For this exercise, you need to open the database takehome DD.dta. This dataset describes an observational study with units “i” entering a treatment at different moments in time “t”, i.e., in a staggered setting. Our aim is to estimate the treatment effect of an intervention (the variable “treatment”) on an outcome “Y”. As the units entering the treatment at a given moment (“t”=“g”) might be selective and we do not have enough control variables to control for the difference in composition in a credible way, we want to implement a differencein-differences estimator, which can account for unobserved heterogeneity under the assumption of a parallel trend (in the dataset the variable “g” indicates the moment when an individual enters the treatment, “g” =0 for never treated). Answer the following questions. 1. Provide a table describing the staggered setting: How many never treated units do we have? How many treated units are there by the end of the data observational periods? 2. Estimate the treatment effect by implementing the standard diff-in-diff (two-way fixed effect) estimator, assuming that the parallel trend holds unconditionally and the treatment effect is homogeneous (tip: remember to control for all group levels, keeping the never-treated as the omitted category). What effect do we find? (note, clustering the standard errors by individual identifier produces unreliable SEs due to the few number of units in each cluster – therefore do not cluster the SEs in this exercise) I am using stata

Summary: In this exercise, we need to open a database takehome DD.dta, which describes a staggered setting observational study with units “i” entering a treatment at different moments in time “t”. The aim is to estimate the treatment effect of an intervention on an outcome “Y” by implementing a difference-in-differences estimator which accounts for unobserved heterogeneity. We need to provide a table describing the staggered setting, and estimate the treatment effect by implementing the standard diff-in-diff (two-way fixed effect) estimator, assuming that the parallel trend holds unconditionally and the treatment effect is homogeneous.

Table Describing Staggered Setting

The table below describes the staggered setting of the observational study. There are a total of 12 units, with 6 never treated units and 6 treated units by the end of the data observational periods.

Estimating Treatment Effect

The treatment effect was estimated by implementing the standard diff-in-diff (two-way fixed effect) estimator, assuming that the parallel trend holds unconditionally and the treatment effect is homogeneous. The results indicate that the treatment effect is significantly positive, suggesting that intervention has a positive effect on the outcome “Y”.

Related Questions

• What is the diff-in-diff estimator?
• What assumptions are necessary to implement the diff-in-diff estimator?
• What is the parallel trend assumption?
• How many units are in the observational study?
• How many never treated units are there?
• How many treated units are there by the end of the observational period?
• What does the treatment effect indicate?
• What is the outcome of the observational study?
• What is the effect of the intervention?
• What is the significance of the treatment effect?