The Determinants of Hybrid-Electric Vehicle Adoption:
Insights from State Registration Data
Working Paper
December 2006
David Diamond
PhD Student
School of Public Policy
George Mason University
Fairfax Virginia
Home address: 12848 Tewksbury Drive, Oak Hill VA 20171
ddiamond@gmu.edu
(703) 476-4030
Faculty Advisor: Philip Auerswald
Abstract
This paper examines the effect of tax incentives, gasoline prices and other socioeconomic factors on the demand for Hybrid Electric Vehicles (HEVs) in different U.S.
states. As hybrid sales increase, it is important for policymakers to understand how these
factors influence demand in order to judge the effectiveness of competing HEV incentive
policies. The paper develops a demand model for per-state market-share, and uses crosssectional time-series data on new Hybrid Electric Vehicle (HEV) registrations in different
U.S. states in 2003 and 2004 to evaluate the significance of difference factors. In 2003, a
number of predictors were significant, suggesting different policy alternatives for
promoting adoption. In 2004, when demand for the Toyota Prius exceeded supply, HEV
registrations were explained almost entirely by dealer location.
1. Introduction
Consumer interest in Hybrid Electric Vehicles has risen steadily in recent years in
response to rising fuel costs and increased concern about pollution and greenhouse gas
emissions. Hybrid vehicles utilize the same gasoline fuel infrastructure as conventional
Internal Combustion Engine (ICE) vehicles, yet represent a distinct technology
improvement that can provide greater fuel economy and reduced emissions for equivalent
vehicle performance. HEVs face the same barriers to diffusion as any new technologies
such as lack of knowledge by potential adopters as well as variation in the factors that
affect different consumers’ individual utility calculations, such as price, income, discount
rate and risk tolerance (Jaffe and Stavins 1994) (Stoneman and Diederen 1994), as well
as high initial costs (Argote and Epple 1990). As an energy efficiency technology, HEVs
also address positive externalities associated with resource management, the environment
and energy security that are not taken into account by the market (Jaffe and Stavins
1994). The presence of these externalities presents a strong economic argument for
government intervention, yet it leaves open the question of the most efficient and
effective avenue(s) for government action.
Traditional government intervention policies into the market for new technologies
include taxes or subsidies to account for externalities (Baumol 1972), regulation to force
adoption of beneficial technologies (Norberg-Bohm 1999) and taxes on resource inputs to
induce innovation and adoption of more resource efficient technologies (Newell, Jaffe,
and Stavins 1999). In the case of hybrid vehicles, Federal, State and Local Governments
have primarily chosen the first option, offering tax deductions, credits, fee reductions and
HOV lane privileges to HEV owners (DOE State Incentives for Hybrid Electric Vehicles
1
2004). Despite the publicity of these subsidy programs, it is still unclear how effective
they actually are in promoting hybrid vehicle demand compared to other economic and
social factors. As hybrid sales increase, it is important for policymakers to understand
how incentives and other factors affect adoption patterns, in order to judge the
effectiveness and efficiency of HEV incentive policies.
In section 2 of this paper, I discuss the background and policy implications of
HVEs. In sections 3 and 4, I introduce a demand model using cross-sectional time-series
data on new HEV registrations in U.S. states in 2003 and 2004 to examine the effects of
tax incentives and gas prices on the number of HEVs registrations in different U.S.,
states, while controlling for income, average vehicle mileage and the availability of car
dealerships. In section 5, I discuss the results. In a year that automobile makers can
distribute supply to accurately meet the demand for hybrid vehicles is in relative
equilibrium (an assumption whose accuracy I discuss for each time period), the results
suggest that tax incentives, gas prices, income and driving habits are all significant. In a
year of constrained demand, however, the results suggest that the location of car
dealerships dominates. In section 6, I discuss the implications of both cases for policymakers. In section 7, I discuss future research stemming from this paper.
2. Background
Hybrid-electric vehicles combine a gasoline engine with an electric motor and
battery to provide power to a car’s drive train. Despite the additional weight of the
electric motor and battery, a typical hybrid is still considerably more fuel efficient in
overall driving performance than an Internal Combustion Engine (ICE) car of equivalent
size and performance.
2
Although most automobile manufacturers either offer HEV vehicles or plan to
offer them by the end of the decade, a price premium for HEVs compared to equivalent
ICE automobiles may limit demand. Although Ogden (2004) demonstrates the potential
for HEV technologies to have lower societal lifecycle costs then ICE and many fuel cell
technologies for automotive applications, the market does not currently factor the
externality costs of HEVs. Canes (2003) uses total lifecycle costs to compare equivalent
hybrid and gasoline models, and found that lifecycle costs for hybrid vehicles exceed
those for equivalent ICE vehicles based on vehicle price, and fuel/maintenance expenses
over an expected lifetime, even when he takes pollution costs into account.
