NBER WORKING PAPER SERIES

OIL, AUTOMOBILES, AND THE U.S. ECONOMY: HOW MUCH HAVE THINGS REALLY CHANGED?

Valerie A. Ramey Daniel J. Vine Working Paper 16067 http://www.nber.org/papers/w16067

NATIONAL BUREAU OF ECONOMIC RESEARCH

1050 Massachusetts Avenue Cambridge, MA 02138 June 2010

The views expressed here are those of the authors and not those of the Board of Governors, or the Federal Reserve System, or the National Bureau of Economic Research. Sam Ackerman provided excellent research assistance. We are grateful for comments from Daron Acemoglu, Jordi Galí, George Hall, James Hamilton, Chris Kurz, Ariel Pakes, and Garey Ramey, and we thank William Lee for sending us his papers. We also thank participants at the 2008 International Society for Inventory Research session, the NBER Twenty-fifth Annual Conference on Macroeconomics, and seminar participants at Statistics Canada and UC San Diego. Valerie Ramey gratefully acknowledges financial support from National Science Foundation grant SES-0617219 through the NBER.NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2010 by Valerie A. Ramey and Daniel J. Vine. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source.Oil, Automobiles, and the U.S. Economy: How Much have Things Really Changed?

Valerie A. Ramey and Daniel J. Vine NBER Working Paper No. 16067 June 2010 , Revised July 2010 JEL No. E2,Q43 ABSTRACT This paper studies the impact of oil shocks on the U.S. economy—and on the motor vehicle industry in particular—and re-examines whether the relationship has changed over time. We find remarkable stability in the response of aggregate real variables to oil shocks once we account for the extra costs imposed on the economy in the 1970s by price controls and a complex system of entitlements that led to some rationing and shortages. To investigate further why the response of real variables to oil shocks has not declined over time, we focus on the motor vehicle industry, which is considered the most important channel through which oil shocks affect the economy. We find that, contrary to common perceptions, the share of motor vehicles in total U.S. goods production has shown little decline over time. Moreover, within the motor vehicle industry, the effects of oil shocks on the mix of vehicle VROG and on capacity utilization appear to have been proportional in recent decades to the effects observed in the 1970s.
Valerie A. Ramey Department of Economics, 0508 University of California, San Diego 9500 Gilman Drive La Jolla, CA 92093-0508 and NBER VRAMEY@UCSD.EDU Daniel J. Vine )HGHUDO5HVHUYH%RDUG 20th Street and Constitution Ave. NW Mail Stop 82 Washington, DC 20551 daniel.j.vine@frb.gov

### Introduction
Between 2002 and mid-2008, the average re al price of gasoline in the United States increased more than two fold after ha ving risen only modestly in the preceding 15 years. Not surprisingly, this run-up led to rene wed interest in the effects of oil shocks on the U.S. aggregate economy. Hamilton’s ( 1983) seminal paper documented the negative effects of oil shocks on the aggregate economy, and numer ous papers since that time have extended or questioned the strength of these effects. 1 Most recently, several authors have argued that the effects of oil price s hocks on U.S. aggregate activity have declined since the mid 1980s (e.g. Herrera and Pesa vento (2009), Blanchard and Galí (2010), Blanchard and Riggi (2009), Edelstein and Kilia n (2009)). These papers have variously attributed the decline to impr oved monetary policy, a smalle r share of oil in production, or more flexible labor markets. Empirica l work has also shown that a more-muted response in the consumption of motor vehicles to energy price shocks has played a large role in obtaining these results (Edelstein and Kilian (2009)). This paper re-examines the extent to which the impact of oil shocks on the aggregate econom y—and on the motor vehicle industry in particular—has changed over time. We first discuss the array of energy co st measures that authors in the literature have used to define oil price shocks, and th en we survey the theoretical contributions from a number of DSGE macro models that include various roles for oil in the economy. Using these models, there are a number of st ructural parameters that reasonably could

1 See, for example, Mork (1989) , Hooker (1996), Hamilton (1996) , Barsky and Kilian (2002), and Hamilton (2003).

2have changed over time and reduced the pote ncy with which oil price fluctuations depress aggregate output. However, all of these macro models assume that the pr ice of oil reflects the true cost of energy for firms and consumers. Wh ile much of the recent empirical work uses published measures of oil and/or gasoline prices as an indicator of the strength of the oil price shocks, we find that these measures neglect the impact of the shortages that occurred in the critical 1 973-74 and 1979 oil shock episodes due to price controls. Using two oil shock measures that include the effects of bot h price and non-price rationing, we re-examine the evidence from vect or autoregressions th at oil disturbances have had less impact on the real economy in the past twenty years than in the preceding decades. The results show that the res ponses of motor vehicle consumption and aggregate output to shortage adjusted oil pri ce shocks appear just as great during the past two and a half decades as they were in the 1970s and early 1980s. On the other hand, even the new measures imply that the impact on nominal variables has become noticeably muted. Why has there been so little change over tim e in the response of motor vehicle consumption to oil price changes? We find that, despite the many innovations in the way the U.S. economy produces and uses motor vehi cles that have occurred over the past 40 years, the primary channels th rough which oil prices directly affect motor vehicles have not changed much over time. Namely, we pres ent evidence that the recent increases in gasoline prices have caused just as much anxiety in consumers now as was observed 30 years ago, and the shifts in demand across vehicl e size classes have al so been as equally

3disruptive to motor vehicle ca pacity utilization since 2000 as they were in the 1970s and early 1980s. The paper proceeds as follows. Section II reviews the data available on various measures of oil prices and discusses how m odern DSGE models accommodate the role of oil in the economy. It also presents evidence that the presence of energy price controls and gasoline shortages in the 1970s may cau se problems in empirical work because published prices in that era do not reflect the true cost of energy. Using measures that include the cost of shortages, we find no evid ence of weaker effects of oil shocks on the real economy. Because we find th at the motor vehicle industry plays a central role in the propagation of the oil shocks, the remainder of the paper studies this industry in detail. Section III examines the role of the motor vehicle industr y in the overall economy. Section IV discusses how gas prices affect vehicle demand and Section V shows the ways in which these shocks affect producti on. Section VI concludes.

I. Oil shoc ks and the U.S. economy We begin by reviewing the behavior of seve ral key m easures of oil prices over the past few decades. After describing how ma cro DSGE models have been used to understand the role of energy costs in the economy, we present evidence that price controls may have led to a wedge between the published price of oil and the true cost of oil during the large oil price shocks in the 1970s. Using vector autoregressions that are similar to those estimated by Blanchard and Galí (2010) and Edelstein and Kilian (2009), we then show how mismeasurement of the true cost of oil in the 1970s may have caused the appearance of structur al instability in the impulse response functions of real output to

4oil price shocks. Using cost measures that acco unt for shortages, we find that the impulse response functions have not changed much over time.

A. Overview of oil prices Figure 1 displays three oil price meas ures: the producer price index for crude petroleum (PPI-oil), the refiner acquisition co st of imported oil (RAQ), and the consumer price index for gasoline (CPI-gas). Ha milton (2003, 2009b) typically uses the PPI-oil measure and Mork (1989) and Barsky and Kilian (2002) use ve rsions of the RAQ

measure. 2 Unfortunately, the RAQ measure starts only in 1974. 3 We include the CPIgas measure because several authors have show n that gasoline is a large share of U.S. petroleum consumption, and gasoline prices are also the most relevant energy price measure for the automobile sector. 4 Oil and gas prices—displayed in log current dollars in the upper panel of Figure 1 (thick solid line) and in log re al index points in the lower panel—have risen notably at several points in history. F our episodes stand out in particular: First, the real price of gasoline rose 27 percent between October 1973 and May 1974, the result of an even larger rise in the price of crude oil after the Yom Kippur War. After falling back a bit over the next four years, the price of crude oi l began to rise again at the end of 1978. By the spring of 1980, the Iranian Revolution and th e Iran-Iraq war led to losses in crude oil

2 Blanchard and Galí (2008) use the price of West Te xas intermediate oil, available from FRED. This series shares the same problems with the PPI measure that we will discuss below.
3 Barksy and Kilian (2002) extend it back to 1971, but we are worried about this extension, for reasons given below.
4 Also, Kilian (2009) highlights the importance of studying gas prices separately from crude oil prices.

5production that pushed up the pr ice of imported oil 71 percen t and the price of gasoline 46 percent. Between 1982 a nd 1985, the nominal price of gasoline grew only modestly until Saudi Arabia abandoned production quotas and the price of crude oil plunged. Real gasoline prices continued to tr end lower after 1985, and, by the end of the 1990s, real gasoline prices had receded to record low levels. This pattern changed abruptly at the beginning of 1999, when OPEC member countries phased in several cuts to production quotas. The real price of gaso line surged 43 percent by the summer of 2001 before the weakening world economy put downward pressure on crude oil prices. The relief was short lived, howev er. Gas prices began to ri se again in early 2002, when political turmoil in Venezuela shut down much of the country’s crud e oil production; real crude prices climbed 588 percent and gasoli ne prices climbed 127 percent by summer 2008, and then collapsed when the financial cris is spread from the housing sector to the rest of the economy and in terrupted aggregate demand.

