Labor productivity growth is generally acknowledged to be procyclical. The author reviews the leading explanations for this, then uses two approaches to compare the time pattern of productivity gains over the business cycle. One approach describes the pattern in terms of the number of quarters of growth since the cycle's trough; the other uses knowledge about the ends of past recoveries to describe the typical pattern of productivity gains as a cycle ages.Labor productivity ; Business cycles

Mark E. Schweitzer

English

Research Papers in Economics

The growth of labor productivity surged
to an average annual rate of 3.1 percent
for the second and third quarters of
1997, after averaging less than 1 percent
a year over the previous decade. Such an
increase this late in an expansion is
unusual and has led some commentators
to expect that the burst of productivity
will persist, but the CBO feels that
is unlikely.
Congressional Budget Office1
[T]he fact that no slowdown [in produc-
tivity growth] is now apparent is evi-
dence . . . that the economy is behaving
as if it remains in a mid-expansion phase,
rather than an end-of-expansion phase.
Economic Report of the President2
P roductivity links the labor force to the
economy’s real output, a key position
that makes productivity growth one of
the most eagerly forecast and analyzed
economic statistics. A prime example is
the current business cycle, in which the
pattern of productivity gains has struck
many observers as extraordinary.3
Economists generally acknowledge that
labor productivity growth is procyclical,
which means that it is higher, on aver-
age, when the economy is expanding
and lower when the economy is con-
tracting. This Economic Commentary
reviews the leading explanations for this
pattern, to see what economic theory
implies for productivity growth. Then it
uses two different approaches to com-
pare the time pattern of productivity
gains over the business cycle. One
approach describes the pattern in terms
of the number of quarters of economic
growth since the business cycle’s trough.
The other approach uses our knowledge
about the ends of past recoveries to de-
scribe the typical pattern of productivity
gains as a cycle ages. 
n n   Why Should Productivity
Be Procyclical?
The procyclical nature of productivity
growth is typically expressed as a posi-
tive correlation between productivity
growth and output growth. In other
words, productivity growth tends to be
above average when real GDP growth is
above average.4  Theory provides at least
three explanations for this business cycle
“fact”: procyclical technology shocks,
increasing returns to scale, and labor
hoarding.5  To this list I would add the
depletion of workforce skills, which can
be linked to labor hoarding. 
Procyclical Technology Shocks. Real
business cycle models of the economy
assume that patterns generally occur
because labor and capital become—for
some mysterious reason—more or less
productive. In this story, “technology
shocks” simply arrive, like manna from
heaven. They can introduce procyclical
productivity growth because periods
with productivity-enhancing shocks also
tend to have higher rates of economic
growth. The shocks can’t be observed,
but they’re measured after the fact as
what remains after accounting for
increases in labor and capital.6 In the
current period, even this measurement is
unclear, making for a time pattern of
productivity that is uncertain but likely
to be correlated with output growth.
July 1998
Federal Reserve Bank of Cleveland
Productivity Gains During Business
Cycles: What’s Normal?
by Mark E. Schweitzer
Economic analysts yearn for a means
of predicting labor productivity
growth. Can they get some help from
business cycle patterns?
ISSN 0428-1276Increasing Returns to Scale. As a
firm’s output increases, the unit cost of
production falls. Instead of changing
over the business cycle, firms’ produc-
tion technologies might just be respond-
ing to higher returns to scale. Clearly,
using equipment and workers that were
previously idle will yield better returns if
these resources had to be paid regardless
of whether they were producing goods.
This would raise measured productivity
without any change in production tech-
niques as the firm increases output in
response to rising demand. Because the
limits of firms’ production technologies
are not known, however, the actual pat-
tern over the business cycle is uncertain.
Labor Hoarding. This explanation
relies on the idea that firms hold similar
numbers of workers whether demand is
high or low, possibly from a wish to
keep the job-specific skills that the firm
might lose by laying off workers. While
this model suggests that productivity
would rise rapidly as the workforce
returned to full production early in the
business cycle, it is unclear what the pat-
tern would be after that point.
Workforce Skills Depletion. Individu-
ally, the workers available to firms may
be more or less productive. At the begin-
ning of an expansion, high unemploy-
ment rates let firms choose the best
workers, who may become more diffi-
cult to hire away from other firms as the
expansion continues. In this case, the
pattern of productivity growth would
depend on occupation-specific skill lev-
