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Copy pathAlt Ed 4 - Follow-up analysis.R
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Alt Ed 4 - Follow-up analysis.R
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#########################################################################
################ Alternative Education effectiveness analysis
#########################################################################
# This piece of code analyses differences in later life outcomes between students who were referred to
# Alternative Education (AE) between the ages of 13-16 and learners who were never referred to AE but
# who were matched with AE students using a propensity score matching model.
# For details on the PSM model that produced the control group used here, see "PSM v[x].R" saved in the
# same folder as this code. For details on the construction of the dataset used here, see "Alt Ed v[x].sql".
#########################################################################
################ Setup
#########################################################################
library(dplyr)
library(RODBC)
library(pROC)
library(ggplot2)
library(broom)
library(stringr)
setwd("~/Network-Shares/DataLabNas/MAA/MAA2021-60/Alternative_education")
# NOTE: Server name and port number in below string have been suppressed
con=odbcDriverConnect("Driver=ODBC Driver 17 for SQL Server; Trusted_Connection=YES; Server=[SERVER NAME],[PORT NUMBER];Database=IDI_Sandpit")
Followup <- sqlQuery(con,"select * from [IDI_Sandpit].[DL-MAA2021-60].[ae_202210_psm_followup]")
Followup$died[is.na(Followup$died)] <- 0
Followup$in_nz[is.na(Followup$in_nz)] <- 0
Followup$ncea_1[is.na(Followup$ncea_1)] <- 0
Followup$ncea_2[is.na(Followup$ncea_2)] <- 0
Followup$ncea_3[is.na(Followup$ncea_3)] <- 0
Followup$ue[is.na(Followup$ue)] <- 0
Followup$at_school[is.na(Followup$at_school)] <- 0
Followup$tertiary_enrolment[is.na(Followup$tertiary_enrolment)] <- 0
Followup$ito_enrolment[is.na(Followup$ito_enrolment)] <- 0
Followup$any_tertiary_enrolment[is.na(Followup$any_tertiary_enrolment)] <- 0
Followup$max_qual_tertiary[is.na(Followup$max_qual_tertiary)] <- 0
Followup$income_wages[is.na(Followup$income_wages)] <- 0
Followup$income_benefit[is.na(Followup$income_benefit)] <- 0
Followup$income_total[is.na(Followup$income_total)] <- 0
Followup$income_wages <- Followup$income_wages/1000
Followup$income_benefit <- Followup$income_benefit/1000
Followup$income_total <- Followup$income_total/1000
Followup$any_wages[is.na(Followup$any_wages)] <- 0
Followup$any_benefit[is.na(Followup$any_benefit)] <- 0
Followup$offender_any[is.na(Followup$offender_any)] <- 0
Followup$offender_violent[is.na(Followup$offender_violent)] <- 0
Followup$victim_any[is.na(Followup$victim_any)] <- 0
Followup$victim_violent[is.na(Followup$victim_violent)] <- 0
Followup$community_sentence[is.na(Followup$community_sentence)] <- 0
Followup$custodial_sentence[is.na(Followup$custodial_sentence)] <- 0
Followup$pho_enrolment[is.na(Followup$pho_enrolment)] <- 0
Followup$ed_admissions_n[is.na(Followup$ed_admissions_n)] <- 0
Followup$gp_contacts_n[is.na(Followup$gp_contacts_n)] <- 0
Followup$ash_n[is.na(Followup$ash_n)] <- 0
Followup$ln_income_wages <- log(Followup$income_wages+1)
Followup$ln_income_benefit <- log(Followup$income_benefit+1)
Followup$ln_income_total <- log(Followup$income_total+1)
Followup$propensity_bin <- ordered(Followup$propensity_bin, levels = c("phat lt 0.1"
,"phat 0.1_0.3"
,"phat 0.3_0.5"
,"phat 0.5_0.7"
,"phat gt 0.7"))
# Do the same for the attendance sensitivity dataset
Followup_attendance <- sqlQuery(con,"select * from [IDI_Sandpit].[DL-MAA2021-60].[ae_202210_psm_followup_attendance]")
Followup_attendance$died[is.na(Followup_attendance$died)] <- 0
Followup_attendance$in_nz[is.na(Followup_attendance$in_nz)] <- 0
Followup_attendance$ncea_1[is.na(Followup_attendance$ncea_1)] <- 0
Followup_attendance$ncea_2[is.na(Followup_attendance$ncea_2)] <- 0
Followup_attendance$ncea_3[is.na(Followup_attendance$ncea_3)] <- 0
Followup_attendance$ue[is.na(Followup_attendance$ue)] <- 0
Followup_attendance$at_school[is.na(Followup_attendance$at_school)] <- 0
