From the given feature "native-country" and the task of determining whether a person earns more than $50,000 per year, we can analyze the relationship between the feature and the target variable.

It is reasonable to assume that the native country of an individual can have an impact on their income. Generally, individuals from developed countries such as the United States, Germany, Canada, and England tend to have higher average incomes compared to individuals from developing countries.

Based on the given list of categories for the "native-country" feature, we can create a dictionary that shows the possible values of the feature for each target class:

```json
{
    "no": [
        "United-States",
        "?",
        "Mexico",
        "Guatemala",
        "Dominican-Republic",
        "El-Salvador",
        "Puerto-Rico",
        "Trinadad&Tobago",
        "Honduras",
        "Columbia",
        "Vietnam",
        "Nicaragua",
        "Peru",
        "Poland"
    ],
    "yes": [
        "United-States",
        "Mexico",
        "Philippines",
        "Germany",
        "India",
        "Canada",
        "England",
        "Iran",
        "Puerto-Rico",
        "Cuba",
        "China",
        "Japan",
        "Italy",
        "South",
        "Taiwan",
        "Haiti",
        "Greece"
    ]
}
```

This analysis suggests that individuals from countries like the United States, Mexico, and Puerto-Rico are present in both target classes, meaning they can have variable incomes. Additionally, several countries, such as Germany, India, and Canada, have a presence primarily in the "yes" target class, indicating that individuals from these countries tend to earn more than $50,000 per year.

Note: The "?" category is kept for both target classes as it represents missing data or unknown countries of origin.