A null result would have been: "We tried to apply Famous Theory to showing that minimum wage has no effect on employment rate but we failed because this and that".
In this case we would expect some studies of the minimum wage to show it increases employment regardless of what the effect of wage rises is in the general case - eg, some official raised the minimum wage while a sector went into a boom for unrelated and coincidental reasons.
If the result is very surprising and contradicts established theory, I wouldn't consider it a null result, even if some parameter numerically measures as zero.
Measurements of some physical quantity are a different kind of experiment, you cannot phrase it as a question about the correlation between two variables. Instead you take measurements and put error bars on them (unless what you're measuring is an indirect proxy for the actual quantity, in which case the null hypothesis and p-value testing does become relevant again).
In this case the difference between before and after raising the minimum wage.
Furthermore, the thing with a null result is that it's always dependant on how sensitive your study is. A null result is always of the form "we can't rule out that it's 0". If the study is powerful then a null result will rule out a large difference, but there is always the possibility that there is a difference too small to detect.
The above is a good point but I would extend it further. I mean, philosophically, you get a positive result from a negative (null) result by merely changing your hypotheses (e.g., something should not cause something else).