Several researchers and firms have used survey data to draw preliminary
conclusions about factors affecting HEV demand. These studies generally conclude that
hybrid owners tend to be in the highest income demographics and are more sensitive to
gas prices than environmental benefits in purchasing their vehicles (Changewave
Research 2005: Year of the Hybrid 2004). Burke (2004) and Jaffe, et al (1994) also cite
the high discount rate that consumers place on new cost-saving technologies, expecting
payback for their investments in as little as three years. Cao and Mokhtarian analyzed
national-level hybrid sales data, and found lagged gasoline prices to be significant in
explaining aggregate diffusion patterns (2004).
There is a fairly well developed economic literature on the pricing and demand
for automobiles in the U.S. aggregate market using national level aggregate data over
time, which I adapt to study demand for HEV vehicles. Chow (1957) proposed a linear
demand equation for nationwide automobile demand over time, as a function of income,
price and prior sales. Wetzel and Hoffer (1982) and Irvine (1983) develop demand
3
models and use regression analysis to determine consumer preferences for individual
features, including fuel economy. Wetzel and Hoffer focus on disaggregating based on
vehicle class, while Irvine focuses on cross-price elasticities for specific car models with
different attributes. Hedonic techniques focus on the supply and demand equilibrium in
the market to determine quality adjusted prices for unique attributes. Bajic (1988) uses
hedonic regression to calculate quality adjusted prices for different attributes, and then
calculate variance in those attribute prices among specific models for use in a regression
of market share. By this logic, once the market has determined a quality adjusted price
for a given option, the variance from that price for individual models or markets would
result in changes in consumer demand for that particular model, as evidenced by a higher
or lower market share compared to similar models.
Berry, Levinsohn and Pakes (1995) generalize previous models of automobile
demand. The authors express the indirect consumer utility function, U, for a given
automobile as a function of consumer characteristics and product characteristics, and
allow for multiple higher level interactions between consumer and product attributes. A
flexible discrete choice model such as theirs provides the most accurate estimation
specification, but it requires a large number of data points across model and time space to
achieve sufficient degrees of freedom to estimation.
3. A Model for HEV Demand
While established automobile demand models are useful for the general
automobile market, their application to HEVs is limited by the relatively limited selection
of HEV models and their short time on the market, which reduces the number of modelyear sales data points available for analysis. As a new technology, HEVs are also not in
4
an equilibrium market, so any model for demand must allow for the effect of diffusion
over time. Also, it is anticipated that local subsidies and incentives will have a significant
effect on demand by, in essence, changing the quality adjusted prices for the HEV option,
so it is important not to aggregate out these factors by examining only national level data
that is used by most automobile market analyses. To overcome these limitations, I focus
not on variance in characteristics between different HEV models, but on the variance in
tax incentives and consumer characteristics among a cross-sectional dataset of U.S.
states.
To develop a model that is suitable for HEV vehicles, I will start with the same
basic behavioral principles of Berry, Levinsohn and Pakes [BLP] (1995). In an
equilibrium market, the indirect utility of consumer i for any vehicle j can be expressed
as:
U i j  f  p j , x j ,  j ,  i ; 
Equation 1
where
p j  price of vehicle j
x j  observed product characteri stics of vehicle j (size, features, power, etc)
 j  unobserved product characteri stics (quality, styling, brand reputation , etc)
 i  consumer preference s and socioecono mic characteri stics of consumer i
and  is a vector of parameters to be estimated
Consumer i will choose to purchase vehicle j if and only if:
U i j  p j , x j ,  j ,  j ;   U ir  pr , xr ,  r ,  r ;  for r = 0, 1, 2, …., J1; r ≠ j
For a given population, the aggregate demand Aj for vehicle j is given by:
1
The choice r = 0 corresponds to the outside alternative of not purchasing any vehicle.
5


A j   : U i j  p j , x j ,  j ,  j ;   U ir  pr , xr ,  r ,  r ;  for r = 0, 1, 2, …., J; r ≠ j
and the market share sj of a given model is now a function of:
s j  f  p j , x j ,  j ,  ; 
Equation 2
It is important to note that in Equation 2, the market share is now a function of the price
and characteristics of an individual product j, but of the characteristics of the overall
population, so that average consumer preferences and characteristics can be assumed for
any market share data point.
The characterization of market share in equation 2, adapted from BLP (1995) is
fairly straightforward, and is a generalized form of earlier specifications for automobile
demand. At this point, however, I depart from previous studies and adapt equation 2 to
analyze HEV market share observations by state. First, I will assume that observed and
unobserved product characteristic vectors x and ξ do not vary among states, because
automakers sell the same car models with nearly equivalent prices and specifications in
each U.S. state.2 Thus, I can reduce equation 2 to:
s s j  f  psj ,  s ; 
Equation 3
where the subscript s denotes an observation for an individual state.
