# Buy and/or Pay Disparity: Evidence from Fully Autonomous Vehicles
## 3 Methodology  
### Data  
In response to our research question, we conducted a comprehensive online survey in Japan from 16 November to 14 December 2015, targeting a diverse demographic to gather insights on fully automated vehicles (FAVs), lifestyle, and environmental concerns. The survey, executed through Nikkei Research Inc., was designed to randomly select participants while mirroring the gender and age distribution of the Japanese population. This approach ensured a representative sample, as evidenced by the 246,642 respondents who were rigorously screened through trap questions to maintain data quality. To ensure the robustness of our findings, we employed multiple data processing methods. These included comparative analyses between the survey results and Japanese Census data, specifically examining the distribution of socioeconomic variables as shown in Table 15. Although we observed minor discrepancies in gender and education levels when compared to the census data, our survey results broadly align with Japan's average demographic trends. This methodology not only underscores the comprehensiveness of our data but also demonstrates our commitment to utilizing varied analytical approaches to validate our findings. Again, we are aware that in 2015, the respondents were less familiar with FAVs than those in 2021. Therefore, we excluded those who answered that they had 'no awareness' of FAVs, which accounted for 14.48% of the entire sample (35,715 observations). Thus, in our model, we only account for the people who were aware of FAV technologies in 2015. Therefore, given that FAVs were not introduced in the market back then and still not introduced in 2021, a substantial change in the result, for example, a change in the sign or implications of the results, is less likely to happen. Therefore, more attention should be given to the signs and relative comparisons of coefficient magnitudes of the latent constructs. Finally, we drop those who selected \"don't know/don't want to answer\" about their individual income (30,156 observations). As a result, we have 180,771 respondents in total. Before the large-scale survey started, a pre-survey was carried out to tune the questionnaires. For the questions related to the FAV purchasing intention, respondents were asked the question: \"Do you want to add a completely self-driving option that allows you to move around when you purchase a car in the future?\". Then, the respondents answered the following questions: \"(1) Purchase for sure, (2) Purchase under certain conditions, (3) Do not purchase, and (4) I don't know\". Given that FAVs are not yet fully introduced to the market in 2015 or 2021, we assume that people who show an affinity to FAVs can be potential consumers in the future. Therefore, we included those who answered (1) and (2) as a group of 'potential consumers' as they show an affinity toward using FAVs. On the other hand, people who answered (3) and (4) are reluctant to purchase FAVs, and we did not consider them potential consumers. Therefore, we coded WTB equal to 1 if a respondent belonged to a potential consumer group and coded WTB as 0 if not. Therefore, our analysis would allow us to see what kinds of factors would shift consumers who belong to (3) and (4) to (1) and (2). We would like to note that we are making a clear distinction between \"adding\" a completely self-driving option and \"purchasing\" FAVs by asking \"Do you want to add a completely self-driving option that allows you to move around when you purchase a car in the future?\". Next, we also asked PV for FAVs. Respondents were asked to write down their PVs freely regardless of the purchase decisions, ranging from 0 JPY to 3.25 million JPY (based on the approximate conversion rate of 1 JPY to 0.0073 USD, the range of PV for FAVs from 0 JPY to 3.25 million JPY, converts to approximately 0 to 23,725 USD).