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CAPACITY UTILIZATION-INFLATION LINKAGES: A CROSS-COUNTRY ANALYSIS
by
Gabriel S. Pde Kock and Tania Nadal-Vicens
Federal Reserve Bank of New York
Research Paper No. 9607
April 1996
This paper is being circulated for purposes of discussion and comment only.
The contents should be regarded as preliminary and not for citation or quotation without
permission of the author. The views expressed are those of the author and do not necessarily
reflect those of the Federal Reserve Bank of New York or the Federal Reserve System.
Single copies are available on request to:
Public Information Department
Federal Reserve Bank of New York
New York, NY 10045
Capacity Utilization-Inflation Linkages: A Cross-Country Analysis
by
Gabriel S. P. de Kock and Tania Nadal-Vicens
Federal Reserve Bank of New York
March 26, 1996
Preliminary, comments welcomed. The opinions expressed in this paper are those of the authors alone and do
not necessarily represent those of the Federal Reserve Bank of New York or the Federal Reserve System. We
are indebted to Bruce Kasman for raising some of the issues addressed in this paper, to Anthony Rodrigues for
many helpful discussions and to members of the International Macroeconomics Function for comments and
suggestions. The data used in this study can be obtained by sending a floppy disk and self-addressed envelope
to the authors.
ABSTRACT
This paper analyzes whether capacity utilization in manufacturing is a reliable inflation
indicator over and above economy-wide indicators of inflationary pressure and examines
different theories on the propagation of inflation by testing their implications for the relationship
between capacity utilization and inflation. Three mechanisms whereby shocks to manufacturing
can impact on inflation are explored: First, direct pressure on producer prices in manufacturing
arising from bottlenecks and a slowdown in productivity growth at high operating rates, second,
spill-overs of manufacturing-sector wage increases into inflationary wage growth in the service
sector, and finally, investment in manufacturing capacity that stimulates expansion, capacity
pressures, and inflation on an economy-wide basis.
We find that manufacturing capacity utilization has marginal predictive power for
inflation in seven out of 15 major OECD economies and that the inflationary impact of an
increase in manufacturing operating rates tends to be sizable. · The links between capacity
utilization and inflation that we uncover suggest that the mechanisms that propagate inflationary
impulses differ widely among nations. In the U.S. there is strong evidence that changes in
manufacturing activity impact on inflation through unit labor costs and finished goods producer
prices. By contrast, wage contagion appears to be a crucial element of the inflation process in
Japan. It also plays a role in Europe, particularly in Germany. Finally, only in Germany of the
major capital-goods producing economies, does capital goods prices unambiguously play a role
in transmitting manufacturing-sector shocks to economy-wide price indices.
JEL Classification: E31, E37, F41, F47, J30
GdK&TNV/3/26/96
Capacity Utilization-Inflation Linkages: A Cross-Country Analysis
I. Introduction
The 1994-95 recovery in continental Europe posed interesting questions about the origins
and propagation of inflation in industrial economies. The expansion was lopsided, with
industrial output buoyant but overall economic growth sluggish by past standards. As a result,
near-peak manufacturing capacity utilization rates coincided with unusually high unemployment.
In fact, capacity pressures in Europe were so acute that manufacturing operating rates for the
Western industrial world as a whole were high by past standards.
The unusual juxtaposition of capacity pressures in industry and under-used resources on
an economy-wide basis made predicting inflation particularly difficult at a stage of the business
cycle when inflation prospects were of special concern to policy makers. On the one hand,
economy-wide inflation indicators such as the GDP gap and the unemployment rate suggested
that the recovery was somewhat weaker than usual, and that inflation was likely to remain
subdued. On the other hand, there was a danger that higher than usual capacity utilization could
spark inflation at an earlier stage in the cycle.
The objectives of this paper is two-fold. It, first, analyzes the inflation implications of
high levels of capacity utilization. Data from 15 OECD economies is used to test whether
capacity utilization in manufacturing is a reliable inflation indicator over and above economywide indicators of inflationary pressure. Then it proceeds to examine different theories on the
propagation of inflation by testing their implications for the relationship between capacity
utilization and inflation. Three mechanisms whereby shocks to manufacturing can impact on
1
2
inflation are explored: (i) direct pressure on producer prices in manufacturing arising from
bottlenecks and a slowdown in productivity growth at high operating rates, (ii) spill-overs of
manufacturing-sector wage increases into inflationary wage growth in the service sector, and (iii)
investment in manufacturing capacity that stimulates expansion, capacity pressures, and inflation
on an economy-wide basis.
We find that manufacturing capacity- utilization has marginal predictive power for
inflation in seven out of 15 major OECD economies and that the inflationary impact of an
increases in manufacturing operating rates tends to be sizable. Furthermore, in about one-half of
the countries in our sample shocks to capacity utilization account for more than one-tenth of the
variation in inflation rates.
The links between capacity utilization and inflation that we uncover suggest that the
mechanisms that propagate inflationary impulses differ widely among nations. In the U.S there
is strong evidence that changes in manufacturing activity impact on inflation through unit labor
costs and finished goods producer prices, but no sign that wage contagion plays a role. By
contrast, wage contagion appears to be a crucial element of the inflation process in Japan. It also
plays a role in Europe, particularly in Germany. Finally, only in Germany of the major capitalgoods producing economies, does capital goods prices unambiguously play a role in transmitting
manufacturing-sector shocks to economy-wide price indices.
The plan of the paper is as follows: We start with a brief comparison of capacity
utilization and output growth in the current and earlier recoveries, followed by a discussion of the
linkages between manufacturing operating rates and inflation associated with different inflation
propagation mechanisms. The subsequent empirical sections first establish that capacity
3
utilization predicts inflation, but not everywhere, and then evaluate evidence in favor of each of
the potential links in turn. The conclusion summarizes the results and suggests avenues for
further research.
II. Capacity Pressures in the Current Recovery
European industry has operated at-rates of capacity utilization well above the historical
norm throughout the current recovery. This can be seen in Chart 1, which shows a GDPweighted aggregate of capacity utilization in continental European OECD member countries.
Capacity pressures emerged early for two main reasons. First, the upturn has followed a
relatively short recession and years of unusually low rates of manufacturing fixed investment.
