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
```