Based on my prior knowledge, the native-country feature may have some influence on the target variable. Certain countries may have higher salaries on average compared to others, which could lead to a correlation between the native-country and the individual's earnings.

Here is the dictionary analyzing the relationship between the native-country feature and the task:

```json
{
    "no": ["United-States", "?", "Mexico", "Dominican-Republic", "Jamaica", "Guatemala", "Vietnam", "Columbia", "Philippines", "Honduras", "Nicaragua", "Peru", "El-Salvador", "Poland", "Haiti", "Portugal", "Ireland", "Scotland", "Ecuador", "Laos", "Taiwan", "Canada", "Outlying-US(Guam-USVI-etc)"],

    "yes": ["United-States", "?", "Mexico", "Philippines", "Germany", "India", "Puerto-Rico", "Poland", "England", "Canada", "China", "Iran", "South", "Italy", "Japan", "Honduras", "Cuba", "Taiwan"]
}
```

Please note that the above list includes the native-country values that have a sufficient representation in the dataset for each target class.