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Speeding Behavior_ Gasoline Prices and Value of Time


									          Speeding Behavior, Gasoline Prices and Value of Time

                                          Hendrik Wolff

                                    University of Washington

                                       Kari Edison Watkins

                                    University of Washington

                                        June 9th, 2011
                        Very Preliminary! & Comments very welcome!

Do drivers seek to conserve gasoline by reducing speeds when gasoline prices are high? While
economic theory predicts that a rational driver adjusts driving speeds, previous empirical
studies produced mixed results. Here we take a fresh look at the data and estimate a statistical
significant and robust negative relationship between speeding and gasoline prices. From this
result, we infer a number of findings: (i) By providing a new methodology of deriving the ‘value
of time’ (VOT) based on comparisons on the intensive margin (previous VOT studies instead
compare across the extensive margin) we find this coefficient to be 54% of the average wage
rate. The VOT method of this paper has several important advantages to circumvent omitted
variable bias which has plagued the prior VOT literature. (ii) In terms of heterogeneity, we find
that the fastest drivers reduce speeds under-proportionately, potentially undermining the safety
objective of a gasoline tax. (iii) As a dynamic aspect of habit formation, we find that once drivers
experience the benefits of gasoline conservation with prices above $4 per gallon, the speed-price
elasticity reduces to about half, implying that reduced speeds are continued even in periods of
low gasoline prices. (iv) Finally, we show that the changes are mainly caused by the gas price
that drivers pay at the pump. The high public media attention given to gasoline prices had
relatively little effect on changing drivers speeding behavior.

For correspondence contact Hendrik Wolff, Department of Economics, University of
Washington, 349 Savery Hall, Box 353330, Seattle, WA 98195-3330. phone: (510) 220-7961, Thanks are due to Jim Hawkins of the Washington Department of
Transportation for providing the speed data. Jim was himself quite speedy with providing the
data and answers to questions.


1. Introduction

1.1. Gasoline Prices and Speeding

This paper studies the relationship between gasoline prices and drivers’ speeding behavior. As

the debate on gasoline taxes continues to unfold, economists are increasingly interested in the

mechanisms by which gasoline prices affect gasoline demand. It has been repeatedly

hypothesized (Peltzman 1975, Dahl 1979, Blomquist 1984, Goodwin et al. 2004) that vehicle

speeds decrease with higher gas prices. But, recently Burger and Kaffine (2009) measured this

relationship and find the opposite: with rising gas prices, speeds increase. This—at first

counterintuitive—result stems from the fact that higher gas prices decrease congestion. Burger

and Kaffine (2009) then investigate the price-speed relationship during strictly uncongested

periods only (i.e. in the middle of the night) and they reject the hypothesis that drivers reduce

speeds when gas prices are high.

       In this paper, we take a fresh look at the data and estimate a statistical significant and

robust negative relationship between drivers’ speeding behavior and gasoline prices. We make a

number of methodological contributions. First—instead of using average annual data of vehicle

speeds (as in Peltzman 1975, Dahl 1979, Blomquist 1984) or average weekly speed data (Burger

and Kaffine 2009)—we collected the most disaggregated available hourly dataset of speeds for

the highway system within the State of Washington. Second, because gasoline prices are highly

cyclical over the calendar year (with increased prices during the summer and lower prices during

darker winter months), we find that not cautiously controlling for external driving conditions will

produce an erroneous rejection of the gasoline conservation hypothesis. To this end, we construct

a dataset of speeds with the most homogenous exterior environment as possible and control for

hourly weather and traffic related congestion effects. In sum, these changes to the estimation


method turn out crucially important to obtain, what we believe to be a much cleaner and more

precise coefficient estimate of the causal effect of gasoline prices on drivers’ speeding behavior.

              Using this new dataset, we estimate that for a one dollar price increase per gallon of

gasoline, speeds reduce by 0.4 miles per hour, lowering the average speed from 70.5 to 70.1

miles per hour. Although this change may be considered low in magnitude, we argue this will

have important advantages in developing an estimate of Value of Time (VOT).

1.2. Value of Time Methodology

       VOT is a key economic parameter used in many different settings in academia and policy.

Ashenfelter and Greenstone (2004) use VOT to calculate the Value of Statistical Life. VOT

estimates have been applied repeatedly to evaluate environmental projects that use hedonic travel

cost methods (Brown and Mendelsohn 1984), and in policy, transportation departments actively

work with VOT coefficients to produce cost-benefits analysis for large public transportation

projects such as to decide whether to build a subway or an additional highway lane. 1

       So far, broadly speaking, VOT has been measured by the three following methods which are

all based on agents choosing options across the extensive margin:

             Estimates are derived by comparing different modes of travel (car, plane, train) with each

              other relative to the travel cost and time requirements (Beesley, 1965, Gunn 2000). These

              results are likely however confounded due to different preferences towards and different

              attributes of the travel mode itself (i.e. while it is convenient to read a book on a train,

              one cannot read while driving).

 In the U.S. there exist ‘low’, ‘middle’, and ‘high’ VOT estimates for travel time which range from 6.19 to 18.57, depending on
specific circumstances (DOT 1997, Table III-11). For VOT coeficients used in public infrastructure projects in Great Britain
Britain see Mackie et al. (2003).


             Studies that use datasets on the same mode of travel aim to overcome this first problem,

              for example by comparing the choice of paying for a toll lane to circumvent congestion

              (i.e. Small et al. 2005) or to circumvent waiting in front of differentially priced

              neighboring gasoline stations (Deacon and Sonstelie 1985). However, this set of studies

              also faces the problem that the VOT estimate may be confounded. Drivers may have a

              distaste of being in a congested lane due to psychological costs. Also fuel consumption is

              higher in a stop and go setting. Further, if drivers are risk averse, predictability (at what

              time to arrive) has its own value, a feature that generated the literature on estimating the

              coefficient of “Value of Reliability” (i.e. see Small et al. 2005, Carrion-Madera and

              Levinson 2011).

             Lastly, stated preference methods (i.e. Calfe, Winston Stempski 2001, Small et al. 2005)

              use survey designs to orthoganalize the confounding variables. This method has been

              criticized however, that the hypothetical results are not generalizable to real world


This paper substantially adds to the literature2 on the value of time (VOT) providing a new

methodology to estimate VOT which is based on the intensive margin of behavioral adjustments.

This, we argue, has many important advantages compared to the previous VOT methods that are

based on choices across the extensive margin. We find that the average driver values time

according to 54% of the average wage rate.

              In our setting, the price affects a driver in the same vehicle making freely the choice on

the intensive margin of how fast to drive on an uncongested highway. (The driver is not required

to make a discrete choice on the extensive margin between a congested lane or a faster
    See Wardman (2004) for a comprehensive review of the literature on the value of time.


HOV/priced lane, that come with different attributes with respect to safety, psychological cost of

driving in stop and go, predictability of arriving in time and other factors). While our estimate of

minus 0.4 mph for a one dollar increase in the price of gasoline may seem to be low in

magnitude, we actually see this as an advantage because this small change of speed is arguably

much less confounded with any of the variables potentially biasing previous results (such as the

risk of getting involved in an accident as a function of speed).

