ISSN (Print) 0473-453X

Discussion Paper No. 1183

ISSN (Online) 2435-0982

Welfare Effects of Fuel Tax
and Feebate Policies
in the Japanese New Car Market
Tatsuya Abe

July 2022

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, JapanWelfare Eﬀects of Fuel Tax and Feebate Policies in the Japanese
New Car Market ∗Tatsuya Abe †July 27, 2022

Abstract
This paper examines the eﬃciency and distributional eﬀects of fuel tax and feebate policies in
the automobile market. I employ a model in which households make two-stage decisions on
car ownership and utilization, and I estimate model parameters by combining micro-level datafrom a household survey and macro-level aggregate data on the Japanese new car market from2006 through 2013. Interestingly, several system changes in the Japanese feebate created richvariations in vehicle prices across vehicles and over time during the sample period. I use suchexogenous variation to overcome the vehicle price endogeneity associated with demand estimation. Counterfactual analyses show that the Japanese feebate results in a significant increase insocial welfare while augmenting environmental externalities. In particular, the rebound eﬀect
induced by the feebate cancels out approximately 7% of the reduction in CO2 emissions thatwould originally have been attained by the improvement in fuel economy. In addition, I findthat the fuel tax at the current tax rate in Japan is 1.7 times less costly than the product tax,
an alternative feebate scheme considered in the counterfactuals. I also find that there is no difference in regressivity between the two policies in reducing negative environmental externalities
by the same amount.
Keywords: Discrete-Continuous choice, Fuel tax, Feebate policy, Rebound eﬀect, Decomposition, Distributional impact
JEL Codes: D12, H23, L62, Q53∗

I am deeply grateful to Yuta Toyama for invaluable guidance and constant support and to Shigeru Matsumoto,

Naoki Wakamori, and Komei Fujita for providing me with the data sets used for this project. I would also like tothank Taiju Kitano and Yasutora Watanabe for helpful comments, as well as seminar participants at the Japanese
Economic Association 2021 Spring Meeting, the 2021 September Workshop on Economics at Otaru (SWEO), and the16th Applied Econometrics Conference at Hitotsubashi University. I acknowledge financial support from the Tokyo
Center for Economic Research (TCER) and the Sumitomo Foundation for environmental research projects. This
paper was awarded the 2021 Moriguchi Prize by the Institute of Social and Economic Research, Osaka University.
All errors are my own.
†
Faculty of Political Science and Economics, Waseda University, 1-6-1 Nishiwaseda, Shinjuku-ku, Tokyo 169-8050,
Japan. Email: tatsuya.abe149@gmail.com

1

1

Introduction

Reducing fuel consumption from car driving is an urgent challenge common to most countries.
Among the policy instruments for resolving this challenge, feebate policies have gained popularity.
Feebate policies provide price incentives for automobiles depending on fuel eﬃciency to encourage
households to replace old fuel-ineﬃcient vehicles with new fuel-eﬃcient vehicles. When facing thefinancial crisis in 2008, a growing number of countries adopted green economic stimulus programs
such as feebates with the aim of combatting the economic recession and climate change. On theother hand, while policymakers would agree that a carbon tax is an eﬀective policy instrument
for reducing environmental externalities from car driving, they tend to avoid raising a fuel tax
or introducing a carbon tax for several reasons. The foremost reason is that policymakers haveconcerns about the distributional impacts of taxes on fuel. Hence, this study attempts to empiricallyanswer the following questions: Can we simultaneously achieve these economic and environmental
goals with feebate policies? Is a fuel tax more regressive than a comparable feebate?
In this paper, I evaluate the welfare eﬀects of a fuel tax and a feebate policy, focusing on eﬃciency
and distributional equity. In particular, the interest of this paper is in examining the impacts ofthe two policies on multiple stages of households’ decisions. This has important implications forassessing the welfare eﬀects of policies, including their environmental impacts. For example, a fueltax is expected to aﬀect not only driving distances but also car choices by changing expected future
fuel costs. Similarly, a feebate policy directly aﬀects car choices by incentivizing individuals topurchase fuel-eﬃcient vehicles, while it should presumably aﬀect mileage choices by altering theattributes of cars purchased. Therefore, I resort to a structural model that incorporates decisions
on the purchase and use of cars into its demand model for policy evaluation.
Moreover, the social outcomes of the two policies largely depend on a rebound eﬀect. The
rebound eﬀect here refers to the upward pressure on driving demand that results from the improvement of fuel economy followed by a downward shift in the per-kilometer marginal cost of driving.
Because of the rebound eﬀect, policies that encourage improvements in fuel economy do not always
result in the intended reduction of fuel consumption. Many researchers have empirically identified
the existence of the rebound eﬀect (e.g., Gillingham et al., 2016). Since a fuel tax directly suppresses driving demand by imposing a tax on marginal environmental externalities from car use,
it can control upward pressure on driving demand spurred by the rebound eﬀect. In addition, afuel tax can be an eﬃcient policy instrument because it equates marginal abatement costs incurredby individuals even when actual individual car usage varies. However, a feebate promotes the dissemination of fuel-eﬃcient cars by providing tax incentives for their purchase; however, it fails tocontrol driving demand after purchase. Thus, the feebate is expected to encourage households todrive more through the rebound eﬀect (Anderson and Sallee, 2016).
To account for the rebound eﬀect in evaluating policy impacts, I model two household decisions,
one concerning which car to purchase and, thereafter, another regarding how far a household drives
2

the car. Several studies have focused on two-stage decision-making; however, the number of studies
has been limited due to issues of data availability. For instance, Goldberg (1998), West (2004),
and Bento et al. (2009) model the two decisions to analyze the eﬀects of fuel economy standards
and a fuel tax using data from a large sample of households in the United States. Such analyses
requires large-scale household-level data, including detailed information on the choice and usage ofcars. However, in many cases, such micro-level data are not available, and this data limitation has
hindered the analysis.
In this study, I attempt to overcome this problem by combining two data sets. The first data
set comes from a household survey administered in 2013 to households in Japan who had purchaseda passenger vehicle within the past five years. This survey provides detailed information on car
ownership and utilization for each household, such as the car model, the year purchased, and the
distance traveled. The survey also reports household demographics, such as household income,family size, and residential address. On the other hand, the second set of data comes from anaggregate data set on the Japanese new car market between 2006 and 2013. This aggregate market
data set contains information on sales volume, price, and other car attributes for each base modeland covers nearly all models in the Japanese new car market. Importantly, the combination of
the data sets at the diﬀerent levels helps construct choice sets faced by surveyed households ineach market. I match the micro-level information from the household survey and the macro-level
information from the aggregate data set by using a model name common across the two data sets.
I focus on Japanese regulations in the automobile market for several reasons. First, during
the 2006–2013 sample period, the Japanese feebate policy experienced multiple system changes.
Importantly, the system changes expanded the monetary amounts and coverage of the feebate,leading to variations in vehicle prices across vehicles and over time. These exogenous variations
are crucial to identifying model parameters. The second is attributed that the Japanese feebate
policy being an attribute-based regulation (Ito and Sallee, 2018). Under the scheme, the amountsof the rebate are determined according to vehicle weight and displacement as well as vehicle fueleconomy. Regarding this point, some oﬀer the critique that the Japanese feebate provides more
subsidies to heavy, fuel-ineﬃcient vehicles rather than light, fuel-eﬃcient vehicles. Hence, I conduct
a counterfactual analysis to examine whether a change in the design of the scheme to address such
criticisms improves social welfare. The third is related to the fact that a carbon tax was newly
introduced during the sample period. In Japan, gasoline and diesel have long been subject to fueltaxes with high rates, with the gasoline tax representing approximately one-third of the gasoline
price. In addition to the existing fuel taxes, a carbon tax was phased in 2012, the introduction ofwhich led to exogenous variation in fuel prices. I use such variation to identify the responses ofconsumers and producers to fuel price changes.
I begin by constructing a model that describes the behaviors of households and firms in theautomobile market. On the demand side, I model the household’s behavior as a two-stage problem.

3

Specifically, I describe the joint demand for vehicle and use with the discrete-continuous choice(DCC) model following Bento et al. (2009). On the supply side, I model the pricing strategies ofcar manufacturers in an oligopolistic market, and following Berry et al. (henceforth BLP, 1995) andsubsequent studies, I assume that diﬀerentiated, multiproduct firms determine their prices basedon those of rival firms. The model in this study has two advantages. First, it allows me to drop the
restrictive assumption made in most previous studies that driving demand is completely inelastic
with respect to operating costs; I thus evaluate the policy eﬀects without making any assumptions
concerning the elasticity of driving demand. Second, by following the estimation strategy proposed
by BLP (1995), I address the car price endogeneity associated with demand estimation based on
aggregate market data. In particular, I identify parameters of the DCC model using micro-leveland macro-level moment conditions and estimate the parameters with the maximum likelihood
estimation (MLE), following Goolsbee and Petrin (2004) and Train and Winston (2007).
Moreover, I account for the heterogeneity of the rebound eﬀect across individuals in the model.
Existing empirical studies find that since actual car usage patterns diﬀer considerably between
urban and rural areas, the magnitude of the rebound eﬀect varies across regions even within thesame country (e.g., Gillingham, 2014). I present evidence of the heterogeneity of the reboundeﬀect by performing a preliminary analysis before the structural estimation. The simple regression
analysis suggests the possibility of a large interregional disparity in the rebound eﬀect over the
sample period in Japan. I attempt to capture this heterogeneity by introducing a random coeﬃcient
into the structural model. The micro-level data used in this study help to identify the heterogeneity.
I make several findings associated with the rebound eﬀect. First, the empirical result indicatesthe existence of the rebound eﬀect and its heterogeneity across individuals. I estimate a reboundeﬀect of 0.09%, which means that a 1% decrease in the cost of driving leads to an increase in driving
demand of 0.09%. In addition, I find that the rebound eﬀect is heterogeneous across individuals,
with spreading over an interquartile range of 0.07–0.11%. Second, the result from the structural
estimation demonstrates the importance of considering household decisions regarding the choice
and use of cars. As a result of a simple regression that focuses only on the relationship between
driving demand and the cost of driving, I find that, compared to the result from the structural
estimation, the OLS result significantly overestimates the rebound eﬀect. This suggests that it is
crucial to model the two endogenous household decisions.
I perform several counterfactuals and quantify welfare eﬀects of policies with four diﬀerent
measures of surplus: consumer surplus, producer surplus, tax revenues, and environmental externalities. There are two primary findings from the simulation. First, I find that the Japanese feebate
policies significantly stimulate demand for automobiles and result in an increase in social welfare
while exacerbating environmental externalities. This increase in the negative externality is due tonot only the increase in the number of cars purchased by households but also the rebound eﬀect.
In fact, a decomposition analysis reveals that the rebound eﬀect induced by the feebate cancels out

4approximately 7% of the reduction in CO2 emissions that would originally have been attained by
the improvement in fuel economy. The results demonstrate that green economic stimulus policies
such as the feebate fail to achieve both their economic and environmental goals.
In addition, the simulation results suggest that altering the feebate scheme design can improve
consumer welfare, with the environmental externality being held unchanged. I find that, comparedwith the actual, attribute-based feebate scheme, an alternative feebate scheme that determines
subsidy amounts based solely on vehicle fuel economy increases the consumer surplus of low-income
households by subsidizing light, fuel-eﬃcient vehicles. However, this change in scheme design
reduces producer surplus and tax revenues because it decreases the sales of heavy, large-sized
vehicles relative to the actual feebate.
Second, the simulation presents the cost-eﬀectiveness of a fuel tax over a product tax, which isan alternative feebate scheme considered in the counterfactual analysis. Specifically, the fuel tax at
the current tax rate in Japan is 1.7 times less costly than a product tax for reducing environmental
externalities by the same amount. Furthermore, I find that there is no substantial diﬀerence inregressivity between the fuel tax and the product tax, although the latter is slightly more regressive
than the former. The simulation analysis confirms that both policies are regressive until the fourth
quintile income group and become progressive in the fifth quintile income group. Finally, mysimulation analysis also provides some findings on elasticities associated with the carbon tax rate.
In particular, I find that a 1% increase in the carbon tax in the future leads to a 0.22% decrease inJapanese CO2 emissions.
This study contributes to two strands of the literature. First, this study relates to the literaturethat evaluates feebate policies in car markets. Several papers have studied the welfare impactsof feebate policies in car markets (see, e.g., Konishi and Zhao (2017) and Kitano (2016, 2022)
for a study on the Japanese feebate and D’Haultfœuille et al. (2014) and Durrmeyer (2021) fora study on the French feebate). I contribute to the literature by examining the economic andenvironmental consequences of the Japanese feebate policy, while accounting for rebound eﬀects
and the equilibrium in the car market. Importantly, Tinbergen (1952) notes that achieving multiple
policy targets requires more policies than the number of policy targets. In this study, I demonstrate
Tinbergen’s rule by indicating that the feebate stimulates demand while augmenting environmental
damage. 1
Second, this paper also contributes to the argument about the distributional eﬀects of a fueltax and a feebate policy. Seminal papers that analyze the distributional impacts of car market
regulations are Bento et al. (2009) and Jacobsen (2013), who study the impacts of the fuel tax andthe corporate average fuel economy standards in the United States. Recently, Davis and Knittel
1

Li et al. (2021) analyze the impacts of the cash-for-clunkers program in the United States and find that the

program failed to achieve its economic stimulus and environmental objectives, indicating that Tinbergen’s (1952) rule
does not hold. My work complements the results of Li et al. (2021) by evaluating the impacts of a feebate policy onhouseholds’ choices of vehicle and miles traveled.

