Using a panel regression, estimate the effect of SCAP on any one of the pollutant levels (SO2, NO2 or SPM)

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Let’s try to investigate the effect of a particular environmental regulation plan on the level of pollution in India.

Prepare the Dataset

This data set contains information on pollution levels in different cities of India. The pollutants are SO2 (sulfur dioxide), NO2 (nitrogen dioxide) and SPM (suspended particulate matter), all in μg/m3 and measured at city-level.

Throughout the years, various environmental regulations were implemented in different cities. One of them is the Supreme Court Action Plan (SCAP) which imposes tougher regulations in some cities and ran from the start of 1997 to the end of 2005. It has a dummy called scap which equals 1 if the city was ever under the Supreme Court Action Plan and 0 otherwise.

District-level socio-economic variables such as population levels, state-level compliance rates of environmental regulations and corruption rates are also included.

The data set is an unbalanced panel as not every district is observed every year.

Question 1(a)

Time for some difference in differences!

First, plot means SO2 level against year (mean SO2 here is taking the mean of all e_mean_so2 observations). Plot one graph showing changes in mean SO2 in cities that were ever under SCAP, and another showing how changes in mean SO2 in cities that were never under SCAP. Add a vertical line at 1997 to indicate the start of SCAP. Then repeat this for mean NO2 levels against year and mean SPM levels against year.

Put the three time series graphs on your answer sheet. They may come in handy later.

Question 1(b)

Using a panel regression, estimate the effect of SCAP on any one of the pollutant levels (SO2, NO2 or SPM). Feel free to design your own regression specification and choose any control variables from the data set. Note that just one regression needs to be run.

Tell us what regression you ran, and why you chose this particular set of controls. Interpret your results briefly. What are the threats to the causal interpretation of your findings? You can also relate to your results in Q1(a).

 

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