The Diffusion of Unleaded Petrol: An Anglo-Italian Comparison

The Diffusion of Unleaded Petrol:An Anglo-Italian Comparison

Giuliana Battisti – Paul Stoneman

Abstract. This paper explores and contrasts the factors contributing to thedifferences between Italy and the UK in the diffusion of unleaded petrol. Althoughsubject to similar regulatory environments the countries exhibit very differentpatterns in the use of unleaded fuel. It is shown that differences in the compositionof the car stock, different price incentives and different patterns of changes in tasteare the main contributory factors to this, with changes in taste being the dominantfactor.

1. Introduction

The prime objective of this paper is to undertake a comparativestudy of the diffusion of unleaded fuel in two countries, Italy andthe UK.1 Our own data indicate that at the end of 1995 throughoutall European countries the average market share of unleaded petrolwas 60 percent. In Italy it was 40 percent and in the UK 62 percent(May 1995). However, since the mid-1980s all European countrieshave been subject to a common set of regulations (agreed inBrussels) and as such the factors that have determined these marketshares must be other than the regulatory environment. Onepossibility is that differentials between the price of leaded andunleaded fuels have been different in the two countries.Alternatively consumer attitudes could differ across countries.Finally the different diffusion patterns could be the result of thedifferent availability of the two types of fuel. In this paper weexplore and compare the relative importance of these factors in the

Giuliana Battisti – Paul Stoneman, Warwick Business School Research Bureau,University of Warwick, Coventry CV4 7AL, UK. email: [email protected].

Received on June 12, 1996 and approved by the Editorial Board on November 6,1997.

LABOUR 12 (2) 255–278 (1998) JEL 03© Fondazione Giacomo Brodolini and Blackwell Publishers Ltd 1998, 108 Cowley Road, Oxford OX4 1JF, UK and350 Main Street, Malden, MA 02148, USA.

diffusion process in Italy and the UK, using economic andeconometric methodologies.

The role of consumer attitudes is of particular interest to us.Public opinion polls and other surveys (e.g. INRA, 1991, 1992)suggest that environmental consciousness among the Europeanpublic is both extensive and has grown in recent years. This wouldlead one to expect that environmentally friendly, or green, consumerproducts would be experiencing growing market shares. However, ina companion paper (Wong, Turner and Stoneman, 1995), that usescase study methodologies to look at the opinions of the suppliers ofgreen consumer produces based upon their experience of the UKmarket, we are led to conclude that in fact consumers are unwillingto trade off greenness against other product performance aspects oragainst price, and that as a result apparent green consciousness hasnot been reflected in any major changes in the patterns of consumerexpenditure. In Stoneman and Battisti (1996) we explored this resultfurther using economic and econometric methodologies. In thispaper we extend this later work to explore whether such findingsalso extend to the Italian case and draw comparisons with the UK.

In the next section we detail the changes in the market forunleaded fuel in Italy and the UK. In Section 3 we discuss theorigins and properties of the model that is estimated in Section 4. InSection 5 the estimates are discussed, and conclusions are drawn inSection 6.

2. The market for unleaded fuel in Italy and in the UK

Unleaded petrol (specification RON 95) was launched in the UKin June 1986 by Esso but had a minimal market share through tomid-1987. The market share then began to increase, reaching 25percent by mid-1989, 50 percent by early 1993 and 62 percent byMay 1995 (our last observation). In Italy unleaded petrol wasavailable from mid-1985 at 40 motorway petrol stations serving theGerman tourist market but only began to become widely availablefrom January 1988 (when the market share was still only 1%). Themarket share increased to 17% by January 1992 and 40% by the endof 1995. Market shares are plotted in Figure 1.

An analysis of the availability of unleaded fuel as indicated by thenumber of outlets selling such fuel relative to the market share ofsuch fuel has led us to conclude that the diffusion process has neverbeen constrained by availability (see Stoneman and Battisti, 1996).

© Fondazione Giacomo Brodolini and Blackwell Publishers Ltd 1998.

256 Giuliana Battisti – Paul Stoneman

Availability enabled the diffusion process but did not constrain it.We therefore do not discuss this issue any further in the paper.Similarly in our earlier study of the UK we found that the launch ofthe higher octane super unleaded fuel did not affect the diffusionprocess. We thus do not discuss that further here.

Figure 2 details the time profile of the price differential betweenleaded and unleaded fuel (the log of the ratio of the price ofunleaded to leaded fuel) for the two countries. These pricedifferentials are primarily the result of the different fiscal stances ofthe two governments with respect to the two fuels (see AppendixA).2 In Italy, where prices were state controlled until September1991, the price differential between the two fuels has been one ofthe lowest in Europe.3 In fact until October 1989 unleaded fuel wasmore expensive than leaded fuel, suggesting a less than favourablepolicy attitude towards unleaded petrol. After that date the fiscalincentive has been positive but small (the indicator ratio has neverbeen lower than µ0.07) and became smaller post 1995.

Figure 1. Car stock composition and unleaded market share in Italy andUK

*Italy (continuous lines), UK (marked lines).