B. Oil shoc ks in macro DSGE models Economists take a keen interest in oil prices because thes e episodes of steep incre ases in prices were often followed by r ecessions. The literature has introduced into macro models four principle channels throug h which oil or energy shocks can lead to recessions: (i) Energy serves as an important input to production; (ii) energy is an important consumption good; (iii) changes in energy prices lead to costly shifts in demand across sectors; and (iv) the policy resp onse to oil price shocks includes monetary tightening, a move that depres ses output. Often layered on top of these channels are forces that multiply and propagate the effects of oil price shocks on aggregate output, such as real wage rigidities (e.g. Bruno a nd Sachs (1982), Blanchard and Galí (2010),

6Blanchard and Riggi (2009)), imperfect co mpetition (Rotemberg and Woodford (1996)), variable utilization rates (Finn (2000)), vintage capital effects (Atkeson and Kehoe (1999), Wei (2009)), and multiplier effects crea ted by externalities across firms (AguiarConraria and Wen (2007)). We will briefly discuss each of these channels and point out which parameters in these DSGE models are suspected to have changed over time.

Energy as an input to production Berndt and Wood (1975), Bruno and Sachs (1982), and Pindyck and Rotemberg (1983) were am ong the first to study energy price shocks in a framework that accommodated energy as an input to production. The strength of this channel is limited, however, by the small share of energy in tota l production costs, even in the 1970s. Finn (1991, 2000) modifies the standard model to reflect the notion that the energy requirements of installed capital are often fixe d, and thus energy must be used in fixed proportions to capital utilizati on. This feature makes output more sensitive to increases in energy prices. In many of the models mentioned above, a decrease in the am ount of oil required to produce a unit of output would reduce the effect of oil shocks on the aggregate

econom y. This result suggests that increases over time in the fuel efficiency of many types of production technology may have weaken ed the relationship be tween of oil price and real output. In ad dition, structural paramete rs not directly relate d to the use of energy can also affect the transmission of energy pr ice shocks in DSGE models. For example, Blanchard and Galí (2010) show that a decline in the rigidity of wages in these types of models reduces the effects of oil price shocks on output.

7Energy as consumption good In addition to the consequences of reduced output in general equilibrium, increases in oil prices also ha ve direct effects on dem and. Fi rst, oil shocks can lead to declines in demand for goods for which consum ption is complementary with purchases of oil. Hamilton (1988) and Wei (2009) use mode ls of demand for motor vehicles to show this effect. Second, oil shoc ks introduce uncertainty into the outlook for future energy prices, and increases in uncertainty can damp en demand for goods if purchases are costly to reverse (Bernanke (1983)). Third, for en ergy consuming capital goods, increases in the price of energy change the desired character istics of the capital in use. Because the energy efficiency of the existing stock of consumer durables available in the short run is largely fixed, demand for new goods can shif t between products in an exaggerated fashion and reflects the widening differential in the relative cost of ownership between different types of goods. For motor vehicl es, smaller and more fuel-efficient models naturally become more desirable. In these types of models, one parameter that has likely changed over tim e is the energy efficiency of consumer durable goods, including motor vehicles and other appliances. When energy efficiency rises or the share of these types of goods in total consumption falls, then we would expect the im pact of oil shocks on output to diminish over time.

Sectoral shifts and costly factor mobility

8Several papers have investigated sectoral shifts as a way in which oil price shocks affect the aggregate output. Davis (1987) and Hamilton (1988) both suggest that oil price shocks have a bigger effect on output if the shocks induce sectoral shifts and factor adjustment is costly. Bresnahan and Ramey (1993) argue that oil shocks can lead to disruptive sectoral shifts even within narrowly defined industr ies. They present empirical evidence that shifts in demand between size classes of automobiles disrupted output in the U.S. automobile industry during the 1970s. In the context of the multi-sector models, it is not clear that the structure of the economy has changed in a way that would weak en the transmission of oil price shocks through the sectoral shifts channel. We fi nd evidence in the motor vehicle industry that this channel remains quite potent.

Monetary Policy Reaction Functions Bernanke, Gertler and Watson (1997) argue that the endogenous response of monetary po licy to an increase in oil prices is an important part of the outsized declines observed in output, a result they showed by usin g a structural vector autoregression and counterfactual experiments with different monetary policy rules. 5 Using a calibrated DGE model, Leduc and Sill (2004) found that 40 percent of the decline in output that follows a positive shock to oil prices reflec ts the systematic component of monetary policy.

5 Hamilton and Herrera (2004) questioned details of their specification. Bernanke, Gertler and Watson (2004) responded and re-estimating their model in a way that attempts to deal with this critique and find results only slightly less strong than in their original paper.

9To look for changes in policy parame ters over time and assess whether these changes may have reduced the impact of o il shocks on output, a number of papers have either simulated monetary DGE models or es timated monetary stru ctural vector auto regressions. Herrera and Pesavento (2009) , Blanchard and Galí (2010), and Blanchard and Riggi (2009) all find eviden ce that oil price shocks have had less impact in recent decades, in part, because of the changes in monetary policy. To summarize, the theoretical literature ha s suggested a variety of ways in which oil shocks affect the econom y. Some of thes e effects should be expected to be weaker now, while other effects can easily be as strong.

C. The importance of non-pri ce rationing in the 1970s In the models described above, it is assume d that the price of oil reflects the true acquisition cost of energy for fir ms and house holds. While the literature on the effects of oil shocks has debated the merits of various measures of oil prices and whether the effects are nonlinear, 6 much of it has missed a potentially important change in the degree to which oil prices reflect oil disruptions . In particular, other than Mork (1989), macroeconomists have not paid much atten tion to the embargoes, price controls, and shortages that marked the oil price disturbanc es in the 1970s. Helb ling and Turley (1975) document that price controls were first imposed on the U.S. domestic oil industry in August 1971 as part of the general imposition of price controls. The controls on other sectors of the economy were pha sed out, but the controls we re made more stringent on the domestic oil industry in response to the OPEC embargo of October 1973. These

6 For example, see the debate between Kilia n and Vigfusson (2009) and Hamilton (2009b).

10complex controls, which imposed a price ceiling on “old” oil that was lower than the one imposed on “new” oil, led to significant disr uptions in the production of domestic oil and held the average domestic price of crude oil belo w the world price. Most of the effects of these controls were felt in the markets for ga soline and diesel fuel . According to some estimates, 20 percent of the gasoline stations ra n out of gas during the height of the crisis (Frum (2000)). Pisarski and de Terra (1975) detail the pol icy responses to the em bargo in various European countries. While most European countries did not impose the types of price controls imposed in the United States, they resp onded with other sorts of controls, such as bans on Sunday driving (Germany, Italy, Ne therlands, Switzerland) and limits on gas purchases (Great Britain, Netherlands, Sw eden, Switzerland). Almost all countries imposed lower speed limits. Multiple oil and gas price controls also helped produce shortages after the Iranian Revolution of 1979. In April 1979, President Carter announced gradua l decontrol of oil prices, but proposed a windfall profits tax. In January 1981, President Reagan signed an order leading to the complete dere gulation of oil and gas prices. To quantify the additional cost imposed on consumers by non-price rationing in the 1970s, Frech and Lee (1987) use data on urba n and rural traffic patterns in California and estim ates of the price elasticity of demand for gasoline from Lee (1980). They estimate that the time cost of the queues adde d between 13 to 84 percen t to the price of a gallon of gasoline between December 1973 and March 1974; the additional time cost implicitly paid by consumers between May 1979 and July 1979 varied from 6 to 33 percent. Thus, the price index for gasolin e shown in Figure 1 potentially understates the

11true cost of gasoline quite severely in peri ods affected by the two oils shocks of the
1970s. The producer price index for crude petr oleum suffers from the sam e problem because of the price controls on domestic crude oil. The refiners’ acquisition cost of imported oil used by Barsky and Kilian (2002) co mes closer to measuring the world price of oil. However, this measure still does not capture all of the additional costs imposed on the U.S. economy by distortions caused by pr ice controls and the entitlement system. The reason is that price controls cause ineffici encies and deadweight loss that are larger than the gap between actual pri ces and market clearing prices. 7 In order to capture the true cost of ga soline during these episodes, we propose two new variables: The first variable augments published gas prices with estimates of the additional time cost during the peri ods of gasoline lines. In particular, we use the average of the rural and urban estimates from Table 1 in Frech and Lee (1987), which compares the time costs per gallon to the published pri ce per gallon of gasoline for the months of December 1973 through March 1974 and May 1979 through July 1979. All told, rationing is estimated to have added between 8 percent (in July 1979) to 67 percent (in March 1974) to the shadow price of a gallon of gasoline. 8 Using these estimates, we

7 For example, Frech and Lee describe inefficienci es in the allocation of gasoline across urban and rural markets. Similarly, Davis and Killian (2009) show th at the total inefficiency costs of price controls on residential natural gas in the U.S. was three times larger than was the estimate of the simple deadweight loss.
8 Frech and Lee’s estimates are based on data from only California, so the question arises as to how California’s shortages compared to the rest of the nation. According to the Feb. 8, 1974 Wall Street Journal , there was no rationing in “New England north of Boston, much of the Midwest, Denver, Nevada, and Southern California. In Northern California, the word was okay on weekdays and in daylight, but

12construct a shortage adjusted index for the re al price of gasoline, which is shown as the dashed line in figure 2. Because the rationing-by-queue cost esti mates likely capture the effect of shortages imperfectly, w e also consider a second measure—the special question posed by Thomson Reuters/University of Michigan in the Survey of Consumer Attitudes. Respondents to the monthly survey are aske d several questions related to car buying conditions. The survey tracks th e portion of respondents who cite the price of gasoline or possible fuel shortages as a reason that car buying conditions are poor. 9 This measure is shown in Figure 3. The portion of consumers that expressed anxiety over fuel prices ramped up sharply at the time of the oil pr ice shocks in the 1970s and early 1980s. Although the rise in real gas prices was much greater in the 2000s than in the 1970s and early 1980s, the consumer sentiment variable hit similar peaks in both periods. A CNN/Opinion Research Poll conducted in June 2008 found that consumers were more concerned about long gas lines than about high prices (C NNMoney.com, June 10, 2008). This may explain why the run-up in gas pr ices in the 2000s, though bigger than the runups in earlier episodes, did not cause a larger effect on consumer sentiment.

otherwise watch out. There were long lines in Wa shington, D.C., and the Philadelphia area...and in New York and New Jersey, where things have been tough for quite a while now.”
9 Other reasons consumers can give for this being a bad time for buying a car ar e: (1) prices of cars are too high; (2) interest rates are too high; (3) can’ t afford to buy; (4) uncertain future; and (5) poor selection or quality of cars.