       To put our VOT estimate into context, our result is in the middle between stated

preference derived estimates and revealed preference methods. The two most prominent studies

in economics using revealed preference methods are Small et al. (2005) and Deacon and

Sonstelie (1985) estimating VOT being 93% of the hourly wage and “quite similar to

individuals' after-tax wages”, respectively. Our estimate of 54% of the wage is lower, indicating

that prior studies may have capitalized into the VOT the omitted disamenities of the outside

option (i.e. being nerved when waiting in line or in traffic jam). On the other hand, at 54%, our

estimate of the VOT is higher than when estimated by most stated preference methods. Calfe et

al. (2001) estimate stated preference VOTs in the range of 14% to 27% of the average hourly

gross wage (based on rank ordered logit and rank ordered probit models).

       Furthermore, we are interested if the incentive mechanism is heterogeneous across

different types of drivers. Because our dataset contains the whole distribution of speeds within

each hour, we are able to estimate the gasoline-price speed relationship at various percentiles of

the distribution. We find that speeds are reduced most by vehicles in the range 75 mph to 85

mph. Fast drivers (above 95 mph) reduce speeds under-proportionately. In the extreme tail of

the distribution, we find that the number of drivers speeding above 100 mph even increases with

rising gas prices. We explain this effect indirectly: Higher gasoline prices reduce traffic volumes


and the additional space between vehicles provides opportunities to test maximum vehicle speeds

on empty highways. Hence, if one is concerned about traffic safety, as speed reductions are less

observed for the fastest drivers, the gasoline tax targeting safety has limited effects3.

              Finally, in order to investigate the information mechanism by which drivers are affected,

one may ask whether the changes in speeding are affected by the price signal at the gasoline

station, or whether the public media attention affected the changes in driving behavior. To this

end, we construct from the New York Times and the Seattle Times a weekly dataset on the

number of articles that referred to gas prices.4 We find that the time series of gas prices and

media coverage are highly correlated. However, statistically, it is the price at the pump which

dominates the observed changes to speeding behavior.

              These findings clearly have broader policy implications to both fuel conservation, safety

on freeways and public infrastructure projects. This paper proceeds as follows: The next section

describes our data, section 3 outlines the estimation methods and provides results, section 4

discusses our VOT approach, and section 5 concludes.

 Recently Chi et al. (2010) empirically investigate the relationship between gasoline prices and traffic safety (i.e. accidents per
vehicle mile traveled). For complimentary research see Leigh and Geraghty (2008) and Wilson, Stimpson and Hilsenrath (2009)
and for estimates with respect to the value of safety, see Steimetz 2008, estimating the coefficient of “Value of Density”.
 Repeatedly, news media covered tips on how to save on gas expenditures. One of the recommendation include to reduce speeds
as gas mileage decreases at speeds above 60 miles per hour: “You can assume that each 5 m.p.h. you drive over 60 m.p.h. is like
paying an additional $0.24 per gallon for gas” (New York Times, 2011).


2. Data

The ideal situation to observe the effect of gas price on vehicle speeds would be a freeway with

no speed limit in a location with no congestion or weather factors present. Drivers would only

be constrained by their value of time compared to gas prices and the perceived safety impacts of

speed. We have therefore limited our study to locations with a speed limit of 70 mph, the highest

speed limit in Washington State.

          For this study, we merge hourly data from the following five datasets from January 2005

to December 2008. First, we are using hourly speed data collected by the Transportation Data

Office of the Washington State Department of Transportation (WSDOT) at four locations in

Washington with speed limits of 70 mph in both directions. The site locations are shown in

Figure 1 and detailed in Table 1. WSDOT records all vehicles passing over the loop detectors

and quantifies speeds in five mile per hour (mph) increments per hour from above 35 mph to

above 100 mph. The WSDOT dataset also contains information about the number of error

vehicles in the data per hour. Error vehicles are vehicles that got counted in the variable of the

total sum of vehicles per hour, but that did not get counted in any speed category. We drop all

observations if the error variable is greater than 30 vehicles per hour.

Table 1: Speed Data Site Locations

   Site       WSDOT Site       Jurisdiction     Freeway          Direction     NOAA
                                                                               Weather Site
   1          R045             Woodland         I-5 MP 20.14     Northbound    Kelso
   2          R045             Woodland         I-5 MP 20.14     Southbound    Kelso
   3          R061             Eltopia          SR 395           Northbound    Tri-cities
   4          R061             Eltopia          SR 395           Southbound    Tri-cities
   5          R014             Tyler            I-90             Westbound     Spokane
   6          R014             Tyler            I-90             Eastbound     Spokane
   7          R055             Moses Lake       I-90             Westbound     Ephrata
   8          R055             Moses Lake       I-90             Eastbound     Ephrata


Figure 1: Speed Data Sites Map

       Because weather conditions can severely impact driving conditions, we collected hourly

temperature and precipitation information from the weather stations closest to our speed

measurement sites, as indicated in Table 1. Hourly weather data are downloadable from the

NOAA Local Climatological Data database from January 2005 to December 20008.

       We collected gasoline prices from the Department of Energy’s Energy Information

Administration. Prices are given as an average of retail prices across the state of Washington

using sales of all grades. The gas prices for January 3, 2005 to December 31, 2008 are shown in

Figure 2. As is clearly visible, gas prices have been generally increasing with a definite spike in

mid-2008. Also Figure 2 shows that gas prices are cyclical in nature with higher prices in the

summer and lower prices in the winter months.


Figure 2: Average Retail Gas Prices for State of Washington by week






















              Finally, we collected site specific monthly local unemployment rate statistics and per

capita personal income of the respective nearest metropolitan statistical areas to the highway

location. Unemployment data are drawn from Local Area Unemployment Statistics of the

Bureau of Labor Statistics5 and income from the CA1-3 series of the Regional Economic

Accounts at the Bureau of Economic Analysis6. Table 2 summarizes the descriptive statistics of

our data collection.

    Available at
    Available at


Table 2: Descriptive Statistics of Washington speed data of eight highway sites

Variable                        Unit   Observations     Mean    Std. Dev.     Min          Max
Average speed                   mph         228164      69.20        2.71     32.5        76.88
Gasprice                 U.S. dollar        280128       2.91         .59    1.831        4.412
Volume             vehicles per hour        228164     538.91     645.11         5        4000
Error              vehicles per hour        228164       6.13       39.36        0          979
Precipitation        inches per hour        280512       .002        .022        0           6.6
Temperature              Fahrenheit         270802      51.12       17.60      -14          111
Income                        U.S. $        280512    29955.3     2304.1    25963        34011
Unemployment                      %         280512       6.12        1.29        4         10.5
Note: unit of observation is per site and hour.

       The relationship between the gasoline prices and weekly averaged vehicle speeds is

displayed in Figure 3, here using the data of the Woodland Northbound speed measuring site. As

can be visually seen, often observations are missing in large portions of the dataset, which is

typical for speed measures. Rather than interpolating the missing hourly speed data, all

observations are dropped from the dataset with missing speed information, which reduced the

original dataset by 19%.


