This working paper has been developed wihin he Alcoa Advancing Susainabiliy Iniiaive o Research and Leverage Acionable Soluions on Energy and Environmenal Economics WP FA15/2013 Economic Crisis and Elasiciies of Car Fuels: Evidence for Spain Mohcine Bakha, José M. Labeaga Xavier Labandeira, Xiral López info@eforenergy.org www.eforenergy.org ISSN nº 2172 / 8437
Economic Crisis and Elasiciies of Car Fuels: Evidence for Spain Mohcine Bakha a, José M. Labeaga a,b,*, Xavier Labandeira a,c, Xiral López a a Economics for Energy. Douor Cadaval 2, 3 E, 36202 Vigo, Spain b Deparamen of Economic Analysis II, UNED. Senda del Rey 11, 28040 Madrid, Spain c Rede and Deparamen of Applied Economics, Universidade de Vigo. Campus As Lagoas, 36310 Vigo, Spain Absrac This paper provides an updaed calculaion of he price and income responsiveness of Spanish consumers of car fuels, wih an explici exploraion of he effecs of he curren economic crisis. We examine separae gasoline and diesel demand models using a se of esimaors, including generalized mehod of momens and bias-correced dynamic fixed-effec models, on a panel of 16 Spanish regions over he 1999-2011 period. The paper confirms he persisence of raher low own-price elasiciies boh for diesel and gasoline and boh in shor and long runs. I also shows ha he crisis has slighly increased he price elasiciy of demand, wih a higher effec on gasoline han on diesel. On he conrary, he crisis has hardly affeced he income elasiciy of car-fuel demand. These updaed resuls are obviously relevan for he curren Spanish debae on he design and implemenaion of energy, environmenal, fiscal and disribuional policies. Moreover, given he duraion and exen of he Spanish economic crisis, our conclusions may be also ineresing and useful from an inernaional perspecive. Keywords: diesel, gasoline, income, price, regions, panel daa JEL classificaion: C23, D12, Q41 * Corresponding auhor: jlabeaga@cee.uned.es This paper could be carried ou wih he economic suppor of he Spanish Minisry of Economy and Compeiiveness hrough is research projec ECO2009-14586-C2-01 (Xavier Labandeira and Xiral López), Alcoa Foundaion (Mohcine Bakha and Xavier Labandeira). The usual disclaimer applies. 1
1. Inroducion From he mid 1990s and unil he oubreak of he 2008 crisis, he demand of car fuels in Spain saw an impressive and unprecedened evoluion: from 1999 o 2007 gasoline and diesel consumpion grew a an average annual rae of respecively 5.1% and 6.5%, reflecing boh he srong growh of he Spanish economy and a limied responsiveness of demand o price changes (which in his period respecively grew a annual average raes of 1.8% and 3.5%). Ye, hree years of deep crisis led o a compleely differen picure: beween 2008 and 2011 gasoline and diesel demand fell a an annual rae of respecively -2.4% and -1.8%, fuelled by srong increases of prices (annual raes of 3.6% and 4.6%). I is clear ha such a boom-and-bus evoluion, as depiced in Figure 1 laer on, brings abou remarkable environmenal, economic or energy effecs. Given he huge changes seen in his marke in he las few years, in his paper we are mainly ineresed in providing an updaed calculaion of he price and income responsiveness of car fuel demand in Spain. The availabiliy of reliable demand elasiciies is a necessary condiion for a proper economic evaluaion of policies and sraegies in his wide area. Given ha Spain is currenly considering a wide reform of is ax sysem ha may incorporae new and more inense axes on car fuels o reduce emissions and energy dependence, or due o he heaed debae on car-fuel pricing, he pracical relevance of his piece research is clearly vindicaed. Even ouside Spain, he resuls of his paper may be useful o anicipae he consequences of a pervasive and long economic crisis on he demand of goods ha are so relevan for welfare and economic developmen. Ye, here are also srong academic reasons for his paper. Alhough some auhors have poined ou ha economic crises are likely o have effecs on he price elasiciies of goods, due for insance o he larger incenives o reac o prices ha are associaed o less availabiliy of income (Eselami e al., 2001), he academic evidence is so far quie limied. I is also rue ha he lieraure generally considers ha he price elasiciy of demand is counercyclical, ha is, sees increases when he economy weakens (van Heerde e al., 2013). This seems o be especially he case in producs wih a low-price elasiciy, as energy goods, and in hose ha accoun for a big share of oal expendiure (Gordon e al., 2013). However, he acual empirical evidence on he variaion of demand elasiciies a imes of economic crisis is very limied, if i exiss a all. 2
