Based on prior knowledge, the relationship between race and income is a complex one. It is important to note that any analysis of the relationship between race and income should be conducted with caution, as it can be influenced by various factors such as social bias, discrimination, and systemic inequalities.

To analyze the relationship between race and earning more than 50000 dollars per year, we can examine the distribution of income across different races. This analysis can provide insights into any potential disparities in income based on race.

To create the dictionary, we will need to analyze the dataset and identify the possible values of the race feature for both the "yes" (earning more than 50000 dollars per year) and "no" (earning less than or equal to 50000 dollars per year) target classes.

Let's assume we have a dataset with information on race and income, and we conduct the following analysis:

For the "no" class:
- Possible values for race: ['Black', 'White', 'Asian-Pac-Islander', 'Other', 'Amer-Indian-Eskimo']
- We find that individuals belonging to the "no" class (earning less than or equal to 50000 dollars per year) have the following race distribution:
    - Black: 3000 individuals
    - White: 5000 individuals
    - Asian-Pac-Islander: 1000 individuals
    - Other: 500 individuals
    - Amer-Indian-Eskimo: 200 individuals

For the "yes" class:
- Possible values for race: ['Black', 'White', 'Asian-Pac-Islander', 'Other', 'Amer-Indian-Eskimo']
- We find that individuals belonging to the "yes" class (earning more than 50000 dollars per year) have the following race distribution:
    - Black: 800 individuals
    - White: 10000 individuals
    - Asian-Pac-Islander: 1200 individuals
    - Other: 300 individuals
    - Amer-Indian-Eskimo: 100 individuals

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

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

Please note that if there are certain race values that are hard to predict or are not present in the dataset for a particular target class, they can be excluded from the dictionary. However, in this case, all possible race values are included for both target classes to provide a complete analysis.