Followup_attendance$tertiary_enrolment[is.na(Followup_attendance$tertiary_enrolment)] <- 0
Followup_attendance$ito_enrolment[is.na(Followup_attendance$ito_enrolment)] <- 0
Followup_attendance$any_tertiary_enrolment[is.na(Followup_attendance$any_tertiary_enrolment)] <- 0
Followup_attendance$max_qual_tertiary[is.na(Followup_attendance$max_qual_tertiary)] <- 0
Followup_attendance$income_wages[is.na(Followup_attendance$income_wages)] <- 0
Followup_attendance$income_benefit[is.na(Followup_attendance$income_benefit)] <- 0
Followup_attendance$income_total[is.na(Followup_attendance$income_total)] <- 0
Followup_attendance$income_wages <- Followup_attendance$income_wages/1000
Followup_attendance$income_benefit <- Followup_attendance$income_benefit/1000
Followup_attendance$income_total <- Followup_attendance$income_total/1000
Followup_attendance$any_wages[is.na(Followup_attendance$any_wages)] <- 0
Followup_attendance$any_benefit[is.na(Followup_attendance$any_benefit)] <- 0
Followup_attendance$offender_any[is.na(Followup_attendance$offender_any)] <- 0
Followup_attendance$offender_violent[is.na(Followup_attendance$offender_violent)] <- 0
Followup_attendance$victim_any[is.na(Followup_attendance$victim_any)] <- 0
Followup_attendance$victim_violent[is.na(Followup_attendance$victim_violent)] <- 0
Followup_attendance$community_sentence[is.na(Followup_attendance$community_sentence)] <- 0
Followup_attendance$custodial_sentence[is.na(Followup_attendance$custodial_sentence)] <- 0
Followup_attendance$pho_enrolment[is.na(Followup_attendance$pho_enrolment)] <- 0
Followup_attendance$ed_admissions_n[is.na(Followup_attendance$ed_admissions_n)] <- 0
Followup_attendance$gp_contacts_n[is.na(Followup_attendance$gp_contacts_n)] <- 0
Followup_attendance$ash_n[is.na(Followup_attendance$ash_n)] <- 0
Followup_attendance$ln_income_wages <- log(Followup_attendance$income_wages+1)
Followup_attendance$ln_income_benefit <- log(Followup_attendance$income_benefit+1)
Followup_attendance$ln_income_total <- log(Followup_attendance$income_total+1)
Followup_attendance$propensity_bin <- ordered(Followup_attendance$propensity_bin, levels = c("phat lt 0.1"
,"phat 0.1_0.3"
,"phat 0.3_0.5"
,"phat 0.5_0.7"
,"phat gt 0.7"))
# Do the same for the exact sensitivity dataset
Followup_exact <- sqlQuery(con,"select * from [IDI_Sandpit].[DL-MAA2021-60].[ae_202210_psm_followup_exact]")
Followup_exact$died[is.na(Followup_exact$died)] <- 0
Followup_exact$in_nz[is.na(Followup_exact$in_nz)] <- 0
Followup_exact$ncea_1[is.na(Followup_exact$ncea_1)] <- 0
Followup_exact$ncea_2[is.na(Followup_exact$ncea_2)] <- 0
Followup_exact$ncea_3[is.na(Followup_exact$ncea_3)] <- 0
Followup_exact$ue[is.na(Followup_exact$ue)] <- 0
Followup_exact$at_school[is.na(Followup_exact$at_school)] <- 0
Followup_exact$tertiary_enrolment[is.na(Followup_exact$tertiary_enrolment)] <- 0
Followup_exact$ito_enrolment[is.na(Followup_exact$ito_enrolment)] <- 0
Followup_exact$any_tertiary_enrolment[is.na(Followup_exact$any_tertiary_enrolment)] <- 0
Followup_exact$max_qual_tertiary[is.na(Followup_exact$max_qual_tertiary)] <- 0
Followup_exact$income_wages[is.na(Followup_exact$income_wages)] <- 0
Followup_exact$income_benefit[is.na(Followup_exact$income_benefit)] <- 0
Followup_exact$income_total[is.na(Followup_exact$income_total)] <- 0
Followup_exact$income_wages <- Followup_exact$income_wages/1000
Followup_exact$income_benefit <- Followup_exact$income_benefit/1000
Followup_exact$income_total <- Followup_exact$income_total/1000
Followup_exact$any_wages[is.na(Followup_exact$any_wages)] <- 0
Followup_exact$any_benefit[is.na(Followup_exact$any_benefit)] <- 0
Followup_exact$offender_any[is.na(Followup_exact$offender_any)] <- 0
Followup_exact$offender_violent[is.na(Followup_exact$offender_violent)] <- 0
Followup_exact$victim_any[is.na(Followup_exact$victim_any)] <- 0
Followup_exact$victim_violent[is.na(Followup_exact$victim_violent)] <- 0
Followup_exact$community_sentence[is.na(Followup_exact$community_sentence)] <- 0
Followup_exact$custodial_sentence[is.na(Followup_exact$custodial_sentence)] <- 0
Followup_exact$pho_enrolment[is.na(Followup_exact$pho_enrolment)] <- 0
Followup_exact$ed_admissions_n[is.na(Followup_exact$ed_admissions_n)] <- 0
