Based on the feature description, the employment feature is a categorical variable with five possible categories:
1. '>=7'
2. '1<=X<4'
3. '4<=X<7'
4. 'unemployed'
5. '<1'

To analyze the relationship between the employment feature and the task of whether a person receives a credit or not, we can look at the distribution of employment categories for each target class.

Based on prior knowledge, we can make the following observations:
- 'unemployed' category might have a higher likelihood of not receiving a credit.
- '>=7' and '1<=X<4' categories might have a higher likelihood of receiving a credit.
- '4<=X<7' and '<1' categories may have an uncertain relationship with receiving a credit.

Using these insights, we can create a dictionary as follows:

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

Please note that we only include the employment categories that have a clear relationship with the target. The uncertain categories, '4<=X<7' and '<1', are not included in the dictionary but could still be accounted for during modeling.