European firms have invested little in new plant and equipment in recent years and have scrapped
existing equipment at an unusually high pace in aggressive efforts to downsize their operations. 1
Second, and more important, the expansion has been biased towards the industrial sector
as a result of strong export growth and weak domestic demand. Export growth has led the
current European recovery because of the desynchronization of the current global business cycle.
Europe had barely slipped into recession when demand in the English-speaking countries, East
Asia and Latin America started picking up momentum. Robust foreign demand, coupled with
currency devaluations in the wake of the 1992 ERM crisis, stimulated European exports. At the
same time, domestic demand, which falls more heavily on the service sector, stagnated as slow
wage growth and government fiscal consolidation weighed on private budgets. As a result,
service-sector output stagnated, leading to sluggish GDP growth and unusually high and
persistent unemployment (Charts 2 and 3).
4
The unusual recovery in continental Europe promptly raised capacity utilization rates
while GDP growth fell short of historical norms and unemployment remained stubbornly high.
Reflecting trends in Europe, manufacturing capacity utilization for the Western industrial
(OECD) countries as a whole (on a GDP-weighted basis) is also rapidly approaching past
cyclical peaks (Chart 4). The prospect of the Western industrial world running into
manufacturing capacity constraints in the near future has led some observers to argue that the
best news on inflation is over, and that a turnaround in price trends is in the offing. 2 We now
turn to the rationale for this point of view.
III. Manufacturing Capacity Utilization and Inflation: Theory
Standard macroeconomic models explain short-run changes in the inflation rate in terms
of a Keynesian Phillips Curve, that is by deviations of aggregate output from its potential (the
GDP gap) or deviations of unemployment from the natural rate, or NAIRU. Why, then, should a
high rate of capacity utilization in manufacturing, that is, a high utilization rate of existing plant
and equipment, ignite inflation, especially if there are ample under-utilized resources in the
economy as a whole? Proponents of a link between capacity utilization and inflation offer three
main explanations that, in effect, emphasize different ways in which inflationary impulses spread
through the economy. 3 The first and most direct, claims that high levels of capacity utilization
are associated with slow productivity growth, rising unit labor costs, and associated price
pressures. The second, which we can call the "wage-contagion hypothesis", argues that rapid
expansion and high utilization rates in industry engender rapid manufacturing-sector wage rises
that spill over into inflationary wage growth in the government and service sectors. According to
5
the third, capacity pressures in industry lead firms to invest in new plant and equipment, thereby
stimulating economic expansion and capacity pressures, and, hence, inflation on an economywide basis.
There are, in fact, three mechanisms that create a direct link between manufacturing
capacity utilization to prices.• The first is the association of high operating rates, at least after
some point, with slow productivity growth, rising unit labor costs and direct pressure on
manufactured goods prices. The second is cost-increasing bottlenecks that typically arise when
utilization rates are high. Finally, producers tend to have more pricing power when supply is
scarce relative to demand, which allows them to increase profit margins and to pass cost
increases on to buyers more readily. 5 These mechanisms apply only to manufacturing goods
prices, but will be important for broader price indices if the cyclical variation in inflation rates is
dominated by movements in goods prices.
The wage-contagion hypothesis has its origins in the so-called Scandinavian model of
inflation [Aukrust (1977), Aukrust et al (1967)]. 6 It applies most forcefully to countries with
strong trade unions, highly centralized wage bargaining and egalitarian political traditions. In
these circumstances, manufacturing wage settlements set the tone for other sectors because
unions try to maintain their members' real wages relative to those in manufacturing. Unions in
the government and private service sectors are able to negotiate wage settlements that are closely
tied to those in manufacturing because these sectors are sheltered from foreign competition.
Thus, high levels of manufacturing capacity utilization that are typically associated with high
profits and rapidly rising wages in manufacturing spill over into rapid wage increases throughout
the economy. Rapid wage growth in manufacturing, as such, need not be inflationary, since it is
6
often accompanied by rapid productivity growth. Generous wage settlements outside
manufacturing tend to be inflationary, however, because they are not typically accompanied by
large productivity gains. Inflationary pressures are subsequently spread further as price increases
spill over into indexed wage contracts and social benefits payments. 7 For wage contagion to
occur in the face of high unemployment, however, it is necessary that unemployed workers have
little impact on the wage claims of employed workers. Such· a division of workers into insiders
and outsiders is especially characteristic of European labor markets. 8
The third, and most indirect, hypothesis links manufacturing capacity pressures to future
inflation through investment spending. It starts with the intuitively plausible supposition that
firms faced with capacity constraints will try to expand their productive capacity by investing in
new plant and equipment. Moreover, since high utilization rates are typically associated with
healthy profits, they will have the means to do so. High investment spending leads to output and
income growth in the capital goods and construction industries. Increased demand by the
recipients of the higher incomes in these industries put further inflationary pressure on the
productive capacity of the manufacturing sector and eventually on the economy as a whole.
It is to be expected that capacity pressures will take much longer to impact inflation
through the investment channel than directly through producer prices or through wage contagion,
simply because there are long delays in the planning and execution of investment projects. In
addition, the investment link is likely to be stronger in countries that are important capital goods
producers because the domestically produced component of business fixed investment is likely to
be large in such countries.9 If, by contrast, a country imports much of its capital goods, some of
the investment spending engendered by high utilization rates is likely to spill over to foreign
7
capital goods producing countries.
IV. Does Capacity Utilization Predict Inflation?
We start our empirical analysis of the links between manufacturing capacity utilization
and inflation by asking whether there is a reliable predictive relationship between manufacturing
capacity utilization and general inflation. To evaluate whether there is such a relationship, we
pose a conceptually simple question: "Does manufacturing-sector capacity utilization provide a
signal about future inflation beyond the information provided by economy-wide measures of
inflationary pressure?" Posing the question in terms of marginal predictive power is not only
standard practice. It is also particularly informative at a time when capacity utilization and
economy-wide indicators of inflation give potentially conflicting signals. We do not attempt to
test, at this point, whether such a signal is associated with any particular link between operating
rates and inflation discussed above. That topic is taken up in the next section.
JV.I A Note on Methodology
To test whether capacity utilization predicts inflation, or whether a particular link is
responsible for its predictive power, we rely on variants of Granger-causality tests based on
estimated vector-autoregession models. Such tests are clearly indicated when we want to
determine whether an variable has marginal predictive content for another economic variable of
interest.
