*This file allows you to replicate the short-term results in "The Power of the Pill for the Next Generation" *Note from Hungerman to himself: the "master file" for this is RR_short_term_1trend_under21_6_09.do ************************************************************************************************************************** *The variable treat_dum, tr_ab, ft2, abft2, agefake14 and abfake14 were constructed from data provided by Melani Guldi, * *Please contact her for the information needed to construct those variables. Or, with her permission, contact Hungerman. * ************************************************************************************************************************** *The variables not included here are: *treat_dum (not included) whether mom had access to pill in year of conception *tr_ab (not included) whether mom had access to abortion *agefake14 (not included) whether a 14 year old had pill access *abfake14 (not included) whether a 14 year old had aboriton access *ft2 (not included) whether women will have access to pill in 2 years *abft2 (not included) whether women will have abortion acces to pill in 2 years *These variables are not included; their construction using Melani Guldi's data is described in the paper. *The unit of analysis is all kids born in a given state and year, to mother's of a given age. *The included variables are * iur_gls -- unemployment as measured in GLS (Gruber, Levine, Staiger)'s QJE paper * pcinc_gls -- percapita income as measured in GLS * crime_gls -- crime rage in GLS * pctnonwh_gls -- perence of state population nonwhite, as in GLS * T* -- state*year trends * S* -- dummies for state of birth * Y* -- year dummies * A* -- dummies for mom's age * astrend* -- mom's age * state dummies. ie, a "linear trend" not in time but in mom's age * dm1l* -- moving averages of the dependent variables (construction described in the paper) * good -- good is a dummy that = 1 if the year is greater than 1963 and if none of the key variables have missing values * state -- state where cohort was born * ys -- index for state and year * year -- year born * momage -- age of mother when cohort was born * womenpop -- number of women in population (ie, number of possible moms--construction of this is described in paper) * kidpop -- number of kids in population (ie, size of cohort) * lfamwelf -- share of kids in a cohort in a household (HH) getting welfare, logged * lonepar -- share of kids in a cohort in single-parent-household, logged * lbelow -- share of kids in a cohort in HH below poverty, logged * lbr -- birthrate (ie, kidpop/womenpop) * lw -- log of womenpop variable (number of potential moms, logged) use "MY DIRECTORY\replication_Power_Generation.dta" *GLOBAL global gls "iur_gls pcinc_gls crime_gls pctnonwh_gls T* S* Y* A*" *Table 1 Regressions reg lbr treat_dum $gls astrend* dm1lbr [aweight = womenpop] if good==1, robust cluster(state) reg lbr treat_dum lw $gls astrend* dm1lbr [aweight = womenpop] if good==1, robust cluster(state) areg lbr treat_dum A* astrend* dm1lbr [aweight = womenpop] if good==1, robust cluster(state) absorb(ys) areg lbr treat_dum lw A* astrend* dm1lbr [aweight = womenpop] if good==1, robust cluster(state) absorb(ys) *Table 2 and Table 3 (panel A) regressions foreach var in famwelf onepar below { reg l`var' treat_dum $gls dm1l`var' astrend* [aweight = kidpop] if good==1 & year>1963, robust cluster(state) areg l`var' treat_dum A* dm1l`var' astrend* [aweight = kidpop] if good==1 & year>1963, robust cluster(state) absorb(ys) areg l`var' treat_dum tr_ab A* dm1l`var' astrend* [aweight = kidpop] if good==1 & year>1963, robust cluster(state) absorb(ys) } *Panel B and C regressions from Table 3 reg lbr agefake14 abfake14 $gls astrend* dm1lbr [aweight = womenpop] if momage>=21 & year>1963, robust cluster(state) areg lbr treat_dum ft2 tr_ab abft2 A* astrend* dm1lbr [aweight = womenpop] if good==1 & year>1963, robust cluster(state) absorb(ys) foreach var in famwelf onepar below { reg l`var' agefake14 abfake14 $gls dm1l`var' astrend* [aweight = kidpop] if momage>=21 & year>1963, robust cluster(state) areg l`var' treat_dum ft2 tr_ab abft2 A* dm1l`var' astrend* [aweight = kidpop] if good==1 & year>1963, robust cluster(state) absorb(ys) } *********Birthweight Regressions************ *These regressions are from a separate file *This file is like the other file with a few trivial changes: * the index for states and years is sy, instead of ys * number of kids in a cohort is birthspop instead of kidpop * llbw is the share of low birthweight birthsin a population, logged. use MY DIRECTORY\replication_Power_Generation_bweight.dta *Table 2 reg llbw treat_dum $gls astrend* dm1llbw [aweight = birthspop] if good==1, robust cluster(state) areg llbw treat_dum A* astrend* dm1llbw [aweight = birthspop] if good==1, robust cluster(state) absorb(sy) *Table 3 areg llbw treat_dum tr_ab A* astrend* dm1llbw [aweight = birthspop] if good==1, robust cluster(state) absorb(sy) reg llbw agefake14 abfake14 $gls astrend* dm1llbw [aweight = birthspop] if momage>=21 , robust cluster(state) areg llbw treat_dum ft2 tr_ab abft2 A* astrend* dm1llbw [aweight = birthspop] if good==1, robust cluster(state) absorb(sy)