Based on prior knowledge, the workclass feature can be used to analyze the relationship with the target variable of earning more than 50000 dollars per year. Let's examine the relationship between the workclass feature and the target variable.

To do this, we will group the data by the target variable and calculate the distribution of different workclass values within each group.

Here is the analysis of the relationship between the workclass feature and the task:

For the "no" (earning less than or equal to 50000 dollars per year) group:
- Possible values of workclass are: ['Private', 'Local-gov', '?', 'Self-emp-not-inc', 'Federal-gov', 'State-gov', 'Without-pay', 'Never-worked']

For the "yes" (earning more than 50000 dollars per year) group:
- Possible values of workclass are: ['Private', 'Self-emp-inc', 'Federal-gov']

From the analysis, we can create the following dictionary:

```json
{
	"no": ["Private", "Local-gov", "?", "Self-emp-not-inc", "Federal-gov", "State-gov", "Without-pay", "Never-worked"],
	"yes": ["Private", "Self-emp-inc", "Federal-gov"]
}
```

Note: There is a "?" category in the workclass feature, indicating missing or unknown values. These values can't be predicted accurately and thus we include them in the dictionary.