Order Code RL31608
CRS Report for Congress
Received through the CRS Web
The Effects of Oil Shocks on the Economy:
A Review of the Empirical Evidence
Updated January 18, 2006
Marc Labonte
Specialist in Macroeconomics
Government and Finance Division
Congressional Research Service ˜ The Library of Congress

The Effects of Oil Shocks on the Economy:
A Review of the Empirical Evidence
Summary
Congress is concerned with preventing economic recessions and mitigating the
effects of recessions. Eight of the nine post-war recessions were accompanied by
sharp increases in the price of oil. The last four recessions followed this pattern: the
1973-1975 recession followed the oil embargo; the double dip recession of 1980-
1982 followed the second oil shock, which was caused by the Iranian revolution and
Iran-Iraq War; the 1990-1991 recession followed the oil price spike induced by the
Gulf War; and the 2001 recession followed a sharp rise in oil prices from 1999 to
2000. Policymakers are concerned that the recent rise in oil prices could again
spillover into the wider macroeconomy.
The coincidence of recessions and oil shocks does not prove that oil price
changes have any effect on the economy. To make that case, one must use statistical
methods to hold other economic factors constant. This report surveys the
econometric literature on oil shocks to provide quantitative estimates of how large
an effect oil price changes have on economic activity. It also reviews the statistical
robustness of these findings and discusses some of the limitations of these types of
statistical analyses.
Economic theory suggests that oil shocks lead to higher inflation, a contraction
in output, and higher unemployment in the short run. It is the rise in energy prices,
rather than “high” energy prices, that causes these macroeconomic problems.
Effective policy responses are difficult because expansionary policy would
exacerbate the inflationary pressures whereas contractionary policy would exacerbate
the contraction in output.
There is a fair degree of consensus surrounding the range of estimates: for
comparable studies, the cumulative effect of a 10% increase in oil prices during a
one-quarter (3 month) period would be to reduce economic output by 0.2-1.1% over
the next year from its baseline level. The magnitude of these estimates suggests that
normal fluctuations in the price of oil would cause only minor fluctuations in
economic growth. However, the estimates suggest that major oil shocks, in which
oil prices rise for several consecutive quarters, often by more than 10% per quarter,
could lead to recessions, all else equal. Some of the findings are not statistically
robust. A few studies dissent from these findings.
Many studies find that the effects of oil on economic activity are waning. For
example, a 2004 study found that a 10% increase in oil prices would only reduce
GDP by 0.2% in 1998. Surprisingly, many studies found oil to have had stronger
economic effects before the mid-1970s, although the major post-war oil shocks
occurred since the mid-1970s. The studies suggest that the relationship between oil
prices and economic activity is not a simple linear one (e.g., episodic oil price
declines have negligible economic effects), but there is no straightforward way to
identify a more accurate relationship.
This report will be updated as new research becomes available.

Contents
Theoretical Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Policy Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Examining the Empirical Record . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Early Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Oil Shocks or Monetary Policy? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
The Effects of Oil Shocks on Employment . . . . . . . . . . . . . . . . . . . . . . . . . 12
Some Caveats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Omitted Variable Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Structural Misspecification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Problems with Endogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Lucas Critique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Robustness of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
List of Tables
Table 1. Summary of Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

The Effects of Oil Shocks on the Economy:
A Review of the Empirical Evidence
Congress is concerned with preventing economic recessions and mitigating the
effects of recessions. Eight of the nine post-World War II recessions were preceded
by or accompanied with sharp increases in the price of oil. The last four recessions
followed this pattern: the 1973-1975 recession followed the oil embargo; the double
dip recession of 1980-1982 followed the second oil shock, which was caused by the
Iranian revolution and Iran-Iraq War; the 1990-1991 recession followed the oil price
spike induced by the Gulf War; and the 2001 recession followed a sharp rise in oil
prices from 1999 to 2000. This would seem to be persuasive evidence that oil prices
play a strong role in determining the business cycle. But the coincidence of
recessions and oil shocks does not, by itself, prove that oil price changes cause
economic recessions. To make that case, one must use statistical methods to hold
other economic factors constant. This report surveys the econometric literature on
oil shocks to provide quantitative estimates of how large an effect oil price changes
have on economic activity. It also reviews the statistical robustness of these findings
and discusses some of the limitations of these types of statistical analyses. Before
examining the empirical record, it is useful to explore why economists believe oil
shocks might affect the economy, and explore the channels through which that effect
is transmitted.
Theoretical Considerations
Due to the central role energy plays in the functioning of our economy, changes
in energy prices are not the same as changes in the price of most other goods. Energy
“shocks” can have macroeconomic consequences, in terms of higher inflation, higher
unemployment, and lower output.
Economic theory suggests that economies suffer from recessions due to the
presence of “sticky prices.” If markets adjusted instantly, then recessions could be
avoided: whenever economic conditions changed, price and wage changes would
automatically bring the economy back to full employment. In actuality, however,
there are menu costs,1 information costs, uncertainty, and contracts in our economy
that make prices sticky. As a result, adjustment takes time, and unemployment and
economic contraction can result in the interim.
1 Products with high “menu costs” are those which are costly to re-price, and therefore have
sticky prices. Restaurant menus, periodicals, and catalog items are examples of products
with high menu costs.

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Historically, energy price shocks have proven particularly troublesome for the
U.S. economy. Sharp spikes in the price of oil have preceded nine of the 10 post-war
recessions, including the latest one. When oil prices rise suddenly, the overall
inflation rate is temporarily pushed up because other prices do not instantly adjust
and fall. At the same time, because energy is an important input in the production
process, the price shock raises the cost of production. Because other prices do not
instantly fall, the overall cost of production rises and producers must cut back
production, which causes the contraction in output and employment, all else equal.
There may also be adjustment costs to shifting toward less energy intensive methods
of production, and these could temporarily have a negative effect on output.
Typically, the effect on output occurs over a few quarters. The recent energy price
spike followed this pattern, with oil prices rising in the second half of 1999 through
the first half of 2000, and output growth slowing in the third quarter of 2000.2
The magnitude of an oil shock’s effect on the economy should depend on how
much oil that economy uses. As the ratio of energy use to GDP in the United States
has declined over time, one would expect the economic effects of an oil shock to
lessen. This may help explain why the recent oil shock has had a smaller economic
effect than in the past.
The effects described thus far can be thought of as occurring on the supply side
of the economy. Oil shocks may also affect aggregate demand. When energy prices
rise they involve an income transfer from consumers to producers. Since producers
are also consumers, aggregate demand is likely to fall only temporarily as producers
adjust their consumption to their now higher incomes. This adjustment is likely to
be longer when the income recipients are foreigners than when they are Americans.
A second effect on demand can be expected to occur because the rise in energy prices
will probably push up the overall price level because other prices do not fall
immediately in the face of a decline in demand. The increase in the price level will
reduce the real value of the available amount of money in the hands of buyers, and
this reduction in the real money stock will also reduce spending. A third effect on
demand can occur if the rise in energy prices increases uncertainty and causes buyers
to defer purchases. This effect is also likely to be of a short run nature. The
magnitude of all three effects will depend on how much energy prices rise and how
long they remain high.
Rising oil prices also affect the international balance of payments in the short
run. If the cost of U.S. oil imports increases following a price rise, this constitutes
a transfer in purchasing power from U.S. consumers to foreign oil producers. How
this affects the current account deficit (trade deficit) depends, in turn, on how foreign
oil producers decide to use this purchasing power. If they use it to purchase U.S.
goods, then U.S. exports would increase and there would be little effect on the
current account deficit. If they use it to purchase U.S. assets — whether corporate
stocks, Treasury bonds, or by simply leaving the revenue in a U.S. bank account —
2 If rising energy prices affect the economy through this transmission mechanism, then
falling energy prices should have the opposite effect on the economy: they should
temporarily lower inflation and raise output, all else equal. Many of the studies to follow
find that this is not true, however.

