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STOCK MARKET DISPERSION AND REAL
ECONOMIC ACTIVITY: EVIDENCE FROM
QUARTERLY DATA
Prakash Loungani, Mark Rush and William Tave
Working Paper Series
Macro Economic Issues
Research Department
Federal Reserve Bank of Chicago
September, 1990 (WP-90-15)
Stock Market Dispersion and Real Economic Activity:
Evidence from Quarterly Data
Prakash Loungani
University of Florida
and
Federal Reserve Bank of Chicago
Mark Rush
University of Florida
and
William Tave
Brown University
September 1990
Earlier drafts of this paper were presented at the NBER Summer
Institute, the Federal Reserve Bank of Chicago and the University
of Florida. We thank James Adams, Herb Baer, Bill Bomberger, Steve
Davis, David Denslow, Martin Eichenbaum, Hesna Genay, Larry Kenny,
Ken Kuttner, David Lilien, Jim Moser, Richard Rogerson, Steve
Strongin, Mark Watson and other seminar participants for extensive
comments. The Financial Institutions Center at the University of
Florida supported this research.
ABSTRACT
We conduct an empirical investigation into the effects that
stock market dispersion has on real economic activity. The results
from fairly standard reduced-form equations
suggest that,
controlling for the effects of monetary and fiscal policy, stock
market dispersion leads to a significant increase in unemployment
and a decline in real GNP and investment. We also report results
from including our stock market measure and a Lilien-type
employment dispersion measure [see Lilien (1982)] in several VAR
systems in which unemployment is used as the indicator of real
economic activity. The performance of the employment-based measure
turns out to be very sensitive to the ordering of the variables in
the system. The stock market dispersion measure always explains a
larger fraction of the variance of unemployment than does the
employment dispersion measure, and the fraction explained is not
sensitive to the ordering of the variables. Even after the
inclusion of an interest rate variable and the Standard & Poor's
500 in the VAR system, stock market dispersion accounts for between
26% and 33% of the variance of unemployment at long horizons.
Prakash Loungani
Economic Research
Federal Reserve Bank of Chicago
230 S. LaSalle St.
Chicago, IL 60690
312-322-8203
Mark Rush
Department of Economics
University of Florida
Gainesville, FL 32611
904-392-0318
William Tave
Department of Economics
Brown University
Providence, RI 02912
1
1. Introduction
David Lilien's (1982) paper has sparked a debate on the extent
to which fluctuations in the aggregate unemployment rate may be
attributed
to
the
reallocation
of
labor
across
sectors.
The
voluminous literature that has followed Lilien can be divided into
two groups: (1) time-series studies which test whether proxies for
the amount of sectoral labor reallocation are correlated with the
aggregate unemployment
rate,
and
(2)
studies which attempt to
measure labor reallocation and its contribution to unemployment
directly by using panel data sets. While this paper belongs to the
first group, it is useful to briefly review the evidence from the
second group of studies.
Lilien appears to have had in mind a model— such as that of
Lucas
and Prescott
sectors
is
(1974)— where
fixed exogenously,
the
but
time
downturns
required to
switch
are marked by
an
increase in the number of workers who experience unemployment as
they switch between sectors. Using data from the Current Population
Survey, Murphy and Topel (1987) present evidence against this early
("search") version of the sectoral shifts hypothesis.
However, one can consider alternate models where the impact of
sectoral shocks is not just on the number of workers who experience
unemployment as they switch sectors, but also on the time it takes
workers to switch sectors. This feature is likely to emerge in
models
that
assign
a prominent
role
to
sector-specific
human
capital. For instance, Topel and Weiss (1985) present a model where
some periods— such as the 1970's and early 1980's— are marked by
2
increased uncertainty about the relative returns to sector-specific
human capital investment, leading to an increase in the time that
displaced workers take to switch sectors.
In Rogerson's
(1989)
model, the impact of sectoral shocks leads to very high durations
of unemployment among older workers who are displaced from their
jobs: The basic idea is that these workers are at an disadvantage
relative to younger workers in that they do not have as long to
reap the benefits of (new) human capital accumulation and hence
require higher wages than do otherwise identical younger workers.
Using the Michigan Panel Study of Income Dynamics, a longitudinal
data set that enables researchers to observe workers' mobility and
unemployment experience over several consecutive years, Loungani
and Rogerson
present
(1989a)
evidence
and Loungani,
consistent
with
Rogerson
these
and
broader
Sonn
views
(1989b)
of
the
sectoral shifts hypothesis that stress the importance of sectorspecific human capital accumulation.
Since most of the panel data sets start around the late 1960's
or early 1970's, they do not offer any evidence on the contribution
of sectoral reallocation to unemployment prior to that period.
Hence,
time-series
in d u stry
d isp e rsio n
studies— which
typically
construct
c r o s s
indices to proxy for the amount of sectoral
reallocation of resources— are a useful source of complementary
evidence.
As discussed in Davis
(1985,
p.32)
and Barro
(1986,
p.138), the use of a dispersion index offers some advantages to
researchers who are interested primarily in determining the impact
of sectoral shocks on broad macroeconomic aggregates such as the
3
aggregate unemployment rate. Barro states that the use of the
dispersion index circumvents "the need to isolate a detailed array
of
many— mostly
preferences
sectors."
unobservable— disturbances
(that)
motivate
reallocations
to
of
technology
resources
and
across
Davis points out that "allocative disturbances from any
particular source are likely to occur rather infrequently over
available sample sizes," which makes it difficult to explicitly
incorporate
variables
that
capture
the
effects
of
allocative
disturbances into an aggregate unemployment equation.
In this paper we construct a measure of the cross-industry
dispersion
in
stock price
of
growth
capital
to proxy
for the
and
undertaken
labor
amount
by
of
sectoral
reallocation
the
economy.
In a well-functioning stock market, the industry stock
price represents the present value of expected future industry
profits.
An increase in the dispersion of stock prices across
industries reflects the occurrence of shocks that are expected to
have differential impacts on industries' profits. If these shocks
are
expected
to be persistent,
productive
resources,
such
as
capital and labor, will be displaced from the industries that are
expected
resources
to
be
are
adversely
not
affected.
immediately
To the
absorbed
extent
into
more
that
these
profitable
industries, the dispersion in stock prices will be followed by a
decline in real economic activity. In Section 2 of the paper, we
present a brief theoretical framework along these lines. We also
present details on the construction of the stock market dispersion
index.
4
While previous studies have focused on the impact of labor
reallocation on unemployment, it is likely that the reallocation of
capital
across sectors is also fairly costly.
plausible
that
the
adjustment
costs
It is therefore
associated
with
capital
reallocation lead to declines in other macroeconomic aggregates. In
Section 3 we show, using quarterly data for the period 1947 to
1987, that an increase in stock market dispersion leads not only to
a statistically significant increase in unemployment but also to a
decline in output and investment.
The results for unemployment bolster our preliminary work on
the relationship between stock market dispersion and unemployment.
Loungani, Rush and Tave (1990) present evidence on the determinants
of U.S. unemployment over a long time period, 1929 to 1987. Using
annual data, we find that unemployment depends on up to three lags
of a stock market dispersion measure. Loungani and Rush
(1990)
construct a stock market dispersion measure using British data for
the period 1912 to 1938. This measure appears to reflect fairly
well the decline of the traditional export industries and the rise
of newer industries and turns out to explain a large fraction of
British interwar unemployment.
Our stock market dispersion index is clearly motivated by
Lilien's use of cross-industry
e m p lo y m e n t
dispersion to proxy for
the intersectoral flow of labor. Many researchers, most notably
Abraham and Katz
(1984,
1986), have questioned Lilien's use of
employment dispersion as a measure of labor reallocation. Their
basic point is that movements in employment dispersion may simply
5
be reflecting the well-known fact that the business cycle has non
neutral effects across industries. The increase in the dispersion
of employment growth rates could reflect,
reallocation,
not
increased labor
but simply the uneven impact of aggregate demand
shocks on temporary layoffs in different industries. Hence there is
an
observational
equivalence
between
the
predictions
sectoral shifts hypothesis and the more traditional
of
the
"aggregate
demand hypothesis."
The main
advantage
of a
stock market
dispersion measure
relative to Lilien's measure is that stock prices respond more
strongly to disturbances that are perceived to be permanent than to
temporary
disturbances,
which
need not
be
true
of employment
changes. The industry stock price represents the present value of
expected profits over a long horizon. The impact of innovations in
industry profits on its stock price will therefore depend on how
long the shocks are expected to be persist.
If the shocks are
purely temporary, the innovations will have little impact on the
present value of expected profits and, hence, will have little
impact on industries'
shocks
are
fairly
stock prices.
persistent,
the
On the other hand,
innovations
will
if the
have
a
significant impact on expected future profits and will lead to
large changes in industries' stock prices. Furthermore, it is these
sorts of persistent shocks that will cause productive resources,
such as capital and labor,
to be displaced from the adversely
affected industries. Hence,
a dispersion index constructed from
industries'
sto c k
p r ic e s
automatically assigns greater weight to
6
permanent structural changes rather than temporary cyclical shocks.
We conjecture,
therefore,
that a stock market based dispersion
measure is less likely than an employment-based measure to reflect
changes in temporary layoffs; this implies that our stock market
dispersion variable is less sensitive than employment dispersion
measures to aggregate demand disturbances that result in large
swings in temporary layoffs.
Rather than rely solely on these conjectures, we put them to
the test
in Section
4 of the paper.
Abraham-Katz
suggest two
methods of resolving the observational equivalence problem that
they identify. The first is to test whether the correlation between
the dispersion index and the aggregate vacancy rate is positive or
negative. Abraham-Katz argue that if the dispersion index is a good
proxy for sectoral shifts this correlation should be positive,
since the reduced labor demand in some sectors will be matched by
increased hiring in other sectors. On the other hand, if dispersion
is attributable to aggregate demand shocks, then this correlation
should be negative since all sectors will reduce their hiring. The
empirical
relationship between dispersion and a proxy
for the
vacancy rate has been investigated in independent work by Brainard
and Cutler (1989) using a stock market dispersion index similar to
ours.1 They find that the impulse response of the vacancy rate
proxy to innovations in their stock market dispersion
consistently
of
one
sign,
and the
standard
errors
"is not
are
large
relative to the coefficients."
This method of resolving the observational equivalence problem
7
suffers from the lack of availability of adequate vacancy data for
the U.S. Instead, researchers are forced to use an index based on
help-wanted advertising in newspapers in 51 cities. An additional
problem is that recent work by Hosios (1988) implies that sectoral
shifts
models that
allow
for both
capital
and labor mobility
generate a negative correlation between dispersion and vacancy
rates. Hence in his model information on vacancy rates cannot be
used
to
distinguish the
aggregate
demand hypothesis
from the
sectoral shifts hypothesis.
