Pigovian Transport Pricing in Practice

This paper reports on the MOBIS experiment, a large-scale randomized controlled trial (RCT) implementing a multi-modal Pigovian transport pricing scheme in urban areas of German- and French-speaking Switzerland. The central research question is whether a first-best transport pricing scheme — one that charges users the full marginal external costs of their travel choices, varying across time, space, and mode — generates meaningful behavioral responses, and how those responses compare to a pure information intervention.

The study recruited participants from urban areas, requiring them to be between 18 and 65 years old and to use a car at least two days per week. After contacting over 90,000 individuals and an initial online screening of 21,800 respondents, 3,656 participants completed the RCT. Each participant agreed to have their daily travel tracked via a smartphone app (“Catch-My-Day”) for eight weeks: four weeks of observation followed by four weeks of treatment. Assignment to treatment and control groups was fully randomized without stratification.

The pricing treatment gave participants a budget equal to their observed external costs during the observation period plus a 20% buffer, from which the external costs of their actual travel were deducted in real time; any remaining balance was theirs to keep. External costs were computed across all modes using official Swiss Federal Roads Office monetization factors, including congestion (via a MATSim-based average marginal cost approach), CO2 climate costs (CHF 136.08/ton), health costs from air pollution (PM10 and NOx), and accident and physical activity effects for active and public modes. Public transport also carried a peak-hour surcharge of CHF 0.10/km for congested zone-pairs. A second “information-only” treatment provided identical information about external costs but imposed no financial charge. A control group received only weekly summaries of kilometers traveled by mode.

The regression framework is a difference-in-differences specification with person, calendar-day, and day-of-study fixed effects, estimated in levels for external-cost outcomes (due to negative values from walking’s net external benefit) and via Poisson Pseudo-Maximum Likelihood for non-negative outcomes.

The pricing treatment reduced total external costs by CHF 0.215 per day (p < 0.01), a 5.1% reduction relative to the control group. The average private cost of transport for the control group during the treatment period was CHF 25.72 per day; the external cost was CHF 4.22 per day, implying that Pigovian pricing raised total transport costs by 16.4% on average. The implied price elasticity of external costs with respect to this price increase is -0.31. The reduction is attributable to mode substitution toward public transport and active modes and to departure time shifting away from peak hours, but not to a reduction in total distance traveled.

The information-only treatment produced a coefficient of -0.087, which is not statistically significant at conventional levels for the full sample. The differential effect of adding pricing to information is -0.127 (marginally significant, p < 0.1), with the pricing increment particularly important for reducing congestion costs. Sensitivity analysis shows that removing the control group and time fixed effects inflates the before-vs.-after elasticity to between -0.57 and -0.71, substantially larger than the preferred estimate of -0.31, underscoring the importance of the experimental design.

Heterogeneity analysis reveals that men respond more strongly than women, German speakers more than French speakers, participants under 30 more than older participants, and those with above-median altruistic values respond significantly even to information alone. Correct knowledge of the definition of external costs (present in 45% of the sample) is a key driver of the pricing treatment effect. These scope conditions — mode availability, urban Swiss context, short 4-week treatment window, mandatory car use eligibility, and the specific external cost monetization framework — bound the generalizability of the elasticity estimate.

Q: What is the main treatment effect of the Pigovian pricing scheme on external transport costs? A: The pricing treatment reduced total external costs by CHF 0.215 per day, which is a 5.1% reduction relative to the control group (p < 0.01). About half of the reduction came from health costs, with congestion and climate costs following in magnitude. The implied elasticity of external costs with respect to the Pigovian price increase is -0.31, meaning a 10% increase in total transport costs from Pigovian pricing would reduce external costs by approximately 3.1% in the short run.

Q: How was the Pigovian price increase calculated, and what was its magnitude relative to private costs? A: The average private cost of transport for the control group during the treatment period was CHF 25.72 per day, and the average external cost was CHF 4.22 per day. The external cost thus represents 16.4% of total (private plus external) transport costs, and dividing the 5.1% reduction in external costs by this 16.4% price increase yields the elasticity of -0.31.

Q: What mechanisms drove the reduction in external costs? A: The reduction resulted from a combination of mode substitution — a shift away from car use toward public transport and active modes — and departure time shifting away from peak hours. Critically, total distance traveled did not decline; the behavioral adjustment operated entirely through changes in how and when people traveled, not in how much.

