Based on my analysis, it is difficult to predict the exact relationship between the native-country feature and the target variable of whether a person earns more than 50000 dollars per year or not. However, we can still examine the data and identify some general patterns.

Here is a dictionary outlining the possible values of the native-country feature for each target class:

```json
{
	"no": ["United-States", "?", "Mexico", "Philippines", "Puerto-Rico", "Vietnam", "Columbia", "India", "Cambodia", "Poland", "Laos", "Outlying-US(Guam-USVI-etc)", "Guatemala", "El-Salvador", "Dominican-Republic", "Honduras", "Mexico", "Haiti", "Ecuador", "Nicaragua", "Peru", "Jamaica", "Trinadad&Tobago", "Honduras", "Scotland"],

	"yes": ["United-States", "Peru", "Guatemala", "Mexico", "Dominican-Republic", "Ireland", "Germany", "Philippines", "Thailand", "Haiti", "El-Salvador", "Puerto-Rico", "Vietnam", "South", "Columbia", "Japan", "India", "Cambodia", "Poland", "Laos", "England", "Cuba", "Taiwan", "Italy", "Canada", "Portugal", "China", "Nicaragua", "Honduras", "Iran", "Scotland", "Jamaica", "Ecuador", "Yugoslavia", "Hungary", "Hong", "Greece", "Trinadad&Tobago", "Outlying-US(Guam-USVI-etc)", "France"]
}
```

Please note that this analysis is based on observation and general patterns. The values included in the dictionary are the ones that appeared more frequently in the dataset. However, it is important to use caution while drawing conclusions based solely on this analysis, as individual cases may vary.