Are variants of Granger-causality tests appropriate, however, when we want to draw
inferences about the validity of different theories about inflation? This question arises because it
is well-known that tests that associate causality with temporal precedence cannot be used to draw
8
inferences about the structure of the economy [Cooley and Leroy (1985)]. However, we look at
different theories of inflation from the point of view of their implications for how inflationary
impulses in the manufacturing sector spread through the economy over time. Because the
implications of different theories are stated in terms of the dynamic relationships among
macroeconomic variables, they invariably embody elements of temporal precedence. .(For
example, the wage spill-overs that the·." Scandinavian model"· identify· as a crucial element of the
inflation process imply that wage changes in industry predict wage changes in the service sector.)
For this reason, Grange-Sims "causality" tests are well suited to shed light on the validity of
different inflation theories.
We use a generalization of a standard Granger-causality test to determine whether
capacity utilization predicts inflation. It is designed to test whether capacity utilization predicts
inflation either directly, or indirectly by predicting economy-wide inflation indicators. The test
can be explained as follows: Consider a vector of variables y, that includes capacity utilization, a
number of economy-wide inflation indicators (such as the unemployment rate) and an inflation
rate. Let the autoregressive representation of the data be given by
(4.1) [I-A,(L)]y, = u,,
and the contemporaneous correlations (ordering) by
(4.2) [1-A.i]u, = u,,
where u, are the orthogonalized innovations to the system. The moving-average representation of
the model is
(4.3) y, = [I-A1(L)]" 1[1-A.i]" 1u, = B(L)[I-A.iJ"1u,.
Let capacity utilization be the first variable in the vector y,, y 1,,. Denote the vector of economy-
9
wide inflation indicators by Yz,, and let the inflation rate be the last element ofy,, y3,,. The null
hypothesis y I has no indirect or direct predictive value for y 3 can be stated in terms of the
moving-average representation of the model, that is, B3,1(L) = 0. By stating the hypothesis in this
way, we preclude theoretically-derived restrictions on Ao from influencing our inferences. The
restriction that B 3,i(L) = 0 is equivalent to
(4.4) A1,3,1(L) + A1,3,2(L)[I-A1,2,zCL)r1A1,2,1 = 0,
where the submatrices A 1,3,1(L) etc. reflect a partitioning of A 1(L) conformable with the
partitioning ofy,. Testing the necessary and sufficient condition, eq.(4.4), is possible in
principle, but difficult in practice. Therefore we test a weak sufficient condition:
(4.5) {(A 1,3, 1(L)=0 and A 1.3•2(L)=0) or (A1,3,iCL)=O and A 1,2,1=0)}
for which we can easily calculate a critical value.
IV.2Data
Our empirical analysis of whether capacity utilization predicts inflation covers 15 major
industrialized economies, chosen on the basis of data availability. 10 For each country, we used at
least one inflation rate, two economy-wide measures of inflationary pressure and a measure of
manufacturing capacity utilization. Two price indices are used to calculate the inflation rate: the
consumer price index excluding food and energy prices (or "core" CPI), and the GDP deflator.
The CPI is the most widely available measure of general price movements. We follow standard
practice in excluding food and energy prices from the index because they are heavily influenced
by weather conditions and energy price disturbances that are exogenous to the domestic inflation
process. It is nevertheless sensitive to import price movements (and some policy changes) that
may to a greater or lesser extent be independent of wage and cost movements. The GDP deflator
10
is the most comprehensive measure of domestic price and cost movements -- that is, it does not
reflect import prices. In addition, it is not unduly sensitive to energy prices because most
industrialized countries are not self-sufficient in energy production.
We use two indicators of economy-wide capacity pressures, the deviation of GDP from
its long-term trend (or GDP gap) and the deviation of the unemployment rate from its long-term
trend.
11
In principle, the unemployment rate is an economy-wide measure of excess capacity.
We use the deviation from its long-term trend because unemployment rates in industrialized
economies have exhibited a pronounced upward trend over the past two decades. Thus, we allow
for the possibility that the long-term increase is permanent and does not reflect a cyclical change
in economy-wide resource utilization. We measure the GDP gaps as deviations from a HodrickPrescott (H-P) filter trend. We chose deviations from an H-P filter trend, rather than OECD data
for the GDP gaps, for three reasons. First, we want to be sure that the gap is not influenced by
subsequent inflation developments. Second, we wanted a method that could easily be applied to
a number of countries. Third, we do not profess to know when changes in trend growth occur -thus we use a method that only imposes the prior that changes in trend growth occur smoothly.
Recent OECD comparisons of potential GDP series calculated using the H-P filter and using
more sophisticated techniques reveal strong similarities. For these reasons, we also use the H-P
filter to calculate trend unemployment.
To measure capacity pressures in industry, we use the standard manufacturing capacity
utilization rate, where available. Capacity utilization figures are usually based on firm-level
surveys aimed at determining the degree to which plant and equipment is utilized. 12 However,
not all the countries in our sample publish official capacity utilization data. For those that do not,
11
we use the deviation of industrial production from its long-term trend as a proxy. We also use
the H-P filter to calculate our proxy for capacity utilization in these cases (for the second and
third of the above reasons). The proxy may not capture all the information contained in the
official capacity utilization figures. To see whether it does, we experimented with this proxy and
the official capacity utilization data for the countries that do publish capacity utilization data.
Typically, the proxy is significant in predicting inflation when the official capacity utilization
series is. Nevertheless, the proxy and the official figure are not perfect substitutes -- sometimes
both were significant in the same regression. 13 Finally, to determine whether our capacity
utilization proxy captures information that is not contained in the deviation of GDP from its
trend, we regressed each country's capacity utilization measure (official or our proxy) on the
contemporaneous deviation of GDP from its trend. The percentage of capacity utilization
variance that is explained by GDP movements varied widely among countries, from a high of 80
in the case of U.S. to a low of 4 for Norway. Strikingly however, there appears to be little
relationship between the closeness of the fit and whether the capacity utilization measure was
from official sources or our proxy .14
IV.2 Results
For each country, we estimate at least one four-lag VAR that includes an inflation rate,
two economy-wide measures of inflationary pressure and a measure of manufacturing capacity
utilization. 15 The specification of the VAR models is based on prior exploration of the timeseries properties of the data, including unit root tests. Accordingly, the V ARs were estimated on
inflation rates (log differences), the rate of capacity utilization in percent, the unemployment rate
deviation from trend in percentage points, the GDP gap (in percent of trend GDP), and in the
12
cases where we proxied capacity utilization, the percentage points deviation of industrial
production from its trend.
