Hi! I have ran a regression analysis, see below. It shows that people who do Economics, Mathematics and Computer Sciences earn significantly higher than others. It also shows that Asian Indian and Pakistani earn significantly more than others. I want to test whether Indian and Pakistani students are self-selecting into the higher paying subjects with "teffects ipwra" and conducted a test.
Code:
reg ln_real_income year_2018_19 year_2019_20 Female Mature first_class lower_second_class third_class un
> classified London arts_humanities social_sciences medicine Economics Law Mathematics Computer_sciences P
> arents_no_degree imd_40pc disability polar1_2 asian_high_paying black_high_paying mixed_high_paying Arab
> Asian_Bangladeshi Asian_Indian Asian_Pakistani black_African black_Caribbean Chinese no_info mixed_whit
> e_asian mixed_white_african mixed_white_caribeean
Source | SS df MS Number of obs = 2,062
-------------+---------------------------------- F(34, 2027) = 31.26
Model | 91.0613268 34 2.67827432 Prob > F = 0.0000
Residual | 173.677701 2,027 .085682141 R-squared = 0.3440
-------------+---------------------------------- Adj R-squared = 0.3330
Total | 264.739027 2,061 .128451736 Root MSE = .29272
---------------------------------------------------------------------------------------
ln_real_income | Coefficient Std. err. t P>|t| [95% conf. interval]
----------------------+----------------------------------------------------------------
year_2018_19 | -.0092687 .015784 -0.59 0.557 -.0402233 .021686
year_2019_20 | -.0586619 .0161195 -3.64 0.000 -.0902744 -.0270494
Female | -.0826012 .0139492 -5.92 0.000 -.1099575 -.0552449
Mature | .0382448 .0370626 1.03 0.302 -.0344398 .1109295
first_class | .0645985 .0166097 3.89 0.000 .0320247 .0971723
lower_second_class | -.0891009 .0381644 -2.33 0.020 -.1639464 -.0142554
third_class | -.058365 .0899435 -0.65 0.516 -.2347564 .1180263
unclassified | .1204162 .0230371 5.23 0.000 .0752373 .165595
London | .1986692 .0141862 14.00 0.000 .1708481 .2264902
arts_humanities | -.2094648 .020395 -10.27 0.000 -.2494621 -.1694675
social_sciences | -.1152332 .0226748 -5.08 0.000 -.1597014 -.0707649
medicine | .0317705 .0263738 1.20 0.228 -.0199521 .0834931
Economics | .247034 .0343026 7.20 0.000 .1797621 .314306
Law | -.0295825 .0362345 -0.82 0.414 -.1006432 .0414783
Mathematics | .2362116 .0347456 6.80 0.000 .1680708 .3043525
Computer_sciences | .3802876 .0369847 10.28 0.000 .3077555 .4528196
Parents_no_degree | .0247399 .0197299 1.25 0.210 -.0139531 .063433
imd_40pc | -.0324318 .02108 -1.54 0.124 -.0737724 .0089089
disability | -.040524 .0205868 -1.97 0.049 -.0808975 -.0001505
polar1_2 | -.0528427 .0230941 -2.29 0.022 -.0981334 -.0075521
asian_high_paying | -.0357674 .0383723 -0.93 0.351 -.1110206 .0394858
black_high_paying | .0939296 .0981908 0.96 0.339 -.0986358 .286495
mixed_high_paying | -.0064315 .0584076 -0.11 0.912 -.1209768 .1081137
Arab | -.0762608 .0990565 -0.77 0.441 -.270524 .1180023
Asian_Bangladeshi | -.0335714 .0946529 -0.35 0.723 -.2191985 .1520557
Asian_Indian | .1352684 .0332033 4.07 0.000 .0701522 .2003846
Asian_Pakistani | .2403407 .0715806 3.36 0.001 .0999616 .3807199
black_African | -.0290208 .0718641 -0.40 0.686 -.1699561 .1119145
black_Caribbean | .0679038 .1301019 0.52 0.602 -.1872437 .3230513
Chinese | .0672874 .0416096 1.62 0.106 -.0143146 .1488894
no_info | .0953633 .0606149 1.57 0.116 -.0235108 .2142374
mixed_white_asian | .0328668 .0405814 0.81 0.418 -.0467188 .1124524
mixed_white_african | -.0501021 .0914115 -0.55 0.584 -.2293724 .1291682
mixed_white_caribeean | .0842579 .0897269 0.94 0.348 -.0917086 .2602245
_cons | 10.28573 .0208051 494.38 0.000 10.24493 10.32654
---------------------------------------------------------------------------------------
Code:
teffects ipwra ( high_paying_subject Female imd_40pc polar1_2 disability) ( Asian imd_40pc p
> olar1_2 disability)
Iteration 0: EE criterion = 5.742e-24
Iteration 1: EE criterion = 1.099e-33
Treatment-effects estimation Number of obs = 2,062
Estimator : IPW regression adjustment
Outcome model : linear
Treatment model: logit
------------------------------------------------------------------------------
| Robust
high_payin~t | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
ATE |
Asian |
(1 vs 0) | .1998188 .0362915 5.51 0.000 .1286888 .2709489
-------------+----------------------------------------------------------------
POmean |
Asian |
0 | .3066177 .0108662 28.22 0.000 .2853202 .3279151
------------------------------------------------------------------------------