The second method— which is essentially the one we follow in
this paper— involves "purging" the dispersion index of movements
that can be attributed to aggregate demand disturbances and then
testing
if
the
significantly
residual
correlated
measure
with
of
economic
dispersion
activity.
is
This
still
method
requires a careful specification of a list of regressors that
adequately capture aggregate demand. Recognizing that stock prices
are
forward-looking,
we include in our list not only standard
aggregate demand shifters
such as money growth and government
spending shocks, but also "information" variables, such as interest
rate spreads and mean stock returns, that have emerged in recent
studies as strong predictors of future economic activity. However,
even
after
controlling
for
the
effects
of these
current
and
potential aggregate demand shifts, innovations in our stock market
dispersion index explain nearly 33% of the variance of unemployment
at long horizons.
dispersion
measure
On the other hand,
explains
less
than
a Lilien-type
5%
of the
e m p lo y m e n t
variance
of
8
unemployment once the aggregate demand shifters are included. To
summarize, the empirical evidence strongly supports our conjecture
that the stock market dispersion index is less susceptible to the
Abraham-Katz critique than Lilien's measure.
2. Stock Market Dispersion and Economic Activity
A. Theoretical Framework
We
begin
by
presenting
a theoretical
framework
that
is
consistent with the key ideas in Lilien (1982), Black (1982) and
Davis
(1987) . For convenience we refer to this framework as the
costly sectoral mobility model.
Consider a n-sector economy with each sector producing a
distinct product using a vector of productive resources or inputs,
Zlt. Profits in each sector are given by,
(1)
7iit = JC(Zl t ) e it
where the ei 's are uncorrelated across sectors, with mean e and
t
(cross-sectional)
standard
deviation
a.
Not
much
significance
should be attached to the particular way in which we specify the
stochastic shocks to the profit function; this framework can be
modified to distinguish among shocks to the sectoral price ("taste
shocks"),
shocks
to
the
marginal
physical
product
of
inputs
("productivity shocks") and shocks to the cost function.
The sectoral stock price equals the sum of discounted expected
future profits over an infinite horizon,
(2)
S it =
(1/p) ( S E ^ J W )
where P is the discount factor and Et is the expectations operator
_x
9
conditional on information available in period t-1.
Long-run equilibrium is characterized by the equality of stock
returns across sectors,
(3)
Ri * = Rt
t
*
for all i
where Ri =log ( S Lt / S
t
i t _!)
and ^ is a weighted average of the sectoral
stock returns. We denote the allocation of inputs across sectors
associated with this long-run equilibrium by Zl * Note that this
t.
target allocation of resources changes over time in response to
realizations of the Bit's.
In the short-run, productive resources move across sectors
if
Zj.t-1
The
-
& i ( z it
~
(Z it
Z it - i) /
with
Z it
~
0
partial-adjustment
Hrt
1
*-»
Zj.t-1
Z it
-
CS3
towards this target allocation as follows:
<
if
a2
<
>
^ it - l
(Xi <
reflects
Z lt *
<
Z it*
1
the
assumption
that
both
capital and labor are partly specialized to a sector and hence the
reallocation process is costly and/or time-consuming. The role of
adjustment costs for capital is emphasized in early work by Eisner
and Strotz (1963), while the quasi-fixity of labor was highlighted
in seminal work by Oi
above
is
the
(1962)
assumption
asymmetric.2 In particular,
reach their
and Becker
that
the
(1964) . Also reflected
adjustment
mechanism
is
contracting sectors are assumed to
long-run equilibrium input levels
faster than the
expanding sectors, so that ax > a2.
Two recent empirical studies provide indirect evidence of the
sector-specificity of labor and capital. Topel (1990, p.17) states
10
that
"when human
capital
is
'general'
in the
sense
of being
portable among activities, a job loss should imply fairly minor and
transitory effects on earning capacity. But with specific capital,
initial losses may be large and persistent." Using data from the
PSID and the Displaced Worker Survey, Topel finds evidence of large
short-run
reductions
in
earnings— 40
percent
for
manufacturing worker— following job loss. Moreover,
change
industry
or
occupation
following
the
the
typical
workers who
job
loss
have
atypically large short run reductions in earnings.3 Grossman and
Levinsohn
(1989)
study the impact of exogenous
changes in the
prices of competing import goods on stock returns in six U.S.
industries. They state (p. 1065) that "when factors are mobile, ..
individual returns may respond little or even positively to adverse
shocks
to
the
particular
sectors
in
which
the
factors
are
employed." They find however that for five of the six industries in
their study, lower-than-expected import prices lead to substantial
declines
in
stock
returns,
suggesting
that
capital
is highly
immobile between sectors in the short run.
We next consider the
changes
in
realizations
Prescott
o,
of
the
the
(1974) model
impact on real economic activity of
(cross-section)
sector-specific
a
standard
shocks.
deviation
In the
of
the
Lucas
and
is assumed to be constant over time and
hence the reallocation of product demand across sectors leads to a
time-invariant natural rate of unemployment. In contrast, Lilien,
Black and Davis suggest that
a
may vary over time, depending on the
nature of the shocks to the economy. In the framework developed
11
above, an increase in a reflects the arrival of shocks that are
expected to have differential impacts on sectoral profits. This
leads to an increase in the stock prices of sectors that investors
believe are going to expand and a decline in the stock prices of
sectors that are expected to contract, thereby causing dispersion
in
the
realizations
of
the
stock
returns.
The
greater
the
difference foreseen in the sectors' prospects, the larger is the
dispersion in stock returns and the larger is the reallocation of
productive resources across sectors that is required to attain the
(new)
long-run
equilibrium.
adjustment mechanism,
Given
our
assumptions
about
the
this reallocation involves an increase in
unemployment, and a decline in aggregate output and investment. As
discussed
in
the
introduction,
the
evidence
from
panel
data
suggests that it is necessary to think of the reallocation process
not just in terms of the
am ount
sectors but also in terms of the
o f
resources that have to switch
it takes resources to switch
tim e
sectors.
Topel
relates
activity.
and Weiss
the
(1985)
dispersion
They assume,
sector-specific.
in
present
stock
as we do,
However,
they
an alternate theory which
market
returns
to
economic
that human capital is partly
interpret
an
increase
in
stock
market dispersion as reflecting an increase in uncertainty about
the relative returns to sector-specific human capital investment.
In the face of this increased uncertainty about which sectors are
going to prosper and which ones are going to decline, "individuals
with less experience and those with greater costs of acquiring
12
sector-specific
human
capital
will
rationally
and
optimally
postpone employment and human capital investment until uncertainty
has been resolved." We refer to the Topel-Weiss framework as the
sectoral uncertainty model.
While the theory underlying their work is distinct from the
costly sectoral mobility model outlined above,
Topel and Weiss
point out that it may be difficult to distinguish between the two
empirically (p. 348):
"In contrast to Lilien, who implies that the o c c u r r e n c e of a
sectoral shock that requires labor to be reallocated raises
unemployment, we argue that t h e p r o s p e c t o f f u t u r e s h o c k s is
a likely candidate for explaining the observed rise in
unemployment, especially among younger individuals. Of course,
to the extent that the occurrence of sectoral shocks is
correlated over time,
a sectoral shock may increase
expectations of future shocks, so it may be difficult to
completely separate the two theories empirically. In this
sense, models of costly sectoral mobility and sectoral
uncertainty
are
complementary
theories
of
rising
unemployment."
B. Construction and Properties of the Stock Market Dispersion Index
This section of the paper describes the construction of the
empirical analog to a. The basic data we used to construct our
measure of the dispersion of stock prices were monthly average
indices of various industries'
Standard and Poors
stock prices,
as constructed by
(1988). The industries, which are defined by
Standard and Poors, range in size from 2 firms to 31 firms and the
indices are computed by weighting each firm's stock price according
to the firm's market value.
Standard and Poors began compiling
these data in 1926; at various times additional industries have
been added (and others subtracted) so that currently Standard and
13
Poors compiles indices for about 85 industries. We used a sample of
60 indices, including most industries with a complete data series
from 1947 through 1987 as well as a few shorter series deemed
important. A list of the industries we used, together with their
starting date, ending date (if relevant), and weight in our index
is given in the appendix.
In calculating the index, we first deflated each index using
the GNP price deflator and then used quarterly averages of the
monthly data.
Then we calculated each indices'
growth rate and
defined our dispersion measure as
(5)
St = [£ wlt(ri - rt)2]12
t
/
where rl is the growth rate of industry i's stock at time t, rt is
t
the growth rate of Standard and Poor's composite listing, and wl
t
is a weight based on the industry's employment. Due to the changing
number
of
available,
industries
the
for which
wi weight
t
given
Standard
an
and Poor's
industry
changed
data
are
as
the
industries included in our dispersion index changed. wl equals the
t
over-all weight for industry i, based on its share of employment
from the entire sample,
(called W±; see the Appendix) divided by
the sum of the W± weights used in period t. Thus we compensated for
the varying number of industries in different years and so S is an
employment-weighted standard deviation of the growth rate of the
industries' stock prices.
14
3. Empirical Results from Reduced-Form Equations
To determine the role our dispersion index plays in affecting
aggregate
economic
activity,
we
start by specifying
a set
of
conventional reduced-form regressions of the type estimated by
Lilien (1982). Our hypothesis is that the greater the difference
foreseen in the industries'
prospects,
the larger will be the
divergence in their stock prices, which will be reflected in an
upward movement in the dispersion index. Moreover, the greater the
difference
foreseen
in
the
industries'
prospects,
the
more
resources must be moved and so the larger will be the resulting
unemployment and decline in real activity. Under both versions of
the sectoral shifts hypothesis, there is reason to expect that an
increase in dispersion will have a persistent impact on economic
activity, i.e., that
la g g e d
values of dispersion will be correlated
with economic activity. Under the costly sectoral mobility model,
this reflects the fact that the reallocation of resources will be
staggered over time due to adjustment costs. Under the sectoral
uncertainty model, the lag length reflects the time it takes for
the uncertainty about sectors' relative prospects to be resolved.
We use changes in government spending and money growth to
capture shocks to aggregate demand. To control for the effects of
changes
in
government
spending,
the
unemployment
regression
includes the ratio of federal government purchases of goods and
services to trend GNP, called GY, while the output and investment
equations include the log of federal purchases, called LF.4 We use
the actual growth rate of the base money supply, called DB, as the
15
monetary variable.5
Unemployment rates trended upwards during the late 1960's and
the 1970's and demographic changes are often thought of as an
important factor in accounting for this rise. To capture this we
include a variable DEMO, which equals the percentage of women in
the total labor force, in the unemployment equation. To account for
the trend growth in output and investment, we include a time trend,
T.6
For all the variables,
except the trend, we included lags.
