Download The macroeconomic impacts of oil price shocks

1.4
The macroeconomic impacts
of oil price shocks
1.4.1 A short history
of a controversial topic
Since the 1973 OPEC (Organization of Petroleum
Exporting Countries) oil embargo, the role of rapid,
unanticipated increases in oil prices has been a topic
of intense interest, among both economists and the lay
public. Considering the magnitude of widespread
national recessions during the 1970s, the controversy
surrounding research on the macroeconomics of oil
price shocks may seem surprising: why would anyone
doubt the capacity of oil price shocks to cause the
major movements in GDP (Gross Domestic Product)
which have been observed in so many countries?
Possibly most important in fueling the controversy
is the small share of GDP that oil and its close
substitutes have comprised in most economies: 1.5%
to 3% prior to the 1973 episode. Experienced
macroeconomists doubted that even a sizeable shock
to such a small part of the economy could have the
observed effects. Second, the 1973 episode itself was
not a clean experiment because a number of other
major factors were emerging at the same time. The
world economy was just getting off the post-Bretton
Woods fixed exchange rate regime. A number of
countries, including the United States, was teetering
on the brink of recession at the time of the 1973
shock; in the United States in particular, monetary
policy tightened right around the time of the 1973
shock. Separating these effects and deciding the role
of oil price shocks in post 1973 movements of GDP,
unemployment rates, and other recessionary indicators
has been technically difficult; the potential answers to
oil’s role have been seen as important components of
diverse doctrinal programs, ranging from fundamental
paradigm shifts in macroeconomics to more purely
political agendas. Many technical results were clouded
by the limited ability to control enough variables to
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test precise hypotheses, while econometric results
were filtered through strong preconceptions about how
the world worked and equally strong desires about how
it should work. Third, some major industrial countries
were not nearly as hard-hit by the 1973 shock as
others, and econometric analysis was unable to offer
widely satisfactory explanations. Putting all these
factors together has fueled a quarter-century of
vigorous research and equally vigorous controversy
about the ability of oil price shocks to cause
macroeconomic recessions.
In 1983, James Hamilton published a paper
identifying a Granger-causal relationship between oil
prices and GDP in the United States during the entire
post-Second World War period through the 1979-80
episode associated with the Iranian Revolution and the
opening of the Iran-Iraq War (Hamilton, 1983). The
author found that oil prices had increased sharply prior
to every recession the United States experienced in the
post-war period. Several years later, McMillin and
Parker (1994) found that decreases in oil prices during
the decades between the First World War and the
Second World War, accompanying the discovery of
new fields and the development of new extraction and
refining technologies, had contributed to economic
growth, even during the difficult decade of the 1930s.
While Hamilton’s paper was ultimately more
influential, the combined findings pushed the evidence
of oil prices’ ability to affect the macroeconomy back
nearly sixty years before the OPEC embargo.
The year 1989 saw two particularly influential
publications on the economics of oil price shocks, with
results pointing in opposite directions. Douglas Bohi
published a monograph that was unable to find a
relationship between oil price movements and
three-digit-SIC (Standard Industrial Classification)
employment in Japan and the United States during the
1973-74 and 1979-80 oil price shocks, from which he
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MINERAL RESOURCES BETWEEN SCARCITY AND GROWTH
concluded that the ensuing recessions were attributable
to inappropriately tightened monetary policy rather
than oil price shocks (Bohi, 1989). Knut Anton Mork
pursued some unresolved results from Hamilton’s
1983 paper, which showed a weakening of the oil
price-GDP relationship during the 1970s (Mork,
1989). With the additional observation of the
non-response to the 1986 oil price collapse, Mork
noticed that oil price increases appeared to impede
economic growth but oil price decreases, at least
during the post-Second World War period, did not
appear to boost growth. Mork introduced the concept
of asymmetric effects of oil prices with separate
variables for increases and decreases. This asymmetric
oil price variable was able to yield a stable relationship
between oil prices and GDP during the full post-war
period. The asymmetric macroeconomic effect of oil
prices has been widely accepted since Mork’s 1989
publication, but the matter of whether the oil
price-GDP relationship has been stable has not been
resolved to the satisfaction of all researchers,
particularly during the 1990s.
