Hello,
I want to estimate the effect of conflict exposure on women's height.
In the data, individuals are born between 1963 and 1996. The conflict started in 1996, meaning that individuals are treated at the same time but at different ages. So, I defined the treated group based on age at the beginning of the conflict in 1996. The cohort groups :
- Individuals aged 0-5 at the beginning of the conflict (born between 1991 to 1996),
-Individuals aged to 6-11 at the beginning of the conflict (born between 1985-1990)
-Individuals aged 12 to 17 at the beginning of the conflict (born between 1979-1984)
- Individuals aged 18 to 33 at the beginning of the conflict (control group –as never treated), born between 1963 to 1978.
I built a binary "exposure to the conflict" variable, namely, affected vs non-affected conflict zone, based on the village of residence. I have repeated cross-sectional data, with years of surveys in 2007 and 2013.
I was wondering if I could apply DID Callaway and Sant’Anna Approach since I do not have individuals born after the conflict in the data, and I am not sure if using the year of birth of individuals is the correct way of defining the time variable .
tab year_birth first_treat
respondent |
's year of | first_treat
birth | 0 1984 1990 1996 | Total
-----------+--------------------------------------------+----------
1963 | 163 0 0 0 | 163
1964 | 477 0 0 0 | 477
1965 | 421 0 0 0 | 421
1966 | 374 0 0 0 | 374
1967 | 384 0 0 0 | 384
1968 | 509 0 0 0 | 509
1969 | 436 0 0 0 | 436
1970 | 522 0 0 0 | 522
1971 | 349 0 0 0 | 349
1972 | 656 0 0 0 | 656
1973 | 599 0 0 0 | 599
1974 | 653 0 0 0 | 653
1975 | 710 0 0 0 | 710
1976 | 661 0 0 0 | 661
1977 | 686 0 0 0 | 686
1978 | 752 0 0 0 | 752
1979 | 721 0 0 0 | 721
1980 | 605 162 0 0 | 767
1981 | 507 144 0 0 | 651
1982 | 807 193 0 0 | 1,000
1983 | 750 250 0 0 | 1,000
1984 | 751 207 0 0 | 958
1985 | 841 284 0 0 | 1,125
1986 | 954 0 240 0 | 1,194
1987 | 883 0 308 0 | 1,191
1988 | 754 0 217 0 | 971
1989 | 654 0 217 0 | 871
1990 | 761 0 238 0 | 999
1991 | 705 0 234 0 | 939
1992 | 763 0 0 233 | 996
1993 | 745 0 0 250 | 995
1994 | 499 0 0 155 | 654
1995 | 646 0 0 205 | 851
1996 | 572 0 0 170 | 742
-----------+--------------------------------------------+----------
Total | 21,270 1,240 1,454 1,013 | 24,977
Thank you in advance,
Emmanuel
I want to estimate the effect of conflict exposure on women's height.
In the data, individuals are born between 1963 and 1996. The conflict started in 1996, meaning that individuals are treated at the same time but at different ages. So, I defined the treated group based on age at the beginning of the conflict in 1996. The cohort groups :
- Individuals aged 0-5 at the beginning of the conflict (born between 1991 to 1996),
-Individuals aged to 6-11 at the beginning of the conflict (born between 1985-1990)
-Individuals aged 12 to 17 at the beginning of the conflict (born between 1979-1984)
- Individuals aged 18 to 33 at the beginning of the conflict (control group –as never treated), born between 1963 to 1978.
I built a binary "exposure to the conflict" variable, namely, affected vs non-affected conflict zone, based on the village of residence. I have repeated cross-sectional data, with years of surveys in 2007 and 2013.
I was wondering if I could apply DID Callaway and Sant’Anna Approach since I do not have individuals born after the conflict in the data, and I am not sure if using the year of birth of individuals is the correct way of defining the time variable .
tab year_birth first_treat
respondent |
's year of | first_treat
birth | 0 1984 1990 1996 | Total
-----------+--------------------------------------------+----------
1963 | 163 0 0 0 | 163
1964 | 477 0 0 0 | 477
1965 | 421 0 0 0 | 421
1966 | 374 0 0 0 | 374
1967 | 384 0 0 0 | 384
1968 | 509 0 0 0 | 509
1969 | 436 0 0 0 | 436
1970 | 522 0 0 0 | 522
1971 | 349 0 0 0 | 349
1972 | 656 0 0 0 | 656
1973 | 599 0 0 0 | 599
1974 | 653 0 0 0 | 653
1975 | 710 0 0 0 | 710
1976 | 661 0 0 0 | 661
1977 | 686 0 0 0 | 686
1978 | 752 0 0 0 | 752
1979 | 721 0 0 0 | 721
1980 | 605 162 0 0 | 767
1981 | 507 144 0 0 | 651
1982 | 807 193 0 0 | 1,000
1983 | 750 250 0 0 | 1,000
1984 | 751 207 0 0 | 958
1985 | 841 284 0 0 | 1,125
1986 | 954 0 240 0 | 1,194
1987 | 883 0 308 0 | 1,191
1988 | 754 0 217 0 | 971
1989 | 654 0 217 0 | 871
1990 | 761 0 238 0 | 999
1991 | 705 0 234 0 | 939
1992 | 763 0 0 233 | 996
1993 | 745 0 0 250 | 995
1994 | 499 0 0 155 | 654
1995 | 646 0 0 205 | 851
1996 | 572 0 0 170 | 742
-----------+--------------------------------------------+----------
Total | 21,270 1,240 1,454 1,013 | 24,977
Thank you in advance,
Emmanuel
