminder of the possibility that not
all households may do a good job of
forecasting. Thinking about household
behavior raises an important empirical

2
A fuller discussion of consumption and the
permanent income hypothesis can be found in
Satyajit Chatterjee’s forthcoming article.

For example, the minutes to the
September 2004 meeting of the U.S.
Federal Open Market Committee
state, “Members perceived several
possible sources of downside risk to
household spending. In particular,
households might hold back on spending in an attempt to increase their
saving, which had fallen to a very low
level relative to income.” In this view,
this “downside risk” to spending could
trigger a slowdown in economic growth
and possibly a recession.
Thus, there are two conflicting
notions about how a low saving rate
can return to normal. One, the “grasshopper theory,” says that consumers
will simply consume less and save
more. The other, the permanent income hypothesis, says that income will
rise while consumption remains stable.
The permanent income theory
actually says something more. It says
that if consumption will be kept the
same when income goes up, consumers should expect their future incomes
to rise and all of the rise in income to
go into saving. So forecasts of income
and saving should tend to mirror one
another. To achieve this goal, the
forecasting equation for saving must be

Business Review Q4 2008 11

FIGURE 2
Twenty Years of Profligacy?
Measured U.S. Personal Saving Rate*
As Reported in 2008 Q1
Percent
14
12
10
Personal Saving Rate in Percent
8
6
4
2
0
-2
651 671 691 711 731 751 771 791 811 831 851 871 891 911 931 951 971 991 011 031 051 071

Years

* Seasonally adjusted.

quite similar to the forecasting equation for income. If this tight relationship between the forecasts holds, the
two forecasts together should perform
better than either separately.3
Campbell found evidence for the
idea that a low saving rate does imply
future growth in income but not for
the stronger claim that income growth
is exactly related to the size of the
drop in the saving rate. However, such
exact tests of hypotheses typically fail
because hypotheses are necessarily
overly simple in their formulation. A

3

The relationship between the two forecasts is
achieved technically through what are called
cross-equation restrictions. In this example, a
low saving rate forecasts a higher rate of income
growth. The low saving rate also forecasts an
increase in the saving rate of about the same
amount. So the coefficient on the low saving
rate should be approximately the same for the
two. For details, see the Appendix, and my
working paper with Tom Stark.

12 Q4 2008 Business Review

statistical rejection can occur because
the simple hypothesis is only approximately true, and the data are sufficiently precise to reject the approximation. In fact, Campbell was testing the
notion that all consumers are ants, and
none are grasshoppers. And that was
rejected.
Peter Ireland has pointed out
that if Campbell’s theory is true, the
personal saving rate should be useful
in forecasting income, in particular,
labor income. He argued that forecasting ability was a good test of an
economic theory; indeed, it shows that
the economic theory can be useful in a
very practical way. That is, the theory
says that saving should help us improve
our forecasts of income. Because this
is true, according to Ireland’s argument, the economic significance of the
hypothesis is validated, even though
statistics may have rejected its nar-

row implications. For example, when
Galileo tested whether two objects of
different weight fell at the same speed,
he ignored the effect of air resistance.
His test, in fact, rejected the hypothesis that the two fell at exactly the
same speed. But his test did show that
the prediction that they would fall at
the same speed was much more accurate than the prediction that they
would fall at a speed proportional
to their weight. Similarly, although
Campbell’s estimation showed that
forecasted saving did not move with
income exactly as the permanent
income hypothesis predicts, Ireland’s
results, as we shall see, showed that
when saving and income were assumed to follow the permanent income
hypothesis, the forecast was better
than if that assumption had not been
made. To put it another way, Ireland
showed that most consumers were ants
rather than grasshoppers, and so, on
balance, for practical purposes, there
are enough ants that the grasshoppers
don’t matter.
Before we get to Ireland’s evidence, however, we shall first discuss
the measurement of saving.
SAVING: INITIAL MEASUREMENT AND REVISION
In a previous Business Review article, I argued that the personal saving
rate may be mismeasured. The main
evidence is that if U.S. personal saving
is unusually low, U.S. wealth should be
falling. However, the opposite has been
true. It is useful to be more precise
about how the personal saving rate is
measured in the U.S.
The personal saving rate is
personal saving as a percentage of
disposable (after-tax) personal income.
Personal saving, in turn, is disposable personal income minus personal
outlays. Disposable personal income
includes some easily measured items,
such as social insurance contributions

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and benefits. Other parts of labor
income, such as other (that is, nonSocial Security) benefits and transfers, are subject to measurement and
conceptual problems. (For example, is
a pension considered income when it is
earned or when it is paid to a worker?)
Wages and proprietors’ income are
subject to underreporting in government records as a result of tax evasion.
Rental income and proprietors’ income
are net income measures that require
estimates of depreciation and other
expenses that are hard to measure
well. Capital gains on equity (other
than from qualified equity stock options) and real estate are not included
in personal income.
Under the current method of
measuring income, we may not be
capturing all of the sources of household income, and this may result in
the appearance of low saving. In that
case, if, in the future, we figure out a
better way to measure income, we will
revise our current estimates of saving
upward.4
How are data revised? Data on
a given quarter’s economic activity are first published in an advance
estimate, late in the first month of the
next quarter. The revised estimate is
published in the second month of a
quarter, followed a month later by a
final estimate. These data are then
generally left unchanged until the following summer, when the latest three
years of national account data are
revised. Initial estimates thus undergo
three summer revisions. Thereafter,
the estimates are changed only in
benchmark revisions, which now occur
every four years. Benchmark revisions
provide an opportunity for the Bureau

of Economic Analysis (BEA) to make
discretionary choices in defining the
items it considers to be part of personal
income. For example, government
pension income is now considered
income when it is earned, rather than
when it is paid out.5 In addition, more
complete data from economic censuses
are included at this time. Most of the
revisions to the saving rate that turn
initially low rates into higher ones occur during benchmark revisions.
Real-Time Data Collected by the
Philadelphia Fed. Researchers Dean
Croushore and Tom Stark pioneered
the collection of data sets in vintages

5
When government pensions were mainly
federal pensions, they were treated this way
because the federal government did not set
aside income at the time the pension obligations
were incurred. However, now most government
pensions are state and local government
pensions, and these governments generally set
aside pension funds when their employees earn
the pensions.

that capture the data as they were
available on a particular date. These
data can be used to show how revisions change our view of economic
processes. (The real-time data used
here as well as a number of other
statistical series can be downloaded
at the following address: http://www.
philadelphiafed.org/research-and-data/
real-time-center/real-time-data/.)
These data show that adjustments
to the measurement of personal saving
have occurred repeatedly in the past.
For example, the average saving rate
from 1980 to 1985 was initially reported as 6.5 percent and is now reported
as 10.4 percent, the highest saving rate
in our current data series. The advance
estimates of the personal saving rate
from the third quarter of 1965 to the
second quarter of 1999 averaged 5.3
percent. But after revisions, as reported
in September 2007, the personal saving
rate over the same period averaged 8.1
percent. Figure 3 shows the advance

FIGURE 3
Before and After: Measured Personal Saving
Rate as First Published and as of 2007 Q4
Percent
14.0
12.0
10.0

2007 Q4 Vintage

8.0
6.0
4.0

Advance Estimates

2.0
0.0
4

In particular, it’s possible that we will eventually adjust our treatment of capital gains, which
have become an important contributor to the
increase in household wealth.

www.philadelphiafed.org

-2.0

Business Review Q4 2008 13

estimates of the personal saving rate as
they were reported when first published, and the latest vintage data, as
we would have seen them last year.
For example, in the fourth quarter
of 1985, when the advance report for
the personal saving rate in the third
quarter of 1985 became available, it
was reported as 2.9 percent. (See the
table in Understanding and Using RealTime Data.) But if we go to the BEA’s
website today, we will find that we now
believe that the personal saving rate
in the third quarter of 1985 was 7.9
percent.
What is the problem in measuring
saving? It turns out that complete data
on income are hard to measure. As the
economy evolves, new types of income
come about. Initially, the new income
may not be reported or may not be
considered income. Over time, as new
sources of data become available and
as old data come to be viewed in a new
light, more income is reported.
These changes in income are
usually recorded in the benchmark
revisions. In our working paper, Tom
Stark and I show that almost all of the
upward revisions to the personal saving rate occur in benchmark revisions.
Which data should we use for our
tests of the permanent income hypothesis? Peter Ireland and Tom Stark and
I simulated forecast exercises. Ireland’s
test focused on how consumers actually behave, to test whether the underlying consumer behavior was primarily
driven by households that conform to
the permanent income hypothesis. To
do this test, we want to use the data
that best reflect the underlying behavior, that is, the most accurate available
data. And those are the latest revised
data, in the most recent vintage.
Forecasting well is not necessarily
the same as understanding consumer
behavior. For understanding consumer
behavior, how poor our latest statistics
are is irrelevant — what we care about

14 Q4 2008 Business Review

is the underlying behavior revealed by
the best statistics, which may be available only in historical data that have
been revised. Ireland used the right
data from the perspective of understanding consumer behavior — the
best and latest available statistics —
but those data are not the best guide
for understanding how to forecast with
the data the forecaster actually has
available.

