Based on the given feature "employment" and the task of determining whether the person receives credit or not, we can analyze the relationship between the feature and the target as follows:

- There are five categories for the employment feature: '>=7', '1<=X<4', '4<=X<7', 'unemployed', and '<1'.
- It is likely that people who are currently employed for more than or equal to 7 years ('>=7') have a higher chance of receiving credit.
- Similarly, individuals employed for 4 to less than 7 years ('4<=X<7') may also have a higher chance of receiving credit.
- On the other hand, people who are unemployed or have been employed for less than 1 year ('unemployed' and '<1') might have a lower chance of receiving credit.
- The category '1<=X<4' does not provide enough information to determine the likelihood of receiving credit.

Using this analysis, we can create a dictionary with the relevant details:

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

Note: The '1<=X<4' category is excluded from the dictionary as it does not provide a clear indication of receiving or not receiving credit.