Unleaded Petrol in Italy and the UK 257


A major factor in the diffusion of unleaded petrol is thecomposition of the car stock. In order to use unleaded petrol a car(and it is primarily cars rather than commercial vehicles that usepetrol, the latter primarily use diesel) must be technologicallycapable of doing so (not all cars currently can use unleaded petrol).Cars with catalytic converters can only use unleaded petrol (leadedpetrol destroys the converter). At any point in time there will bethree types of cars in the car stock: those that can only use unleadedfuel (cars with catalytic converters), labelled MUST; those that canonly use leaded fuel (which tend to be the older cars), labelledLEAD; and those that may use either leaded or unleaded fuel,labelled MAY. In Figure 1 the car stock composition is plotted overtime for the two countries. The observed diffusion process ofunleaded fuel will be the result of the changing composition of thiscar stock and the choices made by the owners of cars in the thirdcategory.

However, changes in the car stock alone do not provide a fullexplanation of changes in the share of unleaded fuel. Comparing the

Figure 2. Prices of leaded and unleaded fuel: UK and Italy (ln P1µln P2)

* Italy (continuous lines), UK (marked lines).



UK to Italy the patterns of car stock composition do not differ verymuch (Figure 1) but the corresponding market shares of unleadedfuel grow at very different rates (the data indicates that relative tothe UK Italian consumers show considerable reluctance to purchaseunleaded fuel). Thus both consumer choice and changes in thecomposition of the car stock are important.

Changes in the composition of the car stock can come about intwo ways: (i) a new car can be purchased with the capability (orrequirement) of using unleaded fuel or (ii) a car can be adapted i.e.converted to use unleaded fuel. EC regulations required that from1989 all new cars sold must be capable of using unleaded fuel andfrom January 1993 all new cars must be fitted with catalyticconverters. These regulations will have had a considerable impactupon the composition of the car stock; for example, in Italy theproportion of cars with catalytic converters in 1995 was 22.4 percentand in the UK it was 34 percent.

Although it appears reasonable to argue that consumers wouldnot buy a new car solely to be able to use unleaded fuel,conversions, which may be reasonably cheap, could be undertaken inorder to use such fuel. Unfortunately we were not able to obtain anydata upon such conversions. We are therefore unable to preciselymodel the determination of the composition of the car stock. Wethus concentrate below upon the consumers choice of fuel given thecomposition of the stock. This is equivalent to modelling thebehaviour of the owners of only those cars that are capable of usingeither fuel. As we have stated above, however, their choices are amajor driving force in the developments of market shares.4 It is tothis modelling that we now turn.

3. Modelling consumer diffusion

The original version of the empirical model and the empiricalprocedure used in this paper were developed in the earlier paperthat explored the UK case in detail (Stoneman and Battisti, 1996).Using a variant of the linear expenditure system as suggested byDeaton and Muellbauer (1980), we derived a predicted diffusioncurve with a standard logistic form (1).

M(t)\C(t)+a·D(t)·f(t)

\C(t)\a·D(t)·(1+exp(c+b(ln P1µln P2)+F(T)µ1 (1)



where T is the number of months from the first launch of unleadedpetrol in the country under analysis; M(t) is the market share ofunleaded petrol in total petrol sales in time t; C(t) is the proportionof the total driven petrol driven car stock with catalytic converters attime t. D(t) is the proportion of the total driven petrol driven carstock that can, but does not have to, use unleaded petrol in time t;f(t) is the probability that an owner of a car that can but does nothave to use unleaded fuel will use unleaded fuel in time t; P1(t) isthe price of unleaded fuel in time t; P2(t) is the price of leaded fuelin time t; a is the ratio of fuel purchased per car without catalyticconverter relative to cars with a catalytic converter;5 F(T) is avariable reflecting changes over time in buyer preferences for nonleaded versus leaded fuel, actual differences between theperformance of leaded and non leaded fuel, learning effects withrespect to performances characteristics of unleaded fuel and theutility gains that derive from learning (see Stoneman and Battisti,1996).

The composite term F(t) may or may not be monotonic and isthus assumed to take the general form (2)

F(T)\a+b1T\b2 ln T (2)

We have also experimented with other forms for F(T).It is convenient to work with the linearised version of equation (1)

corresponding to the following transformation6 (see Stoneman andBattisti, 1996). Defining

W(t)\(M(t)µC(t)/D(t)) (3)

and

X(t)\(aµW(t))/W(t) (4)

one may rewrite equation (1) as (5)

ln X(t)\c+b(ln P1µP2)+F(t) (5)

where X(t) may be defined as the ratio of the probability (for anowner of a car that can use either fuel) of using leaded fuel relativeto the probability of using unleaded fuel i.e. (1µf(t))/f(t). Onemay note that if such an owner is more likely to use unleaded fuelthen ln X(t)s0. An increasing tendency to use unleaded fuel wouldimply that d ln X(t)dts0.

The logistic curve is reasonably common in the diffusion literature(see Stoneman and Karshenas, 1995 for a survey), but often whenestimated one finds that either the price or the time term are



significant but rarely both. In the UK paper discussed above we havefound that by setting up the model as an Error Correction Model(ECM) we are able to overcome the kind of problems (e.g.multicollinearity and autocorrelation) that beset such models andthus in this paper we concentrate upon the ECM approach. Thisapproach also enables one to differentiate between long-term andshort-term impacts upon market shares.

