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Are consumers willing to pay to let cars drive for them? Analyzing response to autonomous vehicles

Author

Listed:
  • Daziano, Ricardo A.

    (Cornell University)

  • Sarrias, Mauricio

    (Universidad Catolica del Norte)

  • Leard, Benjamin

    (Resources for the Future)

Abstract

Autonomous vehicles use sensing and communication technologies to navigate safely and effciently with little or no input from the driver. These driverless technologies will create an unprecedented revolution in how people move, and policymakers will need appropriate tools to plan for and analyze the large impacts of novel navigation systems. In this paper we derive semiparametric estimates of the willingness to pay for automation. We use data from a nation-wide online panel of 1,260 individuals who answered a vehicle-purchase discrete choice experiment focused on energy effciency and autonomous features. Several models were estimated with the choice microdata, including a conditional logit with deterministic consumer heterogeneity, a parametric random parameter logit, and a semiparametric random parameter logit. We draw three key results from our analysis. First, we find that the average household is willing to pay a significant amount for automation: about $3,500 for partial automation and $4,900 for full automation. Second, we estimate substantial heterogeneity in preferences for automation, where a significant share of the sample is willing to pay above $10,000 for full automation technology while many are not willing to pay any positive amount for the technology. Third, our semiparametric random parameter logit estimates suggest that the demand for automation is split approximately evenly between high, modest and no demand, highlighting the importance of modeling fl exible preferences for emerging vehicle technology.

Suggested Citation

  • Daziano, Ricardo A. & Sarrias, Mauricio & Leard, Benjamin, 2016. "Are consumers willing to pay to let cars drive for them? Analyzing response to autonomous vehicles," RFF Working Paper Series dp-16-35, Resources for the Future.
    • Handle: RePEc:rff:dpaper:dp-16-35
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    Citations

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    Cited by:

