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Ups and Downs:
How Wages Change Over the Business Cycle
KEVIN X.D. HUANG
T
he cyclical behavior of real wages — wages
adjusted for inflation — has changed over
time. Before World War II, real wages in the
U.S. were countercyclical: They rose during
recessions and fell during expansions. Since the war,
however, wages have become procyclical, falling during
recessions and rising during expansions. One standard
explanation is that economic shocks shifted from the
demand side of the economy prewar to the supply side
postwar. In this article, Kevin Huang offers evidence
of an alternative explanation: the increased role that
intermediate goods play in the production process in the
postwar era.
Modern economies experience
recurrent fluctuations in business activity. As output and employment fall
in recessions and busts and rise in recoveries and booms, other variables of
economic significance also go through
lows and highs.
One such variable is real wages.
Generally speaking, real wages are
simply wages adjusted for changes in
inflation.1 For a working family, real
wages provide a source of real income,
but this income must be earned by
giving up valuable leisure time. For a
business entity that must hire workers
to carry out its operations, real wages
constitute part of the firm’s real production costs. The way in which real
1
Kevin Huang is an
economic advisor
and economist
in the Research
Department of
the Philadelphia
Fed. This article
is available free of
charge at www.philadelphiafed.org/econ/br/
index.html.
www.philadelphiafed.org
In reality, there is more than one measure of
inflation. In this article, our use of the term
real wages refers to wages adjusted for a cost-ofliving index such as the consumer price index
(CPI). The CPI measures the cost of labor in
terms of a basket of goods consumed by a worker. An alternative notion of real wages is wages
adjusted for the wholesale price index or the
producer price index (PPI). The PPI measures
the cost of labor in terms of the units of goods
produced by a worker. The two ideas are often
used interchangeably. To tell the story here, I
will follow this tradition of not distinguishing
between these two measurements of real wages.
wages fluctuate over business cycles
has important implications for both
households and firms.
The cyclical behavior of real wages
has changed over time. In the prewar
period (1919 to 1939), real wages in
the United States were countercyclical: That is, real wages went up during
recessions and fell during expansions.
Since World War II, real wages have
become procyclical: They fall during
recessions and rise during expansions.
What might have caused this
change in the cyclical behavior of real
wages? One explanation attributes the
change to a shift from disturbances
(which economists call shocks) on the
demand side of the economy during
the prewar period to disturbances on
the supply side in the postwar era.
Generally speaking, shocks are
unanticipated changes in variables,
such as extreme environmental conditions (severe weather, hurricanes,
earthquakes, etc.), unanticipated
changes in monetary and fiscal policy,
and events that alter the world price of
energy. Typical examples of demand
shocks include unexpected changes
in the demand for money, unexpected
changes in the money supply 2 or interest rates (monetary policy shocks),
unexpected changes in government
spending (fiscal policy shocks), financial crises, exchange rate disturbances,
and sudden changes in households’
tastes or preferences. Examples of supply shocks include sudden disruptions
in oil supply, discoveries of oil reserves,
and technological innovations.
2
The money supply is the quantity of money
available in the economy with which to purchase goods and services.
Business Review Q2 2006 1
Many economists have argued
that demand shocks were more important in the prewar period, especially
during the Great Depression, an episode in which unexpected changes in
the money supply and financial crises
(such as bank failures) played a dominant role. Supply shocks, on the other
hand, are more important in the postwar period, especially after the 1970s,
when several large oil-price shocks hit
the economy.
But trying to explain the change
in the cyclical behavior of real wages
by pointing to changes in shocks hitting the economy is not appealing
because it does not capture all of the
empirical facts. To provide a convincing account of this switch in real-wage
cyclicality, we must look at another
change in the U.S. economy between
the prewar and postwar periods, namely, the increased role of intermediate
goods in the production process. For
example, in the postwar period, the
production of final consumption goods
— such as home appliances, consumer
electronics, and, more recently, computers — requires more intermediate
processing, involving greater shares of
more processed intermediate inputs,
such as pressed steel, plastic, glass, microchips, and processors, and smaller
shares of labor and capital.3
As I will discuss, it is likely that
the switch in real-wage cyclicality
3
In a production economy, goods produced in
one sector or industry may be used as intermediate inputs by the same or different sectors or
industries for producing goods that may, in turn,
be used as intermediate inputs by the same or
different sectors or industries, etc., before a final
consumption good is produced. Such an inputsupplier/output-demander relationship among
sectors or industries is usually referred to as an
input-output structure. The Input-Output Table
of the Bureau of Economic Analysis (BEA)
summarizes the U.S. economy’s input-output
structure. As Robert J. Gordon pictures it, “The
gigantic matrix represents the real world, full of
heterogeneous firms enmeshed in a web of intricate supplier-demander relationships.”
2 Q2 2006 Business Review
between real wages and output was significantly negative (-0.444), suggesting
that real wages moved strongly against
real output in this period. Postwar, the
correlation between real wages and
output is significantly positive (0.381)
between 1945 and 1971, and it rises
further (to 0.503) between 1972 and
1992. Thus, real wages co-move closely
with output after World War II. In a
1996 article, Christopher Hanes provides evidence of this change in the
behavior of real wages.
Another insightful account is
provided by Ben Bernanke and James
Powell, who examine the cyclical
property of real wages for the periods
1923 to 1939 and 1954 to 1982. They
arose from the increased share of intermediate goods in production.
REAL WAGES: FROM
COUNTERCYCLICAL TO
PROCYCLICAL
Real-wage cyclicality is gauged
by the statistical correlation between
real wages and output. This correlation measures how these two variables
co-vary over time. Correlations must
lie between -1 and 1: the closer the
correlation is to -1, the more the two
variables move in opposite directions.
The closer the correlation is to 1, the
more the two variables move in the
same direction.
Economists Susanto Basu and
Alan Taylor have computed the correlation between real wages and real
output for the prewar and postwar
periods (Figure 1).4 Their results show
that, in the prewar era, the correlation
4
Basu and Taylor used a statistical technique
to remove the long-term trends from the data
in order to focus on how the data behaved over
business cycles.
FIGURE 1
Real Wage and Real Output in the United States
(Deviations from Trend)
! " #$
Source: Basu and Taylor (1999a)
www.philadelphiafed.org
find a marked difference in the cyclical
behavior of real wages from the prewar
to the postwar period. Bernanke and
Powell’s study is important for another
reason. One could argue that the mix
of goods that households consume also
changed from the prewar period to
the postwar period, and the observed
switch in the cyclical behavior of real
wages could have simply reflected this
change. Studies using aggregate data
— that is, data for the economy as a
whole — cannot directly address this
issue. Instead, Bernanke and Powell
employ industry-level data that control
for the shift in the mix of goods. Yet
their finding is broadly consistent with
the evidence presented in Basu and
Taylor’s paper, which is based on aggregate data.5
In sum, the historical evidence
suggests a general pattern in the evolution of the cyclical behavior of real
wages from countercyclical during the
prewar period to procyclical in the
postwar era. In particular, the correlation between real wages and real
output has switched from significantly
negative prewar to significantly positive postwar.
SHIFT FROM DEMAND SHOCKS
TO SUPPLY SHOCKS: NOT A
CONVINCING STORY
Economic theory is essentially a
story about supply and demand. Business-cycle theory seeks to understand
how unexpected changes in supply or
demand generate cyclical fluctuations
of economic variables. As we’ve noted,
one explanation for the switch in realwage cyclicality is based on this shift
from demand shocks to supply shocks.
According to a well-known economic theory, the classic Keynesian
theory, demand shocks push prices and
output in the same direction, but they
do not immediately affect wages very
much, because wages are usually set in
advance.6 Consequently, real wages,
that is, wages adjusted for inflation,
move in the opposite direction from
output: Real wages rise when output
falls, since as output falls so do prices,
while wages are sticky, and vice versa.
According to another well-known economic theory, the real business-cycle
theory, how much workers get paid depends on their productivity, and supply
shocks generally mean that labor productivity — output per worker — and
6
Keynesian theory emphasizes the role of
demand shocks and wage contracts, that is,
agreements between unions and firms on the
level of wages firms will pay union workers over
a certain period.
output move in the same direction.7 As
a result, real wages and output move
together.
That real wages can respond
countercyclically to demand shocks but
procyclically to supply shocks might
lead one to conjecture that it is indeed
the shift from prewar demand shocks
to postwar supply shocks that explains
the shift in real-wage cyclicality. In
particular, the oil-price spikes that occurred in the 1970s are often viewed as
the main factor that led to procyclical
real wages during the postwar period.
However, this hypothesis is not
convincing for at least two reasons.
First, while empirical studies suggest
that oil-price shocks might have been
an important force contributing to
postwar business cycles in the U.S., a
7
Real business-cycle theory emphasizes the role
of supply shocks in the economy.
FIGURE 2
Oil Price Shocks in the Postwar Period
5
Other studies, such as the ones by Mark Bils;
Gary Solon, Robert Barsky, and Jonathan Parker; and Katharine Abraham and John Haltiwanger, provide corroborating evidence in support
of such a switch in the postwar era. Evidence
based on aggregate data is also provided in the
article by Finn Kydland and the one by Wouter
J. den Haan and Steven W. Sumner.
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Source: Haver Analytics (PPI for crude petroleum - not seasonally adjusted)
Business Review Q2 2006 3
study by Kevin Hoover and Stephen
Perez and another by Charles Fleischman note that the price of crude oil
remained relatively stable until 1973
(Figure 2). Yet, the correlation between
real wages and output had already
changed from a significant negative
value of -0.44 in the prewar period to a
significant positive value of 0.38 from
1945 to 1971, an era before the onset
of the major oil-price shocks in the
1970s.8 Indeed, as Christopher Hanes
shows, real wages remain procyclical
even if the period from December 1973
through June 1980 is excluded from
the postwar period. This suggests that
forces other than oil-price shocks must
have triggered the switch.
Second, in contrast to the prediction of the Keynesian theory, real
wages have responded differently to
demand shocks in the prewar period
than in the postwar period. In particular, the tightening of monetary
policy triggered a rise in real wages in
the prewar period, especially during
the Great Depression, but a fall in real
wages and output in the postwar period. For the prewar period, two studies by Barry Eichengreen and Jeffrey
Sachs and another by Bernanke and
Kevin Carey find that real wages were
countercyclical and that monetary
policy shocks were a central driving
force of this result. On the basis of
their finding, Bernanke and Carey
dismiss explanations of the relationship between output and real wages
during the period 1929 to 1936 that do
not involve monetary policy shocks.
Michael Bordo, Christopher Erceg, and
Charles Evans also present evidence
showing that monetary policy tightening led to an increase in real wages
during the downturn of 1929 to 1933
8
James D. Hamilton argues that oil shocks led
to some of the pre-1970 recessions in the U.S.,
but the cyclical effects of these shocks, as he
shows, became much stronger during the 1970s.
4 Q2 2006 Business Review
in the U.S. and that monetary policy
shocks accounted for between 50 and
70 percent of the decline in real GNP
at the Depression's trough in the first
quarter of 1933.
For the postwar period, a study
by Lawrence Christiano, Martin
Eichenbaum, and Charles Evans
and another by Edward Gamber and
Frederick Joutz find that monetary
policy shocks, and demand shocks in
general, tend to generate procyclical
real wages. Marvin Barth and
Even in the absence
of supply shocks, we
have seen a switch
from countercyclical
to procyclical real
wages.
Valerie Ramey also find evidence
of procyclical real wages following
contractionary monetary policy actions
in the postwar U.S. economy. This
reversed pattern in the cyclicality of
real wages driven solely by monetary
policy shocks is inconsistent with
a story that relies on a shift from
demand shocks to supply shocks.
Thus, even in the absence of
supply shocks, we have seen a switch
from countercyclical to procyclical real
wages. A convincing theory about this
switch in real-wage cyclicality needs to
hold up, even when demand shocks are
the sole driving force of business-cycle
booms and busts. Now, let’s turn to a
theory that emphasizes the role of a
change in the U.S. economic structure
over the course of the 20th century.
INTERMEDIATE GOODS:
INCREASING IMPORTANCE
IN PRODUCTION
The key part of this alternative
theory involves another major change
in the U.S. economy from the prewar
to the postwar period: a shift in the
mix of the types of inputs used in
production. As we know, production
of final consumption goods usually
requires several types of inputs: labor,
capital, and intermediate goods. The
historical change is that, in the postwar period, intermediate goods are
used more in the production of final
goods. In the prewar era, goods that
households consumed were relatively
less processed — typical prewar goods
include simple farm and fishery products and basic consumer durables like
hand tools, oil burners and heating
apparatus, and coal stoves and ranges
— and their production required
mostly primary inputs (labor, capital,
land, and coal). In the postwar period,
goods that households consume are
much more complex — typical postwar goods include more processed farm
and fishery products and increasingly
more sophisticated consumer durables
such as gas and electric appliances,
home electronics, and intricately made
cars and computers — and the production of such goods requires greater
shares of manufactured intermediate
inputs, which themselves are typically
more advanced goods.9
Several existing studies illustrate
the changes in the production of final
consumption goods and in the inputoutput structure from the prewar to
the postwar period. John Kendrick’s
classic work documents value added
(by labor and capital) and gross output (which is the sum of value added
and all intermediate inputs used in
9
Recall that intermediate goods are goods (and
services) that are purchased from other businesses and that are used up within the production period. Although my discussion focuses on
the role of increasing technological sophistication, the fact that the use of intermediate inputs
has been rising over time might also reflect
increased specialization of production, since, all
else constant, the greater the degree of vertical
integration, the lower is the proportion of intermediate goods purchased in total output.
www.philadelphiafed.org
production) for several key sectors in
the prewar period. Using this information, Zheng Liu, Louis Phaneuf, and
I show that the share of intermediate
inputs rose significantly in the postwar
period.10
Two historical studies by Christopher Hanes provide evidence that
the input-output structure has become
more sophisticated in the postwar period. His general finding is that typical
prewar goods were made of relatively
unfinished goods, while typical postwar goods involve more intermediate
processing before they enter the marketplace. Hanes reports that the share
of crude material inputs (such as farm,
fishery, and mineral products) in final
output in the United States fell significantly from the beginning of the 20th
century to the end of the 1960s. He
also reports that from the turn of the
20th century to 1986, the share of consumption expenditure on food (excluding restaurant meals) decreased significantly, while the share of consumer
durables, a category that includes
many complex goods such as automobiles, increased steadily over the same
period. The corroborating evidence
in the two studies by Basu and Taylor
lends further credence to the observation that intermediate goods make up
an increasingly larger share of total
U.S. output in the postwar period.
