Dear all,

I want to examine the effects of children' s characteristics on their parent health outcomes. Since parents with multiple children appear multiple times in the dataset, in regression models I want to weight estimates by the number of children to each parent. The idea is to give equal weight to each parent in my regressions. For a father with three children, for example, who therefore appear three times in the data, each observation of that parent is weighted by one third, so that the sum of the weights equal one. The following is what I have tried, but I am not sure if it is correct or not. Any advice is highly appreciated.

Code
Code:
*** Create weight by the number of children
bys qid: gen wgt = 1/total_child

*** Regression
reg psyc csex cmstt cedu [pw=wgt], vce(cluster qid)
Data
Note: did is the id of parents
Code:
clear
input long qid float psyc byte(csex cmstt cedu) float total_child
 11 0 2 2 7 3
 11 0 1 2 5 3
 11 0 1 2 7 3
 12 0 1 1 5 2
 12 0 1 1 7 2
 13 4 1 2 7 3
 13 4 1 2 7 3
 13 4 1 2 7 3
 14 4 1 2 4 4
 14 4 2 2 6 4
 14 4 2 2 6 4
 14 4 2 2 5 4
 15 0 1 2 4 3
 15 0 2 2 5 3
 15 0 2 2 5 3
 16 0 1 2 5 4
 16 0 1 2 5 4
 16 0 2 2 5 4
 16 0 1 2 7 4
 17 1 1 1 5 2
 17 1 1 2 5 2
 18 0 1 2 7 5
 18 0 2 2 6 5
 18 0 2 2 7 5
 18 0 2 2 5 5
 18 0 2 3 6 5
 19 0 2 2 4 4
 19 0 2 2 6 4
 19 0 2 2 4 4
 19 0 2 2 7 4
110 4 1 2 7 2
110 4 2 2 7 2
111 0 2 2 7 3
111 0 1 2 7 3
111 0 2 2 7 3
112 0 1 2 6 1
113 2 1 2 4 3
113 2 1 2 5 3
113 2 2 2 5 3
114 7 2 2 4 2
114 7 1 2 4 2
115 3 2 2 6 2
115 3 2 2 6 2
116 3 2 2 8 2
116 3 1 1 6 2
117 1 1 1 7 2
117 1 2 2 7 2
118 4 1 1 7 2
118 4 1 2 6 2
119 6 1 1 5 3
119 6 1 2 6 3
119 6 2 2 7 3
120 0 1 1 5 3
120 0 1 2 6 3
120 0 2 2 7 3
121 1 2 2 4 2
121 1 1 2 4 2
122 3 1 2 4 5
122 3 2 2 7 5
122 3 2 5 4 5
122 3 2 5 4 5
122 3 2 2 4 5
123 3 2 2 7 3
123 3 1 2 7 3
123 3 1 1 7 3
124 6 1 3 3 8
124 6 2 2 3 8
124 6 1 2 3 8
124 6 2 2 3 8
124 6 1 2 5 8
124 6 2 2 4 8
124 6 2 2 4 8
124 6 2 2 3 8
125 2 2 2 7 3
125 2 1 2 7 3
125 2 1 2 7 3
126 7 2 2 3 3
126 7 2 2 1 3
126 7 1 2 3 3
127 6 1 2 2 4
127 6 2 2 4 4
127 6 2 2 4 4
127 6 1 2 2 4
128 2 1 2 3 6
128 2 2 2 2 6
128 2 1 2 2 6
128 2 2 2 3 6
128 2 1 2 2 6
128 2 1 3 2 6
129 0 2 2 6 1
130 5 1 2 4 2
130 5 1 2 4 2
131 4 1 1 7 3
131 4 2 2 7 3
131 4 1 2 4 3
132 2 1 1 5 2
132 2 2 2 5 2
133 0 1 1 5 2
133 0 2 1 7 2
134 0 1 2 7 2
end
label values csex LABEL_B119
label def LABEL_B119 1 "Male", modify
label def LABEL_B119 2 "Female", modify
label values cmstt LABEL_B259
label def LABEL_B259 1 "Single", modify
label def LABEL_B259 2 "Married", modify
label def LABEL_B259 3 "Divorced", modify
label def LABEL_B259 5 "Widow", modify
label values cedu LABEL_B269
label def LABEL_B269 1 "No schooling", modify
label def LABEL_B269 2 "Incomplete primary school", modify
label def LABEL_B269 3 "Primary school", modify
label def LABEL_B269 4 "Lower secondary school", modify
label def LABEL_B269 5 "Upper secondary school", modify
label def LABEL_B269 6 "Prof secondary school", modify
label def LABEL_B269 7 "Junior college/university", modify
label def LABEL_B269 8 "Master", modify