Information Technology and Efficiency in Trucking

Transcription

1 Informaton Technology and Effcency n Truckng Phlppe Barla*, Professor Dens Bolduc, Professor Nathale Boucher, Ph.D. Jonathan Watters, M.A. Centre for Data and Analyss n Transportaton (CDAT) Département d économque, Unversté Laval, Québec, QC Canada, G1K 7P4 Abstract In ths paper, we develop an econometrc model to estmate the mpacts of Electronc Vehcle Management Systems (EVMS) on the load factor (LF) of heavy trucks usng data at the operatonal level. Ths technology s supposed to mprove capacty utlzaton by reducng coordnaton costs between demand and supply. The model s estmated on a subsample of the 1999 Natonal Roadsde Survey, coverng heavy trucks travellng n the provnce of Quebec. The LF s explaned as a functon of truck, trp and carrer characterstcs. We show that the use of EVMS results n a 16 percentage ponts ncrease of LF on backhaul trps. However, we also fnd that the LF of equpped trucks s reduced by about 7.6 percentage ponts on fronthaul movements. Ths last effect could be explaned by a rebound effect: hgher expected LF on the returns lead carrers to accept shpments wth lower fronthaul LF. Overall, we fnd that ths technology has ncreased the tonne-klometers transported of equpped trucks by 6.3% and ther fuel effcency by 5%. Key words: Informaton and Communcaton Technology, Effcency, Load factor, Truckng, Energy Effcency JEL codes: O33, Q55, R40 *: Correspondng author, Tel: , e-mal: [email protected]. We gratefully acknowledge the fnancal support of the Quebec Mnstry of Transport and Natural Resources Canada. Jonathan Watters also acknowledges the fnancal support of the Canadan Transportaton Research Forum. P. Barla thanks the Katholeke Unverstet Leuven where he was vstng professor when ths paper was fnalzed. The vews expressed n ths paper only reflect those of ts authors and do not necessarly represent those of the fnancal supporters.

2 Informaton Technology and Effcency n Truckng 1. Introducton The mpact of Informaton and Communcaton Technologes (ICTs) on effcency and productvty has been a central ssue n economcs for several decades. Early studes undertaken n the seventes and eghtes, have found very lttle mpact of ICTs on productvty usng aggregate data leadng to the socalled productvty paradox (for a revew of these early studes see Brynjolsson and Shnku, 1996). More recent studes have used data at the ndustry or frm level and have yelded more encouragng results (see Plat, 2004 for a revew). In many cases ICTs appear to have a postve mpact on productvty but the mportance of the effect vares accordng to frm, market and economy characterstcs. In ths paper, we contrbute to ths lterature by emprcally assessng the mpact of ICTs on the truckng ndustry usng data dsaggregated at the operatonal level. Specfcally, we measure the mpact of new electronc vehcle management systems (EVMS) on capacty utlsaton. The truckng ndustry s facng a complex matchng problem between demand and capacty. A carrer needs to have the rght truck at the rght tme and place n order to respond to a shpper demand. Ths matchng problem necessarly leads to some capacty under-utlzaton wth trucks travellng empty or less than fully loaded. Accordng to the CCMTA (2002), about one out of three heavy trucks travellng on Canada s major hghways are empty and more than half of those wth a charge are not 100% full. EVMS feature two functonaltes that may lower coordnaton costs between capacty and demand: ) they provde real-tme transmsson of the exact postonng of each truck of a carrer s fleet usng GPS technology and ) they enable dspatchers to ntate real-tme communcaton wth drvers va onboard computer. Wth EVMS, dspatchers may therefore be able to coordnate vehcle actvtes n a more effcent manner, thereby ncreasng the trucks load factor (LF). In fact, provders of ths technology nsst n ther promotonal materal on the opportuntes offered by EVMS to reduce empty backhauls. We test the mpact of ths technology on the LF of heavy trucks usng data colleted by the 1999 Natonal 1

3 Roadsde Survey (NRS99). The survey nvolved more than 65,000 drvers ntervewed randomly at 238 roadsde stes throughout the 25,200 klometres of Canada s man road network. We use a sub-sample of NRS99 coverng trucks that have travelled at least n part n the provnce of Quebec. Besde the adopton of EVMS, our econometrc model explanng LF ncludes varables characterzng the truck, trp and carrer. Our analyss s related to a recent study by Hubbard (2003), examnng the mpact of EVMS on the US truckng ndustry. Accordng to Hubbard, EVMS would have ncreased capacty utlzaton rates of equpped trucks by 13% leadng to a 3% ncrease n the ndustry s overall effcency n Besdes focusng on the Canadan ndustry, our analyss dverges from Hubbard by usng data dsaggregatd at the operatonal level. We observe the LF of trucks travellng on specfc trps whereas Hubbard uses measures of capacty utlzaton at the truck level and aggregated over a one year perod (.e. loaded mles and the number of weeks the truck s n use). Therefore, our data allows us to gan a better understandng of how EVMS affect LF dependng upon the trp characterstcs. We test separately the effect of EVMS on fronthaul (F) and backhaul (B) trps. Interestngly, we fnd that f EVMS ncrease LF on B trps by about 16 percentage ponts, ths technology s also assocated wth a 7.6 percentage ponts reducton n F trps LF. These results suggest that EVMS creates a sort of rebound effect. By ncreasng the lkelhood of fndng a backhaul, the EVMS lower the unt cost of a delvery - fxed costs assocated wth the entre trp beng spread over a larger total load thereby promotng acceptance by the carrer of lghter F loads, or trps that requre a longer ntal empty runs (to go pck up the load). Overall, our results suggest that ths technology ncreases capacty utlzaton: we evaluate that tonne-klometres (TKMs) transported would have ncreased by 6.5% for trucks equpped wth ths technology. Takng nto account the (low) adopton rate, ths mples an ndustry-wde ncrease n TKM n the order of 0.83%. Other sgnfcant factors postvely affectng the LF on both types of trps nclude the truck sze, the trp dstance, the traler versatlty and the ntensty of the economc relatonshp between the orgn and destnaton. On B trps, for-hre carrers and owner-operator appear to do better than prvate truckers. Our man results are confrmed when EVMS s allowed to be endogenous. 2

