Experimental Design
For this experiment, we draw on the data from our previous RCT (see AEARCTR-0008212). As preregistered as Experiment 1, we ran an experiment on MTurk, in which we not only asked subjects for demographic information (age, gender, education) but also elicited Big-5 personality traits, preferences on risk, altruism, reciprocity, social comparison, loss aversion and competition. After this elicitation, subjects had to work for 10 minutes on a button-pressing task similar to DellaVigna and Pope (2018). Subjects were randomly assigned to a control group or to one of six different incentive schemes: (1) a piece rate (2) social incentive (3) goal (4) gift (5) bonus loss (6) real time feedback.
Based on the data of this first experiment, we will run a second round of experiments with a different set of subjects on MTurk. We already mentioned this second experiment in the preregistration of the first experiment and will now preregister it in detail.
In the second experiment, we first again elicit the respective subjects’ characteristics (the same characteristics as in the first experiment). We will randomly allocate the subjects to one of three groups: one of two control groups or the treatment group. The first control group is the same as the one in the first experiment, i.e. no incentive will be provided. In the second control group, all workers will work under the scheme that generated the highest average performance in the experiment of the first round, i.e. bonus loss. In the treatment group, workers will be exposed to the scheme that is predicted to yield the highest performance conditional on the specific characteristics of each individual worker. For the predictions, we will use an indirect estimation approach based on random forests, which we trained on the data of the first experiment. We will restrict the set of candidate schemes to three of the six schemes in experiment 1: (1) bonus loss, (2) social incentive and (3) real time feedback. The key expected insights of the experiment are (i) whether and (ii) to what extent algorithmic assignment of the specific incentive scheme adopted can improve performance.
GENERAL EXPERIMENTAL DESIGN
Before participating, subjects will be provided with a brief description of the task (complete a survey and a working task) as well as with the technical requirements (a physical keyboard) and guaranteed payment upon successful submission ($1 flat-pay + $1.50 guaranteed minimum bonus). Furthermore, they will be asked for their consent to participate in the study from which they know they can withdraw at any time.
The final sample will exclude subjects that:
(1) do not complete the MTurk task within 90 minutes of starting;
(2) are not approved;
(3) do not score at least one point;
(4) scored 4000 or more points (since this would indicate cheating)
(5) scored 400 or more points in 1 minute (since this would indicate cheating)
Restriction (2)-(4) are the same as in DellaVigna and Pope (2018). Restriction (1) is similar to the restriction in DellaVigna and Pope (2018), however, the maximum completion time is longer due to the survey included in our study. Restriction (5) is equivalent to restriction (4) broken down to individual minutes for which we will collect data as well.