The Impact of Residential Density on Vehicle Usage and Energy Consumption *

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1 The Impact of Resdental Densty on Vehcle Usage and Energy Consumpton * September 26, 2008 Forthcomng n the Journal of Urban Economcs Davd Brownstone (correspondng author) Department of Economcs 3151 Socal Scence Plaza Unversty of Calforna Irvne Irvne, Calforna, Tel: Fax: Thomas F. Golob Insttute of Transportaton Studes Unversty of Calforna Irvne Irvne, CA * Ths s a revsed verson of Workng Paper UCI-ITS-WP-05-1, Insttute of Transportaton Studes, and Unversty of Calforna Irvne. The authors gratefully acknowledge fnancal support from the Unversty of Calforna Energy Insttute and the Unversty of Calforna Transportaton Center. Kenneth Small, two anonymous referees, and especally Jan Brueckner provded many useful comments on earler drafts, but the authors bear sole responsblty for any remanng errors.

2 The Impact of Resdental Densty on Vehcle Usage and Energy Consumpton Davd Brownstone Thomas F. Golob Unversty of Calforna Irvne Abstract We specfy and estmate a jont model of resdental densty, vehcle use, and fuel consumpton that accounts for both self selecton effects and mssng data that are related to the endogenous varables. Our model s estmated on the Calforna subsample of the 2001 U.S. Natonal Household Transportaton Survey (NHTS). Comparng two Calforna households that are smlar n all respects except resdental densty, a lower densty of 1,000 housng unts per square mle (roughly 40% of the weghted sample average) mples an ncrease of 1,200 mles drven per year (4.8%) and 65 more gallons of fuel used per household (5.5%). Ths total effect of resdental densty on fuel usage s decomposed nto two paths of nfluence. Increased mleage leads to a dfference of 45 gallons, but there s an addtonal drect effect of densty through lower fleet fuel economy of 20 gallons per year, a result of vehcle type choce. JEL Codes: C30, D12, L92, Q58, R14, R41 Keywords: resdental densty, vehcle use, vehcle fuel consumpton, smultaneous equatons, self-selecton.

3 Densty and Vehcle Consumpton 1 1. Introducton and Background Ths paper measures the relatonshp between resdental densty, household vehcle use, and household vehcle fuel use. It contrbutes to a large lterature on the mpact and/or desrablty of low-densty suburban development, frequently called urban sprawl, that has domnated development n the U.S. snce World War II. Increased vehcle usage assocated wth suburbanzaton or urban sprawl has been lnked to ncreasng global warmng, emssons, and other problems (see Pckrell and Schmek, 1999, and Kahn, 2000). Urban sprawl s not smply low densty, but also nvolves scattered development, commercal strp development, or large expanses of sngle-use development. Nevertheless, densty s hghly correlated wth almost all measures of urban sprawl and s the measure used most frequently n ths lterature (see revews by Ewng and Cervero, 2001, and Badoe and Mller, 2000). When used alone as an ndcator of sprawl, densty should therefore be nterpreted as a proxy for access to employment, shoppng, and other travel destnatons. The man reason densty s used so frequently s that t s one of the few ndcators of sprawl that s consstently measured across space and tme, and t s readly avalable n most relevant data sets. Aggregate studes examnng the bvarate relatonshp between vehcle mles traveled and densty fnd a large sgnfcant nverse effect (see Newman and Kenworthy, 1999). These studes are flawed because they do not account for the possblty of resdental self-selecton, whch s the tendency for those households that prefer non-prvate vehcle travel to locate n dense areas wth more transt and shorter trp dstances. Many studes use dsaggregate household data to attempt to control for observable dfferences between households lvng n low and hgh densty areas. One of the best of these s Bento et al. (2005), whch used the 1990 Natonal Personal Transportaton Study to buld dsaggregate models of number of vehcles per household and vehcle mles traveled (VMT) per vehcle. They supplemented the densty measures n the data wth road densty, ral and bus transt supply, populaton centralty, cty shape, jobshousng balance, populaton densty, land area, and clmate. Bento et al. (2005) found that the magntudes of the mpact of any of ther bult envronment measures were frequently statstcally nsgnfcant and small n magntude. Although dsaggregate studes that nclude a rch set of socoeconomc control varables (e.g. Bento et al., 2005) are less subject to resdental self-selecton bas, t s stll possble that resdents of hgh densty areas dffer n some unobservable characterstcs that nfluence ther travel behavor. The only way to deal wth ths possblty s to buld jont models of resdental (or densty) choce and travel behavor. One of the frst studes to do ths was Boarnet and Sarmento (1998). They used the percentage of buldngs bult before 1945, the percentage of buldngs bult between 1945 and 1985, the percentage of resdents more than 65 years old, and the percentage of foregn resdents as nstrumental varables for resdental densty, and they found no stable lnk between densty and VMT. Bhat and Guo (2007) use San Francsco Bay Area data to buld an ambtous jont model of resdental locaton and number of household vehcles. Ther model allows for self-selecton effects (correlaton between the error terms n ther equatons), but after

