3. Experimental Design
- “The rule of the restaurant is that each waiter can choose how much they wish to put in the common pot.”
- “The rule of the restaurant is that each waiter puts at least half of their tips in the common pot.”
- “The rule of the restaurant is that each waiter puts all their tips in the common pot.”
4.1. Mean Contributions Across Subjects Over Time
4.2. Relationship between Contribution and Tips
4.3. Results about Individual Contributions
5. Comparison with Public Good Data
Conflicts of Interest
- the number of tokens you received at each day,
- the number of tokens you shared at each day,
- the number of tokens you received back from the common pot,
- the sum of your earnings at each day,
- the number of tokens accumulated since the beginning of the experiment.
|A. Zero Contributions||B. Positive Contributions||C. Relative Contributions|
|χ2||16.21 ***||27.84 ***||31.48 ***|
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Most literature related to tipping typically studies the behavior of the tipper, i.e., the client in a restaurant (see, e.g., ). Lynn  presents empirical research on the determinants and predictors of restaurant tipping and of national differences in tipping habits. Furthermore, the behavioral data is discussed in the light of economic theory and welfare analysis. Roe  studies different gratuity sharing arrangements and discusses the effects of these arrangements. We are not aware of any lab tip pooling experiments.
Tip-pooling is an important economic phenomenon: “In 2008, a San Diego trial court slapped Starbucks Corporation with a $100 million judgment for violation of California’s tip sharing laws” .
Since we did not elicit believes nor vary the information structure about each other’s sharing we are not able to analyze the reasons behind the behavior of a subject, in particular how believes influence sharing. For related literature, see .
According to de Quidt and colleagues  our rule implication falls under the category “strong demand effect” with the phrase in every period “the rule of the restaurant is … “, similar to “We (experimenters) expect that participants who are shown these instructions will invest more/less in the project than they normally would” as in de Quidt et al. . Zizzo  writes along these lines that Davis and Holt  (p. 26) “do not see a problem with EDE (Experimenter Demand Effect) if ‘explicit suggestion’ is a treatment variable. This is accurate if identifying an EDE is the objective of the experiment.” In our case, we especially want to see the difference in behavior with different request rules.
A way to avoid experimenter demand effect is using anonymity towards the experimenter in a double-blind procedure , which was not implemented in our study.
Subjects knew the exchange rate in advance. We used an artificial currency in case of later replications in other countries with different currencies. This makes comparisons between different data sets more workable (see discussion on currency effects in ). However, this could have produced confounds due to money illusion, meaning that high artificial nominal amounts could have reduced misbehavior or, at the opposite, by encouraging gambling behavior . However, these effects should be small as payoffs are quite low in each round.
This effect might be driven by the way relative contributions are computed, combined with the choice of subjects to treat their tips as integers and not as rational numbers. Excluding the choices following a tip of zero (which do not leave subjects any choice but contributing zero), the lowest possible contribution is 1 ECU, which is the 100% of a 1 ECU tip, the 50% of a 2 ECU tip, the 33.3% of a 3 ECU tip, the 25% of a 4 ECU tip and so on. As a result, subjects have more limited choices for lower tips than for higher tips. Therefore it is more difficult to reveal subjects’ preferences with accuracy when tips are low (mainly between 1 and 5 ECU).
A boxplot provides a summary of a distribution: the box represents the interquartile range of variation (IQR, from the 25th to the 75th percentile, named Q1 and Q3 respectively) and the line within the box is the median (Q2); the lower whisker extends to Q1 − 1.5·IQR and the higher one to Q2 + 1.5·IQR. The black dots are suspected outliers, data points falling outside the two whiskers.
|A. Zero Contributions||B. Positive Contributions|
|χ2(3)||19.97 ***||1372.9 ***|
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