Income-based fare discounts and fare capping for low-income riders: effects on ridership and access

INTRODUCTION

Public transport fares act as a direct price on mobility, and for households at the bottom of the income distribution that price can absorb a disproportionate share of the monthly budget. Qualitative work with low-income riders shows that the fare itself, rather than service quality, is frequently the binding constraint on how many trips a person can take, and riders describe forgoing medical visits, job interviews and social contact when the card runs out.( 1)

The distributional burden is measurable. Analyses that combine travel time, fare and income find that low-income workers face systematically lower job accessibility once the out-of-pocket cost of transit is counted, and that the combined cost of housing and transit can leave little residual income for other needs.( 2-4)

These patterns describe a form of transport poverty in which the monetary cost of travel, rather than the absence of service, is what excludes people from the labour market and from everyday activities, and they help explain why fare affordability has moved from a technical pricing question to a central theme of transport equity policy. Fare capping is a distinct instrument that sets a ceiling on the amount a rider pays over a day, week or month, so that frequent riders effectively convert into pass holders once their cumulative outlay reaches the cap. Means-tested discounts, by contrast, reduce the unit fare for riders who demonstrate income below an eligibility threshold.

Both instruments lower the marginal and the total cost of travel, but they do so for different groups and through different administrative machinery, which is why they are treated together but separately in this review.

The empirical base on these instruments remains fragmented. Two recent syntheses describe the equity methods and fare characteristics studied in the literature but stop short of summarizing effect estimates on behavior, and a scoping review of fare characteristics confirms that the field has spent more effort measuring the fairness of fares than measuring what lower fares do to travel.( 5,6)

Evidence on fare changes more broadly is mixed and suggests that responses may differ by income. Some analyses report that low-income riders are less responsive to fare increases than other groups, while others find the reverse, and fare increases in cities such as Beijing have been shown to redistribute travel and welfare away from lower-income riders.( 7,8)

The wider fare policy literature is dominated by studies of fare evasion, fare structures and the elasticity of aggregate demand, while the specific question of whether an income-based discount changes the travel of the people who receive it has attracted far less attention.

Because several of these programs originated as administrative initiatives rather than research projects, much of the early evidence circulates as agency reports and conference papers rather than as peer-reviewed articles. Fare capping is newer still, and much of the published material describes program design rather than measured ridership or access outcomes.( 9)

The spread of account-based and open-loop payment systems has made capping technically easier to implement than it was a decade ago, yet the behavioral evidence has not kept pace with the deployment. No review to date has assembled the quantitative and qualitative evidence on income-based discounts and capping specifically, which leaves agencies without a consolidated basis for choosing among instruments.

The objective of this review is to synthesize the empirical evidence on the effects of income-based discounted fares and fare capping on ridership, travel frequency and access to opportunities for low-income public transport riders. Three research questions guided the work.

The first asks what designs of income-based fare discounts and fare capping have been evaluated in the literature. The second asks what effects these policies have on ridership and travel frequency among low-income riders. The third asks what effects they have on access to opportunities and on the affordability of travel. By separating the description of the instruments from their behavioral and distributional consequences, the review is intended to give transit agencies and researchers a structured account of what has been tried and what those trials produced. The review deliberately excludes universal fare-free policies from the core synthesis, because their budgetary scale and their population coverage make them a different intervention, but they are retained as the benchmark against which the targeted instruments are judged.

METHODS

This systematic review followed the PRISMA 2020 statement and its search reporting extension, and the search strings were recorded verbatim with their dates and record counts so that the identification stage can be reproduced.(10)

RESULTS

Study selection

The searches identified 431 records, and after 92 duplicates were removed 339 records were screened by title and abstract. Of these, 303 were excluded at the title and abstract stage, 36 reports were sought for retrieval, and 21 could not be retrieved through open access because the publisher full text was behind a paywall without a legitimate open copy. The remaining 15 reports were assessed for eligibility and 15 studies were included in the review (Figure 1).

The title and abstract exclusions were dominated by studies of fare payment technology, fare evasion, and universal fare-free policies, together with records on unrelated topics that shared only the word fare. Scopus contributed 91 of the identified records, OpenAlex contributed 193, Crossref 100 and Semantic Scholar 47. The 21 reports that could not be retrieved were sought but were available only through paywalled publisher platforms.

