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qqtanalysis.R
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rm(list=ls())
setwd('~/dropbox/viennapaper')
# todo: check if larger samples change results
library(data.table)
library(zoo)
library(bit64)
library(countrycode)
library(stringdist)
library(AER)
library(texreg)
source('scripts/qqtheader.r')
mar <- c(4, 3.5, 2.5, 0.5)
clio <- read.csv('~/dropbox/cliodata/allcliodata_raw.csv', stringsAsFactors=F)
# interpolate clio data
clio$lif_ipl <- panel_approx(clio$lif, clio$year, clio$ccode)
clio$gdp_ipl <- exp(panel_approx(log(clio$gdp), clio$year, clio$ccode))
clio$lab_ipl <- exp(panel_approx(log(clio$lab), clio$year, clio$ccode))
clio$edu_ipl <- exp(panel_approx(log(clio$edu), clio$year, clio$ccode))
ss <- read.csv('tabfigs/sumstats_all_harm.csv', stringsAsFactors=F)
# calc patriarchy index
ss$maledom <- rowSums(cbind(patr_transform(ss$femhhh, positive=FALSE),
patr_transform(ss$yngbrd),
patr_transform(ss$olderwives, positive=FALSE),
patr_transform(ss$femnonkin, positive=FALSE)))
ss$gendom <- rowSums(cbind(patr_transform(ss$oldmen),
patr_transform(ss$neolocals, positive=FALSE),
patr_transform(ss$oldjoints),
patr_transform(ss$oldlats)))
ss$patriloc <- patr_transform(ss$mardghtr, positive=FALSE)
ss$sonpref <- rowSums(cbind(patr_transform(ss$lastboy, absolute_min=0.51),
patr_transform(ss$sexratio04, absolute_min=1.05)))
ss$maledom[grepl('usa-18[5-7]', ss$sample)] <- NA
ss$patriloc[grepl('usa-18[5-7]', ss$sample)] <- NA
ss$patri <- ss$maledom / 4 + ss$gendom / 3 + # why not 4, 4 components?
ss$patriloc + ss$sonpref / 2
ss$patri <- round(ss$patri)
# write.csv(ss, 'tabfigs/sumstatus_withpatrind.csv', row.names=F)
datapath <- '~/downloads/data/qqt/cleandata/'
files <- list.files(datapath)
files <- grepr('-', files)
dats <- list()
for (file in files){
datname <- regexprr('-\\w\\w', file)
year <- regexprr('\\d{4}', file)
datname <- paste0(gsub('-', '', datname), year)
x <- fread(paste0(datapath, file), sep=',', header=TRUE) # , data.table=F)
print(file)
print(unique(x$iso3))
print(datname)
assign(datname, x)
rm(x)
}
au1910 <- hhaggregate(au1910, extravrbs='literate')
ca1891 <- hhaggregate(ca1891, extravrbs=c('literate', 'occhisco'))
ca1901 <- hhaggregate(ca1901, extravrbs=c('literate', 'income', 'property', 'ethnic', 'occode', 'occscale'))
hu1869 <- hhaggregate(hu1869, extravrbs='literate')
ie1901 <- hhaggregate(ie1901, extravrbs='literate')
ie1911 <- hhaggregate(ie1911, extravrbs='literate')
ja1884 <- hhaggregate(ja1884, extravrbs=c('literate', 'occhisco'))
kr1918 <- hhaggregate(kr1918, extravrbs=c('literate', 'ethnic'))
us1850 <- hhaggregate(us1850, extravrbs=c('literate', 'occhisco', 'ocscorus', 'race', 'servants'))
us1860 <- hhaggregate(us1860, extravrbs=c('literate', 'occhisco', 'ocscorus', 'race', 'servants'))
us1870 <- hhaggregate(us1870, extravrbs=c('literate', 'occhisco', 'ocscorus', 'race', 'servants'))
us1880 <- hhaggregate(us1880, extravrbs=c('literate', 'occhisco', 'ocscorus', 'race', 'servants'))
us1900 <- hhaggregate(us1900, extravrbs=c('literate', 'occhisco', 'ocscorus', 'race', 'servants'))
us1910 <- hhaggregate(us1910, extravrbs=c('literate', 'occhisco', 'ocscorus', 'race', 'servants'))
ro1867 <- hhaggregate(ro1867)
au1632 <- hhaggregate(au1632)
en1881 <- hhaggregate(en1881, extravrbs='ordergb')
gr1851 <- hhaggregate(gr1851, extravrbs='ordergb')
ro1900 <- hhaggregate(ro1900, extravrbs='occhisco')
sa1827 <- hhaggregate(sa1827, extravrbs='berstel')
sc1881 <- hhaggregate(sc1881, extravrbs='ordergb')
zu1870 <- hhaggregate(zu1870, extravrbs='ethnic')
au10 <- au1910[age > 5 & age < 16 & !schoolage & !is.na(age), ]
au32 <- au1632[age > 5 & age < 16 & !schoolage & !is.na(age), ]
ca91 <- ca1891[age > 13 & age < 16 & !is.na(age), ] # Lotte's over Annelies' coding
ca01 <- ca1901[age > 13 & age < 16 & !is.na(age), ] # Lotte's over Annelies' coding
hu69 <- hu1869[age > 5 & age < 16 & !schoolage & !is.na(age), ]
ie01 <- ie1901[age > 13 & age < 16 & !schoolage & !is.na(age), ] # L/A found no mandatory sch.
