Based on prior knowledge, the feature "employment" represents the current employment status of a person. Analyzing the relationship between the employment status and the task of receiving credit can provide insights into the importance of this feature in predicting credit approval.

To conduct the analysis, we can start by grouping the data based on the target variable ("yes" or "no"), and then check the distribution of employment categories within each group. Here is the analysis:

- For the target class "yes" (person receives credit):
  - Possible values for employment are:
    - '>=7' (employed for 7 or more years)
    - '1<=X<4' (employed for 1 to less than 4 years)
    - '4<=X<7' (employed for 4 to less than 7 years)
    - '<1' (employed for less than 1 year)
    - It is not necessary to include the 'unemployed' category as people without employment are less likely to receive credit. So we can omit this category from the dictionary.

- For the target class "no" (person does not receive credit):
  - Possible values for employment are:
    - '>=7' (employed for 7 or more years)
    - '1<=X<4' (employed for 1 to less than 4 years)
    - '4<=X<7' (employed for 4 to less than 7 years)
    - '<1' (employed for less than 1 year)
    - It is not necessary to include the 'unemployed' category as people without employment are less likely to receive credit. So we can omit this category from the dictionary.

Based on this analysis, we can generate the following dictionary:

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

This dictionary indicates that for both target classes ("yes" and "no"), the feature "employment" can take on values from the categories '>=7', '1<=X<4', '4<=X<7', and '<1'.