Clearly there is no theoretical basis for the number of lags to be
included. The trade-off between more versus fewer lags hinges on
the point that including more lags than justified lowers efficiency
but including fewer biases the results. We expect that the relative
price effects for which we are searching will occur with a fairly
long lag, so at the risk of losing efficiency we included two years
worth of lags for S. We also used eight lags for DB, one lag for
the government spending variables and the demographic variable in
the unemployment equation and,
to capture any inertia that we
failed to explicitly model, two lags of the dependent variables in
each regression. Our main results are robust to several alternate
lag structures.7 In summary, we estimated the following reduced
form regressions:
8
8
1
2
LY = a x + Xb ( i ) DBt.i + I c U J S t . i + X d ( i ) L F t. i + eT + X f L Y ^ i
i=0
i=0
i=0
i=l
8
LI = m
8
+ p jjU JS ,.,
i
2
O
+ S tU JL F t.i + C T + X y L I ^
i=0
i=l
16
8
8
1
1
UN = (X + X p d JD B t.i + S y U J S t.i + X S d J L F t .i + I
i
i=0
i=0
i=0
2
kD O
EM ,...,^
i=0
+ SjlUNt.i
1
where UN = Log(U/[1—U ] ), with U being the unemployment rate, LY is
is
log
of
producers'
real
GNP
and
LI
is
the
log
of
real
investment
in
durable equipment and structures. We hypothesize that
the P's and S's generally should be negative and the b's, d's, £'s,
T's, and K's should be positive. More important,
though,
are the
c's, < ) s and y's which indicate the effect from dispersion. Since
J',
we expect increased dispersion will lower output and investment,
|'
while raising unemployment, the c's and < ) s should be negative and
the y's positive.
For two reasons,
though, we examine mainly the
lagged values of the dispersion variables. First,
the effects of
the more contemporaneous dispersion variables may be reflecting
effects
from
differentially
other,
affect
omitted,
industries.
aggregate
This is,
variables
of course,
that
the point
made by Abraham and Katz. Second, as discussed above, dispersion in
the stock market should lead movements in real economic activity.
Unconstrained Equations
We estimated these regressions for the period 1950-1 to 1987IV. The results from this are reported in Table 1.
[In the table,
S6 indicates the estimated coefficient for St
_6. The other variables
have similar interpretations.] The results from Table 1 show that
the effect of dispersion on output, investment and unemployment is
fairly clear cut. The stock dispersion variables are significantly
17
negative in the output regression at lags two,
six and eight,
in
the investment regression at lags one and eight, and significantly
positive in the unemployment equation at lags one, five, and seven.
The
only puzzle
positive
in
is that the contemporaneous
the
investment
regression
at
S is
the
significantly
10%
level
of
significance. The failure of more individual coefficients to attain
significance may well be because of collinearity because
in all
cases the sum of the coefficients is highly significant at over the
99% confidence level. These results, especially the significance of
the
longer
lagged variables,
provides
evidence
in
favor
of the
sectoral shifts hypothesis.
Constrained Regressions
Because multicollinearity amongst the variables is clearly a
problem,
we
re-estimated
our
regressions
constraining
the
coefficients for DB and S to lie along a second order polynomial.
The results from this estimation are reported in Table 2.
Although
coefficients
this
on
procedure
DB
and
S
does
by
interpretation of the regressions.
not
change
much,
it
the
does
For instance,
sums
of
sharpen
the
our
looking at the
effects from changes in the base money supply, we see that all the
coefficients the regressions have the expected sign and many are
now significantly different from zero. Moreover, all lags of S now
have the "correct" sign and most are significantly different from
zero even up to lags of two years.
It is particularly noteworthy
that in the investment and unemployment regressions, the cumulative
18
effect from S lagged six, seven and eight quarters are larger than
for any other three consecutive quarters.
This large impact
for
what seems ex ante to be quite long lags appears to us as strong
support for the sectoral shocks hypothesis.
4. Sectoral Shifts or Aggregate Demand?
This section is devoted to determining the extent to which our
stock market dispersion index is subject to the same criticisms
that Abraham and Katz
work.
In
the
(1984,
interests
of
1986)
aimed at Lilien's
brevity
we
focus
empirical
largely
on
the
unemployment equation, though similar considerations would hold for
the output and investment equations.
Our empirical work thus far rests on the assumption that the
shocks
to
sectoral
uncorrelated across
profits— the
sectors.
eit's
Hence,
in
equation
movements
(1)— are
in the dispersion
index are assumed to be driven by sectoral shocks alone. However,
as
Abraham-Katz
satisfied
in
differential
point
out,
practice.
impacts
on
this
assumption
Aggregate
sectoral
demand
profits
is
unlikely
shocks
will
which
also
to
be
have
lead
to
movements in the dispersion index. Under certain conditions— which
are spelled out in their paper— aggregate demand shocks can also
lead to a positive correlation between the dispersion index and
aggregate unemployment.
The Abraham-Katz critique points out that treating movements
in dispersion as exogenously given— as was assumed in the reducedform equations estimated in the previous section— may be incorrect
19
under
certain
circumstances.
In
this
section
we
show
that
by
estimating VAR systems, and by imposing alternate orderings on the
contemporaneous innovations, we can gauge the extent to which their
critique is applicable in practice.
A. Comparison with Employment Dispersion
We begin by illustrating the Abraham-Katz critique in a VAR
framework. We construct an alternate measure of denoted SIG; the
difference between S and SIG is that the latter is a measure of the
dispersion of employment growth rates across sectors. We then add
SIG to a VAR system in which the other variables are unemployment
(UN)
and two
aggregate
demand proxies,
the
growth
rate
of the
monetary base (DB) and the ratio of federal government purchases to
trend GNP
(6)
(GY). That is we estimate a m-th order autoregression,
Xt = A * . ! + .....
+
+ et
where Xt is a vector of all the variables in the model
(4x1 in this
case) . As a first step, this allows us to ascertain if movements in
SIG are Granger-caused by other variables in the system.
The results of this estimation are contained in Table 3. The
sample period is 1951:2 to 1987:4. The lag length is picked to be
8 quarters, which is a more generous lag length than that used in
most VAR studies;
however,
pruning the lags does not affect our
results in this table. Panel A shows that lags of SIG are highly
significant in the unemployment equation. However,
case that
lags of the aggregate demand proxies,
it is also the
DB and GY,
are
fairly significant in the employment dispersion equation; the first
20
few lags
of unemployment
are also
significant
in this
equation
though the sum does not attain significance at conventional levels.
Hence,
there appears to be clear evidence of "reverse causality"
running
from
the
other
variables
in
the
system
would
be
to
employment
dispersion.
The
Granger-causality
tests
sufficient
in
detemrnining the extent of the "reverse causality" problem if the
contemporaneous innovations in different variables, i.e., the et's
in equation
(6) above, were independent.
However,
Panel B — which
reports the contemporaneous correlation matrix of the et's— shows
that there is that there is a strong, positive correlation between
innovations unemployment and innovations in SIG. In light of this,
Panel C reports results of the decomposition of variance for the
unemployment and employment dispersion equations using the standard
Choleski factorization under two alternate orderings.
places SIG first in the system,
Ordering 1
followed by GY, DB and UN.
(This,
of course, keeps SIG independent of the contemporaneous values of
UN, GY and DB but allows UN to be affected not only by lags of SIG,
GY,
and
DB
but
also
by
the
contemporaneous
values
of
these
variables.) Hence, with only minor modifications, this equation is
similar
to
the
reduced-form
equation
reported
earlier.
Not
surprisingly, the results support our earlier conclusions and the
views espoused by Lilien. Employment dispersion explains close to
20% of the variance of unemployment, whereas unemployment explains
only about 10% of the variance of dispersion.
This pattern is dramatically altered when SIG is placed last
21
in the system,
as shown in the results
for Ordering 2. Now the
results are closer to the Abraham-Katz view: SIG explains less than
5% of the variance of unemployment while nearly 25% of the variance
of SIG is attributable to unemployment. To summarize, these results
confirm
the
Abraham-Katz
argument
that
it
is
difficult
to
distinguish the view that exogenous sectoral shifts cause some part
of unemployment fluctuations from the view that unemployment causes
increases in dispersion.
Next, we consider the extent to which similar problems arise
when
our
stock
market
measure,
S,
is
used
as
the
measure
of
dispersion. Once again, the sample period is 1951:2 to 1987:4 and
eight lags of each variable are included. The results are reported
in
Table
4.
Panel
A
shows
that
the
sum
of
the
lags
of
S
is
significantly different from zero in the unemployment equation; as
we found in the reduced-from equations, it is the higher-order lags
of S, particularly
lags seven and eight
highly significant.
The evidence for "reverse causality" is much
weaker,
with
equation.
only
Panel
the
B
GY
shows
variable
that
in this
being
there
case,
significant
there
is
that
in
very
are
the
S
little
contemporaneous correlation between the residuals. Panel C presents
variance decompositions for two different orderings,
one in which
S is placed first in the system and one in which it is placed last.
The
key
finding
is
that
the
fraction
of
the
variance
of
unemployment explained by S is not very sensitive to the ordering:
S explains 32% of the variance
(at step 20) if placed last and 38%
if placed first in the system. Also,
less than 2% of the variance
22
of S is attributable to innovations in unemployment. These results
constitute preliminary evidence that S may be less vulnerable than
employment dispersion to the Abraham-Katz critique.
B. Results with Mean Stock Price Growth
Stock
prices
are
forward-looking
and
should
respond
to
expected changes in aggregate demand that may not be reflected in
current
money
growth
or
current
government
spending.
Hence
we
cannot rule out the possibility that the stock market dispersion
index is driven by imminent aggregate demand shocks that we have
omitted that differentially affect industries' fortunes. To explore
this possibility, we augment both our reduced form regressions and
the VAR systems discussed above to include the real growth rate of
the Standard & Poor's 500.
stock
market
dispersion
The idea is that if movements in the
index
are
largely
in
expectation
of
imminent aggregate shocks, then those expectations should also be
reflected in movements
omitted
index,
aggregate
the
in the mean stock price growth.
shocks
inclusion
of
are the
the
factor
mean
driving
stock
price
our
Thus,
if
dispersion
growth
should
eliminate the impact of stock dispersion on aggregate activity.
Table 5 presents the results
include
results,
mean
stock
price
growth,
from augmenting the system to
DSP.
