Gasoline Prices, Vehicle Spending and National Employment:
Vector Error Correction Estimates Implying a Structurally Adapting,
Integrated System, 1949-2011
Danilo J. Santini,
Phone: (703) 678-7656
Argonne National Laboratory
E-mail: dsantini@anl.gov
David A. Poyer,
Phone: (404) 222-2580
Morehouse College
E-mail: dpoyer@morehouse.edu
Presented at:
32nd U.S. and International Associations for Energy Economics
North American Conference
Anchorage, Alaska: July 28-31, 2013
The submitted manuscript has been created by Argonne National
Laboratory, a U.S. Department of Energy laboratory managed by
UChicago Argonne, LLC, under Contract No. DE-AC02-06CH11357.
The U.S. Government retains for itself, and others acting on its behalf, a
paid-up, nonexclusive, irrevocable worldwide license in said article to
reproduce, prepare derivative works, distribute copies to the public, and
perform publicly and display publicly, by or on behalf of the
Government.
Sponsor: J. Ward, Vehicle Technologies Program, U.S. Department of Energy
1
Overview
The Vector Error Correction Model (VECM) separates long-term equilibrium relationships from short
term deviations from equilibrium. What is called the cointegration relationship tests for the existence of a
stable, systematic integrated relationship among levels of variables and estimates correction to deviations
from the cointegrated path. Multiple equilibria are possible as the cointegration path is pushed from
equilibrium via outside forces. In the model estimated by VECM in this paper, the underlying theory is
that reduction of costs of services from highway motor vehicles are a fundamental cause of economic
growth. The theory also argues that deviations from equilibrium are caused by impulses to transportation
fuel prices and elevated transportation fuel price levels. The VECM is readily capable of testing for the
former short term price impulse effect over a few quarters, but not the latter, which is argued to be a more
long-term process.
This paper reports results emerging from several statistical experiments testing the dynamic relationship
among (1) U.S. total employment, (2) real consumption expenditures on motor vehicles and (3) real
gasoline price, from 1949-2011. The results are interpreted in the following way ― reduction of
transportation sector input costs is a fundamental contributor to long-term economic growth. Positive
transport fuel cost deviations from long term reductions are a cause of short-term macroeconomic
declines. The more significant transport cost increases are reversed by adoption of more fuel efficient
technology and return to a new equilibrium growth path.
Transportation technology and economic growth
A significant literature on transport economics has been developed over more than two centuries, in
parallel with widely taught theories of micro- and macro-economics. Historically, the importance of
transportation-related technological change on economic development has not been explicitly considered
(Blaug, 1985). However, in general the importance of technological change on economic growth has
been long recognized (Solow, 1957; Levinson, 1987). Advances in transportation technologies have been
an important contributor to engineering history. Data on the major advances is available.
Clearly with the globalization of national economies the importance of transportation-related
technological advances has become more evident. Recent research examining 22 industrialized nations
from 1962 to 1990 estimates that improvement in container ship technology was as important to growth
in world trade as the reduction in trade barriers (Economist, 2013). Induced indirect benefits were argued
to be as important as the direct cost reduction effects. Faiz (2013) also finds evidence for the “difficult to
trace” causal relationship between rural roads, economic growth and rural prosperity, also finding indirect
effects to be pronounced.
One major question that we raise in this paper is whether our statistical estimates provide
evidence/support that U.S. technological shifts in new light duty vehicle technology that occurred from
1975-1983 helped to insulate the U.S. economy from shocks in energy markets. An important question is
whether there is evidence that the implementation of Corporate Average Fuel Economy standards (CAFE)
played a role in the dynamics of the adjustment process.
2
As can be seen in Figure 1, between 1973 and 1986, there was a dramatic reduction in the fuel
consumption of all vehicles and in the EPA rating-based estimate of fuel consumption of new light duty
vehicles. After 1986, despite a dramatic fall in real gasoline prices, there was only a very slow upward
trend in fuel consumption per new light duty vehicle. This upward trend was reversed starting in 2004,
accompanied by expanding sales of multiple new light duty vehicle powertrain engine technologies –
clean diesel, hybrids, and turbocharged and direct injection gasoline equipped cars and light trucks. The
share of front-wheel drive vehicles, the technology enabling the sharp drop from 1973-86, also reversed a
slow decline from 1990-2004, turning upward in 2005 (U.S. Environmental Protection Agency, 2013).
9
8
Gallons per 100 miles
7
6
5
4
3
All Vehicles (includes heavy trucks)
2
EPA rating of new light duty vehicles (cars and light trucks)
1
2011
2009
2007
2005
2003
2001
1999
1997
1995
1993
1991
1989
1987
1985
1983
1981
1979
1977
1975
1973
0
Figure 1 Comparison of new vehicle on-road fuel use to entire fleet fuel use (per vehicle), 1975-2011
Fuel economy standards vs. gasoline taxes
The adoption of fuel economy standards is consistently regarded by economists as a “second best” option
to taxation (Blumstein and Taylor, 2013). Jacobsen (2013) argues that the use of fuel economy standards
impose an undesirable outcome on consumers of large used-vehicles. However, the potential
macroeconomic effects and policy goals of a fuel economy standard ― reduction of overall fleet gasoline
consumption and lowering of gasoline prices are not considered. If one included an accounting for the
probable CAFE-induced effects on gasoline costs this would reduce the overall cost of ownership of
larger vehicles, which would benefit those who prefer such vehicles. The emergence of the U.S. SUV
followed the 1985-86 gasoline price collapse and the relatively low gasoline prices (in retrospect) that
followed for several years. The SUV emergence occurred with a lag, starting in earnest in the mid-1990s
(U.S. Environmental Protection Agency, 2013, Appendix D). The inroads of Japanese imports were
halted in the 1990s. However, perhaps more research and scholarly exchange is required in this area.