I is well known ha he Spanish economy has been much shaken by he global financial crisis and is afermah, being one of he developed counries ha suffered he sharpes falls in growh and unemploymen afer 2008. As a resul, he crisis saw Spanish energy consumpion plummeing as households cu consumpion spending and Spanish producers scaled down heir purchases of energy inpu. Undersanding he effecs of he crisis on Spanish energy demand, bu paricularly on car-fuel demand, is especially imporan a leas for wo reasons. Firs, fuel consumpion is an imporan source of public revenues and hus a likely source of disrupive effecs on governmen budges. Second, price and income elasiciies of demand are imporan for he choice of domesic energy policies and hus have imporan implicaions for energy and environmenal policies. Therefore, omiing srucural changes in consumer responsiveness o price and income may resul in misleading analysis, advice and policies (Hughes e al., 2008). This paper provides an updaed calculaion of he price and income responsiveness of Spanish consumers of car fuels, wih one of he firs exploraions of he effecs of he curren economic crisis. We examine separae gasoline and diesel demand models using a se of esimaors, including generalized mehod of momens and bias-correced dynamic fixed-effec models, on a panel of Spanish regions covering he 1999-2011 period. We ry o reconcile some apparenly conradicory evidence available for Spain, dealing carefully wih he main economeric problems found for he esimaion of his kind of demand models. The reminder of his paper is srucured as follows. Secion 2 reviews he relaed lieraure. Secion 3 describes he mehodology used in his analysis. Secion 4 presens he empirical analysis discussing he daa used and reporing he core resuls of various esimaion echniques. The las secion concludes. 2. Lieraure review Reduced-form demand models have been exensively used in he specificaion of auomobile fuel demand. Using eiher saic or dynamic forms, in his approach model esimaion can have differen forms depending on he ype of daa available, which can be purely ime-series daa, cross-secion daa or panel daa. In he firs caegory, when daa are purely emporal, he parial adjused model (PAM) sands as one of he preferred alernaives o analyze fuel demand and esimae differen elasiciies. In conras o saic models ha provide average elasiciies, he 3
PAM is a dynamic model ha yields shor and long run elasiciies (Hohakker e al., 1974). The raionale behind his model is ha markes are no perfec and some fricions, such as consumer habis, preclude reaching he proper equilibrium. Thus, a model of his ype can be defined o capure he limied capabiliy of consumers o adjus immediaely o he long-run equilibrium of consumpion in response o change in price, income, populaion and oher facors (Li e al.,2010; Banaszak e al.1999; Al-faris, 1997; Serner and Dahl, 1992). Ye oher approaches, e.g. using co-inegraion echniques, have sressed he need o accoun for he possible non-saionary naure of he ime series. Some iniial evidence poins ino he direcion ha failure o reach saionariy may lead o overesimaion of long-run price elasiciies (Elony and Al-Muairi, 1995; Samimi, 1995; Ramanahan; 1999; Dahl and Kurubi, 2001). In line wih he coinegraion echnique, he auoregressive disribued lag (ARDL) bounds-esing approach developed by Pesaran e al. (2001) has been used in various empirical sudies o deermine long-run elasiciies (Boshoff, 2012; Akinboade e al., 2008; De Via e al., 2006). During he las decade, developmens of panel-daa economeric mehods allowed for he esimaion of energy demand models by combining ime-series and cross-secion daa. The increasing ineres in he use of panel-daa modeling is largely due o he abiliy of his echnique o sor several economeric problems. For insance, panel daa models have offered a soluion o he problem of bias caused by unobserved heerogeneiy, a common issue in fiing he models wih cross secional daa ses. Moreover, panel-daa models can accoun for dynamics ha are difficul o deec wih cross-secional daa (Hsiao, 2003; Balagi, 2001; Wooldridge, 2002). In his conex, he fac ha fuel consumpion decision is made a he household level means ha demographic profiles of households play a major role in auomobile fuel consumpion (Schmalensee and Soker, 1999; Yachew and No, 2001; Kayser, 2000). There are also several applicaions ha used aggregae daa o implemen esimaions a local or regional levels. Balagi e al. (2003) employed a panel daase from 21 French regions and compared he performance of differen ses of homogenous and heerogenous esimaors in he calculaion of gasoline price and income elasiciies. They found ha sandard homogenous esimaors perform beer han heir counerpars, obaining shor-run price and income elasiciies for gasoline of respecively -0.093 and 0.2. Oher European sudies a a regional level confirmed he prevalence of very small gasoline shor-run elasiciies: Balagi and Griffin (1997) found shor-run price and income 4
elasiciies for gasoline o be -0.09 and 0.5, wih a larger long-run price elasiciy of -1.39 1. Liu (2004), who considered a se of differen energy ypes and differeniaes residenial and indusrial secors, used he one-sep GMM esimaor o a PAM (Arellano and Bond, 1987), reporing a larger value (in absolue erms) of he shor-run gasoline price elasiciy and a comparaively lower income elasiciies in he residenial secor. More recenly, Pock (2010) repored very small shor-run effecs of price and income changes in gasoline consumpion (respecively -0.09 and 0.065), which would be probably due o he gradual and generalized swich o diesel cars. Alhough sudies on he demand for fuels in advanced counries are raher abundan in he economic lieraure 2, here is a limied number of papers for he Spanish case. Some auhors have esimaed he price and