Dear Statalists,
I am writing my thesis. Currently, I want to check out if one of my independent variables varies enough to conduct an analysis. To do so, I obviously have to construct that variable. The variable shall be a dummy variable indicating if the person that is responsible for food purchases of the household is female (1) or male (0).
The dataset is an unbalanced panel dataset with t=2 and I am using Stata 16.1.
I have the following information that should suffice to construct the variable, but I don't know how exactly and starting to get a bit mad after trying and searching for an answer for too long:
- hhid (household identifier)
- membid (identifier of household member)
- sex (female=1)
- respshop (membid of the person that is responsible for shopping)
Here you can find my dataex:
I would really appreciate your help also if this might be very trivial for some of you, as I am currently stuck.
Kind regards, Jacob
I am writing my thesis. Currently, I want to check out if one of my independent variables varies enough to conduct an analysis. To do so, I obviously have to construct that variable. The variable shall be a dummy variable indicating if the person that is responsible for food purchases of the household is female (1) or male (0).
The dataset is an unbalanced panel dataset with t=2 and I am using Stata 16.1.
I have the following information that should suffice to construct the variable, but I don't know how exactly and starting to get a bit mad after trying and searching for an answer for too long:
- hhid (household identifier)
- membid (identifier of household member)
- sex (female=1)
- respshop (membid of the person that is responsible for shopping)
Here you can find my dataex:
Code:
* Example generated by -dataex-. For more info, type help dataex clear input long hhid double membid int year float(sex respshop) 101015 1 2012 1 1 101045 1 2012 0 2 101060 2 2012 1 2 101075 1 2012 0 2 101075 2 2012 1 2 101092 2 2015 1 2 101092 2 2012 1 2 101105 2 2012 1 2 101120 1 2012 1 1 101125 1 2015 0 1 101133 2 2015 1 2 101150 1 2012 0 2 101150 2 2012 1 2 101152 1 2015 0 1 101152 2 2015 1 1 101160 1 2015 0 3 101160 3 2015 1 3 101165 1 2012 1 1 101180 1 2012 0 1 101187 1 2015 1 1 101232 2 2015 1 2 101233 1 2015 1 1 102012 1 2012 0 2 102012 2 2012 1 2 102042 1 2012 1 1 102042 1 2015 1 1 102057 1 2012 0 1 102072 1 2012 1 1 102117 1 2012 0 1 102147 1 2012 0 1 102177 1 2012 0 1 102192 2 2012 1 2 102198 1 2012 0 1 102207 1 2012 0 1 102224 1 2012 0 2 102224 2 2012 1 2 102224 2 2015 1 2 102235 1 2015 0 2 102235 2 2015 1 2 102237 1 2015 0 2 102237 1 2012 0 2 102237 2 2015 1 2 102237 2 2012 1 2 102238 1 2015 0 2 102238 2 2015 1 2 102242 2 2015 1 2 102248 1 2015 0 2 102248 2 2015 1 2 102251 2 2015 1 2 102252 2 2012 1 2 102253 2 2015 1 2 102255 2 2015 1 2 102258 2 2015 1 2 102259 1 2015 0 2 102259 2 2015 1 2 102261 1 2015 1 1 102262 1 2015 1 1 103005 1 2015 0 2 103005 1 2012 0 2 103005 2 2012 1 2 103005 2 2015 1 2 103020 1 2012 0 2 103020 2 2012 1 2 103020 2 2015 1 2 103035 1 2012 0 2 103035 2 2012 1 2 103050 1 2012 0 1 103050 1 2015 0 1 103079 2 2015 1 2 103079 2 2012 1 2 103094 2 2012 1 2 103109 1 2012 0 1 103270 1 2015 0 2 103270 2 2015 1 2 103273 2 2015 1 2 103286 1 2015 1 1 104011 1 2015 0 2 104011 2 2015 1 2 104014 1 2015 0 2 104014 1 2012 0 2 104014 2 2012 1 2 104014 2 2015 1 2 104023 1 2015 1 1 104028 1 2015 0 2 104028 2 2015 1 2 104038 2 2015 1 2 104045 1 2012 0 2 104045 2 2012 1 2 104045 2 2015 1 2 104051 2 2015 1 2 104053 1 2015 1 1 104058 1 2015 0 2 104058 2 2015 1 2 104059 1 2015 0 1 104059 1 2012 0 1 104061 1 2015 1 1 104071 2 2015 1 2 104073 2 2015 1 2 104083 1 2015 0 2 104083 2 2015 1 2 end label values year year
Kind regards, Jacob
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