3
Vehicle technology and gasoline price shocks
Gruenspecht, in a 2001 paper, raised concerns about the California “zero emissions vehicle (ZEV)”
program. His major criticism is that vehicle performance standards (emissions and fuel economy) are not
desirable in part because they are accompanied by a delay in purchases that leave older less
technologically sophisticated vehicles on the road longer. We will call this kind of delay the “Gruenspecht
effect.” Earlier, Santini (1988) theoretically had addressed the idea that technological change could lead
to purchase delay, with a model in which a more capital intensive/fuel-efficient vehicle technology first
causes a temporary but sharp drop in sales, though the change later leads to a long-run expansion of the
market. This model assumes that capital and energy are substitutes. For the model to be correct, higher
spending on vehicles must produce lower fuel consumption by those vehicles. Those policy analysts who
evaluate fuel economy standards take this trade-off for granted. However, economists (correctly) do not,
generally treating capital and energy as complements. If vehicle spending and energy consumption are
complements, then when one rises, so does the other (and vice versa). Complementarity can certainly be
readily recognized to exist at the coarsest level – more vehicles will cause more fuel use. Further, with
constant technology, more spending is also usually associated with more fuel use — in particular, the
purchase of a V6 engine instead of a four cylinder will cost more and cause more fuel use per mile.
If in fact motor vehicle spending and gasoline spending are complements under some circumstances and
substitutes in others, it may be difficult to construct a model formulation that will isolate the two effects.
Our model construction was not developed with such a goal in mind, but, as it turns out a case does result
where vehicle spending increases are predicted to cause gasoline price reductions. This is very difficult to
explain if vehicle spending and fuel use are always complementary.
Another explanation for gasoline price-shock-induced declines in vehicle sales noted by Kilian (2008) is
that changes in energy prices create uncertainty about future prices, causing firms to postpone irreversible
investment decisions (Bernanke 1983 and Pindyck 1991). Presumably, this could also apply to
individuals, which happens to be the topic of this paper.
With regard to oil price shocks, using quarterly data, Hamilton in 1983 statistically estimated that oil price
shocks had Granger-caused post WWII recessions up to that time. Recessions followed oil price shocks
by about a year. In 1988, Hamilton asserted that the effects of oil price shocks on the macro-economy
were likely transmitted through variation in passenger car output. He reiterated this opinion in 1996.
Santini (1994), using annual data, also estimated macroeconomic responses of real GDP and
unemployment occurred one year after a transportation fuel cost shock, and had Granger-caused both
post- and pre- WWII recessions (except the Great Depression) with a year lag.
The hypothesis that the automobile sector is the key sector responding to oil price shocks as stated by
Hamilton in 1988 has not yet been widely accepted. Jones, Leiby and Paik (2004) lumped this argument
into a broad, generic “sectoral shifts” hypothesis, concluding that continued research on the hypothesis
would not support any single cause of macroeconomic responses to oil price shocks. In 2008 Kilian
focused precisely on Hamilton’s actual assertion about the central mechanism, with three key
modifications. He argued that gasoline prices were more relevant than oil as a specific cause of
fluctuation in motor vehicle output, that if the hypothesis were correct the determination of gasoline
4
prices should be endogenous, and that if motor vehicles are the key sector, then subsector responses to
gasoline price changes are important. Using bivariate vector autoregression (VAR), he illustrated that
positive gasoline price changes were statistically significant causes of immediate monthly declines in
domestic new vehicle sales, while new imported automobile sales actually rose initially, declining more
modestly a few months after domestic vehicle sales. Kilian noted that when his 1970-2006 sample was
broken into two parts at 1987/88 the response of consumption of new motor vehicles to a gasoline price
increase had dropped sharply after 1987. Responses by unemployment, and other categories of
consumption were also diminished. Kilian attributed the diminished response of motor vehicles to a
change in mix of production capability of domestic and foreign vehicles, resulting in a better ability of
domestic producers to provide smaller more fuel efficient cars in the latter period.
Also in 2008, we estimated with a bi-variate VAR test that the dollar value of domestic motor vehicle
output had been a statistically significant predictor of the rest of GDP from 1970 to 2005. Kilian’s 2008
work supported the existence and importance of the first causal leg of Hamilton’s specific hypothesis that
the automobile sector is the key mechanism in transmitting effects of oil price shocks to the
macroeconomy, while our paper supported the importance of the second leg.
Kilian argued that one of two things had reduced the effect of gasoline price shocks on motor
vehicle consumption. One was that the importance of the motor vehicle industry to the economy
had declined. The other was that
by the late 1980s and 1990s the difference between domestic and foreign auto
producers had been greatly reduced, as domestic auto manufacturers offered small
and energy efficient cars, while foreign manufacturers were beginning to branch
out into the market for jeeps, SUVs, vans and pickup trucks. Thus, the U.S. auto
industry became relatively less vulnerable to energy price increases than in the
1970s (Kilian, p. 25).
Automotive fuel standards should also have contributed to this closure of the difference between
domestic and foreign automakers. This explanation was not offered by Kilian.
Levels vs. impulses
In this paper we use the Vector Error Correction Model instead of Vector Autoregression (VAR)
method we used in 2008. “With a cointegrated system, one should include lagged levels along
with lagged differences” (Hamilton, 1994). The VAR method does not include use of lagged
levels of variables, only lagged differences. In the VECM, the cointegration equation is used to
retrospectively predict — via a cointegrated “Vector” — what values in a given year should be.