income elasiciies a he household level based on complee demand models: Labeaga and López-Nicolás (1997) used a flexible Almos Ideal Demand Sysem (AIDS), wih a special reamen o he problems of zero expendiure and unobserved heerogeneiy, and repored price and income elasiciies for gasoline of respecively -0.536 and 0.429. Labandeira and López-Nicolás (2002) followed he same mehodology for a differen ime spam and yielded -0.08 and 0.99 for respecively price and income elasiciies of car-fuel demand. A sudy of Labandeira e al. (2006), based on a modified form of he AIDS model (Quadraic-AIDS), esimaed price and income elasiciies of household energy goods and found ha he price elasiciy of car fuels ranged beween -0.11 and -0.058, while income elasiciy was beween 1.36 and 1.79. This conrased wih Romero-Jordan e al. (2010), who found larger carfuel price elasiciies, ranging beween -0.64 and -0.32, and income elasiciies (beween 0.92 and 1.45). Such observed differences may be due o he varying ime frames and o he differen mehodological approaches. Oher auhors followed a differen approach by using aggregae daa of peninsular Spanish regions. Danesin and Linares (2013) esimaed he aggregae price and income elasiciies for boh gasoline and diesel. Using a panel daa model based on a se of homogeneous esimaors, hey repored a shor-run gasoline price elasiciy in he (-0.93, -0.29) inerval, and a long-run price elasiciy of -0.69, wih gasoline income elasiciies found o be non-significan. For diesel, shor-run price elasiciy esimaes ranged beween -0.22 and -0.21, while shor-run income 1 These values correspond o he GLS-AR(1), which shows he bes performance. 2 Dahl and Serner (1991), Serner and Dahl (1992), Dahl (1995), Goodwin e al. (2004), de Jong and Gunn (2001), Graham and Glaiser (2002,2004) and Basso and Oum (2007) have provided surveys of he exising lieraure on fuel demand elasiciies. 5
elasiciy were found o be beween 0.35 and 0.46. González-Marrero e al. (2012) used he onesep sysem GMM along wih oher sandard esimaors o compue price and income elasiciies. They found ha one-sep sysem GMM esimaor performs beer han is counerpars, yielding shor and long run gasoline price elasiciies of respecively -0.292 and -0.69, wih non-significan esimaes for diesel and income elasiciies. 3. Mehodology 3.1. Economeric model In his paper we employ a PAM wih a saic represenaion of a long-run demand funcion in which (GAS/CAR)* is he desired fuel (gasoline or diesel) consumpion per vehicle, as in Balagi and Griffin (1983; 1997). Assuming a Cobb Douglas relaionship beween desired demand and is drivers, Equaion (1) defines he long-run demand curve as a funcion of he real price of he car fuel, MG, and a se of covariaes, real income per capia Y and cars per capia CAR. P P CPI To reduce he informaion bias, oal drivers are used insead of oal populaion. The long-run price elasiciy of demand is denoed as β, wihγ and δ as vecors of coefficiens ha describe he responsiveness of he long-run level of demand o he non-price covariaes, and α as a consan. N N GAS CAR * P = α P MG GDP β Y N γ δ CAR N (1) Following Houhakker e al. (1974), a funcion is inroduced o capure he limied capabiliy of consumers o adjus immediaely o he long-run equilibrium level of consumpion in response o a change in price, income and oher variables. Equaion (2) indicaes his limied capabiliy in he form of a parial-adjusmens consrain, designaed by a parameer θ ha capures he year-oyear ineria of habi persisence of fuel consumers, aking values beween zero and one. 6
GAS CAR GAS CAR GAS CAR = GAS CAR 1 1 * θ (2) Adding region and ime subscrips, and he oal flee per km of roads o conrol for he sauraion level of he road nework, he classical dynamic demand equaion for fuel per vehicle is expressed as GAS ln CAR + θϕ ln i, GAS = θ lnα + (1 θ )ln CAR ( SAT) + ui, i, i, 1 Y + θβ ln N i, + θγ ln P P MG CPI i, CAR + θδ ln N i, (3) ( wih u = µ + ε, i 1,..., N, = 1,..,, where denoes a region specific effec and ε is i, i i = T µ i i whie noise. A rend is included in he specificaion so ha echnical progress can differenly affec demand for fuel hrough ime. Under formulaion (3), he shor-run elasiciies of car-fuel demand per car wih respec o per capia income, real price, oal cars per driver and level of sauraion are respecively θβ, θγ, θδ and θϕ. The corresponding long-run responses are given by β, γ, δ and ϕ, wih (1 θ) as he speed of adjusmen o he long-run equilibrium. Noe ha he sock of cars hus eners boh as dependen and independen variable. Therefore, he shor and long run responses of car-fuel demand, relaive o changes in he sauraion level, are respecively (1+θδ) and (1+ δ). Given ha an imporan goal of his paper is o analyze consumer responses o price changes during he ime span 1999-2011, which includes he crisis period, he following empirical model is esed 3 GAS ln CAR i, CAR + θδ ln N GAS = θ lnα + (1 θ )ln CAR i, + θϕ ln i, 1 ( SAT) + θµ Trend + ui, i, + θβ ln Y N i, + ( θγ + λ D crisis )ln P P MG CPI i, (4) ( 3 In he empirical applicaion we also allow for differen income effecs due o he crisis. 7
