estat vif

[Yatharth Garg]

Hi everyone! I am trying to test for multicollinearity among the independent variables in my regression. Basically, one of the regressor is being omitted because of collinarity and I am trying to use

estat vif

after my regression but Stata returns r(321) 'invalid subcommand vif'. But I have checked the syntax multiple times so I am not sure why it is not working.

[Ken Chui]

What kind of regression models? VIF is not universally available, some regression like logit does not work with VIF.

[Yatharth Garg]

I am using Stata 13.1 that might explain why the vif subcommand is not working for me. Kindly advice!

[Ken Chui]

I am using Stata 13.1 that might explain why the vif subcommand is not working for me. Kindly advice!

-estat vif- exists in Stata 13. What I meant in #2 was when you run this code, what regression was submitted above that "estat vif" command. Just to be clear, I'd like to know what is the in the first line below:

** WHAT IS THIS REGRESSION COMMAND? **
estat vif

[Yatharth Garg]

Apologies, I did not see your #2 reply. I am using something similar to this

areg dependent_v independent_v if varlist1==n, absorb(varlist2) cluster(varlist2)

[Yatharth Garg]

And the dependent variable is expressed in terms of log. As you mentioned that estat vif does not work for logit models, is there any other way I could check for collinearity?

[Ken Chui]

Thanks, it seems it's not available for -areg-. Check -help areg postestimation- for more details.

[Jeff Wooldridge]

If a variable is being dropped then there is perfect collinearity, and you need to find out why. Computing the VIFs will not help. You already know the dropped variable is a perfect linear function of the included variables.

This board is much more helpful when you show your data set, Stata commands, and output -- as emphasized in the FAQ.

[Jeff Wooldridge]

If you're using areg, almost certainly a variable is being dropped because it has no variation within the groups defined by "variable2." In any case, VIFs are useless here.

[Yatharth Garg]

Unfortunately, there is nothing similar to VIF for areg postestimation. Hi Mr Wooldridge, apologies, I am including an example of my data set and code in this reply.

