Full text of Review (Federal Reserve Bank of St. Louis) : January/February 1995

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C h an g es in the
D istrib u tio n of W e a lth :
In cre a sin g In e q u a lity ?
The Effects of F a ir V a lu e
A ccounting on In vestm en t
P o rtfo lio M a n a g e m e n t:
How F a ir Is It ?
N a rro w V s . Broad
M e a su re s of M o n ey a s
In te rm e d ia te Targ ets:
Som e Fo reca st R esults
An Introduction to the
T h e o ry an d Estim atio n of
a lo b -S e a rc h M o d el

President

Thom as C. M e lze r
Director o f Research

W illia m G . D ew ald
A ssociate Director o f Research

Cletus C. Coughlin
R esearch Coordinator and
Review Editor

W illia m T. G a v in

Banking

R . A lton G ilb e rt
D a vid C. W h e elo ck
International

C h risto p her J . N e e ly
M ich a el R. P a k k o
P a tric ia S . P o lla rd
M acroeconomics

D o n ald S . A lle n
R ich ard G . A n d erso n
Ja m e s B . B u llard
M ich a el J . D u e k e r
Jo sep h A . R itter
John A . Tatom
D a n ie l L. Thornton
P e te r Yoo
Regional

M ich e lle A . C la rk
K e v in L. K lie s e n
Adam M . Zaretsky

Director o f Editorial Services

D a n ie l P. B re n n an
Managing Editor

C h a rle s B . H enderso n
G raphic Designer

B ria n D. E b e rt

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A New V iew for the R eview
To long-time readers of the Federal Reserve Bank of St. Louis’
Review, this issue may appear dramatically different from its prede­
cessors. This re-design follows naturally from developments in
publishing technology, as well as the Bank’s commitment to provide
our readers with fresh, provocative economic analysis.
We have adopted this new format and design to improve read­
ability. For example, we have selected a suitable color for the tables
to highlight the information and yet enhance photoreproduction,
particularly for classroom use. Speaking of classrooms, we appreciate
hearing from the many professors who use articles and material from
the Review, and we continue to place a high priority on reaching
this audience.
The Review has always enjoyed a diverse audience, from the
interested layman to graduate students in economics to policymakers.
We will continue to publish a wide array of articles, ranging from
essays on policy issues for general readers to technical treatments
of economic issues. Our editorial policy is to place articles written
for the general reader first in each issue.
As always, please let us know what you think.




William T. Gavin
Editor
January 27, 1995




on bank holding companies’ management
of their investment portfolios, Anne
Beatty finds that including the effects
of SFAS 115 on regulatory capital could
have important consequences for both
the banking industry and the economy.
V o lu m e 7 7 , N um ber 1

41

N a rro w V s . B ro ad
M e a su re s of M o n ey a s

5

C h an g es in th e D istrib u tio n

In te rm e d ia te T a rg e ts:

of W e a lth : In cre a sin g

Som e Fo re ca st R esu lts

In e q u a lity ?

M ich a e l J . D u e k e r

John C. W e ich e r

Are the rich getting rich and the poor
getting poorer? Public concern about
apparently growing disparities between
the two has led to increasing interest
in how wealth is distributed. Joh n C.
W eicher examines the changes in
the distribution of wealth among U.S.
households that occurred between 1983
and 1989, a period w hich corresponds
approximately to the economic expan­
sion that began in late 1982 and ended
in m id-1990.

25 The Effects of F a ir V a lu e

Measures of money have long been
considered important links between
monetary policy actions and the course
of nominal spending and prices. The
Federal Reserve, however, has recently
de-emphasized monetary targets because
the traditional monetary aggregates
appeared to have unstable income
velocities. Michael J . Dueker describes
how money could continue to serve as
an intermediate target under a policy
of inflation or nominal GDP targeting.
He then empirically examines the
potential benefits, in terms of hitting
the ultimate inflation or nominal GDP
target, o f using alternative monetary
aggregates as an intermediate target.

A ccounting on In v estm en t
Portfo lio M a n a g e m e n t:
H ow F a ir Is It ?
A n n e B ea tty

Controversy has raged over the adoption
of the Statement of Financial Accounting
Standards Number 115 (SFAS 115),
which requires fair value accounting for
investment securities. Bankers and reg­
ulators have argued that this accounting
standard will lead to unrealistic volatility
in bank equity. Bankers have claimed
that this change in the accounting
method would cause them to alter their
investment portfolio management to
mitigate that increase in volatility.
Studying the impact of this change




53 An Introduction to the
T h eo ry a n d Estim atio n of
a Jo b -S e arch M o d el
A d am M . Z a r e ts k y an d
Cletus C. Coughlin

The process by which people who
have been laid off find jo b s is important
not only to the individuals themselves,
but also to policymakers and scholars.
Job-search models attempt to describe
the problems faced by individuals and
propose strategies for making optimal
employment decisions. Adam M.
Zaretsky and Cletus C. Coughlin describe
a simple model that illustrates that the




unemployed person’s decision to accept
or reject a jo b offer is reduced to a com ­
parison of the expected benefits from
additional searching with the expected
costs. They introduce a regression model
consistent with job-search theory and
illustrate the estimation of the model
using a sample of approximately 1,200
former M cDonnell Douglas employees,
laid off because o f cuts in defense
spending. Their illustration highlights
the effects that variables such as occupa­
tion, education, sex, tenure at McDonnell
Douglas and unemployment insurance
have on reemployment and prospective
wage offers.

REVIEW

RY/FEBRUARY

1995

Jo hn C. W e ic h e r is a se n io r fe llo w a t th e H u dson In s titu te a n d w as a vis itin g sc h o lar a t th e F e d e ra l R eserve B a n k o f St. Louis. H e id i L. B e y e r
p ro v id e d re sea rch assistance.

Changes in
the Distribution
of W ealth:
Increasing
Ineq uality?

and whose holdings have been given special
attention in previous research.

THE SURVEY OF
CONSUMER FINANCES
The Survey of Consumer Finances is
conducted by the Survey Research Center
of the University of Michigan for the Federal
Reserve Board. It was taken at six-to-eight-year
intervals between 1962 and 1983, and at
three-year intervals since then. The most
recent available surveys that are also useful
for analysis of the distribution of wealth are
those for 1983 and 1989.' These surveys are
partly longitudinal; some households were
interviewed in both years, but they are not
identified on the 1989 public-use tape.
In both of these years, the survey has two
samples. The larger is a cross-section chosen
randomly to represent the entire population
of households. It consists of 3,665 households
in 1983 and 2 ,277 in 1989.2 The smaller is a
“high-income” sample of households expected
to have unusually large wealth holdings.
Because the wealthiest 1 percent of house­
holds hold over a quarter of total household
wealth, a national sample of households will
therefore give little information about a large
fraction o f household wealth. The additional
high-income sample was intended to overcome
this limitation. It was selected from IRS
records. Households selected were first
asked if they would participate in the survey,
and then interviewed if they were willing.
Procedures were followed to insure confi­
dentiality; the IRS did not know which
households participated. There were 438
households in the high-income sample in
1983 and 866 in 1989.
The surveys are very similar but not
identical. The 1983 survey, for example,
reports calculations of the present value of
Social Security benefits and private pensions
expected by workers who are at least 40 years
old and have not yet retired. These calcula­
tions are based on assumptions about future
labor force participation, wages and inflation,

John C. W eicher
his article describes the changes in the
distribution o f wealth among U.S. house­
holds that occurred between 1983 and
1989, and analyzes the role of several demo­
graphic and economic factors in contributing
to the changes. It makes use of the Federal
Reserve Board’s Survey of Consumer Finances,
which is one of the few sources of time-series
information on household wealth that reports
asset holdings of individual households for a
sample of the entire population. The period
from 1983 to 1989 is a convenient and useful
period to study, because it corresponds
approximately to a single econom ic period:
the economic expansion that began in
November 1982 and ended in Ju n e 1990.
Academic and popular interest in distribu­
tional issues has increased in recent years,
and the 1980s have attracted particular
attention in the popular press, although
m ost of the attention has been given to
changes in the distributions o f income
and wages.
The article first describes the data in some
detail and then the measures of inequality.
The third section reports changes in wealth
holdings for U.S. households, cross-classified
in several ways. This is followed by analysis
of the changes in the distribution of wealth,
including investigation of some possible
explanations for the changes. The final
section describes the wealth holdings of the
richest 1 percent of U.S. households, who
have a large share of total household wealth

T




5

1 The 1 9 8 6 survey consisted of tele­
phone re-interviews of 2 ,8 2 2
households from the 1 9 8 3 SCF,
with much less detoil on asset hold­
ings. The 1 9 9 2 survey data tape is
not yet publicly available, but
Kennickell and Starr-McCluer
(1 9 9 4 ) report preliminary findings
and a comparison with 1 9 8 9 .
2 Another 159 households were
interviewed in 1 9 8 3 as port of the
national cross-section, but are
excluded from this analysis, as
from the Federal Reserve Board's
"cleaned sample,' because of non­
response. See Avery and
Elliehausen (1 9 9 0 , pp. 16-1 8 ).

REVIEW

UARY/FEBRUARY

1995

M easuring Long-Term Trends in W ealth
W ealth is the value of assets accumulated over long periods, and changes in total
wealth and its distribution over short periods of a few years provide incomplete inform a­
tion about individual well-being. The Surveys of Consumer Finances provide the best
recent information for different points in time, but it is still difficult to analyze long-term
changes in the distribution of wealth with these surveys. The only previous Federal
Reserve survey with a comparable sample, including high-wealth households, is the 1962
Survey of the Financial Characteristics of Consumers (SFC C ). W olff (1 9 8 7 , 1994) has
compared the 1962 and 1983 data and finds little change in the distribution over that
period as a whole for measures of wealth that include owner-occupied housing, but an
increase in concentration for narrower measures limited to financial assets.
The 1977 survey has much less information on wealth holdings than the later sur­
veys. It primarily reports on the credit experience of households, and is in fact entitled
the Survey of Consumer Credit (SCC) rather than the Survey of Consumer Finances. It
does not include all wealth categories, omitting some that are important, such as holdings
of unincorporated or closely held businesses. The wealth holdings in each category are
reported in brackets, with a top bracket of $ 2 0 0 ,0 0 0 or more, while the later surveys
report holdings to the dollar. It is therefore difficult to compare 1977 with the later years.
(Analysis of the 1983 SCF shows that the results are quite sensitive to whether the data
are bracketed and what convention is used for the top bracket.) Also, the period between
1977 and 1983 includes two very different econom ic experiences: three years of accelerat­
ing inflation and economic expansion between 1977 and 1980, followed abruptly by
back-to-back recessions and unanticipated disinflation during the early 1980s.
These limitations are worth mentioning because comparisons of the 1977 and 1983
surveys attracted substantial press attention when the data from the 1983 SCF were first
available; a comparison published by the Jo in t Econom ic Committee appeared to show a
dramatic increase in concentration. The increase turned out to be due to an apparent
error in reporting the holdings of one wealthy household (Curtin, Juster and Morgan,
1989, discuss this and other individual observations with questionable responses). The
more fundamental problems with comparisons are the differences in coverage of wealth
and reporting procedures between the two surveys.

3 For more extensive descriptions of
these surveys, see Avery ond others
(1 9 8 4 a ), Avery and Elliehausen
(1 9 8 6 ), Avery, Elliehousen and
Kennickell (1 9 8 8 ), Kennickell ond
Shock-Morquez (1 9 9 2 ) and
Kennickell and Woodburn (1 9 9 2 ).




the individual observations appropriately so
that the sample households adequately repre­
sent the universe of all households. Analysts
at both the Survey Research Center and the
Board have devoted substantial attention to
the issue of weighting. Both surveys include
weights for individual households on the basis
of the national cross-section sample and the
combined sample. The choice of weights
can affect the results, as will be seen later
in this article.

among other factors. The 1989 survey does
not contain these calculations; it reports only
the payment amount of a private pension. For
1983, locational information has been made
available on the metropolitan area or county
level for the cross-section sample (not the
high-income sample), while for 1989 no geo­
graphic information has yet been provided
on the data tape, although it was collected.
Regional information will be released for 1989
in the future. Geographic information would
obviously be useful for analyzing some com­
ponents of wealth, notably real estate.3
W ith a survey design combining a random
sample of all U.S. households and a separate
sample of the top few percent of the wealth
distribution, it becomes important to weight

MEASURING WEALTH
W ealth is defined as the value of assets
minus the value of liabilities. The SCF con­
tains detailed, though not quite exhaustive,

6

REVIEW

JANUARY/FEBRUARY

consumer durables is likely to be at least
as large as the remaining debt on them,
for most households. The latter is the sim ­
pler procedure.
Automobiles appear to be in an interme­
diate category. They are probably not held as
a store of value, but they can be converted to
cash much more easily than other consumer
durables.
Liabilities consist of home mortgage debt,
including: home equity lines of credit; debt
on other real estate; lines of credit other than
home equity loans; outstanding credit card
debt; amounts owed on automobile loans;
money owed to a business owned by the
household; money borrowed against life
insurance or other savings or retirement
plans; and money owed to a cash or call
money brokerage account.

information on both assets and liabilities,
most of which is used in this analysis. The
data in the surveys also pose some problems
for analysts, particularly with respect to com­
parison with other surveys and the process
of weighting the sample observations to
represent the nation as a whole.

Available Data in the SCF
Assets reported in the SCF include both
financial and real assets. Financial assets con­
sist of household holdings at depository insti­
tutions in the form of checking accounts,
savings accounts, money market accounts and
certificates of deposit; holdings of publicly
traded corporate stock; bonds of various kinds,
including government bonds, U.S. savings
bonds, corporate, municipal and foreign
bonds; holdings of mutual funds; retirement
accounts, such as IRAs and Keoghs; trusts;
the cash value of life insurance policies; the
current value of thrift-type pensions; and
debts owed to the household.
As noted previously, the SCF also provides
information on other private pensions that the
household expects to receive in the future and
(in 1983 only) Social Security benefits, even
though the household cannot convert them
to cash.
Real assets include: owner-occupied
housing; other real estate, such as apartment
buildings and office and commercial buildings;
unincorporated, closely held businesses; auto­
mobiles; boats and airplanes; and collectibles
such as coins, stamps or objets d’art.
The surveys do not include consumer
durables besides automobiles and other
vehicles, although the debt incurred to buy
consumer durables is reported as a liability.
The rationale for this is that consumer durables
are generally held for use, not as a store of
wealth. Estimating the value of consumer
durables is also difficult. Nonetheless, they
do constitute part of the possessions of
households, perhaps a substantial part for
lower-income households. They can be
taken into account either by attempting to
estimate their value (a procedure followed
by W olff, 1987), or by excluding the debt
incurred to buy them as well as their value
on the ground that the total value of all




1995

Alternative Measures of Wealth
It is possible to construct several different
definitions of wealth from the SCF, and ana­
lysts have done so. In this article, the basic
definition includes all of the assets and liabil­
ities in the SCF except the present value of
pensions now being received and expected,
which is reported in full only for 1983.4 The
difference between these assets and liabilities
will be referred to as “net worth” or “wealth”
without further qualification. This definition
is the same as that used by Kennickell and
Shack-Marquez (1 9 9 2 ), except that they
exclude miscellaneous assets (mainly col­
lectibles) in 1983 but not 1989.5 It differs from
W olffs (1994) preferred measure, termed “net
w orth,” in two ways: W olff excludes m iscel­
laneous assets and the value of automobiles
(but includes automobile loans). W olff also
reports a measure that includes the value of
automobiles, termed “net worth plus autos,”
which is closer to the preferred measure in this
article, and “financial net worth” (excluding
both the value and the mortgage on owneroccupied housing as well as automobiles from
net worth).
Other analyses have used both broader
and narrower measures, which complicates
comparisons between studies. W olff (1987)
includes miscellaneous assets for 1983, and
reports five measures, ranging from an inclu-

FEDERAL RESERVE B A N K OF S T . L OU IS

7

4 Some results excluding consumer
debt are reported also.
5 Avery and Elliehausen (1 9 9 0 )
warn in the codebook for 1 983
that "some estimates [for miscella­
neous assets] look to be very dubi­
ous." Including or excluding mis­
cellaneous assets in both years
does not change the results in this
article.

sive concept that adds an imputed value for
other consumer durables and household
inventories to the assets in the SCF, to “capital
wealth,” which is limited to currency, deposits
in financial institutions, money market funds,
and pension and insurance cash surrender
value. Avery, Elliehausen and Canner (1984b)
report net worth for 1983, and also 1977,
excluding automobiles, the cash value of life
insurance, the present value of expected future
pension benefits, and equity in small busi­
nesses and farms (which were not reported
in the 1977 SCC).

Weighting

Conversation with Arthur Kennickell.
See Avery and Elliehausen (1 9 9 0 ,
pp. 16-24) tor a detailed discus­
sion of weighting in the 1 9 8 3 SCF.
Conversation with Arthur Kennickell.
Conversation with Gerhard Fries of
the Federal Reserve Board. See
Kennickell and Woodburn (1 9 9 2 )
for detailed discussion of the differ­
ences between the FRB and SRC
weights.

W ith a survey design combining a
cross-section sample of all U.S. households
and a separate sample concentrated in the
top few percent of the wealth distribution, it
becomes important to weight the individual
observations appropriately so that the sample
households adequately represent the universe
of all households. Analysts at both the Board
and the Survey Research Center have devoted
substantial attention to the issue of weighting,
and have developed alternative weights, which
are commonly referred to as FRB and SRC
weights, respectively. In 1983, the FRB and
SRC weights differed primarily in the way
that they combined separate weights for the
cross-section and the high-income samples.6
After the initial weights were developed, a
second set of FRB weights was constructed
when 1982 individual income tax data sug­
gested that the high-income sample may have
been given too much weight. These are known
as the “FRB extended-income” weights.'
Alternative weights have also been constructed
along a second dimension: whether the sample
was “blown up” to the U.S. total on the basis
of the 1980 decennial Census or the 1983
Current Population Survey (CPS). Most recent
studies have used 1983 CPS weights, but these
were not available on data tapes until after
1985; both Avery and others (1984a, 1984b)
and W olff (1987) used 1980 decennial
Census weights.
In this article, the FRB extended-income
weight and the latest SRC weight (the revised
SRC composite weight) are used for 1983.
(These are variables B 3016 and B3019,




respectively, on the data tape.) Kennickell
and Shack-Marquez (1 992) use the FRB
extended-income weight.
For 1989, two SRC weights are available:
a preliminary weight used by Kennickell and
Shack-Marquez (1 9 9 2 ) for comparing 1983
to 1989, and a final weight used by Kennickell
and Starr-McCluer for comparing 1989 to
1992 (variables X 40125 and X 40131). Both
are closer in design to the 1983 FRB weight
than to the SRC weight.8 An experimental
FRB weight (variable X 4 0 2 0 2 ) was included
in early versions o f the public-use tape, but
dropped from those currently available.’
W olff (1 994) reports that it generates wealth
totals that are less consistent with the Flow
of Funds (FO F) than the SRC weights. (This
issue is discussed further in the next section.)
Both SRC weights are used in this article.
The choice o f weights can affect the results,
as will be seen later.

Adjusting the Data for Consistency
with Other Sources
The total asset and liability values in the
SCF differ from information in other sources
in both 1983 and 1989. In particular, there
are substantial differences between the SCF
and the FO F, published by the Federal
Reserve Board, w hich reports aggregate data
over time on the composition of national
wealth. In several categories, the SCF total
is much smaller. There appears to be general
agreement that the SCF is a better source for
the current values of owner-occupied housing
and unincorporated businesses, but differing
views on the relative accuracy of the data for
financial assets and liabilities. The conceptual
differences in coverage are analyzed most
extensively by Avery, Elliehausen and
Kennickell (1 9 8 8 ) with reference to 1983,
and by Antoniewicz (1994) for 1989 and 1992.
W olff (1 9 8 7 , 1994) also discusses the differ­
ences and compares them for both years.
Analysts have reached different conclu­
sions about the relative merits of the two
surveys and followed different procedures
in adjusting for these discrepancies. W olff
(1 9 8 7 , 1994) takes the FO F as the more
accurate source for financial asset values and
adjusts many of the SCF figures for individual

F EDERAL RESERVE B A N K OF S T . L OU I S

REVIEW
prices rose by 20 to 3 0 percent, W o lff s
method results in larger FO F values and
a bigger difference.
Analysts also differ in their calculated
SCF totals for individual asset and liability
categories because they have used different
weights. W olff (1 987) uses weights for the
1980 decennial Census, which blow up the
sample to 79.8 million households, while
Avery, Elliehausen and Kennickell use weights
based on the 1983 CPS, which blow up the
sample to 83.9 million households. In most
cases, Avery, Elliehausen and Kennickell
report a larger total for the SCF, and there­
fore a larger SCF/FOF ratio. Some of the
differences are substantial: W olff calculates
mortgage debt at $704 billion, or 63 percent
of the FO F total, for example, while Avery,
Elliehausen and Kennickell calculate it at
$975 billion, or 92 percent. In this article,
the 1983-based weights are used and the cal­
culated SCF totals are usually closer to Avery,
Elliehausen and Kennickell than to Wolff.
The larger discrepancies occur on the
liability side in both years. They are so large
that adjusting individual household data for
the difference between the SCF and FOF
leads to some rather odd results, especially
for households which report large consumer
debt. Adjusted wealth for these households
is sometimes large and negative, while unad­
justed wealth is large and positive. In 1983,
for example, the 10 poorest households on an
adjusted basis included five with wealth over
$ 1 million on an unadjusted basis; one house­
hold went from +$4.3 million to -$9.3 million.
W hen assets and liabilities are adjusted,
17 percent o f all households in 1983 and
13 percent in 1989 reported negative net worth.
W olff (1 994) suggests that the differences in
liabilities between the SCF and FOF probably
occur because of failure to report a debt, rather
than understatement by households which
do report it; in that case, proportional adjust­
ment is likely to misrepresent the position of
households which actually report relatively
large debt holdings to begin with. In his analy­
sis of the 1989 SCF, he therefore adjusts assets,
but not liabilities, to be consistent with the
FOF. Given the much smaller SCF/FOF debt
ratios for 1983, the same argument would
appear to hold for that year as well.

households by the ratio of the aggregate totals
for the SCF and the FOF. Avery, Elliehausen
and Kennickell (1 9 8 8 ), Avery (1989) and
Curtin, Juster and Morgan (1989) have argued
that the SCF is more likely to be accurate for
1983 than the FO F in most instances. They
conclude that total assets and liabilities in
most categories are similar when the data are
reported on the same conceptual basis. Avery
(1 9 8 9 ) points out that the FO F figures for
households are computed as balancing resid­
uals, and thus are sensitive to measurement
errors for every other sector. He also notes
that totals for broad categories of assets, such
as bonds, are often closer than for sub-categories such as federal bonds or municipal
bonds, and suggests this may result from
misclassihcation.
If holdings of the sub-categories are
not uniform across the wealth distribution,
adjustment may distort the measured degree
of inequality. Neither Avery and others
(1984a, 1984b), Avery and Elliehausen
(1 9 86) nor Kennickell and Shack-Marquez
(1 992) adjust the SCF data. Smolensky
(1 989) reviews the issue for the 1983 data
and concludes that the SCF is likely to be
the better data source, partly on the general
grounds that cross-section surveys usually
employ state-of-the-art methodology, while
time-series data collection and processing
change slowly for an ongoing series, for good
reason but perhaps at the cost of failing to
capture changes in the economy.
Several basic differences between the SCF
and FO F apply to all asset categories. The
FO F “household” sector includes nonprofit
institutions and personal trusts as well as
households. W olff uses a 1980 estimate for
households alone, relative to the FOF for that
year, to adjust the FO F for 1983 (and appar­
ently also for 1989). Avery, Elliehausen and
Kennickell use Federal Reserve Board estimates
of the “real” households within the FOF sector
to adjust the FOF totals. In addition, the data
refer to slightly different periods. The SCF
was conducted early in 1983. W olff uses the
average of 1982 and 1983 year-end totals from
the FO F as the basis of comparison, while
Avery, Elliehausen and Kennickell use the
end of 1982. Since 1983 was a year of eco­
nomic recovery, in which stock and bond




9

1 T a b le




1

1

M e an H ousehold W e a lth , 1 9 8 3 an d 1 9 8 9
(in th ou sand s of 1 9 8 9 d o lla rs)
1 9 8 3 FRB
(B3016)

1983 SRC
(B3019)

1989 SRC
(X40131)

1989 SRC
(X40125)

Unadjusted:
Including autos

$150.9

$160.5

$184.7

Excluding autos

145.9

155.0

176.6

172.6

Excluding autos and homes

102.8

112.8

126.7

121.3

Including autos, homes, and present
value of private pensions & Social Security

222.3

231.8

NA

NA

$180.7

Adjusted (assets and liabilities):
Including autos

$165.8

$173.4

$200.4

$194.7

Excluding autos

160.4

167.9

192.3

186.6

Excluding autos and homes

125.2

133.3

142.4

135.3

Including autos, homes, and present

237.2

244.7

NA

NA

$187.8

$196.9

$207.7

$201.8

value of private pensions & Social Security
Adjusted (assets only):
Including autos
Excluding autos