4 These results are also mportant n the debate over the development of a durable transportaton system. Indeed, truckng actvtes are a sgnfcant source of envronmental degradaton. They contrbute to urban smog, nose polluton and greenhouse gas (GHG) emssons. For example n Canada, whle total GHG emssons have ncreased by 24% from 1990 to 2003, medum and heavy trucks emssons have jumped by 68.8% durng the same perod and now account for close to 6% of total emssons (see Envronment Canada, 2005 and Natural Resources Canada, 2006). Truckng does, however, also contrbute postvely to economc growth. It s estmated that ths ndustry generated n 2003 close to 50- bllons n revenues and employed some 320,000 full-tme workers n Canada (see Transport Canada, 2003). More mportantly, trucks are a vtal nput for most of the ndustres nsurng the delverng of goods. It s therefore mportant for publc polcy to promote the development of a truckng ndustry that s both effcent and sustanable. Improvements n the load factor of trucks could provde a mean for achevng both objectves a wn-wn soluton à la Porter (see Porter and van der Lnde, 1995). Based on our results, t appears that EVMS have mproved the energy effcency of adoptng trucks by about 5%. The rest of ths paper s organzed as follows. In secton 2, we provde a general overvew of the functonng of the truckng ndustry and the challenges assocated wth matchng capactes and demands. We also develop a smple theoretcal model hghlghtng the potental effects of EVMS dependng on the type of trps. In secton 3, we descrbe the data, the emprcal specfcaton and the estmaton technques. Results are presented and dscussed n secton 4. We conclude n secton Capacty management n the truckng ndustry The truckng ndustry s composed of two man segments: ) for-hre companes whch transport the freght of others for compensaton and ) prvate truckng that nvolves carryng the company s goods. In dollar terms, t s estmated that prvate truckng accounts for about one half of the Canadan ndustry (Nx, 2003). Besdes these two segments, there are also owner-operators who own and drve ther trucks and work on contract ether for-hre or prvate actvtes. 3

5 The productve capacty of a carrer depends upon ts fleet sze and structure. Each truck offers a capacty that can be constraned ether by the shpment weght or volume. Moreover, some shpments requre specalzed tralers such as tanks for carryng lquds. The rate of capacty utlzaton s determned by the porton of tme trucks that are on the road (.e. n use) and ther LF. Our analyss specfcally focuses on the latter aspect. Consderng only the weght constrant, LF can be defned as: CW LF (%) = x 100 [1] MCW wth CW the cargo weght and MCW the maxmal cargo weght a truck can carry. It s easy to understand that LF s a key determnant of a carrer s compettveness. Indeed, the cost per tonne carred clearly declnes wth LF, as several cost components are ether fxed (for example the drver salary) or vary less than proportonally wth LF (e.g. fuel costs). It s also a key determnant of a carrer s energy effcency. The energy effcency assocated wth carryng a shpment weghtng CW, over a dstance D, usng a truck wth capacty MCW can be defned as: TKM CW x D MCW x( LF /100) x D Energy Effency = = = [2]. Energy Energy Energy It s the rato of the output measured by the tonne-klometres carred (TKM) and the energy consumed. By defnton, TKM s the product of the cargo weght and the dstance over whch t s shpped. Usng [1], we mmedately obtan that the level of producton s drectly proportonal to LF. By contrast, the level of energy requred to carry a shpment over dstance D ncreases much less than proportonally. Indeed, t s estmated that a truck fully loaded only consumes about 20% more fuel than f t s empty (see Brdgestone/Frestone, 2006). However, optmzng LF s made complcated by the fact that both demand and capacty are tme, locaton and equpment specfc. Ths complex matchng problem necessarly leads to some underutlzaton of capacty, takng the form of ether empty-runs or less than fully loaded trps. Carrers can reduce these neffcences by tryng to fnd complementary demands. 4

6 To llustrate these matchng actvtes, let s consder the followng smple example: a carrer located n A s contacted to transport a load from A to B (see fgure 1). To optmze ts LF on the whole journey (ABA), the carrer may engage n costly search for complementary demands. 1 Frst, the shpment ntatng the fronthaul trp from A to B whch we wll refer as the F trp - may not fully load a truck, n whch case the carrer may want to group varous shpments. Second, as the demand from a clent s rarely b-drectonal, the carrer needs to fnd a returnng load f t wants to avod an empty backhaul trp from B to A (from now on referred as B trp). Another source of neffcency exsts when the truck base s not close to the shpment orgn requrng an ntal empty run. Smlarly, on return, a truck may be devated n order to pck up a backload. Varous market ntermedares (brokers, web load matchng stes) play a role n coordnatng demands wth capactes. At a carrer level, dspatchers are n charge of optmzng capacty utlzaton (see Hubbard, 2003 for a descrpton of the dspatcher role). Obvously, ther work nvolves tradng off search costs wth the opportunty cost assocated wth a less than full load trp. In the above example, the truck journey s ntated by the fronthaul shpment. In realty, there may be cases where the journey s ntated by a shpment to be carred from B to A, n whch case the AB trp s the jont product for whch the carrer needs to fnd a complementary demand. Ideally, we would lke to know whch trp (F or B) has ntated the journey and whch one s the jont product. Unfortunately, the data does not allow us to dstngush between these two cases. However, B trps (BA n our example) are certanly more lkely to be jont products. Indeed, carrers most often ntate trps n response to local demands. For example, carrers located n A are more lkely to be contacted by shppers n A than n B. Indeed, shppers located n A probably have better nformaton on carrers located n A than n B. Moreover, dealng wth carrers located n A may nvolve longer delvery delay as trucks should frst be sent from A to B before beng able to pck the load. Obvously, ths s not to say that ths type of stuatons does not occur, but smply that t s less lkely and wll usually nvolved specalzed equpments. 1 Search costs not only nclude the cost assocated wth dentfyng complementary demands but also the cost of havng a truck unused durng the search process. 5

