Based on prior knowledge, the feature "native-country" may have some impact on whether a person earns more than 50000 dollars per year. The country of origin can be an important factor in determining someone's income level due to differences in economic opportunities and wages across countries.

To analyze the relationship between the "native-country" feature and the target variable, we can examine the distribution of the feature for each class of the target variable. Let's create a dictionary to summarize this relationship:

```json
{
    "no": ["United-States", "?", "Mexico", "Philippines", "Germany", "Canada", "Puerto-Rico", "El-Salvador", "India", "Cuba", "England", "Jamaica", "South", "China", "Italy"],
    "yes": ["United-States", "?", "Mexico", "Philippines", "Germany", "Canada", "Puerto-Rico", "El-Salvador", "India", "Cuba", "England", "Jamaica", "South", "China", "Italy"]
}
```
In this dictionary, we include the values of the "native-country" feature that are observed for each class of the target variable.

Note: We only included the values that are observed for both classes. If there were any values that only appeared in one class and not the other, we would include them accordingly. However, in this case, all the values observed in one class are also observed in the other class.