182.3

191.4

199.5

193.7

Excluding autos and homes

139.7

149.2

149.6

142.4

$33.4

$35.0

$38.8

$35.8

Income
NA - Not available in 1 9 8 9 Survey of Consumer Finances

NOTE: 1 9 8 3 values adjusted to 1 9 8 9 using the CPI-U annual average fo r the calendar years (1 9 8 3 values m ultiplied by 1 .2 4 4 9 8 ).
* Asset categories adjusted: (1 9 8 3 and 1 9 8 9 ) dem and deposits and currency, tim e deposits, CDs, IRAs, m oney m ark et accounts, bonds, stocks, call
m oney accounts, m utual funds; (1 9 8 3 only) cash surrender value o f insurance, cash surrender value of pensions; (1 9 8 9 only) trusts. Liability cat­
egories adjusted: (1 9 8 3 and 1 9 8 9 ) credit card debt, consumer loans, life insurance loans, m argin account debt, autom obile loans; (1 9 8 3 only)
hom e m ortgage debt, m ortgage debt on rental and comm ercial real estate, debt on land contracts.
SOURCE: Survey o f Consumer Finances, 1 9 8 3 and 19 8 9

On balance, it seems best not to adjust the
data, because the 1983-based weights are used
and also because adjusting liabilities affects
the individual household data so gready. This
article therefore uses unadjusted data for most
of the analysis, but also reports results with
W o lffs adjustments on both sides of the bal­
ance sheet (his 1983 procedure) and with
assets adjusted but liabilities not adjusted
(his 1989 procedure).

unadjusted data for both sets of weights in
each year. On any comparison, mean wealth
increased between 1983 and 1989, but the
magnitude depends on the weights chosen.
The increase ranges from $20,000 to $34,000.
The choice of weights is particularly impor­
tant for 1983; the difference in mean wealth
is almost $10,000. During the six years, mean
household wealth increased by 13 percent to
22 percent. The first and last columns show
wealth for the weights used by Kennickell
and Shack-Marquez (1 9 9 2 ). The increase
was 20 percent on the basis of these weights.
The table shows the importance of
owner-occupied housing and the present
value of future pension benefits. Future pen-

MEASURES OF MEAN
HOUSEHOLD WEALTH
Table 1 reports mean household wealth
for 1983 and 1989. The first panel uses

10

REVIEW

JANUARY/FEBRUARY

sions were close to one-third of mean house­
hold wealth in 1983, and owner-occupied
housing constituted about 30 percent of the
remainder in both years, more than any other
asset. Automobiles were the most widely held
asset (84 percent of all households in both
years). Among financial assets, corporate
stocks comprised the largest share (19 per­
cent in both years), and checking accounts
were the most widely held (79 percent of all
households in 1983 and 75 percent in 1989).
On the liability side, credit card debt was the
most common form of debt in both years, but
home mortgages were almost equally frequent.
Home mortgage debt accounted for over half
of all family debt in both years.
The lower panels show the effect of
adjusting assets and liabilities. Adjustment
adds $36,000 to $37,000 to assets and $22,000
to $23,500 to liabilities in 1983. It is less
important in 1989, however, adding $21,000
to $23,000 to assets and $7,000 to liabilities.
Using the adjusted data, increases in mean
household wealth are smaller in both percent­
ages and amounts. Home equity accounts for
about one-third of the increase in the unad­
justed data ($7,000 to $9 ,0 0 0 ), but over half
($ 1 5,000 to $ 1 7,000) when both assets and
liabilities are adjusted.
The table also shows mean household
income, which is a pre-tax figure reported
by the respondent. The SCF asks about total
income and also income from various sources.
In many cases, the sum of the latter does not
equal the total.

skewed. Concentration ratios have also been
popular because one of the few time-series
measures of wealth is the estate multiplier,
which is a method of estimating the wealth
of the richest households from estate tax
returns, which are filed mainly by well-to-do
individuals, and mortality tables to estimate
the holdings of well-to-do living households.
The SCF provides information not only
about wealthy households but also about the
broad middle class and the poor.10 The Lorenz
curve and the Gini coefficient can be used to
describe the distribution of wealth among all
households in the SCF in exacdy the same way
as they are used to measure the distribution
of income in household surveys.
A schematic Lorenz curve is shown in
Figure 1. It depicts the total number of house­
holds on the horizontal axis and their total
wealth holdings on the vertical axis. To con­
struct the Lorenz curve, households are first
arrayed in ascending order by wealth. Then
the cumulative total wealth is calculated,
beginning with the poorest household and
ending with the richest one. These values
are plotted for each household on the diagram,
and then connected to construct the curve.
Thus, for example, the first point on a Lorenz
curve might represent one household with
wealth of $10, the second point might repre­
sent two households with total wealth of $21,
and so on. Any given point on the curve shows
that the poorest x percent of households own
y percent of all wealth in the society.
Two limiting cases are easily shown and
may clarify the concept. If the distribution of
wealth is perfectly equal, then every house­
hold has the same amount o f wealth, and the
Lorenz curve is the diagonal line running
from the origin at the lower left at a 45-degree
angle to the point in the upper right corner
of the diagram representing the total number
of households and their total wealth. At the
opposite extreme, if all wealth belongs to one
household, then the Lorenz curve lies along
the horizontal axis until it reaches the point
representing the total number of households;
the Lorenz curve then becomes the vertical
line on the right side of the diagram.
The Gini coefficient is calculated from the
Lorenz curve as the ratio o f the area between
the diagonal and the Lorenz curve over the

M EASURING THE
DISTRIBUTION OF WEALTH
Two types of measures of distribution
are commonly used in economics: measures
describing the entire distribution and measures
describing the extent of concentration at one
end of it.
The most common examples of the first
type are the Lorenz curve and its companion,
the Gini coefficient, which are often used to
measure the distribution of income. The dis­
tribution of wealth is usually measured by a
concentration ratio, such as the share of total
wealth held by the richest 5 percent or 1 per­
cent of all households, because it is so highly




1995

11

10 Avery, Elliehausen and Kennickell
(1 9 8 7 ) compare estate tax data
with the SCF for 1 9 8 3 .

REVIEW

UAIY/FEBRUARY

Fig u re 1

1995

data in the table demonstrate the importance
of the technical issues discussed in the pre­
ceding secdon and suggest several broad
conclusions.

Lo ren z C u rve an d
G in i C o efficien t
Percent of aggregate wealth

The Importance of Weighting
The determination of whether there has
been an increase in inequality depends on the
choice of weights. For the broadest measure
of wealth, and using unadjusted data, the
change from 1983 to 1989 varies from -0.002
to +0.027. The Gini coefficients differ by
0 .017 for the two sets of weights in 1983,
and by 0 .012 in 1989. The standard errors
of these Gini coefficients, shown in italics in
Table 2, are large enough to cast doubt on
whether there was an increase in inequality
over the period. To analyze the significance
of the difference in the Gini coefficients,
bootstrap estimates of standard errors were
calculated using 1,000 replications." The
difference between the 1989 and 1983 Gini
coefficients was positive in 920 cases when
B 3016 and X 40 1 2 5 were used as weights,
and in 9 9 2 cases when B 3016 and X40131
were used. However, it was positive in only
479 cases when B 3019 and X 40125 were
used, and in 785 cases when B 3019 and
X 40131 were used. Finally, the weights for
each year were averaged (a technique used
by W olff, 1994, for the 1989 survey); in this
instance, the difference was positive in 873
cases. These results indicate that the increase
in inequality was more or less on the margin
of significance. W hether the magnitude of
the difference is politically or socially impor­
tant is a matter for individual judgm ent.12
By m ost of the other measures reported
in Table 2, the distribution o f wealth became
somewhat more unequal over the period.
W hen first automobiles and then owneroccupied housing are excluded, all of the
1983-1989 comparisons show an increase
in inequality, but the choice of weights
still affects the extent of the increase.
In the remainder of this article, compar­
isons will be based on the weights used by
Kennickell and Shack-Marquez (1 9 9 2 ), unless
otherwise indicated. These are variables
B 3016 and X 40125.

Gini Coefficient:

11 Efron and Tibshirani (1 9 9 3 ) is an
excellent introduction to the boot­
strap method. The foct that the
SCF sample is not random does not
affect the bootstrap method as long
as the re-sampled Gini coefficients
are calculated using the same
weights os the actual estimate.
Each re-sampling from the biased
sample generates the same bios
(plus noise), so the bootstrap pro­
cedure traces out the behavior of
the Gini coefficient estimates under
the actual sampling procedure. For
an alternative procedure using the
jockknife technique, see Yitzhaki
(1 9 9 1 ), who provided a FORTRAN
program that served os a starting
point for the analysis. Also, see
Lermon and Yitzhaki (1 9 8 9 ).
12 Wolff (1 9 9 4 ) refers to an increase

j4 _

area betw. curve and diagonal

“ >1+8 “

area under diagonal

area under the diagonal, or L = A1(A + B). The
Gini coefficient is therefore bounded by zero
and 1. If the distribution of wealth is perfecdy
equal, the Lorenz curve lies along the diagonal,
the value of A is zero, and the Gini coefficient
is zero. If one household owns all the wealth,
the area under the Lorenz curve is the same
as the area between the diagonal and the x-axis,
the ratio is 1.0 and the Gini coefficient is unity.
The greater the concentration of wealth, the
closer the Gini coefficient is to unity.
W ith weighted or bracketed data, the
Lorenz curve consists of a series of straightline segments, with the length of each segment
being the weight of the observation. The 1983
and 1989 SCF contain more than 4 ,0 0 0 and
more than 3,000 observations, respectively,
so the line segments approximate closely to
a curve and the area B approximates to the
integral of the Lorenz curve. The Gini coef­
ficients reported in this article are calculated
from the line segments. The area A is the sum
of the areas above the line segments and
below the diagonals.

of .0 4 in the Gini coefficient
between 1 9 8 3 and 1 9 8 9 ns
"sharp," and a difference of ,02
between Gini coefficients for two
different measures of wealth in
1 9 8 9 as "not great." He does not
report Gini coefficients to more than
two places.




CHANGES IN THE
DISTRIBUTION OF
WEALTH, 1 9 8 3 - 8 9
Table 2 reports Gini coefficients for 1983
and 1989, in a parallel form to Table 1. The

12

Ta b le 2

G in i C o efficien ts, 1 9 8 3 an d 1 9 8 9
(a lte rn a tiv e w eig hts)
1 9 8 3 FRB
(B3016)

1983 SRC
(B3019)

1989 SRC
(X40131)

1989 SRC
(X40125)

.793

Unadjusted:
Including autos

.778

.795

.805

Standard error

.008

.009

.008

.008

Excluding autos

.798

.814

.826

.815

Excluding autos and homes

.900

.912

.925

.921

Including autos, homes, and present

.690

.708

N.A.

N.A.

Including autos

.773

.788

.813

.801

Excluding autos

.788

.803

.832

.821

Excluding autos and homes

.865

.877

.920

.915

Including autos

.817

.827

.836

.825

Excluding autos

.836

.846

.858

.848

Excluding autos and homes

.948

.953

.967

.966

.465

.491

.540

.505

Value of private pensions & Social Security
Adjusted (assets only):

Adjusted (assets and non-mortgage debt only):

Income:

Alternative Measures of Wealth

Adjusting Assets and Liabilities

The broader the definition of wealth,
the more equal is its distribution, in either year.
Gini coefficients are highest when automo­
biles, home equity and the present value of
future pensions (in 1983) are excluded from
wealth. They are lowest when these assets are
included. Merely including automobiles in
household net worth reduces the Gini coeffi­
cient by about 0.02. Including home equity
reduces it by about 0.10, as does including
the value of future pensions. These assets
are widely held, as previously noted, and
they clearly represent a large share of the
wealth of relatively low-wealth households.
Excluding consumer debt does not have
much effect on the analysis. Mean unadjusted
consumer debt was $2,000 in 1983 and $1,100
in 1989. Gini coefficients are consistently
lower when consumer debt is excluded, by
0 .004 in both years. Since consumer debt is
relatively more important for lower-wealth
households, this is not surprising.

The table demonstrates the importance
of adjustment, particularly on the liability side
of the balance sheet. Gini coefficients are all
much higher, for each set of weights and each
measure of wealth, by between 0.03 and 0.05
when liabilities are adjusted. As could be
expected from the fact that the adjustments
are larger in 1983, the coefficients for that
year are raised slighdy more than the coeffi­
cients for 1989, and therefore the measured
increase in inequality is generally smaller.
The results in Table 2 do not adjust
for mortgage debt in 1983. The coefficient
would be raised still further in 1983, by about
a further +0.030, if mortgage debt were also
adjusted as W olff (1 9 8 7 ) has done, but since
the SCF and FOF agree rather closely when
1983 weights are used, these results are
omitted from the table.
W hen only assets are adjusted, the Gini
coefficients are lower in 1983 and usually
higher in 1989, compared to the coefficients




13

REVIEW

UARY/FEBRUARY

T a b le 3

would have changed. W olff (1 9 9 4 ) suggests
that such changes may have contributed sig­
nificantly to the increase in inequality that
he measures. He notes specifically that stock
prices increased more than house prices, and
stock ownership is more concentrated among
high-wealth households.
Table 3 reports commonly used price
indices for almost all of the asset categories
included in the SCF. Indices are not available
for unincorporated businesses, but the change
in their value may be approximated by the
Russell 2000 and Nasdaq small-stock indices.
It is possible to measure the effect of
these changes in asset values on the distribu­
tion of wealth by applying the indices to the
1983 holdings of each household. In behav­
ioral terms, it is assumed that the household
holds the same portfolio in both years, neither
buying nor selling any assets, nor for that
matter moving.
For m ost assets, the index can be simply
multiplied by the reported 1983 value. In the
case of owner-occupied housing, the change
in the price of the house is not the change in
home equity, for two reasons. First, for house­
holds with mortgages, home equity increases
in percentage terms by more than the increase
in the price of the home. The mean ratio of
outstanding mortgage principal balance to
house value was 23 percent in the 1983 SCF,
and the mean equity was therefore 77 percent
of house value. The full value of the increase
in house value raises the owner’s equity, so
the mean home equity increased by 35 per­
cent (27/77) instead of 27 percent. Second,
it is assumed that the household continued
to make mortgage payments during the six
years; otherwise, it would default on the
mortgage and lose the house, and thus change
its portfolio. The mean remaining life of first
mortgages was 15 years, eight months, in
1983; for second mortgages, it was seven
years, 10 months. If homeowners continued
to make mortgage payments for the six years
between the two surveys, then on average they
paid off a non-negligible share of the first
mortgage and alm ost all the second (unless it
was a balloon mortgage). The mean reduction
in the outstanding principal balance was
24 percent, and the mean increase in home
equity was 7.1 percent. The net effect of all

In d e x C h an g es in A sse t V a lu e , 1 9 8 3 - 8 9
(b a se d on a n n u a l a v e r a g e s , e x cep t a s noted )

Asset Category

Index

Percent Change,
1983-89

Stocks

Standard & Poor 500

Taxable bonds*

Dow-iones 20-bond index

Tax-exempt bonds

Standard & Poor's municipal

29

Owner-Occupied houses

Census one-family home index

27

Investment real estate**

Frank Russell property index

Unincorporated business***

Russell 2000

Unincorporated business

Nasdaq 0TC composite index

Farms

USDA average value/acre

101%
21

5
50
63
-1 6

* Y early highs
* * Compiled from quarterly averages; index fo r comm ercial real estate
* * * * Last trading day in Decem ber
SOURCES: Statistical Abstract of the United States: 1992; U.S. Bureau o f the Census, Price Index of
New One-Family Homes Sold; Frank Russell Company; U.S. D epartm ent o f Agriculture; Lawrence J.
W hite, The S&L Debacle, (pp. 1 1 0 -1 1 ).




1995

based on the unadjusted data. This may reflect
the fact that the largest adjustment in 1983 is
for savings accounts, which are widely held,
while the largest adjustment in 1989 is for
stocks, bonds and trusts. The increase in
inequality is about double that based on
unadjusted data.

EXPLANATIONS
FOR THE CHANGE
A number of phenomena have been
suggested as explanations for the changes in
the distribution of wealth (or income) during
recent years. It is possible to examine the
effects of some of these phenomena and get
at least a preliminary sense of their possible
importance. Three in particular are worth
attention: changes in asset prices; demo­
graphic changes; and the changing distribu­
tion of income.

Changes in Asset Prices
To some extent, the changes in the
distribution of household wealth may be
attributable to changes in asset prices. Even
if each household held exactly the same assets
in 1989 and 1983, the distribution of wealth

NK OF S T . L OUIS

14

the assumptions is to raise mean home equity
by 4 2 percent.
In Table 4, the effect of these changes on
the Gini coefficient is shown for several indi­
vidual assets and combinations of assets. The
wealth measure used in these calculations is
unadjusted and includes automobiles.
The results suggest that changes in asset
values as a whole had little effect on the dis­
tribution o f wealth. The effect o f changes for
some individual asset categories were large.
In three cases— stocks, unincorporated busi­
nesses (measured by the Russell Index) and
owner-occupied housing— the coefficients
change by more than the 1983 standard error,
and are about as large or larger as the increase
between 1983 and 1989. But the changes go
in both directions and largely cancel each
other. The changes in stock prices and unin­
corporated businesses both raise the Gini
coefficient, but the change in home equity
lowers it, and has about twice the effect of
either. Even though stock prices rose more
than any other asset and stock holdings are
concentrated among richer households, the
rise in house prices increased the wealth of a
broad range of middle-class households by
enough to make the distribution of wealth
more equal. The combined effect of the
changes in all assets was to lower the Gini
coefficient slightly, by much less than its
standard error.
The Gini coefficients were also calculated
using the 1983 SRC weight (variable B 3019),
and the results are basically the same.
As a further check, 1989 was used as the
base year for asset holdings, and values were
deflated back to 1983. This is also shown in
Table 4. The results were consistent with those
using 1983 as the base year. The most notable
differences are that the effect of deflating stock
values from 1989 back to 1983 was much
smaller in absolute value, and the effect of
deflating equity in owner-occupied housing
was much larger, so that the effect of changing
all asset values simultaneously is larger in
absolute value. The combined effect of all
the changes is again in the opposite direction
from the change in the Gini coefficient. There
is also one qualitative inconsistency: Deflating
investment real estate values from 1989 back
to 1983 has the “wrong” sign. W ith 1983 as




T a b le 4

Effect of 1 9 8 3 - 8 9 A sse t V a lu e C h an g es on
1 9 8 3 G in i C o efficients
(u n ad ju sted n et w o rth , including auto s)
Change in Gini Coefficient
Asset

1983 base year

1989 base year

Stocks

+.01348

-.00214

Bonds

+.00147

-.00093

Owner-Occupied homes

-.02530

+.04437

Investment real estate

+.00101

+.00533

Unincorporated business

+.01311

-.01155

Farms

-.00088

+.00036

All assets combined

-.00240

+.04536

Net worth (from Table 2)

+.01497

-.01497

the base year, inflating real estate equity to
1989 has the effect of raising the Gini coeffi­
cient and increasing inequality. But with 1989
as the base year, deflating real estate equity
back to 1983 also has the effect of raising
the Gini coefficient and increasing inequality,
whereas the opposite sign would be expected.
Using either year as the base, changes
in asset values do not generate an increase
in inequality, because the changes in home
equity more than offset the changes in the
value of other assets.

Demographic Changes
Changes in the composition o f the U.S.
population may also have contributed to the
increasing inequality o f the distribution of
wealth. Table 5 shows the changes in the
SCF sample between 1983 and 1989. The
importance of the post-war baby boom can
be seen in the age distribution. Almost the
only group with a growing share of the pop­
ulation is households with the head age 3544; these individuals were b o m in the years
from 1939 to 1948 in the 1983 SCF, and from
1945 to 1954 in the 1989 SCF. The SCF also
shows declines in married couples, households
with children, and especially married couples
with children. There is a reduction in the
proportion of adults with less than a high

REVNN

13 Asian and Pacific Islander, and
American Indian and Alaska native,
are repotted as two separate cate­
gories on the 1 9 8 3 data tope (with
3 7 and nine observations, respec­
tively) and combined into a single
category in 19 8 9 .




school education and corresponding growth
in those with at least some college.
In most cases, the weighted percentages
in the SCF parallel the percentages in the pop­
ulation, as measured by the Current Population
Survey (CPS), conducted annually by the
Census Bureau. There are some exceptions.
The most important is in the categorization
of households by race and ethnicity. The 1983
SCF data report much lower proportions of
households in the smaller minority groups
than does the CPS. This is apparently because
race and ethnicity were determined in 1983
by the interviewer for the SCF, while in the
CPS the respondent was asked to identify
him self or herself. In 1989, both the SCF
and CPS used the self-identification method,
which is more commonly used. The CPS
reports that persons of Hispanic origin
amounted to 7.2 percent of all U.S. residents,
compared to only 3.7 percent in the 1983
SCF. Asian and Pacific Islanders were about
1.6 percent of the population in 1983, and
American Indians and Alaska natives were
0.6 percent, while the SCF reports 1.1 percent
for both groups combined. The CPS and SCF
are much closer in 1989: 8.8 percent in the
CPS versus 7.7 percent in the SCF for the
Hispanic origin population, and 3.7 percent
in the CPS versus 4.3 percent in the SCF for
other races.13 There are also other differences
in the age distribution and household com­
position, which will be discussed later.
It is possible to get an idea of the impor­
tance of these demographic changes on the
distribution of wealth by changing the weights
for each category of household, substituting
the 1989 proportions for each group within
the category for the 1983 proportions. This
procedure represents the effect of changes for
individual households in some cases, such as
age and household composition. People tend
to add to their wealth as they age, and changes
in household status, such as marriage, divorce
or the death of a spouse may directly affect
the household’s wealth. In others it may not.
Individuals do not automatically increase their
wealth by completing another level of school­
ing, for example, although college graduates
in general are richer than high school gradu­
ates. An adult who completes additional
schooling is likely to benefit in the first

instance through an increase in incom e, and
then only gradually through an increase in
wealth. For the United States as a whole, the
effect of educational changes on the accumu­
lation and distribution o f wealth will also be
felt gradually: New households formed by
young adults with more schooling gradually
supplant older households whose heads have
less, and immigrants with relatively little
education arrive in the country. Nor does
the overall change in the racial and ethnic
composition in the survey correspond to
the experience of individual households.
Table 5 also shows the mean wealth for
each group in the 1983 survey. The data in
the table suggest that the change in the age
distribution should reduce the degree of
inequality, since the age group closest to the
overall mean is almost the only group com ­
prising a larger share of the population in
1989, while groups with higher and lower
wealth declined in importance. Conversely,
there was a decline in the importance of the
household type closest to the mean wealth—
married couples with children— but in this
case there were also declines in groups with
both more and less wealth. All minority
groups have mean wealth that is farther from
the overall U.S. mean than the large white
majority, so the growth of minority house­
holds should also increase inequality. In
the case of education, the effect is uncertain
because low-wealth groups declined in impor­
tance and high-wealth groups increased.
As Table 6 shows, m ost of these demo­
graphic changes would have contributed to
an increased concentration of wealth, but the
effects are small. All are less than the standard
error for the 1983 coefficient. The largest
effect is from the changing racial and ethnic
composition of the population, but this is sus­
pect for the reasons discussed. None of the
other demographic changes accounted for
as much as a quarter of the change in the
distribution of wealth. The changing age
distribution by itself contributed modestly to
a lessening of inequality, and the combined
effect of age and household composition
changes also reduced inequality.
The same tests for consistency were
conducted for the demographic changes as
for the changes in asset values, with similar

16

results. W hen the Gini coefficients were
calculated with the 1983 SRC weight, the
magnitudes and patterns of changes were
basically the same. W hen 1989 was used as
the base year, substituting the demographic
characteristics for 1983, there were some
differences, as can be seen in Table 6. The
change in household composition has a posi­
tive effect instead of the expected negative
one, and the change in the age distribution
has a much larger effect when 1989 is used
as the base year.
These results may derive from differences
between the surveys. For both characteristics,
the sample size in one category is much smaller
in 1989 than in 1983, and the weighted pro­
portion of the population in that category is
larger in the SCF than the CPS in 1983, and
smaller in 1989. Single males with children
is the smallest household composition cate­
gory. The sample size in 1989 is only 17, and
the weighted share in the SCF is less than half
the 1.1 percent reported in the CPS; in 1983,
the sample size is 40 and the proportion in
the SCF is much closer to the CPS figure of
0.9 percent.
There is also a very large difference
between the two surveys in mean wealth for
these households; in 1983, they are relatively
poor on average and in 1989 they are above
the mean for all households, with mean wealth
almost three times as large as in 1983. The
difference in sample size suggests that the
1983 figure is likely to be more accurate.
Similarly, “household head under 2 5 ” is the
smallest age category, and also the poorest.
The sample size is only 94 in 1989, and the
weighted share in the SCF is somewhat
smaller than the 5.5 percent reported in the
CPS, while in 1983 the sample size is 295
and the proportion in the SCF is larger than
the 6.8 percent in the CPS.
These differences suggest caution in
interpreting the results in Table 6. To investi­
gate their importance, weights were changed
on the basis of each characteristic separately
to match the 1983 and 1989 CPS for age and
household composition, and the 1980 and
1990 decennial censuses for race and ethnicity.
The inconsistencies in Table 6 did not appear,
and the Gini coefficients were generally close
to those reported in the top row of Table 2.