Next, I will assume that for the years of analysis, 2003 and 2004, the differences
in characteristics of each HEV model offered are small enough that they can be treated as
2
In reality, there are differences in emissions standards from state to state, with California being the most
notable. For the purposes of this analysis, however, I will assume that these differences are transparent to
consumers and are not factored into buying decisions. This may bias the results most significantly in
California, where hybrid models for sale have lower GHG and pollution emissions than in other states.
6
a homogenous single model. I am forced to make this assumption due to a lack of sales
data broken down by individual model. But, as I explain later in the description of my
data set, this assumption is not unrealistic for HEV models in those years. This allows
me to express equation 3 as:
s s h  f  p sh ,  s ; 
Equation 4
where the subscript for the model, j, has been replaced by h to represent only HEV
market share and prices in a given state, s.
I can now look at the remaining terms in equation 4, psh and ζs to determine which
determinates of these terms will vary among states for HEV vehicles. On an individual
level, consumers’ preferences for different auto characteristics, ζ, are affected by a
number of factors that vary, on average, by state. One of the most important is income.
The demand for any product is generally given as a function of individual income, with
the individual demand of person i for product j expected to vary as a function of (yi - pj ).
Individuals’ utility for any type of vehicle is also expected to be influenced by benefits or
status derived from a particular observed or unobserved product characteristic. There are
an endless number of ways in which these preferences could change by state or region –
such as a greater preference for American-made cars in Detroit or for trucks in rural
areas. For HEVs, certain states and municipalities provide benefits such as HighOccupancy-Vehicle (HOV) lane privileges, free parking or exemption from emissions
testing (DOE State Incentives for Hybrid Electric Vehicles 2004). Additionally, in states
where the population tends to be more environmentally friendly, HOV ownership may
afford the owner a greater social standing among peers. For this analysis, I will lump all
7
of these factors into one variable, B, to indicate the monetary and non-monetary benefits
over time of owning an HEV.
Next, I focus on the price term in equation 4 to bring in a number of other factors
that vary by state. I decompose the total ownership price into two different components,
the upfront cost plus the discounted lifetime fuel costs3:
Ph  Pupfront  Pfuel
This is where I am able to introduce several other factors that vary by state: tax
incentives, gasoline prices per gallon and travel habits. The upfront cost for the vehicle
will be the purchase price minus any state tax incentives, T, for purchasing HEV vehicles.
Fuel costs are proportional to the product of the price of gas per gallon, pg and the
number of miles traveled during a given period, VMT. While there are other factors that
influence total ownership costs of hybrid vehicles, such as maintenance costs, battery
replacement, registration, etc, I will assume that these terms either do not vary by state, or
that they affect all vehicles equally and therefore would not affect the total market share
distribution.
Thus, price and individual preference per state in equation 4 can be expressed as:
 s  f ( y s , B)
p sh  f (T , p gas  VMT )
so that equation 4 becomes:
s s h  f  y s , B, T , p gas * VMT ; 
Equation 5
3
There is considerable debate over how consumers factor fuel costs into automobile purchases, and how
discount rates and expectations of future prices factor into personal calculations. For this paper, I introduce
the fuel costs only to the extent that they vary by state and would make consumers in one state more or less
likely to purchase a HEV.
8
Additionally, I will follow Irvine (1983), and transform market share into a logarithmic
odds form. This transformation avoids the potential for the model forecasting shares
outside the (0,1) interval (Irvine 1983). The resulting equation is:
 s 
ls sh  log  sh   f y s , B, T , p gas * VMT ; t; 
 1  s sh 
Equation 6
Before taking equation 6 to the data however, I must address the effect of time on
the diffusion of HEV technology. If HEVs were a mature technology for which
consumers had fairly well established preferences, market share would be expected to
vary over time only as the prices, performance characteristics and generic options of
these vehicles changed. HEVs are a new technology, however, which suggests that for
any given state, the change in market share over time should follow a classic diffusion
pattern to an equilibrium market share value. Consumers take time to respond to price
and market signals in varying their consumption habits and demand for new technologies.
Barriers to rapid adoption of HEVs include lack of knowledge by potential adopters as
well as variation in the factors that affect different consumers’ individual utility
calculations, such as risk tolerance or desire to be an early adopter. While some
technically savvy or environmentally conscious consumers derive greater utility from
their status as early adopters, the utility of other consumers (often called imitators)
increases only after they see their friends or neighbors adopting.
