Hello everyone,
I really need help.
I am trying to see the impact of a reduction of tariffs on the wage skill premium for a country after trade liberalization. My databse is a panel databse at firm level. The wage skill premium is the wages of skilled workers over the wages of unskilled workers.
I want to use the directors wages as proxy for skilled workers' wages. The problem is that I have 70% of missing values for directors remuneration. Is there an econometric solution/ method I can use ? I saw on internet that there are methods like multiple imputation or maximum likelihood. I am not an expert in econometrics and I don't know if it is possible and which method could be the best one.
Thank you bery much for your help. I really need it because I am bistranded.
sum of directors remunerations variable:
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
directorsr~n | 64909 .0980383 .7602785 0 67.53
sum of salaries and wages variable:
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
salarieswa~s | 64909 6.254965 48.94163 0 3867.4
example of a little part of my dataset:
I really need help.
I am trying to see the impact of a reduction of tariffs on the wage skill premium for a country after trade liberalization. My databse is a panel databse at firm level. The wage skill premium is the wages of skilled workers over the wages of unskilled workers.
I want to use the directors wages as proxy for skilled workers' wages. The problem is that I have 70% of missing values for directors remuneration. Is there an econometric solution/ method I can use ? I saw on internet that there are methods like multiple imputation or maximum likelihood. I am not an expert in econometrics and I don't know if it is possible and which method could be the best one.
Thank you bery much for your help. I really need it because I am bistranded.
sum of directors remunerations variable:
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
directorsr~n | 64909 .0980383 .7602785 0 67.53
sum of salaries and wages variable:
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
salarieswa~s | 64909 6.254965 48.94163 0 3867.4
example of a little part of my dataset:
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input float year byte industry float(compensationtoemployees salarieswages directorsremuneration) 1989 . .77 0 0 1989 . 9.79 0 0 1989 . 1.66 0 0 1989 . .39 0 0 1989 . 17.72 0 0 1989 . 2.31 0 0 1989 . 11.94 0 0 1989 . 4.68 0 0 1989 . .18 0 0 1989 . .48 0 0 1989 . 3.73 0 0 1989 . 1.04 0 0 1989 . 2.01 0 0 1989 . .71 0 0 1989 . 11.96 9.83 0 1989 . 11.32 0 0 1989 . .06 0 0 1989 . 2.34 0 0 1989 . 1.4 0 0 1989 . 3.39 2.81 0 1989 . .08 0 0 1989 . 1.44 0 0 1989 . .89 0 0 1989 . .68 0 0 1989 . 1.24 0 0 1989 . 825.95 0 0 1989 . .32 0 0 1989 . 1.16 0 0 1989 . .4 0 0 1989 . 54.73 0 0 1989 . .71 0 0 1989 . 3.17 0 0 1989 . 5.02 0 0 1989 . 34.11 0 0 1989 . 2.73 0 0 1989 . 2.53 0 0 1989 . 1.53 0 0 1989 . 8.56 0 0 1989 . 1.13 0 0 1989 . 1.2 0 0 1989 . .06 0 0 1989 . .47 0 0 1989 . 16.72 0 0 1989 . 6.52 0 0 1989 . .94 0 0 1989 . 12.7 0 0 1989 . 2.94 0 0 1989 . .21 0 0 1989 . 2.1 0 0 1989 . .64 0 0 1989 . .76 0 0 1989 . .04 0 0 1989 . 2.3 0 0 1989 . 1.48 0 0 1989 . 45.48 0 0 1989 . .36 0 0 1989 . 2.45 0 0 1989 . 10.72 0 0 1989 . 3.15 0 0 1989 . 15.31 0 0 1989 . 2.05 0 0 1989 . 1.14 0 0 1989 . .92 0 0 1989 . 2.5 1.99 .03 1989 . 6.62 0 0 1989 . 5.25 0 0 1989 . .93 0 0 1989 . 202.37 0 0 1989 . 1.86 0 0 1989 . 7.07 0 0 1989 . .34 0 0 1989 . 1.08 0 0 1989 . 5.35 0 0 1989 . 2.05 0 0 1989 . 12.68 0 0 1989 . 1.19 0 0 1989 . 31.62 0 0 1989 . .16 0 0 1989 . 13.62 0 0 1989 . 7.5 0 0 1989 . .79 0 0 1989 . 2.04 0 0 1989 . 11.83 0 0 1989 . 5.38 0 0 1989 . 2.01 0 0 1989 . 1.36 0 0 1989 . .18 0 0 1989 . 1.08 0 0 1989 . .77 0 0 1989 . 1.6 0 0 1989 . .77 0 0 1989 . .22 0 0 1989 . 4.45 0 0 1989 . 6.47 0 0 1989 . 25.08 0 0 1989 . 8.05 0 0 1989 . 4.12 0 0 1989 . 14.42 0 0 1989 . 5.18 0 0 1989 . .32 0 0 end