ie11 <- ie1911[age > 13 & age < 16 & !schoolage & !is.na(age), ] # L/A found no mandatory sch.
ja84 <- ja1884[age > 5 & age < 16 & !schoolage & !is.na(age), ]
kr18 <- kr1918[age > 5 & age < 16 & !schoolage & !is.na(age), ]
us50 <- us1850[age > 5 & age < 16 & !schoolage & !is.na(age), ]
us60 <- us1860[age > 5 & age < 16 & !schoolage & !is.na(age), ]
us70 <- us1870[age > 5 & age < 16 & !schoolage & !is.na(age), ]
us80 <- us1880[age > 5 & age < 16 & !schoolage & !is.na(age), ]
us00 <- us1900[age > 5 & age < 16 & !schoolage & !is.na(age), ]
us10 <- us1910[age > 5 & age < 16 & !schoolage & !is.na(age), ]
ro67 <- ro1867[age > 5 & age < 16 & !schoolage & !is.na(age), ]
en81 <- en1881[age > 5 & age < 16 & !schoolage & !is.na(age), ]
gr51 <- gr1851[age > 5 & age < 16 & !schoolage & !is.na(age), ]
ro00 <- ro1900[age > 5 & age < 16 & !schoolage & !is.na(age), ]
sa27 <- sa1827[age > 5 & age < 16 & !schoolage & !is.na(age), ]
sc81 <- sc1881[age > 5 & age < 16 & !schoolage & !is.na(age), ]
zu70 <- zu1870[age > 5 & age < 16 & !schoolage & !is.na(age), ]
us60$nserv <- us60$fatherservants
us60$servant <- us60$fatherservants > 0
us70$nserv <- us70$fatherservants
us70$servant <- us70$fatherservants > 0
enrldats <- list(zu70, sa27, ro67, ro00, gr51, en81, sc81, ie01, ie11, us50,
# us60, # leave out to fit on 3x7 panel
us70, us80, us00, us10, ca91, ca01, ja84, kr18, hu69, au32, au10)
schoolages <- as.data.frame(matrix(NA, ncol=4, nrow=length(enrldats)))
names(schoolages) <- c('iso3', 'year', 'sample', 'age')
for (i in 1:length(enrldats)){
schoolages$sample[i] <- unique(enrldats[[i]]$smpl)
schoolages$iso3[i] <- unique(enrldats[[i]]$iso3)
schoolages$year[i] <- unique(enrldats[[i]]$year)
schoolages$age[i] <- min(enrldats[[i]]$age) - 1
}
# write.csv(schoolages, 'tabfigs/schoolages.csv', row.names=F)
au10lit <- au1910[age > 6 & age < 16 & !is.na(age), ]
ca91lit <- ca1891[age > 6 & age < 16 & !is.na(age), ]
ca01lit <- ca1901[age > 6 & age < 16 & !is.na(age), ]
hu69lit <- hu1869[age > 6 & age < 16 & !is.na(age), ]
ie01lit <- ie1901[age > 6 & age < 16 & !is.na(age), ]
ie11lit <- ie1911[age > 6 & age < 16 & !is.na(age), ]
ja84lit <- ja1884[age > 6 & age < 16 & !is.na(age), ]
kr18lit <- kr1918[age > 6 & age < 16 & !is.na(age), ]
# us50lit <- us1850[age > 6], na.rm=T)), by=nsib][nsib < 10][order(nsib)]
# us60lit <- us1860[age > 6], na.rm=T)), by=nsib][nsib < 10][order(nsib)]
us70lit <- us1870[age > 9 & age < 16 & !is.na(age), ]
us80lit <- us1880[age > 9 & age < 16 & !is.na(age), ]
us00lit <- us1900[age > 9 & age < 16 & !is.na(age), ]
us10lit <- us1910[age > 9 & age < 16 & !is.na(age), ]
litdats <- list(au10lit, ca91lit, ca01lit, hu69lit, ie01lit, ie11lit,
ja84lit, kr18lit, us70lit, us80lit, us00lit, us10lit)
# pdf('tabfigs/qqt_enrol_allsmpls.pdf', paper='a4r', width=0, height=0)
par(mfrow=c(3, 7), mar=c(1, 2, 3, 0.5), font.main=1)