where Dcrisis a dummy variable equal o 1 beween 2008 and 2011 and zero oherwise. Thus, he shor-run price elasiciy in he crisis period will be θγ + λ, where λ capures he effecs of he crisis period and is expeced o be negaive. In addiion, we included a rend ha proxies he echnological evoluion for gasoline and diesel vehicles. 3.2. Model esimaion To calculae he differen elasiciies of fuel demand we need o esimae Equaions (3) or (4), which is subjec o wo main mehodological challenges: unobserved heerogeneiy as a source of effecs on he explanaory variables, and he presence of a lagged dependen variable in a paneldaa conex. Regarding he former, simply regressing fuel consumpion on a se of regional explanaory variables may lead o biased esimaes unless all relevan variables can be observed. While some of hese conrol variables are periodically published by public eniies or saisical agencies, oher variables are unlikely o be easily accessed or recorded and hus researchers relying solely on observable variables make he assumpion of unconfoundness (Imbens and Woodridge, 2009). In doing so, hey acually produce incorrec measures of demand responsiveness of consumers and infer spurious policy effecs. Neverheless, under he assumpion ha he effecs of ime-invarian facors can be linearly separaed, he regional fixedeffecs will remove he bias induced by observed and unobserved heerogeneiy. A fixed-effec ransformaion such as ime-demeaning all he variables in Equaion (4) has he * form ~ y = ( 1 θ ) ~ y + β ~ x + ε, where ~ 1 y = y y and ~, wih as he vecor of i i x = x i xi xi he ime-varying righ-hand variables. The esimaor from his regression would be consisen only if he curren values of explanaory variables (real price, real income, flee per capia, and roads sauraion) are compleely independen of pas realizaions of he dependen variable (fuel consumpion), i.e., if E( ε is xi) = 0, s,. Obviously, he inclusion of lagged-dependen variables violaes he sric exogeneiy in his equaion. Therefore, he fixed-effecs esimaor is inconsisen and biased in dynamic models. The leas squares dummy variables (LSDV) or wihin-groups esimae is downward biased when considering model (4), and his bias would be especially severe when he auoregressive coefficien small (Nickell, 1981; Roodman 2006). ( 1 θ) 8 is high or he number of ime periods, T, is
To obain consisen esimaes, under he assumpion ha unobserved heerogeneiy exiss bu i is ime-invarian, Equaion (4) is esimaed using a dynamic generalized mehod of momens (GMM) esimaor 4. The basic esimaion procedure consiss of wo essenial seps: firs, he dynamic model in Equaion (4) is ransformed 5 o eliminae he unobserved effecs and subsequenly he model is esimaed using GMM, including lagged values of explanaory variables as insrumens for he curren explanaory variables. For hese insrumens o be valid, hey mus fulfill wo condiions: o provide a source of variaion of curren explanaory variables, and lagged values have o yield an exogenous source of variaion for curren fuel consumpion. In our empirical analysis we furher examine he validiy of he exogeneiy assumpions using a baery of ess. Under he assumpion of exogeneiy, he orhogonaliy condiions saisfy E ( ) E( y ) = 0 s 1 x i s ε i = i s ε i. However he procedure sill has several shorcomings: firs, differencing he equaions in levels may reduce he power of he ess by reducing he variaions in he explanaory variables; second, i has been argued ha variables in levels may be weak insrumens for firs differencing equaions (Arellano and Bover, 1995); and, finally, firsdifferencing may aggravae he effecs of measuremen errors on he dependen variable (Griliches and Hausman, 1986). Arellano and Bover (1995) and Blundell and Bond (1998) suggesed an improved version of he GMM esimaor by also including he equaions in levels in he esimaion procedure. The approach is based on a sysem of equaions ha includes equaions in boh levels and differences, and where he firs-differenced variables are used as insrumens for he equaions in levels. This gives rise o a sysem GMM esimaor ha hinges on esimaing he following sysem, yi y * * i = α + λ y Δ i Δy i 1 xi + β Δx 1 * i + ε i (5) Unforunaely, equaions in levels in his sysem sill conain unobserved heerogeneiy. To deal wih his issue we assume ha any correlaion beween explanaory variables and unobserved 4 This approach was inroduced by Holz-Eakin, Newey and Rosen (1988) and Arellano and Bond (1991), and furher developed in a series of papers including Arellano and Bover (1995) and Blundell and Bond (1998). 5 Tha is, any ransformaion ha permis o rule ou he unobserved componen. Alhough we will laer refer o firs differences, here are oher alernaives such as orhogonal deviaions (see Arellano and Bover, 1995). 9
