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**Hypotheses and expectations** We will explore the effect of development of bribe sizes on the probability of taking a bribe. Based on the results of previous experiments (Bahník & Vranka 2020; Vranka & Bahník, 2018), we expect that participants will be less likely to break the rules and cause harm to the third party when the offered bribe size is low. However, it is not clear how participants will respond to a sudden change in offered bribe’s sizes. It is possible that resisting initial smaller bribes will lead them to continue to refuse bribes even when their size increases. Participants in the low-high condition would then take fewer bribes than in the control condition overall, and specifically of the large bribes. On the other hand, not taking initial small bribes may provide a “moral license” to act more dishonestly later and take a higher number of the bigger bribes. Participants in the low-high condition would then take more bribes than in the control condition overall, and specifically of the large bribes. Similarly, those who encounter larger bribes first may lose any inhibition for accepting them and thus be more likely to accept even the smaller bribes offered later. Participants in the high-low condition would then take more bribes than in the control condition overall, and specifically of the small bribes. Alternatively, they may find the smaller bribes relatively less attractive when compared to larger bribes they encountered previously and thus be more likely to reject the smaller bribes. Participants in the high-low condition would then take fewer bribes than in the control condition overall, and specifically of the small bribes. **Methods** *Participants* Participants will be recruited from a laboratory subject pool to participate in an on-line study. The planned sample size is 300, but the final sample size might slightly differ if multiple people start the experiment at the same time. The experiment will have sufficient power (.80) to detect an effect *d* = 0.23 (assuming an analysis with all 300 participants, and *d* = 0.28 for an analysis using only two groups with 200 participants). A more precise power analysis is complicated by the nature of the repeated measures design of the experiment and by complexity of mixed-effect regression, which will be used for analysis. We will exclude participants with missing data from 3 or more trials, which may occur, for example, in case of a repeated internet connection failure. The analysis will be conducted only with data from participants who will finish the whole study (i.e., get to the final screen of the application). We will also exclude participants who will at least 10 times sort the object according to neither its color, nor its shape, suggesting random responding. *Materials and procedure* The experiment will use a task simulating routine work during which the worker is sometimes given an opportunity to take a bribe (Vranka & Bahník, 2018). In particular, the task is aimed to simulate work of a bureaucrat or a public employee in general. As a part of the task, participants are told to sort objects running on a computer screen according to their color. The objects may have three possible shapes and colors. The sorting is done by pressing one of three keys, each of which is associated with a single color and shape. Shapes and colors associated with the three keys are randomly determined for each trial and they are displayed prominently at the bottom of the screen. At the beginning of the task, 2000 points (corresponding to 200 CZK, ~8 USD) are allotted to a charity. If a key response leads to a wrong assignment to a color, the charity loses 200 points. The loss simulates negative societal effects of not performing given work correctly. Participants get a fixed reward of 3 points for each sorted object, which represents the salary given to a worker for performing his or her job. Finally, in trials where the two sorting criteria are mismatched, there is a 22.5% probability that a given object will be associated with a “bribe”. These objects are shown with a number corresponding to the value of the bribe, which a participant gets if he or she sorts the object according to its shape. The bribe size will vary from **30 to 180** points. Each participant will be given 200 trials of the task; i.e., 200 objects to sort. The number of the trial as well as the money earned for oneself and the charity will be displayed on the screen during the whole task. The responses will determine participants’ reward and money gained (or lost) for the charity. Participants will be randomly assigned to one of three groups: in the control group, bribes will vary from **30 to 180** in all 200 trials. For the low-high group, bribes will vary from **30 to 90** in the first 100 trials and from **120 to 180** in the remaining 100 trials. In the high-low group, the order will be reversed. The experiment will be conducted on-line using a custom-written Python (Django) and Javascript web application running on Participants will be explained the task, complete 10 practice trials and then proceed with the task itself. The points participants earn during the task will be converted to a monetary reward using the conversion rate 10 points = 1 CZK. **Analysis** Trial-level analysis will be conducted using mixed-effect linear regression. The correctness of object classification will serve as the dependent variable. The trials incorrectly sorted according to both shape and color as well as trials without a bribe will be excluded. Random intercepts for participants will be included. Order of the trial and a squared order of the trial (centered and rescaled to range from -0.5 to 0.5) will be included as covariates. The groups will be compared using simple coding, where both low-high and high-low conditions will be compared to the control condition. The model will also include a bribe group coded as -0.5 for bribes 30-90 and 0.5 for bribes 120-180 and its interaction with the condition. Bribe size will be included as a covariate using linear and quadratic contrasts with ordering 30/120, 60/150, and 90/180. An interaction of bribe size and bribe group will be also included in the model. Random slopes for participants will be included for bribe size and bribe group. The script that will be used for analysis can be found in Files. **References** - Vranka, M. A., & Bahník, Š. (2018). Predictors of bribe-taking: the role of bribe size and personality. *Frontiers in Psychology, 9*, 1511. - Bahník, Š., & Vranka, M. A. (2020). Experimental test of the effects of punishment probability and size on the decision to take a bribe. doi: 10.31219/