This sounds like it might be an appropriate situation for multilevel modeling. How many different regions do you have? If there are many (say, dozens or more) you might wish to take such an approach (c.f. Duncan et al., 1998)
On the other hand, educational attainment can be incorporated as a numerical predictor quite successfully, although I always explore its functional relationship with the outcome by (1) using a nonparametric smoothing regression (Beck, 1997; Hastie and Tibshirani, 1987) in order to inform (2) specify a nonlinear (in all likelihood) functional form, usually with nonlinear least squares regression (Davidson, 2004).
If there are relatively few political parties, you might wish to retain the indicator variables for these categories.
Beck, N. and Jackman, S. (1997). Getting the mean right is a good thing: gen- eralized additive models. Working paper, Society for Political Methodology.
Davidson, R. and MacKinnon, J. G. (2004). Econometric Theory and Methods, chapter 6: Nonlinear Regression. New York: Oxford University Press.
Duncan, C., Jones, K., and Moon, G. (1998). Context, composition and heterogeneity: Using multilevel models in health research. Social Science & Medicine, 46(1):97–117.
Hastie, T. and Tibshirani, R. (1987). Generalized Additive Models: Some Applications. Journal of the American Statistical Association, 82(398):371–386.