heerogeneiy is consan over ime (one would suspec ha variables such as real income, flee, and sauraion are correlaed wih unobserved effecs ha characerize each region). In his seing, he sysem GMM esimaor under he exra se of orhogonaliy condiions 6 yields efficien esimaes while conrolling for ime-invarian unobserved heerogeneiy and he dynamic relaionship beween curren values of he explanaory variables and pas values of he dependen variable. Ye he insrumen proliferaion in he GMM sysem does no come wihou a cos. Roodman (2009) argued ha i migh bias he coefficien esimaes of he endogenous variables due o overfiing, diminish he power of he insrumen validiy ess, and produce downward-biased sandard errors. Windmeijer (2005) addressed he laer by a variance correcion for he wo-sep Blundell and Bond (1988) esimaor, which is also considered in his paper. In addiion, Roodman (2009) suggesed esing resuls for sensiiviy o reducions in he number of insrumens. 7 Anoher issue is ha he insrumenal variables are valid for large N, and lile is known abou heir performance in small sample sizes. Under he assumpion of sric exogeneiy of he explanaory variables oher han he lagged dependen variable, Kivie (1995) used an asympoic expansion echnique o correc he biased LSDV esimaor for samples where N is small. In anoher sudy based on Mone Carlo simulaions and deparing from he resuls of Kivie (1995), Judson and Owen (1999) showed ha correced LSDV (LSDVC) ouperformed he GMM approach in erms of bias and efficiency. Laer, Bruno (2005) exended his correcion version of LSDV o be applied in unbalanced panels. In a recen sudy conduced by Flannery and Hankins (2013), he performance of various esimaors on simulaed daases of shor panels in finance were compared and hey concluded ha Blundell-Bond and he bias-correced fixed effecs esimaors of Kivie (1995) had he bes performance. [ ( )] [ ( )] 1 6 E Δxi s µ i + ε = E Δyi s µ i + ε = 0 s 7 The STATA xabond2 command has he abiliy o specify, for GMM-syle insrumens, he limis on how many lags o be included. If T is fairly large (more han 7-8 periods), an unresriced se of lags will inroduce a huge number of insrumens, wih a possible loss of efficiency. 10
4. Empirical applicaion 4.1. Daa Our daase consiss of a panel of 16 Spanish adminisraive regions and covers he 1999-2011 period (annual daa) 8. Variables include fuel consumpion, disaggregaed in regional gasoline and diesel consumpion and obained from he annual repors of Naional Commission of Energy (CNE is acronym in Spanish); real gasoline and diesel prices, obained from he Spanish Minisry of Indusry; regional Spanish populaion, from he Naional Insiue for Saisics (INE is acronym in Spanish); number of gasoline and diesel cars (an imporan deerminan of he long-run evoluion of car raffic), sourced from he General Direcion of Traffic (DGT is acronym in Spanish); oal kilomeers of regional roads, obained from he Minisry of Infrasrucures (Fomeno in Spanish); and household disposable income and price index, boh obained from he INE. As already menioned, he effec of echnical progress on car-fuel consumpion is aken ino accoun by including a rend. 9 Figure 1 depics he evoluion of consumpion and real prices of boh gasoline and diesel beween 1999 and 2011. Since 1999 diesel demand has been seadily increasing, while demand for gasoline has deceleraed is growh since 2001. Indeed, due o a favorable ax regime, diesel now makes more han 80% of Spanish demand of car fuels and hus is in a near-sauraion sage. However, his growh has been sopped by he recen economic crisis, which srongly affeced boh gasoline and diesel demand. Spain annual diesel consumpion in 2011 was around 31 million lires, or 30% lower han i would have been if he pre-2007 rend in diesel consumpion annual growh of 5.6% had coninued. Similarly, Gasoline consumpion in 2011 was abou 6 million lires, or 26% lower han i would have been if he 2004-2006 annual growh of 1.2% had been mainained. In addiion, high unemploymen during he recen economic conracion has reduced disposable income and has srongly affeced car sales and hus he qualiy of he flee. For insance, he growh rae of diesel flee dropped from 10% beween 2004 and 2006 o 1.2% in 2011. 8 Ceua, Melilla and Canary Islands were excluded from his analysis as hey have a special ax regime ha may disor he resuls. 9 We are aware ha in a model esimaed in firs differences he firs difference of a rend is jus a consan. 11
Figure 1 shows ha gasoline and diesel price rends were broadly similar over he period 1999-2011. Real prices increased a a faser rae beween 1999 and 2000, bringing abou concern and demonsraions across Spain and Europe. Afer a price-decreasing inerval beween 2001 and 2003, prices wen up again wih sharp spikes brough abou by he oubreak of he crisis and a srong rebound a he end of he analyzed period. Figure 1. Gasoline and diesel real prices (Euros/lires) and annual consumpion (Million lires) in Spain (1999-2011) 100 90 30 25 20 15 80 70 60 50 10 5 0 99 00 01 02 03 04 05 06 07 08 09 10 11 RP_DIESEL GASO RP_GASO DIESEL Source: The auhors wih daa from Minisry of Indusry and CNE. As his sudy is based on panel daa analysis, i is imporan o assess he variaions of he variables over ime and across regions. Table 1A in he Appendix summarizes he exen of daa variaion, boh iner and inra regionally for he key variables: gasoline and diesel consumpion per car, gasoline and diesel prices per capia, income, cars per capia and road sauraion. For diesel, price and number of cars per capia, variaions were more pronounced wihin he same region across he years han beween regions, whereas he variaion of consumpion per car was more remarkable beween regions han wihin he same region. For gasoline, price and consumpion variaions were more inense wihin each region han beween regions, while he variaion of gasoline vehicles was more remarkable in per capia erms han wihin regions. 12
