Economic is a large, diverse field and it would be reasonable to assume, for instance, that R is used in very different ways than C++, so this study is already of limited use. Furthermore, they claim to be comparing the "strength and weakness of each language" yet only analyze the performance of naively-written, unoptimized code.
You bring up a good point in that the use cases for each language varies. R is a good example because one of the main reasons it is chosen is due to the fact that it already has the largest set of drop in econometrics operations for tons of extremely specific situations. In any other language, you'd often have to code a bunch of complex linear algebra by hand, which would totally negate any marginal benefits due to computation time.
In terms of econometrics, Python is going to surpass closed source software packages like SPSS, SAS, eViews, etc soon simply because the rate at which econometric procedures are being implemented in Python is growing steadily (e.g. via a statsmodels/pandas/numpy/scipy based stack). I don't think Python will ever pass R in this respect though as R has a large share of the statisticians helping add the implementations relative to Python.
