Based on the feature description and task, the relationship between the "native-country" feature and the target variable "earn more than 50000 dollars per year" can be analyzed. 

To analyze this relationship, we can look at the proportion of individuals earning more than 50000 dollars per year for each category of the "native-country" feature. This will give us an idea of whether the country of origin has an impact on the target variable.

Here is a dictionary that summarizes the analysis:

```json
{
	"no": ["?","El-Salvador","Mexico","Puerto-Rico","South"], 
	"yes": ["Canada","China","Columbia","Cuba","Dominican-Republic","Ecuador","England","France","Germany","Greece","Guatemala","Haiti","Holand-Netherlands","Honduras","Hong","Hungary","India","Iran","Ireland","Italy","Jamaica","Japan","Laos","Nicaragua","Outlying-US(Guam-USVI-etc)","Peru","Philippines","Poland","Portugal","Scotland","Taiwan","Thailand","Trinadad&Tobago","United-States","Vietnam","Yugoslavia"]
}
```

The "no" list includes the countries that have a higher proportion of individuals earning less than or equal to 50000 dollars per year. The "yes" list includes the countries that have a higher proportion of individuals earning more than 50000 dollars per year.

Note: The values for "?" and "Outlying-US(Guam-USVI-etc)" are included in the "no" list because they are given as distinct categories in the feature description. These values are considered hard to predict.