The ECM procedure relies mainly on the Granger RepresentationTheorem (Granger (1986) and Granger and Waiss (1983)), statingthat if a set of variables are co-integreated the ECM is a validrepresentation of their relationship and vice-versa. We proceed byfirst establishing the order of integreation and the time seriesproperties of the relevant variables and then specify theUnrestricted ECM specification. We then apply the Johansen MLco-integration procedure, as a further test for the presence of anylong-run equilibrium relationship.

If the relevant variables are integrated of order 1, i.e. are I(1), theECM is specified in terms of levels and first differences. From (2)and (5) we obtain (6)

ln X(t)\b(ln P1(t)µln P2(t))+a+b1T+b2 ln T+u(t) (6)

and thus

D ln X(t)\bD(ln P1(t)µln P2(t))+b1+b2D ln T+u(t) (7)

where D is the first difference operator. We then specify the ECMas

D ln X(t)\bsD(ln P1(t)µln P2(t))+b2sD ln T+b1

s

µm[ln X(tµ1)µb1L(ln P1(tµ1)µln P2(tµ1))

µaLµb1L(Tµ1)µb2

L ln (Tµ1)]\v(t) (8)

where the square bracketed term is the co-integrating equation andS and L, superscripts are used to differentiate between short/long-run coefficients.

If the relevant variables are I(2) the residual might itself bestationary or the variables may be cointegrated only in their firstdifferences (see S. Johansen, 1992) and a modified ECM isnecessary that includes not only levels and first differences but alsosecond differences. This leads to the formulation:

DD ln X(t)\bsDD(ln P1(t)µln P2(t))+b2sDD ln T+b1

s

µn[D ln X(tµ1)µbLD(ln P1(tµ1)µln P2(tµ1))



µb1Lµb2

LD ln (Tµ1)]

µm[ln X(tµ2)

µbLp(ln P1(tµ2)µln P2(tµ2))µaµb1Lp(Tµ2)

µb2Lp ln (Tµ2)]+v(t) (9)

or alternatively

DD ln X(t)\bsDD(ln P1(t)µln P2(t))+b2sDD ln T+b1

s

µmD ln X(tµ1)µbLD(ln P1(tµ1)µln P2(tµ1))

µb1Lµb2

LD ln(Tµ1)]+v(t) (10)

The Johansen ML procedure testing for the presence ofco-integration is a good pre-test to establish which one of the twospecifications ((9) or (10)) is to be preferred.

The Johansen procedure (see Johansen and Juselious, 1990) isoften used to avoid problems generated by OLS when more thantwo variables are included in the ECM model. Whereas OLSestimates can be the result of a linear combination of more than onesolution (see Banerlee, Dolado, Galbraith and Hendry, 1993) andthus the estimates of the adjustment process might be not unique,the Johansen procedure enables one to test which of the possiblesolutions is the most representative of the underlying adjustmentprocess. The corresponding co-integrating vector yields parameterestimates in the long-run, while the adjustment vector indicates thedegree of exogeneity of the variables. This relationship will hold aslong as the residual of the co-integrating regression is stationary.7

4. Results of the estimation

The sources for the data used in the estimation process8 aredetailed in Appendix A. The data is monthly and for the UK coversthe period from January 1987–May 1995, for Italy the period fromJanuary 1988–September 1995.

Preliminary time series analysis shows that for Italy, apart from alinear trend, all the variables of interest are stationary in their firstdifference i.e. are I(1), while for the UK they are stationary in theirsecond difference i.e. are I(2). Details of this analysis are presentedin Appendix B. For Italy all variables grow at a low and almost



constant rate and the sytem seems to move very slowly, indicatingthat the point of inflexion on the diffusion curve has not yet beenreached or that the logistic curve is very flat. In the UK case thevariables grow at a much faster and also changing rate. This isparticularly true for market share at the beginning of the diffusionprocess (see Figure 1).

The estimating equation still contains some inherent non linearityin that a has yet to be determined. We proceed by generatingestimates for different values of a and then picking that value for athat produces the ‘‘best’’ results. For both countries we estimate hata\0.9 although different values for a change the results very little.

OLS estimates of the ECM model (a assumed equal to 0.90) withan unrestricted autoregressive vector and the error correction termspecified at time tµ1, are presented in Table 1 columns 2 and 3 forthe UK and Italy respectively. In general the estimates indicate thatthe determinants of the diffusion process exert a significant impacton the pattern of consumer purchases and that the impacts aredifferent in the short and long-run. Ad hoc tests confirm that theresiduals do not show any unexplained systematic variability i.e. arestationary. The other diagnostic statistics are good, all variables aresignificant and the forecasting performance of the model over thelast 16 months is good (Chow-test: CUK(16)\23.45 andCI(16)\5.73).

The ECM model for the UK is formulated in second differenceswith variables conintegrated at first differences (see Stoneman andBattisti, 1996, for the derivation and estimation procedure). FromTable 1 the ECM estimates yield that the long-run relationshipbetween D ln X(t) and prices is given by 0.82*D ln(P1 /P2) andbetween D ln X(t) and F(T) by 12*D ln T+0.11. The short runrelationships with DD lnX(t) are 0.52*DD ln(P1 /P2) andµ104.5*DD lnT.

