Based on the feature description and the task, we want to analyze how the employment feature relates to whether a person receives credit or not (target variable).

To conduct the analysis, I would first gather data on the target variable (whether a person receives credit or not) and the employment feature. I would then compare the distribution of employment values for the two target classes (yes and no) to understand the relationship.

Here is an example analysis and the resulting dictionary:

Analysis:

- For individuals who receive credit (target class: yes), the possible values of the employment feature are likely to be >=7, 1<=X<4, and 4<=X<7. However, it is hard to predict the specific distribution without additional data.

- For individuals who do not receive credit (target class: no), the distribution of the employment feature could vary. It is possible that those who are unemployed or have employment duration <1 are more likely to be in this target class. Again, the specific values and distribution cannot be determined without additional data.

Dictionary:

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

Note: This analysis is based on general assumptions and prior knowledge. To make more accurate conclusions, actual data and statistical analysis are required.