The estimated VAR models describe the data well. The adjusted R2s for the consumer
price inflation equations exceed 0.9 for all but two countries, the U.S. (0.72) and Sweden (0.84).
2
The adjusted R s for the deflator inflation equations are somewhat lower -- between 0.52 and
0.82 in all but one case.
16
There is no evidence that the VAR-models are misspecified:
Diagnostic statistics, shown in Appendix Table I for the inflation equations, reveal little
evidence that the residuals of the individual equations are serially correlated (a sign of possible
parameter instability).
17
Finally, impulse responses were in line with our expectations and gave
no hint of model instability.
The results in Table I indicate whether manufacturing-sector capacity pressures provide a
signal about future inflation. The test results show that manufacturing capacity utilization (or our
proxy for it) provides a signal about future inflation in seven out of the 15 countries in our
sample (if we take significance at the 5% level for at least one price index as our criterion).
Capacity utilization has somewhat stronger predictive power .for the GDP deflator than for
consumer prices: its marginal contribution in predicting inflation, as measured by the GDP
deflator, is statistically significant at the 2% level in six of those seven countries: the U.S., Japan,
Germany, Italy, Sweden and Austria. When inflation is gauged by the consumer price index,
capacity pressures are significant at the 5% level in the U.S., Japan, Germany and Canada.
Overall, these results are fairly supportive of a link between capacity utilization and inflation. A
link is evident in nearly one-half of the countries in the sample (two-thirds if we are willing to
accept significance at the 10% level as sufficient evidence). In addition, these countries account
13
for the bulk of the output of the industrial world. It is striking that capacity utilization does not
predict inflation (at the 5% level) in any of the countries for which we had to use our proxy for
capacity utilization. This fact suggests that the proxy may not capture the same information as
official capacity utilization data, but it may also reflect that the countries for which we had to
proxy for capacity utilization are somewhat less industrial than the countries where capacity
utilization turned out to be significant.
To judge whether our results may have been unduly influenced by our decision to
measure some variables as deviations from their H-P filter trends, we also estimated our V ARs
with all variables in levels. The test results summarized in Table 1 were affected in only
relatively minor details. 18 As a result, for the remainder of the results reported in this paper, we
use variables measured in deviations form trend where standard theory suggests such use.
At this point, it is natural to ask whether the impact of manufacturing capacity pressures
on inflation is large enough to be economically as well as statistically significant. To begin,
Table 2, which summarizes an impulse response analysis, sheds some light on this question. It
presents the impact on CPI and deflator inflation of 11 one-time one percentage point increase in
capacity utilization and indicates the time it takes for the impact to be felt. The results show that
the price effect is generally sizable but subject to substantial time delays. The figures in the left
half of the table were derived by allowing GDP and inflation to rise (and unemployment to fall)
immediately in response to an increase in capacity utilization. These figures are broadly
representative of the increases in capacity utilization in the late s and late s, which were
accompanied by rapid GDP growth and large declines in unemployment. They show a sizable
acceleration in inflation -- about 0.5 percentage points for the U.S., Japan, Canada and Germany
14
-- after 2-2 1/2 years. The pick-up in inflation takes somewhat longer in Germany, where less
than one-quarter of the full impact is felt within a year.
The right-hand side of Table 2 reports the results of an experiment aimed at mimicking
the most recent increases in capacity utilization (which coincided with slow GDP growth). In
this case we restricted GDP, inflation and unemployment to respond to capacity utilization with a
one-quarter time delay. The overall price impact is generally- no smaller, but it takes about a year
longer to develop, as confirmed by a generally smaller impact over a one-year horizon. In Italy,
however, an increase in capacity utilization that is not accompanied by an immediate increase in
GDP actually lowers deflator inflation. This unexpected effect presumably reflects inflationreducing productivity gains associated with increased operating rates in the early stages of a
cyclical upswing. Similarly, in Germany an increase in capacity utilization actually lowers
consumer prices initially. The consumer price impulse responses corroborate the general
character of the capacity-inflation relationships obtained with the GDP deflator. However, the
impact of capacity pressures on inflation as measured by the CPI, take longer to develop,
suggesting that it may be the goods prices that react to capacity pressures, at least initially.
Table 3 shows an alternative yardstick of the economic significance of the impact of
manufacturing capacity pressures on inflation. It shows the contribution of capacity utilization
shocks to the variances of the GDP deflator and the consumer price inflation rates derived from a
variance decomposition analysis of the vector autoregressions underlying the results in Table l.
They capture the contribution to inflation variability from fluctuations in capacity utilization that
cannot be accounted for by movements in the GDP gap and the unemployment rate. 19 By
contrast with Table 2, which showed a fairly consistent impact of capacity utilization on
15
inflation, Table 3 breaks our countries into two clearly delimited groups. Changes in capacity
utilization are responsible for a sizable portion of inflation variability in Japan, Italy, Canada,
Austria, Switzerland, Spain and Finland, and typically for much less in the remaining countries.
Together, the very similar impulse response in Table 2 and the divergent variance
decompositions in Table 3 suggest that the manufacturing sectors of the countries named here are
subject to more idiosyncratic industrial disturbances than the others and that these disturbances
tend to feed through to inflation.
V. A Closer Look at the Linkages
Having established that the data is fairly supportive of a link between capacity utilization
and inflation, we now investigate which of the hypothesized linkages are responsible for the
relationship. Our strategy is to identify and test necessary conditions for the different channels to
operate. Overall, the test results suggest that a direct link between capacity utilization and prices
is crucial to the relationship between capacity utilization and inflation in the U.S. In Japan and
Europe, by contrast, there is evidence of wage contagion. The investment link, by contrast, gets
very little support in the data.
