Based on the given feature description and task, it appears that the feature "employment" represents the length of present employment in years. Since the feature is categorical, we can analyze its relationship with the target variable "Does this person receive a credit?" by examining the distribution of different employment categories for both the "yes" and "no" classes.

Here is the analysis of the relationship between the feature "employment" and the target variable:

- For the "yes" class (person does receive a credit):
    - Possible values of the feature "employment" for the "yes" class:
        - >=7
        - 1<=X<4
        - 4<=X<7
        - <1

- For the "no" class (person does not receive a credit):
    - Possible values of the feature "employment" for the "no" class:
        - unemployed

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

```json
{
	"yes": [">=7", "1<=X<4", "4<=X<7", "<1"],
	"no": ["unemployed"]
}
```
This dictionary represents the possible values of the feature "employment" for each target class.