Full text of Business Review (Federal Reserve Bank of Philadelphia) : First Quarter 2009

View original document

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

The Peopling of Macroeconomics:
Microeconomics of Aggregate Consumer Expenditures*
BY SATYAJIT CHATTERJEE

ince the 1950s economists have been building
a theory of aggregate consumer spending,
seeking to understand how individual
households choose to spend and how their
choices change when interest rates, the unemployment
rate, and other economic indicators change. Before that
time, economists looked for “economic laws” that would
explain the connection between one set of economic
aggregates and another, without considering the decisions
of individual households. Although the process of
connecting macroeconomic aggregates to individuals’
behavior is far from complete, predictions of aggregate
consumer spending are now rooted in predictions of
individual behavior. In this article, Satyajit Chatterjee
takes readers through a brief historical survey from the
early work on the consumption function to the theory of
aggregate consumer spending in modern macroeconomic
models.

Consumer spending is the largest
single expenditure category in the
final demand for goods and services,
accounting for more than two-thirds of
Satyajit
Chatterjee is a
senior economic
advisor and
economist in
the Research
Department of
the Philadelphia
Fed. This article
is available free
of charge at www.
philadelphiafed.org/research-and-data/
publications/.
www.philadelphiafed.org

gross domestic product (GDP). A clear
understanding of the underpinnings of
consumer spending is a valuable asset
for central bankers and policymakers.
Since the 1950s, macroeconomists
have been engaged in building a theory
of aggregate consumer spending from
the bottom up.1 In this approach, macroeconomists first seek to understand

*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.

how individual households choose to
spend and how their choices change
when interest rates, the unemployment
rate, and other indicators of overall
economic activity change. The relationship between aggregate consumer
spending and indicators of economic
activity is then obtained by aggregating
the predicted changes in the spending
choices of individual households with
respect to changes in indicators of
overall economic activity.
It was not always so. In the early
years of macroeconomics, scholars
looked for enduring empirical relationships (“economic laws”) that connected one set of macroeconomic aggregates to another without explicit reference to the individual decisions that
would make sense of such connections.
This was because economists hadn’t
fully worked out how a household acting rationally in the face of uncertainty
would behave over time — the sort
of knowledge needed to meaningfully
connect macroeconomic aggregates
to the millions of individual choices
that make up those aggregates. But
as economists began to acquire this
knowledge, the process of connecting
macroeconomics to individuals’ behavior started in the 1950s and gathered
steam in the 1970s and 1980s. Although the process of integration is far
from complete, predictions of aggregate
consumer spending are now rooted in
predictions of individual behavior.
The attempt to predict aggregate
consumer spending by first predicting
what individual households would do is

Aggregate consumer spending is total
consumer spending in the economy.
Business Review Q1 2009 1

what I mean by “the peopling of macroeconomics.” The aim of this article
is to give an account of this now halfcentury-long intellectual endeavor. It is
meant to be a (quick!) historical survey
that takes the reader from the early
work on the consumption function
to the theory of aggregate consumer
spending in modern macroeconomic
models.
GENESIS OF THE
CONSUMPTION FUNCTION
The origin of macroeconomics
as a distinct sub-field of economics is
often traced to John Maynard Keynes’s
General Theory of Employment, Interest,
and Money. Published in 1936, the
book sought to explain the reasons for
the economic depression that gripped
the industrialized world after 1929. In
the course of doing so, Keynes introduced a theoretical construct he called
the consumption function. According
to Keynes, the consumption function
was the causal relationship between
annual aggregate disposable (or aftertax) income and annual aggregate
consumer spending.
Keynes asserted that this relationship looked like the brown line shown
in Figure 1. Aggregate consumer
spending was directly and linearly
related to aggregate disposable income.
The point at which the brown line
crosses the black line gives the income
level at which consumer spending is
equal to income. To the left of this
point, spending exceeds income, and
to the right of this point, spending
is less than income. Importantly, the
relationship between income and
spending was a nonproportional one,
with higher incomes associated with
a smaller ratio of spending to income.
To see this, consider the points marked
X and Y on the brown line. At point
X income is $40,000 and spending is
$34,000; at point Y, income is $80,000
and spending is $58,000. Thus, a

2 Q1 2009 Business Review

doubling of income leads to less than
a doubling of spending, which means
that the ratio of spending to income
declines as incomes rise.
Because the consumption function was central to Keynes’s analysis,
the construct attracted a great deal
of attention and soon became the
focus of controversy. The problem was
that Keynes did not explain how the
consumption function could arise from
the choices of individual households
acting rationally. Instead, he defended
his construct as a “psychological law”
that accorded well with common
sense. In Keynes’s favor, the construct
seemed to accord with some facts as
well: Household-level incomes and

points X and Y, a doubling of (per capita) income from $40,000 to $80,000
leads to a doubling of (per capita)
consumer spending from $32,000 to
$64,000 — something that is not true
of the consumption function in Figure
1. Although one might be tempted to
gloss over this difference, the difference was important: Keynes’s theory
assumed that the consumption function looked like the one in Figure 1,
not like the one in Figure 2.
The puzzling difference between
consumption-income relationships
“across households” (cross-section) and
“across time” (time-series) became the
focus of macroeconomic research in
the 1940s and 1950s. By that time,

According to Keynes, the consumption function
was the causal relationship between annual
aggregate disposable (or after-tax) income and
annual aggregate consumer spending.
expenditures were (roughly) related as
shown in Figure 1, with higher income
households spending more than lower
income households but spending proportionately less of their income than
lower income households.
But the household-level evidence
was not definitive because Keynes’s
consumption function was supposed
to hold for aggregate consumer spending and aggregate disposable income
measured at different points in time. The
issue remained unsettled because data
on aggregate consumer spending and
aggregate income for different years
were not readily available. When the
data were eventually assembled, they
showed a relationship like the brown
line shown in Figure 2. Over a long
period of time, the relationship between consumer spending and income
was proportional. As illustrated by the

many economists had accepted
Keynes’s General Theory as being essentially correct, and it became a matter of some urgency to understand why
these relationships differed and how
both could be true at the same time.
Progress came in the form of two studies that pretty much set the stage for
research on the aggregate consumption
function for the next 30 years. One
was by economist Franco Modigliani
and the other by economist Milton
Friedman. Both contributions earned
their progenitors Nobel prizes: Friedman in 1976 and Modigliani in 1985.
RATIONAL CHOICE: AN
ENGINE FOR PREDICTION
Both Friedman and Modigliani
focused on understanding the relationship between spending and income at
the household level, and both sought

www.philadelphiafed.org

FIGURE 1
Keynesian Consumption Function
Consumer Spending
in Thousands of Dollars

100
90
80
70
60

50
40
30

20
10
0
0

100

Income in Thousands of Dollars

FIGURE 2
Relationship Between Spending and
Income Over Time
Per Capita Consumer Spending
in Thousands of Dollars

100
90
80
70
60

50
40
30

20
10
0
0

Per Capita Income in Thousands of Dollars

www.philadelphiafed.org

to achieve this using the model of
rational choice. Rational choice — the
dominant paradigm for thinking about
behavior in economics — posits that
people make decisions to maximize
their well-being subject to the limitations imposed on them by the finiteness of resources. For instance, the
theory of rational choice prescribes
how a family should allocate its finite
income among competing uses in order
to generate the maximum possible
well-being for the family.
Modigliani’s Work. Modigliani
and his student Richard Brumberg
began by studying a very simple individual choice problem. They imagined
a young adult starting out on his
working life at age 20 (say) knowing
(with perfect certainty) that he would
live up to a given age (say, 90 years),
knowing how many of those years
he would work (say, 40), and knowing how much he would earn in each
period of his working life (say, $35,000
each year). Modigliani and Brumberg
assumed that the young adult obtained
the same physical and psychological
benefit (or utility, as economists call
it) from any given amount of spending
in any given year. They also assumed
that, as is customary in economics, the
benefit obtained by the adult from an
additional dollar of spending declines
with the amount already spent that
year: That is, the first dollar spent
in any year gives more benefit than
the second dollar spent in that year
and the second dollar spent in that
year gives more benefit than the third
dollar spent in that year and so on.
Finally, they assumed that the adult
could borrow or save at a bank at a
zero interest rate.
The question they asked was:
What is this individual’s best lifetime
spending plan? The answer is that the
individual should spend his average
lifetime income of $20,000 each year,
where $20,000 is the sum of his in-

Business Review Q1 2009 3

come over his working years ($35,000
multiplied by 40) divided by the
number of years he will live (70, which
is 90 less 20). Because the individual
gets the same benefit from spending in
each year of his life and because every
dollar spent gives less benefit than
the previous dollar spent that year, it
is best for the individual to spend the
same amount every year.2 And if he is
to spend the same amount every year
and live within his means, he must
spend his average lifetime income each
year.
Even though the example was
highly unrealistic, it served to show
that Keynes’s consumption function
(or “psychological law”) had no obvious basis in rational choice. If we could
observe this hypothetical individual
over time, we would see his income
change from $35,000 to zero when
he retires and yet we would see his
spending stay unchanged at $20,000
a year. Contrary to Keynes’s assertion,
an increase or decrease in income need
not be accompanied by an increase
or decrease in spending. In a rational
choice context, current spending need
not respond to a change in current income if that change is fully anticipated
in a previous period.
While these findings raised doubts
about Keynes’s “psychological law,”
they did not resolve the issue of the de-

2
To see why, imagine that the individual plans
to spend $50,000 in 2008 and only $40,000 in
2009. Because the additional benefit from each
dollar spent is declining with the total amount
spent, the benefit obtained from spending the
40,001st dollar in 2008 is more than the benefit
obtained from spending the 50,000th dollar in
2008. Since the benefit obtained from spending
the 40,001st dollar in 2008 is the same as the
benefit obtained from spending the 40,001st
dollar in 2009, the individual can increase his
total benefit by reducing his expenditures by
$1 in 2008 and increasing it by $1 in 2009:
The loss in benefit in 2008 will be more than
compensated by the gain in benefit in 2009.
This sort of logic can be applied repeatedly to
conclude that the best the individual can do is
spend the same amount each year.

4 Q1 2009 Business Review

scriptive realism of the “law.” Perhaps
the “law” was a better description of
reality than rational choice. To be truly convincing, proponents of rational
choice had to show that their theory
explained the facts better than other
alternatives. To prove their point, both
Friedman and Modigliani concentrated on reconciling the differences
between cross-section and time-series
consumption-income relationships.

Contrary to Keynes’s
assertion, an increase
or decrease in
income need not be
accompanied by an
increase or decrease
in spending.
Modigliani and Brumberg’s simple
model is consistent with the differences seen in the data between time-series
and cross-section consumption-income
relationships. In their model, economic
growth causes everyone’s average lifetime income to grow over time. Since
everyone spends their average lifetime
income, economic growth also causes
aggregate spending to grow at the
same rate as average lifetime income.
Therefore, spending and income grow
in proportion to each other. In contrast, the relationship between income
and spending across people alive at
any point in time will be necessarily
nonproportional because even people
without any income (retirees) spend a
positive amount (for more details on
this point, see Reconciling Secular and
Cross-Section Consumption Functions).
Of course, this is a simple example, and one might wonder whether
the rational choice paradigm would
predict these relationships in more

realistic situations. The answer to this
question is a resounding yes, and the
person most responsible for showing
why was Milton Friedman.
FRIEDMAN’S PERMANENT
INCOME HYPOTHESIS
In 1957 Friedman published a
monograph titled A Theory of the
Consumption Function. As an enduring example of the interplay between
economic theory and facts, the treatise
has few equals.3 Friedman distinguished between a household’s permanent income and its actual income
and — with the help of rational choice
theory and empirical facts — argued
that a household tends to spend its
permanent income.4
Friedman defined permanent income as the amount a household could
spend and still maintain its wealth. To
understand what this definition means,
it is helpful to think of some simple
examples. First, imagine a household,
such as a new retiree, that in terms of
resources has only financial wealth.
Suppose that a household has a million
dollars in the bank, and the interest
rate available at the bank is 5 percent.
Then, this household’s annual perma-

3
To quote Friedman’s Nobel citation: “From a
purely scientific viewpoint, one of Friedman’s
most important contributions is his reshaping
of consumption theory with the help of the
hypotheses about ‘the permanent income’, in
place of current annual income, as a decisive
factor in determining total consumption
expenditure. Here an extremely fruitful
distinction is made between households’
temporary income and more permanent income;
Friedman shows that a substantially larger part
of the former income is saved than of the latter.
Friedman has carefully tested this theory on
comprehensive statistical material and gained
interesting results. Friedman’s version of the
consumption function has had a lasting effect
both on theory and on empirical research.”
4
This is simplifying matters somewhat.
Friedman’s permanent income hypothesis is
the assertion that a household’s planned level
of spending will be some proportion of its
permanent income, where the proportion could
fluctuate around unity over time.