13D. The responses of output, consumption, and prices to oil price shocks Using the published measures of energy pr ices and the two vari ables that account for non-price rationing in the 1970s, we now re visit the evidence used to suggest that aggregate activity has been res ponding less to energy pr ice shocks in recent years than it used to in the past. The energy price meas ures we consider are the following: (1) The consumer price index for gasoline; (2) Hamilton’s (2003,2009b) “net oil price increases” 10 ; (3) the CPI index for gasoline that has been augmented with the time cost of rationing-by-queue; and (4) the measure of c onsumer attitudes toward gasoline prices and fuel shortages. Our strategy is as follows: First we sh ow that the impulse response functions from vector autoregressions es timated by Blanchard and Galí (2010) and Edelstein and Kilian (2009) do not change much if published gasoline prices are used in place of the authors’ original oil price meas ures. Second, we show that the impulse response functions based on gasoline price measures that account for the effects of shortages present a different story. We begin by estimating a vector autoregre ssion (VAR) that is similar to the one used by Blanchard and Galí (2010). The VAR system we estimate is given in equation (1). (1) tt t UYLAY +=

− 1 ) ( In the VAR estimated by Blanchard and Galí with quarterly data, includ es the nominal price of oil, the CPI, the GDP deflator , nominal nonfarm compen sation, real GDP and t Y

10 The Hamilton measure is defined as the log change in the price index for gasoline relative to its previous three-year high if it is positive, or zero if it is negative

14nonfarm business hours. In other specifications, they also incl uded the federal funds rate. In our version of their analysis, is built from monthly observations of the following variables (in order): (i) A selected version of one of the oil shock variables, (ii) the consumer price index (CPI), (iii) nominal wages of private production workers, (iv) industrial production, (v) civi lian hours, and (vi) the federal funds rate. A(L) is a matrix of polynomials in the lag operator L

. U is a vector of disturbances. All variables except the sentiment measure and the federal funds rate are in logs. The shock to oil prices is identified using a standard Cholesky decom position. We include a linear time trend and six lags of the variables. The data are monthly and span 1967:1 to 2009:12. t Y

Blanchard and Galí (2010) compare samples that are split between 1983 and 1984, which is the typical split for studies of the Great Moderation. Edelstein and Kilian (2009) study samples split between 1987 and 1988. We choose a split between 1985 and 1986, as this date is between the dates used by these authors, and it also coincides with the rather dramatic change in the nature of the oil market that occurred in 1986. We summarize the results in table 1. As an alternative to showing dozens of impulse response functions from various pe rmutations of oil price measures and estimation periods, table 1 shows the peak resp onse of key macro variables to a shock in each oil price indicator in each period. The oil measure used for each impulse is shown in the first line of each panel in the table. The shock has been normalized so that the size of the increase at its peak is equal to one in both periods. It is important to note, however, that the standard deviations of the (non-normalized) shocks to nominal ga s prices and to Ham ilton’s net price gain measure are more than twice as high in the second period as in the first period. In

15contrast, the standard deviation of the shocks to the shortage-adjuste d gas price is roughly constant across the periods, whereas the standard deviation of the shocks to the measure of consumer sentiment toward gasoline is about 50 percent higher in the second period. The key comparison in Table 1 is the ratio in the last column. For a given shock in the gas cost variable, it shows the ratio of the peak response of the other variables in the second period to the peak response in th e first period. The results for both the standard nominal gas price measure and Ha milton’s nonlinear measure show that the responses of both industrial production and hours are less than half as large in the second period as in the first period. The response of inflation appears to have declined by an even larger proportion than did the real variable s. In contrast, when either the real index of gas prices adjusted for the cost of shor tages or the measure of consumer sentiment about gas prices/shortages are used as the oil price indicator, the peak response of industrial production becomes greater in the second period than in the first period. For hours, the response remains slightly less in the second period than in the first period if we use as the oil price indicator the shortage ad justed gas price index, but it becomes greater if we use the measure of consumer sentiment. 11 However, the response of inflation is still lower in the second period than in the first period even if the shortage adjusted gas price index or the gasoline sentiment measure is used as the oil price indicator. 12

11 We do not show the response of nominal wages and the funds rate because their responses are not significantly different from zero and the dynamic patterns swing from positive to negative in some cases.
12 The results are similar if we substitute the chained price deflator for personal consumption expenditures for the consumer price index.

16To examine more formally the relationship between consumer sentiment toward gasoline and the various gasoline price measur es, we estimate a number of bivariate vector auto regressions and compare the relati onship in the early and late periods. We find that shocks to the published CPI for gasolin e appear to have an effect on sentiment in the early period that is about twice as large as the effect in the late period. In contrast, shocks to the shortage adjust ed gasoline price inde x have about the same peak effect on sentiment in each period. This evidence sugges ts that the presence of shortages in the early period is the key difference between shoc ks to gasoline sentiment and shocks to the published CPI for gasoline. Edelstein and Kilian (2009) find that much of the decline in aggregate activity (or aggregate consum ption, more specifically) th at follows a jump in oil prices comes through demand for motor vehicles. They also show that this cha nnel has weakened over time, thereby reducing the effect of oil shocks on aggregate activity. Because this result is even more likely to have been affected by presence of market distortions in the 1970s, we also re-estimate a VAR si milar to the Edelstein and K ilian (2009) model, using our shortage adjusted measures for the true cost of gasoline. We estimate a trivariate VAR, in which is defined by (i) one of the oil cost indicators, (ii) the log of r eal total consum ption excluding mo tor vehicles, and (iii) the log of real consumption of motor vehicles. In the first set of results, we use as an energy price indicator the Edelstein and Kilian (2009) measure of the purchasing pow er lost to increases in oil prices: This measure scales the changes in real energy prices by the share t Y

17of energy in consumption expenditures. 13 In the second set of re sults, we use as an oil cost indicator the consumer sentiment toward gasoline. In each set of results we estimate the VAR in two sample periods: 1967:1 – 1985:12 and 1986:1 – 2009:12. We normalize each shock so that the peak responses of th e shock variable are equal to one in each sample period. Figure 4 shows the estimates, with f illed dots indicating when the estimated response is more than two standard deviations from zero and open circles indicating when the response is more than one but less th an two standard deviat ions from zero. The panels to the left show responses that us e the Edelstein and Kilian purchasing power series as an oil cost indicator, and the respons es to the shocks are la rgely consistent with those originally reported by Ed elstein and Kilian. Specifically, the response of total consumption falls less sharply in the second samp le period than in the first sample period, though the responses are not statistically significa nt in either period. To the degree that the response has changed, the bottom panel s hows that most of the change comes from the consumption of motor vehicles: The respons e in the consumption of motor vehicles is much less in the second period (the dashed line ) than the first period (the solid line). The column to the right in figure 4 shows the impulse responses obtained when the consumer sentim ent toward gasoline serves as the oil cost indicator. Several comparisons here stand out. First, the res ponses of consumption to these shocks have not diminished between the early and late periods; this holds true for the consumption of

13 We also considered a measure that scaled prices by the average fuel efficiency of the motor vehicle stock and the miles driven by households. The results were similar to those using the Edelstein and Kilian

measure.

18motor vehicles and the consumption of a ll other goods and services. Moreover, the responses in the second pe riod appear to be more persistent than those in the first period. Second, the responses based on these shocks ar e statistically signifi cant, an indication that real activity is more closely related to consumer perceptions of the price of gasoline and its availability than it is to publishe d fuel prices. And third, the decline in consumption of motor vehicles after a ga soline price sentiment shock is many times larger than the response of cons umption excluding motor vehicles. 14 For comparison, we also estimated the impulse response based on other measures of energy prices. Using the Ham ilton measure of net oil price increases, the responses show a significant muting between the early and th e late samples. If we use the shortage adjusted measure of gasoline pr ices, we find results qualitative ly similar to those obtained using consumer sentiment toward gasoline as th e oil cost indicator. The responses to oil shocks are only slightly smaller in the sec ond period than in the fi rst periods; the peak impact on total consumption is 0.9 in the sec ond period relative to th e first, and on motor vehicle consumption is 0.8. To summarize, when oil price shocks are m easured as the shocks to either the published price index for gaso line, the Hamilton net increas e in oil prices, or the Edelstein and Kilian purchasing power meas ure, we confirm the results from the literature that oil shocks have much less of an impact on the economy after 1985 than they did up until 1985. In contrast, when we measure oil shocks as either the shocks to

14 The relative magnitudes of the estimated responses imply that about 30 percent of the decline in total consumption that occurs 15 months after a gasoline price shock comes from the decline in motor vehicle consumption.