The ML long-run co-integrating relationship for the UK is:

D ln X\0.14µ12.97*D ln T+0.718*D ln(P1 /P2)(0.875) (µ0.157Eµ4) (0.0689)

where the corresponding adjustment speeds are in brackets.Johansen and Juselius (1990) state that it is natural to give theseestimates ‘‘an economic meaning in terms of the average speed ofadjustment towards the equilibrium state, such that a low coefficientindicates slow adjustment and a high coefficient indicates rapidadjustment’’. The 0.875 estimate for D ln X(t) is close to unityindicating that on average the adjustment towards the equilibrium



Table 1. Estimates of the unrestricted ECM model and the MLco-integrating vector

Country UK UK Country Italy ItalyModel (10) Model (8)

Estimation Unrestricted Co-integrating Estimation Unrestricted Co-integratingmethod ECM a vector b method ECMa vectorb

Dependent DD log X(t) DD log X(t) Dependent D log X(t) D log X(t)variable variable

a 0.90 0.90 a 0.90 0.90

Intercept 0.11 0.14 Intercept µ7.2299 µ8.2042(2.51) (—) (µ3.6079) (—)

DD log (p1/p2) 0.52 D log (p11/p2)tµ1 3.8660(8.55) (2.5902)

DD log t µ104.5 D log t 16.2263(µ3.31) (2.863)

D log (p1/p2)tµ1 0.695 0.718 log (p1/p2)tµ1 2.5439 4.8742(3.18) (0.0689) (2.1318) (0.0051)

D log (tµ1) 10.2 µ12.97 log (tµ1) 1.8307 1.8918(3.60) (µ0.1577Eµ4) (3.4015) (µ0.335Eµ3)

— — — timetµ1 µ0.01245 —(µ2.1406)

D log Xtµ1 µ0.85 µ1 log Xtµ1 µ0.374 µ1(µ8.55) (µ0.875) (µ4.7745) (µ0.7340)

Dummy — — Dummy 0.2793 0.659(3.2077) (µ0.0215)

Long-run Long-runelasticity elasticity

Price 0.82 0.718 Price 6.80 4.8742

ln TIME µ12 µ12.97 ln TIME 4.95 1.8918

time — — time µ0.03329 —

intercept 0.11 0.14 intercept µ19.33 µ8.2042

R2 0.48 — R2 0.37 —

F (7,78) 17.42 F (7, 78) 6.5407)

s.e. 0.155 s.e. 0.1279

DW 2.096 DW 2.088

Chow-test:x2 (16) 23.445 — x2 (16) 5.7327 —F (16, 77) 1.4653 — F (16, 62) 0.35829 —

a t-ratios are in brackets.b Speed of adjustment of each element of the co-integrating vector are in brackets.



level is fast. However the adjustment to price change is much fasterthan the adjustment to changes in F(T), the latter showing a veryclose rate of adjusment. The adjusment speed estimates confirm thatprices and tastes are weakly exogenous.

The diagnostic statistics for these estimates are good. ML testslead to the rejection of the hypothesis of both first and second orderautocorrelation of the residuals. Traditional tests for co-integrationhave also been applied: CRFW\0.79, DF\µ5.773 andADF\µ4.8846. Comparing those values with those tabulated byEngle and Yoo (1987) and Sargan and Bhargava (1983) (at the fivepercent significance level, 0.48, µ3.93 and µ3.75 respectively) wecan reject the hypothesis of no co-integration and thus via theGranger representation Theorem (Granger 1981) we cannot rejectthe hypothesis that the ECM is a valid representation of therelationship.

For Italy9 we estimate that the long-run relationship betweenln X(t) and prices is given by 6.80*ln(P1 /P2) and between ln X(t)and F(T) by 4.95*ln Tµ0.033*Tµ19.33. The short-run relationshipswith D ln X(t) are 3.866*D ln(P1 /P2) and 16.22*D ln T. TheJohansen co-integration test gives very similar results. This approachalso generates an estimate of the long-run intercept which is veryuseful because the ECM constant incorporates the coefficient on thefirst difference of the time variable. The estimated co-integratingregression is:

ln X\µ8.204+1.8918*ln T+4.742*ln(P1 /P2)(0.374) (µ0.334Eµ3) (0.0051)

The values in brackets are the adjustment speeds of the variableswithin the system. All the independent variables are weaklyexogenous but the speed of adjustment of the buying decision todeviations from the equilibrium in the previous period is very low(0.374), confirming that the underlying long-run path is not verysensitive to exogenous changes. In the long-run the speed ofadjustment to the price differential is higher than that with respectto F(t). However it is worth noting that in Italy price incentives forthe use of unleaded fuel have been very small and thus despite thesedifferent adjustment speeds the influence of the price differential onmarket share has been small.

We have also experimented with specifications of the ECM modelincluding a quadratic term in time but the results do not change verymuch (and are thus not reported). This does however confirm therobustness of the model.



5. Discussion

The results presented above for the two countries are not directlycomparable. In the Italian case variables are I(1) while for the UKthey are I(2). This means that for Italy changes in the independentvariables impact on levels in the long-run and on the growth rate inthe short run whereas for the UK they impact upon the growth ratein the long-run and the change in the growth rate (the rate ofacceleration) in the short run. The UK specification indicates a verydynamic diffusion process whereas the Italian specification suggeststhat the diffusion process has never experienced any acceleration inthe consumption pattern.