www.philadelphiafed.org

nent income is $50,000 — the amount
the household would earn in interest
and therefore could spend without
reducing or augmenting its (financial)
wealth.
The example above imagined a
household, such as a new retiree, with
only financial wealth. What about a
young household that has no financial
wealth but expects to earn income for
many years into the future? Suppose a
household expects to earn $40,000 in
each of the next 20 years and $60,000
in each of the following 20 years (after
which it retires). Suppose it can borrow
from the bank against this income
stream at an interest rate of 5 percent.
Then it is as if this household has
financial wealth of (roughly) $820,000
in the bank today — which is the
discounted value of the household’s
stream of future earnings.5 Then the
same logic as above applies, and the
household can spend about $41,000
annually — which is (roughly) the
annual interest earned on $820,000 —
and still maintain its wealth.
Why would a household wish to
spend its permanent income? Note
that when thinking about how much
a household should spend from one
month to the next, it is fine to imagine
that a household’s circumstances are
similar from one month to the next.
Thus, all else being the same, the
household should spend the same
amount each month. Second, note
that even though a household will
exist for a finite length of time, for
practical purposes it is fine to imagine
that there is no natural end to the
household’s planning horizon. This

5
Discounted (or present) value refers to an
amount of money today that will become a
given amount at a stated point in the future,
depending on the interest rate. For example,
if the interest rate is 10 percent, $100 today
will be worth $110 one year from now. So the
present value of $110 one year from now (when
the interest rate is 10 percent) is $100.

www.philadelphiafed.org

may be because the end is really far
away or because the household cares
about its descendants and its descendants’ descendants and so on, so that
there is literally no end to its planning
horizon. Thus, a household’s decision
problem is to use a finite amount of
wealth to provide for spending over
infinitely many future months. The
only way this household can spend the
same amount each period forever is to
spend the constant interest earned on

A household’s
decision problem is
to use a ﬁnite amount
of wealth to provide
for spending over
inﬁnitely many future
months.
its financial wealth each period, that
is, spend its permanent income.
One can see how Friedman’s
permanent income theory could account for the proportional spendingincome relationship over time and
the nonproportional relationship
across households at a point in time.
If people’s perceptions of their permanent incomes rise with the general rise
in living standards, everyone’s spending will rise in proportion to the rise
in living standards. But if we look at
households at a point in time, there
will be some households whose income
is temporarily above their permanent
income, and those households will save
most of the additional income; there
will also be households whose income
is temporarily below their permanent
income, and they will draw down their
savings to maintain their consumption.
Therefore, the relationship between in-

come and spending across households
at a point in time will naturally tend to
be nonproportional.
Friedman was aware that this
approach to consumer spending
needed to be amended when
uncertainty about future earnings
is taken into account. Because a
household cannot perfectly forecast
its future earnings and because banks
do not lend against the promise of
uncertain future earnings, there is no
way for a household to actually convert
its future income stream into an
equivalent amount of financial wealth.
Nonetheless, Friedman maintained
that there must be some notion of
permanent income to which household
spending is adapted. The level of this
permanent income will be household
specific and will depend on such things
as the household’s expected earnings
and the household’s perception of
future earnings risk as well as the
household’s stock of financial assets.
Although uncertainty about
future earnings played a key role in
Friedman’s theory, the implications of
such uncertainty for rational choice
were only dimly understood at the
time. Friedman did not provide a
rigorous foundation for his ideas. The
result was that while macroeconomists
quickly accepted the distinction
between actual and permanent
income, they ignored Friedman’s
assertion that permanent income was
something not directly observable.
Instead, they took permanent income
to mean the annual interest earned
on the sum of financial and human
wealth, where human wealth was
calculated as the present discounted
value of current and future expected
earnings. A key reason behind the
adoption of this particular definition
was the discovery — made in the
1960s — that under certain conditions
the theory of rational choice implied
that households should set current
Business Review Q1 2009 5

Reconciling Secular and Cross-Section Consumption Functions

uppose that each individual lives for two years. An individual works for the first year of his life and
enjoys retirement in his second and final year. Each year, one one-year-old is “born” and one two-yearold “dies,” so that the total population is always constant at two. There is growth in incomes over time:
Every year, newborns earn 20 percent more than the previous year’s newborns.

TABLE
Income and Spending in a World of Overlapping Generations
Year 0

Gen 0

Year 1

Year 2

Inc.

Spend.

Inc.

Spend.

100

120

Gen 1
Gen 2
Average

…

The table records the relevant data for this hypothetical economy. In the table, columns represent either
income (Inc.) or spending (Spend.) for a particular year.
The generation born in year 0 is denoted Gen 0, the
generation born in year 1 is denoted Gen 1, and so on.
Thus, under the income column for year 0, there is an
entry for 100 in the row representing Gen 0 because
that is what the person born in year 0 earns in that year.
Moving across the same row, the entry under the spending column in year 0 is 50 because that is what Gen 0
spends in year 0 (the rest of his or her earnings are saved).
Continuing to move across, the corresponding entries for
year 1 are 0 and 50, respectively, because Gen 0 retires
in year 1 and earns nothing but spends 50 in year 1 (this
spending is financed by savings accumulated in year 0).
Finally, there are no entries for Gen 0 for years 2 and 3
(and beyond) because Gen 0 is not alive in those years.
Moving down to Gen 1, there are no entries for year 0 or
year 3, since Gen 1 is not alive in those years. For year 1,
the entry under the income column is 120 because Gen

6 Q1 2009 Business Review

Year 3

Inc.

Spend.

Inc.

Spend.

144

…

1 earns 20 percent more than Gen 0. Gen 1 spends 60 in
year 1, and this is recorded under the spending column for
year 1. For year 2 the corresponding entries for income
and spending are 0 and 60, respectively. The situation
is similar for Gen 2. Gen 2 earns 20 percent more than
Gen 1 in year 2 and spends half of his earnings in year
2 and the remaining half in year 3. Naturally, there are
no entries for Gen 2 for years 0 and 1.
We can use the snapshots of the overlapping generation world displayed in the table to reconcile the
shapes of the consumption functions across households
(cross-section) and across time (time series). First, let’s
look at how aggregate per capita income and spending
evolve in this economy. The bottom row of the table
reports the average income and spending in each of the
years for which these averages can be computed from the
information reported in the preceding rows.
Let’s look at year 1. Aggregate per capita income in
year 1 is simply income averaged over the two individuals alive in year 1. The two individuals alive in year 1

www.philadelphiafed.org

Reconciling Secular and Cross-Section Consumption Functions... (continued)

FIGURE A
Per Capita Spending and Income in the
Overlapping Generations Example
Per Capita Consumer Spending
100
90
80
70

60
50

40
30
20
10
0
0

100

Per Capita Income

FIGURE B

Spending and Income Across Households in
the Overlapping Generations Example
Household Spending
120
110
100
90
80
70
60

40
30
20
10
0
0

Household Income

www.philadelphiafed.org

100

110

120

are Gen 0 and Gen 1. Gen 0 has
no earnings in year 1 (because he
or she is retired) and Gen 1 earns
120 units. So, the average income
in year 1 is 60 units (the sum of 0
and 120 divided by 2). Similarly,
the aggregate per capita spending in
year 1 is 55 units (the sum of 50 and
60 divided by 2). Thus, aggregate
per capita consumer spending in
year 1 is 11/12 of aggregate income
in year 1.
In year 2, the two individuals
alive are Gen 1 and Gen 2. Aggregate per capita income is 72 units
(the sum of 0 and 144 divided by 2),
and aggregate per capita spending
is 66 units (the sum of 60 and 72
divided by 2). Once again, aggregate per capita consumer spending
is 11/12 of aggregate per capita
income. Figure A plots aggregate
per capita spending and income at
successive points in time for this
economy. As is evident, income
and spending grow in proportion
to each other over time exactly as
found in the data.
Next, let’s look at the cross-section consumption-income relationship in this economy. Let’s pick year
1. Gen 0 has no income and spends
50 units, and Gen 1 earns 120 units
and spends 60 units. Therefore, the
cross-section consumption-income
relationship for year 1 looks like the
one in Figure B. This relationship
is clearly not proportional and, in
fact, resembles the consumption
function in Figure 1 in the text. If
we were to pick a different year, say,
2, we would get a similar nonproportional relationship except that
it would be shifted upward because
of income growth.

Business Review Q1 2009 7

spending equal to permanent income
calculated in this way. This resulted
in this particular definition becoming
commonly used, and eventually,
the very idea of permanent income
became associated with this particular
definition.
But this interpretation of permanent income turned out to be inconsistent with the evidence. As more extensive aggregate and household-level data
became available for macroeconomists
to analyze, it was found that consumer spending responded too much to
transitory deviations in income from
permanent income defined in this
way to be consistent with the theory’s
predictions.6
UNCERTAINTY, BUFFERSTOCK SAVINGS, AND
SPENDING DYNAMICS
While macroeconomists were busy
testing the permanent income theory
against aggregate and household data
and finding it wanting, others were
concentrating on working out the
implications of rational choice for
decision-making over time when the
future could not be perfectly forecast.
The big hurdle here was that it was
not easy to divine the full implications of rational choice theory because
the theory’s predictions could not be
reduced to a simple formula. Consequently, it was not easy to figure out if
some version of rational choice theory
could explain the data on household
spending and income better than the
permanent income theory.
Two key developments eventually allowed progress to be made. The
first development was something not
intrinsically connected to economics.
It was the increasing availability of

See the article by Robert Hall and Frederic
Mishkin.

8 Q1 2009 Business Review

(and access to) high-speed computers on university campuses and the
concurrent rapid development and
standardization of computer languages
designed to express and solve difficult
numerical problems. Along with a
deeper understanding of the nature

the lingua franca of economics — the
common language economists use
to make sense of behavior in diverse
branches of economics. For instance,
one source from which macroeconomists learned of the numerical
relationship between the benefit from

While macroeconomists were busy testing
the permanent income theory against
aggregate and household data and ﬁnding
it wanting, others were concentrating on
working out the implications of rational choice
for decision-making over time when the
future could not be perfectly forecast.

of rational choice over time, the rapid
improvement in the hardware and
software for numerical computations
permitted macroeconomists to pose,
solve, and simulate rational choice
problems on the computer.
The other, more important
development was connected with the
progress of economics as a discipline.
To solve rational choice problems on
the computer, one must specify the
problem in exact numerical form. For
instance, it is no longer sufficient to assert (as Modigliani and Brumberg did)
that the benefit from an additional
dollar of spending declines with the
amount already spent; it is necessary
to specify how much it declines at any
given level of spending. In other words,
the computer needs to know the exact
numerical relationship between the
benefit from an additional dollar of
spending and the level of spending.
It took macroeconomists decades
to gather this kind of knowledge. The
process was helped by the fact that the
rational choice paradigm had become

an additional dollar of spending and
the level of spending was researchers
trying to understand how fluctuations
in expected rates of return on financial assets affected the growth rate of
consumer spending.
Through this process, it became
possible for macroeconomists to
explore the implications of rational
choice for consumer spending using
computer simulations. Christopher
Carroll and Angus Deaton were
among the pioneers of this research
effort. Their simulations revealed that
households that start without any
financial assets initially consume less
than their earnings in order to accumulate a buffer stock of savings.7 They
do so because savings can protect the
household from temporary shortfalls
in earnings. Since earnings in any
period (a year or a month) are uncer-

7
The discussion in the rest of this section
draws on Christopher Carroll’s article.

www.philadelphiafed.org

tain, there is value to having such a
buffer stock of savings. Then, once the
household accumulates its target buffer
stock of savings, it acts to maintain
that stock over time. Unexpected
increases in income are initially saved
but then gradually spent to bring the
stock of savings down to its target
level. Similarly, an unexpected decline
in earnings is initially met by a reduction in the stock of savings (as the
household tries to maintain spending),
but then the resulting deficit in its
stock of savings is gradually made up
over time.8 Finally, starting at about
age 50, behavior undergoes a significant change: While still working,
households rein in their spending and
begin to accumulate additional savings
to provide for their retirement.
The behavior revealed by these
computer simulations has a simplicity
to it that gives it a ring of truth. But
what makes these predictions compelling for macroeconomists is that the
simulations also explain why spending’s response to transitory fluctuations
in earnings can be larger than that
predicted by the permanent income
theory. When households are working toward accumulating their target
level of buffer-stock savings, their
spending is depressed. The simulations
reveal that in these circumstances a
household that receives an unexpected
transitory increase in income has an
incentive to boost spending from its
depressed level. This happens because
the extra income is used to augment
the household’s savings, and therefore, the household gets closer to (or

8
It is worth noting that macroeconomists were
aware that rational choice theory was consistent
with households’ accumulating assets in order
to meet a potential shortfall in earnings in the
future. What the simulations revealed — and
this came as a surprise — was the centrality
of precautionary or buffer-stock savings in the
household’s spending decisions.

www.philadelphiafed.org

achieves) its target level of buffer-stock
savings. Consequently, the incentive to
curtail spending in order to get to the
target level of buffer-stock savings is
attenuated or eliminated, and spending responds strongly to a transitory
increase in income. This effect is absent in the permanent income theory
because households do not curtail their
spending in order to accumulate a buffer stock of savings.

Carroll’s spending models have to say
about aggregate consumer spending
and savings as well as the movement
of these aggregates over the course
of business cycles. As one might
suspect, the only way to get answers
to these questions is by computer
simulations. But the simulations are no
longer about the behavior of a typical
household but the aggregate behavior
of an entire ensemble of households,

When households are working toward
accumulating their target level of buffer-stock
savings, their spending is depressed.