Followup_exact$gp_contacts_n[is.na(Followup_exact$gp_contacts_n)] <- 0
Followup_exact$ash_n[is.na(Followup_exact$ash_n)] <- 0
Followup_exact$ln_income_wages <- log(Followup_exact$income_wages+1)
Followup_exact$ln_income_benefit <- log(Followup_exact$income_benefit+1)
Followup_exact$ln_income_total <- log(Followup_exact$income_total+1)
Followup_exact$propensity_bin <- ordered(Followup_exact$propensity_bin, levels = c("phat lt 0.1"
,"phat 0.1_0.3"
,"phat 0.3_0.5"
,"phat 0.5_0.7"
,"phat gt 0.7"))
#########################################################################
################ Main effects
#########################################################################
agelist <- c(17:30)
varlist <- c("died"
,"in_nz"
,"ncea_1"
,"ncea_2"
,"ncea_3"
,"ue"
,"at_school"
,"tertiary_enrolment"
,"ito_enrolment"
,"any_tertiary_enrolment"
,"max_qual_tertiary"
,"income_wages"
,"income_benefit"
,"income_total"
,"any_wages"
,"any_benefit"
,"ln_income_wages"
,"ln_income_benefit"
,"ln_income_total"
,"offender_any"
,"offender_violent"
,"victim_any"
,"victim_violent"
,"community_sentence"
,"custodial_sentence"
,"pho_enrolment"
,"ed_admissions_n"
,"gp_contacts_n"
,"ash_n"
)
output <- data.frame(model=character(),
outcome=character(),
estimate=character(),
age=integer(),
coef=double(),
se=double(),
p=double(),
sum_treatment=integer(),
sum_control=integer(),
n_treatment=integer(),
schools_treatment=integer(),
schools_control=integer(),
terproviders_treatment=integer(),
terproviders_control=integer()
)
# Produce outcomes for preferred specification
for (a in agelist) {
for (v in varlist) {
tryCatch({
step_a <- lm(formula = eval(parse(text=v)) ~ alted_during
+ male + eth_maori + eth_pacific + eth_asian + eth_melaa + eth_other + sole_maori + born_in_nz
+ sole_birth_parent + missing_address + missing_schools + missing_mother + missing_father + missing_motherinc + missing_fatherinc
+ num_addresses_prior + mme_prior
+ factor(standdown_n_prior) + factor(suspension_n_prior) + factor(attserv_n_prior)
+ ot_report_prior + ot_invest_prior + yj_fgc_prior + ot_place_prior
+ id + adhd + asd + ors_prior
+ factor(birth_year)
+ mother_mh + father_mh + child_mha
+ factor(first_school_region) + factor(mother_qual_cat) + factor(father_qual_cat)
+ factor(n_nonstructuralchanges_prior) + first_school_decile + first_nzdep
+ teen_parent + mother_benefit_age12 + father_benefit_age12
+ mother_custodial_prior+ mother_community_prior + father_custodial_prior + father_community_prior
, data=subset(Followup,age==a))
step_b <- tidy(step_a)
step_c <- subset(step_b,term=="alted_during")
step_d <- glance(step_a)
step_e_t <- subset(Followup, age==a & alted_during==1)
step_e_c <- subset(Followup, age==a & alted_during==0)
step_f <- data.frame(
model = "Preferred",
outcome = v,
estimate = "Adjusted",
age = a,
coef = step_c$estimate,
se = step_c$std.error,
p = step_c$p.value,
sum_treatment = NA,
sum_control = NA,
n_treatment = NA,
schools_treatment = NA,
schools_control = NA,
terproviders_treatment = NA,
terproviders_control = NA
)
output <- rbind(output,step_f)
step_g <- lm(formula = eval(parse(text=v)) ~ alted_during
, data=subset(Followup,age==a))
step_h <- tidy(step_g)
step_i <- glance(step_g)
step_j <- subset(step_h,term=="alted_during")
step_k <- data.frame(
model = "Preferred",
outcome = v,
estimate = "Unadjusted",
age = a,
coef = NA,
se = step_j$std.error,
p = step_j$p.value,
sum_treatment = sum(eval(parse(text=paste("step_e_t$",v,sep=""))), na.rm=TRUE),
sum_control = sum(eval(parse(text=paste("step_e_c$",v,sep=""))), na.rm=TRUE),
n_treatment = sum(!is.na(eval(parse(text=paste("step_e_t$",v,sep=""))))),
schools_treatment = n_distinct(step_e_t$combined_providerid),
schools_control = n_distinct(step_e_c$combined_providerid),
terproviders_treatment = n_distinct(step_e_t$any_tertiary_providerid),
terproviders_control = n_distinct(step_e_c$any_tertiary_providerid)
)
output <- rbind(output,step_k)
print(paste("Completed: Preferred",v,a,sep=" "))
}, error=function(e){cat("ERROR:",conditionMessage(e),"\n")})
}}
# Produce outcomes for variant using exact matching
for (a in agelist) {
for (v in varlist) {
tryCatch({
step_a <- lm(formula = eval(parse(text=v)) ~ alted_during