for (dat in enrldats){
ttl <- paste(unique(dat$iso3), paste(unique(range(dat$year)), collapse='-'))
dat <- dat[, list(iso3=unique(iso3),
year=paste(unique(range(year)), collapse='-'),
mean_enrol=mean(enrol, na.rm=T),
ci_enrol=sd(enrol, na.rm=T)/sqrt(sum(!is.na(enrol)))),
by=nsib][order(nsib)][nsib < 10]
ciplot(dat$nsib, dat$mean_enrol, dat$ci_enrol, type='l', col=2, bty='l', main='')
title(main=ttl, line=-0.7)
}
dev.off()
# pdf('tabfigs/qqt_lit_allsmpls.pdf', width=8, height=7)
par(mfrow=c(3, 4), mar=c(1, 2, 3, 0.5), font.main=1)
for (dat in litdats){
ttl <- paste(unique(dat$iso3), paste(unique(range(dat$year)), collapse='-'))
dat <- dat[, list(iso3=unique(iso3),
year=paste(unique(range(year)), collapse='-'),
mean_literate=mean(literate, na.rm=T),
ci_literate=sd(literate, na.rm=T)/sqrt(sum(!is.na(literate)))),
by=nsib][order(nsib)][nsib < 10]
ciplot(dat$nsib, dat$mean_literate, dat$ci_literate, type='l', col=2, bty='l', main='')
title(main=ttl, line=-0.7)
}
dev.off()
rows <- sapply(enrldats, nrow)
vrbs <- c("enrol", "nsib", "hhid", "age", "male", "nserv", "urban",
"birthorder", "motheragefirstchild", "spousagegap", "hhid", "smpl")
cols <- length(vrbs)
mstr <- matrix(NA, nrow=sum(rows), ncol=cols)
mstr <- as.data.frame(mstr)
names(mstr) <- vrbs
i <- 1
for (dat in enrldats){
# loop & fill faster than rbind etc.
mstr[i:(i + nrow(dat) - 1), ] <- as.data.frame(dat)[, vrbs]
cat(i, ' through ')
i <- i + nrow(dat) + 1
cat(i, '\n')
}
dim(mstr)
mstr <- mstr[complete.cases(mstr), ]
dim(mstr)
mstr$smpln <- as.numeric(as.factor(mstr$smpl))
table(mstr$smpln)
set.seed(123)
mstr <- mstr[sample(1:nrow(mstr), 50000), ]
table(mstr$smpln)
mstr <- mstr[order(mstr$smpln), ]
# careful, 10000s for 50k obs, 27h for full thing
# careful for lack of hh clustering
# Pr(y_i=1) = logit^{-1}(\alpha_{j[i]} + \beta_{j[i]}x_i)
# \beta \sim N(mu_d, sigma_d)
# \alpha \sim N(mu_a, sigma_a)
# \mu_d ~ normal(0, 100)
# \mu_a ~ normal(0, 100)
datlist <- list(N=nrow(mstr), D=1, K=7, L=length(unique(mstr$smpln)),
enrol=mstr$enrol, smpl=mstr$smpln,
nsib=mstr[, 'nsib', drop=F],
x=mstr[, vrbs[4:10], drop=F])
library(rstan)
options(mc.cores=parallel::detectCores())
fit <- stan('scripts/hierlogit.stan', data=datlist, iter=1000)
save(fit=fit, file='hierlogitpostr.rdata')
load('hierlogitpostr.rdata') # returns fit
betahat <- sumstatsDF(extract(fit)$beta)
gammahat <- sumstatsDF(extract(fit)$gamma)
ahat <- sumstatsDF(extract(fit)$a)
rownames(betahat) <- rownames(ahat) <- unique(mstr$smpl)
par(mfrow=c(3, 7), mar=c(1, 2, 3, 1))
for (smpl in unique(mstr$smpl)){
plot(1:10, invlogit(betahat[smpl, 'q50'] * 1:10 +
ahat[smpl, 'q50'] +
sum(gammahat[, 'q50'] * colMeans(mstr[mstr$smpl==smpl, vrbs[4:10]]))),
xlab='nsib', ylab='P(enrol)', type='l', main=smpl, cex=0.8)
}
betamfx <- list()
for (smpl in unique(mstr$smpl)){
yhat <- ahat[smpl, 'q50'] +