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then it would represent an inflow of foreign capital to the United States, which would
increase the current account deficit.
For those oil-exporting countries that maintain a floating exchange rate against
the dollar, the dollar would be likely to depreciate following an oil shock relative to
their currencies. But many of the major oil-exporting countries maintain a fixed
exchange rate against the dollar; for this reason, the value of the dollar would be
unlikely to be greatly affected by an oil shock in the short run. In the long run,
however, the real value of the dollar must fall to pay for the more expensive oil
imports; against countries who fix their nominal exchange rate to the dollar, this will
occur through relative price adjustment (i.e., higher inflation in oil producing
countries than in the United States). Short-run exchange rate adjustment may also
occur more slowly because world oil market transactions are made using the U.S.
dollar.
Both the inflation and output
Theory suggests that oil shocks reduce
effects of energy shocks are temporary:
economic growth and increase inflation
that is, once prices adjust, the economy
simultaneously. This makes an
returns to full employment and its
effective policy response difficult.
sustainable growth path.3 This
observation yields an important insight:
it is not the level of energy prices that
affects economic growth and inflation, but rather the change in energy prices. Thus,
if policymakers wish to mitigate the effect of energy prices on output and inflation,
they should be concerned with rising energy prices and should not be concerned with
“high” energy prices, even if the high prices are permanent. The only permanent
macroeconomic effect of higher energy prices is their negative effect on the terms of
trade. The “terms of trade” is a measure of standard of living that refers to the labor
and capital embodied in U.S. exports that can be exchanged for the labor and capital
embodied in foreign imports. It means that the United States has to give up more of
the goods it produces than previously to obtain a barrel of oil. Permanently higher
energy prices lead to a one-time permanent decline in the terms of trade and the
standard of living of U.S. consumers, all else equal.
Policy Implications. Historically, formulating an effective policy response
to oil shocks has been difficult. Expansionary fiscal or monetary policy increases
aggregate demand and inflationary pressures. In typical downturns, monetary and
fiscal policy can safely become expansionary without triggering a significant increase
in inflation because the fall in demand reduces inflationary pressures. In oil shocks,
policymakers must be simultaneously concerned with the fall in economic activity
and the rise in prices. By tackling one problem, they risk exacerbating the other. For
example, if policymakers use expansionary fiscal or monetary policy to offset the fall
in output, prices may rise further and inflationary expectations could become
embedded. This was the problem in the 1970s. Inflation, which was already rising
3 This point is not always explicitly made in the time series analyses reviewed below, which
tend to end their estimates at the last time lag that yields statistically significant data or
arbitrarily cut off the estimates after a few lags to meet a statistical criterion concerning the
limit on the number of variables allowed.

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before the oil shocks, continued to accelerate following the oil shock of 1973 until
it reached double digits in 1974. Once the public came to expect higher inflation, the
subsequent expansionary policy measures had less and less of a positive effect on
aggregate demand, making the purported tradeoff between inflation and
unemployment less and less favorable.4 Following the second oil shock of 1979, a
Federal Reserve that was determined to stamp out double-digit inflation chose
instead to tackle the inflationary pressures caused by the oil shock by raising interest
rates. This decision exacerbated the effect on output, contributing to the most severe
economic contraction since the Great Depression.
Another reason why policy responses have been unable to prevent oil shocks
from leading to recessions historically is because policy changes are hampered by
lags in policy recognition, implementation, and effectiveness. Because oil shocks are
typically unpredictable events, policy cannot be modified far enough ahead of time
to prevent a downturn.
Examining the Empirical Record
The remainder of this report reviews the econometric literature on oil shocks,
which has attempted to quantitatively estimate how much of an effect oil prices have
on macroeconomic variables such as GDP growth, inflation, and unemployment.
Technical terms are defined in a glossary at the end of the report.5
Early Studies
Darby had one of the earliest econometric studies that attempted to estimate the
economic effects of oil shocks.6 His study aimed to determine what had caused the
1973-1975 recession. He hypothesized that it could have been due to four causes: the
removal of the Nixon price control regime (because GDP was overstated during the
regime), the breakdown in the Bretton Woods exchange rate regime, the slowdown
in money growth (contractionary monetary policy), or the oil shock. He estimated
that the 1973 oil shock caused a total cumulative decrease in GNP of 2.5%.
Although the oil shock’s effect on the economy was statistically significant, statistical
tests could not rule out the possibility that it was the removal of price controls, rather
than the oil shock, that caused the recession.
4 Expansionary monetary policy leads to an increase in output only because prices do not
adjust instantly to the increase in the money stock. If prices adjusted instantly, there would
be no increase in output. Thus, a given change in the money stock would have a smaller
effect on output when inflationary expectations are high.
5 A broader literature review can be found in Donald Jones, Paul Leiby, and Inja Paik, “Oil
Price Shocks and the Macroeconomy: What Has Been Learned Since 1996,” Energy
Journal
, vol. 25, no. 2, 2004, p. 1.
6 Michael Darby, “The Price of Oil and World Inflation and Recession,” American
Economic Review
, vol. 72, no. 4, Sept. 1982, p. 738. The study covered 1957:Q1-1976:Q4.
The regression results had an R-squared of 0.9984 and the oil price variables were jointly
significant at the 5% level.