+ male + eth_maori + eth_pacific + eth_asian + eth_melaa + eth_other + sole_maori + born_in_nz
+ sole_birth_parent + missing_address + missing_schools + missing_mother + missing_father + missing_motherinc + missing_fatherinc
+ num_addresses_prior + mme_prior
+ factor(standdown_n_prior) + factor(suspension_n_prior) + factor(attserv_n_prior)
+ ot_report_prior + ot_invest_prior + yj_fgc_prior + ot_place_prior
+ id + adhd + asd + ors_prior
+ factor(birth_year)
+ mother_mh + father_mh + child_mha
+ factor(first_school_region) + factor(mother_qual_cat) + factor(father_qual_cat)
+ factor(n_nonstructuralchanges_prior) + first_school_decile + first_nzdep
+ teen_parent + mother_benefit_age12 + father_benefit_age12
+ mother_custodial_prior+ mother_community_prior + father_custodial_prior + father_community_prior
, data=subset(Followup_exact,age==a))
step_b <- tidy(step_a)
step_c <- subset(step_b,term=="alted_during")
step_d <- glance(step_a)
step_e_t <- subset(Followup_exact, age==a & alted_during==1)
step_e_c <- subset(Followup_exact, age==a & alted_during==0)
step_f <- data.frame(
model = "Exact match",
outcome = v,
estimate = "Adjusted",
age = a,
coef = step_c$estimate,
se = step_c$std.error,
p = step_c$p.value,
sum_treatment = NA,
sum_control = NA,
n_treatment = NA,
schools_treatment = NA,
schools_control = NA,
terproviders_treatment = NA,
terproviders_control = NA
)
output <- rbind(output,step_f)
step_g <- lm(formula = eval(parse(text=v)) ~ alted_during
, data=subset(Followup_exact,age==a))
step_h <- tidy(step_g)
step_i <- glance(step_g)
step_j <- subset(step_h,term=="alted_during")
step_k <- data.frame(
model = "Exact match",
outcome = v,
estimate = "Unadjusted",
age = a,
coef = NA,
se = step_j$std.error,
p = step_j$p.value,
sum_treatment = sum(eval(parse(text=paste("step_e_t$",v,sep=""))), na.rm=TRUE),
sum_control = sum(eval(parse(text=paste("step_e_c$",v,sep=""))), na.rm=TRUE),
n_treatment = sum(!is.na(eval(parse(text=paste("step_e_t$",v,sep=""))))),
schools_treatment = n_distinct(step_e_t$combined_providerid),
schools_control = n_distinct(step_e_c$combined_providerid),
terproviders_treatment = n_distinct(step_e_t$any_tertiary_providerid),
terproviders_control = n_distinct(step_e_c$any_tertiary_providerid)
)
output <- rbind(output,step_k)
print(paste("Completed: Exact",v,a,sep=" "))
}, error=function(e){cat("ERROR:",conditionMessage(e),"\n")})
}}
# Produce outcomes for variant using attendance in the matching model (only for ages 17-22)
agelist <- c(17:22)
for (a in agelist) {
for (v in varlist) {
tryCatch({
step_a <- lm(formula = eval(parse(text=v)) ~ alted_during
+ male + eth_maori + eth_pacific + eth_asian + eth_melaa + eth_other + sole_maori + born_in_nz
+ sole_birth_parent + missing_address + missing_schools + missing_mother + missing_father + missing_motherinc + missing_fatherinc
+ num_addresses_prior + mme_prior
+ factor(standdown_n_prior) + factor(suspension_n_prior) + factor(attserv_n_prior)
+ ot_report_prior + ot_invest_prior + yj_fgc_prior + ot_place_prior
+ id + adhd + asd + ors_prior
+ factor(birth_year)
+ mother_mh + father_mh + child_mha
+ factor(first_school_region) + factor(mother_qual_cat) + factor(father_qual_cat)
+ factor(n_nonstructuralchanges_prior) + first_school_decile + first_nzdep
+ teen_parent + mother_benefit_age12 + father_benefit_age12
+ mother_custodial_prior+ mother_community_prior + father_custodial_prior + father_community_prior
, data=subset(Followup_attendance,age==a))
step_b <- tidy(step_a)
step_c <- subset(step_b,term=="alted_during")
step_d <- glance(step_a)
step_e_t <- subset(Followup_attendance, age==a & alted_during==1)
step_e_c <- subset(Followup_attendance, age==a & alted_during==0)
step_f <- data.frame(
model = "Attendance 1999-2004",
outcome = v,
estimate = "Adjusted",
age = a,
coef = step_c$estimate,
se = step_c$std.error,
p = step_c$p.value,
sum_treatment = NA,
sum_control = NA,
n_treatment = NA,
schools_treatment = NA,
schools_control = NA,
terproviders_treatment = NA,
terproviders_control = NA
)
output <- rbind(output,step_f)
step_g <- lm(formula = eval(parse(text=v)) ~ alted_during
, data=subset(Followup_attendance,age==a))
step_h <- tidy(step_g)
step_i <- glance(step_g)