7 The level of effort for fndng complementary demands and probablty of success wll depend upon the truck, load, carrer, trp and market characterstcs. As mentoned above, t s certanly much more dffcult to fnd complementary demands for a load that requres very specalzed equpments. Snce for-hre companes are specalzng n transportaton actvtes, they may have lower search costs and thus could be more successful n avodng empty or less-than full runs. Moreover, prvate companes face legal and nsurance constrants that may lmt ther ablty to serve external demands. The sze of companes may also postvely affect ther abltes to fnd complementary demands. The dstance between A and B s certanly a sgnfcant factor affectng the level of effort a company wll nvest for fndng a load on the return trp. Indeed, the opportunty costs assocated wth returnng empty s certanly ncreasng wth dstance. The demand for transport also depends upon the ntensty of the socal and economc relatonshps that exst between both of the trp extremtes. New satellte-based communcaton and localzaton technologes may also help ncrease the LF by reducng coordnaton costs between demands and supples. As descrbed by Hubbard (2003), tradtonally carrers have reled on a system of check-and-call, where drvers perodcally phone ther dspatcher n order to provde nformaton on ther localzaton. Cell phones now also allow dspatchers to ntate the communcaton. However, snce the late eghtes, EVMS are combnng GPS technologes wth on-board computers. These systems therefore allow precse real-tme localzaton of all a carrer s vehcles as well as drect communcaton wth the drvers. Moreover, the nformaton provded by EVMS may feed software that support dspatchng decsons (e.g. reroutng trucks). Manufacturers of these technologes argue that they are partcularly useful to reduce empty backhauls. 2 For F movements, the mpact of EVMS s less obvous. Frst, t s rare that trucks partally loaded are re-drected n F movements, unless ths has been decded n advance (see Hubbard, 2003). 3 Second, by ncreasng the 2 For example, the web ste of Shaw trackng reports the followng customer testmonal: "Shaw Trackng allows us to reduce our empty mles consderably." ( 3 Varous shpments should ndeed be loaded n the truck n a specfc order takng nto account ther respectve destnaton as well as the weght dstrbuton. 6

8 probablty of fndng a backhaul load, EVMS may generate a sort of rebound effect as llustrated by the smple model below. 4 Let s suppose that a carrer receves orders for shpments from A to B. Each order s characterzed by a weght (or volume) that leads to a specfc load factor LF AB. To keep the analyss smple, we assume that AB LF s unformly dstrbuted on [0,1]. On the returnng trp BA, potental shpments are also dstrbuted so that the resultng load factor BA LF s unformly dstrbuted on [0,1]. However, as argued above, fndng an adequate backhaul load may not be automatc or may requre some watng tme. We therefore assume that the probablty of gettng an order on the return trp s ν < 1. We further suppose that the prce of a shpment depends upon ts weght (or load factor) and that the carrer s a prce taker. Specfcally, we assume that the carrer receves α LF per shpments. When recevng a new order from A to B, the carrer has to decde whether to accept or refuse t dependng upon the whole journey expected proft (π ). Assumng rsk neutralty, t wll accept f: AB BA E[ π ] = αlf c d + ν ( αe[ LF LF LF ] c) 0 [3] wth E [] representng the expectaton, c the costs drectly assocated wth a shpment (.e. the loadng and unloadng costs, the extra fuel cost requred to carry the load, etc.) and d the fxed cost generated by the round trp journey (.e. the trucker s wage, the fuel needed for movng the empty truck, etc.) and BA BA m BA LF m s the mnmal returnng LF that s accepted by the carrer. In fact, on the B trp, the carrer wll accept any load such that α c 0 so that LF BA c m =. Ths mples that α LF BA E[ LF LF BA α c LFm ] = [4]. 2α BA BA + Replacng [4] n [3] determnes the mnmum acceptable load factor on the F trp: 4 Ths effect s ndeed analogous to the well known rebound effect n the energy effcency lterature (see Greenng et al., 2000). For example, mprovements n the fuel effcency of cars lower operatng costs whch n turn stmulate drvng. Ths effect partally counteracts the fuel consumpton reducton assocated wth better fuel ratng. 7

9 LF AB m c + d ν ( α c) = [5]. α 2α To be acceptable, the fronthaul shpment revenues should cover the fxed roundtrp costs, plus the F segment costs mnus the expected net revenues generated by the B segment. The expected load factor on AB s therefore gong to be: E[ LF AB LF AB AB AB 1+ LFm LFm ] = [6]. 2 It s mmedate from [5] and [6] that the expected load factor observed on F trps s declnng wth ν. If EVMS do ncrease ν, we could therefore very well observe that equpped trucks have lower average LF on F trps. Interestngly, our data set offers a unque opportunty to test for ths possblty The Emprcal Analyss 3.1 The Data Our emprcal analyss uses data collected durng the 1999 Natonal Roadsde Survey coordnated by the Canadan Councl of Motor Transport Admnstrators. The man objectve of ths survey was to draw a pcture of heavy trucks actvtes n Canada. More than truck drvers were randomly selected and ntervewed at 238 survey roadsde stes throughout the 25,200 klometres (km) of Canada s man road network. Data was collected durng representatve weeks n the summer and fall of The questonnare ncluded two parts; one compulsory and the other optonal. About 88% of the drvers accepted to respond to both parts of the survey. In ths study, we explot a sub-sample of the NRS99 coverng heavy trucks surveyed at one of the 51 roadsde checkponts n the provnce of Quebec, along wth trucks surveyed elsewhere n Canada, but havng travelled part of ther trp n Quebec. 6 The sample s restrcted to long dstance trps as defned as 5 BA BA BA Note that the backhaul average load factor whch s νe[ LF LF LFm ] should ncrease wth EVMS f ths technology does ndeed help coordnate demand and supply. 6 Admnstratve and confdentally polces prevented us to get access to the whole data set. 8

10 trps of at least 80 km or those connectng two dfferent regons. 7 The Quebec Mnstry of Transport granted us access to ths sub-sample. The man advantage of usng ths sub-sample s that t underwent extensve consstency checks by the Mnstry (see Mnstère des Transports du Québec, 2003). Each observaton corresponds to a truck on a specfc trp and contans a rch set of nformaton on the truck, ts load and the type of company for whch the truck operates. One sgnfcant shortcomng s that t does not contan any nformaton on the carrer sze. We are therefore unable to control for ths factor. Ths could create spurous correlaton between LF and EVMS f, for example, larger carrers are more lkely to adopt ths technology and are also more effcent. We tackle ths ssue n secton 4.3 where EVMS s allowed to be endogenous. Besde the nformaton collected durng the ntervews, a traffc count was realzed at each ste n order to obtan a representatve pcture of the whole populaton. Trucks were classfed accordng to ther type (straght truck, tractor-tralers etc.) and the day and tme of ther passng through the survey stes. Based on these counts, expanson factors (.e. the nverse of the samplng weghts) were assocated to all observatons. A trp, as defned by the NRS99, s a contnuous move by a truck to haul cargo. The trp starts when the frst cargo shpment on-board s pcked-up and ends when the last cargo shpment on-board s delvered.an empty truck s also on a trp, and that trp lasts as long as the truck s empty. It s therefore mportant to underlne that we do not observe the truck complete roundtrp tnerary. The observed segment may also not fully represent a complete F or B trp. Indeed, suppose a carrer sends ts truck from ts base n Montreal to pck up a load n Ottawa to be delvered n Vancouver. Whle conceptually ths could be vewed as one F trp wth two segments, the NRS99 reports two dfferent trps dependng upon where the truck s ntercepted between Montreal and Ottawa, or between Ottawa and Vancouver (the loadng condton changes n Ottawa). Despte these lmtatons, the data base stll offers an opportunty to dstngush between F and B segments. 7 For Quebec, a regon corresponds to an admnstratve regon or a metropoltan census regon. For the rest of Canada and the U.S., a regon s ether a provnce or a state. 9