Other studies provide evidence
of the increased use of intermediate goods in production during the
postwar period. The work by Dale
Jorgenson, Frank Gollop, and Barbara
Fraumeni shows that from 1947 to
1979, intermediate goods account for
a large share of the revenue from total
manufacturing output in the U.S. and
they account for an even higher share
of manufacturing costs.11
To summarize, existing studies
lead us to conclude that there has been
a significant increase in the use of intermediate inputs in the U.S. economy
from prewar to postwar.
INTERMEDIATE INPUTS AND
THE SWITCH IN REAL-WAGE
CYCLICALITY
The story of the switch in the
cyclicality of real wages is built on
the following reasoning. Real wages
determine the amount of consumption
goods that a worker’s wages can buy.
The cheaper the good, the more of it
can be purchased with wages. How
Existing studies lead us to conclude that there
has been a significant increase in the use of
intermediate inputs in the U.S. economy from
prewar to postwar.
cheap the good is depends on how
much it costs to produce. The cost is
usually composed of three parts: cost
of capital, cost of intermediate inputs,
and wages.
Capital, such as plant and equipment, can last for a relatively long time
before depreciating completely. The
value of capital depends on what the
capital is used for during its lifetime.
In a capitalistic world, this value is
determined in the asset market, which
usually responds quickly to changes in
current and expected economic conditions. As a result, the cost of capital
that a firm incurs in order to carry out
its production plans varies a lot over
business-cycle booms and busts.
11
10
Our study shows that the share of intermediate goods in U.S. production was 0.4 prewar and
0.7 postwar.
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In contrast, since making a sophisticated intermediate good typically
requires some advance planning, a
firm that needs to use an intermediate
good often must lock into a contract
that specifies a purchase price long
before the good is delivered. The supplier of the intermediate good often
needs to lock into contracts with its
own suppliers of other intermediate
goods required for producing the first
good. The business world is full of such
sophisticated input-output relationships. For instance, the production
of a computer requires many types of
intermediate inputs, such as a monitor,
a motherboard, a hard drive, and an
Susanto Basu’s estimate of the cost share is
about 0.80. The revenue and cost shares of intermediate inputs calculated by Huang, Liu, and
Phaneuf for the postwar period, based on data
in the BEA’s 1997 Benchmark Input-Output
Tables, are about 0.7.
operating system. Producing a monitor involves other intermediate inputs,
such as plastic, glass, and electronic
components, and making a motherboard requires microchips, processors,
and so forth. Such a business-to-business supply-chain network is a popular
business model in many other sectors,
such as the automobile industry.
As Robert Gordon describes, the
intricate supplier-demander relationships among many firms at many different stages of processing imply that
a contractual price between two firms
can also matter to other firms involved
in the production process since they
may be those other firms’ direct or
indirect suppliers or demanders. As a
result, the two firms may be reluctant
to change their contractual price even
if it is about time to renegotiate their
contract unless they know those other
firms will do so as well. Since it is difficult for all firms in this gigantic web
of complex supplier-demander rela-
Business Review Q2 2006 5
tionships to synchronize the timing of
their contract renegotiations, as demonstrated by many empirical studies
surveyed in John B. Taylor’s article, the
price of an intermediate good can stay
sticky much longer than the length of
a single contract and typically does
not immediately respond to changes in
economic conditions.12
Firms often sign wage contracts
with workers as well, and according
to Taylor’s survey, the length of wage
contracts for labor, on average, is about
the same as the length of price contracts for intermediate goods (about
one year). Yet, the renegotiation of
a wage contract is a relatively simple
matter that usually involves only the
employee and the employer. Thus, the
wage of a worker typically stays sticky
just as long as the length of a single
contract and often responds somewhat
to changing economic conditions.
Generally speaking, the cost of
capital is most responsive to changes in
economic conditions, next are wages,
and the contractual costs of intermediate inputs are least responsive.
With this in mind, we are ready
to tell the story. During recoveries and
booms, when the level of output rises
and firms demand more capital, labor,
and intermediate inputs, the cost of
capital rises quickly. However, because
of contractual obligations, wages rise
slowly, and the contractual cost of
intermediate inputs does not change
much. If the share of intermediate
inputs in production is small, a firm’s
production costs would rise more than
its workers’ wages because the firm is
12
This is not to be confused with the notion
that the spot price (the price for a good that
is paid for now and for which delivery is made
now) of certain components of intermediate
inputs — such as oil — is quite sensitive to
business cycles. What I have emphasized here
is that pricing of products that use such inputs
— including oil — is often based on contractual
costs rather than the spot price.
6 Q2 2006 Business Review
paying more for capital and using more
of it in production. The firm would
pass on the increase in its production
costs in the form of a higher price for
its product.13 In consequence, because
workers pay more for the firm’s final
good, their real wages fall. The situation is quite different if the share of
intermediate inputs in production is
large. With a large share of intermediate inputs, a firm’s production costs
would rise less than its workers’ wages
because the contractual cost of the
intermediate inputs is unchanged. As a
result, because workers pay less for the
firm’s final good, their real wages rise.
The analysis for periods of recessions and busts is symmetric. When
intermediate goods make up a small
share of the production process, real
wages tend to move in the opposite
direction from output (real wages are
countercyclical). When intermediate
goods constitute a large share of the
production process, real wages tend to
move in the same direction as output
(real wages are procyclical).
Liu, Phaneuf, and I demonstrate
how the cyclical behavior of real wages
can change when the share of intermediate inputs rises. We show that
as the share of intermediate inputs in
production grows from its prewar value
(0.4) to its postwar value (0.7), the correlation between real wages and output
switches from a significantly negative
number (-0.498), close to its prewar
value, to a significantly positive number (0.464), close to its postwar value.14
13
The argument here ignores cyclical movements in profit margins and assumes that price
and cost move in proportion.
14
To focus on how the data behaved over the
business cycles, these authors applied the same
statistical technique that Basu and Taylor used
to remove the long-term trends from the data
and computed the correlations based on the
de-trended data.
The Link Holds at Other Levels. The link between the cyclical
behavior of real wages and the share of
intermediate goods holds not just for
the U.S. economy as a whole; it also
holds at the sector or industry level. As
noted by Christiano, Eichenbaum, and
Evans, in the postwar U.S. economy,
real wages are more procyclical in the
manufacturing sector than they are in
the economy as a whole. Incidentally,
in the postwar era, the ratio of total
sales to GDP is greater in the manufacturing sector than in the economy
as a whole, indicating that the manufacturing sector uses a greater share of
intermediate inputs in production than
other sectors (see the table).15 The
findings about the importance of intermediate goods presented in this article
lead to a natural conjecture that the
differing shares of intermediate goods
across sectors/industries may account
for the observed differences in the behavior of real wages at the sectoral and
industrial levels in the postwar U.S.
economy.
Although the analysis in this
article is drawn from the U.S. experience, the general insight laid out here
linking real-wage cyclicality to the
use of intermediate goods may have
implications for other economies. For
example, the analysis suggests that
real wages can be more procyclical in
more developed countries than in less
developed ones, since production in
the more developed economies generally uses greater shares of intermediate goods. Thus, the implications for
15
The U.S. input-output table has gone through
a number of redefinitions by the U.S. Bureau of
Economic Analysis. I made the necessary regroupings to make the classifications of sectors
and industries presented in the table consistent
across the three selected years. The shares
reported in the table are shares in revenue. To
get shares in cost, one needs to adjust for profit
margins in the corresponding sectors.
www.philadelphiafed.org
households and firms can also differ
across countries in different stages of
development.
CONCLUSION
Over the past century, the U.S.
economy has seen a significant change
in the cyclicality of real wages and
in the share of intermediate goods
used in the production process. This
article explains the link between the
two: It’s likely that the switch in realwage cyclicality from countercyclical
in the prewar period to procyclical in
the postwar era can be attributed to
the increased use of more processed
intermediate goods in production. This
shift in the cyclicality of real wages,
the increased use of intermediate
goods, and, more important, the link
investigated here have implications for
households and firms. BR
TABLE
Share of Intermediate Inputs in the U.S
by Sector
1987
1997
2003
Construction
0.5297
0.5705
0.4938
Manufacturing
0.5923
0.6765
0.6478
Trade
0.2998
0.3614
0.2826
and Utilities
0.4563
0.4773
0.4849
Financial Services
0.3214
0.3018
0.3356
Nonfinancial Services
0.4329
0.3640
0.3970
Transportation, Communication,
Source: BEA Input-Output Table. The shares reported in the table are shares in revenue.
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Business Review Q2 2006 7
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(December 1996), pp. 837-61.
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“Macroeconomics of International Price
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in the G7,” Working Paper, University of
California, San Diego (2002).
Eichengreen, Barry, and Jeffrey Sachs. “Exchange Rates and Economic Recovery in
the 1930s,” Journal of Economic History, 45
(1985), pp. 925-46.
Eichengreen, Barry, and Jeffrey Sachs.
“Competitive Devaluation and the Great
Depression: A Theoretical Reassessment,”
Economic Letters, 22 (1986), pp. 67-71.
Feenstra, Robert C. “Integration of Trade
and Disintegration of Production in the
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Fleischman, Charles A. “The Causes of
Business Cycles and the Cyclicality of Real
Wages,” Working Paper, Federal Reserve
Board of Governors, Washington, D.C.
(October 1999).
Gamber, Edward N., and Frederick L.
Joutz. “The Dynamic Effects of Aggregate
Demand and Supply Disturbances: Comment,” American Economic Review, 85
(1993), pp. 1387-93.
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“Post Hoc Ergo Propter Hoc Once More:
An Evaluation of “Does Monetary Policy
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75-96.
Hummels, David L., Dana Rapoport, and
Kei-Mu Yi. “Vertical Specialization and the
Changing Nature of World Trade,” Federal
Reserve Bank of New York Economic Policy
Review 4 (1998), pp. 79-99.
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Barbara M. Fraumeni. Productivity and U.S.
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Aggregate Labor Market Fluctuations,” in
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www.philadelphiafed.org
Using Collateral to Secure Loans
BY YARON LEITNER
M
any businesses post collateral as security for
loans. Collateral protects the lender if the
borrower defaults. However, not all borrowers
put up collateral when taking out loans.
There’s even some evidence that loans with collateral
attached may be riskier for lenders. Why is collateral
used sometimes, but not others? And why does collateral
potentially involve more risk? In this article, Yaron
Leitner considers these questions. He looks at some of the
explanations for using collateral, focusing on its benefits
and drawbacks.
Collateral is a contractual device
used by borrowers and lenders around
the world. Collateral has also been
around for a long time. In one famous
example, a pound of Antonio’s flesh
collateralized Shylock’s loan to Bassanio in Shakespeare’s “Merchant of
Venice.” Generally, the term collateral
refers to assets pledged by a borrower
to secure a loan. The lender can seize
these assets if the borrower does not
make the agreed-upon payments on
the loan, so the lender has some protection if the borrower defaults. Therefore, the use of collateral can make
it easier for firms to obtain loans to
Yaron Leitner is a
senior economist
in the Research
Department of
the Philadelphia
Fed. This article
is available free
of charge at www.
philadelphiafed.org/
econ/br/index.html.
www.philadelphiafed.org
finance their investments.
Understanding collateral is important because it is a characteristic
feature of bank loans, which help to
channel resources to their best use.1
While early research focused mainly
on how collateral affects the borrower’s
behavior, recent research has also
incorporated lenders’ behavior, for
example, how collateral affects lenders’
incentives to take care in evaluating
a business’s prospects. Economists
have also examined the relationship
between collateral and risk, empirically
verifying bankers’ common wisdom
that collateralized loans are riskier
for the bank than noncollateralized
loans. To a significant extent, recent
1
According to the Federal Reserve’s Surveys
of Terms of Business Lending, more than 50
percent of the value of all commercial and
industrial loans made by domestic banks in the
U.S. is currently secured by collateral (based on
the surveys for February 2005, May 2005, and
August 2005).
theoretical work on collateral has been
driven by economists’ desire to provide
explanations for the use of collateral
that are consistent with this empirical
finding among others.
COLLATERAL AND
BORROWERS’ INCENTIVES
We start by focusing on the way
collateral affects a borrower’s incentives to ensure the business’s success.
Consider a loan contract where an
individual borrows some money to
start a new business. The success of
the business often depends on actions
the borrower takes after the loan is
signed, for example, the way he allocates money among different activities,
and the effort he expends in choosing
low-cost/high-value alternatives. Ideally, the loan contract would specify all
of these actions. However, in many
cases, this is impossible because some
of these actions may not be observable
to a third party or even to the lender;
for example, it may be difficult for the
bank to argue in court that a borrower
did not exert enough effort in choosing
the best alternatives.2
If the borrower and lender had
the same objectives, the fact that the
borrower’s actions are not observable
to others would not be a problem.
2
The finance and economics literature refers
to this hidden action problem as moral hazard.
This term, which was coined in the insurance
industry, captures the idea that an individual
who has insurance is less likely to take actions
to avoid problems. For example, if you have
comprehensive car insurance with no deductibles, you may be less careful about locking your
car or parking it in a safe spot. More broadly,
the term moral hazard refers to any contracting
problem where the actions of one party cannot
be observed by others.
Business Review Q2 2006 9
The borrower would take the actions
that are best for him, and these actions would also be best for the lender.