Forecasting well
is not necessarily
the same as
understanding
consumer behavior.
If we want to test how useful the
saving rate is to an economic forecaster, we should use real-time data, which
will repeatedly put us into the situation
of the forecaster: using data that have
not yet been revised.
FORECASTING WITH
MISMEASURED PERSONAL
SAVING
One way economists analyze
statistical relationships in economic
data is to perform an in-sample data
analysis called a regression analysis. In
our case, we want to examine whether
in periods when saving is low, income
rises faster than usual in later periods.
This statistical relationship can then
be used to forecast the behavior of the
consumer.
Peter Ireland’s insight was to argue
that even if not all consumers behave
according to the permanent income
hypothesis, if most of them do so, it
may be better to assume than not assume the hypothesis that saving and
income will rise together and that assumption will produce better forecasts.

The method Ireland used for his
test is called recursive out-of-sample
testing. This method basically asks
over and over (recursively) whether
the relationship in past data successfully forecasts the next piece of data.
This analysis is out-of-sample because
the next piece of data is never in the
sample.
To use a specific example, we
take a base period, say, from the first
quarter of 1959 to the fourth quarter
of 1970, and do a regression analysis.
We then use this regression analysis
to forecast the next quarter’s income
— the first quarter of 1971. We then
compare this to the actual income for
the first quarter of 1971 and measure
the error in this forecast. We then add
the first quarter of 1971 to our data,
lengthening our data one period, and
undertake a new regression based on
data from the first quarter of 1959 to
the first quarter of 1971. We then forecast the new next period: we forecast
income in the second quarter of 1971
and again measure the error in this
forecast.
Continuing to the present, we can
accumulate a long series of forecasts,
the actual data, and the forecast
errors.6 We square the errors, sum
them up, and divide by the number of
forecasts to obtain the mean square error of the forecasts. We then take the
square root to obtain the root mean
square error, a number conceptually
similar to the standard deviation.
The smaller the root mean square
error, the more accurate the average
forecast. When we make forecasts of
income using past income and saving,
we will compare the root mean square
error with the root mean square error
when only past income is used in the

6

The forecast error is the difference between
the actual value and the predicted (forecast)
value of a time series.

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Understanding and Using Real-Time Data

O

rdinarily, data used in economic analysis
are what real-time data users call the
latest available vintage. It reflects the
data that were published by the statistical
agency (in the case of the U.S. personal
saving rate, the Bureau of Economic
Analysis, or BEA) at the time the economic analysis was
performed. As we have stressed, these data may look very
different from those that were available to a forecaster at
some earlier time.
The table on page 16 contains selected portions of a
real-time data matrix. Each column in the full matrix represents a “vintage.” Each vintage contains the data from
the first quarter of 1947 to the quarter before the vintage
date, as it was published at the vintage date.
A forecaster in the fourth quarter of 1985 would have
had available the data in column 2 of the table presented
here and would have thought that the personal saving
rate in the third quarter of 1985 was 2.9 percent. A forecast of real income in the fourth quarter of 1985 would
have been based on this estimate.
In the next quarter, the BEA published a benchmark
revision, and the personal saving rate for the third quarter
of 1985 then appeared to be 3.7 percent. This is shown
in column 3. Because this is a benchmark revision, the
entire history of the personal saving rate has been revised.
Note that even the data from 1947 have been revised.
This would presumably have caused a forecaster using the
personal saving rate in forecasting to redo the regression analysis on which the forecast was based. Again, in
the third quarter of 1986, a summer revision changed
the data for the past three years, and the estimate of the
personal saving rate for the third quarter of 1985 was
reported to be higher yet, 4.2 percent (column 5).

forecast. If saving does help to forecast
income, it will lower the root mean
square error.
Peter Ireland used this “recursive
regression” method to forecast income
using the personal saving rate.7 Using
data from 1959 to 1994, he showed
that the personal saving rate was a
good forecaster of income from 1970
to 1994.

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The data that Peter Ireland used in his paper were
those published by the BEA in the fourth quarter of 1994
(column 6). In Ireland’s work, the estimate of personal
saving for the third quarter of 1985 was 5.4 percent. For
all of his forecasts, Ireland would have used column 6
data. In our real-time forecast analyses, presented here,
we use a different column of data for each forecast. Thus,
we would assume that the forecaster in the fourth quarter
of 1985 would use the data in column 1 to estimate the
forecast equation and to make the forecast. In the first
quarter of 1986, the forecaster would use the data in
column 2. By contrast, Ireland’s forecaster in the fourth
quarter of 1985 uses the data in column 6, up to the row
that says third quarter of 1985, to make a forecast.
Remarkably, the data change further in the latest vintage used in our study, the third quarter of 2005,
which shows a personal saving rate of 7.9 percent in
the third quarter of 1985 (that’s the number still being
reported as of the first quarter of 2008).
An interesting contrast is to look at changes in the
personal saving rate, which changes much less across
vintages compared with the level. In both the real-time
vintage of the fourth quarter of 1985 (column 2), and in
the “latest” vintage of the third quarter of 2005 (column
7), we observe that the personal saving rate in the third
quarter of 1985 is low relative to its neighbors in the
quarter before and the quarter after. In our working paper,
Tom Stark and I present additional evidence that changes
in the personal saving rate are more stable over time than
the level, which is an important reason why changes in
the saving rate have better predictive power than the level
of the saving rate.

To create a benchmark for his
forecasts, he began by using past values
of income growth to forecast future income growth. There is good evidence
that — in part because economies
tend to go through booms and busts —
when income growth is high, it tends
to remain high, and when income
growth is low, it tends to remain low,
a pattern called persistence. Ireland

made recursive out-of-sample forecasts
of income growth using past values

7
When the regressions are formed by
continually enlarging the data set, so that, as
in the example, we always begin from 1959, the
regressions are called recursive. An alternative
technique is “rolling regressions,” where, as we
add more recent data, we drop off the oldest
data, so that the period under consideration is
always the same length.

Business Review Q4 2008 15

Understanding and Using Real-Time Data...(continued)

TABLE
Example of Real-Time Data Personal Saving Rates in Six Vintages,
Selected Observations
1

2

3

4

5

6

7

Vintage
85:Q4

Vintage
86:Q1

Vintage
86:Q2

Vintage
86:Q3

Vintage
94:Q4

Vintage
05:Q3

1947:Q1

5.0%

4.9%

4.9%

4.9%

4.8%

6.1%

1947:Q2

1.4%

1.3%

1.3%

1.3%

1.2%

2.6%

1976:Q1

7.7%

8.2%

8.2%

8.2%

7.9%

9.6%

1976:Q2

7.3%

8.0%

8.0%

8.0%

7.7%

9.6%

1976:Q3

6.7%

7.6%

7.6%

7.6%

7.3%

9.5%

1976:Q4

5.9%

6.9%

6.9%

6.9%

6.6%

8.9%

1984:Q1

6.1%

7.0%

7.0%

6.9%

8.1%

10.3%

1984:Q2

5.7%

6.1%

6.1%

6.0%

7.8%

10.6%

1984:Q3

6.3%

6.7%

6.7%

6.4%

8.4%

11.3%

1984:Q4

6.2%

6.0%

6.0%

6.0%

7.9%

11.0%

1985:Q1

4.5%

4.8%

4.8%

5.2%

6.7%

9.4%

1985:Q2

5.1%

5.9%

5.9%

6.5%

7.8%

10.2%

1985:Q3

2.9%

3.7%

3.7%

4.2%

5.4%

7.9%

1985:Q4

#N/A

4.1%

4.0%

4.4%

6.0%

8.6%

1986:Q1

#N/A

#N/A

4.3%

5.0%

6.5%

8.9%

1986:Q2

#N/A

#N/A

#N/A

5.2%

7.2%

8.9%

Summer
Revision

Ireland
Data

NakamuraStark Latest
Available
Vintage

Date

Benchmark
Revision

16 Q4 2008 Business Review

www.philadelphiafed.org

of income growth and then measured
the forecast error. He repeated this
over the period from 1970 to 1994 and
calculated the root mean square error.
He then made similar forecasts of
future income growth using past values
of income growth and adding past
values of saving. He found that the
root mean square error was lower than
when only past values of income were
used. Moreover, he found that the
forecast error was even lower when he
accounted for the restrictions imposed
by the permanent income hypothesis:
that predicted savings and income
have parallel movements. He took this
to be good evidence that the permanent income hypothesis is true.
However, Ireland used the data as
they were available in 1994. This is not
really a true test of personal saving’s
usefulness in forecasting because we
know that the personal saving rate
as it was available in 1994 differed
substantially from what it looked like
in, say, 1980. So Ireland, making his
forecasts in 1994, used an estimate of
the personal saving rate for the third
quarter of 1985, for example, that was
5.4 percent, while the forecaster in
the fourth quarter of 1985 would have
thought it was 2.9 percent (and, as we
now know, it was later revised to 7.9
percent).
FORECASTING WITH
REAL-TIME DATA
Using real-time data from the
Philadelphia Fed’s data set, we can
make real-time forecasts that use
the data as they were available to an
economist on a series of dates. (To
see how these data are organized, see
Understanding and Using Real-Time
Data. Further information can be
found in the 2000 article by Croushore
and Stark.) Real-time data enable us
to ask: Given that personal saving
has historically been dramatically
mismeasured, would it be a useful