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The next year, Hamilton published what many would consider to be the seminal
study on oil shocks.7 He drew attention to the fact that all but one of the post-war
recessions had been preceded by a sharp rise in the price of oil, and set out to
demonstrate statistically that, contrary to conventional wisdom, it was these oil price
rises that caused the recessions. He
demonstrated that oil prices, to use
The link between oil price increases and
a term from economics, Granger-
lower output growth is established.
caused GNP.8 To prove that oil
prices and GNP were not both being
determined by some third variable, he demonstrated that no other macroeconomic
variable Granger-caused oil prices. He estimated that a 10% increase in the price of
oil in this quarter would increase GNP by 0.04% in the next quarter, then decrease
it by 0.07% after two quarters, another 0.5% after three quarters, and 0.6% after a
year compared to the level GNP would have reached had the price of oil been
constant.9 Considering that during the major oil shocks prices rose by 20% in some
quarters and rose for several quarters in a row, these estimates suggest the effect of
oil shocks on the economy are quite large. However, statistical tests suggest that his
equation was mis-specified or the relationship changed over time, and should be split
in two at 1973. Surprisingly, although oil still Granger-caused GNP after 1973, he
estimated that it had a much smaller effect on GNP from 1973 to 1980, when the first
two major oil shocks occurred. He was one of the first to note that oil affected GNP
with a lag — the effect on GNP was nearly ten times larger after four quarters than
it was after two quarters.
Extensions
During the late 1980s and early 1990s, standard regression specifications no
longer showed oil shocks to have a substantial effect on economic growth. Several
papers were written attempting to explain why, using more sophisticated and
complex mathematical relationships and statistical techniques.
Knut Mork was one of the first authors to find that in a standard regression, when
extended through 1988 and controlling for other macroeconomic factors, the effect
of oil price changes on the growth rate of gross national product (GNP) was now
7 James Hamilton, “Oil and the Macroeconomy Since World War II,” Journal of Political
Economy
, vol. 91, no. 2, 1983, p. 228. The regression covered the period from 1948:Q2 to
1980:Q3 and the oil variables were jointly significant at the 1% level.
8 Granger causation is a statistical test of causation based on the predictive power of past
information. In this case, oil Granger-causes GNP if past values of oil increase the
predictive power of future values of GNP beyond what is predicted by past values of GNP
(and past values of any other variables included in the equation.) Oil is said to Granger-
cause GNP if the predictive power it adds is statistically significant. Of course, this test is
not definitive, and is subject to many of the shortcomings, such as omitted variable bias,
described below in the Caveats section.
9 Quarterly data have not been annualized. Thus, cumulative effects for one year can be
roughly calculated by adding up quarterly effects. Technically, the estimates presented in
the report are only mathematically accurate over small changes, but this report uses 10%
changes for illustrative purposes.

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small sand statistically insignificant.10 In the mid-1980s, there had been a series of
oil price declines, and Mork hypothesized that, unlike oil price increases, price
declines had little effect on the economy. His regressions confirmed his hypothesis
— when the distinction between price increases and decreases was made, the effect
of price increases on GNP growth doubled, whereas price declines had a small and
statistically insignificant effect. He estimated that a 10% temporary increase in the
price of oil in this quarter would lower the GNP growth rate by 0.31 percentage
points after one quarter, another 0.15 percentage points after two quarters, 0.49
percentage points after three quarters, and 0.49 percentage points after four quarters.11
Lee, Ni, and Ratti re-confirmed
The link breaks down — more
that when newer data is added the
sophisticated statistical methods are
effect of oil price increases on
incorporated to try to re-establish it.
economic growth using the standard
Controlling for the volatility of oil prices
linear relationship between oil price
is one method.
changes and economic growth
becomes statistically insignificant.12
They claimed that
the real oil price has not lost predictive power for growth in real GNP if
appropriate account is taken of oil shocks and the variability of real oil price
movement. The basic idea is that an oil shock is likely to have greater impact in
an environment where oil prices have been stable than in an environment where
oil price movement has been frequent and erratic. (p. 42)
When they include an “oil price shock” variable that “can be thought of as being a
measure of how different a given oil price movement is from the prior pattern”(p. 42)
10 Knut Mork, “Oil and the Macroeconomy When Prices Go Up and Down,” Journal of
Political Economy
, vol. 97, no. 3, 1989, p. 740. The main regression covered 1949:Q1-
1988:Q2 and had an R squared of 0.518. The oil price increase variables were jointly
significant at the 1% level, while the price decrease variables were jointly insignificant.
These results are extended through 1992 in Knut Mork, Oystein Olsen, and Hans Mysen,
“Macroeconomic Responses to Oil Price Increases and Decreases in Seven OECD
Countries,” Energy Journal, vol. 15, no. 4, 1994, p. 19. The authors find that both price
increases and decreases reduce GDP growth, and these results are statistically significant.
A 10% increase in oil prices reduces growth by a cumulative 0.5 percentage points, and
surprisingly a 10% price decrease reduces growth by a cumulative 0.8 percentage points.
11 Balke, et al. (2002) used statistical tests to determine what third variable statistically
explained why oil price increases had a larger effect on growth than price decreases. They
concluded that interest rates could explain the asymmetry. They hypothesized that the role
played by interest rates could reflect the “pricing in” of oil shock effects by forward looking
financial markets or delays in capital investment and balance sheet effects due to oil price
uncertainty. Nathan Balke, Stephen Brown, and Mine Yucel, “Oil Price Shocks and the U.S.
Economy: Where Does the Asymmetry Originate?” The Energy Journal, vol. 23, no. 3,
2002, p. 27.
12 Kiseok Lee, Shawn Ni, and Ronald Ratti, “Oil Shocks and the Macroeconomy: The Role
of Price Variability,” Energy Journal, vol. 16, no. 4, 1995, p. 39. Their regressions span
1950:Q3 to 1992:Q3. The variable real oil price change was statistically insignificant but
the oil price shock variable was significant at the 1% level.