betahat[smpl, 'q50'] * mstr$nsib[mstr$smpl==smpl] +
rowSums(gammahat[, 'q50'] * mstr[mstr$smpl==smpl, vrbs[4:10]])
betamfx[[smpl]] <- mean(dlogis(yhat)) * c(ahat[smpl, 'q50'], betahat[smpl, 'q50'], gammahat[, 'q50'])
}
betamfx_nsib <- sapply(betamfx, function(x) x[2])
betahat$mfx <- betamfx_nsib
betahat <- betahat[order(betahat$q50), ]
# overall
data.frame(vrbs[4:10],
est=get_posterior_mean(fit, 'gamma')[, 'mean-all chains'],
sd=apply(extract(fit)$gamma, 2, sd))
pdf('tabfigs/multilev.pdf', height=5)
par(mar=c(3, 14, 1, 1))
dotchart(betahat$q50, xlim=range(betahat))
axis(side=2, at=seq_along(betahat$q50), labels=rownames(betahat), las=1, cex=0.8)
segments(betahat$q05, 1:21, betahat$q95)
abline(v=0, col='gray')
dotchart(betahat$q50, xlim=range(betahat))
axis(side=2, at=seq_along(betahat$q50), labels=rownames(betahat), las=1, cex=0.8)
segments(betahat$q05, 1:21, betahat$q95)
muhat <- sumstatsDF(extract(fit)$mu)
rect(xleft=muhat[2], xright=muhat[4], ybottom=0.5, ytop=21.5, border=2)
dotchart(betahat$mfx, xlim=c(-0.015, 0.005))
axis(side=2, at=seq_along(betahat$q50), labels=rownames(betahat), las=1, cex=0.8)
dev.off()
pooled <- glm(enrol ~ nsib + age + male + nserv + urban
+ birthorder + motheragefirstchild + spousagegap, data=mstr, family=binomial(link='logit'))
mfx(link='logit', pooled)
enrl <- NULL
originalests <- originalses <- NULL
for (dat in enrldats){
m <- glm(enrol ~ nsib + age + male + nserv + urban
+ factor(birthorder)
+ motheragefirstchild + spousagegap - hhid,
data=dat, family=binomial(link='logit'))
originalests <- rbind(originalests, m$coef[c('(Intercept)', 'nsib')])
originalses <- rbind(originalses, summary(m)$coefficients[,2][c('(Intercept)', 'nsib')])
rownames(originalests)[nrow(originalests)] <- unique(dat$smpl)
mbas <- logitmfxest2(m, clustervar1='hhid')
male <- mbas$mfx['male', 1:2]
nsib <- mbas$mfx['nsib', 1:2]
nsrv <- mbas$mfx['nserv', 1:2]
sags <- mbas$mfx['spousagegap', 1:2]
m <- glm(enrol ~ nbro + nsis - hhid + age + male + nserv + urban
+ factor(birthorder)
+ motheragefirstchild + spousagegap - hhid,
data=dat, family=binomial(link='logit'))
ngnd <- logitmfxest2(m, clustervar1='hhid')
nbro <- ngnd$mfx['nbro', 1:2]
nsis <- ngnd$mfx['nsis', 1:2]
ests <- unlist(c(nsib, nbro, nsis, male, nsrv, sags))
enrl <- rbind(enrl, ests)
}
rownames(enrl) <- sapply(enrldats, function(x) unique(x$smpl))
enrl <- as.data.frame(enrl)
names(enrl) <- c('nsib_est', 'nsib_stder', 'nbro_est', 'nbro_stder',
'nsis_est', 'nsis_stder', 'male_est', 'male_stder',
'nserv_est', 'nserv_stder', 'sag_est', 'sag_stder')
enrl$iso3 <- sapply(enrldats, function(x) unique(x$iso3))
enrl$year <- sapply(enrldats, function(x) max(x$year))
enrl$ccode <- countrycode(enrl$iso3, 'iso3c', 'iso3n')
enrl <- cbind(enrl, clio[match(paste0(enrl$ccode, enrl$year), paste0(clio$ccode, clio$year)), ])
