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orKing raper series
Inventories and output volatility
Paula R. W orthing to n
Working Papers Series
Research Department
Federal Reserve Bank of Chicago
Decem ber 1998 (W P -98-21)
FEDERAL RESERVE B A N K
O F CHICAGO
Inventories and output volatility
Paula R. Worthington
Economic Research Department
Federal Reserve Bank o f Chicago
230 South LaSalle
Chicago, Illinois 60604
(312) 322-5802
prw@fihchi.org
This draft: December 1998
Abstract: Analyzing disaggregate data on inventories and sales from the U. S. manufacturing and
trade sector between 1960 and 1997 yields four main findings. First, I find that IS ratios are
somewhat lower after 1984:1 among durable goods manufacturers and durable goods retailers
outside the motor vehicle industry. Second, I find that industries which have lowered their IS
ratios tend to be those in which the variance o f output relative to sales has declined. Third, by
decomposing the variance o f output into its components, I find that the variance o f sales is less
important, and the variance o f inventory investment is more important, after 1984:1 than in earlier
years for the overall manufacturing and trade sector. Finally, the evidence suggests that industries
where IS ratios fell are those where inventory investment volatility played a smaller role in output
volatility in the later period.
The author would like to thank Thomas Klier, Helen Koshy, David Marshall, Dan Sullivan, and seminar participants at
the Federal Reserve Bank of Chicago and the October 1998 meetings of the Illinois Economic Association for valuable
discussions and comments and Kenneth Housinger for excellent research assistance. The views expressed in this paper
are strictly those of the author, and they do not necessarily represent the position of the Federal Reserve Bank of Chicago
or the Federal Reserve System
Introduction
Are business cycles less pronounced now than in earlier years? Several studies offer
evidence o f decreased aggregate volatility in recent years, and business analysts, too, often claim
that future business cycles are likely to feature shorter, less pronounced contractions than earlier
cycles displayed. For example, McConnell and Quiros (1997) present informal evidence that
post-war GDP volatility declined in the early 1980s, and they specifically find evidence o f a one
time decline in the volatility o f post-war GDP in 1984:1. Suggested reasons for such changes in
output volatility or cyclicality are many and varied, but one item on nearly every "short list" o f
factors is the widespread embrace o f just-in-time inventory management techniques by U.S. firms.
For example, the E conom ist (1998) writes:
What is dear, however, is that the economic cycle has become less bumpy than it used to
be...There are several possible explanations for the taming o f the business
cycle....[including] better inventory control through just-in-time techniques and the use o f
computers.
Similarly, McConnell and Quiros (1997) point to a decline in the share o f inventory investment in
GDP fluctuations as a possible source o f the output volatility decline.
In this paper, I investigate the relationship between inventory holdings and output
volatility at the industry level. I use detailed data from the U.S. manufacturing and trade (M&T)
sector from 1960 to 1997, and I relate inventory-sales ratios to several measures o f output and
inventory volatility. I focus on output volatility because swings in business inventory
accumulation have historically accounted for large fractions o f GDP volatility. In particular, I
take as given the breakpoint identified by McConnell and Quiros (1997) and compare inventory
behavior before and after 1983:4. In briefj I find that IS ratios are somewhat lower in the later
period among durable goods manufacturers and durable goods retailers outside the motor vehicle
industry. I also find that in the manufacturing and retail sectors, the industries which have
lowered their IS ratios tend to be the ones whose output variance (relative to the variance o f
sales) has declined. Third, by decomposing the variance o f output into its components, I find that
the variance o f sales is less important, and the variance o f inventory investment is more important,
in the later period than in the earlier period for the overall manufacturing and trade sector. One
prominent exception in the manufacturing sector is the motor vehicles industry, where inventory
investment variance declined. In retailing, the contribution o f inventory investment variance
nearly trebled, rising from 20.7% to 59.3% between the earlier and later periods, suggesting an
increased role for inventory investment fluctuations in that sector. Overall, the evidence suggests
that industries where IS ratios fell are those where inventory investment volatility played a smaller
role in output volatility in the later period.