One of the results of the asymmetric oil price
specification is that both unanticipated oil price
increases and unanticipated decreases can have
disruptive effects on an economy. Lilien’s dispersion
(Lilien, 1982) or sectoral shocks hypothesis offers a
mechanism to account for this behaviour, and Gilbert
and Mork (1986) offered an early explanation for the
phenomenon along similar lines. This effect can
account for the lack of positive response around the
world to the 1986 oil price crash.
1.4.2 Microeconomic mechanisms
that transmit oil price shocks
to the macroeconomy
During the 1990s, following the challenge from Bohi
and the asymmetric relationship identified by Mork,
research focused on transmission mechanisms by
which oil price shocks might cause, or contribute to,
macroeconomic recessions. Several major possible
transmission mechanisms have been identified and, to
varying extents at present, researched; a) labour
markets; b) capital equipment utilization; c) interest
rate channels; d ) uncertainty and investment pauses;
e) the sectoral shocks hypothesis. These mechanisms
need not be mutually exclusive.
Labour market channels. Analysis of labour
market mechanisms has used the concept of aggregate
and allocative channels, the former being the
traditional macroeconomic mechanisms of potential
output effects, income transfers, and wage stickiness,
and the latter involving the closeness of match
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between desired and actual factor input levels across
firms. Research on labour markets has observed that a
given level of unemployment is comprised of far more
extensive destruction and creation of jobs, with the
observed unemployment rate being a net result. Davis
and Haltiwanger (2001) found that oil price shocks
cause more job destruction than job creation in nearly
every industrial sector, with a magnitude about twice
that of monetary shocks. The reallocative impact of the
OPEC shock of 1973Q3 to 1974Q4 amounted to about
11% of the total US manufacturing employment over
the fifteen quarters following that episode. They found
much of this reallocation within four-digit industries,
which suggests that Bohi’s research may have
examined employment changes at too aggregated a
level to find the effects of oil price shocks.
Keane and Prasad (1996) found that oil price
increases depressed real wages for all workers in the
United States but raised the relative wage of skilled
workers. They found that oil price changes did not
appear to cause labour to move into sectors with
relative wage increases. It is possible that oil price
shocks change optimal technologies in industries in
ways that destroy part of workers’ less tangible
skills, inducing them to find employment in
industries requiring skills below their apparent
human capital levels. Part of workers’ human capital
may be firm-specific; thus separation becomes a
more potent force for downward mobility, at least in
the short term. The same study suggests that skilled
labour may be a substitute for energy in many
industries.
Capital equipment utilization. This channel has
not received empirical examination to date, but Finn
(2000) has developed a relatively aggregative model in
which oil price increases depress capital’s future
marginal product, which reduces investment and future
capital stock. Oil price increases thus can have longlived effects on output.
Interest rates. Several studies have found that
interest rates respond to oil price increases (Ferderer,
1996; Hooker, 1996, 1999; Balke et al., 2002). Balke
and co-authors found asymmetric responses of
short- and long-term interest rates to separate positive
and negative oil price shocks, and found evidence
of a choosing of quality in the spread between
four- to six-month period of commercial paper and
six-month Treasury bills. This relationship suggests
a search for security when an oil price increase
heightens uncertainty in the economy.
Uncertainty and investment pauses. Bernanke
(1983) developed a model of irreversible investment in
which an oil price increase heightens uncertainty and
causes firms to defer investment until some of the
uncertainties are clarified. This model has not been
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THE MACROECONOMIC IMPACTS OF OIL PRICE SHOCKS
applied to macroeconomic impacts of oil price shocks
although a number of authors have alluded to the
possibility of such an effect producing the phenomena
they observe.