Q: What was the effect of the information-only treatment? A: The information-only treatment produced a coefficient of -0.087 CHF per day, which was not statistically significant at conventional levels for the full sample. It was statistically significant only for subgroups, notably participants with above-median altruistic values. The differential effect of adding pricing to information (alpha_P minus alpha_I = -0.127) was marginally significant (p < 0.1) and was particularly concentrated in congestion cost reductions, suggesting that the monetary incentive is especially important for internalizing the congestion externality.

Q: Why is the control group critical, and how does removing it affect the estimated elasticity? A: The tracking data show a seasonal negative trend in external costs over the study period; without a control group, this trend would be incorrectly attributed to the treatment, inflating the estimated effect. When both day-of-study and calendar-day fixed effects are removed (approximating a before-vs.-after design without a control group), the estimated elasticity rises to between -0.57 and -0.71, roughly double the preferred estimate of -0.31. This highlights that most prior studies in the literature, which lack control groups, are likely to overestimate treatment effects.

Q: What heterogeneity is observed in the treatment response? A: Men respond more strongly than women to both treatments, with the gender gap particularly pronounced for congestion costs. German speakers respond more strongly than French speakers. Participants under age 30 show stronger responses than older participants. Those scoring above the median on an altruistic values index respond significantly not only to pricing but also to information alone. Participants who correctly defined external costs (45% of the sample) drive the pricing treatment effect; a causal forest analysis confirms knowledge of external costs, age below 30, and language region as key heterogeneity drivers.

Q: How were external costs computed across modes, and what are the key monetization parameters? A: For private road transport, GPS tracks were map-matched using Graphhopper and processed via MATSim modules; emission factors came from the HBEFA 3.3 database, and congestion was assessed via an average marginal cost approach incorporating spillback effects. Externalities were monetized at CHF 136.08/ton for CO2, CHF 515,497–1,358,461/ton for PM10 (rural vs. urban), CHF 7,109/ton for NOx (regional), and a value of travel time savings of CHF 25.77/hour. For other modes, per-km values from the Swiss Federal Roads Office were applied. Walking carries net external benefits (negative external costs), while cycling carries small net external costs because accident costs exceed physical activity benefits.

Q: How was public transport priced in the experiment, and why was it simplified? A: A second-best zonal peak-hour surcharge of CHF 0.10/km was applied to public transport stages between zone-pairs experiencing peak demand, with peak windows set at 7–9 am and 5–7 pm. Full first-best pricing of public transport crowding was deemed infeasible because crowding effects are highly heterogeneous spatially and temporally, often concentrated in very short windows on specific lines, making aggregate distribution unreasonable.

Q: Was there evidence of gaming the mode detection system? A: Because participants could manually correct the app’s algorithmic mode assignments — and the pricing group had an incentive to overclaim low-cost modes — the potential for strategic misreporting was examined. While the analysis could not rule out some gaming, the main results were shown to be robust to excluding potential gamers, suggesting that gaming did not materially distort the treatment effect estimates.

Q: What does the study imply for transport pricing policy? A: The elasticity of -0.31 provides a benchmark for policymakers: a full Pigovian pricing scheme that raises total transport costs by about 16% can be expected to reduce external costs by about 5% in the short run in an urban context. The finding that congestion costs respond more to pricing than to information alone suggests the monetary component is essential for this externality. Heterogeneous responses — particularly the weaker responses by women and French speakers — have distributional implications. The experiment is a proof of concept that first-best transport pricing can generate meaningful behavioral responses, but scaling it would require addressing privacy concerns from GPS tracking, technical infrastructure, and political economy challenges.

Pigovian transport pricing: A pricing scheme that charges each user the marginal external costs of their transport choices — including health, climate, congestion, and noise costs — as they vary across time, space, and mode, intended to internalize the gap between private and social costs of travel.

External costs of transport: Costs borne by society rather than the individual traveler, including congestion (delay imposed on others), climate damages (CO2 emissions), health costs (local air pollution, accidents), and noise; in this paper, computed in real time from tracked trips using official Swiss monetization values.

Average treatment effect (ATE): The difference-in-differences estimate of the causal effect of the pricing or information treatment on outcomes, identified from the randomized assignment and controlling for person, calendar-day, and day-of-study fixed effects.

Mode substitution: The behavioral response in which travelers shift from higher-external-cost modes (primarily car) to lower-external-cost modes (public transport, walking, cycling) in response to pricing, as distinct from reducing total travel distance.

Departure time shifting: The behavioral response in which travelers adjust when they depart to avoid peak-hour congestion surcharges, contributing to reduced congestion externalities without reducing total distance traveled.

Information-only treatment: An experimental arm receiving identical information about external costs as the pricing group but facing no financial charge, used to isolate the informational component of the pricing treatment from the monetary incentive component.

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