Experimental Design Details
Experimental Design (Public)
Training sample data
First, we develop a statistical model (or algorithm) for price discrimination in an artificial market. The good for sale is a lottery with 50% probability of winning £5. In the training sample, we will collect 700 responses to two surveys in random order:
1) Survey similar to the one conveyed by insurance companies (Standard survey). The questions aim to identify the risk preferences of the respondents.
2) Survey, where participants should rate movie genres. (Movies survey).
After that, participants enter the final stage where we elicit the willingness to pay (WTP) for the lottery using multiple price lists. The certain payout varies from £0.2 to £4 in £0.2 increments.
Based on the 700 responses from the training sample we develop two statistical models: the first predicts WTP based on the standard survey, and the second is based on the movies survey. We hypothesize that both standard and movies surveys will have a predictive power about WTP for lotteries. While the model might give different prices, we will truncate the suggested prices to be a minimum £1 and a maximum £3. £1 is chosen to have a lower bound price, which can be interpreted as the “marginal cost” of the product for the firm. This also allows us to see whether subjects buy the lottery when the price is higher than the predicted WTP, which would also point to a strategic response to the survey. The Standard survey is designed to contain questions related to risk aversion, while movie ratings are potentially related to risk preferences through extraversion, which correlates negatively with risk aversion.
There will be two baseline treatments: Baseline Standard and Baseline Movies. In the main experiment, participants will go only through one of the surveys. Baseline Standard refers to the treatment with Standard survey. Baseline Movies refers to the treatment with Movies survey.
Before the survey, participants are warned that the information from the survey will be used for determining the price for the lottery in a later round of the experiment by a statistical model.
After participating in the survey, but before observing the price, participants can decide whether they want to hide the survey responses from the seller (imitating a private browsing option) for the price of £0.1. In case the participant decides to hide the survey responses, the price will be the one that maximizes revenue, given the reservation value of £1 (aka cost for the firm) and the distribution of WTP in the training sample. We refer to it as anonymous price. Participants will be warned that they will learn both individual and the anonymous price at the end of the experiment, to avoid curiosity motives.
Next, participants have to decide whether to buy the lottery at a given price, which is either determined by the algorithm based on survey responses (if the participants decided not to hide the responses) or anonymous. In the case of buying, the lottery is played out and the payoff of the participant for the last round is £5-p, if won or -p if lost. If negative, the payoff will be deducted from the reward of participants for filling out the survey.
In the last task of the experiments, we elicit participants’ beliefs about the lowest and the highest price that the algorithm produced among 300 other participants, given their survey responses. We pay them £0.1 if they are within £0.2 from the lowest price, and £0.1 if they are within £0.2 of the highest price.
Scope information treatments
There will be two scope information treatments: Scope Standard and Scope Movies.
The experiment runs exactly like the Baseline treatments. But before the survey, additionally to the warning that the information from the survey will be used for determining the price, the participants are informed about the lowest and the highest price that the algorithm can select, given their survey responses.