* Example generated by -dataex-. To install: ssc install dataex
clear
input float(logincome urbanisation urbanisation_MG CASTE RO_3) int RO5 float mar long IDPSU
11.007916 .2982456 0 6 1 21 0 10205
   11.045 .6133553 0 4 1 58 1 11301
11.877568        1 0 6 0  0 0 10307
13.802425 .6133553 0 1 0 72 . 11315
12.069107 .6133553 0 1 1 30 1 11305
 12.85396        0 0 6 0 37 1 10504
12.267168 .2982456 0 6 1 27 1 10201
11.104958        0 0 6 1 27 0 10501
13.239257 .6133553 0 2 0 28 1 11313
11.226576 .6133553 0 4 1 34 1 11315
12.745194 .6133553 0 1 0 43 1 11306
13.897643        0 0 6 0 24 1 10502
 11.34805        1 0 6 1 16 0 10305
13.103486        0 0 6 1 12 0 10505
        .        0 0 6 0 25 0 10504
12.340866        0 0 6 0  0 0 11204
11.849183 .6133553 0 4 0 47 1 11315
11.549644        0 0 2 1  1 0 11203
10.850307 .6133553 0 4 1 11 0 11315
 13.33804 .2982456 0 6 1 41 1 10204
11.506907 .6133553 0 2 1 79 . 11310
  12.4965 .6133553 0 2 0 47 1 11309
12.688498 .6133553 0 2 1 50 1 11312
12.261744        0 0 4 1 19 0 11206
11.440354 .6133553 0 1 0 25 0 11311
  11.5436 .6133553 0 2 1 33 0 11315
13.818007        0 0 6 1 47 1 10506
 12.31891        0 0 6 0 27 0 10505
 13.23678 .2982456 0 6 0 40 1 10201
11.302204        1 0 6 1 55 1 10301
10.753638 .6133553 0 2 1 50 . 11304
12.616774        0 0 6 1 33 1 10504
 10.92062        0 0 1 1 12 0 11201
11.127263 .2982456 0 6 1 35 1 10207
12.463425 .6133553 0 2 0 26 1 11304
 11.95761        1 0 6 0 46 1 10307
 11.01205 .2982456 0 6 1 24 0 10205
12.437184        1 0 6 1 46 1 10307
 11.43971 .6133553 0 4 0 31 1 11315
 10.83565        1 0 6 1 22 0 10304
12.328467        1 0 6 1 28 0 10301
12.415928 .2982456 0 6 0 52 . 10204
 12.51017 .2982456 0 6 1 31 0 10201
12.681996 .6133553 0 4 1 65 1 11301
13.897643        0 0 6 1 54 1 10502
 13.03735        1 0 6 1 42 1 10305
  12.5207        0 0 6 0  2 0 11204
13.362954        1 0 6 0 65 . 10302
12.611538        1 0 6 0 45 1 10304
10.590616 .2982456 0 5 0 47 1 10203
13.253654        0 0 2 1 38 0 11203
11.550221        0 0 1 1 24 0 11201
11.886556        0 0 6 0 19 0 10502
10.586584 .2982456 0 6 1  4 0 10205
13.924813 .6133553 0 1 0 32 1 11313
 12.03172        1 0 6 1 30 0 10308
10.968198 .6133553 0 3 0 20 1 11315
12.723526 .2982456 0 6 0 21 0 10203
12.685718 .2982456 0 6 0 28 0 10205
 12.40024        1 0 6 1 21 0 10301
11.320117        0 0 5 1 17 0 11204
  10.1186        0 0 2 0 11 0 11206
12.506177        1 0 6 0 52 1 10304
11.914717 .6133553 0 1 1 42 1 11302
 12.03172 .6133553 0 1 0 26 0 11310
13.141264 .2982456 0 6 0 75 1 10204
12.137955        1 0 6 0 25 0 10301
 12.82759        0 0 6 0 25 1 10502
 12.03172 .6133553 0 1 1 58 1 11310
13.853913 .2982456 0 6 0 40 1 10201
 12.27186 .2982456 0 6 0  7 0 10203
11.885179        0 0 6 1 20 0 10501
 12.03011 .2982456 0 6 0 17 0 10208
11.127263 .2982456 0 6 0 11 0 10207
13.897643        0 0 6 1 32 1 10502
10.874267 .6133553 0 4 0 60 1 11315
11.985426        1 0 6 1  1 0 10305
11.504893 .2982456 0 6 1 36 1 10208
 14.21217 .2982456 0 6 0 32 1 10204
12.366063        0 0 6 1 11 0 10501
 11.83747 .2982456 0 6 1 22 0 10207
 12.96688 .6133553 0 3 0 39 1 11309
13.226363 .6133553 0 1 0 75 . 11311
11.455297 .6133553 0 1 0 28 1 11302
12.283033 .2982456 0 6 1 39 1 10205
13.239257 .6133553 0 2 0 87 . 11313
13.602248        0 0 6 0 34 1 10506
12.439642        1 0 6 1 35 1 10301
13.602248        0 0 6 1 68 1 10506
 12.71387        0 0 6 1 21 0 10502
10.308952 .6133553 0 3 1 59 1 11310
12.919023        1 0 6 1  3 0 10309
 11.57449 .2982456 0 6 0 25 0 10206
 11.96591        0 0 6 1 52 1 10506
13.154444 .6133553 0 2 0 17 0 11307
 9.862665        0 0 6 0  6 0 10506
12.540936 .2982456 0 6 1 12 0 10205
 12.92518 .2982456 0 6 0 42 1 10202
 12.51869        0 0 4 1 35 1 11201
11.877568        1 0 6 1 35 1 10303
end
label values CASTE GROUPS
label def GROUPS 1 "Brahmin 1", modify
label def GROUPS 2 "Forward caste 2", modify
label def GROUPS 3 "OBC 3", modify
label def GROUPS 4 "Dalit 4", modify
label def GROUPS 5 "Adivasi 5", modify
label def GROUPS 6 "Muslim 6", modify

*output for migration
areg logincome urbanisation urbanisation_MG RO_3 RO5 mar if CASTE==1, absorb(IDPSU) cluster(IDPSU)
outreg2 using Migration_output.doc, word dec(2) replace
areg logincome urbanisation urbanisation_MG RO_3 RO5 mar if CASTE==2, absorb(IDPSU) cluster(IDPSU)
outreg2 using Migration_output.doc, word dec(2) append
areg logincome urbanisation urbanisation_MG RO_3 RO5 mar if CASTE==3, absorb(IDPSU) cluster(IDPSU)
outreg2 using Migration_output.doc, word dec(2) append
areg logincome urbanisation urbanisation_MG RO_3 RO5 mar if CASTE==4, absorb(IDPSU) cluster(IDPSU)
outreg2 using Migration_output.doc, word dec(2) append
areg logincome urbanisation urbanisation_MG RO_3 RO5 mar if CASTE==5, absorb(IDPSU) cluster(IDPSU)
outreg2 using Migration_output.doc, word dec(2) append

This is the model I am trying to run! but Stata drops urbanisation because of colleniarity

[Yatharth Garg]

