If Julia is the engine that drives all the critical parts in Python rather than C/C++, then it has a way to get the foot in the door. People will stop and ask: Why am I using Python if I could just use Julia directly?
I'm convinced Julia will eventually be able to be compiled statically, but doing that will likely make it feel decidedly un-Julia like (e.g. no dynamic dispatch, no type inference failure), to the point where a library maker would probably just want to use an actual static language instead.
The main thing holding it back is its ecosystem. R has better libraries and a much better IDE (RStudio), while Python is still the best option for machine/deep learning (and general programming).
However, Julia has much better performance. R is arguably easier for someone with little coding experience to learn, but Julia isn't that much more difficult, and it's much more intuitive to code in than Python.
Julia + RStudio + CRAN/BioConductor would take the cake.
I can't speak for Stata, but literally everybody I know using SPSS use it because of the easy to use GUI that allows quite complex analysis with basically zero programming.
Having some experience with all three of these languages, I find Julia much harder than R and less intuitive than Python. The relatively clean syntax isn't enough to make Julia an easy language.