Knowledge-workers and the sustainable city: the travel consequences of car-related job-perks
Bibliographic record
Abstract
Attracting firms in knowledge and technology intensive (KTI) sectors is highly desired at both the national and the regional level as a powerful engine of economic growth. Due to fierce competition in KTI sectors and national taxation policies, KTI firms often attract high-quality employees by offering car-related job perks as additional incentives to wage. In Canada, car allowance is offered by 46% of the employers to attract highly-skilled workers. In Israel, 61% of knowledge-workers in the KTI sectors receive a company-car with respect to 16% of workers in other sectors. In the U.K., car-related job perks are offered by 18% of the employers. This study focuses on the impact of car-related job-perks on the travel behavior of knowledge-workers. The importance of this issue derives from the impact of the travel behavior of knowledge-workers on congested transportation networks in metropolitan areas, as knowledge-based economy tends to concentrate mainly in metropolitan regions. This study applies discrete choice models in order to analyze the impact of company-cars and car allowances (reimbursement of fuel and parking expenses) on commute and leisure travel of knowledge-workers. The analyzed data consist of 750 observations, retrieved from a revealed-preferences survey among KTI workers who work and reside in the Tel-Aviv metropolitan area in Israel. Results show that car-related job perks are associated with (i) high annual mileage, (ii) high propensity of using the car as main commute mode, (ii) long commute distances and travel times, (iii) high trip chaining frequency in commuting trips, and (iii) high frequency of long-distance weekend leisure trips. Result also show that KTI workers generally prefer the car or non-motorized transport modes over the bus system. These results suggest that the development of sustainable knowledge-based cities should consider (i) the replacement of car-related job perks by other incentives, (ii) the provision of pedestrian and cyclist friendly infrastructures, and (iii) public transport improvements.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".