11 From the Quebec Mnstry of Transport s data set, we have appled several addtonal crtera to construct our own sample. For example, we elmnated observatons for whch nformaton on the analyss varables was mssng, or correspondng to truck confguratons not specfcally desgned for transportaton actvtes (e.g. garbage trucks, tractor wthout traler). The detaled descrpton of the excluson crtera are descrbed n appendx 1. Our fnal sample ncludes 9091 observatons (4,532 F trps and 4,559 B trps) down from an ntal 20,101 sample sze. These observatons correspond to a total of 87,702 trps when takng nto account the expanson factors. Next, we descrbe the emprcal specfcaton. 3.2 The Emprcal specfcaton The general structure of our emprcal model s as follows: LF u, = f ( U, I, C) [7] LF u, s the load factor of truck u on trp. It s explaned as a functon of varables characterzng the truck (U), trp (I) and carrer (C). The selected explanatory varables are expected to affect a carrer s ablty or ts level of effort for fndng complementary demands. We estmate model [7] separately on the sub-samples of observatons correspondng to F and B segments. As explaned above, the ssue of fndng complementary demands s lkely to be more acute on B trps. To classfy an observaton as F or B, we compare the relatve poston of the truck s base wth the trp orgn and destnaton. If the dstance between the truck s base and the trp orgn s less than half the dstance between the base and the trp destnaton, we classfy the observaton as a F segment the truck s ndeed clearly movng away from ts base. Correspondngly, B segments are those for whch the dstance base-destnaton s less than half the dstance base-orgn (.e. the truck s gettng closer to ts base). We elmnate observatons for whch the two dstances are not very dfferent, snce t s much more perlous to classfy those as ether a F or B segment. 10

12 3.2.1 The Load Factor We use as dependent varable the evaluaton of LF provded by the drver durng the ntervew. Fve responses are possble, namely: 0%, 25%, 50%, 75% and 100%. Moreover, f the response s 100%, a follow up queston asks f the truck s full n weght or volume. Ths measure of LF has therefore the advantage of takng nto account both capacty constrants. Obvously, ths measure s an approxmaton of the true LF, that we denote LF * to mean that t s unobserved (latent) The explanatory varables Table 1 brefly descrbes the explanatory varables ncluded n our analyss. As already mentoned, we are partcularly nterested n the mpact of EVMS a dummy varable set to one f the truck has an on-board computer and a satellte dsh. If these systems reduce coordnaton costs, we would expect the varable EVMS to have a postve effect on LF on backhaul. In fronthaul segments, the effect could be somewhat dfferent as already explaned. The truck sze s captured by the number of axles (AXLES). Snce t s lkely that the opportunty cost assocated wth drvng empty ncreases wth the truck sze, we would expect ths varable to have a postve mpact on LF. We also control for the truck base localzaton, dstngushng truck based n the provnce of Quebec, the rest of Canada (ROC) and the US. Gven that the structure of our sample only ncludes trucks havng travelled n Quebec, t s possble that Quebec carrers dspose of an advantage n terms of market knowledge. Also, US trucks are very lkely to carry goods between the US and Canada and should therefore be affected by the trade mbalance that exsts between the two countres. The type of tralers has an mpact on the nature of the cargo that can be transported. The possbltes for poolng several shpments, as well as the probablty of fndng complementary return loads, are more lmted for specalzed equpment. Table 1 presents the classfcaton we use n ths study, and that s partally based on Hubbard (2003). We also control for the nature of the carrer operatons (for-hre versus prvate truckng), as well as f the drver s an owner-operator. Once agan, for-hre carrer and owner-operator may have more 11

13 ncentve and flexblty for fndng complementary demands than prvate carrers for whch transport actvtes are only an nput n ther producton process. However, t s worth mentonng that owneroperators usually operate small companes (on average seven employees, see Nx, 2003) and therefore may have less capabltes for fndng complementary demands. As the opportunty cost of drvng empty ncreases wth DISTANCE, we expect LF to be postvely correlated wth ths varable. We also nclude n our model two varables measurng the mportance of economc relatonshps between the regons of orgn and destnaton of the trp. The varable POPULATION represents, usng a gravtatonal formulaton, the potental nteracton force between the populatons at the orgn and destnaton. For Canada, the populaton data are collected at the level of the census dvson and n the US at the county level. The varable INCOME corresponds to the average of the medan household ncome at the orgn and destnaton The estmaton method By defnton, LF s bounded between 0% and 100% and takes dscrete values that are naturally ordered. For these reasons, we estmate a multnomal ordered probt (MNOP) model (see for example Wooldrdge, 2001). The latent response * LF for observaton s determned accordng to: * ' LF = x β + θ EVMS + u [8] wth ' x representng the vector of the control varables, β beng a parameter vector, and θ the parameter assocated wth the EVMS ndcator. The observed responses are lnked to the latent varable by the followng threshold model: 8 For Canada, the data are once agan gathered at the census dvson level. For the US, we use the county data for Quebec neghbourng States and State level data for the rest of the US. 12

14 LF = f f f f f < LF α < LF 1 α < LF 2 α < LF α < LF 4 3 * * * * * α α α α < [9] wth α1 to α 4 beng threshold parameters to be estmated jontly wth β and θ. We assume that the error terms u are ndependently normally dstrbuted. The samplng structure s taken nto account by weghtng each observaton by the nverse of the expanson factor when constructng the log-lkelhood functon (see Wooldrdge 1999 and 2001). 9 Another econometrc concern s the potental correlaton between the error term and EVMS, whch would bas our results. Unobservable factors (such as the carrer sze) could ndeed affect both LF and the adopton of EVMS. Trucks wth ths technology could also be assgned to roads where t s more dffcult to fnd a backload. We address ths ssue n secton The Emprcal Results 4.1 Prelmnary evdence Before presentng the econometrc results, t s useful to examne some basc descrptve statstcs. Table 2 reports the means and standard devatons for the varables computed over our sample. Clearly, the average LF s hgher on F trps than on B trps, thereby supportng our hypothess that trucks movements are most often ntated by local demands. In fact, the dfference n the percentage of empty trucks explans that ths dfference for the average LF of loaded trucks s very smlar on both type of trps at about 90%. The adopton rates of EVMS are qute lmted, but recall that the data dates back to The 9 If the samplng structure s exogenous, the unweghted results are consstent and generally more effcent than the weghted results. However, f the samplng structure s endogenous, the unweghted results are not consstent whle the weghted results are. In our settng, trucks were randomly chosen at each data collectng ste. However, the localsaton of these stes was dctated by several consderatons (representaton of each provnce, ste convenence, mportance of the traffc flow etc.) whch may lead to an endogenous samplng structure. 13

