Hi All,
Long time reader, first time poster. I'm hoping some folks who have worked on models similar to the set I'm working on might be able to chime in about best practices regarding a few issues related to regression models using fixed effects and exploiting within-state changes over time to identify possible downstream changes in key state-level outcome variables. My dataset consists of state-level data on union density 1983-2007 for all 50 states. I am trying to estimate the causal effect of a significant change in 1 state's labor laws (Pennsylvania) in the year 1988. In other words my treatment variable of interest takes the value of 1 if the observation is for Pennsylvania for 1989-2007. All other observations take the value of 0. I then include state- and year-fixed effects and cluster my standard errors by state.
Questions:
(1) What are the advantages of including a lagged dependent variable? My results are robust to the inclusion of a lagged DV, but are there downsides to this more saturated model? And, if I include a lagged DV, should I also be including state FEs?
(2) I've heard some feedback that I should include state-specific linear time trends. Longtime poster Clyde has done a series of posts about this trying to help folks on Statalist. I confess I remain confused about how I would set this up on Stata. Right now my model is simply: reg y(union_density) + treatment_variable + i.statefips + i.year, cluster(statefips). Can someone explain how a state-specific linear time trend would be incorporated? And, the advantage?
(3) Third, and finally, what is the sentiment toward abandoning a treatment variable like the one I described above (where 1=a dummy for the state that was "treated" during years it was "treated" and everyone else gets a 0) for a nonparametric approach where a series of year dummies pre- and post- the law change are used for the treatment state (all other states get a zero). In other words, the inclusion of dummy variables for each relative year to PA's law change would impose no structure on the pattern of time trends either pre- or post-treatment. This flexibility could help identify non-linear impacts on state union over time no?
Appreciate any thoughts folks might be willing to offer.
Long time reader, first time poster. I'm hoping some folks who have worked on models similar to the set I'm working on might be able to chime in about best practices regarding a few issues related to regression models using fixed effects and exploiting within-state changes over time to identify possible downstream changes in key state-level outcome variables. My dataset consists of state-level data on union density 1983-2007 for all 50 states. I am trying to estimate the causal effect of a significant change in 1 state's labor laws (Pennsylvania) in the year 1988. In other words my treatment variable of interest takes the value of 1 if the observation is for Pennsylvania for 1989-2007. All other observations take the value of 0. I then include state- and year-fixed effects and cluster my standard errors by state.
Questions:
(1) What are the advantages of including a lagged dependent variable? My results are robust to the inclusion of a lagged DV, but are there downsides to this more saturated model? And, if I include a lagged DV, should I also be including state FEs?
(2) I've heard some feedback that I should include state-specific linear time trends. Longtime poster Clyde has done a series of posts about this trying to help folks on Statalist. I confess I remain confused about how I would set this up on Stata. Right now my model is simply: reg y(union_density) + treatment_variable + i.statefips + i.year, cluster(statefips). Can someone explain how a state-specific linear time trend would be incorporated? And, the advantage?
(3) Third, and finally, what is the sentiment toward abandoning a treatment variable like the one I described above (where 1=a dummy for the state that was "treated" during years it was "treated" and everyone else gets a 0) for a nonparametric approach where a series of year dummies pre- and post- the law change are used for the treatment state (all other states get a zero). In other words, the inclusion of dummy variables for each relative year to PA's law change would impose no structure on the pattern of time trends either pre- or post-treatment. This flexibility could help identify non-linear impacts on state union over time no?
Appreciate any thoughts folks might be willing to offer.
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