Based on prior knowledge, the workclass feature can have a relationship with the target variable. 

To analyze the relationship, we need to compare the distribution of workclass values between the individuals who earn more than 50000 dollars per year (yes) and those who don't (no). Let's go through the data and identify the possible values for the workclass feature in each target class:

For target class "yes" (earn more than 50000 dollars per year):

- "Private"
- "Local-gov"
- "Self-emp-not-inc"
- "Federal-gov"
- "State-gov"
- "Self-emp-inc"

For target class "no" (earn less than or equal to 50000 dollars per year):

- "Private"
- "Local-gov"
- "?"
- "Self-emp-not-inc"
- "Federal-gov"
- "State-gov"
- "Self-emp-inc"
- "Without-pay"
- "Never-worked"

Based on the given feature description, it is clear that the workclass values "Without-pay" and "Never-worked" are not relevant for predicting the target variable since they indicate a lack of employment.

Now, let's create the dictionary with the possible workclass values for each target class:

```json
{
	"no": ["Private", "Local-gov", "?", "Self-emp-not-inc", "Federal-gov", "State-gov", "Self-emp-inc"],
	"yes": ["Private", "Local-gov", "Self-emp-not-inc", "Federal-gov", "State-gov", "Self-emp-inc"]
}
```

This dictionary includes all the relevant workclass values for each target class.