These are deducted from the actual values, constructing a set of “errors” for each time period.
Coefficients are estimated for a series of these lagged error estimates. The coefficients are
supposed to predict how the system reacts to these errors ― how it corrects for the past errors.
Thus, the term “error correction” is used.
5
The VECM software that we use in this paper does allow us to test for the existence of
cointegration, and to construct “impulse response functions” that take into account effects of
both lagged levels and lagged errors. To the best of our knowledge, this is the first time that the
VECM has been used within this context.
In Figure 2, real crude oil prices at point of first purchase (or initial sale), real gasoline prices,
and an estimate of real annual expenditure to operate an average vehicle in 2005 dollars are
shown. The highest Post WWII historical unemployment rates have occurred a bit after the
price/cost peaks in 1981 and 2008. However, fortuitously, unemployment rates have actually
dropped while real gasoline costs reached a new peak in 2011. In the case of the first price peak
in 1980 there were “double dip” closely spaced recessions essentially at that point that led to the
deepest decline in real GDP seen since WWII — until the 2008 case. The 2008 peak was
followed immediately by what is now called the “Great Recession”. In both cases, the recessions
halted the price increases, so in a certain sense saying that the recessions occurred at the peak is
deceptive, since the fuel demand drop from the recession causes the end of the price increase.
From 1986 to 2001 the annual cost of operating the average motor vehicle was probably at the
lowest level of the entire period from 1949-2011. This general time period, stretched a few
additional years, has come to be called “the Great Moderation”, due to its relatively infrequent,
relatively mild recessions (Bernanke, 2004). The longest period between post-WWII recessions
was 1991-2002; the second longest from 1961-69 (~ 9 years), and the third longest from 19821990 (~ 8 years). The 1961-69 mini moderation also followed two closely spaced recessions,
and began with small car competition from a foreign manufacturer (the Volkswagen Beetle), with
a compact car response by each of the big 3 domestic manufacturers. Based on the Fig. 2 plot, it
was also a period with relatively low gasoline prices. So, just as the domestic auto industry pushed
back against fuel efficient competition in the 1960s (VW Beetle), leading to a long period without
recession, the process was repeated and sustained during the Great Moderation, with Japanese makes
suffering from the required competition. The Great Moderation period was preceded by a far greater
reduction in fuel consumption than the 1960s case. Thus, anecdotally, periods of low gasoline prices
and low consumer costs of driving experience less frequent recessions than when prices are high.
Perhaps high gasoline price levels cause an urgent technological response by industry and government.
Then if reactions to frequent changes in technology during high gasoline price periods cause fluctuations
in vehicle spending, recessions would become more frequent at that time. The fruits of development of
and introduction of more efficient technology during the periods of high prices could then help push not
only operating costs, but also vehicle costs downward. As Fig. 1 shows, the implementation of enhanced
efficiency in new cars is followed with a lag by improved efficiency in the entire fleet. Once a pulse in
efficiency has been put into place by construction of new factories producing improved vehicles, then
automakers can “coast” and work on process efficiency rather than product efficiency, bringing the cost
of vehicles down. The periods with high gasoline (or generically transportation fuel) prices would be
periods in which substitution of capital for energy was required to move to a period when technology
could once again remain constant for a number of years and energy and capital could complement one
another in supporting widespread economic growth. The quiescent periods when new levels of efficiency
are established may also lead to indirect benefits as productivity is improved throughout the economy via
6
3.5
All gasoline grades
$/gallon except as noted
3.0
Unleaded regular gasoline
Leaded regular gasoline
2.5
Per vehicle annual $/1000
Domestic crude 1st sale
2.0
1.5
1.0
0.5
2009
2006
2003
2000
1997
1994
1991
1988
1985
1982
1979
1976
1973
1970
1967
1964
1961
1958
1955
1952
1949
0.0
Year
Fig. 2 Real gasoline and oil prices and annual vehicle operating costs, 1949-2011
an improved transportation system, in a virtuous cycle such as illustrated earlier by the container ship and
rural roads examples.
Turning to the consideration of impulses, Kilian plotted monthly energy prices from 1970-2006 and noted
that volatility was greater in the 1987-2006 period. Fig. 3 illustrates quarterly price changes, which
exhibit the same behavior. Kilian noted anecdotally that it was not likely that there was a stable
relationship between energy price impulses and vehicle output, since the variability of vehicle sales was
less in the later period, despite the increase in energy price volatility. Fig. 4 illustrates this point visually.
Fig. 5 plots changes in total U.S. employment. Concerning volatility before and after Kilian’s 1987/88
cut point, the employment pattern in Fig. 5 appears much more consistent with Fig. 4 than with Fig. 3.
Thus, although the visual comparison can be misleading, it nevertheless seems more intuitive to expect a
strong relationship between impulses of motor vehicle spending and total employment, than impulses of
gasoline price and motor vehicle spending
Although Kilian stated that his 1987/88 cut point was half way through his sample, ours is not. In terms
of the entire sample, the choice of cut point in terms of the path of all vehicle fleet fuel efficiency in Fig. 1
is close to the 1991 point when the sharp new vehicle fuel consumption reductions from 1977-82 have
worked their way into the entire fleet. In the future, a later cut point of 1991 would be more consistent
with the implications of Fig. 1. However, we do have the advantage of a degree of comparability to
Kilian’s estimates. Further, the choice of 1987/88 properly leaves the gasoline price collapse of 1985-86
in the sample that is estimated to have been affected by the dramatic technological shift to front-wheeldrive in conjunction with the unibody.
7
.2
0
1987q4
-.4
-.2
The Great Recession
1950q1
1960q1
1970q1
1980q1
Quarter
1990q1
2000q1
2010q1
.4
Fig. 3. Proportionate quarterly changes in real gasoline price, 1949-2011.