Sectoral shocks. The sectoral shocks hypothesis
(Lilien, 1982; Hamilton, 1988), known alternatively as
the employment dispersion hypothesis, proposes that a
shock that has differential effects across sectors will
have a larger impact on aggregate unemployment.
Greater dispersion of sectoral shocks increases the
labour reallocation required, which leads to a larger
overall unemployment rate. The reallocative impacts of
any price shock, positive or negative, can cause an
increase in job creation and destruction, leading to an
increase in aggregate employment as time is required
for resources to be reabsorbed elsewhere in the
economy.
Loungani (1986) found evidence supporting the
possibility that oil price shocks were such a sectorally
dispersed event. Other researchers (Keane and Prasad,
1996; Carruth et al., 1998; Davis and Haltiwanger,
2001) have found partial support for this view of the
potency of oil price shocks.
1.4.3 Monetary policy in response
to oil price shocks
Early scepticism that oil comprised a large enough
sector of the economy to be responsible for the
recessions following the price shocks has led to a
number of studies that try to distinguish between the
effects of oil price shocks and monetary policy
shocks around the episodes of 1973-74, 1979-80 and
1990-91. The small number of observations, as well
as the intricacy of the economic interactions
involved in modelling, have rendered it difficult to
obtain clear results. Bernanke and co-authors (1997)
claimed to have established that monetary policy
shocks were the dominant contributor to the
recessions of 1974-75, 1982, and 1991, and that the
preceding oil price shocks were of little
consequence. Their modelling approach, a VAR
(Vector AutoRegression) simulation exercise,
required specification of how private capital markets
operated; a re-examination of their model and data
by Hamilton and Herrera (2004) found that the
Bernanke and co-authors model implied that the
Federal Reserve would have had to depress the
federal funds rate by 900 basis points and that
private investors would have had to overestimate the
funds rate for thirty-six months in a row. Allowing
for a more plausible monetary policy, Hamilton and
Herrera found that monetary policy could do little to
mitigate the effects of oil price shocks.
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The alternative hypothesis, that monetary policy
has systematically responded to oil price shocks and
has been responsible for the post-shock recessions
instead of the oil price shocks, is sometimes called the
systematic monetary policy hypothesis. Hooker (2000)
found that the federal funds rate became less sensitive
to oil price changes at the very time the systematic
monetary hypothesis would have required it to become
more sensitive.
Altogether, current evidence suggests that the
recessions following the past quarter century’s oil
price shocks were due to these shocks, rather than
monetary policy.
1.4.4 What constitutes
an oil price shock?
The analysis of oil price shocks began with a single oil
price variable used in regressions, which was capable
of estimating symmetric effects of oil price changes.
Mork’s (1989) asymmetric approach used separate
variables for oil price increases and decreases. This
construct was able to yield a stable relationship
between oil price shocks and GDP over the entire
post-war period through the late 1980s, but, by the
mid-1990s, it began to perform less effectively in later
sample periods. It became recognized that the term
‘shock’ implied an unexpected change, rather than just
a change, in the oil price, and that the measure of a
genuine shock might well be more intricate than the
asymmetric oil price variable.
Lee and co-authors (1995) were the first authors to
develop a measure of the oil price that accounts for the
surprise component of a particular price change. Their
measure divides the change in each period by an index
of recent volatility of oil prices. Hamilton (2001)
showed that the oil price shock measure in this article
was capable of yielding a stable relationship between
oil prices and GDP over the entire postwar period
through the 1990s.
