I too am not sure exactly what you're asking.
If the variable is truly nominal then the results will be relative to the omitted (reference) category. However, this can be overcome, in Stata 12 at least, with the contrast command which performs anova style contrasts.
This will test whether the mean outcome is the same for all groups of your variable (i.e. does the outcome vary significantly across the categories of your nominal variable?).
Depending on your model you could also use a lrtest to see if the model fit improves significantly when the dummies are added.
The bottom line, though, is that if any dummy is significant then you can safely conclude that your variable -on the whole- has a significant effect.
You can also use Stata's contrast command to see if categories of your variable can be safely combined - this may also be of interest to you.
EDITED to add:
Probably the simplest approach would be to run the model with all the dummies, then simply drop those that aren't significant and keep those that are. This would accomplish pretty much the same thing as creating 50 different contingency tables with 50 separate chi square tests. The problem with either of these approaches is that at the standard .05 alpha level we expect 1 out of every 5 statistically significant effects to actually be null in the population. In other words, there's an error rate associated with significance tests - the error rate is small but when you go data-mining through a huge number of variables then the expected frequency of errors becomes large enough to be a concern.
What you should do in this case is let theory or common sense guide you a bit and then construct a well thought out model and test it. This is a better approach then simply throwing everything including the kitchen sink into the model.
Remember, if you ask, "does occupation matter?" then the answer is yes if any one occupational dummy has a significant effect.
If you must ignore my plea for theory over data mining then one sensible data-driven method of collapsing your categories would be factor analysis. Here again, though, I would recommend confirmatory over exploratory.
EDIT: as I thought about this a bit more, I don't think factor analysis or SEM would be apposite here. So please disregard that bad advice.