Based on prior knowledge, the workclass feature can potentially be related to the target variable of whether a person earns more than $50,000 per year.

To conduct the analysis, we would need a dataset that includes information on both the workclass feature and the corresponding target variable (whether a person earns more than $50,000 per year). By analyzing this dataset, we can observe the relationship between the workclass feature and the target variable and determine how they are linked.

In the absence of a specific dataset, we cannot generate an accurate analysis or create the requested dictionary. However, if we assume that certain workclass values are more likely to be associated with higher incomes, we can make some general observations.

For instance, it is generally expected that individuals working in the "Private" or "Self-emp-inc" workclasses are more likely to earn higher incomes compared to those in other workclass categories. Conversely, individuals in "Without-pay" or "Never-worked" would most likely have lower incomes. 

To create a dictionary based on these assumptions, we would fill in the possible values for each target class:

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

Please note that this is a hypothetical analysis based on general knowledge and assumptions. To provide a more accurate analysis, it is crucial to have access to a dataset that includes actual data on the workclass feature and the target variable.