Based on my prior knowledge, the employment feature can be a good indicator of whether a person receives credit or not. Generally, individuals who have stable and long-term employment are more likely to receive credit, while those who are unemployed or have short-term employment may have lower chances of receiving credit.

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

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

This dictionary specifies the possible values of the employment feature for each target class. The "yes" class includes values that indicate stable and long-term employment, such as ">=7", "1<=X<4", and "4<=X<7". The "no" class includes values that indicate unemployment or short-term employment, such as "unemployed" and "<1".

Please note that this dictionary is based on the general relationship between the employment feature and the task of determining whether a person receives credit. It may not cover all possible scenarios, but it provides a good starting point for analysis.