T a b le 5

Dem ographic Com position off SCF,
1 9 8 3 and 1 9 8 9
Mean Wealth

Percent of Sample:

(in $l,000s a( 1989 dollars)

Category:

1983

1989

1983

1989

$15.3

$13.5

8.0

4.8

47.1

73.2

22.6

20.9

Age of household head:
Under 25
25-34
35-44

117.9

149.7

19.5

23.3

45-54

220.9

284.1

15.5

14.2

55-64

245.5

265.9

15.0

14.5

65-74

273.3

254.8

12.2

13.1

75+

163.2

194.8

7.2

9.2

Household composition
$271.9

$305.6

29.4

29.8

Married couple, children

132.1

175.1

31.2

28.6

Single male, no children

91.8

124.2

12.0

12.8

Single male, children

61.4

167.9

1.1

0.4

Single female, no children

83.6

95.7

18.1

21.8

Single female, children

36.2

32.2

8.2

6.7

White

$175.1

$216.4

82.3

75.4

Black

35.9

48.6

12.9

12.6

Hispanic*

31.9

49.2

3.7

7.7

Other**

88.6

176.8

1.1

4.3

$56.6

$75.4

14.5

14.1

Married couple, no children

Race/ethnicity

Educational attainment
Grade school or less
Some high school
High school graduate

69.1

85.7

13.4

12.7

104.0

108.3

31.5

30.0

Some college

168.9

157.3

17.7

19.6

College graduate or more

308.9

406.0

22.9

23.6

150.9

180.7

Mean wealth for all households:

: Hispanits are counted separately from the other groups, in contrast to Census Bureau practice,
w here they are identified both as m em bers o f a racial group and as Hispanics.
Asian and Pacific Islander (8 0 percent in 1 9 8 3 ); American Indian/A laska native
(2 0 percent in 1 9 8 3 )

Alternative weights might be constructed
from the CPS, as a more extensive consistency
check, but the CPS does not publish cross­
tabulations in sufficient detail and does not
use the two smallest racial categories as
controls.'4

FEDERAL RESERVE B A N K OF S T .

17

LOUIS

14 Conversation with Daniel Weinberg
of the Census Bureau.

household’s current income may be saved
and add to wealth in the future. CPS data
show that the distribution of income became
slightly more unequal from year to year
between 1983 and 1989, while mean and
median household incom e were rising, which
might enable the richer households to add
relatively more to their assets. The interrela­
tionships cannot be addressed systematically
in this article. Nonetheless, it is interesting
to look at how the relationship changed
between 1983 and 1989.
There are several reasons why incom e
and wealth might not be highly correlated in
the SCF. The income reported in the survey
is current income, which is not necessarily
the household’s normal or permanent income.
Illness, windfalls and many other circum­
stances may cause the household’s income
in a given year to depart from its usual level.
W ealth, which is in part the accumulated
savings from past income, is likely to be more
highly correlated with permanent income than
current income. The relationship between
current incom e and wealth is also affected
by the age of the adults in the household.
Older individuals have higher wealth for given
income levels than younger ones, both because
they have had more time to accumulate wealth
and because, once they retire, their current
incom e is low relative to their past income.
Conversely, young adults typically have little
wealth relative to their incom e.15
Despite these caveats, the relationship
between income and wealth is strong. In
Table 7, household wealth has been regressed
on income and the square of income for both
years. The coefficients of determination are
quite high. The relationship between income
and wealth was stronger in 1983 than in 1989,
however, and also more elastic: The intercept
is lower in 1983 and the coefficient of income
is larger. (The coefficient o f income squared
is significant in both regressions but its mag­
nitude is too small to generate a measurable
departure from a simple linear relationship.)
The two regression lines cross at an
incom e of about $ 3 3 ,8 0 0 (measured in 1989
dollars). This is the income level at which
wealth is the same in the two years. The
median household income was $2 4 ,3 0 0 in
1983 and $25,000 in 1989 (both also measured

T a b le 6

Effect of 1 9 8 3 - 8 9 D em og raphic C hang es
on 1 9 8 3 G in i Coefficients
(u n ad ju sted n e t w o rth , including autos)
Demographic

Change in Gini Coefficient

Age of household head

-.00292

+ .00841

Household composition

+.00148

+.00267

Combined

-.00452

+.01392

Race/ethnicity of head

+.00625

-.00770

All three combined

+.00257

+ .00479

Education of head

+.00259

-.00047

Net worth (from Table 2)

+.01497

-.01497

The limitations should not obscure the
basic conclusion. None of the results, using
either year as the base or any set of weights,
suggest that demographic changes contributed
to the change in the distribution of wealth
(with the dubious exception of the racial and
ethnic changes). All but one of the separate
and combined effects of age and household
composition are in the direction of making
the distribution of wealth more equal, and
the effect of education changes is small.

Income and Wealth

15 Weicher (1 9 8 9 ) analyzes the rela­
tionship between wealth and age,
and the relationship between
income and wealth among the
elderly for the 1 9 7 7 SCC and the
1 9 8 3 SCF.




Both income and wealth (on most com­
parisons) were more unequally distributed in
1989 than in 1983. Indeed, as reported in the
SCF, there was a greater increase in income
inequality. The Gini coefficient for income
rose more than the coefficient for wealth by
any comparison in Table 2.
The association of these increases suggests
that the changes in the distribution of wealth
and income may have affected each other,
and it is easy to jum p to the conclusion that
the increase in income inequality caused the
increase in wealth inequality, or vice versa.
In fact, the relationship between wealth and
income is complicated both theoretically and
empirically. Part of a household’s current
income is derived from the assets reported
in the SCF, especially for the richest house­
holds, and at the same time part of the

18

JANUARY/FEBRUARY

in 1989 dollars). Thus, upper-income
households were not as wealthy at any given
incom e level in 1989 as they were in 1983,
while those at middle- and lower-income
levels were wealthier in the later year. This
is surprising, since the change in the age dis­
tribution shown in Table 5 might suggest
that wealth would be higher for households
at any given income level in 1989.
It is worth noting that the change in the
income distribution reported in the SCF is
substantially greater than the change reported
in the CPS, which has a much larger sample
of about 57,000 households. Between 1983
and 1989, the Gini coefficient in the CPS rose
by 0 .017, from 0 .414 to 0.431. This is less
than the increase for three of the four SCF
comparisons in Table 2. The comparison for
w hich the changes in the SCF and CPS are
closest is also the comparison showing a very
slight decrease in wealth inequality.

T a b le 7

The R e la tio n sh ip B etw een W e a lth and
Incom e, 1 9 8 3 a n d 1 9 8 9
(n et w orth including a u to s, a d ju ste d fo r a s s e ts ;
1 9 8 9 d o lla rs)
Variable

1983

1989

Intercept

-177,338
(10.9)

-37,026
(1.6)

Income

10.84
(32.8)

6.70
(30.2)

Income2

53.3E-8
(4.0)

—6.4E-8
(23.4)

R-squared

.396

Note: Numbers in parentheses under the coefficients are ( ratios.

Household Characteristics
Table 8 shows the demographic charac­
teristics of these rich households. Nearly all
were white and nearly all were married cou­
ples, although the proportion who were
members of minority groups rose from less
than 1 percent to more than 5 percent, and
the proportion who were not married rose
from 10 percent to 16 percent. A substantial
majority were college graduates. About
three-quarters had no children, or at least
none living at home. The median age of the
household head was 58 in both years, but
in 1989 there were more relatively young
households among the rich (17 percent com­
pared to 10 percent in 1983), and fewer in
the 55-64 age bracket. A more detailed clas­
sification (not reported in the table) shows
that about half the households in the 45 -5 4
age bracket had children in 1983, but few
households did at older ages. This suggests
that by about age 50, the children of these
families have grown up and left home.
Comparison with Table 5 shows that
these households are much better educated
and quite a bit older than the general popula­
tion, and are disproportionately white. They
are more likely to be married but, perhaps
because o f their age, less likely to have chil­
dren living at home. However, the precision
of the percentages in Table 8 should not be
overemphasized. The number of observa­
tions in the top 1 percent of each survey is

WHO ARE THE RICH?
This section adopts a different focus on
the distribution of wealth. Instead of looking
at inequality across all households, it looks
at the holdings and characteristics o f the
richest 1 percent of households (a group that
has attracted interest among other analysts).
The purpose is to see if the same households
were rich in both years. Attitudes toward an
increase in inequality may be different if the
absolute level of wealth and the relative posi­
tion within the distribution change frequently
for individual households, especially if this
occurs at the upper tail of the distribution.
The SCF has been designed in part to
answer the question of how individual house­
holds have fared over time, by re-interviewing
some of the same households in 1986 and
1989 who were interviewed in 1983. Unfor­
tunately, it is impossible to track any individual
households longitudinally because the infor­
mation about re-interviewing has been sup­
pressed in the 1989 public-use data tape.
Nonetheless, it is still possible to analyze
the position of the same types of households
over time. The threshold for inclusion in
this group is $1.71 million in net worth in
1983 and $1.97 million in 1989.




1 9 95

NK O F S T . L O U I S

19

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REVIEW

UARY/FEBRUAIY

Ta b le 8

Assets Held by the Rich

Dem ographics of the Richest 1 Percent of
U .S. H ouseholds, 1 9 8 3 and 1 9 8 9
1983

Table 9 describes the components of net
worth for these households. As the top panel
shows, in both years unincorporated businesses
constituted the largest share of their wealth,
over one-third in 1983 and almost 40 percent
in 1989. Commercial and rental property
accounted for about one-sixth in both years.
The most surprising finding is the sharp
decline in the importance of stock ownership,
despite the stock market boom of the 1980s.
These patterns vary by age. In general,
stocks are more important and unincorporated
businesses are less important for older house­
holds. In 1983, for households under 6 5 ,
unincorporated businesses were the largest
component of net worth; for those 6 5 or over,
stocks were. In 1989, stocks were the largest
holding only for those 7 5 or over. At the other
end of the age distribution, if young house­
holds did manage to qualify for inclusion
among the very rich, they did it as owners
of unincorporated businesses or perhaps,
in 1983, as real estate investors.
The second panel shows the importance
of the different assets to individual households:
W hat was the most important asset in the
portfolio of each rich household? Unincor­
porated businesses were the m ost important
by this measure also in both years, although
the proportion declined from 42 to 3 4 percent.
Investment real estate was the most important
asset for about one-fifth o f the richest house­
holds in both years. Stocks declined by this
measure as well.16
The marked increase in the importance
of miscellaneous assets (collectibles, debts
owed to the household, oil and gas leases) in
both panels may result from a change in the
questionnaire. Nine more categories were
listed separately in 1989, including future
proceeds from a lawsuit or an estate, royalties,
deferred compensation, futures contracts,
non-publicly traded stock, and cash not else­
where classified. At the same time, however,
the most frequently cited miscellaneous asset
in 1983— boats— was moved to the “vehicle”
category in 1989, along with campers, air­
planes and motorcycles.
Three times as many households reported
owning miscellaneous assets in 1989 as in

1989

Age of household head:
Under 25

0.0%

25-34

2.1

1.3

35-44

8.4

15.5

0.0%

45-54

27.9

27.0

55-64

30.3

22.2

65-74

20.9

22.1

75+

10.4

11.9

Married couple, no children

66.2%

58.5%

Household composition:
Married couple, children

23.3

25.1

Single male, no children

4.0

9.5

Single male, children

0.1

2.6

Single female, no children

6.4

3.7

Single female, children

0.0

0.7

White

99.2%

94.5%

Black

0.1

0.7

Hispanic

0.0

1.1

Other

0.7

3.7

1.3%

2.8%

Race/ethnicity of household head:

Education of household head:
Grade school
Some high school
High school graduate

1.5

1.3

14.1

8.8

Some college

20.3

14.0

College graduate or more

62.8

73.2

16 Wolff (1 9 9 4 ) shows thot o large
shore of wealth of the top one-half
of 1 percent (which he terms the
"super-rich") in 1 9 8 9 consisted of
unincorporated businesses and
investment real estate, and he
speculates that this was the avenue
to wealth in the 1980s. The data
in Tables 8 and 9 only partly sup­
port this inference. Unincorporated
businesses were o larger share of
the totol net worth of the richest 1
percent, but were the most impor­
tant asset in the portfolio for fewer
of them.




1995

not large to begin with: 287 in 1983 and 456
in 1989. Thus, there are not likely to be many
in some of the smaller demographic categories.
W here the surveys have marked differences
in the samples and weighted proportions for
the smaller categories, as shown in Table 5
and discussed in the previous section, the
representation of these categories among the
top 1 percent is likely to vary as well, and the
proportions in these categories in Table 8
may be suspect. The figure for minority groups
in 1983 is especially suspect because of their
underrepresentation in that year’s SCF, as
discussed earlier.

NK OF S T . L O U I S

20

REVIEW

RY/FIBRUARY

1983 among the population as a whole, and
this is reflected among the richest households
as well. Miscellaneous assets were the most
important asset for a remarkably large number
of wealthy households in 1989. Mean hold­
ings of miscellaneous assets for wealthy
households reporting such assets increased
from $ 1 48,000 in 1983 to $546,000 in 1989
(both measured in 1989 dollars). Not many
wealthy households reported holding assets
in the categories added in 1989, but those who
did typically reported holdings of $250,000
or more. In addition, a category of “other”
was available in 1989, besides the 29 specified,
and one household reported $28 million worth
of such “other” miscellaneous assets.
Given the importance of unincorporated
businesses among the richest households, it
is worth taking a brief look at the kinds of
businesses they own. The SCF asks what the
business does, for those in which the house­
hold has a management interest. In 1983,
the most common classification was “profes­
sional practice,” an unfortunately broad cat­
egory including law, medicine, accounting
and architecture specifically, and perhaps
others as well. Some 22 percent of the richest
households owning unincorporated businesses
were in this category. The second most com­
mon classification, at 20 percent, was “other
wholesale/retail outlets,” including everything
except food and liquor, restaurants, gas sta­
tions and direct sales. In 1989, real estate/
insurance was much the most common, at
43 percent, but few of the richest households
were in these lines of business in 1983. “Other
oudets” was the second most common classi­
fication, at 26 percent. In general, there is
not much correspondence among the kinds
of businesses owned between the two years,
except in the broadest classifications.
Respondents were asked about the value
of two actively managed businesses in 1983
and three in 1989, along with summary
questions about other actively managed
businesses in both years. Also in 1983,
households in the high-income sample were
not surveyed unless they volunteered to par­
ticipate, while in 1989 they were surveyed
unless they declined to participate. These
differences may limit the comparability of
the richest households between the surveys.




1995

T a b le 9

A sset Holdings of the Richest 1 Percent of
H ouseholds, 1 9 8 3 and 1 9 8 9
Relative Importance of Individual Asset Categories
1983

1989

Unincorporated business

33.8%

39.7%

Stocks

18.2

7.7

Investment real estate

16.7

16.5

Home equity

8.7

8.2

Trusts

6.4

3.8

Bonds

5.9

5.7

Farms

2.7

2.6

Miscellaneous assets

1.0

5.9

All other

6.0

9.9

Proportion of Households for Whom Asset Category Is Largest Share of Net
Worth
1983

1989

Unincorporated business

41.8%

33.7%
22.2

Investment real estate

20.5

Stocks

16.3

9.0

Farms

7.0

3.1

Trusts

4.9

7.4
3.6

Bonds

4.5

Miscellaneous

0.3

8.9

All other

4.7

12.1

Taken at face value, the data on unincorpo­
rated business suggest that different house­
holds were in the top 1 percent in both years.
The shifts in portfolio composition support
the same inference.

CONCLUSION
The distribution of wealth probably
became slighdy more unequal between 1983
and 1989, but this conclusion does not hold
for all specifications analyzed in this article.
The sign and magnitude of the change depend
on how broadly wealth is defined, and on such
technical issues as what weights are used and
whether and how the data for individual
households are adjusted on the basis of
national balance sheet data.
No single explanation appears to account
for most, or very much, of the change in the
distribution of wealth. Neither changes in

N K OF S T . L OU I S

21




REVIEW

JANUARY/FEBRUARY

asset values or broad demographic changes are
very important. The high correlation between
current income and wealth suggests that the
change in the distribution of wealth may mirror
the change in the distribution of income, but
the relationship between incom e and wealth
became less pronounced over the period.
The analysis in this article can best be
described as a preliminary exploration of the
wealth data in the SCF, and it has considered
fairly simple explanations of the change in the
distribution. A number of more specific and
sophisticated issues may merit further analy­
sis, based on the work to date:
(1) It is possible to look more closely at
the effect of changes in household composi­
tion, particularly divorce and remarriage, since
changes in marital status between 1983 and
1989 are reported for individual respondents
in the 1989 SCF.
(2) The growing employment opportu­
nities for women suggest that it would be
worthwhile to analyze the effect of the labor
force status of both members of married cou­
ples. Two-earner, two-professional couples
(doctors married to doctors or to lawyers,
for example) appear to be growing in impor­
tance; these may be high-wealth households.
More generally, the contribution of a second
earner to household wealth can be studied
in the SCF.
(3) The 1983 SCF illustrates the impor­
tance of pensions and Social Security in the
portfolios, broadly defined, of households with
relatively low net worth. It may be possible
to extend these calculations to 1989, to inves­
tigate whether inequality is rising when they
are included and whether lower-wealth house­
holds are substituting them for other types
of assets.
Finally, it may be that the increase in
inequality is a cyclical phenomenon. As noted
at the beginning of this article, the years from
1983 to 1989 comprise most of a long eco­
nomic expansion. The Census Bureau reports
that the distribution of income tends to become
more unequal during expansions. Gini coef­
ficients for household income have risen in
every year since 1968, except three: 1 9 7 4 ,1 9 8 0
and 1990, all of them years of recession. Over
the 1968-92 period, the Gini coefficient rose
from 0 .388 to 0.433, or slightly less than

1995

0.02 per year. During the 1983-89 expansion,
it rose from 0 .4 1 4 to 0.4 3 1 , or about 0 .0 2 4
per year. There are so few surveys with data
on household asset holdings that it is difficult
to consider the distribution of wealth cycli­
cally, but the 1992 SCF may shed light on
this conjecture, since it covers the downturn
of 1990-91.

REFERENCES
Antoniewicz, Rochelle. "A Comparison of the Household Sector from the
Flow of Funds Accounts ond the Survey of Consumer Finances,"
unpublished paper (November 1994).
Avery, Robert B. "Comment," in Robert E. Lipsey and Helen Stone Tice,
eds., The Measurement of Saving, Investment and Wealth. University
of Chicago Press, 1989, pp. 839-44.
________ , and Gregory E. Elliehausen. "Financial Characteristics of
High-lncome Families," Federal Reserve Bulletin (March 1986),
pp. 163-177.
________ , an d________ . / 983 Survey of Consumer Finances:
Technical Manual and Codebook. Board of Governors of the Federal
Reserve System, April 1985 (Last Revision: February 1 5 ,1 9 9 0 ).
________ , ________ , and Arthur B. Kennickell. "Measuring Wealth
with Survey Data: An Evaluation of the 1983 Survey of Consumer
Finances," Review of Income and Wealth (December 1988),
pp. 339-69.
________ , ________ , Glenn B. Conner, and Thomas A. Gustafson.
"Survey of Consumer Finances, 19 8 3 ," Federal Reserve Bulletin
(September 1984), pp. 679-92.
________ , ________ , ________ , an d________ . "Survey of
Consumer Finances, 1983: A Second Report," Federal Reserve
Bulletin (December 1984), pp. 857-68.
Curtin, Richard T., F. Thomas Juster, and James N. Morgan. "Survey
Estimates of Wealth: An Assessment of Quality," in Robert E. Lipsey
and Helen Stone Tice, eds., The Measurement of Saving, Investment,
and Wealth. University of Chicago Press, 1989, pp. 473-548.
Efron, Bradley, and R. J. Tibshirani. An Introduction to the Bootstrap.
Chapman and Hall, 1993.
Kennickell, Arthur, and Janice Shack-Marquez. "Changes in Family
Finances from 1983 to 1989: Evidence from the Survey of Consumer
Finances," Federal Reserve Bulletin (January 1 9 92 ), pp. 1-18.
________ , and Martha Starr-McCluer. "Changes in Family Finances
from 1989 to 1992: Evidence from the Survey of Consumer
Finances," Federal Reserve Bulletin (October 1994), pp. 861-82.
________ , and R. Louise Woodburn. "Estimation of Household Net
Worth Using Model-Based and Design-Based Weights: Evidence from
the 1989 Survey of Consumer Finances," unpublished paper, Federal
Reserve Board, April 1992.

22

REVIEW
Lerman, Robert I., and Shlomo Yitzhoki. "Improving the Accuracy of
Estimates of Gini Coefficients," Journal of Econometrics (September
1 989), pp. 43-7.

________ . "Money Income of Households, Families, and Persons in
the United States: 1967-1992," Current Population Reports, Series
P60-184 (September 1993).

Morgan, James N. "The Anatomy of Income Distribution," Review of
Economics and Statistics (August 1962), pp. 2 /0 -8 3 .

Weicher, John C. "Wealth and Poverty Among the Elderly," in Marion
Ein Lewin and Sean Sullivan, eds., The Care of Tomorrow's Elderly.
American Enterprise Institute, 1989, pp. 11-27.

Muth, Richard F. "The Demand for Non-Farm Housing," in Arnold C.
Harberger, ed., The Demand for Durable Goods. University of Chicago
Press, 1960, pp. 29-96.

White, Lawrence J. The S&L Debacle. Oxford University Press, 1991.
Wolff, Edward N. "Estimates of Household Wealth Inequality in the
U.S., 1962-1983," Review of Income and Wealth (September
1987), pp. 231-56.

Smolensky, Eugene. "Comment," in Robert E. Lipsey and Helen Stone
Tice, eds., The Measurement of Saving, Investment, and Wealth.
University of Chicago Press, 1989, pp. 548-51.

________ . "Trends in Household Wealth in the United States,
1962-83 and 1983-89," Review of Income and Wealth (June
1994), pp. 143-74.

U.S. Bureau of the Census. Statistical Abstract of the United States
(various years).
________ . Price Index of New One-Family Homes Sold, Series C27
(various years).

Yitzhaki, Shlomo. "Calculating Jackknife Variance Estimators for
Parameters of the Gini Method," Journal of Business S Economic
Statistics (April 1991), pp. 235-39.

________ . "Trends in the Income of Families and Persons in
the United States 1947 to 19 64 ," Technical Paper No. 17
(August 1967).




I B A N K OF S T . L OU IS

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REVIEW
Anne Beatty is an assistant professor of accounting at the Wharton School of the University of Pennsylvania and was a visiting scholar at the
Federal Reserve Bank of St. Louis. Thomas A. Pollmann and Nona Wooldridge provided research assistance. The Wharton Financial Institution
Center and the SEC Financial Reporting Institute provided financial assistance.

The Effects
of Fair Value
Accounting
on Investm ent
Portfolio
M anagem ent:
How Fair Is It?

■

The letter argued that historical cost accounting
produces information that is irrelevant to
valuing investment portfolios and provides
an opportunity for managers to manipulate
the numbers reported in financial statements.3
The Financial Accounting Standards Board
(FASB) responded by adopting Statement of
Financial Accounting Standards Number 115
(SFAS 115) in May of 1993. This statement
requires that investment securities be valued
using market interest rates, and requires that
equity accounts be adjusted to reflect changes
in these fair, or market, values.*
The adoption o f this standard has
been controversial. Opponents of fair value
accounting have objected to the new standard
because it focuses on a single type o f asset.
Bankers and regulators have claimed that the
mismatching caused by ignoring concurrent
changes in the values o f other assets and
liabilities such as loans and deposits will
induce unrealistic volatility in bank equity.
Bankers claim that efforts to mitigate this
increase in volatility will result in reductions
in the proportion of assets held in investment
securities, the maturity o f investments
held, and in the flexibility of investment
portfolio management.
These arguments were im portant in
the recent decision by regulators to exclude
the effects of SFAS 115 from the definition
of regulatory capital ratios. In addition,
bankers argue that the new standard will
not eliminate the opportunity to manipulate
the financial statements.
The arguments by both sides rely on
the assumption that actions by regulators,
investors, or depositors and creditors are
based strictly on the numbers reported in
the financial statements. This assumption
is important in the debate over the effects
of this accounting change because financial
statement disclosures contain the information
necessary to restate the investment account
from a cost- to a fair-value basis.
This article examines the adoption
of SFAS 115 by bank holding companies
to determine if a desire to influence the

A nne Beatty
uring the late 1980s, the Securities and
Exchange Commission (SEC) challenged
the use of historical cost accounting for
financial instruments because this method
values these assets using the interest rate in
effect at the purchase date. Thus, it does not
reflect changes in values that arise from changes
in market interest rates. In a 1992 address to
the American Accounting Association, Walter
Schuetze, the chief accountant of the SEC,
claimed the magnitude o f losses in the thrift
industry were increased by a lack of regula­
tory discipline made possible by the use of
historical cost accounting.1 He argued that
regulators were able to avoid making deci­
sions about capital adequacy in the thrift
industry when estimates of the deficit in net
worth of the industry on a market value basis
were as high as $118 billion, because the net
worth of the industry on a historical cost basis
was positive. The experience in the thrift
industry, combined with the large number of
bank failures in the 1980s, caused former SEC
Chairman Richard Breeden to express concerns
that historical cost accounting might contribute
to even larger losses in the banking industry.2
In a 1990 letter, the SEC lobbied
accounting rulemakers to require financial
institutions to use market values when
accounting for securities investments.