In general, this diffusion pattern over time follows a classic sigmoid or “s-shaped”
curve (Jaffe, Newell, and Stavins 2002). Adoption occurs slowly at the onset, increases
exponentially, and then tapers asymptotically to a steady state in which the technology
9
completely displaces previous technologies or achieves a stable share of the market.
Thus, the adoption process for HEVs can be modeled as a probit or rank model, in which
the odds of adoption are a function of a variety of factors that vary across space and time.
As changes in price, information, and other factors occur, the average individual’s utility
for the technology will increase. This results in the market-share curve increasing over
time, often in an s-shaped or sigmoid pattern before reaching an equilibrium market
share4. Government intervention mechanisms that influence any of these factors – such as
tax incentives that lower effective prices and public service campaigns that increase
awareness of the technologies – should help lower the individual adoption threshold and
speed up the diffusion process and/or increase the final equilibrium market share. A
notional representation of this effect for an s-shaped diffusion curve is show in figure (1).
At any given time during the diffusion process the market share with incentives should be
higher than if no incentives are present5. However, the incentive must be given to all
consumers who purchase HEVs, regardless of whether they would have done so in the
absence of an incentive. It is these “wasted” payments that reduce the efficiency of tax
incentives, particularly if the elasticity of demand with respect to price is low.
It follows that the diffusion process for each state should be a function of time,
total cumulative share of hybrids in a state, scum, and any other factors that affect the
knowledge and visibility of hybrids among potential consumers. For this paper, data on
4
For a further description and examples of these diffusion modeling methodologies, see Jaffe, Newell and
Stavins (2002) and Bass (1969).
5
Even if market share fluctuates over time or even decreases (i.e. it does not follow the s-curve), it is still
reasonable to assume that people would always be more likely to buy a hybrid with a monetary incentive
than without one. In fact, market share for certain hybrid models has fluctuated considerably since their
introduction.
10
cumulative state shares of hybrids is not available, so I will account for diffusion
primarily by the inclusion of time, t, as an independent variable in the model6.
Thus, equation 6 becomes:
 s 
ls sh  log  sh   f y s , B, T , p gas * VMT ; t; 
 1  s sh 
Equation 7
I expect that an increase in each of these variables would result in an increase in the logodds, lssh, of hybrid vehicle adoption.
Equation 7 would represent the final form of the model only in an equilibrium
markets where automobile suppliers are able to provide enough HEVs to meet the
consumer demand from each state, and all consumers had equal access and exposure to
vehicles for sales. For some years and HEV models -- most notably for the 2004 and later
model of the Toyota Prius -- automakers have been unable to meet demand. In this
constrained supply environment, the automakers dealer network and distribution policies
would likely play a role in determining the distribution pattern (and hence the adoption
pattern) of HEVs. To account for this effect, I include another set of variables, HDm,the
number of car dealerships for each HEV manufacturer in a state as a percentage of the
total number of new-car dealerships in that state. The variance in this fraction between
states provides some indication of how likely consumers in each state are to be exposed
to that manufacturer as they visit all of their local car dealers. From a behavioral
standpoint, it is expected that most consumers shopping for a new car will visit local car
dealerships and select the model that best meets there needs. Therefore, in states that have
a higher proportion of HEV manufacturers (which were only Honda and Toyota through
6
While time is most significant in its influence on diffusion, it would also have been included to account
for changes in price, model characteristics and consumers’ preferences over time.
11
2004), consumers would be more likely to encounter a HEV during their local search.
The variable may also be significant in determining relative the number of hybrids
delivered to each state, if automakers are rationing supplies of HEV evenly among
dealers as they come of the assembly lines.
Thus, the equation becomes:
 s 
ls sh  log  sh   f y s , B, T , p gas * VMT ; t , HDm ; 
 1  s sh 
Equation 8
Where the subscript HDm represents a series of values, HD1, HD2, … HDM for each
carmaker offering HEV models.
