Jacob Mincer was a great labor economist at Columbia University. We were colleagues together in the 1990s. He didn't think I was funny but he was very kind to me and to Dora. Here is a Mincer Regression that I doubt he ever ran. I take Federal Employee data from 1998 to 2014 (I'm hiding some details because I'm not ready to publish this yet). I regress log annual nominal earnings on state dummies, a male dummy, dummies for one's rank in the government bureaucracy, year dummies and occupational dummies). Note the 11.8 million observations in the regression. All hail Stata!
Two points to note; first the R2=.96. Kevin Murphy taught us that in private markets that the R2 in the Mincer is never about .35. The U.S government follows its compensating rules very closely. I don't see much discretion here. Note the monotonic returns to seniority (the G variables). Second, note the absence of a Federal male/female wage gap. Men earn .4% more per year. So, if Women earn $75,000 per year, then men earn $75,300.
If my return to labor economics interests you, then you'll have to wait to see what we are really doing here. I'll give you a hint. It involves Berkeley, California.
.
. areg Y S2-S51 male G2-G15 Y2-Y9, absorb(occ)
Linear regression, absorbing indicators Number of obs =
11883541
F( 73,11883006)= 1437775.24
Prob > F = 0.0000
R-squared = 0.9590
Adj R-squared = 0.9590
Root MSE = 0.0941
------------------------------------------------------------------------------
Y | Coef.
Std. Err. t P>|t|
[95% Conf. Interval]
-------------+----------------------------------------------------------------
S2 | -.0584136 .0003898
-149.86 0.000 -.0591776
-.0576496
S3 | -.0014201
.0002753 -5.16 0.000
-.0019597 -.0008806
S4 | -.0045901
.0003787 -12.12 0.000
-.0053323 -.0038479
S5 | .0724196
.000215 336.88 0.000
.0719982 .0728409
S6 | .0250518
.0002655 94.37 0.000
.0245315 .0255721
S7 | .0788722
.0004602 171.38 0.000
.0779702 .0797743
S8 | .0435599
.0007237 60.19 0.000
.0421414 .0449784
S9 | .0327149
.0002164 151.21 0.000
.0322908 .0331389
S10 | .0021632
.0002354 9.19 0.000
.0017018 .0026246
S11 | .0059898
.0002374 25.23 0.000
.0055245 .006455
S12 | -.0453146
.0003261 -138.96 0.000
-.0459537 -.0446754
S13 | -.004479
.0004149 -10.80 0.000
-.0052922 -.0036659
S14 | .0398877
.0002574 154.96 0.000
.0393832 .0403923
S15 | -.0036889
.00032 -11.53 0.000
-.0043162 -.0030617
S16 | -.0069479
.0004509 -15.41 0.000
-.0078318 -.0060641
S17 | -.0088755
.000345 -25.73 0.000
-.0095517 -.0081993
S18 | .0010579
.0003129 3.38
0.001 .0004447 .0016711
S19 | -.0003606
.000319 -1.13 0.258
-.0009858 .0002647
S20 | .0201575
.0004486 44.93 0.000
.0192782 .0210368
S21 | .0401513
.0002201 182.40 0.000
.0397199 .0405827
S22 | .0605932
.0002932 206.67 0.000
.0600186 .0611679
S23 | .0348972
.0002987 116.83 0.000
.0343117 .0354826
S24 | .0272551
.0003521 77.41 0.000
.026565 .0279452
S25 | -.004654
.0003375 -13.79 0.000