Finally, road sauraion variaion was predominanly beween regions, whis he variaion of real income was less pronounced wihin he region han beween regions. 4.2. Resuls Tables 1 and 2 repor he main resuls. We esimae he firs-order auoregressive model by OLS and LSDV as reference specificaions. The coefficiens of he lagged-dependen variable obained from hese wo esimaors provide he bound limis ha are a useful check on he resuls from a heoreically superior esimaor (Bond, 2002). In paricular, while he naïve OLS esimaor overesimaes he coefficien of he lagged dependen variable because regional fixed effecs are no accouned for 10, he LSDV esimaor produces a downward bias. The preceding ables only repor hree alernaives o he reference specificaions: he Anderson-Hsiao (HS), Arellano-Bond (AB) and Blundell-Bond (BB Full) esimaors. The BB Full version involves he use of he full insrumen se available in he daa 11 and, as explained earlier, he model conains a lag of he endogenous variable and several exogenous explanaory variables. We use as insrumens he dependen variable wih a lag of wo or more periods, also considering he resuls for a correced LSDV esimaor (Kivie, 1995; Bruno, 2005). Tables 1 and 2 also supply he heerosckedasiciy-consisen asympoic sandard errors in parenhesis, he -saisic for he linear resricion es under he null hypohesis of nonsignificance, and he Hansen es of he overidenifying resricions. This es is asympoically disribued as χ 2 under he null of no correlaion beween he insrumens and he error erm. Besides, he previous ables repor mi, which is a serial correlaion es of order i (i = 1, 2) using he residuals in firs differences, asympoically disribued as N(0, 1) under he null of no serial correlaion (see for deails Arellano and Bond, 1991). The ess presen no evidence of secondorder auocorrelaion a 5% significance level in he case of gasoline, alhough his is rejeced in he case of diesel 12. Based on he robus Hansen es, he overidenificaion resricions are valid 10 The Hausman es rejecs he null and concludes ha random effecs are no appropriae. 11 We have esimaed several alernaives of he BB model in which we resric he number of insrumens following he procedure oulined in Roodman (2009). They produce prey similar shor and long run elasiciies o he ones presened and discussed laer on. We have also esimaed he model using he LSDV esimaor proposed by Kivie (1995) and Bruno (2005). Alhough we do no presen hese resuls in he paper, hey are available upon reques. 12 We have ried wih insrumens wih lags of hree or more periods, allowing for he presence of measuremen error in he consumpion of diesel, alhough he value of he es for second-order auoregressive residuals fails. 13
a 5% significance level for boh diesel and gasoline 13. The F- and χ 2 - saisics rejec he null hypohesis ha esimaed parameers are joinly equal o zero in he proposed esimaors for diesel and gasoline. Table 1. Esimaes of he diesel dynamic demand model VARIABLES OLS LSDV AH AB BB_Full Lag of diesel consumpion/car 0.981*** 0.738*** 0.733*** 0.581*** 0.657*** (0.0146) (0.0744) (0.0753) (0.0801) (0.100) Trend -0.000597-0.00910** -0.00879** -0.0159*** -0.0158*** (0.00158) (0.00423) (0.00379) (0.0049) (0.00333) Diesel real price -0.106*** -0.0839** -0.0645** -0.0601** -0.0913*** (0.0308) (0.0299) (0.0322) (0.0303) (0.0292) CrisisXprice -0.00496*** -0.00517*** -0.00483*** -0.00488*** -0.00595*** (0.000845) (0.000959) (0.000852) -0.000983 (0.00105) Real Income 0.0489*** 0.298** 0.283*** 0.389*** 0.325** (0.0178) (0.114) (0.0891) (0.141) (0.150) Cars per driver 0.0141 0.0198-0.00471 0.0939 0.0679 (0.0198) (0.0649) (0.0563) (0.0868) (0.0742) Road sauraion -0.00609-0.144-0.153* -0.298*** -0.111** (0.00429) (0.103) (0.0885) (0.0968) (0.0385) Consan 0.359*** 0.599 0.583 1.261** 0.532* (0.133) (0.524) (0.454) (0.521) (0.259) T 12.73*** 8.75*** 4.58** 4.47** 10.91*** m1 m2-3.107*** -2.93-1.968** -1.74* Joinly zero coefficiens 2116*** 862*** 85054*** 3378*** 442*** Hansen 13.57ª 14.66 Observaions 192 192 176 176 192 R-squared 0.988 0.965 Number of ccaa 16 16 16 16 16 Number of insrumens 70 81 ª Hansen es is no compuable here, so we repor Sargan es insead. Noes: Sandard errors in parenheses; *** p<0.01, ** p<0.05, * p<0.1; BB_Full is Blundell and Bond esimaor considering all he insrumens; Hausman es is 17.98, so he random effecs hypohesis is rejeced a a 1% level of significance. Source: he auhors. 13 Since he ess for he validiy of he insrumens do no rejec he null here migh be a misspecificaion problem, even hough we expec ha i does no affec he elasiciy figures. 14
