a. Multiple voters often live within the same household, but it would not be feasible to randomly assign different voters who live in the same households to different mailings; they would show the mailings to each other (creating an issue we will discuss in a future week). Therefore, the authors randomly assigned the data at the household level, such that ail people within a household will always get the same treatment. This is an example of clustering. Should you cluster standard errors when estimating ATE? At which level? [5 points] b. Estimate the ATE of all four treatments (treatmenLcivicduty, etc.) on the outcome variable, voted, using regression. Do not use any controls and do not worry about clustering standard errors yet. [5 points] 0- Now let's correctly cluster the standard errors to account for the fact that it was randomized at the household level. Take the regression you ran in part (b) and take the clustering into account. R, delightfully, does not have a built-in function for computing clustered standard errors. You need to insert the code below, and then call the function in that code using cl(output.from.im.here, cluster.variable.here). Report the standard errors for the estimated effects of the four treatments.[5 points] cl