-.0053154 -.0039926
S26 | -.0053167
.0002705 -19.65 0.000
-.005847 -.0047865
S27 | -.0050044
.0003977 -12.58 0.000
-.0057839 -.0042248
S28 | -.0080507
.0004135 -19.47 0.000
-.0088612 -.0072402
S29 | -.0087135
.0004239 -20.56 0.000
-.0095444 -.0078827
S30 | .039973
.0006492 61.57 0.000
.0387006 .0412455
S31 | .0820698
.0002933 279.85 0.000
.081495 .0826445
S32 | -.0042781
.0003049 -14.03 0.000
-.0048757 -.0036806
S33 | .0549101
.0002444 224.70 0.000
.0544312 .0553891
S34 | .0023059
.0002748 8.39 0.000 .0017673
.0028445
S35 | -.0056885
.000518 -10.98 0.000
-.0067037 -.0046732
S36 | .0127869
.0002525 50.64 0.000
.0122921 .0132818
S37 | -.0063721
.0002892 -22.03 0.000
-.0069389 -.0058053
S38 | .0146571
.0003204 45.74 0.000
.0140291 .0152851
S39 | .0287533
.00024 119.82 0.000
.028283 .0292236
S40 | .043632
.0005334 81.80 0.000
.0425866 .0446775
S41 | .0021588
.0003361 6.42 0.000
.0015 .0028175
S42 | -.0059394
.0004605 -12.90 0.000
-.006842 -.0050369
S43 | -.006146
.0003003 -20.47 0.000
-.0067345 -.0055575
S44 | .0150256
.0002199 68.34 0.000
.0145947 .0154565
S45 | -.011567
.0002976 -38.87 0.000
-.0121502 -.0109838
S46 | -.0255405
.0005998 -42.58 0.000
-.0267161 -.0243649
S47 | .0236543
.0002199 107.57
0.000 .0232233 .0240853
S48 | .0312477
.0002576 121.29 0.000
.0307428 .0317527
S49 | -.0029294
.0003558 -8.23 0.000
-.0036268 -.002232
S50 | .0062762
.0003709 16.92 0.000
.0055491 .0070032
S51 | -.0115276
.0005162 -22.33 0.000
-.0125393 -.0105159
male | .0042255
.0000627 67.34 0.000
.0041025 .0043484
G2 | .1360782
.0007881 172.66 0.000
.1345335 .1376228
G3 | .2481286
.0006704 370.11 0.000
.2468146 .2494426
G4 | .4101902
.0006466 634.40 0.000
.4089229 .4114574
G5 | .5453368
.0006401 851.94 0.000
.5440823 .5465914
G6 | .6643886
.0006434 1032.61 0.000
.6631276 .6656497
G7 | .7809516
.0006386 1222.89 0.000
.7796999 .7822032
G8 | .9128841
.000653 1397.90 0.000
.9116041 .914164
G9 | .9921551
.0006436 1541.51 0.000
.9908936 .9934166
G10 | 1.109301
.0006936 1599.30 0.000
1.107941 1.11066
G11 | 1.185898
.0006445 1840.05 0.000
1.184635 1.187161
G12 | 1.375831
.0006441 2136.07 0.000 1.374569
1.377093
G13 | 1.562526
.0006456 2420.19 0.000
1.56126 1.563791
G14 | 1.741792
.0006504 2678.10 0.000
1.740517 1.743067
G15 | 1.923275
.0006597 2915.38 0.000
1.921982 1.924568
Y2 | .0830945
.0001158 717.55 0.000
.0828676 .0833215
Y3 | .1635206
.0001157 1413.20 0.000
.1632938 .1637474
Y4 | .2433555
.0001182 2058.21 0.000
.2431238 .2435872
Y5 | .3048701
.0001182 2578.50 0.000
.3046383 .3051018
Y6 | .3615331
.0001193 3029.45 0.000
.3612992 .361767
Y7 | .411804
.0001153 3572.55 0.000
.4115781 .4120299
Y8 | .4186785
.0001153 3632.58 0.000
.4184526 .4189044
Y9 | .4336627
.0001191 3640.46 0.000
.4334292 .4338962
_cons | 9.513801
.0006672 1.4e+04 0.000
9.512493 9.515109
-------------+----------------------------------------------------------------
occ | F(461, 11883006) = 3872.969
0.000 (462 categories)