This is falling prey to inferring causality from correlational data. It is just as possible that greater crime rates lead to greater spending on police. These are the standard errors associated with the coefficients. The standard errors can also be used to form a confidence interval for the parameter, as shown in the last 2 columns of this table. If you use a 2 tailed test, then you would compare each p value to your preselected value of alpha.
Coefficients having p values less than alpha are significant.
With a 2 tailed test and alpha of 0.05, you can reject the null hypothesis that the coefficient for maleteen is equal to 0. The coefficient of 1.03 is significantly greater than 0. However, using a 2 tailed test and alpha of 0.01, the p value of 0.019 is larger than 0.01 and the coefficient for maleteen would not be significant at the 0.01 level. This would yield a one tailed p value of 0.00945, which is less than 0.01 and then you could conclude that this coefficient is greater than 0 with a one tailed alpha of 0.01. The coefficient for south is not significantly different from 0 using alpha of 0.05 because its p value is 0.297 is larger than 0.05.