Ordered logistic model with varying intercepts (random effects)

I coauthor a paper with Ozan, a friend who I know when I was TAing Bayesian Methods in ICPSR. The paper tries to see if the 9/11 has any effect on American's attitudes on Muslim. We have dataset from different years: 2 from pre-9/11 periods and 2 from post-9/11 periods. The outcome variable y is ordered categorical in that:

1: very unfavorable; 2: mostly unfavorable; 3: mostly favorable; 4: very favorable

The most common way to model ordinal data like this is to postulate the existence of an underlying latent (unobserved) variable z associated with each response k of y. In other words, we fit such data with ordered logit/probit models. Since the data is time series cross sectional, the reasonable way to model it is to fit a multilevel model (See Andrew Gelman, 2007, Data Analysis using Regression and Multilevel/Hierarchical Models).

Here is the formal expression of the model with varying intercepts (random effects) by year:

Here is the code for BUGS, mologit.txt. A trick to genereate initial values for cutpoints is to assign 0 to each cutpoints. So for our model, it is a 4*3 matrix of 0's. (4 years, 3 cutpoints for each years). If you are using R2WinBUGS, the R code for the inits should be:

inits <- function(){              
   list(C=matrix(0,4,3))       
}  

Our preliminary result suggests that there is no effect of 9/11 on American's attitudes on Muslim. The paper is going to present at this year's MPSA conference at Chicago. The presenting time is on April 5 at 10:00am. We are welcome for comments on the paper.