4 Densty and Vehcle Consumpton 2 controllng for a rch set of covarates they do not fnd any sgnfcant effects. Bhat and Guo fnd statstcally sgnfcant but quanttatvely small mpacts of bult envronment measures (street block densty, transt avalablty, and transt access tme) on vehcle ownershp. We also drectly model the jont choce of densty and VMT to control for potental selectvty, and we also nclude a rch set of socoeconomc varables usng the Calforna subsample from the 2001 Natonal Hghway Transportaton Study descrbed n the next secton of ths paper. We chose to work wth Calforna because t has as much varaton n the key varables as the U.S, but s relatvely homogeneous n clmate, fuel, and vehcle prces. Unlke prevous studes we also explctly model vehcle fuel consumpton to account for the possblty that resdents of hgh densty neghborhoods choose smaller, more fuel effcent vehcles. Ths mght be due to the relatve dffculty of maneuverng and parkng large vehcles n dense neghborhoods. Fang (2008) uses the same data to show that resdents of dense neghborhoods choose fewer trucks and more small cars. Unlke Bhat and Guo (2007) and Fang (2008) we do not explctly model the number of vehcles or ther type. Ths greatly smplfes the econometrcs and allows us to easly deal wth problems caused by non-random data selecton as descrbed n the next secton The thrd secton descrbes our smultaneous equatons model n whch resdental densty, vehcle usage (VMT), and fuel consumpton are jont endogenous varables. The fourth secton descrbes the results, whch are smlar to prevous studes n fndng a statstcally sgnfcant but quanttatvely small mpact of resdental densty. Even though our model allows for jont causalty between the endogenous varables, our preferred model has densty causng VMT (as n Bento et al. 2005) and fuel usage. The fnal secton concludes and argues that the mpacts of ncreased resdental densty are too small to make ncreasng densty a relevant polcy tool for tryng to reduce VMT or greenhouse gas emssons from resdental vehcles.

5 Densty and Vehcle Consumpton 3 2. Data 2001 Natonal Household Transportaton Survey (NHTS) The NHTS s a household-based travel survey conducted every fve years by the U.S. Department of Transportaton. There are 2,583 Calforna (CA) households n the 2001 NHTS sample, representng 9.9% of the total base sample of 26,038. The survey was conducted over a perod of fourteen months endng n May Daly travel was collected usng one-day trp dares for all household members, and data on noncommutng trps of at least 50 mles to the furthest destnaton was collected for a fourweek perod. Household vehcles were defned as all vehcles generally avalable to household members, ncludng motorcycles, mopeds, and recreatonal vehcles. Odometer readngs were obtaned at two dates, generally a few months apart, n order to provde accurate data on annual vehcle mles of travel. The 2001 NHTS s descrbed n detal n exhbts, reports, and codebooks mantaned on the NHTS webste (ORNL, 2004). Vehcle Ownershp and Fuel Usage Ths study focuses on the energy used by all vehcles owned or leased by Calforna households, ncludng vehcles otherwse avalable to households for the general use of household members. The weghted frequences from the NHTS show that 7.5% of Calforna households have no vehcles, 33.8% have one vehcle, 35.0% have two, 15.2% have three, 5.4% have four, and 3.0% have fve or more vehcles. As s usual n surveys of ths type, households wth the fewest numbers of vehcles are underrepresented n the sample. The procedures used to estmate annual fuel usage for each vehcle n the survey are reported n Schpper and Pnckney (2004). Reported and mputed odometer readngs, together wth fuel economy test results for each vehcle make, model and vntage, are adjusted for on-road shortfalls of vehcle dynamometer test results, seasonal varatons, and relatonshps between total mleage and average trp lengths. The resultng annual fuel usage and annual mles traveled varables are much more accurate than those avalable n prevous versons of the NHTS. Snce annual mleage and exact vehcle make, model and vntage are needed to compute fuel usage, 2079 (80.5%) of all Calforna NHTS households have full nformaton on transportaton fuel usage. Snce each household vehcle must be accounted for n order for full energy consumpton nformaton to be computed, the proporton of households wth full nformaton s a decreasng functon of vehcle ownershp level. It s dffcult to collect odometer readngs for households wth many vehcles snce the survey respondent may not have ready access to all of these vehcles when the survey frm calls to collect the data. Full energy nformaton s avalable for the vast majorty of 1- and 2-vehcle households (91% and 86% respectvely), but less than half of all households wth four or more vehcles have avalable energy consumpton nformaton. Snce the number of vehcles s endogenous n our models, ths means that the sample of households wth

