Dear Prof. Hess and Team,
I have conducted a Stated Choice Experiment for the willigness-to-subscribe / willigness-to-pay for a subscription service.
The choice was between 3 plans with different characteristics and prices or the opt-out option.
I have estimated a nested-logit model showing a way too high willigness-to-pay. I would strongly assume this is due to the hypothetical bias.
As I already imagined this might happen, I have included an second experiment in the survey to get (kind of) revealed preference data. In particular, the participants were asked to select a voucher for the subscription service or a lower cash amount as a reward for taking the survey. (With a 10% chance of actually getting this reward.)
The first choice was beween a 30$ voucher and 1$ cash, which was increased in 5$ increments until cash was selected or 30$ in cash was reached.
A substential part of the sample actually selected very low cash amounts despite previously stating their willigness to subscribe for a rather high price.
I am now thinking if there is a sensible way to include this data into my SP model to correct the willingness to pay.
However, in contrast to typical approaches of combining RP and SP data (e.g. Buckell & Hess, 2019) I cannot directly use the cash/voucher price attribute, as it has been a different setting.
Do you have any ideas what might be suitable direction or do you have a paper in mind that could be helpful? My current idea would go into the direction of scaling the price parameter in the SP model, but I am a bit stuck on how to do this.
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Use indirectly related RP data in SP model
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stephanehess
- Site Admin
- Posts: 1049
- Joined: 24 Apr 2020, 16:29
Re: Use indirectly related RP data in SP model
Hi
first, in relation to your SP model, you could try to refine the specification and see if that helps. Feel free to show us your current results on the forum as we may then have more suggestions.
second, in relation to joint "RP"-SP, your datasets look quite different, but you could of course try to have a joint model. However, there is a bigger issue. Your second dataset is potentially affected by endogeneity as you change the values of the attributes depending on what they choose
Stephane
first, in relation to your SP model, you could try to refine the specification and see if that helps. Feel free to show us your current results on the forum as we may then have more suggestions.
second, in relation to joint "RP"-SP, your datasets look quite different, but you could of course try to have a joint model. However, there is a bigger issue. Your second dataset is potentially affected by endogeneity as you change the values of the attributes depending on what they choose
Stephane