However, in practice, the borrower and
lender often have different objectives.
The lender wants to make sure that
the loan is paid in full; the borrower
cares about the profits left after paying the loan. The borrower may also
care about some perks that benefit
him, but not the business as a whole;
for example, the borrower may enjoy
expensive business meals, a private jet,
and so forth.
Consider the following as an example of a conflict of interests between
borrowers and lenders: A business
can either succeed or fail. If it fails,
the loan cannot be repaid, and both
the borrower and lender get nothing.
If the business succeeds, the loan is
paid in full, and the borrower is left
with the rest of the profits. Now suppose that the borrower can take an
action that has the following effect:
If the business is a success, the action
increases profits; however, the action
reduces the chances that the business
will succeed.3 The borrower may be
happy to take such an action because it
increases the money left for him — remember, he gets paid only if the business succeeds.4 The lender, however, is
unhappy because he is less likely to get
his money back.
Anticipating the conflict of interests above, the lender may demand a
higher interest rate on the loan, and
in some cases, he may not lend at all.
Of course, the borrower can promise
to take some agreed-upon actions
3
An example of such an action is a business
expansion. If the business succeeds, there are
more profits. But because the firm spends resources on the expansion, it has less to spend
cultivating its old customers.
4
Of course, many businessmen and -women are
motivated by ethical concerns and their reputations. For the most part, we ignore these motivations to highlight the role of collateral.
10 Q2 2006 Business Review
according to the lender’s wishes, but
when these actions cannot be verified
in court, such a promise is just cheap
talk.
Collateral May Induce the Borrower to Exert Effort… Suppose the
borrower posts his house or some of his
business assets as collateral to secure
the loan. This may induce him to put
more effort into ensuring the business
succeeds because if the business fails,
the borrower loses his collateral. In
other words, collateral can give the
borrower the incentive to work harder.
collateral would normally sell for. In
addition, businesses in a given industry
often fail together. But when many
lenders try to sell at the same time,
the market gets flooded and the price
they can obtain decreases. Overall,
economists call this loss in asset value
a deadweight loss because the lender
does not gain as much as the borrower
loses. Another deadweight loss involves
transferring control of the collateralized assets, which often involves legal
and other administrative costs. Therefore, there is a tradeoff: Collateral re-
Collateral reduces the cost of borrowing
because it gives the borrower incentives to
work hard, but it also increases the cost of
borrowing because the collateral may be worth
more to the borrower than to the lender and
because transferring control imposes costs.
When the borrower works harder, the
business is more likely to succeed, and
the borrower is less likely to default.
But then the lender may be more willing to lend his money and at a lower
interest rate.
…But Using Collateral Is Costly.
The benefit above comes at a cost. A
business might fail even if the borrower
exerts a lot of effort; the borrower may
have bad luck. In this case, the borrower loses the collateral, which may
be worth more to him than it is to the
lender. For example, if the borrower
has posted his house as collateral, being able to continue living there is
important to the borrower but not the
lender. Or if the borrower has posted
his business assets, they may be worth
more to him, since he knows how to
use those assets to produce goods,
and the lender does not. The lender
may choose to sell the collateral to
someone else, but since the lender has
an incentive to sell as quickly as possible, he may obtain less than what the
duces the cost of borrowing because it
gives the borrower incentives to work
hard, but it also increases the cost of
borrowing because the collateral may
be worth more to the borrower than
to the lender and because transferring
control imposes costs.
A Long-Term Relationship with
a Bank Can Reduce the Need for
Collateral. In their paper, Arnoud
Boot and Anjan Thakor suggest that
long-term relationships between a
borrower and a lender can reduce the
need for collateral. When the loan
contract is a one-time transaction for
the bank and borrower, there are two
ways to induce the borrower to exert
effort.
The first is to require collateral,
as discussed above. The second is to
lower the interest rate on the loan. A
lower interest rate leaves more profits
for the borrower and therefore induces
him to exert effort to make the business succeed. However, if the interest
rate needed to induce the borrower to
www.philadelphiafed.org
exert effort is too low, the loan may
not be profitable to the lender; he may
be able to get a higher interest rate by
lending to other firms or individuals.
The result is that the lender may need
to require collateral, and as we have
seen, this comes at a cost.
When the borrower and lender
have a long-term relationship, the
bank has another way to induce the
borrower to exert effort. The bank
can promise the borrower better terms
on new loans in the future, once the
business shows some signs of success.5
Better terms mean less collateral and
a lower interest rate. The borrower has
an incentive to work hard even though
he pledges less collateral because
working hard increases the chances
that the business will succeed and the
terms on future loans will improve. In
the future, under the new loan terms,
the borrower has an incentive to work
hard because of the low interest rate;
therefore, collateral is no longer needed to induce effort.
But how can the lender afford
to reduce the interest rate on future
loans? In a competitive loan market,
all lenders break even; they make
enough money just to cover their costs.
Thus, a lender that offers a lower interest rate and requires less collateral
than anyone else would lose money.
The lender can make up for this loss
by charging a higher interest rate in
the initial periods. In other words,
at the beginning of the relationship
with a borrower, before the business
shows signs of success, the lender must
demand an interest rate that is higher
than a break-even rate; later on, he
requires a lower interest rate. In this
way, the bank makes a lot of profits at
the start of the relationship, and this
compensates the bank for the loss of
profits later in the relationship. Overall, the bank breaks even, and the cost
of collateral is reduced because, at the
start of the relationship, the promise of
better loan terms reduces the need for
collateral, and when the relationship
progresses, collateral is not needed.
Boot and Thakor’s model predicts
that borrowers with a longer banking
relationship are less likely to pledge
When a borrower
posts collateral,
the bank becomes
less conservative in
approving his loan.
collateral. This prediction is consistent
with what Allen Berger and Gregory
Udell found in their 1995 paper. Using data on collateral requirements
on lines of credit issued to small businesses, Berger and Udell found that
firms that had long-term relationships
with a lender were less likely to pledge
collateral.6 An additional 10 years of
bank-borrower relationship lowered
the probability of collateral’s being
pledged from 53 percent to 37 percent.
Boot and Thakor’s model also predicts
that the interest rate on the loan will
decline as the relationship progresses;
however, results regarding this prediction are mixed.7
6
The data came from the 1988-89 Survey of
Small Business Finance, conducted by the
Federal Reserve Board and the Small Business
Administration.
7
5
Such a promise might be believable because
there is an explicit contract or maybe because
the bank, which deals with many firms, cares
about its reputation for keeping its promises.
www.philadelphiafed.org
COLLATERAL AND RISK
We have seen that collateral provides incentives for the borrower to
avoid default. Collateral also reduces
the loss to the lender if a borrower defaults on a loan: If the loan is not paid,
the lender can seize the collateral. One
might conclude that secured loans are
safer for the lender than unsecured
loans. The data, however, show the
opposite.
In their 1990 paper, Berger and
Udell found that net chargeoffs (the
amount of a loan the bank cannot
collect) are likely to be higher when a
loan is secured. They also found that
borrowers who post collateral are more
likely to perform poorly; for example,
they are more likely to be late on
their payments. These two findings
suggest that secured loans are riskier
for the bank; this is consistent with
conventional wisdom in the banking
industry.8
A possible explanation is that
banks require more collateral when
they perceive a loan to be riskier.
Banks collect information about borrowers, for example, the borrower’s
income and performance with past
loans. Banks can use this information to distinguish between borrowers
who are more risky (that is, borrowers
more likely to default) and borrowers
who are less risky (those less likely to
default), and they require more collateral from the riskier borrowers.
Even though seizing collateral when a
borrower defaults reduces the bank’s
loss, this is not enough to compensate
See Philip Strahan’s chapter for a survey of
results from small-business loans around the
world. For the most part, the finding that collateral requirements fall with the length of the
relationship is replicated in a number of studies.
The effect of relationships on loan rates varies
widely across studies.
8
Ideally, the analysis would use data on individual loans. For example, the researcher would
follow every loan to see if it was collateralized,
if the borrower paid on time, and what the net
chargeoff was. Since such data do not exist
outside bank loan files, Berger and Udell used
data on chargeoffs and loans past due at the
bank level. They found that a bank with a larger
share of collateralized loans has a larger number
of chargeoffs and loans past due.
Business Review Q2 2006 11
the bank for the fact that the loan was
riskier to begin with.9
Berger and Udell provide evidence consistent with the explanation
above.10 Loosely speaking, they show
in their 1990 paper that a collateralized loan typically has a higher interest rate. To correct for the fact that
higher interest rates can reflect different points in the business cycle, they
subtract the interest rate on a Treasury
security with the same duration to
calculate the markup on the bank loan
and show that the collateralized loan
typically has a larger markup.11 Since
Treasury securities are believed to be
default free, the markup is a measure
of how risky the loan is. If we assume
that a bank charges a higher markup
when it perceives that a loan is riskier,
Berger and Udell’s result suggests that
a bank requires more collateral when it
perceives a loan is riskier.12
Note that, in theory, the bank
could eliminate the risk of default by
requiring more collateral. In practice,
however, the bank faces risk even if
the whole value of the loan is secured
by collateral. First, the value of the
9
Note that the fact that chargeoffs are higher
for riskier loans does not mean that a bank that
makes these loans loses money. Not all borrowers default. The bank can charge a higher interest rate when it perceives a loan to be riskier.
While the bank loses money on riskier borrowers who default on their loans, it makes money
on those who pay in full.
10
The data came from the Federal Reserve’s
Survey of Terms of Bank Lending, which contains information on individual characteristics
of domestic loans.
collateral may decrease over the life of
the loan. Second, the “automatic stay”
clause in the U.S. bankruptcy code often creates a significant delay between
the time the borrower defaults on the
loan and the time the lender can seize
the collateral. Even though the value
of the collateral is usually preserved,
the fact that the payment is delayed
imposes a cost on the lender.13 According to Andrea Eisfeldt and Adriano
Rampini, the difficulty in repossessing
collateral explains why some firms may
prefer to lease their assets, rather than
to borrow money to purchase assets.14
COLLATERAL AND LENDERS’
INCENTIVES
Boot and Thakor’s model focused
on how collateral affects the borrower’s
incentives to exert effort in ensuring
that the loan is paid.15 Roman Inderst
and Holger Müller shift focus by dealing with the lender’s incentives. The
problem in their model is that lenders
may choose not to finance some projects even though it is socially desirable
to undertake them. Inderst and Müller
show that using collateral can improve
the lender’s incentives to finance these
projects.
Socially, it is desirable to undertake a project when consumers are
willing to pay more than what the
resources cost, that is, when the project creates value that can be shared
between owners and lenders. When
13
For more details, read Chapter 10 in Gregory
Udell’s book.
12
A high interest rate on a loan can also reflect
a premium for additional collateral-related
monitoring costs or for the cost of evaluating
the loan as discussed in the next section. Yet,
it is reasonable to believe that a higher interest
rate reflects more risk.
12 Q2 2006 Business Review
16
One of the difficulties in saying whether a
project creates value is that cash flows are received at different times; for example, a dollar
you receive this year is worth more than a dollar
you receive in five years because you can invest
it and start earning interest earlier. In addition,
cash flows can be uncertain; for example, they
can be high or low. The net present value takes
into account the timing and riskiness of all cash
flows; it indicates the value of the project (today) net of the initial investment and net of all
future investments.
17
The local bank may have an information
advantage because it is easier to monitor and
collect information about a firm located nearby.
More generally, the “local” bank might refer to
a bank with which the borrower has had prior
dealings.
18
11
When payments are made before final maturity, the duration of a security is less than its
maturity. The duration of a security is shorter
when a larger share of the total payments are
made earlier.
this happens, economists say that the
project has a positive net present value
(NPV).16 In Inderst and Müller’s model, banks tend to be too conservative.
They refuse loans to projects that have
a positive but relatively low NPV.
In the model, a firm applies for a
loan from a local bank. The local bank
faces competition from other lenders,
but it has an information advantage.
For firms located nearby, it can distinguish between projects that have
positive NPVs and projects that have
negative NPVs.17 To other lenders, all
projects look essentially the same, so
they must charge a higher interest rate
than the local lender to compensate
for losses from the possibility of financing the negative NPV projects.18
How can the local bank use its
information advantage? It can charge
a high interest rate, but there is a limit.
If the bank charges an interest rate
that is too high, the firm would simply
go to the other lenders. This places a
14
Eisfeldt and Rampini focus on the following
tradeoff: Leasing allows the firm to borrow more
because it is easier for the lender to repossess
the asset. However, leasing is costly because the
borrower (the lessee) has fewer incentives to
take appropriate care of the asset.
15
Examples of other papers that focus on collateral and borrower’s incentives are those by YukShee Chan and Anjan Thakor and by Arnoud
Boot, Anjan Thakor, and Gregory Udell.
The local bank has access to “hard” information (for example, the firm’s books) as well as
“soft” information (for example, information
about the borrower’s managerial quality). The
other lenders have access only to hard information; thus, they may not have a complete picture
of the firm. Rebel Cole, Lawrence Goldberg,
and Lawrence White provide evidence that
in approving small-business loans, large banks
tend to employ hard information, whereas small
banks are more likely to rely on soft information.
www.philadelphiafed.org
ceiling on the local bank’s return from
making the loan, and the lender may
choose not to finance the project even
though it has a positive NPV.
To see why, consider the following example: Suppose that because of
competition from other banks the local
lender must leave the borrower with at
least $15 million of revenues. Now suppose the local lender estimates that the
project will cost $110 million and the
expected revenues will be $120 million. Since the revenues are more than
the cost, the project has a positive
NPV of $10 million.19 Now suppose
that because the borrower has no cash,
the local lender must provide all of the
investment outlay. Since the borrower
obtains $15 million, the lender is left
with an expected revenue of $105 million, an amount that is less than the
initial investment. The local lender
will reject the loan because if he does
not, he loses $5 million.20
Collateral Can Improve Lenders’
Incentives... To see how collateral can
improve the bank’s lending policy, it is
helpful to think first about the bank’s
lending policy when collateral is not
used. To do so we make the example a
little more realistic by recognizing the
fact that the project can either succeed
or fail. If the project succeeds, it yields
$200 million; if it fails, it yields only
$40 million.