6 Densty and Vehcle Consumpton 4 complete energy nformaton s not a random sample. We descrbe the econometrc technques we use to produce consstent estmates later n ths paper. Land Use Denstes The 2001 NHTS provdes several measures of land use related to household locaton. Populaton per square mle and housng unts per square mle are provded at the block group and tract level. Percentage of renter-occuped housng unts s provded at both the block group and tract level, and jobs per square mle are provded the tract level. As expected, these seven land use varables are all hghly correlated. The typcal correlaton between any two s above.7. Vehcle Usage and Land Use As expected, there s a sgnfcant negatve relatonshp between fuel usage and land use densty. Each of the seven land use varables was tested, and the strongest relatonshps were found for dwellng unts per square mle at the census block group level. Consequently, we show only the results for the housng densty varable, but the other sx land use varables exhbt smlar patterns. For (urban) denstes greater than 50 housng unts per square mle, both total annual mleage on all household vehcles and total fuel usage generally declne wth ncreasng housng densty, as shown n Table 1. The dfferences n means for both seres are statstcally sgnfcant, and lnear relatonshps cannot be rejected at the p<.01 level for ether seres. The slope of the curve s greater for fuel consumpton, ndcatng that there s a postve relatonshp between effectve vehcle fuel economy and urban densty. Indeed, effectve fuel economy, measured by the rato of total mleage to total fuel consumpton, ranges from a low of 19.7 mles per gallon for households located n areas wth denstes less than 50 housng unts per square mle, to a hgh of 22.4 mles per gallon for households n areas wth greater than 5,000 housng unts per square mle. These relatonshps are caused n large part by dfferences n household vehcle ownershp levels. As shown n Table 1, vehcles per household ranges from a hgh of 2.2 vehcles per household for households located n areas of dwellngs per square mle, to a low of 1.4 vehcles per household for those located n the hghest densty areas. The dfferences n fuel economy can be attrbuted to vehcle type choce dfferences nvolvng sze and power of cars and to the greater number of pckup trucks, vans, and SUVs n lower densty areas. As shown n Table 1, the lkelhood of ownng one of these three types of trucks ncreases wth decreasng densty, and there s no reversal of the trend at the lowest densty as there s for mleage and fuel usage. Snce 1990 the fleet average fuel economy for lght trucks was no less than 20.2 mles per gallon whle the fleet average fuel economy for cars was no less than 27.5 mles per gallon (see NHTSA, 2008).

7 Densty and Vehcle Consumpton 5 Table 1: Vehcle Characterstcs by Resdental Densty (weghted averages across estmaton sample, 2079 observatons) Housng unts per square < K 1-3K 3-5K >5K mle n Census block group 1K Percentage households resdng n densty group Annual fuel consumpton n gallons Total annual mleage Vehcles per household Percentage households wth at least one truck Average number of drvers Household ncome ($1K) Of course, dfferent types of households choose to lve n areas of dfferent resdental densty. Qute a few socoeconomc and demographc varables were found to descrbe choce of resdental densty n the model presented n the next secton. Two of the varables that stand out are the number of household drvers and average household ncome. The last two rows of Table 1 show that households lvng n more dense neghborhoods have fewer drvers and lower ncome. It s apparent that dfferent types of households choose to lve n areas defned by dfferent resdental denstes. These households have dfferent patterns of actvty partcpaton and travel, and choose to own or lease or otherwse have avalable dfferent numbers and types of vehcles. To account for such selectvty effects of land use on vehcle fuel consumpton, we specfy and estmate a structural equaton model that contans both densty of land use and vehcle usage as endogenous varables. 3. Structural Model of Densty of land Use and Fuel Usage Model Specfcaton We estmate the effects of land use varables on fuel usage by specfyng a smultaneous equaton model wth three endogenous varables and many exogenous varables. The three endogenous varables are: total annual mles drven by all household vehcles (M), total annual household fuel usage measured n gallons of gasolne equvalents per year (F), and housng unts per square mle n census block group descrbed prevously (D). Our preferred model s gven by the equaton system below, where A and B are coeffcent matrces, X s a vector of exogenous household attrbutes, and ε s a vector of resduals wth an unrestrcted correlaton structure. We need to put restrctons on the coeffcent matrces to dentfy the system. We have