www.philadelphiafed.org

forecasting tool?
Forecasting Income with Saving,
with Latest Available Data, and in
Real Time. With data that have been
revised over many years, the relationship between the level of saving and
future income growth is just as the
permanent income hypothesis shows,
as Peter Ireland also showed.
However, if we try to do the same
exercise in real time, the level of the
saving rate is not predictive. I will
show that, in particular, from 1981

less useful in forecasting. In real time
during this period, the level of the
saving rate worsens forecasts, with or
without the restrictions. As we see in
row 2, the forecasts are 4 percent worse
using the level of the saving rate and 1
percent worse adding the restrictions.
An Alternative: Forecasting
with the Change in the Saving Rate.
Thus far, I have focused on the level of
the saving rate as a measure of future
income expectations because the
underlying theory and the data suggest

Using real-time data from the Philadelphia
Fed’s data set, we can make real-time
forecasts that use the data as they were
available to an economist on a series of dates.
to 2005, the level of the saving rate
does not improve forecasts of income
growth. All is not lost, however,
because I will show that changes in the
saving rate can be used in real time to
forecast income growth.
Forecasting in Real Time. Let’s
look at the forecasts using real-time
data, shown in the first row of the
table on page 18. If we look at the
period before 1982 (the first quarter
of 1971 to the fourth quarter of 1981),
before the saving rate started trending
downward, even in real time there is
value to these forecasts, although the
improvement shrinks to 3.7 percent.
There is even a small improvement
from imposing the restrictions of the
permanent income hypothesis.
But when we look at the data after
1981, the level of the saving rate is
much less helpful in forecasting. Looking at row 2, from the first quarter of
1982 to the second quarter of 2005, we
see that when the level of the saving
rate has been falling, it has been much

that the level of the saving rate should
generally be stable. Therefore, when
the saving rate is below average, we
expect it to rise toward the average. A
below-average level of the saving rate,
according to the permanent income
hypothesis, implies that income is
expected to rise, causing saving to
rise. But, as we have seen, the most
recent level is typically too low and
likely to be revised higher. Thus, the
level might be misleading. Perhaps we
should try the change in the saving
rate. Even if the level is low because of
mismeasurement, a downward change
might be telling us that income is
expected to increase.
It is true that in the absence of
substantial measurement error, the
change in the saving rate is unlikely
to be as informative as the level of the
saving rate. If we look at the latest
revised data, in the heavily revised period from 1971 to 1981, we see that the
level of the saving rate reduces the root
mean square error 12 percent. When

Business Review Q4 2008 17

TABLE
Forecasting Real Disposable Income Growth with Real-Time Data:
Ratios of Forecast Errors, Forecasts with Saving Relative to Forecasts
with Only Past Income Growth*
(1)
Level of Saving

(2)
Permanent Income
Hypothesis
Restrictions on Level
of Saving

(3)
Change in Saving

(4)
Permanent Income
Hypothesis
Restrictions on
Change in Saving

0.963

0.954

0.950

0.944

1.040

1.010

0.943

0.935

1971:Q1 – 1981:Q4
1. Real time
1982:Q1 – 2005:Q2
2. Real time

* Lags chosen using the Akaike information criterion.

we add the restrictions of the permanent income hypothesis, we reduce
the root mean square error 16 percent. With the latest revised data, the
change in the saving rate does not do
as well in this period, reducing the root
mean square error 10 to 12 percent,
depending on whether we impose the
restrictions of the permanent income
hypothesis.
Thus, with good revised data,
the change in the saving rate is not
as informative about future changes
in income as is the level of the saving
rate. The theory points us to the right
form for the data.
But as noted before, this does not
tell us about the situation a forecaster
faces. If we look at that same period
but make forecasts using real-time
data, we see that the level of the
saving rate reduces the root mean
square error only 3.7 percent, and the

18 Q4 2008 Business Review

permanent income hypothesis restrictions add only a small improvement,
reducing the root mean square error
4.6 percent. We make better forecasts
with the change in the saving rate,
which produces a 5 percent improvement without the restrictions of the
permanent income hypothesis and 5.6
percent with them.
If we look at the more recent
period, from 1982 to 2005, the level of
the saving rate performs quite poorly
in forecasting. In real time in this
period, the level of the saving rate
worsens forecasts with or without the
permanent income hypothesis restrictions, as we have seen. By contrast,
the change in the saving rate performs
well, reducing the root mean square error 5.7 percent without the permanent
income hypothesis restrictions and 6.5
percent with it.
Thus, using changes in the saving

rate in real time, a forecaster could
have made a better forecast of future
income than using only past data on
income. This is true whether or not
the permanent income hypothesis restrictions are imposed. By contrast, the
level of the saving rate, despite attractive theoretical properties and despite
the fact that the level of the saving
rate does well with the latest revised
data overall, would not have been a
good choice in a forecasting equation
over the past 20 years.
Why might the change in the
saving rate be better in real time than
the level of the saving rate? It turns
out that the change in the saving rate
is subject to smaller revisions than the
level of the saving rate. Technically,
this is because revisions tend to have
a cumulative impact on the levels.
Consequently, the changes are more
reliable than are the levels.

www.philadelphiafed.org

CONCLUSION
I have made three points in this
article. First, I argued that when the
saving rate falls, it is more likely to be
evidence that households expect faster
real income growth in the future,
rather than evidence that they are
spending too much and will have to
cut back on consumption.
Second, I showed that the personal saving rate has typically been
substantially revised and usually up-

ward. The Philadelphia Fed’s real-time
data set gives us the data we need to
show this. Since a low personal saving
rate can occur because of mismeasurement and may well be revised upward,
in practice, the level of the personal
saving rate does not help us forecast
real income growth.
Finally, I showed that, guided by
this insight, forecasters can use the
change in the saving rate rather than
the level as a forecasting tool. Al-

though this technique does not work
as well as having better data would,
it does enable economists to improve
their forecasts.
So I have shown that real-time
data can be quite useful for improving
forecasting when revisions are large. By
using real-time data, economists can
sometimes figure out how current data
can be valuably employed, even when
poorly measured. BR

TECHNICAL APPENDIX
Permanent Income Hypothesis Restrictions
Campbell’s version of the permanent income hypothesis that we are testing is a two-equation system that predicts
changes in income and the level of the saving rate. The system relates these to past values of income and the level of the
saving rate.
Formally, the system is
ΔYl t = a (L )ΔYl t −1 + b (L ) St −1 + u1t
St = c (L )ΔYl t −1 + d (L ) St −1 + u2t

where Yl t is real labor income per capita at time t, St is real saving per capita, and Δ is the first-difference operator. The
p
i
terms a( L), b( L), c( L), and d ( L) are polynomials in the lag operator, given by, for example, a ( L) = Σ i =1 ai L , p is the lag
length, and the ut are forecast error terms.
Whenever the expected permanent increase in real labor income occurs, the saving rate is expected to rise
at the same time. The permanent income hypothesis says that these two expected increases are closely related;
econometrically, this relationship is called a cross-equation restriction because it relates coefficients across the two
equations. The intuition behind these cross-equation restrictions is that a current decrease in saving must imply a future
predictable permanent increase in real labor income and a future predictable saving increase. The 2p restrictions on the
coefficients of the lag operators are
ci = ai , i = 1,..., p
d1 = b1 + (1 + r )
di = bi , i ≥ 2,

where r represents a constant real interest rate. See Peter Ireland’s article for a more detailed description.

www.philadelphiafed.org

Business Review Q4 2008 19

REFERENCES

Campbell, John Y. “Does Saving
Anticipate Declining Labor Income? An
Alternative Test of the Personal Income
Hypothesis,” Econometrica 55 (November
1987), pp. 1249-73.