Before
several points should be noted. First,
we
discuss
the
it turns out that
the government spending variable, GY, is no longer significant in
the unemployment
equation
in the
augmented system and hence we
exclude this variable from the system.
In any case,
including GY
23
does not affect our main conclusions.
brevity,
Second,
in the interest of
we only report the results for a system in which both S
and SIG are included simultaneously. Third, we continue to use the
monetary base as the measure of money whereas many VAR studies use
Ml; however, we obtain qualitatively similar results if we replace
the base by Ml (a change which also involves starting the sample in
1959
rather than
1948).
Hence the
estimated system consists
of
unemployment, UN, the monetary base, DB, mean stock price growth,
DSP, and the two dispersion measures, S and SIG. The lag length is
set
at
eight
for
the
S variable
and
four
for
all
the
other
variables.
Panel A shows that lags of stock market dispersion continue to
be
significant
inclusion
of
at
DSP
about
does
a
not
5%
level.
eliminate
Panel
the
B
shows
importance
that
of
S
the
for
unemployment. While the stock market mean is fairly important at
the shorter forecast horizons, stock market dispersion continues to
account for between 34% and 39% of the variance of unemployment at
the longer horizons.
Barro
between
mean
(1989)
has
recently
stock price
growth
investigated
the
and aggregate
relationship
investment
using
reduced-from equations similar to those we use in Section 3. He
finds that lagged stock price growth has strong explanatory value
for
the
(growth
rate
of)
investment
and,
moreover,
that
this
variable dominates other predictors of investment such as q and
measures of cash flow.8 In a companion paper,
provides
recent evidence
Barro
confirming the well-known
(1988b)
also
link between
2 4
mean
stock price
growth and subsequent movements
in output. In
light of these results, it is interesting to briefly return to the
reduced-form
framework
and
see
whether
the
inclusion
of
stock
market dispersion has any impact of Barro's findings for output and
investment.
Table
6 presents
the
results
from
augmenting
the
unconstrained reduced form regressions to include the growth rate
of the S&P 500; Table 7 presents similar results from a constrained
system, where the coefficients for DB, DSP, and S are constrained
to lie along a second order polynomial. In both Tables we see that
mean stock price growth has a strong effect on output,
and
unemployment:
Many
of
the
individual
investment
coefficients
significantly different from zero and, except for output,
are
so too
are the sums of the coefficients.
Including the growth rate of stock prices seemingly reduces
the impact of our dispersion variable. In particular, for both the
unconstrained investment and unemployment regressions the sum of
our dispersion variables is no longer significantly different from
zero at conventional levels.
However,
it is important to notice
that this reduction takes place among the contemporaneous and first
few lags of dispersion.
If we examine only the last four lagged
coefficients
find
we
again
that
the
sums
are
significantly
different from zero: In the investment regression, the F-statistic
for the sum of the last four dispersion coefficients is 2.43 and in
the
unemployment
regression the F-statistic
is 2.29.
Given
our
emphasis on the lagged coefficients,
we find the point that the
lags
Looking
remain
significant
reassuring.
now
to
the
output
25
regression,
well
as
we can see that the sum of all the coefficients— as
the
sum
of
just
the
last
four
coefficients— is
significantly different from zero. Moreover, when we constrain the
coefficients
in Table
7,
the
sums
as well
as
the
last
several
coefficients again emerge as significant.
C. The Role of Interest Rate Spreads
Following the work of Sims
(1980), who drew attention to the
strong predictive power of the commercial paper rate for output, it
has become customary to include some measure of interest rates in
VAR systems that attempt to test whether movements in money affect
real activity. Sims
interest
rates
(1982) and McCallum
rather than monetary
(1986) suggest that it is
growth
rates
that
properly
capture Federal Reserve actions, which may account for their being
informative about the future of the real economy. However, a flurry
of recent papers has shown that measures of interest rate spreads—
differences between interest rates on alternative financial assets-dominate
measures
of
the
level
of
interest
rates
as
robust
predictors of economic activity.9 While the measure of the spread
used differs across studies,
the measure that appears to perform
the best is the difference between the short-term commercial paper
rate and the short-term Treasury bill rate. In prediction equations
for real GNP, Friedman and Kuttner (1989) find that the sum of the
interest rate spread variables is significant at the .001 level or
better in all their specifications.
Stock and Watson
(1989)— who
examined the information contained in a wide array of variables in
26
constructing
a
new
index
of
leading
indicators— find
that
the
spread outperforms nearly every other variable in forecasting the
business cycle. Bernanke (1990) provides preliminary evidence that
the reason the spread works so well in predicting economic activity
is that it combines information about the stance of monetary policy
and, to a lesser extent, expected default risk.
To the extent that the stock market dispersion index is also
responding
to
information
about
the
future
course
of
monetary
policy, including the interest rate spread in the VAR system should
weaken its correlation with unemployment. Table 8 reports results
obtained by adding the measure of the spread used by Friedman and
Kuttner, the difference between the 4-to-6 month commercial paper
rate and the 3 month Treasury Bill rate, which we call IRS, to the
VAR system discussed earlier.
Panel A reports the F-tests for the unemployment equation. All
the variables included in the system are significant and,
as in
Friedman and Kuttner's work with output, the interest rate spread
is significant at better than a .001 level. Panel B reports the
variance decomposition of unemployment for two different orderings.
Ordering 1 places the employment dispersion first in the system and
the
stock
positions.
dispersion
last
whereas
Ordering
2
reverses
Several conclusions are apparent. First,
these
the interest
rate spread explains a much larger fraction of the variance than
the
monetary
base.
Second,
the
contribution
of
employment
dispersion is relatively modest, ranging from 3% to 9% at step 20.
The most important conclusion, from our perspective, is that stock
27
market dispersion continues to account for a large fraction of the
variance of unemployment; at step 20, for instance, between 25% and
33% of the variance is attributable to movements in S.
D. Have We Adequately Controlled for Aggregate Demand ?
In
the
preceding
monetary base growth
stock price growth
sections
we
have
used
four
variables—
(DB), government spending changes
(DSP)
and the interest rate spread
(GY), mean
(IRS)— to
capture the state of current and future aggregate demand.
In this
section we conduct some tests suggested by Abraham and Katz
(1984,
pp. 17-20) to detemine whether these variables adequately control
for the impact of aggregate demand fluctuations on sectoral stock
price growth.1
0
As before,
let Sl denote the stock price index for industry
t
i at time t and define rl = log
t
. We regress rl on the
t
aggregate demand variables:
(7 )
rl = Yo + YiDBt + Y2GYt + y 3D SPt + Y4 IR S t + T i
t
lt
where the T lt/s are residuals.
|
We then construct a stock market
index based on the residuals from equation
(8)
(x):
Sp
urged, = [Z wit( i t - T t)2]1 2
t
Tl
j
/
where T t is a weighted average of the “
]
Hit's.
We also estimate these equations allowing lagged values of the
aggregate
demand proxies— as
well
as
current
values— to
affect
sectoral stock price growth;
(9 )
We
r it = Yo + S y u D B t.i + SY2iGYt.i + ^ D S P ^
picked
two
alternate
lag
lengths,
4
+ EY4iIRSt_i + T i'it
and
8 . The
two
Spu g d
re
28
measures
corresponding
correlated
equation
with
the
(7) . This
to
one
is
these
lag
lengths
constructed
shown
using
in Panel
A
of
turn
the
to
be
highly
residuals
Table
9.
from
Hence
the
subsequent work only uses the estimated equations (7) and the Spu
rged
measure given in (8) .
If the four variables— GY, DB, DSP and IRS— do a good job of
capturing
the
common
factors
that
underlie variations
in
stock
market returns then the correlation among the T lt's should be much
|
lower than the correlation among the rit's. Whether or not this is
indeed the case is investigated in Panel B of Table 9.
The top number in each cell of the table gives the
correlation
between
the
r^'s
for
eight
industries
simple
which
were
randomly chosen from our set of 60 industries. The bottom number
gives the corresponding correlation between the T it's. As shown, the
)
correlation between the rit's is uniformly positive— the average
correlation is 0.42— which indicates that some common factors do
underlie the variations in sectoral stock price returns. However
the correlation between the T , ' s is almost always close to zero,
h.
suggesting that the four variables adequately control for aggregate
demand. The cases where the correlation between the elt's is non
zero tend to be cases where the two industries belong to the same
broader industry group,
e.g.,
the
for
residual
returns
"Aluminum" and "Copper." Note that
"Auto"
and
"Oil"
are
negatively
correlated, as one might expect in a period dominated by strong oil
price shocks.
Finally,
we
present
results
obtained
from
including
Spu
rged
29
instead of S in the VAR system. Panel A of Table 10 shows that the
sum
of
the
lagged
values
of
the
"purged"
dispersion
index
is
significant at a 2% level of significance. Panel B shows that Sp r a
ugd
explains 22% of the variance of unemployment if placed last in the
system and 33% if placed first.
Figure
1
plots
the
impulse
response
of
unemployment
innovations in the other variables of the system. As shown,
to
Sp r e
ugd
has a strong impact on unemployment with the peak occurring around
lag 10. The impact is also fairly persistent; for instance, at lag
12 the impact of monetary base innovations is essentially zero but
the impact of Sp r e is still at half its peak effect.
ugd
V.
CONCLUSIONS
A multi-sector economy is subject to a variety of shocks that-
-initially at least— affect only one or a few sectors. Many recent
papers
investigate the impact of such sector-specific shocks,
prominent
example being Grossman and Levinsohn's
(1990)
empirical
study
in
the
capital
in
competing
of
import
the
goods
impact
on
of
variations
returns
to
a
careful
prices
six
of
U.S.
industries. Their study complements Grossman's (1987) earlier work
on
the
employment
competition.
sectoral
While
shocks,
and
wage
our
focus
our
goal
in
effects
is
also
this
of
on
paper
variations
the
is
in
impacts
different:
import
of
We
such
are
interested in determining the extent to which sectoral shocks can
lead to changes in broad macroeconomic aggregates such as real GNP,
aggregate investment and aggregate unemployment. Recent theoretical
30
work emphasizes two channels through which this can occur. First,
if physical
capital
reallocation
and human
of resources
affected by
sectoral
uncertainty
about
out
shocks
the
capital
of
are
sector-specific,
industries
can be costly.
relative
returns
that
are
Second,
to
the
adversely
if there
is
sector-specific
investment, firms and workers may delay making any investment until
the uncertainty is resolved.
Instead of explicitly modelling specific shocks,
Lilien's
we
follow
(1982) innovative use of a dispersion index to proxy for
the intensity of sector-specific shocks.
