[Zahid Khan]

Thank you, Clyde, for the reply, I understand your point but how come the following example data allows the estimation without dropping an additional year while it has a similar variable this time lmw which is constant within a year?
-----------------------

* Example generated by -dataex-. To install: ssc install dataex
clear
input float(logspike lmw median_age median_education share_illeterate share_retired share_students share_unmarried share_women share_agri share_services share_trading share_manufacturing year)
-3.4170706  7.824046   35 10  10.90684 2.3139918  2.023752 26.797155  13.73886 4.3713713  44.90681   13.2719  4.224636 2004
 -2.841785  8.294049   36 10  10.11645  .5896513  2.232368  23.83671  12.69499  .4683629   52.9002   4.71407  3.142393 2006
-3.8485096  8.433811   35 10  8.807885     1.994 2.0053542 25.155243  12.78523  5.743135  39.88345 11.423695  2.910006 2008
 -3.060271  8.987197   35 10  9.257348 1.1297612  3.469716  24.63409 14.048172  2.913295  57.50639  7.941474  5.878557 2010
-3.1668806 9.1049795   37 12 11.313846 1.1189824  3.246734   23.5888  15.25079  2.674123  42.88018 1.3306923  5.635561 2012
 -2.626447  9.392662 35.5 10 14.250965   1.44758  5.981085 24.215256 18.896103  5.309056  41.92178 11.290117 4.3161345 2014
 -3.035263  7.824046   35  8  24.94258  2.411582 2.0687857 23.043375  5.596759 18.071205 37.827724 16.069403   9.58853 2004
 -1.979691  8.294049   33 10  23.63444 1.3409512 1.1733979 29.169844  6.616175  2.940082   39.2728  11.80261 11.010347 2006
 -4.603168  8.433811   40 10   29.7847  5.690811  1.241238  20.39517  6.313539  30.08603  18.02271   11.8409  7.477448 2008
-2.4328504  8.987197   36  8  24.49669 2.2567503 2.0568764  25.74037  7.116378 13.233109  29.95527 17.699556  6.658371 2010
-2.5248394 9.1049795   37  9 21.075203  3.399511 2.2731442  22.63762   7.94648 21.418705 21.197315  .4158455  6.402743 2012
-2.2615483  9.392662   40  9  29.85759 4.2597065  3.772423 17.042093 17.281464 35.977978 17.591042   14.6199  7.165493 2014
 -2.913256  7.824046   36 10 16.486708  2.691559 1.8031697  26.97494  8.780628  8.136788  38.99605 17.440176  6.688824 2004
 -2.641675  8.294049   35 10   14.3521 2.2873812 1.0050443 29.273617  7.893749  .5711666  44.88651 16.005455  6.434148 2006
 -4.411202  8.433811   37 10  15.11578  2.526976  .7542983  26.42409  8.041978 11.740806  30.21699  19.36924  6.276433 2008
 -2.472361  8.987197   36 10  14.27757  2.270407 2.1635392  25.77645 10.269762  7.460679  41.17196 18.242336  6.679374 2010
-2.9419296 9.1049795   37 10  11.80966  3.051733  3.081607  24.10761 11.634595  8.838715 30.151203  .7680569  11.12961 2012
 -2.433613  9.392662   38 10   11.3621  3.106778  2.462341  22.41693 14.661825 11.769317 25.401844 17.888924 12.493567 2014
 -2.901879  7.824046   37 10 23.114164  2.536887  1.723712  29.60225 13.199022 28.670237 27.447746  12.94578  6.789503 2004
-2.3008292  8.294049   34 10 16.297745  1.700985 .16370514 36.159004  4.049959  .9187898 38.144917  11.26712  9.229283 2006
-3.7418625  8.433811   37  9  19.25018  3.790027  .3587158 25.588404 13.397354  26.12445 36.957302 11.687654 4.1275344 2008
 -2.429707  8.987197   38  9  17.66565  2.946066  1.514906  23.62282  8.056461  20.51382  27.93592  21.62195  6.391588 2010
-2.8944786 9.1049795   40 10 16.300997 4.5193305  .6651658 20.167477 10.316284 22.684656  20.22245  .3725536  7.009204 2012
-2.3074121  9.392662   37 10 20.160286   2.91207  1.628566  20.05286 17.811663  21.99719  18.74904  19.99981  6.625844 2014
-3.0689826  7.824046   40  9 14.696598  8.845533  .6403158  20.53451  6.657221  24.68303  32.90722 13.904582  5.199502 2004
-3.2580965  8.294049   34 10 10.231947 1.4687244 .20539355  31.30534  5.409117 1.3399057  57.55589 11.989794 3.2144725 2006
 -4.204693  8.433811   40 10  20.77222  6.464825 .12221632   20.6369 15.578804  35.50686  17.28283 13.592597  4.731545 2008
 -2.587422  8.987197   40 10 12.990064  4.909758 1.4079636 20.700485 12.615027 16.141537  29.78276 21.304056  6.897037 2010
 -2.659523 9.1049795   40 10  18.40334   7.13759  1.463623  19.88619 15.253842  26.20398 24.162725  .3173602  4.914416 2012
-2.3167696  9.392662   42  9  27.86186    6.7485  1.767647 15.834953  25.91438  48.56338 14.435417 14.183154  4.597822 2014
 -2.655118  7.824046   38  8  36.86613  5.415364  .7064816 23.754173  5.475322  27.38824  47.85018 11.228417 4.1117926 2004
 -2.642745  8.294049   37 10 27.956715 1.3213423 .09172598  23.07722  6.744209  4.397186  43.11872  22.25546  7.272947 2006
 -3.074465  8.433811   38  9 29.515635  4.562873  .4083143 20.508543  6.041085  32.70047  25.94048 17.309027  6.294824 2008
  -2.58499  8.987197   38 10    29.097  1.893685 1.0775973  22.90376  6.862245  31.18125 22.865385 13.586557  9.271658 2010
 -2.975113 9.1049795   38  9  28.05855 2.3109894   .677605 20.734507  6.676054 29.086473  19.31948 .21634085  7.013789 2012
 -2.707024  9.392662   38  8 33.578148 4.3502154 2.0265534  21.05648  10.27356   29.3577 17.129723   19.5642 10.744636 2014
 -2.484907  7.824046 37.5  8 35.470394 1.2951858 .52347237  20.66788 2.9510536  25.73541  13.35516  21.43323  3.099942 2004
-2.0949457  8.294049   35  9  24.65865  .6374565    .40857 27.506796 3.5997574  5.693882  38.14806 19.875296  2.655157 2006
-3.6287756  8.433811   38  9  31.44732 1.4250277         0 18.995642 1.8721256  39.28391 22.796656 10.799778  5.287941 2008
-2.5282254  8.987197   40  9  43.43942  3.954788 1.7468052 15.364724  7.088504  52.79863 17.865355  9.960211  7.857956 2010
end