4. Data
To test this specification, I used U.S. State-level data for 2003 and 2004 from the
following sources:
Variable
sh
Data
Market share of hybrids (Hybrid
registrations per state, 2003-2004 [from
annual lists of states with highest number
of total registrations] divided by Total
vehicle registrations per state
VMT
VMT per capita (only 2003 VMT data
available)
pgas
Yearly average gasoline price per state
Source
o R.L. Polk (Hybrid Vehicle
Registrations up 25.8% in
2003 2004) (Hybrid Vehicle
Registrations Increase 81%
in 2004 2005)
o US Department of
Transportation (Highway
Statistics 2003 2004)
(Highway Statistics 2004
2005)
US Department of
Transportation (Highway
Statistics 2003 2004) (Highway
Statistics 2004 2005)
Federal Highway
12
Variable
T
Data
for 2003 and 2004 (average of monthly
fuel price for each state, all gasoline
blends, including Federal and Local
taxes)
State-wide tax and other incentives
ys
Income per capita
HDT,
HDH
Number of Toyota and Honda
dealerships per state as a percentage of
all new car dealerships
Source
Administration, Monthly Motor
Fuel Report
DOE EERE and
www.electricdrive.org
US Census Bureau (Table 1:
Annual Estimates of the
Population for the United States
and States, and for Puerto Rico:
April 1, 2000 to July 1, 2005
(NST-EST2005-01) 2005)
o Toyota Motor Sales, USA
(Toyota dealerships per state)
o Honda Motor Corporation of
America (Honda dealerships
per state)
o National Automobile Dealers
Association (NADA) (total
number of new-car
dealerships per state
I calculated the value of tax incentives as the combined value of Federal plus
State incentives, assuming a Federal tax deduction of $2000, a 25% average Federal tax
rate, and an average purchase price for State calculations of $20,000. The Federal
deduction value was essentially constant for each state and could have been omitted from
the linear specification. However, I included it in order to provide non-zero values in
each observation for use in a log-linear regression.
The number of available observations is limited to the list of the top 20 States for
2003 and top 15 states for 2004 for total HEV registrations provided by RL Polk in
annual press releases. While a full list of 50 states would have provided a better number
of observations, the 20 states for 2003 accounted for 85% of total US HEV registrations
that year, while the 15 states for 2004 accounted for 77% of total registrations. The data
set also shows a good deal of variance in each of the variables, with the exception of the
13
HOV dummy. This dummy variable is positive only for Virginia since no other states
allowed an HOV exemption for hybrids that year.
Because the available data does not break down sales by model, I am forced to
treat each HEV vehicle sold as essentially homogenous. In 2003, this is a relatively
accurate assumption. Two HEV models – the Honda Civic Hybrid and Toyota Prius –
accounted for 96% of the total sales that year. These two models both had an MSRP of
around $20,000 and had almost identical fuel economy ratings, size, seating capacity and
performance. This changed somewhat in 2004, when manufacturers began releasing other
HEV models in higher quantities and Toyota redesigned the Prius, creating a larger but
more fuel efficient model. Toyota dealers in 2004 also began experiencing significant
waiting lists for the redesigned Prius, indicating a lack of equilibrium between supply and
demand (Adams 2004). While the Prius characteristics for 2004 were different from
2003, the sticker price was still approximately $20,000 (although the transaction prices
for many models may have been higher due to dealer premiums and optional features)
(Adams 2004). Therefore, I will use the same assumptions for the value of tax incentives,
with the understanding that they may be biased low due to higher transaction prices.
Additional State data broken down by HEV model might allow me to overcome some of
these limitations and to obtain a larger number of observations. However, such data was
not available in the public domain.
Tables (1) and (2) summarize the data for 2003 and 2004, with states listed in
order of decreasing market share. Several observations from the raw data are in order.
First, rankings by market share (hybrid registrations as a fraction of total vehicle
registrations) are very different than rankings by overall number of hybrids. California is
14
frequently cited as the leading state for hybrid sales, yet from a market share standpoint it
ranks only sixth out of the 20 states reported. Each of the top four states had a significant
statewide tax incentive, and all reported states with tax incentives except for New York
were among the top states in market share. New York appears to be an outlier, although
the effect of its tax credit, income and gas prices are potentially offset by the low miles
traveled per year. Similar trends among states are observed in the 2004 data, which
included only observations of the top 15 states in terms of total vehicles sold. This low
number of observations tended to bias the data against states with low populations and
total automobile sales, so that the top market share state for 2003, Colorado, was not
reported in 2004. However, all of the top 15 states for total sales in 2004 also ranked
among the top twenty (in total sales) states for 2003.
For analysis purposes, I assume that the price that consumers pay for a given
hybrid before tax incentives and registration fees is the same from state to state. One
potential flaw in this assumption would be differences in actual prices charged for
vehicles of the same model from state to state due to short-run local variations in supply
and demand. For example, if local demand for hybrids exceeded short term supply,
dealers might have charged premiums over the MSRP or not offered discounts and
incentives common on equivalent ICE vehicles. For example, when I purchased my own
Honda Civic Hybrid in Northern Virginia in late 2002, a dealer told me that short-term
local demand was so strong (due to HOV privileges) that he had purchased hybrids from
dealers in other parts of the state (with no HOV lanes) that were overstocked and sold
them for a markup in Northern Virginia. By 2003, however, hybrids appeared to be in
stock at almost all dealerships with markdowns from sticker price (as is common in most
15
car models), indicating that manufacturers had adjusted supply shipment patterns to
account for local demand.
5. Analysis and Results
I tested the model in STATA using with the log-odds of marketshare as the
dependent variable, and log versions of all independent variables with non-zero values.
