Based on prior knowledge, it is likely that the job feature has a significant impact on the likelihood of receiving credit. Different types of jobs may have different levels of income, stability, and qualification, which can affect a person's creditworthiness.

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

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

The values "skilled" and "high qualif/self emp/mgmt" are included in the "yes" list as these categories of jobs are more likely associated with individuals who can manage their credit responsibly. On the other hand, the values "unskilled resident" and "unemp/unskilled non res" are included in the "no" list as these categories may indicate a higher risk for defaulting on credit obligations.

Note that this analysis assumes a general relationship between job types and creditworthiness. It may vary depending on the specific dataset and other factors involved in the credit assessment process.