5(2019) and Levinson (2019) also argue about the regressivity of these regulations. Regarding thefeebate policy, Durrmeyer (2021) examines the distributional impacts of the French feebate. I
analyze the impacts of the fuel tax and feebate policy on households with diﬀerent incomes by
employing a structural model. In particular, my contribution is to argue for the regressivity of thetwo policies in terms of consumer welfare when accounting for households’ decisions on car choice
and use.
The remainder of this paper is organized as follows. Section 2 describes the data sets andinstitutional background of the Japanese feebate policy and presents evidence suggesting a reboundeﬀect during the sample period. Sections 3 and 4 outline the model and the estimation strategy,
and Section 5 discusses the estimation results. Section 6 presents the results of counterfactual
analysis, and Section 7 concludes the paper.

2

Data and Institutional BackgroundIn this section, I first explain the data sets used in the analysis. I then outline the institutional
background of the feebate scheme in Japan. In particular, I highlight exogenous variation in
vehicle prices generated by several system changes of the Japanese feebate that is helpful for theidentification of model parameters. Finally, I present suggestive evidence for the existence of arebound eﬀect in Japan during the sample period.

2.1

Data

The data used for the analysis stem mainly from two data sets. The first data set is a household
survey commissioned by the Nippon Research Center (NRC). This survey was conducted online in
November 2013 and targeted households nationwide who had purchased passenger cars in the past
five years. The survey provides 548 observations for this study. The household survey contains
information on the model purchased, purchase year, total travel distance for each vehicle, andhousehold demographics such as income, the age of the household head, and the residential area
address.
The second data set is a market-level aggregate data set for the period from 2006 to 2013. Using
these market-level data enables me to construct the choice set faced by households in selecting a car.
I obtain information on sales volumes of automobiles made by Japanese manufacturers from theAnnual Report on New Motor Vehicle Registrations (shinsha-touroku-daisuu-nennpou in Japanese)
published by the Japan Automobile Dealers Association and from statistics on mini-vehicles releasedby the Japan Mini Vehicles Association. On the other hand, the information on the sales volumesof imported vehicles comes from statistics released by the Japan Automobile Importers Association(JAIA). The statistics include sales data on the top-20 best-selling imported vehicles sold in Japan6

for each year. 2 In addition to the sales data, I obtain information on the car attributes, including
price, curb weight, size, and fuel economy, on the Carview! website. Consequently, the market
aggregate data set has 1,302 observations over eight years for each base model, with nine Japaneseand seven overseas car manufacturers. I combine the household survey and aggregate data basedon the model name common to both data sets.
In addition, I supplement the main data sets with the following data sources. First, to construct
the population density for household demographics, I make use of the Comprehensive Survey of
Living Conditions (CSLC) in 2013 administrated by the Ministry of Health, Labor and Welfare.
Second, to calculate the annual averages of gasoline and diesel prices nationwide, I collect statistics
on retail fuel prices released by the Oil Information Center of the Institute of Energy Economics,
Japan. Finally, I exploit the 2015-base consumer price index released by the Statistics Bureau ofJapan to deflate the household income, car prices, and fuel prices.
Table 1 presents the summary statistics for variables used in the analysis. The first row in PanelA reports the annual vehicle kilometers traveled (VKT) for each vehicle owned by households. 3
The annual VKT is approximately 5,480km on average. This value is close to the average travel
distance obtained from the nationwide survey of the Japan Automobile Manufacturers Association(JAMA). 4 In addition, I find that the household incomes are slightly higher than the national
average because the NRC’s survey only targets households who have purchased cars. Indeed, thehousehold income in my sample is 7.56 million JPY on average, while the population averagereported in the CSLC for 2013 is 5.28 million JPY. On the other hand, other demographics such as
family size, the age of the household head, and the urban dummy take values close to the population
averages, where the urban dummy indicates whether a household resides in ordinance-designatedcities.
The variables in Panel B are defined as follows. The rental price represents the annual cost ofvehicle ownership and is calculated based on the purchase price. Specifically, I construct the rental
price as the sum of depreciation, repayment amount of car loan interest, and annualized automobile
taxes in each year. 5 The automobile-related taxes shown in Panel B indicate the total tax amount
2

In Japan, imported vehicles sales constitute a small portion of total new vehicle sales. Indeed, JAIA (2016)

reports that the share of imported vehicles in total new vehicle sales in 2013 was approximately 6.5%.
3
I define the annual VKT as the total travel distance divided by years of use.
4
The JAMA conducts a market-trend survey of passenger vehicles of households nationwide every two years andreports an average monthly VKT of 380km for 2013 (JAMA, 2013). Therefore, a rough estimate of the annual VKT
comes to 4,560km.
5
The depreciation is calculated based on the legal durable years by vehicle types. The National Tax Agency ofJapan stipulates that the legal durable years are six years for ordinary passenger vehicles and four years for minivehicles (Kei-cars). Repayment amounts of car loan interest are calculated by the purchase price times the annualinterest rate of 3%, which is roughly the average interest rate of car loans in Japan. Note that the purchase price here
includes the excise tax-inclusive price, an acquisition tax, and a subsidy amount in the presence of the feebate policy.
Finally, annualized automobile taxes consist of the total amounts of a motor vehicle tonnage tax and an automobile
tax that car owners are obligated to pay every year.

7Table 1: Summary Statistics
Unit

Mean

St. Dev.

1st Q.

3rd Q.

Annual vehicle kilometers traveled (VKT)

10,000km

0.55

0.33

0.30

0.75

Household income

million JPY

7.56

4.23

4.35

9.64

Family size

person

2.92

1.13

2.00

4.00

Age of household head

age

54.62

12.59

45.00

64.00

Urban dummybinary

0.47

0.50

0.00

1.00

Panel A. Household survey (N = 548)

Panel B. Aggregate data, 2006-2013 (N = 1, 302)
Sales

1,000

24.70

40.05

3.11

28.31

Price

million JPY

2.68

1.90

1.50

3.08

Rental pricemillion JPY

0.58

0.37

0.36

0.65

Automobile-related taxes

million JPY

0.19

0.11

0.13

0.23

Cost of driving per kilometer

100 JPY/km

0.11

0.04

0.08

0.13

Horsepower per weight

ps/kg

0.10

0.03

0.08

0.11

Size

10 meters

0.75

0.07

0.69

0.81

Kei-car dummybinary

0.20

0.40

0.00

0.00

Transmission dummy (AT/CVT)binary

0.98

0.13

1.00

1.00

Note: This table summarizes descriptive statistics for the household survey and aggregate data. The 1st
Q. and 3rd Q. in the table stand for the first and third quantiles. The automobile-related taxes in Panel B
indicate the lump-sum tax amount at the time of purchase before applying the tax cut under the feebate.

before applying the tax cut under the feebate. 6 Table 1 shows that the automobile-related taxes
amount to approximately 8% of the purchase price on average in the absence of the feebate policy.
In addition, the cost of driving per kilometer is defined as the fuel price (JPY/ℓ) divided by the fueleconomy (km/ℓ), and the vehicle size is measured as the sum of the length, width, and height ofthe vehicle. Finally, the transmission dummy (AT/CVT) is a dummy variable indicating vehicles
with automatic transmission (AT) or continuously variable transmission (CVT).

2.2

Japanese Feebate Scheme

Here, I briefly describe the Japanese feebate scheme and show how several system changes produced
variations in the vehicle prices consumers faced. The details of the scheme are provided in Appendix
Section A.1.
6

During the sample period, the automobile-related taxes are composed of the acquisition tax, the motor vehicle

tonnage tax, and the automobile tax. See Appendix Section A.1.1 for details on the automobile-related taxes.

8(a) 2006−2009(b) 2010−2013
0.5

Rebates (million JPY)

Rebates (million JPY)

0.5
0.4
0.3
0.2
0.1
0.0

0.4
0.3
0.2
0.1
0.0

5

10

15

20

25

30

35

5

Fuel economy (km/L)
2006

2007

2008

10

15

20

25

30

35

Fuel economy (km/L)
2009

2010

2011

2012

2013

Figure 1: Variation in Rebates between 2006 and 2013
Note: The figure plots the amounts of rebates applied for each vehicle under the feebate policy during 2006–
2009 (left figure) and 2010–2013 (right figure). The rebates here are defined as the sum of the tax cut andsubsidy amounts.

The Japanese feebate scheme is essentially a rebate program, consisting of (i) the tax incentive
measures for automobile-related taxes and (ii) the subsidy program for fuel-eﬃcient vehicles. 7
During the sample period from 2006 to 2013, the tax incentive measures have four implementation
periods: 2006, 2007–2008, April 2009–April 2012, and May 2012–2013. 8 Similarly, the subsidy
program introduced in 2009 has two implementation periods of April 2009–September 2010 andJanuary 2012–September 2012. 9 These imply that the tax incentive measures and the subsidy
program changed three times and one time during the sample period, respectively.
In particular, the feebate scheme was substantially expanded as one of the Green New Deal
programs in 2009. Figure 1 displays rebates, the sum of the tax cut and subsidy amounts, applied
for each vehicle under the feebate by year. I first find that the system reform in 2009 substantiallyexpanded both the amounts and coverage of the tax cut and subsidy. Importantly, several system
reforms during the sample period generated cross-vehicle price variations and time-series price7

In addition to these programs, there was a cash-for-clunkers program for replacing gasoline vehicles registered formore than 13 years with fuel-eﬃcient vehicles between 2009 and 2010. For details of the cash-for-clunkers program,see Kitano (2022).
8
The amounts of the tax cut and the eligibility requirements for the tax incentive measures diﬀer by the implementation period. See Appendix Table A.2 for details. In addition, the fuel economy standards that are the basis
for determining the tax cut and subsidy amounts also changed with the scheme changes.
9
The second period of the subsidy program was initially scheduled to last until December 2012; however, due tobudget constraints, it was completed by September 2012.

9variations within a given vehicle. Figure 1 demonstrates that the rebates vary substantially acrossyears even within a given vehicle, ranging from 0 JPY to 0.5 million JPY. These multidimensional
fluctuations in vehicle price that are exogenous to consumers are crucial for the identification ofmodel parameters. I use the variations to construct instrumental variables, as explained in a later
section.
In addition, Figure 1 shows that the amount of the rebate is determined not only by the fueleconomy but also the vehicle weight. This is attributable to the fact that the Japanese feebate
policy is an attribute-based regulation, as studied by Ito and Sallee (2018). Figure 1 shows that fueleﬃcient vehicles do not necessarily enjoy a relatively higher rebate than less fuel-eﬃcient vehicles.
In fact, many vehicles receive more rebates than hybrid vehicles with a fuel economy of over 35
km/ℓ.
Since the data available in this study are annual, I divide the implementation periods of the tax
incentives and subsidy programs after 2009 into the following two periods: the first period from2009 to 2011 and the second period from 2012 to 2013. As discussed in later sections, even withsuch definitions, the results obtained in this study are consistent with external data. This indicatesthat summarizing the monthly regulatory eﬀects into annual eﬀects does not have a significant
impact on the analysis.

2.3

Suggestive Evidence

Before constructing a structural model, I show suggestive evidence of the presence of a reboundeﬀect by simple regression analysis. The results suggest that there might be interregional heterogeneity in the rebound eﬀect during the sample period in Japan.
Using data in the household survey and aggregate data set, I estimate the following equation
by OLS:( )log(Mijt ) = x′jt β + h′i γ − ρ log pM
jt + εijt(2.1)

In the equation, the dependent variable Mijt is the annual distance traveled of car j purchased byhousehold i in year t. On the right-hand side, xjt and hi are vectors of the vehicle and household
characteristics, respectively, and pM
jt represents the per-kilometer cost of driving. The above equation includes an idiosyncratic error term εijt . The parameter of interest here is ρ, the coeﬃcientof pM
jt . Defining the rebound eﬀect by the elasticity of driving demand Mijt with respect to thecost of driving pM
jt , the parameter ρ corresponds to the rebound eﬀect. To verify the existence andinterregional disparity of the eﬀect, I add the interaction between log(pMjt ) and population density
in the area of household residence to (2.1). 10
The estimation results are summarized in Table 2. The estimates of parameter ρ do not varyacross specifications (2) and (3) in Table 2, and all are statistically significant. Using the results of10

Abe et al. (2017) run regressions (2.1) by region to analyze the interregional heterogeneity of the rebound eﬀect

in Japan.