Interestingly, the simulations
also reveal that the predictions of the
permanent income theory continue to
be relevant once a household reaches
its target level of savings. As the
buffer-stock of savings is approached,
households act more like future
uncertainty does not matter – just as
the permanent income theory had
assumed. Of course, the household
behaves this way because it has accumulated a buffer stock of savings to
counter the risk of lost earnings. That
being said, it is important to note that
because households are continually
buffeted by shocks to earnings and
are therefore accumulating or drawing
down financial assets, the fraction that
behaves according to the permanent
income theory in the simulations is a
minority.
CYCLICAL IMPLICATIONS
OF BUFFER-STOCK SAVINGS
MODELS
Macroeconomists and policymakers are interested in what Deaton’s and

an ensemble whose summed behavior
has measurable effects on the cyclical
behavior of market prices and interest rates. Since the cyclical behavior
of market prices and interest rates,
in turn, affects the behavior of each
household in the ensemble, the challenge for the simulation is to properly
account for the feedback from behavior
to market prices and back to behavior.
The “feedback” problem prevented macroeconomists from analyzing the business-cycle implications of
buffer-stock savings behavior until, in
an important paper, Per Krusell and
Anthony Smith showed how the problem could be solved. They developed
a procedure for reliably compressing
the amount of information required by
the computer to keep track of feedback
effects. With this innovation, macroeconomists are now able to simulate
the behavior implied by rational choice
of a large ensemble of interacting
households living through expansions
and recessions.
The simulations reveal that cycli-

Business Review Q1 2009 9

cal fluctuations in aggregate consumer
spending and aggregate income are
more tightly linked than the permanent income theory implies. This
makes intuitive sense: The tighter link
is a consequence of the fact that those
households whose spending is depressed because they are in the process
of accumulating their target level of
buffer-stock savings will increase their
spending more when income is temporarily high (as it is in an expansion).
CONCLUSION
Macroeconomics studies the
structure and performance of an
economy as a whole. Although the
founding documents of economics
have a decidedly macroeconomic

focus — Adam Smith wrote about
the wealth of nations, after all — the
development of economics as a modern
discipline has been a long and arduous
effort to understand and predict the
behavior of individual decision-making
units, such as households and business
firms.
For historical reasons, macroeconomics began life with a rather
tenuous connection to the principles of
rational choice, in part because John
Maynard Keynes explicitly rejected
rational choice – and its correlate of
competitive markets – as a framework
unsuitable for explaining the Great
Depression. But it was also because of
the broad scope and general complexity of the subject matter; it is a field

that invites theorizing at the macro
rather than at the micro level.
But fortunately for the development of macroeconomics, there was
one very important point of contact
between macro- and microeconomics,
namely, the consumption function. To
make sense of this function, macroeconomists had to think seriously
about individual behavior. And so began the “peopling of macroeconomics.”
The process has gone on now for more
than 50 years, and, to quote Angus
Deaton, it has “generated some of the
best science in economics.” This article
has endeavored to give a glimpse of
this fascinating and ongoing intellectual journey. BR

REFERENCES

Carroll, C. “Buffer Stock Savings and
the Life-Cycle/PIH.,” Quarterly Journal of
Economics, 112 (1997), pp. 1-56.
Deaton, A. “Savings and Liquidity
Constraints,” Econometrica, 59 (1991)
pp. 1221-48.
Friedman, Milton. A Theory of the
Consumption Function. Princeton:
Princeton University Press, 1957.

10 Q1 2009 Business Review

Hall, R.E., and F.S. Mishkin. “The
Sensitivity of Consumption to Transitory
Income: Estimates from Panel Data on
Households,” Econometrica, 50 (1982), pp.
461-81.
Krusell, Per, and Anthony Smith.
“Income and Wealth Heterogeneity and
the Macroeconomy,” Journal of Political
Economy, 106:5 (1998), pp. 867-97.

Modigliani F., and R. Brumberg. “Utility
Analysis and the Consumption Function:
An Interpretation of Cross-section Data,”
in Kenneth K. Kurihara, ed., PostKeynesian Economics. New Brunswick,
N.J.: Rutgers University Press, 1954, pp.
388-436.

www.philadelphiafed.org

Accounting for Cross-Country Differences
In Income Per Capita*
BY AUBHIK KHAN

iving standards, as measured by average
income per person, vary widely across
countries. Differences in income result in
large disparities in spending on goods and
services by people living in different economies. What
makes some countries rich and others poor? Furthermore,
what determines income per person in a country, and why
are these factors unevenly allocated across the world? In
this article, Aubhik Khan outlines a framework for growth
accounting to account for cross-country differences in
income. The current consensus is that differences in per
capita income across countries don’t arise primarily from
differences in the quantities of capital or labor, but rather
from differences in the efficiency with which these factors
are used.

Living standards, as captured
by average income per person, vary
dramatically across countries. These
differences in income result in large
disparities in spending on goods and
services by people living in different
economies. The typical person in

Aubhik Khan
is an associate professor of economics at Ohio State
University. When
he wrote this article he was a senior
economic advisor
and economist in
the Philadelphia
Fed’s Research
Department. This article is available free of
charge at www.philadelphiafed.org/researchand-data/publications/.
www.philadelphiafed.org

a poor country has not only less
consumption of food and housing but
also less education and poorer health,
when compared with a typical person
living in a rich country. There are also
sharp differences in life expectancy
and infant mortality between rich
and poor countries, both falling with
income per capita.
In an effort to illustrate the
magnitude of these differences in
income, let’s examine real gross
domestic product (GDP) per capita
using the cross-country data available

*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.

from the Penn World Tables.1 For
2004, the most recent year for which
this measure exists, there are data
on per capita GDP for 82 countries.
Aside from Luxembourg, which is
anomalous, the richest fifth in this
group have an average income per
capita of $32,142.2 The poorest fifth
have an average income per capita
of only $1,422. Thus, the 16 poorest
countries for which we have data for
2004 have an average income that
is 23 times less than that of the 16
richest countries. This means that the
typical person living in these poorer
countries must survive on $4 each
day. In the absence of government
subsidies, it is difficult to imagine how
an individual could buy enough food
and shelter in the U.S. to survive with
this income.
What makes some countries
relatively rich while others are
unimaginably poor? More generally,
what are the determinants of income
per person in an economy, and why
are these inputs allocated so unevenly
across the world? Why are some
countries always at the bottom of the
tables, while others rapidly close the
gap between themselves and richer
nations? We are compelled to ask

The Penn World Tables, prepared by Alan
Heston, Robert Summers, and Bettina Aten,
facilitate cross-country comparisons by calculating real GDP per capita for a large set of countries using a common set of international prices.
It is widely used for cross-country comparisons
because it assigns the same value to any particular commodity or service regardless of country.

2
Luxembourg is anomalous not only because of
its size but also because its income per capita,
$54,285, is far beyond that of the rest of the rich
world. The next richest country, the United
States, has an income per capita of $39,535.

Business Review Q1 2009 11

such questions because their answers
might give policymakers a chance to
implement a dramatic improvement in
living standards in poorer countries.
Nobel laureate Robert E. Lucas writes:
“The consequences for human welfare
involved in questions like these are
simply staggering: Once one starts to
think about them, it is hard to think
about anything else.”
Economists have studied sources
of cross-country differences in
income for more than 200 years. In
the 1950s, Nobel laureate Robert
Solow developed a framework for
growth accounting that has been
used extensively by economists to
account for cross-country differences
in income. Researchers in this field
have achieved a remarkable degree
of consensus that differences in per
capita income across countries don’t
arise primarily because of differences
in the quantities of capital or labor but
rather because of differences in the
efficiency with which these factors are
combined. Further research on the
underlying sources of these differences
has provided further insights.
ACCOUNTING FOR CROSSCOUNTRY DIFFERENCES IN
INCOME PER CAPITA
Accounting for cross-country
differences in income is a daunting
task. Why is one country richer or
poorer than another? One could
think of a host of reasons involving
differences in skills; technologies;
economic policies; natural
endowments, including land, climate,
and the frequency of natural disasters;
political stability and human rights;
the role of women in the workforce;
and many other phenomena.
Whether studying the reasons
for changes in Great Britain’s income
over the course of the Industrial
Revolution or why Bangladesh is
poorer than Thailand, economists

12 Q1 2009 Business Review

begin by studying production in each
country. The total value of all goods
and services produced in the nation
— real GDP — can be attributed to
one of three sources: capital, labor, and
total factor productivity. The manner
in which differences in the levels of

an economist would assume that the
value of a tractor, as capital, is 15 times
the value of a plough. A hypothetical
economy that had only ploughs and
tractors, 10 of the first and two of the
second, would have a total capital
stock of $40,000.

Whether studying the reasons for changes
in Great Britain’s income over the course of
the Industrial Revolution or why Bangladesh
is poorer than Thailand, economists begin by
studying production in each country.
these factors translate into differences
in real GDP is determined through the
aggregate production function.
AGGREGATE PRODUCTION
FUNCTION
Before describing the production
function, let’s review the factors
of production listed above. At the
simplest level of aggregation, capital
and labor are arguably always present
in the production of any commodity —
whether restaurant meals, economics
lectures, or other goods and services.
Capital. Capital is the sum
of all different types of equipment
and structures used in production.
Examples of equipment include
both ploughs and tractors and both
motorcycles and buses. This suggests
the first problem in growth accounting,
one that affects all of macroeconomics:
How do you add up different goods
to arrive at a total stock? If we want a
single measure of all of the capital in
the economy, how many ploughs make
a bus? We need a way to assess the
value of each commodity. Economists
often use market prices as a measure
of value. Thus, if a plough costs
$1000 and a tractor costs $15,000,

Simple aggregation as described
above cannot be directly applied to
the measurement of capital because
we don’t count the quantities of
different types of capital existing in
an economy. We don’t know how
many ploughs there are in Great
Britain because there is no direct
measurement of existing stocks. In
contrast, there is direct measurement
of flows. We count the output of every
firm, and thus we have a good estimate
of how many new ploughs are made
each year. Thus, while we lack data on
the stocks of capital, we do have data
on investment in these stocks.
Economists infer a measure of
capital stock through the aggregate
flow of investment using what is
known as the perpetual inventory
method. In its simplest application,
this assumes that all capital goods
lose a constant fraction of their value
as they deteriorate through use.
Known as physical depreciation, this
notion captures both breakdowns and
obsolescence, not only of machinery
but of all forms of capital. The
existence of depreciation implies that
there must be gross investment to
simply maintain the existing capital

www.philadelphiafed.org

stock because some of it is lost each
year. A conventional estimate of
the average depreciation rate for the
United States is around 6 percent.
The capital stock this year
is calculated as the sum of the
nondepreciated fraction of capital from
last year and current gross investment.
This method relies on an initial guess
for capital, but depreciation reduces
the importance of this guess over
time. The perpetual inventory method
determines the total capital stock
existing today as the weighted sum of
all past investments, with the weight
on past investment declining over time
because of depreciation.
Aside from physical capital,
economists have also tried to address
cross-country differences in intangible
capital.3 Examples include spending
on research and development, training
employees, creating new businesses and
other forms of organizational capital,
and the accumulated experience and
know-how of productive organizations.
Most of these investments in intangible
capital are not counted in national
income and product accounts. This
omission understates the importance
of broad capital in production.
Labor. Labor is as diverse as
capital. In most studies of crosscountry income differences, labor
input is measured as the total stock
of human capital. Human capital is
simply the quality-adjusted stock of
workers, just as physical capital is the
stock of equipment and structures used
in production. The stock of human
capital in an economy divided by the
number of workers gives an average

The 2002 book by Stephen Parent and Edward
Prescott provides a more extensive discussion
of the issues involving the measurement of
intangible capital. They conclude that differences in intangible capital cannot, by itself,
explain much of the cross-country differences
in income.

www.philadelphiafed.org

measure of the skill of the workforce.
This leaves open the issue of
how to measure the average level
of skills. One common approach
is to use cross-country data on the
average years of schooling provided
by Robert Barro and Jongwha Lee

TFP represents the
efﬁciency with which
inputs, capital and
labor, are used.
As such, it is often
interpreted as a
measure of the
effectiveness of the
technologies used in
an economy.
in 1993. However, a person’s years
of schooling are not a measure of
his or her skill but a measure of the
quantity of time invested in acquiring
skills. To convert years of schooling
into a level of human capital, the
returns to schooling are often used.
This approach, developed by Jacob
Mincer and described in his 1974
book, assumes that people with
higher levels of human capital are
paid higher wages because they are
more productive in their jobs and, as a
result, more valuable to their employer.
By examining the increase in real
wages arising from an additional year
of schooling, economists can use what
are known as Mincerian regressions to
convert the average years of schooling
in a country into a measure of average
human capital per worker. Typical
estimates of the increase in real wages
from an additional year of schooling
are about 10 percent.