Unlike Lilien, however,
we use the dispersion in stock price growth across industries—
rather than employment growth dispersion— to measure the intensity
of sectoral shifts. The results from fairly standard reduced-form
equations
suggest that,
controlling for the effects
base growth and fiscal policy,
of monetary
stock market dispersion leads to a
significant increase in unemployment and a decline in real GNP and
investment.
While these initial results give strong support for a sectoral
shifts explanation of unemployment,
robustness,
1986)
particularly
critique
of
in light
Lilien's
it is necessary to test their
of Abraham and Katz's
employment
dispersion
(1984,
index.
Our
principal empirical findings are as follows:
(i)
Using
a
VAR
framework,
contemporaneous
we
correlation
find
that
between
there
the
is
a
strong
innovations
unemployment and innovations in employment dispersion.
in
This
makes it very difficult to distinguish empirically a model in
3 1
which
exogenous
unemployment
shifts
in
from a model
employment
dispersion
in which the
causality
cause
runs
the
other way. Hence we confirm the basic Abraham-Katz finding,
albeit in a different empirical framework.
stock
(ii) When
sectoral
market
dispersion
reallocation,
there
is
used
is
as
the
little
measure
evidence
of
that
unemployment Granger-causes movements in the stock dispersion
index. On the other hand, after controlling for the effects of
standard
aggregate
growth and changes
demand
shifters
such
as
in government purchases,
monetary
base
innovations
in
stock market dispersion account for between 32% to 38% of the
variance of unemployment at long horizons.
(iii) We recognize that stock prices are forward-looking and hence
our dispersion index may be influenced not only by the current
state of aggregate demand,
as reflected in money growth and
government spending, but also by the future state of aggregate
demand. This leads us to expand our VAR system to include two
"information" variables that have emerged in recent studies as
robust predictors of economic activity.
the mean return on the stock market
Fischer and Merton
(1984)]
These variables are
[see Barro
(1988,
1989) ,
and an interest rate spread— the
differential between the short-term commercial paper rate and
the
short-term month
Kuttner
Bernanke
(1989),
(1990),
Stock
Treasury bill
and Watson
rate
(1989)].
[see Friedman
As
the spread appears to reflect
discussed
and
in
largely the
stance of monetary policy. However, even after controlling for
32
the effects
of these additional variables
on unemployment,
innovations in stock market dispersion account for between 25%
and 33% of the variance of unemployment at long horizons.
(iv) The set of four variables— DB, GY, DSP and IRS— does a good
job of capturing the common
stock price movements.
factors that underlie sectoral
Regressions
of sectoral
stock price
growth on these variables yield residuals that are virtually
uncorrelated across industries.
(v)
Finally, we construct a proxy for sectoral shifts, Spurged that
is purged of the influence of aggregate demand. This measure
continues to account for between 22% and 31% of the variance
of unemployment.
33
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38
Table 1: UNCONSTRAINED REGRESSIONS
O U T PU T
IN V E S T M E N T
UNEM PLO YM ENT
STANDARD
COEFFICIENT ERROR
C
.83***
.16
STANDARD
COEFFICIENTERROR
C
-.88***
.26
DB
.15
DB1
.18
DB2 -.12
.11
DB3
DB4 -.05
.11
DB5
D B 6 -.02
DB7
.11
.13
DB8
.13
.13
.14
.14
.13
.12
.12
.12
.10
DB
DB1
DB2
DB3
DB4
DB5
DB6
DB7
DB8
DB
-2.29***
DB1
-.58
DB2
2 .12***
DB3 -2.63***
-.14
DB4
.47
DB5
.62
DB6
DB7 -3.62***
DB 8
.80
.25
I
.30
.32
.33
.32
.31
.29
.29
.28
.25
-1.26***
•
.62**
I
-.01
1 .20***
-.88**
.15
.40
-.25
-.06
.96***
-.24
GO
STANDARD
COEFFICIENT ERROR
C
.82***
.22
.09
.10
.11
.11
.11
.11
.11
.10
.09
S
SI
S2
S3
S4
S5
S6
SI
S8
.12
.46*
-.11
.21
-.18
.52*
-.19
.69**
.15
.24
.27
.29
.29
.29
.30
.29
.28
.24
.60***
.2 1
I
1.67***
.54
-.014
-.050
-.097**
.031
-.075
.034
-.078*
.001
-.066*
.037
.043
.045
.047
.046
.046
.045
.043
.038
S
SI
S2
S3
S4
S5
S6
SI
S8
I
-.312***
.086
I
LF
LF1
T
.057
LF
.014
.025
-.029
LF1
-.014
.056
-.035
.025
T .0012***
.0002
.0008***
.0002
1.14***
-.24***
R2 .9994
=
.09
.08
LI1
LI2
S E = .0091
- 5.25*** 1 .40
.16*
-.30***
.05
-.12
.05
-.12
-.06
-.06
-.19**
S
SI
S2
S3
S4
S5
S6
S7
S8
LY1
LY2
I
.81
.88
.86
.87
.84
.79
.80
.77
.66
1.30***
-.42***
.08
.08
R2 .997 6SE= .0217
=
GY
GY1
-•1.57
1.68
DEMO
.045**
DEMOl -.039**
UNI
1.48***
UN 2
-.66***
1 .14
1 .17
.020
.020
.07
.07
R:! 9708SE= .0584
=.
*** indicates significance at the 1% level; ** at the 5% level
and * at the 10% level.
39
Table 2: CONSTRAINED REGRESSIONS
O U T PU T
IN V E ST M E N T
UNEM PLO YM ENT
STANDARD
COEFFICIENT ERROR
.21
.87***
C
STANDARD
COEFFICIENT ERROR
94 * * * .17
C
STANDARD
COEFFICIENTERROR
•1.04***
.27
C
DB
DB1
DB2
DB3
DB4
DB5
DB6
DB7
DB8
.16*
.10*
.05
.02
.02
.02
.05*
.09***
.15***
DB
DB1
DB2
DB3
DB4
DB5
DB6
DB7
DB8
I
.66**
S
S4
S5
S6
S7
S8
-.047**
-.040**
-.035***
-.032**
-.030**
-.031**
-.034***
-.039***
-.046**
I
- .3 3 4 * * *
LF
LF1
T
LF
.017
-.089
.058
.023
LF1
.034
.024
.058
-.040*
.0002
T .0013***
.0003
.0008***
S3
1 .10***
-.20***
LY1
LY2
•
I
I
CM
9994
.19
.11
.07
.08
.09
.08
.07
.08
.14
DB
-•1.41***
DB1
-.91.***
DB2
-. 54***
DB3
-.31
DB4
-.22
DB5
-.26
D B 6 -.43**
DB7
-.75***
DB 8 -■
1.20
.52
.30
.21
.22
.25
.24
.21
.23
.38
I
1 .5 6 * * *
.51
I
- ■6.01***
1 .4 7
.024
.016
.013
.014
.014
.013
.012
.014
.022
S
SI
S2
S3
S4
S5
S6
S8
-.061
-.052
-.049
-.052
-.061*
-.076**
-.098***
-.125***
-.159***
.063
.041
.033
.036
.037
.035
.031
.037
.059
S
SI
S2
S3
S4
S5
S6
S7
S8
.33**
.22**
.15***
.11
.11
.14
.22***
.33***
.48***
.16
.11
.09
.09
.09
.09
.08
.10
.16
.084
I
- .7 3 2 * * *
.2 1 9
2 .0 9 * * *
.5 6
C
M
SI
S2
.40**
.25**
.14*
.07
.04
.05
.10
.19**
.33**
.08
.05
.03
.03
.04
.03
.03
.03
.06
.08
.08
SE=.0091
SI
LI 1
LI2
1 .21***
- .
34
R2 .9970
=
***
.08
.08
SE= .0234
I
GY
GY1
- 1.57
1.75
1.16
1.19
DEMO
.029
DEMOl -.022
UNI
1.40***
UN 2
-.59***
R2 .9638
=
.020
.020
.07
.07
SE =.0621
*** indicates significance at the 1% level; ** at the 5% level;
and * at the 10% level.
40
Key:
SIG = employment dispersion
GY = govt, purchases/trend GNP
DB =: monetary base growth
UN =: unemployment rate
Table 3
VAR System: SI 6 6Y DB UN
Sample Period: 1951:1 to 1987 :4
F-TESTS : UN
Panel A
VARIABLE
SIG
GY
DB
UN
F-STAT.
4.70
1.94
3.75
189.45
F-TESTS: SIG
SIGN. : VL.
L
.00005
.05946
.00003
.00000
F-STAT.
3.18
2.71
2.63
1.62
SIGN. LVL.
.0027
.0089
.0110
.1275
Entries are F-statistic values and significance levels of the
hypothesis that 8 lags of the variable can be excluded. from the
unemployment and employment dispersion equations.
Panel B
CORRELATION MATRIX OF RESIDUALS
VARIABLE
1 . 000
SIG
GY
DB
UN
2
4
8
12
20
DB
UN
- 0.114
- 0.005
0.026
#
#
1.000
0.313
- 0.128
- 0.091
1.000
•
Panel . C
STEP
GYI
SIG
•
1.000
•
DECOMPOSITION OF VARIANCE: ORDERING 1
SIG
15.8
10.9
16.6
18.3
17.7
UN
GY
1.4
4.0
7.5
11.6
13.5
DB
UN
2.2
3.2
6.9
7.2
7.4
80.5
82.0
68.9
62.8
61.4
SIG
95.9
83.7
80.6
76.7
74.9
SIG
GY
DB
1.0
7.9
9.0
8.6
8.6
0.1
2.5
3.4
5.1
5.1
UN
3.0
5.9
7.0
9.5
11.3
DECOMPOSITION OF VARIANCE: ORDERING 2
STEP
GY
UN
DB
UN
SIG
2
4
8
12
20
1.2
3.6
6.9
10.9
13.0
3.7
4.6
9.3
9.8
9.9
94.2
91.4
80.7
74.8
72.7
SIG
0.8
0.3
3.1
4.5
4.6
GY
0.8
7.4
8.6
8.2
8.2
DB
UN
1.7
3.7
4.5
6.1
6.1
16.5
19.4
19.3
21.2
22.5
SIG
81.0
69.4
67.6
64.9
63.1
Entries show percentage of forecast variance of unemployment and
employment dispersion at different horizons attributable to
innovations in the variables of the system. Ordering is as shown
41
Key:
S =
GY =
DB =
UN =
Table 4
VAR System: S GY DB UN
Sample Period: 1951:2 to 1987:4
stock market dispersion
govt, purchases/trend GNP
monetary base growth
unemployment rate
F-TESTS: UN
Panel A
F-STAT.
2.61
1.24
3.04
165.29
VARIABLE
S
GY
DB
UN
F-TESTS : S
SIGN. L V L .
