I have panel data on adult women and their husbands, I have a variable (family_id) which is the same for men and women in the same family unit (husbands and wives). Each adult reports his or her own employment status, I would like to create a new variable for the wives based on the employment status of their husbands, i.e. a binary variable "husband_is_unemployed". For the life of me I cannot figure out how to create a variable that summarize properties of the other members of the same family, as is needed above, even using the following: https://www.stata.com/support/faqs/d...ng-properties/ can anyone please advise?
To describe the data:
My initial simple thought was something like the below (where male is 1) but of course this won't apply to the wife, who I am anxious to create the variable on spousal employment for
To describe the data:
Code:
. tab gender Gender | Freq. Percent Cum. ------------+----------------------------------- Female | 1,102 62.61 62.61 Male | 658 37.39 100.00 ------------+----------------------------------- Total | 1,760 100.00 . tab binary_employment_y0 Binary No | Employment? | Freq. Percent Cum. ----------------+----------------------------------- Some Employment | 1,019 73.05 73.05 No Employment | 376 26.95 100.00 ----------------+----------------------------------- Total | 1,395 100.00 . tab family_id family_id | Freq. Percent Cum. ------------+----------------------------------- 5 | 1 0.06 0.06 7 | 2 0.11 0.17 8 | 1 0.06 0.23 9 | 2 0.11 0.34 10 | 2 0.11 0.45 12 | 1 0.06 0.51 14 | 2 0.11 0.63 15 | 2 0.11 0.74 18 | 2 0.11 0.85 21 | 2 0.11 0.97 22 | 1 0.06 1.02 23 | 2 0.11 1.14 25 | 2 0.11 1.25 26 | 2 0.11 1.36 28 | 2 0.11 1.48 30 | 1 0.06 1.53 31 | 1 0.06 1.59 32 | 2 0.11 1.70 33 | 1 0.06 1.76 35 | 2 0.11 1.87 36 | 2 0.11 1.99 38 | 2 0.11 2.10 40 | 2 0.11 2.22 41 | 2 0.11 2.33 47 | 2 0.11 2.44 48 | 2 0.11 2.56 57 | 1 0.06 2.61 58 | 2 0.11 2.73 61 | 2 0.11 2.84 63 | 1 0.06 2.90 64 | 2 0.11 3.01 66 | 2 0.11 3.13 67 | 1 0.06 3.18 68 | 2 0.11 3.30 74 | 2 0.11 3.41 80 | 1 0.06 3.47 81 | 2 0.11 3.58 82 | 1 0.06 3.64 83 | 2 0.11 3.75 86 | 2 0.11 3.86 87 | 1 0.06 3.92 88 | 2 0.11 4.03 89 | 2 0.11 4.15 91 | 1 0.06 4.20 93 | 1 0.06 4.26 98 | 2 0.11 4.37 103 | 2 0.11 4.49 108 | 2 0.11 4.60 110 | 2 0.11 4.72 112 | 1 0.06 4.77 116 | 2 0.11 4.89 124 | 2 0.11 5.00 134 | 2 0.11 5.11 135 | 2 0.11 5.23 137 | 2 0.11 5.34 138 | 2 0.11 5.45 141 | 1 0.06 5.51 142 | 2 0.11 5.62 147 | 2 0.11 5.74 153 | 2 0.11 5.85 154 | 2 0.11 5.97 155 | 2 0.11 6.08 156 | 1 0.06 6.14 etc....... ------------+----------------------------------- Total | 1,760 100.00 . The frequency above provides a clue that it refers to the relationship between study members because when women and men are married it is two, but when the woman is single it is one.
Code:
generate maleunemployed_y0 =. replace maleunemployed_y0 = 1 if binary_employment_y0==1 & gender == 1 replace maleunemployed_y0 = 0 if binary_employment_y0==0 & gender == 1 generate partnerunemployed_y0 =. replace partnerunemployed_y0 = 1 if maleunemployed_y0==1 & gender == 0 replace partnerunemployed_y0 = 0 if maleunemployed_y0==0 & gender == 0 sort family_id browse id family_id gender maleunemployed_y0 partnerunemployed_y0 binary_employment_y0
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