Output. After the measurement
of inputs, we have to address
the measurement of output. The
production of different goods and
services is summed using market
prices, whenever possible, as a measure
of their value. This is similar to the
approach described to aggregate the
capital stock. It allows us to arrive at
aggregate measures of total production
in economies that produce an amazing
diversity of goods and services. In what
follows below, we will use per capita
GDP, the level of goods and services
produced per person, interchangeably
with income per person.4
Total Factor Productivity. There
is one last element in the aggregate
production function. Economists
have found that two countries with
identical levels of capital and labor
do not produce identical levels of
output. More generally, given the
stock of capital and labor, the level of
output produced by these inputs varies
substantially.5 This variation exists
both over time within a country and
across countries at a specific time. This
phenomenon is described as variation
in total factor productivity (TFP). TFP
represents the efficiency with which
inputs, capital and labor, are used.
As such, it is often interpreted as a
measure of the effectiveness of the
technologies used in an economy.
Economies with higher TFP are
believed to produce using more
efficient technologies that provide

4
This is not exactly correct because income per
person is actually better represented by gross
national product, or GNP, rather than GDP.
The difference between these two measures of
income per person arises when the citizens of a
nation have sources of income from production
outside their own nation. Furthermore, in the
Penn World Tables, the market prices are actually international prices based on a weighted
average of prices calculated for each country.
5
This is also known as the Solow residual, since
it was first isolated by Robert Solow.

Business Review Q1 2009 13

more goods and services for any given
level of capital and labor.
TFP is not directly measured.
Instead, its level is determined by
dividing GDP by a benchmark level of
output, that is, the level of output that
would exist if TFP were one.
But how do we know the level
of output when TFP is one? This
is where the aggregate production
function enters the analysis, providing
a benchmark measure of output from
the factors of production: capital and
labor. Many forms of the production
function have been used in economics,
but growth accounting usually uses the
Cobb-Douglas production function.
(See The Cobb-Douglas Production
Function.)
Permanent increases in an
economy’s TFP are thought of as
technological progress. This is because
such a change implies that the
economy can produce more output
using the same stocks of physical
and human capital. In other words,
the economy is using a new, more
productive technology.
In truth, aside from differences
in the level of technology, TFP differs
across countries for many other
reasons. Differences in other factors of
production, not directly measured, are
just one such reason. Thus, the levels
of raw materials and energy used in
production are implicitly captured by
TFP. If two countries have the same
capital and labor, but the first has
twice the level of raw materials and
energy as the second, then TFP will
be higher in the first country than in
the second. As we shall learn below,
much recent research into growth
accounting focuses on the causes of
differences in TFP.
CROSS-COUNTRY
DIFFERENCES IN INCOME
Economists are primarily
interested in explaining differences in

14 Q1 2009 Business Review

The Cobb-Douglas Production Function

hen computing the level of output that will be produced given
a stock of capital and level of labor hours, economists often
apply a relationship known as the Cobb-Douglas production
function. If Y is used to denote output, K is the variable that
represents capital, and L stands for labor, the Cobb-Douglas
production function is the relationship:

Y = AK αL1¯α
Here α is a coefficient between 0 and 1 that captures the percentage
change in output that results from an additional unit of capital. It is also
known as capital’s share. Similarly, in the above version of the Cobb-Douglas
function, labor’s share is 1¯α. The sum of shares is then equal to 1, which implies that if we increase both capital and labor by some proportion, output will
also rise by that same proportion.
The share term, α, is calculated using data on either the income earned
by capital or the income earned by labor. Under the assumption that factors of
production are paid competitively, the share of total production that is paid to
labor will equal 1¯α.
If there is imperfect competition, and firms have monopoly power, then
1¯α will exceed the share paid to workers. However, provided we have a measure of firms’ markups of price over cost, we can still use labor income data to
derive the coefficient, α.
Given the direct measurement of output, Y, the capital stock, K, the stock
of human capital, L, and the coefficient α, the level of TFP is given by A. It
is the fraction of output that cannot be explained by the stock of capital and
labor.
The form of the Cobb-Douglas production function implies that, in
competitive markets, the share of income paid to capital and labor will be
constant. This is broadly supported by empirical evidence showing that, over
long periods of time, there has been little change to the share of income paid
to labor and capital.

income per person, or, more formally,
real GDP per capita. It is, of course,
no mystery if a country twice the size
of another produces twice as much.
All else equal, this would arise simply
because one country had twice the
number of people, and thus twice
the workers, of the other. There need
be no difference in TFP or capital
per worker. The question of why one
country contains twice the people
compared with another country may
still be of interest to social scientists.

However, the more limited goal of
growth accounting is to explain
differences in income per person.
A simple reshuffling of the
aggregate production function allows
us to attribute production per person
to either capital per person, TFP, or
the average level of human capital in
an economy. In this way, we can use
the aggregate production function
described above to break down crosscountry differences in income and, as a
result, to begin to answer the primary

www.philadelphiafed.org

question of economic development:
Why are some countries richer than
others?
Differences in TFP Explain
Most of the Variation in Income.
The tangible wealth of a nation is
contained in its physical capital;
intangible wealth lies in human
capital and in TFP. A reader may have
believed that most of the differences
in income per person across countries
may be explained by differences
either in the quantity of physical
capital per worker or in the skills of
the workforce. However, the startling
finding from growth accounting over
the past decade is that the majority of
cross-country differences in income
per person arise through differences
in TFP. Most researchers believe that
measurable inputs such as physical and
human capital explain less than half
of the difference in income. Rather,
it is the level of technology used that
explains the majority of this difference.
While the list of researchers who have
made important contributions to this
debate is lengthy, three influential
papers are the 1997 work by Peter
Klenow and Andrés RodriguezClare, the 1998 lecture by Edward
C. Prescott given at the University of
Pennsylvania, and the 1999 study by
Robert Hall and Chad Jones. Across
these studies TFP is found to explain
between 50 and 75 percent of the
observed differences in income per
capita.
The figure, which is derived using
data made available by Francesco
Caselli, shows the relationship between
TFP and income per capita in 1996.
As explained by Hall and Jones,
who derived a similar figure using
1988 data, the figure shows that the
differences in income across countries
is very similar to the corresponding
differences in total factor productivity
— that fraction of output that cannot
be explained by capital and labor. The
correlation between output per worker
www.philadelphiafed.org

and total factor productivity exceeds
80 percent.
Differences in Capital and Labor
Are Less Striking. If differences in
technology, as captured by TFP, are
the primary determinant of differences
in income, physical and human
capital are less important explanatory
variables. It is certainly true that
richer economies have more capital
per worker. However, the extent of the
cross-country variation in capital per
worker is not large enough to explain
most of the observed differences in
income. To see this, we again use
the data set developed by Francesco
Caselli for his chapter in the Handbook
of Economic Growth. Across the 94
countries in his sample, the richest
20 percent had income per capita
that was almost 22 times that of the
poorest 20 percent. However, after he
adjusted for the importance of capital
in production, the differences in the
ratio of capital to output across these

two groups of countries was somewhat
less than two-fold.
Still there is more capital used in
richer countries, and the underlying
reasons for this are an important issue.
However, this does not appear to be
because savings or investment rates are
higher in richer countries. As shown
by Chang-Tai Hsieh and Peter Klenow
in their 2007 paper, when measured
using domestic prices, savings rates do
not vary systematically with average
income. Instead, it appears that poorer
countries are less efficient at producing
investment goods relative to goods
used for consumption.
It’s also true that richer countries
have higher levels of skills per worker.
However, the Mincerian approach
to calculating skills does not lead to
cross-country variation in the stocks
of human capital, which suggests a
much larger role for human capital
in explaining income differences
than that found for physical capital.

FIGURE
Logarithm of Output per Worker (lny)
11.5
10.5
9.5
8.5
7.5
6.5
ln(y) = - 2.6066+1.4183ln(A)
2
R = 0.8245

5.5
4.5
5.5

6.5

7.5

8.5

9.5

Logarithm of Productivity Measure (InA)

Source: Data set from Francesco Caselli “Accounting for Cross Country Differences in Income,”
and available at http://personal.lse.ac.uk/casellif/.
Business Review Q1 2009 15

Returning again to the data used by
Francesco Caselli, the ratio of average
human capital in the richest fifth of
nations, relative to that in the poorest
fifth, was about two, very similar in
size to differences in capital.
Adding It All Up. The apparent
unimportance of measurable inputs
leads to the following conclusion. In
general, to explain why one country is
poorer than another, you must explain
why it has lower TFP. How large are
these differences in TFP? The data
used by Francesco Caselli suggest
that the ratio of TFP between the
richest and the poorest 20 percent of
countries is more than five-fold. When
taken alongside differences in physical
and human capital, this explains the
difference in overall GDP per capita.
Remember that the ratio of per
capita income between the richest
and poorest 20 percent of countries
is about 20. The Cobb-Douglas
production function gives us an
accounting identity that breaks this
difference down into the product of
three other ratios: (i) capital divided
by output and adjusted for a term
reflecting capital’s share of production,
(ii) labor, and (iii) TFP. Their values
are (i) 1.85, (ii) 2.06, and (iii) 5.36 and
their product is 1.85×2.06×5.36 =
20.4.6
Subsequent work re-examining
the sources of cross-country income
differences has largely confirmed the
original findings that TFP explains
most of the difference we see. In
reaching this consensus, economists
have carefully tried to address

6
The reason that there is a small difference
between the product of these ratios, which is
20.4, and the ratio of per capita GDP between
the poorest and richest 20 percent of economies, which is 21.82, is somewhat technical.
This discrepancy, a result of something known
as Jensen’s inequality, arises because the product
of the average of the ratios is not equal to the
average of the product of the ratios.

16 Q1 2009 Business Review

problems that might arise from errors
present in the measurement of inputs
and output. These efforts have led to
better measures of schooling and more
precise calculations of human capital.
There have also been corrections
for the quality of goods and services
produced in rich and poor countries.
The implications of different aggregate
production functions, other than
the conventional Cobb-Douglas,

Subsequent work reexamining the sources
of cross-country
income differences has
largely conﬁrmed the
original ﬁndings that
TFP explains most of
the difference we see.

have been studied. Researchers have
also corrected for different levels of
market versus home production across
countries. In poorer economies, more
goods and services are produced at
home, using time-intensive methods of
production, than in the marketplace.
Omitting the value of such home
production, which is not included
in national accounts, exaggerates
the income disparity between rich
and poor countries. This research is
summarized in the survey by Francesco
Caselli and in Peter Klenow’s 2006
plenary address to the Society for
Economic Dynamics.
EXPLAINING DIFFERENCES
IN TFP
As I’ve described above, a
consensus has developed on the

primary importance of cross-country
differences in TFP for explaining
differences in income per capita.
However, the accounting methodology
used to arrive at this consensus has
presented a problem. Since TFP is
inferred as a residual and not directly
measured as physical or human
capital are, attributing differences in
income to differences in TFP does not
completely answer the question of why
countries differ. All we have really
found is that these differences cannot
be attributed to measured differences
in physical or human capital. They
lie somewhere else. Economists have
started to examine the causes of
differences in TFP across countries.
Looking Behind the Aggregate
Production Function. An important
early contribution to this research
was made by Stephen Parente and
Edward C. Prescott, who, in their 1999
paper, described how the adoption
of more productive technologies
may be hampered because groups
of people have vested interests in
protecting existing, but less productive,
technologies. Following their work,
a large body of research has arisen.
Some of this work looks inside the
production function for the economy.
This research seeks to examine how
factors affecting the production
decisions of individual firms add up to
differences in output at the aggregate
level. Instead of attempting a full
survey of this literature, I mention two
recent examples.
One interesting line of research
studies how taxes and other
distortions, such as employment
protection policies, can reduce TFP.
For example, in their paper, Diego
Restuccia and Richard Rogerson study
the effect of taxes and subsidies that
favor some firms relative to others.
They find that such policies lead to
too much capital and labor being
used by some plants that benefit

www.philadelphiafed.org

from subsidies. By moving capital
and labor from productive plants to
unproductive plants, such policies can
lead to a reduction of between 30 and
50 percent in an economy’s TFP. This
research provides an example of how
TFP is not necessarily determined by
technological know-how alone but is
also affected by economic policies.
Amartya Lahiri and Kei-Mu Yi
also emphasize the role of economic
policies in explaining the different
economic performance of two Indian
states, West Bengal and Maharashtra.
Economic development in these two
states poses an interesting puzzle. In
1960 West Bengal’s GDP per capita
exceeded that of Maharashtra, but by
1993 its GDP was barely two-thirds
that of Maharashtra. Lahiri and Yi
use this case study as a means to
get behind the aggregate production
function. In their model, there are
separate production functions for
agriculture, manufacturing, and
services. They conclude that West
Bengal has fallen behind Maharashtra
because TFP in manufacturing and
services has grown more slowly.
Returning to our language above,
there has been less technological
progress in West Bengal. Lahiri and
Yi suggest that growth in TFP has
been lower in West Bengal because
labor and industrial regulations have
hindered growth in business TFP. In
general, policies that stifle innovation
or the adoption of new, more efficient
technologies slow TFP growth. This,
in turn, reduces the growth of income
per capita.
Re-examining the Role
of Human Capital. Recently,
researchers have begun to question the

www.philadelphiafed.org

importance of TFP. In two separate
papers, Rodolfo Manuelli and Ananth
Seshadri and, separately, Andres
Erosa, Tatyana Koreshkova, and Diego
Restuccia have argued that human
capital has not been properly measured
in existing studies.
They have two main insights. The
first is that human capital investment
in a country is not independent of the
level of TFP. Second, they argue that
human capital investment requires
not only years of schooling but also
goods and services such as schools and
teachers. This, in turn, suggests that
years of schooling are an incomplete
measure of human capital because
the quality of the human capital is
neglected.
Households make educational
investments based on the returns to
education, and these investments
involve not only the time spent in
school but also real goods devoted
to education. This implies that
the standard Mincerian approach
to inferring the stock of human
capital may understate crosscountry differences. These authors
argue that a different approach to
measuring human capital, one where
households explicitly undertake
schooling decisions that vary across
countries in response to the economic
environment, leads to much larger
differences in quality-adjusted human
capital across countries. This, in
turn, reduces the direct role of TFP.
Indeed, they find that cross-country
differences in human capital are
the leading source of differences in
income. However, it remains true that
these differences in human capital are
driven by differences in TFP. It’s just

that the required differences in TFP
become far smaller.
CONCLUSION
Economists account for crosscountry differences in income per
person using the method known as
growth accounting. It breaks down
real GDP per capita into capital per
worker, human capital per worker, and
the level of technology, or TFP. TFP
is the level of output that remains to
be explained after accounting for the
role of physical and human capital.
Measuring the levels of these inputs
across countries, we find that most of
the cross-country variation in income
per person is attributable to differences
in TFP. Poorer economies are poorer
not because they have less capital and
lower skills per worker but because
they use these inputs less efficiently
than wealthier economies.
Many things can affect a nation’s
TFP. For example, economic policies,
such as taxes or subsidies, may impede
the efficient distribution of capital
and labor across firms, which will
lower TFP. Alternatively, they may
prevent the adoption of the most
efficient technologies and thus lower
TFP. However, to the extent that
the technology is much more readily
transferable across countries than
physical or human capital, why would
one country suffer the loss in output
associated with using an inferior
technology? If, instead, TFP differs
because of policies that hinder the
growth of business, why allow such
policies to persist when the gains to
correcting them are so large? BR

