Hostname: page-component-7b9c58cd5d-v2ckm Total loading time: 0 Render date: 2025-03-23T17:08:24.895Z Has data issue: false hasContentIssue false
  • English
  • Français

Learning to accept welfare-enhancing policies: an experimental investigation of congestion pricing

Published online by Cambridge University Press:  14 March 2025

Nicholas Janusch ,
Stephan Kroll ,
Christopher Goemans ,
Todd L. Cherry  and
Steffen Kallbekken
Nicholas Janusch*
Affiliation:
California Energy Commission, 1516 Ninth Street, MS-22, Sacramento, CA 95814, USA
Stephan Kroll
Affiliation:
Department of Agricultural and Resource Economics, Colorado State University, Fort Collins, CO 80523, USA
Christopher Goemans
Affiliation:
Department of Agricultural and Resource Economics, Colorado State University, Fort Collins, CO 80523, USA
Todd L. Cherry
Affiliation:
Department of Economics, Appalachian State University, Boone, NC 28608, USA Center for International Climate Research, Pb. 1129 Blindern, Oslo 0318, Norway
Steffen Kallbekken
Affiliation:
Center for International Climate Research, Pb. 1129 Blindern, Oslo 0318, Norway
*
nicholas.janusch@energy.ca.gov
Get access Rights & Permissions [Opens in a new window]

Abstract

Welfare-enhancing policies such as congestion pricing are argued to improve efficiency in situations with externalities. Unfamiliarity and lack of any personal experience with such policies, however, can hinder their implementation; particularly the ex-ante uncertainties of incidences of gains and losses as well as debates regarding equity concerns and how to recycle revenues often stymies implementation. This paper employs a laboratory experiment with heterogeneous users to investigate the effectiveness and acceptability of a toll in a six-player-two-route congestion game. To measure acceptability and how it is affected by experience with the toll, we conduct referenda before, during, and after subjects experience a congestion problem and a toll. The experiment employs a 2×2 design that varies two treatments: the rate of revenue reallocation and the level of information before the final vote. After an experiential learning phase, congestion pricing is found to curb congestion effectively, and although some subjects do not vote in their monetary self-interest initially, the majority does so after experiencing the congestion pricing policy. Data on worldviews and beliefs are collected and matched to voting behavior to examine the evolution of how experience determines acceptability. Some worldviews and beliefs can predict voting behavior and the timing of when an individual finds a toll (un)acceptable.

Keywords

AcceptabilityCongestion pricingPolicy trialLab experimentRevenue recyclingVoting behavior

JEL classification

R48: Government Pricing and Policy C92: Laboratory, Group Behavior D91: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making‡
Type
Original Paper
Information
Experimental Economics , Volume 24 , Issue 1 , March 2021 , pp. 59 - 86
Copyright
Copyright © 2020 Economic Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10683-020-09650-2) contains supplementary material, which is available to authorized users.