15 ntercepted trucks are mostly based n Quebec whch s hardly surprsng gven our sample structure. Vans domnate n terms of truck traler type. Only about 20% of the observatons relates to prvate truckng. Ths can be explaned by the fact that the survey targets long dstance trps, whle prvate truckng mostly specalzes n local freght transportaton. The average trp dstance s about 450 km. Table 3 llustrates the relatonshp between LF, the adopton rate of EVMS and some key explanatory factors. Hgher LFs are assocated wth EVMS, larger trucks, for-hre operatons, owneroperators and dstance traveled. Moreover, Quebec-based trucks and US trucks would have lower LFs than trucks from the ROC. EVMS appears to be mostly nstalled on for-hre van-type trucks travellng on long dstances. US and ROC based trucks are also more lkely to use ths technology. Lookng more closely at the mpact of EVMS by trp dstance classes, Table 4 suggests that ths technology s assocated wth mproved capacty utlzaton on B trps. On F segments, however, the relatonshp s less obvous, as t appears that equpped trucks have slghtly lower LFs and are less lkely to be fully loaded on longer roads. Ths result s consstent wth a rebound effect. Obvously, t s dangerous to conclude from these partal correlatons. We therefore turn next to the econometrc results. 4.2 The econometrc results Table 5 presents the econometrc results assocated wth the F and B trps. We report the results obtaned by smple OLS and by MNOP. Snce the mpacts of the explanatory varables may vary wth the type of trucks, we also report the results obtaned usng only observatons pertanng to van tractor-traler (MNOP Van). From these results, we can assess whch factors have a sgnfcant postve or negatve mpact on LF. In order to evaluate the magntude of the effects, we also present n fgure 2, the result of a smulaton 14

16 based on the MNOP results. 10 Ths fgure llustrates the change n the LF resultng from changes n the varous explanatory wth respect to a reference case. 11 From these tables and fgures, we can draw the followng conclusons. EMVS s ndeed assocated wth a reducton of LF on F trps n the order of 7.6 percentage pont. On B-trps, EMVS has a postve and sgnfcant effect on LF. Our smulaton ndcates that trucks equpped wth EVMS would have LF about 16 percentage ponts hgher than non-equpped trucks. Thus, f ths technology actually ncreases LFs on B trps, t also seems to ncte carrers to accept lower LF on F trps. The mpact appears somewhat more pronounced when estmatng the model wth the van subsample. In ths case, the technology ncrease backhaul LF by 22.5 percentage ponts and reduces fronthaul LF by 14.4 percentage ponts. As expected, the LF s postvely affected by the truck sze, partcularly on F trps. For example, a truck wth seven axles has on average a 15.7 percentage pont hgher LF than a fve axles truck. Whle trucks from the ROC do not appear to have very dfferent LF than Quebec trucks, US trucks have lower LFs on F trps. Trade mbalance between the US and Canada may explan ths result. As expected, more specalzed tralers have lower LFs. Prvate carrers have LFs that are about 5% lower on average than for-hre truckers n B trps. Interestngly, owner-operators have somewhat hgher backhaul LF. As expected, dstance s a major factor affectng LFs on both types of segments. A 1000 km (resp km) trp has on average a load factor hgher by 14 (25) percentage ponts compared to a truck travellng on a 450 km journey. INCOME has a postve mpact on F trps whle POPULATION postvely affects backhaul LF. In order to better assess the overall mpact of EVMS, we smulate the change n the TKMs transported assocated wth ths technology. To that end, we estmate the TKMs for each of the 10 Except for the OLS model, the magntude of the effects cannot be drectly nferred from the value of the estmated coeffcents. 11 The reference case corresponds to an average trp of 450 km, between two areas of 226,000 habtants each wth medan ncome of 45,000$. The reference truck has fve axles, no EVMS, s a van based n Quebec and s operated by a for-hre carrer. 15

17 observatons n our sample usng the results of our econometrc model, and then re-compute the TKMs that would have been carred f no truck was equpped wth EVMS. 12 We fnd that ths technology has allowed adoptng trucks to ncrease ther output by 6.3%, holdng constant the suppled capacty. Ths mples an overall ncrease n the ndustry capacty utlzaton of about 0.83%. Our results are somewhat smaller than those reported by Hubbard (2003). Indeed, recall that Hubbard fnds that adoptng trucks ncrease ther output by 13%, whch translates nto a 3% ndustry-wde capacty utlzaton mprovement. The larger overall mpact s obvously partally explaned by the hgher adopton rate n the US (25% of equpped trucks n Hubbard data versus 10% n ours). A hgher adopton rate may also lead to hgher benefts f there are learnng and network effects. In fact, Hubbard s analyss suggests that benefts n the US were larger n 1997 than n 1992 when the adopton rate was lower. Fnally, we can translate our results n terms of energy effcency gans; EVMS would have ncreased fuel effcency of adopters by close to 5% and by 0.66% at the ndustry level. 13 The mportance of ths effect should, however, be contrasted wth the 40% mprovement n energy effcency that the Canadan truckng ndustry has experenced snce 1990 (see Natural Resources Canada, 2006). Several alternatve specfcatons were tested, leadng to results that were very much n lne wth those reported n table Frst, the model was estmated wth a measure of LF based on the cargo weght and the maxmum loadng capacty of the truck rather than the drver estmate. Besdes not takng nto account the volume constrant, ths varable s also an approxmaton snce the maxmal loadng capacty of the truck s not drectly measured, but rather t s nferred based on the truck characterstcs. 15 In any case, the correlaton between both measures of LF s close to 0.8 and the econometrc results lead to smlar conclusons. Second, the model was estmated wthout accountng for the samplng weghts. Whle the estmated coeffcents are statstcally dfferent (thereby suggestng that the unweghted results 12 See the numerator of [2] for the formulaton used to compute TKM. 13 We assume that a 1% ncrease n LF leads to a 0.2% ncrease n fuel consumpton. 14 All results are avalable from the correspondng author upon request. 15 It s computed as the dfference between the weght of the truck full and empty. The former s evaluated as the number of axles tmes kg (.e. the maxmal weght per axles authorzed by the Canadan regulaton), the latter s based on the weght of empty trucks n the current survey and n the smlar survey realzed n

