kenya_dat2<-kenya_dat[,c("contracept","urban_stat","x1993","x1998","x2003","x2008","x2014","more_than1_union","husband_home","kid_desire","femwork","hs_or_more","cheb","sonsathome","dausathome","sonsdied","dausdied","literacy","mig_rurl_urb","mig_urb_rurl","geo_ke1989_2014","fridge_stat","phone","tv_stat","radio_stat","newspaper","car_stat","bike_stat","motorcycle_stat")]
imp<-mice(data=kenya_dat2,seed = 1234)
##
## iter imp variable
## 1 1 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 1 2 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 1 3 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 1 4 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 1 5 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 2 1 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 2 2 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 2 3 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 2 4 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 2 5 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 3 1 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 3 2 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 3 3 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 3 4 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 3 5 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 4 1 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 4 2 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 4 3 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 4 4 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 4 5 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 5 1 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 5 2 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 5 3 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 5 4 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
## 5 5 more_than1_union husband_home kid_desire femwork hs_or_more literacy fridge_stat phone tv_stat radio_stat newspaper car_stat bike_stat motorcycle_stat
kenya_dat3<-complete(imp)
modernity.pc<-prcomp(~fridge_stat+phone+tv_stat+radio_stat+newspaper+car_stat+bike_stat+motorcycle_stat,data=kenya_dat3,center=T,scale=T,retx=T)
summary(modernity.pc)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 1.510 1.0876 1.0044 0.9553 0.86248 0.84431 0.80126
## Proportion of Variance 0.285 0.1479 0.1261 0.1141 0.09298 0.08911 0.08025
## Cumulative Proportion 0.285 0.4329 0.5590 0.6731 0.76605 0.85516 0.93541
## PC8
## Standard deviation 0.71885
## Proportion of Variance 0.06459
## Cumulative Proportion 1.00000
scores<-data.frame(modernity.pc$x)
scores$name<-rownames(modernity.pc$x)
kenya_dat3$name<-rownames(kenya_dat3)
kenya_dat3<-merge(kenya_dat3, scores, by.x="name", by.y="name", all.x=F)
tail(names(kenya_dat3), 20)
## [1] "literacy" "mig_rurl_urb" "mig_urb_rurl"
## [4] "geo_ke1989_2014" "fridge_stat" "phone"
## [7] "tv_stat" "radio_stat" "newspaper"
## [10] "car_stat" "bike_stat" "motorcycle_stat"
## [13] "PC1" "PC2" "PC3"
## [16] "PC4" "PC5" "PC6"
## [19] "PC7" "PC8"
modernity.pc$rotation
## PC1 PC2 PC3 PC4 PC5
## fridge_stat 0.47826164 -0.2769190 0.1822003 -0.02739979 0.05198816
## phone 0.39696863 -0.2816384 0.1443698 -0.13292768 0.19396320
## tv_stat 0.45239770 0.1126950 -0.1944750 0.25526395 0.04266343
## radio_stat 0.26241427 0.5251392 -0.4085084 -0.02816912 0.60751642
## newspaper 0.36600403 0.1347650 -0.4372899 0.10193297 -0.73134246
## car_stat 0.43252876 -0.2095035 0.2781477 -0.16168380 0.03515345
## bike_stat 0.10149554 0.5558230 0.3370797 -0.68587313 -0.22156582
## motorcycle_stat 0.09389016 0.4295717 0.5992074 0.63927488 -0.05991801
## PC6 PC7 PC8
## fridge_stat 0.20196988 -0.16926356 -0.767130520
## phone -0.80311165 -0.04511198 0.199845420
## tv_stat 0.25539445 -0.67031430 0.404089348
## radio_stat -0.03261830 0.30496486 -0.156688500
## newspaper -0.17451178 0.27199307 -0.083488254
## car_stat 0.44629856 0.53876711 0.418128196
## bike_stat -0.01267849 -0.21783722 -0.003096312
## motorcycle_stat -0.13465530 0.12363916 -0.043840874
#urban only model
fit1b<-glmer(contracept~urban_stat+(1|geo_ke1989_2014), family=binomial, kenya_dat3,
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
fit11b<-glmer(contracept~urban_stat+x1993+x1998+x2003+x2008+x2014+more_than1_union+husband_home+kid_desire+femwork+hs_or_more+(1|geo_ke1989_2014), family=binomial, kenya_dat3,
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 0.00420155 (tol =
## 0.001, component 1)
fit2b<-glmer(contracept~urban_stat+x1993+x1998+x2003+x2008+x2014+more_than1_union+husband_home+kid_desire+femwork+hs_or_more+cheb+sonsathome+dausathome+sonsdied+dausdied+(1|geo_ke1989_2014), family=binomial, kenya_dat3,
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 0.00523655 (tol =
## 0.001, component 1)
fit3b<-glmer(contracept~urban_stat+x1993+x1998+x2003+x2008+x2014+more_than1_union+husband_home+kid_desire+femwork+hs_or_more+cheb+sonsathome+dausathome+sonsdied+dausdied+PC1+PC2+PC3+(1|geo_ke1989_2014), family=binomial, kenya_dat3,
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 0.00518432 (tol =
## 0.001, component 1)
fit4b<-glmer(contracept~urban_stat+x1993+x1998+x2003+x2008+x2014+more_than1_union+husband_home+kid_desire+femwork+hs_or_more+cheb+sonsathome+dausathome+sonsdied+dausdied+PC1+PC2+PC3+literacy+mig_rurl_urb+mig_urb_rurl+(1|geo_ke1989_2014), family=binomial, kenya_dat3,
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 0.00629405 (tol =
## 0.001, component 1)
stargazer(fit1b,fit11b,fit2b,fit3b,fit4b,type="html")
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Dependent variable:
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contracept
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(1)
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(2)
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(3)
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(4)
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(5)
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urban_stat
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0.533***
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0.318***
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0.440***
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0.372***
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0.349***
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(0.020)
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(0.022)
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(0.023)
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(0.024)
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(0.025)
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x1993
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0.232***
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0.369***
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0.353***
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0.346***
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(0.046)
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(0.047)
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(0.047)
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(0.048)
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x1998
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0.301***
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0.500***
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0.496***
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0.496***
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(0.045)
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(0.046)
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(0.046)
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(0.047)
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x2003
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0.230***
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0.430***
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0.429***
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0.430***
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(0.045)
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(0.046)
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(0.046)
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(0.047)
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x2008
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0.514***
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0.719***
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0.685***
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0.677***