    1. Gu, Yewen & Goez, Julio C. & Mario, Guajardo & Wallace, Stein W., 2019. "Autonomous vessels: State of the art and potential opportunities in logistics," Discussion Papers 2019/6, Norwegian School of Economics, Department of Business and Management Science.
    2. Andreja Pucihar & Iztok Zajc & Radovan Sernec & Gregor Lenart, 2019. "Living Lab as an Ecosystem for Development, Demonstration and Assessment of Autonomous Mobility Solutions," Sustainability, MDPI, vol. 11(15), pages 1-21, July.
    3. Peng Liu & Run Yang & Zhigang Xu, 2019. "Public Acceptance of Fully Automated Driving: Effects of Social Trust and Risk/Benefit Perceptions," Risk Analysis, John Wiley & Sons, vol. 39(2), pages 326-341, February.
    4. Kishore Bhoopalam, A. & van den Berg, R. & Agatz, N.A.H. & Chorus, C.G., 2021. "The long road to automated trucking: Insights from driver focus groups," ERIM Report Series Research in Management ERS-2021-003-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    5. Jingya Gao & Andisheh Ranjbari & Don MacKenzie, 2019. "Would being driven by others affect the value of travel time? Ridehailing as an analogy for automated vehicles," Transportation, Springer, vol. 46(6), pages 2103-2116, December.
    6. Sobolewski, Maciej, 2021. "Measuring consumer well-being from using free-of-charge digital services. The case of navigation apps," Information Economics and Policy, Elsevier, vol. 56(C).
    7. Xu Kuang & Fuquan Zhao & Han Hao & Zongwei Liu, 2019. "Assessing the Socioeconomic Impacts of Intelligent Connected Vehicles in China: A Cost–Benefit Analysis," Sustainability, MDPI, vol. 11(12), pages 1-28, June.
    8. Mustapha Harb & Yu Xiao & Giovanni Circella & Patricia L. Mokhtarian & Joan L. Walker, 2018. "Projecting travelers into a world of self-driving vehicles: estimating travel behavior implications via a naturalistic experiment," Transportation, Springer, vol. 45(6), pages 1671-1685, November.
    9. Mohamed Alawadhi & Jumah Almazrouie & Mohammed Kamil & Khalil Abdelrazek Khalil, 2020. "A systematic literature review of the factors influencing the adoption of autonomous driving," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(6), pages 1065-1082, December.
    10. Xiaobei Jiang & Wenlin Yu & Wenjie Li & Jiawen Guo & Xizheng Chen & Hongwei Guo & Wuhong Wang & Tao Chen, 2021. "Factors Affecting the Acceptance and Willingness-to-Pay of End-Users: A Survey Analysis on Automated Vehicles," Sustainability, MDPI, vol. 13(23), pages 1-12, November.
    11. Eric Williams & Vivekananda Das & Andrew Fisher, 2020. "Assessing the Sustainability Implications of Autonomous Vehicles: Recommendations for Research Community Practice," Sustainability, MDPI, vol. 12(5), pages 1-13, March.
    12. Kenichiro Chinen & Yang Sun & Mitsutaka Matsumoto & Yoon-Young Chun, 2020. "Towards a Sustainable Society through Emerging Mobility Services: A Case of Autonomous Buses," Sustainability, MDPI, vol. 12(21), pages 1-20, November.
    13. Yoo, Sunbin & Kumagai, Junya & Kawabata, Yuta & Keeley, Alexander & Managi, Shunsuke, 2021. "Willingness to Buy and/or Pay Disparity: Evidence from Fully Autonomous Vehicles," MPRA Paper 108882, University Library of Munich, Germany.
    14. Iva Bojic & Dániel Kondor & Wei Tu & Ke Mai & Paolo Santi & Carlo Ratti, 2021. "Identifying the Potential for Partial Integration of Private and Public Transportation," Sustainability, MDPI, vol. 13(6), pages 1-16, March.
    15. Mourad, Abood & Puchinger, Jakob & Chu, Chengbin, 2019. "A survey of models and algorithms for optimizing shared mobility," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 323-346.
    16. Adnan, Nadia & Md Nordin, Shahrina & bin Bahruddin, Mohamad Ariff & Ali, Murad, 2018. "How trust can drive forward the user acceptance to the technology? In-vehicle technology for autonomous vehicle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 819-836.
    17. Anne Collin & Afreen Siddiqi & Yuto Imanishi & Eric Rebentisch & Taisetsu Tanimichi & Olivier L. de Weck, 2020. "Autonomous driving systems hardware and software architecture exploration: optimizing latency and cost under safety constraints," Systems Engineering, John Wiley & Sons, vol. 23(3), pages 327-337, May.
    18. Monika Stoma & Agnieszka Dudziak & Jacek Caban & Paweł Droździel, 2021. "The Future of Autonomous Vehicles in the Opinion of Automotive Market Users," Energies, MDPI, vol. 14(16), pages 1-19, August.
    19. Akshay Vij, 2019. "Understanding consumer demand for new transport technologies and services, and implications for the future of mobility," Papers 1904.05554, arXiv.org.
    20. Peng Jing & Gang Xu & Yuexia Chen & Yuji Shi & Fengping Zhan, 2020. "The Determinants behind the Acceptance of Autonomous Vehicles: A Systematic Review," Sustainability, MDPI, vol. 12(5), pages 1-26, February.
    21. Leard, Benjamin, 2018. "Consumer inattention and the demand for vehicle fuel cost savings," Journal of choice modelling, Elsevier, vol. 29(C), pages 1-16.
    22. Scott Kaplan & Ben Gordon & Feras El Zarwi & Joan L. Walker & David Zilberman, 2019. "The Future of Autonomous Vehicles: Lessons from the Literature on Technology Adoption," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 41(4), pages 583-597, December.

    More about this item

    Keywords

    willingness to pay; autonomous vehicle technology; discrete choice models; semiparametric heterogeneity;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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