MOST IMPORTANT FINDINGS Clause Samples

MOST IMPORTANT FINDINGS. When the regression model was conducted on all 308 Flemish municipalities, only the control variables had a trend to significantly predict the variance in the conditional grants per capita. In the linear regressions, the control variables (2012: 12,8%; 2013: 9,8%) came up to have a considerably higher explanation value than the independent variables (2012 and 2013: 1,6%). Furthermore, the master’s thesis did not confirm the fact that the home bias is stronger closer to the elections since no big differences were detected between the results from 2012 and 2013. Remarkably, when a closer look was being taken at the home town of the minister president or vice minister president, a negative association was being discovered. This thus means that the home town of the minister president or vice minister president appeared to receive less conditional grants. Eventually, the master’s thesis results appeared to be different when the analyses were only applied on the municipalities with 30000 inhabitants or more. The statistical results showed that the model was able to explain 38,9% (2012) and 53,9% (2013) of the variance in the dependent variable, remarkably higher than the model for all 308 Flemish municipalities. The home bias thus appeared to be stronger in the larger cities since the independent variables remained significant after the addition of the control variables. The overall conclusion of this master’s thesis is thus that the home bias is not very noticeable when applied on all 308 Flemish municipalities. However, the impact seems to increase when the population size increases since the results from the 47 largest municipalities noticed stronger predicting values.