Hi Satalist Users,

I currently working on an applied project for my BSc.I am trying to estimate the effects of smoking on wages, using Longitudinal Data. As this is my first time using Stata and Im relatively new to econometric analysis, I would appreciate your help some help to get in the right direction.

Specifically I am using ln(hourly_wage) as a dependent variable and the following independet variables:
age and dummies on Smoking (Current_Smoker), Marriage (Married), Education (Education) and whether an individual lives in London (London_Region). Based on relevant literature I am expecting a negative coefficient on smoking and positive coeffecients on the rest.

Using the hausman test, I have to use a Fixed Effects model. However, in running both an RE and a FE, while I am getting the expected sings, the p-values are significant under RE but insignificant under FE. Any ideas as to why is that/Alternatives to proceed with.

Below is an example of the data I am using and the FE/RE model.

Thanks for reading and apologies if this question is too elementary.

Best,
Konstantinos

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float ln_hourly_wage int age float(Current_Smoker Educated Married London_Region)
         . 40 0 0 0 0
         . 41 0 0 0 0
-1.5998685 27 . 1 0 1
  1.938442 31 0 0 0 0
 1.4868356 32 0 0 0 0
  2.187459 34 0 0 0 0
 -1.466337 32 1 0 0 0
         . 43 . 0 0 0
         . 44 0 0 0 0
         . 45 0 0 0 0
         . 46 0 0 0 0
         . 26 0 0 0 0
  2.135531 43 0 0 0 1
 2.3328905 44 0 0 0 1
 2.2285895 48 0 0 0 0
 2.0928645 49 0 0 0 0
 2.7593434 50 0 0 0 0
 2.5510464 51 0 0 0 0
 2.0794415 52 0 0 0 0
 -1.466337 25 0 1 0 0
  1.634014 26 0 1 0 0
  2.012822 36 1 0 1 0
  1.941505 38 1 0 1 0
  1.974081 39 1 0 1 0
         . 34 0 0 0 0
         . 35 0 0 0 0
         . 36 0 0 0 0
 2.6621404 35 0 1 0 0
 2.8668995 37 0 1 0 0
         . 57 . 1 1 0
         . 58 0 1 1 0
         . 59 0 1 1 0
         . 60 0 1 1 0
  2.815501 30 0 1 1 0
 3.0727496 32 0 1 1 0
 3.0908585 33 0 1 1 0
  3.142028 34 0 1 1 0
  2.618958 45 1 1 1 0
 2.6634095 46 1 1 1 0
   2.86012 47 1 1 1 0
 2.5700946 35 1 0 1 0
  2.588787 37 0 0 1 0
  2.610766 38 1 0 1 0
   2.70206 39 0 0 1 0
  2.717156 40 0 0 1 0
   3.02489 39 0 1 0 0
   3.08373 40 0 1 0 0
  2.862104 43 0 1 0 0
  3.024423 30 0 1 1 0
  3.111434 31 0 1 1 0
 3.0202274 33 0 1 1 0
-1.5998685 44 0 1 1 0
-1.5998685 44 0 1 1 0
-1.5998685 45 0 1 1 0
 -1.466337 46 1 1 1 0
  2.540996 52 0 0 1 0
         . 53 0 0 1 0
 2.6597795 47 0 1 1 0
 -1.466337 48 0 1 1 0
 1.9348603 49 0 1 1 0
 2.3611286 50 0 1 1 0
   2.81445 44 0 1 1 0
  2.572438 45 0 1 1 0
  2.647983 46 0 1 1 0
  2.705969 48 0 1 1 0
   2.97104 28 0 1 1 0
 2.8081336 30 0 1 1 0
 3.3866694 32 0 1 0 0
  3.465736 33 0 1 1 0
 3.5352485 34 0 1 1 0
  3.491053 35 0 1 1 0
 3.5352485 36 0 1 1 0
  2.946461 39 0 1 1 0
 3.0608716 40 0 1 1 0
 3.0334725 42 0 1 1 0
  3.129784 44 0 1 1 0
 3.1297834 43 0 1 1 0
  3.182203 44 0 1 1 0
 3.1891665 45 0 1 1 0
  2.993651 46 0 1 1 0
   3.19914 47 0 1 1 0
  2.307622 59 0 1 0 0
  2.600979 60 0 1 0 0
 2.6227384 53 0 1 1 0
  2.664375 54 0 1 1 0
  2.729984 55 0 1 1 0
 -1.466337 56 0 1 1 0
 -1.466337 57 0 1 1 0
  3.075173 42 0 1 1 0
         . 55 0 1 1 0
         . 56 0 1 1 0
   3.50858 57 0 1 1 0
   3.62263 52 0 1 1 0
         . 53 0 1 1 0
 3.3449435 54 0 1 1 0
   3.61207 55 0 1 1 0
 3.3211546 56 0 1 1 0
 2.1594841 50 1 1 1 0
         . 51 1 1 1 0
 1.7361553 52 1 1 1 0
end
label values age i_age_dv