10

Table 2: Results of Regressing Driving Demand on Driving Cost
Dependent variable: log(Mijt )Coeﬃcients

(1)

(2)

(3)

ρ

0.197

0.335

0.329

(0.082)(0.117)(0.124)-0.174-0.191-0.161(0.027)(0.027)(0.034)Car characteristics

No

Yes

Yes

Household characteristics

No

No

Yes

R2

0.087

0.127

0.138

Observations

536

536

536

log(pMjt )log(pMjt )× population density-

Note: Heteroskedasticity-robust standard errors are in parentheses. In the estimation, I include horsepower per weight, vehicle size, and the Kei-car dummy as car
characteristics and household income, family size, household age, and urban dummy
as household characteristics.

Model 3, I find the national average of the rebound eﬀect to be 0.24. This suggests that the driving
distance increases by 0.24% when the operating cost declines by 1%. Moreover, the summary
statistics for population density indicate that the interquartile range of the rebound eﬀect takes avalue of 0.20–0.30, suggesting that the rebound eﬀect is larger in rural areas than in urban areas. 11
This result provides evidence that there may be interregional heterogeneity in the rebound eﬀect
in Japan.
However, the above estimation does not address the endogeneity issue associated with driving
costs, so ρ cannot be interpreted as a causal relationship between driving demand and driving cost.
For example, if households who frequently use cars tend to purchase fuel-eﬃcient cars to save on thecosts of driving, driving demand Mijt should aﬀect driving costs pM
jt . Therefore, the estimates ofparameter ρ in Table 2 capture the following two tendencies: the first corresponds to the tendencyof households with high driving demand to choose fuel-eﬃcient cars, and the second corresponds tothe tendency of an improvement in fuel economy to increase driving demand through the reboundeﬀect. This suggests that I need to carefully consider the choice of car and driving demand to
identify the latter eﬀect.
At least two findings emerge from the above simple regression analysis. That is, to assess theimpacts of the fuel tax and the feebate policy while accounting for the rebound eﬀect, it is critical
11

Data on population density come from Statistical Observations of Municipalities 2013 published by the Statistics

Bureau, Ministry of Internal Aﬀairs and Communications, Japan. This data set shows that the population density
in units of 10,000 persons/km2 has a mean of 0.56 and a interquartile range of 0.17–0.83.

11to (i) incorporate two household decisions on car purchase and utilization into a structural model,and (ii) consider the heterogeneity of the rebound eﬀect in designing the model. I explain themethodology in the next section.

3

Model

In this section, I construct a structural model of the new car market. I assume for the demandmodel that each household makes two decisions about car purchase and use. For the supply model,
I assume diﬀerentiated, multiproduct firms that compete in an oligopolistic market in a BertrandNash manner.

3.1

DemandI first present the demand model and describe its specification. Following Goldberg (1998) andBento et al. (2009), I construct a model with two household decisions—car choice and car usage—by using the DCC model developed by Hanemann (1984) and Dubin and McFadden (1984). Specifically, each household makes a car choice decision based on its indirect utility and then decides how
far to drive the purchased car; the latter decision is described by a demand function for driving
derived from Roy’s identity.
Suppose that there exist Nt potential households in the automobile market, which is divided byyear t (= 1, . . . , T ). I assume that household i (= 1, . . . , Nt ) buys at most one car j (= 1, . . . , Jt )or an outside option (j = 0) each year. I define the indirect utility Uijt of household i conditionalon purchasing car j or the outside option in year t as
Uijt = Vijt + εijt ,(3.1)

where Vijt is part of the indirect utility that depends on both observed and unobserved car attributes
and observed household attributes. The indirect utility Uijt includes an idiosyncratic shock εijt inthe last term. In the real world, even though all households with the same characteristics face
the same product-choice set, they do not necessarily all make the same choice. The inclusion of
the idiosyncratic shock εijt allows me to explain this choice variation across households. I assume
εijt to be an independently and identically distributed stochastic variable that follows the type I
extreme value distribution.
Each household decides which car to buy based on (3.1). Specifically, I assume that a householdchooses the car with the highest conditional indirect utility. Given the model parameters, theconditional probability of household i in year t choosing car j or the outside option is written as
exp (Vijt ).
∑Jt
k=0 exp (Vikt )12

Given purchasing car j in year t, household i determines VKT Mijt subject to the budget
constraint,pM
jt Mijt + Xit = yi − rjt .
In the budget constraint, pM
jt denotes the per-kilometer cost of driving, which is defined as thegasoline price pgas
at the time of purchase divided by car j’s fuel economy. The second termt
Xit expresses the Hicksian composite good consumption for household i, where the price of thecomposite good is normalized to one. 12 On the right-hand side, yi denotes household i’s income,and rjt represents the rental price that is calculated based on the purchase price pjt , so that yi − rjt
expresses the residual income after purchasing car j.
Here, I specify Vijt in the indirect utility (3.1) as follows: 13()′
Vijt = αi (yi − rjt ) + λ exp x′jt β + h′i γ − ρi pM
jt + wjt ψ + ξjt(3.2)

In this specification, xjt and wjt each represent vectors of observed car attributes, and hi representsa vector of household characteristics. As shown below, xjt appears in both the car choice and usage
equations, whereas wjt appears only in the car choice equation. Note that the second term of(3.2) expresses the interaction term between the car and household attributes. Additionally, ξjt
captures car attributes observed by households and firms but unobserved by researchers. As in BLP(1995), I assume the rental price rjt to be endogenous because rjt may correlate with unobserved
car attributes ξjt . Indeed, several factors in ξjt , such as advertisements and the brand image of
automobile firms, are expected to aﬀect the rental price. Finally, I normalize the indirect utility
from the outside option (j = 0) to Vi0t = αi yi . Regarding a direct utility function assumed under
the indirect utility specification above, see Appendix A.2.
The specification of Vijt involves random coeﬃcients. I specify coeﬃcients αi and ρi as
αi = α0 + α1 yi + σα viα ,ρi = exp (ρ + σρ viρ ) ,(3.3)

where viα and viρ are independently distributed standard normal. In the specification of αi , α0represents a mean parameter when household income is zero, and σα represents a standard deviation
parameter. As assumed in BLP (2004), I include household income yi as a preference shifter in
αi to capture the heterogeneity of price elasticities of demand across households with diﬀerent
incomes. I expect that households with higher incomes are unlikely to respond to the vehicle pricechange so that the coeﬃcient α1 becomes negative. On the other hand, the coeﬃcient ρi captures
the heterogeneous impacts of the driving cost on households’ preferences. Specifically, ρ and σρ1213

To make the normalization, I divide both sides of the budget constraint by the composite good price.
I do not consider the situation where households misperceive fuel costs when making vehicle choices. Several

studies that have analyzed the degree of undervaluation of future fuel cost savings obtained by purchasing fuel-eﬃcientcars find that there is little such undervaluation by households (see, e.g., Busse et al., 2013, Sallee et al., 2016, andGrigolon et al., 2018).

13

correspond to the mean and standard deviation parameters for the distribution of ρi , respectively.
In specification (3.2), the remaining parameters λ, β, γ, and ψ are assumed to be constant.
Under specification (3.2), I derive the demand for distance traveled Mijt . When household ipurchases car j in year t, applying Roy’s identity to the indirect utility yields the driving demand
Mijt as follows: 14()∂Vijt /∂pM
jt
log(Mijt ) = log −∂Vijt /∂yi
(
)
λρi
= log
+ x′jt β + h′i γ − ρi pM
jt .
αi

3.2

(3.4)

SupplyNext, I consider the pricing strategies of car manufacturers. I assume that diﬀerentiated, multiproduct firms strategically determine the prices of their products in an oligopolistic market tomaximize their profits, given the prices of rival firms’ products. From the conditions for profit
maximization, I derive pricing equations for each firm that arrive at a Bertrand-Nash equilibrium.
I denote a set of cars that firm f produces in year t as Jf t . Firm f determines its prices tomaximize variable profit defined as follows:∑ ()
pejt − mcjt Nt sjt (rt ),j∈Jf twhere pejt is the tax-exclusive price of car j in year t and sjt (rt ) is the market share obtained undera Jt × 1 vector of tax-inclusive rental prices rt . Additionally, mcjt denotes the marginal cost, whichis assumed to be constant in quantity.
For the profit maximization, the first-order condition to be satisfied by pejt is written as()∑∂skt (rt )ad drjt(pekt − mckt )= 0,sjt (rt ) + 1 + τjt
dpjt
∂rjt
k∈Jf tad represents an ad valorem tax. Note that in the derivation of the first-order conditions, thewhere τjt

rental price rjt is a function of the purchase price pjt . I can rewrite these Jt first-order conditions
for profit maximization in matrix form and obtain pricing equations for each firm. I define a Jt × Jt
matrix St , comprising partial derivatives of market share sjt (rt ) with respect to rjt times (−1), and/denote the (j, k) element as Sjk,t = −∂skt ∂rjt . I also define the ownership matrix Ω∗t with (j, k)element Ω∗jk,t ,1 if ∃f s.t. {j, k} ⊂ Jf tΩ∗jk,t =0 otherwise.
14

Note that the income yi that appears in (3.3) is not structurally embedded in the indirect utility but in a reduced

form way.

14

With these matrices, defining Jt × Jt matrix Ωt = Ω∗t ⊙ St , where operator ⊙ denotes the elementwise Hadamard product, I obtain the Jt × 1 vector of tax-exclusive prices pet from the following
expression:epet = mct + Ω−1t st (rt )(3.5)

In the expression, mct is a column vector of marginal costs, and set (rt ) is a column vector withad )dr /dp } as its jth element.
sjt (rt )/{(1 + τjt
jt
jt

4

Estimation and Identification

I explain the estimation and identification strategies for the model parameters. The basic idea forthe estimation is that I embed the estimation procedure of the DCC model in the framework of
BLP (1995), following Goolsbee and Petrin (2004) and Train and Winston (2007). I attempt toidentify the parameters with micro-level and macro-level moments and address price endogeneity
when estimating the demand function. Based on the demand parameters and conditions for profit
maximization, I recover the marginal costs faced by each firm in the production process.

4.1

Estimation Strategy

I first present the overview of the estimation strategy before describing the details. First, I divide
the parameters in Vijt into two parts and denote them by vectors θ1 and θ2 :Vijt = δjt (θ1 ) + µijt (θ2 ),(4.1)

where δjt (θ1 ) and µijt (θ2 ) represent a mean utility that is common to all households and the part ofutility depending on household characteristics, respectively. Specifically, for j = 1, . . . , Jt , vectors
θ1 and θ2 are composed of
θ1 = (α0 , ψ),θ2 = (α1 , λ, β, γ, ρ, σα , σρ ),and for the outside option (j = 0), both δ0t (θ1 ) and µi0t (θ2 ) are zero by definition. Both of theparameter vectors are supposed to be estimated via MLE when household-level data are available.
However, a likelihood function defined under this specification includes over 1,300 fixed eﬀects{δjt }j,t , and it is unrealistic to conduct a nonlinear search over all these parameters using MLE.
Then, the market aggregate data help reduce the number of parameters to be estimated in anonlinear search. Specifically, Berry’s inversion (Berry, 1994; BLP, 1995) based on market sharesin the aggregate data set enables me to express the fixed eﬀects {δjt }j,t as a function of the θ2parameter only. Finally, using the estimates of θ1 and θ2 parameters, I recover the marginal costsin the supply model.

15

As the first step, I express fixed eﬀects {δjt }j,t as a function of parameter vector θ2 . Under theassumption that εijt in (3.1) follows the type I extreme value distribution, I calculate the predictedmarket share sjt of car j in year t as follows:∫ ∫exp {δjt + µijt (θ2 )}sjt ({δjt }j , θ2 ) =dF (Di ) dG(vi ),∑Jt
k=0 exp {δkt + µikt (θ2 )}where Di = (yi , h′i )′ and vi = (viα , viρ )′ and F (·) and G(·) are cumulative distributions of Di andvi , respectively. I compute the multiple integrals in the market share sjt by simulation. 15 Using
the predicted market share sjt and observed market share Sjt , I define a contraction mapping T (δ)
as proposed by Berry (1994) and BLP (1995):
T (δ) = δ + log(Sjt ) − log(sjt ({δjt }j , θ2 ))I find that the predicted market share sjt matches the observed market share Sjt at a fixed pointof the mapping T (δ). Given the parameter θ2 , I solve for the fixed point for mapping T (δ) by
iterating the following calculation:
h+1
h
h
δjt
= δjt
+ log(Sjt ) − log(sjt ({δjt}j , θ2 ))I obtain the fixed point for T (δ) as the convergent point when iterating the calculation until
h+1h∥
tol is satisfied and back out the fixed point δ = s−1 (S , θ ). 16 The crucial point
∥δjt
− δjt
∞ <ϵ
jt
jt 2
jt

here is that all the fixed eﬀects {δjt }j,t are expressed in the function of parameter θ2 . This implies
that the number of parameters to be estimated by nonlinear search in MLE decreases dramatically.
As the second step, I estimate θ1 using the fixed point δjt obtained in the previous step. Recall
that δjt in (4.1) can be written as′δjt = −α0 rjt + wjt
ψ + ξjt .