Business Review Q1 2009 17

REFERENCES

Barro, Robert J., and Jongwha Lee.
“International Comparisons of Education
Attainment,” Journal of Monetary
Economics 32 (1993), pp. 363-94.
Caselli, Francesco. “Accounting for
Cross-Country Income Differences,” in P.
Aghion and S. Durlauf (eds.), Handbook of
Economic Growth, North Holland (2005).
Erosa, Andres, Tatyana Koreshkova,
and Diego Restuccia. “How Important Is
Human Capital? A Quantitative Theory
Assessment of World Income Inequality,”
Working Papers tecipa-280, University of
Toronto, Department of Economics (2007).
Hall, Robert, and Chad Jones. “Why
Do Some Countries Produce So Much
More Output per Worker Than Others?”
Quarterly Journal of Economics, 114 (1999),
pp. 83-116.
Heston, Alan, Robert Summers, and
Bettina Aten. Penn World Table Version
6.2, Center for International Comparisons
of Production, Income, and Prices at the
University of Pennsylvania (September
2006); (http://pwt.econ.upenn.edu).
Hsieh, Chang-Tai, and Peter J. Klenow.
“Relative Prices and Relative Prosperity,”
American Economic Review, 97 (2007), pp.
562-85.

18 Q1 2009 Business Review

Klenow, Peter K. “Income Differences
Across Countries,” plenary address to the
Society for Economic Dynamics, July 6,
2006, Vancouver, Canada (available at
www.klenow.com/KlenowSED.pdf).
Klenow, Peter, and Andrés RodriguezClare. “The Neoclassical Revival in
Growth Economics: Has It Gone Too Far?”
in National Bureau of Economic Research
Macroeconomics Annual, Cambridge, MA:
MIT Press (1997).
Lahiri, Amartya, and Kei-Mu Yi. “A
Tale of Two States: Maharashtra and
West Bengal,” Federal Reserve Bank of
Philadelphia Working Paper 06-16/R
(April 2008); available at http://www.
philadelphiafed.org/research-and-data/
publications/working-papers/2006/wp0616.pdf.
Lucas, Robert E., Jr. “On the Mechanics
of Economic Development,” Journal
of Monetary Economics, 22 (1988), pp.
3-42. Reprinted as Chapter 1 in Lectures
on Economic Growth, Cambridge, MA:
Harvard University Press (2002).

Mincer, Jacob. Schooling, Experience, and
Earnings. New York: Columbia University
Press (1974).
Parente, Stephen L., and Edward. C.
Prescott. “Monopoly Rights: A Barrier to
Riches,” American Economic Review, 89
(1999), pp. 1216-33.
Parente, Stephen L., and Edward C.
Prescott. Barriers to Riches. Cambridge,
MA: MIT Press (2000).
Prescott, Edward C. “Needed: A Theory
of Total Factor Productivity,” International
Economic Review, 39 (1998), pp. 529-49.
Restuccia, Diego, and Richard Rogerson.
“Policy Distortions and Aggregate
Productivity with Heterogeneous Plants,”
Review of Economic Dynamics, 4 (October
2008), pp. 707-20.
Solow, Robert M. “Technical Change
and the Aggregate Production Function,”
Review of Economics and Statistics, 39
(1957), pp. 312-20.

Manuelli, Rodolfo, and Seshadri, Ananth.
“Human Capital and the Wealth of
Nations,” Working Paper, University of
Wisconsin-Madison (April 2007).

www.philadelphiafed.org

Rethinking the Implications of Monetary Policy:
How a Transactions Role for Money Transforms the
Predictions of Our Leading Models*
BY JULIA K. THOMAS

ver the past several decades, economists have
devoted ever-growing effort to developing
economic models to help us understand
how changes in interest rates brought
about by monetary policy actions affect the production
and provision of goods and services in the economy.
Although New Keynesian models have broad appeal in
explaining how changes in the money stock can affect
business activity, these models generate results that are
inconsistent with what we know about how interest rates
move with policy-induced changes in the money stock.
In this article, Julia Thomas argues that by extending the
New Keynesian model to reintroduce money’s liquidity
role, we can resolve some of the remaining divorce
between economic theory and the patterns observed in
the workings of actual economies.

Each meeting of the FOMC is met
with widespread interest by everyone
from financial market participants
on Wall Street, to real estate agents,

Julia Thomas is an
associate professor
of economics
at Ohio State
University. When
she wrote this
article she was an
economic advisor
and economist in
the Philadelphia
Fed’s Research
Department. This article is available free of
charge at www.philadelphiafed.org/researchand-data/publications/.
www.philadelphiafed.org

to the cashier at your local grocery
store. People perceive changes in the
FOMC’s target for the federal funds
rate — the interest rate at which banks
borrow and lend to each other, usually
overnight, through the federal funds
market — as relevant and important
in their everyday lives. Business people
view changes in this interest rate as
an important determinant influencing

*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.

everything from car and home sales to
consumer spending over the Christmas
holiday season. Whenever business
conditions are widely perceived to be
weak, most people welcome cuts in the
federal funds rate.
Despite these observations,
however, the means through which
changes in an interest rate affect
business activity is, in fact, far from
obvious. Over the past few decades,
economists have devoted ever-growing
effort to developing formal economic
models to help us understand precisely
how changes in interest rates brought
about by monetary policy actions affect
the production and provision of goods
and services throughout the economy.
While there are several different types
of models describing how monetary
policy actions drive short-run changes
in total employment and GDP, a
growing consensus has emerged.
Most often, when an economic model
is used as an additional tool with
which to analyze the consequences of
alternative monetary policy actions, it
is drawn from a class of models known
as New Keynesian (or sticky price)
models.
New Keynesian models have broad
appeal because they provide a relatively
simple explanation for how changes in
the stock of money can affect business
activity and because they are, in some
respects, quite consistent with what
economists know about how actual
changes in the money stock affect
the economy. Unfortunately, though,
versions of these models capable of
generating realistic effects of changes
in the money stock for production and
employment are, at their most basic
level, inconsistent with what we know
Business Review Q1 2009 19

about how interest rates move with
policy-induced changes in the stock of
money.
This article argues that, by
extending the New Keynesian model
to reintroduce an abandoned liquidity
role of money found in earlier models,
we can resolve some of the remaining
divorce between our economic theory
and the patterns we observe in the
workings of actual economies.1 What
is this role of money? It is the idea,
from classical economics, that money
serves a special purpose in allowing
transactions to take place between
buyers and sellers, since it is the only
financial asset universally accepted as
a means of payment. Other assets, such
as stocks and bonds, are typically not
accepted as a means of payment and
cannot be directly used to buy goods
and services. Thus, in contrast to
money, these nonmonetary assets are
relatively illiquid.
When we introduce the classic
liquidity role of money into the New
Keynesian model, and we acknowledge
the fact that it is costly to convert
nonmonetary assets into monetary
ones (and vice versa), we arrive at a
richer model that is consistent with
our knowledge of how interest rates
are affected by changes in the stock
of money. At the same time, the
mechanics of the New Keynesian
model become more complicated with
this improvement, because the level of
an individual’s monetary assets takes
on an independent role in his or her
spending decisions. Exploring the
effects of changes in monetary policy
in this richer environment, we find
that the overall magnitude of these
effects and the rate at which they

The expanded model we pursue throughout
this discussion is drawn from my article with
Robert King, which builds upon my work with
Aubhik Khan.

20 Q1 2009 Business Review

spread throughout the economy can
depend importantly on how much
money is typically held and how
rapidly it changes hands, on average.
In short, our extended theoretical
model offers new insights about how

Macroeconomists
generally associate
an easing of monetary
policy with a cut in
interest rates.
the effects of monetary policy are
transmitted throughout the economy.
WHAT HAPPENS FOLLOWING
A CHANGE IN MONETARY
POLICY?
For many economists, at the
most basic level, the changes in the
economy associated with a change
in monetary policy may be traced to
changes in the rate at which the supply
of money grows over time, rather than
to movements in the interest rate.
Indeed, the means through which
central banks actually move their key
interest rates is through open market
operations, wherein government
bonds — a nonmonetary asset — are
exchanged for money. For example,
the monetary authority can reduce the
overall level of money in the economy
by undertaking an open market sale of
government bonds for money.2 In the
process of such a contractionary open
market operation, the overall supply
of bonds for sale is increased, which
puts downward pressure on the price at
which each bond is sold. This, in turn,

2
See the article by Frederic Mishkin or Dean
Croushore’s book for a more thorough discussion of the implementation of open market
operations.

increases the difference between a
bond’s payoff at maturity (its par value)
relative to its purchase price today,
ultimately raising the rate of return on
bonds — that is, the interest rate.
Macroeconomists generally
associate an easing of monetary policy
with a cut in interest rates. As Nobel
Laureate Milton Friedman put it in
his 1968 presidential address to the
American Economic Association,
“The initial impact of increasing the
quantity of money at a faster rate
than it has been increasing is to make
interest rates lower for a time than
they would otherwise have been.”
Indeed, there is such consensus about
the inverse relationship between shortterm interest rates and the growth rate
of the aggregate money supply that the
relationship has been given a name:
the liquidity effect.3
There is even greater consensus
that changes in nominal variables,
such as the interest rate, have notable
consequences for the paths of real

3
Most evidence of the liquidity effect is indirect,
in that the relationship is inferred by examining economic data through the lens of complex
empirical models beyond the scope of this article.
However, Seth Carpenter and Selva Demiralp
directly establish the existence of the liquidity
effect at a daily frequency by studying the forecast
errors made at the New York Fed’s Trading Desk
in conducting open market operations on behalf
of the Federal Reserve System. Using these errors
to identify exogenous changes in the supply of reserves to the banking system, the authors establish
a negative and statistically significant correlation
between unanticipated changes in high-powered
money and the federal funds rate. Elsewhere,
John Cochrane provides direct evidence that
the liquidity effect exists for broader measures of
money and interest rates. He examines changes
in the growth of M1 (total currency and checkable deposits) and in the nominal yields on U.S.
Treasuries between October 1979 and November
1982 (a historical episode throughout which the
Federal Reserve expressly targeted the quantity
of money held by commercial banks). Cochrane
uncovers statistically significant negative effects
of M1 growth on both three-month Treasury bill
rates and 20-year Treasury bond rates and thereby
establishes that increases in the rate of money
growth are associated with declines in nominal
interest rates lasting up to one year.

www.philadelphiafed.org

variables like GDP and employment.
Such real effects arising from a
change in monetary policy are termed
nonneutralities. Perhaps the most
celebrated example of nonneutrality
is the observation that reductions in
inflation caused by contractionary
monetary policy are associated with
temporary increases in unemployment,
a relationship termed the Phillips
curve tradeoff.4
NEW KEYNESIAN MODELS
It is not easy to reproduce the
patterns in the movements of money,
interest rates, employment, and
output observed in actual economies
within our economic models; however,
doing so is an important step toward
understanding why these patterns
arise and how they may be influenced
by monetary policy. To generate
nonneutralities in our models, we
must first find a way to overcome their
tendency to exhibit a related, and
quite opposite, phenomenon known
as the neutrality of money. This
term applies whenever changes in an
economy's money stock are transmitted
immediately into the overall level of
prices and have no effect at all on the
real quantities of goods and services
produced and sold.
Neutrality of Money. To illustrate
the neutrality of money, consider
the following simple example of a
remote island with a single good and
a single currency. Let us assume that
the mango is the only good valued
by inhabitants of the island and that

4
This relationship is named after Alban William
Phillips, who documented an inverse relationship
between changes in unemployment and nominal
wages in the United Kingdom across roughly 100
years of data. However, some argue that acknowledgment should instead go to Irving Fisher, who
had suggested a similar relationship roughly 20
years earlier. The relationship was theoretically
formalized to consider its policy implications by
Paul Samuelson and Robert Solow.