References

Anas, A, & Lindsey, R (2011). Reducing urban road transportation externalities: Road pricing in theory and in practice. Review of Environmental Economics and Policy, 5(1), 66–88. 10.1093/reep/req019CrossRefGoogle Scholar
Anderson, L., Holt, C. A., & Reiley, D. (2008). Congestion pricing and welfare. In Environmental Economics, Experimental Methods, chapter 15, pages 280–292. Routledge.Google Scholar
Börjesson, M, Eliasson, J, Hugosson, MB, & Brundell-Freij, K (2012). The Stockholm congestion charges-5 years on. Effects, acceptability and lessons learnt. Transport Policy, 20, 1–12. 10.1016/j.tranpol.2011.11.001CrossRefGoogle Scholar
Carattini, S, Carvalho, M, & Fankhauser, S (2018). Overcoming public resistance to carbon taxes. Wiley Interdisciplinary Reviews: Climate Change, 9(5), e531.Google ScholarPubMed
Cherry, TL, Kallbekken, S, & Kroll, S (2014). The impact of trial runs on the acceptability of environmental taxes: Experimental evidence. Resource and Energy Economics, 38, 84–95. 10.1016/j.reseneeco.2014.06.005CrossRefGoogle Scholar
Cherry, TL, Kallbekken, S, & Kroll, S (2017). Accepting market failure: Cultural worldviews and the opposition to corrective environmental policies. Journal of Environmental Economics and Management, 85, 193–204. 10.1016/j.jeem.2017.05.004CrossRefGoogle Scholar
Cherry, TL, McEvoy, DM, & Westskog, H (2019). Cultural worldviews, institutional rules and the willingness to participate in green energy programs. Resource and Energy Economics, 56, 28–38. 10.1016/j.reseneeco.2017.10.001CrossRefGoogle Scholar
Dal Bó, E, Dal Bó, P, & Eyster, E (2017). The demand for bad policy when voters underappreciate equilibrium effects. The Review of Economic Studies, 85(2), 964–998.CrossRefGoogle Scholar
De Borger, B, & Proost, S (2012). A political economy model of road pricing. Journal of Urban Economics, 71(1), 79–92. 10.1016/j.jue.2011.08.002CrossRefGoogle Scholar
Dechenaux, E, Mago, SD, & Razzolini, L (2014). Traffic congestion: An experimental study of the Downs-Thomson paradox. Experimental Economics, 17(3), 461–487. 10.1007/s10683-013-9378-4CrossRefGoogle Scholar
Denant-Boemont, L., & Hammiche, S. (2009). Public transit capacity and users’ choice: An experiment on Downs-Thomson paradox. In Proceedings of the Kuhmo NECTAR conference on transportation economics: annual conference of the international transportation economics association.Google Scholar
Dixit, VV, Ortmann, A, Rutström, EE, & Ukkusuri, SV (2017). Experimental economics and choice in transportation: Incentives and context. Transportation Research Part C: Emerging Technologies, 77, 161–184. 10.1016/j.trc.2017.01.011CrossRefGoogle Scholar
Douglas, M, & Wildavsky, A (1983). Risk and culture: An essay on the selection of technological and environmental dangers, Berkeley: University of California Press 10.1525/9780520907393CrossRefGoogle Scholar
Falk, A, & Heckman, JJ (2009). Lab experiments are a major source of knowledge in the social sciences. Science, 326(5952), 535–538. 10.1126/science.1168244CrossRefGoogle Scholar
Fernandez, R, & Rodrik, D (1991). Resistance to reform: Status quo bias in the presence of individual-specific uncertainty. American Economic Review, 81(5), 1146–1155.Google Scholar
Figureau, A-G, Montginoul, M, & Rinaudo, J-D (2015). Policy instruments for decentralized management of agricultural groundwater abstraction: A participatory evaluation. Ecological Economics, 119, 147–157. 10.1016/j.ecolecon.2015.08.011CrossRefGoogle Scholar
Fischbacher, U (2007). z-Tree: Zurich toolbox for ready-made economic experiments. Experimental Economics, 10(2), 171–178. 10.1007/s10683-006-9159-4CrossRefGoogle Scholar
Fullerton, D (2011). Six distributional effects of environmental policy. Risk Analysis, 31(6), 923–929. 10.1111/j.1539-6924.2011.01628.xCrossRefGoogle ScholarPubMed
Gu, Z, Liu, Z, Cheng, Q, & Saberi, M (2018). Congestion pricing practices and public acceptance: A review of evidence. Case Studies on Transport Policy, 6(1), 94–101. 10.1016/j.cstp.2018.01.004CrossRefGoogle Scholar
Hall, JD (2018). Pareto improvements from lexus lanes: The effects of pricing a portion of the lanes on congested highways. Journal of Public Economics, 158, 113–125. 10.1016/j.jpubeco.2018.01.003CrossRefGoogle Scholar
Hartman, J. L. (2006). A route choice experiment involving monetary payouts and actual waiting times. mimeo, Department of Economics, University of California Santa Barbara, Santa Barbara, CA 93106.Google Scholar
Hartman, J. L. (2007). The relevance of heterogeneity in a congested route network with tolls: An analysis of two experiments using actual waiting times and monetized time costs. mimeo, Department of Economics, University of California Santa Barbara, Santa Barbara, CA 93106.Google Scholar
Hartman, JL (2012). Special issue on transport infrastructure: A route choice experiment with an efficient toll. Networks and Spatial Economics, 12(2), 205–222. 10.1007/s11067-009-9111-1CrossRefGoogle Scholar