* Example generated by -dataex-. To install: ssc install dataex
clear
input float(logincome urbanisation urbanisation_MG CASTE RO_3) int RO5 float mar long IDPSU
13.602248          0         0 6 0 34 1  10506
 12.51869          0         0 4 1 35 1  11201
 12.23157   .2982456         0 6 1 20 0  10203
13.406037   .6133553         0 1 1  4 0  11308
13.141264   .2982456         0 6 1 55 1  10204
11.647456   .6133553         0 4 1 26 0  11303
10.634653          0         0 5 0 58 1  11204
12.696797   .2982456         0 6 0 49 1  10206
12.363076          1         0 6 0 17 0  10308
12.676076   .6133553         0 2 1 75 .  11311
11.932294   .2982456         0 6 1 37 1  10201
11.925035   .6133553         0 1 0 33 1  11308
 12.23174          0         0 1 1 20 0  11201
 12.79247          0         0 6 0 12 0  11204
 13.72646          1         0 6 0 16 0  10305
 11.26641   .2982456         0 6 0 30 1  10203
12.439642          1         0 6 0 33 1  10301
 12.93362   .6133553         0 3 0 32 .  11305
 12.65076    .333045         0 2 1 39 1  20210
12.197738    .333045         0 3 0 52 1  20212
13.007047   .2364217         0 1 0 24 1  21006
 11.56143  .17322835         0 2 1 23 0  20506
11.132104  .26127526         0 2 0 27 1  20406
12.068877  .17322835         0 4 1 24 0  20510
12.587655   .2364217         0 4 1 33 1  21007
11.865833  .17322835         0 4 1 22 0  20501
12.133502   .3412798         0 2 1 86 1  21101
 12.01097   .2364217         0 2 1 39 1  21007
11.724806  .17322835         0 4 0 32 1  20507
12.075395    .333045         0 2 1 53 .  20207
11.149226    .333045         0 2 0 60 1  20205
13.433525          0         0 2 1  2 0  20601
11.354024          0         0 1 0 15 0  20604
11.796147          0         0 1 0 36 1  20104
 10.75475   .3412798         0 2 1 61 1  21105
 10.39452          0         0 4 0  8 0  20806
10.811746    .333045         0 2 1 24 0  20206
 11.10736          0         0 7 1 43 1  30801
13.387279   .3007246         0 7 1 30 1  30306
 11.06194          0         0 7 1 60 1  31606
 11.03489   .4038817         0 3 0 28 1  30104
10.691945          1         0 4 0 70 1  30404
13.387279   .3007246         0 7 0 60 1  30306
 10.08789   .1688126         0 2 1  9 0  30707
10.905956  .27672035         0 4 1 60 1  30202
 10.89117          0         0 4 1 11 0  31601
12.853883   .3007246         0 7 1 20 0  30305
 11.05089  .10143493         0 7 1  3 0  31112
 11.85779   .4353963         0 3 0  0 0  30913
11.656246  .10143493         0 4 0 19 0  31101
11.646144   .4353963         0 3 1 45 1  30913
 11.34569  .27672035         0 4 1 58 1  30208
 11.98293   .4038817         0 4 0  4 0  30108
 12.89922          1         0 7 1 26 0  30401
11.983866          0         0 4 0 24 .  31605
 11.01699   .4038817         0 7 1 17 0  30104
12.528156   .4353963         0 7 1 70 1  30901
12.644327   .4353963         0 4 0 26 0  30908
11.512925  .10143493         0 4 1  7 0  31105
11.157107          0         0 3 1 21 0  30601
 13.02924   .4038817         0 7 1 38 1  30112
10.491274          0         0 4 1 75 1  30506
11.184422          1         0 4 1 70 .  31002
11.758417  .10143493         0 3 1 10 0  31113
  11.0021   .4038817         0 2 0 37 .  30106
11.703712   .4353963         0 7 0 31 .  30913
 10.89674          0         0 4 0  3 0  31607
11.184422  .27672035         0 4 1 11 0  30208
11.514923          0         0 7 1 18 0  31603
11.571195          1         0 4 1 20 0  30404
11.390758  .27672035         0 4 1 65 .  30209
11.805968   .1688126         0 4 1 13 0  30707
11.208436   .1688126         0 7 1 45 1  30701
11.661345   .4038817         0 4 1 14 0  30104
11.931636          1         0 3 0 23 0  40403
12.682307          1         0 7 1 27 1  40401
 12.18689          1         0 6 1 23 0  40406
 11.04771  .13305323         0 4 1 27 1  51308
 10.90412          0         0 2 0 34 1  50901
11.221905          1         0 6 0 14 0  51203
 10.64766   .4036511         0 4 1  8 0  51103
 11.56281          0         0 2 0 60 .  50906
12.711926   .4036511         0 1 0 13 0  51101
12.813117   .4036511         0 4 0 21 0  51104
10.522585   .4036511         0 4 1 18 0  51102
 11.20697          0         0 3 0  0 0  50801
10.491274   .1765719         0 1 0  4 0  60207