18 are not consstent), the sgn and statstcal sgnfcance of the varables of nterest are smlar. Thrd, the model was re-estmated usng only trps wth a dstance superor to 450 km. Once agan, our man conclusons reman unchanged and the estmated coeffcent on EVMS s very smlar. Fourth, we ntroduced for each commercal axs fxed effects to make sure that our varables are not capturng the mpact of unobservable factors. 16 The estmated coeffcents for EVMS are very smlar n both specfcatons. 17 Ffth, the effects are comparable when estmatng a model, explanng the probablty of a truck beng full or havng a load (.e. estmatng a probt model wth FULL or LOADED as dependant varables). Fnally, EVMS may be endogenous. We tackle ths concern n the next sub-secton. 4.3 Endogenety of EVMS adopton EVMS could be correlated wth the error term, thereby basng n our results. Ths correlaton could result from mssng varables that are affectng both LF and EVMS such as for example the carrer sze. It may also be that carrers assgn ther equpped trucks to roads dependng LF. Dealng wth ths ssue s made dffcult by the fact that the adopton rate s low (t s more dffcult to predct a rare event). Moreover, both LF and EVMS are dscrete varables whch requre estmatng an endogenous swtchng model. In fact, we add to the latent LF response model [8] and [9], the followng latent model for explanng EVMS adopton: EVMS * = ' γ + υ [10] z 0 EVMS = 1 f EVMS otherwse * 0 [11] 16 Ideally, we would want to defne these axes as precsely as possble, however, for statstcal reasons, we need to have enough observatons per axs. For ths reason, we defne a commercal axs based on the regon of orgn and destnaton. The regons are defned as follows: ) the provnce of Quebec s dvded n four sectors usng a dvson defned n the NRS99; ) the rest of Canada s dvded n three parts: the provnce of Ontaro (.e. Quebec s man economc partner n Canada), the West (Brtsh Columba, Alberta, Saskatchewan, Mantoba and the North Western Terrtores) and the Atlantc provnces ) the US s dvded n two: the North-East s separated from the rest of the US. 17 Obvously, as expected, the estmated coeffcents assocated wth varables that vary lttle across commercal axes were affected by the ntroducton of the fxed effects. 17

19 wth ' z the vector of explanatory varables. Furthermore, t s assumed that a shared random effect nduces dependence between the error terms n [8] and [10]: u υ = ε + ζ = λε + τ where ε, τ and ζ are ndependently normally dstrbuted wth mean 0 and varance 1 and λ s a free parameter. In ths model, the correlaton between the two latent equaton error terms s gven by: ρ = λ. 2( λ 2 + 1) EVMS s exogenous f ρ = If the model can be estmated wthout any nstruments t s, however, recommended to specfy some excluson restrctons (.e. varables affectng EVMS but not LF). We propose usng as nstruments varables ndcatng f the truck s equpped wth: ) an electronc drve log that automatcally records the hours of operaton (LOG); ) an electronc vehcle dentfcaton tag that can be read by equpment located n the roadway (TAG); ) a trpmaster provdng nformaton about the best route (TRIPMASTER). These electronc equpments are not desgned to have an mpact on LF but they are lkely to be postvely correlated wth the adopton of EVMS. We also use a varable ndcatng f the truck has ant-lock brakes (ANTI). Indeed, ths varable should provde an ndcaton of the truck age. 19 Newer trucks are certanly more lkely to be equpped wth new up-to-date technologes such as EVMS. Fnally, we also use a bnary varable set to one f the drver has receved at least a one day tranng course on the usage of electronc equpments n the last three years (TRAINING). The results are reported n Table 6. For both F and B trps, we cannot reject the hypothess that ρ = 0 at any sgnfcance level. The estmated coeffcents are farly close to those estmated n Table 5, except, however, for the coeffcent assocated wth EVMS. For F trps, the negatve effect s somewhat larger but becomes statstcally not sgnfcant. For B trps, t s postve sgnfcant and much larger than 18 The model s estmated by maxmum lkelhood usng the ssm wrapper program n Stata created by Mranda and Rabe-Hesketh (2006). 19 In the US antlock brakes become mandatory on heavy trucks n

20 n table 5 (1.16 versus 0.421). Note, however, that the EVMS model has dffculty to correctly predct adopton. Indeed, the model only predcts a probablty of adopton hgher than 0.5 for 63 B observatons whle there are 467 equpped trucks. In fact, the average predcted probablty of adopton computed over the sample of equpped trucks on B trps s only Thus the average mpact of EVMS n ths model s lkely to be best evaluated by consderng a change n the predcted LF for EVMS ncreasng from 0 to 0.24 (rather than from 0 to 1). In ths case, the mpact s relatvely comparable to the one measured n Table 5. Lookng at the adopton results, we fnd that the probablty of havng an EVMS s postvely and statstcally lnked to all our nstruments. It also ncreases wth the trp dstance. Prvate carrers and owner-operators are less lkely to adopt ths technology. The probablty of adopton s also lower for specalzed tralers. These results are n lne wth Hubbard (2000). Trucks from the ROC are more lkely to have ths technology than Quebec trucks. For US trucks, the evdence s less clear: on F trps they have a much hgher probablty, whle on B trps t seems to be the opposte. 5. Concluson Usng data dsaggregated at the operatonal level, we have shown that ICT mproves capacty utlzaton n the truckng ndustry. Whle the overall mpact s relatvely lmted due to a low adopton rate, the effect on adopters s not neglgble. Snce t s possble that benefts are ncreasng over tme wth learnng and dffuson, t would be nterestng to reevaluate the effect of EVMS usng more recent data. Our results also llustrate, once agan, the dffculty n measurng the mpact of ICT on aggregate productvty measures. Indeed, we have found evdence that whle the technology mproves capacty utlzaton on backhaul, t also leads carrers to accept fronthaul shpments that may not have been accepted wthout ths technology. Ths could be vewed as an mprovement n the qualty of the servces offered. Obvously, ths rebound effect lmts the postve envronmental consequences of ths technology. In future research, t would be worthwhle to further test ths rebound effect usng a more structural approach, where the fronthaul load factor drectly depends upon the expected load factor on the 19

21 return. Ths approach would, however, requre observng the complete truck journey rather than only one segment, as n our data. Fndng a proper source of dentfcaton would also be a challenge. 20