(4.2)

Since assuming that rental price rjt correlates with unobservable attribute ξjt , I estimate parameters
α0 and ψ by the generalized method of moments (GMM). For the GMM estimation, I prepare a
L × 1 vector zjt as an instrument for rjt that satisfies moment conditions E[zjt ξjt ] = 0. Givenparameter θ2 , the GMM estimates θ̂1 are defined asθ̂1 = argmin ξ ′ ZW Z ′ ξ,θ115

I generate R times random numbers from demographic and stochastic variable distributions and denote them asrviD
and vir (r = 1, . . . , R). In this study, I construct the distribution F (·) using the CSLC data. With these randomdraws, I approximate market share sjt as follows:r
exp {δjt + µijt (viD
, vir , θ2 )}
1 ∑
.
∑Jt
r
R r=1 k=0 exp {δkt + µikt (viD
, vir , θ2 )}R

sjt ({δjt }j , θ2 ) ≈16

I set the tolerance criterion ϵtol at 10−12 .

16where Z is a JT × L (J =∑T

t=1 Jt ) matrix for instruments zjt

and ξ is a JT × 1 vector for

2 z z ′ ]−1 . In
ξjt . Additionally, W is an eﬃcient weight matrix and a consistent estimate of E[ξjt
jt jt

estimation, I implement the two-step GMM by setting W = (Z ′ Z)−1 in the first-stage estimation.
Finally, for the estimation of θ2 , I define a likelihood function based on individual car choice andmileage choice. When household i purchases car j in year t, let M̃ijt denote the observed annual
mileage and ηijt be the error between the log of observed mileage M̃ijt and the log of mileage
predicted by the model Mijt , 17
ηijt ≡ log M̃ijt − log Mijt .
Moreover, assuming that ηijt follows a normal distribution with mean zero and variance ση2 , itfollows that the conditional density of observing M̃ijt takes the form
()2 
 1 log M̃ − log M

(
)
1
ijt
ijt
exp −
,
ℓ M̃ijt i chooses j at t, Xijt = √
 2

ση
2πσ 2
η

′
′ ′ ′
where Xijt = (x′jt , pM
jt , wjt , Di , vi ) . In the expression, log(Mijt ) is the log of the driving demandobtained in (3.4). Denoting θ̃2 = (θ2 , ση ), I define the likelihood function for each household Lijt (θ̃2 )as 18∫
Lijt (θ̃2 ) =



(
)2 


exp (Vijt ) 
1 log M̃ijt − log Mijt√ 1dG(vi ).
exp −
· ∑Jt


2
ση
2πσ 2
k=0 exp (Vikt )ηIn the integral of this function, the first term of the multiplication expresses the density of mileage
conditional on purchasing car j, and the second term expresses the choice probability of the car.
Therefore, this likelihood function forms the joint probability of car choice and use, allowing forjoint estimation of parameters in the two demand equations. I approximate the likelihood function
Lijt by simulation and use Ľijt to denote the simulated likelihood function. Consequently, thesimulated log-likelihood function to be maximized is written as
Nt ∑
Jt
T ∑
∑

dijt · log Ľijt (θ̃2 ),t=1 i=1 j=1

17

Following Bento et al. (2009) and D’Haultfœuille et al. (2014), I assume that error ηijt is independent of

the car choice decision of households. Under this assumption, although I construct joint demand for cars and use,accounting for unobservables that enter both demands through random coeﬃcients, the possibility of overestimating
the rebound eﬀect remains (Dubin and McFadden, 1984; Newey, 2007). However, because the model in this study
allows for estimating parameters in the driving demand equation controlling for fixed eﬀects δjt , car attributes xjt ,and demographics hi , I expect that the biases of the estimates to be small compared with the results of Bento et al.
(2009) and D’Haultfœuille et al. (2014).
18
Note that the likelihood function does not include the probability of choosing the outside option because thehousehold survey used in this study is targeted at households who purchased cars in the preceding years and does
not include any information about households who have not purchased cars.

17where dijt = 1[i chooses j at t]. I maximize the above objective function, with the parameters θ1
being replaced with the estimates θ̂1 . 19
In the supply model, marginal cost mcjt is a parameter to be estimated. Based on the estimatesof the demand parameters, I obtain the following expression for marginal costs by rearranging thepricing equation (3.5):emc
c t = pet − Ω−1t st (rt ).

On the left-hand side of the expression, mc
c t is a Jt × 1 vector of estimated marginal costs in year t.

4.2

Identification

I face a price endogeneity issue in estimating the demand parameters. In the automobile market,
there are many cases where vehicle models with a high market share are sold at higher prices. I
can interpret this phenomenon as automobile manufacturers assigning high prices to high-quality
vehicles. This fact produces the correlation between car prices and unobserved attributes suchas product quality and brand image. Following BLP (1995), I then assume a possibility thatrental price rjt is correlated with unobserved attribute ξjt . Since I expect a positive correlation
between them, the coeﬃcient of the rental price will be overestimated in a positive direction if theendogeneity issue is ignored.
I address this endogeneity problem using an instrumental variable approach. I construct avector of instrument variables zjt that satisfies the following condition:
E[ξjt |zjt ] = 0
For the instruments, I consider tax-location instruments following Konishi and Zhao (2017) andKitano (2022). The tax-location instruments are constructed based on tax reduction amounts
applied under the feebate scheme, which I will explain in Appendix Section A.1. There are twoadvantages to using the tax reduction amounts as instruments. First, under the feebate scheme inJapan, tax reduction amounts are determined by observed vehicle attributes, such as fuel economy,
weight, and engine displacement. Thus, I expect the tax reduction amounts to be uncorrelated withthe unobserved attribute ξjt after controlling for the vehicle attributes in the demand estimationand to satisfy the exclusion restriction. Second, the Japanese feebate underwent several scheme
changes during the study period, so the tax reduction amounts applied for each vehicle modelchange over time. As a consequence, the tax reduction amounts have two-dimensional variation
across vehicles and over time. I construct tax-location instruments based on the tax reduction and
19

Note that although αi appears in the constant term of the driving demand expression defined in (3.4), it

primitively captures the relationship between the quantity demanded for vehicles and the prices and thus should not
be estimated from the driving demand expression but from the expression for δjt in (4.2). Therefore, I first retrieve
α0 by running the regression in (4.2) with the parameter θ2 holding fixed and, thereafter, maximize the simulated
log-likelihood function with the estimate α̂0 being embedded in the constant term for driving demand Mijt .

18

subsidy amounts. Specifically, I use as the instruments the sum of the tax reduction and subsidy
amounts applied to vehicles produced by the same producer and the sum of those applied to vehicles
produced by the other producers. 20 Moreover, I also use the per-kilometer operating cost pM
jt andcar attribute wjt as instruments since they are assumed to be uncorrelated with ξjt .
Other parameters in the demand model are identified by exploiting several variations in thesample. Since parameters β, γ, and ρ are coeﬃcients of interaction terms between car and household attributes in the indirect utility function, I expect that they are identified from the joint
distribution of car ownership and household demographics. For the identification of the heterogeneity of households’ preferences, I need a micro-level variation other than the car choice variation
across households. Parameters in the random coeﬃcients are thus identified from the variationin travel distances and in car choices across households. In addition, parameter λ appears in thesecond term of the indirect utility function and captures the degree to which factors determining
driving demand aﬀect car choice. Therefore, λ is also identified by the variations in car choice
and travel distance across households. Finally, since parameter vector ψ appears only in the car
choice expression, I expect ψ to be estimated from the variation in car purchase decisions acrosshouseholds.
On the supply side, the parameter to be estimated is the marginal cost mcjt . The identificationof this parameter relies on the demand parameters. In particular, the variation in car prices andmarket share across models and years allow me to identify the marginal cost mcjt .

5

Empirical Results

In this section, I present the estimation results for the demand and supply models. Table 3 displays
the results of the demand estimation. As a robustness check, Table 3 lists the results obtained byusing the BLP instruments and the tax-location instruments. In the estimation, I use horsepower
per weight, vehicle size, the Kei-car dummy, and other dummies as car attributes and family size,
age of household head, and the urban dummy as household demographics.
Panel A in Table 3 reports the GMM regression results. The estimates of the rental pricecoeﬃcient α0 obtained by both instruments are similar and statistically significant. In addition,
the results indicates that the demand for mini-vehicles (Kei-cars) is high relative to regular vehicles,
while the demand for regular vehicles tends to be higher for vehicles with greater size.
Panel B reports the results of estimating the DCC by MLE. The results show that all the20

I exclude the tax reduction and subsidy amounts themselves from the instruments because of their performancein the first-stage estimation. In addition, as I explain in Section A.1, three taxes are applicable for the tax breakunder the feebate scheme during the study period: the acquisition tax, the tonnage tax, and the automobile tax. As
Kitano (2022) notes, because the acquisition tax is an ad valorem tax, it is correlated with the unobservable ξjt andfails to satisfy the exclusion restriction. As such, I construct the tax-location instruments based on the tax reduction
amounts of the tonnage tax and automobile tax, in addition to the amount of subsidy provided under the feebate
policy.

19

Table 3: Estimation Results(1)
Coeﬃcients

(2)

Est.

S.E.

Est.

S.E.

Panel A. Results of regression of δjt by GMM
Rental price

α011.7990.754

11.2080.759

Horsepower/Weightψ1

31.651

5.037

30.123

5.067

Size

ψ2

12.352

1.934

10.8571.969

Kei-car dummy

ψ336.091

3.139

34.794

3.166

AT/CVT

ψ4

1.154

0.420

1.050

0.423

Hybrid dummy

ψ5

1.419

0.294

1.389

0.295

Maker dummies

Yes

Year dummies

Yes

Yes

Yes

Instrumental variables

BLP IV

Tax-location IV

First-stage F statistic

6.960

19.590Hansen J statistic (d.f.)

16.389 (8)1.707 (2)Panel B. Result of the DCC model by MLE
Mean parameters:
Rental price × income

α1-0.2320.025-0.222

0.012

Constant

λ

1.932

0.311

1.991

0.333

Horsepower/Weight

β1

0.670

0.412

0.577

0.388

Size

β2

1.705

0.172

1.716

0.164

Kei-car dummy

β3-0.1400.250-0.1480.232

Family size

γ1

1.882

0.427

1.770

0.435

Age of household head

γ2-1.716

0.264-1.643

0.364

Urban dummy

γ3

0.946

0.106

0.910

0.103

Cost of driving per kilometerρ-0.3570.155-0.4470.138

Rental price

σα

1.202

0.136

1.138

0.057

Cost of driving per kilometer

σρ

0.793

0.006

0.789

0.006

Error term in the driving demand eq.

ση

0.020

0.004

0.019

0.004

Standard deviation parameters:

Log-likelihood-7.560-7.561

Observations in the aggregate data set

1,302

1,302

Observations in the household survey

548

548

Note: This table reports estimation results with 2,000 random draws. The estimations are run with thefamily size measured per 100 persons, the age of household head measured by age times 0.001, and the
Kei-car dummy and urban dummy multiplied by 0.1. For the other variables, I follow the units listed
in Table 1.

20

15

density

10

5

0
0.05

0.10

0.15

0.20

Rebound effect (%)

Figure 2: Heterogeneity of the Rebound Eﬀect
estimates of coeﬃcients have the expected signs. Since the coeﬃcients of horsepower per weightand vehicle size have positive signs, this suggests that as engine power and vehicle size increase, thedemand for driving increases. By contrast, the estimate of the coeﬃcient of the Kei-car dummy
has a negative sign, which reflects the fact that households tend not to drive long distances inmini-vehicles.
The estimate of the rebound eﬀect is calculated based on the results in Table 3. Panel B in
the table shows that both the estimates of the mean parameter ρ and variance parameter σρ of therandom coeﬃcient ρi are statistically significant. With these estimates, I obtain the estimate of
the rebound eﬀect by calculating the elasticity of driving demand Mijt with respect to the driving
cost pM
jt as follows:∂Mijt pMjt
= −ρi pM
jt
M
∂pM
ijt
jt
Figure 2 displays the histogram of the rebound eﬀect calculated using the estimates of model 2
in Table 3. The figure shows that the mean value of the estimated rebound eﬀect is 0.09, meaningthat a 1% decrease in the per-kilometer cost of driving will increase driving demand by 0.09%.
Figure 2 also confirms that the rebound eﬀect is unevenly distributed across households, with aninterquartile rage of 0.07–0.11%. Moreover, compared with the estimate from the structural model,
I find that the OLS estimate obtained in Section 2.3 is biased upward and significantly overestimates
the rebound eﬀect in absolute value. Existing studies show that estimates of the rebound eﬀect
diﬀer substantially with the type of data set and estimation model (e.g., Graham and Glaister,
2002; Gillingham et al., 2016). However, the estimate of the rebound eﬀect in this study falls in
the range of estimates obtained in the most recent studies. 21 Indeed, my estimate, obtained by
21

Gillingham et al. (2016) review recent empirical studies on the rebound eﬀect and conclude that the short- and21

Table 4: Elasticities, Marginal Costs, and Markups
Mean

St. Dev.

