Based on prior knowledge, the feature "job" can potentially have an impact on whether a person receives credit or not. Different types of jobs may indicate different levels of income, stability, and ability to repay the credit. 

To perform the analysis, we need to examine the relationship between different job categories and the target variable "Does this person receive a credit?". By segregating the data based on job categories and observing the distribution of credits received or not, we can determine the relationship between the feature and the target.

Here is the dictionary generated based on the analysis:

```json
{
    "yes": ["skilled", "high qualif/self emp/mgmt"],
    "no": ["unskilled resident", "unemp/unskilled non res"]
}
```

In this case, individuals with jobs classified as "skilled" or "high qualif/self emp/mgmt" are more likely to receive credit. On the other hand, individuals with jobs classified as "unskilled resident" or "unemp/unskilled non res" are less likely to receive credit. The other job categories, not mentioned in the dictionary, can be considered as potentially uncertain or inconclusive in predicting the credit status.

Please note that this analysis is based on general assumptions, and the actual relationship between the "job" feature and the target variable may vary depending on the specific dataset and context.