Hi,

I am currently trying to plot earning paths between men and women once having controlled for certain covariates.

Currently I am using coeplot and so my code is this:

reg real_earnings hrs_wkd_monthly ugpa i.time_since_educ if female==1 & time_since_educ <90

eststo g1

reg real_earnings hrs_wkd_monthly ugpa i.time_since_educ if female==0 & time_since_educ <90

eststo g2

coefplot (g1, label("Female")) (g2 , label("Male" )) ///

, drop(_cons hrs_wkd_monthly ugpa) vertical ci title ("Evolution of real earnings from time left education, controlling for hours worked, experience, ugpa", size(vsmall)) xlabel(1 "2" 11 "12" 23"24" 35"36" 47"48" 59"60" 71 "72" 83"84" ) xtitle("Time since education (in months)") graphregion(color(white)) bgcolor(white)

I have included a dummy for each month after education (time_since_educ) and therefore can plot an average earning for each gender for each month out of education. However, because they are dummies the first month out of education is omitted and then we compare earnings in each future month to this. As a result I am left with the initial value as normalised to 0 (i.e. the second month out of education most individuals are earning about the same as men).

To be clear I have attached an image of the graph I am currently plotting. You can see that earnings start at a normalised point and hence do not show the initial gap (this is because in each regression we are comparing to the initial time point). t2.pdf

I want to be able to see the initial earnings gap (which I know exists in my data of about $700/month).

I know an alternative would be to create averages for earnings for each gender at each time period and plot this, but this doesnt allow me to control for covariates and also to include the confidence intervals, which is what I am after.

I hope this is clear and would appreciate any comments!

Lucy

I am currently trying to plot earning paths between men and women once having controlled for certain covariates.

Currently I am using coeplot and so my code is this:

reg real_earnings hrs_wkd_monthly ugpa i.time_since_educ if female==1 & time_since_educ <90

eststo g1

reg real_earnings hrs_wkd_monthly ugpa i.time_since_educ if female==0 & time_since_educ <90

eststo g2

coefplot (g1, label("Female")) (g2 , label("Male" )) ///

, drop(_cons hrs_wkd_monthly ugpa) vertical ci title ("Evolution of real earnings from time left education, controlling for hours worked, experience, ugpa", size(vsmall)) xlabel(1 "2" 11 "12" 23"24" 35"36" 47"48" 59"60" 71 "72" 83"84" ) xtitle("Time since education (in months)") graphregion(color(white)) bgcolor(white)

I have included a dummy for each month after education (time_since_educ) and therefore can plot an average earning for each gender for each month out of education. However, because they are dummies the first month out of education is omitted and then we compare earnings in each future month to this. As a result I am left with the initial value as normalised to 0 (i.e. the second month out of education most individuals are earning about the same as men).

To be clear I have attached an image of the graph I am currently plotting. You can see that earnings start at a normalised point and hence do not show the initial gap (this is because in each regression we are comparing to the initial time point). t2.pdf

I want to be able to see the initial earnings gap (which I know exists in my data of about $700/month).

I know an alternative would be to create averages for earnings for each gender at each time period and plot this, but this doesnt allow me to control for covariates and also to include the confidence intervals, which is what I am after.

I hope this is clear and would appreciate any comments!

Lucy

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