Difference in Marginal rate of substitution between MNL and Mixed logit models
Posted: 13 Feb 2024, 16:06
Dear Prof. Stephane,
I have a question concerning the difference in the marginal rate of substitution values for the Multinomial Logit (MNL) and Mixed Logit models. Specifically, I am calculating 'Value of Risk Reduction,' which is the additional travel time food delivery riders are willing to undertake for a 10% reduction in the risk of a crash.
For the Mixed Logit model, I have assumed a log-normal distribution for both parameters (positive for risk reduction and negative for time). Here are the values I have obtained for both models:
Value of Risk Reduction (MNL_mean): 1.07 (90% CI: 0.28 - 1.85)
Value of Risk Reduction (Mixed_mean): 7.9 (90% CI: 1.64 - 14.1) - with a standard deviation of 53
While it is evident that the Mixed Logit model provides a better fit, I want to know what explains the difference in the values obtained (almost 7 times higher in the case of the Mixed Logit model). Could you please provide insight on this?
Thank you for your time and assistance.
Best regards,
K.
I have a question concerning the difference in the marginal rate of substitution values for the Multinomial Logit (MNL) and Mixed Logit models. Specifically, I am calculating 'Value of Risk Reduction,' which is the additional travel time food delivery riders are willing to undertake for a 10% reduction in the risk of a crash.
For the Mixed Logit model, I have assumed a log-normal distribution for both parameters (positive for risk reduction and negative for time). Here are the values I have obtained for both models:
Value of Risk Reduction (MNL_mean): 1.07 (90% CI: 0.28 - 1.85)
Value of Risk Reduction (Mixed_mean): 7.9 (90% CI: 1.64 - 14.1) - with a standard deviation of 53
While it is evident that the Mixed Logit model provides a better fit, I want to know what explains the difference in the values obtained (almost 7 times higher in the case of the Mixed Logit model). Could you please provide insight on this?
Thank you for your time and assistance.
Best regards,
K.