Based on my analysis, the native-country feature may have some relationship with the target variable (earning more than 50000 dollars per year). Here is the dictionary with the possible values of the native-country feature for each target class:

```json
{
  "no": [
    "?",
    "Peru",
    "Guatemala",
    "Mexico",
    "Dominican-Republic",
    "Ireland",
    "Germany",
    "Thailand",
    "Haiti",
    "El-Salvador",
    "Puerto-Rico",
    "Vietnam",
    "South",
    "Columbia",
    "Japan",
    "India",
    "Cambodia",
    "Poland",
    "Laos",
    "England",
    "Cuba",
    "Italy",
    "Canada",
    "Portugal",
    "China",
    "Nicaragua",
    "Honduras",
    "Scotland",
    "Jamaica",
    "Ecuador",
    "Yugoslavia",
    "Hungary",
    "Greece",
    "Trinadad&Tobago",
    "Outlying-US(Guam-USVI-etc)",
    "France",
    "Holand-Netherlands"
  ],
  "yes": [
    "United-States"
  ]
}
```

Please note that the target class "no" contains a variety of native-country values, while the target class "yes" is mostly represented by individuals from the United States.