22 Appendx 1. Crtera used to select the observatons n the sample From the ntal data set provded by the Quebec Mnstry of Transport, we elmnate observatons correspondng to: Small trucks (.e. trucks wth less than fve axles) whch are more lkely to be affected by local transportaton actvtes. We also elmnate non standard truck confguraton (.e. one tractor wth more than one traler) whch s lkely to face dfferent operatonal constrants. Courer and less-than-truckload servces (as well as peddle run). Contrary to full truckload companes, courer and less-than-truckload (LTL) carrers are specalzed n relatvely small freght, and usually operate wthn a hub-and-spoke network: freghts from varous clents are frst shpped nto a termnal before beng dspatched to other termnals and then delvered to ther fnal destnaton. For these types of servces, optmzng LF s somewhat less mportant than provdng frequent and on-tme delveres. The mpact of varous explanatory varables on ther actvtes s therefore lkely to be dfferent than on those of full truckload carrers. Moreover, snce relatvely few observatons were related to courer and LTL operatons n our dataset, t was not possble to estmate the model separately for these observatons. Trps for whch t was dffcult to determne f t was a fronthaul or backhaul trp. Specfcally, we elmnate trps for whch the dstance truck base-orgn and truck base-destnaton were relatvely smlar. Indeed, t s hazardous to classfy these trps as fronthaul and backhaul. Trps wth a dstance less than 80 km. 21

23 Table 1. Varable Defnton. LF Varable FULL LOADED Truck characterstcs EVMS AXLES Truck s base QUEBEC ROC USA Type of traler VAN CONTAINER REFRI LOGGING SPECIALIZED II. Carrer s Characterstcs FOR-HIRE PRIVATE OWNER-OP II. Trp s Characterstcs DISTANCE POPULATION INCOME Descrpton Load factor of the truck as evaluated by the drver. Fve responses are possble: 0%, 25%, 50%, 75% and 100%. Bnary varable sets to 1 f LF=100% and 0 otherwse. Bnary varable sets to 1 f LF 0 and 0 otherwse. Bnary varable sets to 1 f the truck s equpped wth an on-board computer and a satellte dsh. Number of axles. Bnary varable sets to 1 f the truck s regstered n the provnce of Quebec. Bnary varable sets to 1 f the truck s regstered n the rest of Canada. Bnary varable sets to 1 f the truck s regstered n the U.S. Bnary varable sets to 1 for van type traler. Bnary varable sets to 1 for contaner type traler. Bnary varable sets to 1 for refrgerated van type traler. Bnary varable sets to 1 for traler desgned to carry logs. Bnary varable sets to 1 f the traler s a platform, a hopper, a dump or a tank. Bnary varable sets to 1 for-hre carrer. Bnary varable sets to 1 f prvate carrer. Bnary varable sets to 1 the truck s drven by an owner-operator. Total trp dstance. It s the shorter dstance ( as-the-crow-fles) between the orgn and destnaton based on these two ponts longtude and lattude. Populaton n the area of orgn tmes the populaton n the area of destnaton dvded the square of the trp dstance. Sources: for Canada Statcan, for the US, US Census Bureau. Average of the orgn and destnaton household medan ncomes. Sources: for Canada Statcan, for the US, US Census Bureau. 22

24 LF (%) B & F trps Table 2. Sample Characterstcs Varables Mean (std) (*) F trps 75.4 (38.3) 64.4 (44.0) FULL (%) B & F trps B trps B trps 54.8 (46.4) 64.0 (47.9) 54.3 (49.8) LOADED (%) B & F trps F trps B trps 45.9 (49.8) 83.1 (37.4) 71.7 (45.0) F trps 61.7 (48.5) EVMS (%) 8.4 (27.7) AXLES 5.6 (0.81) QUEBEC (%) 78.9 (40.7) ROC (%) 17.1 (37.7) US (%) 3.8 (19.2) VAN (%) 48.0 (49.9) CONTAINER (%) 3.9 (19.5) REFRI (%) 10.3 (30.4) LOGGING (%) 3.5 (18.3) SPECIALIZED (%) 33.9 (47.3) FOR-HIRE (%) 78.2 (41.2) PRIVATE (%) 21.7 (41.2) OWNER-OP (%) 20.0 (40.0) DISTANCE (km) (480.9) POPULATION 2.4 x 10 6 (5.2 x 10 6 ) INCOME (en ml. CA$) 45.5 (9.4) NUMBER OF TRIPS (*) : The expanson factor s used to weght each observaton. 23

25 EVMS Yes No Table 3. Average LF and EVMS Adopton Rate as a Functon of Key Varables Varables Average LF (*) (%) EVMS adopton rate (*) QUEBEC 63.1 ROC 72.0 US 58.1 VAN Yes 70.4 No 58.9 FOR-HIRE Yes 66.8 No 55.9 OWNER-OP Yes 68.9 No 63.3 DISTANCE < 450 km 54.9 > 450 km 80.2 (*) : The expanson factor s used to weght each observaton Table 4. Impact of EVMS on Capacty Utlzaton LF FULL LOADED EVMS EVMS EVMS No Yes No Yes No Yes F trps Dstance< Dstance> B trps Dstance< Dstance>

26 Table 5. Estmaton results (standard error) F trps B trps Varable OLS MNOP MNOP Vans EVMS * *** (0.031) (0.108) (0.133) OLS MNOP MNOP Vans 0.132*** 0.406*** 0.584*** (0.02) (0.09) (0.124) AXLES 0.073*** (0.011) 0.231*** (0.045) 0.244*** (0.085) (0.015) (0.041) (0.062) QUEBEC Reference Reference Reference Reference Reference Reference ROC (0.021) (0.077) (0.106) (0.025) (0.069) (0.101) USA *** (0.04) *** (0.123) *** (0.195) (0.058) (0.150) (0.279) VAN Reference Reference -- Reference Reference Reference CONTAINER (0.050) (0.171) (0.054) (0.149) REFRI ** *** (0.028) (0.095) (0.042) (0.117) LOGGING (0.073) (0.220) (0.058) (0.160) SPECIALIZED -0.09*** *** *** *** -- (0.021) (0.073) (0.025) (0.068) FOR-HIRE Reference Reference Reference Reference Reference Reference PRIVATE (0.024) (0.081) (0.121) * (0.027) * (0.075) (0.009) OWNER-OP (0.025) (0.086) (0.116) 0.050* (0.026) 0.118* (0.071) (0.09) Log(DISTANCE) 0.163*** (0.016) 0.538*** (0.048) 0.619*** (0.074) 0.170*** (0.015) 0.466*** (0.043) 0.631*** (0.062) Log(POPULATION) (0.003) (0.013) (0.019) 0.009** (0.004) 0.027** (0.011) 0.059*** (0.017) Log(INCOME) 0.09*** (0.036) 0.351*** (0.133) 0.07 (0.175) (0.049) (0.135) (0.179) α 1 α 2 α 3 α 4 Logpseudolkelhood Wald Test (0.713) 4.64 (0.711) 4.79 (0.712) 5.14 (0.711) 4.25 (1.09) 4.49 (1.08) 4.65 (1.09) 4.94 (1.08) 3.12 (0.68) 3.25 (0.68) 3.34 (0.68) 3.57 (0.68) 3.39 (0.92) 3.55 (0.92) 3.67 (0.92) 3.87 (0.92) χ (13) =241 χ (9) =109 χ (13) =272 χ (9) =154 R-squared Pseudo R-squared (McFadden) Numb.. obs * : sgnfcant at 10%, ** : sgnfcant at 5%, *** : sgnfcant at 1% In parenthess, robust standard errors (Hubber and Whte estmators). α 1, α 2, α 3, α 4 represent the threshold parameters 25