.0116
.2807
.0038
.0000
F-STAT.
4.31
3.21
0.69
0.98
SIGN. LVL.
.0001
.0025
.6942
.4522
Entries are: F-statistic: values and significance levels of the
hypothesis that 8 lags of the variable can be excluded from the
unemployment and stock dispersion equations.
panel B
CORRELATION MATRIX OF RESIDUALS
VARIABLE
1.000
S
GY
DB
UN
Panel C
STEP
2
4
8
12
20
GYI
S
- 0.022
#
1.000
DB
UN
0.046
- 0.044
0.101
- 0.131
- 0.085
1.000
•
•
1.000
•
DECOMPOSITION OF VARIANCE: ORDERING 1
S
4.9
11.5
25.1
37.5
36.7
UN
GY
1.0
1.6
1.9
3.9
7.5
DB
2.0
3.3
8.2
7.5
7.6
UN
92.1
87.8
64.8
51.1
48.1
S
97.5
92.9
84.6
80.9
75.2
S
GY
1.2
4.9
10.3
13.9
16.6
DB
0.8
1.7
3.0
3.9
6.7
UN
0.5
0.5
0.9
1.3
1.5
S
DB
UN
S
DECOMPOSITION OF VARIANCE: ORDERING 2
STEP
2
4
8
12
20
GY
1.0
1.8
2.2
4.4
8.1
UN
DB
1.7
2.8
7.0
6.2
6.4
UN
95.4
88.9
71.0
56.7
53.6
S
1.8
6.4
19.8
32.7
31.9
GY
1.5
5.0
10.4
14.0
16.5
0.7
1.7
2.7
3.8
6.2
0.9
1.0
1.2
1.7
1.8
97.0
92.3
85.6
80.6
75.5
Entries show percentage of forecast variance of unemployment and
stock market dispersion at different horizons attributable to
innovations in the variables of the system. Ordering is as shown.
42
Table 5
VAR System: S SI 6 DSP DB UN
Sample Period: 1951:2 to 1987:4
Key: S
SIG
DSP
DB
UN
Panel A
VARIABLE
s
SIG
DSP
DB
UN
=
=
=
=
=
stock market dispersion index
employment dispersion index
growth rate of S&P 500
monetary base growth
unemployment rate
P-TESTS: UN
F-STATISTIC
1 .96
4. 96
4. 05
2 .58
496. 57
SIGNIF. LEVEL
.0576
.0010
.0041
.0408
.0000
Entries are F-statistic values and significance levels of
hypothesis that 4 lags of the variable (8 in the! case of ;
be excluded from the unemployment equations.
Panel B
DECOMPOSITION OF VARIANCE: ORDERING 1 (SIG DSP1 DB UN S)
STEP
S
SIG
2
4
8
12
20
1.7
4.4
11.4
27.1
33.8
12.0
5.3
5.7
6.2
5.6
DSP
DB
4.0
25.2
32.2
28.8
27.5
0.8
1.6
5.5
4.6
4.4
UN
81.5
69.9
45.2
33.3
28.6
DECOMPOSITION OF VARIANCE: ORDERING 2 (S DSP DB UN SIG)
STEP
S
SIG
DSP
DB
2
4
8
12
20
2.6
6.1
14.3
31.1
39.0
0.4
1.1
1.5
1.9
2.0
2.5
15.6
26.1
21.8
20.2
2.4
3.3
9.8
9.6
8.4
UN
92.2
73.8
48.3
35.5
30.4
Entries show percentage of forecast variance of unemployment at
different horizons attributable to innovations in the variables
of the system. Ordering is as shown in parenthesis (...).
43
Table 6: UNCONSTRAINED REGRESSIONS WITH STOCK PRICE GROWTH
OUTPUT
INVESTMENT
STANDARD
COEFFICIENT ERROR
.23
.45**
C
STANDARD
COEFFICIENT ERROR
.17
.74***
C
STANDARD
COEFFICIENTERROR
C
■ •07***
1
.26
.13
.13
.13
.13
.13
.12
.12
.11
.11
DB
DB1
DB2
DB3
DB4
DB5
DB 6
DB7
DB 8
-.11
1 .22***
-.70**
.12
.36
-.24
-.02
1 .01***
-.16
.31
.31
.33
.31
.31
.29
.29
.28
.26
DB
2 .20**
DB1
-.98
DB2
1.96**
DB3 -•2.49***
DB4
-.44
DB5
.30
DB 6
.65
DB7 -•3.53***
DB 8
.42
.25
I
1.48***
.51
.004
DSP
DSP1 .032***
DSP2 .034***
DSP3 -.002
DSP4 .007
DSP5 -.007
DSP6 -.001
DSP7 -.012
DSP8 -.004
.011
.012
.012
.012
.012
.012
.012
.011
.012
DSP
-.019
DSP1
.035*
DSP2
.087***
.037
DSP3
DSP4
.023
DSP5
.033
.028
DSP6
DSP7
.032
DSP8
.018
- 6.31*** 1.61
.02
.07
DSP
DSP1 — .22 * * * .07
DSP2 -.18**
.08
DSP3 -.19**
.08
DSP4 -.09
.08
DSP5 -.11
.08
DSP6 -.03
.08
DSP7 -.10
.08
DSP8 -.08
.08
I
.050
.041
I
S
SI
S2
S3
S4
S5
S6
S7
S8
.006
-.060
-.093**
.015
-.063
.028
-.090**
.003
-.068*
.037
.042
.043
.046
.045
.045
.044
.043
.037
S
SI
S2
S3
S4
S5
S6
SI
S8
.208**
-.256**
.062
-.097
.079
-.056
-.073
-.040
-.166
I
-.323***
.094
I
-.340
LF
LF1
T
.036
-.061**
.0004*
.026
.026
.0002
LF
LF1
T
-.044
.058
-.001
.056
.0009***
.0003
DB
.01
.22*
DB1
DB2 -.12
.10
DB3
DB4 -.07
DB5
.11
.03
DB6
DB7
.06
.07
DB8
.39
I
R2 9995 SE=.0087
:
=.
LI1
LI2
1 .22***
-.32***
I
- 1 .00***
.30
.089
.101
.107
.107
.108
.107
.106
.101
.089
S
SI
S2
S3
S4
S5
S6
SI
S8
-.08
.41
-.19
.14
-.33
.34
-.17
.53**
.15
.24
.26
.28
.28
.28
.29
.29
.27
.24
.233
I
.59
.098
.09
.09
R‘ 9980SE= .0209
!
=.
GY
GY1
•
.09
.08
E
o
1.08***
-.12
.84
.86
.84
.85
.82
.77
.79
.75
.69
0
0
LY1
LY2
.275***
.027
.027
.028
.029
.029
.028
.029
.028
.029
UNEMPLOYMENT
-1.62
2.09*
DEMO
.042**
DEMOl -.034*
UNI
1.36***
UN 2
-.50***
1.13
1.16
.019
.019
.08
.08
R 2 .9755SE =.0555
:
=
*** indicates significance at the 1% level; ** at the 5% level;
and * at the 10% level.
4 4
Table 7: CONSTRAINED REGRESSIONS WITH STOCK PRICE GROWTH
INVESTMENT
STANDARD
COEFFICIENT ERROR
.63***
STANDARD
COEFFICIENTERROR
-1.23***
.27
C
CM
CM
•
OUTPUT
STANDARD
COEFFICIENT ERROR
.92*** .17
C
DB
DB1
DB2
DB3
DB4
DB5
DB6
DB7
DB8
.07
.04
.03
.03
.03
.05*
.08**
.2
1*
.08
.05
.03
.03
.04
.03
.03
.03
.06
DB
DB1
DB2
DB3
DB4
DB5
DB 6
DB7
DB 8
I
.55**
.24
X
.018**
DSP
DSP1 .016**
DSP2 .014**
DSP3 .011*
DSP4 .007
DSP5 .003
DSP6 - . 0 0 2
DSP7 -.007
DSP8 -.013
.009
.006
.005
.006
.006
.005
.005
.006
.009
DSP
DSPl
DSP2
DSP3
DSP4
DSP5
DSP6
DSP7
DSP8
X
.041
c
.1 0
.046
.48***
.29**
.16**
.07
.04
.07
.15**
2 9* * *
.48***
.20
.11
.07
.08
.09
.08
.07
.09
.16
DB
DB1
DB2
DB3
DB4
DB5
DB6
DB7
DB 8
-1.79***
-1.16***
-.71***
-.43**
-.32
-.38
-.61***
-1 .01***
-1.59***
2.05***
.53
X
- 8 .00*** 1.67
.002
.022*
.038***
.048***
.053***
.053***
.047***
.036**
.020
.022
.015
.013
.014
.015
.015
.014
.017
.024
DSP
DSPl
DSP2
DSP3
DSP4
DSP5
DSP6
DSP7
DSP8
X
.32***
.10
-.017
-.006
-.002
-.006
-.019
-.039
-.067**
-.102***
-.146***
.062
.042
.036
.038
.039
.036
.032
.037
.057
X
s
.242
S
SI
S2
S3
S4
S5
S6
S7
S8
-.041*
-.036**
-.033**
-.031**
-.032**
-.033**
-.037***
-.042***
-.049**
.024
.016
.014
.014
.015
.014
.013
.014
. 0 2 2
S
SI
S2
S3
S4
S5
S6
S7
S8
X
-.333***
.093
X
-.403*
LF
LF1
T
.026
-.051**
.0005**
.023
.024
LF
LF1
T
-.120** .057
.057
.064
.0011***
.0003
LY1
LY2
1.06***
-.12*
R2 .9994
=
. 0 0 0 2
.08
.08
SE= .0088
UNEMPLOYMENT
LI1
LI2
1 .11***
-.24***
.08
.07
R2 .9973SE=.0226
=
.56
.33
.21
.21
.24
.23
.21
.25
.43
-.08
-. 12***
-.14***
-.16***
-.16***
-.15***
— .14***
-.11**
-.07
.06
.04
.04
.04
.04
.04
.04
.05
.07
- 1 .1 1 ***
.30
SI
S2
S3
S4
S5
S6
SI
S8
.21
.08
.01
-.04
-.03
.02
.11
.25**
^44***
.16
.10
.09
.10
.10
.09
.09
.10
.15
X
1.06**
.62
GY
GY1
-1.29
1.70
.034*
DEMO
DEMO1-.024
UNI
1.27***
UN 2
-.46***
1.13
1.16
.019
.019
.08
.07
R2 9674SE=. 0596
=.
*** indicates significance at the 1% level; ** at the 5% level
and * at the 10% level.