Facts and theories about inventories
Economists care about inventory behavior because, historically, swings in inventory
investment have played a prominent role in cyclical fluctuations. In brie£ inventory investment is
highly volatile and contributes significantly to recessionary declines in GDP, and inventory-sales
(IS) ratios are strongly countercyclical, rising during recessions and falling in expansions. In fact,
inventory disinvestment is a central part o f cyclical contractions. Table 1 reports the average
post-war contribution o f changes in inventory accumulation ("inventory disinvestment") to the
peak-to-trough decline in GDP during contractions. The table shows that the decline in inventory
investment accounted for 76 percent o f GDP's decline in the average post-war recession. The
table reveals three features o f the data. First, the manufacturing and retail sectors dominate the
wholesale trade sector, accounting for most o f the inventory effect, with retailers accounting for
2
about one- third (,26/.76) o f the total contribution.1 Second, firms in the durable goods sectors
account for most o f the impact. Third, during the two most recent recessions, the role o f durable
goods manufacturers was quite muted, as their inventory disinvestment during those episodes
accounted for a below-average 11% o f the total contraction in GDP. In contrast, the retail sector
was o f little consequence in the 1981-1982 recession, but key in the 1990-1991 recession. On
balance, table l's evidence suggests that durables goods inventories held by manufacturers and
retailers are key to any analysis o f the cyclical behavior o f inventories.
The tw o major competing models o f inventory behavior, production smoothing models
and S,s threshold-type models, offer competing predictions about tw o key aspects o f inventories,
namely the variance o f output relative to sales and the correlation between sales and inventory
investment; see Fitzgerald (1997) or Homstein (1998) for useful discussions.2 In production
smoothing models, output is predicted to be less (more) variable than sales, when shocks are
solely on the demand (cost) side. Such models also typically predict a negative covariance
between sales and inventory investment. In contrast, generalized S,s models do not offer
predictions on these points (Homstein (1998)), though with specific assumptions about
aggregation and other model features, such models do offer specific predictions. For example,
McCarthy and Zakrajsek (1997) develop an S,s model in which output is predicted to be more
^Although not shown in the table, the shares for the three categories o f inventories held by
manufacturers, namely materials, goods in process, and finished goods, confirm Blinder and
Maccini's (1991) finding that finished goods inventories account for little o f the total contribution
(6% post-war average) despite being the focus o f much economic research. In contrast, goods in
progress and, to a lesser extent, materials and supplies held by manufacturers are more important.
^ o r retailers and wholesalers, the terms "production" and "output" are taken to mean
deliveries o f goods from their suppliers. In this paper, I will use these terms interchangeably to
denote output in the manufacturing sector and deliveries in the trade sector.
3
variable than sales and in which sales and inventory investment should be uncorrelated.
Data
The data used inthis paper are quarterly inventories and sales (shipments) data, in chained
1992 billions ofdollars, from the U.S. Department ofCommerce, for the manufacturing and trade
sectors. Manufacturing includes 21 separate industries (essentially2-digit SIC industries), the
merchant wholesale sector includes 19 industries, and the retailtrade sector includes 13 sectors.3
I construct output as the sum of shipments and inventory investment, and the inventory-sales ratio
is denominated in months. Because itisthe business cycle aspects ofinventory investment which
are of most interest, the data are detrended using the Hodrick-Prescott filter.4
Have inventory-sales ratios fallen?
Table 2 reports inventory-sales (IS) ratios by sector over several alternative time periods,
with period means reported in columns 1 through 3 and cyclical highs reported in columns 4
through 6.s Turning firstto the means, we find that IS ratios are higher in durable goods
industries than in nondurable goods industries and that ratios are highest among durable goods
manufacturers. Further, comparing the earlyperiod (1960:1-1983:4) to the later period (1984:11997:4), we see that IS ratios have not fallen overall; in fact, they have risen. Only among durable
goods manufacturers, on average, did IS ratios fall,with the greatest declines occurring in SIC
3Data on the detailed sectors isavailable in manufacturing and retail from 1959 onwards;
detailed wholesale sectors have data only from 1967 forward. Unless otherwise noted, the paper's
calculations will use the 1960-1997 period, thus will include wholesale trade only at the level of
durable and nondurable goods.
4See Hodrick and Prescott (1997), and also see Homstein (1998) for a more general
discussion of detrending, extracting the appropriate frequencies from the data, and so on.
5The ratios are constructed from the data prior to detrending.
4
industries 35 (industrial and commercial machinery and computer equipment), 371 (motor
vehicles and equipment), and 38 (instruments). In the retail sector, motor vehicle IS ratios rose
from 1.55 to 1.85 months, while allother durable goods retailers saw IS ratios fall.
