Why two dimensions MCA that may represent 40% of variance is enough to constructing socioeconomic scores? The research paper I am putting the link to and many others when using MCA to formulate socioeconomic scores, they tend to use only two dimensions eventhough most of the variance is not represented by these two dimensions. why? is it because it is easier to group the variables into socioeconomic groups when using two dimesnions? or it is arbitrary and it just depends on the statistician and what he may think as the optimal approach?
the link of the research paper (full text not available) :
https://www.researchgate.net/figure/Multiple-correspondence-analysis-A-Spatial-distributions-of-dimensions-by-socioeconomic_fig1_342853820
Thanks.

(I condensed our discussion in the comments into an answer)
It's because 2 is the number of dimensions a computer screen has.
At 40% variance, 2 dimensions is not enough to visualize everything that's going on, but it's the best we can do (until Neurolink allows us to see in 10D :))
To reference this fact, you should cite the Eckart–Young–Mirsky theorem which tells us the precise sense(s) in which taking the first two principle components is optimal (or, more precisely, when the Singular Value Decomposition is optimal in terms of rank-2 matrix approximation). Wiki on Low-rank approximation is a good place to start: https://en.wikipedia.org/wiki/Low-rank_approximation
