Based on my prior knowledge, the occupation of an individual can have an impact on their earning potential. Some occupations may generally have higher salaries than others. 

To analyze the relationship between the feature "occupation" and the target variable "Does this person earn more than 50000 dollars per year?", we can examine the distribution of occupations for different income levels.

Here is the generated dictionary:

```json
{
	"no": ["Machine-op-inspct", "Farming-fishing", "Protective-serv", "?", "Other-service", "Craft-repair", "Adm-clerical", "Handlers-cleaners"],
	"yes": ["Prof-specialty", "Exec-managerial", "Tech-support", "Sales", "Transport-moving", "Priv-house-serv", "Armed-Forces"]
}
```

The "no" list contains the possible values of the occupation feature for individuals who do not earn more than 50,000 dollars per year. These occupations generally have lower income levels.

The "yes" list contains the possible values of the occupation feature for individuals who do earn more than 50,000 dollars per year. These occupations generally have higher income levels.

Note that the "?" occupation value is included in the "no" list as it represents unknown or missing data.