Columns 4 through 6 offer another perspective on whether IS ratios have dropped in
recent years. The table shows that the overall M & T IS ratio peaked at about the same point in
each ofthe three recessions reported inthe table: 1.48 in the 1973-1975 recession, and 1.53 in
the 1981-1982 and 1990-1991 recessions. From that perspective, littlehas changed. However,
durable goods firms saw the cyclical highs fallin the last recession relative to the preceding one,
especially inthe manufacturing sector. Furthermore, the three broadest sectors exhibit different
patterns: in manufacturing, the cyclical maximum fell; in wholesale, itwas basicallyunchanged;
and in retail, itrose. Again, one interesting aspect ishow motor vehicle-related inventories
behaved: in manufacturing (SIC 371), the cyclical high fellfrom 1.06 to 0.86, comparing the
1981-1982 and 1990-1991 recessions, while inthe retailtrade sector, motor vehicle inventories
reached cyclical highs of 1.92 in 1981-82 and 2.08 in 1990-1991.
On balance, then, the evidence points to declining IS ratios among durable goods
manufacturers and durable goods retailers excluding motor vehicles. Outside ofthese groups, IS
ratios were at best flat, at worst up somewhat. This compares to earlierwork by Ben Salem and
Jacques (1996) and Hirsch (1996), who find that inventory-sales (IS) ratios have declined in the
manufacturing sector, but that ratios have risen in the wholesale and retailtrade sectors.
The variance of output relative to sales
In this section ofthe paper, I examine the variance of output relative to the variance of
sales, and I relate this relative output variance to IS ratios. In brief I find some evidence that
5
industries with high IS ratios are those whose output variance isrelativelyhigh. I also find that in
the manufacturing and retail sectors, the industries which have lowered their IS ratios tend to be
the ones whose relative output variance has declined; the opposite seems to be true in the
wholesale sector. This establishes, in an unstructured way, a connection between lower IS ratios
and decreased output volatility, at leastin the manufacturing and retail sectors.
Table 3 reports the ratio ofthe variance of output to the variance of sales for the broad
sectors studied here. The ratio exceeds 1 in allcases, as output ismore volatile than sales. This is
especially true inthe durable goods sectors. Comparing the early and later periods, I find that
output volatilityrelative to sales has risen in allcases.6 However, the disaggregate data indicate
that eight ofthe 21 manufacturing industries experienced declines, most notably several durable
goods industries, including SICs 32 (stone, clay and glass), 35 (industrial machinery and computer
equipment), 36 (electronic equipment), 371 (motor vehicles), and 37-exduding 371 (all other
transportation equipment). In the retail sector, although only one disaggregate industiy (other
durable goods retailers) showed a decline in relative output variability, the overall retail sector
excluding motor vehicles experienced a decline from 3.12 to 2.05. This highlights the importance
of retail motor vehicles, inwhich output variabilityrose from 2.74 to 4.16. Note the overlap
between the sectors where IS ratios have declined and sectors where output volatilityhas
declined: for example, SIC 35 (industrial machinery) had itsmean IS ratios fellfrom 3.32 to 2.14,
while SIC 371 (motor vehicles) had itsIS ratio fallfrom 0.98 to 0.63; both industries experienced
declines in output volatility. In the retail sector, motor vehicle IS ratios rose from 1.55 to 1.85 as
6As we shall see intable 5 below, the variance of output has declined in absolute terms;
table 3's relative variance measure has risenbecause the variance of sales has declined even
further.
6
output volatilityrose considerably. For motor vehicles, itappears that IS ratios and output
volatilityrelative to sales volatilityhave fallenin the manufacturing sector but risen in the retail
trade sector.
Table 4 relatesthe relativevariance of output to IS ratios in a more formal way. For each
detailed sector, I compute the relativevariance of output, firstover the fullsample period and
then separately for the early and laterperiods. I also compute the mean and maximum IS ratio for
those time periods. Table 4'stop panel reports the cross-sectional correlation coefficients
between the IS ratio and the relative variance measure, for allindustries together as well as
separately for the three broad sectors. In manufacturing and retail, the correlation islarge and
positive, suggesting that high IS ratios are associated with high output volatility; the correlation is
weaker in the wholesale sector. The bottom panel addresses the issue ofwhether those sectors
that lowered their IS ratios are those whose output volatility declined. For each industry, I
calculate the ratio ofthe early to laterperiod output volatility, the ratio ofthe early to lata*mean
IS ratios, and the ratio ofthe 1981-1982 cyclical high to the 1990-1991 cyclical high IS ratio.
