Based on prior knowledge, it is difficult to predict whether gender alone can determine whether a person earns more than $50,000 per year or not. However, we can still analyze the relationship between gender and the target variable.

To conduct the analysis, we would need a dataset that includes the feature "gender" and the corresponding target variable "Does this person earn more than 50000 dollars per year?". Without a dataset, it would be impossible to determine the possible values of the feature "gender" for each target class. However, we can make some assumptions based on general knowledge. 

Assuming that both genders are equally represented in the dataset, the possible values of the feature "gender" for the target class "no" (earn less than $50,000 per year) could be ['Male', 'Female']. Similarly, the possible values of the feature "gender" for the target class "yes" (earn more than $50,000 per year) could also be ['Male', 'Female']. 

Therefore, the dictionary for this feature and task would look like:

```json
{
	"no": ["Male", "Female"],
	"yes": ["Male", "Female"]
}
```

Please note that this analysis is based on assumptions and general knowledge. Without a specific dataset, it is difficult to determine the exact relationship between the feature "gender" and the target variable.