Dear Statalist,

I am running the following regression with an interaction between a categorical education variable and continuous variable. The categorical variable takes on the values (1 = no education, 2 = primary education, 3 = secondary education, 4 = university education). When I include imports non-interacted as a control, the categorical variable omits the base group (Output1).
However, when I use total trade as the control instead, it keeps the base group (Output 2). How can I understand this, and is it possible to interpret the coefficients on the interaction in Output 2?

Thank you in advance.
Ray

Code:
 reg lrhourlywage schoolinglevel1#c.ltotalexports_china ltotaltrade_china ltotaltrade_eu15 ltotaltrade_usa ltotaltrade_mca ltotaltrade_row i.schoolinglevel1 i.region i.occupationgroup i.isic1 i.establishmentsize _2015 _2016 _2017 _2018 [fweight = factor] , vce(cluster region) 
, vce(cluster region)
Code:
 
Linear regression                               Number of obs     =  1,517,202
                                                F(4, 5)           =          .
                                                Prob > F          =          .
                                                R-squared         =     0.4382
                                                Root MSE          =      .4583

                                                          (Std. Err. adjusted for 6 clusters in region)
-------------------------------------------------------------------------------------------------------
                                      |               Robust
                         lrhourlywage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------------------+----------------------------------------------------------------
schoolinglevel1#c.ltotalexports_china |
                                   2  |   .0055916   .0055252     1.01   0.358    -.0086114    .0197947
                                   3  |   .0146709   .0162778     0.90   0.409    -.0271724    .0565142
                                   4  |   .0720004   .0125011     5.76   0.002     .0398653    .1041356
                                      |
                  ltotalexports_china |   -.027375    .012245    -2.24   0.076    -.0588519    .0041019
                  ltotalimports_china |   .0288434    .024478     1.18   0.292    -.0340792     .091766
                     ltotaltrade_eu15 |  -.0073836   .0710008    -0.10   0.921    -.1898971    .1751298
                      ltotaltrade_usa |   .0914549   .0475511     1.92   0.112    -.0307791     .213689
                      ltotaltrade_mca |    .038949   .0462096     0.84   0.438    -.0798365    .1577346
                      ltotaltrade_row |   .0495398   .0261668     1.89   0.117    -.0177242    .1168038



Output 2
Code:
Linear regression                               Number of obs     =  1,517,202
                                                F(4, 5)           =          .
                                                Prob > F          =          .
                                                R-squared         =     0.4383
                                                Root MSE          =     .45823

                                                          (Std. Err. adjusted for 6 clusters in region)
-------------------------------------------------------------------------------------------------------
                                      |               Robust
                         lrhourlywage |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------------------------+----------------------------------------------------------------
schoolinglevel1#c.ltotalexports_china |
                                   1  |  -.0315226   .0112729    -2.80   0.038    -.0605005   -.0025447
                                   2  |  -.0256822   .0071686    -3.58   0.016    -.0441096   -.0072549
                                   3  |  -.0167331   .0064504    -2.59   0.049    -.0333145   -.0001517
                                   4  |   .0404786   .0129226     3.13   0.026       .00726    .0736971
                                      |
                    ltotaltrade_china |   .1049092   .0460189     2.28   0.072    -.0133862    .2232047
                     ltotaltrade_eu15 |   .0343422   .0780796     0.44   0.678    -.1663679    .2350523
                      ltotaltrade_usa |   .1433543   .0451913     3.17   0.025     .0271863    .2595224
                      ltotaltrade_mca |  -.0104562   .0550203    -0.19   0.857    -.1518903     .130978
                      ltotaltrade_row |   .0377084   .0232289     1.62   0.165    -.0220033    .0974202