I am working with an event study approach to see if the effect of becoming a parent on earnings is different for men vs women. the year of the first child birth (when individual i becomes a first time parent) is event time t = 0 and the rest is indexed. I leave event dummy t=-1 out of the regression so that the other event time dummy variables measure the effect relative to right before the child birth. time window is t=-2 to t=5. to make dummy variables, event time is not set at t=-2 to t=5, but at t=1 to t=8. female = 1 for female and 0 for male. income is gross earnings.
I run the following regressions to estimate this:
Code:
clear all program eventstudy reg income ib2.eventtime i.age i.year, robust cluster(id) predict income_p, xb generate b = . replace b = 0 if eventtime==2 generate se = . replace se = 0 if eventtime==2 foreach i in 1 3 4 5 6 7 8 { replace b = _b[`i'.eventtime'] if eventtime==`i' replace se = _se[`i'.eventtime'] if eventtime==`i' } generate income_c = income_p - b end use "sampleddefinitive.dta" runby eventstudy, by(female) verbose collapse income income_p income_c b se, by(female eventtime) generate gap = b/income_c generate gapse = se/income_c generate cilow = gap - 1.96*gapse generate cihigh = gap + 1.96*gapse reshape wide income income_p income_c b se gap gapse cilow cihigh, i(eventtime) j(female) generate penalty = (b1-b2)/income_c2
If the latter is the case, is it then best to use 1 regression and use interaction effects? If so, is this the correct regression and how do I test whether there is a difference?
Code:
regress income i.age i.year ib2.eventtime (ib2.eventtime i.age i.year)#i.female i.female, robust cluster(id)
Thank you in advance

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