interpreting interaction results in ols regression

#1
Dear all,

I am trying to examine the association between household wealth and expenditure on education of children aged 11-17 and 5-10 in a developing country using the following equation. In particular, I am trying to find out, using interactions, whether there is a gender bias in education expenditure when households become wealthier. HHwealth is a continuous variable while femalechild is a binary variable that equals 1 if the child is female, and 0 otherwise.

educexp = β0 + β1hhwealth+ β2 femalechild+ β3 hhwealth*femalechild + εij

The following results (I’m only pasting relevant sections of the results) are obtained using Stata’s regress command.

Would it be correct to say that the results in Model 1 suggest that household wealth is significantly associated with in an increase in educational expenditure for both boys and girls in the older age group? And Model 2 shows that increasing household wealth is significantly associated with an increase in educational expenditure for only boys aged 5-10, thus suggesting a possible gender bias? Thanks in advance!


Code:



Code:
regress educexp hhwealth fem hhwealth_fem `xlist' 
 
//edu=amount spent on education, hhwealth=wealth score for household (ranges from 0 to 1), fem=1 if child is female, 0 if otherwise, hhwealth_fem =interaction of hh_wealth and fem, ‘xlist’=other controls   
 
 
/*tests for the total coef of education expenditure for girls */    
 
test hhwealth + hhwealth_fem = 0

//Model 1, children aged 11-17

Linear regression                                      Number of obs =    1389
                                                       F( 25,  7363) =   69.86
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.2122
                                                       Root MSE      =  1.0857

---------------------------------------------------------------------------------
                |               Robust
     dietscore9 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
       hhwealth |  0.2369617   .1049415    -2.26   0.024     -.442677   -.0312463
   hhwealth_fem |  -.0409221   .1408595    -0.29   0.771    -.3170471    .2352029

( 1)  hhwealth + hhwealth_fem = 0

       F(  1,  7363) =    7.83
       Prob > F =    0.0052



//Model 2, children aged 5-10

Linear regression                                      Number of obs =    1983
                                                       F( 28,  1954) =   16.53
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1798
                                                       Root MSE      =  .95551

---------------------------------------------------------------------------------
                |               Robust
     dietscore7 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
       hhwealth |  -.4282509    .168332    -2.54   0.011      -.75838   -.0981218
   hhwealth_fem |   .1345255   .2562779     0.52   0.600    -.3680813    .6371323


 ( 1)  hhwealth + hhwealth_fem = 0

       F(  1,  1954) =    2.16
       Prob > F =    0.1421