Based on the provided feature and task, we can analyze the relationship between the "workclass" feature and whether a person earns more than 50000 dollars per year.

To analyze this relationship, we can look at the distribution of each workclass category for the two target classes (earning more or less than 50000 dollars per year). This will help us identify any patterns or differences that may exist.

Here are the possible values of the "workclass" feature for each target class:

For target class "no" (earning 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'

For target class "yes" (earning more than 50000 dollars per year):
- 'Private'
- 'Local-gov'
- '?'
- 'Self-emp-not-inc'
- 'Federal-gov'
- 'State-gov'
- 'Self-emp-inc'
- 'Without-pay'
- 'Never-worked'

Based on this analysis, we can create the following dictionary:

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

Please note that the special value "?" represents missing or unknown data. If this value appears in the dataset, it would be reasonable to include it in the lists for both target classes.