Based on the information provided, the feature "employment" represents the present employment in number of years. It is a categorical variable with the following categories: ['>=7', '1<=X<4', '4<=X<7', 'unemployed', '<1'].

To analyze the relationship between the "employment" feature and the task of determining whether the person receives credit or not, we can inspect the distribution of each employment category within the target classes "yes" and "no".

Assuming we have access to a dataset with the target variable and the "employment" feature for each data point, we can count the occurrences of each employment category for both "yes" and "no" classes. Let's assume the counts are as follows:

yes:
- '>=7': 120
- '1<=X<4': 80
- '4<=X<7': 60
- 'unemployed': 30
- '<1': 10

no:
- '>=7': 50
- '1<=X<4': 40
- '4<=X<7': 20
- 'unemployed': 30
- '<1': 5

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

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

In this case, all employment categories are present in both target classes.