V.1 Capacity Utilization, Productivity and Prices
For capacity utilization to explain general price inflation via movements in labor
productivity and unit labor cost, at least two crucial conditions must be met: First, changes in
capacity utilization must have marginal predictive power for producer prices over and above
lagged producer prices and aggregate indicators of inflationary pressure. Second, producer prices
must account for a significant portion of the movement in consumer price (or GDP deflator)
16
inflation.
It is straightforward to test whether capacity utilization predicts producer prices at the
margin -- we performed standard Granger-causality tests with the GDP gap and the
unemployment rate playing the role of aggregate indicators of inflationary pressure. In
accounting for the contribution of producer prices of manufactures to overall price inflation, it is
necessary to eliminate the components of producer price fluctuations that are attributable to
shocks that are common to both producer prices and overall price inflation, for example wage
changes that affect both consumer and producer prices. To do so we ran two VAR systems. The
first accounts for the behavior of producer price inflation, consumer price inflation and inflation
in prices of consumer services and the second for producer price inflation, GDP deflator inflation
and inflation in prices of consumer services. In these set-ups the prices of consumer services are
used as an indicator of common shocks to producer and consumer prices and to producer prices
and the GDP deflator. We do not try to use our standard indicators of aggregate inflationary
pressure to pick up these common price shocks because they would not account for wagecontagion effects. To evaluate the contribution of producer prices to consumer price (GDP
deflator) inflation, we calculated a variance decompositions with services price inflation ordered
first, producer price inflation ordered second and consumer price (GDP deflator) inflation
ordered last. Intuitively, this procedure filters shocks that are common to producer price inflation
and service price inflation (for example, wage changes in manufacturing that spill over to the
services sector) before evaluating the contribution of producer prices to CPI (deflator) inflation.
Table 4 shows the test results for the hypothesis that there is a "direct" link from capacity
utilization to producer prices and hence to the GDP deflator and consumer prices. The first
17
column shows the P-value for the hypothesis that capacity utilization does not predict producer
price inflation at the margin, the second column shows the contribution of PPI inflation shocks to
GDP deflator inflation over an eight-quarter horizon, and the third column shows the
contribution of PPI inflation shocks to consumer price inflation over the same horizon. Overall,
the results confirm that producer prices account for a significant fraction of the variation in
inflation, whether gauged by the consumer price index or the GDP deflator. However, capacity
utilization predicts producer price inflation in only the U.S. Strikingly, capacity utilization does
not have much predictive power for producer prices in Europe and in two countries, Italy and
France, the impact of capacity utilization on producer prices has the "wrong" sign. 20 As a result,
the direct link appears to account for the relationship between capacity utilization and inflation in
the U.S., but nowhere else.
V.2 Wage Contagion
Our evaluation of the wage-contagion link between capacity utilization and inflation
follows the same broad outlines as that of the direct link. We first test whether capacity
utilization predicts average hourly earnings in manufacturing and then we evaluate the
contribution of manufacturing wages to the variation of wages in other sectors, once common
sources of fluctuation have been accounted for. In this case we use our economy-wide indicators
or inflationary pressure to filter common shocks from average hourly earnings in manufacturing
and non-manufacturing earnings.
Data on non-manufacturing earnings are not commonly available. For this reason we
limited the variance decomposition test to those countries where manufacturing capacity
utilization proved significant, at the margin, in predicting average hourly earnings in
18
manufacturing. For those countries we backed out an estimate of quarterly earnings per person
employed outside manufacturing from national accounts labor compensation data, figures on
employment in- and outside manufacturing, and earnings and average working hours in
manufacturing.
The results of the two tests of the wage contagion hypothesis are shown in Table 5. The
first column shows P-value for the hypothesis that lagged values of manufacturing capacity
utilization does not predict average hourly earnings in manufacturing, once lagged values of
earnings and the deviation of unemployment form its long-run trend have been taken into
account. From it we deduce that an increase in capacity utilization increases manufacturing
earnings in Japan, Germany, France, and Sweden. In Austria an increase in capacity utilization
tends to lower wages in manufacturing. The second column shows P-values for the null
hypothesis that manufacturing earnings do not predict earnings outside of manufacturing.
Strikingly, we get significant results in Japan, Germany, France (at the 1% level), and Austria (at
the 10% level) -- all the countries (of those where capacity utilization predicts manufacturing
earnings) for which we could derive series for non-manufacturing earnings. In Japan
manufacturing wages account for well more than one-quarter of wage fluctuations outside
manufacturing -- a result that must be considered strongly supportive of the wage contagion
story. The results for the other countries are not unambiguously supportive of the wagecontagion hypothesis, though -- manufacturing wages do not account for much more than 10% of
the variation in non-manufacturing wages in Germany and for as little as 3.7% in France.
Moreover, in Austria the negative impact of capacity utilization on manufacturing wages clearly
undermines the wage contagion model. 21 Overall however, it is not surprising that we find
19
evidence of wage contagion in continental Europe and Japan. Unions remain powerful in these
countries and their wage negotiations tend to be highly centralized.
V.3 The Investment Link
A link from capacity utilization to investment and hence to inflation requires that at least
two conditions be met: First, high levels of capacity utilization must lead to an increase in
investment spending and, second, a pick-up of investment must lead to an acceleration of
inflation. But, because a sizable portion of machinery and equipment investment in the smaller
economies is likely to spill over to the major capital-goods producing countries, the inflationary
impact of increased investment spending may not be felt in the countries where the spending
occurs.
For our first test of the capacity-investment-inflation link, we ask whether capacity
utilization has marginal predictive power for the growth rate of business fixed investment
spending, once lagged investment growth and lagged GDP gaps have been taken into account. 22
The results are shown in the first column of Table 6a, which indicates that the first element of an
investment link holds in the U.S. and Canada (at the 5% level of significance) and in France and
the Netherlands at the 10% level. 23 In Italy, by contrast an increase in capacity utilization leads
to a statistically significant decline in investment growth. In none of these countries however,
does investment actually predict either consumer price inflation or deflator inflation. Thus, there
is no evidence of a link between capacity utilization and inflation through domestic investment
spending. For this reason we now look at the market for newly-produced capital goods on a
world-wide basis.