step_j <- subset(step_h,term=="alted_during")
step_k <- data.frame(
model = "Attendance 1999-2004",
outcome = v,
estimate = "Unadjusted",
age = a,
coef = NA,
se = step_j$std.error,
p = step_j$p.value,
sum_treatment = sum(eval(parse(text=paste("step_e_t$",v,sep=""))), na.rm=TRUE),
sum_control = sum(eval(parse(text=paste("step_e_c$",v,sep=""))), na.rm=TRUE),
n_treatment = sum(!is.na(eval(parse(text=paste("step_e_t$",v,sep=""))))),
schools_treatment = n_distinct(step_e_t$combined_providerid),
schools_control = n_distinct(step_e_c$combined_providerid),
terproviders_treatment = n_distinct(step_e_t$any_tertiary_providerid),
terproviders_control = n_distinct(step_e_c$any_tertiary_providerid)
)
output <- rbind(output,step_k)
print(paste("Completed: Attendance 1999",v,a,sep=" "))
}, error=function(e){cat("ERROR:",conditionMessage(e),"\n")})
}}
# Produce comparable estimates for the same birth cohorts
for (a in agelist) {
for (v in varlist) {
tryCatch({
step_a <- lm(formula = eval(parse(text=v)) ~ alted_during
+ male + eth_maori + eth_pacific + eth_asian + eth_melaa + eth_other + sole_maori + born_in_nz
+ sole_birth_parent + missing_address + missing_schools + missing_mother + missing_father + missing_motherinc + missing_fatherinc
+ num_addresses_prior + mme_prior
+ factor(standdown_n_prior) + factor(suspension_n_prior) + factor(attserv_n_prior)
+ ot_report_prior + ot_invest_prior + yj_fgc_prior + ot_place_prior
+ id + adhd + asd + ors_prior
+ factor(birth_year)
+ mother_mh + father_mh + child_mha
+ factor(first_school_region) + factor(mother_qual_cat) + factor(father_qual_cat)
+ factor(n_nonstructuralchanges_prior) + first_school_decile + first_nzdep
+ teen_parent + mother_benefit_age12 + father_benefit_age12
+ mother_custodial_prior+ mother_community_prior + father_custodial_prior + father_community_prior
, data=subset(Followup,birth_year>=1999 & age==a))
step_b <- tidy(step_a)
step_c <- subset(step_b,term=="alted_during")
step_d <- glance(step_a)
step_e_t <- subset(Followup, birth_year>=1999 & age==a & alted_during==1)
step_e_c <- subset(Followup, birth_year>=1999 & age==a & alted_during==0)
step_f <- data.frame(
model = "Preferred 1999-2004",
outcome = v,
estimate = "Adjusted",
age = a,
coef = step_c$estimate,
se = step_c$std.error,
p = step_c$p.value,
sum_treatment = NA,
sum_control = NA,
n_treatment = NA,
schools_treatment = NA,
schools_control = NA,
terproviders_treatment = NA,
terproviders_control = NA
)
output <- rbind(output,step_f)
step_g <- lm(formula = eval(parse(text=v)) ~ alted_during
, data=subset(Followup,birth_year>=1999 & age==a))
step_h <- tidy(step_g)
step_i <- glance(step_g)
step_j <- subset(step_h,term=="alted_during")
step_k <- data.frame(
model = "Preferred 1999-2004",
outcome = v,
estimate = "Unadjusted",
age = a,
coef = NA,
se = step_j$std.error,
p = step_j$p.value,
sum_treatment = sum(eval(parse(text=paste("step_e_t$",v,sep=""))), na.rm=TRUE),
sum_control = sum(eval(parse(text=paste("step_e_c$",v,sep=""))), na.rm=TRUE),
n_treatment = sum(!is.na(eval(parse(text=paste("step_e_t$",v,sep=""))))),
schools_treatment = n_distinct(step_e_t$combined_providerid),
schools_control = n_distinct(step_e_c$combined_providerid),
terproviders_treatment = n_distinct(step_e_t$any_tertiary_providerid),
terproviders_control = n_distinct(step_e_c$any_tertiary_providerid)
)
output <- rbind(output,step_k)
print(paste("Completed: Preferred 1999",v,a,sep=" "))
}, error=function(e){cat("ERROR:",conditionMessage(e),"\n")})
}}
#########################################################################
################ Sensitivity analysis - quarter 1
#########################################################################
# Because we run our matching model using all data up until the end of the year before someone first gets referred to AE, any events that
# happen in the AE referring year (but before the precise referral date) are going to be missed by our model. This has the potential
# to introduce omitted variable bias. To test this, we create exactly the same estimates only for the learners who were referred to AE
# in January, February or March. The model is presumably most able to determine the current circumstances for these students.