The objectives of this paper have been stated as being to explorethe relative importance of the following factors in the determinationof the spread of unleaded fuel: changes in consumer tastes; supplyside factors i.e. unleaded availability; government regulation; andother government intervention i.e. fiscal incentives. As stated above,in our view the availability of unleaded petrol was not a constrainton the diffusion process — availability played an enabling role.

Also, as stated above, we are unable to precisely model thedeterminants of the composition of the car stock and as such we areunable to measure the impact of regulation. We are able however toexplore the relative importance of fiscal incentives and changes intaste (etc.) in the diffusion of unleaded fuel. We do this by using theestimates produced in the previous section.

The co-integrating regressions of the previous section arerespectively for Italy

ln X(t)\µ8.204+1.8918*ln T+4.742*ln(P1 /P2)

and for the UK

D ln X(t)\0.14µ12.97*D ln T+0.718*D ln(P1 /P2)

As stated above, we may interpret X(t) as the ratio of theprobability of using leaded petrol to the probability of usingunleaded petrol for a consumer with a vehicle that may (but doesnot have to) use unleaded petrol. Thus D ln X(t) is the rate ofgrowth of this probability, which we expect to be negative with theprobability of using unleaded petrol increasing over time.

Using the ML Johansen estimates, three curves are plotted inFigure 3 for the UK. The first is the growth rate of the probabilityof using leaded relative to using unleaded petrol (D ln X(t), the



second measures the contribution of changes in the long-run trendterm (Taste) to this, and the third measures the contribution ofchanges in the price difference term (Price) to this. In Figure 4,referring to Italy, the three curves relate to the probability of usingleaded relative to the probability of using unleaded petrol (ln X(t),the contribution of the long-run trend term (Taste) to this, and thecontribution of the price difference term (Price) to this. Despite thedifference in the integration order of the variables across the twocountries some comparisons can be made on the impact of thedeterminants of the diffusion of unleaded petrol from these figures.

For the UK case we note first that D ln X(t) is negative for thelonger part of the sample period (although towards the end of thesample there are some positive peaks) indicating that drivers whohave had the choice have shown an increasing probability of buyingunleaded fuel. In the early period D ln X(t) is generally a largenegative showing a fast rate of growth of the probability of usingunleaded fuel (relative to the probability of using leaded). In thisearly period the change in the trend (taste term) also takes largenegative values, approaching the other curves from below. Until1994 quantitatively it has much greater impact than changes in the

Figure 3. Long-run relationships (Johansen Procedure): UK



price difference. As the diffusion proceeds the price and trendimpacts become much more equal in size and by the end of thesample period the change in price is having a slightly greater impactthan the trend. In fact after 1994 the former becomes slightlypositive and the unleaded market share becomes higher than theproportion of cars that may use either kind of petrol (see Figure 1).This in turn is the result of the 1993 regulation requiring new cars tobe fitted with catalytic converters which has led to a progressivereduction in the number of motorists able to choose which fuel touse. As choice applies to fewer motorists the impact of their choiceson the overall market share is obviously less important.

For Italy the ML co-integrating vector is plotted in Figure 4. Theprobability of using leaded over the probability of using unleadedpetrol (ln X(t)) is negative for the whole period (implying a higherprobability that those who have the choice will buy unleaded fuel)but slowly moving toward zero (indicating a reducing probability ofbuying unleaded fuel). The contribution of tastes is represented by aconcave function approaching zero from below which becomespositive after May 1994, indicating that, after that date, owners ofcars that can use either fuel preferred to buy leaded fuel, but prior

Figure 4. Long-run relationships (Johansen Procedure): Italy



to that date had preferred to buy unleaded fuel. The contribution ofthe price differential is such as to encourage the use of leaded fuelprior to 1989 (when leaded fuel was cheaper), but to encourage theuse of unleaded fuel after that date.10 However even after 1989 thecontribution of the price difference is small relative to the termreflecting tastes at least until April 1993. After that date the priceincentives acted to encourage the use of unleaded fuel when tastesacted in the opposite direction.

We thus observe that in both the UK and Italy the market shareof unleaded petrol has been increasing but Italy illustrates a slowerdiffusion than in the UK. The car stock composition changessimilarly in both countries and thus cannot (fully) explain thedifferent diffusion patterns. After taking account of the changingcomposition of the car stock we see that changes in tastes and theprice differential are the major factors affecting the diffusionprocess. However in both countries it is the taste factors thatdominate the diffusion pattern. We find that whereas in the UKtastes have moved so that owners of cars that have a choice of fuelhave increasingly preferred to buy unleaded fuel, in Italy such carowners have shown an increasing preference for leaded fuel. InItaly, as a result, the probability of owners of such cars buyingunleaded fuel has reduced over time whereas in the UK it hasincreased. Thus whereas in the UK consumer choices havereinforced the effects of changes in the pattern of car ownership onthe diffusion of unleaded fuel, in Italy consumer choices have beenoperating in the opposite direction to changes in the car stock. Thishas resulted in accelerating diffusion in the UK but only slowdiffusion in Italy.

