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The demand for road diesel in Canada
Energy Economics 43 (2014) 316–322
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The demand for road diesel in Canada☆ Philippe Barla ⁎, Mathieu Gilbert-Gonthier, Jean-René Tagne Kuelah Center for Data and Analysis in Transportation (CDAT), Département d'économique, Université Laval, Québec, QC G1V 0A6, Canada
a r t i c l e
i n f o
Article history: Received 15 November 2011 Received in revised form 4 March 2014 Accepted 10 March 2014 Available online 26 March 2014 JEL classification: R40 Q41 Q38 R48 R41 Q54
a b s t r a c t In this paper, we estimate the demand for road diesel in Canada using aggregate annual data for the period 1986–2008. Using a partial adjustment model (PAM), we find short and long run price elasticities of − 0.43 and −0.8 respectively. However, using cointegration techniques, we obtain price elasticities that are 30 to 50% lower. The short run elasticity with respect to GDP per capita is evaluated at 0.5 with the PAM and 1.55 with cointegration. In the long run, both estimation techniques indicate an income elasticity around 0.9. Our results underline the importance of controlling for the expansion of the primary sector which has been characterizing the Canadian economy in the 2000s. © 2014 Elsevier B.V. All rights reserved.
Keywords: Road diesel Demand Elasticity Partial adjustment model Cointegration
1. Introduction Literally hundreds of researches have studied the demand for gasoline (for reviews see Basso and Oum, 2007; Dalh, 2012; Goodwin et al., 2004; Graham and Glaister, 2004). Much less effort has been made studying the demand for road diesel fuel. Yet, in many countries, the consumption of road diesel has been increasing much faster than gasoline, fuelled in part by the rapid expansion of trucking. For example, in Canada, while gasoline consumption has been multiplied by 1.5 over the 1971–2008 period, road diesel consumption has grown by a factor of 7.4 during the same period. If gasoline remains the principal source of energy for road transportation activities at 54.4% in 2008, diesel is second at 32.5%, up from 25% in 1990. The fewer number of studies on road diesel demand could be due in part to the heterogeneity of users in many countries. For example, in Europe, road diesel is used not only by trucks but also by a large share of automobiles (about 50% of new cars sold in 2008, see Comité des constructeurs français d'automobiles, 2008). In Canada, however, the share of light duty vehicles (automobiles ☆ The authors acknowledge the financial support of Natural Resource Canada (Accord de contribution CDAT2009-12). The views expressed in this paper are however solely ours. We also thank an anonymous referee for the very useful suggestions. ⁎ Corresponding author at: Département d'économique, Université Laval, Pavillon J.-A.-DeSève, 1025 av. des Sciences-Humaines, Québec, Québec, G1V 0A6, Canada. E-mail address: [email protected] (P. Barla).
http://dx.doi.org/10.1016/j.eneco.2014.03.008 0140-9883/© 2014 Elsevier B.V. All rights reserved.
and light trucks) in road diesel consumption was a meager 1.3% in 2008. In fact, medium and heavy trucks consumed over 91% of road diesel while the balance is fuelling busses (6.7%). This makes the analysis of road diesel easier and more relevant as the main driving force behind road diesel demand is trucking activities. The main objective of this paper is therefore to measure how the price of diesel and the level of economic activities are affecting the demand for road diesel fuel in Canada. Specifically, we estimate the demand for road diesel in Canada using aggregate annual data for the period 1986–2008. We focus the analysis on short and long run price and income elasticities. Besides contributing to the understanding of the demand for diesel, this paper also contributes by comparing the results obtained using two different methodological approaches. First, we estimate a reduced-form partial adjustment model (PAM). This is the approach most often used in the literature on gasoline demand but it may be affected by spurious correlation. Second, we estimate an error correction model (ECM) based on cointegration techniques. This approach is more recent and would be more adequate for time series that are not stationary. In the case of gasoline demand, Graham and Glaister (2004) and Basso and Oum (2007) compare the results obtained in the literature with these two approaches. Their analysis suggests that cointegration analysis usually leads to smaller price elasticities (between 25 and 30% smaller), particularly in the long run. One of the shortcomings of these comparisons is that they are based on studies that use different data from different
P. Barla et al. / Energy Economics 43 (2014) 316–322
317
Table 1 Overview of recent researches on diesel demand using aggregate data. Reference
Data
Method
Elasticitiesa
Dalh (2012)
Review of 34 studies
Static models only
Gonzalez-Marrero et al. (2012) Boshoff (2010)
Panel: 16 Spanish regions 1998–2006
Dynamic panel methods (GMM) for gasoline and diesel ADRL model
Price: −0.16 Income: near 1 Price: n/s Income: n/s Price: −0.13 (long run) Income: 1.51 (long run) Few significant coefficients for diesel and unexpected signs Price: −0.62
Bhattacharyya and Blake (2009) Iootty et al. (2009) Iwayemi et al. (2010) Pedregal et al. (2009) Pock (2007)
Polemis (2006) De Vita et al. (2006) Liu (2004) Belhaj (2002)
Dahl and Kurtubi (2001)
a
Demand for petroleum product demand including diesel fuel — South Africa 1998–2008 (quarterly) Demand for petroleum products — Middle East and North African countries 1982–2005 Brazilian demand for automotive fuel including diesel — 1970–2005 Oil product demand including total diesel — Nigeria 1977–2006 Demand for oil products demand including transportation diesel — Spain 1984–2006 (monthly) Gasoline and diesel — 14 European countries — 1990–2004
Demand for road energy demand including diesel — Greece 1978–2003 Quarterly data 1980–2002 Namibian total diesel consumption
PAM models AIDS model and vector error correction model Cointegration Unobserved components model PAM model — Dynamic panel methods
Cointegration Cointegration techniques
Energy products demand including automotive diesel in OECD countries 1978–1999 Demand for gasoline and diesel — Morocco 1970–1996
Dynamic panel techniques (one-step GMM) PAM model — fuel consumption and vehicle stock
Demand for petroleum products including automotive diesel — Indonesia — 1970–1995
PAM and Cointegration
Price: −0.41/n.s Income: 0.8/n.s. Price: −0.025/0.37 Income: 0.28/1.06 Price: −0.14/large variations across methods Income: 0.6/~1.2 (large variation across methods) Price: −0.07/−0.44 Income: 0.44/1.18 Price: n/s Income: ~2 Price: −0.09/−0.26 Income: 0.425/1.2 Price: −0.18/−0.62 Income: n/s but indirect effect via the stock of vehicle PAM: Price: −0.18/−0.64 Income: 0.57/2.06 Cointegration: Price: n/s/−0.67 Income: 2.14/2.16
When available, short run/long run elasticities.
countries and/or periods. The difference in the results may therefore be due not only to the methodologies but also to the difference in the data and context. In this paper, we compare the two approaches using the same data. The rest of this paper is organized as follows. In Section 2, we review the recent literature on diesel demand and provide some background on the Canadian setting. In Section 3, we describe our data and in Section 4 our methodology. The results are presented in Section 5 and we conclude in Section 6.
2. Background Table 1 provides an overview of recent researches on diesel demand using aggregate data.1 We observe a variety of methodological approaches using quite different data. Some studies focus on total diesel demand, other on transportation diesel or road transportation diesel demand. Also note that the institutional context varies across countries with some countries facing diesel price regulations or tax policies favoring the use of diesel over gasoline. It is therefore not so surprising that results are very variable. Overall, however, it appears that the impact of price is generally rather small. From her review, Dalh (2012) reports an average price elasticity of about − 0.16. The impact of income (or GDP) seems much stronger particularly in the long run. Some studies find an elasticity around 2. Dalh (2012) obtains an average elasticity near one. Few studies estimate cross price elasticities between gasoline and diesel and those that do usually find very small effects. Interestingly, Dahl and Kurtubi (2001) compare results obtained using a PAM and cointegration techniques. They find a smaller price effect and a
1 See also Dalh (2012) who analyses price and income elasticities from 140 gasoline and 34 diesel demand studies (static models only).
larger income effect using cointegration in the short run. Results for the long run income effect are very similar using both approaches. Some recent studies have adopted a more disaggregated approach to better understand the observed trends in road freight energy use. Sorrell et al. (2009) uncover the main factors that explain the decoupling of UK road freight energy consumption and GDP over the 1989–2004 period. The decline in the value of domestically manufactured goods relative to GDP is the main factor explaining over 30% of the decoupling. Other factors include reductions in empty running (14%) and improved energy efficiency (8.6%). Kamakaté and Schipper (2009) analyze the trend in truck freight energy use in 5 OECD countries from 1973 to 2005. They decompose the trend in energy use to identify the effects of activity, relative modal share and energy intensity. The analysis reveals that GDP growth and the share of trucking remain closely linked. Geography and the structure of the economy (e.g. the importance of raw materials) explain some of the cross country variations. Whyte et al. (2013) show that disaggregated models by commodity groups and truck weight class improve explanatory power compared to aggregated models. Indeed, different sectors experience distinct trends and react differently to various economic indicators.