The table'sbottom panel reports the cross-sectional correlation between the early to later output
volatilityratio and the earlyto laterperiod IS ratio. Again, manufacturing and retail show
positive correlations, suggesting that industries whose IS ratios fellare indeed those industries
whose output volatility (relativeto sales) declined. The wholesale sector issomewhat different,
showing a negative correlation.
Covariance of sales and inventory investment
Table 5 contains the covariances between sales (S) and inventory investment (CB1) for the
broad sectors studied here, again for the fullperiod as well as for the early and later periods. In
7
allcases, the covariance ispositive over the foil sample period, and the covariance declines
between the early and laterperiods. In retail, the covariance actually becomes slightlynegative in
the laterperiod, implying that inventory investment declines when sales are rising.
Because we are ultimately interested in output volatility, decomposing the variance of
output into itscomponents isuseful. Since output isthe sum of sales and inventory investment,
the variance ofoutput equals the sum ofthe variance of sales, the variance of inventory
investment, and twice the covariance between sales and inventory investment. Table 6 reports, in
levels and in percent terms, the components for the overall manufacturing and trade sector for the
different time periods studied.
Several patterns emerge from the table. First, the variance ofoutput has declined; this is
true for the disaggregate industries in the manufacturing and wholesale trade sectors, as well as
for the retail sector excluding motor vehicles. Much ofthe decline isdue to a decline in the
variance of sales, which occurred in allbut eight ofthe 34 manufacturing and retail industries. In
percentage terms, the variance of sales isless important in the laterperiod, accounting for 50.9%
of total output variance, compared to 66.2% of the total during the earlierperiod. Second, the
variance ofinventory investment has risen for the M & T sector overall, as well as for the
manufacturing, wholesale trade, and retailtrade sectors independently. However, nine of 21
manufacturing industries show a decline inthe variance of inventory investment, though as a share
oftotal output variance, inventory investment variance has risen in nearly allindustries. The one
prominent exception is, again, SIC 371, the motor vehicles industry, where the share oftotal
output variance accounted for by inventory investment variance fellfrom 7.8% to 3.5% between
the earlier and laterperiods. In retailing, the variance of inventory investment rose in allindustries
8
but one (lumber stores), and as a percentage of total output variance, the contribution of
inventory investment variance rose from 20.7% to a whopping 59.3% between the earlier and
laterperiods. In that sense, we can say that inventory investment volatility has become a more
pronounced factor in the retail sector. Furthermore, this increase isnot solely due to motor
vehicles; itisprominent throughout the sector.
In fact, computing correlations between IS ratios and the shares of inventory investment
variance in total output variance, similarto the exercise in table 4, shows that inthe
manufacturing and retail sectors, the industrieswhere IS ratios fellthe most are those in which
inventory investment variance accounted for smaller shares oftotal output variance; the
correlation isespecially strong in the retail sector. In the wholesale trade sector, the correlation is
negative.
Discussion and conclusions
In thispaper, I use detailed manufacturing and trade sector data to examine several
measures ofinventory behavior before and after 1984:1, a point identified by previous researchers
as the time of a one-time decline in G D P volatility. Because movements inwholesale trade
inventory investment are, on average, less important in business cycle fluctuations than are
movements in manufacturing and retailinventory accumulation, I emphasize the key results from
the lattertwo sectors.
First, I find some evidence that IS ratios were lower after 1984:1 than inthe earlier period
among durable goods manufacturers and durable goods retailers excluding motor vehicles; outside
ofthese groups, IS ratios were at best flat, at worst up somewhat. Second, output ismore
variable than sales in allindustries over each time period examined, by and large consistent with
9
previous research. Comparing the early and later periods, I find that output volatilityrelative to
sales has risen overall, but that several durable goods manufacturing industries show declines,
noticeably several whose IS ratios have declined over time. In fact, simple correlations show that
in the manufacturing and retail sectors, the industries which have lowered their IS ratios tend to
be the ones whose relative output variance has declined; the opposite seems to be true in the
wholesale sector.
