Based on prior knowledge, the workclass feature represents the employment status of an individual. To analyze its relationship with the target variable (earning more than $50000 per year), we can look at how different workclass values are distributed among the two target classes.

Here is the analysis:

1. For individuals earning less than or equal to $50000 per year (target class: "no"), the possible workclass values could include: 'Private', 'Local-gov', '?', 'Self-emp-not-inc', 'Federal-gov', 'State-gov', 'Without-pay', and 'Never-worked'.

2. For individuals earning more than $50000 per year (target class: "yes"), the possible workclass values could include: 'Private', 'Local-gov', '?', 'Self-emp-not-inc', 'Federal-gov', 'State-gov', and 'Self-emp-inc'.

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

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

Please note that the workclass value '?' is present in both target classes as it represents missing or unknown data.