2.1. The Canadian setting In many countries, the share of diesel-fuelled cars in the vehicle fleet has considerably increased in recent years. This “dieselisation” has been linked to tax differentials between gasoline and diesel (Mayeres and Proost, 2001) and more recently to CO2 emissions reduction policies (Bonilla, 2009; Daly and Gallachóir, 2011; Rogan et al., 2011). However, in Canada, we observe the opposite as the share of road diesel consumed by cars and light duty trucks has steadily declined from 4.3% in 1990 to 1.3% in 2008. During the same period, the share of diesel consumed by medium and heavy trucks has jumped from 83.5% in 1990 to 91% in 2008.
8 4
10000
6
12000
sh_primary
14000
10
16000
12
P. Barla et al. / Energy Economics 43 (2014) 316–322
8000
GDP associated with truck transportation (million $ of 2002)
318
1985 1985
1990
1995
2000
2005
1990
1995
Year
2000
2005
2010
2010
Year Fig. 2. Share of the primary sector in total GDP (%), 1986–2008.
Fig. 1. GDP associated with the truck transportation industry (in million $ of 2002), 1986–2008.
The growth in the Canadian trucking industry is explained by the same global changes experienced elsewhere: the development of international trade, the “just in time” supply chain management and the increased fragmentation of manufacturing processes. Several policy changes also have had an impact on the Canadian trucking industry and thereby on diesel sales (see Barla, 2010). In 1987, the industry was deregulated leading, at least initially, to a reduction in the number of trucking companies. This could have contributed to the decline in diesel sales observed in the early 90s. Overall, the deregulation appears to have intensified competition and fostered efficiency (see Statistics Canada, 1995). In 1989, the Canada–US Free Trade Agreement and later, in 1994, the North American free trade agreement (NAFTA) have led to a significant increase in trade between Canada, the US and Mexico.2 Since trucking is the predominant transportation mode between these trading partners, these agreements have stimulated international trucking activities. In fact, Canadian trucking traffic has, to some extent, been reoriented from an east–west to a north–south pattern (see Statistics Canada, 1995). The sharp increase in trucking activities can be visualized in Fig. 1.3 Based on the available fragmented evidence, the top industries carrying commodities by trucks in Canada remain the same over time (see Statistics Canada, 1995, 2004). They include: motor vehicles and parts, petroleum and coal products, minerals, wood products, chemical products, and food manufacturing.4 It is however very likely that the relative shares in trucking activities of these sectors have changed over time. Indeed, one of the most profound changes in the Canadian economy over the period has been the rapid expansion of the tar sand industry in Alberta starting in the early 2000s. The petroleum extraction industry relies heavily on trucks both in the production process and product distribution. In fact, the consumption of road diesel in Alberta has increased by over 60% from 2000 to 2008.5 Also note that Canadian mining activities have also progressed over the same period. Overall, the development of mining and oil and gas extraction has led to an increase in the share of the primary sector in total GDP as can be observed in Fig. 2. Controlling for this transformation of the Canadian economy 2 From 1990 to 2004, the total value of international trade within North America has doubled in real terms (see Barla, 2010). 3 Note that Fig. 1 only shows for-hire trucking activities. 4 These industries represented over 50% of tonnage hauled in 1995. Unfortunately, this figure is not available for other time periods. 5 Over the same period, the Canadian road diesel consumption has only increased by 25%. The share of the Alberta road diesel consumption in Canada has risen from 16.4% in 2000 to 21% in 2008.
turns out to be important for the econometric analysis. Indeed, the development of the petroleum extraction industry is related to the price of oil and is therefore also correlated with the price of diesel. Ignoring this transformation would result in a downward bias in the price elasticities as shown in Section 5.6 Finally, we should also mention that in the mid-nineties, Canadian provinces joined the International Fuel Tax Agreement (IFTA) which insures that carriers pay diesel tax based on the distance driven in each jurisdiction rather than where the diesel has been purchased. This could have stimulated sales in Canada where the tax rates on diesel are usually higher than in the US.
3. Data The main data sources are Statistics Canada and MJ Ervin & Associates. Diesel consumption data is provided by the Road Motor Vehicle Survey which collects information on net sales of diesel for which road taxes were paid. Our data covers the 1986–2008 period. Unfortunately, over this period, some provinces have eliminated road fuel taxation at some point in time leading to artificial shifts in net sales. Specifically, Alberta has abolished road fuel taxation from 1978 to 1987 and Saskatchewan from 1982 to 1987. In order to correct for these problems, we use domestic sales of diesel as provided by Statistics Canada data on monthly supply and disposition of refined petroleum products. This series is complete and does not depend on taxation rules. Domestic sales are however not directly comparable to net sales of road diesel as they include all uses of diesel (e.g. off-road, rail, industrial usage). For each of the three provinces with missing data, we therefore regress net sales of road diesel on domestic sales and use the predictions to complete our series on net sales of road diesel. For the price of diesel, we use the average retail price provided by MJ Ervin & Associates based on retail prices in the major Canadian cities. This measure is clearly imperfect as it only covers part of Canada.7 Furthermore, it does not capture the fact that major trucking companies do not pay the full price displayed at retail stations. We expect, however, that this price measure should be closely correlated with actual prices paid. All the other variables used in the analysis are obtained from Statistics Canada (e.g. GDP, population). Figs. 3 to 5 illustrate respectively the changes in per capita road diesel sales, the average price of diesel and the level of GDP per capita over the 1986–2008 period in Canada. From these figures, it clearly appears 6 We thank an anonymous referee for suggesting to control for changes in the Canadian economic structure. 7 The list of cities included also varies over time.
1985
1990
1995
2000
2005
annee
70
80
90
100
110
319
60
Estimated Average Price of Road Diesel in Canada ($ of 2002)
500 450 400 350 300
Per Capita Net Sales of Road Diesel (liters)
P. Barla et al. / Energy Economics 43 (2014) 316–322
1985
1990
1995
2000
2005
annee
Fig. 3. Per capita net sales of road diesel in Canada, 1986–2008. Fig. 4. Estimated average price of road diesel in Canada, 1986–2008.
