差分需求體系模型之建構與選擇－－以臺灣地區總和食品需求為例

Measuring the Economic Benefits from Recreation by Using the Mixed Logit Model

本研究以臺灣地區總和食品需求為例，探討四種差分需求體系模型（Rotterdam需求體系、差分AIDS需求體系、CBS需求體系、及NBR需求體系）之設定，利用兩套總合食品資料，運用反覆似乎無關迴歸估計需求結構參數，並求算相關價格彈性及支出彈性，以進行不同需求模型間的選擇與比較。研究結果顯示，不論是利用糧食平衡表資料或是利用年報資料推估的消費資料，在模型的設定選擇上，皆以Rotterdam需求體系較佳，體系RS分別為0.97與0.91；此外，研究也發現資料的處理方式與產品分類明顯地影響需求彈性估計值。

Discrete response models have been applied to many fields such as resource management, transportation studies, commodity brand studies, and recreation studies. The mixed logit model (MXL) is interesting because it is flexible, in a sense that it is a generalization of all discrete response models based on random utility maximization. The MXL can capture the random heterogeneous preference of the consumers, which is its unique strength among discrete response models.Typically, the ultimate goal of recreation studies is to obtain economic benefits related to changing site attributes or availability. The literature states that the welfare effects obtained by using the MXL are significantly affected by the interpretation of its random terms. One interpretation denotes the randomness as capturing taste differences among the consumers; another denotes the random terms as capturing measurement errors. The literature finds that the welfare effects the welfare effects based on the first interpretation are all substantially larger than those the welfare effects based on the second interpretation. This study uses a different data set. With more sites and under more distributions of the random parameters, I find that the interpretation of its random terms sometimes significantly affects the welfare effects. However, the welfare effects based on the first interpretation is not always larger than the welfare effects based on the second interpretation.This study also uses empirical data to illustrate that, when the travel cost is a random parameter, the welfare effects based on the first interpretation is significantly biased. A third formula based on the same interpretation is appropriate. These findings help understanding the welfare measurement when using the MXL. Therefore, it helps the application of the MXL in the study of natural recreational resource management as well as in other fields.