Price discrimination through hidden city options? A data-driven study on the extent and evolution of skiplaggability in the global aviation system
Bibliographic record
Abstract
The application of revenue management in airlines, mainly driven by profitability seeking and an increased competition, has led to the evolution of so-called booking ploys, where passengers exploit technical loopholes to reduce their ticket fares significantly. One of these booking ploys is hidden city ticketing, also called skiplagging: A passenger who wants to travel from A to B books a multi-segment itinerary A–B–C while deliberately taking the first segment A–B only. The legal status of such ploys is uncertain, but airlines argue that such strategies reduce the profit and accordingly try to prevent such cases through their conditions of carriage. Given the significant potential fare savings, the popularity of skiplagging services is ubiquitous. Existing studies on this subject have mostly focused on theoretical models reproducing the effect of skiplagging and also discussed various legal and moral aspects. In this study, we investigate the existence of skiplagging opportunities in the global aviation system. Given worldwide airfare data for the years 2010 to 2021, we perform a data-driven analysis to identify spatial regions and temporal periods of skiplaggability. Such a quantification is, to the best of our knowledge, unique in the scientific literature. We find that skiplaggability is largely driven by hub airports and the extent to which they host dominating airlines. Particularly, we identify disadvantages for passengers living in hub cities with dominant hub airlines, apart from their paying hub premiums. Moreover, we have identified a significant shift of skiplaggability from the US (in the early 2010s) towards Asia (2015+). We believe that the outcome of our study helps policy makers to perform more informed decision making regarding hidden city options, by better understanding the recent scope and extent of this phenomenon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".