www.philadelphiafed.org

local suppliers typically harvest and
sell 50 mangos each week. The single
currency used to purchase these goods
is the seashell; that is, islanders buy
and sell mangos using only seashells.
There are 100 seashells on the island
this week, as in many previous weeks,
and all mangos are sold (and all
seashells are exchanged for mangos)

actively watching for the outcome
of each meeting of the FOMC,
economists generally accept that
changes in the supply of money induce
temporary movements in output,
employment, and the real return to
holding assets measured in units of
consumption — the real (or inflationadjusted) interest rate, to which we

It is not easy to reproduce the patterns in
the movements of money, interest rates,
employment, and output observed in actual
economies within our economic models.
precisely once each week. Under these
circumstances, the price of a mango
will be two seashells.
Next, let us suppose that a nearby
hurricane causes 100 additional
seashells to wash up on the island’s
beaches next week, unexpectedly
doubling the supply of currency (or
money). This would seem to imply
twice as many island dollars next week
chasing after the same weekly harvest
of 50 mangos. So what will happen to
the price of a mango? One possibility
is that it will immediately rise to
four seashells, thereby doubling the
island price level, with no change in
the number of mangos harvested and
sold. If this happens, the rise in the
money supply will have simply led to
a proportionate rise in the price level,
with no consequence at all for the
island’s real activity – its employment
and GDP – and we have a textbook
case of the neutrality of money.
Nonneutrality of Money. In
contrast to the scenario suggested
above, most economists are convinced
that actual economies exhibit shortterm departures from the neutrality
of money. Like the many individuals

will return later in this article.5 Let
us reconsider our island economy of
seashells and mangos in light of this
consensus view.
If the price of each mango does
not immediately double in response to
the unexpected doubling of seashells
on the island, the quantity of mangos
supplied must rise to prevent unfilled
demand for mangos and undesired
idle seashells. But how might this
happen? New Keynesian models have
a simple answer to the question. They
assume that the firms supplying goods
and services — in our example, the
islanders gathering mangos — cannot

5
Economists use rich empirical methods to study
the joint movement of interest rates, prices, and
output. Their findings suggest that a persistent
increase in the nominal interest rate is initially
accompanied by a small decline in the growth rate
of output, with little or no change in the growth
rate of the price level. Over the course of several
subsequent quarters, it is followed by declines
in both output growth and inflation. At some
point thereafter, the changes in the quantities
of goods and services produced in response to
the change in the interest rate eventually vanish. For further discussion, see the articles by
Lawrence Christiano, Martin Eichenbaum, and
Charles Evans; Harald Uhlig; and Robert King
and Mark Watson.

Business Review Q1 2009 21

always change their prices at will.
Rather, some must honor prices that
they set in the past.6
For simplicity, suppose that
one-third of the mango sellers on our
island are able to change their prices
in any given week, with a single crayon
used to reset prices on cardboard
advertisements alternating between
each of the three groups of sellers on
the island each week. In this case,
when the new seashells arrive, the
average price of a mango will not
immediately jump to four seashells.
Instead, the island price level will
rise only part way in the first week,
since only one-third of all sellers can
respond to the increase in the supply
of seashells with an increase in their
prices.
Assuming that all sellers are
forced to supply the quantity of
mangos that is demanded of them at
their posted prices (or that they face
sufficiently harsh penalties for not
doing so that they choose to comply),
the staggered price adjustment
described above is all that is needed
to break the neutrality of money
in our island economy. With the
average price in the economy not
initially doubling, and assuming that
all consumers on the island spend
their extra seashells, the total demand
for mangos will rise above the usual
weekly supply of 50, and more mangos
will have to be harvested. As a result,
mango suppliers (and their employees)
will work more relative to the normal
level of labor effort on the island, and
more fruit will be sold. Put another
way, given temporary price stickiness
among a fraction of sellers, the

6
See the articles by William Kerr and Robert
King; Bennett McCallum and Edward Nelson;
and Michael Woodford for analytically tractable
examples of the basic New Keynesian environment.

22 Q1 2009 Business Review

unexpected increase in the amount of
currency on the island will have real
effects, raising employment and/or the
average hours worked per employee, as
well as total production (real GDP).
The real effects of the rise in
the island’s money supply are not
permanent, however. Instead, the
initial week’s high level of real activity
will begin to subside as the economy’s
price level continues responding to

rate! This is where the problems begin
for the basic New Keynesian model.
Interest Rate Movements. In
contrast to the liquidity effect observed
in actual economies, the formal
relationships between money, interest
rates, inflation, and output at the core
of the New Keynesian model lead it
to predict that the interest rate rises
when the money supply is expanded.
Why do interest rates move the wrong

In contrast to the liquidity effect observed in
actual economies, the formal relationships
between money, interest rates, inﬂation, and
output at the core of the New Keynesian model
lead it to predict that the interest rate rises
when the money supply is expanded.
the doubled supply of seashells. In
the following week, as an additional
one-third of sellers are able to raise
their prices, the average price of a
mango will rise further. Thus, while
total demand will remain higher than
usual, it will be less so than initially,
and total mango production and sales
will move nearer to their customary
level. Eventually, as all sellers have
had the opportunity to respond to the
new economic conditions, the island
price level will reach precisely double
its original level, and the quantity
of mangos harvested each week will
return to the same 50 as existed before
the hurricane.
The example above illustrates how
unexpected increases in the money
supply can temporarily stimulate
economic activity. However, its
mechanics are very different from the
way we usually think of a change in
monetary policy. Note, in particular,
that our example never even
mentioned a change in the interest

way in the model? To understand this,
we must consider a key relationship
between (nominal) interest rates and
real interest rates: the Fisher equation,
named after Irving Fisher.7 The
Fisher equation says that the interest
rate — the ratio of the dollar payoff on
an asset relative to its dollar purchase
price — is approximately equal to the
sum of the real interest rate and the
expected rate of inflation. To see why
it is natural that this equation should
hold, at least approximately, we begin
with a broad definition of the real
interest rate. The real interest rate is
the ratio of an asset’s payoff in units
of future consumption of goods and
services relative to the consumption

7
Fisher’s exposition of the relationship in The
Theory of Interest, published in 1930, is now out
of print. However, it is available online at the Library of Economics and Liberty (www.econlib.org/
library/classics.html). The topic is also routinely
covered in most macroeconomics texts; see, for
example, Robert Barro’s book.

www.philadelphiafed.org

that must be forgone today for its
purchase; in other words, it is the
return on savings measured not in
money but in goods and services.
Returning to the island analogy
above, let us suppose that our islanders
are able to borrow and save. In particular, if an inhabitant saves 10 seashells
this week, an island banker will lend
them to some other islander and return to the original lender 11 seashells
next week. Thus, the weekly nominal
interest rate is 10 percent. Suppose
also that the price of a mango will
rise over the course of the week from
one seashell to 1.01 seashells; in other
words, the weekly rate of inflation is 1
percent. Under these circumstances,
a mango forgone this week implies
one seashell of savings deposited with
the island banker that will return 1.1
seashells next week (each worth 1/1.01
mangos), allowing the lender to buy
1.089 additional mangos at that time.
Notice that the real interest rate, measured in units of island goods, is then
approximately 9 percent. In this way,
we have arrived at the key relationship
defined by Irving Fisher; the interest
rate on our island is roughly equal to
the sum of the real interest rate and
the inflation rate.
Given the discussion above, it
is straightforward to summarize why
the basic New Keynesian model fares
poorly with regard to the liquidity effect. In the basic model economy, an
increase in the money supply implies
very little change in the real interest rate. However, at the same time,
it leads to comparatively substantial
increases in future inflation rates.
Referring back to the Fisher equation, it is then natural that the model
should predict that the interest rate
initially rises when the supply of money
in the economy is expanded. This is
a somewhat disconcerting feature of
our standard model, given the broad
consensus regarding the liquidity effect

www.philadelphiafed.org

— the inverse relationship between
changes in interest rates and changes
in the money supply observed in actual
economies.
EXTENDING THE MODEL:
TRANSACTIONS ROLE FOR
MONEY AND INFREQUENT
PORTFOLIO ADJUSTMENTS
Basic New Keynesian models
fail to reproduce the liquidity effect
essentially because they place no
emphasis on the nature of the open
market operations that implement
monetary policy.8 We can correct this
problem if we extend our theoretical
model to reflect the fact that
individuals hold both liquid assets,
broadly interpretable as money, as well

acknowledge the fact that individuals
hold low-yield liquid assets, or money,
because they must draw on them for
transactions. Quite simply, goods and
services can be purchased only with
money (which we might think of as
currency, checkable deposits, and time
and savings deposits). At the same
time, individuals also choose to hold
higher-yield nonmonetary assets, such
as government bonds, as a means of
saving. While these assets cannot be
used directly for transactions, they
pay significantly higher rates of return
than money.
Various events — some expected,
some unexpected — occasionally lead
people to adjust their asset portfolios,
moving wealth out of bonds (illiquid

When there is a change in the quantity of
bonds in the economy, it affects those people
who are active in the bond market at that time,
whether directly or through their brokers.
as illiquid assets, such as stocks and
government bonds, and we also take
account of the fact that individuals
infrequently adjust their portfolios
between these two types of assets.
In this extension of the model,
we allow money to serve a particular
purpose not reflected in the basic New
Keynesian environment. Here, we

More elaborate versions of these models do succeed in generating a liquidity effect. However,
Bill Dupor, Jing Han, and Yi-Chan Tsai raise an
inherent tension regarding this success. They find
that the additional assumptions needed to make
the basic New Keynesian model consistent with
the observed responses in interest rates, inflation,
and output following changes in monetary policy
have the unfortunate consequence of making it
inconsistent with observed responses following
nonmonetary disturbances.

assets) into money (liquid assets), or
vice versa. When an individual puts
a down payment on a mortgage, she
may do so by converting CDs or other
high-yield assets into money that is
deposited into her bank account and
then write a check from that account
to make the down payment. However,
for the average person, such events
are relatively infrequent. Thus, in any
given month, most individuals are not
actively adjusting their asset portfolios
— or what we will loosely term “active
in the bond market.”
When there is a change in the
quantity of bonds in the economy, it
affects those people who are active
in the bond market at that time,
whether directly or through their
brokers. It is with these individuals

Business Review Q1 2009 23

that the monetary authority conducts
an open market operation.9 For
example, the monetary authority might
repurchase bonds from them and pay
for the bonds by making deposits into
their bank accounts. When these
individuals are induced to sell bonds
and receive the associated payments
of money into their bank accounts,
the overall supply of money in the
economy is increased. However, the
full rise in the stock of money does
not find its way into economic activity
right away. Instead, much of it remains
in the recipients’ bank accounts for
some time.
It is precisely the fact that most
people are active in the bond market
only occasionally in our extended
model that implies that a change
in the overall money supply is not
immediately transmitted throughout
the economy. Most of the individuals
involved in the expansionary open
market operation from above do
not expect to sell more bonds in
the near future, so they save much
of the current increase in their
bank accounts to finance their
expenditures over future months and
boost their spending only gradually.
Thus, the injection of new money
into the economy does not lead to
an immediate equivalent increase in
aggregate spending but instead induces
a more protracted rise in spending
as more and more of the additional
money is drawn from the recipients’
accounts.
The slow increase in overall
nominal spending in our extended

9
For expositional convenience, we proceed
through the remainder of this discussion as
though the monetary authority directly interacts
with individuals when conducting open market
operations. In reality, of course, interactions
between the Federal Reserve System and individuals are not direct, since the Desk actually
conducts open market operations through the
primary dealers.

