Based on prior knowledge, we can assume that higher levels of education are generally associated with higher earnings. We can analyze the relationship between the feature "educational-num" and the target variable "Does this person earn more than 50000 dollars per year?" to understand how the feature influences the target.

To create the dictionary as requested, we need to determine the typical educational-num values for each target class.

For the target class "no" (earning less than or equal to 50000 dollars per year), some possible typical educational-num values could be:
- 1 (preschool)
- 9 (11th grade)
- 10 (12th grade, no diploma)
- 11 (high school diploma)
- 12 (some college, no degree)

For the target class "yes" (earning more than 50000 dollars per year), some possible typical educational-num values could be:
- 13 (associate's degree)
- 14 (bachelor's degree)
- 15 (master's degree)
- 16 (professional school degree)
- 17 (doctorate degree)

Now, let's create the dictionary:

```json
{
	"no": [1, 9, 10, 11, 12],
	"yes": [13, 14, 15, 16, 17]
}
```

This dictionary shows the possible typical educational-num values for each target class.