Based on prior knowledge, we can analyze the relationship between the "native-country" feature and the task of whether the person earns more than 50000 dollars per year. 

It is expected that the "native-country" feature can have some relationship with the target variable. Generally, individuals from countries with higher average incomes are more likely to earn more than 50000 dollars per year. However, we should take into account that this relationship may not hold true for all individuals.

To create the dictionary, we need to analyze the distribution of "native-country" values for each target class separately. Here is the analysis:

For the target class "no" (earning less than or equal to 50000 dollars per year):
- United-States: The majority of individuals in the dataset are from the United States, indicating that a significant proportion of people earning less than 50000 dollars per year are from the United States. Hence, we can include this value in the dictionary for the "no" class.
- Other countries: It is hard to predict the relationship between other countries and the target class "no" without further analysis. We should include all other countries available in the dataset in the dictionary for the "no" class.

For the target class "yes" (earning more than 50000 dollars per year):
- United-States: Although individuals from the United States are likely to have higher incomes on average, there may also be a significant proportion of individuals from other countries who earn more than 50000 dollars per year. Hence, we should include this value in the dictionary for the "yes" class.
- Other countries: Similar to the "no" class, it is hard to predict the relationship between other countries and the target class "yes" without further analysis. We should include all other countries available in the dataset in the dictionary for the "yes" class.

Based on this analysis, the dictionary should be formatted as follows:

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

Note: The analysis assumes that the dataset accurately represents the relationship between the "native-country" feature and the target variable. Further analysis could be conducted using statistical techniques to validate the significance of this relationship.