0
.2
1987q4
-.2
The Great Recession
1950q1
1960q1
1970q1
1980q1
Quarter
1990q1
2000q1
2010q1
Fig. 4. Proportionate quarterly changes in real personal consumption of motor vehicles, 1949-2011.
8
.02
0
.01
1987q4
-.02
-.01
The Great Recession
1950q1
1960q1
1970q1
1980q1
Quarter
1990q1
2000q1
2010q1
Fig. 4. Proportionate quarterly changes in total U.S. employment, 1949-2011.
Model Estimates
We use the Vector Error Correction methodology (VECM) to construct our first tri-variate model, to more
appropriately test the theories discussed, within an estimated dynamic system. The estimates are not only
a test of the theories discussed, but also a test of the value of VECM in their examination. The estimate
of, and test for co-integration is our first.
Our variables were downloaded from EconStats. The total national employment variable was the Bureau
of Labor Statistics “Employment Level” quarterly variable LNS12000000Q. Real vehicle spending was
obtained by deflating the Bureau of Economic Analysis (BEA) “Personal Consumption Expenditures by
Major Type of Product” accounts variable expenditures on “Motor vehicles and parts” (DMOTRC1) by
the price index for Motor vehicles and parts (DMOTRG3). This was multiplied by 100.
In addition to DMOTRG3, information to develop real gasoline price involved use of two of the BEA
“Price Indexes for Personal Consumption Expenditures by Major Type of Product and by Major
Function”. These were the indexes for (1) gasoline & other energy goods (DGOERG3) and (2) total
personal consumption expenditures (DPCERG3). The former was divided by the latter. The logarithmic
form of each variable was used in the estimated models.
9
Four experiments were conducted and five models estimated:
1. Restricted assuming that real gasoline price does not belong in the cointegration
equation – over the 1949q2 to 2011q3 period (one-period model)
2. Unrestricted normalizing on employment and testing for cointegration with motor
vehicle spending and real gasoline price – over the 1949q2 to 1987q4 and 1988q1 to
2011q3 periods (two-period model)
3. Semi-restricted: same cointegrating structure for the two-period models, but unrestricted
error coefficients over the 1949q2 to 1987q4 and 1988q1 to 2011q3 periods.
4. ϋber-restricted: same cointegrating structure from the two-period model imposed on the
restricted model (error coefficients as well as the cointegrating equation restricted to be
the same throughout the sample period)
All models were estimated using maximum likelihood procedure. Each model estimated coefficients for
lagged deviations from the cointegrating equation prediction (vector errors) for four quarters. The
aggregate dynamic response of the system, including both vector and error components, is called an
“impulse response function”. Details on statistical results regarding parameter estimates are available on
request from the authors. We concentrate in this paper on presentation and interpretations of the impulse
response functions, which are readily produced by the software used and are presented after an overview
discussion of the four models tested and compared.
The model in experiment number one excluded gasoline prices from the cointegrating equation. This
would be consistent with an assumption that gasoline prices are entirely exogenous and not influenced by
U.S. actions, at least with respect to employment and real vehicle spending. Results were not at all
consistent with theories and hypotheses reviewed.
Experiment number two differed in two ways. First, the sample was broken into two subsamples at the
1987/88 break point and second, real gasoline prices were allowed into the cointegration equation, with
normalization on total employment. In comparison to model 1, this is essentially a test of the Kilian
hypothesis that gasoline prices are endogenous. Hamilton notes the potential importance of the
normalization decision. “… choosing which variable to” normalize on “might end up making a material
difference … for the evidence one finds for cointegration among the series.” (Hamilton, 1994, p. 589).
We used the full information maximum likelihood estimation method proposed by Johansen to “avoid
this normalization problem” (Hamilton, 1994, p. 590). Once experiment number two was conducted there
was an amazing similarity in the empirical estimate of the cointegrating equation parameters for the two
periods.
Given the similarities of the cointegration system parameter estimates when the time periods were
separately estimated, in the third model the same cointegrating structure was imposed for the entire period
— gasoline prices were endogenous to the cointegrating relationship normalizing on employment. The
coefficients of the deviations from the cointegrating equation predictions (errors) were allowed to differ
between the first and second period.
10
Finally, in the fourth model, both the cointegrating equation and coefficients of the deviations from the
cointegrating equation predictions (errors) were required to be the same throughout the full sample
period.
Tests were conducted under the following null conditions:
1. Overall structure is the same for the Post-War period (1949q2 to 2011q3)
2. The cointegrating equation is the same in the two-period model.
3. The structure for cointegrating equation from the two-period model on the one-period model
Chi square test was applied to a comparison of model 2 against model 4. This test rejected the hypothesis
that model 2 offered no improvement over model 4, rejecting the null hypothesis that the same dynamic
and cointegrating structure was an adequate representation of the behavior of the system over the entire
post-war period. When model 2 and model 3 were compared there was no evidence of a difference
between the two models, which was supportive of the existence of the same cointegrating relationship
over the entire period. Model 2 and model 3 are essentially identical.
Between these two essentially identical models, the most readily produced impulse response functions are
from the separate subperiod tests in experiment 2. Both quarterly and cumulative impulse responses are
presented. Although the models were estimated with four quarters of errors, we chose to plot the impulse
response functions over a period of eight quarters. Historical estimates have found that recessions follow
energy price impulses in about four quarters. Therefore, if we had not plotted eight quarter impulse
responses we might not have adequately understood the dynamics of the system with respect to the
anticipated relationship between real gasoline price impulses and employment.
