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Table S1: Multilevel model for EU support. Bayesian and ML estimates.

Figure S1: Performance of point and interval estimates of individual level covariate effect inline image in hierarchical linear and probit models (type III/IV). Displayed are relative bias (in %) of estimates and 95% confidence interval non-coverage as a function of the number of countries used, and ML and Bayes estimation.

Figure S2: Performance of point and interval estimates of country level covariate effect inline image in hierarchical linear and probit models (type III/IV). Displayed are relative bias (in %) of estimates and 95% interval non-coverage as a function of the number of countries used, and ML and Bayes estimation.

Figure S3: Performance of point and interval estimates of country level covariate effect inline image in hierarchical linear and probit models (type V/VI). Displayed are relative bias (in %) of estimates and 95% interval non-coverage as a function of the number of countries used, and ML and Bayes estimation.

Figure S4: Performance of point and interval estimates of individual level covariate random coefficient inline image in hierarchical linear and probit models (type V/VI). Displayed are relative bias (in %) and 95% interval non-coverage as a function of the number of countries used, and ML and Bayes estimation.

Figure S5: Performance of point and interval estimates of country*individual level interaction effect inline image in hierarchical linear and probit models (type V/VI). Displayed are relative bias (in %) and 95% interval non-coverage as a function of the number of countries used, and ML and Bayes estimation.

Figure S6: Effect of intraclass correlation on performance of point estimates of country level effects inline image in a hierarchical probit model (type VI). Displayed is relative bias (in %) of the estimate as a function of three levels of intraclass correlations, the number of countries used, and ML and Bayes estimation.

Figure S7: Effect of different variance component priors on performance of point and interval estimates of inline image in a hierarchical linear model (type III). Displayed is 95% interval noncoverage as a function of the number of countries used, and inverse gamma and Gelman standard deviation uniform priors.

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