Dear, with the following data:

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float(lntobin pwomen boardsize findependent logsale segmentcount)
-.017516488         0 11 .8181818  7.618251  6
  .12475272         0 11 .8181818   7.37419 12
-.016281042         0 11 .8181818  7.416138 18
   .1741487 .08333334 12 .8333333  7.477378 18
   .3389418 .08333334 12 .8333333  7.466399 18
  .08878828 .08333334 12 .8333333  7.626473 18
   .3576642  .0909091 11 .9090909 10.621083 15
   .3279516 .15384616 13 .9230769 10.601125 15
   .3400276 .15384616 13 .9230769  10.65034 15
   .2206872 .16666667 12 .9166667 10.704165 10
   .1878603        .2 10        0  10.73134  5
  .11441843        .2 10       .9  8.102224  2
   .1143407        .2 10       .9   8.14747  4
  .20094657  .1818182 11 .9090909  8.158125  6
  .15798543        .1 10       .9  8.159215  6
    .217275        .1 10       .9  8.160142  6
   .2351784  .1818182 11 .9090909  8.179003  6
  .21915583        .2 10       .9  8.213719  4
    .225777  .1818182 11        0  8.152258  2
   .7601307  .3636364 11 .9090909 10.593477 21
   .5852639  .3636364 11 .9090909  9.991864 42
   .6417528  .3636364 11 .8181818 10.217934 50
   .8903543  .3333333 12 .8333333 10.328036 28
   .4800359 .14285715  7 .7142857  6.234762 27
   .6661467 .14285715  7 .7142857  6.304359 27
   .4916412      .125  8      .75  6.325297 27
  .05608032 .14285715  7 .5714286  6.458837 18
   .2571971        .1 10       .8   8.59822 10
   .4196974        .1 10       .7 8.5752735 20
   .4057164  .0909091 11 .7272727  8.613594 30
   .6225287        .2 10       .6  8.291797 30
  1.4030088 .22222222  9 .6666667  8.359838 30
  1.2907536       .25  8      .75  8.580919 30
  1.5674034 .22222222  9 .7777778  8.775703 20
   .1087137  .3333333 12 .8333333  10.50769  4
   .2705342  .3333333 12 .8333333 10.968253 12
   .3405285  .3846154 13 .8461539 11.005875 12
   .3163855  .3333333 12 .9166667 11.053348  8
   .5802096  .3333333 12 .9166667 11.009522  4
   .5037185 .27272728 11 .9090909  9.170736 11
    .623949 .27272728 11 .9090909   9.22822 24
    .765121 .16666667 12 .9166667 9.2533045 36
   .7700632        .2 10       .9  9.199775 35
   .8828155       .25  8     .875 9.1616125 32
   .8072674       .25  8     .875  9.010376 30
   .8526819       .25  8     .875  9.097194 20
  .25676554        .3 10       .8  8.446127  3
  .41635615 .27272728 11 .8181818  8.509967  8
   .6560078        .4 10       .9  8.588211 12
   .7763644  .4545455 11 .9090909  8.630165 12
   .5901425  .4545455 11 .9090909  8.687948 12
   .4026812 .44444445  9 .8888889  8.978786 12
   .2950791  .4545455 11 .9090909  9.019664  8
   .5058599        .1 10       .8  7.352441  2
   .4850742 .14285715  7 .8571429  7.400743  4
   .5901712 .14285715  7 .8571429  7.446702  6
   .6101473 .14285715  7 .8571429   7.54163  6
   .4182765 .14285715  7 .8571429  7.571268  6
  .23310487 .14285715  7 .7142857  7.624082  6
   .2245757 .14285715  7 .8571429  7.706523  4
  .28666762  .2857143  7        0  7.697621  2
  .05849881      .125  8     .875  8.714489  9
  .02568183      .125  8     .875  8.767649  9
   .6297287        .1 10       .9 10.536487  7
   .7847168       .25 12 .9166667 10.572726 14
   .8463459       .25 12 .9166667 10.604256 22
   .8096887       .25 12 .9166667 10.560515 23
   .8198145       .25 12 .9166667  10.57903 24
   .9738825 .23076923 13 .8461539 10.609897 24
   .8560433       .25 12 .9166667   10.6407 16
  1.0402052       .25 12        0 10.510777  8
     .80136         0  9 .7777778  7.357927 10
   .7419222         0  8      .75  7.491087 20
   1.345916      .125  8     .875  7.736962 36
  1.4949583      .125  8     .875  8.088991 36
  1.3172176      .125  8     .875  8.098339 35
  1.4328263 .11111111  9 .7777778  8.202866 34
   1.248179 .11111111  9 .7777778  8.260493 22
  1.0762497 .22222222  9        0  8.124683 11
 -.07162579 .15384616 13 .7692308 10.537177  7
  .05197859 .10526316 19 .9473684 10.011624 15
-.029052453 .14285715 14 .9285714  9.281451 23
 -.17600115 .14285715 14 .9285714  8.800264 24
   .1664449  .1818182 11 .9090909  8.468423 24
   .1561371 .16666667 12 .9166667  8.606302 24
  .09665885 .16666667 12 .9166667  8.751949 16
   .4281915  .1818182 11        0  8.778788  8
   .0934423       .25  8      .75  8.366432  9
  .25248697       .25  8      .75  8.478303 17
   .2312367 .22222222  9 .7777778  8.523732 25
   .2410163  .3333333  9 .7777778  8.496541 25
   .4223332        .4 10       .9  8.545722 27
   .3076631        .3 10       .9  8.604032 29
  .14714135        .3 10       .9  8.770625 20
  .10163653 .27272728 11 .7272727  7.248016 15
  .22053432 .27272728 11 .7272727  7.431952 15
   .2408325        .3 10        0  7.626816  5
   .3098805      .125  8        0  6.530162  7
  .09622293 .08333334 12 .8333333  9.612132  7
   .1124422  .2142857 14 .8571429  9.639327 13
end
I performed two regressions:

-reg lntobin pwomen boardsize findependent logsale segmentcount i.SIC i.year, cluster(CUSIP) - a simple OLS with industry and year fixed effects and errors clustered at firm (CUSIP) level

After performing the following command for the variable GVKEY, which like CUSIP, accounts for individual firms
destring GVKEY, replace
xtset GVKEY

I regressed:
-xtreg lntobin pwomen boardsize findependent logsale segmentcount i.year, fe vce(robust) - a firm FE regression with year dummies

Now, the R-squared of the first regression is substantially higher than the R-squared of the firm FE regression. As feedback, my supervisor commented that firm-FE regression always have higher R-squared than industry-effects regression. Is there an explanation why I do not find a higher R-squared in the regression?