Croushore, Dean, and Tom Stark. “A
Real-Time Data Set for Macroeconomists:
Does the Data Vintage Matter?,” Review of
Economics and Statistics 85 (August 2003),
pp. 605-17.

Kilian, Lutz, and Atsushi Inoue, “InSample or Out-of-Sample Tests of
Predictability, Which One Should We
Use?” Econometric Reviews 23 (November
2004), pp. 371-402.

Chatterjee, Satyajit. “The Peopling of
Macroeconomics: Microeconomics of
Aggregate Consumer Expenditures,”
Federal Reserve Bank of Philadelphia
Business Review (forthcoming).

Croushore, Dean, and Tom Stark. “A
Real-Time Data Set for Macroeconomists,”
Journal of Econometrics 105 (November
2001), pp. 111-30.

Nakamura, Leonard. “Investing in
Intangibles: Is a Trillion Dollars Missing
from GDP?,” Federal Reserve Bank of
Philadelphia Business Review (Fourth
Quarter 2001).

Croushore, Dean. “Forecasting with RealTime Macroeconomic Data,” in Graham
Elliott, Clive W.J. Granger, and Allen
Timmermann, eds., Handbook of Economic
Forecasting, Amsterdam: North-Holland,
2006.
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.

20 Q4 2008 Business Review

Flavin, Marjorie. “The Adjustment of
Consumption to Changing Expectations
of Future Income,” Journal of Political
Economy 89 (October 1981), pp. 974-1009.
Hall, Robert E. “Stochastic Implications
of the Life Cycle-Permanent Income
Hypothesis: Theory and Evidence,” Journal
of Political Economy 86 (December 1978),
pp. 971-87.
Ireland, Peter N. “Using the Permanent
Income Hypothesis for Forecasting,”
Federal Reserve Bank of Richmond
Economic Quarterly 81 (Winter 1995), pp.
49-63.

Nakamura, Leonard, and Tom Stark,
“Mismeasured Personal Saving and the
Permanent Income Hypothesis,” Federal
Reserve Bank of Philadelphia Working
Paper 07-8 (February 2007); available
at: http://www.philadelphiafed.org/
research-and-data/publications/workingpapers/2007/wp07-8.pdf
Stark, Tom, and Dean Croushore.
“Forecasting with a Real-Time Data
Set for Macroeconomists,” Journal of
Macroeconomics 24 (December 2002), pp.
507-31.

www.philadelphiafed.org

Growing Slowly, Getting Older:*
Demographic Trends in the Third District States
BY TIMOTHY SCHILLER

N

ational trends such as slower population
growth, an aging population, and
immigrants as a larger component of
the population are mirrored in the
Third District states (Pennsylvania, New Jersey, and
Delaware). These trends are likely to persist and perhaps
even accelerate well into the future. In this article,
Tim Schiller reviews these trends and their possible
interaction with health-care and retirement benefit
programs nationally and in the Third District states.

Since the last census in 2000,
estimates suggest that U.S. population
growth has slowed, the population has
aged, and immigrants have become a
larger component of the population.
These national trends have also been
evident in the three states of the Third
Federal Reserve District: Pennsylvania,
New Jersey, and Delaware. These
trends are likely to persist — and even
accelerate — well into the future. The
major economic consequence of these
demographic changes is a slowdown
in the rate of employment growth.
Coupled with an aging population,
slow growth in the working population

Tim Schiller is a
senior economic
analyst in the
Philadelphia Fed’s
Research Department. This article
is available free
of charge at www.
philadelphiafed.
org/research-anddata/publications/business-review/.
www.philadelphiafed.org

will present challenges for government
budgets, particularly public retirement
and health-care programs. The
magnitude of these challenges cannot
be determined exactly; it will depend
on actual demographic developments
and on how the benefit programs
evolve. Nevertheless, estimates can be
made based on current demographic
trends and existing benefit programs.
This article will review these trends
and their possible interaction with
benefit programs nationally and in the
Third District states.
THIRD DISTRICT
POPULATION: SLOWER
GROWTH AND OLDER
Population Growth Has Slowed.
Annual estimates of the national
population and the population of the

*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.

Third District states indicate that
growth since 2000 has been slower
than growth between the census
years 1990 and 2000.1 As of 2007,
the U.S. population was about 7
percent larger than it was in 2000,
the result of an annual growth rate
of about 1 percent. This was slower
growth than the annual rate of
about 1.2 percent between 1990 and
2000. In the region, the increase in
population from 2000 to 2007 was 1.2
percent in Pennsylvania, 3.2 percent
in New Jersey, and 10.4 percent in
Delaware. Among the three states,
only Delaware’s population growth
was faster than the nation’s (Table 1).
Delaware ranked 10th among the 50
states and the District of Columbia in
population growth since 2000, and it
was the only state in the northeastern
region of the country to have
population growth above the national
rate.
Population growth in the nation
is the result of two factors: natural
increase (births minus deaths) and
net international migration (people
moving into the country minus people
moving out). Since 2000, growth in
the national population has been
due to natural increase and net
international migration in the same

1

The Census Bureau makes annual estimates
of the national and state populations. See
the U. S. Bureau of the Census citation in
the References for website information. The
bureau’s analysis of its estimates of current
population and projections of future population
indicates that both tend to be lower than actual
population counts. Nevertheless, the estimates
and projections give a fair picture of the trend
in actual population over time, which is the
subject of this article. For a discussion of the
accuracy of projections, see the working paper
by Ching-li Wang.
Business Review Q4 2008 21

TABLE 1
Population Change 2000 - 2007
Percent

Population
2007

28.4
23.5
18.5
16.6
15.9
14.6
14.2
13.0
12.6
10.4
9.9
9.7
9.5
9.0
8.9
8.3
8.2
7.9
7.2
6.5
6.2
6.1
6.0
5.9
5.9
5.7

2,565,382
6,338,755
2,645,330
9,544,750
1,499,402
23,904,380
18,251,243
4,861,515
9,061,032
864,764
4,407,709
6,468,424
3,747,455
683,478
7,712,091
1,969,915
6,156,719
36,553,215
301,621,157
1,315,828
957,861
5,618,344
2,834,797
1,283,388
522,830
5,197,621

Nevada
Arizona
Utah
Georgia
Idaho
Texas
Florida
Colorado
North Carolina
Delaware
South Carolina
Washington
Oregon
Alaska
Virginia
New Mexico
Tennessee
California
United States
New Hampshire
Montana
Maryland
Arkansas
Hawaii
Wyoming
Minnesota

Percent
South Dakota
Missouri
Kentucky
Oklahoma
Wisconsin
Indiana
Alabama
Nebraska
Illinois
Maine
Kansas
New Jersey
Connecticut
District of Columbia
Mississippi
Iowa
Vermont
New York
Massachusetts
Michigan
Pennsylvania
Ohio
Rhode Island
West Virginia
North Dakota
Louisiana

Population
2007

5.5
5.0
4.9
4.8
4.4
4.4
4.1
3.7
3.5
3.3
3.2
3.2
2.8
2.8
2.6
2.1
2.0
1.7
1.6
1.3
1.2
1.0
0.9
0.2
-0.4
-3.9

796,214
5,878,415
4,241,474
3,617,316
5,601,640
6,345,289
4,627,851
1,774,571
12,852,548
1,317,207
2,775,997
8,685,920
3,502,309
588,292
2,918,785
2,988,046
621,254
19,297,729
6,449,755
10,071,822
12,432,792
11,466,917
1,057,832
1,812,035
639,715
4,293,204

Source: U.S. Census Bureau.

proportions as growth from 1990 to
2000: 60 percent of the growth in
population was from natural increase
and 40 percent from net international
migration. For the states, there is the
additional component of population
change: movement of people from
one state to another, called internal
migration.
A look at all of the components
of population change since 2000 in

