Hi everyone,
I have been trying to run regression analysis on the effect of ethnicity and gender on future employment, without much success. None of the p-value is significant (please see below). I have considered treating it as panel data as I have observations from 3 years and conduct a fixed effect model, to single out the unobserved heterogeneities however, I am confused as to which one I should set as panel variable in the "xtset" command, I have previously set salary as the panel var, however, it is the independent variable that cannot be the panel var.
Futhermore, many of my controlled variables are string, do I need to generate new variables and replace them with numeric variables one by one before conducting "xtreg" ?
Please also see below an extract of my data, open to any suggestion on how I can improve the current regression analysis. Thanks!
I have been trying to run regression analysis on the effect of ethnicity and gender on future employment, without much success. None of the p-value is significant (please see below). I have considered treating it as panel data as I have observations from 3 years and conduct a fixed effect model, to single out the unobserved heterogeneities however, I am confused as to which one I should set as panel variable in the "xtset" command, I have previously set salary as the panel var, however, it is the independent variable that cannot be the panel var.
Futhermore, many of my controlled variables are string, do I need to generate new variables and replace them with numeric variables one by one before conducting "xtreg" ?
Please also see below an extract of my data, open to any suggestion on how I can improve the current regression analysis. Thanks!
Code:
reg salary year genderx mature
Source | SS df MS Number of obs = 3,426
-------------+---------------------------------- F(3, 3422) = 0.51
Model | 1.4826e+13 3 4.9419e+12 Prob > F = 0.6750
Residual | 3.3125e+16 3,422 9.6801e+12 R-squared = 0.0004
-------------+---------------------------------- Adj R-squared = -0.0004
Total | 3.3140e+16 3,425 9.6759e+12 Root MSE = 3.1e+06
------------------------------------------------------------------------------
salary | Coefficient Std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
year | -6990.206 65826.15 -0.11 0.915 -136052.7 122072.3
genderx | -128063.3 106348.3 -1.20 0.229 -336575.9 80449.25
mature | -70051.69 238444.9 -0.29 0.769 -537560.4 397457
_cons | 314171.5 210461.1 1.49 0.136 -98470.57 726813.5
------------------------------------------------------------------------------
Code:
* Example generated by -dataex-. For more info, type help dataex clear input long salary str36 ethnicity str6 domicile float year 0 "Chinese" "UK" 1 0 "Chinese" "UK" 1 0 "White" "UK" 1 0 "White" "UK" 1 0 "White" "UK" 1 0 "Mixed - White and Asian" "UK" 1 0 "White" "UK" 1 0 "Black or Black British - African" "UK" 1 0 "White" "UK" 1 0 "White" "UK" 1 0 "White" "UK" 1 0 "White" "EU" 1 0 "White" "UK" 1 0 "White" "UK" 1 0 "White" "UK" 2 0 "White" "UK" 2 0 "White" "UK" 2 0 "White" "UK" 2 0 "White" "UK" 2 0 "Chinese" "non-EU" 2 0 "White" "UK" 2 0 "White" "UK" 2 0 "Black or Black British - Caribbean" "UK" 2 0 "White" "UK" 3 0 "Black or Black British - African" "UK" 3 0 "White" "UK" 3 0 "White" "UK" 3 0 "Other Asian background" "UK" 3 0 "Other Asian background" "UK" 3 0 "White" "UK" 3 0 "Information refused" "UK" 3 1 "White" "UK" 3 1 "Mixed - White and Asian" "UK" 3 1 "Mixed - White and Black Caribbean" "non-EU" 3 10 "White" "UK" 1 14 "White" "UK" 1 17 "White" "UK" 1 23 "White" "UK" 1 26 "White" "UK" 2 28 "White" "UK" 1 30 "Chinese" "non-EU" 1 40 "White" "EU" 1 40 "White" "UK" 1 40 "White" "UK" 1 40 "White" "UK" 2 45 "White" "UK" 1 48 "Asian or Asian British - Indian" "UK" 1 50 "White" "UK" 1 50 "White" "UK" 1 50 "White" "UK" 1 91 "White" "UK" 1 100 "White" "UK" 3 120 "Asian or Asian British - Indian" "non-EU" 1 200 "White" "UK" 3 300 "Chinese" "non-EU" 1 520 "White" "UK" 2 600 "White" "UK" 3 684 "White" "UK" 3 800 "Asian or Asian British - Pakistani" "UK" 2 850 "White" "UK" 3 900 "White" "UK" 2 1000 "White" "UK" 2 1000 "White" "UK" 3 1000 "Mixed - White and Asian" "UK" 3 1200 "Mixed - White and Black African" "UK" 2 1200 "White" "UK" 2 1200 "White" "UK" 2 1200 "White" "UK" 3 1350 "White" "non-EU" 2 1500 "White" "UK" 1 1500 "White" "UK" 1 1500 "Arab" "UK" 1 1500 "White" "UK" 2 1500 "White" "UK" 2 1680 "White" "EU" 1 1800 "White" "UK" 3 2000 "Mixed - White and Black African" "UK" 1 2000 "White" "UK" 1 2000 "White" "UK" 1 2000 "White" "UK" 2 2000 "White" "UK" 2 2000 "White" "UK" 2 2500 "White" "UK" 2 2600 "White" "UK" 3 3000 "Mixed - White and Asian" "UK" 1 3000 "White" "UK" 1 3000 "White" "UK" 1 3000 "White" "EU" 1 3000 "Mixed - White and Black Caribbean" "UK" 1 3000 "White" "UK" 1 3000 "White" "UK" 2 3000 "Asian or Asian British - Indian" "non-EU" 2 3000 "White" "UK" 2 3000 "White" "UK" 2 3000 "White" "non-EU" 3 3252 "White" "UK" 3 3300 "White" "UK" 3 3423 "White" "UK" 2 3500 "White" "EU" 2 3500 "White" "UK" 2 end
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