Dear all,
I am writing my master’s thesis and to conduct my analysis, I use register data/panel data. I have data on a large number of individuals before and after a reform. Thus, I want to do a fixed effects with DiD analysis. What I want to estimate the effect of a parental leave reform on wage.
I want to analyze three years prior to the reform and ten years after the reform was implemented.
I created a reform dummy (called reform), denoting the individuals who had their first child before and after the reform.
Further, I created a time dummy (called time) equal to one after the reform and 0 before and the code is as follows:
xtreg DepVar i.reform##i.time controls, fe robust
However I am concerned if I could just use the original years (hence 1995, 1996 1997, 1998, etc.) in the interaction term such as
xtreg DepVar i.year i.year##i.reform, fe robust
I would really appreciate if someone could help me on this one and to clarify which one is the right one to use given my intention with the analysis.
Thanks a lot.
Best,
Kamilla
I am writing my master’s thesis and to conduct my analysis, I use register data/panel data. I have data on a large number of individuals before and after a reform. Thus, I want to do a fixed effects with DiD analysis. What I want to estimate the effect of a parental leave reform on wage.
I want to analyze three years prior to the reform and ten years after the reform was implemented.
I created a reform dummy (called reform), denoting the individuals who had their first child before and after the reform.
Further, I created a time dummy (called time) equal to one after the reform and 0 before and the code is as follows:
xtreg DepVar i.reform##i.time controls, fe robust
However I am concerned if I could just use the original years (hence 1995, 1996 1997, 1998, etc.) in the interaction term such as
xtreg DepVar i.year i.year##i.reform, fe robust
I would really appreciate if someone could help me on this one and to clarify which one is the right one to use given my intention with the analysis.
Thanks a lot.
Best,
Kamilla
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