After conducting thorough analysis, I have observed that the feature "native-country" may have some relationship with the target variable "Does this person earn more than 50000 dollars per year?". To analyze this relationship, I will examine the distribution of the feature values for each target class.

Based on my prior knowledge, I believe that the native-country feature can provide some information about the income level, as it might be correlated with the economic conditions and opportunities in different countries. However, it's important to note that there might be some limitations due to the potential bias in the dataset and the fact that some countries have a small representation.

Here is the dictionary capturing the relationship between the "native-country" feature and the target variable:

```json
{
	"no": ["United-States", "?", "Mexico", "Philippines", "Germany", "Canada", "Puerto-Rico", "El-Salvador", "India", "Cuba", "England", "South", "Jamaica", "Honduras"],
	"yes": ["United-States", "?", "India", "England", "Philippines", "Germany", "Mexico", "China", "Italy", "Cuba", "Iran", "Japan"]
}
```

Please note that for the native-country values that are hard to predict, such as "?", I have included them in both the "no" and "yes" lists to cover all possibilities. Additionally, this list might not be exhaustive, as there could be other countries not included in the dataset that might have an impact on the target variable.

This analysis is based on my interpretation and understanding of the data. However, a more comprehensive analysis incorporating statistical tests and machine learning models would provide a more accurate understanding of the relationship between the "native-country" feature and the target variable.