27 Table 6. Estmaton results wth endogenous EVMS adopton. F trps B trps Varable LF eq. EVMS eq. LF eq. EVMS eq. EVMS *** -- (0.531) (0.382) AXLES 0.234*** (0.04) ** (0.072) (0.042) (0.068) QUEBEC Reference Reference Reference Reference ROC (0.08) 0.364*** (0.100) (0.070) 0.214*** (0.111) USA *** (0.141) 0.563*** (0.171) (0.155) ** (0.189) VAN Reference Reference Reference Reference CONTAINER (0.178) *** (0.250) (0.155) (0.241) REFRI *** (0.09) (0.172) (0.129) *** (0.126) LOGGING (0.228) (0.443) (0.169) *** (0.225) SPECIALIZED *** (0.082) *** (0.124) *** (0.087) *** (0.131) FOR-HIRE Reference Reference Reference Reference PRIVATE (0.084) (0.136) (0.07) (0.146) OWNER-OP (0.084) (0.113) 0.164** (0.078) *** (0.120) Log(DISTANCE) 0.549*** (0.053) 0.303*** (0.061) 0.422*** (0.055) 0.261*** (0.06) Log(POPULATION) (0.014) 0.046** (0.018) 0.025** (0.011) (0.01) Log(INCOME) 0.363*** (0.139) (0.191) (0.140) 0.08 (0.193) LOG *** (0.146) *** (0.134) TAG (0.164) *** (0.134) TRIPMASTER *** (0.236) *** (0.223) ANTI *** (0.110) *** (0.102) TRAINING *** (0.146) *** (0.090) CONST *** (1.02) *** (0.949) α 4.58*** 2.96*** 1 (0.75) (0.71) α 4.75*** 3.08*** 2 (0.75) (0.71) α 5.89*** 3.17*** 3 (0.75) (0.71) α 5.25*** 3.39*** 4 (0.75) (0.71) Log-pseudolkelhood Wald ch2 (30) Table 6. Estmaton results wth endogenous EVMS adopton (cont.). ρ LHR test for ρ =0 2 χ (1) = 0 χ (1) = 0 Standard devaton n parenthess. * : sgnfcant at 10%, ** : sgnfcant at 5%, *** : sgnfcant at 1% 2 26

28 Fgure 1. F (Fronthaul) trp A = Truck base B (Backhaul) trp B 27

29 Fgure 2. Smulaton results usng column (2) estmates from Tables 4. REV POP DIST=2000 DIST=1000 OWNER-OP PRIVATE SPEC. LOGGING B-trps F-trps REFRI CONTAINER. US ROC 7 AXLES EVMS Varaton n % ponts The mpacts are computed wth respect to an average reference trp of 450 km, between two area of 226,000 habtants each wth medan ncome of 45,000$. The reference truck has fve axles, no EVMS, s a van regstered n Quebec and s operated by a for-hre carrer. 28

30 References Brynjolsson E. and Y. Shnku (1996), Informaton Technology and Productvty: A Revew of the Lterature, n Marvn Zelkowtz, ed., Advances n computers, Vol. 43, New York: Academc Press. Brdestone/Frestone Commercal Truck Tres (2006), Truck Tres.com, avalable onlne February CCMTA (Canadan Councl of Motor Transport Admnstrators) (2002), 1999 Natonal Roadsde Study, Truck Traffc Estmate Software Package, Canada. Envronnement Canada (2005), Inventare canaden des gaz à effet de serre , avalable on lne Greenng L., D. Greene and C. Dfglo (2000), Energy effcency and consumpton the rebound effect a survey, Energy Polcy 28, p Hubbard, T. N. (2005), The Demand for Montorng Technologes: The Case of Truckng, The Quarterly Journal of Economcs, Vol. 115 No. 2, p Hubbard, T. N. (2003), Informaton, Decson, and Productvty: On-Board Computer and Capacty Utlzaton n Truckng, The Amercan Economc Revew, Volume 93, Issue 4, pages Plat D. (2004), The ICT productvty paradox: Insghts from mcro data, OECD Economc Studes 38, p Porter, M. E. and C. van der Lnde (1995), Towards a New Concepton of the Envronmental- Compettveness Relatonshp, Journal of Economc Perspectves, 9, Mnstère des Transports du Québec (2003), Les déplacements nterurbans de véhcules lourds au Québec, Enquête sur le camonnage de 1999, Aprl, 147 pages. Mranda A. and S. Rabe-Hesketh (2006), Maxmum lkelhood estmaton of endogenous swtchng and sample selecton models for bnary, ordnal, and count varables, Stata Journal, StataCorp LP, vol. 6(3), p

31 Nx F. P. (2003), Truck Actvty n Canada A profle, prepared for Motor Carrer Polcy Branch, Transport Canada. Natural Resources Canada (2006), Energy Effcency Trends n Canada, Ottawa. Transport Canada (2003). Les transports au Canada, Rapport Annuel 2003 et 2004, avalable on lne, Wooldrdge, J. M. (1999), Asymptotc Propertes of Weghted M-Estmators for Varable Probablty Samples, Econometrca, Vol. 67, No. 6, p Wooldrdge J.M. (2001), Econometrc Analyss of Cross Secton and Panel Data, The MIT Press, Cambrdge, Massachusetts, 2001, 752 pages. 30