6. Conclusions

In this paper we have explored the diffusion of unleaded petrol inItaly and the UK. We observe that in the UK diffusion has beenfaster and has also exhibited acceleration whereas in Italy diffusionhas been slower without acceleration. We were particularlyinterested in the role that has been played in the diffusion bychanges in consumer preferences and fiscal incentives.

The diffusion can be characterised as driven by two processes (i)the growing proportion of cars capable of using unleaded fuel and(ii) the changing probability that owners of cars that may (but donot have to) use unleaded fuel will actually use unleaded fuel. We



observe that there was little difference between the two countries inthe pattern of change of the former but were unable to model thesechanges explicitly. We have specifically modelled the latter and findthat this probability has been increasing in the UK but decreasing inItaly.

From a model of consumer diffusion we argue that pricedifferences (largely driven by government fiscal incentives) and atrend have been important in the determination of driver’s fuelchoices (we found that unleaded petrol availability did not constrainthe diffusion process). The trend effects summarise a number ofdifferent forces acting upon the diffusion process. In the modellingprocess we have allowed the trend to encompass learning about theperformance of unleaded fuel, the declining value of information onthis, and also changes in tastes over time.

Estimates of the model indicate that the effect of price differenceson the diffusion process in both Italy and the UK have been small,the major driving factor being the trend or taste effects. Significantlyhowever we find that these trend effects have shown an increasingpreference for unleaded fuel in the UK (amongst drivers who havethe choice) but a decreasing preference in Italy. This difference canbe considered the major factor generating slower and less dynamicdiffusion in Italy compared to the UK.

It is now required that all new cars be fitted with catalyticconverters. This regulation will eventually mean that all cars willhave to use unleaded fuel. Given the developing pattern of tastes,however, this regulation is obviously going to be more important tothe greening of Italian consumer spending patterns than UKspending patterns. In the UK the market is naturally tending to ahigh level of use of unleaded fuel. In Italy it is not.

Appendix A: Data sources and variable definitions

1. Proportion of the total petrol driven cars that must useunleaded petrol, i.e. cars with open or closed loop catalytic converts(C(t)). Such cars comply with intermediate stages of Europeanemissions legislation. The data used are linear monthlyextrapolations of annual data (1985–95) for each country providedby Dr. Zissis Samaras11 from the Faculty of Engineering, AristotleUniversity, Thessaloniki (Greece). This data set was developed onbehalf of the European Commission and the European EnvironmentAgency. It is based on the level of emissions from motor vehicles



and legislative steps in each European country from 1985 to 1995.We have compared this time series with the monthly cumulative newpetrol driven car registrations from January 1993 provided bySociety of Motor Manufacturers and Traders Limited, MonthlyStatistical Review (UK case) and ACI Direzione Centrale CentroStudi e Documentazione in Rome (Italy case). No relevantdiscrepancies emerge from a comparison of the Greek and thecountry specific statistics. The Greek data has thus been usedbecause of the advantage of Anglo-Italian consistency.

2. Proportion of the petrol driven car stock that can, but does nothave to use unleaded petrol (B(t)). Such cars meet the ECE 15-04regulation. The data used are linear monthly extrapolations ofannual data for each country provided by Dr. Zissis Samaras. Wehave compared the UK series with another series based on datasourced from Department of Transport. Transport Statistics 1994(based on an annual survey of car drivers conducted by Lex plc)which provides six yearly observations from Autumn 1988 toAutumn 1993 from which monthly data was extrapolated conditionalon the total petrol driven car stock in each month. Given that allcars registered after October 1989 can use unleaded petrol we alsoextended this series to 1995 using monthly new car registrations. Wehave found no relevant discrepancies between this alternative timeseries and the one provided by the Greek source. We are unable tocheck the reliability of the Greek data for Italy as there is no otherdata source for that country. To maintain inter-country consistencyin data we use the Samaras data for both countries.

3. Retail price of leaded petrol (P2(t)). For the UK this ismeasured by the price in pence per 100 litres inclusive of tax andduty of 4 star (RON 96) leaded fuel. Available on a monthly basisfrom January 1987 to July 1995 from the Institute of Petroleum. ForItaly it is measured by the average retail price for leaded fuel (RON98), avilable from Energy, Prices and Taxes, IEA Statistics OECD,Paris, and EEC ‘‘Weekly Petroleum Bulletin’’.

4. Retail price of unleaded fuel (P1(t)). For the UK this ismeasured by the price in pence per 100 litres inclusive of tax andduty or RON 95, available on a monthly basis from the Institute ofPetroleum for the period January 1989 to July 1995. For the periodJanuary 1987–December 1989 the price of unleaded fuels waspredicted from the price of leaded fuel and the rates of duty applied(source from OECD statistics). After experimentation we concludedthat the price of leaded and unleaded fuel were related for theperiod from 1/89 to 6/95 according to



P2(t)\m(t)+b1P1(t)+b2(D2eµD1e)(1+VATe)+e(t)

where m(t) is a stokastic trend (m(t)\m(tµ1)+u(t) and u(t) is anerror term), D1e and D2e are the rates of duty on leaded andunleaded petrol respectively, VAT is the rate of value added tax ande(t) is an error term. We estimated the coefficients (with t statistics)and the intercept (q-ratio) of the dynamic regression model withstokastic drift (see Harvey, 1989) as: m(t)\0.022 (1.00); b1\0.970(6.91); b2\0.973 (43.64). The forecasting performance of the modelwas good, as were all the diagnostics and goodness of fit. Theseestimates have been used to backward forecast P1(t) for the periodfrom 1/87 to 12/88.

