To analyze the relationship between the "native-country" feature and the target variable (earning more than $50,000 per year), we can examine the distribution of different native countries among the two classes: "yes" and "no".

Based on prior knowledge, we can expect that individuals from certain countries may have higher or lower incomes compared to others, due to factors like economic development, education, and job opportunities.

Here is the analysis and the resulting dictionary:

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

	"yes": ["United-States", "?", "Mexico", "Philippines", "Puerto-Rico", "India", "Cuba", "England", "Canada", "China", "Germany", "Italy", "Dominican-Republic", "Japan", "Greece", "Vietnam", "Scotland", "El-Salvador", "Poland", "Iran", "Taiwan", "Ireland", "Haiti", "Cambodia"]

}
```

Please note that the list of countries provided in each class is not exhaustive and includes the most frequent or notable values observed in the dataset. The "?" category represents instances where the native-country information is missing.

This dictionary provides a summary of the relationship between the "native-country" feature and the target variable, indicating the possible values of the "native-country" for each target class.