Hamilton (1996) introduced the NOPI (Net Oil
Price Increase) concept, with a variable that used only
positive changes which reached a level greater than
had been reached within the previous year. The
one-year NOPI measure yielded a stable oil price-GDP
relationship through the date of publication, but
subsequent analysis of the NOPI concept found that
extending the one-year time period to three years
improves the stability of the oil price-GDP
relationship. The three-year NOPI measure identifies
an oil price increase as a shock only if the price
surpasses the highest observed price during the
previous three years, and the size of the shock is only
the percentage by which the three-year peak is
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MINERAL RESOURCES BETWEEN SCARCITY AND GROWTH
exceeded. Hamilton (2003), recognizing the
simultaneity between oil supply and price,
experimented with a physical measure of oil supply
disruption, with results quite similar to those obtained
with the three-year NOPI variable.
1.4.5 The econometrics
of oil price shocks
Early econometric analysis of oil price shocks
relied on ordinary least squares regression
estimation, generally augmented with
autocorrelation corrections. These analyses were
generally structural estimations, from which
elasticities could be derived readily. Hamilton
(1983) introduced the newer time-series technique
of VAR to the subject, to accommodate the timeseries character of macroeconomic data, and most
analyses since then have used VARs (error
correction models, another time series technique,
have been used in oil price-macro analyses as well.)
Because of the lag structure of VARs, the regression
coefficients are not interpretable as elasticities. The
coefficients of the impulse response function
calculated from a VAR are technically the partial
elasticities to the dependent variable with respect to
the independent variable, and accordingly the sum
of the lagged IFR (Impulse Response Functions)
coefficients could be interpreted as roughly
comparable to an elasticity (of GDP to oil prices),
but the results of the IFR calculation are dependent
on the method of triangularization of the VAR in a
way that introduces an element of arbitrariness into
the estimation that has been difficult to measure
(Hamilton, 1994).
While the analysis of oil price shocks has turned to
mechanisms that convert these price movements into
large and sustained output and employment
movements, the time series econometrics has not kept
up with its ability to test structural hypotheses.
Structural approximations with VARs are constructed
with triangularization methods that imply causation,
but the results are sensitive to the ordering of
variables. Coefficient values are not readily
interpretable as magnitudes of coefficients in
structural economic models, and the interpretation of
impulse response functions may not be entirely clean
measures of the effect of a variable of interest. This is
an area where improvement in application should be
expected in the coming decades. In the meantime,
hypothesis testing of structural relationships such as
can be conducted with cross sectional data is not a
straightforward matter in oil shock-macroeconomy
research.
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1.4.6 Non-US evidence
Much of the empirical estimation of oil price shocks'
impacts on aggregate economic performance has been
conducted with US data. Nonetheless, some work was
done early on non-US data, and research has been
published recently on examination of European
countries. Burbidge and Harrison (1984) used a VAR
approach with a symmetric oil price change
specification to compare the response of the United
States, the United Kingdom, Canada, Japan, and
Germany following the 1973 and 1979-80 oil price
shocks. They found that oil price shocks affected all
these countries, but that Japan was more strongly
affected by the 1979-80 shock than the 1973 one.
Mork and co-authors (1994) estimated VARs with
Mork’s (1989) asymmetric oil price specification for
seven OECD (Organization for Economic Cooperation
and Development) countries from 1967 to 1992.
France, the United Kingdom, West Germany, Canada,
and Japan exhibited much of the temporal pattern of
response as found in the United States, but Norway,
being a major oil producer, had a positive response to
oil price shocks.
Several European studies have appeared recently.
Using a symmetric oil price specification over the
period 1989-99, Papapetrou (2001) reported impulse
response functions for effects of oil price changes on
Greek industrial production and employment of
⫺0.027 and ⫺0.008. These relatively small impacts
excluded the periods of the large oil shocks of the
1970s, but oil prices still accounted for about 20% of
the forecast error of industrial production and around
10 to 20% for employment, considerably larger than
the impact of interest rates. Cuñado et al. (2003) used
a VAR approach to study fourteen European countries,
comparing the performance of alternative oil price
specifications. Hamilton’s NOPI specifications and
Mork specifications yield significant impacts of oil
price shocks in eleven cases, and the Lee and
co-authors (1995) variance-conditioned specification
in thirteen instances. The negative effect of an oil price
shock on industrial production typically reached its
peak about six quarters after a shock, and recovery
was reached by ten to twelve quarters. Miguel and
co-authors (2003) simulated a real business cycle
model, much like Finn’s (2000), to study impacts of oil
price shocks on Spain’s economy. They emphasized
the exogeneity of the interest rate to the Spanish
economy, an important difference from the US case.