that the sales of diesel and GDP are very closely linked. It is particularly evident during the 1990–1991 recession. 4. Methodology First, we estimate a dynamic reduced form demand model. Specifically, we use a partial adjustment model (PAM) which allows for inertia in the reaction of consumers. At time t, the unobserved desired level of diesel demand per capita Dcap⁎t is assumed to be given by:
LnDcapt ¼ β0 þ β1 LnP t þ β2 LnGDPcapt þ β3 LnShP rimaryt þ β4 Trend þ ut
ð1Þ with Pt the real price of diesel ($ of 2002), GDPcapt the per capita level of GDP ($ of 2002) and Sh_Primary the share in % of the primary sector in total GDP. Per capita demand and GDP are used in order to filter out the effect of population growth.8 The share of the primary sector is included in order to control for the Canadian boom in mining and oil extraction, which may have had an impact on diesel demand based on empirical evidence discussed in Section 2. The potential effects of changes in several other sectors and industries, including manufacturing, were also investigated but no significant impact was observed, justifying their exclusion from the model. Finally, a trend was included to the model in order to capture long-run changes in the reliance on trucking. The actual level of demand takes into account the fact that economic agents cannot immediately adjust to the desired level. For example, following an increase in diesel price, a trucking company may decide to renew its aging truck fleet. This process is however likely to take some time. The partial adjustment process is assumed to take the following form: LnDcapt −LnDcapt−1 ¼ λ
LnDcapt −LnDcapt−1
þ νt :
the short and long-run impact of the other explanatory variables. Note that the variable GDPcapt should be interpreted, in our context, more as an indicator of the level of economic activity which determines the demand for trucking than a traditional consumer income effect. For ease of exposition, we do however refer to λβ2 as the short run income elasticity and β2 as the long run income elasticity. The above reduced form dynamic model has the advantage of being simple and has been extensively used in the literature on gasoline demand (see Basso and Oum, 2007). However, it may be subject to spurious correlation if the time series are not stationary. The second approach is therefore based on cointegration techniques and the estimation of an error correction model which should, in principle, avoid this problem. Specifically, we adopt the Engel and Granger procedure (for details see for example Enders, 2004). In this approach, we first need to test for the stationarity of the time series and determine their order of integration. A time series follows a stationary process if its mean and variance are constant over time. Moreover, the covariance between two points in time should only depend upon the time distance between these two points. A series is said to be integrated of order I(d) if it is not stationary but its dth difference is. Modified Dickey–Fuller tests (or DF-GLS tests) are used to assess the order of integration.9 We also confirm the results using the augmented Dickey–Fuller (ADF) and Phillips–Perron tests. If the time series are I(d) then it is possible that they are cointegrated meaning that a long run relationship still exists between these series even if they are not stationary (i.e. they have a common attractor). In our setting, the long run relationship has the following form: LnDcapt ¼ θ0 þ θ1 LnP t þ θ2 LnGDPcapt þ θ3 LnShP rimaryt þ θ4 Trend þ ϵ t :
ð2Þ
By substituting Eq. (1) into Eq. (2), we obtain the following relationship which can be estimated: LnDcapt ¼ λβ 0 þ λβ1 LnðP t Þ þ λβ 2 LnGDPcapt þ λβ 3 LnShP rimaryt
If the error term ϵt is stationary, we can reject the hypothesis of the absence of cointegration of the series. In this case, the coefficients can be estimated by OLS and θ1 and θ2 measure the long run price and income elasticities respectively. To find short run effects, an error correction model (ECM) is estimated. It has the following specification:
þλβ 4 Trend þ ð1−λÞLnDcapt−1 þ ν t þ λut : The short run price elasticity is measured by λβ1 while the long run price effect is obtained by multiplying λβ1 by λ1. The same holds true for
8
Population has grown by 27% over the 1986–2008 period.
ΔlnDcapt ¼ γ0 þ γ1 ΔLnP t þ γ2 ΔLnGDPcapt þ γ3 ΔLnShP rimaryt þγ4 ΔLnDcapt−1 þ γ 5 ΔLnP t−1 þ γ6 ΔLnGDPcapt−1 þγ7 ΔLnShP rimaryt−1 þ γ 8 ^ϵ t−1 þ θt 9
See for example Stock and Watson (2007).
P. Barla et al. / Energy Economics 43 (2014) 316–322
30000
35000
Table 3 Results of stationarity tests. Variables
DF-GLS
LnDcap ΔLnDcap LnP ΔLnP LnGDPcap ΔLnGDPcap LnShPrimary ΔLnShPrimary
−0.06 (1, nt) −2.70⁎⁎⁎ (0, nt) −1.06 (0) −5.11⁎⁎⁎ (1) −3.59 ⁎⁎ (5) −2.28⁎ (0, nt) −0.4 (2) −5.9⁎⁎⁎ (1)
In parenthesis, the number of lags and nt for ‘no trend’. The number of lags is determined by the Ng–Perron procedure. ⁎ Significant at the 10% level. ⁎⁎ Significant at the 5% level. ⁎⁎⁎ Significant at the 1% level.
25000
GDP per capita ($ of 2002)
40000
320
1985
1990
1995
2000
2005
2010
annee Table 4 Results of the cointegration model.
Fig. 5. GDP per capita, 1986–2008.
Table 2 Results for the partial equilibrium model. Variable
Coefficient
Std. errora
LnPt LnGDPcapt LnShPrimaryt LnDt − 1 Trend Constant R-square Long run price elasticityb Long run income elasticityb
−0.43⁎⁎⁎ 0.50⁎⁎ 0.25⁎⁎⁎ 0.45⁎⁎ 0.009⁎⁎⁎ −18.7⁎⁎⁎ 0.98⁎ −0.78⁎⁎ 0.90⁎⁎⁎
0.06 0.29 0.059 0.16 0.003 4.93
⁎ Significant at the 10% level. ⁎⁎ Significant at the 5% level. ⁎⁎⁎ Significant at the 1% level. a Hubber/White standard error robust to heteroskedasticity and serial correlation of the error terms. b Significance evaluated by using a non-linear Wald-type test.
with γ1 and γ2 measuring the short run price and income elasticities and γ8 the speed of the adjustment process toward the long run equilibrium. The lags are necessary to eliminate serial correlation in the error terms. Note that we only report the results of our most preferred specification but the role of alternative determinants was explored. For example, we tested a variable measuring the share of imports and exports in total GDP. We also tried the share of trade with the US and Mexico in total GDP. The impact of the price of gasoline was also examined as well as the level of unemployment. We also test for the effect of a dummy set to one after 1995 and its interaction with the trend to capture the impact of NAFTA and the International Fuel Trade Agreement. We have also tested alternative ways to capture the changes in the Canadian economic structure. 5. Empirical results Table 2 presents the results from the partial adjustment model (PAM). Tables 3 to 5 present the results from the cointegration analysis. Starting with the results of the PAM, we obtain a very good fit with an Rsquared of 98%. The level of inertia is not very high with a coefficient on LnDt − 1 at 0.45. This implies that the ratio of long to short run effects is evaluated at 1.8 which is in the lower part of values obtained by other studies.10 We find a positive trend indicating an autonomous growth
10 1 Recall that this ratio is measured by λ1 which is, in our case, equal to ð1−0:45 Þ. From Table 1, this ratio varies from a low 1.48 to 6.28 in other studies. For gasoline, this ratio is usually between 2 and 4.
Variable
Coefficient
Std. error
LnPt LnGDPcapt LnShPrimaryt Trend Constant Engle–Granger test (4 lags)a
−0.42⁎⁎⁎ 0.96⁎⁎⁎ 0.23⁎⁎ 0.015⁎⁎⁎ −33.2⁎⁎ −4.69⁎⁎
0.08 0.20 0.07 0.003 4.63
⁎ Significant at the 10% level. ⁎⁎ Significant at the 5% level. ⁎⁎⁎ Significant at the 1% level. a Test critical values: −5.014 at 1% level and −4.150 at 5% level.
rate of about 0.9% per year in diesel consumption per capita. The short run and long run price elasticities are statistically significant at −0.43 and −0.78 respectively. These values are larger than those obtained in other studies (see Table 1), particularly for the short run effect. This could be due to the fact that we are controlling for the changing structure of the economy. It is indeed interesting to note that if we do not control for the share of the primary sector (i.e. the variable LnSh_Primary is excluded), we obtain much lower price elasticities (−0.18 and −0.31 respectively). In this case, the variable LnPt is picking up the impact of the missing variable Sh_Primary resulting in a downward bias.11 The impacts of GDP per capita are slightly stronger than the price effects with a short run effect at 0.5 and a long run impact at 0.9. These values are in the lower range of those obtained in other studies, particularly for the long run. Once again, the inclusion of the variable Sh_Primary turns out to be important. Without this variable, the income elasticities are 0.76 and 1.32. The impact of Sh_Primary is positive and statistically significant. Moreover, the magnitude of its impact is quite important: a 10% increase in the share of the primary sector is associated with a 2.5% increase in diesel consumption in the short run and a 4.5% increase in the long run. Next, turning to the cointegration results, the stationarity tests results in Table 3 clearly suggest that LnDcap, LnP and LnSh_Primary are I(1). For LnGDPcap, the evidence is more blurry. The DF-GLS test suggests that LnGDPcap would be I(0), but with 5 lags included (which leaves only 17 observations). Phillips–Perron and ADF tests do not reject the hypothesis of a unit root. When the first difference is taken, we reject the presence of a unit root at the 10% significance level with zero lag with the DF-GLS test. While these tests do not strongly allow us to conclude that LnGDPcaps is indeed I(1), we still proceed with the cointegration analysis. The results of the long run model are presented in Table 4. We reject the hypothesis of a unit root for the residuals at the 5% level suggesting
11 The correlation between the variables LnPt and LnSh_Primary is positive at 0.92. Recall that the Canadian oil extraction industry is dependent on high world oil prices.