enrl <- cbind(enrl, ss[match(rownames(enrl), ss$sample), ])
enrl <- enrl[rev(rownames(enrl)), ]
pdf('tabfigs/nsib_v_gdp.pdf')
plot(nsib_est ~ gdp_ipl, data=enrl,
bty='l', xlab='GDPpc', ylab='nsib coef.', col=2)
abline(lm(nsib_est ~ gdp_ipl, data=enrl), untf=T)
dev.off()
pdf('tabfigs/nsib_v_edu.pdf')
plot(nsib_est ~ edu_ipl, data=enrl,
bty='l', xlab='Av. years of education', ylab='nsib coef.', col=2)
abline(lm(nsib_est ~ edu_ipl, data=enrl), untf=T)
dev.off()
pdf('tabfigs/nsib_v_lif.pdf')
plot(nsib_est ~ lif_ipl, data=enrl,
bty='l', xlab='lif', ylab='nsib coef.', col=2)
abline(lm(nsib_est ~ lif_ipl, data=enrl), untf=T)
dev.off()
pdf('tabfigs/nsib_v_patr.pdf')
plot(nsib_est ~ patri, data=enrl,
bty='l', xlab='Patr. Index', ylab='nsib coef.', col=2)
# pigs if factor
m <- lm(nsib_est ~ patri, data=enrl)
summary(m)
abline(lm(m), untf=T)
dev.off()
pdf('tabfigs/nsib_v_maledom.pdf')
plot(nsib_est ~ maledom, data=enrl,
bty='l', xlab='Male dom. index', ylab='nsib coef.', col=2)
# pigs if factor
m <- lm(nsib_est ~ maledom, data=enrl)
summary(m)
abline(lm(m), untf=T)
dev.off()
pdf('tabfigs/male_v_patr.pdf')
plot(male_est ~ patri, data=enrl,
bty='l', xlab='Patr. Index', ylab='Male coef.', col=2)
abline(lm(male_est ~ patri, data=enrl), untf=T)
dev.off()
pdf('tabfigs/male_v_maledom.pdf')
plot(male_est ~ maledom, data=enrl,
bty='l', xlab='Male dom. index', ylab='Male coef.', col=2)
abline(lm(male_est ~ maledom, data=enrl), untf=T)
dev.off()
pdf('tabfigs/nsibcoefs.pdf')
par(mfrow=c(1,1))
dotchart(enrl$nsib_est, labels=rownames(enrl))
segments(enrl$nsib_est + 2*enrl$nsib_stder, 1:nrow(enrl), enrl$nsib_est - 2*enrl$nsib_stder, 1:nrow(enrl))
# dotplot(x=enrl$nsib_est, ses=enrl$nsib_stder*2, labels=rownames(enrl),
# mar=c(4, 9, 3.5, 0.5))
abline(v=0)
dev.off()
rownames(originalses) <- rownames(originalests)
originalests <- as.data.frame(originalests)
originalses <- as.data.frame(originalses)
originalests <- originalests[match(rownames(betahat), rownames(originalests)), ]
originalses <- originalses[match(rownames(betahat), rownames(originalses)), ]
pdf('tabfigs/multilev2.pdf', height=5)
par(mar=c(3, 14, 1, 1))
dotchart(betahat$q50, xlim=range(betahat))
axis(side=2, at=seq_along(betahat$q50), labels=rownames(betahat), las=1, cex=0.8)
segments(betahat$q05, 1:21, betahat$q95)
abline(v=0, col='gray')
dotchart(betahat$q50, xlim=range(betahat))
axis(side=2, at=seq_along(betahat$q50), labels=rownames(betahat), las=1, cex=0.8)
segments(betahat$q05, 1:21, betahat$q95)
muhat <- sumstatsDF(extract(fit)$mu)
rect(xleft=muhat[2], xright=muhat[4], ybottom=0.5, ytop=21.5, border=2)
dotchart(betahat$q50, xlim=range(betahat))
points(originalests$nsib, seq_along(originalests$nsib), col='gray')
axis(side=2, at=seq_along(betahat$q50), labels=rownames(betahat), las=1, cex=0.8)
segments(betahat$q05, 1:21, betahat$q95)
abline(v=0, col='gray')
