Hi everyone !

I'm currently working on the effect of the german minimum wage of 2015 (8.5 EUR per hour) using a nonlinear difference-in-differences model using panel data. More precisely i'm interested in the effect of a minimum wage on the employment retention probability. My treatment group consits of the people whose wage will have to increase in order to comply with the new minimum wage (<8.5 EUR), while my comparison groupe is composed by the people who earn a wage slightly above the minimum wage (between 8.5 and 12.5). I first estimated a logit diff-in-diff model as following
Code:
 clear all
use sample_1315.dta

*generate diff-in-diff variable
gen time = (welle>=2015) & !missing(welle)
label variable time " 1= after reform 0= before reform"
gen treated = (hourly_wage_contract <8.5)   & !missing(hourly_wage_contract)
label variable treated " eligible to minimum wage"
gen interaction= treated*time

gen full_time_experience_sqr= (full_time_experience)^2 
gen part_time_experience_sqr= (part_time_experience)^2 
gen age_sqr=(age)^2

gen year2013 =(welle==2013)
gen year2014 =(welle==2014)
gen year2015 =(welle==2015)

logit employed i.(treated time interaction)  i.year2013  if hourly_wage_contract < 12.5  /* only with year controls*/
margins, dydx(interaction)  at(treated==1 time==1)