22 Q4 2008 Business Review

the three Third District states reveals
that natural increase has been an
important factor, but the other factors
have had different effects in each state.
(See the Figure.) In Pennsylvania,
net international migration was the
component that contributed the most
to growth. Natural increase was much
less in the state than it was in the
nation, but it contributed 75 percent of
the total increase, a greater share than

in the nation. Pennsylvania has the
third lowest natural increase (among
the 50 states and District of Columbia)
in population; West Virginia had an
actual decrease (deaths exceeded
births); and Maine’s natural increase
was less than Pennsylvania’s. The
two components contributing
to an increase in Pennsylvania’s
population — natural increase and net
international migration — were offset

www.philadelphiafed.org

FIGURE
Components of Population Change 2000-2007
Percent of 2000 population
6

4

2

0

-2

-4

-6

US
Natural Increase

DE

NJ

Net International Migration

PA
Net Internal Migration

Source: U.S. Census Bureau

to some extent by negative net internal
migration (more people moved out to
other states than moved in), which
subtracted from the state’s population
between 2000 and 2007. Another
way to look at this effect is to note
that net internal migration offset 33
percent of the total population growth
from 2000 to 2007. For the years
since 2000 compared with the 1990s,
international migration became a more
important positive factor and internal
migration became a less important
negative factor in Pennsylvania’s
population growth.
In New Jersey, net international
migration added to the state’s
population between 2000 and 2007,
but it was offset by a virtually equal
amount of negative net internal
migration (more people moved out to
other states than moved in from other
states). Consequently, on net, New
Jersey’s population growth was almost
equivalent to its natural increase. The
www.philadelphiafed.org

offsetting effects of international and
internal migration in New Jersey for
the years since 2000 were similar to
their effects during the 1990s.
Delaware had a natural increase
that nearly matched the nation’s, net
international migration that was less
than the national gain, and significant
net internal migration. Both net
international and net internal
migration have been somewhat more
important for Delaware’s population
growth in the years since 2000 than
they were in the 1990s. Compared
with the average of other states,
natural increase and net international
migration contributed proportionately
more to growth in Pennsylvania and
New Jersey and less in Delaware.
Net internal migration contributed
proportionately more to growth in
Delaware and subtracted from growth
in Pennsylvania and New Jersey.
Increase in Foreign-Born
Percentage of Population and an

Older Population. The percentage
of the population that is foreignborn has increased, and the
population has gotten older. Both
of these developments represent the
continuation of long-term trends in
the nation as well as in the Third
District states. With international
migration accounting for nearly half of
the increase in the national population
since 2000, it is not surprising that
the foreign-born percentage of the
national population increased to
about 13 percent from 11 percent.
The percentage-point increase was
smaller in Pennsylvania (from 4.1
percent to 5.1 percent), which has long
had a smaller percentage of foreignborn population than the nation.
The percentage-point increase was
somewhat larger in Delaware (from
5.7 percent to 8.1 percent). Like
Pennsylvania, Delaware has long had
a smaller percentage of foreign-born
population than the nation, but also
like Pennsylvania, Delaware has seen
an increasing share of its population
growth come from an increase in
foreign-born residents. The percentagepoint increase in the foreign-born
population of New Jersey (from 17.5
percent to 20.1 percent) was greater
than the national increase. New Jersey
has long had a larger percentage of
foreign-born population than the
nation.
The national birth rate, which
has been declining for many years,
has continued to do so at a slow
pace in the years since the start of
this century. With the slowdown in
the birth rate, the median age of the
national population rose, as did the
median age of the population in each
of the Third District states. Although
the median age of people immigrating
into the country each year is younger
than the median age of the current
total population, the numbers of
immigrants and the difference in

Business Review Q4 2008 23

median age have not been sufficient
to keep the median age from rising
nationally or in the three states.
Since 2000, the median age
nationally has increased from 35.3
to 36.6 (Table 2). The median age
in each of the Third District states is
above the national median. Among
the three states, it is highest in
Pennsylvania, 39.7 years, making the
state the sixth highest for median age
among all the states and the District
of Columbia. The median age in New
Jersey is 38.4 years (11th highest),
and in Delaware it’s 37.9 years (13th
highest).
Another measure of the
population’s age is the percentage at or
above certain ages. A common cutoff
for this measure is 65. The percentage
of the population 65 years and over
in the nation has not changed much
since 2000, rising from 12.4 percent
to 12.6 percent. In the region, the
percentage of the population 65 years
and over has increased since 2000
in Delaware but declined slightly
in Pennsylvania and New Jersey.
Nevertheless, the percentage is greater
in each state than in the nation (15.2
percent in Pennsylvania. 13.1 percent
in New Jersey, and 13.6 percent in
Delaware).
Besides the population age
65 and over as a share of the total
population, another key measure of
this age group’s significance is its
size in relation to the population age
20 to 64. This is because the 65 and
over population is much more likely
not to be in the workforce, while the
20 to 64 population largely makes
up the workforce. The nonworking
older population relies, in part, on
the younger working population
for its support. This is true to the
extent that future social obligations
toward the elderly — such as Social
Security payments and public medical
expenditures — are not fully funded

24 Q4 2008 Business Review

by past savings. (It is mitigated to the
extent that savings have been set aside
either by the individual or on his or
her behalf in public trust funds.) For
this reason, the ratio of the population
65 and over to the population 20 to
64 is called the old-age dependency
ratio. For the national population, this
ratio has decreased slightly since 2000,
from 21.1 percent to 20.9 percent. This
decline is due, at least in part, to the
rising immigrant population, which
has added to the 20 to 64 population.
Our region has seen a similar decline
in the old-age dependency ratio. However, the ratio in each state remains
above the national ratio (Table 3).
At the other end of the age
spectrum, there is the youthdependency ratio: the ratio of the
population under 20 to the population
20 to 64. This ratio has also declined

since 2000. The decline in the youthdependency ratio is a consequence of a
declining birth rate and of immigration
of 20 to 64 year olds. In our region,
the youth-dependency ratio declined
in each state, and it has remained
below the national ratio, reflecting the
region’s older population.
Labor Force Growth Slower in
Nation, But Mixed in Region. Labor
force growth in the nation since 2000
has been slower than it was in the
1990s, but in the region, the trends
have been mixed. The labor force is
the number of persons working or
available for work. Although the age
group most likely to be employed is
the 20- to 64-year-old group, the labor
force includes all workers or potential
workers regardless of age. For the
nation, labor force growth was 1.3
percent annually during the 1990s

TABLE 2
Age Measures
Actual

Projected

2000

2007

2010

2020

2030

Median age

35.3

36.6

37.0

38.0

39.0

Percent 65 and over

12.4

12.6

13.0

16.3

19.7

Median age

38.0

39.7

40.0

40.6

42.1

Percent 65 and over

15.6

15.2

15.5

18.8

22.6

Median age

36.7

38.4

38.9

39.6

40.8

Percent 65 and over

13.2

13.1

13.7

16.4

20.0

Median age

36.0

37.9

39.4

41.5

43.6

Percent 65 and over

13.0

13.6

14.1

18.3

23.5

US

Pennsylvania

New Jersey

Delaware

Source: U.S. Census Bureau. 2000 Census, 2007 Population Estimates, and State
Interim Population Projections

www.philadelphiafed.org

interest. The implications of future
labor force growth and changes in
the dependency ratio for the Third
District states are discussed below in
light of demographic and economic
projections.

TABLE 3
Dependency Ratios
Actual
2000

Projected

2007

2010

2020

2030

US
Youth

48.5

45.4

44.9

46.2

48.3

Old Age

21.1

20.9

21.7

28.4

36.3

Youth

46.1

35.9

41.1

42.0

44.8

Old Age

27.1

24.4

26.0

32.9

42.4

Youth

45.5

37.6

42.2

42.2

43.8

Old Age

22.2

20.7

22.5

27.9

35.9

Youth

47.1

38.0

42.3

43.4

45.5

Old Age

21.9

21.7

23.4

32.1

44.7

Pennsylvania

New Jersey

Delaware

Notes:

Youth dependency ratio is population under 20 years old as a percent of
population 20-64 years old. Old-age dependency ratio is population 65
years old and over as a percent of population 20-64 years old.