19the price of gasoline adjusted for the cost of shortages or as the shocks to consumer sentiment toward gasoline, we find that the impact of these shocks on real activity has either diminished only slightly or has beco me larger in the late r period. Lastly, all measures of energy price shocks produce result s that suggest the motor vehicle industry is a key part of the transmission mechanism be tween oil shocks and real activity. Thus, the remainder of the paper presents eviden ce that, although the moto r vehicle industry has changed in many ways over the pa st 40 years, this sector con tinues to act as an important propagation mechanism between oil price shocks and real activity.

II. The contribution of the motor vehi cle sector to the U.S. Economy The contraction in the size of the Detroit three autom akers in recent decades often leaves the impression that the contribution of the auto indu stry to the U.S. economy has declined significantly. In this section we present some measures of the contribution of the entire domestic motor vehicle industry (the portion operated by the Detroit firms as well as the portion operated by other firms) to the U.S. economy and to the business cycle. Figure 5 shows two measures of the contri bution of m otor vehicle output to U.S. GDP: Panel A shows the quarterly values of a statistic commonly referred to as “gross motor vehicle output,” and panel B shows a nnual estimates of the domestic value added of motor vehicle and parts manufacturing, a na rrower view of the industry’s contribution
to GDP. 15 The lower line in each of these graphs displays each measure of output as a

15 “Gross motor vehicle output” in the NIPAs is the retail value of motor vehicles sold to final consumers (households, businesses, and governments) and the wholesale value of vehicles invested in

20share of total GDP, and the upper lines plot these measures as a share of either goods GDP (for gross motor vehicle output) or to tal value added from goods manufacturing (for motor vehicle and parts manufacturing value a dded). Shares are calculated from nominal expenditures data reported in the National In come and Product Accounts (NIPAs). The dashed lines in the figure represent the 95 percent confidence interval for the sample mean of each line in two sample periods: 1967 to 1985, and 1986 to 2007 (before the financial crisis had affe cted vehicle sales). Two features of the graphs in figure 5 are worth noting: First, m otor vehicle output drops abruptly in recessions, and many of these recessions followed large increases in gasoline prices. As shown in ta ble 2, the motor vehicle sector alone accounts for between 14 and 22 percent of the variance of the quarterly changes in real GDP, depending on the time period. Even after the great moderation, these figures continue to exceed the moderate size of the motor vehicle sector. The second feature of the gra ph that is worth noting is that the size of the m otor vehicle industry as a share of the U.S. economy does not sh ow a downward trend that is as striking as one might expect. As a share of total GDP (the top line in Panel A), motor vehicles represented about 4 percent of the U.S. econom y between 1967 and 1985, and that figure declined to 31⁄2 percent between 1986 and 2007. As a share of the goods producing sectors of the U.S. economy, however , motor vehicle output actually increased

inventories. This series is adjusted for net exports of motor vehicles and has the advantage of capturing all of the value added from the production process as well as from the distribution of motor vehicles to final demand, including the wholesale and retail margins. This measure is not the same as “gross output of motor vehicles and parts” in the U.S. industry accounts, which is the sum of all sales and receipts in the industry, including sales of intermediate inputs to firms in other (or in the same) industries.

21between the two periods, from about 101⁄2 pe rcent in the early sample, to about 111⁄2 percent in the more recent sample. Manufacturing value added gives a similar picture: Motor vehicle and parts manuf acturers accounted for 5.7 percent of U.S. manufacturing value added in the 1970s a nd early 1980s, and this share slipped to 5.2 percent in the more recent period. However, as shown by the dashed lines, the decline between the two periods is not very pronounced rela tive to its high volatility. 16 All told, the motor vehicle sector has been a modest but relativ ely stable share of the goods producing sector over the past 40 years, and the declines that have occurred in its contribution to total GDP mostly reflect an increase in the size of the services sector. Most importantly, the auto indus try continues to induce swings in aggregate activity that far exceed its modest size.

III. Oil shoc ks and the demand for motor vehicles We now describe theories of how gasolin e prices affect vehicle demand and then present evidence in som e detaile d auto industry data that co nsumers adjust their vehicle buying patterns in response to changes in gasolin e prices. In addition, we show that this behavior has not changed mu ch over the past 40 years. A rather large literature has developed—m uch of it in the late 1970s—to analyze how households respond to changes in gaso line prices by m aking adjustments to their

16 One additional measure of motor vehicle output that we examined (but do not report) is motor vehicle and parts output. This wider view of the industry is intended to help control for the value of imported intermediate inputs to motor vehicle production that have risen over time. (See Kurz, and Lengermann (2008), and Klier and Rubenstein (2009).) Using this adjusted measure, motor vehicle output was 121⁄2 percent of goods GDP in both the early and late periods, and it declined from 43⁄4 percent of total GDP in the early period, to 33⁄4 percent in the later period.

22vehicle stock and to their driv ing behavior (e.g. Dahl (1979)). 17 More recently, Wei (2009) casts the vehicle purch asing decision in a general equilibrium framework, in which households invest in trans portation capital with a particular level of fuel efficiency and then combine it with gasoline to produce the good that ultimately enters their utility functions—personal vehicle trav el. Because consumers are forward looking, changes in gasoline prices lead to dynamic effects on th e vehicle stock and average fuel efficiency. Following a gasoline price shock, households respond in the short run mostly by reducing travel, though estim ates from the literatu re suggest the response in the short run is quite low (e.g. Hughes, Knittel and Sperling (2006)). Over long horizons, households adjust their vehicle technology and reduce further their consum ption of gasoline. Using her DSGE model calibrated to US fuel and vehicle consumption data, Wei (2009) finds that vehicle purchases and total miles trav eled decline after a permanent shock to gasoline prices, though the equilibrium fuel e fficiency of new vehicles increases. These theories suggest that permanen t increases in gaso line prices lead households to reduce vehicle trav el in the shor t run and then to repl ace their vehicle stock in the long run. In the very-long run, gasolin e prices can also impact where households choose to live and work. To see the effects of these decisions on vehicle travel over the past 40 years, figure 6 plots total vehicle m iles traveled per household in the United States between 1970 and 2009. Two features in the figure are noteworthy: First, households nowadays consume a significantly larger amount of trav el than they did in the early 1970s: The

17 The CBO study from January 2008, “Effect of Gasoline Prices on Driving Behavior and Vehicle Markets,” surveys much of the literature from the 1970s.

23average household drove approximately 1,500 miles per month in 1970, and that figure has increased 50 percent, to almost 2,200 miles per month in the period 2000-2007. Second, households do cut back on travel when gasoline prices incr ease, though part of the decline in travel likely also reflects the deterioration in the broa der economy that also occurs at these times. While households now drive more each m onth than they did in the early 1970s, they do so in vehicles that are, on average, mo re fuel efficient. Figure 7 shows data from the U.S. Department of Transportation on the average fuel efficiency of the registered stocks of cars and light trucks (which include SUVs and vans). As seen in the plot, the average fuel economy for each type of vehicle has increased over the sample, though much of the gains occurred in the 1980s, after the United States introduced Corporate Average Fuel Economy (CAFE) standards that were met by reducing the average weight of cars and introducing some technological improvements to engine design. One feature of figure 7 that receives lots of attention is the m arked slowdown (or near halt) in the rate of improvement for average fuel economy that occurred in the 1990s. This was an era of relatively cheap gasoline, and, likely as a result, demand shifted away from cars and toward larger sports utility vehicles. 18 Studies that more carefully take into a ccount vehicle size and engine horsepower, su ch as Knittel (2009), conclude that the technologi cal frontier of fuel-economy/ vehicle-weight/engine-power possibilities continued to expand over this period, but these improvements are obscured in aggregate data by the shift in sales across vehicle size classes.

18 The average fuel efficiency of vehicles flowing into the stock eac h year (i.e., new sales) actually decreased during the 1990s, as the mix in sales shifted toward light trucks.

24Shifts in vehicle demand across vehicl e size classes often occur when gasoline prices move dramatically, a stylized fact that was discussed by Bresnahan and Ramey (1993). To see evidence of this, figure 8 shows the domestic market shares of vehicles of various sizes. 19 The panel to the left shows the ke y market shares in the 1970s and early 1980s. The domestic market share for standard-size cars fell noticeably in 1973 and did not stabilize until almost two years later. 20 When the second oil pr ice shocks hit in 1979, this market share fell even further. The ma rket share of small cars moved in the opposite direction on both occasions. The panel to the right shows m arket shares of key vehicle segments in the 2000s. The patterns in market shares since 2000 have been similar to those observed in the earlier episodes of sharp gas prices increases, though the scope of the variety of products available has grown considerably since the 1970s . The market share of full size pickups, utility vehicles and vans fell more than 15 percentage point s between its peak in 2004 and early 2009. Small cars and the new cross utility vehicle segment picked up most of this market share. 21

19 This graph focuses on the market for domestically produced goods. An additional effect not shown in the graph is the shift to imported cars when oil pr ices increase; this move o ccurred in both the in 1980s and in the 2000s. The share of imported vehicles ro se from about 15 percent in the mid 1970s to about 25 percent in the first half of the 1980s. The import share then fell back as foreign automakers began to establish manufacturing operations in North America, and the import share fell below 10 percent by 1996, The market share of imported vehicles turned up again in the late 1990s, and the share moved up from 17 percent in 2000, to 26 percent in 2009.
20 The domestic market share excludes vehicl es imported from outside North America.
21 A cross-utility vehicle is a utility vehicle that is a ssembled on a car chassis. They are classified by the industry as a light truck.