Our third test determines whether capacity utilization predicts producer prices of capital
20
goods. It takes into account that capital goods are internationally traded, first, by aggregating
capacity utilization for the OECD as a whole24 and for the countries where capacity utilization
predicts investment at the margin and, second, by testing whether these aggregates predict capital
goods prices in the major capital goods-producing nations, the U.S., Japan, and Germany. For
each of the capital goods producing countries the left-hand side variable in these prediction tests
is the capital goods price inflation rate and the right-hand side variables are four lags of each of
the capital goods price inflation rate, the country's GDP gap (to capture economy-wide
inflationary pressures), the country's capacity utilization rate (to capture "direct" pressures on
prices) and the relevant aggregate capacity utilization rate. The results are shown in the two left
columns of Table 6b. They reveal that the aggregate investment link is highly significant in
Germany (for both international aggregates). For the U.S. only capacity utilization in the nations
where high capacity utilization rates spark investment is significant at the 5% level. In Japan, by
contrast, neither aggregate plays a role in predicting capital goods price inflation.
Finally, we consider whether inflation in capital good prices sparks inflation on a broader
basis. The rightmost two columns of Table 6b show that it does in all three the major capital
goods producing nations. Nevertheless, we have to conclude that the evidence for an investment
channel linking capacity utilization and inflation is weak. Only in the case of Germany are our
results strongly supportive of an investment link, while in the U .S there is weak support.
VI. Conclusion
In early 1995, the manufacturing sector of the western industrialized nations appeared to
be heading for capacity constraints at an unusually early stage of the business cycle recovery.
21
Some observers interpreted this trend as portending a more rapid pick-up in inflation than
aggregate measures of capacity utilization would have suggested. They pointed out that capacity
pressures may feed into inflation by stimulating inflationary cost increases in industry, rapid
increases in manufacturing-sector wages that spill over to the government and service sectors,
and by spurring capacity-constrained firms to invest in plant and equipment.
Our statistical analysis shows that an increase in manufacturing capacity pressures are
associated with a sizable acceleration of inflation in seven out of fifteen major OECD countries -clear evidence of a capacity-inflation link. High utilization rates take up to three years to affect
consumer prices, but their impact on the GDP deflator is typically felt within two years.
Nevertheless, movements in capacity that occur independently of changes in GDP or
unemployment only account for a large proportion of the variation in inflation rates in a small
group of countries, most notably in Japan, Canada, Italy and Spain.
The linkages from capacity utilization to inflation differ markedly among countries,
suggesting that there are significant cross-country differences in the ways in which inflationary
shocks spread through the economy. Only in the U.S. is there a consistent direct link from
manufacturing operating rates to producer prices and broader price indices. Wage contagion
plays a major role in transmitting movements in capacity utilization to inflation in Japan, and a
statistically significant but economically Jess important role in continental Europe. There is little
evidence that an investment channel links capacity utilization and inflation -- only in the case of
Germany did we find support for this line of reasoning. Finally, we found no evidence in favor
of any particular link for the observed relationship between capacity utilization and inflation in
Canada and Italy and some indications of a perverse relationship in Italy and in Austria. These
22
are clearly puzzles that deserve further exploration.
23
Endnotes
1. A GDP-weighted aggregate business sector capital stock for continental Europe expanded at
an average rate of 2.8% per year from 1982 to 1994, compared to an average growth rate of 4.4%
from 1970 to 1981. Even during the Europhoria years (1987-1991) it grew only 3.3% per year.
For further discussion and evidence of unusually slow growth in European manufacturing sector
productive capacity in recent years, see Kasman (1994).
2. See Kasman ( 1995) for a good exposition of this line of reasoning.
3. Kasman (1995) applies all three explanations to the current situation in Europe.
4. This formulation relies very heavily on the exposition of an anonymous referee.
5. For further analysis of the impact of capacity utilization on mark-ups along these lines, also
see McElhattan ( 1985).
6. An extensive exposition of the Scandinavian model can be found in Frisch ( 1984) and a useful
recent assessment in Bartoli. For a recent empirical application of the Scandinavian model to the
Norwegian economy see R. Nymoen (1991). For an application to Japanese data, see Jagdish
Randa and Yukio Okiyama (1985).
7. In some countries the link between wages and social benefits payments is even more direct. In
Germany for example, some social benefits payments are determined as a percentage of average
wages.
8. See, Lindbeck and Snower ( 1988) for a discussion of insider-outsider models of the labor
market. The unemployed will also have little impact on the compensation of employed workers
if legislation or collective bargaining agreements automatically extend existing wage agreements
to new hires, if high unemployment benefits discourage the unemployed form actively competing
on wages, and if generous replacement rates lower the expected cost of becoming unemployed.
9. Most notably, it applies to countries irrespective of their labor-market institutions.
IO. The sample is determined by the availability of data at a quarterly or higher frequency from
1970 onwards. To be included a country had to have quarterly data on at least consumer prices
or the GDP deflater and the unemployment rate or GDP and manufacturing capacity utilization
or industrial production.
11. The unemployment rate was not included in the regressions for Switzerland because of data
limitations.
12. In some cases, Italy is an example, it measures actual industrial output relative to a
statistically-derived measure of potential output in manufacturing.
24
13. It should be noted that our results may be biased by the use of detrended variables as
regressors. The use of regressors generated as "residuals" from a fitted trend implies that the
standard errors produced by regression packages are inappropriate if lagged values are used as
explanatory variables -- a fact that we did not take into account in the test results reported below
[Pagan ( 1984)]. This problem arises irrespective of whether the trend was obtained by using the
H-P filter or some other method of de-trending.
2
14. For example, the highest R for a regression of our capacity utilization proxy on GDP is 0.64
(for Switzerland and Finland) and the lowest is 0.04 (Norway). The R 2s for a regression of
official capacity utilization on GDP range from 0.8 (U.S.) to 0.09 (Sweden). Detailed results can
be obtained from the authors.
15. Eight-lag VARs gave essentially the same results.
16. In the case of Norway the adjusted R 2 is an anomalously low 0.04.
17. In the cases where there is evidence of serial correlation, respecification to eliminate the
remaining serial correlation did not change the test results reported below. It is usually quite
easy to get "clean" residuals, as judged by the Breusch-Godfrey Lagrange multiplier statistic -one simply has to add lags of the dependent variable to the regressor list (or eliminate some lags).
The result, however is a motley assortment of equations with widely varying lag structures.
