I am interested in exploring how different characteristics of national pension systems are related to each other. I have used MCA for a dataset in which the rows are countries and the columns are different features of pension systems. However, I am not sure how to interpret the distances between points in the SPSS Joint Plot of Category Points. Using a symmetrical normalization, do the distances between points representing categories of different variables say something about how these categories are associated? Does a shorter distance mean a higher level of association?
The standard visualisation is the biplot. The interpretation depends on the details of the technique applied but will usually lean on some notion of inner product. But since I don't know what SPSS does when you ask for MCA then I hesitate to offer more concrete advice. Nevertheless you'll surely find all you need to interpret them in the (free) book Biplots in Practice, specifically chapters 9-10.
However, if you're wondering how to interpret its output then you might profitably first revise your theory of correspondence analysis. Greenacre's CA in Practice is a good applied text. Ch. 9 covers biplots and ch. 16-20 revise the multi-way extensions of simple correspondence analysis (they are short chapters). That should provide enough background to see what SPSS is offering you.
As @ttnphns points out, a two way table implies simple rather than multiple correspondence analysis. Then things are indeed easier (but still see references above).