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Contrasting the recommendations of participatory value evaluation and cost-benefit analysis in the context of urban mobility investments

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  • Mouter, Niek
  • Koster, Paul
  • Dekker, Thijs

Abstract

Participatory Value Evaluation (PVE) is a new method to assess the desirability of government projects. In a PVE, individuals select their preferred portfolio of government projects given a constrained public budget. Individuals’ preferences for (the impacts of) government projects can be determined based on these choices. The obtained preferences can be used to rank government projects in terms of their desirability. Cost-Benefit Analysis (CBA) is an alternative appraisal method used to assess the desirability of government projects. CBA establishes the desirability of public projects through analyzing people’s trade-offs between their private income and impacts of public projects. The primary objective of this paper is to investigate whether CBA and PVE lead to different policy recommendations in the context of urban mobility investments. We conducted CBAs and a PVE for 16 urban mobility investment projects and find indicative evidence that projects which focus on improving traffic safety and improvements for cyclists/pedestrians rank higher in the PVE, whereas car projects rank higher in the CBA analysis.

Suggested Citation

  • Mouter, Niek & Koster, Paul & Dekker, Thijs, 2021. "Contrasting the recommendations of participatory value evaluation and cost-benefit analysis in the context of urban mobility investments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 54-73.
    • Handle: RePEc:eee:transa:v:144:y:2021:i:c:p:54-73
    DOI: 10.1016/j.tra.2020.12.008
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    5. Bhat, Chandra R. & Mondal, Aupal & Pinjari, Abdul Rawoof & Saxena, Shobhit & Pendyala, Ram M., 2022. "A multiple discrete continuous extreme value choice (MDCEV) model with a linear utility profile for the outside good recognizing positive consumption constraints," Transportation Research Part B: Methodological, Elsevier, vol. 156(C), pages 28-49.
    6. Hernandez, Jose Ignacio & van Cranenburgh, Sander & Chorus, Caspar & Mouter, Niek, 2023. "Data-driven assisted model specification for complex choice experiments data: Association rules learning and random forests for Participatory Value Evaluation experiments," Journal of choice modelling, Elsevier, vol. 46(C).
    7. Bhat, Chandra R., 2022. "A closed-form multiple discrete-count extreme value (MDCNTEV) model," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 65-86.
    8. Hung Tran & Tien Mai, 2023. "Network-based Representations and Dynamic Discrete Choice Models for Multiple Discrete Choice Analysis," Papers 2306.04606, arXiv.org.
    9. Caputo, Vincenzina & Lusk, Jayson L., 2022. "The Basket-Based Choice Experiment: A Method for Food Demand Policy Analysis," Food Policy, Elsevier, vol. 109(C).
    10. Vittadini, Giorgio & Sturaro, Caterina & Folloni, Giuseppe, 2022. "Non-Cognitive Skills and Cognitive Skills to measure school efficiency," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    11. Hössinger, Reinhard & Peer, Stefanie & Juschten, Maria, 2023. "Give citizens a task: An innovative tool to compose policy bundles that reach the climate goal," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).

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