45
Table 8
VAR System: S DSP SIG IRS DB UN
Sample Period: 1951:2 to 1987:4
Key: S
DSP
SIG
IRS
DB
UN
Panel A
VARIABLE
S
DSP
SIG
IRS
DB
UN
=
=
=
=
=
=
stock market dispersion index
growth rate of S&P 500
employment dispersion index
interest rate spread
monetary base growth
unemployment rate
F-TESTS: UN
F-STATISTIC
2.00
2.10
5.52
3.14
3.35
532.60
SIGNIF. LEVEL
.0520
.0838
.0004
.0171
.0122
.0000
Entries are F-statistic values and significance levels of the
hypothesis that 4 lags of the variable (8 lags in the case of S)
can be excluded from the unemployment equations.
Panel B
DECOMPOSITION OF VARIANCE: ORDERING 1 (DSP SIG IRS DB UN S)
STEP
2
4
8
12
20
S
1.2
2.7
8.9
20.3
24.6
DSP
3.6
18.1
29.6
26.8
26.2
SIG
10.7
4.6
5.4
5.6
4.9
IRS
5.9
12.9
12.9
14.5
16.1
DB
0.7
1.8
5.8
14.5
16.1
UN
77.9
59.9
37.4
27.9
23.5
DECOMPOSITION OF VARIANCE: ORDERING 2 (S DSP IRS DB UN SIG)
STEP
2
4
8
12
20
S
2.0
4.9
12.8
27.0
33.1
DSP
3.1
16.4
26.1
22.5
21.5
SIG
0.4
0.7
1.2
1.8
1.9
IRS
4.5
10.7
9.7
9.6
10.4
DB
1.6
2.8
9.0
8.9
7.6
UN
88.3
64.4
41.1
30.3
25.5
Entries show percentage of forecast variance of unemployment at
different horizons attributable to innovations in the variables
of the system. Ordering is as shown in parenthesis (...).
46
Table 9
Panel A: Correlation matrix of alternate Spargsd measures
Spurged
(4 lags)
C
‘
-'purged
(8 lags)
c
‘
-'purged
0.936*
(no lags)
0.902*
C
‘
-'purged
(4 lags)
••
0.972*
)
Panel B : Correlation matrix of rlt's and T lt's
Entr.
Auto.
.47*
.01
Copp.
Alum.
Drug
.44*
-.01
.53*
.13
.21*
-.41*
.44*
-.09
.30*
-.07
.57*
.11
.40*
.05
.33*
-.11
.47*
.02
.39*
.07
.63*
.30*
.63*
.40*
.42*
.09
.52*
.25*
.23*
-.03
.47*
.11
.37*
-.06
.42*
.09
.24*
-.07
.43*
.00
.48*
.25*
.21*
-.11
.29*
-.20*
••
.26*
-.05
.47*
.05
Copp.
••
Alum.
••
•
#
•
•
••
••
•
•
•
Drug
•
•
•
•
•
•
•
•
•
•
•
•
•
Media
••
••
Coal
Coal
.43*
.12
Entr.
Oil
Oil
.36*
-.01
* denotes that the null hypothesis that the correlation is zero
can be rejected at a significance level of .01
Kev to abbreviated industry names:
A u t o .= Automobiles; Entr.= Entertainment; Copp. = Copper;
Alum.= Aluminum; Oil = Domestic Oil; Media = Broadcast Media
47
Table 10
VAR System: Spurgad DSP SIG IRS DB UN
Sample Period: 1951:2 to 1987:4
Key: Spurged = "purged" stock market dispersion index
DSP = growth rate of S&P 500
SIG = employment dispersion index
IRS = interest rate spread
= monetary base growth
DB
= unemployment rate
UN
F-TESTS: O
N
Panel A
VARIABLE
Spurged
DSP
SIG
IRS
DB
UN
F-STATISTIC
2.39
3.30
5.52
2.78
5.16
426.53
SIGNIF. LEVEL
.0207
.0137
.0004
.0307
.0007
.0000
Entries are F-statistic values and significance levels of the
hypothesis that 4 lags of the variable (8 lags in the case of S)
can be excluded from the unemployment equations.
Panel B
DECOMPOSITION OF VARIANCE: ORDERING 1 (DSP SIG IRS DB ON Sp r < l
ugK)
STEP
2
4
8
12
20
Spurqed
1.3
3.6
1 0 .6
20.5
2 2 .1
DSP
4.7
23.2
38.9
34.8
34.8
SIG
9.6
3.7
5.4
5.6
5.3
IRS
4.5
1 0 .0
8 .1
1 1 .0
1 2 .6
DB
3.1
5.3
9.5
7.7
6.9
UN
76.7
54.1
27.4
20.3
18.2
DECOMPOSITION OF VARIANCE: ORDERING 2 (SpU g < DSP IRS DB UN SIG)
rBj
STEP
Spurqed
2
2.4
4
6 .8
8
12
20
16.0
28.9
31.4
DSP
3.4
20.3
33.1
28.2
28.2
SIG
0.5
0.7
2.3
3.1
3.1
IRS
3.3
7.7
5.3
6.3
7.4
DB
5.1
7.2
13.7
11.9
UN
85.2
57.3
29.7
1 0 .6
19.3
2 1 .6
Entries show percentage of forecast variance of unemployment at
different horizons attributable to innovations in the variables
of the system. Ordering is as shown in parenthesis (...).
IMPULSE RESPONSE OF UN
-.0 7 5
48
APPENDIX I :
CONSTRUCTION OF THE D ISPE R SIO N INDEX
To assemble our measure of the dispersion of stock market
prices, we used 60 industrial indices compiled by Standard and
Poor's. The following listing, arranged by length of the data
series, gives the starting and, if relevant, ending dates as well
as the employment weight for each industry used:
INDUSTRY
OIL-COMPOSITE
MACHINERY (AGRICULTURAL)
AUTOMOBILES
COMPUTER SYSTEMS
ENTERTAINMENT
INVESTMENT COS (CLOSED END)
RETAIL STORES (DEPARTMENT STORES)
RETAIL STORES (FOOD CHAIN STORES)
COPPER
MACHINERY (CONSTRUCTION & MAT. HAND.)
OIL (CRUDE PRODUCERS)
BUILDING MATERIALS
COAL
DRUGS
FINANCIAL (PROPERTY-CASUALTY INSURANCE)
HOUSEHOLD PRODUCTS
MACHINERY (DIVERSIFIED)
MONEY CENTER BANKS
PAPER
RETAIL STORES (COMPOSITE)
SHOES
STEEL
TIRES AND RUBBER GOODS
TRANSPORTATION (RAILROADS)
MACHINE TOOLS
CHEMICALS
CONTAINERS (METAL & GLASS)
FOODS
HEAVY DUTY TRUCKS & PARTS
TEXTILE PRODUCTS
TRANSPORTATION (AIRLINES)
UTILITIES (ELECTRIC POWER COMPANIES)
ELECTRONIC MAJOR COMPANIES
AEROSPACE/DEFENSE
BEVERAGES (SOFT DRINKS)
TEXTILES (APPAREL MANUFACTURERS)
BEVERAGES (DISTILLERS)
FINANCIAL (PERSONAL LOAN)
BEVERAGE S (
BREWERS)
ALUMINUM
DOMESTIC OILS
INTERNATIONAL OILS
STAR1’ END
YEAR YEAR. .
. w,
.004614
1926 —
1926 1985 .007786
1928 —
.048679
1930 —
.026044
1930 —
.008573
.001387
1930 —
.044414
1930
1930 —
.023748
1930 1986 .009005
1930 1985 .007786
1930 1985 .004614
1932 —
.009658
1932 -- .000850
1932 —
.032236
1932 —
.000669
1932 —
.032236
1932 —
.007786
1932 —
.021462
1932 —
.012355
1932 —
.044414
1932 —
.002114
1932 —
.009005
1932 —
.019075
1932 —
.017221
1933
.007786
1934 —
.032236
1934 —
.011508
.014427
1934
1934 —
.048679
1934 —
.010624
1934 —
.013094
1934
.013124
1934 1986 .026044
1936 —
.048679
.014427
1936 —
1936 —
.010188
1936 1986 .014427
1939 -- .001387
1940
.014427
1941 —
.009005
1943 —
.031571
.031571
1943 —
—
—
—
—
—
49
OIL WELL & EQUIPMENT SERVICE
ELECTRICAL EQUIPMENT
GOLD MINING
HOUSEHOLD FURNISHINGS & APPLIANCES
MAJOR REGIONAL BANKS
METALS MISCELLANEOUS
NATURAL GAS PIPE LINES
PAPER CONTAINERS
NATURAL GAS DISTRIBUTORS
PUBLISHING
BROADCAST MEDIA
TRANSPORTATION (TRUCKERS)
FINANCIAL (SAVINGS & LOAN HOLD. COS.)
HOMEBUILDING
TRANSPORTATION (AIR FREIGHT)
ELECTRONICS (SEMICONDUCTORS)
COMPUTER SOFTWARE & SERVICES
HOSPITAL MANAGEMENT
The
1943 —
.007786
1945 —
.026044
1945
.004582
1945 —
.026044
—
1945
.021462
1945
.009005
1945
.013124
1945
.012355
1945 1984 .013124
1946
.011392
1947
.005688
1957 —
.011936
.001387
1959 —
1965 —
.001766
1965 —
.013094
1970 —
.026044
1978 —
.025904
1978
.020130
—
—
—
—
—
-
-
—
weights used to construct S were derived from the
Standard and Poor's Compustat II 1968-1987 Annual Aggregate
Industrial File computer data tape.
This tape lists, among other
data, annual employment for each industry.
The industries are
organized by four-digit codes similar to the SIC codes, though
the industry break-down is not exactly the same as in the
Standard and Poor's Security Price Index, from which the stock
data were obtained. However, the composition of these industries
were the same for two-digit industries. Thus, we needed to make
some approximations. We wanted weights based on data near the
center point of our sample period. Thus, we started by using the
four digit industries and calculated the industry's average
employment figure using data between 1968 to 1972. If all of
these years were missing data, we used the employment figure from
the year closest to 1972. These four-digit industry weights were
then grouped into the two-digit industry and the share of
employment accounted for by each two-digit industry was
calculated. Finally, to give our w1 this share was divided by
#
the number of our sixty industries that fell within each of the
two-digit categories. Thus, similar industries that fall within
the same two digit classification, eg FOOD and BEVERAGES, have
the same employment weight.
51
FOOTNOTES
1. Brainard and Cutler (1989) regress industry stock growth on mean
stock price growth and use the residuals from these regressions to
construct their stock market dispersion index. However, despite
this difference, the correlation between their index and ours is
high: 0.66 in levels and 0.74 in logs.
