Gasoline Prices, Vehicle Spending and National Employment

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:

David A. Poyer,

Phone: (404) 222-2580

Morehouse College

E-mail:

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 Energylaboratory 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

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 formershort 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 contributortolong-term economic growth.Positive transport fuel cost deviations from long term reductions area 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 importanceof 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 researchexamining 22 industrialized nations from 1962 to 1990 estimates that improvement in container ship technology was as important to growth inworld trade as the reduction intrade 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/supportthat 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 isevidence that the implementation of Corporate Average Fuel Economy standards (CAFE) played a role in the dynamics of the adjustment process.

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 trendwas 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).

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 probableCAFE-induced effects on gasoline costs this wouldreduce 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.

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 addressedthe ideathat 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 1988has 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 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 1987/88the 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. Kilianattributed thediminished 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 otherwas 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.

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 peakin 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 1982-1990 (~ 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

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-wheel-drive in conjunction with the unibody.