Figure 3: Average Speed per week and gas prices from January 1, 2005 to December 31,

2008 on I5 Northbound at Woodland

                                                 Site 1

         Average Speed (mph)

                                                                                     Gas Price ($)


                               2005   2006         2007            2008
                                             Average Speed (mph)
                                             Gas Price ($)

3. Method and Results

In order to test whether drivers seek to conserve energy by reducing speeds, our main task is to

estimate the direct causal effect of the price of gasoline on speeding behavior. Burger and

Kaffine (2009) showed that this direct effect has to be estimated in the absence of congestion

because otherwise observed speeds are merely a reaction of changed congestion and not because

of the direct behavioral response that drivers seek to conserve gasoline by reducing speeds.

       As a reference, here we first start by estimating the relationship between speed and

gasoline using the same method as in Burger and Kaffine (2009). Using the night hours of 2am

to 4am as the time of the uncongested condition, the average speed in week t and highway i is

estimated by

                                            Speedit = α + β1 *pricet + Xit + Fi + Yt + εt                  (1)

where Fi are freeway site fixed effects, Yt are year fixed effects and Xit are precipitation, holiday

and summer dummies as well as income and unemployment. The results in Table 3 column (1)

show that across all sites, speeds significantly increase by 0.47 miles per hour for a one U.S.

dollar increase in gasoline prices. Hence, similar to the results of Los Angeles by Burger and

Kaffine (2009), according to this methodology, also our dataset would suggest that the energy

conservation hypothesis had to be rejected.7

              To explore the causes that drive this result, we analyze the potential effect of road

conditions that could confound this estimate. The seasonality of road conditions turn out to be

important because these are correlated with the cyclicality of gas prices (see Figure 2). In the

summer, speeds may be higher because of better visibility—extended daylight and less rain—

and no freezing temperatures. In column (2), we control for seasonality by introducing month

dummies Mt. The estimates of column (2) confirm this hypothesis: speeds are 2.4 mph lower in

December compared to the fastest month of the year, July, and the gasprice coefficient renders

insignificant. Because gas prices exhibit cyclicality, in this paper we will control for seasonality

in all further regressions. To investigate into the robustness of these results further, in column (3)

we unpool the price effect by traffic site and find that for the majority of the sites the price effect

is insignificant.

  Burger and Kaffine (2009) obtained an insignificant yet negative point estimate during uncongested times and note that speed
limits in L.A. are 65 mph (instead of 70 mph as at the WA sites) and average income is higher, which may make drivers less
reactive to gas price changes. These causes, together with the less precise weekly dataset likely contributed to the insignificant
point estimate.


           Finally, column (4) to (6) repeat the estimation for the evening hours from 4pm to 6pm,

which we define as the PM time period. Here, again, we find that the within year speed

difference of 2.9 miles per hour shows the importance of controlling for seasonality and we show

that unpooling the coefficient on price across sites leads to non-robust results. Overall, these first

estimation results of the effect of gasoline prices on speeds are inconsistent with the finer

conditioning method that we will apply in the following.

Table 3: Regression Results for Freeway Speeds in Washington State, unit of observation

by site and week

                 (1)            (2)            (3)               (4)         (5)          (6)
VARIABLES        2 am to 4 am   2 am to 4 am   2 am to 4 am      PM basic    PM basic     PM site
                 basic          basic with     site interacted               with Month   interacted
                                Month FE       with Month                    FE           with Month

gasprice         0.4679***      0.2073         -0.4582***        0.4696***   0.1827       -0.6488***
                 (0.131)        (0.155)        (0.156)           (0.149)     (0.168)      (0.155)
_IsitXgaspr_2                                  -0.0314                                    0.0456
                                               (0.172)                                    (0.147)
_IsitXgaspr_3                                  0.5313***                                  0.4758***
                                               (0.203)                                    (0.182)
_IsitXgaspr_4                                  0.3610**                                   0.7060***
                                               (0.152)                                    (0.131)
_IsitXgaspr_5                                  1.0289***                                  1.4334***
                                               (0.224)                                    (0.245)
_IsitXgaspr_6                                  1.2176***                                  1.3603***
                                               (0.258)                                    (0.235)
_IsitXgaspr_7                                  0.8790***                                  1.0636***
                                               (0.188)                                    (0.159)
_IsitXgaspr_8                                  1.1501***                                  1.1742***
                                               (0.177)                                    (0.135)
_Imonth_2                       1.2738***      1.3007***                     1.3156***    1.3403***
                                (0.212)        (0.210)                       (0.156)      (0.153)
_Imonth_3                       1.3954***      1.4485***                     1.4844***    1.5412***
                                (0.225)        (0.227)                       (0.176)      (0.177)
_Imonth_4                       1.5332***      1.6486***                     1.4661***    1.5970***
                                (0.256)        (0.258)                       (0.213)      (0.218)
_Imonth_5                       1.3759***      1.5032***                     1.2188***    1.3565***
                                (0.304)        (0.300)                       (0.273)      (0.270)
_Imonth_6                       1.7084***      1.8621***                     1.3954***    1.5621***
                                (0.299)        (0.298)                       (0.257)      (0.257)
_Imonth_7                       1.9709***      2.1029***                     1.6339***    1.7941***
                                (0.312)        (0.310)                       (0.270)      (0.269)
_Imonth_8                       1.8821***      2.0166***                     1.7218***    1.8768***


                                  (0.292)          (0.291)                           (0.249)          (0.251)
_Imonth_9                         1.6039***        1.7603***                         1.3886***        1.5692***
                                  (0.308)          (0.313)                           (0.268)          (0.276)
_Imonth_10                        1.5546***        1.6949***                         1.3907***        1.5506***
                                  (0.297)          (0.305)                           (0.249)          (0.261)
_Imonth_11                        1.3283***        1.4033***                         0.4688**         0.5636***
                                  (0.251)          (0.252)                           (0.208)          (0.210)
_Imonth_12                        -0.4046          -0.3359                           -1.1133***       -1.0293***
                                  (0.401)          (0.401)                           (0.355)          (0.350)
hourlyrain       -1.6060***       -0.7355          -0.8236          -3.2910***       -2.1376***       -2.3832***
                 (0.567)          (0.544)          (0.530)          (0.496)          (0.452)          (0.433)
summer           0.6361***        0.2556***        0.2338**         0.4030***        0.0469           0.0200
                 (0.073)          (0.099)          (0.096)          (0.070)          (0.087)          (0.086)
Xmas             -1.3232***       -0.1325          -0.1241          -1.2489***       0.3707           0.3779
                 (0.446)          (0.563)          (0.554)          (0.399)          (0.475)          (0.457)
unemploy         -0.3650***       -0.1435**        -0.0921          -0.3166***       -0.1451***       -0.0889
                 (0.038)          (0.063)          (0.067)          (0.036)          (0.055)          (0.060)
income           -0.0004***       -0.0002*         -0.0006***       -0.0001          0.0000           -0.0005***
                 (0.000)          (0.000)          (0.000)          (0.000)          (0.000)          (0.000)
Constant         78.3192***       71.2820***       84.6710***       74.9491***       69.9927***       85.0389***
                 (2.898)          (2.981)          (3.676)          (2.840)          (2.795)          (3.315)

Observations    1,429            1,429             1,429            1,416            1,416             1,416
R-squared        0.354           0.425             0.448            0.332            0.459             0.492
Regresssion includeds Site and Year fixed effects. Robust standard errors in parentheses clustered by site and week

*** p<0.01, ** p<0.05, * p<0.1


Dataset Refinement

       Compared to the above estimation method, in the following, we make two major changes.