dotchart(originalests$nsib, labels=rownames(originalests), xlim=c(range(betahat)))
segments(originalests$nsib + 2*originalses$nsib, 1:nrow(originalests), originalests$nsib - 2*originalses$nsib, 1:nrow(originalests), col='gray')
# dotplot(x=enrl$nsib_est, ses=enrl$nsib_stder*2, labels=rownames(enrl),
# mar=c(4, 9, 3.5, 0.5))
abline(v=0)
dev.off()
pdf('tabfigs/nsibcoefs_nomfx.pdf')
par(mfrow=c(1,1))
dotchart(originalests$nsib, labels=rownames(originalests), xlim=c(range(betahat)))
segments(originalests$nsib + 2*originalses$nsib, 1:nrow(originalests), originalests$nsib - 2*originalses$nsib, 1:nrow(originalests), col='gray')
# dotplot(x=enrl$nsib_est, ses=enrl$nsib_stder*2, labels=rownames(enrl),
# mar=c(4, 9, 3.5, 0.5))
abline(v=0)
dev.off()
pdf('tabfigs/malecoefs.pdf')
par(mfrow=c(1,1))
dotchart(enrl$male_est, labels=rownames(enrl))
segments(enrl$male_est + 2*enrl$male_stder, 1:nrow(enrl), enrl$male_est - 2*enrl$male_stder, 1:nrow(enrl))
abline(v=0)
dev.off()
enrlsv <- enrl[!is.na(enrl$nserv_est), ]
pdf('tabfigs/nservcoefs.pdf')
par(mfrow=c(1,1))
dotchart(enrlsv$nserv_est, labels=rownames(enrlsv))
segments(enrlsv$nserv_est + 2*enrlsv$nserv_stder, 1:nrow(enrlsv), enrlsv$nserv_est - 2*enrlsv$nserv_stder, 1:nrow(enrlsv))
abline(v=0)
dev.off()
pdf('tabfigs/sagcoefs.pdf')
par(mfrow=c(1,1))
dotchart(enrl$sag_est, labels=rownames(enrl))
segments(enrl$sag_est + 2*enrl$sag_stder, 1:nrow(enrl), enrl$sag_est - 2*enrl$sag_stder, 1:nrow(enrl))
abline(v=0)
dev.off()
pdf('tabfigs/brosiscoefs.pdf', width=10)
par(mfrow=c(1, 2))
dotchart(enrl$nbro_est, labels=rownames(enrl), col=1, xlim=c(-0.07, 0.07)) #, pch='B')
segments(enrl$nbro_est + 2*enrl$nbro_stder, 1:nrow(enrl), enrl$nbro_est - 2*enrl$nbro_stder, 1:nrow(enrl))
title(main='Brothers')
abline(v=0)
# points(enrl$nsis_est, 1:nrow(enrl) - 0.2, col='red', pch='S')
# segments(enrl$nsis_est + 2*enrl$nsis_stder, 1:nrow(enrl)-0.2, enrl$nsis_est - 2*enrl$nsis_stder, 1:nrow(enrl)-0.2, col=2)
dotchart(enrl$nsis_est, labels=rownames(enrl), col=1, xlim=c(-0.07, 0.07)) #, pch='B')
segments(enrl$nsis_est + 2*enrl$nsis_stder, 1:nrow(enrl), enrl$nsis_est - 2*enrl$nsis_stder, 1:nrow(enrl))
title(main='Sisters')
abline(v=0)
dev.off()
pdf('tabfigs/brosiscoefs_alt.pdf')
par(mfrow=c(1, 1))
dotchart(enrl$nbro_est, labels=rownames(enrl), col=1, xlim=c(-0.07, 0.07), pch='')
points(enrl$nbro_est, 1:nrow(enrl) + 0.25, pch='B')
segments(enrl$nbro_est + 2*enrl$nbro_stder, 1:nrow(enrl)+0.25, enrl$nbro_est - 2*enrl$nbro_stder, 1:nrow(enrl)+0.25, col=1)
points(enrl$nsis_est, 1:nrow(enrl) - 0.25, col='red', pch='S')
segments(enrl$nsis_est + 2*enrl$nsis_stder, 1:nrow(enrl)-0.25, enrl$nsis_est - 2*enrl$nsis_stder, 1:nrow(enrl)-0.25, col=2)
abline(v=0)
dev.off()
ivlist <- list()
for (dat in enrldats){
smpl <- unique(dat$smpl)
try(
ivlist[[smpl]] <- ivreg(enrol ~ nsib + age + male + nserv + urban
+ factor(birthorder)