8 Densty and Vehcle Consumpton 6 chosen to dentfy our system prmarly by restrctng the A matrx to the recursve system shown below. We also mpose enough restrctons on the B matrces to dentfy the system (see Table 3), but these restrctons are based on removng nsgnfcant varables. The excluded varables from the B matrx are therefore weak nstruments, and estmates are essentally unchanged when all exogenous varables are ncluded n each equaton. In the context of our model, resdental self-selecton mples postve correlatons between the structural errors (ε ). We cannot reject the null hypothess that all of the error correlatons are zero, and ths s consstent wth other studes (e.g. Bhat and Guo, 2007) that condton on a rch set of socoeconomc varables. Note that ths fndng of no sgnfcant error correlatons does not mean that there are not self-selecton effects, but t does mply that the ncluded socoeconomc varables capture these effects. M = A D + B X + ε 1,3 1 1, F = A M + A D + B X + ε 2,1 2,3 2 2, D = B X + ε 3 3, Our recursve model starts by assumng that the choce of resdental densty s only a functon of exogenous household characterstcs. Ths s equvalent to assumng that households frst choose ther resdental locaton and then choose ther vehcle holdngs and drvng patterns condtonal on ths choce. Densty, whch s a proxy for access to employment and other destnatons, affects total mles drven, but after controllng for exogenous socodemographc factors fuel usage s assumed to not drectly affect total mles drven. Densty also affects fuel usage snce households n denser neghborhoods choose more fuel effcent vehcles. Densty s postulated to affect fuel usage by both decreasng total mles drven and ncreasng vehcle fuel economy. The exogenous varables n the structural equatons model are desgned to capture all socodemographc characterstcs related to choce of densty and vehcle use (see the defntons n Table 2). In many cases we nclude ndcator varables of common household types along wth counts of chldren, workers, and drvers. Ths combnaton of count and ndcator varables allows for flexble nonlnear mpacts of key socodemographc characterstcs. A contnuous varable was constructed for ncome by usng the mdponts of the 10 categores used n the survey nstrument, wth $170,000 assumed for the top category, and $35,000 assgned for mssng ncomes. All of the overdentfyng restrctons n our preferred model passed the specfcaton tests descrbed below. In partcular, we could fnd no economcally or statstcally sgnfcant backward lnks from fuel usage to land use densty. Note however that removng any of the exogenous characterstcs from the model leads to rejecton of the null hypothess that the error correlatons are all zero. Ths hghlghts the necessty of ncludng a rch set of socodemographc controls to avod resdental self-selecton bas.

9 Densty and Vehcle Consumpton 7 Weghtng and Estmaton Methodology As dscussed n the prevous secton, our estmaton sample, whch requres full energy nformaton, s not a random sample of any populaton. The strongest factor causng mssng energy nformaton s the number of vehcles n the household, and ths s closely related to the endogenous varables n our model. Ths means that the estmaton sample s effectvely stratfed on an endogenous varable, whch mples that standard estmaton methods wll yeld based coeffcent estmates and nferences. There are two basc approaches to gettng vald estmates n ths stuaton (see Wooldrdge, 2002, Chapter 17): the structural approach and the weghtng approach. The structural approach adds an explct equaton explanng whether a household has complete energy nformaton and then estmates ths equaton together wth the structural equatons model descrbed above. The weghtng approach uses weghted estmaton where the weghts compensate for the dfferent probabltes of havng complete energy nformaton. The weghtng approach s almost always neffcent, but unlke the structural approach t doesn t rely on functonal form assumptons that are hard to justfy. We began by tryng the structural approach usng Heckman s (1979) two-step estmaton method. Ths method starts wth a separate bnomal probt model of whether the household has complete energy nformaton. Under the assumpton that all of the errors n the system are normally dstrbuted, the Mll s rato estmated from ths probt equaton can then be added to the substantve structural equatons model to control for the bas caused by non-random samplng. When appled to our data ths showed that there was no substantal bas. However, small changes n model specfcaton led to strong rejectons of the no bas hypothess. A smple Ramsey test for the jont normalty assumpton can be carred out by addng the square and cubed Mll s rato, and ths test strongly rejected the jont normalty assumpton. We therefore adopted the weghted estmaton approach, and we estmated the weghts so that the weghted dstrbuton of the number of vehcles (categorzed by 0, 1, 2, 3, 4, and 5 or more vehcles per household) n our sample of 2079 households wth complete energy nformaton matched the dstrbuton n the entre sample of 2583 Calforna households n the NHTS. The resultng weghts range from.8 for the 0 vehcle households to 6.4 for the 5 or more vehcle households. Note that we dd not use any addtonal exogenous socoeconomc nformaton about the households to mprove the weghts snce we drectly control for these exogenous factors n our structural equatons models. Addng these adjustments to the weghts would reduce the effcency of the weghted estmaton methods, but t s mportant to adjust the weghts when usng the estmates for populaton projectons and smulatons. Our structural model s: y = Ay Cov + Bx ( ε ) = Ω + ε The weghted estmator we use s defned by:

10 Densty and Vehcle Consumpton 8 mn w 1 (( I A) y Bx ) Ω (( I A) y Bx ) Where the weghts, w, are the nverse probablty of selecton. The covarance of the weghted estmator above s gven by: V Ψ = Λ = = Ψ E 1 E ΛΨ 1 2 w L ( θ,x) θ θ wl ( θ,x) wl ( θ,x θ n ) θ Once the weghts are estmated, then most standard software for structural equatons models can perform the weghted estmaton. Unfortunately these softwares typcally 1 use Ψ to estmate the covarance of the estmator, and ths s clearly based. We therefore use a wld bootstrap (Horowtz, 2002) to generate standard errors for our weghted estmates. Ths bootstrap works by takng the vector of estmated resduals,, for each observaton and multplyng by: e ( 1-5) 2 wth Probablty = ( 1 + 5) ( 2 5) ( 1 + 5) 2 wth Probablty = 1- ( 1 + 5) ( 2 5) Ths mples that across the bootstrap repettons the resduals wll have mean equal to e and covarance equal to e e, whch s the same approxmaton used to derve Whte heteroskedastc-consstent standard errors. Ths bootstrap procedure has the advantage that t wll yeld consstent standard errors even f the errors n the model are heteroskedastc. We used 200 bootstrap teratons, although we checked our fnal results usng 1000 bootstrap teratons, and the results were very stable. We found that 1 the ncorrect standard errors ( Ψ ) were downward based by from %, and the weghted estmates are statstcally and operatonally sgnfcantly dfferent from unweghted estmates n many specfcatons. One drawback of usng weghted estmatons s that they are not equvalent to maxmum lkelhood, so standard lkelhood rato tests of overdentfyng restrctons cannot be used. We mplemented a bootstrap test for overdentfyng restrctons (ncludng the restrctons on the resdual correlaton matrx) by bootstrappng the dfference between the restrcted and unrestrcted reduced forms for the varous models we examned. The reduced form s gven by: y = Cx + μ and the overdentfyng (or structural) restrctons are gven by: C = I A B Cov ( ) 1 1 ( μ ) = ( I A) Ω( I A) 1