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(0.044)
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(0.045)
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(0.045)
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(0.046)
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x2014
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0.975***
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1.155***
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1.189***
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1.204***
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(0.038)
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(0.039)
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(0.040)
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(0.040)
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more_than1_union
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-0.262***
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-0.175***
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-0.137***
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-0.108***
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(0.038)
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(0.038)
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(0.038)
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(0.039)
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husband_home
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0.154***
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0.115***
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0.081***
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0.105***
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(0.021)
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(0.022)
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(0.022)
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(0.022)
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kid_desire
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-0.931***
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-0.572***
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-0.560***
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-0.490***
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(0.019)
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(0.022)
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(0.022)
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(0.023)
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femwork
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0.688***
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0.645***
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0.624***
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0.580***
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(0.019)
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(0.020)
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(0.020)
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(0.020)
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hs_or_more
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0.334***
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0.433***
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0.287***
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0.244***
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(0.023)
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(0.024)
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(0.025)
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(0.026)
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cheb
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0.017**
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0.028***
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0.069***
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(0.009)
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(0.009)
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(0.009)
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sonsathome
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0.212***
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0.217***
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0.223***
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(0.011)
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(0.011)
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(0.012)
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dausathome
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0.161***
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0.167***
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0.162***
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(0.011)
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(0.011)
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(0.011)
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sonsdied
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-0.215***
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-0.211***
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-0.223***
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(0.025)
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(0.025)
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(0.025)
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dausdied
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-0.188***
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-0.185***
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-0.187***
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(0.027)
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(0.027)
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(0.027)
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PC1
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0.109***
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0.082***
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(0.007)
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(0.007)
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PC2
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0.167***
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0.115***
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(0.009)
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(0.009)
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PC3
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-0.078***
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-0.048***
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(0.009)
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(0.009)
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literacy
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0.906***
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(0.031)
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mig_rurl_urb
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0.152***
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(0.047)
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mig_urb_rurl
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0.263***
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(0.050)
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Constant
|
-1.453***
|
-2.048***
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-2.854***
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-2.829***
|
-3.711***
|
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(0.277)
|
(0.333)
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(0.402)
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(0.365)
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(0.326)
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Observations
|
70,289
|
70,289
|
70,289
|
70,289
|
70,289
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Log Likelihood
|
-40,142.900
|
-36,601.590
|
-35,813.480
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-35,496.350
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-35,023.030
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Akaike Inf. Crit.
|
80,291.800
|
73,229.180
|
71,662.960
|
71,034.700
|
70,094.050
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Bayesian Inf. Crit.
|
80,319.280
|
73,348.260
|
71,827.850
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71,227.070
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70,313.900
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Note:
|
p<0.1; p<0.05; p<0.01
|