25IV. The response of motor vehicle production Shocks that affect motor vehicle sales of ten lead to changes in the rate of production that are quite abrupt , and the high level of vo latility in motor vehicle production has been studied extensively in th e literature on inventories and production scheduling. It is well-unders tood how changes in aggregate vehicle demand can lead to reductions in production. In this s ection, we show how changes in the composition of demand, such as those induced by gas price increases, can leadto further declines in output. We begin by presenting an inventory model in which segment shifts lead to capacity m ismatches and thereby reduce outp ut. We then study this channel using detailed auto industry data by vehicle size class. We find that segment shifts are an important channel through which oil shocks a ffect the U.S. motor vehicle industry and that the importance of this channe l has not declined much over time.

A. Segment sh ifts and the co nstraints on capacity Bresnahan and Ramey (1993) speculated that a shift in demand from one vehicle
segm ent to another, with no decline in overall demand, can lead to a decrease in production and capacity utilizati on. They also argued that the variance of days’ supply (i.e. the inventory-sales ratio ) across segments reflects mismatches between capacity and demand for vehicles in some segments. In order to formalize this hypothesis, we consider a simple model of a profitmaximizing monopolist that sells cars to two segments of demand. For each segment i , the monopolist chooses the price, P t , and schedules production using regular time hours,
RH t , and overtime hours, OH t . These choices determine expected sales, S t , and expected

26end-of period inventories, I t . The firm maximizes the expe cted present discounted value of profits, given as equation (2).
(2) 0 0 11 2 2 0 [ ] t tt t t t t E P S P S Cost β ∞

= Π = + − 

## ∑
The costs of production and invent ory holding are give n by equation (3).
(3) 12 1 2
2 2 1 11 11 2 21 2 2 1 1 ( )( 2 2 ttt t

tt t Cost RH RH OH OH IS I S ) t

t γ γ γω γω αφ α φ

− − =⋅ +⋅ +⋅⋅ +⋅⋅

+ −+ − Inventory stocks evolve according to equation (4), (4) 2 , 1, 1 = −++=

− iSOHRHII itit it itit and the use of regular time hours is limited by the capacity constrai nts in equation (5). (5) 2,1, =≤ iKRH it The parameters of the model satisfy the following restrictions: 0 < β < 1; γ > 0; ω > 1; α i , φ

i > 0; K > 0. Relative to the familiar linear-quadratic production smoothing model, the inventory holding costs in equation (3) are th e sam e, but the marginal costs of production are somewhat more complicated: Marginal costs in this model are flat when the monopolist uses regular time hours but rise when the firm must increase its workweek of capital and use overtime hours or a second shift. This assumption induces a key asymmetry in marginal costs. Finally, the sales processes for segments 1 and 2 are described by equations (6) and (7). (6) 1 1 s eg agg t A Bt t t S Pu θ θ =− + + u (7) 2 2 s eg agg t A Bt t t S Pu θ θ =− − + u
27There are two types of demand shifts in these sales equations: The first, s eg t u , is a variable that shifts demand away from one segmen t and toward the other, while the second, , is a variable that shifts the demand curves for all types of ve hicles in the same direction. We assume that each of thes e shift variables follows an AR(1) process as shown in equations (8) and (9). agg t u

(8) 1 seg seg t t u u t ρ ε

− =⋅ + (9) agg agg t t u u t ρ η =⋅ + The autocorrelation parameter ρ lies between 0 and 1, and the shocks ε and η are white
noise. An increase in gasoline prices affects vehicle sales through both and agg t u s eg t u . To study the effects of the s econd type of shock—the shift in sales between segments— we simulate the model and evaluate the optim al paths of key choice variables. To calibrate the simulation, β and φ i are set to match the averages observed in the data for interest rates and days’ supply for li ght vehicles. We choose values for K , θ A , θ B , and γ to generate a price elasticity of demand of - 1.5 at the steady-state le vel of output, a figure that is in the range of empirical estimates for total vehicle demand. Finally, we set α B

22 i

a nd ω so that the premium on overtime hours or second shift hours is ten percent. The 23

22 Berry, Levinsohn, and Pakes (1995) find much hi gher elasticities (in absolute value) for particular models.
23 The statutory overtime premium is 50 percent, wher eas shift premia are typically 5 to 10 percent. Trejo (1991) has found that the implicit overtime premium is substantially lower than 50 percent. Thus, our assumption of a 10 percent premium is within the relevant range.

28simulation considers shocks that shift the intercept of the demand curve by 10 percent, with ρ equal to 0.75. 24 Because plants face the same cost functi on in each segment, the effects of an aggregate shock on inventories and production are the same in each segment. And,because the inventory-sales ratios change by equal amounts in each segment, the crosssection variance of the days’ supply remains zero. This is not the case for shocks that shift demand between segments, a scenario th at is shown in figure 9. If demand for segment 1 cars shifts up and demand for se gment 2 cars shifts down by an equal amount, total production falls because the rise in output in segment 1 does not fully offset the fall in output in segment 2. The asymmetric res ponse of production reflects the increase in marginal costs that occurs at the capacity cons traint. Mirroring this pattern, the price of vehicles in segment 1 rises by an amount that is different from the decline in the price of vehicles in segment 2. All told, segment shifts reduce total production, sales, and capacity utilization, and they drive up the variance of days’ supply across vehicle segments. Some of these results depend critically on the increase in marginal cost that occurs when production exceeds the level of capacity. 25 If costs were instead quadratic, as is assumed in the standard production smoot hing model, then marginal costs would be linear, capacity utilization would have no e ffect on production costs, and segment shifts

24 The values of the parameters are as follows: β = 0.997, φ i = 2.5 for i = 1, 2, K = 40, θ A = 100, θ B = 1, γ = 19.85, α i = 0.1, and ω = 1.1.
25 Several authors in the capacity utilization literature define full capacity as the point beyond which marginal production costs begin to rise too rapidly. See Klein (1960) and Corrado and Mattey (1996).

29would not reduce total production. 26 The variance of days’ s upply would still increase in the case of quadratic costs, but the asym metric response of production to positive and negative demand shocks requires the marginal cost function to exhibit so me curvature.

B. Evidence of capacity co nstraints and se gment shifts in th e auto industry To see the effects of segment shifts in the detailed auto industry data, figure 10 plots day’s supply for vehicles in selected size classe s in the early and la te periods. In the earlier period, days’ supply of standard cars grew to uncomfortably high levels at the tim e of both oil shocks, and days’ supply fo r small cars moved down. Similarly, in the later period, days’ supply for full size trucks, va ns and utilities climbe d to critically high levels between 2000 and 2008, while days’ supply for small cars and cross utility vehicles moved down between 1998 and 2000 before edging back up in 2002. As the shift in demand between segments accelerated again at the end of 2004, days’ supply for small vehicles receded, and several of the mode ls in these segments were reported to be in short supply. 27 The onset of the financ ial crisis in the second half of 2008 appears to have been a common shock that pushed up da ys’ supply for almost all vehicle segments.

26 See, for example, equation (7) of Ramey and Vine (2006).
27 The patterns in sales and days’ supply between 2000 and 2009 were influenced by occasional inventory clearance events, often targ eted toward full size trucks and S UVs. The Detroit manufacturers have typically dominated the full size truck market se gments and therefore faced significant loss in market share as sales of these vehicles sagged over this peri od. The large dip in days’ supply in late 2001 reflects the advent of zero-interest financing, and the plunge in stocks in 2005 coincides with the extension of these firms’ employee-discount programs to all customers.

30To measure these supply imbalances on a more general scale, we calculate the variance of days’ supply across size categories, D S V , that was described earlier. The formula is shown in equation (10).
(10) 

# ( ) 11 2

1 ; 1, 2,...,11 i DS i A t t t t A i t I V DS DS i I

= = −=



## ∑

i t I denotes inventories on hand in vehicle segment i at the end of period t , A t I is the aggregate inventory stock, is days’ supply for each segment , i t DS A t DS denotes aggregate days’ supply, and i ranges from 1 to 11, covering five car segments (subcompact, compact, intermediate, full-size and luxury) and six truck segments (compact pickups, full-size pickups, small vans, large vans, cro ss utility vehicles, and full-size utility vehicles). An increase the variance of da ys’ supply across segments indicates that the imbalance between the composition of cap acity and the composition of demand has become worse. 28 The variance of days’ supply is plotte d in F igure 11 from January 1972 to March 2009. Large spikes in the variance correspond quite closely with the increases in fuel prices discussed ea rlier. Also, the severity of some of the su pply-demand imbalances that occurred after 2002 appear even greater than the magnitudes observe d in the early 1980s. To investigate the empirical relationship betw een these spik es in the variance of days’ supply, the cost of gasoline, a nd movements in capacity utilization, we estimate a VAR in

28 In the theoretical model presented above, the va riance of days’ supply in steady state was zero, because the parameters of the cost function were the same for each vehicle segment. In actual industry data, vehicle types often show distinct long-run average inventory-sales ratios. This implies that the crosssectional variance can be positive in steady state.