24 Q1 2009 Business Review

model reduces the upward pressure on
inflation relative to that in the basic
New Keynesian model. How might
this alter the model’s performance with
regard to the liquidity effect? Recalling
the Fisher relationship from above,
we know that the more gradual rise in
inflation increases the likelihood that
the interest rate will fall in response to
a money injection. All that is required
for this to happen is that the real
interest rate exhibit a fall of sufficient
magnitude to outweigh the initial rise
in inflation.

real interest rate while simultaneously
reducing the upward pressure on
inflation, and thus it has the ability to
reproduce the liquidity effects we see
following expansionary open market
operations in actual economies.
MONEY VELOCITY IN THE
EXTENDED MODEL
To reconcile reductions in shortterm nominal interest rates with
expansionary monetary policy that
stimulates output and employment
over the short run, we have extended

To reconcile reductions in short-term nominal
interest rates with expansionary monetary
policy that stimulates output and employment
over the short run, we have extended the
New Keynesian model to introduce an explicit
transactions role for money, alongside
infrequent trading of bonds by the typical
individual.
This brings us to the fall in the
real interest rate. For the increase in
the money supply to find its way into
general economic activity, individuals
participating in the open market
operation must be induced to increase
their spending and thus their real
consumption of goods and services.
This can only happen, however, if
the opportunity cost of an increase
in their current consumption (the
forgone return of a greater increase
in consumption next month) is not
intolerably high. To ensure that this
is the case, the real interest rate
must fall relative to its average level,
which is precisely what happens in
our extended version of the New
Keynesian model. On balance, our
extended model delivers a fall in the

the New Keynesian model to introduce
an explicit transactions role for money,
alongside infrequent trading of bonds
by the typical individual. However,
the repercussions of this extension go
beyond merely resolving the problem
of the absent liquidity effect. In fact,
the new elements we have introduced
into the model can have large and
important implications for the way
in which monetary policy affects the
economy, because they, in turn, create
a prominent role for movements in the
velocity of money.
Velocity Defined. The velocity
of money is another classic feature
of models of the monetary economy
that has been largely ignored in New
Keynesian models. It is a very basic
concept reflecting the average number

www.philadelphiafed.org

of times a unit of money is used within
a specific time period, and it lies at
the heart of traditional monetary
theory. To compute velocity, we need
only take the ratio of total nominal
spending on goods and services relative
to the overall stock of money in the
economy. This observation comes
straight from the velocity equation
MV = PY, wherein M represents the
aggregate money stock, V is velocity,
P is the aggregate price level, and Y
is real aggregate output. Notice that
by simply rearranging the velocity
equation, we have V = PY/M.
Let us consider our island
economy once again. There, within
a typical week, all seashells changed
hands exactly one time, with a total
of 100 available seashells being used
to buy 100 seashells' worth of mangos.
Thus, the weekly velocity of money
was one. Now, let us suppose that,
when the extra 100 seashells wash
onto the island in the week of the
hurricane, only one person is out on
the beach to receive the unexpected
“money injection,” so that he is
the only inhabitant to receive any
additional money or even know of it.
If we further suppose that this islander
spends only 50 of the extra seashells
this week and tucks the remainder
away for future use (holding them
idle in his hut for quick and costless
access), total nominal spending on the
island will rise to only 150 seashells out
of a total seashell supply of 200. Thus,
the average number of times any one
seashell changes hands in the week
will be 150/200, implying a money
velocity of 0.75.
In our example above, when
only one islander was on the beach to
receive the unexpected injection of
seashells, and he chose to hold half of
the injection idle rather than immediately spending it or investing it in
island bonds, we saw that the velocity
of money dropped from its average

www.philadelphiafed.org

weekly level of 1 to 0.75. This is analogous to what happens in our extended
version of the New Keynesian model
following an expansionary open market operation. Because only a fraction
of all individuals actually take part in
the open market operation, and those
individuals that do participate elect
to save much of the increased money
stock in their bank accounts to finance

When an open market
operation increases
the bank balances of
individuals who are
trading bonds, their
spending rises, but it
rises by less than the
increase in their bank
accounts.

near-term expenditures, there too
velocity falls with an increase in the
money supply.
How Changes in Velocity
Influence the Transmission of
Monetary Policy. Changes in
velocity over time can have important
consequences for the rate at which
nominal phenomena, such as
unexpected movements in the supply
of money, transmit themselves into real
effects. In the basic New Keynesian
model, where money has no distinct
role in facilitating transactions,
movements in velocity do not feed
back into the operation of the real
economy. It is true that money helps
to determine the interest rate through
the interaction of money demand and
the aggregate money supply. However,
once the interest rate is determined,
the aggregate quantity of money and

the velocity of money have no further
role.10 Put another way, changes in
interest rates always affect output,
employment, and inflation in the same
way, irrespective of the money supply
and the resulting number of times each
currency unit is used.
In our expanded model, by
contrast, individuals’ bank balances
help determine their spending over
and above their total income or
wealth. An individual with a total
wealth of $1000, but with only $100
currently available as money in her
bank account, will spend less on
nondurable goods this week than will
another individual who has the same
$1000 but who holds it entirely in
her bank account. Because money
is necessary for transactions in our
expanded model economy, the role
of the aggregate money stock and its
velocity does not end with the interest
rate. Rather, the quantity of money
that individuals hold and the rates
at which they spend it have a direct
influence on the aggregate demand
for goods and services even after the
interest rate has been determined.
Thus, we cannot anticipate the
changes in production, employment,
and inflation that will follow a given
change in monetary policy by simply
knowing the implied path of interest

This is essentially because the system of equations governing the model has only a single equation involving the demand for real balances, and
that equation is effectively quarantined from the
rest of the economy in that it links real balances
only to the nominal interest rate and the money
growth rate. Apart from the money demand
equation, there is a core block of equations that
contain no monetary variables at all but that
together determine output, inflation, and the
real interest rate as a function of the interest rate.
In the most basic formulation of the model, this
block of equations is simply (1) an Euler equation
describing households’ optimal savings behavior,
(2) the Fisher relation discussed above, and (3) a
Phillips curve relating current inflation and the
aggregate supply of goods and services to expected
inflation.

Business Review Q1 2009 25

rates; instead, we must also know how
individuals’ money holdings and their
money spending rates (velocities) will
respond to the change in policy.
When an open market operation
increases the bank balances of
individuals who are trading bonds,
their spending rises, but it rises by
less than the increase in their bank
accounts. Thus, we see a rise in the
fraction of the money supply sitting
idle awaiting future use, money
changes hands less frequently than
before, and velocity falls. Unlike
the basic New Keynesian model,
where changes in velocity have
no independent influence on the
economy, the decline in velocity in our
expanded model has an important role
in shaping the economy’s response to
the expansion of the money supply.
When velocity falls, there are fewer
dollars in circulation for undertaking
transactions than there would be
otherwise. This places a restraint on
the economy’s overall demand for
goods and services and thus dampens
the initial rise in production and
employment. Moreover, recalling our
money velocity equation from above,
we know that the fall in velocity
(V) means that aggregate nominal
spending (PY) initially rises by less
than the rise in the money supply (M).
Thus, the fall in velocity helps to
restrain the rise in the aggregate price
level, and the inflation rate rises by less
than it would were velocity unchanged
or irrelevant (as in the basic model).
Over time, as the individuals
who participated in the open market
operation begin to spend more and
more of the extra money they are
holding, aggregate velocity begins
to rise back toward its normal level.
Over the early part of this transition,
as more and more money balances
enter circulation, aggregate demand
continues to rise, thereby propping
up the responses in employment and

26 Q1 2009 Business Review

output. At the same time, the rises
in aggregate nominal spending must
also serve to prop up the inflation
rate. Thus, we see that, while the
initial decline in velocity dampens the
initial changes in both real quantities
and inflation, these subsequent
upward movements in velocity serve
to protract those changes. For this
reason, our economy’s responses to
an open market operation cannot be
completed until velocity has recovered
to its normal level, when the full
increase in the money supply has found

slowly, and the fall in aggregate
velocity following an expansionary
open market operation only reinforces
this fact. In that setting, it will take
far longer for the full effects of the
same increase in the aggregate money
supply to be transmitted through the
economy, since it will take far more
time for the new balances to fully enter
into circulation.
The movements in velocity arising
in our expanded New Keynesian
model are, in truth, an attempt to
formalize Milton Friedman’s views

When velocity falls, there are fewer dollars in
circulation for undertaking transactions than
there would be otherwise.
its way into circulation and individuals
have resumed their usual spending
rates.
As indicated above, the time it
takes for an open market operation to
flow throughout our model economy
will depend on how long it takes
for velocity to return to its ordinary
level. In a setting where velocity is
initially very high, money changes
hands very frequently. There, despite
some resulting decline in velocity
as described above, the effects of
a change in monetary policy that
are unique to our expanded model
are likely to vanish rapidly. This
is because new money held by
individuals participating in an open
market operation will not be left
idle for long but will instead rapidly
enter circulation. After that has
happened, the aggregate responses
in our expanded model economy will
closely resemble those of the basic
New Keynesian model. By contrast,
a setting with low initial velocity is
one where people spend their money

on the transmission of monetary
policy. In his words, “The initial effect
of a change in monetary growth is
an offsetting movement in velocity,
followed by changes in the growth of
spending initially manifested in output
and employment, and only later in
inflation.”11 If the nominal interest
rate is cut when velocity is low, we will
observe a slow and gradual response
in output, employment, and prices
in our model economy. However,
the transmission of an expansionary
change in policy will look quite
different if velocity is high. In that
case, the increase in money supply
corresponding to a nominal interest
rate cut will quickly find its way into
circulation, yielding a more abrupt rise
in production and employment and
more quickly bringing about the full

This passage is drawn from Friedman’s testimony
to the House of Commons Select Committee in
1979; for the full text, see the 1980 reference to
Friedman.

www.philadelphiafed.org

implied rise in inflation.
By extending the New Keynesian
model to correct its prediction
regarding the liquidity effect, we
have arrived at a richer setting where
movements in the velocity of money
over time themselves feed back
through the economy to influence
how much and for how long changes
in monetary policy affect real activity.
As a result, our expanded theory
suggests that central bankers must be
attentive to more than just the change
in the nominal interest rate and a
simple Phillips curve relationship in
considering the effects of a change
in policy. They must also take into
account the ways in which velocity will
affect the transmission of monetary
policy. Since velocity is, in part,
determined by individuals’ bank
account balances, these balances
become relevant as we anticipate the
consequences of a policy change.
Moreover, our theory suggests that
we need to know something about
individuals’ willingness to alter their
money spending rates over time, since

www.philadelphiafed.org

this too will influence how velocity
responds to a change in the growth
rate of the money supply.
CONCLUSION
Economists use New Keynesian
models to study how short-term
nonneutralities allow monetary policy
to affect real economic activity. The
basic New Keynesian model explains
how changes in money supply can
yield temporary changes in output and
employment. However, it does not
explain why nominal interest rates fall
when the central bank increases the
money supply through an open market
operation. We have discussed an
extension of the model that corrects
this problem by introducing an explicit
transactions role for money and taking
into account the fact that individuals
adjust their portfolios of bonds and
money infrequently.
This more complex model
reconciling the New Keynesian
theory with a liquidity effect exhibits
important changes in the velocity
of money over time. These changes

vary from one economy to another
depending on how much money
individuals need to hold against their
coming spending and depending on
how willing they are to alter their
money savings patterns in response
to changes in aggregate conditions.
Our theory predicts that the effects
of changes in monetary policy will
depend on both the average velocity in
an economy as well as its movements
over time. Thus, to anticipate the
effects of a particular change in policy,
we need to be able to predict how
velocity will evolve in response to the
change in the nominal interest rate.
On balance, when we extend our
standard model to achieve greater
realism with regard to interest rate
movements, we find that monetary
policy becomes a more complicated
exercise than we may have thought
and that it cannot be well understood
without explicit attention to the
determinants underlying the
overall demand for money balances
throughout the economy. BR

Business Review Q1 2009 27

REFERENCES

Alvarez, Fernando, Andrew Atkeson, and
Patrick Kehoe. “Money, Interest Rates
and Exchange Rates with Endogenously
Segmented Markets,” Journal of Political
Economy, 110:1 (2002), pp. 73-112.
Barro, Robert J. Macroeconomics, 5th Edition.
Cambridge, MA: The MIT Press, 1997.
Carpenter, Seth, and Selva Demiralp.
“The Liquidity Effect in the Federal Funds
Market: Evidence from Daily Open Market
Operations,” Journal of Money, Credit, and
Banking, 38:4 (2006), pp. 901-20.
Christiano, Lawrence J., Martin Eichenbaum,
and Charles L. Evans. “Monetary Policy
Shocks: What Have We Learned and to
What End?” In Michael Woodford and John
B. Taylor, eds., Handbook of Macroeconomics
IA. Amsterdam: Elsevier Science, 1999.
Cochrane, John H. “The Return of the
Liquidity Effect: A Study of the Short-Run
Relation between Money Growth and
Interest Rates,” Journal of Business and
Economic Statistics, 7:1 (1989), pp. 75-83.
Croushore, Dean. Money and Banking: A
Policy-Oriented Approach. Boston: Houghton
Mifflin Company, 2006.
Dupor, Bill, Jing Han, and Yi-Chan Tsai.
“What Do Technology Shocks Tell Us About
the New Keynesian Paradigm?,” Ohio State
University Working Paper (2007).

28 Q1 2009 Business Review

Friedman, Milton. “The Role of Monetary
Policy,” American Economic Review, 58:1
(1968), pp. 1-17.

Mishkin, Frederic S. The Economics of Money,
Banking, and Financial Markets, 8th edition.
Addison Wesley, 2006.

Friedman, Milton. “Memorandum to U.K.
Treasury and Civil Service Committee
Regarding ‘Enquiry into Monetary Policy’,”
House of Commons, United Kingdom (July
1980).

Phillips, A. W. (1958). “The Relationship
between Unemployment and the Rate of
Change of Money Wages in the United
Kingdom 1861-1957,” Economica, 25 (1958),
pp. 283-99.

Kerr, William, and Robert G. King. “Limits
on Interest Rules in the IS Model,” Federal
Reserve Bank of Richmond Quarterly Review,
82:2 (1996), pp. 47-75.

Samuelson, Paul A., and Robert M. Solow.
“Analytical Aspects of Anti-Inflation Policy,”
American Economic Review Papers and
Proceedings, 50:2 (1960), pp. 177-94.

Khan, Aubhik, and Julia K. Thomas.
“Inflation and Interest Rates with Endogenous
Market Segmentation,” Federal Reserve
Bank of Philadelphia Working Paper 07-1
(2007).

Uhlig, Harald. “What Are the Effects of
Monetary Policy on Output? Results from an
Agnostic Identification Procedure,” Journal
of Monetary Economics, 52:2, pp. 381-419
(March 2005).