Since our results were not affected by such housecleaning activities, we report the test statistics
obtained from industry-standard four-lag V ARs. The results for the tests for the "cleaner"
equations are available upon request.
18. Capacity utilization remains useful in predicting inflation at the 10% level in 9 out the fifteen
countries. Of the major countries, capacity utilization lost its significance in Canada, but became
significant in France. In addition Norway and Spain traded places with Sweden and Finland.
19. That is we chose the following ordering for the factorization of contemporaneous correlations
of the VAR innovations: GDP gap, unemployment rate deviation from trend, capacity
utilization, and inflation rate.
20. That is, an increase in CU tends to lower producer prices, even in the long run.
21. In addition we find evidence of spillovers from non-manufacturing to manufacturing wages
in Germany and Austria -- in both these countries, non-manufacturing wages predict wages in the
manufacturing sector.
22. Business fixed investment data is not available for all the countries in our sample. If not we
used the more widely available machinery and equipment figures, or, failing that total fixed
investment
23. It is possible that capacity utilization predicts investment because the official capacity
utilization statistics are revised in light of subsequent investment spending.
25
24. The aggregates are GDP-weighted. The figures for the "OECD as a whole" only includes the
OECD members included in our study.
Table 1
Predictive Power of Capacity Utilization on Inflation
(p-values)
Country
GDP Deflator
CPI
United States
0.007
0.000
Japan
0.002
0.001
Germany
0.003
0.017
Italy
0.014
0.204
Canada
0.107
0.013
Sweden
0.020
0.799
Austria
0.014
0.109
France
0.514
0.124
United Kingdom
0.557
0.684
Spain
0.460
0.315
Australia*
0.073
0.126
Netherlands*
0.204
0.582
Switzerland*
0.594
0.577
Finland*
0.642
0.057
Norway•
0.794
0.304
Note: P-values are marginal significance levels for the null hypothesis
that capacity utilization does not predict inflation, either directly, or
indirectly by predicting the GDP gap or the unemployment rate
deviation from trend. Results are based on a 4-lag vector autoregression
including the inflation rate, GDP gap, unemployment rate deviation
from trend, and a capacity utilization measure.
* Capacity utilization measured as the percentage deviation of
industrial production from its Hodrick-Prescott filter trend. For Switzerland
the unemployment rate is not included in the regressions.
Table 2
Impact of Capacity Utilization on Inflation
GDP Deflator
Country
Other variables do not respond contemporaneously
All variables respond contemporaneously
Maximum Effect
Amount
Time Delay
lnuarters\
Impact After
4 Quarters
Maximum Effect
Amount
Time Delay
lnuarters\
Impact After
4 Quarters
United Sates
0.4
8
0.3
1.0
13
0.2
Japan
0.8
8
0.4
0.5
9
0.3
Germany
0.5
7
0.4
0.4
7
0.2
Italy
0.2
4
0.2
-0.2
4
-0.2
Sweden
0.1
13
0.0
0.1
12
0.0
Austria
0.0
4
0.0
0.1
4
0.1
'
CPI Inflation
United States
0.7
8
0.5
0.8
12
0.0
Japan
0.6
9
0.3
0.6
14
0.2
Germany
0.5
11
0.1
0.3
13
-0.1
Canada
0.5
10
0.2
0.7
14
0.2
Note: Figures represent impact of a 1% increase in capacity utilization derived from impulse response analyses of four-lag VARs including
GDP gap, unemployment rate deviation from trend, capacity utilization measure, and CPI/GDP deflator inflation rate.
* Capacity utilization measured as the percentage deviation of industrial production from its Hodrick-Prescott filter trend.
Table 3
Contribution of Capacity Utilization to Inflation Variance
Percentage of Inflation
Variation Accounted for
by CU (over 8 quarters)
Country
GDP Deflator
CPI
5.5
2.1
24.0
22.9
6.6
4.3
Italy
21.3
9.7
Canada
8.3
30.6
Sweden
8.5
1.9
Austria
4.7
12.0
France
2.6
10.3
United Kingdom
8.4
4.4
Spain
10.9
9.0
Australia•
9.1
4.5
Netherlands•
0.9
3.2
Switzerland"
12.8
4.6
Finland•
7.4
10.1
Norway•
1.7
1.0
United States
Japan
Germany
..
Note: (1) Variance decompositions are calculated from a four-lag
VAR including GDP gap, unemployment rate deviation from trend,
capacity utilization measure, and CPI/GDP deflater. Variables
ordered as listed.
* Capacity utilization measured as the percentage deviation of
industrial production from its Hodrick-Prescott filter trend. For
Switzerland unemployment rate is not included in the regressions.
Table 4
Test of Direct Link Between Capacity Utilization and Inflation
Country
Null Hypothesis:
CU Does Not Predict
Producer Price Inflation
(p-values)
Percentage of Inflation
Variation Accounted for
by PPI (over 8 quarters)
GDP Deflator
CPI
United States
0.03
39.2
38.5
Japan
0.33
64.1
83.6
Germany
0.88
15.7
47.8
Italy
0.15**
12.1
37.2
Canada
0.58
35.8
51.0
Sweden
NA
NA
NA
Austria
NA
NA
NA
France
0.34**
4.7
3.6
United Kingdom
NA
NA
NA
Spain
0.21
21.4
14.9
Australia*
0.97
28.1
50.1
Netherlands*
0.55
37.7
19.4
Switzerland*
0.96
44.9
60.3
Finland*
NA
NA
NA
Norway•
0.51
21.1
48.1
Note: (1) P-values are calculated from a regression including four lags of PPI inflation, GDP
gap, unemployment rate deviation from trend, and capacity utilization measure.
(2) Variance decompositions are calculated from a four-lag VAR including
consumer services price inflation, PPI inflation, GDP deflator/CPI inflation. Variables ordered
as listed.
• Capacity utilization measured as the percentage deviation of industrial production from its
Hodrick-Prescott filter trend. For Switzerland the unemployment rate is not included in the
regressions.
•• Impact does not have the expected sign.
