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The ICC is important to indicate to what extent the clustering of data, if not taken into account, will violate the assumption of independent observations within groups and result in inflated standard errors of coefficients, and a greater likelihood of seeing nonexistent relationships (Type I errors). Even as small an intraclass correlation as 0.01, with about 50 observations within each area, will inflate the Type I error from the posited a =.05 to about a =.11 (Kreft and DeLeeuw, 1998).

Secondly, a model was constructed which explained within-area variance, followed by a model explaining between-area variance (Bryk and Raudenbush, 1992). Four inequality variables were specified as main effects for the final model, in which cross-level interactions with significant micro variables were fitted (Snijders and Bosker, 1999). Continuous variables were centered around the grand mean to minimize cross-level correlations, all other variables were dichotomized.