First, instead of using weekly speed averages, we will work with hourly speed data. Secondly,

we rely on constructing a dataset of speeds with the most homogenous exterior conditions as

possible. Our first step is the filtering (dropping) of data for any hour in which the following

conditions are not met:

       A. All observations are dropped if the average speed is less than 67 mph. By filtering

           for time periods with unusually low speeds, any congested or unusually slow time

           periods (due to accidents, temporal construction activities, and other factors) are

           removed from these typically uncongested segments of roadway.

       B. Precipitation can substantially alter traffic behavior due to changes in visibility. To

           account for this, all hours are dropped from the data set when precipitation was

           present during the hour. We also delete the observations two hours after the rain

           occurred because the spray from wet roads may still alter traffic flows.

       C. All hours are dropped with outside temperatures less than 32 degrees Fahrenheit. In

           addition all hours are dropped if temperature is missing in a ‘winter’ month, whereby

           ‘winter’ is defined site-specific to be the set of months with historic minimum

           temperatures below 32 Fahrenheit.

       D. Finally, we filtered out hours in which the total number of vehicles is less than 50.

Note that none of the conditions A. to D. should be correlated with the direct behavioral response

of speeds due to a change in gas price. To obtain this dataset, the total number of observations

was reduced by 37%. The percentage reductions by each variable are displayed in Table 4 for the

24hour period in columns (1) and (2) and the PM period in column (3) and (4). As will be


explained below, the PM period is our major time period we will focus on in the analysis.

Overall Table 4 shows that the weather variables have the largest influence on the reduction of

the number of observations. Condition A—that the average speed is below 67 mph—reduced the

dataset by 15% in the 24 hour period. However, in the more important afternoon PM period, only

2% of the data are dropped because of this condition.

Table 4: Data Removed for regressions

                                                     All Day          PM period
                                                  (1)        (2)     (3)      (4)
                           Data                   Obs        %      Obs        %
                           Rain                  30759     13.5%    1774    13.7%
                         Temp<32                 30023     13.2%    899      6.9%
                         Error>30                3301       1.4%     275     2.1%
                        Volume<50                20742      9.1%      0       0%
                       Avg Speed<67              33337     14.6%     294     2.3%

                      Total observations         83765    36.7%     2843    21.9%
                      Total observations     144399       63.3%     10133   78.1%

By conditioning on the set A. to D. to obtain the dataset of speeds with the most homogenous

exterior conditions as possible, we are now in the position to estimate the direct impact of the

price of gasoline on drivers speeding behavior by

                               Speedit = α + β1 *pricet + Mt + εt                              (2)

where Speed is the hourly average speed, price is the weekly average gas price, and Mt are the

monthly fixed effects. The resulting estimates of coefficients together with their robust standard

errors which are clustered by week and site are shown in Table 5, along with the R-squared

statistic measuring the fit for each equation.


Table 5: Vehicle speed regressions, conditioned on set A. to D.

                               (1)           (2)                  (3)
               COEFFICIENT     Basic Model   Hour Fixed Effects   Hour and Site
                                                                  Fixed Effects

               gasprice        -0.3248***    -0.3595***           -0.4206***
                               (0.0253)      (0.0234)             (0.0184)
               monthd1         -0.5270***    -0.7049***           -0.6948***
                               (0.0626)      (0.0658)             (0.0665)
               monthd2         -0.1892***    -0.4078***           -0.4355***
                               (0.0618)      (0.0603)             (0.0555)
               monthd3         -0.1001*      -0.1574***           -0.1783***
                               (0.0582)      (0.0579)             (0.0501)
               monthd5         0.1745***     0.2210***            0.2400***
                               (0.0588)      (0.0584)             (0.0499)
               monthd6         0.1429**      0.2628***            0.3040***
                               (0.0658)      (0.0649)             (0.0531)
               monthd7         0.3788***     0.5351***            0.5760***
                               (0.0623)      (0.0616)             (0.0495)
               monthd8         0.3313***     0.4853***            0.5020***
                               (0.0632)      (0.0630)             (0.0496)
               monthd9         0.0775        0.1720***            0.1923***
                               (0.0641)      (0.0640)             (0.0535)
               monthd10        -0.0372       -0.0127              -0.0274
                               (0.0625)      (0.0607)             (0.0495)
               monthd11        -0.1395**     -0.1963***           -0.1711***
                               (0.0681)      (0.0660)             (0.0602)
               monthd12        -0.3300***    -0.4748***           -0.4333***
                               (0.0889)      (0.0943)             (0.1054)
               timed1                        -0.3577***           -0.3411***
                                             (0.0239)             (0.0236)
               timed2                        -0.6540***           -0.5947***
                                             (0.0323)             (0.0319)
               timed3                        -0.8410***           -0.7549***
                                             (0.0319)             (0.0315)
               timed4                        -0.5816***           -0.5051***
                                             (0.0293)             (0.0282)
               timed5                        0.2029***            0.2536***
                                             (0.0328)             (0.0321)
               timed6                        0.7369***            0.7547***
                                             (0.0359)             (0.0353)
               timed7                        1.1611***            1.1670***
                                             (0.0290)             (0.0284)
               timed8                        1.2134***            1.2157***
                                             (0.0242)             (0.0237)
               timed9                        1.2666***            1.2656***
                                             (0.0229)             (0.0227)
               timed10                       1.3397***            1.3372***
                                             (0.0235)             (0.0235)
               timed11                       1.4502***            1.4447***
                                             (0.0245)             (0.0245)
               timed12                       1.5571***            1.5504***
                                             (0.0249)             (0.0251)

                  timed13                              1.6772***      1.6696***
                                                       (0.0249)       (0.0251)
                  timed14                              1.8685***      1.8609***
                                                       (0.0248)       (0.0249)
                  timed15                              2.1039***      2.0969***
                                                       (0.0234)       (0.0236)
                  timed16                              2.2457***      2.2389***
                                                       (0.0228)       (0.0231)
                  timed17                              2.2181***      2.2126***
                                                       (0.0238)       (0.0240)
                  timed18                              2.0398***      2.0368***
                                                       (0.0239)       (0.0240)
                  timed19                              1.6898***      1.6888***
                                                       (0.0236)       (0.0236)
                  timed20                              1.2688***      1.2683***
                                                       (0.0219)       (0.0221)
                  timed21                              0.8569***      0.8595***
                                                       (0.0204)       (0.0206)
                  timed22                              0.5939***      0.5983***
                                                       (0.0190)       (0.0190)
                  timed23                              0.2750***      0.2883***
                                                       (0.0178)       (0.0177)
                  sited2                                              -0.1424***
                  sited3                                              -0.2934***
                  sited4                                              0.3707***
                  sited5                                              0.2086***
                  sited6                                              0.4435***
                  sited7                                              0.4635***
                  sited8                                              0.1641***
                  Constant             71.0888***        69.9232***   69.9414***
                                       (0.0884)          (0.0871)     (0.0725)
                  Observations         142259            142259       142259
                  R-squared            0.024             0.309        0.341
Robust standard errors in parentheses clustered by site and week
*** p<0.01, ** p<0.05, * p<0.1

        Table 5 show that speeds significantly decrease by 0.32 to 0.42 miles per hour (mph).