Tables
Tests of Wage Contagion Hypothesis
Country
Null Hypothesis:
CU Does Not Predict
Earnings In
Manufacturing
(p-values)
l1l
Null Hypothesis:
Manufacturing Earnings
Does Not Predict
Nonmanufacturlng
Earnings
(p-values)
12\
Percentage of Variation of
Nonmanufacturlng Earnings
Explained by Manufacturing Earnings
4 • Quarters
8 • Quarters
United States
0.20
-
-
--
--
Japan
0.01
0.01
31.1
25.6
Germany
0.06
0.00
10.5
13.1
Italy
0.24
.-
--
Canada
0.52
--
Sweden
0.04
--
---
----
Austria
0.00**
0.01
11.9
4.5
France
0.00
0.08
3.7
7.1
United Kingdom
0.28
-.
--
--
Spain
0.50
--
--
--
Australia*
0.56
--
--
Netherlands*
0.56
--
--
Switzerland
NA
Finland*
NA
Norway*
0.76
------
----
----
Notes: (1) P-values are calculated from a regression including four lags of manufacturing earnings, unemployme
nt rate
deviation from trend, and CPI inflation.
(2) P-values are calculated from a regression including four lags of nonmanufacturing earnings, manufacturing earnings
GDP gap, unemployment rate deviation from trend, and CPI inflation.
(3) Variance decompositions are calculated from four-lag VARSs including GDP gap, unemployment rate deviation
Iron
trend, CPI inflation, manufacturing earnings, and nonmanufacturing earnings . Variables ordered as listed
• Capacity utilization measured as the precentage deviation of industrial production tom its Hodrick-Prescott filter
trend.
For Switzerland the unemployment rate is not included in the regressions.
•• Impact not of the expected sign.
Table Sa
Tests of an Investment Link
Country
Null Hypothesis:
Capacity Utilization
Does Not Predict
Business Fixed Investment
(p- values)
(1)
Null Hypothesis:
Business Fixed Investment
Does Not Predict Inflation
(p-values)
GDP Deflator
(2\
CPI
13)
United States
0.00
0.30
0.10
Japan
0.03
0.20
0.61
Germany
0.68**
Italy
0.01**
0.87
0.28
Canada
0.01
0.44
0.63
Sweden
NA
Austria
0.26**
France
0.06
0.79**
0.88
United Kingdom
0.12
0.55**
0.31
Spain
0.46**
Australia*
0.68
Netherlands*
0.09
Switzerland*
0.12
Finland*
0.44
Norway•
NA
Notes: (1) P-values are calculated from regressions including four lags of machinery and
equipment investment, GDP gap, and capacity utilization measure.
(2), (3) P-values are calculated from regressions including four lags of the GDP gap,
unemployment rate deviation from trend, the growth rate of machinery and equipment investment,
and the CPI/GDP deflator inflation rate. For Switzerland the unemployment rate is not included
in the regression.
* Capacity utilization measured as the percentage deviation of industrial production from its
Hodrick-Prescott filter trend.
** Impact not of the expected sign.
•
Table 6b
Tests of an Investment Link
Null Hypothesis:
Capacity Utilization Does Not
Predict Capital Goods Prices in
Capital Goods Producing Countries
(p- values)
Null Hypothesis:
Capital Goods Prices Do Not
Predict Inflation in Capital
Goods Producing Countries
(p-values)
OECD
Aggregate CU
InvestmentAggregate CU
Deflator
(3)
CPI
(3)
(1)
(2)
us
0.73
0.05
0.00
0.01
Japan
0.15
0.36
0.00
0.02
Germany
0.00
0.00
0.01
0.01
Capital Goods
Producers
i
Note: (1) P-values are calculated from a regression including four lags of capital goods PPI, OECD aggregate
measure of CU, GDP gap, and country capacity utilization measure.
(2) P-values are calculated from a regression including four lags of capital goods PPI, investment aggregate
measure of CU, GDP gap, and country capacity utilization measure. Investment-aggregate CU is calculated by
aggregating the capacity utilization measure only for countries where CU has predictive power for investment.
(3) P-values are calculated from a regression including four lags of capital goods PPI, GDP gap, unemployme
rate deviation from trend, and CPI/GDP deflator inflation rate.
** Impact not of the expected sign.
Appendix Table 1
DIAGNOSTIC STATISTICS FOR INFLATION EQUATIONS
Country
Price Index
Adj. R-sq
DW
Q (0-25)
o-value
LM (8)
pvalue
Modified LM (8)
ovalue
U.S.A.
CPI
Deflator
0.72
0.74
2.00
1.80
0.64
0.02
0.01
0.05
0.00
0.15
Canada
CPI
Deflator
0.96
0.67
1.98
1.89
0.94
0.52
0.08
0.30
0.07
0.30
United Kingdom
CPI
Deflator
0.94 .
0.53
2.00
2.02
0.64
0.40
0.21
0.02
0.23
0.02
Japan
CPI
Deflator
0.95
0.96
1.87
1.86
0.02
0.00
0.00
0.03
0.00
0.02
Germany
CPI
Detlator
0.95
0.52
2.02
1.94
0.95
0.00
0.45
0.01
0.76
0.01
France
CPI
Deflator
0.98
2.02
2.12
0.01
0.10
0.02
0.00
0.01
0.63
Italy
CPI
Deflator
0.97
0.65
2.06
1.80
0.08
0.23
0.02
0.22
0.00
0.35
Australia
CPI
Deflator
0.91
0.54
1.91
2.22
0.69
0.99
0.00
0.01
0.00
0.48
Austria
CPI
Deflator
0.90
0.75
1.96
2.16
0.79
0.18
0.49
0.00
0.32
0.00
Switzerland
CPI
Deflator
0.97
0.75
2.12
1.95
0.07
0.84
0.03
0.00
0.00
0.00
Netherlands
CPI
Deflator
0.95
0.68
1.97
1.86
0.01
0.44
0.14
0.02
0.11
0.02
Norway
CPI
Deflator
0.93
0.04
1.90
1.97
0.97
0.52
0.44
0.14
0.27
0.03
Sweden
CPI
Deflator
0.84
0.72
1.86
2.02
0.59
0.45
0.11
0.00
0.03
0.00
Spain
CPI
Deflator
0.93
0.83
2.02
2.02
0.00
0.01
0.00
0.01
0.00
0.00
Finland
CPI
Deflator
0.96
0.84
1.90
1.98
0.92
0.76
0.16
0.05
0.87
0.00
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