Source: U.S. Census Bureau

but only 1.0 percent in the years since
2000.2 In Delaware, labor force growth
has been slower since 2000 than in the
1990s: 0.9 percent versus 1.4 percent.
In contrast, recent labor force growth
in both Pennsylvania and New Jersey
has been slightly faster than growth
in the 1990s: 0.5 percent versus 0.4
percent in Pennsylvania, and 0.7
percent versus 0.5 percent in New
Jersey. Nevertheless, these growth rates
remain below those of Delaware and
the nation.
However, many factors other than
demography affect the size of the labor
2
Labor force and employment data for the
nation and the states are produced by the
Bureau of Labor Statistics. See the U.S. Bureau
of Labor Statistics website information in the
References.

www.philadelphiafed.org

force, both nationally and in the states.
These factors include public policies
and economic developments that can
have positive or negative effects on
labor markets at the national, state,
and local levels. Regardless of the
origin of the factors influencing the
size of the labor force, these factors
have important implications, both
nationally and at the state and local
levels. Perhaps the most imperative
of these is the effect of labor force
size on government fiscal conditions
because the size of the labor force
relative to the total population is the
major factor determining government
revenues in relation to government
spending capacity. This is why the
dependency ratio, described above, is
a demographic measure of significant

POPULATION PROJECTIONS:
EVEN SLOWER GROWTH AND
MORE AGING
Slower Projected Population
Growth in Nation and Region.
The Census Bureau projects slower
population growth in the nation
and in the Third District states for
the decades ahead, compared with
population growth from 1990 to
2000. National population growth of
around 1.3 percent annually in the
1990s has slowed to around 1 percent
and is projected to slow to just under
1 percent for the 10 years to 2010
and to continue at around that rate
until 2030, the horizon for the census
projections.3
The Census Bureau projects that
Pennsylvania’s population will grow at
around its current rate of 0.2 percent
a year until 2020 and then at a slower
rate from 2020 to 2030. Projections for
New Jersey’s rate of growth show that
it will stay around its current rate of
approximately 0.5 percent, or slightly
faster, to 2030. Projections indicate
that Delaware will continue to be the
fastest-growing of the three states,
although the Census Bureau projects
that Delaware’s current growth rate
of around 1.5 percent a year will fall
below 1 percent by 2030. Pennsylvania
was the sixth most populous state in
2000 and is projected to still hold that
rank in 2030. New Jersey is projected
to move from ninth to 13th, and
Delaware is projected to remain in 45th

3
See the U.S. Bureau of the Census website
for projections. The website address is in the
References. The website includes both national
and state population projections.

Business Review Q4 2008 25

place. States in the West and South
are projected to be the fastest growing,
as they were from 1990 to 2000.
Projections indicate that
international immigration will
continue to play a large role in the
national population’s increase and in
the population growth of Pennsylvania
and New Jersey. Delaware’s population
growth is projected to result mostly
from natural increase and net inward
internal migration. Pennsylvania and
New Jersey are projected to experience
net outward internal migration.
While population projections made
in the past have done fairly well in
comparisons with eventual census
counts, they have tended to underpredict growth, especially for fastgrowing states.4 For example, in the
Third District, the population in
2000 was projected to be around
12.2 million in Pennsylvania and the
actual census count was 12.3 million
(an under-projection of around 0.6
percent); the New Jersey projection was
around 8.2 million versus an actual
count of 8.4 million (a 2.9 percent
under-projection); and the Delaware
projection was around 760,000 versus
an actual count of around 780,000
(an under-projection of around 2.3
percent).5
Older Population Nationally
and Third District States Among
the Oldest. The national population is
projected to get older, on average, and

4

Census Bureau projections have tended
to under-predict state population growth,
especially in fast-growing states, because annual
state population estimates have tended to be
too low, and state-to-state migration has been
greater than expected. For a discussion of the
accuracy of projections, see the working paper
by Ching-li Wang.

5

The Census Bureau’s high growth projections
for each state were used to compute the
percentage under-projection versus the actual
count in each state.

26 Q4 2008 Business Review

the Third District states are projected
to be among those with the oldest
populations. The national median age
is projected to rise, and the percentage
of the population 65 and older is
projected to increase (Table 2). The
old-age dependency ratio is projected
to increase (Table 3). The median age
in each of the Third District states
is projected to increase through the
30 years from 2000 to 2030, and the
percentage of the population 65 and

annual rate of 0.8 percent versus 1.2
percent).6
The slower projected labor force
growth is primarily the result of the
aging of the population, a process
that will move more potential workers
into the age groups that have had
lower labor force participation rates
historically.7 Although the BLS
projects an increase in the labor
force participation rate of the older
population, this will not be sufficient

The slower projected labor force growth
is primarily the result of the aging of the
population, a process that will move more
potential workers into the age groups
that have had lower labor force participation
rates historically.
older and the old-age dependency ratio
will increase in each state. Delaware is
projected to overtake Pennsylvania in
measures of age. This will be at least
partially the result of low international
immigration into Delaware, since
immigrants tend to be younger than
the current population.
Economic Significance of
Population Trends. The economic
significance of the trends of slower
population growth and an aging
population will be seen in the effects
those trends have on labor force
growth, which is projected to be slower
in the years immediately ahead than
in the past. Demographic factors
alone determine the dependency
ratio, as described earlier, but other
factors influence the actual size of the
labor force. Taking all factors into
consideration, the most recent Bureau
of Labor Statistics (BLS) projections
indicate that growth in the labor force
from 2006 to 2016 will be slower than
growth from 1996 to 2006 (a projected

to offset the drop in the overall
participation rate that occurs as
potential workers move to and beyond
the traditional retirement age of 65,
when the participation rate is expected
to fall by half (in 2016).
With slower labor force growth
ahead, employment growth will be
slower as well, even if the economy
is at full employment (as assumed for
the purpose of the BLS projections).
The BLS projects payroll employment
growth of 1 percent a year from 2006
to 2016, slower than the 1.3 percent

6

The Bureau of Labor Statistics makes national
labor force and employment projections every
two years. For the latest projections, consult
the Bureau’s Monthly Labor Review, November
2007. Projections are summarized in the article
by James C. Franklin.

7
The labor force participation rate is the
number of people in a given age category who
are in the labor force — as defined above — as
a percent of the total number of people in that
age category.

www.philadelphiafed.org

annual rate from 1996 to 2006.8
Employment growth in the three
Third District states is also projected
to be slower in the future. State labor
departments project declines in the
growth rate of employment.9 For
Pennsylvania, employment growth
from 2004 to 2014 is projected to be
0.7 percent per year versus 0.8 percent
a year from 1994 to 2004. For New
Jersey, growth is projected to be 1
percent a year versus 1.2 percent. For
Delaware, growth is projected to be 1.2
percent a year versus 1.8 percent.
Adverse Effects of Increased
Dependency Ratio and Slower Labor
Force Growth. Slower labor force and
employment growth and an increasing
old-age dependency ratio have adverse
economic implications for the nation
and for the states. The increase in
the dependency ratio and the slowing
labor force growth in the nation
are the reasons that the currently
projected level of Social Security
benefits will soon outstrip the taxes
required to pay them. (Social Security
refers to the Federal Old-Age and
Survivors Insurance — OASI—and
the Federal Disability Insurance Trust
Funds — DI — collectively referred
to as OASDI.) It is projected that the
annual cost of OASDI will exceed
OASDI annual tax revenue beginning

8

The projection for household survey
employment is 0.8 percent from 2006 to
2016, slower than the 1.3 percent annual rate
from 1996 to 2006. The household survey of
employment includes farm workers and the selfemployed. Employment among these groups is
projected to grow more slowly than employment
of workers at business firms, which is measured
in the payroll survey.

in 2017, after which the shortfall will
be covered by redemptions of special
obligations of the Treasury that make
up the trust fund assets. The assets
of the DI fund are projected to be
exhausted in 2026 and the assets
of the OASI fund in 2042.10 Other
federal government benefits, such as
Medicare and Medicaid, are similarly

Federal social welfare programs are projected
to become a much larger portion of the
federal budget and to grow in relation to gross
domestic product, portending an increasing
burden on the economy regardless of whether
these programs are financed by dedicated
taxes or general revenues.
in jeopardy. For example, the Federal
Hospital Insurance Trust Fund is
projected to be exhausted in 2019.11
Other parts of the Medicare program
(collectively known as Supplemental
Medical Insurance) do not use trust
funds; instead, this program requires
that revenue be matched to costs
annually. The cost of this portion of
Medicare is also projected to increase
rapidly. Consequently, total Medicare
expenditures are projected to increase
from 3 percent of GDP in 2006 to
11 percent by 2081. Thus, even aside
from trust fund issues, federal social
welfare programs are projected to
become a much larger portion of the
federal budget and to grow in relation
to gross domestic product, portending

9

State employment projections are made by
state labor and industry departments. These
projections are made after the national
projections are issued. Consequently, the most
recent state projections do not extend as far
as the most recent national projections. See
the References for website information for
projections for Pennsylvania, New Jersey, and
Delaware.

www.philadelphiafed.org

an increasing burden on the economy
regardless of whether these programs
are financed by dedicated taxes or
general revenues.
State government financial
obligations vary, but many states
also face future difficulties paying for
benefits, especially state portions of
Medicaid payments and payments

10
See the annual report of the Board of Trustees
of the Federal Old-Age and Survivors Insurance
and Federal Disability Insurance.

for health-care benefits for state and
local government employees and
retirees.12 For some states, government
retiree pensions will also present fiscal
challenges. The GAO projects the
sum of all state and local government
operating budgets to be in deficit
by 2015. However, most states and
local governments are required to
maintain balanced budgets in most
years, so future fiscal difficulties could
necessitate urgent action at that time.
Among the Third District states,
New Jersey appears to be the least
prepared to make future payments,
although none of the three states
has fully funded future obligations.
Analysis by the Pew Center on the
States estimates that the New Jersey
state employee pension system is
slightly less than 80 percent funded
(one of 20 states below that level) and

11

See the annual report of the Board of Trustees
of the Federal Hospital Insurance and Federal
Supplemental Medical Insurance.

