Dear Statalist members,

I have done a Poisson fixed effects panel regression (Stata 13) regressing the number of high skilled employees on different explanatory variables. I am a little confused as to interpret my coefficients, I had no problems with dummy variables (taking the example from Wooldridge (2016:546) using[ exp(b1)-1]*100), but have one variable that is a firms export share in percentage as total numbers (so 80 if the firms export share is 80%, not 0.8).

I have found a forum entry somewhere (I cannot find it anymore:/) that has told me to use the formula [exp(b1*10)-1]*100 to calculate the effect of a ten percentage point increase on the dependent variable but in the case of the regression below for a subsample, this would mean that a ten percent increase in the export share, e.g. from 40 to 50 per cent would result in a [exp(0.02191*10)-1]*100=24.5 per cent decrease in the expected number of high skilled employees, and that seems way too high. So I am wondering if this formula might be wrong. I have checked several books but could not find an example that matches mine.

For logged variables (as turnover below) I have found a formula suggesting that a ten percent increase in turnover results in a [exp(0.55537*ln(1.1)-1]*100=5.4 per cent increase in the expected number of high skilled employees. This number seems more reasonable but generally it does not make sense that an increase in export share has a so much larger effect than the same increase in turnover...

Does anyone have the correct formulas to interpret these two coefficients?

Best,
Helen


Code:
 Conditional fixed-effects Poisson regression    Number of obs     =      5,778
Group variable: idnum                           Number of groups  =      1,141

                                                Obs per group:
                                                              min =          2
                                                              avg =        5.1
                                                              max =         12

                                                Wald chi2(19)     =     124.27
Log pseudolikelihood  = -11946.676              Prob > chi2       =     0.0000

                                  (Std. Err. adjusted for clustering on idnum)
------------------------------------------------------------------------------
             |               Robust
   highskill |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   investict |   -0.00127    0.01990    -0.06   0.949     -0.04028     0.03774
product_inno |    0.00352    0.02504     0.14   0.888     -0.04556     0.05260
process_inno |   -0.00309    0.03642    -0.08   0.932     -0.07447     0.06830
  lnturnover |    0.55537    0.06823     8.14   0.000      0.42163     0.68910
   lnavwages |   -0.08005    0.04703    -1.70   0.089     -0.17223     0.01213
  collective |   -0.01919    0.02391    -0.80   0.422     -0.06606     0.02768
 exportshare |   -0.02191    0.01100    -1.99   0.046     -0.04346    -0.00035
  investment |   -0.00179    0.00110    -1.63   0.102     -0.00395     0.00036
             |
        year |
       2008  |   -0.03672    0.03296    -1.11   0.265     -0.10132     0.02789
       2009  |   -0.03015    0.03442    -0.88   0.381     -0.09762     0.03732
       2010  |   -0.03578    0.03690    -0.97   0.332     -0.10810     0.03654
       2011  |    0.00830    0.03712     0.22   0.823     -0.06446     0.08106
       2012  |    0.06552    0.04054     1.62   0.106     -0.01394     0.14497
       2013  |    0.03576    0.04118     0.87   0.385     -0.04495     0.11647
       2014  |    0.06289    0.05151     1.22   0.222     -0.03807     0.16386
       2015  |    0.08398    0.05862     1.43   0.152     -0.03091     0.19886
       2016  |    0.06948    0.05608     1.24   0.215     -0.04044     0.17939
       2017  |    0.09956    0.06558     1.52   0.129     -0.02897     0.22810
       2018  |    0.08761    0.06331     1.38   0.166     -0.03648     0.21169
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