They found oil price shocks could account for 60% of
the business cycle fluctuations from 1970 through
1998.
Bachman and Jacquet (1998), in unpublished work
conducted for the US Department of Energy’s Office of
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THE MACROECONOMIC IMPACTS OF OIL PRICE SHOCKS
the Strategic Petroleum Reserve, conducted VAR
analyses of oil price shocks on fifteen developed and
developing countries of the Pacific Rim: the United
States, Canada, Mexico, Chile, Australia, Japan, the
Philippines, Indonesia, Singapore, Hong Kong,
Malaysia, Thailand, Taiwan, South Korea and China.
Few other studies have targeted many of these countries.
They used the Mork asymmetric oil price specification
and found negative responses of GDP in the first and
second years after both oil price increases of 100% and
decreases of 50% across many of these countries.
1.4.7 The impact of oil prices
on the macroeconomy
The most recent OLS (Ordinary Least Square)
non-VAR estimate of the oil price-GDP elasticity for
the United States is Mory’s (1993) estimate of ⫺0.055.
This amount is very close to the sum of lagged oil
price coefficients Mork and co-authors (1994)
estimated for the United States with a VAR, ⫺0.054,
but the lag coefficients component in the denominator
of the reported coefficients is unknown. This number
need not be a reliable estimate of the impact of oil
price shocks on US GDP. The Mork and co-authors
article reports VAR estimates of the oil price-GDP
relationship for about a dozen OECD countries, and
the sum of the lagged oil price coefficients varies
considerably across countries. However, because of the
construction of those coefficients, inferences from
direct comparison of their magnitudes are hazardous.
The sum of Hamilton and Herrera’s (2004) impulse
response coefficients over forty-two months for the
United States is ⫺0.055, using the one-year NOPI
measure of oil price shocks. The sum of impulse
response coefficients over eight quarters in Hamilton
(2003), estimated for the United States over 1997-98,
is -0.116 using the three-year NOPI and ⫺0.535 using
the method developed by Lee and co-authors (1995) to
measure oil price surprises. Thus, the three-year NOPI
indicates a sizeable GDP response when the hurdle of
the three-year previous high oil price is exceeded.
European studies show a similar range of responses,
but with the variability to be expected among
countries with different industrial structures and
monetary policies.
Government agencies and agencies such as the
IMF (International Monetary Fund) and OECD not
infrequently report oil price-GDP elasticity
estimates that are actually simulation results. The
results have varied between ⫺0.002 and ⫺0.01.
These numbers are not to be taken seriously. The
large simulation models do not attempt to use
asymmetric oil price change specifications nor
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develop measures of the surprise content of some oil
price changes. These models also specify oil only as
a commodity input into production functions in
ways that would not distinguish it from coffee or
sugar. None of the mechanisms that research has
found to be plausible transmission mechanisms
capable of converting oil price shocks into
disproportionately large GDP movements are
present in these models (Jones et al., 2004)
Raymond and Rich (1997), using a
regime-switching model, found evidence that oil price
shocks appear not to cause an economy to change
from a growth regime to a recession regime. However,
they did find statistically significant evidence that the
NOPI measure of oil price shocks has a strong
depressing effect on the growth rate in low-growth
periods. These findings are consistent with the more
casual observations that the great oil price shocks of
the last quarter of the twentieth century occurred when
economies were either teetering on the brink of
recession or entering into what could otherwise have
been a milder recession.
Altogether, it appears that oil price shocks have not
precipitated these recent recessions – they may have
tipped earlier ones – but they have helped incipient
downturns become full-scale recessions.
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