The Vector Error Correction Model (VECM) addresses some of our theoretical questions. It separates
long-term equilibrium relationships from short term deviations from equilibrium. The cointegration
relationship tests for the existence of a stable, systematic integrated relationship among levels of variables
and estimates correction to deviations from the cointegrated path. Multiple equilibria are possible as the
cointegration path is pushed from equilibrium via outside forces. In the model estimated by VECM in
this paper, the underlying theory is that reduction of costs of services from highway motor vehicles are a
fundamental cause of economic growth. The theory also argues that deviations from equilibrium are
caused by (1) impulses to transportation fuel prices and (2) elevated transportation fuel price levels. The
VECM is readily capable of testing for the latter short term price impulse effect over a few quarters, but
not the latter, which is argued to be a more long-term process. The test for structural differences between
the 1949-87 and 1988-20011 period show a unique behavior of responses of real gasoline prices to real
vehicle spending that is difficult to explain unless vehicle spending and fuel consumption were substitutes
in the first period.
Cointegration equation results
Parameter estimates for cointegration relationships are shown in Table 1.The estimated cointegration
equation(s) for experiments 3-4 consistently predicted a stable, positive long-term relationship among the
levels of all three variables. The results strongly support Kilian’s hypothesis that real gasoline prices are
11
endogenous and that real motor vehicle spending and national employment have been cointegrated in a
stable long-term fashion over the entire estimation period. There is no evidence of a diminishing
importance of the automobile industry in the estimates and tests behind this system of equations.
Impulse response results
Impulse responses of the other two variables to a 1% change in the specified variable are plotted in Fig. 5.
Values of six cumulative impulse responses for each of the two subperiods in experiment 2 are shown in
Table 1. When any of the estimates of the error correction coefficients are statistically significant with a
p-value of 0.10 or less, the quarter number for which the significant coefficient was estimated is noted,
with the sign of the coefficient in parentheses. While our focus for the VECM estimates is the net
impulse response functions, examination of the pattern of error correction coefficients across periods and
for the full sample can reveal which of the relationships changed most dramatically, and which
relationships remained fairly stable. Variable names used in the table are: RMVE (real motor vehicle
expenditures); N (employment); Gas (real gasoline price).
Motor vehicle spending on employment
Error response coefficient estimates of the effect of motor vehicle spending on employment are unstable
(Table 1). Apparently, the net error correction effect is not large and the consistent cointegration
relationship dominates. The implication of the impulse response coefficients is that motor vehicle
spending remained just as important to total national employment in the second period as the first.
Perhaps this is a result of compensating effects. Although direct employment in the motor vehicle
industry has clearly declined dramatically, indirect multiplier effects must remain strong. The loss of
direct employment may be offset by the domestic motor vehicle industry vehicle mix composition effects
noted by Kilian. Since fewer losses of domestic production to imports occurred in the second period, this
could have offset the direct employment losses otherwise occurring in the auto industry when consumers
Table 1 Summary of selected statistical results of Experiment 2 and Experiment 4
Coefficient type
Variables &
direction (if
applicable)
1st period
8 qtr
CIRF
Coefficients
significant in
1st period @
10%
N/A
Yes
Yes
2(-)
1(+),2(-),4(-)
Noneb
4 (-)
4(+)
none
2nd period 8
qtr CIRF
Coefficients
significant in
2nd period @
10%
N/A
Yes
Yes
2(+), 3(+)
Nonec
1(-)
1(+), 2 (+)
1(+)
2(+)
4th
Experiment
Coefficients
Cointegration
N
1.0
1.0
N/A
Cointegration
RMVE
-0.38a
-0.41 a
Yes
Cointegration
Gas
-0.19 a
-0.21 a
No
Error response
RMVE on N
0.37^
0.31^
None
Error response
N on RMVE
6.82^
5.08^
1(+),2(-),4(-)
Error response
Gas on RMVE
-6.23^
-1.96^
1(-)
Error response
Gas on N
-0.26^
-0.05^
4 (-)
Error response
N on Gas
20.28^
36.96^
none
Error response
RMVE on Gas
-0.97^
5.16^
3(-)
^Cumulative Impulse Response over 8 quarters
a
Though negative, these “cointegrating vector” coefficients imply a positive net relationship among variables – see
Hamilton, pp. 586-590. Normalization (restricting N=1) is discussed there.
b
The p-value for the negative 1st quarter coefficient is 0.11, and the coefficient is highest of all quarters
c
The p-value for the positive 1st quarter coefficient is 0.22, and the coefficient is highest of all quarters
12
Figure 5 Estimated Quarterly Impulse Response Functions (Response to 1% Impulse)
reduced motor vehicle spending. When consumers reduced spending on vehicles in total, they did not
reduce spending on domestic vehicles as much as in the past, so when total spending was reduced,
downward pressure on direct domestic vehicle employment was less in the second period. While this is
inferred, further statistical testing can confirm its probable validity.
13
Employment on motor vehicle spending
Error response coefficient estimates of the effect of employment on motor vehicle spending consistently
have the same sign for the three largest (in absolute value) coefficients (quarters 1, 2 and 4). The second
time period effects are smaller than for the first and are statistically insignificant (Table 1). In all tests
illustrated in Table 1 the first quarter coefficient is largest in absolute value and is positive. The second
and fourth quarter coefficients are consistently negative, tending to wash out the short term positive error
correction effect. The implications are that new jobs and immediate automobile purchases are strongly
associated with one another. The long-term cointegration relationship between employment and motor
vehicle spending is consistent and positive. The cumulative impulse response obtained by adding each of
the individual quarter impulse responses (Fig. 5) is very much the same in the first and second period.