2. For a discussion of asymmetries in adjustment costs of quasifixed factors, see Nickell (1978), Leban and Lesourne (1980), Weiss
(1986) and Courtney (1989).
3. The losses are actually larger for occupational change than
industry change, which is consistent with the comments of
(1987) . See Loungani, Rogerson and Sonn for evidence on
contribution
of
occupational mobility
to total
weeks
unemployment.
for
Oi
the
of
4. Two points about our specification deserve mention. First,
since we include time trends in the output and investment
regressions, which is equivalent to detrending all the
independent variables, the specification of the government
spending variable is actually quite similar to that in the
unemployment regression. Second, the distinction between
permanent and temporary changes in spending is important in
theory [see Barro (1981 and 1988a), Denslow and Rush (1989) and
Aiyagari, Christiano and Eichenbaum (1990)] and empirical
applications that include major wars. However, our sample period
includes only the Korean and Vietnam wars; neither of these
seemed sufficiently important relative to total output to enable
us to distinguish between temporary and permanent government
spending.
5. Our choice of the base as the measure of money is motivated by
the possible endogeneity of broader monetary aggregates such as Ml.
See King and Plosser (1984) and Rush (1986) for a further
discussion of this issue. Studies that use quarterly data, starting
with Barro and Rush (1980) and up to the more recent Frydman and
Rappoport (1987), tend to find that all changes in the money
supply— not just unexpected changes— matter for real activity.
Hence we do not pursue a decomposition of base growth into expected
and unexpected components.
6. There is still a lot of dispute over whether macro aggregates
such as GNP are difference-stationary, as suggested by Nelson and
Plosser (1982), or trend stationary, as suggested in many other
studies such as Diebold and Rudebusch (1988). Faced with this
uncertainty, we opted for the traditional approach of assuming
52
trend stationarity.
7. For instance, we increased the number of lags for S and DB to
twelve and sixteen; increased the lags for government spending and
DEMO to four; and increased the lags for the dependent variable to
three and four. Individually and jointly the added lags rarely
attained standard levels of significance.
8. For alternate views of the investment process that stress the
role of cash flow variables, see Fazzari, Hubbard and Petersen
(1988) .
9. In addition to the studies cited in the main text of the paper,
the role of interest rate spreads is investigated in Laurent (1988,
1989), Estrella and Hardouvelis (1989) and Strongin (1990).1
0
10. It is quite likely that variables such as DSP are responding to
events such as oil price shocks, which are not pure aggregate
demand shocks. In fact, Davis (1985), Loungani (1986), Hamilton
(1988) and Kowalczyk and Loungani (1990) provide theoretical and
empirical evidence on the impact of oil price shocks on the
sectoral reallocation of resources. However, in order to be as fair
as possible to the Abraham-Katz view, we prefer to "over-control"
for the effects of aggregate demand on sectoral stock returns by
treating all movements in DSP as being "aggregate-demand-driven."
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Taxation of Public Utilities Sales: State Practices
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WP-86-1
D ia n e F . S ieg e l a n d W illia m A . T esta
Measuring Regional High Tech Activity with Occupational Data
WP-87-1
A len k a S . G iese a n d W illia m A . T esta
Alternative Approaches to Analysis of Total Factor Productivity
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WP-87-2
R o b e rt H. S ch norbu s a n d P h ilip R . Isra ilev ic h
Industrial R&D An Analysis of the Chicago Area
WP-87-3
A len ka S. G iese a n d W illia m A . T esta
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WP-89-1
W illiam A . T esta
Unemployment Insurance: A State Economic Development Perspective
WP-89-2
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WP-89-3
A len ka S. G iese
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P h ilip R . Isra ile v ic h a n d W illia m A. T esta
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WP-89-5
A len ka S .G ie se
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A New Approach to Regional Capital Stock Estimation:
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Alenka S. Giese and Robert H. Schnorbus
WP-89-6
Why has Illinois Manufacturing Fallen Behind the Region?
William A. Testa
WP-89-7
Regional Specialization and Technology in Manufacturing
Alenka S. Giese and William A. Testa
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Christopher Erceg, Philip R. Israilevich and Robert H. Schnorbus
WP-89-9
Regional Energy Costs and Business Siting Decisions:
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David R. Allardice and William A. Testa
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William A. Testa
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Philip R. Israilevich
WP-89-10
WP-89-12
WP-90-1
Regional Regulatory Effects on Bank Efficiency
Douglas D. Evanoffand Philip R. Israilevich
WP-90-4
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Geoffrey JD. Hewings
WP-90-5
Institutional Rigidities as Barriers to Regional Growth:
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Michael Kendix
WP-90-6
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Douglas D. Evanoff, Philip R. Israilevich and Randall C. Merris
WP-89-11
2
Working paper series continued
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W P-89-12
D o u g la s D . E v a n o ff
Are Some Banks too Large to Fail? Myth and Reality
WP-89-14
G eo rg e G . K au fm an
Variability and Stationarity of Term Premia
W P-89-16
R am o n P . D e G en n aro a n d J a m e s T. M o se r
A Model of Borrowing and Lending with Fixed and Variable Interest Rates
WP-89-17
T hom as M o n d sch ea n
Do ‘'Vulnerable" Economies Need Deposit Insurance?: Lessons from the
U.S. Agricultural Boom and Bust of the 1920s
W P-89-18
C h a rle s W. C a lo m iris
The Savings and Loan Rescue of 1989: Causes and Perspective
WP-89-23
G eo rg e G. K au fm an
The Impact of Deposit Insurance on S&L Shareholders' Risk/Retum Trade-offs
WP-89-24
E lijah B r e w e r III
Payments System Risk Issues on a Global Economy
W P-90-12
H e rb e rt L . B a e r a n d D o u g la s D . E v a n o ff
MACRO ECONOMIC ISSUES
Back of the G-7 Pack: Public Investment and Productivity
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WP-89-13
D a v id A . A sch a u e r
Monetary and Non-Monetary Sources of Inflation: An Error
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W P-89-15
K en n eth N . K u ttn er
Trade Policy and Union Wage Dynamics
WP-89-19
E llen R . R issm a n
3
Working paper series continued
Investment Cyclicality in Manufacturing Industries
W P-89-20
B ru ce C. P e te rse n a n d W illia m A . S tra u ss
Labor Mobility, Unemployment and Sectoral Shifts:
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WP-89-22
P ra k a sh L ou n gan i, R ic h a rd R o g e rso n a n d Y ang-H oon Sonn
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WP-90-2
L a w re n c e J . C h ristia n o a n d M a rtin E ich en bau m
Money Supply Announcements and the Market's Perception
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WP-90-3
S teven S tron gin a n d V efa T arhan
Sectoral Shifts in Interwar Britain
W P-90-7
P ra k a sh L o u n g a n i a n d M a rk R u sh
Money, Output, and Inflation: Testing the P-Star Restrictions
WP-90-8
K en n eth N . K u ttn e r
Current Real Business Cycle Theories and Aggregate Labor
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L a w re n c e J. C h ristia n o a n d M a rtin E ich en bau m
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WP-90-10
S. R a o A iy a g a ri, L a w re n c e J. C h ristia n o a n d M a rtin E ich en bau m
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WP-90-11
B en ja m in M . F ried m a n a n d K en n eth N . K u ttn er
Real Business Cycle Theory: Wisdom or Whimsy?
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M a rtin E ich en bau m
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WP-90-14
S teven S tro n g in
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Working paper series continued
Stock Market Dispersion and Real Economic Activity:
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P ra k a sh L o u n g a n i , M a rk R u sh a n d W illiam T ave
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R o b e r t D . L a u ren t
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WP-90-16
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George J. Benston and George G. Kaufman
SM-86-1
The Equilibrium Approach to Fiscal Policy
David Alan Aschauer
SM-86-2
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SM-86-3
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Elijah Brewer, III and Cheng Few Lee
SM-86-4
Wage Growth and Sectoral Shifts: New Evidence on the
Stability o f the Phillips Curve
Ellen R.Rissman
SM-87-1
Testing Stock-Adjustment Specifications and
Other Restrictions on Money Demand Equations
Randall C. Merris
SM-87-2
The Truth About Bank Runs
SM-87-3
G eo rg e G . K a u fm a n
On The Relationship Between Standby Letters of Credit and Bank Capital
Gary D. Koppenhaver and Roger Stover
Alternative Instruments for Hedging Inflation Risk in the
Banking Industry
Gary D. Koppenhaver and Cheng F. Lee
SM-87-4
SM-87-5
The Effects of Regulation on Bank Participation in the Market
Gary D. Koppenhaver
SM-87-6
Bank Stock Valuation: Does Maturity Gap Matter?
Vefa Tarhan
SM-87-7
6
Staff Memoranda continued
Finite Horizons, Intertemporal Substitution and Fiscal Policy
SM-87-8
D a v id A la n A sch a u e r
Reevaluation of the Structure-Conduct-Performance
Paradigm in Banking
SM-87-9
D o u g la s D . E v a n o ff a n d D ia n a L . F o rtie r
Net Private Investment and Public Expenditure in the
United States 1953-1984
SM-87-10
D a v id A la n A sch a u e r
Risk and Solvency Regulation of Depository Institutions:
Past Policies and Current Options
SM-88-1
G e o rg e J. B en sto n a n d G e o rg e G . K au fm an
Public Spending and the Return to Capital
SM-88-2
D a v id A sch a u e r
Is Government Spending Stimulative?
SM-88-3
D a v id A sch a u e r
Securities Activities of Commercial Banks: The Current
Economic and Legal Environment
SM-88-4
G eo rg e G. K au fm an a n d L a r r y R. M o te
A Note on the Relationship Between Bank Holding Company
Risks and Nonbank Activity
SM-88-5
E lija h B r e w e r , III
Duration Models: A Taxonomy
SM-88-6
G. O . B ie rw a g , G eo rg e G. K au fm an a n d C ynthia M . L a tta
Durations of Nondefault-Free Securities
G . 0 . B ie rw a g a n d G e o rg e G. K au fm an
Is Public Expenditure Productive?
D a v id A sch a u e r
SM-88-7
Staff Memoranda continued
Commercial Bank Capacity to Pay Interest on Demand Deposits:
Evidence from Large Weekly Reporting Banks
SM-88-8
E lija h B rew e r, III a n d T h om as H . M o n d sch ea n
Imperfect Information and the Permanent Income Hypothesis
SM-88-9
A b h ijit V. B a n e rje e a n d K en n eth N . K u ttn er
Does Public Capital Crowd out Private Capital?
SM-88-10
D a v id A sch a u e r
Imports, Trade Policy, and Union Wage Dynamics
SM-88-11
E llen R issm a n
8
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