DISCUSSION

The question that opened this review was whether lower fares, delivered through income-based discounts or caps, actually change how much low-income riders travel and what they can reach. The evidence assembled here indicates that they generally do, and that the size of the response depends on how the discount is delivered, to whom, and over what horizon.

Regarding P1, the included programs show a shift from broad first-generation fare reductions toward targeted, means-tested instruments and toward caps that reward frequency.(9,25) This mirrors the design arguments advanced in the wider literature, where targeted subsidies are described as more efficient than flat reductions because they concentrate benefit on riders with the highest marginal utility of travel, and where referential pricing has been proposed to signal the value of reduced-fare programs relative to the regular fare.(26,27)

Conversely, the persistence of distance-based and flat fare analyses in the core set shows that many systems continue to pursue equity through fare geometry rather than through income-based eligibility, an approach whose distributional consequences remain contested because horizontal and vertical equity pull in opposite directions and because high-income riders can benefit from every fare scheme.( 21,28)

The modelling studies of distance-based fares are retained in the core set because they answer the same distributional question through fare geometry, but they should be read as scenario analysis rather than as evaluations of an implemented policy. The absence of African and Asian evaluations is also notable given the size of the transit markets in those regions, and it cautions against assuming that the enrollment models developed for United States agencies will transfer to systems with different welfare registration infrastructure.

With respect to P2, the direction of the ridership evidence is consistent. Discounted and capped fares increased trips or card use in the randomized trial, the discontinuity study and the event study, and the elasticity near -0.20 estimated for Belo Horizonte sits close to the central values reported for urban transit more broadly.( 12,13,24)

The finding that bus rapid transit responded more strongly than conventional lines is consistent with studies showing heterogeneous fare elasticities across corridors and time periods, and with fare savings that vary across the network.( 29,30)

The King County result that part of the treatment effect reflected payment substitution, rather than new travel, is a caution that card use alone can overstate behavioral change, and it echoes evidence that fare complexity and the payment instrument itself shape recorded demand.( 31)

The attenuating effect observed in Bogota over time also parallels the wider finding that short-run responses to fare changes decay, and that low-income riders may respond differently than other groups to price signals, so cross-sectional elasticities should be applied to targeted programs with care.( 7,32)

The absence of long-run estimates in the included studies means the persistent component of these ridership gains remains unknown. The revenue counterpart of these gains is rarely neutral: the fare capping and reduced-fare syntheses both note that discounts and caps reduce farebox revenue unless they are offset by new trips or by dedicated subsidy, so the choice of discount level carries a fiscal consequence that the ridership estimates alone do not settle.

The finding that even a modest, temporary fare reduction produces measurable travel suggests that the income effect on low-income demand is real and not merely an artifact of pass purchase, although the payment substitution in the randomized trial shows that both channels operate simultaneously.

As for P3, the access evidence, though less quantified than the ridership evidence, points in the same direction. Participants reported reaching destinations they otherwise could not reach, and the half-price and subsidy evaluations linked lower fares to reduced financial stress and improved access to work and services.( 12,15,16)

This aligns with studies that model fare budgets and accessibility jointly, which find that the fare, not only service supply, determines how much of the city low-income riders can actually use.( 2,33,34)

The incidence results from Madrid and the Costa Rica audit add a complementary caution: fare reductions do not automatically benefit low-income riders unless the subsidy is designed around them, because existing fare structures can be regressive in practice.( 17,23)

Employer-based subsidies, which are less available to the lowest-income workers, and youth concessions illustrate the same design dependence for targeted programs, in that a subsidy only reaches the intended group when enrollment and eligibility are actively managed.( 35-37)

One measurement caveat is that most access findings in the core set are perceived or modelled rather than observed through travel diaries or activity data, so the access gains are more suggestive than the ridership gains, and they depend on how respondents interpret a fare that is only one component of the total cost of reaching an opportunity. Whether these perceived access gains materialize as more employment or better health is not established by the included studies, and the link from a cheaper trip to a better life outcome remains an inference rather than a measured chain.

The consistency across designs points to a plausible mechanism. A fare reduction relaxes the budget constraint of low-income households, lowering the marginal cost of each additional trip and shifting the point at which travel is rationed.

The concentration of effects among economically active users and frequent riders is consistent with this interpretation, because these are the riders for whom the saved fare translates directly into more job search, more shifts worked or more errands completed. The attenuation over time observed in Bogota suggests that part of the initial response reflects novelty or pent-up demand rather than a permanent change in the daily travel pattern, which is why short-run elasticities overstate long-run responses in this setting.

