Hi all,

I am looking at the effect of gender inequality (measured by GII which is a 0 to 1 scale, where higher values indicate greater inequality) on economic growth (measured by GDP per capita growth, as a %). As the two variables aren't measured in the same form I was wondering how I interpret the effect of GII on growth when looking at my coefficients, and also how to interpret the interaction term between GII and income (measured by natural log of GDP per capita) given I have interacted two variables measured differently?

Code:
xtreg Growth lagGII lagIncomeln GII_Income i.Year, fe robust

Fixed-effects (within) regression               Number of obs     =      2,276
Group variable: CountryID                       Number of groups  =        114

R-sq:                                           Obs per group:
     within  = 0.1394                                         min =         18
     between = 0.0410                                         avg =       20.0
     overall = 0.0255                                         max =         20

                                                F(22,113)         =      14.49
corr(u_i, Xb)  = -0.9338                        Prob > F          =     0.0000

                            (Std. Err. adjusted for 114 clusters in CountryID)
------------------------------------------------------------------------------
             |               Robust
      Growth |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      lagGII |  -50.08883   12.19639    -4.11   0.000    -74.25208   -25.92559
 lagIncomeln |  -7.186864   1.354488    -5.31   0.000     -9.87035   -4.503379
  GII_Income |   5.313213   1.206568     4.40   0.000     2.922784    7.703641
             |
        Year |
       1997  |  -.3565464   .3871003    -0.92   0.359    -1.123462    .4103691
       1998  |  -.9939671   .4270054    -2.33   0.022    -1.839942   -.1479923
       1999  |   -.847875   .4163987    -2.04   0.044    -1.672836    -.022914
       2000  |  -.0046979   .4400977    -0.01   0.992    -.8766109    .8672151
       2001  |  -.9246834   .4146138    -2.23   0.028    -1.746108   -.1032587
       2002  |     -.5211   .4825815    -1.08   0.283    -1.477181    .4349809
       2003  |   .2614466   .4714257     0.55   0.580    -.6725327    1.195426
       2004  |   1.514311     .45475     3.33   0.001     .6133698    2.415253
       2005  |   1.131297   .4277166     2.64   0.009      .283913     1.97868
       2006  |   2.066314   .4628423     4.46   0.000      1.14934    2.983288
       2007  |   2.169376   .4984575     4.35   0.000     1.181842    3.156911
       2008  |   .4962147   .5488308     0.90   0.368     -.591118    1.583548
       2009  |  -2.813979   .4936407    -5.70   0.000     -3.79197   -1.835987
       2010  |   1.726426   .4774053     3.62   0.000     .7805997    2.672252
       2011  |   1.158964   .6006279     1.93   0.056    -.0309881    2.348916
       2012  |   .9286711   .5992796     1.55   0.124    -.2586098    2.115952
       2013  |   .7256153   .6654279     1.09   0.278    -.5927175    2.043948
       2014  |   1.278339   .5844081     2.19   0.031     .1205209    2.436156
       2015  |   .8467109   .6811747     1.24   0.216     -.502819    2.196241
             |
       _cons |   69.04605    12.6388     5.46   0.000     44.00631    94.08578
-------------+----------------------------------------------------------------
     sigma_u |  5.5939725
     sigma_e |  3.3023023
         rho |  .74156902   (fraction of variance due to u_i)
------------------------------------------------------------------------------
Is there a way I can work this out or is it easier to standardise my coefficients, if so how do I do this?

Many thanks,

Hellie