  9.66323  .24048443         0 6 1 87 1  61811
11.500853  .23310024         0 4 0 17 0  61902
9.1049795  .24048443         0 3 0 22 1  61812
 12.10487   .1575246         0 2 1 24 0  61703
 8.349957          0         0 2 1 31 1  60406
 9.743905          0         0 1 0  4 0  60408
11.486582          0         0 2 1 24 1  60501
 11.20754   .1765719         0 6 0 50 .  60202
9.1049795  .24480712         0 2 0 52 .  61206
11.530766          0         0 4 0 12 0  60504
11.762906          0         0 2 0  7 0  61302
10.834667   .1765719         0 3 0 40 1  60201
11.356272  .11832947         0 3 1 13 0  60807
12.452542          0         0 6 1 24 1  60101
11.755872          0         0 2 1 38 1  60605
10.645425   .1765719         0 3 1 32 1  60201
11.620883          0         0 4 1 19 0  60501
 11.59035          0         0 4 1 47 1  61003
12.205072  .11832947         0 4 1  0 0  60806
13.621113  .23310024         0 2 0 75 .  61901
11.619085   .3035714         0 2 0  1 0  60910
11.695247   .1765719         0 4 0 14 0  60201
10.968198   .1765719         0 3 0 25 0  60209
13.164848  .11832947         0 1 1 19 0  60808
11.620883          0         0 4 0 21 0  60501
12.308358          0         0 2 0 27 1  60407
11.922848  .11832947         0 3 1 35 1  60804
11.177732          0         0 3 1 13 0  60504
10.723268          1         0 6 1 25 1  60301
11.448494   .1765719         0 4 0 20 1  60204
12.163687  .24048443         0 4 0 70 .  61805
10.714417  .24048443         0 6 0 30 1  61806
 11.05089   .1765719         0 4 1 27 0  60201
13.114532          0         0 2 0 45 1  60506
 9.798127  .24480712         0 2 0 14 0  61204
12.206073  .11832947         0 1 1  6 0  60808
10.730947          0         0 4 0 37 1  60401
11.760957  .23310024         0 4 0 25 1  61905
13.116144          0         0 2 1  5 0  60506
 13.82645   .1765719         0 2 1  0 0  60207
 12.01878          0         0 2 0 65 1  60505
12.300018          0         0 4 1  0 0  61306
13.490196          0         0 3 1 42 1  61002
12.100712          1         0 4 0 11 0  71013
11.334134          1         0 4 1  2 0  70102
12.729025          1         0 2 0 11 0  71026
 11.92867          1         0 4 0 19 0  70103
15.261724          1         0 6 0 67 1  71004
  11.5288          1         0 4 0 62 .  71008
11.338573          1         0 4 1 48 1  71039
11.703546          1         0 2 0 28 1  70401
11.474185          1         0 3 1 36 1  71015
12.386726          1         0 1 1 36 .  70401
13.263863          1         0 3 1 59 1  71020
12.375815          1         0 4 0 25 0  71038
11.763684          1         0 3 1 17 0  70704
11.849398          1         0 3 0 15 0  70903
12.183315          1         0 3 0 17 0  70107
 12.25009          1         0 6 0 36 .  71030
11.522876          1         0 4 0 15 0  71028
11.283512          1         0 4 0 13 0  71028
 11.48812          0         0 3 0 16 0  70201
13.715143          0         0 3 0  0 0  70201
12.465066          1         0 3 1 61 1  70101
11.744038          1         0 2 0 27 1  71034
12.492754          1         0 4 0 15 0  71037
11.289782          1         0 4 0 40 .  70901
11.938193          1         0 3 0  4 0  70101
11.170435          1         0 3 0  0 0  71101
12.267168          1         0 3 1 27 1  70301
11.810062          1         0 3 0 27 1  71036
  13.7547          1         0 4 0 41 .  71013
9.2103405          1         0 3 0 20 1  71008
 10.80304        .04         0 3 1 16 0  81104
 11.06484          0         0 3 1  0 0  80802
10.714417   .2814433         0 5 1 21 0  82003
 10.91181          0         0 3 0 75 .  80502
 11.09741          1         0 6 0 33 .  83002
11.760645   .3247745         0 4 1 17 0  81507
10.729854          0         0 2 0 12 0  80505
  12.7994  .14285715         0 1 1 50 1  80602
11.131665   .2347561         0 3 1 28 1  83209
 10.89674  .14285715         0 2 0 58 1  80610
11.468712          0         0 2 1 25 1  80805
  12.9244   .3314543         0 4 0 50 1  81304
11.159816          0         0 1 1  6 0  80503
10.668956        .04         0 4 0 32 1  81104
 11.17381  .14285715         0 3 1 32 1  80604
11.487095  .14285715         0 4 1  7 0  80604
 10.70549  .23435226         0 5 1  8 0  82603