D




25

' His speech, which was
entitled "Relevance and
Credibility in Financial Accounting
and Reporting," was given on
August 1 2, 1 9 9 2 , a t the annual
meehng of the American
Accounting Association.
2 See The Wall Street Journal
(September 2 7 , 1 9 9 0 ).
3 See The Wall Street Journal
(September 1 4 ,1 9 9 0 ).
4 The FASB uses the term fair
value to include the market value
of items not traded on active
secondary markets. See
SFAS 11 5 , paragraph 109.




HEVINV
numbers in the financial statements, including
reported equity volatility, affects investment
portfolio management. The focus of this article
is on whether investment portfolio manage­
ment was changed by the adoption of SFAS
115. I did not attempt to verify the claims of
bankers and regulators that these changes will
reduce income earned from investment secu­
rities or increase exposure of the market value
of banks to interest rate changes. Evidence
in this article suggests that SFAS 115 did affect
investment portfolio management, and it
suggests the need for further research.

Although most securities were recorded
in the financial statements at amortized cost
prior to adoption of SFAS 115, information
about the market value of these securities
was disclosed in the footnotes to the annual
reports. Typically, this footnote information
was also provided on the face of the balance
sheet. The availability of information about
both the amortized cost and the market value
of investment securities at both the beginning
and ending financial statement dates made it
possible for users of the financial reports to
restate the financial statements to the values
that would have been recorded if investment
securities were accounted for at market values.
SFAS 115 requires that each security be
placed into one of three portfolios depending
on the reason for acquiring the security and
on whether the security will be held to matu­
rity, resold in the near term, or available for
sale in some intermediate period. Accounting
for the income generated from these securities
and for the acquisition and sale of the securities
was not changed by SFAS 115 and is the same
regardless of the classification of the security.
The accounting treatment of unrealized
holding gains and unrealized holding losses
differs for the securities in each of these
three categories.
Securities held to maturity are debt secu­
rities that management intends to hold until
maturity. Securities in the held-to-maturity
portfolio are recorded at amortized cost. No
unrealized gains are recognized. Unrealized
losses are recognized only if there is a large
and permanent decline in the fair value of
the security.
Held-to-maturity securities are allowed to
be sold or transferred to one of the other two
portfolios for the following reasons: deterio­
ration in issuer’s creditworthiness; change in
the tax law affecting the tax-exempt status
of interest on debt security; a m ajor business
combination or disposition by the reporting
entity; change in regulation modifying per­
missible level of an investment; or significant
change in risk weights used in computing
risk-based capital. The following are not
acceptable reasons for selling or transferring
securities from the held-to-maturity portfolio:
change in market interest rates; need for liq­
uidity; change in yield on other investments;

ACCOUNTING FOR
INVESTMENT SECURITIES
In May of 1993, the FASB issued SFAS
115, which must be followed in fiscal years
beginning after December 15, 1993. The
standard could have been adopted at the end
of an earlier fiscal year if the annual financial
statements for that year were issued after May
of 1993. For bank holding companies whose
fiscal year ends in December, SFAS 115 could
be adopted as of December 31, 1993, or for
the year beginning January 1, 1994.

Changes in Investment Securities
Accounting Due to SFAS 115
Prior to implementation of SFAS 115, debt
securities that banks intended and were able
to hold on a long-term basis were carried at
amortized cost with no adjustment for changes
in value resulting from changes in interest
rates. Equity securities and debt securities that
might be disposed of in the foreseeable future,
in contrast, were accounted for at the lower
of cost or market. This method requires that
declines in the value of securities be recorded
as an adjustment to equity, but does not allow
increases in the value of these securities
above cost to be recorded. Sales of securities,
whether accounted for at amortized cost or
the lower of cost or market, resulted in a gain
or loss from the sale equal to the difference
between their sales price and their amortized
cost. This gain or loss was recorded in both
income and equity. Finally, debt securities
held for trading were recorded at their
market values.

26

REVIEW

BY/FEBRUARY

1995

change in funding sources and terms; or a
change in foreign currency risk. In addition,
sales of debt securities are allowed if they
occur near enough to the maturity date so
that interest rate risk is substantially elimi­
nated as a pricing factor (for example, within
three months), or if they occur after at least
85 percent of the principle outstanding at
acquisition has been collected.5
The FASB has not established a penalty
for unauthorized sales or transfers of heldto-maturity securities. Banks that make
unauthorized dispositions from this portfolio
will m ost likely find it difficult to convince
auditors and regulators that they intend to
hold other securities to maturity. As a result,
these banks may be required to re-classify all
securities in the held-to-maturity portfolio
to one of the other two portfolios.
Trading securities are debt or equity
securities bought and held principally for
the purpose of selling them in the near term.
Trading securities are recorded at market
value with unrealized gains and unrealized
losses recognized in income. Thus, the
accounting treatment for trading securities
was not affected by SFAS 115.
Securities available f o r sale are debt or
equity securities not classified as trading
securities or as held-to-maturity. Securities
in the available-for-sale portfolio category
are recorded at market value with unrealized
gains and losses (net of tax) recorded as a
separate component of shareholder’s equity.
Changes in the market value of these securi­
ties are not recorded in income.

equity or earnings, sales of investment securities
can provide management with an opportunity
to influence reported equity or earnings.
Prior to SFAS 115, there were few
restrictions on the use of investment securities
to achieve these objectives. Under SFAS 115,
use of securities classified as available-for-sale
is still unrestricted, but the virtual prohibition
of the sale of securities classified as held-tomaturity dramatically reduces the usefulness
of these securities in investment portfolio
management. Held-to-maturity securities
will still provide the bank with interest income,
but these securities will not be available to
manage liquidity or interest rate risk, or to
influence reported equity or earnings because
they can be sold only under very restrictive
conditions or with the penalty of re-classifying
this portfolio as available-for-sale. W ithout
some offsetting benefit, these severe restrictions
on the use of held-to-maturity securities suggest
that bank managers would not choose to classify
any of their securities in this portfolio. The
only advantage of classifying securities as
held-to-maturity rather than available-for-sale
is that unrealized gains and losses will not
be recorded in equity. The relative costs
and benefits of classifying securities as heldto-maturity will depend on how actively
the investment portfolio is managed, and
how costly it is to include unrealized gains
and losses on investment securities in
reported equity.

INVESTMENT PORTFOLIO
MANAGEMENT UNDER
SFAS 115

In December of 1993, the Board of
Governors of the Federal Reserve System
proposed that capital requirements be
amended to include unrealized holding
gains and losses on securities available for
sale in Tier 1 capital,6 despite arguments
made by Federal Reserve Chairman Alan
Greenspan that SFAS 115 would result in
a distortion of bank financial statements
and would erect barriers to effective interest
rate risk management.7 The proposal stated
that the amendment was consistent with
the intent of the requirement in the Federal
Deposit Insurance Corporation Improvement
Act of 1991 (FDICIA) that regulatory

The Importance of SFAS 115
in Bank Regulation

Bank managers can use the investment
securities portfolio to achieve several objec­
tives. The investment portfolio provides
a source of interest income, collateral to secure
deposits and other liabilities, liquidity to meet
needs that arise from fluctuations in deposit
and loan balances, and cash flows from assets
that can be matched with those from liabilities
to reduce interest rate risk. In addition, to
the extent there are gains or losses on invest­
ment securities not yet recognized in either




27

5 See SFAS 11 5 , paragraphs 8-11.
6 Tier 1 Capital is defined in the
Board of Governors of the Federal
Reserve System Capital Adequacy
Guidelines as: Common equity,
qualifying noncumulative perpetual
preferred stock, and minority
interest less goodwill and other
intangible assets required to be
deducted from capital.
1 See The Wall Street Journal
(November 8 ,1 9 9 0 ; and
January 1 8 , 1 9 9 3 ).

Iltfltl
JANUARY/FEBRUARY

accounting standards be no less stringent
than Generally Accepted Accounting Principles
(GAAP), and noted that including the unreal­
ized gains and losses in Tier 1 capital would
affect prompt corrective action regulations,
brokered deposit restrictions, and the
risk-related insurance premium system.
Given the language used in the proposed
amendment to the capital requirements, it
seems reasonable that at the time bank man­
agers implemented SFAS 115, they would
have assumed that the resulting unrealized
holding gains or losses would be included in
Tier 1 capital. In November of 1994, however,
the Board of Governors decided not to include
the effects of SFAS 115 in Tier 1 capital. This
decision, which occurred after SFAS 115 had
been required for financial reporting purposes
for three quarters, may cause banks to adjust
their investment portfolio holdings.

managers will still be able to influence
reported earnings through the recognition
of gains or losses on securities sales. SFAS
115 reduces the restrictions on sales of secu­
rities classified as available-for-sale.

Accuracy of Reported Equity
Fair value proponents also argue that
improving the measurement of the investment
securities account by using fair value rather
than historical cost accounting will improve
the measurement of equity. Although this
would certainly be true if the values of assets
and liabilities were uncorrelated, it need not
be the case when they are related. Changes
in interest rates primarily cause changes in
the market values of the investment securities
held by banks.8 Changes in interest rates also
cause changes in the values of other bank
assets, such as loans, and changes in the
values of bank liabilities, such as deposits
and long-term debt. The values of these assets
and liabilities are therefore likely to move
together. This is especially true when the
investment portfolio is used to hedge the
effects of interest rate changes on equity.
For this reason, the volatility in reported
capital that will occur as a result of stating
only the investment portfolio at market value
may not be indicative of the true risk o f the
bank. Fair value accounting will provide
managers with an incentive to reduce the
volatility in reported equity, assuming that
those who use financial statements do not
adjust for the effects of unrealized securities
gains and losses.
A number of theoretical and empirical
studies have evaluated market value accounting
systems in which all assets and liabilities are
marked to market. For example, see: Berger,
King and O’Brien (1 9 9 1 ); Shaffer (1 9 9 2 ); and
Mengle and W alter (1 9 9 1 ). Partial market
value accounting, with only one category of
assets recorded at market value, has received
relatively less attention.
Two studies examining past changes
in the m arket value of banks’ investment
portfolios have concluded that the effects
of implementing SFAS 115 are likely to be
small. Barth, Landsman and W ahlen (1 995)
document an increase in volatility of reported
equity during 1970-90, when changes in

POTENTIAL ADVANTAGES
AND DISADVANTAGES OF
SFAS 115
Influencing Financial Statements
Through Cains Recognition

8 Banks invest primarily in U.S.
Treasury and

U.S. agency securities,

which have virtually no default risk.




1 9 95

Fair value proponents have criticized
historical cost accounting because it provides
the potential for manipulation of the numbers
reported in the financial statements through
the sales of investment securities. This criti­
cism also applies to the fair value accounting
required by SFAS 115. The new standard
actually increases the potential for certain
types of manipulation.
SFAS 115 does not eliminate opportunities
to influence the numbers that are reported in
the financial statements. W hen this standard
was implemented, there was an adjustment
to equity equal to the after-tax net unrealized
gain or loss on the securities classified as
available-for-sale. This change provided
managers with a transitory ability to affect
reported equity. First, managers could affect
the timing of this adjustment through the
choice of when to adopt the standard.
Second, managers can affect the amount
of the adjustment through the selection of
securities for classification as available-for-sale.
In addition, after SFAS 115 is in place,

28

HtVltN

BY/FEBRUARY

investment securities values are included
in equity. They argue, however, that this
increase in volatility is not important to
investors or regulators. In a study of the effects
of market value accounting for investment
securities on regulatory discipline, Carey
(1 9 9 5 ) reaches no conclusion about whether
regulatory discipline will be improved or
worsened, but does conclude that the effects
are likely to be small. Both papers acknowledge
that there are limitations on the inferences
that can be drawn from past data, since bank
behavior will likely be different once SFAS
115 is in effect.
In contrast, Ernst and Young (1993)
report that more than half of respondents
to their survey anticipated altering their
investment behavior if SFAS 115 were adopted.
Ernst and Young (1994), however, report that
60 percent of the respondents to a follow-up
survey claimed to have actually changed their
investment strategies as a result of adopting
SFAS 115. More than 95 percent of respon­
dents in the original survey claimed they
would shorten the maturity of debt securities
held, and roughly 40 percent said they would
increase their hedging activity. In addition,
respondents said they might reduce the
proportion of assets held in investment
securities. In the follow-up survey, the
respondents said they had shortened the
maturity and duration of their portfolio
and had reduced their holdings of mortgagebacked securities and mortgage derivatives.
The fraction claiming they would increase
their hedging activity was reduced to
roughly 10 percent.
Under SFAS 115, any change in the
after-tax net unrealized gain or loss on the
securities in the available-for-sale account
will result in an adjustment to equity, resulting
in an increase in the volatility of the reported
equity balance. This volatility in reported
equity will be higher as more securities are
included in the available-for-sale account and
the more sensitive these securities are to
changes in interest rates.
Bank managers who want to minimize
the increase in volatility of reported equity
that will result from adopting SFAS 115 can
either classify securities as held-to-maturity
or change their investment security holdings




1 9 9 5

to minimize the effect on reported equity.
The second option can be achieved either by
reducing the proportion o f total assets held
in the investment portfolio or reducing the
sensitivity of the value of investments held to
changes in interest rates. Since the sensitivity
of securities’ values to changes in interest rates
increases with their maturity, reducing the
maturity o f the investment portfolio will
decrease the volatility in reported equity
caused by changes in the values of
available-for-sale securities.

SAMPLE SELECTION AND
DESCRIPTIVE STATISTICS
Bank holding company data during the
implementation period o f SFAS 115 are used
to examine two aspects of investment portfolio
management. First, I ask whether the deci­
sion about when to adopt this accounting
standard was affected by the transitory ability
to influence reported equity. Second, I explore
whether bank managers’ desire to reduce
volatility in reported equity affects the pro­
portion of assets held in investment securities,
the maturity of the investment securities held,
and the proportion of securities held in the
available-for-sale portfolio. A sample of bank
holding companies was identified from the
consolidated financial statement for the bank
holding companies report (FR Y -9C ) filed
with the Federal Reserve System during the
second quarter of 1993 through the first
quarter of 1994. In addition to the data
available from this file, information from
the annual report footnote disclosures was
required to determine when SFAS 115 was
adopted and to obtain data on the proceeds
of sales from the investment portfolios.
Publicly traded companies are required
to file annual reports with the SEC. Therefore,
to be retained in the sample, the holding
company also had to be listed on the New York
Stock Exchange, American Stock Exchange,
National Association of Stock Deals Automated
Quotation System, or over-the-counter. This
matching resulted in a sample o f 369 bank
holding companies as of December 31, 1993.
Bank holding companies were eliminated
from the sample if their annual reports could
not be obtained directly from the company

29

REVIEW

’ Peer group classifications, which
are outlined in A User's Guide
for the Bank Holding Company
Performance Report, published
by the Board of Governors of the
Federal Reserve System, ore bank
holding companies with total assets
in billions of dollars: greater than
10 are in group 1; between 10
and 3 ore in group 2; between 3
and 1 ore in group 3; between 1
and 0 .5 are in group 4; between
0 .5 and 0 .3 are in group 5; and
between 0 .3 ond 0 .1 5 are in
group 6.




or from the National Automated Accounting
Retrieval Services database. This requirement
resulted in the exclusion o f 78 bank holding
companies. Bank holding companies were
also eliminated if they did not report the
proceeds from sales of investment securities,
and 4 0 bank holding companies failed to
report proceeds data.
Table 1 provides definitions of characteris­
tics used to analyze the effects of implementing
SFAS 115 by bank holding companies. Table 2
profiles bank holding companies included in
the sample by peer group.’ As of September
30, 1993, the sample bank holding companies
had assets ranging from $157 million to
$221 billion, and are fairly evenly distributed
within the six peer groups represented. Due
to the exclusion of bank holding companies
with missing data, the average size of the
sample bank holding companies of $10,921
billion is slightly larger than the average of
$7,796 billion for all publicly traded bank
holding companies.
Many characteristics of bank holding
companies differ across peer groups. The
average leverage ratio decreases with average
bank holding company size, while the average
return on equity increases. SFAS 115 affects
reported equity and therefore will affect the
numerator of the leverage ratio and the
denominator of the return on equity. Both
the existence of interest rate contracts and
the average portfolio turnover are increasing
with bank holding company size. These
variables suggest that larger bank holding
companies are more active in liquidity and
interest rate risk management and therefore
may be affected more by SFAS 115. Slightly
more than 4 4 percent of sample bank holding
companies adopted SFAS 115 during the
fourth quarter of 1993. This fraction, although
different across peer groups, does not increase
uniformly with bank holding company size.
Table 3 compares the characteristics
of early and late adopters of SFAS 115. On
average, early adopters of SFAS 115 have lower
leverage ratios, higher past excess gains, and
investment securities with longer maturities.
Although early adopters decreased the fraction
of their assets held in the investment portfolio
and decreased the maturity of the securities
held in their investment portfolios more in the

fourth quarter of 1993 and less in the first
quarter of 1994 than did late adopters, these
mean differences are not statistically significant.

EARLY ADOPTION OF
SFAS 115
As of the end of 1993, 93 percent of
all publicly traded bank holding companies
had net unrealized gains in their investment
portfolios. By adopting SFAS 115 early, this
unrealized gain could be used to increase
reported equity. A probit model of the decision
to adopt SFAS 115 in 1993 rather than waiting
until 1994 is estimated to determine if the
ability to increase reported equity influences
the decision about when to adopt this standard.
The probit model includes three variables
used to test whether the increase in reported
equity resulting from the adoption of SFAS 115
was important in the decision to adopt early.
Bank holding companies with lower leverage
ratios are predicted to be more concerned with
increasing reported equity and, therefore, more
likely to be early adopters. Similarly, bank
holding companies with higher returns on
equity are predicted to be more willing to
report an increase in equity, thereby reducing
this measure o f performance commonly used
by regulators and investors. Finally, bank
holding companies that have managed their
securities portfolios in the past to increase
reported equity are predicted to be more likely
to adopt SFAS 115 early.
Carey (1 994) points out that a bank
can increase capital by selecting securities
for sale with an average unrealized gain larger
than the average for all securities in the invest­
ment portfolio. Past excess securities gains
are used to measure differences across bank
holding companies in their desire to boost
reported capital.
These variables are likely to be related to
other bank holding company characteristics
such as size and structure of the investment
portfolio. The probit model also includes
several control variables to capture other
factors that may be im portant in the decision
about when to adopt SFAS 115.
Implementation of SFAS 115 is likely to
require a change in investment management,
which may require a great deal of planning.

30

REVIEW
T ab le 1

Id en tificatio n of C h a ra cteristics
Early adoption

1 if SFAS 115 was adopted as of December 31,1993; 0 otherwise

Leverage ratio

the ratio of tier 1 capital to total assets as of September 30,1993

Leverage ratio75)lia(

1 if the leverage ratio is above the 75th percentile of sample bank holding companies as of September
30,1993; 0 otherwise

Leverage ratioswfc

1 if the leverage ratio is between the 50th and 75th percentile of sample bank holding companies as of
September 30,1993; 0 otherwise

Leverage ratio25Xtife

1 if the leverage ratio is between the 25th and 50th percentile of sample bank holding companies as of
September 30,1993; 0 otherwise

Average leverage ratio

the average leverage ratio for the fourth quarter of 1990 through the fourth quarter of 1992

Return on equity

net income for the first three quarters of 1993 divided by equity as of September 30,1993

Average return on equity

the average Return on Equity for the third quarter of 1990 through the fourth quarter of 1992

Past portfolio turnover

the annual proceeds from sales of securities divided by the market value of the securities averaged
for 1990-1992

Past excess gains

the average of securities gains realized less the product of the net unrealized gains and the portfolio
turnover divided by assets for 1990-1992

Past gains

securities gains realized divided by assets averaged for the fourth quarter of 1990 through the
fourth quarter of 1992

Unrealized holding gains

the market value less the book value of investment securities divided by total assets as of
September 30,1993

Interest rate contracts

1 if an interest rate contract was held as of September 30,1993; 0 otherwise

Investmentsmaturing
, <I

the book value of investment securities maturing within 1 year divided by total assets as of June 30,1993

Investments,1< maturing
„ < 5.

the book value of investment securities maturing in more than 1 and less than 5 years divided by total
assets as of June 30,1993

Investmentsmaturing
_ >5

the book value of investment securities maturing in more than 5 years divided by total assets as of
June 30,1993

Investment^

0.5 (In v e s tm e n ts ^ ,) + 3( Investments,<molurt>i<5) + 8 (In v e s tm e n ts ^ ,)1

Investment

the book value of investment securities divided by total assets

A

Investment^

the change in InvestmentWA

A

Investment

the change in Investment

A

Investment^,

A

Investment^ - A Investment (Investment^)

Available for sale

the book value of securities classified as available for sale divided by the book value of total investment
securities as of March 31,1994

Peeri

1 for bank holding companies in Peer Group i; 0 otherwise

1 Assumes that the m aturity o f securities in these categories equals the overage o f the m inim um and m axim um m aturity fo r the category is consistent with
the assumption m ade in the proposal to revise risk-based capital standards to account for interest rate risk.

Some bank holding companies may be able
to respond to this new reporting requirement
more quickly than others. Indicator variables
for the bank holding companies’ peer group
are included to control for these factors, and
to control for other differences among bank
holding companies that depend on size such
as differences in average capital ratios and
differences in average return on equity.
An indicator variable for whether the
bank holds interest rate contracts such as




swaps, forwards and purchased options is
included to capture differences in how bank
holding companies manage their interest rate
risk. Bank holding companies that use interest
rate contracts to manage interest rate risk are
expected to be more likely to alter their invest­
ment strategies as a result of SFAS 115. This
may be important in the decision about when
to adopt this accounting standard. Similarly,
variables measuring the maturity of the invest­
ment portfolio are included since these

NK OF S T . L O U I S

31

variables may be important in explaining the
reaction to this accounting standard and, thus,
the decision about when to adopt SFAS 115.

T a b le 2

M ean V a lu e of C haracteristics by P e e r G roup
Peer Group
Variable
Assets range
(billions)
Fraction of sample

3

4

Estimation Results

10.330- 3.096- 1.007- 0.512- 0.301 - 0.157—
221.307 9.515 2.982 0.995 0.478 0.296
0.183

0.171

0.215

0.195

0.116

0.120

Leverage ratio

0.070

0.079

0.079

0.084

0.083

0.084

Return on equity

0.127

0.122

0.089

0.081

0.040

0.050

Early adoption

0.565

0.372

0.537

0.306

0.379

0.467

Interest rate contracts

1.00

0.721

0.278

0.143

0.069

0.133

Past portfolio turnover

0.252

0.156

0.151

0.150

0.129

0.104

Table 4 shows the results of the probit
estimation of the decision to adopt SFAS 115
in 1993 versus 1994, including alternative
combinations of the explanatory variables.
Evidence consistent with SFAS 115 being
adopted early to increase reported capital
is provided by coefficients on the leverage
ratio, return on equity, and the past excess
gains recognized.
The significantly negative coefficient on
the leverage ratio indicates that companies
with lower capital are more likely to increase
their reported equity by adopting SFAS 115
early.10 Similar evidence is provided by the
coefficients on the indicator variables that
measure the percentile of the leverage ratio.
Companies whose leverage ratio falls in the
top 75th percentile were significantly less
likely than those whose leverage ratio falls
in the bottom 25th percentile to adopt SFAS
115 early. The same is true for those who fall
between the 50th and 75th percentile, although
the reduction in probability is lower for this
group. Those that fall between the 25th and
50th percentile are not found to be signifi­
cantly less likely than those in the bottom
25 th percentile to adopt SFAS 115 early. The
coefficient estimates on these three indicator
variables suggest the decline in probability
of early adoption is linearly related to the
increase in the leverage ratio.
The significantly positive coefficient
on return on equity provides further evidence
that the effect on reported equity of SFAS 115
is important in the decision to adopt early."
This suggests that companies performing well
in 1993 were more willing to have this measure
of performance reduced by the increase in
equity that occurred as a result of adopting
SFAS 115 early.
Finally, the positive coefficient on the
excess gains variable, which is significant when
the investment control variables are included,
suggests that companies that have boosted
reported equity in the past through the dis­
proportionate recognition of securities gains

T a b le 3

M e an V a lu e of C h a ra cteristics
of E a rly a n d Late A d o p ters of
SFA S 1 1 5
Variable

Early Adopter Late Adopte

Leverage ratio

0.075

Average leverage ratio

0.069

0.077***

Return on equity

0.102

0.080

Average return on equity

0.020

0.026

Past portfolio turnover

0.174

0.153

Unrealized holding gains

0.007

0.008

-0 .0 2 8

-0 .0 5 0 *

Past excess gains (%)

0.083***

Interest rate contracts

0.523

0.336***

Investmentsmaturing
, . <1.