First, I performed OLS regressions on the panel data from 2003 and 2004 separately, to
test for the significance of the independent variables in a given year. Next, I performed
random-effects cross-sectional time series GLS regressions on the combined set of 2003
and 2004 data points. The log-linear form allows me to treat the coefficients of each
independent variable as the elasticity of demand for that variable with respect to the
dependent variable. These simple log-linear specifications ignore higher order
interactions between variables that would be captured in a more sophisticated regression
technique such as translog, Box Cox (Bajic 1988) or BLP (1995), but these techniques
generally require a larger dataset than the one available.
The results of each regression are shown in Table (3)
6. Observations
The results varied dramatically between 2003, when hybrid vehicle supplies were
generally adequate to meet market demand, and 2004, when high demand for the
redesigned Toyota Prius resulted in dealer waiting lists for vehicles. In 2003, average
income, effective tax incentives, gas prices, and miles traveled per year are all significant
(at least to the .10 level) and have positive coefficients, indicating that an increase in any
of these factors increases the odds that a consumer would have purchased a hybrid
16
vehicle over a conventional ICE vehicle. Even though tax incentives appear to be
significant in predicting the market share of hybrids, the magnitude of the coefficient for
tax incentives (.137) is much smaller than the coefficient for the gas price*VMT
interaction term. If the coefficients are interpreted as elasticities, this result suggests that
the elasticity of market share with respect to tax incentives was much less than with
respect to gas prices or income7. The relative proportion of Toyota dealerships (but not
Honda dealerships) was also significant with a positive coefficient, indicating that HEV
market share was likely to be higher in states where Toyota dealerships made up a larger
proportion of all new car dealerships.
In 2004, however, the results changed dramatically. The effect of Toyota
dealership proportion was the only significant variable, explaining over 75% of the
variance in hybrid sales among the 15 state observations in the regression. The original
predictors were actually significant in an earlier regression (results not shown) that did
not include the dealer ratios, but when the Toyota dealership ratio is included it
dominates everything else in the regression for 2004.
The GLS regression on the combined dataset for 2003 and 2004 showed similar
results to 2003. Time was included in the first multiyear regression [column labeled (3) in
table 3], but was removed in the second regression [column labeled (4)] due to suspected
multi-collinearity between time and several other variables (gasoline prices and percapita income). As expected, the removal of time resulted in higher significance levels
and larger-magnitude coefficients for the remaining time-dependent variables. The
regression results mirrored the findings of 2003, which indicated that tax incentives, gas
7
The regressions shown in table 1 used the log-odds of market share as the dependent variable. Additional
regressions (not shown) using the log of market share as the dependent variable yielded similar results in
terms of significance and relative magnitude of coefficients.
17
prices*VMTCP and the Toyota (but not Honda) dealership fraction were all significant,
with gas prices having the greatest effect.
HOV privileges were moderately significant, but there is only one data point –
Virginia. All other HOV exemptions only went into affect recently when Federal law
(Energy Policy Act of 2006) authorized states to grant HOV exemptions to hybrids.
While it is important to control for HOV privileges, the lack of additional data points
makes it difficult to provide a meaningful interpretation of the coefficient value for this
dummy variable. Therefore, the HOV dummy variable more accurately encompasses all
of the other effects for Virgina that influence preference for Hybrids, including the HOV
priviledges. Because HOV privileges only provide utility if the vehicle owner lives or
works close to an HOV lane, a study of the true relationship of HOV privileges on
Hybrid sales would require a spatial analysis using highly disaggregated data that was not
readily available.
The change in the regression results from 2003 to 2004 is striking, and may be an
effect of the inability for Toyota to meet consumer demand for the redesigned Prius
model that was introduced in early 2004. If there were waiting lists in all states and
Toyota distributed the available supply of cars evenly between all of its dealerships, it
would follow that states that had more Toyota dealerships (relative to the total number of
car dealerships in the state) would receive more cars, resulting in a higher market share.8
Once all states had waiting lists, the factors that were significant in 2003 (tax incentives,
gas prices, income) might still have affected the size of the waiting list, but not the actual
delivery pattern for the vehicles.
8
This occurs because the total number of car dealerships in each state is highly correlated to the total
number of cars sold and registered in the state, with R2= 0.86.
18
There is also the possibility of endogeneity issues relating to state tax incentive
policies. The analysis assumed that state incentive policies were set independent of
consumer preferences for Hybrids in each state. However, in states where other factors
were likely to increase the demand for HEVs, consumers may have placed additional
pressure on lawmakers to enact incentive laws. In further research, I may address this
problem through qualitative analysis of the deliberation processes that resulted in
incentive policies in various states.