11.218554          1         0 2 1 36 1  81406
12.483705          1         0 6 0 54 1  80303
 10.78932          0         0 4 0 50 1  80401
10.658435  .26223776         0 6 1 24 1  82402
12.620514   .3314543         0 4 1 24 0  81307
 12.58515          0         0 4 1 38 1  82503
13.519246          0         0 3 0 20 0  80803
 11.26894  .26223776         0 5 0  6 0  82411
 8.699514          0         0 3 0 14 0  80505
12.238782  .26223776         0 4 0 19 1  82411
 9.927057  .23435226         0 5 1 44 1  82602
 10.99876          0         0 4 0 16 0  80801
10.807706  .23435226         0 2 1 14 0  82607
 12.36376          0         0 3 0  7 0  80502
 12.38752   .3314543         0 3 0 12 0  81308
 11.24831          1         0 3 0 15 0  82905
11.980113          0         0 3 0 25 1  80701
11.946136          0         0 3 1 16 0  80504
12.042377          1         0 6 0 14 0  81208
  11.0137          0         0 2 1  8 0  80403
 10.95954   .2814433         0 3 1 41 1  82010
11.373663   .2814433         0 5 0 46 .  82003
10.986986          1         0 3 0 17 0  81406
10.434115  .26223776         0 3 0 28 1  82403
11.558568        .04         0 3 1 12 0  81104
11.093387          0         0 3 1 10 0  80702
12.726196          0         0 3 0  0 0  80504
11.127263   .3247745         0 6 1 13 0  81510
10.706161   .2347561         0 3 1 40 0  83204
 11.83007   .3247745         0 2 1 15 0  81507
 10.87047  .26223776         0 3 1 55 1  82403
11.956168          0         0 4 1 14 0  80803
11.712596  .23435226         0 3 0 18 .  82601
12.299108          0         0 1 0 50 .  80902
 5.857933  .23435226         0 5 0  2 0  82603
 12.24011   .3314543         0 3 1  9 0  81304
 13.50783   .3314543         0 3 0 17 0  81304
10.658905          0         0 5 0 47 1  80703
11.809022   .2814433         0 4 1  2 0  82005
11.272216   .3247745  .3247745 3 1 42 1  81502
 10.58532  .26223776         0 3 0  0 0  82403
10.682813          0         0 3 1 15 0  80706
12.010392          0         0 3 0 25 1  80406
10.824168  .23435226         0 5 0 10 0  82602
12.282988          0         0 1 1 26 1  80701
 10.47164   .2347561         0 6 0 10 0  83208
10.817777          0         0 3 0  5 0  82501
11.505397   .3314543         0 3 1 30 .  81309
12.179472          0         0 2 0 18 .  80801
11.272216   .3247745         0 3 0 62 .  81502
 11.81835          1         0 4 1 46 1  81403
 11.07287          0         0 4 1 60 1  80506
 13.13172   .3247745         0 6 0 22 0  81513
 11.09559          0         0 3 1  5 0  94704
10.850405          0         0 3 0 50 1  93301
10.844275          0         0 3 0 39 1  96803
10.596635          1         0 6 0  2 0  96203
10.308952          1         0 6 1 12 0  96203
10.351374          0         0 4 0 55 1  96603
 9.920836          0         0 4 0  5 0  92903
 8.919988          0         0 3 1 65 1  93001
11.200678          0         0 6 1 12 0  94903
11.865412          0         0 4 0 36 1  90602
 11.87416          0         0 6 1 90 1  95406
11.411447   .1969925         0 1 1 12 0  96501
 11.95761          0         0 1 1 32 1  96804
 9.291459          0         0 3 0 18 .  95003
10.173514          0         0 5 0 11 0  96804
 10.45161          0         0 3 1 36 1  96801
11.701063          1         0 6 0 38 1  96201
12.528156   .1969925         0 1 1 48 1  96510
12.405743          1         0 6 1 17 0  91005
 10.89674          0         0 3 0  5 0  95102
11.042922   .6284404         0 6 1 50 1  93407
 10.54902          0         0 3 1 37 1  94903
10.571317   .3543092  .7086183 6 1 24 1  90306
11.913123   .3616715         0 5 1 58 1  96703
 11.60368   .1969925         0 6 0 24 1  96510
 11.99102          0         0 4 1 34 1  90207
10.795075   .3543092         0 4 0  5 0  90310
 12.51135   .4301598         0 6 0 25 1  90402
 10.88669          1         0 2 1 15 0  91505
 11.11632   .1969925         0 4 1 83 1  96503
 11.73607          1         0 6 0 21 0  91506
11.192307   .1604938         0 3 0 67 1  92302
11.192307   .1604938         0 3 1 15 0  92302
10.842518          0         0 4 0 22 0  93301
 11.11543   .4301598         0 6 1 25 0  90402
10.506436   .1604938         0 4 0 19 1  92303
 10.04325          0         0 6 1 42 1  96801