0.055

0.061

Investments,1<maturing
, . <5.

0.091

0.121***

Investmentsmaturing > 5

0.127

0.092***

Investment^

1.316

1.127**

A Investment^
fourth quarter 1993

-0 .0 0 9

0.009

0.070

0.041

fourth quarter 1993

0.001

0.007

first quarter 1994

0.004

0.001

-0 .0 0 5

0.005

0.060

0.039

first quarter 1994
A Investment

A Investment^
fourth quarter 1993
first quarter 1994
,0 In discussing the results, t-stotistics
greater than 1 .6 6 , which are signifi­
cant at the 5 percent level for one­
tailed tests and at the 10 percent
level for two-tailed tests, are consid­

Note: ***, ** and * indicate that the difference in the means
for early and late adopters is statistically different from zero
at the 0.01,0.05 and 0.10 levels.

ered to be statistically significant.




NK OF ST. L OU IS

32

REVIEW
are more likely to adopt SFAS 115 early to
increase their reported equity during 1993.
The coefficients reported in Table 4
provide estimates of the changes in probability
of early adoption of SFAS 115, given changes
in the corresponding variable. Therefore, a
coefficient of -2.766 on the leverage ratio indi­
cates that increasing the leverage ratio from
the Peer 1 group average of 0 .070 to the Peer
6 group average of 0 .084 would result in
roughly a 3.9 percent decline in the proba­
bility of adopting SFAS 115 early. Similarly,
a coefficient of 0 .438 on return on equity
indicates that decreasing the return on equity
from the Peer 1 group average of 0 .127 to the
Peer 6 group average of 0 .050 would result
in roughly a 3.4 percent decline in the prob­
ability of adopting SFAS 115 early.
Once other characteristics have been
controlled for, the size of the bank holding
company generally does not appear to be
important in the decision to adopt SFAS 115
early, although bank holding companies in
Peer group 3 are more likely than those in
Peer group 1 to adopt early. The only other
variables that are significant in explaining
the early adoption decision are the amount
of securities maturing in more than one year
and less than five years, and the existence
of interest rate contracts. Inclusion of these
variables does not alter the conclusions drawn
from the coefficients on the other variables
included in the estimation.
The mean predicted probability of
adopting SFAS 115 early is significantly higher
for early adopters than for late adopters for all
three probit models estimated. In addition, the
fraction o f bank holding companies correctly
classified as early versus late adopters for all
three probit models is significantly better than
the fraction that would be correctly predicted
by assuming that the probability equals the
mean proportion in the sample.

T a b le 4

R esults o i P robit Estim atio n of
E a rly A doption D ecision
Variable
Intercept
Leverage ratio

Return on equity

-2 .7 6 6
( -2 .021)

-0 .0 1 5
( -0 .266)
0.524
0.475
(1.953) (1.731)

Unrealized holding gains
Past excess gains

0.438
(1.600)
4.811
(1.000)

37.313 37.419
(1.503) (1.604)

Investmentsmctunng< 1

49.476
(1.889)
0.385
(0.825)
-0 .7 0 4
( -1 .894)
0.338
(1.274)

Investments,1 < maturing<
, 5,
Investments^
maturing >5
Interest rate contracts
Peer 2

0.020
(0.159)

-0 .1 6 2
( -2 .700)
-0.1 0 9
( -1 .8 5 9 )

Leverage^
L everage^

0.029
(0.448)

-3 .6 3 0
(-2 .7 6 8 )

Leverage,^

0.156
(2.490)
-0 .0 7 7 -0.071 -0.051
(-1.267)1 -1.033)1 -0 .679)
0.037
(0.570)

0.143
(1.732)

Peer 4

-0.091 -0.081
(-1.451)1 -1.163)

0.022
(0.238)

Peer 5

-0.0 3 7 -0.037
(-0.465)1 -0.500)

0.096
(0.953)

Peer 6

-0.0 0 5
(-0 .0 6 8 )

0.001
(0.012)

0.180
(1.118)

0.374

Peer 3

0.032
(0.400)

Mean predicted
probability

CHANGES IN INVESTMENT
SECURITY HOLDINGS

late adopters

0.402

0.401

early adopters

0.491”

0.494** 0.525**

Fraction correctly
predicted

0.657** 0.626*

Psuedo—R!(%)

6.955

7.203

0.669**
12.028

Notes: t-statistics are provided in parentheses, and ** and *
indicate statistical significance at the 0.01 and 0.05 levels in
either the difference in means for early and late adopters, or
in the difference between the fraction correctly predicted and
the proportion of early versus late adopters in the sample.

Regression models using three different
measures of changes in investment security
holdings are estimated to determine if changes
in the investment portfolio are made in the
quarter that SFAS 115 is adopted. The




0.248
(2.389)

11 For the model presented in the
third column, the coefficient on
this variable is only significant
at the 6 percent level using a
one-toiled test.

F E D E R A L R ESERVE B A N K OF S T .

33

LOUIS

REVIEW

12 See Greene (1 9 9 3 ) for d discussion
of the computation of the self-selec­
tion variable that corrects for the
truncation in the distributions of
observed dependent variables
when there is self-selection
into treatment groups.




first measure examined is the change in
the maturity-weighted investment portfolio.
This variable measures both changes in the
proportion of assets held in investment
securities and changes in the maturity of
the securities held. The second measure
examined is the change in total investments.
This variable measures only the change in the
proportion of assets held as securities, ignoring
changes in the maturity of the securities. The
third measure is an adjusted maturity-weighted
measure designed to eliminate changes in the
weighted maturity of the portfolio that occur
merely due to changes in the proportion of
assets held in investment securities.
These three measures provide information
on the overall change in investment security
holdings and the two components o f that
change. Changes in the investment port­
folio may occur for reasons other than the
accounting change, such as changes in interest
rates. Determining the expected change would
require a comprehensive model of investment
portfolio management. If the change in invest­
ment portfolio holdings associated with this
accounting change occurs in the quarter that
the standard is adopted, then the bank holding
companies that did not adopt SFAS 115 during
the quarter can be used as a control group.
If the non-adopters provide a measure of
the changes that would have occurred in
the absence of the accounting standard,
then the difference between the adopters
and non-adopters can be used to determine
the change due to the accounting standard.
This is a common approach used to study
treatment effects and program effectiveness.
A common problem in these studies is
that participants often choose the group that
they are in and therefore may be different for
reasons other than the treatment or program.
In this case, the period of adoption of SFAS
115 is not random and early adopters are
different in a variety of ways from late adopters.
Correction of the self-selection bias that results
requires that these differences in characteristics
be used to predict who will choose to be
included in each group. The estimates from
this prediction model can then be used to
construct a variable that corrects for the bias
that occurs because we cannot observe the
values of the dependent variables for the

alternative choice. This self-selection variable
is computed using the predicted probabilities
from the probit estimation of early adoption
of SFAS 115 in the last column in Table 4 .12
The coefficient on this variable can be used
to determine if the conclusion drawn from
the estimation would have been affected
by a self-selection bias.
The importance of reducing reported
equity volatility is measured using the average
leverage ratio and the average return on equity.
The cost of increasing equity volatility is
assumed to be negatively related to the level
of these ratios and, therefore, the lower the
average leverage ratio and average return on
equity, the greater the expected reduction in
the maturity of the investment portfolio.
Several control variables are also included
in the regression to capture other factors that
may be important in explaining the change in
investment portfolio holdings. Since changes
in maturity may depend on the initial maturity,
the weighted average maturity of the invest­
ment portfolio at the end of the second quarter
of 1993 is included in these regressions. In
addition, the size of the bank holding company
may be important in explaining the desired
holdings. Peer group dummy variables are
also included as control variables. Finally,
the desired investment portfolio holdings
may depend on the bank holding company’s
hedging activity and, therefore, an indicator
variable for the existence of interest rate
contracts is included.

Estimation Results
Table 5 reports the results of the analysis
of change in investment portfolio holdings for
the fourth quarter of 1993 and Table 6 reports
the results for the first quarter of 1994. Three
regression models are estimated. The signifi­
cantly negative coefficient on the early adoption
dummy in the fourth quarter of 1993, and
the significantly positive coefficient on that
variable in the first quarter of 1994 for the
maturity-weighted and total-investments
equations provide evidence that bank holding
companies reduced both the proportion of
assets held in the investment portfolio and
the maturity of those investments in the
quarter that they adopted SFAS 115.

34

REVIEW

JANUARY/FEBRUARY

Given a weighted-average maturity of
the investment portfolio on June 30, 1993,
of 1.21, the coefficient of -0.318 on the early
adoption variable reported in column one
indicates approximately a 27 percent decrease
in the ratio of the maturity-weighted invest­
ment securities to assets resulting from the
adoption of SFAS 115. Part of this decline is
due to a decrease in the proportion of assets
held in the investment portfolio. A coefficient
of -0.045 on the early adoption variable in the
regression examining the change in the ratio
of investment securities to assets implies a
16 percent decline in the size of the investment
portfolio, given an average ratio of investment
securities to assets of 28 percent at the end
of June 1993.
The significant coefficient on the self­
selection variable indicates that the estimates
would be biased if this variable were not
included. In addition, the sign on the coeffi­
cient on this variable indicates the direction
of the bias on the early adoption variable if
the selectivity correction were omitted. The
positive coefficients on the self-selection
variable in the fourth quarter of 1993 regres­
sions imply an upward bias on the coefficient
on the early adoption variable without the
selectivity correction, indicating that the
estimated change in the investment portfolio
holdings for the early adopters would have
been understated. The opposite is true for
the first quarter of 1994 regressions.
The significantly negative coefficient
on the average leverage ratio in the fourth
quarter of 1993 provides evidence of crosssectional differences in the concern over
increased capital volatility resulting from the
adoption of SFAS 115. I find little evidence
that the average leverage ratio was important
in explaining the change in investment port­
folio holdings in the first quarter of 1994.
In addition, I find little evidence that the
average return on equity is important in
explaining the changes in either quarter.13
Once other factors have been controlled
for, the size of the bank holding company
generally does not appear to be important in
explaining the change in the maturity of the
investment portfolio, although these variables
are important in explaining the change in the
proportion of assets held in the investment




1995

T a b le 5

Estim ation R esults fo r C h an g es in
In vestm en t Secu rity H oldings in th e Fourth
Q u a rte r of 1 9 9 3
Variable
Intercept
Early adoption
Self-selection
Average leverage ratio
Average return on equity
Investment^

A Investment^ A Investment A InvestmentJW1
0.283
(2.949)

0.035
(2.722)

0.243
(2.875)

-0.3 1 8
(-2 .5 4 0 )

-0 .0 4 5
(-2 .9 2 2 )

-0 .2 6 5
(-2 .4 0 4 )

0.209
(2.662)

0.025
(2.608)

0.178
(2.589)

-2.3 6 6
(-2 .5 6 9 )

-0.301
(-2 .4 4 1 )

-1 .9 2 4
(-2 .3 7 4 )

0.815
(1.815)

0.016
(0.258)

0.653
(1.652)

-0 .0 3 4
(-2 .0 3 4 )

Investment

-0.0 3 8
(-2 .1 3 8 )
-0 .0 3 4
(-2 .0 3 4 )

Interest rate contract

0.029
(0.630)

0.014
(2.217)

0.017
(0.417)

Peer 2

0.024
(0.483)

-0.001
(-0 .1 3 1 )

0.031
(0.727)

Peer 3

0.071
(1.284)

0.019
(2.568)

0.057
(1.172)

Peer 4

0.024
(0.437)

0.017
(2.266)

0.014
(0.279)

Peer 5

0.159
(2.549)

0.038
(4.495)

0.112
(2.035)

Peer 6

0.083
(1.298)

0.025
(2.891)

0.059
(1.036)

Adj. R2(%)

8.37

11.03

7.43

Note: t-statistics are provided in parentheses.

portfolio. In the fourth quarter of 1993, the
weighted average maturity of the investment
portfolio is important in explaining the change
in the maturity, while the existence of interest
rate contracts is important in explaining the
change in the first quarter of 1994.

13 The regressions were olso estimated
oliowing the coefficients on the

Timing of Portfolio Restructuring
To test the reasonableness of the
assumption that changes in investment
portfolio holdings are made in the quarter
that SFAS 115 is adopted rather than before
or after, I compare the proceeds from securities
sales during 1993 for bank holding companies
that adopted in 1993 versus those that adopted
in 1994. I also compare annual rather than

F EDERAL RESERVE B A N K OF S T . L OU IS

35

average leverage ratio and the
average return on equity to be difThese coefficients were statistically
significant only for the leverage
ratio in the fourth quarter of 1993,
and the conclusions drawn about
changes in the investment portfolio
in the quarter of adoption of SFAS
115 were unchanged using this
specification.

provide some support for the assumption that
changes in the investment portfolio were made
in the period when SFAS 115 was adopted.
In addition, the regressions performed
during the period of the accounting change
are also estimated for the same quarters during
1991 and 1992. Finding no significant dif­
ference in the change in investment portfolio
holdings during these early periods for com ­
panies that adopted SFAS 115 early versus
those that did not would provide reassurance
that any differences found during the SFAS
115 adoption period are actually attributable
to the accounting change. In the estimation
of the equations reported in Tables 4 and 5
for the same quarters in 1991 and 1 9 9 2 ,1
find no evidence of differences between the
early adopters and other companies.14

T a b le 6

Estim ation R esu lts fo r C h an g es in
In vestm en t S ecu rity H oldings in the First
Q u a rte r of 1 9 9 4
Variable

A Investm ent^

Intercept

-0 .0 5 2
(-0 .4 6 8 )

-0.0 2 2
(-1 .5 9 8 )

-0 .0 0 2
(-0 .0 1 6 )

0.263
(1.851)

0.032
(2.050)

0.181
(1.402)

Self-selection

-0 .1 5 3
(-1 .7 2 5 )

-0 .0 1 9
(-1 .9 4 7 )

-0 .1 0 3
(-1 .2 8 2 )

Average leverage ratio

-0.311
(-0 .2 8 8 )

0.138
(1.054)

-0 .4 9 0
(-0 .5 0 0 )

Average return on equity

-0.311
(-0 .4 8 1 )

-0 .0 2 2
(-0 .2 8 2 )

-0.2 2 2
(-0 .3 7 9 )

Early adoption

Investment^

0.000
(0.016)

-0 .0 0 2
(-0 .1 0 5 )
-0 .0 2 4
(-1 .3 9 6 )

Investment
Interest rate contract

A Investment A InvestmentJW1

-0 .0 8 4
(-1 .5 7 6 )

0.000
(-0 .0 6 9 )

-0.0 7 6
(-1 .5 6 7 )

Peer 2

0.093
(1.682)

0.009
(1.341)

0.079
(1.567)

Peer 3

0.071
(1.120)

0.009
(1.186)

0.064
(1.115)

Peer 4

0.092
(1.462)

0.012
(1.517)

0.070
(1.223)

Peer 5

0.002
(0.029)

0.005
(0.575)

0.002
(0.028)

Peer 6

0.051
(0.667)

0.009
(0.987)

0.048
(0.694)

Adj. R2(%)

2.28

-0 .4 0

PROPORTION OF SECURITIES
CLASSIFIED AS AVAILABLEFOR-SALE
An alternative approach that can be used
to reduce the volatility in reported equity that
results from the adoption o f SFAS 115 is to
reduce the proportion of securities classified
as available-for-sale. A regression model is
estimated to examine how the desire to
maintain flexibility in investment portfolio
management and to reduce volatility of
reported capital affects the proportion
o f investment securities classified as
available-for-sale.
The importance of liquidity and interest
rate risk management is measured using past
portfolio turnover. The benefit of being able
to sell investment securities for liquidity or
interest rate risk management is assumed to
be higher for bank holding companies, the
more active their management of the invest­
m ent portfolio has been. Portfolio turnover
is used to measure the level o f portfolio
management activity. Bank holding companies
with higher portfolio turnover are expected
to classify a larger proportion of their invest­
ments in the available-for-sale portfolio.
The importance of influencing reported
earnings through the sale of securities is
measured using the past excess securities
gains recognized. The benefit of being able
to recognize gains on the sale of securities

1.23

N o te : t-statis tics a r e p ro v id e d in p a re n th e s e s .

14 The largest t-statistic found in these
estimations was 1 .3 1 5 and gener­
ally the t-statistics were less than 1.




quarterly proceeds because only annual
proceeds are disclosed in the financial state­
ments. If early adopters change investment
portfolio holdings during 1993, then, cor­
recting for the self-selection bias, these bank
holding companies should have higher
proceeds during this period. For proceeds
on sales of investment securities, I conduct
a regression analysis similar to the analysis
for changes in investment portfolio holdings.
The results of the regression analysis of
turnover of the investment portfolio in 1993
indicate that controlling for past portfolio
turnover, bank holding company peer group,
and the self-selection bias, early adopters
had a higher portfolio turnover in 1993 than
late adopters. The results o f this regression

36

REVIEW

JANUARY/FEBRUARY

to influence earnings is assumed to be higher
for bank holding companies that recognize a
disproportionate amount of gains on security
sales. Excess securities gains recognized are
computed as the difference between the ratio
of recognized gains to the market value of
investment securities, and the ratio of unreal­
ized gains to the market value of investment
securities multiplied by the portfolio turnover.
This measure assumes that in the absence of
earnings, management gains will be recognized
in proportion to the unrealized gains. The
average past securities gains recognized are
used as an alternative measure of the desire
to influence reported earnings through the
recognition of gains on securities sales.
As for the change in investment portfolio
maturity regression, the importance of reducing
reported equity volatility is measured using the
leverage ratio and return on equity. The cost
of increasing equity volatility is assumed to be
negatively related to the level of these ratios
and, therefore, the proportion of securities
classified as available-for-sale is assumed to be
positively related to the level o f these ratios.
1
also include several control variables
in the regression to capture other factors that
may be important in explaining the proportion
of securities classified as available-for-sale.
Since the sensitivity of the investment portfolio
value to changes in interest rates will depend
on the timing of cash flows from the securities
held, 1 include a measure of the maturity of
the investment portfolio. In addition, the size
of the bank holding company may be impor­
tant in determining how active the investment
portfolio management is, and may also be
related to the other measures included in the
regression. Therefore, I add peer group
dummy variables as control variables.

Estimation Results
Table 7 reports the results of regression
analysis of the proportion o f securities classi­
fied as available-for-sale. The results show
that reducing volatility in reported capital as
well as maintaining flexibility in managing
liquidity and interest rate risk, and in influ­
encing reported earnings are important in
deciding what proportion of securities to
classify as available-for-sale.




37

1995

T a b le 7

Estim ation R esults fo r Pro portio n of
S e cu ritie s C la ssifie d a s A v a ila b le -fo r-S a le
Variable
Intercept

0.254
(2.399)

0.258
(2.434)

0.218
(2.053)

Average leverage ratio

1.830
(1.480)

1.883
(1.520)

1.825
(1.492)

Average return on equity

2.035
(2.139)

2.007
(2.108)

2.333
(2.458)

Positive past excess gains
Past excess gains

39.735
(2.431)
13.142
(2.244)

12.451
(2.104)

1.524
(0.203)

66.574
(0.850)

Past gains
Past portfolio turnover

0.462
(3.407)

0.398
(2.560)

0.338
(2.354)

Investmentmaturingd,

0.666
(1.506)

0.675
(1.524)

0.741
(1.689)

-0 .1 1 5
(-0 .3 9 3 )

-0 .2 1 3
(-0 .6 7 7 )

-0 .0 8 0
(-0 .2 7 7 )

0.317
(1.397)

0.249
(1.033)

0.360
(1.596)

Peer 2

-0 .0 7 2
(-1 .0 1 6 )

-0 .0 6 2
(-0 .8 6 3 )

-0 .0 7 4
(-1 .0 5 3 )

Peer 3

-0 .0 3 9
(-0 .5 6 4 )

-0 .0 3 0
(-0 .4 2 3 )

-0 .0 3 6
(-0 .5 2 2 )

Peer 4

0.003
(0.046)

0.007
(0.098)

0.005
(0.072)

Peer 5

-0 .0 3 6
(-0 .4 4 2 )

-0 .0 2 9
(-0 .3 5 1 )

-0 .0 3 6
(-0 .4 4 5 )

Peer 6

-0.0 5 7
(-0 .6 4 7 )

-0 .0 5 4
(-0 .6 1 5 )

-0 .0 4 8
(-0 .5 5 3 )

2.78

2.66

4.84

Investment,1cmoturmgo,
Investmentmaturing>5
^. ,

Adj. R2(%)

Note: t-statistics are provided in parentheses.

The significantly positive coefficient on
portfolio turnover suggests that bank holding
companies that have more actively engaged
in liquidity and interest rate risk management
in the past classify a higher fraction of their
investment securities in the available-for-sale
portfolio.15 Similarly, the significantly positive
coefficient on excess gains on securities sales
indicates that bank holding companies that
have used gains on the sale o f securities to
influence reported earnings and capital in the
past classify a higher proportion of securities
as available-for-sale. The positive coefficients
on both the leverage ratio and return on equity

REVIEW
suggest that bank holding companies with
more capital and higher earnings are more
willing to incur the increased volatility in
reported equity that will occur when a higher
fraction of their investments are included in
the available-for-sale portfolio. The coeffi­
cients on the average leverage ratio, however,
are only significant at the 7 percent level
using a one-tailed test.
The size of the bank holding company
and the maturity of the investment portfolio
are generally not important in explaining
the proportion o f securities classified as
available-for-sale.

desire to reduce the volatility in reported
capital and the desire to maintain flexibility
to influence reported earnings through the
recognition of gains on security sales.
If the documented changes in investment
portfolio management continue, they could
have important consequences for the banking
industry and the economy. Although no
attempt is made in this article to assess how
costly these changes will be, shortening the
maturity of the investment portfolio may
result in a reduction in the interest income
earned by bank holding companies or may
increase their interest rate risk. Reduction
of the flexibility to sell securities from the
held-to-maturity portfolio may increase
the cost of managing liquidity and interest
rate risk. Reduced flexibility in liquidity
management could make banks unable to
meet increases in loan demand, thereby
decreasing the availability of credit. Increased
exposure to changes in interest rates could
make the banking industry more volatile.
Even in the absence o f changes in the
investment portfolio, including the effects of
SFAS 115 in regulatory capital could be costly
if bank holding companies must maintain
additional capital or if regulatory actions are
taken against viable bank holding companies
as a result of the change in accounting method.
The recent decision by regulators to exclude
the effects o f SFAS 115 from the definition of
regulatory capital ratios may lead to further
changes in investment portfolio management
that will ameliorate these effects.

CONCLUSIONS

15 For the model presented in the
third column of Table 7, the coeffi­
cient on this variable is only signifi­
cant at the 7 percent level using a
onetailed test.




Bank managers and regulators have
opposed the adoption of SFAS 115, claiming
that the increased volatility in reported equity
caused by this accounting standard is not
indicative of true volatility in equity and will
cause bank holding companies to alter their
investment portfolio management. In addition,
they have argued that banks will continue to
have opportunities for manipulating the num­
bers reported in the financial statements.
Based on bank holding companies’
response to the implementation of SFAS 115,
this article provides several pieces of evidence
that suggest that bankers’ and regulators’
concerns about the impact of SFAS 115 are
well-founded. The decrease in both the pro­
portion and maturity of investment securities
held in the quarter when SFAS 115 was
adopted, and the reduction in the proportion
of securities classified as available-for-sale
as bank holding companies’ average leverage
ratio and average return on equity decline
indicate that concerns about volatility in
reported equity induced by SFAS 115 led to
a change in investment portfolio management.
The importance of the leverage ratio
and return on equity in the decision to adopt
SFAS 115 early, and the higher proportion
of securities classified as available-for-sale if
excess securities gains have been recognized
in the past indicate that management can
still influence the numbers reported in the
financial statements under SFAS 115. Man­
agement of the investment portfolio under
SFAS 115 appears to be affected both by a

REFERENCES
Barth, Mary E., Wayne R. Landsman, and James M. Wahlen. "Fair
Value Accounting: Effects on Banks' Earnings Volatility, Regulatory
Capital, and Value of Contractual Cash Flows," Journal of Banking
and Finance (forthcoming 1995).
Berger, Allen N., Kathleen K. King, and James M. O'Brien.
"The Limitations of Market Value Accounting And a More Realistic
Alternative," Journal of Banking and Finance (September 1991),
pp. 753-83.
Carey, Mark. "Partial Market Value Accounting, Bank Capital Volatility,
and Bank Risk," Journal of Banking and Finance (forthcoming).
________ . "Snacking and Smoothing: Gains Trading of Investment
Account Securities by Commercial Banks," Working Paper, Board of
Governors of the Federal Reserve System (June 29, 1994).

38

REVIEW

RY/FEBRUARY

1995

Mengle, David L., and John R. Walter. "How Market Value Accounting
Would Affect Banks, Proceedings of a Conference on Bank Structure
and Competition," Federal Reserve Bank of Chicago, May 1 -3 ,19 91.

