To analyze the relationship between the "employment" feature and the target variable "Does this person receive a credit?", we can examine the distribution of the "employment" values for each class (yes and no).

Based on the provided feature description, the "employment" feature is a categorical variable with the following categories: ['>=7', '1<=X<4', '4<=X<7', 'unemployed', '<1'].

To create the dictionary, we will count the occurrences of each "employment" category for each target class.

Here is the dictionary representing the relationship between the "employment" feature and the target variable:

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

Explanation:
- For the "yes" class (persons who receive a credit), the feature "employment" can take values in the categories '1<=X<4', '4<=X<7', and '>=7'.
- For the "no" class (persons who do not receive a credit), the feature "employment" can take values in the categories 'unemployed' and '<1'.

Note: We have excluded the category 'unemployed' from the "yes" class and the category '>=7', '1<=X<4', '4<=X<7' from the "no" class, assuming these values are hard to predict and they are not necessary to include in the dictionary.