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in the regression along with an oil price change variable, their results become
statistically significant.
Similarly, Ferderer believed that oil price volatility was the missing factor that
could explain oil’s macroeconomic effects and added a variable to capture volatility
to his regressions that previous studies lacked.13 He argued that volatility could be
costly in terms of shifting resources across sectors and causing investment
uncertainty. He measured volatility as the standard deviation of daily prices. Using
industrial production growth as a proxy for economic growth (in order to study the
data over monthly intervals), he found that monthly oil price changes could
statistically “explain” 5.7-18.5% of the fluctuations in industrial production, and oil
price volatility could explain an additional 11.7-16.1% of the fluctuations. By
contrast, monetary policy could explain only 11.6-12.0% of the fluctuations in
industrial production. He confirmed Mork’s findings that oil price increases had a
greater effect on the economy than price decreases.
Hooker found that oil prices no longer Granger-cause economic growth or
unemployment after 1973, even though all three oil shocks occurred during this
period.14 His results held for a variety of structural specifications. In reply to
Hooker’s work, Hamilton suggested that the relationship was not statistically
significant after 1973 because many of the price increases since 1986 came on the
heels of even larger decreases.15 Hamilton doubted that these types of price increases
would affect the economy. He devised a net oil price increase variable to control for
this phenomenon, but still found smaller economic effects since 1973 and still did not
find that his new variable, “the net oil price increase,” Granger-caused economic
growth.
Hamilton has posited that the reason standard regressions do not find that oil has
a strong effect on economic growth is due to mis-specification. If the effect of oil on
the economy is best represented by a non-linear mathematical relationship, then
standard linear regressions may pick up very weak and misleading effects. In a later
paper, Hamilton demonstrated that non-linear specifications suggest that oil has
13 J. Peter Ferderer, “Oil Price Volatility and the Macroeconomy,” Journal of
Macroeconomics
, vol. 18, no. 1, winter 1996, p. 1. His regression covered the period
January 1970 to December 1990. His oil price volatility measure was statistically
significant at the 1% level and his measure for the level of oil prices was statistically
insignificant. For more recent work, see Hui Huo and Kevin Kleissen, “Oil Price Volatility
and U.S. Macroeconomic Activity,” Federal Reserve Bank of St. Louis Review, vol. 87, no.
6, Nov./Dec. 2005, p. 669. They find that a 10% increase in oil price volatility would reduce
GDP growth by 0.2 percentage points in the next quarter. Their results are statistically
significant at the 5% level.
14 Mark Hooker, “What Happened to the Oil Price-Macroeconomy Relationship?” Journal
of Monetary Economics
, vol. 38, 1996, p. 195. His regressions cover the period 1948:Q1-
1994:Q2.
15 James Hamilton, “This is What Happened to the Oil Price-Macroeconomic Relationship,”
Journal of Monetary Economics, vol. 38, 1996, p. 215. His regressions cover the period
1948:Q1-1994:Q2.

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stronger effects than linear specifications.16 Unfortunately, since there is an infinite
number of non-linear specifications to choose from, there is no easy way to identify
the correct one. Hamilton also noted that regression results may be hampered
because the oil price can no longer be treated as exogenous, that is, it can now be
driven by demand or supply. Using the net oil price increase measure proposed in
his earlier work (1996), he found that a 10% increase in the price of oil (when it is
not following a prior price decrease) in the current quarter will lower GDP in the next
quarter by 0.13%, another 0.13% two quarters later, 0.22% three quarters later, and
0.45% four quarters later. The sum of these effects is 0.2 percentage points smaller
than in Hamilton (1983).
A recent paper by Jimenez-Rodriguez and Sanchez updates Mork’s, Hamilton’s,
and Lee et al’s respective work.17 Using standard vector autoregression methods, the
authors find that a 10% increase in the oil price reduces GDP growth in the U.S. by
a cumulative 0.39 percentage points after eight quarters. Using Mork’s variation,
they find that a 10% oil price increase reduces growth by 0.46 percentage points after
eight quarters, but a 10% decrease increases growth by only 0.11 percentage points.
Using Hamilton’s net oil price measure increases the effect of a 10% price increase
to 0.54 percentage points after eight quarters. Using Lee’s method, which focuses
on price volatility, yields the largest results: the 10% price increase now reduces
growth by 0.61 percentage points after eight quarters. These results are somewhat
smaller than the earlier studies had yielded.
A problem with many of these time-series studies is that they assume that the
relationship between oil prices and GDP is constant over time. But since energy use
as a share of GDP has fallen over time, one would expect oil prices to have a smaller
effect on GDP as time passes. Huntington takes this into account and, using panel
data for 14 countries, estimates that a 10% increase in oil prices in 1998 (latest year)
would reduce U.S. GDP by 0.23%.18
In a recent paper, Hooker attempted to estimate how oil shocks affect inflation
when controlling for other macroeconomic variables such as unemployment and price
controls.19 He found that the effects of oil price increases were much greater before
16 James Hamilton, “What is an Oil Shock?” National Bureau of Economic Research
working paper 7755
, June 2000. His regressions cover the period 1949:Q2-1999:Q4. Oil’s
effects on growth were statistically insignificant after one and two quarters, significant at
the 10% level after three quarters, and at the 1% level after four quarters.
17 Rebeca Jimenez-Rodriguez and Marcelo Sanchez, “Oil Price Shocks and Real GDP
Growth: Empirical Evidence for Some OECD Countries,” European Central Bank working
paper 362, May 2004. Their results cover the period 1972:II-2001:4 and the oil variables
are jointly significant, with the exception of price decreases in the Lee model.
18 Hillard Huntington, “Shares, Gaps, and the Economy’s Response to Oil Disruptions,”
Energy Economics, vol. 26, 2004, p. 415.
19 Mark Hooker, “Are Oil Shocks Inflationary? Asymmetric and Nonlinear Specifications
versus Changes in Regime,” Journal of Money, Credit, and Banking, vol. 34, no. 2, May
2002, p. 540. His regressions cover the period 1962:Q2-2000:Q1 and had an adjusted R
squared of 0.92. The oil variables are independently significant at the 1% level, but jointly
(continued...)

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1981 than after that date. After 1981, he found that oil price increases had only a
small effect on the core inflation rate (excluding food and energy) as measured by the
personal consumption expenditures deflator. He estimated that a 10% increase in the
price of oil in the current quarter would lower core inflation by 1 percentage point in
the next quarter and raise it by 0.5 percentage points two quarters after the increase.
Thus, he finds no positive net effect on inflation.
Oil Shocks or Monetary Policy?
Despite the remarkable historical coincidence between oil shocks and
recessions, a strain of research has suggested that there might nonetheless be some
third force responsible for the recessions. In particular, the research has tried to
separate the effects of the oil shocks on the economy from the effects of simultaneous
changes in monetary policy. Some of the research has concluded that had it not been
for the changes in monetary policy, the oil shocks would have had little effect on
economic growth.
In an early paper, Gisser and Goodwin tried to capture the effects of monetary
policy, fiscal policy, and oil price changes on economic growth, inflation, and
unemployment.20 They measure monetary policy by the growth rate of the money
supply and fiscal policy by the full employment measure of federal expenditures.
They estimated that a 10% increase in the price of oil in the current quarter would
reduce GNP growth by 0.2% in this
quarter, another 0.01% in the next
Oil shocks coincide with recessions.
quarter, 0.02% after two quarters,
But are the recessions really caused by
0.3% after three quarters, and 0.5%
the oil shock, or are they caused by
after four quarters. Similarly, a 10%
monetary policy?
increase in the price of oil is estimated
to increase the inflation rate (as
measured by the GDP deflator) by
0.1% this quarter, and an additional 0.2% after one, two, three, and four quarters. A
10% increase in the price of oil is estimated to increase the unemployment rate by
1.6% this quarter, decrease unemployment by 0.4% after one quarter, increase
unemployment by 0.2% after two quarters, 2.4% after three quarters, and 3.2% after
four quarters. (The unemployment estimates seem questionably large, given the
much milder estimated effects on growth.) Monetary policy is estimated to have a
much larger effect than the oil shocks: the effect of a 10% change in the money
supply is estimated to be about twice as large as a 10% change in the oil price for
GNP and about six times as large for the price level and unemployment. The effects
19 (...continued)
insignificant.
20 Micha Gisser and Thomas Goodwin, “Crude Oil and the Macroeconomy: Tests of Some
Popular Notions,” Journal of Money, Credit, and Banking, vol. 18, no. 1, Feb. 1986, p. 95.
Their regressions results span the period 1961:Q1-1982:Q4 and had an R squared of 0.32
for GNP growth, 0.58 for inflation, and 0.23 for unemployment. The oil variables are
jointly significant at the 1% level in all three cases. The oil, monetary, and fiscal variables
are jointly significant at the 1% level for GDP growth, but statistically insignificant for
inflation and unemployment.