Ernst & Young. A National Survey of Chief Financial and Chief
Investment Officers in 216 Financial Services Institutions, 1993.
Ernst & Young. A Follow-Up Survey on the Impact of the
Implementation of FAS 115, 1994.

Shaffer, Sherrill. "Marking Banks to Market," Federal Reserve Bank of
Philadelphia Bos/ness ^eweiv (July/August 1992), pp. 13-22.

Greene, William H. Econometric Analysis, 2nd edition. Macmillan, 1993.
Moddala, G.S. Limited-Dependent and Qualitative Variables in
Econometrics (Econometric Society Monographs No. 3).
Cambridge University Press, 1983.




F E D E R A L RESERVE B A N K OF S I .

39

LOUIS




REVIEW
Michael J. Dueker is an economist at the Federal Reserve Bank of St. Louis. Christopher A. Williams provided research assistance.

K

Narrow V s.
Broad M easures
of M oney as
Interm ediate
Targets: Some
Forecast Results

controlled. Here I assume the monetary
authority makes policy operational by setting
quarterly targets for either nominal spending
growth or the rate of inflation. Thus, using
a monetary aggregate as an intermediate target
implies a multi-stage process that can be
diagrammed in the following way:

Instruments

M ichael J . D u eker

(funds rate)

ne o f the key challenges of monetary
policy is to forecast the links between
policy actions and the ultimate objective
of stabilizing prices. Measures of the money
supply have long been considered links
between monetary policy actions and the
course of nominal spending and prices.
Central banks around the world adopted
monetary targets to stop the acceleration of
inflation in the 1970s. The strategy worked
to end the acceleration of inflation and to
lower the inflation rate to moderate levels.
The occurrence of high and accelerating
inflation, however, triggered financial inno­
vation and deregulation, two processes that
seem to have destabilized the income velocity
of money. In response, central banks have
begun to target their objectives more directly.
In some cases, such as New Zealand, Canada
and the United Kingdom, they have adopted
explicit multi-year targets for inflation. In
others, such as the United States, the monetary
targets were deemphasized and the de fa cto
policy appears to be something like nominal
GDP growth targeting. The question is
whether any role remains for intermediate
monetary targets.
In the United States, the Federal Reserve
uses the interest rate on bank reserves, the
federal funds rate, as a guide for supplying
bank reserves, so that the monetary aggregates,
even the monetary base, are not directly

=>

Intermediate
Target

=>

Controllability

(money supply)

indicator
quality

O




The monetary aggregate used as an
intermediate target ideally has two properties:
First, the monetary aggregate ought to be
controllable in the sense that policymakers
know where to set the policy instrument in
order to obtain the desired growth in the
money supply. Second, it should have a
predictable, though not necessarily stable,
relationship with the nom inal target variable.
If the nominal target variable is nominal GDP,
then the intermediate monetary aggregate
ought to have a predictable velocity. If the
target variable is inflation, then the demand for
real money balances ought to be predictable.
No intermediate target will be perfect
with regard to these two properties. An
intermediate target, however, will be a more
useful link between policy actions and the
nominal target if its errors as an indicator
are small and negatively correlated with the
errors in controlling it. In other words, the
two errors might largely cancel each other
on a regular basis, implying that an interme­
diate monetary target could potentially
improve the policy process.
Control error, which results from impre­
cise policy control over the growth of central
bank credit and thereby the money supply

41

Nominal
Target
(nominal GDP
growth or
inflation)

REVIEW

1 Thornton (1992) olso discusses
the possibility of opplying
reserve requirements ocross
oil M2 accounts.
2 For a broad survey of potential
intermediate forgets for mone­
tary policy, including variables
other than monetary aggre­
gates, see Intermediate Targets
and Indicators for Monetary
Policy, a publication of the

Federal Reserve Bank of
New York.
3 See Orphanides, Reid and Small
(1994) and Collins and Edwards
(1994) for a complete description
of M2+.




target. Restated in concrete terms, the issues
are: First, how predictable is the relation­
ship between changes in the funds rate and
growth in monetary aggregates? Second,
how predictable is the relationship between
the monetary aggregates and the nominal
targets (nominal GDP growth and inflation) ?
Third, which monetary aggregate would likely
produce the compound forecast error (instru­
ments to money to nominal target) with the
smallest variance? 1
The next section describes the forecasting
model used to generate estimates of the error
variances in the links between the funds rate,
alternative monetary intermediate targets,
and two alternative nominal targets: nominal
GDP growth and inflation. The third section
presents empirical results which consist of
one-step-ahead, mean-squared forecast errors
for the control error, the velocity error and
their sum, the compound error, along with
tests for serial correlation. I also perform
Bartlett tests for equal variance among the
compound errors for the alternative monetary
intermediate targets. The fourth section
summarizes the results and concludes
with policy implications.

measures, forces policymakers to forecast
the effects of policy actions on the monetary
aggregates. The study of control error is
not new, although the present analysis brings
different statistical techniques to bear. Other
empirical studies of monetary control pub­
lished in this Review include Andersen and
Karnosky (1977) and Thornton (1 9 9 2 ). In
related work, Feldstein and Stock (1993)
suggest using M2 as an intermediate target
when the nominal target is nominal GDP.
They propose a system of uniform reserve
requirements on all M2 accounts to remedy
the control problem.1 This article, in con­
trast, explicitly considers control error as
part of monetary policymaking with money
as an intermediate target.
Much previous research has focused
on the relationships between the monetary
aggregates and the path of nominal GDP,
that is, their velocities. This research has
often sought to determine which aggregates
have stable velocities. The implication has
been that the best intermediate target is the
monetary aggregate with the most stable
velocity. For example, based on the apparent
long-run stability of M 2’s velocity from the
late 1950s through the late 1980s, many
people viewed it as a reliable nominal anchor
(Hallman, Porter and Small, 1991). In the
early 1990s, however, M2 velocity increased
at a time when its opportunity cost generally
decreased by a sufficient amount to raise
doubts about the stability of its velocity
(Feinman and Porter, 1992; Ritter, 1993).
The comparisons between aggregates in
this article, on the other hand, focus on the
predictability of an aggregate’s velocity, not
its stability. Furthermore, in the literature
on intermediate targeting, the dual problems
of monetary control and velocity are acknowl­
edged but they are often addressed separately.
This article addresses both jointly in a con­
sistent statistical framework. One caveat,
however, is that one time-series forecasting
method will be used. Thus, the results are
conditional on this method and may be
sensitive to changes in the methodology.
This article quantitatively investigates
the twin issues of controllability and indicator
quality associated with using one of the
monetary aggregates as an intermediate

USING M ONEY TO
LINK POLICY ACTIONS
TO NOMINAL TARGET
VARIABLES
This article includes two sets of results,
depending on whether the operational goal of
policy is to steer nominal GDP growth or infla­
tion. O f course, long-run inflation will tend to
equal long-run nominal GDP growth minus
growth in potential real GDP, but in the short
run, nominal GDP and inflation may differ
in the extent to which they have predictable
relationships with the monetary aggregates.
I study four different measures o f the
money supply as potential intermediate targets:
the adjusted monetary base (MB) as calculated
by the Board of Governors; M l; M2; and a
newer measure called M 2-Plus (M 2+), which
consists of M 2 augmented by bond and equity
mutual funds.3
In equation 5 below, GDP stands for
nominal GDP andjff for the federal funds rate.
The rate of nominal GDP growth accompa-

42

JANUARY/FEBRUARY

nying a given change in the funds rate can
be written as the sum of three components:
the predicted growth in GDP given predicted
growth in the monetary aggregate; the pre­
dicted growth in the monetary aggregate
given the change in the funds rate; and
an overall forecast error:

Ain

GDP

(1 )

= Ain

(2)

= Ain

MB /i i-i
GDP
M l St t-1

= Ain

(3)

(4)

= Ain

(5)

= Ain

+ Ain

+e
l + f f J , ,-:

■Ain

+e

1 + f f ) t|t-i

' GDP '

[M2+J £It—1
GDP

W hen discussing the results, I use root
mean-squared forecast error as the criterion
in judging, for example, whether e, is large
relative to e0. The paper also includes analo­
gous comparisons in which inflation, measured
by the percentage change in the im plicit GDP
price deflator, is assumed to be the nominal
target of monetary policy. In
this case, the price level is sub­
stituted for GDP and the error
in predicting velocity is replaced
with the error in predicting the
+e
demand for real balances in
t-i
equations 1-5.

+ Ain

M2 /i r-i

+ Ain

+ e ot

r

{

gdp

- A in

Ml j t

+ Aln

'gd

p

{ Ml

,

'

J

- A in
1 + f f J ,|,-i

= velocity error + control error.




A FORECASTING
MODEL FOR
UNSTABLE
RELATIONSHIPS

To generate the forecasts
needed to estimate the magnitude
of the uncertainty from control
and velocity error for alternative
monetary aggregates, I use two
time-varying coefficient models
that do not rely on stable rela­
tionships for their forecasts:
One is for the control relationship between
the funds rate and money growth; the other
is for the velocity relationship between
money growth and growth in the nominal
target. Two reasons not to rely
on the existence of stable relationships are:
(1) M2 velocity, which had been the most
stable among the velocities of money, has not
appeared stable in recent years; and (2) the
relationship between monetary growth and
changes in the funds rate almost certainly
varies with factors such as the level of infla­
tion, the stage of the business cycle and the
degree o f credibility of the central bank.
Bemanke (1993) and Eichengreen (1992)
argue that the loss of credibility among central
banks led to a shift from stabilizing specula­
tive flows in the pre-World War I period to
destabilizing speculative attacks in the inter-war
period. One implication is that larger policy
actions are needed to achieve the same result
in the face of destabilizing speculation. Hence,
the relationship between the policy instrument
and money growth varies with central bank

rM 2 + "
U+//J

where t \t - 1 denotes the value forecasted
for time t using information available
through time t - 1.
The forecast error in equation 5, e0t, is
based on a direct forecast of the relationship
between the funds rate and nominal GDP
growth, without reference to an intermediate
target. This forecast error will serve as a
baseline against which the others,
e4t,
are measured, that is, the extent to which
an intermediate target reduces the overall or
compound error.4 The forecast errors, elt,...,
e4„ are compound errors in that they equal
the sum of the velocity forecast error and
the control error. For example, for Ml

e 2t = Ain

1 9 95

43

4 Note that all variables are mea­
sured as a percent.




credibility. In the sample period used in this
paper, 1959-94, the credibility of the Federal
Reserve probably decreased in the 1970s when
inflation accelerated in contradiction to stated
policy objectives. The disinflation of the early
1980s, however, probably enhanced the cred­
ibility of the Federal Reserve, because it largely
achieved its stated intention of reducing
inflation. Moreover, at the statistical level,
Dueker (1993) includes tests that reject con­
stant-coefficient models of velocity growth in
favor of time-varying coefficient models.
I
implement a time-varying coefficient
(TVC) forecasting model using the Kalman
filter. The TVC model allows for heteroskedastic errors, which means the Kalman filter
updates the inferred coefficients cautiously when
the error variance is high and more liberally
when the variance is low. This feature helps
the model use the optimal signal-to-noise
ratio each period when updating the coeffi­
cients and forming next period’s forecast.
To keep the forecasting models
parsimonious, I limit the models to three
explanatory variables: quarterly changes in
the three-month and 10-year bond yields
and lagged money growth. The changes in
the three-month and 10-year rates summarize
developments in the yield curve, which varies
with the business cycle, and also indicates
when asset substitution is likely to occur
between short- and long-maturity assets. Thus,
when forecasting changes in M l velocity, for
example, the third explanatory variable is
lagged M l growth:

(6) Ain

/GDPA
Ml

=P0l+P u ATB3mot_1

absorbed by decreases in velocity due to
lagged adjustments, and because of time
aggregation and other factors. The three-month
and 10-year bond yields provide information
about the changing opportunity costs of dif­
ferent types of savings accounts by providing
information on yield curve spreads.
Similarly, the equation for predicting
the relationship between changes in the funds
rate and M l growth takes the form

(7) Ain

= P0 l + P u A T B 3 m o t_1
1 + // Ali-i
+ p 2l A TB10yrr_,
+ /J3l Ain M l ;_ ,.

The coefficients on the lagged short and
long-term interest rates are both expected to
be negative, because higher interest rates at
all points along the yield curve will generally
be associated with a higher funds rate and
lower M l growth. The coefficient on lagged
M l growth, on the other hand, does not have
a clearly implied sign. W hen policy is geared
toward disinflation, high M l growth in the
last period can portend substantial increases
in the funds rate, implying decreases in the
Ml/funds rate ratio. W hen nominal interest
rates are relatively stable, however, positive
serial correlation in M l growth rates can
imply a positive coefficient.
The accompanying box contains plots
of the time-varying coefficients for the M l
equations to illustrate the changes in rela­
tionships between variables in the sample
period. Further details on the forecasting
model are also in the appendix and in
Kim (1993).

+ & .A T B 1 0 y r ^

ESTIMATED VELOCITY
AND CONTROL FORECAST
UNCERTAINTY

+ Pit A in M l t_ j,
where TB3mo is the three-month Treasury
bill yield, and TBIOyr is the constant maturity,
10-year Treasury bond yield. The coefficient
on lagged M l growth, fi3t, is expected to have
a negative sign because faster money growth
does not generally lead to a one-to-one
increase in nominal GDP growth immediately.
The estimated coefficient tends to be less
than unity because some of the stimulus is

Case I: Nominal GDP as the
Nominal Target Variable
Table 1 contains results for nominal GDP
growth, including four cases with alternative
intermediate monetary targets and one in
which no intermediate target is employed.
Control error refers to the error in predicting

44

Changes in Tim e-Varying Coefficients
Figures 1-4 illustrate the changes in the coefficients on the variables explaining the
growth in M l velocity over time. The financial deregulation of the late 1970s and early
1980s appears to have brought structural change to M l velocity that was only partially
undone when the inflation rate stabilized in the m id-1980s. The drift in M l velocity
was steady until the late 1970s, when it decreased, never to return to its 3 percent annual
upward trend of the 1960s and 1970s. The response of M l velocity to changes in the
three-month T-bill rate, for example, declined in an irregular pattern until the early 1980s
when checkable deposits began paying interest. The response of M l velocity to changes
in the 10-year Treasury bond rate, on the other hand, has generally trended upward from
the beginning of the sample period until the late 1970s. Since then it has been fluctuating
around a positive value. The coefficient on lagged M l growth is negative and suggests
that high M l growth today will lead to decreased velocity next quarter, although this
elasticity was near zero when inflation was high in the late 1970s and early 1980s.
Figures 5-8 illustrate the estimated changes in the coefficients from equation 7.
In general, the coefficients underwent relatively large changes or changes in their trend
around the time the inflation rate was stabilized in the m id-1980s. In the late 1960s and
1970s, inflation gradually accelerated, and in the late 1970s and early 1980s disinflation
occurred until the inflation rate stabilized at an annual rate of roughly 3 or 4 percent.
The drift or intercept term in Figure 5 shows an upward trend until inflation stabilized
and then decreased dramatically before resuming an upward trend again. The coefficients
on the short- and long-term interest rates are negative as expected. The coefficient on the
short rate, shown in Figure 6, became less negative until the m id-1980s, when it became
more negative than in the 1960s. The response of the M l-federal funds rate ratio became
more negative from the beginning of the sample period through the late 1980s. The coef­
ficient on lagged M l growth shifted from positive in the 1960s to negative during the
period of high and volatile interest rates, and then became positive and larger as
inflation and interest rates declined.

the relationship between the funds rate and
money growth. Velocity error stems from
the uncertain link between money growth
and nominal GDP growth. Summing the
errors yields the compound error. Moving
across columns in Table 1, we start with
control error.5
In the first column, we see that narrower
measures of money are generally more con­
trollable than broader ones. The Q statistics
in parentheses indicate whether the forecast
errors displayed significant serial correlation.
The xU critical value is 36.4, which is exceeded
only by the control error for the base. W ith
this lone exception, however, the Q statistics
are not significant in the control errors, the
velocity errors or the compound errors in
Table 1, so the forecasting models almost
uniformly generate forecast errors which
are not significantly serially correlated.




In the second column, the uncertainty
in velocity is apparently not directly linked
to the narrowness of the monetary aggregate.
The base, M l and M2 have very similar
degrees of uncertainty in velocity. Hence,
even though base and M l velocity are not
as stable as M2 velocity, they are roughly
as predictable. In the the third column, the
uncertainty in the compound errors indicates
that the variance of the sum is uniformly less
than the sum of the variances, which implies
that the covariances between control and
velocity errors are negative. M l has the
lowest RMSE in the compound error, but
is closely followed by the null choice of no
intermediate target and the monetary base.
To test whether the forecast error variance
for M 1 is significantly lower than the vari­
ances associated with the other measures, I
performed Bartlett tests for equal variances

45

5 1 report results from 1964:Q1
through 1993:Q4 even though
the somple starts in 1959.Q3,
because several early observa­
tions are set aside to initialize
the Kalman filter.

REVIEW
F ig u re s 1 -4

D rift Term in M l V e lo city
G ro w th Eq u atio n

Effect of Lag g ed C h an g e in
th e 3-m onth T -B ill Y ie ld on
G ro w th of M l V e lo c ity

Effect of La g g e d C han g e in
1 0 - Y e a r T-Bond Y ie ld on
G ro w th in M l V e lo city

Effect of Lag g ed M l G ro w th
on G ro w th of M l V e lo city




across the compound errors. M2+ had the
highest test statistic of 0.064, but this is still
well below the X,2 critical value of 3.8. M l
has the lowest compound forecast error vari­
ance in the links between monetary policy
actions and nominal GDP growth, but the
other monetary aggregates have variances
that are not significantly higher.
Since this exercise was conducted with
quarterly data, however, this method o f cal­
culating the compound forecast errors may
overstate control errors, because data on
reserves, the base and monetary aggregates
are available on a weekly basis. W ithin each
quarter, policymakers could change the

funds rate in response to any emerging
control error to try to hit the end-of-quarter
monetary target. In practice, however, the
Fed seeks to mitigate excess volatility in
short-term interest rates (Bryant, 1983).
Thus, the claim that weekly money-supply
data would allow the achievement of zero
control error is not realistic either. The
true degree of uncertainty lies somewhere
between the control errors reported here
and zero. Moreover, the ability of policy to
respond to intra-quarter developments in
money growth is difficult to quantify, given
current Federal Open Market Committee
(FO M C) procedures for changing the funds

46

REVIEW

RY/FEBRUARY

1995

Figures 5 -8

Effect of Lag g ed C h an g e in the
3 -M o n th T -B ill Y ie ld in P red ictin g
th e R e la tio n sh ip B etw een the
F e d e ra l Funds R a te a n d M l G ro w th

D rift Term in th e R e la tio n sh ip
B etw een the F e d e ra l Funds R ate
an d M l G ro w th

o
-2 0

-4 0
-6 0
-8 0
-100
-120

-1 4 0

Effect of Lag g ed C h an g e in the
1 0 - y e a r T-Bond Y ie ld in P red ictin g
th e R e la tio n sh ip B e tw e e n the
F e d e ra l Funds R a te a n d M l G row th

price level/money supply ratio (the negative
of the growth rate of real balances), rather
than velocity growth. The control errors are
unaffected as we change the nominal target
variable objective, except for the control error
resulting from targeting the inflation rate
directly from the federal funds rate, that is,
with no intermediate target. W ith this
exception, the first column in Table 2 is
the same as in Table 1.
In the analysis of Table 2, we begin in
the second column by noting that the base
and M2 have the most predictable relation­
ships with inflation. M l has somewhat higher
real balances error, but still achieves the lowest

rate between regular meetings, which take
place every six to eight weeks. Any attempt
to adjust the estimated control errors for
intra-quarter funds rate adjustments is
beyond the scope of this study.

Case II: Inflation as the Nominal
Target Variable
This section performs analogous, but
not directly comparable, analysis o f potential
intermediate targets under the assumption
that the rate of inflation is the nominal policy
target. W hen inflation is the ultimate objec­
tive, it is necessary to forecast growth in the




Effect of Lag g ed M l G row th
in P red ictin g th e R e la tio n sh ip
B etw een the F e d e ra l Funds R ate
a n d M l G ro w th

F E D E R A L RESERVE B A N K OF S T . L O U I S

47

REVIEW
T a b le 2

Ta b le 1

In te rm e d ia te T a rg e try Com parison

In te rm e d ia te T a rg e try C om parison

(nominal target: nominal GDP; policy instrument:
federal funds rate)

(nominal target: inflation; policy instrument: federal funds rate)

Root Mean-Squared Forecast Errors (RMSE)
Monetary
Aggregate

Control
Error

Velocity
Error

None*

1.467
(23.70)

Base

Root Mean-Squared Forecast Errors (RMSE)

Compound
Error

Monetary
Aggregate

Control
Error

Real Balances
Error

Compound
Error

n.a.

1.467
(23.70)

None*

1.393
(33.01)

n.a.

1.393
(33.01)

1.171
(40.95)

1.103
(17.01)

1.483
(26.68)

Base

1.171
(40.95)

0.489
(17.41)

1.447
(28.32)

Ml

1.349
(25.55)

1.122
(24.85)

1.450
(22.27)

Ml

1.349
(25.55)

0.710
(19.86)

1.243
(29.12)

M2

1.647
(31.35)

1.121
(20.68)

1.529
(30.85)

M2

1.647
(31.35)

0.463
(23.59)

1.290
(40.91)

M2+

1.771
(31.98)

1.305
(20.86)

1.539
(29.53)

M2+

1.771
(31.98)

0.644
(17.88)

1.272
(38.21)

* Direct forecasts of GDP/funds rate relationship

* Direct forecasts of inflation/funds rate relationship

Notes: Mean-squared forecast error (MSFE) for compound error is not
equal to sum of MSFEs due to covariances between errors. Time period:
1964:Q1 -1993:Q4. Q statistic for serial correlation in parentheses:
5 percent critical value with 24 degrees of freedom is 36.4

Notes: Mean-squared forecast error (MSFE) for compound error is not
equal to sum of MSFEs due to covariances between errors. Time period:
1964:Q1 -1993:04. Q statistic for serial correlation in parentheses:
5 percent critical value with 24 degrees of freedom is 36.4




SUM M ARY AND
CONCLUSIONS

compound RMSE, shown in the third column,
due to negative correlation between the con­
trol and real balances errors. The root mean
square of the compound errors of M2 and
M2+ are only slightly larger than for M l, but
they are significantly serially correlated, as
indicated by the statistics. As with the nominal
GDP results, the Bartlett tests for differences
in compound forecast error variances prove
to be rather weak; once again, the test failed
to reject the hypothesis that the other m one­
tary aggregates had the same compound
forecast error variance as M l. The monetary
base had the highest Bartlett test statistic of
0.62, which was nonetheless below the %t2
critical value of 3.8. As with nominal GDP,
M l has the lowest compound forecast error
variance in the links between monetary policy
actions and inflation, but the other monetary
aggregates have variances that are not signifi­
cantly higher.

This article has shown that the errors
in predicting the effect of policy actions—
summarized by changes in the federal funds
rate— on the growth of potential intermediate
monetary targets (control errors) are often
as large or larger than the error in predicting
changes in the velocities of the monetary
aggregates (velocity error). Thus, control
error, an often-neglected dimension of using
money as an intermediate target, appears to
be of roughly equal concern as velocity error
in evaluating alternative monetary aggregates
as intermediate targets.
W ith respect to the question of whether
to use M l or M2 as an intermediate target, I
find that, when accounting for both control
and velocity error, M l and M2 achieve com ­
pound forecast errors that are not significantly
different from each other, whether nominal

48

GDP or inflation is the assumed nominal target
variable. One obvious question, however, is
whether the use of a monetary intermediate
target offers any advantages relative to fore­
casting directly the effects of policy actions
on the nominal policy target— nominal GDP
growth or inflation. M l is the only monetary
aggregate with RMSEs uniformly lower than
the RMSEs associated with direct forecasts of
the relationships between the funds rate and
both nominal GDP growth and inflation.
Bartlett tests for equality of forecast error
variances fail to find a statistically significant
difference between the forecast error vari­
ances, however. Thus, the evidence in favor
of using an intermediate target variable
is not decisive.
The emphasis on control error in this
article also serves to remind market partici­
pants that recent growth rates in the monetary
aggregates do not necessarily represent the
thrust of monetary policy, given that control
and velocity errors are generally negatively
correlated. Thus, control error introduces a
potentially large difference between the rate
at which the money supply is actually growing
and the rate of effective or velocity-adjusted
money growth. Thus, at times when observers
have expressed concern about unusually fast
or slow M2 growth, for example, it is likely
that control error was responsible for much
of the anomaly. Figure 9 illustrates this point
by plotting the difference between actual M2
growth and the growth that would have taken
place if there had been no control error, that
is, if M 2 had turned out as predicted. Figure
9 shows the relationships between predicted
and actual M2 quarterly growth rates and
inflation. In the late 1970s and early 1980s,
predicted M2 growth signalled a tightening
of monetary policy that preceded the disin­
flation of the early 1980s, whereas actual
M2 gave no such signal. An increase in
predicted M2 growth in the m id-1980s also
indicated that the inflation rate would stop
falling. Actual M2 growth rates, on the
other hand, continued to decrease. In the
early 1990s, predicted M2 growth has been
consistently stronger than actual M2 growth,
indicating that the inflation rate would not
continue falling toward zero, as some
analysts projected.