P. Barla et al. / Energy Economics 43 (2014) 316–322 Table 5 Results of the error correction model. Variable
Coefficient
Std. error
ΔLnPt ΔLnGDPcapt ΔLnShPrimaryt ΔLnDcapt − 1 ΔLnPt − 1 ΔLnGDPcapt − 1 ΔLnShPrimaryt − 1 ϵt − 1 Constant
−0.15⁎ 1.55⁎⁎⁎
0.08 0.26 0.06 0.19 0.13 0.32 0.09 0.29 0.007
0.05 0.12 −0.09 −0.54 0.07 −0.56⁎⁎ 0.009
that the series are indeed co-integrated. The long run price elasticity is −0.42 while the long run income elasticity is 0.96. We find an autonomous increase in diesel consumption of 1.5% per year. The elasticity of diesel with respect to Sh_Primary is 0.25. Table 5 presents the results obtained from the error correction model (short run effects). We find a very low short run price elasticity at 0.15. In contrast, the short run income elasticity is quite important at 1.55. Interestingly, the short run income elasticity is higher than the long run value which may reflect that with time, companies become more fuel efficient in carrying additional freights generated by an increase in GDP. The share of the primary sector does not appear to have a significant effect in the short run. The adjustment rate to the long run value is evaluated at 56% meaning that more than 50% of the adjustment occurs within the first year.
5.1. Comparing the results from the two approaches Table 6 compares the short and long run elasticities obtained from both approaches. We find lower price effects with cointegration both in the short and long run. For the short run income elasticity, the value is three times larger with cointegration than with the PAM. The values are very similar for the long run effect of income. This pattern is actually quite similar to Dahl and Kurtubi (2001) which also estimate diesel demand with these two techniques (see Table 1). Moreover recall that Basso and Oum (2007) and Graham and Glaister (2004) also conclude from their literature review on gasoline demand that cointegration often leads to much lower long run price elasticities. They conclude that PAM models would tend to overestimate long term elasticity by 25 to 30%. In our case for diesel, we find a long term overestimation of the price effect of 52%. As mentioned earlier, these differences may be due to the non-stationarity of the time series leading to spurious effects when using PAM. It is however important to stress that cointegration techniques also have weaknesses. For example, as pointed out by Basso and Oum (2007), tests for stationarity and cointegration are well known to be weak, particularly in small samples. They also rely on the hypothesis of the absence of structural breaks (i.e. the existence of a stable long term relationship). A recent study on gasoline demand (Hughes et al., 2008) suggest that the values of the long run price elasticities of gasoline demand may have declined over time as a Table 6 Summary of the elasticities. PAM (1)
Cointegration (2)
Ratio (2)/(1)
Price Short run Long run
−0.43⁎⁎⁎ −0.80⁎⁎
−0.15⁎ −0.42⁎⁎⁎
0.35 0.52
Income Short run Long run
0.50 0.91⁎⁎
1.55⁎⁎⁎ 0.96⁎⁎⁎
3.1 1.05
⁎ Significant at the 10% level. ⁎⁎ Significant at the 5% level. ⁎⁎⁎ Significant at the 1% level.
321
result of a changing urban and economic environment.12 Unfortunately, it would be very perilous to test for structural breaks with our small time-series datasets. Finally, recall that the evidence that LnGDPcap is I(1) is weak which may cast some doubts about the cointegration results. Overall, however, the elasticities obtained with cointegration are closer to the values obtained in other studies.13 6. Conclusion In this research, we estimated the price and income elasticities for road diesel demand in Canada. One of the main advantages of the Canadian context is that road diesel users are almost only trucks. We also compared the results obtained using two different methodological approaches, one based on a PAM and one on cointegration techniques. In all cases, we find that the demand of diesel is price inelastic in the short run. The long run price elasticity estimates vary depending on models: − 0.8 with the PAM and − 0.4 with the error correction model. Our analysis also stressed the importance to control for changes in the Canadian economy and particularly the growing impact of mining and oil product extraction. The level of economic activity does appear to be a major determinant with a long run elasticity close to 1. We also find a positive trend reflecting the increased reliance on trucking. The comparison of the results obtained from both techniques confirms what has been observed in the literature on gasoline demand. The PAM appears to overestimate the impact of prices. In our case, the long run overestimation would be in the order of 50%. For the impact of income, the PAM leads to a much smaller effect in the short run while both approaches lead to long run effects that are comparable. At this stage, we consider that the results obtained using cointegration are more reliable for two reasons: i) there is strong evidence that the series are non-stationary and ii) elasticities obtained with cointegration are more in line with those obtained in previous studies. Nevertheless, we are reluctant to definitively reject PAM results. It would certainly be worthwhile to further assess these two approaches. For example, with longer time series than those used in this paper, it would be very interesting to compare the forecasting performance of each approach as it is, in fine, one of the main objectives of these demand analyses. In future research, it would also be worthwhile to estimate a more structural model explaining the main determinants of the demand for road diesel such as the number of trucks, the distance driven or the fuel efficiency of the truck fleet. References Barla, P., 2010. Greenhouse gas issues in the North American trucking industry. Energy Effic. 3 (2), 123–131. Basso, L., Oum, T.H., 2007. Automobile fuel demand: a critical assessment of empirical methodology. Transp. Rev. 27 (4), 449–484. Belhaj, M., 2002. Vehicle and fuel demand in Morocco. Energy Policy 30, 1163–1171. Bhattacharyya, S., Blake, A., 2009. Domestic demand for petroleum products in MENA countries. Energy Policy 37, 1552–1560. Bonilla, D., 2009. Fuel demand on UK roads and dieselisation of fuel economy. Energy Policy 37 (10), 3769–3778. Boshoff, Willem H., 2010. Petrol, diesel fuel and jet fuel demand in South Africa: 1998–2009. Available at SSRN: http://ssrn.com/abstract=1661021. Comité des constructeurs français d'automobiles, 2008. L'industrie automobile française : analyse et statistiques. Available at: http://www.ccfa.fr/IMG/pdf/CCFA_COMPLET.pdf. Dahl, C., Kurtubi, 2001. Estimating oil product demand in Indonesia using a cointegration error correction model. OPEC Rev. (March), 1–25. Dalh, C.A., 2012. Measuring global gasoline and diesel price and income elasticies. Energy Policy 41, 2–13. Daly, H., Gallachóir, B., 2011. Modelling future private car energy demand in Ireland. Energy Policy 39 (12), 7815–7824. De Vita, G., Endresen, K., Hunt, L.C., 2006. An empirical analysis of energy demand in Namibia. Energy Policy 34, 3347–3463. Enders, W., 2004. Applied Econometric time series, Wiley Series in Probability and Statisticssecond edition. 12 Obviously, structural breaks would also bias the results obtained with a PAM specification. 13 It should however be noted that other studies do not control for possible changes in the economy structure. As our results suggest this could eventually bias their results.
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Gonzalez-Marrero, R.M., Lorenzo-Alegria, R., Marrero, G.A., 2012. A dynamic model for road gasoline and diesel consumption: an application for Spanish regions. Int. J. Energy Econ. Policy 2 (4), 201–209. Goodwin, P., Dargay, J., Hanly, M., 2004. Elasticities of road traffic and fuel-consumption with respect to price and income: a review. Transp. Rev. 24 (3), 275–292. Graham, D.J., Glaister, S., 2004. Road traffic demand estimates: A review. Transp. Rev. 24 (3), 261–274. Hughes, J.E., Knittel, C.R., Sperling, D., 2008. Evidence of a shift in the short-run price elasticity of gasoline demand. Energy J. 29 (1), 93–114. Iootty, M., Pinto, H., Ebeling, F., 2009. Automotive fuel consumption in Brazil: applying static and dynamic systems of demand equations. Energy Policy 37, 5326–5333. Iwayemi, A., Adenikinju, Babatunde, M.A., 2010. Estimating petroleum products demand elasticities in Nigeria: a multivariate cointegration approach. Energy Econ. 32, 73–85. Kamakaté, F., Schipper, L., 2009. Trends in freight energy use and carbon emissions in selected OECD countries from 1973 to 2005. Energy Policy 37 (10), 3743–3751. Liu, G., 2004. Estimating energy demand elasticities for OECD countries: a dynamic panel data approach. Discussion Papers No. 373. Statistics Norway.
