Can the subsidy program change the customer base of next-generation vehicles?
〇王 佳星1； 松本 茂
Jiaxing Wang and Shigeru Matsumoto
1. Background and research objective
Since the promotion of hybrid electric vehicles (HEVs) is efficient to reduce energy consumption
and greenhouse gas emissions from the transportation sector, various countries have introduced
preferential tax treatments and rebate programs for HEVs. The impacts of such subsidies on HEV sales
have been examined in Canada (Chandra et al. 2010) and the United States (Gallagher and Muehlegger2011). Further, a similar subsidy program called the “Eco-car program” was introduced in Japan; Iwataand Matsumoto (2016) confirm that the program rapidly expanded the sales of HEVs. Although it isimportant to know the types of households that are likely to be attracted by a specific subsidy program,no literature has yet identified the households that switched from conventional gasoline vehicles toHEVs. In this study, we compare the vehicle choice between three sampling periods(before/during/after the Eco-car program) and examine whether the rebate program changed thecustomer base of HEVs.
2. Data and research method
For the empirical analysis, we use micro-level data from the Japanese National Survey of Family
Income and Expenditure (NSFE), which was collected in 2009 and 2014. NSFE collects data on
households’ socioeconomic characteristics, information related to houses, etc. In addition, NSFE alsocollects vehicle-related information such as the number of vehicles owned, the year of purchase ofeach vehicle, and the type of vehicle.
We focus on the households that purchased only one new vehicle between 2004 and 2014. We
conduct empirical analyses based on the Multinomial Logit Model. We estimate the probability ofselecting one of the following three types of vehicles: HEV, compact gasoline vehicle (CGV), andregular gasoline vehicle (RGV). To examine whether new types of households began purchasing HEVwith the Eco-car program, we divide the data into three sampling periods: before, during, and after theEco-car program. We then estimate the following equation for three sampling periods separately,Pr(y𝑖𝑡 = 𝑗) =exp⁡(𝑩𝑗𝑠 𝑿𝑖 − 𝛾𝑗𝑠 𝑝𝑗𝑡 + 𝜃𝑗𝑠 𝑔𝑡 )3
∑𝑗=1 exp⁡(𝑩𝑗𝑠 𝑿𝑖 − 𝛾𝑗𝑠 𝑝𝑗𝑡 + 𝜃𝑗𝑠 𝑔𝑡 )where 𝑩𝑗𝑠 , 𝛾𝑗𝑠 , and 𝜃𝑗𝑠 are period-specific coefficients; 𝑿𝑖 is the vector of socioeconomics
characteristics of household 𝑖 ; 𝑝𝑗𝑡 is the weighted average price of type 𝑗 ’s vehicle at time ofpurchase 𝑡; 𝑔𝑡 is the gasoline price at time of purchase 𝑡. We choose HEV as the base outcome.
 Aoyama Gakuin University. 4-4-25 Shibuya, Shibuya-ku, Tokyo, 150-8366, Japan1

Table 1. Changes (RRRs) in the marginal impact of household characteristics on vehicle selection.
Variables

Before Eco-Car programDuring Eco-Car program

After Eco-Car program

Vehicle = CGV Vehicle = RGV Vehicle = CGV Vehicle = RGV Vehicle = CGV Vehicle = RGV

Gasoline Price

0.959

***

0.968

0.988

0.979

Household income

0.749

**

1.109

0.583

***

0.898

0.804

*

1.002

Gender of household head dummy

0.602

***

0.940

0.565

***

0.823

0.646

***

0.896

Net wealth

0.971

0.971

0.930

***

0.952

***

0.963

**

0.947

Age of household head

0.982

0.977

***

1.014

***

0.990

**

Household size***

1.368

***

1.156

**

1.312

***

***

**

1.020

0.978

***

0.997

1.117

1.348

***

1.244

Detached house dummy

1.078

0.823

1.412

**

1.035

Home owner dummy

0.741

0.923

0.524

***

0.765

*

0.802

Electricity bills per capita

0.882

***

1.026

1.001

1.069

***

0.932

3.88E+11 *5870071

0.000

0.004

Constant

0.954

0.844

***

0.974
0.705
***

922.143

N101584149

3004

Log likelood-7368.137-4046.655-3093.565

**

1.006
4E+37

Notes: 1) ***, **, * indicate significance at 1, 5, and 10% level, respectively; 2) RRRs capture the relative risks, which are the ratios of the vehicle
choice probabilities; 3) dummies of 47 prefectures and average weighted price of each type of vehicles are concluded in the model.

3. Main findings
The results presented in Table 1 suggest that the impact of household characteristics on HEV
selection has changed during three purchase periods. We find that the impact of gasoline price has
become smaller after the Eco-car program. We also find that higher income households who used
CGVs before the Eco-car program switched to HEVs during the Eco-car program. In contrast,
household income does not play an important role on the choice between HEV and RGV. In addition,
we find that households with large net wealth tend to choose HEVs over both CGVs and RGVs.
Households who own a house tend to choose HEVs over CGVs or RGVs during and after the Eco-car
program, while households living in a detached house tend not to choose HEVs over CGVs during theEco-car program. Finally, we find that RGV households consume more electricity than remaining twohouseholds.
4. Conclusion
In this study, we analyzed the micro-level data of vehicle selection from NSFE and found how
the Eco-car program affected the vehicle selection of households. These findings shall give policy
implications to the further promotion of next-generation vehicles.
References
Chandra, A; Gulati, S; Kandlikar, M 2010. “Green drivers or free riders? An analysis of tax rebates forhybrid vehicles.” Journal of Environmental Economics and Management 60: 78-93.
Iwata, K and Matsumoto, S 2016. “Use of hybrid vehicle in Japan: An analysis of used car market data.”
Transportation Research Part D 46: 200-206.
Gallagher, K and Muehlegger, E 2011. “Giving green to get green? Incentives and consumer adoption ofhybrid vehicle technology.” Journal of Environmental Economics and Management 61(1): 1-15.

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