10.404263   .6284404         0 4 0 52 1  93406
12.581113   .3543092         0 2 1 62 1  90307
12.354492          1         2 3 1 20 0  91503
 11.03489   .4301598         0 6 1 60 1  90406
10.322198   .3543092         0 6 0 11 0  90308
 11.82041          1         0 1 0  0 0  91901
11.829924          1         0 2 1 40 1  90703
10.849337          0         0 3 1  9 0  93001
10.289022          0         0 4 1 41 1  93002
 13.29519          1         0 2 1 53 1  91902
13.260385   .6284404         0 2 0 22 0  93405
11.783715          0         0 2 1 55 1  93301
11.656662          0         0 4 1  3 0  92903
 11.73679   .6284404         0 4 0 35 1  93403
11.599562          0         0 4 1 20 0  90207
 9.937889          0         0 4 1 10 0  94904
11.177033          0         0 3 1 62 .  94005
10.919805          0         0 4 1 26 1  90602
10.873338   .4301598         0 4 0 22 1  90411
  9.66903          0         0 3 1 23 .  95004
10.691717          0         0 4 0  8 0  96601
11.141862          1         0 4 0 42 1  90701
 10.45161   .4301598         0 4 0  4 0  90404
 9.784141          0         0 6 1 65 .  95004
  11.0021          1         0 6 1  5 0  90702
11.661345          1         0 6 0  9 0  96303
 9.701983          0         0 3 0 36 1  92901
 11.39245          0         0 3 0 55 1  94506
11.352522          0         0 3 1  4 0  95905
 9.990399   .1604938         0 3 0 20 1  92304
11.012545          0         0 3 1 60 .  95904
11.695247          1         0 6 1 35 1  94601
10.208395   .1969925         0 3 0 35 1  96504
 11.68385          0         0 3 1  3 0  94703
11.872137          0         0 3 1 26 1  94702
12.591335          1         0 6 0 18 0  96202
10.893641          0         0 3 1 40 1  95403
11.103753          0         0 4 0 13 0  90207
12.595408   .4041667         0 2 1  4 0  91409
11.005427   .6284404         0 6 1 20 0  93409
11.400988   .1969925         0 2 0 17 0  96507
10.497643          0         0 6 0 52 .  94803
 9.949416   .1604938         0 4 0 20 0  92307
11.182057   .2937788         0 4 1 22 1  92405
 10.11557   .6284404         0 4 0 54 1  93402
11.372514          0         0 4 0 14 0  94205
 10.44287   .4301598         0 3 0 19 1  90401
11.140556          0         0 4 0  3 0  92905
12.742566          1         0 2 0 41 1  90703
12.003957          1         0 6 1 20 0  90906
11.383386          1         0 3 1 20 0  91504
10.415413          0         0 4 0  7 0  96603
12.105865          0         0 3 1 48 1  90508
 10.14506          0         0 3 1 40 1  94801
11.472103          0         0 6 0 25 1  90504
10.778956   .2340824         0 6 0  0 0  90102
 11.29228          0         0 1 1 74 1  94802
11.082911   .4301598         0 4 1  6 0  90411
 11.67844   .4301598         0 6 0 70 .  90409
10.729634   .3616715         0 3 0 61 1  96701
10.379566          0         0 3 1 35 1  93001
10.189418          0         0 3 0 38 1  93302
 9.348536   .2937788         0 4 0 11 0  92403
        .          0         0 3 1  8 0  92904
 12.65076          1         0 2 0 62 1  91006
 9.569063   .1604938         0 3 0  6 0  92306
10.123225          0         0 2 1 15 0  90507
11.315693          0         0 2 0  4 0  95906
10.788494          0         0 4 1 40 1  94905
10.768485          0         0 3 0 28 .  94401
10.496815          1         0 6 0 32 1  96302
10.939338          0         0 3 0 21 .  95403
 10.83958          1         0 3 0 45 1 102802
11.462905  .05714286         0 6 0 10 0 102708
11.576838          0         0 6 0 46 1 101607
  11.0021          0         0 6 0  7 0 100504
 9.653101          0         0 5 1  8 0 100501
10.923237  .05714286         0 4 1 62 1 102701
10.170457          0         0 3 1 22 0 100502
 10.81537          0         0 3 1  5 0 100604
 9.908475  .57722175         0 3 0 20 0 103201
 12.50132  .57722175         0 2 0 29 1 103211
10.435585          0         0 4 0  7 0 101608
11.925035          0         0 3 1  7 0 101204
 11.50136          0         0 2 0 50 1 101407
10.819778          1         0 6 0 32 1 101701
 9.773664          0         0 3 1 59 1 101403
 10.79794          0         0 3 1 58 1 101406
11.859348          1         0 3 1 39 1 103003
10.736397          0         0 3 0 40 1 100605