11 Densty and Vehcle Consumpton 9 Our test statstc s gven by: 1 ( C C ) ( C C ), R U R U where CR are the restrcted reduced form estmates, CU are the unrestrcted reduced form estmates, and s the bootstrap varance estmate of ( CR CU ). If the restrctons are correct then ths statstc follows a Ch-squared dstrbuton wth degrees of freedom equal to the number of restrctons. Ths test appears to work well snce t ruled out many possble model specfcatons. Fnally, we also mplemented a smple Hausman(1978) test for the null hypothess that the weghts are actually exogenous. Ths test compares the weghted estmates wth standard maxmum lkelhood estmates gnorng the weghts. When appled to our preferred model ths test also does not reject the null hypothess that the weghts are exogenous, but, as wth the structural Heckman test, ths result s very senstve to slght changes n model specfcaton. We therefore decded to be conservatve and use the weghted estmates for our emprcal results. Although neffcent, they are consstent under the wdest array of assumptons about the underlyng data generaton process. 4. Estmaton Results The best model uses housng densty at the census block level, although the other sx land use varables also produce acceptable models and smlar results. The structural equaton model was estmated usng weghted three-stage least squares wth bootstrapped standard errors as descrbed n Secton 3, and the results are gven n Table 3. Note that the estmates n Table 3 are computed under the assumpton that the structural errors are uncorrelated. The overdentfyng restrctons for ths model cannot be rejected at any usual level of confdence. Table 4 gves the restrcted reduced form estmates correspondng to the structural model n Table 3. The reduced form gves the total mpact of the exogenous varables on endogenous varables. Note that the excluson restrctons mposed on the structural model n Table 3 mply dfferent excluson restrctons on the restrcted reduced form n Table 4 due to the nonlnear relatonshp between the two models. The squared multple correlatons for the structural equatons are 0.11 for housng densty, 0.37 for annual mleage, and 0.95 for annual fuel usage. For the reduced-form equatons, the squared multple correlatons are 0.11 for housng densty (same as the structural R 2 because there are no endogenous varable effects on housng densty), 0.37 for annual mleage, and 0.42 for fuel usage.

12 Densty and Vehcle Consumpton 10 Table 2 Varable Descrptve Statstcs of the Varables of the Structural Equaton Model (Weghted sample, N = 2079) Mean Std. Dev.* Annual household fuel consumpton n gallons Total mleage per year for all household vehcles Thousand dwellng unts per sq. mle - Census block group Annual household ncome n unts of $10, Number of chldren n household Number of workers n household worker household worker household or-more-worker household 0.13 Number of drvers n household drver household drver household or-more-drver household 0.18 Respondent has only college degree 0.53 Respondent has postgraduate degree 0.15 Respondent s retred 0.23 Youngest chld at least and at least 2 adults not retred 0.05 Sngle-person household not retred 0.14 Race s Asan 0.07 Race s Hspanc 0.11 Race s Black 0.05 Race s mxed Whte & Hspanc 0.06 * Varables wth mssng Std. Dev. are dummy varables defned as =1 f condton s true and =0 otherwse

13 Densty and Vehcle Consumpton 11 Table 3: Structural Regresson Coeffcents (bootstrapped t-statstcs n parentheses) Explanatory varable Dwellng unts per sq. mle n unts of 1,000 census block group Total mleage per year on all household vehcles Annual household ncome n unts of $10,000 Number of chldren n household Number of workers n household 1-worker household 2-worker household 3-or-more-worker household Number of drvers n household 1-drver household 2-drver household 3-or-more-drver household Respondent has only college degree Respondent has postgraduate degree Respondent s retred Youngest chld at least and at least 2 adults not retred Sngle-person household not retred Race s Asan Race s Hspanc Race s Black Race s mxed Whte & Hspanc Household fuel usage per year n gallons (-6.15) (17.3) 13.3 (4.41) 40.0 (4.2) -117 (-1.64) 97.3 (1.25) 252 (1.69) 384 (1.54) 65.7 (3.35) (-2.22) (-3.03) (-1.43) (-1.25) (-1.01) Endogenous varable Total mleage per year on all household vehcles (-4.97) 255 (1.04) 8493 (1.88) (2.24) (2.11) (3.64) (-1.19) (-1.3) (-0.78) 3729 (0.59) (-1.66) (-1.38) (-0.86) Dwellng unts per sq. mle n unts of 1,000 - census block group (-1.99) (-5.43) (2.42) (-0.77) (-2.34) (-2.42) (-1.68) (-3.04) (-3) (1.37) (3.11) (4.24) (4.89) (3.87)