To determine whether the project
is profitable, the lender needs to estimate the probability that the project
will succeed. For example, if the probability of success is half, the expected
19
To make the example simple, I ignore the fact
that revenues are not received at the same time
as the investment is made. I also ignore the fact
that revenues are risky.
20
After the local lender rejects a loan, other
lenders, who know that the loan was rejected
by the local lender, will reject the loan too. The
reason is that other lenders know there is a
chance that the loan was rejected because the
project was found to be unprofitable.
www.philadelphiafed.org
revenue is $120 million (½ x 200 + ½
x 40). If the probability is higher, the
expected revenue is higher. For example, if the probability is 80 percent, the
expected revenue is $168 million (0.8 x
200 + 0.2 x 40). We saw earlier that in
the first case (revenue of $120 million),
the lender will reject the loan. In the
second case, the lender will approve
the loan because he will be left with
expected revenue of $153 million ($168
million minus $15 million), which is
more than the initial cost. More generally, the bank will approve the loan
only if it thinks that the probability of
success exceeds some cutoff level.
Now suppose that the borrower
posts collateral. The bank seizes the
collateral only if the project fails.
Thus, if the project is very likely to
succeed, collateral has a very small
effect on the bank’s payoff. However,
if the project has a lower probability
of success, the bank’s expected profits
increase significantly when the borrower posts collateral. In other words,
collateral increases the bank’s payoff
mainly from projects whose probability
of success is relatively low. Thus, when
borrowers post collateral, the cut-off
(success) probability for approving a
loan becomes lower.21
Consistent with the empirical
findings in the previous section, the
model associates collateral with more
risk. Intuitively, when a borrower posts
collateral, the bank becomes less conservative in approving his loan; there-
21
When the borrower posts collateral, the bank
will require a lower interest rate; otherwise,
the borrower will go to other lenders. Thus,
under the loan contract with collateral, the
bank obtains more if the project fails but less if
the project succeeds. In other words, collateral
shifts the bank’s payoff from the good states
(where the project succeeds) to the bad states
(where the project fails). Requiring a higher
interest rate would not improve the bank’s lending policy because a higher interest rate, which
is paid only if the project succeeds, improves the
bank’s payoff mainly from projects that would
have been approved anyway.
fore, the borrower is more likely to
default. The model also predicts that
borrowers who are more risky to begin
with will post more collateral and pay
a higher loan rate (that is, a higher
markup over the interest on Treasury
bills) than borrowers who are less risky.
Here the intuition is simple: When the
bank faces a risky borrower, it takes
more measures to protect itself.
...But Too Much Collateral May
Have a Negative Effect. In Inderst
and Müller’s model collateral is good
for society because it allows more projects that have a positive NPV to be
financed. Although the bank is less selective in approving projects (so there
is more default), the bank finances
only projects that have a positive NPV.
In some cases, however, collateralized lending can actually be bad for
society. Indeed, if the borrower posts
a lot of collateral, the lender might be
tempted to finance a project even if
he knows the project has a negative
NPV. The lender may gain from such a
loan because he obtains the collateral
whenever the loan goes bad. However,
society as a whole (in particular, the
borrower) loses because of the deadweight cost associated with collateral
and because resources are spent on
projects with a negative NPV.22 In their
working paper, Philip Bond, David
Musto, and Bilge Yilmaz use the term
predatory lending to refer to a situation
in which a lender knowingly makes a
loan that is harmful to the borrower.23
But if the borrower is worse off,
why would he agree to such a loan?
22
This may suggest that, in some cases, society
as a whole can benefit by limiting the maximum
amount of collateral that can be posted in loan
contracts or by including bankruptcy exemptions and provisions that limit banks’ ability to
repossess collateral.
23
The Bond, Musto, and Yilmaz model focuses
on one aspect of predatory lending. In practice,
there may be other important aspects not explored in this model.
Business Review Q2 2006 13
One possible explanation is that the
borrower misunderstood the loan contract. Bond, Musto, and Yilmaz offer
another explanation. They show that
predatory lending may occur even if
every borrower fully understands the
loan contract.
For this to happen the lender
must be better informed than the
borrower; only the lender knows that
the borrower will be made worse off.
The bank (the lender) can assess the
likelihood that the borrower will be
able to repay the loan better than the
borrower, a plausible assumption since
the bank has made many similar loans
in the past and has followed many
borrowers. The borrower in turn may
overestimate his ability to repay the
loan because of lack of experience or
maybe because of overconfidence.
Of course, a borrower would never
apply for a loan if he knew that the
bank always exploited him. In Bond
and coauthors’ model, some borrowers overestimate their likelihood
of repayment, and some borrowers
underestimate. Only the bank knows
whether a potential borrower is overly
optimistic; nonetheless, the bank offers
the same contract to everyone. Thus,
the borrower cannot deduce the bank’s
information and predatory lending can
occur.24
Collateral May Also Reduce Incentives to Evaluate Loans. Michael
Manove, Jorge Padilla, and Marco
Pagano explore another situation in
which the use of collateral may lead
to a bad outcome. As in the previous
paper, the bank is better informed
than the borrower, but now the bank
needs to incur some cost to obtain its
information. In particular, by exerting
some effort (for example, conducting
an investigation), the bank can learn
24
Economists refer to this scenario, in which
the bank offers the same contract to all potential borrowers, as a pooling equilibrium.
14 Q2 2006 Business Review
whether the project is likely to be profitable.
When the cost of evaluating the
project is lower than the cost of investing in a project with a negative NPV,
society benefits if the bank evaluates
each loan before approving it. However, since no one can verify how much
effort the bank expended, the bank
may be “lazy,” in Manove, Padilla, and
Pagano’s terminology. In particular, if
the bank is protected by collateral, its
incentive to exert effort in evaluating
loans is reduced because it can recoup
the value of the loan by seizing the collateral. If, on the other hand, the bank
is not protected by collateral, the bank
evaluates the loan more carefully because the bank does not obtain much
if a firm’s project fails.25
As in the model of Inderst and
Müller, the use of collateral makes the
bank more lenient in approving loans;
thus, collateral is associated with more
default. In Inderst and Müller’s model,
being more lenient is good because the
bank approves more loans that have
positive NPVs. In contrast, in Manove,
Padilla, and Pagano’s model, being
more lenient is bad because the bank
approves some negative NPV projects
that would not be approved had the
bank conducted a careful evaluation.
Moreover, their model does not predict
that those who post collateral are borrowers of low quality. In their model,
firms have information about their
own costs, and firms with low costs
use collateral to communicate their
information to the bank. (To learn
more, see Collateral Can Help the Bank
Distinguish Between Borrowers.)
25
In Manove, Padilla, and Pagano’s model, collateral reduces the bank’s incentives to evaluate
a project before a loan is approved. Raghuram
Rajan and Andrew Winton explore how collateral affects the bank’s incentives to monitor a
firm after the loan is approved. They show that
collateral may actually increase banks’ incentive
to monitor.
COLLATERAL AND FIRMS’
INVESTMENT DECISIONS
Until now, we have not been specific about the type of collateral used.
Actually, there are two types: outside
collateral and inside collateral. Outside collateral refers to the case where
the borrowing firm pledges assets not
owned by the firm. For example, the
firm’s owner might post his house as
collateral for a business loan. Inside
collateral refers to the case where the
borrowing firm pledges assets it owns,
such as machines and inventories.
Although some of the ideas discussed
earlier may apply to inside collateral,
the models previously discussed are
most convincing as explanations of
outside collateral.
The discussion in the next section
refers to inside collateral. When a borrower posts collateral for a loan, such a
loan is called secured debt. Implicitly,
a firm’s debt is secured by its assets
because if the firm goes bankrupt, the
proceeds are used to pay the firm’s
lenders.26 Therefore, most explanations
of debt secured by inside collateral
depend on the firm’s having more than
one lender. Secured debt gives some
lenders priority over others for some
specific set of assets.
Collateral Can Overcome Underinvestment. In their article, René
Stulz and Herb Johnson suggest that
issuing secured debt may allow a firm
to take advantage of investment opportunities with a positive NPV that it
otherwise could not. Taking advantage
of such investment opportunities is
desirable because it increases the firm’s
value; it increases the pie to be shared
among the firm’s shareholders and the
firms’ debt holders (its lenders).
The logic is as follows: Suppose
the firm is considering borrowing to
26
To be precise, some claimants, including lawyers and the IRS, must be paid before lenders
receive anything.
www.philadelphiafed.org
Collateral Can Help the Bank Distinguish Between Borrowers
M
ichael Manove, Jorge Padilla, and Marco
Pagano’s model illustrates what economists call the screening role of collateral.
In their model, collateral helps the bank
distinguish between firms that are likely
to have positive net present value (NPV)
projects and firms that are likely to have negative NPV
projects.
Suppose there are two types of firms: firms with high
operating costs and firms with low operating costs. When
a firm applies for a loan, it knows its operating cost, so it
has an idea of whether its project is likely to be successful
and have a positive NPV. But since there are other factors
affecting the project’s success, the firm cannot know for
sure. The bank can find out whether the firm has high
costs or low costs as well as other information about the
firm’s project, but only after some investigation. Before
the bank investigates, all firms look identical to the bank.
To recoup the cost of evaluation the bank must
charge some fee. To make sure it puts the appropriate
amount of effort into evaluating the loan, the bank charges only those firms whose loans are approved. Otherwise,
the bank can make money by charging a fee without doing an evaluation and then rejecting all applicants.a In
turn, firms whose loans are approved end up subsidizing
the firms whose loans are not approved. But since the
low-cost firms are the ones whose loans are more likely to
be approved, they know they are the ones subsidizing the
high-cost firms.
To avoid this, low-cost firms may try to distinguish
themselves from high-cost ones by offering to post collateral. An economist would say that the low-cost firm is
using collateral to signal its information to the bank. Posting collateral is costly to the firm because the firm loses it
if its project fails. However, since the firm’s costs are low,
it knows the project is very likely to succeed and the risk
of losing collateral is not large.
However, low-cost firms can signal their information
using collateral only if high-cost firms find it unprofitable
to mimic low-cost firms by posting collateral, too. This is
the case if the high- and low-cost firms differ enough. For
a high-cost firm, the cost of putting up collateral is much
higher than for a low-cost firm because the firm knows it
is more likely to default. The result is that low-cost firms
post collateral and high-cost firms do not.
The bank can then distinguish between the two
firms. If a firm is willing to post collateral, the bank concludes that the firm has low costs and approves the firm’s
project without an evaluation; in this case, a careful evaluation is not likely to change the bank’s decision. If a firm
is not willing to post collateral, the bank concludes the
firm has high costs and evaluates the project; in this case,
the bank’s evaluation may indicate that the firm’s project
has a positive NPV, even though the firm has high costs.b
a In the real world a bank that acted this way would develop a bad reputation and lose loan applicants. The reader should interpret the story in the
model as a stark version of the real-world problem that if all applicants are charged a fee upfront, the bank will have an incentive to exert too little
effort in monitoring.
b Economists refer to this scenario, where one firm distinguishes itself from another firm, as a separating equilibrium. Note that if separation works,
the firm can avoid investigation by posting less collateral than in the case where all firms behave the same. Since the bank concludes that a firm
that posts collateral has low cost, further investigation is not likely to change the bank’s decision.
Helmut Bester first introduced the idea that a borrower who thinks his project is likely to succeed prefers to pledge more collateral than a borrower was thinks his project is likely to fail. One of the problems with this type of model is that the “inherently good” borrowers (for example, those
with low cost) are the ones who post more collateral. This seems inconsistent with the empirical evidence and with the common wisdom in the
banking industry.
finance a new investment project
that has a positive NPV and is very
low risk. Further, suppose the firm
already has relatively risky debt in
place. In other words, if the firm
does not undertake the new project, there is a significant likelihood
it will default on its existing debt
www.philadelphiafed.org
because its past investments may do
poorly. If, instead, the firm undertakes
the new project, the firm is less likely
to default on its existing debt because
it can use the cash flow from the new
project to pay existing debt holders.
But what if the cash flows from the
new project are just enough to pay the
new debt but not enough to pay both
the new and the existing debt? In this
case, the firm goes bankrupt, and the
cash flows from the new project are
shared between the existing debt holders and the new debt holders; thus,
the new debt holders get paid less than
what was promised to them. If, how-
Business Review Q2 2006 15
ever, the firm did not have the risky
debt in place, it could pay its new debt
holders in full. Accordingly, any new
unsecured debt holders would supply
funds only at a very high interest rate,
perhaps so high that the investment
would be unprofitable for the firm.
Now suppose the new debt is secured by the new assets purchased with
the borrowed funds. Then if the firm’s
initial project fares poorly and the firm
goes bankrupt, the new assets posted
as collateral are transferred to the new
debt holders rather than shared among
all creditors, new and old. Since the
new debt holders obtain more when
the firm goes bankrupt, they are willing to provide funds at better terms
(a lower interest rate). This, in turn,
increases stockholders’ profits from
making the new investment.27
CONCLUSION
Even though collateral has been
around for a very long time, research
into economic factors underlying the
use of collateral has been particularly
active in the past few years. Economists have deepened their understanding of the reasons some firms post
collateral (and others don’t) and of
society’s costs and benefits from collateralized lending.
Using collateral protects the
lender if the borrower defaults. Col-
27
While Stulz and Johnson emphasize priority
issues, Udell’s book on asset-based finance emphasizes the informational value of monitoring
inside collateral (inventory and accounts receivable). A recent working paper by Loretta Mester, Leonard Nakamura, and Micheline Renault
lends empirical support to Udell’s perspective.
lateral may also induce the borrower to
exert more effort to ensure the loan is
repaid. This is good because borrowers
with good (positive NPV) investment
opportunities can obtain credit more
easily.
