Back to History

Fields Changed

Registration

Field Before After
Last Published May 12, 2020 05:35 AM May 18, 2020 03:52 AM
Intervention (Public) Group members discuss which number of surplus hours they want to state. Treatment-Group: The chat text is evaluated by a pre-trained classifier, labeling the group as either honest or dishonest. Based on the label, 2 out of 7 groups in each session are chosen to be controlled. Control-Group: 2 out of 7 groups are randomly chosen to be controlled. For the out-of-sample performance of the pre-trained classifier, out-of-context chat-data is collected. In this new online-experiment, participants work for a fictitious company in pairs of two. In an online chat, they discuss which number of surplus hours they want to state. Groups are controlled if reports do differ and/or if their group is one of the 30% percent of randomly controlled groups in each session. If the inspections show that an individual's report was not truthful, (s)he needs to pay a fine.
Intervention Start Date November 26, 2019 May 19, 2020
Experimental Design (Public) Participants work for a fictive company in teams of two. Both group members have to state their surplus hours. Both members have to report the same amount of surplus hours. The group being controlled depends either on the classifier's result (treatment) or is randomly chosen. In both conditions, 2 out of 7 groups are chosen to be controlled. If reports differ, the group is always controlled. Participants work for a fictive company in groups of two. Both group members are informed about the surplus hours they worked. They subsequently get the opportunity to chat about the amount of surplus hours they want to state. The reports are controlled if the group members' reports differ and / or if the group is one of the 30 percent of randomly chosen groups to be controlled. If a group is controlled and the number of stated surplus hours is not the same as the actually worked surplus hours, each group member as to pay a fine.
Randomization Method Participants draw a seat number. Treatments are alternated each session, where the initial treatment to start with is chosen randomly. The experimental setting does not involve treatment. In order to minimize waiting-times in the online-experiment, participants are grouped by the time they log in to the experiment.
Randomization Unit Treatment and control conditions are alternated between sessions. In each session, 30 percent of the groups are randomly chosen to be controlled.
Sample size (or number of clusters) by treatment arms 100 participants (50 groups) in each treatment (2 treatments overall). This experiment does not involve treatments.
Additional Keyword(s) machine learning, natural language processing, compliance machine learning, natural language processing, compliance, behavioral taxation
Back to top