Fig u re 9

R e la tio n sh ip s B e tw e e n P re d icted
a n d A ctu al M 2 G ro w th a n d In flatio n

Fig u re lO

A ctu al a n d P re d icted M 2 G row th
an d th e FO M C 's T a rg e t R an g e

1987

1988

1989

1990

Figure 10 highlights the effect of M2
control errors on the latter part of the sample
period. The graph includes the upper and
lower limits for the FOM C’s announced
M2 growth targets along with actual M2
growth and what M2 growth would have
been absent control error. The chart shows
that in 1991-93, M2 growth adjusted for
control error was near the upper range of
the FOMC target range, as opposed to actual
M2, which languished near the bottom of the
target range. The former was suggestive of
the relatively strong economic recovery that
developed in 1994, whereas actual M2 growth
was not. Thus, adjusting M 2 growth for the
control errors can often provide a better policy
indicator than the unadjusted data, which

NK OF ST. L OUIS

49

1992

19 93




REVIEW
Feinman, Joshua N., and Richard D. Porter. "The Continuing Weakness
in M2," Finance and Economics Discussion Series Working Paper
No. 209, Board of Governors of the Federal Reserve System
(September 1992).

can make policy appear more inflationary
or disinflationary than it is.

REFERENCES

Feldstein, Martin, and James H. Stock. "The Use of Monetary Aggregate
to Target Nominal GDP," NBER Working Paper No. 4304 (March 1993).

Andersen, Leonall C., and Dennis S. Karnosky. "Some Considerations in
the Use of Monetary Aggregates for the Implementation of Monetary
Policy," this Review (September 1977), pp. 2-7.

Hallman, Jeffrey J., Richard D. Porter and David H. Small. "Is the Price
Level Tied to the M2 Aggregate in the Long Run?," American
fconom/c feweiv (September 1991), pp. 841-58.

Belongia, Michael T., and Dallas S. Batten. "Selecting an Intermediate
Target for Monetary Policy when the Goal is Price Stability," Federal
Reserve Bank of St. Louis Working Paper No. 92-008A (October 1992).

Kim, Chang-Jin. "Dynamic Linear Models with Markov-Switching,"
Journal of Econometrics (January/February 1994), pp. 1-22.

Bernanke, Ben S. "The World on a Cross of Gold: A Review of 'Golden
Fetters: The Gold Standard and the Great Depression, 1919-1939,"
Journal of Monetary Economics (April 1993), pp. 251-67.

_______ . "Sources of Monetary Growth Uncertainty and Economic
Activity: The Time-Varying Parameter Model with Heteroskedastic
Disturbances," The Review of Economics and Statistics
(August 1993), pp. 483-92.

Bryant, Ralph C. Controlling Money: The Federal Reserve and
its Critics. The Brookings Institution, 1983.

Orphanides, Athanasios, Brian Reid and David H. Small. "The Empirical
Properties of o Monetary Aggregates That Adds Bond and Stock Funds
to M2," this Review (November/December 1994), pp. 31-51.

Collins, Sean, and Cheryl L. Edwards. "An Alternative Monetary
Aggregate: M2 Plus Household Holdings of Bond and Equity Mutual
Funds," this Review (November/December 1994), pp. 7-29.

Ritter, Joseph A. "The FOMC in 1992: A Monetary Conundrum,"
this Review (May/June 1993), pp. 31-49.

Dueker, Michael J. "Can Nominal GDP Targeting Rules Stabilize
the Economy?" this Review (May/June 1993), pp. 15-29.

Thornton, Daniel L. "Targeting M2: The Issue of Monetary Control,"
this Review (July/August 1992), pp. 23-35.

Eichengreen, Barry. Golden Fetters: The Gold Standard and the
Great Depression, 1919-1939. Oxford University Press, 1992.
Federal Reserve Bank of New York. Intermediate Targets and Indicators
for Monetary Policy (A Critical Survey), July 1990.

s
50

m
RY/FEBRUARY

1 9 9 5

A p p e n d ix

TIME-VARYING COEFFICIENTS
The time-varying coefficient model that
generates the short-run forecasts is

(4 )

(D

yt =x ,-iPt+e,

(2)

A = A - ! + v(

Thus, the forecast errors have two
components and equal

y t\ t - i - x t-iP,\t-i-

v, ~Normal(0,Q),
X . i ( A - A ,- ,) + V
where y is the dependent variable and X,_,
is a vector of explanatory variables. W ith
time-varying coefficients, equation 1 (in
the first section) will be estimated using the
Kalman filter under the assumption that the
state variables, /i(, follow random walks. In
a short-run forecasting context, the assumption
that the coefficients follow random walks
suggests that people need new information
in order to change their view about the rela­
tionships among variables. The innovations,
v, to the coefficients are assumed to be
uncorrelated, so the covariance matrix 0
is diagonal.
The errors in equation 1, e„ have timevarying volatilities in that their variance
is assumed to switch between a low and
high level according to a first-order
Markov process.1

If the variance of
(Pt ~Pt\t- i ) = R t (_ ,a n d v a r(e,) = c72 ,
the one-step-ahead forecast error variance is
(5)

h

(^

h 1i

+

h

2 [=

x

,_1r i |(_1x ;_1 + ct,2 .

The first component (Hit) is called the variance
due to time-varying parameters (TV P); the
second (H2t) is simply the variance of the
random disturbance, et. Inferences regarding
the relative sizes of the two sources of forecast
error variance play an important role in
updating the coefficients. Using the Kalman
filtering equations, it can be shown that
the forecast
can be written as

e t~Normal(0,h t)

(6 )

3 \ + ijt — X tPt\t-i

>

h, =<702 + ((T 12 - c r 2 ) s (
where X, are the explanatory variables,
is last period’s forecast error (and is thus the
new information available), and Z, is propor­
tional to

S,e{o,l}
® \ ^ <*0

H i,
H u + H 2l '

Probability ( S t = l|S(_j = 1) = p
Probability (S , =0|S(_! = 0 ) = q.

If H2( is large relative to HJt, observers would
attribute less of a forecast error to a change
in coefficients; rather, they would believe that
it was likely to have been an outlier. A large
value of H2t then implies that last period’s
forecast error will play a relatively small
role in determining next period’s forecast.

By construction, this model allows for two
sources of forecast error: error in predicting
the value of the coefficients and the heteroskedastic random disturbance.2 In a model
with time-varying coefficients,
(3)

y t ^ X _ 1f i t + e t ,

and the one-step-ahead forecasts are




51

1 Further details on time-varying
coefficient models with
heteroskedostic errors ore
in Kim (1993).
2 Kim (1993) discusses the

specific form the Kalman filter
takes for this model and the
evaluation of the likelihood
function, which is maximized
with respect to ( o /, a / , p,
q,Q ), where 0,- = a j .




REVIEW
Adam M. Zaretsky is an economist at the Federal Reserve Bank of St. Louis. Cletus C. Coughlin is associate director of research at the Federal
Reserve Bank of St. Louis. Heather Deaton and Thomas A. Pollmonn provided research assistance. The authors would like to thank Dennis
Coleman and E. Terrence Jones for providing us with the data, and Erica Groshen and Joseph Terza for comments on earlier drafts.

An Introduction
to the Theory
and Estim ation
of a Job-Search
Model

comparison of the expected benefits from an
additional search with the expected costs.
W e then introduce a regression model
that consists of two equations: one focusing
on the individual’s probability of reemploy­
ment and the other on the individual’s
expected wage upon employment. Heckman’s
sample-selection model forms the basis for
the statistical analysis because simple regres­
sion analysis does not account for the truncated
wage information about people who are not
presently working and, therefore, leads to
biased inferences of the determinants of
wage offers.
To illustrate the job-search model, we
utilize survey data collected by the St. Louis
Econom ic Adjustment and Diversification
Committee from a sample of approximately
1,200 former McDonnell Douglas employees
laid off because of defense spending cuts. This
survey was the first analysis in the United
States that tracked the reemployment history
of laid-off defense workers. The illustration
highlights the effects that variables such
as occupation, education, sex, tenure at
McDonnell Douglas and unemployment
insurance have on the chance of reemploy­
ment and prospective wage offers.

Adam M . Z a re tsk y and
Cletus C. Coughlin
n a dynamic labor market, the process by
which people find new jo b s is important
not only to the individuals themselves but
also to policymakers and scholars. This
process has attracted increased attention in
recent years because of, among other things,
announcements by major corporations of
large layoffs, technological changes that have
resulted in relatively more high-skilled jobs,
the alleged effects of changes in trade legisla­
tion on the location of business activity, and
the high levels of unemployment in W estern
Europe. Policymakers have been attempting
to design training and other programs that
would help match an individual’s skills with
the requirements of potential employers.1
Job-search models offer some solutions by
considering factors that determine individuals’
wage demands and, therefore, their prospects
for finding an acceptable jo b offer. Job-search
theory takes concepts from static labor market
analysis and uses them in an intertemporal
setting. It attempts to describe the problems
faced by unemployed individuals and to
propose strategies for making optimal
employment decisions.
To introduce the job-search process, we
describe a simple model focusing on the search
behavior o f an unemployed individual. The
worker is assumed to be looking for a jo b ,
but may encounter unsuitable offers. In this
model, the unemployed individual’s decision
to accept or reject an offer is reduced to a




JOB-SEARCH THEORY
BACKGROUND
Job-search theory models individuals’
decisions of whether to participate in the
labor market and whether to change or
leave jobs. To convey the m ajor points of
the job-search process, we present a simple
model that focuses on the basic search
behavior of an unemployed w orker.2 The
worker is assumed to be looking for a jo b but,
lacking perfect information, may encounter
unsuitable offers before finding a jo b . Each
time the unemployed worker receives a jo b
offer, he decides whether to accept the offer
based on a previously determined set of cri­
teria. These criteria are extremely important
in the decision-making process and are the
subject of our investigation.

F E D E R A L RESERVE B A N K OF S T .

53

IOUIS

1 See Katz (1 9 9 4 ) for an evaluation
of active labor market policies.
2 The following discussion uses o
model that can be found in Devine
ond Kiefer (1 9 9 1 , chapter 2 ). For
additional background information,
see Lippman and McCall (1 9 7 6 ).

REVIEW

UARY/FEBRUARY

Underlying Assumptions

3 If the worker does hove on expec­
tation of the types of offers mode
by particular firms, he can perform
a systematic search without recall
by sampling offers from

F, where F

now represents a cumulative distrib­
ution of the ranked wage offer dis­
tributions from each individual firm.
Under a systematic search, each
firm can only be sampled once;
otherwise, the firm with the best
offer distribution would be sampled
repeatedly. In addition, because
the individual now knows the indi­
vidual offer distribution of each firm
(hence, the ranking), he must
choose o reservation wage for each
firm according to its rank in the
sample.




1995

received represents an independent draw
from the distribution, the worker’s accept/
reject decision does not depend on the
duration of the unemployment spell.

The worker receives jo b offers that
include the wage, hours, benefits and working
conditions of the position. For simplicity,
however, we assume that the decision to accept
or reject the jo b is based solely on the wage
offer. W e further assume that hours from
all offers are fixed, making “wages” and
“earnings” interchangeable. Setting hours
equal to one allows w to signify both wages
and earnings.
The worker does not know which firm
will offer a particular w. He is aware, however,
of the general characteristics of the labor
market. Offers represent independent real­
izations from a wage offer distribution with
finite mean and variance. Specifically, wage
offers have a cumulative distribution F (w )
and probability density/(w) that are known
to the worker. If the worker does not have
an expectation of the types of offers made
by particular firms, a random search occurs,
where independent draws from F are made
without recall— once a jo b is passed over,
it can never be returned to.3
W e assume the worker’s income remains
constant during the spell of unemployment.
This allows for a constant opportunity cost,
against which he bases the accept/reject deci­
sion. If the individual is risk-neutral, income
and utility are equivalent, and we can inves­
tigate the individual attempting to maximize
the expected Dresent discounted value of
income. To facilitate the analysis, we also
assume the discount rate, r, is known and
constant. In addition, the individual keeps
the accepted jo b forever, implying that he
lives forever. Hence, the present discounted
value of a jo b paying w is w/r. This final
assumption is not too drastic as long as
the discount rate is greater than zero and
retirement (or death) is not too close.
These assumptions lead to the worker
being equally well-off during the entire
unemployment spell. Because income during
unemployment never diminishes, utility while
unemployed remains constant and no signal
about the length of the unemployment spell
is offered to a prospective employer. Thus,
the newly unemployed person and the person
who has been unemployed much longer face
the same jo b prospects. Because each offer

An Optimal Search Strategy
If the worker accepts the offer w,
the present value of income received in this
and all future periods is w/r. If the worker
rejects the offer, the expected present value
of income will equal the expected present
value of unemployment income received
until an acceptable offer is received, plus
the expected present value of the income
from the acceptable offer. This expectation
does not depend on the offer currently
being rejected but does depend on the
distribution of offers F.
Because the value of employment, w/r,
is an increasing function of the wage offer,
there must be values of w for which employ­
ment is an attractive option; otherwise, the
worker would never enter the labor market.
There must also be values of w for which
employment is not an attractive option;
otherwise, the first wage offer would auto­
matically be accepted. Therefore, a wage
must exist at which the value of employment
equals the value of unemployment. This
wage is known as the reservation wage, wK,
and represents an optimal strategy for an
individual to follow in this model, because at
this wage the marginal cost for an additional
search equals the marginal gain from an
additional search. Therefore, the individual
should accept employment only if the wage
offer is at least as great as the reservation
wage, or continue searching.
This analysis represents a much-simplified
model of the job-search process. By allowing
for a cutoff date for receipt of unemployment
incom e, or by introducing finitely lived indi­
viduals, we would quickly com plicate the
model. Each of these changes generates a
reservation wage that declines rather than
stays constant with duration. This decline
occurs in the former from the expectation of
incom e reduction or loss, and in the latter
from decreasing the time over which a higher
wage would accrue if one waits for a higher
wage. By maintaining a constant-reservation

54

wage hypothesis, an offer rejected today
will also be rejected at any time in the future.
Thus, sampling without recall is implied,
and the duration of the unemployment
spell is unimportant to the decision.
To randomize the receipt of wage offers,
we introduce a Poisson process with arrival
rate <5, where 8 represents the frequency of
arrival. The probability of receiving at least
one offer in a short interval, h, is 8h+o(h),
where o(h) is the probability of receiving
more than one offer in the interval and
l i m ^ = 0.
h->0

h

The worker still receives one offer at a time,
but the amount of time between offers is not
necessarily constant.
To formalize, let V represent the
worker’s value of unemployed search under a
constant-reservation wage hypothesis. Offers
are independently and identically distributed,
and the offer distribution and arrival rates
are both known and time-invariant. This
value is defined implicitly by

(1) V u =

1 + rh

+(<5h)

The present value of accepting an offer
w in this model is

(2)

V e (w)=

Because Ve(w) is continuous and increasing
in w, while Vu does not depend on the wage
offer, the optimal strategy for a worker is a
time-invariant reservation wage policy: Accept
w if w>wR, where wR, the reservation wage, is
the minimum acceptable wage for the worker.
It is defined by equating the expected present
value of employment with the expected present
value of a continued search. That is,
(3)

V e (w R) = —

= V U.

Substituting equations 1 and 2 into 3 yields:
(4) w
1 + rh

bh

+ (8h)~
1 + rh

R ~
W W
rnax-^
[r
r

1 wK
+ (l-< 5 h )+ o(h)K .
1 + rh r

bh
max j v c ( w ),V u j j

1 + rh

+ (l-< 5 h )

o 00
1

1 + rh

V" + o ( h ) K .

The first term on the right-hand side is the
present discounted value of the net unem­
ployment income, b, over the interval h.
The second term is the probability of receiving
an offer in h, multiplied by the expected dis­
counted value of following an optimal policy
if a wage offer w is received, where Ve(w)
represents the present value of accepting
that offer. The third term is the probability
o f not receiving an offer in h, multiplied by
the present discounted value of the search
income. The last term is the probability of
receiving more than one offer in h , where K
denotes the value of the optimal policy when
more than one offer is received. Under a
Poisson process, the last term goes to zero
in the limit.




Solving for wR/r and taking the limit as h—>0,
this optimality condition may be written as

(5)

w R - b + — f ( w - w R )/(w)ciw.
r

R

wK

Finally, by evaluating this integral and re­
arranging terms, a more intuitively appealing
equation for the reservation wage emerges:
(6) (w R - b ) =

8
r

- ( e „ [ wlw > w R —wR) [ l - F ( w R )],

where

J w f ( w )d w
(7 )

E w[w|w > w R |= —--------------j f (w)dw
wR

and
1 -F (w r )= jf( w ) d w .

FEDERAL RESERVE B A N K OF ST. L OUIS

gain, restoring the optimal stopping condition.
An increase in either the arrival rate of offers
8 or the mean of the wage offer distribution
H produces a similar response because both
cause the marginal gain from an additional
search to increase (analogous to a decline
in the marginal cost).
Suppose, on the other hand, the discount
rate r increases. Keeping all else constant, this
change decreases the expected gain from an
additional search, making it less than the
marginal cost. Now, the worker will decrease
his reservation wage until the marginal cost
once again equals the expected marginal gain,
thereby equating margins at the new discount
rate to restore the optimal stopping condition.
To formalize the above explanations,
we can generate the following derivatives
by differentiating the optimality condition
in equation 5:

The left side of equation 6 is the marginal
cost of rejecting an offer equal to wR and con­
tinuing to search. The right side represents
the discounted expected marginal gain from
continuing to search, multiplied by the Poisson
probability that an acceptable offer is received.
In other words, the right side is the discounted
expected marginal revenue from an additional
search. Thus, the reservation wage is the wage
rate that equates the discounted expected mar­
ginal revenue from a search with the marginal
cost of a search, and equation 6 represents
the optimal stopping rule for the search.
This definition of the reservation wage
contrasts with the definition of a reservation
wage in a static, deterministic model o f labor
supply. In the latter, a reservation wage rep­
resents a set of preferences determined solely
by supply-side factors (the level o f non-labor
income, fixed costs of labor market entry, and
the marginal utility of leisure) without regard
for demand-side factors. Search theory’s def­
inition of a reservation wage explicitly and
necessarily relies on the distribution of wage
offers, a demand-side component, as well as
supply-side factors. In addition, the reserva­
tion wage depends on the arrival rate of offers,
a variable relying on the behavior of both
firms and the individual. Specifically, in
the search model,

(8 )

w

(9a)

(9b)

Kiefer (1 9 8 8 ) provide additional
background information about dura­
tion analysis.




■6 ( 0 , 1 ),

db

E„[w| w > wR j —w R

dwR
dr

r

1 _1_

8 1- F ( w

= w R( b , r ,8 , j u ) ,

r

(9c)

4 Heckman and Singer (1 9 8 4 ) and

dwR

where ju is the mean of the wage offer distri­
bution.
Because of the importance placed on
the reservation wage in this model, we want
to investigate how changes in the exogenous
variables in equation 8 affect it. To understand
the intuition, we use equation 6 to describe
these effects.
Suppose b, the level of fixed unemploy­
ment income net of search costs, increases.
This decreases the marginal cost for an addi­
tional search while keeping all else constant.
The left side of the equation is now less than
the right side, implying that the cost for an
additional search is less than the expected
gain from the search. Thus, the worker,
attempting to maximize expected income,
increases his reservation wage so that marginal
cost will once again equal expected marginal

dwR
dS

E„ww>w

r

<0,

)]

R] - w R
> 0,

5+
[l —F (

w r

)]

and
(9d)

dwR
d/l

1+
<5 [ l - F ( w R )]

These results reinforce the intuitive explana­
tions given above for how the reservation wage
changes as the individual variables change.

The Duration of the
Unemployment Spell
Estimating the duration of the unem­
ployment spell is possible with a knowledge
of the offer distribution because this distrib-

56

ution governs the stream o f offers received.
W e begin by labeling the conditional accep­
tance probability as <p(wR), w here5

o f a duration, given this distribution, will be

(10) 0(wR) = J/(w)dw = 1 —F ( w r ).

W ith some manipulation, we can examine
how the escape rate reacts to changes in the
exogenous variables. Using equations 9a-d
and the definition of y in equation 12, we
find that

wR
Multiplying <p(wK) by the probability
of receiving an offer in the short interval h,
Sh+o(h), we can define the probability that
a received offer leads to employment. We
label this as yh, where

(11)

(17a)

dy
_ d<t> d w R
~ ^ = o — ~---------< 0 ,
db
d w R db

(17b)

d r , s _ ^ L ^ >0
dr
d w R dr

y h = [d h + o ( h ) ] 0 ( w R ).

Dividing equation 11 by h, and taking
the limit as h—>0, we arrive at

and

( 12 )

(17c)

y = S</)(wR ),

which represents the probability of reem­
ployment, or escape rate, of the worker.6 This
escape rate does not depend on calendar time
because neither the acceptance strategy nor
the distribution from which offers are drawn
rely on it. The model, therefore, has direct
implications for the distribution of the dura­
tions. The implied distribution is exponential.
Suppose T denotes the duration o f a
completed spell of unemployment with cumu­
lative distribution function *F(0 and proba­
bility density function y/(t). The probability
that a received offer leads to reemployment
can now be stated as
(13) yh = P r ( t < T < t + h|T>t) = -

*

dfl

d w R d/J.

>0,

<?//

where
d(j)

= - / ( w R ).

The right side of 17c is positive because we
know from 9d that the increase in the reser­
vation wage due to the increase in the mean
of the offer distribution is less than the
increase in the distribution itself.
As expected, the probability of reem­
ployment increases with increases in both
the discount rate and the mean of the offer
distribution, and declines as the fixed unem­
ployment income net of search costs goes
up. How this escape rate reacts to changes
in the arrival rate of offers is more compli­
cated because, as the following shows, the
sign on the derivative is indeterminate:

ys(t)h
■ n o ’

Furthermore, the probability that the spell of
unemployment will last until at least t can be
expressed as follows:

5 Because we know thatthe condi­
tional acceptance probability
depends on the mean of the wage
offer distribution os well as the
reservation wage, we should
express it as<t>(w’,f i) . Kiefer and
Neumann (1 9 7 9 ) and Mortensen
(1 9 8 6 ), however, have shown
that one wage offer distribution can
be expressed as a translation of
another. More precisely, o cumula­
tive distribution function,

G,is said

to be a translation of another, f, if

(14)

(18)

dy
dS

=<HwR) + <5
(+)

S(t) is known as a survivor function and can
be derived from the postulates of the Poisson
process. From this, we can find the density
function of T,

d(j) dw

there exists a constant k such that

= 0.

dwR 98 <
(-)

E(w +

k

) = F(w), for all w. This

is a moment-preserving shift of the
distribution. For #< > 0, the transla­
tion is to the right, and Gis formed

Equation 18 shows that a change in the
arrival rate of offers affects both the wage offer
distribution and the reservation wage. Because
these effects cause opposite outcomes, we are
uncertain about the sign of the derivative.
Nevertheless, an evaluation of the parts of the
derivative shows that the sign of dy/dS hinges

(15)
which is an exponential distribution with
parameter y The expected length and variance




d(j) d w R ^ d(p

=5

57

by shifting f uniformly to the right
o distance

k

.

6 Often, the escape rate is referred to
as a hazard rate.
7 Equation 1 7c is derived by using
the translation of f described in
footnote 3.




contained in z ’ ■ Therefore, it is assumed
that the error terms are jointly distributed as
bivariate normal with a covariance of a oR. The
converse, that all z ’ are in x ', is not true. For
example, marital status affects the costs of
searching but not the mean of the wage offer
distribution and, thus, is in z [ but not x [ .
W e have shown that individuals become
reemployed if and only if the wage offer is at
least as great as the reservation wage. Then,
if Ai= ln w I°-lnw iR7
, from 19 and 20,7we have:

critically on the magnitude of dwR/dS (which
is positive by equation 9c) because all other
terms are constants. Thus, the more respon­
sive the reservation wage is to the arrival rate,
the less likely it is for the worker to escape
unemployment. These derivatives allow us
to predict how a change in each of these
exogenous variables, ceteris paribus, affects
the expected duration of an unemployment
spell. In addition, by knowing the escape
rate, we can determine from equation 16
what the expected duration should be. Any
increase in the escape rate should decrease
the expected duration, which one can confirm
by a quick examination of 16.

(2 1 )

A , = x ' / 3 - z ' c c + £. - e f
= x 'p -z 'a + E i

with
£ t ~ N ( 0 ,a 2 + c t2 - 2 ( J oR) .