Measurement deserves explicit attention because the included studies span two very different data regimes. Studies using card, ticket or administrative data measure actual taps or trips and therefore capture behavior, but they cannot distinguish a genuinely new trip from a payment that simply moved from one medium to another, as the King County trial demonstrated. Survey studies capture self-reported trips and perceived access, which are closer to the experience of the rider but vulnerable to recall and social desirability.

Modelling studies, in turn, produce counterfactual estimates that are only as good as the elasticity inputs they borrow from older work. The triangulation across these regimes is a strength of the review, but it also means that no single number in this synthesis carries the same meaning as the next, and readers should weight the causal estimates above the rest.

Several sources of heterogeneity deserve attention. Fare-free and universal low-fare programs, although excluded from the core set, provide the most relevant comparison. Tallinn and Kansas City showed ridership gains but with substantial revenue and fiscal costs, the German 9-Euro-Ticket produced large but short-lived ridership responses, and fare-free policies for specific groups such as older adults raise questions of sustainability and horizontal fairness.( 38-41)

The populations, time horizons and outcome measures also differed across the included studies, from a six-month subsidy in a single United States county to permanent programs across fifty agencies, which limits any single pooled estimate. Fare increases in Beijing and the Lisbon fare reform further illustrate that distributional outcomes depend on the baseline fare structure being replaced and on how the change is framed to riders.( 8,42-44)

The definition of low income also varied across studies, from a fixed poverty-line multiple in the United States programs to a subsidy card issued through the national welfare register in Bogota, and this variation affects who is counted as a beneficiary in each estimate.

The comparison between discounts and capping deserves its own reading. A means-tested discount lowers the price of every trip for a defined group, which is the direct instrument for vertical equity but requires income verification and enrollment. A cap, by contrast, is universal in operation but progressive in incidence, because it delivers the largest savings to the riders whose cumulative monthly outlay is highest.

The included fare capping studies support this framing, in that the burden was redistributed toward frequent riders, while the reduced-fare syntheses show that means-tested programs only reach their intended group when agencies partner with social service organizations to manage enrollment.( 9,14,18,25)

The two instruments are therefore complements rather than substitutes, and the most coherent designs combine a base discount with a cap that protects the frequent low-income rider.

The generalizability of these findings to the Global South warrants caution. The Bogota and Belo Horizonte studies show that the direction of the response holds outside high-income systems, and the Costa Rica audit shows that fare inequity can be measured and corrected in a system with informal and semi-regulated operators, but the administrative machinery of means testing and capping presumes a fare media and a data infrastructure that many cities do not yet have. In systems where fares are paid in cash and routes are operated by small private providers, the instruments described here may be harder to implement than in the smart-card environments that dominate the included evidence, and this is a practical limit to transferability rather than a statistical one.

The certainty of the overall evidence is low to moderate. The three strongest designs agree on the direction of the effect, but only two of them were quasi-experimental or randomized, the follow-up was short in all of them, and the abstract-based extraction for part of the included set means some estimates could not be verified against a full text. This pattern matches the state of the wider fare policy literature, in which causal evaluations of fare changes remain scarce relative to modelling and descriptive work.

The findings should therefore be read as a consistent signal in favor of income-based discounts and capping, rather than as a precisely estimated effect size that can be transferred from one city to another. A future meta-analysis will only become possible if primary studies begin to report elasticities or trip changes on a common scale, with standard errors, and with the same follow-up horizon.

Standardizing the low-income threshold used to define eligibility would further help, because the present heterogeneity in definitions is itself a barrier to pooling. These reporting improvements are inexpensive relative to the cost of the programs themselves, and they would convert the next generation of evaluations into directly comparable evidence.

CONCLUSIONS

The included evidence describes a family of income-based fare discounts and capping schemes whose designs range from means-tested half-fare programs to daily, weekly and monthly caps. Discounted and capped fares were associated with higher ridership and travel frequency among low-income riders in most studies that measured these outcomes. The evidence also indicates that these policies widened access and reduced the financial burden of travel, with the strongest effects reported among riders experiencing transport poverty. The certainty of this evidence is limited by predominantly observational designs, short follow-up and abstract-based extraction for part of the included set.

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