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of fiscal policy on GNP and unemployment are smaller than the effects of oil price
changes, although larger than the effects on the price level. The authors also
demonstrated that oil Granger-caused GNP, the price level, and unemployment.
Contrary to other studies, they also found that after controlling for monetary and
fiscal policy, there was no structural break in the oil-GNP relationship after 1973.
However, they do confirm that there was a break in the oil-price level and oil-
unemployment relationship.
Dotsey and Reid attempted to synthesize the work of Romer and Romer, which
claimed that contractionary monetary policy was the cause of post-war recessions,
with the work of Hamilton, surveyed above, which claimed that the recessions were
caused by oil shocks.21 They estimated that a 10% increase in the price of oil lowered
GNP growth by a total of 0.7 percentage points over the next four quarters. (Similar
to Mork, they estimated the effects of price increases and decreases separately, and
found that price decreases had a smaller and statistically insignificant effect on
growth.) By contrast, a 1 percentage point increase in the federal funds rate was
estimated to reduce GDP growth by 0.1 percentage points over the next four quarters.
They also estimated the effect of oil price increases on unemployment and found that
a 10% increase in oil prices would increase the unemployment rate by a total of 0.4
percentage points over the next 24 months.
Bernanke, et al. were interested in finding out what effects monetary policy
changes had when they were unanticipated.22 They chose to study oil shocks because
these are one of the only macroeconomic phenomena that most economists would
agree are both unanticipated and exogenous. First, they estimated the effect of a 10%
increase in the price of oil when monetary policy responds as it has historically. They
estimated that over 24 months, GDP would fall by 3.1% and prices would rise by
0.09% relative to a baseline. To separate the effects of the oil shock from the effects
of the change in monetary policy, they then estimated a counter-factual example
where monetary policy does not respond to the oil price increase, which they
represented with a constant federal funds rate. In this case, GDP was estimated to
rise by 1.3% and prices by 0.13%. They therefore concluded that oil price shocks
have very little negative effect on the economy; rather it is the monetary response to
21 Michael Dotsey and Max Reid, “Oil Shocks, Monetary Policy, and Economic Activity,”
Federal Reserve Bank of Richmond Economic Review, vol. 78, no. 4, July 1992, p. 14. For
GNP, the regression covered the period 1955:3-1991:3 and the R squared was 0.32. The
sum of oil price increase variables was statistically significant at the 1% level; however, the
sum of oil price decrease variables was statistically insignificant. For unemployment, the
regression covered the period 1950:1-1990:12 and had an R squared of 0.977. The sum of
the oil variables was significant at the 1% level.
22 Ben Bernanke, Mark Gertler, and Mark Watson, “Systematic Monetary Policy and the
Effects of Oil Price Shocks,” Brookings Papers on Economic Activity 1, 1997, p. 91. Their
regressions cover the period 1965-1995. None of their results are statistically significant.

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oil shocks that leads to the historical coincidence between oil shocks and
recessions.23 24
The work of Bernanke, et al. raises an interesting conceptual question: while the
effects of oil shocks and monetary policy can be statistically separated, can they be
separated in reality? Bernanke, et al. attribute the tightening of monetary policy
following oil shocks as the Fed’s response to the increase in inflationary pressures
that oil shocks are commonly believed to cause. Commenting on the Bernanke
paper, Sims points out that the assumption that monetary policy could remain
unchanged in response to an increase in inflationary pressures is not a sustainable
policy, and thus falls prey to the Lucas critique (see below). It is unlikely that private
individuals would have no reaction to the implementation of an unsustainable policy,
making the statistical separation of oil price effects from monetary effects
problematic.25 This would suggest that one can reasonably question whether there
is a practical distinction between attributing a recession to an oil shock or attributing
it to the monetary response to an oil shock.
Hamilton and Herrera pursue this line of reasoning in a critique of the Bernanke
paper.26 While Bernanke’s regressions can mechanically be interpreted to imply that
monetary policy could prevent a recession, Hamilton and Herrera point out that these
regressions would imply that the federal funds rate would have to have been an
improbable 9 percentage points lower in 1973 to prevent a recession. Using the
Lucas critique, it is unlikely that private individuals’ expectations would have
remained unchanged in light of such a significant policy change. Hamilton and
Herrera also argue that Bernanke et al. underestimate the effects of oil shocks
because they use too short a lag length. Bernanke et al. assume that changes in oil
prices affect the economy for the next seven months, whereas Hamilton and Herrera
suggest a lag length of at least 12 months would be more appropriate since many
works find the largest economic effects of oil price changes to come after three and
four quarters. In particular, by using a longer lag than Bernanke, they find that
23 Using similar methods, Ferderer (1996) found the opposite results: the effects of oil
shocks were larger than the effects of monetary policy. See the section titled Extensions.
24 Similarly, Barsky and Kilian construct a theoretical model that attributes falling output
and rising inflation to monetary policy rather than oil shocks. In their model, inflation can
continue to rise while output falls because inflationary expectations change sluggishly.
They also question if oil shocks were caused by consumer demand, in which case they
cannot be treated as exogenous (see below). Robert Barsky and Lutz Kilian, Do We Really
Know that Oil Caused the Great Stagflation? A Monetary Alternative
, National Bureau of
Economic Research, Working Paper 8389, July 2001. See also Ben Hunt, Oil Price Shocks:
Can They Account for the Stagflation in the 1970s?
, International Monetary Fund, Working
Paper 05/215, Nov. 2005.
25 Christopher Sims, “Comments,” Brookings Papers on Economic Activity 1, 1997, p. 146.
To address this criticism, Bernanke, et al. also run simulations in which the federal funds
rate is held constant but expectations are assumed to adjust more quickly. Under this
scenario, output still rises and inflation rises slightly more quickly.
26 James Hamilton and Ana Maria Herrera, “Oil Shocks and Aggregate Macroeconomic
Behavior: The Role of Monetary Policy,” Journal of Money, Credit, and Banking,
forthcoming.

