viol_inc hat = 33.21 (5.29) + 12.43lockout (7.01) – 4.19Yr2015 (2.98) – 3.32 (lockout x Yr2015) (1.85) N = 181, R^2 = 0.128 Where:
We are interested in analysing the effect of the lockout laws introduced in some areas of the Sydney CBD and nearby surrounds on the number of alcohol-related violent incidents.
Suppose we have two samples of data on the number of violent incidents in a number of local areas of the Sydney CBD and nearby surrounds. Pubs, hotels and clubs located in a subset of these areas became subject to the lockout laws when they were introduced in 2014. The first sample is from 2010 before the introduction of the lockout laws, and the second is from 2015 after the introduction of the law. The hypothesis we wish to test is that the introduction of the lockout laws reduces violent incidents in the areas in which the lockout laws were put in place.
We use a difference-in-difference model on the pooled data from 2010 and 2015. We find the following results:
viol_inc hat = 33.21 (5.29) + 12.43lockout (7.01) – 4.19Yr2015 (2.98) – 3.32 (lockout x Yr2015) (1.85)
N = 181, R^2 = 0.128
Where:
- viol_incis the number of violent incidents in the local area
- lockoutis an indicator variable equal to 1 if the area is one in which the lockout laws were introduced, and equal to 0 otherwise
- Yr2015is a dummy variable equal to 1 if the year is 2015 and 0 otherwise
Using the information above, answer the following 3 questions.
[i] Which local areas are the treatment group, and which are the control group?
[ii] From the results in Model (D1), what is the average number of violent incidents in 2010 across the local areas that were not covered by the lockout laws?
[iii] From the results in Model (D1), what is the average number of violent incidents in 2015 across the local areas that were covered by the lockout laws?
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