# Buy and/or Pay Disparity: Evidence from Fully Autonomous Vehicles
## 1 Introduction  
The rapid advance of autonomous vehicles (AVs) and fully autonomous vehicles (FAVs) implies the arrival of the era of completely driverless cars. Starting from Google's self-driving car project in 2009, famous companies such as Uber, Apple, and Tesla have been challenging the development of autonomous vehicles. This is because shifting to FAVs from vehicles without any automation function would have numerous benefits if properly used, including the prevention of accidents due to human errors [1, 2], alleviation of congestion [3, 4], and reduction of emissions from traffic [5, 6, 7].  
Due to these benefits, the market for fully automated vehicles begin to increase and the market experts predict the market share of FAV technology worldwide to reach 15-20% by 2025 [8], calling for the policies and future blueprints for the gradual shift to FAV. Drafting such standards would require understanding which factors would encourage or discourage potential consumers from adopting autonomous vehicles and, at the same time, how they evaluate them in monetary terms. Such an understanding requires including people's intentions and behaviors, determined by attitude and perceptions, which requires constructing latent factors [9]. Furthermore, people can have multiple attitudes simultaneously; for example, people can fear FAVs but at the same time appreciate the merits of FAVs. Thus, given that people simultaneously perceive benefits and fears from FAVs, s/he (as an inclusive, gender-neutral pronoun, intended to inclusively represent \"she or he\") may express high levels of WTB and PV because s/he appreciates the benefits more than s/he fears FAVs. Furthermore, such different attitudes might be correlated.  
In this study, we constructed four categories of latents, considering their correlations, that express the attitudes and behaviors of people through an extensive literature review and estimated their relationship to WTB and PV. We also considered socioeconomic factors, such as income, gender, household size, and car-related factors, such as car ownership and car type. We chose structural equation modeling (SEM), a widely known methodology, to scrutinize people's psychometric intentions, which allows for the identification of latent factors and simultaneous estimation of latents with exogenous variables. Figure 1 shows our study structure. First, we identified attitudes, which are expressed in latents, according to the behaviors, and estimated the relationship between intentions and decisions.  
Data-wise, we conducted a survey of more than 180,000 respondents in Japan, which contained questions regarding WTB and PV of FAVs, individual characteristics such as income, gender, commuting time and ages, environmental awareness, and opinions on the advantages and concerns regarding FAVs. Then, we constructed four empirical models that answer our research questions. Our results indicate that while environmental concerns enhance the likelihood of purchasing FAVs, they simultaneously diminish their perceived value due to the perception that FAV adoption does not directly benefit environmental conservation. On the other hand, individuals who recognize the benefits of FAV technology demonstrate a higher willingness to buy and assign greater value to these vehicles. Conversely, apprehensions regarding FAV technology correlate with a lower inclination to purchase and a reduced perceived value. These insights are pivotal in informing the development of effective policies and strategies for the adoption of FAVs.  
The remainder of this paper is structured as follows. Section 2 provides a background in terms of industry and policy. The data and model are presented in Section 3. Section 4 shows the empirical results. Section 5 discusses our findings and provides policy implications. Section 6 concludes our paper.