els and might vary according to which
occupations hiring was concentrated in.
We need not choose one of these expla-
nations in preference to the rest because
they are not mutually contradictory. In
fact, these simple models don’t tell us
much about the pattern of productivity
growth we would expect to see over a
business cycle.
n n    Quarterly Time 
Patterns, 1949–97
Other difficulties in interpreting pro-
ductivity growth patterns become clear
when we look at U.S. data. Figure 1
shows productivity growth in the eight
business cycle expansions since the
Bureau of Labor Statistics started meas-
uring nonfarm business productivity in
1947.7  It also shows the current expan-
sion. The figure has too many lines to
let us interpret individual patterns, but
the quarterly variation in productivity is
plainly large enough to permit all sorts
of time patterns. At almost any number
of quarters after the business cycle
trough, there have been declines as well
as substantial growth. 
On careful inspection, however, regular-
ities in the data show a procyclical ten-
dency. In particular, some of the very
highest productivity growth rates—and
never any declines—have occurred in
the second and third quarters following
the trough. Even beyond that point, the
slope is slightly negative. Two major
problems make it hard to interpret the
raw data. First, quarterly productivity
growth is highly variable: The standard
deviation of productivity growth in com-
pleted recoveries is over 4 percent, so
that growth within any given quarter
could be negative or well over 6 percent.
Second, the length of business cycles
also varies considerably, from five to 36
quarters since the 1950s. 
One way to solve the first problem is to
focus on more than one quarter at a time
in order to average out part of the sub-
stantial variation in each. Figure 2 uses
the statistical technique of regression to
smooth out some of the variation shown
in figure 1 and to distinguish a clearer
cyclical productivity pattern.8  The typi-
cal business cycle is based on the eight
cycles completed since 1947. The cur-
rent business cycle, smoothed using the
same technique, clearly had a slow start
followed by  two spurts of productivity
growth. Both estimates, though certainly
not identical, are susceptible to statistical
variation because of data limitations.
The error band shown in figure 2 indi-
cates the range of typical (95 percent
confidence) patterns consistent with the
eight business cycles used in the regres-
sion. About 95 percent of the time, our
estimate of a smooth productivity
growth rate would fall within the band.
In our short history of business cycles,
only the beginning of the current expan-
sion is statistically distinctive.9
n n    Age Patterns 
of Business Cycles
The wide variation in the length of
business cycles, as well as the com-
mon descriptions attached to them
(early, mid, late), suggest judging pro-
ductivity growth not by the number of
quarters from the start, but by how far
into the expansion phase the economy
has gone. For an expansion that will
last only two years, the seventh quarter
is late; in the current cycle, it is not
even the midpoint. Furthermore, if
recoveries with strong productivity
growth tend to last longer, then, as the
sample of recoveries is thinned by
recessions, productivity growth esti-
mates will tend to rise with the number
of quarters since the last trough.
Figure 3 shows estimates of the produc-
tivity growth pattern relative to the frac-
tion of the expansion that has been com-
pleted.10  This way of accounting for
time fits the data better, because early
phases of the expansion show stronger
productivity growth that eventually
heads toward zero.11  It is also evident
from this perspective that the decline in
productivity growth occurs more evenly
over the expansion. Again, the variability
of the data must be considered. While the
typical pattern tends to flatten out after
the first half of the expansion, the data
are consistent with a smoothed produc-
tivity growth trend that could dip nearly
to zero and then return to over 4 percent.
It is hard to place the current expansion
in this context because we can’t know
how far through the cycle we are until it
ends. Figure 3 shows one possibility—
that the current expansion will last as
long as that of the 1980s (32 quarters).
This would put us about 80 percent
through the expansion (which is essen-
tially the CBO’s view). However, the
patterns of previous recessions yield little
to support strongly either the CBO’s or
the Administration’s assertions about
timing. Shifting the current expansion to
the left relative to history (halfway
through the expansion would be the
Administration’s estimate) still leaves the
current high-trend productivity growth
only slightly above the historical norm.
n n    Conclusion
Compared to previous expansions, the
present one is interesting but hardly
pathbreaking. The variation inherent in
measured labor productivity makes the
current pattern essentially consistent
with the previous eight. Interestingly,
the slow start of productivity growth in
this expansion stands out more than the
relatively high growth rates of the last
few quarters.
The case for using productivity growth to
date an expansion—or for using the age