14 Densty and Vehcle Consumpton 12 Table 4: Reduced Form Coeffcents (bootstrapped t-statstcs n parentheses) Exogenous varable Annual household ncome n unts of $10,000 Number of chldren n household Number of workers n household 1-worker household 2-worker household 3-or-more-worker household Number of drvers n household 1-drver household 2-drver household 3-or-more-drver household Respondent has only college degree Respondent has postgraduate degree Respondent s retred Youngest chld at least and at least 2 adults not retred Sngle-person household not retred Race s Asan Race s Hspanc Race s Black Race s mxed Whte & Hspanc Household fuel usage per year n gallons 24.2 (2.92) 55.0 (5.12) -129 (-1.79) 422 (2.77) 761 (3.42) 1274 (2.93) 596 (4.10) -128 (-.86) -315 (-1.07) -265 (-.59) (-2.22) (-3.03) 129 (.60) -400 (-1.60) (-1.31) -199 (-2.17) -172 (-1.54) (-3.93) (-3.14) Endogenous varable Total mleage per year on all household vehcles 276 (1.12) 271 (3.51) -211 (-1.91) 8493 (1.88) (2.24) (2.11) (3.59) (-.96) (-1.12) (-.65) 4208 (.67) (-1.55) -256 (-1.30) (-1.64) (-1.11) (-3.51) -835 (-2.87) Dwellng unts per sq. mle n unts of 1,000 - census block group (-1.99) (-5.43) (2.42) (-.77) (-2.34) (-2.42) (-1.68) (-3.04) (-3.00) (1.37) (3.11) (4.24) (4.89) (3.87)

15 Densty and Vehcle Consumpton 13 Interpretaton of Results The Effects of Land Use Densty The model mples that, f two households are dentcal n all aspects measured by the exogenous varables n the model, but one household s located n a resdental area that that s 1,000 housng unts per square mle more dense, the household n the denser area wll drve 1171 mles per year less than the household n the less dense area. Ths s the net effect of vehcle ownershp level and trp patterns. The household n the denser area wll consume 64.7 fewer gallons of fuel, and ths effect of resdental densty on fuel usage s decomposed nto two paths of nfluence. The mleage dfference of 1171 mles leads to a dfference of 44.7 gallons (usng gallons per mle, the estmated drect effect of mleage on fuel consumpton, mplyng a fuel economy of 26.2 mles per gallon). However, there s an addtonal drect effect of densty on fuel consumpton of 20 gallons per 1,000 housng unts per square mle. Ths s due to the relatonshp between resdental densty and fleet fuel economy, a result of vehcle type choce. Exogenous Varable Effects Number of Drvers As expected, the number of household drvers has a strong nfluence on household annual mleage and fuel consumpton. However, the number of drvers also affects the choce of resdental densty. Thus, the total effect on mleage s due to both a drect effect and an effect channeled through resdental densty. In turn, the effect on fuel consumpton s a sum of a drect effect, an effect channeled through mleage, and an effect channeled through resdental densty. The total effects on each of the three endogenous varables are nonlnear, as captured by up to four varables: a contnuous number of drvers varable, and dummy varables for one-drver, two-drver and threeor-more-drver households. Drvers per household has a negatve dmnshng margnal effect on choce of resdental densty. All else held constant, the model predcts that a household wth one drver wll locate n a resdental area that s less dense by 840 dwellng unts per square mle, when compared wth a household wth no drvers; a household wth two drvers wll locate n a resdental area that s less dense by about 450 dwellng unts per square mle, when compared wth a household wth one drver; and the dfference n densty between two- and three-drver households declnes to about 200 dwellng unts per square mle. The nfluence of drvers per household on annual vehcle usage and fuel consumpton does not exhbt such dmnshng margnal effects, and the man nonlneartes nvolve the effects of more than two drvers. Based on the reduced form results n Table 4, addng the frst drver n the household ncreases annual mleage by 10,100, and addng an addtonal drver leads to an addtonal 8,700 mles per year. From two to three drvers per household the added mleage per year s 15,100 mles, and from three to