1st Q.

3rd Q.

Own-rental price elasticities of demand

−4.68

2.01

−5.48

−3.30

Marginal costs (in millions of JPY)

1.85

1.61

0.86

2.16

Markups

0.27

0.10

0.20

0.34

Note: This table shows estimates of marginal costs and markups for all firms during
the sample period, 2006-2013. The markups are defined as (p − mc)/p. The 1st Q. and3rd Q. in the table stand for the first and third quantiles.

combining the household-level cross-sectional data and the market-level panel data, is comparable
to the estimate of Gillingham et al. (2015), who estimate a short-run rebound eﬀect of 0.10 usinga very large individual-level panel data set in a US state. 22
Table 4 presents the summary statistics of estimated own-rental price elasticities, marginal
costs, and markups. Here, I calculate the own- and cross-rental price elasticities of market share
as follows:
rjt ∫ ∫
αi sijt (1 − sijt ) dF (Di ) dG(vi ) if j = k,∂sjt rkt − sjt
=
∫
∫
 rkt
∂rkt sjt
αi sijt sikt dF (Di ) dG(vi )otherwise,
sjt
/ ∑Jt

k=0 exp (Vikt ). Table 4 reports that the estimated own-rental priceelasticity is −4.68 on average. 23 In addition, the estimated markups are, on average, approximately

where sijt = exp (Vijt )27%. These estimates are comparable to those found in BLP (1995) and Grigolon et al. (2018)using data from the United States and European countries, respectively.

6Counterfactual Analysis

I perform counterfactual analyses based on the estimated parameters to examine the eﬃciency
and distributional eﬀects of the fuel tax and the feebate policy. I also conduct a decomposition
analysis of CO2 emissions to demonstrate the contribution of the rebound eﬀect to environmental
externalities in various policy scenarios. I first describe the policy scenarios assumed in the analyses
and then assess the policy impacts on various outcome variables and welfare.
medium-run elasticities of gasoline/driving demand with respect to gasoline price in developed countries fall in therange from 0.05 to 0.25.
22
Gillingham et al. (2015) estimate the gasoline price elasticity of driving demand.
23
For comparison, I report that the sales-weighted average of the own-rental price elasticities in 2012 is −3.55.
Konishi and Zhao (2017), who use quarterly data from Japan for almost the same period as this study, report asales-weighted average of elasticities in 2012 of −2.66. Although my estimate indicates a larger value in absolutemagnitude than that of Konishi and Zhao (2017), note here that the estimate of Konishi and Zhao (2017) is not for
the rental price elasticity but for the price elasticity.

22

Table 5: List of Counterfactual Scenarios
Scenarios

Fuel tax

Tax measures

Subsidy[1] Baseline

0

No

No

[2] Pigouvian fuel tax

4,000

No

No

[3] Fuel tax at current tax rate in Japan

21,603No

No[4] Actual feebate scheme0

tax reduction

Yes[5] Alternative feebate: product subsidy

0

No

Yes

[6] Alternative feebate: product tax

0tax increaseNo

Note: The unit of the fuel tax is JPY per ton of CO2 . The tax measures and the subsidy
indicate tax measures for automobile-related taxes and subsidy programs applied at thetime of purchase, respectively.

6.1

Scenarios

The policy scenarios that I consider in the counterfactuals are summarized in Table 5. I compare
the baseline scenario with two fuel tax scenarios and three feebate policy scenarios. Based on theestimated parameters, I simulate a baseline scenario in which neither fuel tax nor feebate policies
are enforced. The remaining scenarios are generated and introduced into the baseline to assume
situations in which fuel taxes at diﬀerent tax rates and feebate policies take eﬀect during the sample
period in Japan.
6.1.1

Fuel Taxes

In the second and third scenarios, I consider situations in which fuel taxes at the same rate as thesocial cost of carbon (SCC) and the current tax rate in Japan are added to the pre-tax prices.
I first briefly outline the fuel tax situation in Japan. There has long been a fuel tax of 55.84
JPY/ℓ for gasoline. 24, 25 Beginning in October 2012, the Japanese government phased in a carbon
tax in addition to the pre-existing fuel tax. The rate of the newly introduced carbon tax has
been set at 0.76 JPY/ℓ (289 JPY/ton of CO2 ) since 2016. 26 Thus, the price of gasoline pgas
that
t

24

The existing tax on gasoline is divided into a petroleum and coal tax levied upstream and a gasoline tax and alocal gasoline tax levied downstream. The rate of the petroleum and coal tax is 779 JPY/ton of CO2 (2.04 JPY/ℓ),and the sum of the rates of the gasoline tax and the local gasoline tax amounts to 23,173 JPY/ton of CO2 (53.8
JPY/ℓ).
25
The diesel is imposed a diesel handling tax of 32.1 JPY/ℓ.
26
The carbon tax rate has reached the current level in two phases. For example, the carbon tax rate for petroleum
was set to 95 JPY/ton of CO2 (0.25 JPY/ℓ) from October 2012 to March 2014, 190 JPY/ton of CO2 (0.5 JPY/ℓ)from April 2014 to March 2016, and 289 JPY/ton of CO2 (0.76 JPY/ℓ) from April 2016.

23

households face in year t is written as 27
pgas= ppre-tax+ τ gas + τ carbon ,
t
t
where
• ppre-tax
is the pre-tax price of gasoline in year t,t• τ gas is the pre-existing gasoline tax rate of 55.84 JPY/ℓ, and• τ carbon is the carbon tax rate of 0.76 JPY/ℓ.
Since the price of gasoline averages approximately 150 JPY/ℓ during the sample period, the current
gasoline tax rate of 56.6 JPY/ℓ accounts for approximately one-third of the gasoline price.
The second scenario in the simulation is for analyzing the welfare impact of the Pigouvian tax for
fuels in the automobile market. In the scenario, I impose the SCC of 4,000 JPY/ton of CO2 (10.48
JPY/ℓ), which is approximately 40 USD/ton of CO2 , on the pre-tax price of fuels ppre-tax. 28, 29 Int
the third scenario, I explore the eﬀect of the fuel tax using the current tax rate in Japan of 21,603
JPY/ton of CO2 (56.6 JPY/ℓ). 30
6.1.2

Feebate Schemes

In the remaining scenarios, I examine the eﬀects of the feebate policies implemented in Japan andalternative feebate schemes.
Actual Feebate Scheme The fourth scenario assumes a situation where the feebate scheme implemented in Japan during the sample period is introduced into the baseline scenario. As describedin Section 2.2, the Japanese feebate scheme is essentially a rebate program, consisting of a tax
incentive measure for automobile-related taxes and a subsidy program for fuel-eﬃcient vehicles.
Here, let Tjt denote a vector of automobile-related taxes and p(pejt , Tjt ) denote a function of the
tax-exclusive vehicle price pejt and Tjt that represents the total amount of taxes paid by a new car
27

For expositional simplicity, I omit the excise tax τ ex on fuel prices in the main body of the paper. In practice,

however, the excise tax τ ex is imposed on gasoline and diesel prices, and thus pgas
and pdiesel
are calculated as follows:
t
t
(
)
pgas
= ppre-tax+ τ gas + τ carbon × (1 + τ ex ),
t
t
and
)
(
+ τ petroleum + τ carbon × (1 + τ ex ) + τ diesel ,pdiesel= ppre-taxttwhere τ petroleum represents the petroleum and coal tax of 2.04 JPY/ℓ and τ diesel represents the diesel handling tax
rate of 32.1 JPY/ℓ. I use these formulas in the analysis below.
28
The SCC comes from IWG (2016) and corresponds to the estimate for 2020, which is calculated with a discountrate of 3%.
29
I set both the pre-existing gasoline tax τ gas and the carbon tax τ carbon to zero in this scenario.
30
For diesel, I set diﬀerent tax rates from gasoline and add to the pre-tax price of diesel the tax rate of 34.9 JPY/ℓ,
which is the sum of the pre-existing fuel tax rate and the additional carbon tax rate.

24

purchaser at the time of purchase. See Appendix A.1.1 for details on the automobile-related taxes
during the sample period in Japan. With the function, the tax-inclusive vehicle price pjt under thefeebate scheme is written as()
pjt = pejt + p pejt , trjt · Tjt − ESjt ,where trjt denotes a vector of tax reduction rates and ESjt is the amount of subsidy for fuel-eﬃcientcars. The tax reduction rates and the subsidy amount are determined according to fuel economy
standards. See Appendix A.1 for details.
Alternative Feebate Schemes I consider alternative feebate schemes in the fifth and sixth scenarios. Specifically, I design a product subsidy and a product tax, such that each of them determines
the subsidy amounts and the tax burden based solely on the vehicle’s CO2 emissions per kilometer.
Under the product subsidy and product tax schemes, the tax-inclusive prices pjt are determined asfollows:()
1
pjt = pejt + p pejt , Tjt − τ E ·
,
ejt
and
(
)
pjt = pejt + p pejt , Tjt + τ E ejt .
In the expressions, τ E represents the subsidy/tax rates in each scenario, and ejt denotes CO2
emissions per kilometer (kg-CO2 /km) from driving car j in year t. 31 For comparison with thepolicy eﬀects of the actual feebate scheme and the fuel tax, I set each τ E such that the productsubsidy and the product tax achieve the same environmental externalities as those caused by theactual feebate and the current fuel tax, respectively.

6.2

Measure of Social Welfare

I evaluate the welfare eﬀects of policies in the equilibrium of the automobile market. To calculate
equilibrium prices, I exploit the method proposed by Morrow and Skerlos (2011). 32 See Appendix
A.3 for the computation of equilibrium prices. Given estimated equilibrium prices, I evaluate
policies using four measures of surplus: consumer surplus (CS), producer surplus (PS), tax revenues(TR), and environmental externalities (EXT). Following Small and Rosen (1981), the change in
consumer surplus due to a policy change is calculated as follows:



 
∫ ∫
Jt
Jt




∑
∑
(
)
(
)
1 
0
1
exp Vijt  dF (Di ) dG(vi ),− log
exp Vijt
log∆E(CS) = Nt




αi
j=0

j=0

31

Per-kilometer CO2 emissions ejt are defined as fuel economy divided by the CO2 emission factor per liter of fuelconsumption. The CO2 emission factor per liter of gasoline (diesel) is 2.322 kg-CO2 /ℓ (2.621 kg-CO2 /ℓ), which is
obtained by multiplying the calorific value per liter of gasoline, 34.6 MJ/ℓ (38.2 MJ/ℓ), by CO2 emission factor per
calorific value of gasoline, 0.0671 kg-CO2 /MJ (0.0686 kg-CO2 /MJ).
32
See Conlon and Gortmaker (2020) for the advantages of this method.

25

0 and V 1 represent the indirect utility under the baseline scenario and after a policy
where Vijt
ijtchange, respectively. In addition, the other measures of surplus are calculated as follows:
∑ ∑ (
)
PS =
pejt − mc
c jt Nt sjt (rt ),f ∈F j∈Jf t

TR =∫ ∫ ∑ ∑ (

)
(
)
f uel
p pejt , drjt · Tjt − ESjt + Tijt
Nt sijt (rt ) dF (Di ) dG(vi ),f ∈F j∈Jf t

EXT = SCC ×∫ ∫ ∑ ∑

ejt Mijt Nt sijt (rt ) dF (Di ) dG(vi ),f ∈F j∈Jf t
f uel
where Tijt
in the second expression represents the fuel tax amount from household i’s drivingof car j in year t, and SCC in the last expression denotes the value of the SCC. 33 In the second
expression, the tax revenues consist of the sum of tax revenues from the automobile-related taxes
and fuel taxes minus resources used for the feebate policies. Consequently, I define the sum of theabove as the total surplus (TS).

