ApolloChoiceModelling forum

Hi all,

I'm estimating an MNL model for a DCE with two alternatives representing different levels of monitoring oil spills from drilling rigs and an opt-out option.

One of the attributes in the model is the number of species types saved as a result of monitoring, with the levels being:
1 = Fish,
2 = Fish + Invertebrates,
3 = Fish + Invertebrates + Mammals.

When this attribute was included as a categorical ordinal variable, maintaining its natural ordering, its coefficient was non-significant.

However, when I used three dummy variables and kept the coefficient for 'fish' (b_fish) fixed, the coefficients for 'fish + invertebrates' and 'fish + invertebrates + mammals' were positive and negative (respectively) and significant.

Given these results, if there is evidence of quadratic preferences, would it make sense to incorporate both a linear and a quadratic parameter for the attribute while treating it as a categorical ordinal variable in the utility specification?

Anat

Hi Anat

when you say "categorical ordinal", I think you are in fact still treating the attribute as linear in your first specification, given that you are estimating a single coefficient.

It is only when you dummy code it that you are in fact treating it as categorical, and are able to capture the non-monotonic trend.

With only three levels, there is no benefit at all to use linear + quadratic; you are already using the most flexible possible treatment

Stephane

Thank you Stephane
This attribute was indeed dummy coded. I also understand why, with three levels, it is not possible to estimate quadratic preferences.

My question is: is there any way to isolate and elicit preferences for 'not harming sea invertebrates' and 'not harming sea mammals', independently?
(See the levels below):

Fish
Fish + Invertebrates
Fish + Invertebrates + Mammals
I was considering the following approach (for a model with two generic alternatives + opt-out):
The model runs, but I am unsure if this formulation makes conceptual sense.

Anat.

Anat

doing this inside the model is not going to help as it can only lead to the same results as what you get.

Basically, if you fix fish (beta1) to 0 and estimate the other two parameters, then beta2 will tell you the added utility for Invertebrates compared to fish (which you indicated was positive). The fact that beta3 is negative means that Fish + Invertebrates + Mammals is worse than fish. That could be because mammals is so negative that it exceeds the benefit of invertebrates, but it could also be due to a negative interaction between them

Stephane

Thanks a million Stephane!