Ison, S, & Rye, T (2005). Implementing road user charging: The lessons learnt from Hong Kong. Cambridge and Central London. Transport Reviews, 25(4), 451–465. 10.1080/0144164042000335788CrossRefGoogle Scholar
Kahan, DM, Jenkins-Smith, H, & Braman, D (2011). Cultural cognition of scientific consensus. Journal of Risk Research, 14(2), 147–174. 10.1080/13669877.2010.511246CrossRefGoogle Scholar
Kahan, DM, Peters, E, Wittlin, M, Slovic, P, Ouellette, LL, Braman, D, & Mandel, G (2012). The polarizing impact of science literacy and numeracy on perceived climate change risks. Nature Climate Change, 2(10), 732–735. 10.1038/nclimate1547CrossRefGoogle Scholar
Kallbekken, S, Kroll, S, & Cherry, TL (2011). Do you not like Pigou, or do you not understand him? Tax aversion and revenue recycling in the lab. Journal of Environmental Economics and Management, 62(1), 53–64. 10.1016/j.jeem.2010.10.006CrossRefGoogle Scholar
Knight, FH (1924). Some fallacies in the interpretation of social cost. The Quarterly Journal of Economics, 38(4), 582–606. 10.2307/1884592CrossRefGoogle Scholar
Light, T (2009). Optimal highway design and user welfare under value pricing. Journal of Urban Economics, 66(2), 116–124. 10.1016/j.jue.2009.05.003CrossRefGoogle Scholar
Mahendra, A., Grant, M., & Swisher, M. (2012). Effective approaches for advancing congestion pricing in a metropolitan region—a primer on lessons learned and best practices (no. fhwa-hop-12-030). Technical report, Federal Highway Administration.Google Scholar
Morgan, J, Orzen, H, & Sefton, M (2009). Network architecture and traffic flows: Experiments on the Pigou-Knight-Downs and Braess Paradoxes. Games and Economic Behavior, 66(1), 348–372. 10.1016/j.geb.2008.04.012CrossRefGoogle Scholar
Nishi, A, Shirado, H, Rand, DG, & Christakis, NA (2015). Inequality and visibility of wealth in experimental social networks. Nature, 526(7573), 426 10.1038/nature15392CrossRefGoogle ScholarPubMed
Pigou, AC (1920). The economics of welfare, London: McMillan.Google Scholar
Pigou, AC (1937). Socialism versus capitalism, London: Macmillan.Google Scholar
Rey, D, Dixit, VV, Ygnace, J-L, & Waller, ST (2016). An endogenous lottery-based incentive mechanism to promote off-peak usage in congested transit systems. Transport Policy, 46, 46–55. 10.1016/j.tranpol.2015.11.010CrossRefGoogle Scholar
Salant, S, & Seegert, N (2018). Should congestion tolls be set by the government or by the private sector? The Knight-Pigou debate revisited. Economica, 85(339), 428–448. 10.1111/ecca.12259CrossRefGoogle Scholar
Schade, J, & Baum, M (2007). Reactance or acceptance? Reactions towards the introduction of road pricing. Transportation Research Part A: Policy and Practice, 41(1), 41–48.Google Scholar
Schrank, D., Eisele, B., & Lomax, T. (2019). 2019 Urban Mobility Report. Texas A&M Transportation Institute and INRIX, Inc..Google Scholar
Seale, DA, Parco, JE, Stein, WE, & Rapoport, A (2005). Joining a queue or staying out: Effects of information structure and service time on arrival and staying out decisions. Experimental Economics, 8(2), 117–144. 10.1007/s10683-005-0872-1CrossRefGoogle Scholar
Selten, R, Chmura, T, Pitz, T, Kube, S, & Schreckenberg, M (2007). Commuters route choice behaviour. Games and Economic Behavior, 58(2), 394–406. 10.1016/j.geb.2006.03.012CrossRefGoogle Scholar
Small, KA, & Verhoef, ET (2007). The Economics of Urban Transportation, Abingdon: Routledge 10.4324/9780203642306CrossRefGoogle Scholar
Swanson, J., & Hampton, B. (2013). What do people think about congestion pricing?: A deliberative dialogue with residents of metropolitan Washington. Metropolitan Washington Council of Governments, Washington, D. C.: National Capital Region Transportation Planning Board.Google Scholar
Wijayaratna, KP, Dixit, VV, Denant-Boemont, L, & Waller, ST (2017). An experimental study of the online information paradox: Does en-route information improve road network performance?. PLoS ONE, 12(9), 1–17. 10.1371/journal.pone.0184191CrossRefGoogle ScholarPubMed
Winslott-Hiselius, L, Brundell-Freij, K, Vagland, Åsa, & Byström, C (2009). The development of public attitudes towards the Stockholm congestion trial. Transportation Research Part A: Policy and Practice, 43(3), 269–282.Google Scholar
Ziegelmeyer, A, Koessler, F, & Denant-Boemont, L (2008). Road traffic congestion and public information: An experimental investigation. Journal of Transport Economics and Policy, 42(1), 43–82.Google Scholar
Supplementary material: File

Janusch et al. supplementary material 1

Janusch et al. supplementary material 1
Download Janusch et al. supplementary material 1(File)
File 5.3 MB
Supplementary material: File

Janusch et al. supplementary material 2

Janusch et al. supplementary material 2
Download Janusch et al. supplementary material 2(File)
File 1.3 MB