13.384727          0         0 2 1 20 0 100205
11.461632  .57722175         0 4 1 39 1 103209
11.164785  .05714286         0 6 0 15 0 102708
 11.10736  .57722175         0 1 0 13 0 103205
10.924138  .57722175         0 3 1 19 0 103202
11.058795          1         0 6 1 29 1 103002
 11.91572          1         0 3 1 17 0 102602
10.886502   .4023569         0 3 1 51 . 102204
10.348173          1         0 3 0 30 1 103501
  10.3791          0         0 4 0 45 1 101202
 11.15625          1         0 3 0 17 0 102805
10.561525          0         0 2 0 72 . 101607
10.983597          0         0 3 1  5 0 100202
10.330714          0         0 3 0 30 1 100503
 9.644717          0         0 4 1 11 0 100506
 9.740969  .57722175         0 4 0 34 . 103206
11.492723  .57722175         0 3 1 12 0 103209
 11.15052  .57722175         0 3 0 16 0 103213
11.225244  .57722175         0 6 0 55 . 103209
 10.69274          0         0 4 0  7 0 100502
11.225297          0         0 2 0 36 1 101204
 10.86092          0         0 3 1  7 0 101406
  11.5158          0         0 4 1 18 0 100507
 8.877661          0         0 3 1  0 0 101201
10.229188          0         0 2 1 10 0 100203
 9.798127          0         0 6 1  6 0 101401
11.151238          0         0 3 0 15 0 101402
11.021085          0         0 3 0 28 1 103102
10.678215          0         0 3 1 28 1 101402
 10.90779          0         0 3 1  5 0 100604
12.624783  .05714286         0 6 1 68 1 102702
 9.903487          1         0 3 1  0 0 103501
11.500427          1         0 6 0 16 0 103502
 12.88917   .4023569         0 3 1 32 0 102207
11.695247   .7852883         0 3 1 44 1 110402
 11.84582   .7852883         0 5 0 48 1 110401
 12.97618   .7852883         0 2 1  4 0 110403
 10.20081   .7852883         0 3 0  3 0 110403
12.100712    .252994         0 5 1 38 1 120407
11.227906    .252994         0 5 0 55 1 120408
 12.16525    .252994         0 5 0 20 0 120402
11.758128   .3956693         0 5 0 24 0 130302
 11.71709   .4927235         0 2 0 42 1 140701
12.528156   .3314121         0 5 1 47 1 150304
11.741653   .3314121         0 5 0 27 0 150304
 10.68739   .3314121         0 5 1 47 1 150305
10.747208   .2100109         0 5 1 22 0 160105
11.472103   .2100109         0 2 0 42 1 160101
11.695247   .2100109         0 3 0 42 1 160103
10.317086   .2100109         0 3 1 12 0 160104
11.092854   .2100109  .4200218 2 1 25 0 160101
10.382822   .2100109         0 2 0 12 0 160106
12.372857  .22594753         0 2 0 35 0 170501
13.093964  .22594753         0 5 1 29 1 170605
 11.95761  .22594753         0 5 0 30 1 170603
 11.88449  .22594753         0 5 0 48 1 170701
        .          0         0 6 0 12 0 180204
 10.65726          0         0 6 0 40 . 180207
10.360912   .1636219         0 2 0  2 0 180603
 12.25914          0         0 5 0 70 . 180207
12.437184  .11953727         0 5 0 12 0 180903
  11.9694          0         0 6 0 25 1 180209
        .  .11953727         0 6 0 15 0 180905
12.305692   .3088235         0 4 0 44 0 181703
11.695247          1         0 2 0 26 1 182101
 9.528067          0         0 2 1  8 0 180201
 11.05089          0         0 6 0 10 0 180203
 9.767095  .11953727         0 5 0 29 1 180909
 9.042631   .1636219         0 5 1 32 1 180602
12.628067          1         0 3 0 48 1 181402
11.126233  .11953727         0 5 1 55 1 180912
12.197032   .3088235         0 2 0  0 0 181705
 13.01698   .1636219         0 6 1 34 . 180610
 11.03004  .11953727         0 6 1 32 1 180908
10.542706          0         0 5 0 40 1 180206
 11.55981          0         0 6 1 64 1 180208
13.221303   .1636219         0 2 1 60 1 180611
 10.86092   .3088235         0 3 1  9 0 181706
12.297193   .1636219         0 5 1 34 1 180601
11.084448   .1636219         0 4 0  9 0 180607
13.835313   .3088235         0 2 0  4 0 181705
11.589887   .1636219         0 5 1 20 0 180601
10.348173   .3088235         0 6 1 43 1 181709
12.225875          1         0 6 0 38 1 180302
12.483705          1         0 1 0 44 1 181901
 10.42228   .3088235         0 4 0 55 1 181701
 9.442087    .474824         0 2 0  2 0 190921
 10.22557  .05788177         0 4 0  2 0 190802
10.849337          1         0 2 1 10 0 191206
11.164785          1         0 2 0 33 1 191609
