We cross-randomize website visitors interested in a car insurance to offers that vary the pre-selected default deductible (4000NOK, 6000NOK, 8000NOK) as well as the insurance premium with mark-ups/downs equal to -8%, -4%, 0, +4%, +8% relative to the prices in the standard menu. This design allows us to study a number of questions in the short term and slightly longer horizon.
In the short term, we will investigate whether the premium and/or default deductible affects the likelihood to purchase insurance. We will also study whether the default deductible affects the choice of deductible, where we predict that a higher default deductible leads to a higher chosen deductible, and thus less insurance cover.
In the long term we will investigate insurance claims, customer churn and overall profitability for the company. A particular focus will be on how the price dimension impacts selection (adverse or advantageous) and how the default deductible dimension impacts moral hazard.
Using the random variation in the price premium, we notice that adverse (advantageous) selection should, ceteris paribus, imply fewer (more) claims, on average, among those who were randomly offered a low price compared to those who got a high price. We will investigate if this is the case.
Moral hazard measures to what extent the coverage of the insurance influences the probability of having a claim. If moral hazard is present we should observe more claims exceeding the maximum deductible - ceteris paribus - among those who have a low deductible. The problem with observational data is that the deductible is chosen and the propensity to choose a low deductible may correlate with non-observed individual characteristics that may also have a separate effect on the probability of having an accident. We aim to get around this problem by using the randomly assigned default deductible to instrument for the chosen deductible. With exogenous variation in the choice of deductible we can investigate moral hazard by comparing the number of claims above the maximum deductible (i.e., 8000NOK) using both an ITT and IV estimation strategy.
The experiment has been running since September 2020 and we will end recruitment in January 2023, based on predictions that we then have reached the desired number of website visitors and customers (see power calculations). The set of customers will be followed for at least another two years in order to reach the desired number of observations to identify effects on claim behavior, customer churn and overall profitability (see power calculations for details).
Important notice: Before September 2021 (i.e. during the first year of the experiment) there was a technical issue that made it impossible to extract data on website visitors that did not proceed to purchase insurance. To study treatment effects on insurance demand we will therefore use two complementary methods. i) First, we will conduct a proportion test, testing whether the observed empirical proportions of customers (i.e. website visitors that purchase insurance) in the respective treatment groups coincide with the expected proportions given the pre-assigned randomization. Based on this test we can, for example, elicit price elasticities of demand. ii) Second, we will restrict the sample to the period September 2021 to January 2023, i.e., where we have complete information on the choices of all website visitors, to estimate how treatment affects the likelihood to buy insurance using standard regression analysis. We expect similar results from the two different methods, and will base our conclusions on the method that turns out to be most powerful ex post.