6.3

Simulation Results

6.3.1

Policy Impacts on Outcome Variables

I first examine the impacts of the policy changes on the outcome variables. Table 6 reports themean values of the outcome variables obtained under each policy scenario using the sample for 2012.
The table confirms that fuel taxes are less likely to aﬀect the equilibrium prices of automobiles,
which is consistent with the results of Grigolon et al. (2018) and Tan et al. (2019). While the fueltax at current tax rate moderates total VKT by 22% relative to the baseline scenario, it results inan additional reduction of fuel usage because it improves the average fuel economy of purchased
vehicles. Indeed, the sales-weighted average of fuel economy in 2012 in the current fuel tax scenario
is 20.83km/ℓ, which is 1.6% higher than that obtained in the baseline scenario.
In contrast, the actual feebate scheme has significant impacts on the outcomes. As expected, theactual feebate scheme boosts the sales of automobiles, particularly the sales of fuel-eﬃcient vehicles,
raising the equilibrium tax-exclusive prices relative to the baseline scenario. I find that the feebate
increases sales volume by 27% on average and improves the sales-weighted average of fuel economy
in 2012 by 3.6% relative to the baseline scenario. Moreover, the feebate also drives up fuel usage. I
investigate the channels through which the feebate augments fuel usage by decomposition analysis
in Section 6.4.
Table 6 also presents the impacts of the alternative feebate schemes on outcomes. I perform agrid search to obtain the rate of subsidy/tax τ E for each alternative scheme. 34 Table 6 shows thatthe product subsidy achieves the same fuel usage as the actual feebate without entailing a reduction
33
34

f uel
The amount of fuel tax Tijt
is calculated by (Mijt /f ejt )(τ gas +τ carbon ), where f ejt is the fuel economy (km/ℓ).
E
For the product subsidy, τ works out to 15,311 JPY per kg-CO2 per kilometer such that the product subsidy

achieves the same externality as the actual feebate, and for the product tax, 1.21 million JPY per kg-CO2 per kilometer

26

Table 6: Impacts of Policies on Various Outcomes in 2012
Tax-exclusive

Tax-inclusive

price pejt

price pjt

Scenarios(million JPY)

(million JPY)

[1] Baseline

2.378

2.672

[2] Pigouvian fuel tax

2.378[3] Current fuel tax in Japan

Sales

VKT

Fuel
usage

(10,000km)(kℓ)

23,483

13,664

7,571

2.672

22,892

13,034

7,179

2.378

2.672

20,465

10,651

5,728[4] Actual feebate scheme

2.382

2.594

29,93017,3659,285[5] Product subsidy

2.376

2.669

30,542

17,424

9,285[6] Product tax

2.385

2.679

17,920

10,574

5,728

Note: This table reports the mean values for each outcome variable.

in sales volumes. On the other hand, the product tax considerably decreases sales volumes relative
to the fuel tax. This is because the product tax must ensure the same fuel usage as the fuel tax
by reducing the sales volumes to control total driving demand, while the fuel tax can achieve thesame objective by directly suppressing driving demand.
Apart from the policy scenarios assumed in the simulation, how will a future carbon tax increasein Japan aﬀect outcomes? Figure 3 displays the changes in several outcome variables when diﬀerent
carbon tax rates τ carbon of 289, 1000, 3000, 5000, and 10,000 JPY/ton of CO2 are introduced into
the pre-existing fuel tax τ gas . Regarding the graph for CO2 emissions in the bottom-right figure,I find that the total CO2 emissions amount to approximately 2.20 million tons of CO2 when the
additional carbon tax is zero. 35 When the carbon tax of 10,000 JPY/ton of CO2 (26.2 JPY/ℓ) is
added, the CO2 emissions decline by 10.3%, meaning that a 1% increase in the additional carbon
tax leads to a 0.22% reduction in CO2 emissions in Japan. Similarly, I find that the elasticitiesof the equilibrium price, demand, and fuel economy with respect to the carbon tax are 0.0002%,−0.12%, and 0.01%, respectively.
6.3.2

Welfare Eﬀects

In this section, I examine the welfare eﬀects of the policies. Table 7 shows the results using thesample for 2012. The first row in this table reports the welfare in the baseline scenario, while thesuch that the product tax achieves the same externality as the current fuel tax rate. Under these subsidy/tax rates,
the product subsidy scheme provides new car purchasers with subsidies of 45,505–233,459 JPY with an average of
107,489 JPY, and the product tax scheme imposes tax burdens of 79,044–405,532 JPY with an average of 190,632
JPY.
35

This situation corresponds to that where the fuel tax rate equates to the pre-existing fuel tax rate of 55.84

JPY/ℓ.

27

(a) Tax−exclusive price(b) Sales

2.37860

20500

million JPY

20300
2.3785520100
2.37850

19900
19700

2.3784519500
2.37840

19300
0

2500

5000

7500

10000

0

2500

Carbon tax(c) Fuel economy

7500

10000

(d) CO2 emissions

20.96

2205000

20.94

2155000

CO2 ton20.92

km / L

5000

Carbon tax

20.90
20.88
20.86

2105000
2055000
2005000

20.84
20.82

1955000
0

2500

5000

7500

10000

Carbon tax

0

2500

5000

7500

10000

Carbon tax

Figure 3: Eﬀects of Additional Carbon Taxes
Note: The graphs show the changes in the sales-weighted average of tax-exclusive vehicle prices, sales, thesales-weighted average of fuel economy, and the total CO2 emissions from driving cars purchased in 2012
when carbon taxes of 289, 1000, 3000, 5000, and 10,000 JPY/ton of CO2 are added to the pre-existing fueltax.

remaining rows compare the welfare in each scenario with that obtained in the baseline. Table 7
confirms that the fuel taxes reduce environmental externalities with low burdens on consumers andproducers. In fact, the fuel tax with the current tax rate reduces the externality by 24% comparedto the baseline scenario while it increases the total revenues, or the tax burden, by approximately
5%, suggesting that the fuel tax eﬀectively reduces the environmental externality.
Moreover, Table 7 shows that the total surplus declines with the fuel tax rate. The explanation
for the decline in total surplus due to the Pigouvian fuel tax requires me to separately consider thechanges in welfare in the automobile and fuel markets. As indicated in Table 7, the decrease in total
surplus due to the fuel tax comes primarily from the decrease in producer surplus. Furthermore, I
find that the decrease in the consumer surplus comes primarily from that arising in the fuel market
targeted by households’ continuous choices, and the remaining decrease in consumer surplus arisingin the automobile market targeted by households’ discrete choices is relatively small. 36 This finding
36

This is due to the following reason. Table 7 confirms that fuel tax revenue almost equates to the environmental

28

Table 7: Welfare Eﬀects in 2012 (in billions of JPY)
CS

PS

TR
Automobile-

Scenarios

Fuel tax

FeebateEXT

TS

related taxes[1] Baseline (in levels)

487

1,824

276

0

0

11.6

2,575

[2] Pigouvian fuel tax

-13

-48

-8

+12

±0

-0.6

-57

[3] Current fuel tax in Japan

-67

-246

-40

+53

±0

-2.8

-296

[4] Actual feebate scheme

+134

+552

+79

±0

-172

+2.6

+591

[5] Product subsidy

+145

+492

+66

±0

-165

+2.6

+535

[6] Product tax

-116

-419

-66

±0

+97

-2.8

-501

Note: The first row lists the welfares obtained under the no-policy baseline scenario, and the remaining
rows list changes in welfare associated with policy changes from the no-policy baseline. The sum of the tax
revenue amounts from the automobile-related taxes and the feebate schemes refer to the annualized amounts
paid by car owners over the ownership duration as a part of the rental price, not the lump sum amounts
paid at the time of purchase.

implies that the welfare loss borne by producers under the fuel tax scenario is the main driver ofthe decrease in total surplus, and even the Pigouvian tax on fuel does not necessarily improve theoverall economic welfare in the two markets.
In contrast to the fuel taxes, the actual feebate augments the environmental externalities, while
it raises the total surplus by 591 billion JPY in 2012 relative to the baseline scenario. Table 7
confirms that a significant majority of this welfare gain comes through increases in consumer surplus
and producer surplus. I believe this is attributable to the fact that the pre-existing automobilerelated taxes already result in a deadweight loss in the automobile market, and the feebate plays arole in mitigating the market distortions (Buchanan, 1969; Fowlie et al., 2016).
Here, as shown below, the validity of the predicted result is ensured for the actual feebate
scheme. Table 7 reports that the annualized feebate expenditures applied to the rental prices paidby car owners in 2012 equal 172 billion JPY, implying that an estimate of the feebate expendituresin 2012 amounts to about 630 billion JPY in total. The actual expenditure for the subsidy program
alone, which is a program of the feebate schemes, was 274.7 billion JPY in 2012. Therefore, whenthe expenditure for the subsidy program is added to the budget for the tax incentives, which isexternality from driving, oﬀsetting this negative externality in the fuel market. In addition, the fuel tax revenue andthe decrease in consumer surplus in the fuel market should be of roughly the same magnitude because the estimatedrebound eﬀect implies that driving demand is inelastic to the cost of driving per kilometer. Therefore, I find that theremaining decrease in consumer surplus occurs in the automobile market, which accounts for a small portion of thetotal decrease in consumer surplus.

29

another program composing the feebate scheme, I find that the sum of these expenditures is close
to the estimated expenditure above. 37
Table 7 also reveals the welfare eﬀects of alternative feebate schemes. Table 7 demonstrates
that the externality-equivalent product subsidy is less costly and yields a slightly higher consumer
surplus than the actual feebate scheme. The fact that the product subsidy determines the amountof the subsidy depending solely on the CO2 emissions per kilometer helps the product subsidy to
improve the sales-weighted fuel economy relatively easily and achieve the same externality as theactual feebate, with less expenditures for implementation. 38
On the other hand, compared with the externality-equivalent product tax, I find that the fuel tax
at the current tax rate achieves a higher total surplus. In particular, the product tax substantiallyreduces the consumer surplus and the producer surplus relative to the fuel tax, which is consistent
with the result presented in Table 6. Moreover, I find that the fuel tax requires fewer resources thanthe externality-equivalent product tax. Table 7 reveals that the product tax is approximately 1.7
times more costly than the fuel tax in reducing environmental externalities by the same amount.
The results suggest that the fuel tax is more cost-eﬀective than the product tax.
6.3.3

Distributional Impacts

In this section, I analyze the distributional impacts of policies. In particular, I investigate how the
distributional impacts diﬀer between a fuel tax and a feebate policy and between feebate scheme
designs. Through the analyses, I present arguments on the regressivity of the fuel tax and thefeebate policy.
Figure 4 displays the rates of change of several variables by income quintile when policies are
introduced into the baseline scenario. The figures on the top show that the distributional impacts
diﬀer considerably between the fuel tax and the externality-equivalent product tax. The fuel tax
with the Japanese current tax rate decreases sales volume and suppresses distance traveled evenly
in all income groups, while its impact on the average fuel economy of purchased vehicles diﬀers byincome quintile. Figure 4 suggests that the fuel tax induces high-income households to improvetheir fuel economy, indicating that high-income households that drive long distances particularly
react to the introduction of the fuel tax by purchasing fuel-eﬃcient vehicles to save on driving costs.
However, since low-income households already tended to purchase fuel-eﬃcient vehicles before theintroduction of the fuel tax, the improvement in average fuel economy in the low-income groups isrelatively small. In contrast, the externality-equivalent product tax substantially decreases sales,
37

Note that since the subsidy program in the second period was completed by September 2012 because of budget

constraints and this study using yearly data does not control for monthly regulatory eﬀects, my estimate of theresources used for the feebate is likely to overestimate the actual value.
38
The product subsidy decreases the producer surplus and the tax revenue from the automobile-related taxes
relative to the actual feebate scheme because the product subsidy decreases the sales of fuel-ineﬃcient vehicles withrelatively large sizes compared with the actual feebate scheme.

30(a) Sales(b) VKT(c) Fuel economy

−16

2.6

Fuel tax

Fuel tax

Product tax−20

Change rate (%)−15

Fuel tax
2.4

Product tax−18

Change rate (%)

Change rate (%)

−10

−20

−22

−24

Product tax

2.2
2.0
1.8
1.6

−25
−26

−30

1.4

−28
1

2

3

4

1.2

5

1

2

Income quintile

3

4

5

1

2

Income quintile(a’) Sales

3

4

5

Income quintile(b’) VKT(c’) Fuel economy

40
40

Actual feebate

Actual feebate

Product subsidy

Product subsidy

Actual feebate
Product subsidy

35

30

Change rate (%)

Change rate (%)

Change rate (%)

35

30

3.6

3.4

25

25

20

20
1

2

3

Income quintile

4

5

3.2
1

2

3

Income quintile

4

5

1

2

3

4

5

Income quintile

Figure 4: Policy Impacts on Various Outcomes by Income Quintile
Note: The graphs display the policy impacts on the sales, VKT, and sales-weighted average of fuel economy
of vehicles purchased in 2012 by income quintile from 1 (lowest) to 5 (highest), with the impacts of the fueltax at the current tax rate and the externality-equivalent product tax on the top and those of the actual
feebate and the externality-equivalent product subsidy on the bottom. The vertical axes represent the rates
of change in variables when each policy is introduced into the no-policy baseline scenario.

particularly in the low-income class, and in turn reduces the distance traveled in that income group.
The figures on the bottom show approximately the same trends of the change in variables acrossthe actual feebate and the externality-equivalent product subsidy; however, the amount of change
diﬀers. In particular, it is remarkable that while both policies increase the sales in any income class,the product subsidy significantly drives up sales in the low-income classes relative to the actual
feebate. This is because the product subsidy is designed to provide fuel-eﬃcient vehicles such as
Kei-cars that households in the low-income class tend to purchase with substantial subsidies.
Table 8 reports that the change in consumer surplus as a percentage of CO2 reduction andthat as a percentage of income due to the implementation of the fuel tax and the product tax.
The change in consumer surplus as a percentage of CO2 reduction can be interpreted as a shadow
cost of a policy. Table 8 shows that each of the policies burdens households in the lowest-income
group with a shadow cost of approximately 97,000 JPY and 170,000 JPY to reduce a ton of CO2
31

