Based on prior knowledge, we can analyze the relationship between the "employment" feature and the target variable "Does this person receive a credit?".

From the feature description, it is clear that "employment" represents the present employment duration in number of years. The categories for this feature are: ['>=7', '1<=X<4', '4<=X<7', 'unemployed', '<1'].

To analyze the relationship, we can look at the distribution of the feature values for each target class. We will identify the possible values of the "employment" feature for the target class "yes" (receive credit) and "no" (does not receive credit).

Based on the analysis, the possible values of the "employment" feature for the target class "yes" are: ['>=7', '1<=X<4', '4<=X<7']. 

For the target class "no", the possible values of the "employment" feature are: ['unemployed', '<1'].

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

```json
{
	"yes": [">=7", "1<=X<4", "4<=X<7"],
	"no": ["unemployed", "<1"]
}
```

This dictionary represents the possible values of the "employment" feature for each target class.