As some observers watch for signs of a possible U.S. economic growth slowdown, product substitution and spending on consumer staples compared to discretionary items will come under examination, as a slowdown would tend to favor the former and harm the latter.
It might be intuitive that product substitution is higher for discretionary purchases such as airline tickets than for ride shares, which arguably more essential than leisure travel. And studies tend to support that notion.
For ride sharing, surge pricing seems to result in relatively modest rates of rider substitution.
Study | Context / Location | Main Findings on Rider Substitution |
Transportation Research Part C (Singapore) | Singapore—Grab vs. taxi services (ScienceDirect) | Estimated cross‑price elasticity ≈ 0.26 → a 10% surge in ride share price is associated with a 2.6% increase in traditional taxi bookings. Suggests modest substitution. |
Rice University / Kinder Institute (NYC) | Uber during concert event in New York City (Kinder Institute) | Surge pricing doubled driver supply and led some riders to avoid booking entirely; higher‑valuation riders remain. Indicates some riders choose alternatives or delay. |
Uber “Under the Hood” case study (NYC) | After sold‑out concert (Madison Square Garden) vs. NYE surge glitch (Medium, Uber) | Without surge pricing many requests go unfulfilled; with surge, demand dampens—a takeaway is that some riders choose not to request rides in surge conditions. |
“On Rider Strategic Behavior…” (theoretical/experimental) | Modeling rider relocation and spillovers (arXiv) | Riders walk out of surge zones or shift to adjacent cheaper zones, redistributing demand and lowering peak prices in equilibrium. |
Dynamic pricing consumer welfare (China) | Nanjing, China—citywide ride‑hailing data (ScienceDirect) | Surge pricing reduces consumer surplus significantly; spatial/temporal heterogeneity shows riders avoid surges—some choosing other travel modes or forgoing trips. |
Reddit anecdotal evidence | US urban areas | Frequent reports of users switching to taxis, waiting, or canceling Uber when surge rates are high: |
As you might expect, substitution is quite a bit higher for leisure travelers considering buying an airline ticket, compared to ride sharing purchases. Business travel, as you also would expect, features much less substitution, compared to leisure travel.
Study & Year | Context / Location | Method or Data | Findings on Passenger Substitution or Demand Response |
Meyer et al. (2024) | One Western short‑haul route, anonymous airline | (SAGE Journals) | Price elasticity varies over time-to-departure and passenger segment. Leisure segment is highly elastic—larger drop in demand when fares rise—suggesting those passengers delay or cancel, potentially shifting modes when prices high. |
ProQuest cross‑modal elasticities (US) | Traffic on multiple U.S. corridors (e.g. Chicago–Orlando; LA–SF) | Cross-elasticity analysis of air, road, and rail demand (ProQuest) | A 10% fare increase reduces air travel ~ 6–7%, and leads to small rail uptake (~ 14% on specific routes), modest substitution to road/rail. |
Puller & Taylor (2012) | US airline markets, business vs leisure | Day-of-week and advance-purchase discount experiments (Transport Research A) (Wikipedia, pitjournal.unc.edu) | Leisure demand highly elastic (~ –1.9), whereas business ~ –0.38. High fares deter leisure travelers substantially—some may choose alternatives. |
Luttmann (2019) | U.S. airlines, fare differences by origin income endpoints | Analysis of directional price discrimination by income (ScienceDirect, ResearchGate) | Higher fares from wealthier endpoints lower elasticity—richer markets absorb price rises with less substitution; poorer markets more likely to switch modes or forego travel. |
Zhang & Cooper / Fiig et al. (2009–2018) | Models incorporating substitutable flight itineraries | Choice-model simulation (MNL, PODS) (ResearchGate) | When high-fare itineraries surge, some passengers switch to lower-priced flight alternatives (off-time, different stops) rather than modes, indicating internal substitution within airline offerings. |
Gatti‑Pinheiro et al. (2022) | Airline revenue management simulations | “Earning-while-learning” RL‑type models comparing revenue-maximizing fare experimentation vs static YM (arXiv) | Frequent price experimentation yields better revenue long-term—but excess fluctuation can harm demand prediction quality, implying passengers may delay buying or avoid volatile fares. |
Though some amount of ride sharing might not happen if an economic slowdown happens, the impact will likely be muted, compared to discretionary consumer airline travel, which is highly elastic.
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