------------------


When I run the following code

xtreg logspike lmw median_age median_education share_illeterate share_retired share_students share_unmarried share_women share_agri share_services share_trading share_manufacturing i.year , fe vce(cluster districtself)

and stata gives following output

Fixed-effects (within) regression               Number of obs     =        588
Group variable: districtself                    Number of groups  =         98

R-sq:                                           Obs per group:
     within  = 0.5873                                         min =          6
     between = 0.0021                                         avg =        6.0
     overall = 0.4835                                         max =          6

                                                F(17,97)          =      39.47
corr(u_i, Xb)  = -0.1135                        Prob > F          =     0.0000

                                 (Std. Err. adjusted for 98 clusters in districtself)
-------------------------------------------------------------------------------------
                    |               Robust
           logspike |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
                lmw |  -1.210841   .4791554    -2.53   0.013    -2.161832   -.2598498
         median_age |   -.003657   .0131017    -0.28   0.781    -.0296601    .0223462
   median_education |  -.0499525   .0266452    -1.87   0.064    -.1028358    .0029308
   share_illeterate |   .0052515   .0025465     2.06   0.042     .0001974    .0103056
      share_retired |   .0027109    .018899     0.14   0.886    -.0347984    .0402203
     share_students |   .0319675   .0146223     2.19   0.031     .0029462    .0609888
    share_unmarried |  -.0008118    .005405    -0.15   0.881    -.0115391    .0099155
        share_women |  -.0021351   .0059738    -0.36   0.722    -.0139914    .0097211
         share_agri |  -.0114931   .0032325    -3.56   0.001    -.0179087   -.0050775
     share_services |  -.0080453   .0030779    -2.61   0.010     -.014154   -.0019365
      share_trading |  -.0054309   .0045622    -1.19   0.237    -.0144856    .0036237
share_manufacturing |  -.0064336   .0054526    -1.18   0.241    -.0172555    .0043883
                    |
               year |
              2006  |   .9074726   .2433482     3.73   0.000     .4244939    1.390451
              2008  |  -.0278337   .2958549    -0.09   0.925    -.6150239    .5593565
              2010  |   1.295703   .5182802     2.50   0.014       .26706    2.324346
              2012  |   1.431845   .5979957     2.39   0.019     .2449892    2.618701
              2014  |   2.055355   .7541802     2.73   0.008     .5585157    3.552194
                    |
              _cons |   7.737046   3.839832     2.01   0.047      .116042    15.35805
--------------------+----------------------------------------------------------------
            sigma_u |  .21358472
            sigma_e |  .36282614
                rho |  .25735142   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------

