Based on the given feature "employment, Present employment, in number of years" and the task "Does this person receive a credit? Yes or no?", we need to analyze how the feature and target are related.

To begin, let's analyze the possible relationship between the feature "employment" and the target variable.

Since we do not have any specific information about the relationship between the feature and the target, it is difficult to make accurate predictions. However, we can make some assumptions based on common knowledge:

1. People who are employed for a longer duration (>= 7 years) may have a higher likelihood of receiving credit.
2. Individuals who are employed for a shorter duration (< 1 year) may have a lower probability of receiving credit.
3. People who are unemployed may have a lower likelihood of receiving credit.

Based on these assumptions, we can create the following dictionary:

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

The "yes" list includes the features "employment" values that are more likely to result in receiving credit, while the "no" list includes the features "employment" values that are less likely to result in receiving credit.

Please note that this is a general analysis and the actual relationship between the feature and the target variable may vary depending on the specific dataset and context.