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.
I am not sure whether I have understoof teffects ipwra correctly: I assumed that being female, being the 40% most impoverished in the country (IMD_40pc), being in areas with the lowest higher education participation (POLAR1_2) and having disability affects one's choice to do higher paying subjects at university. In the results below, does it indicate that being an Asian means that one is 19.98% more likely to choose higher paying subjects?
My questions are: how to I correctly use teffects ipwra to test self-selective effects, and, how would I interpret the teffects ipwra results? Thank you in advance! Any help would be very appreciated!
Code:
reg ln_real_income year_2018_19 year_2019_20 Female Mature first_class lower_second_class third_class unclassified London arts_humanities social_sciences medicine Economics Law Mathematics Computer_sciences Parents_no_degree imd_40pc polar1_2 disability2 asian_high_paying black_high_paying mixed_high_paying Arab Asian_Bangladeshi Asian_Indian Asian_Pakistani black_African black_Caribbean Chinese Gypsy no_info mixed_white_asian mixed_white_african mixed_white_caribeean other_asian other_black other_ethni other_mixed
Source | SS df MS Number of obs = 2,062
-------------+---------------------------------- F(37, 2024) = 26.69
Model | 86.8227337 37 2.34656037 Prob > F = 0.0000
Residual | 177.916294 2,024 .087903307 R-squared = 0.3280
-------------+---------------------------------- Adj R-squared = 0.3157
Total | 264.739027 2,061 .128451736 Root MSE = .29648
---------------------------------------------------------------------------------------
ln_real_income | Coefficient Std. err. t P>|t| [95% conf. interval]
----------------------+----------------------------------------------------------------
year_2018_19 | -.0066294 .015992 -0.41 0.679 -.0379919 .024733
year_2019_20 | -.0538776 .016335 -3.30 0.001 -.0859126 -.0218425
Female | -.0859628 .014141 -6.08 0.000 -.1136952 -.0582304
Mature | .0472211 .0375194 1.26 0.208 -.0263595 .1208018
first_class | .0610436 .0168296 3.63 0.000 .0280385 .0940488
lower_second_class | -.1135958 .0386488 -2.94 0.003 -.1893913 -.0378002
third_class | -.0846431 .0910253 -0.93 0.353 -.2631562 .0938699
unclassified | .0835025 .0228795 3.65 0.000 .0386327 .1283723
London | .2113647 .014288 14.79 0.000 .183344 .2393854
arts_humanities | -.2518371 .0198149 -12.71 0.000 -.2906967 -.2129774
social_sciences | -.15883 .0221505 -7.17 0.000 -.2022701 -.11539
medicine | .0107031 .0266903 0.40 0.688 -.0416403 .0630465
Law | -.0879987 .035868 -2.45 0.014 -.1583408 -.0176566
Mathematics | .1945213 .0347577 5.60 0.000 .1263567 .262686
Computer_sciences | .3357554 .0369588 9.08 0.000 .2632741 .4082366
Parents_no_degree | .0271592 .0200198 1.36 0.175 -.0121024 .0664207
imd_40pc | -.0335472 .0214549 -1.56 0.118 -.0756231 .0085287
disability2 | .0365298 .0208912 1.75 0.081 -.0044407 .0775002
polar1_2 | -.0557097 .0234141 -2.38 0.017 -.1016281 -.0097914
asian_high_paying | .0250073 .0401323 0.62 0.533 -.0536976 .1037122
black_high_paying | .1971525 .0983907 2.00 0.045 .0041948 .3901101
mixed_high_paying | .0534454 .0601928 0.89 0.375 -.0646009 .1714918
Arab | -.0710604 .1003542 -0.71 0.479 -.2678686 .1257478
Asian_Bangladeshi | -.0545143 .0959663 -0.57 0.570 -.2427173 .1336887
Asian_Indian | .1449718 .0348664 4.16 0.000 .0765941 .2133495
Asian_Pakistani | .2208525 .0728182 3.03 0.002 .078046 .363659
black_African | -.0453323 .0728138 -0.62 0.534 -.1881302 .0974656
black_Caribbean | .0543343 .1317912 0.41 0.680 -.2041262 .3127948
Chinese | .0471459 .0428536 1.10 0.271 -.036896 .1311877
no_info | .1089667 .0613981 1.77 0.076 -.0114434 .2293768
mixed_white_asian | .0226828 .0415478 0.55 0.585 -.058798 .1041637
mixed_white_african | -.0553939 .0928383 -0.60 0.551 -.2374625 .1266748
mixed_white_caribeean | .0853587 .0910051 0.94 0.348 -.0931147 .263832
other_asian | .0721104 .0624849 1.15 0.249 -.050431 .1946518
other_black | -.0657053 .2972024 -0.22 0.825 -.6485599 .5171492
other_ethni | .0761133 .0772964 0.98 0.325 -.0754754 .227702
other_mixed | -.0011103 .0534764 -0.02 0.983 -.1059848 .1037642
_cons | 10.28388 .027373 375.69 0.000 10.2302 10.33757
---------------------------------------------------------------------------------------
Code:
. teffects ipwra ( high_paying_subject Female imd_40pc polar1_2 disability) ( Asian imd_40pc polar1_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
------------------------------------------------------------------------------
My questions are: how to I correctly use teffects ipwra to test self-selective effects, and, how would I interpret the teffects ipwra results? Thank you in advance! Any help would be very appreciated!
