Based on the given task and feature, the relationship between the feature "employment" and the target variable "Does this person receive a credit?" can be analyzed as follows:

To analyze the relationship, we can look at the distribution of the employment categories within each target class. We will check if there are any significant differences in the distribution between the two classes.

- For the target class "yes" (person receives credit), the possible values of the feature "employment" can be:

['>=7', '1<=X<4', '4<=X<7', '<1']

- For the target class "no" (person does not receive credit), the possible values of the feature "employment" can be:

['unemployed', '1<=X<4', '4<=X<7', '<1']

It is not necessary to include the category 'unemployed' in the "yes" list, as it is unlikely that unemployed individuals would receive credit. Similarly, it is not necessary to include the category '>=7' in the "no" list, as it is unlikely that individuals with long-term employment would be denied credit.

Based on this analysis, the dictionary can be formatted as follows:

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