16 Densty and Vehcle Consumpton 14 four t s 13,800. The effects of the number of drvers on fuel usage follow the same trend, but the rates of ncrease per drver are slghtly greater. Ths s due to an addtonal postve drect effect of the number of drvers on fuel usage, ndcatng a lowerng of fleet fuel economy as a functon of the number of drvers. Number of Workers There s a postve lnear effect of the number of workers on resdental densty. Households wth more workers tend to lve n hgher densty areas, ceters parbus. As n the prevous case of household drvers, the total effects of number of workers on annual mleage and fuel usage are both nonlnear, each beng captured by three varables: a contnuous varable and dummy varables for one-worker, two-worker and three-or-more-worker households. However, n contrast to number of drvers, the greatest margnal effect for number of workers s the dfference n mleage and fuel consumpton attrbuted to the dfference between two to three workers, whch s sgnfcantly greater than the dfferences between one and two workers, and somewhat greater than the dfference between zero and one worker. The model mples that ncreases n total household mleage are generally shorter for the second worker n the household and longer for the thrd worker, n comparson to the frst worker. Fuel consumpton per worker generally tracks annual mleage, wth the excepton that fuel consumpton s more lnear than mleage n the range of zero to two workers, mplyng that frst workers generally use more fuel effcent vehcles. Income The model predcts that fuel usage ncreases lnearly wth ncome, and ths s caused by all three factors. Hgher ncome translates nto: (1) choce of lower densty resdental locaton, (2) greater total drvng dstances, ndependent of the greater dstances caused by lower denstes, and (3) lower overall fuel economy of the household fleet. Number of Chldren Fuel usage ncreases wth number of chldren due to two factors. Larger famles tend to choose lower resdental densty, whch n turn ncreases total mleage. In addton, fuel economy decreases as a functon of the number of chldren, due to ncreased lkelhood of a least one van or SUV n the household fleet. Educaton Only two educaton dummy varables were found to be sgnfcant. Households headed by a respondent wth a college degree tend to have a vehcle fleet wth greater overall lower fuel economy than ther less educated counterparts. Ths effect s accentuated f the household s headed by a respondent wth a postgraduate degree.

17 Densty and Vehcle Consumpton 15 Lfe Cycle Effects Retred two-person households tend to lve n lower-densty resdental areas. However, the postve nfluence of lower resdental densty on fuel consumpton s partally offset by a vehcle fleet wth hgher fuel economy, probably due to a lower lkelhood of vans, pckup trucks and SUVs. Households wth older chldren choose to lve n lower densty areas. In Calforna, many chldren over sxteen years of age have drvng lcenses, so the effects of ths varable on vehcle usage and fuel consumpton should be combned wth the household drvers varables. If an addtonal household drver s a chld years of age, the model predcts that the addtonal vehcle usage and fuel consumpton wll be less than f the drver s not such a chld. Fnally, non-retred sngle-person households also tend to lve n hgher densty areas. Ths translates nto lower annual mleage and fuel consumpton strctly through the drect effect of land use densty. Race and Ethncty Four race and ethncty varables were determned to have sgnfcant effects on choce of resdental densty and moblty. Households whch are solely Black, solely Asan, solely Hspanc, or mxed Whte and Hspanc, all tend to resde n hgher-densty areas, compared to other households, predomnately solely Whte households. Ths leads to lower vehcle usage and fuel consumpton for all of these groups. In addton, there are possble drect travel and fuel economy effects for Asan and Hspanc households, but these effects are not estmated wth precson. Further research s needed to mprove our understandng of these and other demographc nfluences on resdental transportaton fuel consumpton. 5. Conclusons and Drectons for Further Research We specfed a smultaneous equaton model that accounts for self selecton effects n estmatng the nfluence of resdental densty on household vehcle annual mleage and fuel consumpton. Ths model was estmated usng a method that corrects for mssng data that s non-random and related to the endogenous varables. Once we ncluded a complete set of socodemographc control varables, we could not reject the hypothess that there are not sgnfcant self-selecton effects (smlar to Bhat and Guo, 2007). We fnd that densty drectly nfluences vehcle usage, and both densty and usage nfluence fuel consumpton. Comparng two households that are smlar n all respects except resdental densty, a lower densty of 1,000 (roughly 40% of the mean value) housng unts per square mle mples a postve dfference of almost 1,200 mles per year (4.8%) and about 65 more gallons of fuel per household (5.5%). Ths total effect of resdental