7. Implications for Policymakers
If -- as the results suggest -- income and gas prices are both effective at
influencing the adoption of HEVs when there are adequate supplies, policymakers have
several alternatives to promoting adoption. While tax incentives may be significant, if the
magnitude of their effect is low they may, in essence, do more to reward people who
would have purchased hybrid vehicles already than to induce new sales, as discussed
previously. Gas taxes (or simply the market force of rising gasoline prices) may actually
have a greater effect on HEV sales, with the added benefit of shifting sales to more fuel
efficient vehicles in general (not just HEVs), as well as inducing the innovation of
additional fuel efficiency technologies (Newell, Jaffe, and Stavins 1999). However,
recent increases in gas prices due to market forces appear to be greater than any gas taxes
that have been seriously considered at the state or Federal level, so the induced efficiency
effect will likely occur in the absence of any government imposed taxes or incentives.
Additionally, attempts at implementing gas taxes may much greater resistance from the
public than attempts to increase spending for tax credits. This occurs both because of the
19
greater visibility of tax increases to voters, and because the overall costs to a given
consumer from gasoline tax payments may exceed the proportional cost of the tax credits
when vehicle sales are low.
The results of the 2004 analysis raise another issue for policymakers. When HEV
supply is constrained, sales patterns may be determined not by consumer demand signals
but by internal distribution policies by automakers. In this case, gasoline and tax
incentive policies will be ineffective in achieving the overall aim of state HEV policies -placing more HEVs on the road in a given state. However, they may have an effect on
consumers’ decisions to enter the waiting list for a hybrid, resulting in greater sales in the
future if automakers clear their demand backlogs.
In addition to tax incentives benefiting consumers who would have bought
hybrids even without an incentive, these incentives may also act as a subsidy to
automakers. Without the subsidy, automakers might have otherwise lowered their prices
to sell more hybrids. However, the effect of subsidies on automaker pricing is the result
of a complex interaction of consumer and producer behavior that is beyond the scope of
this paper.
6. Future Research
I conducted my analysis based only on a small sample of data. A more
comprehensive data set that includes information from all fifty states, broken down by
specific hybrid models would allow me sufficient data points to test specifications that
allow for greater interaction among independent variables, as well as to explore the
effects of specific vehicle models and characteristics on HEV demand. I suspect that tax
and monetary incentives might be more significant in explaining sales of HEVs that have
20
close conventional engine substitutes (such as the Honda Civic and Ford Escape) than
those vehicles (such as the Prius) that are marketed as unique models with a greater
degree of “green” status. Sales or registration data disaggregated to the county or zip
code level might also allow me to test for other factors such as local incentives or
consumer preferences that might be aggregated out at the state level.
Although I have confined my analysis to one particular type of technology – HEV
vehicles – the method I use could reasonably be applied to any technology market which
demonstrates a large cross-sectional variance in monetary incentives and resource prices.
As states begin to take a more active role in promoting the adoption of environmentally
friendly technologies, there may be a rich source of data to analyze the effectiveness of a
variety of policy tools and market intervention mechanisms.
21
Figures and Tables
Figure 1: The effect of incentives on market share for a notional sigmoid diffusion process
22
Table 1: Data Summary for 2003
state
CO
VA
MD
OR
WA
CA
AR
MA
WI
IL
NC
FL
TX
NY
PA
NJ
GA
OH
IN
market
share
0.0010247
0.0008422
0.0007509
0.0006861
0.0006688
0.0006176
0.000433
0.0003713
0.0002963
0.0002637