Mayeres, I., Proost, S., 2001. Should diesel cars in Europe be discouraged? Reg. Sci. Urban Econ. 31 (4), 453–470. Pedregal, D.j, Dejuan, O., Gomez, N., Tobarra, M.A., 2009. Modelling demand for crude oil products in Spain. Energy Policy 37, 4417–4427. Pock, M., 2007. Gasoline and diesel demand in Europe: new insights. Working Paper 202. Institute for Advanced Studies, Vienna. Polemis, M.L., 2006. Empirical assessment of the determinants of road energy demand in Greece. Energy Econ. 28, 385–403. Rogan, F., Dennehy, E., Daly, H., Howley, M., Gallachóir, B., 2011. Impacts of an emission based private car taxation policy — first year ex-post analysis. Transp. Res. A 45 (7), 583–597. Sorrell, S., Lehtonen, M., Stapleton, L., Pujol, J., Champion, T., 2009. Decomposing road freight energy use in the United Kingdom. Energy Policy 37 (8), 3115–3129. Statistics Canada, 1995. Trucking in Canada, No. 53-222-XIB. Statistics Canada, 2004. Trucking in Canada, No. 53-222-XIE. Stock, J.H., Watson, M.W., 2007. Introduction to Econometrics, second edition. Pearson. Whyte, K., Daly, H., Gallachóir, B., 2013. Modelling HGV freight transport energy demand in Ireland and the impacts of the property construction bubble. Energy 50, 245–251.
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Energy Economics journal homepage: www.elsevier.com/locate/eneco
The demand for road diesel in Canada☆ Philippe Barla ⁎, Mathieu Gilbert-Gonthier, Jean-René Tagne Kuelah Center for Data and Analysis in Transportation (CDAT), Département d'économique, Université Laval, Québec, QC G1V 0A6, Canada
a r t i c l e
i n f o
Article history: Received 15 November 2011 Received in revised form 4 March 2014 Accepted 10 March 2014 Available online 26 March 2014 JEL classification: R40 Q41 Q38 R48 R41 Q54
a b s t r a c t In this paper, we estimate the demand for road diesel in Canada using aggregate annual data for the period 1986–2008. Using a partial adjustment model (PAM), we find short and long run price elasticities of − 0.43 and −0.8 respectively. However, using cointegration techniques, we obtain price elasticities that are 30 to 50% lower. The short run elasticity with respect to GDP per capita is evaluated at 0.5 with the PAM and 1.55 with cointegration. In the long run, both estimation techniques indicate an income elasticity around 0.9. Our results underline the importance of controlling for the expansion of the primary sector which has been characterizing the Canadian economy in the 2000s. © 2014 Elsevier B.V. All rights reserved.
Keywords: Road diesel Demand Elasticity Partial adjustment model Cointegration
1. Introduction Literally hundreds of researches have studied the demand for gasoline (for reviews see Basso and Oum, 2007; Dalh, 2012; Goodwin et al., 2004; Graham and Glaister, 2004). Much less effort has been made studying the demand for road diesel fuel. Yet, in many countries, the consumption of road diesel has been increasing much faster than gasoline, fuelled in part by the rapid expansion of trucking. For example, in Canada, while gasoline consumption has been multiplied by 1.5 over the 1971–2008 period, road diesel consumption has grown by a factor of 7.4 during the same period. If gasoline remains the principal source of energy for road transportation activities at 54.4% in 2008, diesel is second at 32.5%, up from 25% in 1990. The fewer number of studies on road diesel demand could be due in part to the heterogeneity of users in many countries. For example, in Europe, road diesel is used not only by trucks but also by a large share of automobiles (about 50% of new cars sold in 2008, see Comité des constructeurs français d'automobiles, 2008). In Canada, however, the share of light duty vehicles (automobiles ☆ The authors acknowledge the financial support of Natural Resource Canada (Accord de contribution CDAT2009-12). The views expressed in this paper are however solely ours. We also thank an anonymous referee for the very useful suggestions. ⁎ Corresponding author at: Département d'économique, Université Laval, Pavillon J.-A.-DeSève, 1025 av. des Sciences-Humaines, Québec, Québec, G1V 0A6, Canada. E-mail address: [email protected] (P. Barla).
http://dx.doi.org/10.1016/j.eneco.2014.03.008 0140-9883/© 2014 Elsevier B.V. All rights reserved.
and light trucks) in road diesel consumption was a meager 1.3% in 2008. In fact, medium and heavy trucks consumed over 91% of road diesel while the balance is fuelling busses (6.7%). This makes the analysis of road diesel easier and more relevant as the main driving force behind road diesel demand is trucking activities. The main objective of this paper is therefore to measure how the price of diesel and the level of economic activities are affecting the demand for road diesel fuel in Canada. Specifically, we estimate the demand for road diesel in Canada using aggregate annual data for the period 1986–2008. We focus the analysis on short and long run price and income elasticities. Besides contributing to the understanding of the demand for diesel, this paper also contributes by comparing the results obtained using two different methodological approaches. First, we estimate a reduced-form partial adjustment model (PAM). This is the approach most often used in the literature on gasoline demand but it may be affected by spurious correlation. Second, we estimate an error correction model (ECM) based on cointegration techniques. This approach is more recent and would be more adequate for time series that are not stationary. In the case of gasoline demand, Graham and Glaister (2004) and Basso and Oum (2007) compare the results obtained in the literature with these two approaches. Their analysis suggests that cointegration analysis usually leads to smaller price elasticities (between 25 and 30% smaller), particularly in the long run. One of the shortcomings of these comparisons is that they are based on studies that use different data from different
P. Barla et al. / Energy Economics 43 (2014) 316–322
317
Table 1 Overview of recent researches on diesel demand using aggregate data. Reference
Data
Method
Elasticitiesa
Dalh (2012)
Review of 34 studies
Static models only
Gonzalez-Marrero et al. (2012) Boshoff (2010)
Panel: 16 Spanish regions 1998–2006
Dynamic panel methods (GMM) for gasoline and diesel ADRL model
Price: −0.16 Income: near 1 Price: n/s Income: n/s Price: −0.13 (long run) Income: 1.51 (long run) Few significant coefficients for diesel and unexpected signs Price: −0.62
Bhattacharyya and Blake (2009) Iootty et al. (2009) Iwayemi et al. (2010) Pedregal et al. (2009) Pock (2007)
Polemis (2006) De Vita et al. (2006) Liu (2004) Belhaj (2002)
Dahl and Kurtubi (2001)
a
Demand for petroleum product demand including diesel fuel — South Africa 1998–2008 (quarterly) Demand for petroleum products — Middle East and North African countries 1982–2005 Brazilian demand for automotive fuel including diesel — 1970–2005 Oil product demand including total diesel — Nigeria 1977–2006 Demand for oil products demand including transportation diesel — Spain 1984–2006 (monthly) Gasoline and diesel — 14 European countries — 1990–2004
Demand for road energy demand including diesel — Greece 1978–2003 Quarterly data 1980–2002 Namibian total diesel consumption
PAM models AIDS model and vector error correction model Cointegration Unobserved components model PAM model — Dynamic panel methods
Cointegration Cointegration techniques
Energy products demand including automotive diesel in OECD countries 1978–1999 Demand for gasoline and diesel — Morocco 1970–1996
Dynamic panel techniques (one-step GMM) PAM model — fuel consumption and vehicle stock
Demand for petroleum products including automotive diesel — Indonesia — 1970–1995
PAM and Cointegration
Price: −0.41/n.s Income: 0.8/n.s. Price: −0.025/0.37 Income: 0.28/1.06 Price: −0.14/large variations across methods Income: 0.6/~1.2 (large variation across methods) Price: −0.07/−0.44 Income: 0.44/1.18 Price: n/s Income: ~2 Price: −0.09/−0.26 Income: 0.425/1.2 Price: −0.18/−0.62 Income: n/s but indirect effect via the stock of vehicle PAM: Price: −0.18/−0.64 Income: 0.57/2.06 Cointegration: Price: n/s/−0.67 Income: 2.14/2.16
When available, short run/long run elasticities.
countries and/or periods. The difference in the results may therefore be due not only to the methodologies but also to the difference in the data and context. In this paper, we compare the two approaches using the same data. The rest of this paper is organized as follows. In Section 2, we review the recent literature on diesel demand and provide some background on the Canadian setting. In Section 3, we describe our data and in Section 4 our methodology. The results are presented in Section 5 and we conclude in Section 6.