King, Robert G., and Julia K. Thomas.
“Breaking the New Keynesian Dichotomy:
Asset Market Segmentation and the
Monetary Transmission Mechanism,”
Working Paper (2007).

Woodford, Michael. “How Important Is
Money in the Conduct of Monetary Policy?,”
NBER Working Paper 13335 (2007).

King, Robert G., and Mark W. Watson.
“Money, Prices, Interest Rates and the
Business Cycle,” Review of Economics and
Statistics, 78:1 (1996), pp. 35-53.
McCallum, Bennett T., and Edward M.
Nelson. “An Optimizing IS-LM Specification
for Monetary Policy and Business Cycle
Analysis,” Journal of Money, Credit, and
Banking, 31:3 (1999), pp. 296-316.

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/.

FIRM DYNAMICS, PRIVATE INFORMATION, AND THE GENERATION OF NEW
TECHNOLOGY
The authors present a theory of spinoffs
in which the key ingredient is the originator’s
private information concerning the quality
of his new idea. Because quality is privately
observed, by the standard adverse-selection
logic, the market can at best offer a price that
reflects the average quality of ideas sold. This
gives the holders of above-average-quality ideas
the incentive to spin off. The authors show that
only workers with very good ideas decide to spin
off, while workers with mediocre ideas sell them.
Entrepreneurs of existing firms pay a price for
the ideas sold in the market that implies zero
expected profits for them. Hence, firms’ project
selection is independent of firm size, which,
under some additional assumptions, leads to
scale-independent growth. The entry and growth
process of firms leads to invariant firm-size
distributions that resemble the ones for the U.S.
economy and most of its individual industries.
Working Paper 08-26, “Spinoffs and the
Market for Ideas,” Satyajit Chatterjee, Federal
Reserve Bank of Philadelphia, and Esteban RossiHansberg, Princeton University, and Visiting
Scholar, Federal Reserve Bank of Philadelphia
TESTING FOR DATA RATIONALITY
Rationality of early release data is typically
tested using linear regressions. Thus, failure to

www.philadelphiafed.org

reject the null does not rule out the possibility
of nonlinear dependence. This paper proposes
two tests that instead have power against generic
nonlinear alternatives. A Monte Carlo study
shows that the suggested tests have good finite
sample properties. Additionally, the authors
carry out an empirical illustration using a realtime data set for money, output, and prices.
Overall, they find strong evidence against data
rationality. Interestingly, for money stock, the
null is not rejected by linear tests but is rejected
by the authors’ tests.
Working Paper 08-27, “Information in the
Revision Process of Real-Time Data Sets,” Valentina
Corradi, University of Warwick; Andres Fernandez,
Rutgers University and Universidad de Los Andes;
and Norman Swanson, Rutgers University,
and Visiting Scholar, Federal Reserve Bank of
Philadelphia
NONRESPONSE BIAS IN CPI MEASURES
FOR RENTS
Until the end of 1977, the U.S. consumer
price index for rents tended to omit rent
increases when units had a change of tenants
or were vacant, biasing inflation estimates
downward. Beginning in 1978, the Bureau of
Labor Statistics (BLS) implemented a series
of methodological changes that reduced this
nonresponse bias, but substantial bias remained
until 1985. The authors set up a model of
nonresponse bias, parameterize it, and test it

Business Review Q1 2009 29

using a BLS micro-data set for rents. From 1940 to 1985,
the official BLS CPI-W price index for tenant rents rose
3.6 percent annually; the authors argue that it should have
risen 5.0 percent annually. Rents in 1940 should be only half
as much as their official relative price; this has important
consequences for historical measures of rent-house-price
ratios and for the growth of real consumption. (Revision
forthcoming in Review of Economics and Statistics.)
Working Paper 08-28, “Rents Have Been Rising, Not
Falling, in the Postwar Period,” Theodore Crone, Swarthmore
College; Leonard I. Nakamura, Federal Reserve Bank of
Philadelphia; and Richard Voith, Econsult Corporation
DESIGNING MONETARY POLICY FOR
THE EURO AREA
In this paper, the authors aim to design a monetary
policy for the euro area that is robust to the high degree
of model uncertainty at the start of monetary union and
allows for learning about model probabilities. To this end,
they compare and ultimately combine Bayesian and worstcase analysis using four reference models estimated with
pre-EMU synthetic data. The authors start by computing
the cost of insurance against model uncertainty, that is,
the relative performance of worst-case or minimax policy
versus Bayesian policy. While maximum insurance comes
at moderate costs, they highlight three shortcomings of
this worst-case insurance policy: (i) prior beliefs that would
rationalize it from a Bayesian perspective indicate that such
insurance is strongly oriented toward the model with highest
baseline losses; (ii) the minimax policy is not as tolerant
of small perturbations of policy parameters as the Bayesian
policy; and (iii) the minimax policy offers no avenue for
incorporating posterior model probabilities derived from data
available since monetary union. Thus, the authors propose
preferences for robust policy design that reflect a mixture
of the Bayesian and minimax approaches. They show how
the incoming EMU data may then be used to update model
probabilities and investigate the implications for policy.
Working Paper 08-29, “Insurance Policies for Monetary
Policy in the Euro Area,” Keith Kuester, Federal Reserve Bank
of Philadelphia, and Volker Wieland, Goethe University of
Frankfurt

30 Q1 2009 Business Review

CHOOSING THE OPTIMAL
MONETARY POLICY INSTRUMENT
Currently there is a growing literature exploring the
features of optimal monetary policy in New Keynesian
models under both commitment and discretion. This
literature usually solves for the optimal allocations that are
consistent with a rational expectations market equilibrium,
but it does not study how the policy can be implemented
given the available policy instruments. Recently, however,
King and Wolman (2004) have shown that a time-consistent
policy cannot be implemented through the control of
nominal money balances. In particular, they find that
equilibria are not unique under a money stock regime.
The authors of this paper find that King and Wolman’s
conclusion of non-uniqueness of Markov-perfect equilibria
is sensitive to the instrument of choice. Surprisingly, if,
instead, the monetary authority chooses the nominal interest
rate, there exists a unique Markov-perfect equilibrium. The
authors then investigate under what conditions a timeconsistent planner can implement the optimal allocation by
just announcing his policy rule in a decentralized setting.
Working Paper 08-30, “On the Implementation of MarkovPerfect Interest Rate and Money Supply Rules: Global and
Local Uniqueness,” Michael Dotsey, Federal Reserve Bank of
Philadelphia, and Andreas Hornstein, Federal Reserve Bank of
Richmond
BUSINESS CYCLE COSTS AND
FLUCTUATIONS IN UNEMPLOYMENT
This paper develops a real business cycle model
with labor market search and matching frictions, which
endogenously links both the cyclical fluctuations and the
mean level of unemployment to the aggregate business cycle
risk. The key result of the paper is that business cycles are
costly for all consumers, regardless of their wealth, yet that
unemployment fluctuations themselves are not the source of
these costs. Rather fluctuations over the cycle induce higher
average unemployment rates as employment is non-linear
in job-finding rates and past unemployment. The authors
first show this result analytically in special cases. They then
calibrate a general equilibrium model with risk-averse assetholding and liquidity-constrained workers to U.S. data. Also

www.philadelphiafed.org

under these more general circumstances, business cycles
mean higher unemployment for all workers. The ensuing
costs of cycles rise further for liquidity-constrained agents
when replacement rates are lower or when workers’ skills
depend on the length of (un)employment spells.
Working Paper 08-31, “The (Un)Importance of
Unemployment Fluctuations for Welfare,” Philip Jung,
Mannheim University, and Keith Kuester, Federal Reserve Bank
of Philadelphia
DOES RESTRICTING ACCESS TO EXPENSIVE
CREDIT HARM CONSUMERS?
Many policymakers and some behavioral models hold
that restricting access to expensive credit helps consumers by
preventing overborrowing. The author examines some shortrun effects of restricting access, using household panel survey
data on payday loan users collected around the imposition
of binding restrictions on payday loan terms in Oregon.
The results suggest that borrowing fell in Oregon relative to
Washington, with former payday loan users shifting partially
into plausibly inferior substitutes. Additional evidence
suggests that restricting access caused deterioration in the
overall financial condition of the Oregon households. The
results suggest that restricting access to expensive credit
harms consumers, on average.
Working Paper 08-32, “Restricting Consumer Credit
Access: Household Survey Evidence on Effects Around the
Oregon Rate Cap,” Jonathan Zinman, Dartmouth College, and
Visiting Scholar, Federal Reserve Bank of Philadelphia
IS EFFICIENCY IMPORTANT IN
UNDERSTANDING INSTITUTIONAL
DEVELOPMENT?
Are efficiency considerations important for
understanding differences in the development of
institutions? The authors model institutional quality as the
degree to which obligations associated with exchanging
capital can be enforced. Establishing a positive level of
enforcement requires an aggregate investment of capital
that is no longer available for production. When capital
endowments are more unequally distributed, the bigger
dispersion in marginal products makes it optimal to invest
more resources in enforcement. The optimal allocation of

www.philadelphiafed.org

the institutional cost across agents is not monotonic and
entails a redistribution of endowments before production
begins. Investing in enforcement benefits primarily agents
at the bottom of the endowment distribution and leads to a
reduction in consumption and income inequality. Efficiency,
redistribution, and the quality of institutions are thus
intricately linked and should be studied jointly.
Working Paper 08-33, “Efficient Institutions,” Thorsten
Koeppl, Queen’s University; Cyril Monnet, Federal Reserve
Bank of Philadelphia; and Erwan Quintin, Federal Reserve
Bank of Dallas
LABOR MARKETS’ ROLE IN EURO
AREA MONETARY POLICY
In this paper, the authors explore the role of labor markets for monetary policy in the euro area in a New Keynesian
model in which labor markets are characterized by search
and matching frictions. They first investigate to which
extent a more flexible labor market would alter the business
cycle behavior and the transmission of monetary policy.
They find that while a lower degree of wage rigidity makes
monetary policy more effective, i.e., a monetary policy shock
transmits faster onto inflation, the importance of other labor
market rigidities for the transmission of shocks is rather
limited. Second, having estimated the model by Bayesian
techniques, the authors analyze to which extent labor market
shocks, such as disturbances in the vacancy posting process,
shocks to the separation rate, and variations in bargaining
power, are important determinants of business cycle fluctuations. Their results point primarily towards disturbances in
the bargaining process as a significant contributor to inflation and output fluctuations. In sum, the paper supports current central bank practice which appears to put considerable
effort into monitoring euro area wage dynamics and which
appears to treat some of the other labor market information
as less important for monetary policy.
Working Paper 09-1 “The Role of Labor Markets for Euro
Area Monetary Policy,” Kai Christoffel, European Central
Bank, Frankfurt; Keith Kuester, Federal Reserve Bank of Philadelphia; and Tobias Linzert, European Central Bank, Frankfurt

Business Review Q1 2009 31

LONG-TERM SOVEREIGN DEBT:
ARGENTINA AS A TEST CASE
The authors present a novel and tractable model of longterm sovereign debt. They make two sets of contributions.
First, on the substantive side, using Argentina as a test case
they show that unlike one-period debt models, their model of
long-term sovereign debt is capable of accounting for the average spread, the average default frequency, and the average
debt-to-output ratio of Argentina over the 1991-2001 period
without any deterioration in the model’s ability to account
for Argentina’s cyclical facts. Using their calibrated model
the authors determine what Argentina’s debt, default frequency, and welfare would have been if Argentina had issued
only short-term debt. Second, on the methodological side,
the authors advance the theory of sovereign debt begun in
Eaton and Gersovitz (1981) by establishing the existence of
an equilibrium pricing function for long-term sovereign debt
and by providing a fairly complete set of characterization
results regarding equilibrium default and borrowing behavior.
In addition, they identify and solve a computational problem
associated with pricing long-term unsecured debt that stems
from nonconvexities introduced by the possibility of default.
Working Paper 09-2, “Maturity, Indebtedness, and Default
Risk,” Satyajit Chatterjee, Federal Reserve Bank of Philadelphia,
Burcu Eyigungor, Koç University

32 Q1 2009 Business Review

WHOLESALE FUNDS, MARKET DISCIPLINE,
AND LIQUIDITY RISKS
Commercial banks increasingly use short-term wholesale
funds to supplement traditional retail deposits. The existing literature mainly points to the “bright side” of wholesale
funding: sophisticated financiers can monitor banks, disciplining bad ones but refinancing solvent ones. This paper
models a “dark side” of wholesale funding. In an environment with a costless but imperfect signal on bank project
quality (e.g., credit ratings, performance of peers), short-term
wholesale financiers have lower incentives to conduct costly
information acquisition and instead may withdraw based
on negative but noisy public signals, triggering inefficient
liquidations. The authors show that the “dark side” of
wholesale funding dominates the “bright side” when bank
assets are more arm’s length and tradable (leading to more
relevant public signals and lower liquidation costs): precisely
the attributes of a banking sector with securitizations and
risk transfers. The results shed light on the recent financial
turmoil, explaining why some wholesale financiers did not
provide market discipline ex-ante and exacerbated liquidity
risks ex-post.
Working Paper 09-3, “The Dark Side of Wholesale Funding,” Rocco Huang, Federal Reserve Bank of Philadelphia, and
Lev Ratnovski, International Monetary Fund

Full text of Business Review (Federal Reserve Bank of Philadelphia) : First Quarter 2009

FRASER