Based on prior knowledge, it is important to note that gender alone may not be a strong predictor of whether a person earns more than 50000 dollars per year. Income is influenced by various factors such as education, occupation, and experience. However, we can still analyze the relationship between gender and income to see if there are any observable patterns.

To analyze this relationship, we need access to a dataset that includes gender and income information for individuals. Without access to such data, we cannot provide specific details for the dictionary. However, we can provide a general approach to constructing the dictionary.

```json
{
	"no": ["Male", "Female"], 
	"yes": ["Male", "Female"]
}
```

Since gender is a categorical variable with two possible values (Male and Female), in the absence of data analysis, we assume that both genders can have individuals who earn more or less than 50000 dollars per year. Thus, we include both values in both 'no' and 'yes' lists, indicating that there is no clear pattern between gender and income.

It's important to note that this assumption may not hold true in every dataset. Without analyzing the dataset, we cannot provide more specific insights or create a dictionary that is tailored to the relationships present in the data.