The resuls indicae ha he ime-rend esimaes have he righ sign and are saisically significan for diesel bu no for gasoline. I suggess ha echnological advances in diesel engines reduced vehicle fuel consumpion by 1% per year, which demonsraes he progressive increase in energy efficiency in diesel echnologies during he las few years. Regarding he influence of he crisis on he price elasiciy of car-fuel demand, our resuls suppor he idea ha consumers are more price-responsive a imes of crisis. To reach his conclusion, we perform he null hypohesis of linear resricion H0: θγ + λ = 0 o check wheher he coefficien θγ + λ is saisically differen from zero 14. Recall ha during he crisis period, where he dummy variable akes he value of 1, he elasiciy coefficien is θγ + λ. The null hypohesis is rejeced for all he esimaors, supporing he above-menioned finding. The resuls indicae ha he shor-run price elasiciy of diesel during he crisis period is beween -0.097 and -0.065, wich is 0.005 bigger han during he non-crisis period. Similarly, he shor-run price elasiciy of gasoline demand is shown o be 0.01 larger, in absolue value, during he crisis period. The price elasiciies obained in his sudy are broadly in line wih hose repored by he academic lieraure. Our diesel resuls are similar o hose found by Labandeira el al. (2006), bu lower han he repored by Danesin and Linares (2013). The differences wih he laer are probably due o heir shorer period of analysis and o heir consideraion of wo ypes of diesel (95 and 97 ocanes) insead of one (diesel 95). However, he size of our gasoline elasiciies depend on he mehod used o esimae he model, which deserves furher explanaion. In his sense, he esimae of he lagged dependen variable in he AH model, 0.641, is higher han ha yielded by LSDV esimaor, while he esimaed shor-run price and income elasiciies are higher. In addiion, he resuling long-run price and income elasiciies from he AH model are higher han he ones corresponding o LSDV. Looking a he BB-version esimaors, collapsing and conrolling he number of lags used as insrumens did no improve he gasoline resuls. Indeed, he dynamic coefficiens fall ouside he limis and he AR(2) es yields a value of 2.38, which means ha he null hypohesis of no second-order correlaion is rejeced a 5% significance level. This correlaion in urn affecs he validiy of he insrumens ha is also corroboraed wih he Hansen-es. Therefore, in his conex i is appropriae o use LSDVC, which has proved o be suiable o 14 In order o allow for non-linear effecs of he crisis we have also esed he ineracion wih he squared erm of he price, bu he resuls are non-significan. 15
correc he bias in he LSDV esimaor and in he case of small samples where he GMM esimaor lacks efficiency 15. Table 2. Esimaes of he gasoline dynamic demand model VARIABLES OLS LSDV AH AB BB_Full Lag of gasoline 0.760*** 0.620*** 0.641*** 0.683*** 0.631*** consumpion/car (0.0299) (0.0412) (0.0671) (0.0989) (0.0459) Trend -0.00976*** -0.00377-0.00731-0.0131-0.000507 (0.00283) (0.00467) (0.00524) (0.0146) (0.00279) Gasoline real Price -0.0364-0.107* -0.0622-0.136** -0.149*** (0.0589) (0.0611) (0.0671) (0.0503) (0.0338) CrisisXprice -0.00379-0.0104*** -0.00805** -0.0116** -0.0123*** (0.00333) (0.00356) (0.00400) (0.00414) (0.00253) Real Income 0.0285 0.300*** 0.263** 0.161 0.0548* (0.0194) (0.0814) (0.107) (0.369) (0.0270) Cars per driver -0.00654 0.00720-0.0465-0.542-0.00620 (0.0174) (0.0956) (0.107) (0.343) (0.0172) Road sauraion -0.0249*** -0.0495-0.0656-0.174-0.0365*** (0.00528) (0.0835) (0.0943) (0.292) (0.00633) Consan 0.0916-0.283-0.297 0.398** (0.248) (0.459) (0.580) (0.171) T 0.45 3.56* 1.05 8.31** 8.87*** m1-3.02*** -3.05*** m2 1.89* 1.75* Joinly zero coefficiens 297*** 176*** 99042*** 95*** 390*** Hansen 13.56 15.86 Observaions 192 192 176 176 192 R-squared 0.919 0.880 Number of regions 16 16 16 16 Number of insrumens 49 83 Noe: The Hausman es is 11.57, herefore he random effecs hypohesis is rejeced a a 5% level of significance. Source: The auhors. The resuls on price effecs repored by Table 3 indicae, as expeced, ha price elasiciy is inelasic in he shor-run and more elasic in he long run. The resuls are, o some degree, smaller han hose surveyed by Graham and Glaiser (2002) and Basso and Oum (2007), which fall beween -0.2 and -0.3 for he shor erm and beween -0.6 and -0.8 for he long erm. 15 Though Alvarez and Arellano (2003) sae ha he consisency of he GMM esimaor should no be a problem when T/N ends o c, wih 0<c<=2, a condiion ha holds in our analysis. 16
However, hey are in line wih hose repored by Pock (2010) for he EU conries (shor-run: -0.106; long-run: -0.408), and Balagi e al. (2003) for French regions: -0.093 and -0.329 for respecively shor and long run price elasiciies. Our shor-run price elasiciies is also similar o hose repored by Balagi and Griffin (1997) for 18 OECD counries, including Spain. Ye he sudies specifically conduced for Spain have repored slighly higher values of boh shor and long run price elasiciies (González Marrero e al., 2012; Danesin and Linares, 2013). Table 3. Shor and long run elasiciies of diesel and gasoline demand OLS LSDV AH AB BB_Full Diesel shor-run (crisis period) -0.111-0.088-0.069-0.065-0.097 shor-run (non-crisis period) -0.106-0.083-0.064-0.06-0.091 Long-run (crisis period) -5.837-0.336-0.258-0.155-0.283 Long-run (non-crisis period) -5.579-0.317-0.240-0.143-0.265 Gasoline shor-run (crisis period) -0.039-0.117-0.070-0.147-0.161 shor-run (non-crisis period) -0.036-0.107-0.062-0.136-0.149 Long-run (crisis period) -0.163-0.308-0.195-0.464-0.436 Long-run (non-crisis period) -0.150-0.282-0.173-0.429-0.404 Source: The auhors. To furher explore he effec of he crisis on car-fuel demand responsiveness we performed a esimaion ha, by ineracing income wih a dummy ha conrols for he crisis period, evaluaed wheher he