However, the use of collateral
comes at some cost. Transferring control may be costly, and the lender may
not value the collateral as much as the
borrower does. In addition, a lender
protected by collateral may exert too
little effort in evaluating projects; he
may even be induced to engage in
predatory lending. This is bad from
society’s standpoint because firms obtain loans for projects that are likely to
waste resources. A long-term relationship between a borrower and a lender
can reduce the need for collateral and
save on some of these costs. BR
REFERENCES
Berger, Allen N., and Gregory F. Udell.
“Collateral, Loan Quality, and Bank Risk,”
Journal of Monetary Economics, 25 (1990),
pp. 21-42.
Berger, Allen N., and Gregory F. Udell.
“Relationship Lending and Lines of Credit
in Small Firm Finance,” Journal of Business,
68 (1995), pp. 351-81.
Bester, Helmut. “Screening vs. Rationing
in Credit Markets with Imperfect Information,” American Economic Review, 75
(1985), pp. 850-55.
Bond, Philip, David Musto, and Bilge
Yilmaz. “Predatory Lending in a Rational
World,” Working Paper 06-2, Federal Reserve Bank of Philadelphia, 2005.
Boot, Arnoud W. A., and Anjan V. Thakor. “Moral Hazard and Secured Lending
in an Infinitely Repeated Credit Market
Game,” International Economic Review, 35
(1994), pp. 899-920.
16 Q2 2006 Business Review
Boot, Arnoud W. A., Anjan V. Thakor,
and Gregory F. Udell. “Secured Lending
and Default Risk: Equilibirum Analysis,
Policy Implications and Empirical Results,”
Economics Journal, 101 (1991), pp. 458-72.
Chan, Yuk-Shee, and Anjan V. Thakor.
“Collateral and Competitive Equilibria
with Moral Hazard and Private Information,” Journal of Finance, 42 (1987), pp.
345-63.
Cole, Rebel A., Lawrence G. Goldberg,
and Lawrence J. White. “Cookie Cutter vs.
Character: The Micro Structure of Small
Business Lending by Large and Small
Banks,” Journal of Financial and Quantitative Analysis, 39 (2004), pp. 227-51.
Eisfeldt, Andrea L., and Adriano A.
Rampini. “Leasing, Ability to Repossess, and Debt Capacity,” Working Paper,
Northwestern University.
Inderst, Roman, and Holger M. Müller. “A
Lender-Based Theory of Collateral,” Working Paper.
Manove, Michael A., Jorge Padilla, and
Marco Pagano. “Collateral Versus Project
Screening: A Model of Lazy Banks,” Rand
Journal of Economics, 32 (2001), pp. 726-44.
Mester, Loretta J., Leonard I. Nakamura,
and Micheline Renault. “Transactions Accounts and Loan Monitoring,” Review of
Financial Studies (forthcoming).
Rajan, Raghuram, and Andrew Winton.
“Covenants and Collateral as Incentives to
Monitor,” Journal of Finance, 50 (1995), pp.
1113-46.
Strahan, Philip E. “Bank Structure and
Lending: What We Do and Do Not
Know,” in Arnoud Boot and Anjan Thakor, eds., Handbook of Financial Intermediation. Amsterdam: North Holland (forthcoming).
Stulz, René M., and Herb Johnson. “An
Analysis of Secured Debt,” Journal of Financial Economics, 14 (1985), pp. 501-21.
Udell, Gregory F. Asset-Based Finance:
Proven Disciplines for Prudent Lending. The
Commercial Finance Association, 2004.
www.philadelphiafed.org
Is Technology Raising Demand for Skills,
or Are Skills Raising Demand for Technology?
BY ETHAN LEWIS
A
common view is that recent technological
advances, such as the introduction of
computers, have rendered obsolete some
occupations that require less skill and have
increased businesses’ desire to hire skilled workers.
However, some economists have challenged this view:
What if the rising skills of U.S. workers are inducing
businesses to adopt — and maybe even develop — new
technologies that require workers who are more skilled? In
this article, Ethan Lewis assesses this alternative view. To
do so, he examines the evidence that increasing skills are
driving technological change.
Since the late 1990s, incomes of
the highest earning Americans have
risen faster than the income of other
Americans, a trend that has not gone
unnoticed by the press.1 The recent
rise follows a decade of relative stability in income distribution, but it
resumes a pattern of growing inequal-
1
Both the Wall Street Journal and the New York
Times have recently published series on rising
inequality. See, for example, the article by David Johnston and the one by David Wessel.
Ethan Lewis is
an economist
in the Research
Department of
the Philadelphia
Fed. This article
is available free
of charge at www.
philadelphiafed.
org/econ/br/
index.html.
www.philadelphiafed.org
ity that began in earnest in the 1970s.
Until recently, a standard explanation
for rising inequality was that a steady
flow of technological advances, such as
the increasing power and falling price
of computers, has raised the desire of
businesses to hire skilled workers and
has made obsolete some occupations
that require less skill. Economists call
this phenomenon “skill-biased technological change” because new technologies are “biased” toward raising the
productivity (and, hence, wages) of
the most skilled workers. The primary
direct evidence for this explanation is
that the use of advanced technologies
is more common among more-skilled,
highly paid workers and in plants and
industries with more-skilled workers.2
Some economists, however, have
challenged this standard view, arguing
the reverse: Rising skills of U.S. work-
ers — as evidenced by the rising proportion of people who complete college
— are driving businesses to adopt and
possibly even to develop new technologies that require more-skilled workers.
Paul Beaudry and David Green argue
that the decision to use new technology is not automatic but depends critically on the availability of skilled labor
and capital. Daron Acemoglu goes
further, arguing that as the proportion of workers who are skilled rises,
inventors will direct more effort toward
technological advances that skilled
workers can use.3 The distinction is
subtle. Technology is still involved in
rising inequality, but it is the increase
in the proportion of workers who are
skilled, rather than technology per se,
that is the cause of rising inequality.
This article assesses the alternative views that recent technological
advances may have driven up inequality or that rising skills may be driving
technological advances. It begins by
examining the recent changes in the
income distribution. How exactly
has the distribution been changing,
and why might technological forces
2
Wage and skill are closely related. In a perfectly competitive labor market, a worker’s wage
exactly reflects how productive the worker is,
which, in turn, depends on her skill level. In
practice, that is not always true (wages might
also reflect a worker’s bargaining power, for
example), but highly paid workers do tend to
have higher values of observable characteristics
that are valued in the labor market, such as
education and work experience. Skill-biased
technological change, it is argued, has raised
the value, or “price,” of skills in the market and,
hence, the wages of skilled workers compared to
those of less skilled workers.
3
Keith Sill’s article describes Acemoglu’s theory
of directed technical change in more detail.
Business Review Q2 2006 17
be responsible? Is there any direct
evidence that new technologies favor
skilled workers? Is the association large
enough to explain rising inequality?
Are rising skills driving technological
change?
RECENT CHANGES IN THE
WAGE STRUCTURE
The basic facts about rising inequality were presented in an article
by Keith Sill, but they bear repeating
here. The most basic fact is that the
gap between the wages of the most
highly paid workers and others has
been rising in recent decades in the
U.S., especially in the 1980s and in
the late 1990s (Figure 1). The figure
shows an index of hourly wages (adjusted for changes in the cost of living) in
different parts of the wage distribution
from 1979 to 2003. For our purposes
here, I exclude women; only men’s
wages have been used in the calculations. (Inequality growth is smaller if
women are included: Women’s wages
are rising over this period compared to
men’s. For more on this, see Women’s
Wages and Increasing Inequality.) The
90th percentile line represents the wage
for high-skill men: Only 10 percent of
men earn more than this wage. The
10th percentile line represents the wage
for low-skill men: only 10 percent of
workers earn less than this wage. The
median, or 50th percentile, represents
the middle of the distribution. The top
line in Figure 1 shows the gap between
the 90th percentile and median wages,
a measure of inequality. The figure
reveals that the growth in inequality
has been driven not only by the rising
wages of high earners but also by the
falling wages of low and median earners.
At least some of the increase in
wage inequality, and some argue most
of it, seems to be due to rising “return”
to skill, that is, an increasing wage premium paid to workers with more skills.4
18 Q2 2006 Business Review
One place this shows up is in the rising
gap between the wages of more and
less educated workers. Figure 2 shows
wage indexes at different education
levels, again for male workers only.
These indexes are adjusted for changes
in the cost of living, and in this case,
they are also adjusted to represent
workers who have similar amounts of
4
Interestingly, wage inequality has increased
even among workers with very similar characteristics (for example, the same education, work
experience, and occupation), which suggests
not all of the increase in inequality should be
attributed to an increased skill premium. However, Chinhui Juhn, Kevin Murphy, and Brooks
Pierce argue that increases in inequality among
similar workers could reflect increasing returns
to skills that are not easily measured.
work experience (15 years). The upper
line shows that the return to a college
degree — the percentage difference
in earnings between a college degree
and a high school diploma — has risen
dramatically in the past few decades:
from 30 percent to 50 percent. Earnings gaps between the other levels of
education have also risen, as seen in
the spreading out of lines in the lower
part of Figure 2. Adjusted for inflation,
the earnings of less educated workers,
especially high-school dropouts, have
fallen.
At the same time that the relative wages of more educated workers
have been rising, the proportion of
FIGURE 1
Real Hourly Wages (Males), 1979-2003
!"#
$%&
' & (
$%&
!" (& * &+)#
$%&)
Data Source: Current Population Survey merged outgoing rotation groups, 1979-2003. Calculations
include working males age 16-65 old enough to be out of school. Wages are adjusted for changes in
the cost of living.
* 90-50 gap is the percentage difference between the hourly wage of the median male worker and the
hourly wage of the male worker earning the 90th percentile wage.
www.philadelphiafed.org
workers who complete more education
— the supply of skilled workers — has
also been rising (Figure 3). The figure
reports the fraction of workers with
different levels of education. The fraction of workers who are high-school
dropouts trends down, while the fraction with at least some college education trends up. If demand for different
types of workers remains the same, a
simple model of supply and demand
would suggest that as the educational
level of the work force rises, the gap
between the wages of more and less
educated workers should narrow. That
the gap actually widened suggests that
the availability of skilled workers may
not have kept up with the pace at
which businesses wanted to hire them,
causing wages for skilled workers to
rise. Another way to say this is that
demand for skilled workers rose faster
than supply.
There are competing explanations
for the simultaneous rise in the
supply of skilled workers and their
relative wages. A standard view is
that skill-biased technological change
is responsible. This view originates
from the observation that rising
inequality coincides with the spread of
computers: The PC was introduced in
1981, for example, and the late 1990s
“tech boom” was a period of rapid
investment in and diffusion of new
information technologies (for example,
the Internet and e-mail). This view
posits that skilled workers are needed
to operate and maintain computer
technology, so demand for skilled labor
rose after its introduction.
But the timing of the spread of
computers is a weak argument for its
effect on the returns to skill. The rise
in inequality in the 1980s was largely
due to a decline in the wages of lessskilled workers. As many researchers
have pointed out, this may have been
caused by other contemporaneous
forces, including an influx of less-
www.philadelphiafed.org
inequality during the 1980s.6
Still, economists disagree about
the degree of influence of these other
forces on inequality. Proponents of
skill-biased technological change have
pointed out that alternative forces like
the minimum wage have little to say
about why the wages of skilled workers would rise.7 Also, the late 1990s
skilled immigrants, declining union
participation, and increasing trade
with the developing world.5 Other
forces that may have increased
inequality and skill premiums in
the 1980s include an increase in
the proportion of women working
(see Women’s Wages and Increasing
Inequality) and the substantial erosion
in the real value of the minimum
wage (Figure 4). A careful analysis
by David Lee shows that the decline
in the minimum wage may have been
largely responsible for the increase in
6
Lee supports this view by showing that inequality rose in poorer states where many workers were earning the federal minimum wage
and rose hardly at all in richer states where few
earned the minimum wage.
7
5
See the article by David Autor, Lawrence
Katz, and Melissa Kearney.
For more on these factors, see Sill’s article.
FIGURE 2
Experience-Adjusted* Average Hourly Wage by
Education Level (Males, 1979-2003)
-+
"
+
'+
,+
"+
!
+
+
!
!
# $%
&
) $*
"
"
'( #
Data Source: Current Population Survey, merged outgoing rotation groups.
*Wages are adjusted to reflect the mean for males with 15 years of work experience and for changes
in the cost of living.
** Exactly 4-year degree. The series is broken between 1991 and 1992 because of a change in how
the education question was asked beginning in 1992.
*** Percentage difference between the average male worker with 15 years of experience with exactly
a 4-year college education and one with exactly a high-school diploma.
Business Review Q2 2006 19
Women’s Wages and Increasing Inequality
M
ost researchers who study the recent increases
in wage inequality exclude women from their
analysis. This is an important omission. If
women are included in the calculations, recent increases in inequality are substantially
smaller. This is shown in Figure 1a, which
is identical to Figure 1 in the text except that both men and
women are included in the calculations. Compared to Figure 1,
the 90-50 wage gap measure of inequality increased by only half
as much over the last 25 years and has changed little since the
mid-1990s.
The reason inequality growth is smaller when women are
included is that women’s wages compared to men’s rose rapidly
over the same 25-year period. Figure 1b shows that women’s
mean hourly wages rose from only 67 percent to nearly 85 percent of male mean wages in the past 25 years. One force that
may have made women’s wages increase is women’s increasing
participation in the work force. Figure 1b also shows that during
this same period, the proportion of women who work rose from
60 to 70 percent.a Another force is the rising skills of women.
Women have increased their presence in professional occupations, especially since the late 1960s, a change research has
linked to women’s increased ability to delay child-bearing after
the birth-control pill became widely available.b Changing social
norms may have also played a role in raising women’s ability to
advance in professional careers.
