this article talks about why one should consider centering for continuous variables in a multilevel/panel/HLM model.
http://www.ssicentral.com/hlm/techdocs/Centering.pdf
Two main advantages of centering the predictors are:
Obtaining estimates of and other effects that are easier to interpret, so that the
statistical results can be related to the theoretical concerns that motivate the research.
Removing high correlations between the random intercept and slopes, and high
correlations between first- and second-level variables and cross-level interactions (for a
detailed discussion of this aspect, the reader is referred to Kreft and de Leeuw, (1998),
pp. 135 to 137).
enjoy!