of an expansion to predict productivity—
is weak, despite a statistically significant
pattern of productivity increases (higher
in the early quarters of the expansion,
then increasingly stable until the veryend). How can this be? First, there are
very few recoveries for which we have
data on productivity growth. Second, the
length of recoveries is highly uncertain.
And third, the data on productivity
growth are highly variable from quarter
to quarter. The approach taken here—
looking at the patterns in a smooth under-
lying component—favors finding an
interpretation of patterns in the data. If
the quarterly figures were used on their
own, the inference would be much less
certain. A reasonable error band on the
productivity data in the current quarter,
based on the length of the expansion
phase, is broad, ranging from less than
–6 percent to more than 8 percent. So,
although productivity is one number we
would fervently wish to forecast with
confidence, its pattern over the business
cycle is not very informative. 
n n    Footnotes
1. Congress of the United States, Congres-
sional Budget Office, The Economic and
Budget Outlook: Fiscal Years 1999–2008.
Washington, D.C.: U.S. Government Printing
Office, 1998; or http://www.cbo.org.
2. Economic Report of the President, Feb-
ruary 1998. Washington, D.C.: U.S. Govern-
ment Printing Office, 1998, p. 81.
3.  The “New Economy” adherents, who
argue that a permanent change in U.S. busi-
ness cycle patterns has already occurred,
focus attention on the productivity numbers
and their failings. For a careful analysis of
this interpretation, see John B. Carlson and
Mark E. Schweitzer, “Productivity Measures
and the ‘New Economy,’” Federal Reserve
Bank of Cleveland, Economic Commentary,
June 1998.
4. Finn E. Kydland and Edward C. Prescott,
“Business Cycles: Real Facts and a Monetary
Myth,” Federal Reserve Bank of Minneapolis,
Quarterly Review, vol. 14, no. 2 (Spring
1990), measure the contemporaneous correla-
tion as 0.51, where 1.0 would indicate per-
fectly proportional comovements and zero
would indicate no comovement. These figures
are for detrended data and use household
hours and GNP for constructing a productivity
measure, instead of using the published series,
in order to match the series typically used in
real business cycle models. For long-run pat-
terns, the difference between their results and
published statistics should be small.
5. The list is that of Ben S. Bernanke and
Martin L. Parkinson, “Procyclical Labor Pro-
ductivity and Competing Theories of the
Business Cycle: Some Evidence from Inter-
war U.S. Manufacturing Industries,” Journal
of Political Economy, vol. 99, no. 3 (June
1991), pp. 439–59.
FIGURE 1 PRODUCTIVITY GROWTH BY NUMBER
OF QUARTERS INTO EXPANSION
FIGURE 2 PATTERN OF PRODUCTIVITY GROWTH BY QUARTER
FIGURE 3 PATTERN OF PRODUCTIVITY GROWTH
BY AGE OF EXPANSION
SOURCES: U.S. Department of Labor, Bureau of Labor Statistics; National Bureau of Economic
Research, Inc.; and author’s calculations.6.  Economists call this the Solow residual.
7. NBER dating from trough to peak is used
throughout. The earliest productivity data are
not used because the data for the expansion of
October 1945–November 1948 would be
incomplete. 
8.  To combine information from surrounding
quarters flexibly, we regressed productivity
growth on a four-term quadratic expansion for
the number of quarters since the trough. This
approach measures a smooth but flexible
underlying trend in productivity. The regres-
sion is further augmented to adjust for a per-
manent slowdown in productivity growth after
1973, which has been the focus of consider-
able research. Using a dummy variable, this
regression estimates a slowdown in productiv-
ity growth of 2 percent after 1974. An alterna-
tive approach, using business-cycle fixed
effects, yielded very similar estimates of the
time patterns of productivity growth.
9.  More detailed statistical tests of difference
could very well accept the hypothesis that
these early low-productivity growth points
are consistent with the existing pattern, once
the error in the current period estimates is
included and heteroskedasticity is allowed
for. I did not undertake these because the
conclusion of this Economic Commentary is
qualitative.
10. Figure 3 employs the same statistical
technique used to smooth out some of the
quarterly variation in figure 2. Again, the
regression includes a four-term quadratic
expansion of the time variable (in this case,
the fraction of the business cycle completed)
and an adjustment for the slowdown in pro-
ductivity.
11. The R2 for the fraction regression is
0.195, compared to 0.184 for the regression
on the number of quarters.
Mark E. Schweitzer is an economist at the
Federal Reserve Bank of Cleveland. The
data cited here are current as of July 30th.
The views stated herein are those of the
author and not necessarily those of the
Federal Reserve Bank of Cleveland or of the
Board of Governors of the Federal Reserve
System.
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Productivity gains during business cycles: what's normal?

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Productivity gains during business cycles: what's normal?

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