31which. includes four variables: (i) consumer sentiment about gasoline prices or shortages; (ii) aggregate days-supply for dome stic vehicles; (iii) the variance of days supply across segments (defined in equation ( 10)); and (iv) capacity utilization for light motor vehicle assembly. t Y

29 The VAR also includes six lags and a linear time trend. Oil shocks in the VAR play two roles: Fi rst, oil shocks reduce aggregate de mand and dampen sales for all types of vehicles. In the production model de scribed earlier, this role resembles the aggregate shock that drives up days’ supply for the entire industry and reduces capacity utilization for all segments . Second, high gas pri ces lead to segment shifts in demand away from larg e vehicles and toward small vehicles. This role leads to mismatches in capacity and drives up th e variance of days’ supply across vehicle segments. Our production model also shows how shocks in this ro le can reduce capacity utilization when capacity cons traints push up marginal costs for the products in demand. Figure 12 shows the resp onses of the variables in to a shock to the measure of consumer sentiment toward gasoline (meani ng a higher percentage of consumers were worried about high gas prices), which is ordered first in th e VAR. The VAR is estimated over the full sample from January 1972 to March 2009. As seen in the figure, both the level and variance of days-s upply increase after a sentiment shock, indicating that shocks to gasoline sentiment affect demand both as an aggregate shock and as segment shifting shock. According to the last panel, cap acity utilization also falls significantly. t Y

29 The production theory presented above suggests that one should also include vehicle prices for each segment, but unfortunately we lack data on segmen t specific vehicle prices over much of the necessary history.

32Has the relationship between these variable s changed over time? To answer this question, we compare the impulse response f unctions from VARs estimated separately for the 1972—1985 and 1986—2009 periods and plot the results in figure 13. The shocks to consumer sentiment toward gasoline have been normalized to reach a peak of 1.0 in both periods. As seen in the figure, the peak responses of both the level and variance of days’ supply are about 30 percent larger in th e early period th an in the later period. The response of capacity ut ilization, however, is about th e same in both periods. Finally, using the VAR estimates and a set of counter-f actual experiments, we parse the response of capacity utilization to oil shocks in to portions that reflect the aggregate demand channel and the segment shifts channel. In one experiment, we plot the response of utilization to a gasoline sentiment shock after we have shut down the segment shifts channel by replacing the esti mated coefficients of the days’ supply equation in the VAR with zeros. The differe nce between the baseli ne response and this counter-factual response reveals the contribution of the aggreg ate demand channel to the transmission of gasoline shocks to capacity utiliza tion. In a second e xperiment, we shut down the segment shifts channel by setting th e coefficients of the variance of days’ supply equation to zero. The third ex periment shuts down both channels. The panels in figure 14 show the results of the each experim ent; the solid lines represent the baseline response of capacity utiliz ation to a gasoline sentiment shock, and the dashed lines represent one of the counter factual responses. In the first panel, the counterfactual response is only half as large as the base line response, a comparison that suggests the contraction in demand for all vehicles plays an important role in transmitting oil shocks to motor vehicle production. The counterfactual response in the second panel

33is also only about half as large when the segment shifts channel is shutdown, indicating that this channel is al so important. The counterfactual re sponse in the third panel remains very close to zero, suggesting that omitted channels are not too important. The counter-factual exercises indicate that the level and varian ce of days’ supply channels are about equally im portant in transm itting oil shocks to motor vehicle output. These results imply that oil shocks have both aggregate effects and segment shift effects. Moreover, the relationship between these variab les appears to have been stable over time.

V. Conclusions This paper has studied the impact of oil shocks on the U.S. economy and its motor vehicle industry and has exam ined whether th ese relationships have changed over time. We have found that, once the costs of queuing are added to the prices paid for gasoline during the gasoline shortages in the 1970s, real output in the United States has been as sensitive to oil price shocks since the mid 1980s as it ha d been in the 1970s and early 1980s. The effect on inflation, however, has diminished over time. We have also found that the motor vehicl e industry plays an i mportant role in propagating oil price shocks to the rest of the U.S. economy and that, despite the many innovations in the ways motor vehicles are produced and consumed, the primary channels through which oil prices directly affect dema nd for motor vehicles have not weakened much over time. Specifically, the abrupt shif ts in demand across vehicle size classes that stem from oil shocks have been as disrupt ive to the supply-demand relationship in the motor vehicle industry since the mid 1980s as they were in the 1970s and early 1980s. Our results may impact the debate over which changes in the U.S. economy have led to the decline in GDP vol atility since the m id 1980s, or the “Great Moderation.” If

34the relationship between oil shocks and real output had, in fact, become weaker since the mid 1980s, then this stylized f act could have bolstered argume nts that structural change had reduced the sensitivity of output to these types of s hocks. Our results, to the contrary, suggest that this particular relationship has be en stable over time. The diminished impact of oil shocks on infl ation, however, may s upport the theory that monetary policy has played a role in reducing vol atility. Finally, ou r results point to another change in government policy that may also have reduced the volatility of output since the 1970s: A decline in the propensity to use price controls.

35Work Cited Aguiar-Conraria, Luis, and Yi Wen. 2007. Unde rstanding the Large Negative Im pact of Oil Shocks. Journal of Money, Credit and Banking 39(4): 926-944. Atkeson, Andrew, and Patrick J. Kehoe. 1999. M odels of Energy Use: Putty-Putty versus Putty-Clay. The American Economic Review 89(4): 1028-1043. Barsky, Robert B. and Lutz Kilian. 2002. Do We Really Know That Oil Caused the Great Stagf lation? A Monetary Alternative. NBER Macroeconomics Annual 2001 16:
137-183. Bernanke, Ben S. 1983. Irreversibility, Uncertainty, and Cyclical Investment. Quarterly Journal of E conomics. 98: 85-106. Bernanke, Ben S., Mark Gertler, and Ma rk W atson. 1997. Systematic Monetary Policy and the Effects of Oil Price Shocks. Brookings Papers on Economic Activity . 1:
91-142. Berndt, Ernst R., and David O. Wood. 1975. Technology, Prices, and the Derived
De mand for Energy. Review of Economics and Statistics. 57: 259-68. Berry, Steven, James Levinsohn, and Ariel Pake s. 1995. Automobile Prices in Market Equilibrium. Econometrica 63: 841-890. Blanchard, Olivier J. and Jo rdi Galí. 2007. The Macroecono m ic Effects of Oil Price Shocks: Why are the 2000s so Different from the 1970s? International Dimensions of Monetary Policy . ed. Jordi Galí and Mark Gertler. 373-421. Blanchard, Olivier J. and Marianna Riggi. 2009. W hy are the 2000s so Different from the 1970s? A Structural Interpretation of Cha nges in the Macroeconomic Effects of

36Oil Prices. NBER worki ng paper no. 15467. Cambridge, MA: National Bureau of Economic Research. Bresnahan, Timothy F. and Valerie A. Ram ey. 1993. Segment Shifts and Capacity Utilization in the U.S. Automobile Industry. American Economic Review 83: 213-
218.

------------------------------------ -. 1994. Output Fluctuations at the Pla nt Level. The Quarterly Journal of Economics. 109: 593-624. Bruno, Michael and Jeffrey Sachs. 1982. Input Price Shocks and the Slowdown in
Econom ic Growth: The Case of UK Manufacturing. Review of Economic Studies.
49: 679-705. Congressional Budget Office. 2008. Effects of Gasoline Prices on Dr iving Behavior and Vehicle Markets. A CBO study. Congre ssional Budget Offi ce: Washington, DC. Corrado, Carol and Joe Mattey. 1997. Capacity Utilization. Journal of Economic Perspectives

. 1: 151 - 167. Dahl, Carol A. 1979. Consumer Adjustment to a Gasoline Tax. Review of Economics and
Statis tics. 61(3): 427-432. Davis, Steven. 1987. Fluctuations in the Pace of Labor Reallocation. Carnegie Roch ester Conference Series on Public Policy. 27: 335-402. Davis, Lucas W. and Lutz Kilian. 2009. The Allo cativ e Cost of Price Ceilings in the U.S. Residential Market for Natural Gas. Univ ersity of Michigan Working Paper: Ann Arbor, MI.