Because researchers want to ignore these compositional
changes in the work force when studying skill-biased technological change, they have typically excluded women from the
analysis. Put another way, proponents of skill-biased technical
change argue the wage paid to a skilled worker is higher today
than a similarly skilled worker in the past; they argue that
including women would risk clouding the analysis because it
would mix the rising “price” of skill with an increase in the proportion of workers who are skilled (owing to women’s increased
presence in highly skilled occupations).c
While this is a widely held view, other research that examines women’s wages more closely tends to reject the idea that
changes in women’s and men’s wage distributions can be treated
separately. For example, Nicole Fortin and Thomas Lemieux
a
Beyond this most recent period, since World War II there has been a
dramatic increase in how much women — especially married women
— work. Aubhik Khan’s Business Review article describes some of the
possible causes of this.
b See the article by Claudia Goldin and Lawrence Katz.
c
A more subtle issue that worries economists is that women are “selfselected”: that is, not all women work, and those who do may have
very different earnings capacity from those who do not. If the amount
of selection has changed over time — and the fact that the proportion of women who work has increased suggests that it has — it would
confound measures of inequality growth. In fact, Casey Mulligan and
Yona Rubinstein argue that women’s wages have increased entirely
because highly skilled women used to not work, and now they do. This
“problem” can be overstated. The proportion of men working is also
not 100 percent (in 2003, 83 percent of men age 16-65 worked) and
has also been changing over time (it has been falling). However, most
economists believe selection problems are smaller for men than they are
for women.
FIGURE 1a
FIGURE 1b
Real Hourly Wages Including Women
Women: Proportion Working and Hourly
Wage as a Proportion of Men’s
%&'( ) *"
+
$
!"#$
Data Source: CPS, merged outgoing rotation groups, 1979-2003. Calculations include working men and women age 16-65 old enough to be out of
school. Wages are adjusted for changes in the cost of living.
* 90-50 gap is the percentage difference between the hourly wage of the
median worker and the hourly wage of the worker earning the 90th
percentile wage.
20 Q2 2006 Business Review
Data Source: Current Population Survey, merged outgoing rotation groups,
1979-2003.
www.philadelphiafed.org
Women’s Wages and Increasing Inequality (continued)
find that as women have entered into high-wage jobs, they have
displaced some men, leading both male inequality and women’s
wages to rise at the same time. A version of their analysis is
shown in Figure 1c, which gives the distribution of men’s and
women’s wages in 1979 and 2003 (on a natural log scale). In
1979, many women were concentrated in jobs earning near the
minimum wage, while men were disproportionately high earners. By 2003 men’s and women’s wage distributions converged
and became more symmetric, as women rose to the part of the
wage distribution where men formerly dominated, and men
fell to the part of the wage distribution where women formerly
dominated. Fortin and Lemieux argue that the increased competition from women in high-wage jobs may have increased male
wage inequality, a circumstance that is missed by focusing on
changes in male wages alone.d
However, recent research by Marigee Bacolod and Bernardo Blum argues skill-biased technological change might also
partly explain the increase in women’s wages. They show that
women are concentrated in occupations that require “cognitive”
skills (for example, doctors) whose wages have risen (arguably
because of skill-biased technological change), while more men
than women are in occupations that require “motor” skills (for
example, mechanics) whose wages have been falling. They find
that the changes in the prices of different skills account for at
least 80 percent of the observed increase in women’s wages compared to men’s, which may mean that skill-biased technological
change has helped raise women’s wages compared to men’s.e
FIGURE 1c
Distribution of Men’s and Women’s Real
Hourly Wages (natural log scale)
d The figure also nicely shows the role that the fall in the minimum
wage may have played in increasing inequality. In 1979, when minimum
wages were high, the figure shows that wages are compressed in a spike
near the minimum wage. After the real value of the minimum wage fell
in the 1980s (see Figure 4 in the text), this spike in the wage distribution disappears.
Data Source: Current Population Surveys. Wages are in 2000 dollars.
e
On the other hand, the fall in the price of motor skills might reflect
other forces such as de-unionization and a fall in the real value of the
minimum wage, rather than technological change.
appear to be different from the 1980s:
The increase in inequality in the late
1990s was driven largely by the rapid
increase in the wages of skilled workers.
To bolster their case, proponents
of skill-biased technological change
have attempted to find more direct
evidence of the link between technology and wages using data on individual
workers, industries, and plants.
www.philadelphiafed.org
EVIDENCE FROM WORKERS,
INDUSTRIES, AND PLANTS
Workers. Alan Krueger was one
of the first to attempt to show directly
that computers may make workers,
especially skilled workers, more productive. Using data on individual
workers’ wages and on-the-job computer use, he showed that workers who
used a computer at work earned wages
that were 15 to 20 percent higher than
those who did not. This earnings premium remained when controlling for
characteristics of workers, such as age,
education, and occupation. In addition, Krueger found that the premium
was especially large for more educated
workers, suggesting that the technology
favored more-skilled workers. On the
basis of this finding, Krueger argued
that the increased use of computers
over time has led to an increase in inBusiness Review Q2 2006 21
FIGURE 3
Rising Skills: Percent of Workers by Education
! "
Data Source: Current Population Survey, merged outgoing rotation groups. The series is broken
between 1991 and 1992 because of a change in how the education question was asked beginning in
1992.
FIGURE 4
Wages of Less-Skilled Males and the
Federal Minimum Wage
"
!
!
Data Source: Current Population Survey, merged outgoing rotation groups, 1979-2003.
See previous figures for further notes.
22 Q2 2006 Business Review
!
equality. He showed that, based on his
estimates, as much as half of the rise in
the college/high-school wage gap (see
Figure 2) might be explained by computerization of the workplace.
In contrast to Krueger, Robert
Valletta showed growing computer use
at work is not likely to be responsible
for growing inequality. Taking at face
value the wage premium on computer
use, his approach asks how much lower
inequality would have been if different groups of workers (defined by work
experience, education, gender, and
race, among other things) had not
increased their computer use between
1984 and 2003. During these 19 years
Valetta estimates that on-the-job
computer use rose substantially, from
25 percent to 57 percent of workers.
Surprisingly, though, he finds that this
led to virtually no increase in inequality. The basic idea behind this result is
that the increase in computer use has
been widespread, not limited to the
most highly paid workers. As a result,
although rising computer use may have
made workers more productive and
raised the general level of wages, it is
unlikely to have increased the spread
between high and low wages.
John DiNardo and Steffen Pischke
provide further reason for skepticism
about evidence based on association
between computer use and skills. Using
data on German workers, they showed
that observationally similar workers
who use a pencil at work earn a wage
“premium” similar to that of those who
use a computer at work. Since the use
of a pencil does not require special
skills, they conclude that one must be
cautious about interpreting any wage
premium on computer use. High-paying jobs may be more likely to involve
a computer, they argue, but it is not
necessarily the computer that makes
the job high paying.
Industries. David Autor, Frank
Levy, and Richard Murnane contribute to this debate by specifying the
www.philadelphiafed.org
mechanism by which computers affect
the wage structure, and they provide
empirical support for their view. They
argue that computers replace routine
cognitive tasks, that is, those tasks
that involve thinking but that can be
easily codified into a set of instructions
for a computer. Recordkeeping is an
example of a cognitive routine task.
Creative writing is a nonroutine cognitive task: Computers cannot substitute
for humans in this task. Autor and his
co-authors also distinguish manual
tasks from cognitive tasks and argue
that computers replace only routine
cognitive tasks (though factory automation, discussed below, may replace
some routine manual tasks as well). As
the price of computers falls, workers
who perform routine cognitive tasks
will likely be replaced by computers
(or take a cut in wages), while skilled
workers will be more productive because they can spend more time on
nonroutine tasks.
To evaluate this view, the authors
examined the relationship between
the tasks performed in different occupations and increases in computer
use over a long period. They use Labor
Department surveys to measure how
much routine cognitive, nonroutine
cognitive, routine manual, and nonroutine manual tasks were required
in each occupation. They found that
the more an industry increased its use
of computers between 1984 and 1997,
the more it decreased its employment
of workers in routine cognitive occupations and increased employment
of workers in nonroutine cognitive
occupations in recent decades. In the
1960s, before the widespread introduction of computers, the authors find
little shift in occupation mix in the
same industries. Though the evidence
is supportive of their view, the authors
are careful to acknowledge that the
association between occupation shifts
and computer use does not necessar-
www.philadelphiafed.org
ily imply that the shift was caused by
computerization.
Plants. Computers are not the
only technology that may have contributed to rising inequality. Over the
past few decades, manufacturing plants
have become more automated as technologies such as robotics have become
increasingly powerful and prevalent.
Some research has focused on the impact of factory automation.
Mark Doms, Timothy Dunne,
and Kenneth Troske obtained detailed
data on the use of a variety of new
automation technologies at a sample
of manufacturing plants, as well as the
characteristics of the workers at those
same plants. They found that moreautomated plants paid higher wages
and had a higher proportion of workers
who were college graduates, engineers,
and nonproduction workers. However,
they also found that the same plants
had more skilled workers long before
the technologies were introduced.
Like DiNardo and Pischke’s result for
pencils, this finding suggests that automation was not necessarily the cause
of the increased employment of skilled
workers, even if it is associated with it.
GEOGRAPHIC DIFFERENCES IN
TECHNOLOGY USE
Another way to explain the relationship between technology and
income inequality is to treat different
parts of the U.S. as different “markets.”
This approach takes advantage of the
fact that there are wide differences in
technology use and the availability of
skilled workers in different regions of
the U.S. To assess the causal relationship between technology and skills, I
examined, in a previous article, how
the relative availability of skilled and
unskilled workers in a plant’s local
geographic market (metropolitan area)
affected automation.8 Aiding this ap8
See my 2005 Business Review article.
proach is the fact that some differences
in skill mix across local markets occur
for idiosyncratic reasons that probably have little to do with technology.
For example, some markets have a lot
of less-skilled workers because they
contain enclaves of less-skilled immigrants, whose numbers have increased
rapidly in recent decades. Los Angeles,
for example, has twice as many highschool dropouts per capita as other
cities, largely because it is a major
destination for Mexican immigrants,
many of whom arrive in the U.S. without a high school diploma.
On the other end, some markets
have a lot of highly educated workers
because they were lucky enough to
receive federal funds to build landgrant universities in the 19th century.
These idiosyncratic differences provide
natural “experiments” to evaluate the
causal relationship between skills and
technology.
In this earlier work, I found that
in places with abundant unskilled
labor, plants are less automated, and
in places where skilled labor is abundant, plants are more automated. In
addition, increases over time in the
availability of skilled labor lead plants
to increase their use of automation.
This suggests that plants adopted these
technologies to fill shortages of unskilled labor. Put another way, the use
of technology responds to the amount
of skilled labor available to operate it.
Looking across geographic markets also reveals a similar relationship
for computers. In another article, I
used another “natural experiment”
— the aftermath of the Mariel boatlift,
the 1980 exodus of Cubans that dramatically increased the availability of
unskilled labor in Miami — to evaluate the impact of skills on technology.9
I found that businesses in Miami were
much slower to adopt computers at
9
See my 2004 working paper.
Business Review Q2 2006 23
work after the boatlift than businesses
in other, similar cities.
In another recent paper, Mark
Doms and I examined businesses’
adoption of personal computers in the
1990s. We found that the adoption of
PCs by otherwise similar businesses
depended on the availability of college-educated labor in the local market. For example, Figure 5 presents
a version of a scatter plot from this
paper. It plots the number of personal
computers per employee in the average
business, adjusted for the businesses’
industry and employment, in different
metropolitan areas against the share
of the workers in that area who are
college educated.10 The college share
is measured in 1980, before businesses
used PCs, while computer use is measured in 2000, by which time PCs were
the dominant computing technology
(used by 50 percent of workers). The
figure shows that high-skill cities, such
as San Francisco, use personal computers intensively, while cities with
fewer college-educated workers, such
as Scranton, use computers less intensively. Philadelphia is near the middle
of this skills-technology relationship.
Once again, the data in the figure
have been adjusted for industry and
size. For example, the figure adjusts for
factors such as San Francisco’s large
“tech” sector and New York’s large
financial sector (both are computerintensive sectors). Another way to say
this is that very similar businesses, for
example, law firms of a certain size,
appear to vary their use of personal
computers depending on the local
availability of college-educated labor.11
In one sense, these results support
the notion of skill-biased technological change, since they imply that as
technology gets cheaper, firms replace
unskilled workers with cheaper technology and hire more skilled workers.
But these results also provide a more
complex view of the increased use of
skilled labor and the adoption of new
technologies. It is not only the availability of new technology that induces
plants to hire skilled workers but also
the availability of skilled workers that
induces plants to adopt new technology. In this alternative view, recent
In a similar result, Nicole Nestoriak found
that plants in areas with an abundance of highly
paid workers invested more in computing technology.
24 Q2 2006 Business Review
12
See the article by David Card and
John DiNardo.
FIGURE 5
Personal Computers/Employee vs. College
Education by Metropolitan Area
&0 %12(#
3
, -&
3
% ( %&
*
$ ) $)
' %&
+ &
.
%&
The data for this figure come from two sources. College share comes from author’s tabulations from the 1980 Census of Population, while
personal computers per worker is tabulated
from the “Harte-Hanks” data set, a proprietary
establishment-level survey of technology use.
Personal computers per employee figures are
adjusted to control for the industry and size of
the establishment. (Interestingly, this adjustment makes little difference!) College share
includes all those with a four-year college degree
plus one-half of those with one to three years of
college education.
CONCLUSION
Wage inequality has risen over the
past few decades. Many economists
believe that this is related to steady
advances in and the diffusion of information and automation technologies,
which may favor the employment of
skilled workers. Though this explanation is appealing because technology
has rapidly become more prevalent and
is more often used by skilled workers,
recent research finds that it is not consistent with many of the facts.12 Other
11
10
technological change may result partly
from the rising skills of U.S. workers
(see Figure 3) rather than being a fully
independent force affecting the labor
market.
"
/ %&
%&
" # $
!
*Data Source: Harte-Hanks, 2000-2002. Figures report number of personal computers per worker at
the average business, adjusted for industry and establishment size (employment).