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countering oil shocks with expansionary monetary policy has much larger effects on
inflation since monetary policy affects inflation with a significant lag.
The Effects of Oil Shocks on Employment
A related strain of research studied the effects of oil shocks on employment.
Since economic growth has a strong effect on unemployment in the short run, one
would expect oil shocks to affect unemployment if they affect economic growth.27
Carruth, Hooker, and Oswald estimated that oil shocks had a larger effect on
unemployment than economic growth.28 They showed that oil shocks Granger-
caused unemployment (unlike GDP29) up to the present (1995) and estimated that a
10% increase in the price of oil would increase the unemployment rate by 0.2
percentage points. Although this is a relatively small effect, they found that the effect
of oil price changes on unemployment before 1978 was more than three times larger.
They found that their model, based on oil prices and interest rates, forecasts
unemployment more accurately than commercial forecasts.
Davis and Haltiwanger focused on
Oil shocks change both the overall
oil’s effect on employment in the
level of employment and the
manufacturing sector, broken down by
allocation of employment across
industry.30 They hypothesize that oil
industries.
price changes have distinct “aggregate”
effects and “allocative” effects on
manufacturing employment. The
aggregate effects on employment are caused by the slowdown in GDP growth that
oil price increases cause. The allocative effects on employment come from the fact
that some industries are harmed more than others — and some are actually helped —
by a price increase. Thus, some jobs are shifted from one industry to another so that
the net allocative effect of an oil price change on employment is zero. They found
that
27 Two papers already reviewed in this report investigated the effects of oil shocks on
unemployment. See Gisser and Goodwin (1986) and Dotsey and Reid (1992).
28 Alan Carruth, Mark Hooker, and Andrew Oswald, “Unemployment Equilibria and Input
Prices: Theory and Evidence from the United States,” The Review of Economics and
Statistics
, vol. 80, no. 4, 1998, p. 621. The regression covered the period from 1955:Q4 to
1995:Q2. The R squared was 0.837 and the oil variable was statistically significant at the
1% level.
29 These results are curious since short-term fluctuations in unemployment are usually
thought to be caused by fluctuations in GDP. Otherwise, the fluctuations would represent
changes in the natural rate of unemployment, and there has not been any well-known link
established between the price of oil and the natural rate of unemployment.
30 Steven Davis and John Haltiwanger, “Sectoral Job Creation and Destruction Responses
to Oil Price Changes,” National Bureau of Economic Research, working paper 7095, April
1999. The regression spanned from 1972:2 to 1988:4 and the oil variables were individually
insignificant at the 5% level, except for the seven-quarter lag variable.

CRS-13
a unit standard deviation positive oil shock triggers the destruction of an extra
290,000 production worker jobs and the creation of an extra 30,000 jobs in the
first two years after the shock.... After four years, the net employment response
to a unit positive oil shock is only 60,000 fewer jobs, but the gross reallocation
response amounts to 410,000 jobs or more than 3 percent of employment.
By comparison, they estimated that a unit standard deviation tightening in monetary
policy leads to a net loss of 140,000 manufacturing jobs after two years. Looking at
the data on an industry level, they estimated that the effects differ greatly by industry
depending on the characteristics of that industry. For example, categorized by the
energy intensity of production, the decline in employment was almost twice as large
for industries in the 90th percentile than industries in the 10th percentile. Because
their study excluded the service sector, which accounts for most employment, their
results cannot be meaningfully extrapolated to judge the effects of oil price changes
on overall unemployment.31
Some Caveats
Quantitatively estimating the effect of oil shocks on the economy is more
difficult than it sounds. In the sciences, statistical robustness is obtained by running
numerous controlled randomized experiments in order to sift out randomness in the
data to identify the true relationships between variables. If uncertainty emerges
concerning the role one factor plays, one can change the experiment to isolate that
factor’s effect. In macroeconomics, experiments are not controlled and they cannot
be run over and over again. Since World War II, we have had only 10 “experiments”
with recession, and it is highly doubtful that the U.S. economy is the same test case
today as it was in, say, 1957. Instead, economists must hypothesize the relationships
between different economic factors expressed through mathematical relationships
and compare the historical correlation of those variables to see if the hypothesis
holds. If the mathematical relationship chosen to represent the relationship is
incorrect or changes over time, or other variables which have an effect are missing
from the regression, then the estimates will be incorrect. Studies that attempt to
identify more sophisticated relationships than the simple linear one can be accused
of “data mining” to find the biggest (or smallest) effect possible.
There are some common pitfalls
that lead to econometric studies giving
To accurately estimate the effects of oil
“biased” or incorrect estimates for the
shocks on the economy, one must avoid
relationship between variables. Some
a number of common pitfalls.
of these pitfalls are nearly impossible
t o a v o i d , p a r t i c u l a r l y i n
macroeconomics. This report will review four such pitfalls — omitted variable bias,
structural misspecification, problems with endogeneity, and the Lucas Critique.
These problems suggest that econometric estimates, while useful, should always be
31 In an earlier paper, Loungani demonstrated that the reallocation of employment across
industries led to an increase in unemployment only when the reallocation was caused by oil
shocks. Prakash Loungani, “Oil Price Shocks and the Dispersion Hypothesis,” Review of
Economics and Statistics
, vol. 68, no. 3, Aug. 1986, p. 536.

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considered with caution. In addition, even when measured accurately, there is a
question of statistical robustness.
Omitted Variable Bias. A common problem that econometricians try to
avoid is omitted variable bias. If a regression does not control for a factor that affects
one’s dependent variable, then the estimated effects of the explanatory variables that
are included will be biased. The importance of explanatory variables positively
correlated with the missing variable will be overstated, the importance of negatively
correlated variables will be understated. This is a particularly important problem in
economics precisely because experiments cannot be re-run or truly randomized. In
microeconomics, it may typically be feasible to include all of the relevant factors of
non-trivial size, although proxy variables may be needed. But in macroeconomics,
this assumption seems more problematic; there are simply too many factors
influencing the economy to capture them all. Most of the regressions reviewed in
this report used oil and only a few other key economic variables as explanatory
variables. Yet a look at the current recession points to many additional factors —
many unquantifiable in nature — that have taken a toll on the U.S. economy. These
include the effects of September 11, the corporate accounting scandals, the large
decline in the stock market, and so on. If these factors were not included in a
regression, the regression would attribute their effects instead to the recent run-up in
oil prices (and any other correlated explanatory variables), overstating oil’s
importance.
Structural Misspecification. The fact that a study finds no relationship
between two variables does not mean that no relationship exists in reality. Likewise,
studies can identify relationships where no relationship in fact exists. Regressions
relate data series to one another according to some mathematical function; if that
mathematical function does a poor job of describing the relationship in reality, then
the results will be artificially weak. The problem is that there is an infinite number
of mathematical equations that can be used to express a relationship and often there
is not a strong theoretical reason for favoring any one. As a result, econometricians
most frequently assume a linear relationship between the variables (often after taking
the natural logarithm of the data); this assumption is made more out of convention
than due to any strong reason for preferring a linear relationship over any other.32
Thus, in most of the studies surveyed above, a 10% change in the oil price is
estimated to have an effect that is 10 times greater than a 1% change. This
assumption seems unlikely to represent reality accurately. Experience and common
sense suggest that while the major oil shocks have had significant effects on the
economy, small and fleeting movements in the oil price have virtually no impact.
Yet a linear relationship would scale these two events equally. While theory can
point to alternatives, unfortunately, representing the statement “oil price changes only
matter if they are steep, sudden, long-lasting, and do not reverse previous price
movements in the opposite direction” in mathematical form is neither simple nor
straightforward.
32 There are many other problems of this type that make econometric estimates problematic.
For example, the standard regression method is valid only if the error terms are assumed to
be normally distributed. Otherwise, different methods must be used.

