First, check if the dataset contains any missing values. Next, compute the mean of outcome (Y) by region without repeatedly using the mean() function.

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Question 1

We begin by loading the data and complete the following tasks. First, check if the dataset contains any missing values. Next, compute the mean of outcome (Y) by region without repeatedly using the mean() function. You must only use ONE line of code. Report the results and say something about them.

 

Question 2 (20 points)

The authors employed the Difference-in-differences (DiD) approach to estimate the change in administrative costs in control and treatment municipalities, before and after the reform.

Create a time-series plot of the mean outcome by year.  The horizontal axis is the years whereas the vertical axis is the mean outcome. You should have two lines connecting data points (each data point should be marked by a symbol): one for the treatment group and the other for the control group. Add a vertical dashed line on the year 2007 so that the pre- and post-treatment periods are clear. Lastly, properly label x and y axes. Do not forget to use different colors and add text labels to differentiate two lines.

 

Question 3 (20 points)

What is the identification assumption for the DiD approach? How can we investigate this? Does the plot you created in the previous question confirm that the assumption is likely to hold in this application?

 

Question 4 (40 points)

Using the data from years 2007 (the last year of pre-treatment period) and 2008 (the first year of post- treatment period), compute the DiD estimate of the effect of municipal merger on administrative costs. Report a point estimate as well as its 95% confidence interval. Make sure to use the qnorm function to get the appropriate quantiles. Briefly interpret the result.

 

Question 5 (Extra credit up to 20 points)

In Questions 2 and 3, we examined the validity of using the DiD estimator by a visual inspection. This question asks you to investigate the validity by calculating DiD estimates in pre-treatment periods (that is, conduct a placebo test). If the outcomes from both treated and control groups moved in tandem before the amalgamation happened, the DiD estimate should be zero. Follow the following steps to complete this question:

1. Define a function named calc_did() that takes the following arguments: data (the dataframe used), pre_year (the pre-treatment time period used for the DiD estimation), and post_year (the post- treatment time period used for the DiD estimation). The calc_did() function returns a vector with three elements that correspond to the lower bound of the 95% confidence interval, the point estimate, and the upper bound of the 95% confidence interval.

2. Use the function that you defined in the Step 1 and confirm that your answer is the same as the previous question. Run calc_did(data = data, pre_year = 2007, post_year = 2008).

3. Check the following outcomes and briefly comment on the results: calc_did(data = data, pre_year

= 2006, post_year = 2007) and calc_did(data = data, pre_year = 2005, post_year = 2006).

 

 

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