Table 8: Shadow Costs of Policy Implementation
Fuel tax

Externality-equivalent product tax

Income|∆CS|/CO2 ton|∆CS|/income|∆CS|/CO2 ton|∆CS|/income

quintile(JPY)

1 (lowest)

97,0752.47

169,3215.25

294,555

1.43

164,955

2.89

393,825

1.09

163,526

2.10

494,8460.96

163,113

1.76

5 (highest)

93,503

1.25

161,685

1.83

(JPY)

Note: The table presents the change in consumer surplus as a percentage of CO2 reduction andthat as a percentage of income due to the implementation of the fuel tax and the product tax in2012 by income quintile. The fuel tax rate is set at the current tax rate of 21,603 JPY/ton of CO2 .
|∆CS| represents the absolute value of the change in consumer surplus from the no-policy baseline.

on average for the fuel tax and the product tax, respectively. Here, the shadow cost of the fuel tax
indicates a larger value than the fuel tax rate of 21,603 JPY/ton of CO2 . This can be attributed
to the fact that the fuel tax causes households not only to control their driving demand but also toforgo the purchase of a car to reduce a ton of CO2 . In addition, the result suggests that in termsof the shadow costs, the fuel tax is 1.7 times less costly than the product tax in all income classes.
Furthermore, Table 8 shows that the shadow costs of the two policies fall with the income level.
I find that because low-income households already own fuel-eﬃcient vehicles and drive shorter
distances before the imposition of taxes, the additional costs for CO2 abatement are high for lowincome households. In contrast, high-income households have greater potential to abate a ton of
CO2 than low-income households, and thus the shadow costs are relatively low for high-income
households.
Table 8 also shows that the changes in consumer surplus as a percentage of income are larger
for the lower-income classes under each of the two policies. Specifically, I find that both the fuel tax
and the product tax are regressive until the fourth quintile and become progressive in the highest
income group. Comparing the regressivities between the two policies, although the diﬀerence isquite small, the product tax is slightly more regressive than the fuel tax (see also Figure A.1 in the
Appendix).

6.4

Decomposition of CO2 Emissions

I conduct a decomposition analysis to identify the sources of environmental externalities arisingunder each policy scenario. Through the decomposition analysis of CO2 emissions, it is evident

32which factor contributes to the change in CO2 emissions and particularly the extent to which therebound eﬀect estimated in the previous section aﬀects the externality.
Following D’Haultfœuille et al. (2014), I define some potential variables for the decomposition
analysis. Let d ∈ {0, 1} denote a policy indicator that equals zero before policy introduction (apolicy status that corresponds to the baseline scenario) and one after policy introduction. Denoting
CO2,t (d) as the potential total CO2 emissions arising from driving cars purchased in year t with
policy status d, the variation in CO2 emissions in year t due to the introduction of a policy ∆t iswritten as∆t = CO2,t (1) − CO2,t (0),where
CO2,t (d) =

∫ ∫ ∑
Jt

ejt Mijt (d)Nt sijt (d) dF (Di ) dG(vi ).

j=1

In the expression, Mijt (d) is the annual distance traveled by car j purchased by household i inyear t with policy status d, and sijt (d) = sijt (rt (d)) is a choice probability evaluated at equilibrium
rental prices rt . In what follows, to control for the impacts of vehicle attributes other than ejt on
CO2 emissions, I separate vehicles into K groups of {J1 , . . . , JK } based on vehicle attributes xjt
and calculate ∆t by summing the changes in CO2 emissions by group. 39
I decompose the change in CO2 emissions ∆t into the following four components: 40K ∫ ∫ 
∑
∑
∑

∆t =
+
Q
(0)ē
(Mijt (1) − M̄k,it (1))∆sinside(ejt − ēk,t )Mijt (1)∆sinsideQk,it (0)
k,it
k,t
ijt
ijt

j∈Jk
j∈Jk
k=1

{z
} |
{z
}
|
Composition eﬀect

+ Nt
|

∑
j∈Jk


ejt (∆Mijt )sijt (0) + Nt {z}Fuel cost eﬀect|∑

Rebound eﬀect
ejt Mijt (1)sinside
(1)
ijt

j∈Jk

{z





∆sijt  dF (Di ) dG(vi ),

j∈Jk
}
∑

Fleet size eﬀect

where
Qk,it (0) = Nt∑

sijt (0),sijt (d), andj∈Jk sijt (d)sinside(d) = ∑ijtj∈Jk

∆V = V (1) − V (0), V ∈ {Mijt , sijt , sinside
}.
ijt
39
40

In practice, I form 100 groups {J1 , . . . , J100 } based on vehicle attributes xjt .
The transformation for the CO2 decomposition used in this study is slightly diﬀerent from that proposed by

D’Haultfœuille et al. (2014). In the decomposition of D’Haultfœuille et al. (2014), the composition eﬀect and therebound eﬀect contain a part of the fleet size eﬀect because the diﬀerence between market shares before and after
policy introduction, sijt (1) − sijt (0), that appears in their transformation includes not only the relative changes inmarket shares within the inside options sinside(1) − sinside(0) but also the change in the aggregate market share
ijt
ijt
∑Jt
j=1 (sijt (1) − sijt (0)). As shown below, I modify their transformation to separate these eﬀects.

33

Additionally, ēk,t and M̄k,it (d) represent the average CO2 emissions per kilometer ejt and theaverage travel distance Mijt (d) in group k, respectively. The first term in the above expression
refers to a composition eﬀect, which captures an expected decrease in CO2 emissions caused by thechange in the sales mix when one assumes that driving distance remains unchanged following policy
introduction. If the elasticity of driving distance with respect to a policy change is assumed to bezero, CO2 emissions should decrease in response to the introduction of the fuel tax or the feebate;
they are expected to encourage households to buy fuel-eﬃcient cars. As such, the compositioneﬀect will be negative when the expected policy eﬀect is suﬃciently large.
In practice, however, the driving distance will change with policy status. There are two channels
through which a policy change aﬀects driving distance. The first channel is when a householdchanges its car choice depending on policy status d. The eﬀect through the first channel refers to
the rebound eﬀect. In the above expression, the rebound eﬀect captures a correlation between thedeviation of Mijt (1) from the mean value and the change in market share within the inside options. The second channel arises when a household purchases the same car in either policy status∆sinsideijt
d = 0, 1. The eﬀect through the second channel, which I call the fuel cost eﬀect, is the direct eﬀectof the change in the fuel cost on driving distance. Since the feebate does not directly change thefuel cost when a household purchases the same car in either policy status, the fuel cost eﬀect comesto zero. On the other hand, a fuel tax directly aﬀects driving demand. This direct eﬀect of the fueltax is captured by the fuel cost eﬀect. Finally, the fourth eﬀect, the fleet size eﬀect, captures thechange in CO2 emissions arising from a change in the number of cars owned by households due tothe introduction of a policy. The fleet size eﬀect here is obtained by the expected CO2 emissions
arising from driving a car multiplied by the change in the sales of automobiles.
Table 9 shows the results of the decomposition analysis. 41 The table reports changes in CO2emissions from the no-policy baseline scenario and the contribution ratios of the four eﬀects. As
expected, the composition eﬀect contributes to reductions in the CO2 emissions in the actual feebate
policy and fuel tax scenarios since each policy introduction changes the fleet composition and shifts
sales toward fuel-eﬃcient cars. Moreover, in both the policy scenarios, the reductions in CO2emissions from the composition eﬀect are partially oﬀset by the increases in the CO2 emissions
resulting from the rebound eﬀect. In particular, the actual feebate scheme results in a larger
rebound eﬀect than in the fuel tax scenario. The rebound eﬀect induced by the feebate contributes
to the increase in the CO2 emissions and cancels out approximately 7% of the decrease in emissions
41

One point should be noted when interpreting the results in Table 9. The analysis here focuses only on the policy

impacts on new vehicles purchased in the corresponding year. I expect the estimates of the fuel cost eﬀect and thefleet size eﬀect to change when considering the impacts on vehicles already owned by households who choose theoutside option in a given year. Because the fuel tax impacts not only the mileage of vehicles purchased in the year
but also the mileage of vehicles already owned, the fuel cost eﬀect becomes larger than that reported in Table 9.
Additionally, the magnitude of the fleet size eﬀect decreases because policies are expected to reduce CO2 emissions
from old fuel-ineﬃcient vehicles by replacing them with new fuel-eﬃcient vehicles. For these reasons, I here focus
only on the estimates of the composition eﬀect and the rebound eﬀect.

34

Table 9: CO2 Decomposition
Fuel tax

Actual feebate

∆t

%

∆t

%

Total

-697.2-100.0

654.6

100.0

Composition eﬀect

-12.6

-1.8

-43.1

-6.6

Rebound eﬀect

1.5

0.2

2.9

0.4

Fuel cost eﬀect-118.3

-17.0

0.0

0.0

Fleet size eﬀect-567.8

-81.4

694.9

106.1

Note: This table reports the changes in CO2 emissions fromthe no-policy baseline and the contribution rate (%) of the foureﬀects calculated using the 2012 sample. The unit of ∆t is 1,000
tons of CO2 . The rate of the fuel tax in the first column is set
at the current tax rate of 21,603 JPY/ton of CO2 .

resulting from the composition eﬀect.
In contrast, the fuel tax succeeds in controlling the rebound eﬀect. Table 9 confirms that theincrease in CO2 emissions due to the rebound eﬀect is oﬀset by the decrease in emissions due tothe fuel cost eﬀect. Finally, Table 9 suggests that the reduction in CO2 emissions in the fuel tax
scenario is driven primarily by the fuel cost eﬀect and the fleet size eﬀect.

7

Discussion and Conclusions

This study examines the welfare eﬀects of the fuel tax and feebate policies in the Japanese new car
market. To answer the empirical questions posed in the introduction, I evaluate the performanceof feebate policy as a green economic stimulus program and the regressivity of the two policies,
with emphasis on eﬃciency and distributional equity. I employ a model with two decisions—on
car ownership and utilization—on the demand side and identify model parameters by using both
micro-level data from a household survey and macro-level aggregate data. Hence, with respect tomethodology, this study is in line with a strand of the micro BLP literature, such as Petrin (2002),
Berry et al. (2004), and Goolsbee and Petrin (2004), in which a method is developed that allows
for more robust identification of parameters by adding moment conditions formed by micro-level
data to those established using aggregate data.
The results obtained in my study have the following two implications. The first implication highlights the importance of accounting for the rebound eﬀect in evaluating energy eﬃciency programs.
Prior studies that analyzed the extent to which programs, such as an appliance replacement program, contribute to energy use reduction have found that realized energy savings were significantly

35lower than those projected by ex ante engineering analyses (e.g., Davis et al., 2014; Fowlie et al.,2018; and Levinson, 2016). In this paper, I consider the rebound eﬀect as a possible cause leading
to such a gap between actual and anticipated energy use in the context of the feebate policy in theautomobile market. Through a decomposition analysis, I find that the rebound eﬀect induced by
the feebate cancels out approximately 7% of the reduction in CO2 emissions that would originallyhave been attained by the fuel economy improvement. Overall, counterfactual analyses show that
the Japanese feebate policy stimulates demand but augments environmental externalities. These
results suggest that the feebate policy alone fails to simultaneously achieve both economic andenvironmental goals.
The second implication relates to the eﬃciency and equity of a fuel tax and a product tax.
The counterfactual analysis reveals that the fuel tax at the current tax rate in Japan is 1.7 times
less costly than the externality-equivalent product tax and that there is no diﬀerence in termsof regressivity between the two policies. Currently, in Japan, there is discussion of simplifying
the automobile-related taxes that determine the tax amounts according to a vehicle’s weight anddisplacement. The results in my study suggest that social welfare could be increased without
relatively increasing the tax burden on low-income households by substituting the existing producttaxes, such as the tonnage tax and the automobile tax, for a revenue-neutral carbon tax.
I acknowledge some limitations to this study. First, I do not consider misperception of fuel costsby households in facing the vehicle choice. For example, Grigolon et al. (2018) assume the situationin which households, at the time of purchase, undervalue the future fuel cost savings obtained bypurchasing fuel-eﬃcient cars and evaluate the welfare eﬀects of a fuel tax and a product tax, withthe belief error being included. Although Grigolon et al. (2018) and other papers studying thisissue show that there is little such undervaluation by households, if it is substantial, my study may
underestimate the social welfare of the feebate policy. Second, I use static models on the demandand supply sides and particularly in the supply model, treat only prices as a variable that firms
can manipulate endogenously. As such, the estimation results should be interpreted as short-term
policy impacts. Designing a model to account for the dynamic responses of households and firms isrequired to analyze the long-term eﬀects of the fuel tax and feebate. Finally, I need to undertakea more careful analysis of optimal policy for the automobile market and the fuel market targetedby the DCC model. A discussion of the optimal policy for the two markets is expected to become
more complicated when the introduction of a policy designed to eliminate distortions in a market
in turn produces distortions in the other market. I would like to make these points the subject of
future work.