2. Background Table 1 provides an overview of recent researches on diesel demand using aggregate data.1 We observe a variety of methodological approaches using quite different data. Some studies focus on total diesel demand, other on transportation diesel or road transportation diesel demand. Also note that the institutional context varies across countries with some countries facing diesel price regulations or tax policies favoring the use of diesel over gasoline. It is therefore not so surprising that results are very variable. Overall, however, it appears that the impact of price is generally rather small. From her review, Dalh (2012) reports an average price elasticity of about − 0.16. The impact of income (or GDP) seems much stronger particularly in the long run. Some studies find an elasticity around 2. Dalh (2012) obtains an average elasticity near one. Few studies estimate cross price elasticities between gasoline and diesel and those that do usually find very small effects. Interestingly, Dahl and Kurtubi (2001) compare results obtained using a PAM and cointegration techniques. They find a smaller price effect and a
1 See also Dalh (2012) who analyses price and income elasticities from 140 gasoline and 34 diesel demand studies (static models only).
larger income effect using cointegration in the short run. Results for the long run income effect are very similar using both approaches. Some recent studies have adopted a more disaggregated approach to better understand the observed trends in road freight energy use. Sorrell et al. (2009) uncover the main factors that explain the decoupling of UK road freight energy consumption and GDP over the 1989–2004 period. The decline in the value of domestically manufactured goods relative to GDP is the main factor explaining over 30% of the decoupling. Other factors include reductions in empty running (14%) and improved energy efficiency (8.6%). Kamakaté and Schipper (2009) analyze the trend in truck freight energy use in 5 OECD countries from 1973 to 2005. They decompose the trend in energy use to identify the effects of activity, relative modal share and energy intensity. The analysis reveals that GDP growth and the share of trucking remain closely linked. Geography and the structure of the economy (e.g. the importance of raw materials) explain some of the cross country variations. Whyte et al. (2013) show that disaggregated models by commodity groups and truck weight class improve explanatory power compared to aggregated models. Indeed, different sectors experience distinct trends and react differently to various economic indicators.
2.1. The Canadian setting In many countries, the share of diesel-fuelled cars in the vehicle fleet has considerably increased in recent years. This “dieselisation” has been linked to tax differentials between gasoline and diesel (Mayeres and Proost, 2001) and more recently to CO2 emissions reduction policies (Bonilla, 2009; Daly and Gallachóir, 2011; Rogan et al., 2011). However, in Canada, we observe the opposite as the share of road diesel consumed by cars and light duty trucks has steadily declined from 4.3% in 1990 to 1.3% in 2008. During the same period, the share of diesel consumed by medium and heavy trucks has jumped from 83.5% in 1990 to 91% in 2008.
8 4
10000
6
12000
sh_primary
14000
10
16000
12
P. Barla et al. / Energy Economics 43 (2014) 316–322
8000
GDP associated with truck transportation (million $ of 2002)
318
1985 1985
1990
1995
2000
2005
1990
1995
Year
2000
2005
2010
2010
Year Fig. 2. Share of the primary sector in total GDP (%), 1986–2008.
Fig. 1. GDP associated with the truck transportation industry (in million $ of 2002), 1986–2008.
The growth in the Canadian trucking industry is explained by the same global changes experienced elsewhere: the development of international trade, the “just in time” supply chain management and the increased fragmentation of manufacturing processes. Several policy changes also have had an impact on the Canadian trucking industry and thereby on diesel sales (see Barla, 2010). In 1987, the industry was deregulated leading, at least initially, to a reduction in the number of trucking companies. This could have contributed to the decline in diesel sales observed in the early 90s. Overall, the deregulation appears to have intensified competition and fostered efficiency (see Statistics Canada, 1995). In 1989, the Canada–US Free Trade Agreement and later, in 1994, the North American free trade agreement (NAFTA) have led to a significant increase in trade between Canada, the US and Mexico.2 Since trucking is the predominant transportation mode between these trading partners, these agreements have stimulated international trucking activities. In fact, Canadian trucking traffic has, to some extent, been reoriented from an east–west to a north–south pattern (see Statistics Canada, 1995). The sharp increase in trucking activities can be visualized in Fig. 1.3 Based on the available fragmented evidence, the top industries carrying commodities by trucks in Canada remain the same over time (see Statistics Canada, 1995, 2004). They include: motor vehicles and parts, petroleum and coal products, minerals, wood products, chemical products, and food manufacturing.4 It is however very likely that the relative shares in trucking activities of these sectors have changed over time. Indeed, one of the most profound changes in the Canadian economy over the period has been the rapid expansion of the tar sand industry in Alberta starting in the early 2000s. The petroleum extraction industry relies heavily on trucks both in the production process and product distribution. In fact, the consumption of road diesel in Alberta has increased by over 60% from 2000 to 2008.5 Also note that Canadian mining activities have also progressed over the same period. Overall, the development of mining and oil and gas extraction has led to an increase in the share of the primary sector in total GDP as can be observed in Fig. 2. Controlling for this transformation of the Canadian economy 2 From 1990 to 2004, the total value of international trade within North America has doubled in real terms (see Barla, 2010). 3 Note that Fig. 1 only shows for-hire trucking activities. 4 These industries represented over 50% of tonnage hauled in 1995. Unfortunately, this figure is not available for other time periods. 5 Over the same period, the Canadian road diesel consumption has only increased by 25%. The share of the Alberta road diesel consumption in Canada has risen from 16.4% in 2000 to 21% in 2008.
turns out to be important for the econometric analysis. Indeed, the development of the petroleum extraction industry is related to the price of oil and is therefore also correlated with the price of diesel. Ignoring this transformation would result in a downward bias in the price elasticities as shown in Section 5.6 Finally, we should also mention that in the mid-nineties, Canadian provinces joined the International Fuel Tax Agreement (IFTA) which insures that carriers pay diesel tax based on the distance driven in each jurisdiction rather than where the diesel has been purchased. This could have stimulated sales in Canada where the tax rates on diesel are usually higher than in the US.
3. Data The main data sources are Statistics Canada and MJ Ervin & Associates. Diesel consumption data is provided by the Road Motor Vehicle Survey which collects information on net sales of diesel for which road taxes were paid. Our data covers the 1986–2008 period. Unfortunately, over this period, some provinces have eliminated road fuel taxation at some point in time leading to artificial shifts in net sales. Specifically, Alberta has abolished road fuel taxation from 1978 to 1987 and Saskatchewan from 1982 to 1987. In order to correct for these problems, we use domestic sales of diesel as provided by Statistics Canada data on monthly supply and disposition of refined petroleum products. This series is complete and does not depend on taxation rules. Domestic sales are however not directly comparable to net sales of road diesel as they include all uses of diesel (e.g. off-road, rail, industrial usage). For each of the three provinces with missing data, we therefore regress net sales of road diesel on domestic sales and use the predictions to complete our series on net sales of road diesel. For the price of diesel, we use the average retail price provided by MJ Ervin & Associates based on retail prices in the major Canadian cities. This measure is clearly imperfect as it only covers part of Canada.7 Furthermore, it does not capture the fact that major trucking companies do not pay the full price displayed at retail stations. We expect, however, that this price measure should be closely correlated with actual prices paid. All the other variables used in the analysis are obtained from Statistics Canada (e.g. GDP, population). Figs. 3 to 5 illustrate respectively the changes in per capita road diesel sales, the average price of diesel and the level of GDP per capita over the 1986–2008 period in Canada. From these figures, it clearly appears 6 We thank an anonymous referee for suggesting to control for changes in the Canadian economic structure. 7 The list of cities included also varies over time.
