To analyze the relationship between the "native-country" feature and the target variable, we can examine the distribution of the "native-country" values for each target class ("yes" and "no").

Here is the dictionary with the analysis:

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

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

Based on the analysis, we can see that the "native-country" feature values are mostly the same for both target classes ("yes" and "no"). It indicates that the "native-country" feature may not have a strong predictive power in determining whether a person earns more than 50000 dollars per year.