12

See the report by the Government
Accountability Office (GAO).

Business Review Q4 2008 27

that the provision for state employeeretiree health benefits is practically
unfunded. Pennsylvania and Delaware
state employee pension systems are
estimated to have greater funding, but
neither state has full funding for state
employee-retiree health benefits.13

an aging population will tend to limit
employment growth in the future.
Slower employment growth, in turn,
will tend to limit economic growth.
While the issue is a national
one, some regions, states, and local
areas will face more difficulty than
others. Future population growth is
projected to be stronger in parts of the
country that have already experienced
relatively strong population growth,
namely, the South and West, and less
strong elsewhere. So regions in the
North and East, including the three
states of our region, are more likely
than other parts of the country to face
difficulty as a result of demographic
trends.
Slower growth in the number

of workers will necessitate faster
growth in productivity per worker to
maintain or improve the growth in
total income that will be required to
finance growing obligations. A key to
higher productivity is greater human
capital, which improves individual
earning power and is important for
regional economic improvement
as well. Human capital appears to
boost regional economic growth by
attracting more and better-educated
workers to areas that already have
large concentrations of workers with
higher-level educations.14 BR

Board of Trustees, Federal Old-Age and
Survivors Insurance and Federal Disability
Insurance Trust Funds. Annual Report,
2007. Washington, D.C.: U.S. Government
Printing Office.

Government Accountability Office. State
and Local Governments: Growing Fiscal
Challenges Will Emerge During the Next 10
Years. Washington, D.C.: Government
Accountability Office, 2008.

Schiller, Timothy. “Human Capital
and Higher Education: How Does Our
Region Fare?” Federal Reserve Bank of
Philadelphia Business Review (First Quarter
2008).

Board of Trustees, Federal Hospital
Insurance and Federal Supplemental
Medical Insurance Trust Funds. Annual
Report, 2007. Washington, D.C.: U.S.
Government Printing Office.

New Jersey. Department of Labor and
Workforce Development. http://lwd.dol.
state.nj.us/labor/lpa/pub/pub_index.html.

U.S. Bureau of Labor Statistics. http://
www.bls.gov

SUMMARY
Recent demographic trends
are likely to continue and even
accelerate in years after 2010. That
means slower population growth, an
older population, and an increasing
percentage of foreign-born residents.
Both slower population growth and
13

See the report by the Pew Center on the
States.

14

See my Business Review article.

REFERENCES

Delaware. Office of Labor Market
Information. http://www.delawareworks.
com/OOLMI/welcome.shtml.
Franklin, James C. “An Overview of BLS
Projections to 2016,” Monthly Labor Review
(November 2007), pp. 3-12.

28 Q4 2008 Business Review

Pennsylvania. Center for Workforce
Information and Analysis. http://www.
paworkstats.state.pa.us/gsipub/index.
asp?docid=399.
Pew Center on the States. Promises with a
Price. Philadelphia: Pew Charitable Trusts,
2007.

U.S. Bureau of the Census. http://www.
census.gov/popest/estimates.php
U.S. Bureau of the Census. http://www.
census.gov/population/www/projections/
popproj.html
Wang, Ching-li. “Evaluation of the Census
Bureau’s 1995-2025 State Population
Projections,” Working Paper 67, U.S.
Census Bureau (2002).

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/.

FILING FOR BANKRUPTCY: HOW
DO HOMEOWNERS FARE?
This paper provides the first in-depth
analysis of the homeownership experience
of households in bankruptcy. The authors
consider households who are homeowners
at the time of filing. These households
are typically seriously delinquent on their
mortgages at the time of filing. The authors
measure how often they end up losing their
houses in foreclosure, the time between
bankruptcy filing and foreclosure sale, and
the foreclosure sale price. In particular,
they follow homeowners who filed for
Chapter 13 bankruptcy between 2001 and
2002 in New Castle County, Delaware,
through October 2007. They present three
main findings. First, close to 30 percent of
the filers lost their houses in foreclosure
despite filing for bankruptcy. The rate rose
to over 40 percent for those who were 12
months or more behind on their mortgage
payment, about the same fraction as
among those who entered into foreclosure
directly. Second, filing for bankruptcy
allowed those who eventually lost their
houses to foreclosure to remain in their
houses for, on average, an additional year.
Third, although the average final sale price
exceeded borrowers’ own estimates at the
time of filing, the majority of the lenders
suffered losses. These findings are pertinent
to the recent debate over the bankruptcy
code on mortgage modification. Finally, the
paper also reports circumstances related to
www.philadelphiafed.org

the loan, borrower, and lender that make it
more or less likely that a certain result will
take place.
Working Paper 08-14, “The
Homeownership Experience of Households
in Bankruptcy,” Sarah W. Carroll, formerly
Federal Reserve Bank of Philadelphia, and
Wenli Li, Federal Reserve Bank of Philadelphia
UNEMPLOYMENT: A RIGHT-TOMANAGE BARGAINING SCHEME
If the Mortensen and Pissarides model
with efficient bargaining is calibrated to
replicate the fluctuations of unemployment
over the business cycle, it implies a far
too strong rise of the unemployment rate
when unemployment benefits rise. This
paper explores an alternative right-tomanage bargaining scheme. This also
generates the right degree of fluctuations of
unemployment but at the same time implies
a reasonable elasticity of unemployment
with respect to benefits.
Working Paper 08-15, “The Elasticity
of the Unemployment Rate with Respect to
Benefits,” Kai Christoffel, European Central
Bank, Frankfurt, and Keith Kuester, Federal
Reserve Bank of Philadelphia
EVIDENCE OF DIVERGENT
BEHAVIOR IN RETURN AND
VOLATILITY SPILLOVERS
The authors provide a simple and
intuitive measure of interdependence
of asset returns and/or volatilities. In
Business Review Q4 2008 29

particular, they formulate and examine precise and
separate measures of return spillovers and volatility
spillovers. The authors’ framework facilitates study of
both noncrisis and crisis episodes, including trends and
bursts in spillovers, and both turn out to be empirically
important. In particular, in an analysis of 19 global
equity markets from the early 1990s to the present,
they find striking evidence of divergent behavior in the
dynamics of return spillovers vs. volatility spillovers:
Return spillovers display a gently increasing trend but
no bursts, whereas volatility spillovers display no trend
but clear bursts.
Working Paper 08-16, “Measuring Financial Asset
Return and Volatility Spillovers, with Application to
Global Equity Markets,” Francis X. Diebold, University
of Pennsylvania, and Visiting Scholar, Federal Reserve
Bank of Philadelphia, and Kamil Yilmaz, Koc University,
Istanbul
GENERATING FORECASTS FOR NONCORE
VARIABLES
This paper develops and illustrates a simple method
to generate a DSGE model-based forecast for variables
that do not explicitly appear in the model (noncore
variables). The authors use auxiliary regressions
that resemble measurement equations in a dynamic
factor model to link the noncore variables to the
state variables of the DSGE model. Predictions for
the noncore variables are obtained by applying their
measurement equations to DSGE model-generated
forecasts of the state variables. Using a medium-scale
New Keynesian DSGE model, the authors apply
their approach to generate and evaluate recursive
forecasts for PCE inflation, core PCE inflation, and the
unemployment rate along with predictions for the seven
variables that have been used to estimate the DSGE
model.
Working Paper 08-17, “DSGE Model-Based
Forecasting of Non-Modelled Variables,” Frank
Schorfheide, University of Pennsylvania, and Visiting
Scholar, Federal Reserve Bank of Philadelphia; Keith
Sill, Federal Reserve Bank of Philadelphia; and Maxym
Kryshko, University of Pennsylvania
PAYDAY LENDING AND PERSONNEL
PERFORMANCE
Does borrowing at 400 percent APR do more
harm than good? The Pentagon asserts that payday
loans harm military readiness and successfully lobbied
30 Q4 2008 Business Review