crisis affeced he relaionship beween income and consumpion of car fuels. The resuls show ha he shor and long-run income elasiciies of diesel are no significan when esimaed wih BB version esimaors, while he LSDVC esimaor yields a saisically significan value of 0.16 for he income esimae. However, he coefficiens of he ineracion erm show ha he income elasiciy of diesel demand is lower afer 2008, indicaing ha consumers would have reduced heir consumpion a 1% in response o a hypoheical increase in heir income during his period. Income esimaes have he correc sign and are saisically significan for gasoline, alhough wih differen significance level and magniude. The esimaed values of he ineracion erm coefficien are highly significan and consisen, showing ha a heoreical increase in income would cause 2% more of gasoline consumpion before he crisis han afer. 17
Moreover, he speed of adjusmen values esimaed from he esimaors are close and belong o he bound limis 16, which means ha boh diesel and gasoline consumpion adjus owards heir long-run equilibrium levels a a relaively slow rae, wih abou 35% of he adjusmen occurring wihin he firs year. This resul is in line wih he findings of Danesin and Linares (2013), who sugges four years for long-run equilibrium o be resored. As expeced, he resuls also demonsrae ha road sauraion negaively affecs car-fuel consumpion, showing a negaive correlaion beween road congesion and mobiliy. However, he respecive magniudes of road sauraion coefficiens, hough no significan for gasoline, reveal a non-negligible effec on diesel consumpion per car. Neverheless, i mus be noed ha any impac assessmen of road improvemen on mobiliy and fuel consumpion would require addiional variables, such as vehicle-miles ravelled, and a raher differen approach ha is beyond he scope of his paper. 5. Conclusions In his paper we have presened he resuls from various specificaions of a dynamic demand model for gasoline and diesel (for car use) esimaed on Spanish regional daa for 1999 o 2011. The paper showed ha, afer he oubreak of he economic crisis, price and income changes have had an addiional effec on car-fuel demand in Spain. Pu in oher words, consumer response was found o be more elasic during he 2008-2011 recessive period han in he years before he crisis. A consisen finding across he differen esimaors employed in he analysis is ha he diesel (gasoline) price elasiciy is 0.005 (0.01) larger wih respec o he pre-crisis levels. Besides, esimaed income elasiciies for diesel and gasoline were respecively 1% and 2% lower during he crisis han in he preceding (pre-crisis) years. The paper hus suggess ha he significan reducion of car-fuel consumpion and he concomian fall in sales and ax revenues, seen in Spain during he crisis, were parly due o changed values of price and income elasiciies. I is clear ha he behavior of Spanish car-fuel demand afer he oubreak of he crisis responded boh o soaring fuel prices and o srong economic difficulies for households (wage reducions, unemploymen, ec.) and firms (a shrinking 16 The values yielded by diesel models are ouside he bound limis deermined by he OLS and LSDV esimaors, alhough hey generally remain close. 18
inernal demand). However, our resuls indicae ha hese effecs were exacerbaed by a modificaion of he demand elasiciies. This indicaes ha he use of pre-crisis elasiciies o anicipae he effecs of changes (associaed or no o public policies) would provide inaccurae resuls, as can be easily esed for he Spanish case wih he pre-crisis exising (ex-ane) empirical evidence and real price, income and consumpion daa. In addiion, he larger (relaive o diesel) change of gasoline elasiciies observed by his paper suggess ha privae rips have been adjused wih more inensiy during he crisis, as diesel cars are also used wih commercial and indusrial purposes. Moreover, he quaniaive and qualiaive changes seen in he sock of vehicles afer he oubreak of he crisis provide an indicaion of he uncerainies and difficulies faced by Spanish households, wih imporan environmenal and energy implicaions. In view of all he preceding, our findings sress he imporance of accurae panel daa esimaion wih a special emphasis on he reamen of insrumen proliferaion problem in he GMM esimaor and bias correcion in he LSDV esimaor. We feel ha our empirical approach provides updaed, robus and reasonable price and income elasiciies of carfuel demand in Spain, in line wih hose obained by, among ohers, Balagi and Griffin (1997), Balagi e al. (2003) and Pock (2010) for differen developed counries. In a momen of pressing disribuional consrains and imporan changes in he Spanish energy and ax domains, largely relaed o he severe and pervasive economic crisis hemselves, i is paricularly imporan crucial o have accurae esimaes of he responsiveness of demand o price and income changes. Indeed, our findings sugges ha sraegies and policies relaed o car-fuel consumpion need o be fully informed so ha adapaion o a shifing socio-economic conex can proceed in a swif, cos-efficien and equiable manner. This general message, ogeher wih he findings ha can be derived from he deph and persisence of he Spanish crisis, make he paper ineresing and useful for a wide inernaional audience. 19