.

if we take what you said in #15 then should it have not dropped a year here too? due to lmw which is constant within a year.

[Clyde Schechter]

Your data example does not include the panel variable, districtself. So I just added it in--the correspondence between the values I imputed and the real values may be incorrect, but for present purposes that doesn't matter. When I then run your regression I get:

. xtreg logspike lmw-share_manufacturing i.year, fe vce(cluster districtself)
note: 2014.year omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =         40
Group variable: districtself                    Number of groups  =          7

R-squared:                                      Obs per group:
     Within  = 0.8525                                         min =          4
     Between = 0.7720                                         avg =        5.7
     Overall = 0.2873                                         max =          6

                                                F(6, 6)           =          .
corr(u_i, Xb) = -0.6404                         Prob > F          =          .

                                  (Std. err. adjusted for 7 clusters in districtself)
-------------------------------------------------------------------------------------
                    |               Robust
           logspike | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------+----------------------------------------------------------------
                lmw |   .0156496   .1783912     0.09   0.933     -.420858    .4521572
         median_age |   -.138412   .0989398    -1.40   0.211     -.380509     .103685
   median_education |  -.0195938   .1128303    -0.17   0.868    -.2956797    .2564921
   share_illeterate |  -.0297899   .0233445    -1.28   0.249    -.0869119    .0273321
      share_retired |   .1277706   .0959802     1.33   0.231    -.1070844    .3626256
     share_students |   .0903001    .098844     0.91   0.396    -.1515624    .3321625
    share_unmarried |  -.0148899   .0313451    -0.48   0.652    -.0915885    .0618088
        share_women |   .0560934   .0404521     1.39   0.215    -.0428892     .155076
         share_agri |  -.0014602   .0128844    -0.11   0.913    -.0329873    .0300668
     share_services |  -.0105384   .0064131    -1.64   0.151    -.0262306    .0051538
      share_trading |  -.0001185   .0171469    -0.01   0.995    -.0420755    .0418385
share_manufacturing |  -.0236564   .0336202    -0.70   0.508    -.1059221    .0586093
                    |
               year |
              2006  |   .5392263   .3790262     1.42   0.205    -.3882173     1.46667
              2008  |  -1.034594   .2487087    -4.16   0.006    -1.643162   -.4260259
              2010  |   .3391307   .2320094     1.46   0.194    -.2285758    .9068373
              2012  |  -.0719455    .472225    -0.15   0.884    -1.227438    1.083547
              2014  |          0  (omitted)
                    |
              _cons |   2.735972   4.490421     0.61   0.565    -8.251693    13.72364
--------------------+----------------------------------------------------------------
            sigma_u |  .67132177
            sigma_e |  .34158628
                rho |  .79434152   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------

Notice that in addition to the base category, year 2014 is omitted.

There are two possible conclusions I can draw. One is that your belief that lmw is supposed to be constant within years is incorrect. The other, more likely, is that you have an error in your full data set. Somewhere in your full data set you have a year (perhaps more than one) for which lmw is not constant. To find it:

by year (lmw), sort: gen byte inconsistent = (lmw[1] != lmw[_N])
sort year districtself
browse year districtself lmw if inconsistent

[Zahid Khan]

Thank you very much for the detailed explanation of the underlying issue, I have figured that out, as there was a problem with the variation in the variable which was possibly generated slightly wrong way. It was the lmwhs variable which I generated slightly wrong way. For the time being, the problem is solved. Thank you