for a binding 36 percent APR cap on loans to military
members and their families (effective October 1, 2007).
But existing evidence on how access to high-interest
debt affects borrower behavior is inconclusive. The
authors use within-state variation in state lending laws
and exogenous variation in the assignment of Air Force
personnel to bases in different states to estimate the
effect of payday loan access on personnel outcomes.
They find significant average declines in overall job
performance and retention and significant increases
in severely poor readiness. These results provide some
ammunition for the private optimality of the Pentagon’s
position. The welfare implications for military members
are less clear-cut, but the authors’ results are consistent
with the interpretation that payday loan access causes
financial distress and severe misbehavior for relatively
young, inexperienced, and financially unsophisticated
airmen. Overall job performance declines are also
concentrated in these groups, and several pieces of
evidence suggest that these declines are welfarereducing (and not the result of airmen optimally
reducing effort given an expanded opportunity set);
for example, performance declines are larger in high
unemployment areas with payday lending.
Working Paper 08-18, “In Harm’s Way? Payday Loan
Access and Military Personnel Performance,” Scott Carrell,
University of California, Davis; and Jonathan Zinman,
Dartmouth College, and Visiting Scholar, Federal Reserve
Bank of Philadelphia
MEASURING BUSINESS CONDITIONS:
ESTIMATING THE STATE OF REAL
ACTIVITY
The authors construct a framework for measuring
economic activity at high frequency, potentially in
real time. They use a variety of stock and flow data
observed at mixed frequencies (including very high
frequencies), and they use a dynamic factor model that
permits exact filtering. They illustrate the framework in
a prototype empirical example and a simulation study
calibrated to the example.
Working Paper 08-19, “Real-Time Measurement of
Business Conditions,” S. Boragan Aruoba, University
of Maryland, and Visiting Scholar, Federal Reserve
Bank of Philadelphia; Francis X. Diebold, University of
Pennsylvania, and Visiting Scholar, Federal Reserve Bank
of Philadelphia; and Chiara Scotti, Federal Reserve Board

www.philadelphiafed.org

TRANSMITTING MONETARY POLICY
THROUGH THE BANK LENDING CHANNEL
This study shows that during Paul Volcker’s drastic
monetary tightening in the early 1980s, local banks
operating in only one county reduced loan supply
much more sharply than local subsidiaries of multicounty bank holding companies in similar markets,
after controlling for bank (and holding company) size,
liquidity, capital conditions, and, most important, local
credit demand. The study allows cleaner identification
by examining 18 U.S. “county-banking states” where
a bank’s local lending volume at the county level was
observable because no one was allowed to branch across
county borders. The local nature of lending allows us to
approximate and control for the exogenous component
of local loan demand using the prediction that counties
with a higher share of manufacturing employment
exhibit weaker loan demand during tightening (which
is consistent with the interest rate channel and the
balance-sheet channel of monetary policy transmission).
The study sheds light on the working of the bank
lending channel of monetary policy transmission.
Working Paper 08-20, “The Effect of Monetary
Tightening on Local Banks,” Rocco Huang, Federal
Reserve Bank of Philadelphia
MACROECONOMIC FLUCTUATIONS AND
CORPORATE DEFAULT
This paper studies the relation between
macroeconomic fluctuations and corporate defaults
while conditioning on industry affiliation and an
extensive set of firm-specific factors. Using a logit
approach on a panel data set for all incorporated
Swedish businesses over 1990-2002, the authors find
strong evidence for a substantial and stable impact
of aggregate fluctuations. Macro-effects differ across
industries in an economically intuitive way. Out-ofsample evaluations show their approach is superior to
both models that exclude macro information and best
fitting naive forecasting models. While firm-specific
factors are useful in ranking firms’ relative riskiness,
macroeconomic factors capture fluctuations in the
absolute risk level.
Working Paper 08-21, “Firm Default and Aggregate
Fluctuations,” Tor Jacobson, Sveriges Riksbank; Rikard
Kindell, Svenska Handelsbanken; Jesper Linde, Sveriges
Riksbank and CEPR; and Kasper Roszbach, Sveriges
Riksbank, and Visiting Scholar, Federal Reserve Bank of
Philadelphia
www.philadelphiafed.org

LEISURE AMENITIES AND URBAN
DEVELOPMENT
The City Beautiful movement, which in the early
20th century advocated city beautification as a way
to improve the living conditions and civic virtues
of the urban dweller, had languished by the Great
Depression. Today, new urban economic theorists
and policymakers are coming to see the provision
of consumer leisure amenities as a way to attract
population, especially the highly skilled and their
employers. However, past studies have provided
only indirect evidence of the importance of leisure
amenities for urban development. In this paper the
authors propose and validate the number of leisure
trips to metropolitan statistical areas (MSAs) as a
measure of consumers’ revealed preferences for local
leisure-oriented amenities. Population and employment
growth in the 1990s was about 2 percent higher in
an MSA with twice as many leisure visits: the third
most important predictor of recent population growth
in standardized terms. Moreover, this variable does a
good job of forecasting out-of-sample growth for the
period 2000-2006. “Beautiful cities” disproportionally
attracted highly educated individuals and experienced
faster housing price appreciation, especially in supplyinelastic markets. Investment by local government
in new public recreational areas within an MSA was
positively associated with higher subsequent city
attractiveness. In contrast to the generally declining
trends in the American central city, neighborhoods
that were close to “central recreational districts” have
experienced economic growth, albeit at the cost of
minority displacement.
Working Paper 08-22, “City Beautiful,” Gerald A.
Carlino, Federal Reserve Bank of Philadelphia, and Albert
Saiz, The Wharton School, University of Pennsylvania,
and Visiting Scholar, Federal Reserve Bank of Philadelphia
MEASURING GROWTH AND INTANGIBLE
INVESTMENT IN THE CHANGING U.S.
ECONOMY
In this paper the author focuses on three related
and difficult areas of the measurement of national
income. He argues that the economic theory underlying
measurement of these items is currently controversial
and incomplete.
Working Paper 08-23, “Intangible Assets and National
Income Accounting,” Leonard I. Nakamura, Federal
Reserve Bank of Philadelphia
Business Review Q4 2008 31

MODELING PREDATORY LENDING WITH
AND WITHOUT COMPETITION
Regulators express growing concern over predatory
loans, which the authors take to mean loans that
borrowers should decline. Using a model of consumer
credit in which such lending is possible, they identify
the circumstances in which it arises both with and
without competition. The authors find that predatory
lending is associated with highly collateralized loans,
inefficient refinancing of subprime loans, lending
without due regard to ability to pay, prepayment
penalties, balloon payments, and poorly informed
borrowers. Under most circumstances competition
among lenders attenuates predatory lending. They
use their model to analyze the effects of legislative
interventions.
Working Paper 08-24, “Predatory Mortgage Lending,”
Philip Bond, The Wharton School, University of
Pennsylvania, and Visiting Scholar, Federal Reserve Bank
of Philadelphia; David K. Musto, The Wharton School,
University of Pennsylvania; and Bilge Yilmaz, Graduate
School of Business Stanford University
SELECTING FACTOR PROXIES
In economics, common factors are often
assumed to underlie the co-movements of a set of
macroeconomic variables. For this reason, many
authors have used estimated factors in the construction
of prediction models. In this paper, the authors
begin by surveying the extant literature on diffusion
indexes. They then outline a number of approaches

32 Q4 2008 Business Review

to the selection of factor proxies (observed variables
that proxy unobserved estimated factors) using the
statistics developed in Bai and Ng (2006a,b). The
authors’ approach to factor proxy selection is examined
via a small Monte Carlo experiment, where evidence
supporting their proposed methodology is presented,
and via a large set of prediction experiments using the
panel data set of Stock and Watson (2005). One of
their main empirical findings is that their “smoothed”
approaches to factor proxy selection appear to yield
predictions that are often superior not only to a
benchmark factor model, but also to simple linear time
series models, which are generally difficult to beat in
forecasting competitions. In some sense, by using the
authors’ approach to predictive factor proxy selection,
one is able to open up the “black box” often associated
with factor analysis, and to identify actual variables that
can serve as primitive building blocks for (prediction)
models of a host of macroeconomic variables, and that
can also serve as policy instruments, for example. The
authors’ findings suggest that important observable
variables include various S&P500 variables, including
stock price indices and dividend series; a one-year
Treasury bond rate; various housing activity variables;
industrial production; and exchange rates.
Working Paper 08-25, “Seeing Inside the Black Box:
Using Diffusion Index Methodology to Construct Factor
Proxies in Large Scale Macroeconomic Time Series
Environments,” Nii Ayi Armah, Rutgers University, and
Norman R. Swanson, Rutgers University, and Visiting
Scholar, Federal Reserve Bank of Philadelphia.

www.philadelphiafed.org


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