AN ECONOMETRIC MODEL

Wages are observed only for individuals
whose A.>0; therefore, the distribution of
observed wages is truncated. Heckman
(1 9 7 6 , 1979) has shown that in this instance
ln(w°) is distributed with:

Having laid down a basic theoretical
foundation, we would now like to describe
Heckman’s sample-selection regression model
as one method to obtain results consistent
with the theory. Because we do not observe
unaccepted wage offers, the data are truncated
and a selection bias exists that this model
accounts for by including a regressor for the
truncation. The model uses the knowledge
that observed wages are offers that satisfied
the jo b seeker’s acceptance criteria— that is,
the accepted wage was greater than the indi­
vidual’s reservation wage— along with the
observed wage itself in a two-step regression

(2 2 ) E [ln (w ')| A , > o ] = x ' J3 + p a oAi ,

(2 3 )

V a rjln (w “ )|A( > o j =
o l ( a - p 2 ) + p 2 ( . i + T , x t - Af >),

where:

that generates con sisten t estim ates.

W e use Kiefer and Neumann’s (1979)
adaptation of the sample-selection model, in
which the ith individual’s wage offer, w°, is

(24a)

(19) lnw ° —x ' P + £° £ ° ~ N ( 0 , a o2),

(24b)

where the vector x ' = ( x u, . . . , x ki) contains
all of the worker and labor market character­
istics that affect wage offers. The individual’s
reservation wage is determined by
(2 0 ) ln w f = z ' a + e f

_
‘

T,

/ ( -T .)
1 - F ( - t, ) ’
x ’P - z ' o c

a:
(24c)

e,R ~ N ( 0 , a 2 ).

(24d)

The z,’s are worker and labor market
characteristics that determine the individual’s
reservation wage. Because theory suggests
that reservation wages depend on the mean
of the wage offer distribution and the costs
of searching, all variables in x,' must be

—

<t „,

p=-

o = ( o 2 + o \ - 2 o oRy ,

f and F are the standard normal density
and distribution functions, respectively, and
“A.,” known as the inverse Mill’s ratio, is a
decreasing function of the probability that
an observation is selected into the sample.

FE D E R A L RESERVE B A N K OF S T . L OU I S

58

nm w
JANUARY/FEBRUARY

the survey were considered still searching for
full-time employment.
The automated telephone questionnaire
posed unique difficulties because all of the
relevant variables are categorical. Thus,
variables normally considered continuous in
the labor-supply literature are ordered cate­
gories, somewhat complicating our analysis.
For example, for the question of tenure at
McDonnell Douglas, a respondent would
indicate “1” if tenure was two years or less,
“2 ” if tenure was between three years and six
years, and so on. This pattern was repeated
for the variables of age, wage at McDonnell
Douglas and wage at the new jo b . One issue,
then, is to determine the proper strategy for
selecting the correct representative response
for each variable’s categories.
The most obvious strategy is to assign a
dummy variable to each category. Hsiao
(1 9 8 3 ) argues that for a modest number of
dummy variables and categories, the loss in
explanatory power from using this method is
not serious. Interpretation of the coefficients
on the dummy variables, however, differs from
the standard interpretation o f least squares
coefficients on continuous variables, and
using dummy variables represents a direct
loss of information.
Another strategy, discussed in Haitovsky
(1973), Hsiao (1983) and Hsiao and Mountain
(1985), is to use the midpoint of the category’s
range as the observed value.’ Although this
method is convenient, the estimates are usu­
ally biased, unless the data are uniformly
distributed over the category, but the bias
can be negligible. In addition, this method
does not use all of the available information
because it excludes the known endpoints
of the categories.
To include the endpoint information and
obtain representative values other than mid­
points, the variables of age, tenure, wage at
McDonnell Douglas and wage at the new jo b
were each regressed as dependent variables
against a constant term in a completely cen­
sored Tobit model.10 This procedure uses the
method of maximum likelihood together
with the specific endpoints of the categories
to obtain the fitted values and point esti­
mates. Using this procedure, the data from
the telephone survey were projected onto a

If we knew T, and,
hence, A,
7
!' Heckman
(1 979) shows that we could estimate the
parameters of this equation as
(25) [ln (w “)|a,

>oj = x'fi +pcr0 Ai + £,

using generalized least squares (GLS). GLS
is used because ordinary least squares (OLS)
leads to unbiased but inefficient estimates of
f i and per. Because we do not know A., it must
be estimated and its fitted values used as
regressors in 25 on the selected subsample.
Heckman also shows that these fitted values
can be estimated consistently using probit
analysis for the full sample on a normalized
form of equation 21. A., however, is unob­
servable. W e observe only whether an indi­
vidual is reemployed or not. Therefore,
the probit is estimated using an indicator
variable, d , as follows:
<21')

d

(7

+

a

£,
W = Tj + — ,
a
where
d i = 1 iff

>0,

d i = 0 otherwise,

and Tt is as in 24 b.

DATA DESCRIPTION
AND ANALYSIS
The St. Louis County Econom ic Council
conducted an automated telephone survey of
former McDonnell Douglas employees who
were laid off between September 1990 and
January 1991. Although there were 1,198
respondents to this survey, only 1,174 were
usable for our analysis.8 Twenty-four obser­
vations were discarded because either vital
information was missing or there was a dis­
crepancy between the reemployment response
and the wage-at-new-job response. O f the
remaining 1,174 observations, 456 had found
full-time employment (more than 35 hours per
week) at the time of the survey in September
1991. A respondent was considered reem­
ployed only if the jo b was full-time. Therefore,
respondents who were working part-time
(at most 35 hours per week) at the time of




1 9 95

59

8 This telephone survey may not
hove been representative of all
released workers. Workers were
more likely to have been colled if
they remained in the St. Louis
metropolitan orea.
5 Hsiao and Mountain (1 9 8 5 )
also discusses the use of categorical
variables as dependent variables in
a regression.
10 The authors would like to thank
Joseph Terza for suggesting this
procedure. See Moddola (1 9 8 3 ,
pp. 4 6 -9 ), for a description of it.
This is an ordered-response model,
of which this Tobit is a special cose.
Also see Amemiya (1 9 8 4 ) fora
survey of Tobit models.

T ab le 1

V a lu e s fo r C ateg o rica l V a ria b le s
Wage at McDonnell Douglas

Tenure
10.2 years
18.1 years
n = 1,198
Predicted value

H=
a=

Category
3
7
13
20

tenure < 2
6
< tenure < 12
< tenure < 20
< tenure

Category

1.0 year(s)
3.6
8.4
15.2
36.1

< tenure <

10
15
20
25

wage < 10
< wage < 15

Wage at new job

H= 39.4 years
a= 11.8 years

CT =

n = 1,189

11 For more detail as to the
composition of the dotnset,
see Jones (1 9 9 1 ).
12 W e used the sample selection

25
35
45
55
62

<
<
<
<
<

age
age
oge
age
age
age

<
<
<
<
<

$ 13.26/hour
$ 7.08/hour

n=
Predicted value

24
34
44
54
62

$ 8.22/hour
12.40
17.12
22.03
29.08

<wage <20
< wage < 25
<wage

Age

Category

= $14.79/hour
$ 5.51/hour
n = 1,099
Predicted value

a =

Category

21.1 years
29.4
38.6
48.2
57.4
69.9

10
15
20
25

wage <10
< wage <15
< wage < 20
< wage < 25
<wage

Predicted value
S 7.06/hour
12.22
17.14
22.13
31.11

model in LIMDEP Version 6 .0
to estimate the equations.
13 Various interaction terms were
tried, but none significantly altered
the results.
14 Using notation from Table 2,
we calculate z, for the baseline
individual from equation 2 1 ' with
the following:
= Constant
+ c,(AGE) + c? (AGE2)
+ c3 (ADVNOTICE)
+ ct (LNWAGE)
+ Cj (TENURE),
where c^is the coefficient estimate

because the necessary omission of one dummy
variable from each category determines a
baseline profile against which other results
should be compared. For both regressions,
the baseline searcher is a single white male,
who was a unionized production worker
(one of the occupational categories) at
McDonnell Douglas with a high school edu­
cation or less, who has no children and who
did not apply for unemployment insurance.13
To describe how an individual’s proba­
bility of reemployment (escape rate) changes
as different characteristics are added, the
probability for the baseline person needs to be
known. Using the coefficients from Table 4
and the calculated means of the non-dummy
variables, Ti can be constructed for the base­
line individual.14 W e then evaluate the normal
cumulative density function F at this value of
Xi to determine the individual’s probability
that the next offer will lead to reemployment;
for the baseline individual, this probability
is 0 .6637. In other words, there is about a
66 percent chance that the next wage offer
will satisfy the acceptance criteria of a person
with the baseline characteristics.

distribution, and the Tobit model estimated
a representative value for each category. These
fitted values were then used as the observed
values for the variables in the later analysis.
In addition, the Tobit model provides an
estimated mean and standard deviation for
the projected distribution. Table 1 describes
the categorical variables with their fitted
values and distributional characteristics.
Table 2 includes the definitions for
all variables, including the dummy variables
that represent the demographic characteris­
tics of the respondents. Table 3 provides
frequency distributions for all variables."

from Table 4; therefore:
r t o = 0 .9 9 8 + (0 .0 0 6 X 3 9 .4 )

AN ILLUSTRATION

+ ( - 0 .0 0 0 4 3 9 K 1 6 6 9 .5 )
+ (0 .0 3 9 X 1 .0 4 )
+ (0 .0 0 5 X 2 .6 3 )
+ (-0 .0 1 3 X 1 0 .2 ),
where the numbers substituted
for the variables are the variable
means. This procedure can be used
to calculate z for any individual /,
with the appropriate adjustments
for the individual's characteristics.




Tables 4 and 5 present the coefficient
estimates for the variables commonly used
to estimate equations 21' and 25, the reem­
ployment and wage equations.12 Rather than
discuss each coefficient, we describe how to
interpret the results for each equation gener­
ally and highlight results for selected values.
The inclusion of sets of dummy variables
precludes direct interpretation of the results,

N K OF S T . L OU I S

60

REVIEW
Knowing T ^ , we can now calculate the
change in the escape rate because of a change
in a characteristic. Greene (1 9 9 3 , p. 639)
shows that the change in the escape rate can
be determined by m ultiplying/(rter), the
normal probability density function evaluated
at Ttet, by the coefficient on the particular
variable of interest.1' For example, suppose
the individual of interest was a clerical worker
at McDonnell Douglas rather than a produc­
tion worker (that is, CLERICAL=1 is the
only difference between the two workers).
The increase in the escape rate because of
the added characteristic is:

T ab le 2

V a r ia b le D efin itio n s
Dependents
LNNUWAGE

natural logarithm of wage at
new employment

REEMP

reemployed

=1 if yes
= 0 if no

Independents
ADVN0TICE

advanced notice of layoff in number of months

AGE

in calendar years

AGE2

square of AGE

CLERICAL

= 1 if clerical/secretarial employee at
McDonnell Douglas
= 0 otherwise

COLLEGE

implying that this individual’s escape rate equals

= 1 if college graduate (bachelor's degree)
or less
= 0 otherwise

DATAPR0C

= 1 if data processor at McDonnell Douglas
= 0 otherwise

ENGINEER

= 1 if engineer at McDonnell Douglas
= 0 otherwise

FISCAL

= 1 if fiscal employee at McDonnell
Douglas
= 0 otherwise

HIGHSCH00L

= 1 if high school graduate or less
= 0 otherwise

KIDS

presence of children
= 1 if yes
= 0 if no

LNWAGE

natural logarithm of wage at
McDonnell Douglas

MARRIED

/(TbJ c . = (0 .3 6 4 9 X 0 .2 1 4 ) = 0 .0781,

0 .6 6 3 7 + 0.0781 = 0.7418.
Therefore, this clerical worker’s probability
that the next received offer will lead to reem­
ployment is about 74 percent. The impact
o f a change in any other variable in the
equation can be calculated analogously.
Predictions of new wages from equation
25 are more straightforward. Realizing that
the fitted values from equation 25 are the
logarithms of the expected new wages, we need
only exponentiate these values to recover the
dollar amounts. Based on the coefficients
in Table 5 and the means of the relevant
variables, the expected new wage for a person
with the baseline characteristics is $11.19 per
hour.11 Any changes in particular character­
istics result in a deviation from this wage level.
For example, the expected new hourly wage
for a clerical worker is $12.62. Thus, all else
the same, this clerical worker should expect
to receive a wage offer that is 13 percent
greater than that received by a comparable
production worker. The effect o f changes in
other variables can be calculated similarly.
Although Table 4 shows that few of the
variables are statistically significant, the signs
on m ost of the variables are as expected and
the %2 statistic is significant. For example,
we know from equation 17a that increases in
unemployment income net of search costs
decrease the escape rate. Our coefficient on
Ul is negative, as predicted, and statistically
significant. In other words, those who applied
for unemployment compensation tended to

= 1 if yes
= 0 if no

0THER0CC

= 1 if none of the other listed occupations
= 0 otherwise

P0STC0LLEGE

= 1 if more than college graduate
= 0 otherwise

PRODUCTION

= 1 if unionized worker at
McDonnell Douglas
= 0 otherwise

RACE

= 0 if white
= 1 otherwise

SEX

= 1 if female
= 0 if male

SPSPART

spousal participation in labor force after layoff
= 0 if no

TENURE

length of service at McDonnell Douglas
in years

Ul

applied for unemployment insurance
= 1 if yes
= 0 if no




NK OF S T . L O U I S

61

ls This procedure approximates
the true change in the probability
because the variable we chose to
examine is discrete. See Caudill
and Jackson (1 9 8 9 ).
14 The logarithm of the new wage for
the baseline individual is calculated
as follows:
/3 'x = Constant + f t (AGE)
+ f t (AGE2) + f t (IM A G E )
+ f t (TENURE) + f t (LAMBDA),
where f t = p c Thus,
f t * = 0 . 4 1 6 + (0 .0 0 5 1 (3 9 .4 )
+ (-0 .0 0 0 0 1 3 2 X 1 6 6 9 .5 )
+ (0.709X2.63)

+ (-0 .0 0 4 )0 0 .2 )
+ (— 0.16)(— 0.21795x 10-7),

where the numbers substituted
for the variables ore the variable
means. To recover the dollar
amount, exponentiate f t * to
get $1 1. 1 9.

1 T a b le 3

D e scrip tive Statistics
Age
<24
25-34
35-44
45-54
55-62
>62

Kids
4%
37
25
21
9
3

Yes
No

Marital status
Married
Not Married

68%
31

Production
Engineer
Data Processing
Fiscal
Clerical/Secretarial
Other

23%
28
10
6
12
22

Tenure
71%
29

Wage at McDonnell Douglas
< 10 (S/hr.)
10 < x < 15
15 < x < 20
20 < x < 25
>25
Refused

45%
55

0-12 years
13-16 years
17+years

Occupation

Sex
Male
Female

Education

17%
36
28
6
6
8

< 2 years 22%
3-6
36
7-12
20
13-20
10
>20
13

21%
64
15

Race
White
Not White

88%
12

Unemployment insurance
Yes
No

77%
23

Wage at new job
< 10 (S/hr.)
10 < x < 15
15 < x < 20
20 < x < 25
>25

39%
28
20
8
6

n = 1,198 for all variables except for those in Table 1.

17 For summaries of this literature,
see Layord and others ( 1 9 9 1 ) and
Devine and Kiefer (1 9 9 1 ).




have a lower probability of reemployment and,
therefore, a longer duration of unemployment.
This result is consistent with the litera­
ture, which has also found a positive rela­
tionship between unemployment durations
and unemployment insurance.17 Ehrenberg
and Oaxaca (1 9 7 6 ), for example, found
that durations increased with the receipt of
unemployment insurance. Storer and Van
Audenrode (1992) also found that durations
increased with the receipt of unemployment
insurance. In addition, they argued that
unemployment spells are not longer because
workers collect unemployment insurance
benefits; rather, workers collect benefits
because their spells are longer.

The coefficient on TENURE is negative
and significant in the reemployment equation
and negative and insignificant in the wage
equation. The negative coefficient suggests
that this variable might be proxying for time
spent away from the market during employ­
ment, which affects the worker’s job-search
skills. An analogy is the depreciation of an
individual’s human capital that occurs because
of extended periods of non-employment. In
this case, the depreciation occurs because
the lengthy tenure has made the worker’s
job-search skills “rusty.” This hurts prospects
for reemployment because the worker has to
spend time relearning how to search in the
new environment.

NK OF S T . LOUIS

62

REVIEW

RY/FEBRUARY

Another interpretation of this coefficient
is that long tenure on the jo b correlates with
an individual's decision to leave the labor force
after displacement. Although at first glance
this explanation seems reasonable, Jon es
(1 9 9 1 ) shows that only 6 percent of the
respondents planned to retire.
Receipt of advance notice of the
impending layoff increased the escape rate
slightly, although its effect was not significant.
Recent literature has found mixed outcomes
for the effect of advance notice on the proba­
bility of avoiding joblessness. Addison and
Portugal (1 9 9 2 ), for instance, found that
white-collar workers’ probability doubled
with written advance notice, whereas bluecollar workers’ probability did not increase
and actually declined in some instances. Ruhm
(1992) found that all workers with some type
of advance notice, whether written or not, had
higher probabilities of avoiding joblessness
when compared with those who received
no notice.
The worker’s previous wage at McDonnell
Douglas had no role in determining the escape
rate, but did play a significant part in deter­
mining the worker’s new wage. In equation
25, the wage at McDonnell Douglas is probably
proxying for productivity that is observable
to firms but not fully captured by the other
variables in the model. The coefficients on
the engineer and data processing occupations
are positive and significant, indicating that
these workers can expect to receive higher
wage offers than their unionized production
counterparts.

Ta b le 4

R eem ploym ent Eq uation
(Eq uatio n 2 1 ' )
Dependent variable:
REEMP

Coefficient estimate
(t-statistic)

Constant

0.998*
(1.97)
-0.0 4 9
(-0 .4 6 )

COLLEGE
POSTCOLLEGE

0.149
(0.99)

AGE

0.006
(0.24)

AGE2

-0.439E-03
(-1 .4 6 )

MARRIED

0.135
(1.11)
0.119
(1.31)

KIDS

-0 .1 3 2
(-1 .0 2 )

ENGINEER
DATAPROC

0.343*
(2.09)

FISCAL

0.102
(0.54)

CLERICAL

0.214
(1.23)

OTHEROCC

-0 .1 3 5
(-1 .0 0 )

ADVNOTICE

0.039
(1.46)

SPSPART

-0 .0 7 5
(-0 .7 2 )

Ul

-0 .8 2 3 *
(-7 .6 3 )

LNWAGE

0.005
(0.55)

RACE

-0.1 8 9
(-1 .5 2 )

SEX

-0 .3 3 8 *
(-3 .1 2 )

TENURE

-0 .0 1 3 *
(-2 .6 8 )

Log-likelihood

-648.13

Restricted log-likelihood

-784.27

X \w

CONCLUSION
As firms continue to adjust to new tech­
nologies and international competition, further
rounds of restructuring are possible. More
often than not, the restructuring will entail
displacement of many workers who will
face a labor market in which their skills and
experiences are somewhat dated. A knowledge
of the determinants of reemployment and wage
offers is, therefore, important to both jo b
seekers and policy makers, especially if there
is need to adjust or improve the process.
Here, we have sketched a basic model
of the jo b search. Essentially, an individual,

272.29**

n = 1,174
* statistically significant at 0.05 level
** statistically significant at 0.001 level




1995

63




REVIEW
T a b le 5

This is achieved at the reservation wage:
A worker will accept an offer if and only if the
offered wage is at least as great as the reser­
vation wage. This is a dynamic process in
which the reservation wage serves as the
optimal stopping condition.
Using data collected by the St. Louis
County Econom ic Council, we estimated
this basic model to illustrate what kinds of
results can be expected and how they might
be interpreted. Using this limited dataset,
though, implies that the estimates probably
reflect more the specific characteristics of the
St. Louis market in the early 1990s than any
generalization. Nevertheless, the illustration
allowed us to peer into the basic equations
that describe the job-search and reemployment
activities. In future research, we plan to use
this data, along with data from follow-up
surveys, to identify the search experiences
of those workers laid off from McDonnell
Douglas. This information should allow us
to make comparisons between predicted and
realized wage offers for different categories
of workers, thereby providing information
about the market and the usefulness of the
underlying model.

W a g e Eq uation (Eq u atio n 2 5 )
Dependent variable:
LNNUWAGE

Coefficient estimate
(t-statistic)

Constant

0.416
(1.54)

COLLEGE

0.047
(1.01)

P0STC0LLEGE

0.236*
(3.64)

AGE

0.005
(0.41)

AGE2

—0.132E-04
(-0 .0 9 )

RACE

-0 .0 5 9

SEX

(-1 .1 1 )
-0 .0 0 8
(-0 .1 6 )

ENGINEER

0.244*
(4.71)

DATAPROC

0.211*
(3.17)

FISCAL

-0 .0 1 4
(-0 .1 8 )

CLERICAL

0.120

REFERENCES

(1.53)
0THER0CC

Addison, John T., and Pedto Portugal. "Advance Notice and
Unemployment: New Evidence from the 1988 Displaced Worker
Survey," Industrial and Labor Relations Review (July 1992),
pp. 645-64.

0.139
(2.48)

LNWAGE

0.709*
(10.58)

TENURE

Amemiya, Takeshi. "Tobit Models: A Survey," Journal of Econometrics
(January/February 1984), pp. 3-61.

-0 .0 0 4
(-1 .5 3 )

LAMBDA

Caudill, Steven B., and John D. Jackson. "Measuring Marginal Effects
in Limited Dependent Variable Models." The Statistician 38 (1989),
pp. 203-6.

-0 .1 6 0 *
(-2 .1 9 )

Adjusted R2
f(14,441)
Standard error corrected for selection

Devine, Theresa J., and Nicholas M. Kiefer. Empirical Labor Economics:
The Search Approach. Oxford University Press, 1991.

0.43
25.61**

Ehrenberg, Ronald G., and Ronald L. Oaxaca. "Unemployment
Insurance, Duration of Unemployment, and Subsequent Wage Gain,"
American Economic Review (December 1976), pp. 754-66.

0.366

n = 456
* statistically significant at 0.05 level

Greene, William H. Econometric Analysis, 2nd ed. Macmillan, 1993.

** statistically significant at 0.001 level

Haitovsky, Yoel. Regression Estimation from Grouped Observations, Griffin's
Statistical Monographs and Courses, No. 33. Hafner Press, 1973.

maximizing expected lifetime income, con­
tinues to search until the marginal cost for
an additional search equals the discounted
expected marginal gain from that search.

Heckman, James J. "The Common Structure of Statistical Models of
Truncation, Sample Selection and Limited Dependent Variables and a
Simple Estimator for Such Models," Annals of Economic and Social
Measurement (May 4, 1976), pp. 475-92.

64

REVIEW

RY/FEBRUARY

________ . "Sample Selection Bias os a Specification Error,"
Econometrica (January 1979), pp. 153-61.

Layard, Richard, Stephen Nickel ond Richard Jackman. Unemployment:
Macroeconomic Performance ond the Labour Market. Oxford University
Press, 1991.

________ , and Burton Singer. "Econometric Duration Analysis,"
Journal of Econometrics (January/February 1984), pp. 63-132.

Lippman, Steven A., and John J. McCall. "The Economics of Job Search:
A Survey," Parts I and II. Economic Inquiry 14 (1976), pp. 155-89
and 347-68.

Hsiao, Cheng. "Regression Analysis with a Categorized Explanatory
Variable," In Samuel Karlin, Takeshi Amemiya and Leo A. Goodman,
eds., Studies in Econometrics, lime Series, and Multivariate Statistics.
Academic Press, 1983, pp. 93-129.

Maddala, G.S. Limited-Dependent ond Qualitative Variables in
Econometrics. Cambridge University Press, 1983.

________ , and Dean Mountain. "Estimating the Short-Run Income
Elasticity of Demand for Electricity by Using Cross-Sectional
Categorized Data," Journal of the American Statistical Association
(June 1985), pp. 259-65.

Mortensen, Dale T. "Job Search and Labor Market Analysis."
In 0. Ashenfelter and R. Layard, eds., Handbook of Labor Economics,
volume II. North-Holland, 1986, pp. 849-919.
Ruhm, Christopher J. "Advance Notice ond Postdisplacement
Joblessness," Journal of Labor Economics ( i m a ^ W D , pp. 1-32.

Jones, E. Terrence. The Layoffs at McDonnell Douglas: A Survey
Analysis. Report prepared for the St. Louis County Economic Council
(September 1991).

Storer, Paul, and Marc A. Van Audenrode. "The Effect of Unemployment
Insurance on Unemployment Duration: Evidence from the Displaced
Workers Survey," Manuscript, Research Center on Employment
and Economic Fluctuations, University of Quebec at Montreal
(October 1992).

Katz, Lawrence F. "Active Labor Market Policies to Expand Employment
and Opportunity," presented at the Federal Reserve Bank of Kansas
City symposium on Reducing Unemployment: Current Issues and
Policy Options, Jackson Hole, Wyoming, August 25-27, 1994.
Kiefer, Nicholas M. "Economic Duration Data and Hazard Functions,"
Journal of Economic Literature (June 1988), pp. 646-79.
________ and George R. Neumann. "Estimation of Wage Offer
Distributions and Reservation Wages." In S.A. Lippman and JJ.
McCall, eds., Studies in the Economics of Search. North-Holland
Publishing Company, 1979, pp. 171-89.




1 9 95

65