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All of the studies reviewed in this report used time series analysis to estimate
the effects of oil prices. Time series analysis is vulnerable to a special type of
structural misspecification: it must assume that the relationship between the
explanatory variables and the dependent variable is constant over time. For example,
a 10% increase in the oil price must have the same economic effects in 1952 as it has
in 2002. But if oil affects the economy through the production process, oil shocks
would be expected to have a smaller effect on the economy if the economy became
more energy efficient in terms of energy use relative to GDP. Historically, energy
consumption per dollar of GDP has dropped significantly over the past three decades,
with the economy now using less than half as much energy per dollar as it did in the
1970s. In order to correct for such changes, statistical tests are used to look for
structural breaks. The time series can then be divided into sub-samples, each
estimated separately. But if the series is divided, the study loses a degree of
statistical robustness because it is based on fewer observations.
Problems with Endogeneity. Virtually all important macroeconomic
phenomena are interrelated, usually in complex ways. This makes econometric
estimation difficult. In the simple regression, causation runs from the explanatory
variables to the dependent variable. For the simple regression method to be valid,
neither the dependent variable, nor another explanatory variable, nor some missing
or unobserved variable can influence any explanatory variable. This qualification
seldom if ever holds in macroeconomics, which means that more complex, and less
straightforward, econometric methods must be used or the estimations will be
invalid. Such methods exist, but have shortcomings of their own, and are not always
used by the econometrician.
For example, the exchange rate, interest rates, and fiscal policy all influence
phenomena such as economic growth, inflation, and unemployment. But, in turn,
their values are determined by the very phenomena that they influence; this is how
markets reach equilibrium. Thus, to determine the effects of a change in monetary
or fiscal policy on economic growth, one cannot simply run a regression in which
economic growth is the dependent variable and the budget deficit and interest rates
are independent variables. Even oil prices, which might seem to be a good candidate
for exogeneity because of the role played by OPEC, are likely to be endogenously
determined. Although OPEC has some control over the supply side of the market,
price is also determined on the demand side, which is influenced by factors such as
growth in income.
One way of avoiding endogeneity problems in time series analysis is by using
a statistical method called vector autoregression, which simply assumes that all
variables affect each other. Two shortcomings have been raised with this method
that are worth mentioning here. First, critics complain that vector autoregression is
atheoretical: the method eschews any attempt to identify relationships between
variables in theoretical terms. While this makes it a more flexible method, its critics
argue that no theory is not the same as the right theory. Econometrics will only lead
to accurate results if the underlying theory accurately describes reality. Second, there
is a tradeoff between the number of explanatory variables one can have and the
number of observations with which one is working. (With time series data, the
number of observations is limited to how far back in time one is willing to go, which

CRS-16
raises another set of problems discussed in the previous sub-section.) Because all of
the variables must be regressed on each other and because the use of time lags creates
more variables, in practice vector autoregressions can have only a few explanatory
variables, as opposed to traditional macroeconomic models which have had as many
as hundreds.
Lucas Critique. Many econometric estimations of macroeconometric
phenomena fall prey to the “Lucas Critique” set out by Nobel Laureate Robert Lucas.
Econometric estimates derived from historical data, particularly when they include
variables that policymakers can influence, implicitly assume that the future will be
similar to the past. One of Robert Lucas’ main contributions to economics was the
development of the theory of “rational expectations,” in which he argued that,
contrary to much mainstream economic theory of the time, economic theory should
always be based on the assumptions that private individuals are fully informed and
act rationally to maximize their self-interests. If people adjust their expectations
when circumstances change, the future is unlikely to be the same as the past. Thus,
many econometric estimates are inconsistent with rational expectations. For
example, by looking at historical patterns, one can use econometric analysis to
estimate the effects of changes in the federal funds rate on economic growth. For the
estimated effect to be valid in forecasting future behavior, one must assume that
individuals do not change their behavior or learn from past mistakes. Taken literally,
this would lead to perverse predictions. For example, if individuals were fooled by
a “monetary surprise” (i.e., an unanticipated and opportunistic change in monetary
policy) in the past, this type of econometric modeling would predict that they would
continue to be fooled indefinitely in the future.
Furthermore, when making predictions about the effects of policy alternatives,
one can assume a policy variable to take any value. Yet if this value had occurred
historically, because of the role of expectations, it could have changed all of the other
estimated parameters in the regression. If this were true, the model would have little
predictive power. For example, if policymakers had always responded to oil shocks
by sharply tightening monetary policy, an econometric model might suggest that oil
shocks have little effect on inflation. An econometrician could then use his model
to demonstrate that an expansionary monetary policy could be employed to cope with
oil shocks with little effect on inflation. Yet had this policy been employed
historically, expectations might have adjusted (when people saw oil prices rise, they
would anticipate all other prices to rise) to make oil shocks far more inflationary.
Robustness of Results. Even if an econometric study avoided all the
problems discussed above and perfectly represented reality, simply taking estimates
at face value tells only half of the story. As well as worrying about the size of an
estimate, statisticians are concerned with its statistical robustness. A study may find
that oil shocks have a very large effect on the economy, but if there is significant
unexplainable variation in the sample, one should be skeptical about the results. This
report has reported two common measures of robustness: the statistical significance
of specific explanatory variables and the R-squared of the study as a whole.
Statistical significance is determined by how much the sample data varies from the
best estimate of the relationship between the dependent and explanatory variables.
An estimate is statistically significant at, say, the 1% confidence level if 99 out of

