Based on my prior knowledge, the feature "native-country" can provide some insights on a person's income level. There might be variations in income between different countries due to differences in economic development, job opportunities, and average wages.

To create the dictionary, I will analyze the relationship between the "native-country" feature and the target variable ("Does this person earn more than 50000 dollars per year? Yes or no?") by examining the distribution of native countries for each target class.

Here is the dictionary:

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

Explanation:
- For the "no" class (people earning less than or equal to 50000 dollars per year), the most frequently occurring native countries are "United-States", "?", "Mexico", "Philippines", and "Germany". Other countries in the list have lower frequencies but could still be indicative of lower income levels.
- For the "yes" class (people earning more than 50000 dollars per year), the most frequently occurring native countries are also "United-States", "?", "Mexico", and "Philippines". However, we also observe the presence of countries like "Germany", "Canada", "India", "England", "Iran", "Japan", "China", and "Italy". These countries might represent a mix of native-born individuals with higher income levels and individuals with international backgrounds working in higher-paying occupations.

Note: The presence of "?" indicates missing or unknown data for the native country, which is common in datasets. It is important to handle missing values appropriately during data preprocessing.