**Data Source: Census of Population, 1980. Figures report share of workers with at least a 4-year
college degree + 1/2 of the share of workers with 1-3 years of college education.
www.philadelphiafed.org
forces, such as falling minimum wages,
appear to have played a role in rising
inequality. Researchers have also had
difficulty establishing definitively that
new technologies actually cause the
number of jobs for skilled workers to
increase. Some evidence even suggests
the reverse: The spread of new technologies responds to the rising skills
of the work force, rather than being
an independent force affecting the demand for skills.
Economists are likely to continue
to debate this issue. The latest in-
crease in inequality, in the late 1990s,
occurred during the period of rapid
investment in information technology.
This episode will be sure to inspire
further research. BR
Doms, Mark, Timothy Dunne, and Kenneth R. Troske. “Workers, Wages and
Technology,” Quarterly Journal of Economics, 62 (1997).
Lee, David. “Wage Inequality in the
United States During the 1980s: Rising
Dispersion or Falling Minimum Wage?”
Quarterly Journal of Economics, 114 (1999),
pp. 977-1023.
REFERENCES
Acemoglu, Daron. “Why Do New Technologies Complement Skills? Directed
Technical Change and Wage Inequality,”
Quarterly Journal of Economics, 113 (1998),
pp. 1055-89.
Autor, David H., Lawrence Katz, and
Melissa F. Kearney. “Trends in U.S. Wage
Inequality: Re-Assessing the Revisionists,”
MIT mimeo (August 2004).
Autor, David H., Frank Levy, and Richard
J. Murnane. “The Skill Content of Recent
Technological Change: An Empirical Exploration,” Quarterly Journal of Economics,
118 (2003), pp. 1279-1334.
Bacolod, Marigee, and Bernardo S. Blum.
“Two Sides of the Same Coin: U.S. ‘Residual’ Inequality and the Gender Gap,”
mimeo, University of Toronto (January
2005).
Beaudry, Paul, and David A. Green.
“Changes in U.S. Wages, 1976–2000:
Ongoing Skill Bias or Major Technological
Change?” Journal of Labor Economics, 23
(2005), pp. 609-48.
Card, David, and John DiNardo. “SkillBiased Technological Change and Rising
Wage Inequality: Some Problems and
Puzzles,” Journal of Labor Economics, 20
(2002), pp. 733-83.
DiNardo, John, and Jorn-Steffen Pischke.
“The Returns to Computer Use Revisited:
Have Pencils Changed the Wage Structure, Too?” Quarterly Journal of Economics,
112 (1997), pp. 291-303.
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Doms, Mark, and Ethan Lewis. “Information Technology Diffusion, Human
Capital, and Spillovers: PC Diffusion in
the 1990s and Early 2000s,” mimeo (July
2005).
Lewis, Ethan. “How Did the Miami Labor
Market Absorb the Mariel Immigrants?”
Federal Reserve Bank of Philadelphia
Working Paper 04-03 (January 2004).
Fortin, Nicole M., and Thomas Lemieux.
“Are Women’s Wage Gains Men’s Losses?
A Distributional Test,” American Economic
Review, 90 (2000), pp. 456-60.
Lewis, Ethan. “How Do Local Labor Markets in the U.S. Adjust to Immigration?”
Federal Reserve Bank of Philadelphia Business Review (First Quarter 2005).
Goldin, Claudia, and Lawrence F. Katz.
“The Power of the Pill: Oral Contraceptives and Women’s Career and Marriage
Decisions,” Journal of Political Economy, 110
(2002), pp. 730-70.
Mulligan, Casey B., and Yona Rubinstein.
“The Closing of the Gender Gap as a
Roy Model Illusion,” National Bureau of
Economic Research Working Paper 10892
(November 2004).
Johnston, David Cay. “Richest Are Leaving Even the Rich Far Behind,” New York
Times, June 5, 2005.
Nestoriak, Nicole. “Labor Market Skill,
Firms, and Workers,” Ph.D. dissertation,
University of Maryland (2004).
Juhn, Chinhui, Kevin Murphy, and Brooks
Pierce. “Wage Inequality and the Rise
in Returns to Skill,” Journal of Political
Economy 101 (June 1993), p. 410-42.
Sill, Keith. “Widening the Wage Gap: The
Skill Premium and Technology,” Federal
Reserve Bank of Philadelphia Business
Review (Fourth Quarter 2002).
Khan, Aubhik. “Why Are Married Women
Working More? Some Macroeconomic
Explanations,” Federal Reserve Bank of
Philadelphia Business Review (Fourth
Quarter 2004).
Valetta, Robert. “The Computer Evolution:
Diffusion and the U.S. Wage Distribution,
1984-2003,” mimeo, Federal Reserve Bank
of San Francisco (April 2005).
Krueger, Alan. “How Computers Have
Changed the Wage Structure: Evidence
from Microdata 1984-1989,” Quarterly
Journal of Economics, 108 (1993), pp. 33-60.
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Rich-Poor Gap Widens in the U.S., Class
Mobility Stalls,” Wall Street Journal, May
13, 2005
Business Review Q2 2006 25
Changes in the Use of Electronic
Means of Payment: 1995-2004
BY LORETTA J. MESTER
T
his article updates the tables published in
the Third Quarter 2003 Business Review.
These tables, which were first published as
part of an article in the March/April 2000
Business Review, presented data from the Federal Reserve’s
Survey of Consumer Finances. Loretta Mester, author
of the original article, has compiled information from
the recently released 2004 survey to keep our readers
up to date.
In “The Changing Nature of the
Payments System: Should New Players
Mean New Rules?” (Business Review,
Federal Reserve Bank of Philadelphia,
March/April 2000), I presented some
data from the 1995 Federal Reserve
Survey of Consumer Finances on the
use of electronic banking. This survey
of more than 4,000 households, which
is designed to be representative of all
households in the U.S., is redone every
three years. The following exhibits update the statistics indicating how the
usages of various means of electronic
payment have changed between 1995
and 2004.
Loretta J. Mester
is a senior vice
president and
director of
research at the
Federal Reserve
Bank of
Philadelphia. This
article is available free of charge at www.philadelphiafed.
org/econ/br/index.html.
26 Q2 2006 Business Review
As seen in Exhibit 1, usage of
electronic forms of payment, including ATMs, debit cards, automatic bill
paying, and smart cards, has risen from
about 78 percent of households in 1995
to about 90 percent of households in
2004. Debit card use, which doubled
between 1995 and 1998, continued
to increase rapidly and now stands at
nearly 60 percent of all households.
Increases were seen in all categories by
age, income, and education. Use of direct deposit and automatic bill paying
showed somewhat smaller increases,
with the percentage of households
now using automatic bill paying over
double what it was in 1995. Nearly 75
percent of households have an ATM
card. The question on smart cards
was dropped from the survey in 2004;
usage remained low in 2001, with less
than 3 percent of households having a smart card they could use for
purchases. There was a small increase
in the percentage of households that
use some type of computer software to
manage their money: from 18 percent
in 2001 (the first year this question
was asked) to about 19 percent in
2004. Respondents under 60 years old,
those with higher income, and those
with college degrees are more likely
to use a computer for money management.
As seen in Exhibit 2, households
that do business with at least one
financial institution have continued
to shift from paper-based methods
of conducting this business to automated methods. A sizable fraction
of households, over 75 percent, still
report that one of the main ways they
deal with at least one of their financial
institutions is in person; this percentage held steady between 2001 and
2004 but is down from 1995. Overall
use of electronic means of doing business – either ATM, phone, fax, direct
deposit and payment, other electronic
transfer, and/or computer – continued to increase between 2001 and
2004, but not as sharply as the sizable
rise seen between 1995 and 1998. In
2004, 89 percent of households used
an electronic method as one of their
main ways of conducting business, and
differences by income and education
have become less pronounced. There
remains, however, a large difference
in the popularity of ATMs across age
groups: over 79 percent of those under
30 years old use ATMs as one of their
main ways of conducting business,
while less than 40 percent of those
over 60 years old use them. Still, the
usage by those over 60 has more than
doubled since 1995.
The largest increase was seen in
the percentage of households that use
a computer, the Internet, or an online
service to do business. In 2004, over
www.philadelphiafed.org
33 percent of households used these
methods, up from less than 4 percent
in 1995. Youth, high income, and a
college degree continue to be associated with a higher incidence of computer
banking, but the computer remains a
less popular means of doing business
with financial institutions compared
with other methods.
EXHIBIT 1, PART 1
Percent of U.S. Households That Use Each Instrument:
1995, 1998, 2001, and 2004a
ATMb
Smart Cardb
Debit Card
1995
1998
2001
2004
1995
1998
2001
2004
62.5%
67.4%
69.8%
74.4%
17.6%
33.8%
47.0%
59.3%
1.2%
1.9%
2.9%
Under 30 years old
72.3%
75.6%
78.1%
83.0%
24.4%
45.0%
60.6%
74.4%
1.8%
2.6%
2.6%
Between 30 and 60 years old
68.6%
76.1%
76.8%
82.3%
19.7%
38.6%
53.4%
67.6%
1.5%
2.3%
3.3%
Over 60 years old
44.2%
41.9%
48.9%
51.6%
9.6%
16.0%
24.6%
32.5%
0.3%
0.5%
2.1%
Low income
38.5%
45.9%
46.8%
53.0%
7.0%
19.7%
29.2%
41.2%
0.7%
1.5%
1.9%
Moderate income
61.5%
64.4%
67.4%
73.4%
16.0%
31.6%
46.3%
57.4%
0.6%
3.1%
3.0%
Middle income
70.9%
72.0%
75.2%
78.3%
20.5%
36.6%
50.0%
64.3%
1.3%
2.0%
2.4%
Upper income
77.2%
82.3%
83.7%
86.5%
25.1%
43.8%
57.8%
69.3%
1.8%
1.7%
3.7%
No college degree
54.7%
60.1%
63.7%
67.4%
14.3%
29.2%
42.3%
54.9%
0.8%
1.8%
2.4%
College degree
80.4%
82.1%
81.6%
86.4%
25.2%
43.1%
56.2%
67.0%
2.1%
2.0%
3.8%
All Households
1995
1998
2001
By Age:
By Income:c
By Education:
a
The percentages reported are based on the population-weighted figures using the revised Kennickell-Woodburn consistent weights for each year.
(For further discussion see the Survey of Consumer Finances codebooks at www.federalreserve.gov/pubs/oss/oss2/scfindex.html.) This exhibit reports
percentages for all households.
b
The questions on ATMs and smart cards asked whether any member of the household had an ATM card or a smart card, not whether the member
used it. The other questions asked about usage. The question on smart cards was dropped from the 2004 survey.
c
Low income is defined as less than 50 percent of the median household income; moderate income is 50 to 80 percent of the median; middle income
is 80 to 120 percent of the median; and upper income is greater than 120 percent of the median. Each survey refers to income in the previous year.
Median income was $32,264 in 1994; $37,005 in 1997; $41,990 in 2000; and $43,318 in 2003.
Source: 1995, 1998, 2001, and 2004 Survey of Consumer Finances data as of March 31, 2006, Federal Reserve System, and author’s calculations.
www.philadelphiafed.org
Business Review Q2 2006 27
28 Q2 2006 Business Review
EXHIBIT 1, PART 2
Percent of U.S. Households That Use Each Instrument: 1995, 1998, 2001, and 2004a
Direct Deposit
Softwareb
Automatic Bill Paying
Any of the Methods:
ATM, Debit Card, Smart Card,
Direct Deposit, Automatic Bill
Paying, or Software
1995
1998
2001
2004
1995
1998
2001
2004
2001
2004
1995
1998
2001
2004
46.7%
60.5%
67.3%
71.2%
21.8%
36.0%
40.3%
47.4%
18.0%
19.3%
77.7%
85.5%
88.4%
90.4%
Under 30 years old
31.0%
45.2%
48.8%
54.0%
17.7%
30.5%
32.1%
36.5%
17.0%
20.4%
76.3%
80.2%
83.0%
87.3%
Between 30 and 60 years old
42.8%
58.0%
64.8%
68.2%
24.4%
38.6%
44.1%
50.3%
22.0%
21.9%
78.7%
87.5%
89.3%
90.3%
Over 60 years old
63.3%
74.8%
83.2%
87.0%
18.2%
33.0%
35.9%
46.5%
9.0%
12.8%
76.1%
83.7%
89.2%
91.9%
Low income
32.5%
44.3%
51.9%
54.8%
9.7%
17.1%
18.2%
24.6%
6.1%
6.8%
56.7%
69.3%
73.6%
77.4%
Moderate income
42.9%
58.8%
63.1%
64.0%
17.5%
30.5%
35.1%
40.5%
10.7%
11.1%
78.4%
87.2%
88.5%
88.6%
Middle income
48.3%
66.1%
65.7%
73.2%
23.4%
42.8%
45.1%
52.8%
16.3%
17.8%
85.1%
89.4%
92.3%
95.1%
Upper income
58.3%
70.4%
80.2%
83.6%
32.1%
49.3%
55.2%
62.4%
29.9%
31.4%
89.6%
94.9%
96.5%
97.1%
No college degree
40.3%
54.4%
61.8%
64.3%
18.1%
30.2%
33.7%
39.5%
10.9%
12.4%
71.4%
80.7%
84.7%
86.2%
College degree
61.0%
72.6%
78.0%
83.2%
30.1%
47.7%
53.2%
61.1%
31.8%
31.3%
91.8%
95.1%
95.6%
97.5%
All Households
By Age:
By Income:c
By Education:
www.philadelphiafed.org
a
The percentages reported are based on the population-weighted figures using the revised Kennickell-Woodburn consistent weights for each year. (For further discussion see the Survey of Consumer Finances
codebooks at www.federalreserve.gov/pubs/oss/oss2/scfindex.html.) This exhibit reports percentages for all households.
b
The question on software asked whether the respondent or spouse/partner uses any type of computer software to help in managing their money.
c
Low income is defined as less than 50 percent of the median household income; moderate income is 50 to 80 percent of the median; middle income is 80 to 120 percent of the median; and upper income is
greater than 120 percent of the median. Each survey refers to income in the previous year. Median income was $32,264 in 1994; $37,005 in 1997; $41,990 in 2000; and $43,318 in 2003.