37Deacon, Robert T. and Jon Sonstelie. 1985. Ra tioning by Waiting and the Value of Time: Results from a Natural Experiment. Journal of Political Economy. 93(1): 627-
647. Edelstein, Paul and Lutz Kilian. 2009. Ho w Sensitiv e are Consumer Expenditures to Retail Energy Prices? Journal of Monetary Economics 56: 766-779. Finn, Mary G. 1991. Energy Price Shocks, Ca pacity Utilization and Business Cycle Fluctuations. Discussion Paper 50. In stitute for Em pirical Macroeconomics, Federal Reserve Bank of Minneapolis. Finn, Mary G. 2000. Perfect Competition and the Effects of Energy Price Increases on
Econom ic Activity. Journal of Money, Credit and Banking . 32: 400-416. Frech III, H.E. and William C. Lee. 1987. The W elfare Cost of Rationing-by-Queue Across Markets: Theory and Estimates from the U.S. Gasoline Crises. Quarterly Journal of Economics. 102: 97-108. Frum, David. 2000. How We Got Here: the 70s. Basic Books. New York, NY. Graham, Daniel and Stephen Glaister. 2002. Th e Dem and for Automobile Fuel: A Survey of Elasticities. Jounral of Transport Economics and Policy. 36: 1-26. Hamilton, James D., 1983. Oil and the Macroeconom y since WWII. Journal of Political Economy 91: 228-248.
----------------. 1988. A Neoclassical Model of Une mploy ment and the Business Cycle. Journal of Political Economy. 96: 593-617. ----------------. 2003. What Is an Oil Shock? Journal of Econometrics. 113: 363-398.
----------------. 2009a. Causes and Consequences of the Oil Shock of 2007-08. Brookings Papers on Economic Activity. 1: 215-261.

38----------------. 2009b. Yes, the Response of the U.S. Economy to Energy Prices is Nonlinear. UCSD working paper, University of California, San Diego: La Jolla,
CA. Hamilton, James D., and Ana Maria Herr era. 2004. Oil Shocks and Aggregate

m acroeconomic Behavior: The Role of Monetary Policy. Journal of Money, Credit and Banking. 36(2): 265-86. Helbling, Hans H. and James E. Turley. 1975. Oil Price C ontrols: A Counterproductive Effort. Federal Reserve Bank of St. Louis Review. November: 2-6. Herrera, Ana María and Elena Pesavento. 2009. Oil Price S hocks, Systematic Monetary Policy, and the ‘Great Moderation’. Macroeconomic Dynamics 13: 107-137. Hooker, Mark A. 1996. What Happened to th e Oil Price-Macroeconom y Relationship? Journal of Monetary Economics. 38(2): 195-213. Hughes, Jonathan E., Christopher R. Knitte l, an d Daniel Sperling. 2006. Evidence of a Shift in the Short-Run Price Elasticity of Gasoline Demand. Research Report UCD-ITS-RR-096-16. University of Californi a, Davis: Institute of Transportation Studies: Davis, CA. Kilian, Lutz. 2010. Explaining Fluctuations in Gasoline P rices: A Joint Model of the Global Crude Oil Market and the U.S. Retail Gasoline Market. The Energy Journal 31: 103-128. Kilian, Lutz and Robert J. Vi gfusson. 2009. Pitfa lls in Estim ating Asymmetric Effects of Energy Price Shocks. CEPR Discussion Paper no. DP7284. Center for Economic Policy Research: London.

39Klein, Lawrence R. 1960. Some Theoretical I ssues in the Measurement of Capacity.
Econometrica. 28:2: 272-286. Klier, Thomas H., and James M. Rubenstei n. 2009. Im ports of Intermediate Parts in the Auto Industry—A Case Study. Paper prepar ed for the conference “Measurement Issues Arising from the Growth of Globalization.” Washington, DC. Knittel, Christopher R. 2009. Automobiles on Steroids: Product Attribute Trade-offs and Technological Progress in the Autom ob ile Sector. NBER working paper no. 15162. Cambridge, MA: National Bureau of Economic Research. Kurz, Christopher, and Paul Lengermann. 2008. Outsourcing and U.S. Economic Growth: The Role of I mported Intermedia te Inputs. Paper prepared for the 2008 World Congress on National Accounts and Economic Performance Measures for Nations. Arlington, VA. Leduc, Sylvain, and Keith Sill. 2004. A Quan titative Analysis of Oil-Price Shocks, System atic Monetary Policy, and Economic Downturns. Journal of Monetary
Economics . 51: 781-808. Lee, William C. 1980. Demand for Tr avel and the Gaso line Crisis. Transportation Research Record . No. 764: 38-42. Li, Shanjun, Christopher Timmins, and Roge r von Haefen. 2008. Ho w do Gasoline P rices affect Fleet Fuel Econo my? NBER Working Paper no. W14450. Cambridge, MA: National Bureau of Economic Research. McAlinden, Sean P., Kim Hill, and Bernard Swiecki. 2003. Econom ic Contribution of the Automotive Industry to the U.S. Econo my—An Update. Ann Arbor, MI: Center for Automotive Research.

4041 McConnell, Margaret M., and Gabriel Perez- Quiros. 2000. Output Fluctuations in the United States: W hat has Cha nged Since the Early 1980s? American Economic
Review. 90(5): 1464-76. Mork, Knut Anton. 1989. Oil and Macroeconomy W hen Prices Go Up and Down: An Extension of Hamilton’s Results. Journal of Political Economy. 97(3): 740-44. Pindyck, Robert S., and Julio J. Rotember g. 1983. Dyna mic Factor Demands and the Effects of Energy Price Shocks. American Economic Review. 73(5): 1066-79. Pisarski, Alan E. and Niels de Terra. 1975. Am erican and European Transportation Responses to the 1973-74 Arab Oil Embargo. Transportation. 4: 291-312. Ramey, Valerie A. and Daniel J. Vine. 2006. D eclining Volatility in the U.S. Automobile Industry. American Economic Review . 96(5): 1876-89. Rotemberg, Julio J., and Michael Woodford. 1996. Im perfect Competition and the Effects of Energy Price Increases on Economic Activity. Journal of Money, Credit and Banking . 28(4): 550-77. U.S. Department of Energy. 2009. Transportation Energy Databook: Edition 28

. Washington, DC: U.S. Departm ent of Energy. Wei, Chao. 2009. A Dynamic General Equilibri um Model of Driving, Gasoline Use and Vehicle Fuel Efficiency. George Washi ngton University working paper. George Washington University. Washington, DC.42 Table 1 The Peak Effects of Oil Shocks on U.S. Variables

Estimation period: Jan. 1967 to Dec. 1985 Jan. 1986 to Dec. 2009
Peak effect Month of

peak Peak effect Month of

peak Ratio

(1) (2) (3) (4) (3)/(1) Nominal gas price 1.000 3 1.000 2 Industrial production -0.202 24 -0.091 20 0.45
Hours -0.099 26 -0.035 32 0.35
CPI 0.184 19 0.051 25 0.28

Hamilton measure 1.000 1 1.000 1 Industrial production -0.005 24 -0.002 11 0.41
Hours -0.002 26 -0.001 24 0.39
CPI 0.003 13 0.001 2 0.25
Gas price adjusted for the cost of

shortages 1.000 1 1.000 2 Industrial production -0.072 25 -0.091 20 1.27
Hours -0.038 27 -0.035 32 0.92
CPI 0.084 20 0.051 2 0.61
Consumer sentiment about gas 1.000 3 1.000 1 Industrial production -0.002 27 -0.003 31 1.67
Hours -0.001 27 -0.002 37 1.97
CPI 0.002 15 0.001 2 0.30
Note: The impulse responses are based on vector autoregressions with monthly data. The variables include: (1) a selected oil price measure, (2) industrial production, (3) hours, (4) the headline CPI, (5) nominal wages, and (6) the federal funds rate. The VARs included six lags and a linear time trend. Shocks to oil prices are defined using a standard Cholesky decomposition with oil ordered first. Th e oil shocks are standardized to be th e same size in each sample period. The standard deviations for the shocks to each oil price measure are as follows: The nomi nal gas price shock is .013 in the early period and .038 in the late period. The shock to the Hamilton measure is .797 in the early period and 1.751 in the late period

. The shock to the gas price adjusted for the cost of shortages is .045 in the early period and .038 in the late period. The sho ck to gasoline sentiment is 1.364 in the early period and 2.028 in the late period.43 Table 2 Fluctuations in Real Gross Domestic Product (GDP)

Period Share of GDP (percent) Standard deviation (percent quarterly changes, annual rate) Share of GDP volatility (percent)

1967 to

1985 100 4.3 100

1986 to 2007 100 2.1 100 Goods and services

1986 to 2009Q3 100 2.5 100

1967 to

1985 37 9.2 54

1986 to 2007 30 5.0 51 . . Goods

1986 to 2009Q3 30 5.6 50

1967 to

1985 4.0 38.1 22

1986 to 2007 3.5 19.2 14 . . Motor vehicles

1986 to 2009Q3 3.3 24.9 16

Note: Data are from the National Income and Product Accounts . Share of GDP volatility attri butable to each component is calculated as 100 less the variance of growth contribution of GDP excluding each component relative to the variance of total GDP. Figures for the early period exclude 1970Q4, when a long strike severely reduced motor vehicle output and sales.Figure 1. Petroleum Prices January 1967 through March 2010
Panel A: Price indexes Panel B: Real price indexes

1970 1975 1980 1985 1990 1995 2000 2005 2010 -100 -50 0 50 100 150 200 250 300 Log index (1990 = 100) Gasoline CPI Crude oil PPI (W561) Refiners acquisition cost - imported crude oil

1970 1975 1980 1985 1990 1995 2000 2005 2010 -100 -50 0 50 100 150 200 250 300 Log index (1990 = 100) Gasoline CPI Crude oil PPI (W561) Refiners acquisition cost - imported crude oil

Note: Data come from the Bureau of Labor Statistics. The r eal indexes normalize the changes in petroleum prices by the changes in headline consumer price infl ation. Refiners acquisition cost da ta begin in 1974. For each series x , the log index is calculated as 

### ( ) () () ( ) 

### [ ] 100 100 log log 1990 xt x +⋅ −
.

Figure 2. Real Gasoline Prices January 1967 through March 2010

1970 1975 1980 1985 1990 1995 2000 2005 2010 40 60 80 100 120 140 160 180 Log index (1990 = 100) Gasoline CPI Gasoline CPI adjusted for shortages

Note. The dashed line represents the CPI for g asoline augmented with the shadow cost of waiting time in gas lines in 1973, 1974, and 1979 as estimated by Frech and Lee (1987). The log index is calculated as in figure 1.

