Table 6. The estimation result of a binary logit model for trip to mountain village as a dependent variable.

Variable Basic model Interaction model (1) Interaction model (2)
COVID-19 outbreak −1.729*** −0.984*** −2.019***
Age (base=20s)
          30s (30~39) 0.265 0.419* 0.265
          40s (40~49) 0.135 0.397 0.133
          50s (50~59) 0.272 0.589** 0.272
          60s (60~69) 0.677*** 0.964*** 0.679***
Age*COVID-19    
          30s (30~39) -0.532  
          40s (40~49) -0.902**  
          50s (50~59) -1.132***  
          60s (60~69) -0.978**  
Gender (male=1) −0.100 −0.099 −0.100
Children (with children=1) 0.573*** 0.578*** 0.576***
Job (office job=1) 0.116 0.116 0.117
Education (base=not enrolled in university)    
          Enrolled in or graduated from university 0.242 0.248 0.241
          Enrolled in or graduated from graduate school −0.140 −0.137 −0.140
Income (base=under 2 million KRW)    
          2~4 million KRW 1.206*** 1.203*** 1.207***
          4~6 million KRW 1.346*** 1.344*** 1.348***
          6~8 million KRW 1.079*** 1.075*** 1.077***
          More than 8 million KRW 0.916*** 0.910*** 0.917***
Residential area (in Seoul Metropolitan area=1) 0.217* 0.217* 0.071
Residential area*COVID-19   0.531**
Importance of forest (base=not important)    
          Slightly important 0.934*** 0.930*** 0.932***
          Important 1.278*** 1.275*** 1.277***
          Very important 1.284*** 1.282*** 1.284***
Constant −3.536*** −3.756*** −3.463***
Obs. 2000 2000 2000
LR chi2 (prob > chi2) 308.07 (0.000) 316.60 (0.000) 312.07 (0.000)
Pseudo R2 0.148 0.152 0.150
Log likelihood −889.563 −885.298 −887.560
Note: p<0.1*, p<0.05**, p<0.01***