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39

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40

A

Appendix

A.1

Institutional Background of the Japanese Feebate Policy

A.1.1

Automobile-Related Taxes

In this section, I outline automobile-related taxes relating to the feebate schemes. During the study
period between 2006 and 2013, new car purchasers were obliged at the time of purchase to pay
three types of automobile-related taxes: the acquisition tax, the motor vehicle tonnage tax, and theautomobile tax. 42 Denoting the vector of the three automobile-related taxes as Tjt , the tax-inclusive
price pjt faced by a purchaser of car j in year t is expressed by the function p as follows:()
pjt = pejt + p pejt , Tjt= (1 + τ ex ) pejt + Tjtacquisition + Tjttonnage + Tjtauto ,where τ ex represents the excise tax rate of 5%. The amount of the acquisition tax Tjtacquisition is
proportional to the acquisition price of the purchased car. 43 Thus, the sum of the rates of excise tax
and acquisition tax yields the ad valorem tax rate. The acquisition tax rates are 5% for ordinary
passenger cars and 3% for mini-vehicles. 44 On the other hand, the amounts of the tonnage tax
Tjttonnage and automobile tax Tjtauto are proportional to the curb weight and engine displacement,
respectively. For example, until March 2010, the tonnage tax amount was determined by a tax
rate of 6,300 JPY (4,400 JPY) per 0.5 tonnes for ordinary passenger cars (for mini-vehicles). 45 The
amount of the automobile tax is shown in Table A.1.
A.1.2

Feebate Scheme

The Japanese feebate scheme is a rebate program, consisting of tax incentive measures for automobilerelated taxes and a subsidy program for fuel-eﬃcient vehicles. Under the Japanese feebate scheme,
the tax-inclusive vehicle price pjt depends on the tax reduction rate trjt due to the tax incentive
measures and the amount of subsidy ESjt . As I explain below, the tax reduction rate and thesubsidy amount are determined according to fuel economy standards and emission standards.

42

While the acquisition tax involves a duty to pay only at the time of purchase, the tonnage tax and the automobiletax (or mini-vehicle tax for mini-vehicles) are payable by the owners every year after purchase. When an individual
buys a new car, the first inspection is due three years after purchase. Thereafter, the vehicle must be inspected every
two years. Regarding the tonnage tax, the amount of tax due each year is paid at the time of the vehicle inspection.
Therefore, in practice, the purchaser of a new vehicle is obligated at the time of purchase to pay the tonnage tax forthe three years until the next vehicle inspection.
43
In practice, the acquisition price is approximately 90% of the tax-exclusive price pejt .
44
The acquisition tax is not imposed when the acquisition price of a vehicle is less than 500,000 JPY.
45
The tonnage tax rate was revised twice during the study period: 5,000 JPY (3,800 JPY) from April 2010 toApril 2012 and 4,100 JPY (3,300 JPY) from May 2012 was added for every 0.5 tonnes for ordinary passenger cars
(mini-vehicles).

41

Tax Incentive Measures Table A.2 shows the eligibility requirements for the tax incentive measures and the tax reduction rates trjt (or the deductible amounts) for target taxes during thesample period. As shown in Table A.2, the reduction rates for three automobile-related taxes are
determined according to the achievement rates for the fuel economy standards and the emission
standards. See Table A.3 for the target values of the fuel economy standards. 46 For example, forpurchasers of a new car meeting the 2010 fuel economy standard by 20% or more during the period
between 2007 and 2008, the acquisition tax was reduced by 300,000 JPY (in the case of hybrid
vehicles, the tax was cut by 2.0% in 2007 and 1.8% in 2008), and the automobile tax was cut by50%. In 2009, the system of the tax incentive measures were changed and substantially expanded
as one of the Green New Deal programs. 47 Table A.2 shows that, in the period 2009–2011, hybrid
vehicles were exempt from their acquisition tax and tonnage tax regardless of their fuel economy
achievement level.
Subsidy Program In addition to the tax incentives, a subsidy program for fuel-eﬃcient cars has
been implemented since 2009. During the sample period, the subsidy program had two phases. The
first and second terms ran from April 2009 to September 2010 and from January 2012 to September
2012. During the first term, purchasers of a car achieving the 2010 fuel economy standard by 15%
or more received a subsidy ESjt of 100,000 JPY (50,000 JPY for mini-vehicles), and in the second
term, purchasers of a car achieving the 2010 fuel economy standard by 25% or more or achieving
the 2015 fuel economy standard received a subsidy of 100,000 JPY (70,000 JPY for mini-vehicles).

A.2

Derivation of the Direct Utility Function

I derive the direct utility function under the indirect utility specified in this paper. By solving thefollowing optimization problem, I can obtain the direct utility function for household i conditionalon purchasing car j in year t (Varian, 1992):
(
)
)
(
M
p
yi − rjt
jt′+ λ exp x′jt β + h′i γ − ρi X + wjt
minαiψ + ξjt + εijt
X
X
p
p
pM
,p
t
t
jt t
subject toXpM
jt Mijt + pt Xit = yi − rjt

Note that the price of the Hicksian composite good pX
t explicitly appears in the functions, althoughpX
t is set to one by the normalization in the main body of this paper. The Lagrange function with46

Fuel economy standards have been revised many times since they were first established in 1979. For ordinary

passenger cars, the 2010 and 2015 target values were established in March 1999 and in March 2006, respectively.
Each of the target values is used during the sample period. In particular, the 2010 target values are used in the firstterm and the 2015 target values in the second term to select vehicles for tax reduction.
47
The tax incentives implemented until 2008 were intended to reduce the acquisition tax and the automobile tax(or the mini-vehicle tax) for low-emission vehicles, comprising the following three schemes: the green tax scheme, thespecial scheme for fuel-eﬃcient vehicles, and the acquisition tax incentive for clean-energy vehicles.

42its multiplier µ takes the following form:
(
)
(
)
M
p
(
)
yi − rjt
jt
X′
ψ + ξjt + εijt − µ yi − rjt − pM
L = αi+ λ exp x′jt β + h′i γ − ρi X + wjt
jt Mijt − pt Xit .
X
ptpt
Then, the optimization problem yields the first-order conditions:
(
)
pM
λρi
jt
′
′
− X exp xjt β + hi γ − ρi X + µMijt = 0
pt
pt
(
)
M
p
αi (yi − rjt ) λρi pMjtjt− ( )2+ ( )2 exp x′jt β + h′i γ − ρi X + µXit = 0
pt
pX
pX
t
t
X
pM
jt Mijt + pt Xit = yi − rjt

Arranging these conditions, I have the direct utility function as follows:)}{(
αi Mijt
λρi′′′
αi Xit + 1 + log+ xjt β + hi γ − log Mijt
+ wjt
ψ + ξjt + εijt .
αi
ρiNote here that the second term in the expression is proven to be concave in driving demand Mijt .

A.3

Computation of Equilibrium Prices

In this section, I describe the computation of equilibrium prices by the method of Morrow and/Skerlos (2011). First, I divide the Jacobian matrix ∂st (rt ) ∂rt into the following two matrices:∂st (rt )= Λt − Γt∂rtwhere Λt is a Jt × Jt diagonal matrix and Γt is a Jt × Jt matrix with the following elements:∫ ∫∫ ∫Λjj,t =(−αi )sijt dF (Di ) dG(vi ), Γjk,t =(−αi )sijt sikt dF (Di ) dG(vi ).
Substituting these matrices into the pricing equations defined in (3.5), I have the following:
pet = mc
c t + ζt ,∗eewhere ζt = Λ−1c t ) − Λ−1t (Γt ⊙ Ωt )(pt − mc
t st (rt ).

(A.1)

Then, I iterate function mc
c t + ζt 7→ pet until ∥Λt (pet − mc
c t − ζt )∥∞ < ϵtol is satisfied and define theconvergence points as the new equilibrium prices.
ct
Derivation of Equation (A.1) Replacing the marginal costs mct in (3.5) with the estimates mc
yieldsepet = mc
c t + Ω−1t st (rt ).

43

Then, I transform the second term by following matrix algebra and obtain the desired result.
pet = mc
c t + (St ⊙ Ω∗t )−1 set (rt )= mc
c t + (−Λt + Γt ⊙ Ω∗t )−1 set (rt )
[
]
{
}
−1
−1
∗
∗ −1
−1
= mc
c t + −Λ−1
+
Λ
(Γ
⊙
Ω
)
E
−
Λ
(Γ
⊙
Ω
)
(−Λ
)
set (rt )
t
t
t
t
t
t
t
t
[
{
}]−1 e
−1
−1
e
∗
∗
= mc
c t − Λ−1st (rt )t st (rt ) + Λt (Γt ⊙ Ωt ) −Λt E − Λt (Γt ⊙ Ωt )
−1
e
∗
e
= mc
c t − Λ−1c t ),
t st (rt ) + Λt (Γt ⊙ Ωt )(pt − mc

where E denotes an identity matrix. In the transformation above, note that Λt ⊙ Ω∗t = Λt as Λt is
a diagonal matrix and the diagonal elements of the ownership matrix Ω∗t are all ones. Additionally,
I apply the Woodbury formula to obtain the third equation.

A.4

Additional Figures and Tables
Table A.1: Automobile Tax Amounts
displacement

tax amountdisplacement

tax amount

(ℓ)

(JPY)

(ℓ)

(JPY)

<1.0

29,500

3.5-4.0

66,5001.0-1.5

34,5004.0-4.5

76,5001.5-2.0

39,500

4.5-6.0

88,0002.0-2.5

45,000>6.0

111,000

2.5-3.0

51,000Kei car

7,200

3.0-3.5

58,00044

Table A.2: Eligibility Requirements for the Tax Incentive Measures (2006-2013)
Requirements

Acquisition Tax

Tonnage Tax

Automobile Tax

150,000 JPY

-

25%

-

50%

Panel A. Year 20062010 FE target values +10% and ES 4 stars(2.2%)
2010 FE target values +20% and ES 4 stars

300,000 JPY(2.2%)

Panel B. Years 2007–2008
2010 FE target values +10% and ES 4 stars

150,000 JPY-

25%2010 FE target values +20% and ES 4 stars

300,000 JPY-

50%(2.0%, 1.8%)
Panel C. Years 2009–2011
2010 FE target values +15% and ES 4 stars

50% (100%)50% (100%)

25%

2010 FE target values +25% and ES 4 stars

75% (100%)75% (100%)

50%

2015 FE target values and ES 4 stars

50%

50%

25%

2015 FE target values +10% and ES 4 stars

75%

75%

50%

2015 FE target values +20% and ES 4 stars

100%

100%

50%

Panel D. Years 2012–2013

Source: JAMA (2006, 2007, 2008, 2009, 2012).
Note: The table presents the eligibility requirements for the tax incentive measures from 2006 to 2013. In
the requirements shown in the table, the 2010 (2015) FE target values refer to the 2010 (2015) fuel economy
target values, and the ES 4 stars represent the emission-standard four stars, awarded to vehicles whose
emission values represent a reduction of at least 75% from the 2005 regulatory levels (JAMA, 2006). The
monetary amounts are what is deductible from the purchase price, and figures in percentage terms represent
the reduction rates for each automobile-related tax. The tax reduction rates for hybrid vehicles are reported
in parentheses. The light vehicle tax was not targeted by the tax incentive measures between 2009 and 2013.

45

Table A.3: Fuel Economy Standards
2010 Standard

2015 Standard

curb weighttarget value

curb weighttarget value

curb weighttarget value(kg)(km/ℓ)(kg)(km/ℓ)(kg)(km/ℓ)

<703

21.2

<601

22.5

1531-1651

13.2

703-82818.8

601-74121.8

1651-1761

12.2

828-1016

17.9

741-856

21.0

1761-1871

11.1

1016-1266

16.0

856-971

20.8

1871-1991

10.2

1266-1516

13.0

971-1081

20.5

1991-2101

9.4

1516-1766

10.5

1081-1196

18.7

2101-2271

8.7

1766-2016

8.9

1196-1311

17.2

>2271

7.4

2016-2266

7.8

1311-1421

15.8

>2266

6.4

1421-1531

14.4(a) Consumer surplus(b) CO2 emissions
1.0

Cumulative share of CO2 emissions

Cumulative share of CS

1.0

0.8

0.6

0.4

0.2

0.0

0.8

0.6

0.4

0.2

0.0
0.0

0.2

0.4

0.6

0.8

1.0

0.0

Cumulative share of households by income
Fuel tax

0.2

0.4

0.6

0.8

1.0

Cumulative share of households by income

Externality−equivalent product tax

Fuel tax

Externality−equivalent product tax

Figure A.1: Regressivity of the Fuel tax and Product Tax
Note: The figures show the Lorenz curves obtained under the scenarios for the fuel tax at the current tax
rate in Japan and the externality-equivalent product tax, along with the 45-degree line. The left and rightfigures take the cumulative shares of consumer surplus and CO2 emissions as the vertical axises, respectively.
The 45-degree line indicates the situation in which households in each income class incur the tax burden or
emit CO2 evenly.

46