**Hypotheses and expectations** We will test whether the possibility of being punished for taking a bribe decreases the probability of taking a bribe. According to the standard economic analysis of effects of punishment, the probability of committing a crime should decrease with increasing probability and severity of punishment (Becker, 1968). While the deterrence effect of increasing the probability of punishment seems to be well established, the effect of increasing the severity of punishment is less clear (Nagin, 2013). In some circumstances, introducing a small penalty for undesirable behavior may even lead to an increase of the penalized behavior (Gneezy & Rustichini, 2000). In observational data of the rates of corruption, both the probability and severity of punishment seem to decrease the rate of corrupt behavior (Goel & Rich, 1989). Nevertheless, measurement of corrupt behavior in the real world is imprecise and an experimental study of the effects of punishment probability and severity may clarify their effects on the probability of taking a bribe. The results of the experimental study will be compared with predictions of the standard economic model. Apart from this main goal, the study will also assess the effect of punishment on perceived morality of decisions in the task. We will also study the effect of HEXACO traits and risk aversion on the decision to take a bribe or the effect of punishment. We will test: - the effect of the punishment size on the probability of taking a bribe - the effect of the punishment probability on the probability of taking a bribe - the interaction of the punishment probability and size on the probability of taking a bribe - the effect of punishment size on the perception of taking a bribe - the effect of punishment probability on the perception of taking a bribe - the association between honesty-humility (H-H) and the probability of taking a bribe Becker, G. S. (1968). Crime and Punishment: An Economic Approach. Journal of Political Economy, 76, 169-217. Gneezy, U., & Rustichini, A. (2000). A fine is a price. The Journal of Legal Studies, 29, 1-17. Goel, R. K., & Rich, D. P. (1989). On the economic incentives for taking bribes. Public Choice, 61, 269-275. Nagin, D. S. (2013). Deterrence in the twenty-first century. Crime and Justice, 42, 199-263. **Methods** *Participants* Participants will be recruited from a laboratory subject pool to participate in a batch of studies, the first of which will be the present study. The planned sample size is 500, but the final sample size might slightly differ because participants are invited some time in advance and the experiment is administered in groups of up to 17. The experiment will have sufficient power (.80) to detect an effect d = 0.18 (assuming an analysis with all 500 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. *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 40 to 190 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. Punishment will be given with a certain probability to a participant who does not sort an object according to its color in a trial with a bribe. That is, a participant who takes a bribe and harms the charity at the same time. Participants will be randomly divided into four groups according to the probability of punishment. A control group without punishment will have the probability of 0%, three experimental groups will have a probability of 1%, 5%, and 25% that participants will be punished after taking a bribe. Each experimental group with non-zero probability of punishment will be further divided into three categories according to the severity of punishment. For one part of the participants, the punishment will mean that the task ends and they cannot earn any further reward. For the remaining participants, the punishment will mean a loss of 40 or 400 points from the reward they had earned. Participants in the experimental groups will be randomly assigned to one of the three punishments. The experiment will be conducted in a lab on a computer using a custom-written Python program. Participants will be explained the task and participants in the experimental group will be informed about the probability and type of punishment. Participants will have a 1/5 chance that the points they earned 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 logistic regression. The correctness of object classification will serve as a binary dependent variable. The trials incorrectly sorted according to both shape and color as well as trials where the two criteria are aligned will be excluded. Random intercepts for participants and trials 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 effect of the punishment size and punishment probability on the probability of taking a bribe will be tested with models where a presence of a bribe will be included as a predictor and its interaction with punishment size and punishment probability will be used to test the hypotheses. We will use linear and polynomial contrasts for size and probability of punishment. A triple interaction of punishment size, probability, and bribe presence will be included in a different model which will test the interaction of punishment size and probability without the control group. The group where punishment will lead to termination of the task will be analyzed in another separate model, in which it will be compared to the other three punishment sizes. Random slopes for presence of a bribe will be included for participants. The association of H-H with the probability of taking a bribe will be analyzed using the interaction of bribe presence with H-H. Similar models will be conducted with a bribe size rather than bribe presence as a predictor to examine the effect of the bribe size as well. The perception of taking a bribe will be analyzed with a regression on a participant level. A composite score will be computed by averaging answers to all the perception questions (with some of the answers reversed). Punishment size and probability and their interaction will serve as predictors.