The cumulative two year response of motor vehicle spending to employment is 7% for each 1% increase
in employment in the base quarter in the first period, and 5% in the second (Table 1)
Gasoline price changes on motor vehicle spending
The largest absolute value of the coefficient for the response of vector errors is consistently for the first
quarter. The coefficient is negative and is statistically significant in two of the three cases, and is nearly
significant in the third case (period 1). Considering both cointegration relationship and error responses,
the estimated response of motor vehicle spending to a 1% gasoline price impulse is consistently
immediately negative and remains negative in all quarters (Fig. 5). For the first period there is evidence
that a recovery might begin beyond two years, though all that is happening after 8 quarters is a reduction
of negative effect.
In the second period there is a sharp reduction in the two year cumulative effect of a 1% gasoline price
impulse on motor vehicle spending. In the first period, a cumulative two year inverse effect of 6% is
predicted (Table 1). This drops to just 2% in the second period. Interestingly, however, this cannot be
due to the cointegration equation, which is about the same in both periods, nor to the cumulative effect of
the four error response coefficients, which sum to -0.25 in the first period, and a slightly larger -0.31 in
the later period. The VECM system is complex and chains of effects can be difficult to sort out. As in
Granger causality VAR models, lagged effects of the transformations of the dependent variable (not
presented here) are also estimated. Statistically, in this model, support for the hypothesis that positive
gasoline price impulses cause negative responses of motor vehicle spending is stronger since 1987 than
before.
Gasoline price changes on employment
Error response coefficient estimates of the effect of gasoline price changes on employment are unstable
(Table 1). Those coefficients that are estimated to be statistically significant both have a different lag
point and a different sign across the two periods (Table 1). Nevertheless, the pattern of the response of a
1% impulse of gasoline price is temporally consistent (Fig. 5). There is a very small percentage change in
the second year, after negligible positive change in the first year. The axis choice on the plot is chosen to
visually emphasize this. Thus, there is no evidence that the initial reaction of motor vehicle sales after a
gasoline price impulse could be caused by the strong immediate link of employment to motor vehicle
spending. Delayed negative employment effects in the second year may compound a motor vehicle
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spending problem that has already arisen in response to a gasoline price impulse, but it is not possible that
the mechanism of cause of first year motor vehicle spending decline is a first year decline in employment.
Employment changes on gasoline prices
Error response coefficient estimates of the effect of employment on gasoline price are positive for three of
four quarters and the four quarter sums are also consistently positive. The subperiod estimates each have
one statistically significant positive coefficient, but the timing of these is as far apart as possible (Table
1). The significant coefficient for the first quarter in the second period is 4.7, while the significant
coefficient for the fourth quarter in the first period is 0.89. Since the cointegrating equation is the same,
the large difference in early vs. late impulse response in Fig. 5 is clearly dominated by the coefficients for
the error terms. The overall cumulative impulse response of gasoline price to a 1% change in
employment is large, at 21% in the first period, and 37% in the second. Employment — particularly new
jobs — must have a relatively large effect on fleet fuel consumption. Given the results for effects of
employment on motor vehicle spending, along with these results for employment on gasoline prices, it
appears that newly employed individuals must be considerably more likely to purchase vehicles (in order
to reliably get to and from the new job) and to use those vehicles to enjoy the new increase in disposable
income (driving for the purpose of recreation and entertainment), leading to an unusually large increment
in fuel consumption, thus driving up demand for gasoline. It may also be true that the majority of vehicle
purchases associated with new jobs are purchases of used cars, which have been less fuel efficient than
new cars during most of the sample period.
Considering both these results and the cointegration equation results, it certainly appears that Kilian’s
contention that gasoline prices are endogenous was correct.
Motor vehicle spending on gasoline price
This is the only case where the signs of the impulse response predictions are different from the first to
second period. Error response coefficient estimates are only statistically significant in two of twelve
cases among those examined for Table 1. In those two cases, different quarters are involved and the
coefficients have different signs. The third quarter coefficient is negative in all three models and is
estimated to be statistically significantly negative in the fully constrained full period model. The
estimated cumulative two year response to a 1% increase in motor vehicle spending in the first period is
estimated to be a 1% reduction in gasoline price. In the second period the sign is reversed and the
magnitude of the effect quintuples (Table 1). Nevertheless, the response of gasoline price to increased
motor vehicle spending in this system of equations is far less than its percentage response to a 1%
increase in employment.
If the only operative effect were complementarity between vehicle and gasoline spending, one would
think that the estimated response of gasoline price to vehicle spending would be larger than its response to
employment. As noted earlier, gasoline use can be either complementary to vehicle spending or can be
substituted by additional vehicle spending, when switching technologies (diesel or hybrid for
conventional gasoline, front-wheel drive unibody vehicles for rear-wheel drive). Since both effects exist
within the sample, then the response of gasoline price to fuel demand changes associated with vehicle
purchases could logically be less than for employment induced increases. In addition to contributing to
purchases of additional vehicles, a portion of employment increases should also increase utilization of
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presently owned vehicles as commuting to work becomes necessary. So, while increases in spending on
vehicles can reduce fuel use when a new more efficient vehicle is used to replace an old inefficient
vehicle, increased employment without a vehicle replacement must inevitably increase fuel use. Since
vehicle spending is accounted for in the system of equations, the only estimated gasoline price effects of
employment would arise from increased fuel use/demand.
Thus, the 5% response of gasoline price to a 1% increase in vehicle spending, compared to its 37%
increase in response to a 1% increase in employment is probably also evidence that the fuel savings
substitution effect exists when new vehicles replace old. Though the decline is small in the second
period, fleet fuel consumption nevertheless goes down, so some efficiency enhancing substitutions are
implied (Fig. 1).