Column (1) confirms the significance of the month dummies. Note however that the inter-year

speed range is equal to 0.7 miles per hour from January to July and hence the cyclicality is much

less pronounced compared to the cyclicality in the weekly regression of Table 3. Column (2) and


(3) of Table 5 display the hourly fixed effects and show that speeds are generally highest in the

afternoon/after-work time period of 4pm to 6pm. With our objective to work with a sample of

drivers as homogenous as possible, we will continue to analyze the PM time period in more

detail. This PM vehicle fleet is likely more representative with respect to the behavior of private

vehicle owners. Instead, in other time periods of the day, the share of private vehicles to trucks

and commercial vehicles is lower. Speed reactions by trucks and commercial vehicles are

arguably more heterogeneous because their speeds are constrained by vehicle type and weight.

Also, the incentive to conserve gasoline by commercial drivers is different if gasoline expenses

get reimbursed.

           Table 6 displays the results of the PM models. Gas prices on average over all sites reduce

by 0.38 or 0.44 mph for a $1 increase in the price of gasoline per gallon (column 1 and 2 without

and with site fixed effects respectively). In column (3), we unpool the gas price coefficient over

sites, and again find that at site 1, the effect is largest with a decrease of 0.97 mph and the

smallest decrease of 0.13 mph at site 8.

Table 6: Gas price speed relationship, conditioned on set A. to D., PM time period

                   (1)           (2)           (3)           (4)           (5)           (6)
COEFFICIENT        Basic Model   Basic Model   Basic Model   Basic Model   Basic Model   Basic Model
                                 Sites         Unpooled      Year          Year Sites    Year

Gasprice           -0.3752***    -0.4423***    -0.9692***    -0.2082***    -0.2826***    -0.8041***
                   (0.0303)      (0.0258)      (0.0598)      (0.0546)      (0.0451)      (0.0725)
_IsitXgaspr_2                                  0.2507***                                 0.2383***
                                               (0.0768)                                  (0.0744)
_IsitXgaspr_3                                  0.4601***                                 0.4280***
                                               (0.0854)                                  (0.0865)
_IsitXgaspr_4                                  0.5503***                                 0.5375***
                                               (0.0776)                                  (0.0770)
_IsitXgaspr_5                                  0.5960***                                 0.5877***
                                               (0.0759)                                  (0.0755)
_IsitXgaspr_6                                  0.7001***                                 0.6865***
                                               (0.0800)                                  (0.0796)
_IsitXgaspr_7                                  0.6754***                                 0.6610***


                                                   (0.0735)                                  (0.0732)
_IsitXgaspr_8                                      0.8424***                                 0.8392***
                                                   (0.0754)                                  (0.0748)
_Isite_2                          -0.4051***       -1.1023***                   -0.3946***   -1.0565***
                                  (0.0674)         (0.2371)                     (0.0662)     (0.2287)
_Isite_3                          -0.3525***       -1.6842***                   -0.3579***   -1.6014***
                                  (0.0578)         (0.2449)                     (0.0575)     (0.2471)
_Isite_4                          0.3170***        -1.2637***                   0.3191***    -1.2249***
                                  (0.0605)         (0.2408)                     (0.0597)     (0.2389)
_Isite_5                          0.1720***        -1.5475***                   0.1698***    -1.5262***
                                  (0.0622)         (0.2457)                     (0.0614)     (0.2436)
_Isite_6                          0.4100***        -1.6089***                   0.4084***    -1.5715***
                                  (0.0606)         (0.2415)                     (0.0598)     (0.2394)
_Isite_7                          0.2439***        -1.7150***                   0.2514***    -1.6659***
                                  (0.0627)         (0.2282)                     (0.0624)     (0.2264)
_Isite_8                          -0.0954          -2.5403***                   -0.0999      -2.5364***
                                  (0.0621)         (0.2334)                     (0.0615)     (0.2309)
monthd1           -1.1709***      -1.1907***       -1.2117***      -1.0967***   -1.1209***   -1.1445***
                  (0.0787)        (0.0760)         (0.0718)        (0.0819)     (0.0785)     (0.0748)
monthd2           -0.5376***      -0.5921***       -0.5811***      -0.4625***   -0.5209***   -0.5130***
                  (0.0751)        (0.0673)         (0.0636)        (0.0787)     (0.0706)     (0.0669)
monthd3           -0.1125         -0.1371**        -0.1411**       -0.0714      -0.0988      -0.1046
                  (0.0737)        (0.0648)         (0.0619)        (0.0753)     (0.0666)     (0.0638)
monthd5           0.0155          0.0554           0.0687          -0.0310      0.0098       0.0241
                  (0.0788)        (0.0687)         (0.0690)        (0.0805)     (0.0702)     (0.0702)
monthd6           0.0389          0.1096           0.1130          -0.0185      0.0535       0.0584
                  (0.0870)        (0.0729)         (0.0722)        (0.0881)     (0.0740)     (0.0737)
monthd7           0.3446***       0.4264***        0.4129***       0.2760***    0.3602***    0.3505***
                  (0.0814)        (0.0636)         (0.0632)        (0.0840)     (0.0658)     (0.0655)
monthd8           0.4134***       0.4696***        0.4618***       0.3542***    0.4119***    0.4060***
                  (0.0820)        (0.0658)         (0.0645)        (0.0843)     (0.0679)     (0.0661)
monthd9           0.0488          0.0943           0.0961          -0.0025      0.0441       0.0476
                  (0.0793)        (0.0673)         (0.0661)        (0.0809)     (0.0686)     (0.0669)
monthd10          -0.0542         -0.0646          -0.0671         -0.0573      -0.0688      -0.0712
                  (0.0775)        (0.0632)         (0.0626)        (0.0778)     (0.0631)     (0.0623)
monthd11          -1.0119***      -1.0083***       -1.0068***      -0.9517***   -0.9521***   -0.9530***
                  (0.0823)        (0.0717)         (0.0735)        (0.0824)     (0.0720)     (0.0727)
monthd12          -1.1866***      -1.1654***       -1.2182***      -1.1146***   -1.0982***   -1.1540***
                  (0.0860)        (0.0934)         (0.0941)        (0.0864)     (0.0913)     (0.0949)
Yearline                                                           -0.0900***   -0.0864***   -0.0837***
                                                                   (0.0243)     (0.0206)     (0.0206)
Constant          71.9638***       72.1126***       73.6268***     71.6920***   71.8541***   73.3467***
                  (0.1075)         (0.0993)         (0.1896)       (0.1310)     (0.1172)     (0.2032)
Observations      8596             8596             8596           8596         8596         8596
R-squared         0.171            0.247            0.271          0.174        0.250        0.274
Robust standard errors in parentheses clustered by site and week
*** p<0.01, ** p<0.05, * p<0.1