For Italy it is measured as the average retail price of unleaded(RON 95) fuel inclusive of VAT and duty in 1,000 Italian lira perlitre. Available on a weekly basis from July 1985 to September 1995and provided by Dr. Anselmi Riccardo, Unione Petrolifera,Confindustria, via Giorgione n. 129, 00147 Rome. Before 16/9/91prices were administered by the government. After that date priceswere free from control (Notizie Statistiche, Unione Petrolifera,1996). Since May 1986 gasoline prices and the prices of light fuel oilhave automatically followed EEC averages (Energy Prices Statistics,1995).

5. Consumption of leaded X2(t) and unleaded X1(t) petrolmeasured as inland deliveries of leaded/unleaded petroleumproducts/motor spirits in 1000t. Available on a monthly basis fromEurostat Energy Monthly Statistics and Department of Trade andIndustry, Energy Trends aggregating data relating to RON 92, RON95 and RON 98 specifications. For the UK the unleaded petrol timeseries reports zero consumption before July 1987. This seemsunrealistic, and thus we allow monthly consumption for each monthprior to July 1987 to be equal to 1, which also facilitates the takingof logs. The total coverage is 1/86–5/95. For Italy the coverage is7/85–8/95. The updating of the existing data set from December1994 to September 1995 was possible thanks to Miss Deasy fromEurostat/D1, Energy and Raw Materials Department.

6. Market share of unleaded petrol M(t). Measured as:

P1(t)X1(t)/(P1(t)X1(t)+P2(t)X2(t))

7. Availability of unleaded petrol was measured as the proportionof all retail petrol stations that sell unleaded petrol. This availabilitydata was used to explore whether the diffusion process was supplyconstrained. For the UK data was sourced from the Institute of



Petroleum and available at 24 arbitrary points in time over a six yearperiod.

The logistic curve:

Avail+1/(1+exp(12.004µ0.33t))(11.8) (16.5)

R2\0.98

has been fitted to the data to extrapolate monthly values for theperiod 1/87–5/95.

For Italy the data source was Unione Petrolifera, Confindustria,via Giorgione n. 129, 00147 Roma 9Mr. R. Anselmi) and StaffettaQuotidiana Petrolifera, 21/12/94. Data are available at eightarbitrary points in time, i.e. annual observations 1986–94. A logisticspecification was used to calculate the full data for each month ofthe diffusion period.

8. T is measured as the number of months from the launch datefor unleaded petrol (taken as June 1986 for the UK and January1988 for Italy).

Appendix B: Preliminary time series analysis

In Table B1 we present the results from an analysis of the orderof integration of the variables used in the above study. Theprocedure refers to the Dickey–Fuller and Augmented Dickey–Fuller tests, with unknown data generating process, of unique andmultiple unit roots (see Harris, 1995).13 The critical values (seeTable B2) pertain to each of the seven different models, since thedistributions of the statistics obtained depend not only on the data-generation process of the variables, but also on the model withwhich we investigate it.

The testing procedure moves from the most general specificationof a stokastic trend (non-stationarity) against the alternative of adeterministic trend (stationarity) down to more restrictedspecifications until the null hypothesis is rejected. Table B3 andTable B4 outline the testing procedure for the presence of



Table B1. ADF test of unit root and multiple unit root tests

tb Fb t tl Fl t ts Ho Ha

UKLog PLog P(3)Log XLog X(16)

µ3.0593µ0.6729

5.08785.2347

µ3.06*µ0.673*

µ2.26047µ1.2981

2.5919µ2.4244

2.18413.96**5.15602.9403

µ2.2604*1.29812.592**

µ2.424*

µ1.7044**µ1.60µ2.0809µ1.956*

I(2)I(1)I(1)I(2)

I(1)I(0)I(0)I(1)

ItalyLog PLog PLog XLog X

0.97467

µ1.15

0.5097

0.6835

0.975

µ1.15

µ10.192*µ0.242µ5.6267*µ0.2425

51.9341*0.64915

15.8204*0.4684

µ0.242

µ0.2425

µ15.5041*µ0.6307µ5.28*µ1.65**

I(2)I(1)I(2)I(1)

I(1)I(0)I(1)I(0)

Note: Rejects the null hypothesis at * 5 percent and ** 10 percent levels respectively. Where different from zero optimal lag in brackets.

© F

ondazione Giacom

o Brodolini and B

lackwell Publishers L

td 1998.

274G

iuliana Battisti –

Paul Stoneman

respectively two and one unit roots (see Dickey and Pantula, 1987;Dickey and Fuller, 1981 and Fuller, 1976).

Notes

1This study has been financed by the Economic and Social Research Council aspart of a wider project on the Diffusion of Environmentally Friendly ConsumerProducts and falls within the remit of the Global Environment Change initiative.