18 Densty and Vehcle Consumpton 16 densty on fuel usage s decomposed nto to two paths of nfluence. Increased mleage leads to a dfference of 45 gallons, but there s an addtonal drect effect of densty through lower fleet fuel economy of 20 gallons per year, a result of vehcle type choce. Unfortunately for those wshng to use land use plannng to control resdental vehcle use, t s very dffcult to ncrease the densty of an establshed urban area by 40%. Downs (2004, Chapter 12) shows that ncreasng the densty of an exstng metropoltan area by 40% requres extreme denstes of new and nfll development. Bryan, Mnton, and Sarte (2007) have recently developed a consstent hstorcal database of U.S. cty and regonal denstes. These data show that only 30 out of 456 ctes ncreased populaton densty more than 40% between 1950 and 1990, and the medan cty n ths sample decreased populaton densty by 36%. The ctes that dd ncrease populaton densty by more than 40% are smlar to Santa Ana, Calforna. They experenced large ncreases n low-ncome mmgrants nto very tght housng markets. The ncrease n denstes n these ctes was largely accommodated by crammng more people nto the exstng housng stock. Of course, ncreasng dwellng unt densty s even harder than ncreasng populaton densty. As expected, the most mportant exogenous nfluences are number of household drvers and number of workers, but educaton and ncome also are sgnfcant. Isolatng the effects of number of workers on fuel consumpton allows the development of models amed at forecastng the effects of employment levels on resdental transportaton energy consumpton. There are also demographc, race, and ethncty effects, as retred households are more lkely to lve n less dense resdental areas, and sngles and nonwhte households are more lkely to lve n denser areas. Ths research can be usefully extended n a number of drectons. Adjunct geographc locaton nformaton can be merged nto the NHTS dataset to provde more nformaton about the households neghborhood characterstcs. For those households n major metropoltan areas t mght be possble to obtan nformaton on accessblty to publc transportaton. An expanded model can then be developed to jontly determne publc transt accessblty along wth resdental densty and transportaton energy use. Detaled geographc nformaton can also be utlzed to emprcally examne the clam that balancng the number of resdences and jobs wthn a communty wll reduce resdental transportaton fuel use. Tract-level Census data could be used to develop measures of jobs-housng mbalance for each of the NHTS Calforna sample members and then test whether these measures have any sgnfcant mpact on vehcle use and fuel use. The present method for handlng the endogenous sample selecton caused by mssng energy nformaton also nvtes mprovement. Ideally both the structural and weghtng methods should yeld the same quanttatve results. The structural method should yeld more effcent estmates f the equatons explanng the mssng data process are correctly specfed. The problem s lkely due to the jont normalty assumpton requred

19 Densty and Vehcle Consumpton 17 by standard structural methods. Bhat and Eluru (2008) have developed a promsng new methodology usng copulas can be used to relax ths assumpton. Fnally, the present research concentrates on Calforna, usng only that porton of the NHTS natonal sample. Ths work can be expanded to the natonal level, both as a check on the stablty of the models and to emprcally examne the clam that Calforna drvng behavor has unque characterstcs that cannot be captured by standard socoeconomc measures.

20 Densty and Vehcle Consumpton 18 References Badoe, D. and E. Mller Transportaton land use nteracton: emprcal fndngs n North Amerca, and ther mplcatons for modelng. Transportaton Research D 5, Bento, A.M., M.L. Cropper, A.M. Mobarak and K. Vnha, The mpact of urban spatal structure on travel demand n the Unted States. Revew of Economcs and Statstcs 87, Bhat, C.R. and J.Y. Guo A comprehensve analyss of bult envronment characterstcs on household resdental choce and auto ownershp levels. Transportaton Research B 41, Bhat, C.R., and N. Eluru, A copula-based approach to accommodate resdental self-selecton effects n travel behavor modelng. Techncal Paper, Department of Cvl, Archtectural & Envronmental Engneerng, The Unversty of Texas at Austn, June. Boarnet, M. and Sarmento, S Can land-use polcy really affect travel behavor? A study of the lnk between non-work travel and land-use characterstcs. Urban Studes 35, Bryan, K.A., B. D. Mnton, and P. G. Sarte The evoluton of cty populaton densty n the Unted States, Federal Reserve Bank of Rchmond Economc Quarterly 93, Data accessed from on August 25, Downs, A Stll stuck n traffc: copng wth peak-hour traffc congeston, The Brookngs Insttuton, Washngton, D.C. Ewng, R. and R. Cervero Travel and the bult envronment: a synthess. Transportaton Research Record 1780, Fang, A., A dscrete-contnuous model of households' vehcle choce and usage, wth an applcaton to the effects of resdental densty. Transportaton Research B 42, Hausman, J.A., Specfcaton tests n econometrcs. Econometrca 46, Heckman, J.J., Sample selecton bas as a specfcaton error. Econometrca 47, Horowtz, J. L., The bootstrap, n E. E. Leamer and J.J. Heckman (Eds.), Handbook of Econometrcs, Vol. 5, Elsever, Amsterdam, Chapter 52. Kahn, M. E., The envronmental mpact of suburbanzaton. Journal of Polcy Analyss and Management 19, Newman, P. and J. Kenworthy Costs of automoble dependence: global survey of ctes. Transportaton Research Record 1670,

21 Densty and Vehcle Consumpton 19 NHTSA, CAFÉ overvew frequently asked questons. U.S. Natonal Hghway Transportaton Safety Agency webste. Accessed September 13, ORNL, The 2001 Natonal Household Transportaton Survey. Oak Rdge Natonal Laboratory for the U.S. Department of Transportaton. Accessed September 14, Pckrell, D. and P. Schmek, Growth n motor vehcle ownershp and use: evdence from the Natonal Personal Transportaton Survey. Journal of Transportaton and Statstcs 2, Schpper, M.A. and D.S. Pnckney, Supplementng the 2001 Natonal Household Travel Survey wth energy-related data. Presented at the 2004 Annual meetng of the Transportaton Research Board, January 11-15, Washngton, DC. Wooldrdge, J.A., Econometrc analyss of cross secton and panel data. MIT Press, Cambrdge, MA.

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