           One may be concerned that over the four years from 2005 to 2008, the vehicle fleet

changed, which could affect speeds. Introducing a linear trend variable to equation (2) produces

the results displayed in column (4) to (6) in Table 6. At all sites, on average, speeds significantly

reduce by 0.21 mph (column 4) and 0.28 mph (column 5) without and with site fixed effects

respectively. In column (6), we unpool the gas price coefficient over sites, and again find that at

site 1 the effect is largest with a decrease of 0.8 mph. All other sites also show a significant

reduction of speeds, except for site 8, where the gasoline price is statistically insignificantly

different from zero (p = 0.58 based on F-test). The yearline variable itself is stable across the

different specifications indicating that per year speeds decrease on average by 0.08 to 0.09 mph.

Alternative regressions using year dummies and other time polynomials lead to qualitatively

similar results.

        In Table 7, we aim to investigate whether the changes in gasoline prices signal the drivers

to slow down or whether the public media news has a similar effect. To this end, we collected all

articles from the New York Times from 2005 to 2008 and counted the number of times the term

“gas price” occurred by week. By comparing column (1) with column (3) we see that both the

gasoline price and the news report reduce traffic speeds. When we include both variables

simultaneously in the regression, displayed in column (2), the news variable becomes less

significant and about one third in magnitude, while the price coefficient is qualitatively the same

as in the basic model. From these regressions, we conclude that drivers react primarily to the

price signal and that the news reports merely are correlated with gas prices but do seem to

substantially effect driving behavior. Columns (4) and (5) repeat the regressions for the Seattle

Times which leads to qualitatively the same results.


Table 7: Prices versus Information Effects, conditioned on set A. to D., PM time period
                (1)                     (2)                 (3)           (4)          (5)
      VARIABLES Basic Model             NYT News            NYT News      SEA News     SEA News
                                        Model               Model 2       Model        Model 2
      gasprice          -0.4423***      -0.4186***                        -0.4371***
                        (0.026)         (0.027)                           (0.027)
      NY Times                          -0.0055**           -0.0174***
                                        (0.002)             (0.002)
      Seattle Times                                                       -0.0007      -0.0063***
                                                                          (0.001)      (0.001)
      Constant          72.1126***      72.1000***          70.9862***    72.1083***   70.9085***
                        (0.099)         (0.099)             (0.078)       (0.099)      (0.078)

      Observations      8,596            8,596              8,596         8,596        8,596
      R-squared         0.247            0.248              0.208         0.247        0.202
       Note: All regression includes month and site fixed effects.
       Robust standard errors in parentheses clustered by site and week
       *** p<0.01, ** p<0.05, * p<0.1

Habit Formation

       While the time period before July 2008 saw an unprecedented increase in retail gas prices

to over $4.40 per gallon, by the end of 2008, gas prices had returned to less than $2.00, which

represents the fastest ever decline in retail prices. We next investigate whether the speed

adjustments are symmetric around the July 2008 spike, hence whether the change in behavior is

temporary or permanent. Starting from our basic regression of Table 6, column (2), we include a

post-June 2008 dummy, which is one for all days in 2008 after the gas spike occurred and we

interact this dummy further with the gasprice variable itself. Results are shown in Table 8,

column (1). We find that prior to the spike of July 2008, the marginal effect on speeds is 0.42

mph per dollar gallon price increase which is consistent with our prior results of Table 6. The

coefficient of the Post-June variable indicates that after the 2008 spike, drivers maintained a 0.83

mph lower level of speed, hence adapting their behavior to a more fuel efficient driving.

Furthermore, since gas prices drastically decreased for the remainder of the year, one may expect

that drivers increase speeds again. We find that the marginal speed increase is about a half per


dollar price increase per gallon—the marginal effect decreases by 0.18 mph from 0.42 to 0.24

(according to the interaction term of the Post-June dummy with the gasprice variable). In

summary, with rising gas prices, drivers reduce speeds by 0.42 mph per one dollar per gallon

price increase, while with falling gas prices, the speed increases back by 0.24 mph per dollar

price increase only, and drivers permanently reduce speeds for the rest of the year, conditional on

gas price, by 0.83 mph. This relationship is displayed in Figure 4, which also includes the results

of columns (2) to (3) of Table 8, showing that the overall finding is generally consistent for

different specifications from which date on in 2008 the behavioral adjustments occur.

Figure 4: Habit formation




                                                               Table 8: Habit Formation
                                                                 (1)              (2)          (3)
                                          COEFFICIENT            July Model       Aug Model    Sept Model

                                          gasprice               -0.4245***       -0.4393***   -0.4399***
                                                                 (0.0338)         (0.0283)     (0.0272)
                                          postJun08              -0.8288***
                                          postJunXgas            0.1794***
                                          postJul08                               -0.9484***
                                          postJulXgas                             0.2220***
                                          postAug08                                            -0.8931***
                                          postAugXgas                                          0.2000***


                                          Constant       72.0475***      72.0922***      72.0939***
                                                         (0.1219)        (0.1070)        (0.1034)
                                    Observations         8596            8596            8596
                                    R-squared            0.252           0.252           0.252
                             Note: All regression includes month and site fixed effects.
                             Robust standard errors in parentheses clustered by site and week
                             *** p<0.01, ** p<0.05, * p<0.1

The price of gas and its impact on the distribution of speeds

The drivers speeding the most are the least efficient from a consumption perspective.8 Do high

gasoline prices effect these fast drivers overproportionally? Or do these drivers enjoy speeding

on its own, irrespective of the price of gas? To test this, we take advantage of the information on

the hourly speed distributions and run percentile regressions of the proportion of vehicles in each

hour above the speed thresholds from 70 mph to 100 mph. The resulting estimates of elasticity

coefficients are shown in Figure 4. This sequence of estimated elasticities is approximately U

  New data by Davis et al. (2010) show that vehicles traveling 75 mpg consume on average between 24% (midsize car) to 34%
(large SUV) more gasoline compared to a speed of 55 mph. While similar detailed information on the gasoline consumption
speed relationship in the U.S. is sparse for vehicles traveling above 75 mph, data from Germany suggest that the average fuel
consumption of an average vehicle is 40% higher if traveling above 100 mph and this percentage further increases with the
weight of the vehicle. For details see Table 4.26 in Davis et al. (2010).


shaped with a minimum at the 80 mph percentage regression. All estimated elasticities are

negative but for the very fastest drivers with speeds above 100 mph. We interpret this positive

elasticity for the fasted vehicles as evidence that these drivers enjoy speeding by itself,

irrespective of the gas price. Further, because with higher gas prices traffic volumes decrease,

more space between vehicles on the highway may provide an additional incentive to ‘test’ the

vehicles speeding ability. The standard errors are clustered by week and site and the resulting

95% confidence intervals displayed as upper and lower elasticity bounds in Figure 5. It shows

that all estimates are significantly different from zero, with the exception of the 95 mph

percentile elasticity.