44Figure 3. Consumer Sentiment toward Gasoline Share of respondents to the Thomson Reuters/University of Mich igan Survey of Consumer Sentiment that cite high gasoline prices or shortages of gasoline as reas ons that car buying conditions are poor January 1970 through April 2010

1970 1975 1980 1985 1990 1995 2000 2005 2010 0 5 10 15 20 25 30 Percent

Note. The gasoline price question was asked on a quarterly basis p rior to January 1978 and the series was extrapolated to a monthly frequency by the authors.

45Figure 4. Responses of Cons umption to Two Gasoline Shocks Shoc k to the gas price Shock to sentiment toward gasoline

. Response of gas price Response of sentiment toward gasol ine

-0.5 0.0 0.5 1.0

6 12 18 24 30 36 42 48Index (1=peak)

Months 1972 - 1985 1986 - 2009

-0.5 0.0 0.5 1.0

6 12 18 24 30 36 42 48Index (1=peak)

Months 1972 - 1985 1986 - 2009

Response of consumption excl. motor vehicles Response of consumption excl. motor vehicles

-5 -4 -3 -2 -1 0 1

6 12 18 24 30 36 42 48Percent

Months 1972 - 1985 1986 - 2009

-0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20

6 12 18 24 30 36 42 48Percent

Months 1972 - 1985 1986 - 2009

Response of consumption of motor vehicles Response of consumption of motor vehicles

-20 -15 -10 -5 0 5

6 12 18 24 30 36 42 48Percent

Months 1972 - 1985 1986 - 2009

-1.00 -0.80 -0.60 -0.40 -0.20 0.00 0.20 0.40 0.60 0.80 1.00

6 12 18 24 30 36 42 48Percent

Months 1972 - 1985 1986 - 2009

Note. Solid dots indicate periods in which the responses are more than 2 standard deviations from zero, and open circles indicate periods in which the responses are between 1 and 2 standard deviations from zero. 46Figure 5. Motor Vehicle Industry in the U.S. Economy Panel A: Output Panel B: Manufacturing Value Added 1967Q1 through 2009Q4 1967 through 2007

1970 1975 1980 1985 1990 1995 2000 2005 2010 0 2 4 6 8 10 12 14 16 Percent

Share of total GDP Share of goods GDP

1970 1975 1980 1985 1990 1995 2000 2005 2010 0 1 2 3 4 5 6 7 8 Percent

Share of total GDP Share of goods manuf. value added

Note. Dashed lines represent 95% confidence intervals for the sample means in two periods: 1967 to 1985, and 1986 to 2007. Ex penditure shares are based on nominal data. For value added, motor vehicles and parts is defined on a NAICS basis from 1977 through 2007; earlier periods are plotted as best changes from the SIC definition. Goods manufacturing includes agriculture, forestry, fishing, and hunting; mining; construction; and manufacturing.

Figure 6. Vehicle Distance Traveled per Household January 1970 to October 2009

1970 1975 1980 1985 1990 1995 2000 2005 1450 1500 1550 1600 1700 1800 1900 2000 2100 2200 2300 Miles, ratio scale

Note. Data on vehicle miles are from Tra ffic Volume Trends , Office of Highway Policy Information in the Department of Transportation. Data on the number of households in the United States are from the U.S. Census Bureau. Data were smoothed with a 12-month moving average. 47Figure 7. Average Fuel Economy for the U.S. Light Vehicle Stock December 1970 to December 2009

1970 1975 1980 1985 1990 1995 2000 2005 8 10 12 14 16 18 20 22 24 Miles per gallon

Cars

Light trucks All light vehicles

Note. Data from the Tra nsportation Energy Databook: Edition 28 , U.S. Department of Energy. Data points for light trucks for 2003 and 2004 were interpolated. Light vehicles include cars and light trucks.

Figure 8. Domestic Sales Shares for Selected Vehicle Segments Percent of domestic vehicles sold January 1972 through December 1984 January 1996 through No vember 2009

1972 1974 1976 1978 1980 1982 1984 10 20 30 40 50 Percent

Standard cars Small cars

1996 1998 2000 2002 2004 2006 2008 2010 10 20 30 40 50 Percent

Full-size trucks, vans and utilities

Small cars and cross utilities

Note: Shares are calculated from U.S. sa l es of domestic light vehicles. Domestic light vehicles are defined as vehicles produced in North America. The category “small cars” include compact and subcompact cars; “standard cars” include full size and luxury cars. Cross-utility vehicles are sma ll utility vehicles assembled on a car chassis.
48Figure 9. Responses to a Segm ent-shifting Shock to Sales Response of sales Response of prices

-10 -8 -6 -4 -2 0 2 4 6 8 10

2 4 6 8 10 12 14 16 18 20 22 24Percent

Months Small Large

-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12

2 4 6 8 10 12 14 16 18 20 22 24Percent

Months Small Large

Response of capacity utilization Response of days’ supply

70 85 100 115 130

2 4 6 8 10 12 14 16 18 20 22 24Percent

Months Small Large

59 60 61 62 63 64 65

2 4 6 8 10 12 14 16 18 20 22 24Days

Months Small Large

Response of the variance of days’ supply

-100 -75 -50 -25

0 25 50 75 100

2 4 6 8 10 12 14 16 18 20 22Variance

Months Note. Graphs show impulse responses from the production model presented in equations (2) through (9).
49Figure 10. Domestic Days’ Supply of Selected Vehicle Segments January 1972 through December 1984 January 1996 th rough November 2009

1972 1974 1976 1978 1980 1982 1984 20 40 60 80 100 120 Days

Standard cars

Small cars

1996 1998 2000 2002 2004 2006 2008 2010 20 40 60 80 100 120 Days

full-size trucks, vans and utilities

Small cars and cross-utilities

Note: Days’ supply is calculated with end of month inventories and the three-month moving average of sales. Inventories include finished vehicles held at dealerships, assembly plants and vehicles in transit. Domestic vehicles refer to vehicles produced in North America. “Small car s” include compact and subcompact cars, and “standard cars” include full size and luxury cars. Cross-utility vehicles are small utility vehicles assembled on car chassis.

Figure 11. Variance of Domestic D ays’ Supply across Vehicle Segments January 1972 through December 2009

1975 1980 1985 1990 1995 2000 2005 2010 0 300 600 900 1200 1500 days squared

Note: Domestic days’ supply is calculated from U.S. inventories and sales of vehicles assembled in North America. Variance is calculated across 5 car segments (subcompact, compact, inte rmediate, full-size, and luxury) and 6 light truck segments (compact vans, full-size vans, compact pickups, full-size pickups, cross-utility vehicles, an d standard utility vehicles).

50Figure 12. Responses to a Shock to Consumer Sentiment toward Gasoline Combined sample: 1972 through 2009
Response of sentiment toward gasoline Response of days’ supply

-1 0 1 2 3

6 12 18 24 30 36 42 48Percent

Months -1 0 1 2 3

6 12 18 24 30 36 42 48Days

Months

Response of the variance of days’ supply Response of capacity utilization

-20

0 20 40 60 80

6 12 18 24 30 36 42 48Variance

Months -3 -2 -1 0 1

6 12 18 24 30 36 42 48Percentage point

Months

Note. Dashed lines enclose 95% confidence intervals.

51Figure 13. Responses to a Shock to Consumer Sentiment toward Gasoline Split samples: 1972 to 1985 and 1986 to 2009 Response of sentiment toward gasoline Response of days’ supply

-0.5 0.0 0.5 1.0

6 12 18 24 30 36 42 48Index (1=peak)

Months 1972 - 1985 1986 - 2009

-0.5 0.0 0.5 1.0

6 12 18 24 30 36 42 48Days

Months 1972 - 1985 1986 - 2009

Response of the variance of days’ supply Response of capacity utilization

-10

0 10 20 30

6 12 18 24 30 36 42 48Variance

Months 1972 - 1985 1986 - 2009

-1.0 -0.5 0.0 0.5

6 12 18 24 30 36 42 48Percentage point

Months 1972 - 1985 1986 - 2009

Note. Solid dots indicate periods in which the responses are more t han 2 standard deviations from zero, and open circles indicate periods in which the responses are between 1 and 2 standard deviations from zero.

52Figure 14. Response of Capacity Utilization to Shocks to Consumer Sentiment toward Gasoline
Scenario: Level of days’ supply held constant Scenario: Variance of days’ supply held co nstant

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2

6 12 18 24 30 36 42 48Percentage point

Months Baseline

Counterfact.

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2

6 12 18 24 30 36 42 48Percentage points

Months Baseline

Counterfact.

Scenario: Level and variance of days’ supply held constant

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2

6 12 18 24 30 36 42 48Percentage points

Months Baseline

Counterfact.

53