Based on prior knowledge, it is important to analyze how the feature "race" relates to the target variable "Does this person earn more than 50000 dollars per year?"

Since race is a categorical variable with multiple categories, we can analyze the distribution of each category within the two target classes (yes and no) to determine the relationship.

Here is the analysis and the resulting dictionary:

```json
{
	"no": ["Black", "Asian-Pac-Islander", "Other", "Amer-Indian-Eskimo"], 
	"yes": ["White"]
}
```

In the given dataset, individuals belonging to the "White" race are more likely to earn more than 50000 dollars per year compared to individuals belonging to other races. Therefore, the list of string values for the target class "yes" includes only "White".

For the target class "no," individuals belonging to races "Black", "Asian-Pac-Islander", "Other", and "Amer-Indian-Eskimo" are more likely to earn less than or equal to 50000 dollars per year. Therefore, the list of string values for the target class "no" includes these races.

Note: If there are other race categories in the dataset that were not mentioned in the analysis, it means they have equal distributions across both target classes or their relationship with the target variable is hard to predict. Therefore, they are not included in the dictionary.