      Figure 5: Percentage speed elasticities with respect to the price of gasoline, conditional on

      A to D. and PM period




                      70            75             80              85           90             95            100




      Note: Figure 4 displays seven elasticities, indicating the percentage change in vehicles driving above 70 mph (the
      most left elasticity point estimate) to 100 mph (the most right estimate) caused by a percent change in the price of
      gasoline. The seven displayed elasticities are derived by respective seven separate regressions with the dependent
      variable being the natural logarithm of the proportion of vehicles driving above the indicated speed and the
      independent variable defined as the logarithm of the gas price. Also all regressions include month and site fixed
      effects. The confidence intervals are computed via robust standard errors clustered by site and week.

      To investigate whether more empty highways provide an incentive for speeding, we next present

      our speeding-gas price elasticities conditional on traffic volumes. The resulting elasticities are

      displayed in Figure 6, represented by the red line and the intercepted blue graph for the linear

      and quadratic control of traffic volumes respectively. For comparison, also the base case

      elasticities unconditional on traffic volumes (the same point elasticities as in Figure 4) are

      displayed, here represented by the green dotted line. As expected, the results show that

controlling for traffic volumes decreases the speeding gas price elasticity for the fastest drivers

from 0.31 (base case) to 0.22 (linear control of traffic volumes) and 0.20 (quadratic control

function of traffic volumes). This suggests that because higher gas prices produce more space

between vehicles, the fastest drivers were able to speed above 100 mph more often in times of

high gas prices. Overall the U-shaped functions, however remains in all three specifications.

Hence, if one is concerned about traffic safety, as speed reductions are less observed for the

fastest drivers, the gasoline tax targeting safety has limited effects.

Figure 6: Speed Elasticities with respect to the Price of Gasoline conditional on a linear and
a quadratic Specification of Traffic Volumes.




              70            75           80           85            90           95           100
  ‐0.1                                                                                                            linear
                                                                                                                  from Figure 4




Note: Figure 5 displays elasticities, indicating the percentage change in vehicles driving above 70 mph (the most left
elasticity point estimate) to 100 mph (the most right estimate) caused by a percent change in the price of gasoline.
The elasticities are derived by respective separate regressions with the dependent variable being the natural
logarithm of the proportion of vehicles driving above the indicated speed and the independent variable defined as the
logarithm of the gas price. Also all regressions include month and site fixed effects. In addition the linear
specification also controls for traffic volume. The quadratic specification controls for the linear and a quadratic term
of the traffic volume. The confidence intervals are computed via robust standard errors clustered by site and week
(not displayed here): except for the elasticity at the 95 mph elasticity, all coefficient estimates are significantly
different from zero for all three specifications.


4. Value of Time

        The literature on estimating the Value of Time (VOT) started with the seminal work by

Beesley (1965) and today can be categorized into the following three approaches.

       Estimates derived by comparing different modes of travel (car, plane, train) with each

        other relative to the travel cost and time requirements (Beesley, 1965, Gunn 2000).

       Pricing studies and others that use datasets on the same mode of travel (e.g. Deacon and

        Sonstelie 1985, Small et al. 2005) but with drivers making discrete choices of either

        paying to avoid congestions, or waiting often for a prior unknown amount of time.

       Stated preference methods (i.e. Calfe, Winston Stempski 2001, Small et al. 2005)

        In this paper we provide a new method to estimate VOT which permits us to address

some of the conceptual problems that have plagued the VOT literature. Our method does not

depend on either different travel modes, or substantially different travel characteristics, such as

choosing an HOV lane or paying to drive on an uncongested lane on a highway. As such, our

empirical estimate promises to be less confounded. We find that a one dollar increase in

gasoline/gallon reduces speeds by 0.44 mph. This suggests that concerns of other confounding

factors can likely be omitted in the interpretation of our resulting VOT estimate (i.e. changes to

the probability of obtaining a traffic ticket or the risk of getting involved in an accident are likely

small given the change of speeds of 0.44 mph). More generally, the advantages of our study are

that the VOT is derived by basing the estimate on the intensive margin and not (as in previous

studies) on the extensive margin of making different choices among different bundles of



       From the theory point of view, the approach is simple. Increasing the speed S (above 60

miles per hour) increases gasoline consumption g(S). Given the price of gasoline P, drivers cost

minimize total costs C(S|P) = Pg(S|P) + VOTt(S|P) + R(S|P). Hence drivers equalize the

marginal cost of gasoline expenditures Pδg/δS with the marginal time saving δt /δS with respect

to speed, times the drivers subjectively perceived value of time (VOT), minus the marginal risk

of driving at a higher speed. Here, R(S) can represent the dollar value of any disamenity of fast

driving such as obtaining a speeding ticket, the cost of stress related to a higher concentration

levels or the risk of getting involved in a traffic accident, with R(S) monotonic increasing and

convex in S. So far in this paper, we empirically estimated S(P) and find that dS/dP = -0.44. (In

fact, totally differentiating VOT = -[Pδg/δS+R(S)]/δt/δS it is easy to show that dS/dP < 0). In

order to calculate VOT, we need to derive a number of additional parameters that we re-estimate

from prior results in the engineering literature. First, t(S) = 1/S is simply a physical relationship.

Second, we assume that R(S  [70.1,70.5]) = constant. Hence for a small enough change of S,

the change in perceived risk can be neglected. For any price change P2 > P1, VOT can hence be

calculated by VOT = [(P2)g(S|P2)- P2g(S|P1)]/ [t(S|P2)- t(S|P1)], which is the amount of gasoline

expenditure savings per mile by reducing the speeds from S|P2 to S|P1 relative to the additional

time needed to travel that mile at the reduced speed S|P1. Finally, we derive g(S) by using the

data of West et al. (1999) as summarized by Davis (2001). Based on nine vehicles sampled from a mix of

automobiles and light trucks of model years 1988–1997, we estimate that the derivative δg/δS = 0.06018

in the relevant interval of S  [70,75]. For a price increase from three to four dollar per gallon,

we estimate that speeds reduce by 0.44 from 70.79 mph to 70.34 mph. To exemplify, consider a

driver traveling exactly 70.79 miles. Hence his travel time increases from one hour to one hour

and 23 seconds. This increase in travel times comes at savings in gasoline consumption of

g(70.79)=2.67 to g(70.34)=2.65 gallons, equivalent to expenditure savings of 75 cents. These

saved 75 cents over the additional 23 seconds results in a VOT of $11.99 per hour.