1985
1990
1995
2000
2005
annee
70
80
90
100
110
319
60
Estimated Average Price of Road Diesel in Canada ($ of 2002)
500 450 400 350 300
Per Capita Net Sales of Road Diesel (liters)
P. Barla et al. / Energy Economics 43 (2014) 316–322
1985
1990
1995
2000
2005
annee
Fig. 3. Per capita net sales of road diesel in Canada, 1986–2008. Fig. 4. Estimated average price of road diesel in Canada, 1986–2008.
that the sales of diesel and GDP are very closely linked. It is particularly evident during the 1990–1991 recession. 4. Methodology First, we estimate a dynamic reduced form demand model. Specifically, we use a partial adjustment model (PAM) which allows for inertia in the reaction of consumers. At time t, the unobserved desired level of diesel demand per capita Dcap⁎t is assumed to be given by:
LnDcapt ¼ β0 þ β1 LnP t þ β2 LnGDPcapt þ β3 LnShP rimaryt þ β4 Trend þ ut
ð1Þ with Pt the real price of diesel ($ of 2002), GDPcapt the per capita level of GDP ($ of 2002) and Sh_Primary the share in % of the primary sector in total GDP. Per capita demand and GDP are used in order to filter out the effect of population growth.8 The share of the primary sector is included in order to control for the Canadian boom in mining and oil extraction, which may have had an impact on diesel demand based on empirical evidence discussed in Section 2. The potential effects of changes in several other sectors and industries, including manufacturing, were also investigated but no significant impact was observed, justifying their exclusion from the model. Finally, a trend was included to the model in order to capture long-run changes in the reliance on trucking. The actual level of demand takes into account the fact that economic agents cannot immediately adjust to the desired level. For example, following an increase in diesel price, a trucking company may decide to renew its aging truck fleet. This process is however likely to take some time. The partial adjustment process is assumed to take the following form: LnDcapt −LnDcapt−1 ¼ λ
LnDcapt −LnDcapt−1
þ νt :
the short and long-run impact of the other explanatory variables. Note that the variable GDPcapt should be interpreted, in our context, more as an indicator of the level of economic activity which determines the demand for trucking than a traditional consumer income effect. For ease of exposition, we do however refer to λβ2 as the short run income elasticity and β2 as the long run income elasticity. The above reduced form dynamic model has the advantage of being simple and has been extensively used in the literature on gasoline demand (see Basso and Oum, 2007). However, it may be subject to spurious correlation if the time series are not stationary. The second approach is therefore based on cointegration techniques and the estimation of an error correction model which should, in principle, avoid this problem. Specifically, we adopt the Engel and Granger procedure (for details see for example Enders, 2004). In this approach, we first need to test for the stationarity of the time series and determine their order of integration. A time series follows a stationary process if its mean and variance are constant over time. Moreover, the covariance between two points in time should only depend upon the time distance between these two points. A series is said to be integrated of order I(d) if it is not stationary but its dth difference is. Modified Dickey–Fuller tests (or DF-GLS tests) are used to assess the order of integration.9 We also confirm the results using the augmented Dickey–Fuller (ADF) and Phillips–Perron tests. If the time series are I(d) then it is possible that they are cointegrated meaning that a long run relationship still exists between these series even if they are not stationary (i.e. they have a common attractor). In our setting, the long run relationship has the following form: LnDcapt ¼ θ0 þ θ1 LnP t þ θ2 LnGDPcapt þ θ3 LnShP rimaryt þ θ4 Trend þ ϵ t :
ð2Þ
By substituting Eq. (1) into Eq. (2), we obtain the following relationship which can be estimated: LnDcapt ¼ λβ 0 þ λβ1 LnðP t Þ þ λβ 2 LnGDPcapt þ λβ 3 LnShP rimaryt
If the error term ϵt is stationary, we can reject the hypothesis of the absence of cointegration of the series. In this case, the coefficients can be estimated by OLS and θ1 and θ2 measure the long run price and income elasticities respectively. To find short run effects, an error correction model (ECM) is estimated. It has the following specification:
þλβ 4 Trend þ ð1−λÞLnDcapt−1 þ ν t þ λut : The short run price elasticity is measured by λβ1 while the long run price effect is obtained by multiplying λβ1 by λ1. The same holds true for
8
Population has grown by 27% over the 1986–2008 period.
ΔlnDcapt ¼ γ0 þ γ1 ΔLnP t þ γ2 ΔLnGDPcapt þ γ3 ΔLnShP rimaryt þγ4 ΔLnDcapt−1 þ γ 5 ΔLnP t−1 þ γ6 ΔLnGDPcapt−1 þγ7 ΔLnShP rimaryt−1 þ γ 8 ^ϵ t−1 þ θt 9
See for example Stock and Watson (2007).
P. Barla et al. / Energy Economics 43 (2014) 316–322
30000
35000
Table 3 Results of stationarity tests. Variables
DF-GLS
LnDcap ΔLnDcap LnP ΔLnP LnGDPcap ΔLnGDPcap LnShPrimary ΔLnShPrimary
−0.06 (1, nt) −2.70⁎⁎⁎ (0, nt) −1.06 (0) −5.11⁎⁎⁎ (1) −3.59 ⁎⁎ (5) −2.28⁎ (0, nt) −0.4 (2) −5.9⁎⁎⁎ (1)
In parenthesis, the number of lags and nt for ‘no trend’. The number of lags is determined by the Ng–Perron procedure. ⁎ Significant at the 10% level. ⁎⁎ Significant at the 5% level. ⁎⁎⁎ Significant at the 1% level.
25000
GDP per capita ($ of 2002)
40000
320
1985
1990
1995
2000
2005
2010
annee Table 4 Results of the cointegration model.
Fig. 5. GDP per capita, 1986–2008.
Table 2 Results for the partial equilibrium model. Variable
Coefficient
Std. errora
LnPt LnGDPcapt LnShPrimaryt LnDt − 1 Trend Constant R-square Long run price elasticityb Long run income elasticityb
−0.43⁎⁎⁎ 0.50⁎⁎ 0.25⁎⁎⁎ 0.45⁎⁎ 0.009⁎⁎⁎ −18.7⁎⁎⁎ 0.98⁎ −0.78⁎⁎ 0.90⁎⁎⁎
0.06 0.29 0.059 0.16 0.003 4.93
⁎ Significant at the 10% level. ⁎⁎ Significant at the 5% level. ⁎⁎⁎ Significant at the 1% level. a Hubber/White standard error robust to heteroskedasticity and serial correlation of the error terms. b Significance evaluated by using a non-linear Wald-type test.
with γ1 and γ2 measuring the short run price and income elasticities and γ8 the speed of the adjustment process toward the long run equilibrium. The lags are necessary to eliminate serial correlation in the error terms. Note that we only report the results of our most preferred specification but the role of alternative determinants was explored. For example, we tested a variable measuring the share of imports and exports in total GDP. We also tried the share of trade with the US and Mexico in total GDP. The impact of the price of gasoline was also examined as well as the level of unemployment. We also test for the effect of a dummy set to one after 1995 and its interaction with the trend to capture the impact of NAFTA and the International Fuel Trade Agreement. We have also tested alternative ways to capture the changes in the Canadian economic structure. 5. Empirical results Table 2 presents the results from the partial adjustment model (PAM). Tables 3 to 5 present the results from the cointegration analysis. Starting with the results of the PAM, we obtain a very good fit with an Rsquared of 98%. The level of inertia is not very high with a coefficient on LnDt − 1 at 0.45. This implies that the ratio of long to short run effects is evaluated at 1.8 which is in the lower part of values obtained by other studies.10 We find a positive trend indicating an autonomous growth
10 1 Recall that this ratio is measured by λ1 which is, in our case, equal to ð1−0:45 Þ. From Table 1, this ratio varies from a low 1.48 to 6.28 in other studies. For gasoline, this ratio is usually between 2 and 4.
