Based on prior knowledge, we can analyze the relationship between the "employment" feature and the task of whether a person receives a credit or not.

Possible hypotheses:
1. People who are currently employed for a longer duration (>=7 years) are more likely to receive a credit.
2. People who are currently employed for a shorter duration (1<=X<4, 4<=X<7, or <1 year) are less likely to receive a credit.
3. Unemployed individuals may have lower chances of receiving a credit compared to employed individuals.

Based on these hypotheses, we can generate a dictionary with the possible values of the "employment" feature for target classes "yes" and "no".

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

Please note that this analysis is based on general assumptions and prior knowledge, and further exploration with actual data may be required to validate these hypotheses.