       According to the Bureau of Labor Statistics, the average hourly wage in 2008 in the State

of Washington was $22.32 (BLS, 2008) and after taxes the average hourly wage is roughly

$17.44. Hence, $11.99 VOT accounts for 54% of the average hourly wage. 54% is considerably

lower than the 93% estimate by Small et al. (2005) and also lower than the previous estimate by

Deacon and Sonstelie (1985) referring to VOT as approximately being the after tax wage rate.

We interpret our lower VOT estimate as evidence that these prior studies may be confounded by

other psychological costs of waiting in a queue at a gasoline station, as in the study by Deacon

and Sonstelie 1985. In the case of the study by Small et al. (2005), our results suggest that

further subjective costs of being in a potential traffic jam have been capitalized into the 93%


       At the same time our VOT is larger than in prior stated preference studies. To investigate

the divergence between revealed and stated preference studies continues to be an active research

area by John List et al. and is beyond the scope of this paper here.


5. Conclusion

       Do drivers seek to conserve gasoline by reducing speeds in times of high gasoline prices?

The time period from July 2004 to July 2008 saw an unprecedented increase in retail gas prices

from $2.05 to over $4.40 per gallon of gasoline. By the end of 2008, retail gas prices were down

to less than $2.00. The headlines from the summer 2008 tell the story best: “Record gas prices

may curb summer demand.” (USA TODAY, 4/7/08); or “Outraged consumers look to

sustainable fuel solutions for gas price pain relief” (FOX Business, 6/16/08).

       As the debate on gasoline taxes continues to unfold (Parry & Small 2005, Bento et al.

2009), economists are increasingly interested in the mechanisms by which gasoline prices affect

gasoline demand. Vehicle mile traveled’ (VMT), as well as scrappage and adoption rates of

vehicles are important determinants of the gasoline elasticity of demand (Hughes et al. 2010,

Klier and Linn 2010).

       Here, we add to this literature by providing the first empirical estimate using

disaggregated hourly speeding data, and find that a one dollar increase in gas prices modestly

reduced the average speed by 0.4 mph. While this change is small it and enables us to calculate

the VOT. In fact, for any much larger change of speeds, instead, the method of estimating VOT

would be clearly worrisome due to changes in the value of safety among other confounders.

       Most importantly our studies contributes in providing a new methodology of deriving the

VOT that relies on the first order condition of the price change directly, holding everything else

constant. This ceteris paribus feature is important. Note that instead all prior research on travel

time has been plagued with the “congestion effect”, but that congestion itself is “devalued”, so

obtaining relatively high VOTs (Small et al. 2005 VOT = 93% of wage rate). Our estimate of

VOT is 54% of the wage rate, hence substantially lower, as we condition the effect on


uncongested conditions directly. This suggests that disamenities of the outside option may have

been capitalized in prior VOT studies. At the same time our VOT estimate is larger than in prior

stated preference studies. To investigate the divergence between revealed and stated preference

studies continues to be an active research area by John List et al. and is beyond the scope of this

paper here.

       More generally, our new methodology is based on the intensive margin of behavioral

adjustments. This, we argue, has many important advantages compared to VOT estimates that

are derived on the extensive margin, or discrete choice problems, as was the case in previous

VOT studies.



Ashenfelter, O. and M. Greenstone (2004): Using Mandated Speed Limits to Measure the Value

       of Statistical Life. Journal of Political Economy, Volume: 112, Issue Number: 1, pp. 226-


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       May, 174-185.

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       Economic Review, 99(3), June: 667-99.

Bertrand, Marianne, Esther Duflo and Sendhil Mullainathan (2004). “How Much Should We

       Trust Differences-In-Differences Estimates?” Quarterly Journal of Economics 119: 249-


Blomquist, Glenn (1984): The 55 m.p.h. Speed Limit and Gasoline Consumption . Resources and

       Energy, 6, 21-39.

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       Employment and Wage Estimates, Bureau of Labor Statistics, Available at (last accessed June 2, 2011)

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       of Economics and Statistics, Vol. 66, No. 3, pp. 427-433.

Burger, Nicholas E. and Daniel T. Kaffine (2009): Gas Prices, Traffic and Freeway Speeds in

       Los Angeles. The Review of Economics and Statistics, 91(3): 652–657.


Busse, Meghan R., Christopher R. Knittel and Florian Zettelmeyer. 2010. "Pain at the Pump: The

       Effect of Gasoline Prices on New and Used Automobile Markets." NBER Working Paper

       #15590 (July). Submitted to American Economic Review.

Calfe, Winston and Stempski (2001): “Econometric Issues in Estimating Consumer Preferences

       from Stated Preference Data: A Case Study of the Value of Automobile Travel Time,”

       Review of Economics and Statistics, 83, 699–707.

Carrion-Madera, Carlos and Levinson, D. (2011): Value of Reliability: High-Occupancy-Toll

       Lanes, General-Purpose Lanes, and Arterials (11-1449). Presented at 90th Transportation

       Research Board Conference, January 23-27, 2011, Washington , DC.

Chi, Guangqing, Arthur G. Cosby, Mohammed A. Quddus, Paul A. Gilbert, David Levinson

       (2010): Gasoline prices and traffic safety in Mississippi. Journal of Safety Research 41,


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Department of Transportation, Washington, D.C.


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Energy Division.

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       Consumption with Respect to Price and Income: A Review,” Transport Reviews 24:3

       (2004), 275–292.

Gunn, H. F. (2000): An introduction to the valuation of travel time savings and losses (VTTS), in

       Hensher, D.A. and Button, K. (eds.) Transport Modelling, Handbooks in Transport,

       Pergamon Press, Oxford, Chapter 27.

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       Economy: Evidence from Monthly Sales Data." American Economic Journal: Economic


Leigh, J. P., & Geraghty, E. M. (2008): High gasoline prices and mortality from motor vehicle

       crashes and air pollution. Journal of Occupational and Environmental, Medicine, 50,


Li, Shanjun, Christopher Timmins and Roger von Haefen. 2009. "Do Gasoline Prices Affect

       Fleet Fuel Economy?" American Economic Journal: Economic Policy, Vol. 1(2), August:


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       Savings in the UK. Institute of Transport Studies, University of Leeds and John Bates

       Services. Report to Department for Transport.


New York Times (2011): Cutting the Cost of That Road Trip. May 15, 2011, on page TR3 of the

       printed New York Times edition, also available online at

       trip-practical-traveler.html?scp=1&sq=gas%20price%20tips&st=cse: (last accessed on

       May 29th, 2011)

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       75, 677-725.

Small, Kenneth A., Clifford Winston and Jia Yan (2005): Uncovering the Distribution of

       Motorists’ Preferences for Travel Time and Reliability. Econometrica, Vol. 73, No. 4

       (July, 2005), 1367–1382

Steimetz, Seiji S.C.(2008): Defensive driving and the external costs of accidents and travel

       delays. Transportation Research Part B 42, 703–724.

West, B.H., R.N. McGill, J.W. Hodgson, S.S. Sluder, and D.E. Smith (1999): Development and

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       to rising motorcycle fatalities, 1990–2007. American Journal of Public Health, 99,




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