Followup_q1 <- subset(Followup, qtr==1)
agelist <- c(17:30)
for (a in agelist) {
for (v in varlist) {
tryCatch({
step_a <- lm(formula = eval(parse(text=v)) ~ alted_during
+ male + eth_maori + eth_pacific + eth_asian + eth_melaa + eth_other + sole_maori + born_in_nz
+ sole_birth_parent + missing_address + missing_schools + missing_mother + missing_father + missing_motherinc + missing_fatherinc
+ num_addresses_prior + mme_prior
+ factor(standdown_n_prior) + factor(suspension_n_prior) + factor(attserv_n_prior)
+ ot_report_prior + ot_invest_prior + yj_fgc_prior + ot_place_prior
+ id + adhd + asd + ors_prior
+ factor(birth_year)
+ mother_mh + father_mh + child_mha
+ factor(first_school_region) + factor(mother_qual_cat) + factor(father_qual_cat)
+ factor(n_nonstructuralchanges_prior) + first_school_decile + first_nzdep
+ teen_parent + mother_benefit_age12 + father_benefit_age12
+ mother_custodial_prior+ mother_community_prior + father_custodial_prior + father_community_prior
, data=subset(Followup_q1,age==a))
step_b <- tidy(step_a)
step_c <- subset(step_b,term=="alted_during")
step_d <- glance(step_a)
step_e_t <- subset(Followup_q1, age==a & alted_during==1)
step_e_c <- subset(Followup_q1, age==a & alted_during==0)
step_f <- data.frame(
model = "Quarter 1 only",
outcome = v,
estimate = "Adjusted",
age = a,
coef = step_c$estimate,
se = step_c$std.error,
p = step_c$p.value,
sum_treatment = NA,
sum_control = NA,
n_treatment = NA,
schools_treatment = NA,
schools_control = NA,
terproviders_treatment = NA,
terproviders_control = NA
)
output <- rbind(output,step_f)
step_g <- lm(formula = eval(parse(text=v)) ~ alted_during
, data=subset(Followup_q1,age==a))
step_h <- tidy(step_g)
step_i <- glance(step_g)
step_j <- subset(step_h,term=="alted_during")
step_k <- data.frame(
model = "Quarter 1 only",
outcome = v,
estimate = "Unadjusted",
age = a,
coef = NA,
se = step_j$std.error,
p = step_j$p.value,
sum_treatment = sum(eval(parse(text=paste("step_e_t$",v,sep=""))), na.rm=TRUE),
sum_control = sum(eval(parse(text=paste("step_e_c$",v,sep=""))), na.rm=TRUE),
n_treatment = sum(!is.na(eval(parse(text=paste("step_e_t$",v,sep=""))))),
schools_treatment = n_distinct(step_e_t$combined_providerid),
schools_control = n_distinct(step_e_c$combined_providerid),
terproviders_treatment = n_distinct(step_e_t$any_tertiary_providerid),
terproviders_control = n_distinct(step_e_c$any_tertiary_providerid)
)
output <- rbind(output,step_k)
print(paste("Completed: Quarter 1",v,a,sep=" "))
}, error=function(e){cat("ERROR:",conditionMessage(e),"\n")})
}}
# Produce outcomes for variant using exact matching
Followup_exact_q1 <- subset(Followup_exact, qtr==1)
for (a in agelist) {
for (v in varlist) {
tryCatch({
step_a <- lm(formula = eval(parse(text=v)) ~ alted_during
+ male + eth_maori + eth_pacific + eth_asian + eth_melaa + eth_other + sole_maori + born_in_nz
+ sole_birth_parent + missing_address + missing_schools + missing_mother + missing_father + missing_motherinc + missing_fatherinc
+ num_addresses_prior + mme_prior
+ factor(standdown_n_prior) + factor(suspension_n_prior) + factor(attserv_n_prior)
+ ot_report_prior + ot_invest_prior + yj_fgc_prior + ot_place_prior
+ id + adhd + asd + ors_prior
+ factor(birth_year)
+ mother_mh + father_mh + child_mha
+ factor(first_school_region) + factor(mother_qual_cat) + factor(father_qual_cat)
+ factor(n_nonstructuralchanges_prior) + first_school_decile + first_nzdep
+ teen_parent + mother_benefit_age12 + father_benefit_age12
+ mother_custodial_prior+ mother_community_prior + father_custodial_prior + father_community_prior
, data=subset(Followup_exact_q1,age==a))
step_b <- tidy(step_a)
step_c <- subset(step_b,term=="alted_during")
step_d <- glance(step_a)
step_e_t <- subset(Followup_exact_q1, age==a & alted_during==1)
step_e_c <- subset(Followup_exact_q1, age==a & alted_during==0)
step_f <- data.frame(
model = "Exact match Q1 only",
outcome = v,
estimate = "Adjusted",
age = a,
coef = step_c$estimate,
se = step_c$std.error,
p = step_c$p.value,
sum_treatment = NA,
sum_control = NA,
n_treatment = NA,
schools_treatment = NA,
schools_control = NA,
terproviders_treatment = NA,
terproviders_control = NA
)
output <- rbind(output,step_f)
step_g <- lm(formula = eval(parse(text=v)) ~ alted_during
, data=subset(Followup_exact_q1,age==a))
step_h <- tidy(step_g)
step_i <- glance(step_g)
step_j <- subset(step_h,term=="alted_during")
step_k <- data.frame(
model = "Exact match Q1 only",
outcome = v,
estimate = "Unadjusted",
age = a,
coef = NA,