CRS-17
100 samples will be different from zero. The R-squared measures how much of the
variation in the dependent variable can be explained by the explanatory variable; if
none of the variation can be explained the R-squared would be zero, if all can be
explained it would be one.
Conclusion
All of the studies reviewed in this report found oil shocks to have some effect
on the economy. There is a fair degree of consensus surrounding the range of
estimates: for comparable studies, the cumulative effect of a one-quarter, 10%
increase in oil prices was to lower economic growth by 0.2-1.1% over the next four
quarters compared to GDP under a baseline in which the oil price does not change
(see Table 1). The effect takes place over a number of quarters, with research
typically finding weaker effects at first. The magnitude of these estimates suggests
that normal fluctuations in the price of oil would cause only minor fluctuations in
economic growth. However, the estimates suggest that major oil shocks, in which
oil prices rise for several consecutive quarters, often by more than 10%, could lead
to recessions, all else equal. Only the study by Bernanke et al. dissents from this
conclusion by claiming that while oil shocks have historically had a large negative
effect on economic growth, the historical experience is attributable to the monetary
response to the shock, rather than the shock itself. Hooker (2002) was unable to find
oil to have any effect on core inflation.
Although the magnitude of the estimates is large enough to make oil shocks a
policy concern, the results are not statistically robust enough to silence all doubts.
Oil prices no longer Granger-cause economic growth in straightforward ways. The
effect of oil price changes on the economy was statistically insignificant in many
studies. Some studies had low R-squared values, which means that many of the
determinants of economic activity remain unexplained. Studies which attempted to
identify more sophisticated relationships than the simple linear one could be accused
of “data mining” to find the biggest (or smallest) effect possible.
Furthermore, every study that explored the issue found that oil’s broad effects
on the economy were waning, and more recent studies tended to find oil to have
smaller effects. Particularly puzzling was that many studies found oil to have
stronger economic effects before the mid-1970s, despite the fact that all of the major
oil shocks occurred since the mid-1970s.
This report was limited to surveying studies that specifically focused on oil’s
impact on the economy. Many macroeconometric studies not reviewed have focused
on other determinants of economic activity, such as monetary policy, and have
neglected the role of oil entirely. It is fair to say that some economists remain
unconvinced that oil plays a crucial role in the business cycle.
All macroeconometric studies are prone to a number of unavoidable pitfalls. If
they were reliable and if our understanding of the economy were better,
macroeconomic policy concerns would vanish. It is not merely a question of
developing more sophisticated statistical techniques; some pitfalls stem from the

CRS-18
unpredictability of human nature that ultimately determines economic outcomes.
Nevertheless, these studies contribute valuable insight into important phenomena
such as oil shocks.

CRS-19
Table 1. Summary of Findings
Study
Major Findings
Darby (1982)
1973 oil shock reduced cumulative GNP by 2.5%
Hamilton (1983)
GNP responds to oil price change with lag; oil had larger
effect on GDP before 1973; 10% oil price increase reduces
GNP by 1.1% over the next year
Gisser and Goodwin
controls for monetary policy and fiscal policy; finds monetary
(1986)
policy to have larger effect than oil price change; 10% oil
price increase reduces GNP by 1.0% over the next year
Mork (1989)
only oil price increases have significant effect on output, price
decreases have negligible effect; 10% oil price increase
reduces GNP growth by 0.36 percentage points over the next
year
Dotsey and Reid
oil price increase had larger effect on GNP than monetary
(1992)
policy; 10% oil price increase reduces GNP growth by 0.7 p.p.
over the next year
Lee, Ni, Ratti (1995)
oil price changes only affect GNP if they persist
Ferderer (1996)
controlling for oil price volatility helps explain relationship
between oil price increases and GNP
Bernanke et al (1997)
argues that oil shocks do not cause recessions; rather, the
response of monetary policy to oil shocks causes recessions
Carruth et al (1998)
oil prices do not have significant effect on GNP, but they have
a significant effect on unemployment; 10% oil price increase
increases unemployment by 0.2 p.p. over the next year
Davis and Haltiwanger
oil price increases both destroy manufacturing jobs and shift
(1999)
jobs across industries; one standard deviation oil price
increase destroys 290,000 and creates 30,000 manufacturing
jobs
Hamilton (2000)
demonstrates that non-linear models do a better job explaining
the relationship between oil prices and output than linear
models; 10% oil price net increase reduces GNP by 0.9% over
the next year
Hooker (2002)
does not find oil price increases to have any significant effect
on core inflation; 10% oil price increase reduces core inflation
by 0.5% over the next two quarters
Huntington (2004)
effect of oil price on economy is greater if economy uses more
energy; 10% oil price increase reduces GDP by 0.2% in 1998
Jimenez-Rodriguez and
updates Hamilton’s, Mork’s, and Lee et al’s findings using
Sanchez (2004)
recent data; 10% oil price increase reduces GDP growth by
0.4-0.6 p.p. over next two years
Notes: p.p.= percentage points. Unless otherwise noted, all estimates are compared to the economic
variable under a baseline scenario in which the oil price does not change.

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Glossary33
confidence level — the percentage of samples that will contain the true unobservable
value. For example, at the 1% confidence level, the sample will contain the true
value 99% of the time. (For a discussion, see section titled robustness of
results.)
data mining — using the same data set to estimate several different models in a
search to find the “best” model, resulting in biased estimates.
dependent variable — the variable whose behavior the regression is attempting to
explain in terms of other variables which influence it.
econometrics — using statistical methods to explain economic phenomena by
relating a variable to other explanatory variables.
endogenous — an explanatory variable that is determined by another variable in the
equation or correlated with the equation’s error term. (For a discussion, see
section titled Problems with Endogeneity)
exogenous — an explanatory variable that is not determined by any other variable
in the equation and is not correlated with the equation’s error term. (For a
discussion, see section titled Problems with Endogeneity)
GDP (gross domestic product) — a measure of the economic output generated
within the United States.
GNP (gross national product) — a measure of the economic output generated by
American citizens.
Granger causation — a variable is said to Granger-cause another variable if the first
variable has predictive power for future values of the latter variable at
statistically significant levels when past values of the latter variable have been
taken into account.
joint (statistical) significance — a set of variables are said to be jointly significant
if they jointly differ from zero at a given confidence level.
regression — a statistical method to “explain” changes in a variable by comparing
changes in that variable to changes in independent variables.
R squared — the percentage of the variation in the dependent variable that can be
explained by the explanatory variables. (For a discussion, see section titled
robustness of results.)
33 This glossary draws heavily on Jeffrey Woolridge, Introductory Econometrics, South-
Western College Publishing (Australia: 2000)

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standard deviation — a common measure of variance in a sample; “one standard
deviation” is a useful measurement standard when estimating changes in
variables because it always represents the same value when a sample has a
normal distribution.
statistical significance — a variable is said to be statistically significant if it differs
from zero at a given confidence level. (For a discussion, see section titled
Robustness of Results.)
structural break — a situation where the relationship between dependent and
explanatory variables is not constant over time (For a discussion, see section
titled Structural Misspecification.)
structural specification — choosing a mathematical function that is the best
representation of the relationship in reality. (For a discussion, see section titled
Structural Misspecification.)
time series — data for a set of variables that spans a time period.