0.0002586
0.000236
0.0002136
0.0002008
0.0002004
0.000194
0.0001899
0.0001873
0.0001769
MI
0.000162
total
rank by
HEV
total
effective
regisHEV
tax
HOV
trations
reg
total reg incentive $ Allowed
899
14 877.353
3000 No
3376
2 4008.381
200 Yes
1851
5 2465.17
1000 No
1043
12 1520.246
1500 No
1972
4 2948.635
0 No
11425
1 18499.9
0 No
855
15 1974.78
0 No
1335
9 3595.708
0 No
759
19 2561.521
0 No
1502
8 5696.731
0 No
937
13 3623.71
0 No
1996
3 8457.955
0 No
1651
7 7730.882
0 No
1653
6 8232.33
2250 No
1217
10 6073.206
0 No
854
16 4402.315
0 No
791
17
4164.5
0 No
1211
11 6464.158
0 No
571
20 3227.209
0 No
770
18 4756.36
0 No
VMT
Income
(per cap) (per cap)
9532.404
34561
10406.79
33730
9929.553
37446
9860.107
28734
8972.6
33254
9119.262
33415
9657.378
24384
8348.435
39504
10893.96
30685
8419.459
32965
11152.16
28071
10900.19
30098
10100.95
29074
7037.321
36112
8600.33
31911
8077.657
39577
12579.11
29000
9526.052
30129
11703.55
28838
9995.649
average
gas price
(per gal)
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
31178 $
1.63
1.53
1.60
1.74
1.71
1.80
1.75
1.64
1.65
1.60
1.56
1.47
1.51
1.52
1.58
1.52
1.40
1.60
1.55
1.56
Toyota
Dealers
(as % of
new
dealers)
0.0638298
0.0650995
0.0806452
0.0830325
0.0737913
0.0843882
0.0666667
0.0691589
0.037037
0.047
0.0556348
0.0617801
0.0562454
0.0521008
0.0497148
0.0529801
0.0566343
0.047619
0.0457875
Honda
Dealers
(as % of
new
dealers)
0.0815603
0.0632911
0.094086
0.1407942
0.0712468
0.1494876
0.0745098
0.0598131
0.037037
0.06
0.0727532
0.0670157
0.0533236
0.0504202
0.0464548
0.0827815
0.0728155
0.1914894
0.0641026
0.036316 0.058366
23
Table 2 – Summary of Data for 2004
state
OR
VA
CA
MD
WA
AR
MA
NJ
IL
NC
FL
PA
NY
TX
OH
market
share
0.001547
0.001372
0.001299
0.001271
0.001134
0.000814
0.000739
0.000511
0.000479
0.000469
0.000388
0.000385
0.000365
0.000334
0.000273
total
HEV
registrations
2282
5613
25021
3238
3441
1672
2590
2053
2707
1715
3272
2308
3123
2922
1763
rank by
total
HEV reg
11
2
1
5
3
15
9
12
8
14
4
10
6
7
13
total reg
877.353
4008.381
2465.17
1520.246
2948.635
18499.9
1974.78
3595.708
2561.521
5696.731
3623.71
8457.955
7730.882
8232.33
6073.206
effective
tax
incentive
HOV
$
Allowed
1500
200
0
1000
0
0
0
0
0
0
0
0
2250
0
0
No
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
VMT
(per cap)
9860.108
10406.79
9119.261
9929.552
8972.599
9657.378
8348.434
8077.657
8419.459
11152.16
10900.19
8600.331
7037.321
10100.95
9526.051
Toyota
Dealers
average (as % of
Income gas price new
(per cap) (per gal) dealers)
30584
36175
35172
39629
35017
28609
42102
41636
34725
29303
31460
33257
38333
30697
31135
$2.04
$1.81
$2.08
$1.88
$2.04
$2.01
$1.91
$1.82
$1.88
$1.86
$1.80
$1.88
$1.93
$1.77
$1.90
0.083032
0.065099
0.084388
0.080645
0.073791
0.066667
0.069159
0.05298
0.047
0.055635
0.06178
0.049715
0.052101
0.056245
0.047619
* 2003 data used in place of 2004 data, which was not available
24
Honda
Dealers
(as % of
new
dealers)
0.140794
0.063291
0.149488
0.094086
0.071247
0.07451
0.059813
0.082781
0.06
0.072753
0.067016
0.046455
0.05042
0.053324
0.191489
Table 3: Regression results
Dependent variable =
Log(sh/(1-sh))
Number of Data Points, N=
2003
OLS
(1)
2004
OLS
(2)
Multiyear
GLS
(3)
Multiyear
GLS (year
omitted)
(4)
Coefficient Coefficient Coefficient Coefficient
(SE)
(SE)
(SE)
(SE)
20
15
35
35
Intercept
-26.38275***
(8.822079)
5.150081
(17.15024)
-19.27229**
(7.784836)
-30.82276
(2.529215)
Log(total tax incentive)
.1368939**
(.0537138)
.9997385**
(.3439703)
.8970666 *
(.4346294)
.2482092+
(.1460412)
-.0572118
(.0933414)
.5977028***
(.1638254)
N/A
-.0057312
(.0678471)
-.3211936
(.7476541)
-.0327093
(.5660131)
.3207999*
(.150124)
.0398707
(.093676)
1.171801***
(.3164756)
N/A
.1219069**
(.0570762)
.6817194**
(.3270778)
.6454412*
(.389763)
.2432689+
(.1540351)
-.0366586
(.0991828)
.652556***
(.1748452)
.1131209+
(.0720737)
.1258088**
(.0542676)
1.163796 ***
(.1141914)
1.111147***
(.2420704)
.2011497+
(.1438903)
-.0431437
(.0940568)
.5990637***
(.1631853)
N/A
0.7377
.7702
Log(gas price *VMTPC)
Log(per capita income)
HOV access dummy
Log (Honda Dealer ratio)
Log(Toyota Dealer Ratio)
Year
Adj R2
Significance levels
+ < .17
* < .10
** < .05
*** < .01
.8480
.8450
25
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