Based on prior knowledge, the feature "capital-loss" refers to the monetary loss an individual incurred in a year due to various reasons (e.g., selling stocks, real estate, or other investments at a loss). To analyze the relationship between this feature and the task of earning more than $50,000 per year, we'll analyze the distribution of "capital-loss" for both target classes ('no' and 'yes').

Here's the dictionary with the analysis of typical "capital-loss" values for each target class:

```json
{
	"no": [-1, 0, 1015, 1564, 1902],
	"yes": [-1, 0, 1648, 1876, 1977]
}
```

The values are as follows:

For the target class 'no':
- -1: This typically represents a missing or unknown capital loss value.
- 0: This means the individual did not incur any capital loss in the last year.
- 1015: This value could represent a relatively small capital loss.
- 1564: This value could represent a moderate capital loss.
- 1902: This value could represent a higher capital loss.

For the target class 'yes':
- -1: This typically represents a missing or unknown capital loss value.
- 0: This means the individual did not incur any capital loss in the last year.
- 1648: This value could represent a relatively small capital loss.
- 1876: This value could represent a moderate capital loss.
- 1977: This value could represent a higher capital loss.

Please note that these values are only examples and there could be other values for each target class.