Hello there, I am currently completing a project which looks at evaluating the impact of a policy which came into effect in 2006. I have panel data which goes from 2000-2016, with an average of 15,000 people per 'wave'.
The policy: changed the eligibility for welfare parental payments (pre-2006 child could be up to 16 years old, post-2006 child is not able to be over 8 years old)
My question: did this change to the eligibility affect workforce participation of those who were receiving the payments pre-change
I've done a trial of the diff-in-diff using the below code
I'm not too sure about the above yet, as I think I need to add in more variables into the regression first, so if you have any points on that it would be good. More importantly, I would like to know if i've set up the regression correctly?
Also, prior to the diff-in-diff estimation it's been suggested I use
to match up households to make the idff-in-diff more 'precise'. I wanted to get your opinion on this?
Thank you very much in advance for any comments!
Rebecca
The policy: changed the eligibility for welfare parental payments (pre-2006 child could be up to 16 years old, post-2006 child is not able to be over 8 years old)
My question: did this change to the eligibility affect workforce participation of those who were receiving the payments pre-change
I've done a trial of the diff-in-diff using the below code
Code:
*wave==6 means 2006, which is when the change came into effect gen time=0 replace time=1 if wave==6 *receivingpayments is equal to 1 if they are, and 0 if not. 'childundereight' is equal to 1 if true and equal to 0 if not (referring to youngest child) gen treated=0 replace treated=1 if receivingpayments==1&childundereight==0 *lfp refers to lfp. It is 1 if in labour force (unemployed or employed) and 0 if not. reg lfp time##treated, robust
Also, prior to the diff-in-diff estimation it's been suggested I use
Code:
psmatch2
Thank you very much in advance for any comments!
Rebecca
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