se = step_j$std.error,
p = step_j$p.value,
sum_treatment = sum(eval(parse(text=paste("step_e_t$",v,sep=""))), na.rm=TRUE),
sum_control = sum(eval(parse(text=paste("step_e_c$",v,sep=""))), na.rm=TRUE),
n_treatment = sum(!is.na(eval(parse(text=paste("step_e_t$",v,sep=""))))),
schools_treatment = n_distinct(step_e_t$combined_providerid),
schools_control = n_distinct(step_e_c$combined_providerid),
terproviders_treatment = n_distinct(step_e_t$any_tertiary_providerid),
terproviders_control = n_distinct(step_e_c$any_tertiary_providerid)
)
output <- rbind(output,step_k)
print(paste("Completed: Exact Q1",v,a,sep=" "))
}, error=function(e){cat("ERROR:",conditionMessage(e),"\n")})
}}
# Produce outcomes for variant using attendance in the matching model (only for ages 17-22)
Followup_attendance_q1 <- subset(Followup_attendance, qtr==1)
agelist <- c(17:22)
for (a in agelist) {
for (v in varlist) {
tryCatch({
step_a <- lm(formula = eval(parse(text=v)) ~ alted_during
+ male + eth_maori + eth_pacific + eth_asian + eth_melaa + eth_other + sole_maori + born_in_nz
+ sole_birth_parent + missing_address + missing_schools + missing_mother + missing_father + missing_motherinc + missing_fatherinc
+ num_addresses_prior + mme_prior
+ factor(standdown_n_prior) + factor(suspension_n_prior) + factor(attserv_n_prior)
+ ot_report_prior + ot_invest_prior + yj_fgc_prior + ot_place_prior
+ id + adhd + asd + ors_prior
+ factor(birth_year)
+ mother_mh + father_mh + child_mha
+ factor(first_school_region) + factor(mother_qual_cat) + factor(father_qual_cat)
+ factor(n_nonstructuralchanges_prior) + first_school_decile + first_nzdep
+ teen_parent + mother_benefit_age12 + father_benefit_age12
+ mother_custodial_prior+ mother_community_prior + father_custodial_prior + father_community_prior
, data=subset(Followup_attendance_q1,age==a))
step_b <- tidy(step_a)
step_c <- subset(step_b,term=="alted_during")
step_d <- glance(step_a)
step_e_t <- subset(Followup_attendance_q1, age==a & alted_during==1)
step_e_c <- subset(Followup_attendance_q1, age==a & alted_during==0)
step_f <- data.frame(
model = "Attendance 1999-2004 Q1 only",
outcome = v,
estimate = "Adjusted",
age = a,
coef = step_c$estimate,
se = step_c$std.error,
p = step_c$p.value,
sum_treatment = NA,
sum_control = NA,
n_treatment = NA,
schools_treatment = NA,
schools_control = NA,
terproviders_treatment = NA,
terproviders_control = NA
)
output <- rbind(output,step_f)
step_g <- lm(formula = eval(parse(text=v)) ~ alted_during
, data=subset(Followup_attendance_q1,age==a))
step_h <- tidy(step_g)
step_i <- glance(step_g)
step_j <- subset(step_h,term=="alted_during")
step_k <- data.frame(
model = "Attendance 1999-2004 Q1 only",
outcome = v,
estimate = "Unadjusted",
age = a,
coef = NA,
se = step_j$std.error,
p = step_j$p.value,
sum_treatment = sum(eval(parse(text=paste("step_e_t$",v,sep=""))), na.rm=TRUE),
sum_control = sum(eval(parse(text=paste("step_e_c$",v,sep=""))), na.rm=TRUE),
n_treatment = sum(!is.na(eval(parse(text=paste("step_e_t$",v,sep=""))))),
schools_treatment = n_distinct(step_e_t$combined_providerid),
schools_control = n_distinct(step_e_c$combined_providerid),
terproviders_treatment = n_distinct(step_e_t$any_tertiary_providerid),
terproviders_control = n_distinct(step_e_c$any_tertiary_providerid)
)
output <- rbind(output,step_k)
print(paste("Completed: Attendance 1999 Q1",v,a,sep=" "))
}, error=function(e){cat("ERROR:",conditionMessage(e),"\n")})
}}
# Produce comparable estimates for the same birth cohorts
for (a in agelist) {
for (v in varlist) {
tryCatch({
step_a <- lm(formula = eval(parse(text=v)) ~ alted_during
+ male + eth_maori + eth_pacific + eth_asian + eth_melaa + eth_other + sole_maori + born_in_nz
+ sole_birth_parent + missing_address + missing_schools + missing_mother + missing_father + missing_motherinc + missing_fatherinc
+ num_addresses_prior + mme_prior
+ factor(standdown_n_prior) + factor(suspension_n_prior) + factor(attserv_n_prior)
+ ot_report_prior + ot_invest_prior + yj_fgc_prior + ot_place_prior
+ id + adhd + asd + ors_prior
+ factor(birth_year)
+ mother_mh + father_mh + child_mha
+ factor(first_school_region) + factor(mother_qual_cat) + factor(father_qual_cat)
+ factor(n_nonstructuralchanges_prior) + first_school_decile + first_nzdep
+ teen_parent + mother_benefit_age12 + father_benefit_age12
+ mother_custodial_prior+ mother_community_prior + father_custodial_prior + father_community_prior
, data=subset(Followup_q1,birth_year>=1999 & age==a))
step_b <- tidy(step_a)