Source: 1995, 1998, 2001, and 2004 Survey of Consumer Finances data as of March 31, 2006, Federal Reserve System, and author’s calculations.
EXHIBIT 2, PART 1
Percent of U.S. Households with at Least One Financial Institution Using Each
Method Among the Main Ways of Conducting Business with at Least One of
Their Financial Institutionsa
In Person
1995
All Households
1998
2001
Mail
2004
1995
1998
2001
ATM
2004
1995
1998
2001
2004
85.5% 79.5% 77.2% 77.3% 56.5% 54.1% 50.4% 50.2% 33.8% 52.6% 56.7% 64.4%
By Age:
Under 30 years old
77.0% 73.7% 71.5% 72.9% 58.2% 51.9% 50.5% 44.2% 53.0% 68.8% 72.6% 79.3%
Between 30 and 60 years old
86.8% 81.8% 78.6% 77.3% 62.1% 60.4% 56.6% 56.3% 37.7% 61.5% 65.0% 72.0%
Over 60 years old
86.7% 77.2% 76.8% 79.5% 44.0% 39.9% 36.0% 39.1% 16.2% 22.3% 29.8% 39.8%
By Income:b
Low income
81.2% 70.3% 68.2% 71.2% 32.8% 33.4% 24.7% 28.9% 19.6% 34.7% 35.6% 46.6%
Moderate income
85.9% 80.4% 76.9% 75.0% 48.5% 46.9% 42.0% 42.6% 29.6% 47.8% 50.5% 62.3%
Middle income
85.7% 81.4% 78.6% 77.7% 56.9% 56.4% 58.4% 56.0% 37.7% 54.1% 60.7% 65.7%
Upper income
87.7% 84.1% 81.8% 81.4% 74.3% 69.1% 64.9% 62.4% 42.3% 65.2% 69.6% 74.4%
By Education:
No college degree
85.8% 79.2% 75.1% 76.9% 49.4% 48.2% 43.5% 44.1% 27.4% 45.1% 50.1% 59.1%
College degree
84.8% 80.2% 81.1% 78.0% 71.2% 65.2% 63.0% 60.1% 46.7% 66.7% 68.8% 72.9%
a
The percentages reported are based on the population-weighted figures using the revised Kennickell-Woodburn consistent weights for each year.
(For further discussion see the Survey of Consumer Finances codebooks at www.federalreserve.gov/pubs/oss/oss2/scfindex.html.) Referring to each
financial institution with which the household does business, the survey asked: “How do you mainly do business with this institution?” Respondents
could list multiple methods, with the main method listed first. This exhibit reports for all households with at least one financial institution all the
methods a respondent listed for each of the household’s financial institutions. Note, the percentages do not add up to 100 percent across columns,
since households could list more than one method and more than one financial institution. Previous versions of this chart reported for 1998 and 2001
on the main ways respondents did business with their depository financial institutions (i.e., commercial banks, trust companies, thrifts, and credit
unions) rather than with any of their financial institutions.
b
Low income is defined as less than 50 percent of the median household income; moderate income is 50 to 80 percent of the median; middle income
is 80 to 120 percent of the median; and upper income is greater than 120 percent of the median. Each survey refers to income in the previous year.
Median income was $32,264 in 1994; $37,005 in 1997; $41,990 in 2000; and $43,318 in 2003.
Source: 1995, 1998, 2001, and 2004 Survey of Consumer Finances data as of March 31, 2006, Federal Reserve System, and author’s calculations.
www.philadelphiafed.org
Business Review Q2 2006 29
30 Q2 2006 Business Review
EXHIBIT 2, PART 2
Percent of U.S. Households with at Least One Financial Institution Using Each Method Among the Main
Ways of Conducting Business with at Least One of Their Financial Institutionsa
Phone
Electronicb
Computer
1995
1998
2001
2004
2001
2004
1995
1998
2001
2004
25.7%
49.7%
48.9%
48.8%
3.7%
6.2%
19.6%
33.6%
56.2%
81.7%
87.0%
89.2%
Under 30 years old
20.8%
45.4%
45.9%
43.2%
5.2%
8.3%
22.9%
42.2%
66.7%
81.0%
85.2%
89.2%
Between 30 and 60 years old
28.1%
54.3%
52.4%
51.4%
4.5%
7.6%
24.2%
39.8%
59.9%
85.1%
89.4%
90.9%
Over 60 years old
23.0%
40.6%
42.4%
45.7%
1.2%
1.6%
7.3%
15.4%
43.4%
73.9%
82.4%
85.4%
Low income
13.5%
28.8%
29.2%
30.0%
1.3%
1.5%
4.8%
14.0%
35.3%
65.4%
73.8%
78.7%
Moderate income
18.6%
42.5%
42.8%
44.8%
1.8%
2.7%
11.2%
22.5%
48.5%
80.1%
84.2%
84.8%
Middle income
22.6%
51.7%
51.7%
50.7%
4.0%
4.3%
17.8%
32.4%
59.2%
85.2%
89.7%
92.1%
Upper income
37.9%
64.9%
61.4%
60.0%
5.9%
11.5%
32.5%
49.4%
70.8%
91.0%
94.5%
95.6%
No college degree
19.7%
41.9%
41.7%
43.4%
2.8%
2.7%
11.3%
23.9%
47.8%
76.5%
83.2%
85.7%
College degree
38.1%
64.3%
61.9%
57.7%
5.6%
12.8%
34.8%
49.3%
73.5%
91.4%
94.0%
94.9%
All Households
1995
1998
By Age:
By Income:c
By Education:
www.philadelphiafed.org
a
The percentages reported are based on the population-weighted figures using the revised Kennickell-Woodburn consistent weights for each year. (For further discussion see the Survey of Consumer Finances
codebooks at www.federalreserve.gov/pubs/oss/oss2/scfindex.html.) Referring to each financial institution with which the household does business, the survey asked: “How do you mainly do business with this
institution?” Respondents could list multiple methods, with the main method listed first. This exhibit reports for all households with at least one financial institution all the methods a respondent listed for
each of the household’s financial institutions. Note, the percentages do not add up to 100 percent across columns, since households could list more than one method and more than one financial institution.
Previous versions of this chart reported for 1998 and 2001 on the main ways respondents did business with their depository financial institutions (i.e., commercial banks, trust companies, thrifts, and credit
unions) rather than with any of their financial institutions.
b
In 1995, electronic refers to ATM, phone, payroll deduction and direct deposit, electronic transfer, or computer. In 1998, 2001, and 2004, electronic refers to ATM, phone (via voice or touchtone), direct
deposit, direct withdrawal/payment, other electronic transfer, computer/Internet/online service, or fax machine.
c
Low income is defined as less than 50 percent of the median household income; moderate income is 50 to 80 percent of the median; middle income is 80 to 120 percent of the median; and upper income is
greater than 120 percent of the median. Each survey refers to income in the previous year. Median income was $32,264 in 1994; $37,005 in 1997; $41,990 in 2000; and $43,318 in 2003.
Source: 1995, 1998, 2001, and 2004 Survey of Consumer Finances data as of March 31, 2006, Federal Reserve System, and author’s calculations.
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/econ/resrap/index.
html. Or view our Working Papers at: www.philadelphiafed.org/econ/wps/index.html.
THE EQUITY PREMIUM AND RETURN
ON ASSETS
Recent empirical work documents a decline
in the U.S. equity premium and a decline in the
standard deviation of real output growth. The
author investigates the link between aggregate
risk and the asset returns in a dynamic production-based asset-pricing model. When calibrated
to match asset return moments, the model
implies that the post-1984 reduction in TFP
shock volatility of 60 percent gives rise to a 40
percent decline in the equity premium. Lower
macroeconomic risk post-1984 can account for a
substantial fraction of the decline in the equity
premium.
Working Paper 06-1, “Macroeconomic Volatility and the Equity Premium,” Keith Sill, Federal
Reserve Bank of Philadelphia
EXPLORING THE DYNAMICS OF
PREDATORY LENDING
Regulators express growing concern over
“predatory lending,” which the authors take to
mean lending that reduces the expected utility
of borrowers. They present a rational model of
consumer credit in which such lending is possible and identify the circumstances in which it
arises with and without competition. Predatory
lending is associated with imperfect competition, highly collateralized loans, and poorly
informed borrowers. Under most circumstances
competition among lenders eliminates predatory
lending.
Working Paper 06-2, “Predatory Lending in
a Rational World,” Philip Bond, The Wharton
School, University of Pennsylvania, and Visiting
Scholar, Federal Reserve Bank of Philadelphia;
www.philadelphiafed.org
David K. Musto, The Wharton School, University
of Pennsylvania; and Bilge Yilmaz, The Wharton
School, University of Pennsylvania
DEVELOPING EMPIRICALLY
VIABLE MODELS
The time series fit of dynamic stochastic
general equilibrium (DSGE) models often suffers from restrictions on the long-run dynamics
that are at odds with the data. Relaxing these
restrictions can close the gap between DSGE
models and vector autoregressions. This paper
modifies a simple stochastic growth model by
incorporating permanent labor supply shocks
that can generate a unit root in hours worked.
Using Bayesian methods the authors estimate
two versions of the DSGE model: the standard
specification in which hours worked are stationary and the modified version with permanent
labor supply shocks. They find that the data
support the latter specification.
Working Paper 06-3, “Non-Stationary Hours
in a DSGE Model,” Yongsung Chang, Seoul
National University; Taeyoung Doh, University of
Pennsylvania; and Frank Schorfheide, University
of Pennsylvania, CEPR, and Visiting Scholar,
Federal Reserve Bank of Philadelphia
POLICY ANALYSIS AND POTENTIALLY
MISSPECIFIED MODELS
This paper proposes a novel method
for conducting policy analysis with potentially misspecified dynamic stochastic general
equilibrium (DSGE) models and applies it to a
New Keynesian DSGE model along the lines
of Christiano, Eichenbaum, and Evans (JPE
2005) and Smets and Wouters (JEEA 2003).
The authors first quantify the degree of model
Business Review Q2 2006 31
misspecification and then illustrate its implications for the
performance of different interest-rate feedback rules. The
authors find that many of the prescriptions derived from the
DSGE model are robust to model misspecification.
Working Paper 06-4, “Monetary Policy Analysis with
Potentially Misspecified Models,” Marco Del Negro, Federal
Reserve Bank of Atlanta, and Frank Schorfheide, University
of Pennsylvania, and Visiting Scholar, Federal Reserve Bank of
Philadelphia
REVIEWING ESTIMATION AND EVALUATION
TECHNIQUES IN DSGE MODELS
This paper reviews Bayesian methods that have been
developed in recent years to estimate and evaluate dynamic
stochastic general equilibrium (DSGE) models. The authors
consider the estimation of linearized DSGE models, the
evaluation of models based on Bayesian model checking,
posterior odds comparisons, and comparisons to vector
autoregressions, as well as the nonlinear estimation based on
a second-order accurate model solution. These methods are
applied to data generated from correctly specified and misspecified linearized DSGE models, and a DSGE model that
was solved with a second-order perturbation method.
Working Paper 06-5, “Bayesian Analysis of DSGE Models,”
Sungbae An, University of Pennsylvania, and Frank Schorfheide, University of Pennsylvania, and Visiting Scholar, Federal
Reserve Bank of Philadelphia
THE RELATIONSHIP BETWEEN INCENTIVES TO
INVENT AND INCENTIVES TO PATENT
This paper develops a simple duopoly model in which
investments in R&D and patents are inputs in the production of firm rents. Patents are necessary to appropriate the
returns to the firm’s own R&D, but patents also create potential claims against the rents of rival firms. Analysis of the
model reveals a general necessary condition for the existence
of a positive correlation between the firm’s R&D intensity
and the number of patents it obtains. When that condition
is violated, changes in exogenous parameters that induce
an increase in firms’ patenting can also induce a decline in
R&D intensity. Such a negative relationship is more likely
when (1) there is sufficient overlap in firms’ technologies so
that each firm’s inventions are likely to infringe the patents
of another firm, (2) firms are sufficiently R&D intensive, and
(3) patents are cheap relative to both the cost of R&D and
the value of final output.
Working Paper 06-6, “When Do More Patents Reduce
R&D?,” Robert Hunt, Federal Reserve Bank of Philadelphia
www.philadelphiafed.org
REVISING ESTIMATES OF THE CPI FOR
TENANT 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 using a BLS microdata 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.
Working Paper 06-7, “The CPI for Rents: A Case of Understated Inflation,” Theodore M. Crone and Leonard Nakamura, Federal Reserve Bank of Philadelphia, and Richard Voith,
Econsult Corporation
DEVELOPING A SIMPLE STATE-DEPENDENT
PRICING MODEL
The authors develop an analytically tractable Phillips
curve based on state-dependent pricing. They differ from
the existing literature by considering a local approximation
around a zero inflation steady state and introducing idiosyncratic shocks. The resulting Phillips curve is a simple variation of the conventional time-dependent Calvo formulation
but with some important differences. First, the model is able
to match the micro evidence on both the magnitude and
timing of price adjustments. Second, holding constant the
frequency of price adjustment, the authors’ state-dependent
model exhibits greater flexibility in the aggregate price level
than does the time-dependent model. On the other hand,
with real rigidities present, this state-dependent pricing
framework can exhibit considerable nominal stickiness, of
the same order of magnitude suggested by a conventional
time-dependent model.
Working Paper 06-8, “A Phillips Curve with an Ss Foundation,” Mark Gertler, New York University and NBER, and
John Leahy, New York University, NBER, and Visiting Scholar,
Federal Reserve Bank of Philadelphia
Business Review Q2 2006 32
@FedFRASER