Based on my analysis, the feature "native-country" may have some relationship with the task of determining if a person earns more than $50000 per year. 

To create a dictionary showing the relationship between the feature and task, I will examine the distribution of the "native-country" values for each target class and identify the values that are more prevalent in each class.

```json
{
	"no": ["United-States", "?", "Mexico", "Philippines", "Puerto-Rico", "Germany", "Dominican-Republic", "Canada", "El-Salvador", "India", "Cuba", "England", "Jamaica", "Iran", "Italy", "Vietnam", "South", "Poland", "Columbia", "Haiti", "Portugal", "Taiwan", "Nicaragua", "Honduras", "Scotland", "Laos", "Ecuador", "France", "Ireland", "Hong"],

	"yes": ["United-States", "?", "Mexico", "Philippines", "Puerto-Rico", "Germany", "Dominican-Republic", "Canada", "El-Salvador", "India", "Cuba", "England", "Jamaica", "Iran", "Italy", "Vietnam", "South", "Poland", "Columbia", "Haiti", "Portugal", "Taiwan", "Nicaragua", "Honduras", "Scotland", "Laos", "Ecuador", "France", "Ireland", "Hong"]
}
```

Please note that the above lists include the values present in both the "yes" and "no" target classes. The feature "native-country" does not have any specific values that are exclusively associated with one target class or the other.