Determinants of Gratuity Size in the Czech Republic: Evidence from Four Inexpensive Restaurants in Brno
Bibliographic record
Abstract
Abstract The paper presents the results of the first study exploring what factors influence tipping in restaurants in the Czech Republic. It shows that the tipping norm evolved here into a form that has some features similar to the tipping norms known in the USA, Canada, and Israel, but there are also striking differences. As in the three countries, the gratuity increases with the bill size but the gratuity as percentage of the bill is much lower here. The bill size explains here a lower part of the gratuity variability too. Also, the service quality results in customers being more generous with their tips, and though the increase in gratuity seems to be small, it rises with a group size. Strikingly, the regular patrons tip significantly less in the Czech Republic and they stiff more often. This supports the hypothesis that the relationship between the customer frequency and the gratuity size is an artifact of a missing variable, and the regular patrons tip differently because they belong to a different social group than occasional customers. Also, the customers paying by card stiff more often here and the interaction between the amount on the bill and use of payment card is statistically insignificant. The group size lowers the percentage gratuity, which supports the diffusion of the responsibility hypothesis. There are differences between genders: Male customers leave bigger tips than female customers, and female waitresses earn more than their male colleagues. The time spent at the table, consumption of alcoholic beverage, and smoking do not change the gratuity size but it may be affected by the weather conditions. The customers tip less and stiff more often when they order a lunch special. They round the total expenditures, not the gratuities, which creates the magnitude effect.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".