Third, I decompose the variance of output into itscomponents and find that much ofthe
decline in output variability after 1984:1 isdue to declines in salesvariability, which, in percentage
terms, isless important in the later period. The share oftotal output variance accounted for by
inventory investment variance has risen for the M & T sector overall. One prominent exception in
the manufacturing sector isthe motor vehicles industry, where inventory investment variance
became less important inthe later period. In retailing, the contribution ofinventory investment
variance nearly trebled, rising from 20.7% to 59.3% between the earlier and later periods,
suggesting an increased role for inventory investment fluctuations in that sector. More formally,
correlations between IS ratios and the shares accounted for by inventory investment variance are
positive inthe manufacturing and retail sector, suggesting that industries where IS ratios fellare
those where inventory investment volatility played a smaller role in output volatilityin the later
period.
Finally, the motor vehicle industry stands out as sector worth further study. In the
manufacturing sector, motor vehicle IS ratios fell, output volatility fell, and inventory investment
volatilitybecame less important a factor in overall output volatility. In the retail motor vehicle
sector, the opposite was true on all counts. Ifinventories and volatility have just been pushed
10
"downstream", then itishard to argue that, for the economy as a whole, changes in inventory
management in one sector ofthe economy imply smoother aggregate output paths in the years
ahead.
In conclusion, this paper has established a cross-sectional correlation between IS ratios,
output volatility, and inventory volatility. This isa useful first step in addressing the extent to
which recent changes in inventory management techniques may have "tamed" the business cycle.
Of course, as Homstein (1998) notes, attributing overall inventory investment volatilityto
individual sectors isdifficultbecause ofthe covariance across sectors, and I cannot conclude that
changes in inventory management techniques, as revealed through lower IS ratios, are responsible
for declines in output volatility. However, the cross-sectional evidence does point to a connection
between lower inventory holdings and decreased output volatility. Future research must address
the covariance issue to make more progress in understanding the implications of new inventory
management techniques forthe business cycle.
11
References
Ben Salem, Melika, and Jean-Francois Jacques, "About the stabilityof the inventory-sales ratio:
an empirical study with U.S. sectoral data." A p p lie d E c o n o m ic s L e tte rs 3 (1996): 467469.
Blinder, Alan S., and Lotus J.Maccini, "Taking Stock: A Critical Assessment ofRecent Research
on Inventories." J o u r n a l o f E c o n o m ic P e rs p e c tiv e s 5,1 (Winter 1991): 73-96.
T h e E c o n o m is t.
"The business cycle: puncture ahead." December 5,1998, p. 90.
Fitzgerald, Terry J., "Inventories and the Business Cycle: An Overview."
Federal Reserve Bank of Cleveland, 33,3 (1997): 11-22.
E c o n o m ic R e v ie w
,
Hirsch, Albert A., "Has inventory management in the U.S. become more efficient and flexible" A
macroeconomic perspective." I n te r n a tio n a l J o u r n a l o f P r o d u c tio n E c o n o m ic s 45 (1996):
37-46.
Hodrick, Robert J.,and Edward C. Prescott. "Postwar U.S. Business Cycles: An Empirical
Investigation." J o u r n a l o f M o n e y , C r e d it , a n d B a n k in g 29,1 (February 1997): 1-16.
Homstein, Andreas. "Inventory investment and the business cycle." Federal Reserve Bank of
Richmond E c o n o m ic Q u a r te r ly 84,2 (Spring 1998): 49-71.
McCarthy, Jonathan, and Egon Zakrajsek, "Microeconomic Inventory Adjustment and Aggregate
Dynamics." Working paper, Federal Reserve Bank ofN e w York, November 1998.
McCarthy, Jonathan, and Egon Zakrajsek, "Trade Inventories." Working paper, Federal Reserve
Bank ofN e w York, December 1997.
McConnell, Margaret M , and Gabriel Perez Quiros, "Output Fluctuations in the United States:
What Has Changed Since the Early 1980s?" Working paper 9735, Federal Reserve Bank
ofN e w York, November 1997.
12
Table 1
Inventory investment's share of recessionary declines in GDP
Percent
mean
Total change in business inventories
manufacturing
durable goods
nondurable goods
merchant wholesale
durable goods
nondurable goods
retail
durable goods
nondurable goods
76
35
28
7
5
5
0
26
23
4
1981:1-1982:4
31
12
12
-0
3
2
1
-1
-7
7
1990:3-1991:1
49
6
10
-3
6
1
4
28
28
0
Notes: raw data are in billions of chained 1992 dollars. Shares are computed by sector for each postwar recession; the
mean over all recessions is reported in column 1, and shares for the most recent two recessions are reported in columns
2 and 3.