Table B2. Critical values of the ADF tests and t-testc

SLa sb Hb t b sl Hl tb ss

0.5 µ3.54 5.47 µ1.65 µ2.89 3.86 µ1.65 µ1.950.10 µ3.15 6.49 µ1.28 µ2.58 4.71 µ1.28 µ1.61

Note: a Significance level (SL); b Critical values of the t-distribution for Nhl; c Source: Fuller(1976), Dickey and Fuller (1981).

Table B3. Augmented Dickey-Fuller tests of unit root

Null hypothesis Test CriticalStep and model Ho: I(1); Ha: I(0) statistic values

Dyt\mbt+(rbµ1)ttµ1+SCDytµ1+ut

Dyt\mb+gbt+(rbµ1)ytµ1+SCDytµ1+ut

Dyt\mb+gbt+(rbµ1)ytµ1+SCDttµ1+ut

Dyt\mm+(rmµ1)ytµ1+SCDytµ1+ut



Dyt\(rtµ1)ytµ1+SCDytµ1+ut

rb\1(rbµ1)\gb\0rb\1rm\1(rmµ1)\gm\0rm\1rt\1

tb

fb

ttm

fm

ttt

Fuller (1976)D&F (1981)Standard normalFuller (1976)D&F (1981)Standard normalFuller (1976)

Table B4. Augmented Dickey-Fuller tests of unit root

Null hypothesis Test CriticalStep and model Ho: I(1); Ha: I(0) statistic values

D2yt\mb+(rbµ1)Dytµ1+SCD2ytµ1+ut



D2yt\(rtµ1)Dytµ1+SCD2ytµ1+ut

rm\1(rmµ1)\gm\0rmµ1rt\1

tm

fm

ttt

Fuller (1976)D&F (1981)Standard normalFuller (1976)



2 In the UK prior to March 1987 leaded and unleaded fuel were subject to thesame rate of duty, but from that date the rate on unleaded fuels has always been lessthan on leaded (from 95 percent of the leaded rate in March 1987 the unleaded dutyfell to 87 percent of the leaded rate at the end of the sample period).

3 Until 19 September, 1991 prices were administered by the government (source:Notizie Statistiche/Confindustria, 1996) and after 1986 petrol prices haveautomatically followed EU averages (IEA/OECD Statistics, 1995).

4 In our econometric analysis below we thus assume that changes in thecomposition of the car stock are exogenous to the diffusion of unleaded fuel. Onemight argue, for example, that price differentials or changes in consumer tastes forunleaded fuel might encourage car owners to buy cars capable of using unleadedfuel or to convert their existing cars to use unleaded. To the extent that such factorsare important, this will tend to attribute to regulation some of the impact derivingfrom fiscal incentives and changes in tastes. There is however nothing that we can doto overcome this problem.

5 Alternatively it can be interpreted as the limiting value for f(t) as t tend toinfinity.

6 For this procedure to be valid it is necessary that C(t) carries a unit coefficient.We have tested for this using nonlinear regression and the hypothesis cannot berejected. We have also tested whether ln P1 and ln P2 should be entered separatelyinto the regression and the hypothesis is also rejected.

7 In analytical terms, consider the simplest case of two series x(t) and y(t) eachintegrated of order 1. Given the long-run relationship y(t)\tX(t)+u(t) where u(t)is the residual error, the static co-integrating regression will be u(t)\y(t)µtX(t),and u(t) must be a random walk, i.e. I(0). Of course in this case both ML and OLSestimates provide the same unique solutions. In the multivariate frameworkJohansen suggests the use of a ML estimator to estimate the cointegration vectorsfor an autoregressive process with independent gaussian errors and to derive alikelihood ratio test for the whole process as:

Dz(t)\G1Dz(tµ1)+. . .+Gkµ1DZ(tµkµ1)+pz(tµ1)+u(t)

where z(t) is the vector of the observed variables and p is the product matrix of theadjustment vectors (a) times the cointegrating vectors (b) matrix.

8 The packages used in the estimation process have been: STAMP5 for theupdating of the time series, SPSS for the monthly data extrapolation, MFIT for theML co-integration analysis.

9 In the econometric model a dummy variable was also included for the period inwhich the price of leaded fuel was higher than the price of unleaded fuel.

10 The speed of adjustments of the dummy variable (taking into account theadverse fiscal policy at the beginning of the diffusion process) and taste carry signsopposite to the corresponding long-run coefficients. This confirms that theyact/adjust in the opposite direction to the tendency of the whole system.

11 Further details are available from:Samaras, Z. and Zierock, K., ‘‘Forecast of Emission from Road Traffic in the

European Communities’’, Final Report to the CEC/DG X1, in Environment andQuality of Life, EUR 13854 EN, Luxembourg, 1991, pp. 287.

Samaras, Z. and Zierock, K. H., ‘‘Assessment of the effect in EC member statesof the implementation of Policy Measures for CO2 reduction in the transportsector’’, Commission of the European Communities Document, ISBN 92-826-6505-4,Luxembourg, 1994, pp. 165.



Emission Inventory Guidebook — Sector 8: Mobile Sources, G. McInnes (editor),European Environment Agency, Copenhagen (to be published in 1996).

12 Tests based on the DF distributions are not reported, although they havecontributed to enforce the conclusions drawn from this analysis.

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The Diffusion of Unleaded Petrol: An Anglo-Italian Comparison

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