Dear all,

I am using the -margins- command to estimate the impact of several binary explanatory variables on log of contribution (in $)(all my explanatory variables are binary). I use the following command:

Code:
margins, dyex(*) predict(ystar(0 .) eq(#3)) atmeans
And receive the following result:
HTML Code:
Conditional marginal effects                    Number of obs     =      4,974
Model VCE    : Robust

Expression   : E(lny3*0<lny3), predict(ystar(0 .) eq(#3))
dy/ex w.r.t. : max_10 max_20 line_5 age40_65 above65 female below100000 _149999    _199999    married
envdonor farmeryes perfield sughigh
at           : max_10          =    .3365501 (mean)
max_20          =    .3329312 (mean)
line_5          =    .4989948 (mean)
age40_65        =    .6410097 (mean)
above65         =    .3049866 (mean)
female          =    .2427064 (mean)
below100000     =     .153114 (mean)
_149999         =    .3368508 (mean)
_199999         =    .2563625 (mean)
married         =    .7696129 (mean)
envdonor        =    .3187746 (mean)
farmeryes       =    .5028146 (mean)
perfield        =          .5 (mean)
sughigh         =    .5006031 (mean)


Delta-method
dy/ex   Std. Err.      z    P>z     [95% Conf. Interval]

max_10    .0631488   .0587649     1.07   0.283    -.0520282    .1783258
max_20    .0939937   .0572245     1.64   0.100    -.0181643    .2061518
line_5   -.0602471   .0689658    -0.87   0.382    -.1954175    .0749234
age40_65    .7288097   .3987188     1.83   0.068    -.0526647    1.510284
above65    .3905804   .1965155     1.99   0.047     .0054171    .7757436
female   -.0632363   .0924284    -0.68   0.494    -.2443926      .11792
below100000    .0536459   .0590108     0.91   0.363    -.0620131    .1693049
_149999    -.174858   .0965162    -1.81   0.070    -.3640264    .0143103
_199999    -.060281   .0737858    -0.82   0.414    -.2048985    .0843366
married    .0042625   .3035701     0.01   0.989    -.5907239    .5992489
envdonor           0  (omitted)
farmeryes    .0747624   .0698082     1.07   0.284    -.0620592    .2115839
perfield   -.0302773   .0667833    -0.45   0.650    -.1611702    .1006156
sughigh    .0542216   .0663194     0.82   0.414    -.0757621    .1842053
My question is, how do I interpret the coefficients? For example, say for the variable max_10 (I know that it's not significant), do I interpret that max_10 increases the expected contribution by 6 percentage points keeping all other variables constant at their means? Would this also be the average partial effect for the population rather than the average marginal effect?

Thanks,
Anwesha