Fig. 1 shows that the first period involved adoption of considerably more fuel efficient new vehicles than
did the second. The first period was one during which capital and energy became substitutes when
industry response to CAFE and high gasoline prices essentially forced 1980s consumers, on average, to
purchase a much more efficient new technology when replacing the old. It was also argued that vehicle
and fuel capital can be substitutes only when alternative powertrain technology is available to allow the
switch. Fig. 1 shows that a dramatic switch was made in the 38 year long first estimation period, but not
in the 24 year long second period. Sharp increases in fuel economy required by the new round of CAFÉ
standards did not start until the very end of the second estimation period (Edmunds, 2013). Perhaps in 14
years an estimate of such a model for 1988 to 2025 will result in estimation of a negative reaction of
gasoline price to vehicle spending. As far as estimation of the long run effects of CAFE standards is
concerned, the first period is the only one for which such a test is possible. Fig. 1 shows some small fuel
use reduction potential in new vehicles since 2004, but little in the entire highway fleet so far. The
consumer use weightings in the new vehicle ratings may not match with the way vehicle owners actually
drive.
While the important question is not necessarily whether a CAFE standard accomplishes an absolute
reduction in gasoline prices, it is imperative that it accomplish a reduction compared to what prices would
have otherwise been. We do not have to rely on this argument, however, since the estimated impulse
response function for the first period does imply that additional vehicle spending decreased gasoline
prices. Any analysis of a fuel consumption standard that fails to estimate the benefits of reduced fuel
prices (Jacobsen, 2013) is inaccurate. Standards that require new vehicle purchasers to purchase more
efficient vehicles than they otherwise would ultimately benefit all drivers and the economy as a whole by
reducing gasoline prices. Analysts of the desirability of such standards routinely consider the initial cost
increases in comparison to the net benefits of reduced lifetime cost, but they seldom (if ever) incorporate
an estimate of effects of reduced gasoline price, nor of potential indirect benefits resulting from greater
availability of disposable income for purchases other than gasoline.
Implications and Interpretations
At the outset, the primary motivation was to test whether the estimated model would produce dynamic
results consistent with a causal path from real gasoline price to motor vehicle spending, to national
employment. In addition to the question of the magnitude of impulses and their period-to-period stability,
the timing of impulses was important. If recessions tend to start about four quarters after significant
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gasoline price impulses, then national employment effects should show up after about four quarters and
later. This is what the model simulates. If motor vehicle spending is a cause of the employment change
in the recession, the response of motor vehicle expenditures must begin before the response of
employment. Fig. 5 shows that this is what happens. This is consistent with Kilian’s estimates for
monthly unit sales. Reactions of vehicle spending to a gasoline price impulse start immediately, then
employment impacts follow. The evidence from the impulse response predictions is that a decline in
employment initially induced by a decline in vehicle spending can compound the initial vehicle spending
effect by causing a second round of declines in vehicle spending, etc. Consistent with the classic
formulation of a multiplier effect, the reductions in employment caused by the initial gasoline-priceimpulse-induced vehicle spending reduction cause a second round reduction of vehicle spending
manifested by the prompt reaction of vehicle spending to employment. In the meanwhile, the initial
negative response of vehicle spending to the gasoline price impulse persists for many quarters.
There is no evidence of a diminishing importance of the automobile industry with respect to national
employment levels.
These results are strongly supportive of the specific sectoral shifts hypothesis advocated by James
Hamilton, which focuses on motor vehicles. With the benefit of the important post-2004 statistical
evidence (Jones, Leiby and Paik, 2004) provided by another recession, along with proper focus of the
investigation, we think that the fundamental argument of Hamilton’s specific sectoral shifts hypothesis
can soon be proven.
A question for econometrics is how important is the use of VECM instead of VAR in conducting
investigations of the sectoral shifts hypothesis and/or correctly revealing the facts of the theoretical
relationship.
In addition to isolating the vehicle sector as the overall sector that shifts most dramatically in response to
gasoline price increases, Kilian has suggested that sub-sector compositional effects are also important, as
consumers shift from larger vehicles to smaller vehicles. Thus, Kilian suggests a corollary to Hamilton’s
sectoral shifts hypothesis, what we term a sector composition hypothesis. Kilian asserted that the
domestic motor vehicle industry was protected (through 2008) from the early 21st century gasoline price
impulses by its ability to domestically produce relatively more small, fuel efficient motor vehicles than in
the past. We believe this is correct, and believe that policies that promoted domestic production of
smaller cars provided a cushion that mitigated effects of the Great Recession. However, this should be
analyzed by estimation of appropriate models rather than simply asserted. Our model does not contain the
needed subsector detail.
Even though public policy with regard to the composition effect may have reduced the magnitude of
decline of sales of motor vehicles in the Great Recession, the second period statistical evidence is that the
motor vehicle sector’s shifts, transmitted through personal consumption declines, caused a significant
portion of the collapse into the Great Recession.
We note that the more robust recovery from the double dip recessions after 1980 was associated with a
dramatic drop in gasoline price. Such a drop has not yet been realized in support of the Great Recession
recovery.
17
Macroeconomists have proven unable to predict recessions. Apparently the response preferred at the
moment is to improve the characterization of the financial sector which is said to be helpful “when
studying how an economy in ‘equilibrium’ responds to things like a spike in the price of petrol”
(Economist, 2013b, p. 75). We agree with the quote of Hyun Song Shin, that “macro is an empirical
subject” that “cannot forever remain impervious to the facts.” (Economist, 2013b, p. 75) If Hamilton’s
1988 sectoral shifts hypothesis is correct, better understanding the financial sector alone will not be
enough. It may be the wrong sector to start with.
