Hello,

I would like to clarify the following uncertainty: I am analysing the impact of technology on different occupations and use panel data on 11 industries between 2006-2017. I have created a fixed effects model in order to look for the similar effect across all industries after accounting for individual effects, however, I would also like to look later into the different effect of computer_use for each industry. Hence, I decided to use the interaction of computer_use with industry dummies for the second model.
However, when I use the latter regression, other variables that were significant in the first model become now insignificant. Can I still use the first regression to interpret the effect of other variables, and from the latter just refer to different effects of computer_use?
Or does it make my results uncomparable?
Code:
. xtreg nonrout using_computer lngva  price_computer total_internet_access sharedegre
> e sharehigher shareother, fe vce(robust)

Fixed-effects (within) regression               Number of obs     =        120
Group variable: industry1                       Number of groups  =         10

R-sq:                                           Obs per group:
     within  = 0.3276                                         min =         12
     between = 0.4580                                         avg =       12.0
     overall = 0.4408                                         max =         12

                                                F(7,9)            =      16.27
corr(u_i, Xb)  = 0.5375                         Prob > F          =     0.0002

                                   (Std. Err. adjusted for 10 clusters in industry1)
------------------------------------------------------------------------------------
                   |               Robust
           nonrout |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
    using_computer |   .0014271   .0004206     3.39   0.008     .0004755    .0023786
             lngva |  -.0193869   .0317928    -0.61   0.557    -.0913072    .0525334
    price_computer |   .0014037   .0009901     1.42   0.190     -.000836    .0036434
total_internet_a~s |   .0041153   .0022304     1.85   0.098    -.0009303    .0091609
       sharedegree |   .0926562   .1112741     0.83   0.427    -.1590632    .3443756
       sharehigher |  -.2771514   .1359427    -2.04   0.072    -.5846752    .0303723
        shareother |   .1583427   .0836769     1.89   0.091    -.0309475    .3476329
             _cons |    .200577   .5024723     0.40   0.699    -.9360942    1.337248
-------------------+----------------------------------------------------------------
           sigma_u |   .1681399
           sigma_e |  .01373558
               rho |  .99337076   (fraction of variance due to u_i)
------------------------------------------------------------------------------------


Now - with the interaction term:

. xtreg nonrout c.using_computer#i.industry1 lngva  price_computer total_internet_acc
> ess sharedegree sharehigher shareother, fe vce(robust)

Fixed-effects (within) regression               Number of obs     =        120
Group variable: industry1                       Number of groups  =         10

R-sq:                                           Obs per group:
     within  = 0.4646                                         min =         12
     between = 0.0007                                         avg =       12.0
     overall = 0.0008                                         max =         12

                                                F(6,9)            =          .
corr(u_i, Xb)  = -0.7915                        Prob > F          =          .

                                   (Std. Err. adjusted for 10 clusters in industry1)
------------------------------------------------------------------------------------
                   |               Robust
           nonrout |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
         industry1#|
  c.using_computer |
    Accommodation  |  -.0019848   .0006782    -2.93   0.017    -.0035191   -.0004505
Administrative ..  |   .0011701   .0002696     4.34   0.002     .0005603    .0017799
     Construction  |   .0019143   .0003306     5.79   0.000     .0011663    .0026623
Financial and I..  |  -.0067784   .0034683    -1.95   0.082    -.0146244    .0010675
Information and..  |   .0032694   .0010298     3.17   0.011     .0009398     .005599
    Manufacturing  |   .0017933   .0013022     1.38   0.202    -.0011526    .0047392
Professional, S..  |  -.0004355   .0008249    -0.53   0.610    -.0023016    .0014306
      Real Estate  |   .0019032   .0002549     7.47   0.000     .0013266    .0024799
Transportation ..  |  -.0000783   .0002766    -0.28   0.784    -.0007041    .0005475
  Wholesale trade  |  -.0000112   .0014483    -0.01   0.994    -.0032876    .0032651
                   |
             lngva |   .0000949   .0355875     0.00   0.998    -.0804097    .0805995
    price_computer |   .0013431   .0009608     1.40   0.196    -.0008305    .0035166
total_internet_a~s |   .0037761   .0023165     1.63   0.138    -.0014641    .0090163
       sharedegree |   .0639118   .1022921     0.62   0.548     -.167489    .2953126
       sharehigher |  -.2078717   .1323905    -1.57   0.151    -.5073598    .0916164
        shareother |   .0901104   .0656804     1.37   0.203     -.058469    .2386898
             _cons |    .127795    .461287     0.28   0.788    -.9157087    1.171299
-------------------+----------------------------------------------------------------
           sigma_u |   .3108439
           sigma_e |  .01283018
               rho |  .99829925   (fraction of variance due to u_i)
------------------------------------------------------------------------------------

. 
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