How to test if change is significant across multiple categories? Here is a look at my data. We asked the same respondents (n=~400) to provide us with their current and future consumption as a proportion of total expenditure. Plotted here are the mean proportions of total expenditure for each category for "Now" and "Later", respectively current and future.  
What I'm looking for is a statistical method that I can use to test if the other categories are increasing while the largest category is decreasing. The graph shows this, to some degree, but I would like to make sure that it is statistically valid.  

 A: There are subtle issues involving the difference between designed comparisons and post-hoc comparisons, of which this likely is an example.
If, before collecting the data, you anticipated this kind of pattern, you could employ a simple nonparametric test.  The null hypothesis would be that all changes are due to chance with the alternative being that a specified category was increasing and the other eight categories were decreasing.  Under the null, positive changes have a 50% chance of occurring, implying the chance of the alternative is $(0.50)^8(1 - 0.50)^1$ = $0.002$: highly significant evidence for the alternative.
The analysis for a post-hoc observation is difficult because we can't even get started with describing the situation.  Exactly what kind of pattern would you happen to notice and considered worthy of testing?  So many are possible, with no accurate description available, that all we can say (from experience) is that (a) it is highly likely that any interested investigator would notice some pattern in the data and (b) a post-hoc hypothesis test could be constructed to "demonstrate" the "high significance" of that pattern, exactly as I did above.  For these reasons, applying hypothesis tests after the fact to support claims of "statistical validity" for exploratory results is frowned upon.  (Among statisticians, who should know better, it is called "data snooping" or worse.)
One way out is to conduct your analysis with c. half the data, randomly selected.  Look for any patterns you like.  Construct an appropriate suite of hypothesis tests for those patterns and then apply them to the held-out data only.  This is in the spirit of the scientific requirement for replication.  If you don't do this, then you would be obliged to repeat your experiment to confirm whatever you're seeing in the data you currently have.
A: Given the additional information you've subsequently posted I'm not sure any statistical test is going to be that informative.  If you had a strong prediction of a pattern such as this or similar, this is such a low probability event that you're pretty much set just getting these data.  With an N of 400 almost any tests will most definitely be significant.  Some good descriptive stats like confidence intervals would be very useful.
I would suggest that caution be made in your description of the downward trend being remotely meaningful.  It's such a tiny amount that, yeah, if your N is big enough it will be significant.  But is that tiny drop in percentage meaningful?  I think the more meaningful statement is that it's not an increase like the others and that it is staying roughly flat.  Don't try to change the story of really small effects with statistical tests.