In statistically evaluating his sectoral shifts hypothesis Hamilton has highlighted the impact of oil price
shocks, then unanticipated oil price shocks on the automobile industry, because the industry is seen as a
transfer mechanism for the effects of this exogenous event. Kilian asserted that the correct hypothesis
would use gasoline price impulses as the starting point, and that gasoline prices are probably endogenous.
Though we doubt that endogenous feedbacks alone explain most gasoline price shocks, our methods and
results strongly support both elements of Kilian’s opinion. Our results imply that future analyses should
attempt to isolate exogenous and endogenous components of gasoline price shifts.
Though we wished to give the sectoral shifts hypothesis a fair test, our perspective on the role of
transportation vehicles is actually fundamentally broader. We assume that variations in the domestic
output of motor vehicles are a fundamental cause of isolated recessions, double dip recessions, Great
Recessions and (in Santini’s case [1988,1989]) Depressions before WWII. Oil or gasoline prices are only
one of many possible causes of the fluctuation in output of motor vehicles. Transportation cost shocks,
broadly conceived, are the primary cause of dramatic fluctuation in output of motor vehicles. In this
paper we add discussion of society’s desire for increased capital spending to obtain lesser fuel
consumption as a cause of reduction and fluctuation of motor vehicle production during the time the
transition takes place. In the case of this paper, agreements between industry and government to pursue
exploitation of this trade-off (Edmunds, 2013) for the long-term interests of both (CAFE) are a core topic.
The conceptual problem of technology shock, however, does not require such agreements. If industry and
motor vehicle purchasers relatively suddenly decide that they want new technology to be introduced, then
declines in sales and production capacity of the old technology may create sharp overall declines in motor
vehicle output as the transition is accomplished.
So, in addition to recommending continued study of gasoline price impulses as the cause of sharp declines
in motor vehicle spending and output, we caution that there are many different candidate causes of
changes in sales of motor vehicles, including:
Purchase delay effects, including:
Gruenspecht effects (standards imposing safety, emissions, and/or fuel
efficiency technology)
Bernanke and Pindyck uncertainty effects
Santini capital for energy substitution effects
Depreciation allowances
Subsidies
Taxes and fees
Vehicle and network performance capabilities
Interest rates
18
Credit rating systems
Strikes
Natural disasters
Income
Employment
Emergence of delayed evidence on vehicle reliability and safety
Fuel reformulation or switching
Fueling infrastructure technology
Vehicle operating network infrastructure funding and design
Enabling technology feasibility shocks (such as battery technology, fuel cells,
steam engines, electric street railways, internal combustion engines)
All of these are studied in the transportation economics and/or engineering literature. However, they are
seldom considered as potential causes of sharp fluctuations in motor vehicle output when they
dramatically change.
Regarding transportation economics, we are sympathetic to economic historian Blaug’s 1985 puzzlement
that the economics discipline had not folded the assessment of transport cost into mainstream economic
theory. Given the recent research on growth impacts of container ships, we are emboldened to ask why a
discipline that has for multiple centuries emphasized the value of low cost movement of goods (tariff
reduction in support of “free” trade) has failed to consider the methods by which engineers, politicians,
lawyers, and corporate managers repeatedly develop and implement the technologies that enable efficient
low cost trade of final goods and supply of inputs that in turn support long-term growth of the macroeconomy, even at the expense of short term difficulties.
Descriptively, we note that the three longest periods between recessions since WWII were each preceded
by a shift of production of domestic motor vehicles toward smaller compact cars that aided the industry in
reducing market share of competing small imported cars. The latter widely spaced pair of recessions
during what has been called the Great Moderation followed a reversal of market share of Japanese
vehicles. The first of the three longest stretches between Post WWII recessions followed industry
response to the success of the German Volkswagen Beetle. Kilian’s sector composition hypothesis argues
that matching foreign competitor’s small and efficient vehicle strengths reduces losses to domestic
competition when gasoline prices rise. This diminishes the odds of collapse of domestic vehicle
production and therefore, of sub-sector-shift-induced recessions. It may also reduce the odds of
recessions because the mix of vehicle sizes tilts toward smaller more fuel efficient vehicles than otherwise
would have been sold, putting downward pressure on average vehicle purchase and operating costs,
gasoline prices, and thus needed expenditure per vehicle for productive services rendered (commuting to
work, delivering goods and services).
Conclusions
Clearly the ability to steadily reduce cost of ownership and use of motor vehicles year-by-year is
consistent with enhanced economic efficiency, a fundamental contributor to economic growth. Periodic
displacements from the equilibrium path induced by transportation costs occasionally rising significantly
relative to the long-term cointegrated path require structural adjustment to return to the path. These
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adjustments are accomplished via introduction of new efficiency-enhancing transportation system
technology. Thanks to its ability to test data available via years of dedicated collection efforts by
numerous government employees supporting the National Income and Product Accounts, the VECM
seems to capture the post WWII nature of this process quite well.
The results are supportive of a focused, specific sector shift hypothesis that assigns a response of the
economy to transportation fuel cost increases acting via their influence on the equipment that uses the
fuel, motor vehicles. They strongly support Hamilton’s 1988 hypothesis that the transfer mechanism for
effects of oil price shocks on the macroeconomy passes through motor vehicles. Kilian’s 2008 contention
that if motor vehicles are important gasoline prices are more appropriate is also supported, as is his
contention that gasoline prices should be endogenous. Though additional empirical clarification is
desirable, the results also support Santini’s argument (1988, 1989) that fuel efficiency enhancing
transportation technology shifts are both macro-economically problematic in the short term, but also
foundational to economic growth in the long term.
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