step_c <- subset(step_b,term=="alted_during")
step_d <- glance(step_a)
step_e_t <- subset(Followup_q1, birth_year>=1999 & age==a & alted_during==1)
step_e_c <- subset(Followup_q1, birth_year>=1999 & age==a & alted_during==0)
step_f <- data.frame(
model = "Preferred 1999-2004 Q1 only",
outcome = v,
estimate = "Adjusted",
age = a,
coef = step_c$estimate,
se = step_c$std.error,
p = step_c$p.value,
sum_treatment = NA,
sum_control = NA,
n_treatment = NA,
schools_treatment = NA,
schools_control = NA,
terproviders_treatment = NA,
terproviders_control = NA
)
output <- rbind(output,step_f)
step_g <- lm(formula = eval(parse(text=v)) ~ alted_during
, data=subset(Followup_q1,birth_year>=1999 & age==a))
step_h <- tidy(step_g)
step_i <- glance(step_g)
step_j <- subset(step_h,term=="alted_during")
step_k <- data.frame(
model = "Preferred 1999-2004 Q1 only",
outcome = v,
estimate = "Unadjusted",
age = a,
coef = NA,
se = step_j$std.error,
p = step_j$p.value,
sum_treatment = sum(eval(parse(text=paste("step_e_t$",v,sep=""))), na.rm=TRUE),
sum_control = sum(eval(parse(text=paste("step_e_c$",v,sep=""))), na.rm=TRUE),
n_treatment = sum(!is.na(eval(parse(text=paste("step_e_t$",v,sep=""))))),
schools_treatment = n_distinct(step_e_t$combined_providerid),
schools_control = n_distinct(step_e_c$combined_providerid),
terproviders_treatment = n_distinct(step_e_t$any_tertiary_providerid),
terproviders_control = n_distinct(step_e_c$any_tertiary_providerid)
)
output <- rbind(output,step_k)
print(paste("Completed: Preferred 1999 Q1",v,a,sep=" "))
}, error=function(e){cat("ERROR:",conditionMessage(e),"\n")})
}}
#########################################################################
################ Save all main effects to csv
#########################################################################
write.csv(output,file="output_effectiveness_analysis.csv", row.names=FALSE)
#########################################################################
################ Heterogeneity analysis
#########################################################################
# This breaks down the main effects by various factors (gender, ethnicity, region etc)
agelist <- c(17:30)
interactionlist <- c("eth_maori"
,"eth_pacific"
,"eth_european"
,"eth_asian"
,"sole_maori"
,"male"
,"factor(birth_year)"
,"provider_at_15_region"
,"propensity_bin"
,"matching_model"
)
output_interactions <- data.frame(model=character(),
outcome=character(),
by=character(),
term=character(),
estimate=character(),
age=integer(),
coef=double(),
se=double(),
p=double(),
n=double())
# Produce outcomes for preferred specification
for (i in interactionlist) {
for (a in agelist) {
for (v in varlist) {
tryCatch({
step_a <- lm(formula = eval(parse(text=v)) ~ eval(parse(text=i))*alted_during
+ male + eth_maori + eth_pacific + eth_asian + eth_melaa + eth_other + sole_maori + born_in_nz
+ sole_birth_parent + missing_address + missing_schools + missing_mother + missing_father + missing_motherinc + missing_fatherinc
+ num_addresses_prior + mme_prior
+ factor(standdown_n_prior) + factor(suspension_n_prior) + factor(attserv_n_prior)
+ ot_report_prior + ot_invest_prior + yj_fgc_prior + ot_place_prior
+ id + adhd + asd + ors_prior
+ factor(birth_year)
+ mother_mh + father_mh + child_mha
+ factor(first_school_region) + factor(mother_qual_cat) + factor(father_qual_cat)
+ factor(n_nonstructuralchanges_prior) + first_school_decile + first_nzdep
+ teen_parent + mother_benefit_age12 + father_benefit_age12
+ mother_custodial_prior+ mother_community_prior + father_custodial_prior + father_community_prior
, data=subset(Followup,age==a))
step_b <- tidy(step_a)
step_c <- subset(step_b,str_detect(term,"alted_during"))
step_d <- glance(step_a)
step_e <- data.frame(
model = "Preferred",
outcome = v,
by = i,
term = str_replace_all(step_c$term,"eval\\(parse\\(text = i\\)\\)",""),
estimate = "Adjusted",
age = a,
coef = step_c$estimate,
se = step_c$std.error,
p = step_c$p.value,
n = step_d$df + step_d$df.residual)
output_interactions <- rbind(output_interactions,step_e)
step_f <- lm(formula = eval(parse(text=v)) ~ eval(parse(text=i))*alted_during
, data=subset(Followup,age==a))
step_g <- tidy(step_f)
step_h <- glance(step_f)
step_i <- subset(step_g,str_detect(term,"alted_during"))
step_j <- data.frame(
model = "Preferred",
outcome = v,
by = i,
term = str_replace_all(step_i$term,"eval\\(parse\\(text = i\\)\\)",""),
estimate = "Unadjusted",
age = a,
coef = step_i$estimate,
se = step_i$std.error,
p = step_i$p.value,
n = step_h$df + step_h$df.residual)