 9.406729  .20939335         0 6 0 34 1 190601
 9.880731    .474824         0 2 1 34 1 190921
11.210333          0         0 6 1 22 0 190708
11.309585          1         0 3 1 50 1 191303
11.995352          1         0 3 1 32 1 191608
11.284744          0         0 4 0 45 1 190701
10.361546  .59172076         0 2 0 42 1 191103
10.259062  .05788177         0 6 0 22 0 190801
 10.84017    .474824         0 4 0 42 1 190922
10.631036          0         0 4 1 49 1 190107
11.962727    .474824         0 4 1 25 1 190909
10.201738    .474824         0 4 1 35 1 190904
11.711777          1         0 1 1 62 1 191202
11.800358          0         0 4 1 34 1 190708
11.255708    .474824         0 4 0 16 0 190908
10.348814  .27573964         0 6 1 15 0 191008
11.018465          0         0 4 0 11 0 190102
 10.29011  .16473617         0 2 0 64 . 190204
11.338573          1         0 2 1 41 1 191805
10.993731          0         0 4 0 43 1 190107
        .  .59172076 1.1834415 4 1 19 0 191107
10.096172          0         0 5 1 11 0 221502
10.019937          0         0 3 0  1 0 220702
 9.392662    .318328         0 3 0 65 1 220102
 9.642123    .318328         0 5 0 34 1 220103
 10.85773          0         0 3 0 62 1 220905
10.966473     .43875         0 1 0 25 0 220205
10.914925   .3820225         0 3 0  9 0 221004
11.695247   .3344948         0 1 0 14 0 220406
12.560245   .6068223         0 4 0 28 1 221105
 9.546813   .3344948         0 5 1 70 1 220405
10.776035   .3820225         0 3 1 39 1 221003
10.196903   .3820225         0 3 1  6 0 221007
11.934684          0         0 6 1 21 1 220901
 9.945589          0         0 3 1 13 0 220903
 10.07954          0         0 5 0 47 1 220905
10.966473     .43875         0 1 0 40 0 220205
 9.772809   .3344948         0 5 0 12 0 220404
10.517159          0         0 3 1 14 0 234404
  11.9117          0         0 1 0 55 . 230502
 11.57025          0         0 3 0 27 1 233509
11.435936          0         0 3 0 65 1 232301
10.818778          0         0 4 0 12 0 230801
 10.92062          0         0 3 1 11 0 232002
12.260324          0         0 1 1 12 0 233603
 10.47164          0         0 3 1 35 1 233602
 9.510445          0         0 4 0 65 . 231202
10.511893          0         0 5 1 45 1 233506
11.407565          1         0 3 1 20 0 232604
 9.445492   .3539683  .3539683 5 1 22 1 232703
10.302935          0         0 4 0 65 . 232301
 9.148465          0         0 4 0 53 . 232006
11.267408          0         0 5 1  1 0 231707
 12.42296          0         0 4 1  4 0 232302
10.884648          0         0 4 0  0 0 230801
10.681665    .347079         0 4 1  6 0 230205
10.325482  .26302084         0 4 0 45 1 232102
10.075337          0         0 4 1  4 0 232805
10.568107   .3539683  .3539683 5 1 45 1 232705
10.940225          0         0 2 0 25 1 272607
11.338573   .2130584         0 4 0 14 0 273111
 9.833601    .299396  .5987921 3 1 24 1 292603
10.866662  .19053876         0 3 0 23 0 291705
11.209114          0         0 4 0 78 . 290502
11.150088          0         0 3 1 85 1 291601
11.116916          0         0 6 0 45 1 291301
12.613203          0         0 3 1 55 1 291601
10.264652          0         0 3 1 50 1 291302
10.819778  .19053876         0 6 0 26 1 291709
10.668956          1         0 5 0 34 1 290104
12.395452   .3343949         0 3 0 28 1 291203
 10.20869          0         0 3 0  4 0 291302
10.407289          0         0 5 0 48 1 291301
 11.70767   .3343949  .6687898 6 1 28 1 291211
end
label values CASTE GROUPS
label def GROUPS 1 "Brahmin 1", modify
label def GROUPS 2 "Forward caste 2", modify
label def GROUPS 3 "OBC 3", modify
label def GROUPS 4 "Dalit 4", modify
label def GROUPS 5 "Adivasi 5", modify
label def GROUPS 6 "Muslim 6", modify
label def GROUPS 7 "Christian, Sikh, Jain 7", modify

This might be a better sample size for my data, the variable urbanisation_MG has 202,000 observations with value '0'. And only 3,388 with either 1 or 2

[Jeff Wooldridge]

Sort your data by IDPSU and you'll see that urbanization does not vary within IDPSU. Which variable do you care about? I bet urbanization is just a control variable, in which case it's fine to just drop out. And you're supposed to show your output; using outreg2 doesn't help.