Variable
Coefficient
Std. error
LnPt LnGDPcapt LnShPrimaryt Trend Constant Engle–Granger test (4 lags)a
−0.42⁎⁎⁎ 0.96⁎⁎⁎ 0.23⁎⁎ 0.015⁎⁎⁎ −33.2⁎⁎ −4.69⁎⁎
0.08 0.20 0.07 0.003 4.63
⁎ Significant at the 10% level. ⁎⁎ Significant at the 5% level. ⁎⁎⁎ Significant at the 1% level. a Test critical values: −5.014 at 1% level and −4.150 at 5% level.
rate of about 0.9% per year in diesel consumption per capita. The short run and long run price elasticities are statistically significant at −0.43 and −0.78 respectively. These values are larger than those obtained in other studies (see Table 1), particularly for the short run effect. This could be due to the fact that we are controlling for the changing structure of the economy. It is indeed interesting to note that if we do not control for the share of the primary sector (i.e. the variable LnSh_Primary is excluded), we obtain much lower price elasticities (−0.18 and −0.31 respectively). In this case, the variable LnPt is picking up the impact of the missing variable Sh_Primary resulting in a downward bias.11 The impacts of GDP per capita are slightly stronger than the price effects with a short run effect at 0.5 and a long run impact at 0.9. These values are in the lower range of those obtained in other studies, particularly for the long run. Once again, the inclusion of the variable Sh_Primary turns out to be important. Without this variable, the income elasticities are 0.76 and 1.32. The impact of Sh_Primary is positive and statistically significant. Moreover, the magnitude of its impact is quite important: a 10% increase in the share of the primary sector is associated with a 2.5% increase in diesel consumption in the short run and a 4.5% increase in the long run. Next, turning to the cointegration results, the stationarity tests results in Table 3 clearly suggest that LnDcap, LnP and LnSh_Primary are I(1). For LnGDPcap, the evidence is more blurry. The DF-GLS test suggests that LnGDPcap would be I(0), but with 5 lags included (which leaves only 17 observations). Phillips–Perron and ADF tests do not reject the hypothesis of a unit root. When the first difference is taken, we reject the presence of a unit root at the 10% significance level with zero lag with the DF-GLS test. While these tests do not strongly allow us to conclude that LnGDPcaps is indeed I(1), we still proceed with the cointegration analysis. The results of the long run model are presented in Table 4. We reject the hypothesis of a unit root for the residuals at the 5% level suggesting
11 The correlation between the variables LnPt and LnSh_Primary is positive at 0.92. Recall that the Canadian oil extraction industry is dependent on high world oil prices.
P. Barla et al. / Energy Economics 43 (2014) 316–322 Table 5 Results of the error correction model. Variable
Coefficient
Std. error
ΔLnPt ΔLnGDPcapt ΔLnShPrimaryt ΔLnDcapt − 1 ΔLnPt − 1 ΔLnGDPcapt − 1 ΔLnShPrimaryt − 1 ϵt − 1 Constant
−0.15⁎ 1.55⁎⁎⁎
0.08 0.26 0.06 0.19 0.13 0.32 0.09 0.29 0.007
0.05 0.12 −0.09 −0.54 0.07 −0.56⁎⁎ 0.009
that the series are indeed co-integrated. The long run price elasticity is −0.42 while the long run income elasticity is 0.96. We find an autonomous increase in diesel consumption of 1.5% per year. The elasticity of diesel with respect to Sh_Primary is 0.25. Table 5 presents the results obtained from the error correction model (short run effects). We find a very low short run price elasticity at 0.15. In contrast, the short run income elasticity is quite important at 1.55. Interestingly, the short run income elasticity is higher than the long run value which may reflect that with time, companies become more fuel efficient in carrying additional freights generated by an increase in GDP. The share of the primary sector does not appear to have a significant effect in the short run. The adjustment rate to the long run value is evaluated at 56% meaning that more than 50% of the adjustment occurs within the first year.
5.1. Comparing the results from the two approaches Table 6 compares the short and long run elasticities obtained from both approaches. We find lower price effects with cointegration both in the short and long run. For the short run income elasticity, the value is three times larger with cointegration than with the PAM. The values are very similar for the long run effect of income. This pattern is actually quite similar to Dahl and Kurtubi (2001) which also estimate diesel demand with these two techniques (see Table 1). Moreover recall that Basso and Oum (2007) and Graham and Glaister (2004) also conclude from their literature review on gasoline demand that cointegration often leads to much lower long run price elasticities. They conclude that PAM models would tend to overestimate long term elasticity by 25 to 30%. In our case for diesel, we find a long term overestimation of the price effect of 52%. As mentioned earlier, these differences may be due to the non-stationarity of the time series leading to spurious effects when using PAM. It is however important to stress that cointegration techniques also have weaknesses. For example, as pointed out by Basso and Oum (2007), tests for stationarity and cointegration are well known to be weak, particularly in small samples. They also rely on the hypothesis of the absence of structural breaks (i.e. the existence of a stable long term relationship). A recent study on gasoline demand (Hughes et al., 2008) suggest that the values of the long run price elasticities of gasoline demand may have declined over time as a Table 6 Summary of the elasticities. PAM (1)
Cointegration (2)
Ratio (2)/(1)
Price Short run Long run
−0.43⁎⁎⁎ −0.80⁎⁎
−0.15⁎ −0.42⁎⁎⁎
0.35 0.52
Income Short run Long run
0.50 0.91⁎⁎
1.55⁎⁎⁎ 0.96⁎⁎⁎
3.1 1.05
⁎ Significant at the 10% level. ⁎⁎ Significant at the 5% level. ⁎⁎⁎ Significant at the 1% level.
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result of a changing urban and economic environment.12 Unfortunately, it would be very perilous to test for structural breaks with our small time-series datasets. Finally, recall that the evidence that LnGDPcap is I(1) is weak which may cast some doubts about the cointegration results. Overall, however, the elasticities obtained with cointegration are closer to the values obtained in other studies.13 6. Conclusion In this research, we estimated the price and income elasticities for road diesel demand in Canada. One of the main advantages of the Canadian context is that road diesel users are almost only trucks. We also compared the results obtained using two different methodological approaches, one based on a PAM and one on cointegration techniques. In all cases, we find that the demand of diesel is price inelastic in the short run. The long run price elasticity estimates vary depending on models: − 0.8 with the PAM and − 0.4 with the error correction model. Our analysis also stressed the importance to control for changes in the Canadian economy and particularly the growing impact of mining and oil product extraction. The level of economic activity does appear to be a major determinant with a long run elasticity close to 1. We also find a positive trend reflecting the increased reliance on trucking. The comparison of the results obtained from both techniques confirms what has been observed in the literature on gasoline demand. The PAM appears to overestimate the impact of prices. In our case, the long run overestimation would be in the order of 50%. For the impact of income, the PAM leads to a much smaller effect in the short run while both approaches lead to long run effects that are comparable. At this stage, we consider that the results obtained using cointegration are more reliable for two reasons: i) there is strong evidence that the series are non-stationary and ii) elasticities obtained with cointegration are more in line with those obtained in previous studies. Nevertheless, we are reluctant to definitively reject PAM results. It would certainly be worthwhile to further assess these two approaches. For example, with longer time series than those used in this paper, it would be very interesting to compare the forecasting performance of each approach as it is, in fine, one of the main objectives of these demand analyses. In future research, it would also be worthwhile to estimate a more structural model explaining the main determinants of the demand for road diesel such as the number of trucks, the distance driven or the fuel efficiency of the truck fleet. References Barla, P., 2010. Greenhouse gas issues in the North American trucking industry. Energy Effic. 3 (2), 123–131. Basso, L., Oum, T.H., 2007. Automobile fuel demand: a critical assessment of empirical methodology. Transp. Rev. 27 (4), 449–484. Belhaj, M., 2002. Vehicle and fuel demand in Morocco. Energy Policy 30, 1163–1171. Bhattacharyya, S., Blake, A., 2009. Domestic demand for petroleum products in MENA countries. Energy Policy 37, 1552–1560. Bonilla, D., 2009. Fuel demand on UK roads and dieselisation of fuel economy. Energy Policy 37 (10), 3769–3778. Boshoff, Willem H., 2010. Petrol, diesel fuel and jet fuel demand in South Africa: 1998–2009. Available at SSRN: http://ssrn.com/abstract=1661021. Comité des constructeurs français d'automobiles, 2008. L'industrie automobile française : analyse et statistiques. Available at: http://www.ccfa.fr/IMG/pdf/CCFA_COMPLET.pdf. Dahl, C., Kurtubi, 2001. Estimating oil product demand in Indonesia using a cointegration error correction model. OPEC Rev. (March), 1–25. Dalh, C.A., 2012. Measuring global gasoline and diesel price and income elasticies. Energy Policy 41, 2–13. Daly, H., Gallachóir, B., 2011. Modelling future private car energy demand in Ireland. Energy Policy 39 (12), 7815–7824. De Vita, G., Endresen, K., Hunt, L.C., 2006. An empirical analysis of energy demand in Namibia. Energy Policy 34, 3347–3463. Enders, W., 2004. Applied Econometric time series, Wiley Series in Probability and Statisticssecond edition. 12 Obviously, structural breaks would also bias the results obtained with a PAM specification. 13 It should however be noted that other studies do not control for possible changes in the economy structure. As our results suggest this could eventually bias their results.
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