Table 2 Inventories-Sales Ratios
Number of months
Ma&mum ratio
Meaq,ratjQ
1960:11997:4
1960:11231:4
1984:11997:4
1973:4197&1
1981:31982:4
1990:31991:1
1.36
1.33
1.42
1.48
1.53
1.53
1.51
1.52
1.50
1.78
1.78
1.66
manufacturing-durable gds
1.93
1.96
1.86
2.39
2.47
2.15
manufacturing-nondurable gds
1.13
1.13
1.13
1.22
1.20
1.18
1.15
1.07
1.30
1.14
1.36
1.38
wholesale trade-durable gds
1.56
1.50
1.67
1.77
2.09
1.80
wholesale trade-nondurable gds
0.79
0.71
0.94
0.70
0.82
0.98
1.24
1.14
1.41
1.31
1.31
1.47
retail trade-durable gds
1.94
1.91
2.00
2.32
2.22
2.20
retail trade-nondurable gds
0.91
0.82
1.08
0.90
0.93
1.07
manufacturing and trade
manufacturing
wholesale trade
retail trade
Columns 1-3 report the mean inventories-sales (IS) ratio, in monhts, for the time periods listed in the column headings.
Columns 4-6 report the maximum IS ratio reached in the three contractions listed in the column headings.
Table 3 Ratio of Variance o f Output to Variance o f Sales
1960:1-
1984:1-
1S32A
1S2L4
1.59
1.51
1:96
1.63
1.55
2.13
manufacturing-durable gds
1.88
1.79
2.24
manufacturing-nondurable gds
1.34
1.24
2.07
1.48
1.38
1.95
wholesale trade-durable gds
1.65
1.50
2.23
wholesale trade-nondurable gds
1.50
1.36
1.79
1.94
1.81
2.24
retail trade-durable gds
2.48
2.20
3.02
retail trade-nondurable gds
1.76
1.75
1.78
1960:11997:4
manufacturing and trade
manufacturing
wholesale trade
retail trade
Table 4 Correlation between inventories-sales ratio and relative variance o f output
Correlation between Var(Q)/Var(S) and:
full sample
manufacturing
wholesale*
retail
Mean IS ratio
.585
.682
.274
.688
Maximum IS ratio
.556
.742
.246
.693
Correlation between (Va^QyVar^X^Va^QyVar^S))*, and:
full sample
manufacturing
wholesale*
retail
Mean IS ratio^^/
Mean IS ratio^
.306
.253
-.178
.436
Max IS rattOiMcj.iMj.j/
Max IS ratiotM03.IMi.i
.175
.288
-.200
.178
The IS ratios and and relative variance measures are computed over the 1967:1-1997:1 period for the wholesale trade
sector, all others use data from 1959:1-1997:4.
Notes: Var(QyVar(S) and IS ratios are computed separately by industry. The top panel reports the cross-sectional
correlation between the mean (maximum) IS ratio and the relative output variance (Var(Q)/Var(S)). For the bottom
panel, the ratio of the early period (1960:1-1983:4) relative variance to the later period (1984:1-1997:4) relative
variance is computed; similarly, the ratio of the early to later period IS ratio is computed. The table reports the
correlation between these two ratios (line 3), as well as the correlation between the relative variance ratio and the ratio
ofthe cyclical maxima (1981-1982 vs. 1990-1991).
Table 5 Covariance o f sales and inventory investment
1960:11997:4
1960:1-
19?3;4
1984:11997:4
27.75
32.48
18.27
9.30
11.35
5.41
manufacturing-durable gds
5.82
7.14
3.30
manufacturing-nondurable gds
0.28
0.33
0.18
1.26
1.55
0.66
wholesale trade-durable gds
0.80
1.01
0.38
wholesale trade-nondurable gds
0.11
0.06
0.19
1.14
1.91
-0.32
retail trade-durable gds
0.37
0.79
-0.41
retail trade-nondurable gds
0.13
0.27
-0.14
manufacturing and trade
tTiflUiifflcfiipng
wholesale trade
retail trade
Note: the covariance of sales and inventory investment is computed separately for each sector over the time periods
indicated.
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