+ motheragefirstchild + spousagegap
- hhid | . -nsib + hhtwin, data=dat)
)
}
ivests <- sapply(ivlist, function(m) m$coef['nsib'])
names(ivests) <- names(ivlist)
sort(enrl[names(ivests),1] - ivests)
dotchart(sort(ivests))
abline(v=0)
parlits <- NULL
names <- NULL
for (dat in enrldats){
if (!'fatherliterate' %in% names(dat)){
next
}
m <- glm(enrol ~ nsib - hhid + age + male + nserv + urban
+ factor(birthorder)
+ fatherliterate + motherliterate
+ motheragefirstchild + spousagegap - hhid, data=dat, family=binomial(link='logit'))
cfs <- logitmfx2(m, clustervar1='hhid')
dad <- cfs$mfx['fatherliterate', 1:2]
mom <- cfs$mfx['motherliterate', 1:2]
ests <- unlist(c(dad, mom))
parlits <- rbind(parlits, ests)
names <- c(names, unique(dat$smpl))
}
rownames(parlits) <- names
parlits <- as.data.frame(parlits)
names(parlits) <- c('dadlit_est', 'dadlit_stder', 'momlit_est', 'momlit_stder')
parlits <- parlits[rev(rownames(parlits)), ]
pdf('tabfigs/parentlitcoefs.pdf', width=10)
par(mfrow=c(1, 2))
dotchart(parlits$momlit_est, labels=rownames(parlits), main='mother', xlim=c(0, 0.3), mar=c(1, 1, 1, 1))
points(parlits$dadlit_est, 1:nrow(parlits), col='gray')
segments(parlits$momlit_est + 2*parlits$momlit_stder, 1:nrow(parlits), parlits$momlit_est - 2*parlits$momlit_stder, 1:nrow(parlits))
dotchart(parlits$dadlit_est, labels=rownames(parlits), main='father', xlim=c(0, 0.3))
points(parlits$momlit_est, 1:nrow(parlits), col='gray')
segments(parlits$dadlit_est + 2*parlits$dadlit_stder, 1:nrow(parlits), parlits$dadlit_est - 2*parlits$dadlit_stder, 1:nrow(parlits))
dev.off()
extensions <- NULL
names <- NULL
for (dat in enrldats){
if (sum(is.na(dat$nupw)) > 0){
next
}
m <- glm(enrol ~ nsib - hhid + age + male + nserv + urban
+ factor(birthorder)
+ nupw + nlat
+ motheragefirstchild + spousagegap - hhid, data=dat, family=binomial(link='logit'))
cfs <- logitmfx2(m, clustervar1='hhid')
nupw <- cfs$mfx['nupw', 1:2]
nlat <- cfs$mfx['nlat', 1:2]
ests <- unlist(c(nupw, nlat))
extensions <- rbind(extensions, ests)
names <- c(names, unique(dat$smpl))
}
rownames(extensions) <- names
extensions <- as.data.frame(extensions)
names(extensions) <- c('nupw_est', 'nupw_stder', 'nlat_est', 'nlat_stder')
extensions <- extensions[rev(rownames(extensions)), ]
pdf('tabfigs/nupwcoefs.pdf')
par(mfrow=c(1,1))
dotchart(extensions$nupw_est, labels=rownames(extensions))
segments(extensions$nupw_est + 2*extensions$nupw_stder, 1:nrow(extensions), extensions$nupw_est - 2*extensions$nupw_stder, 1:nrow(extensions))
abline(v=0)
dev.off()
pdf('tabfigs/nlatcoefs.pdf')
par(mfrow=c(1,1))
dotchart(extensions$nlat_est, labels=rownames(extensions))
segments(extensions$nlat_est + 2*extensions$nlat_stder, 1:nrow(extensions), extensions$nlat_est - 2*extensions$nlat_stder, 1:nrow(extensions))
abline(v=0)
dev.off()
lits <- NULL
for (dat in litdats){
m <- glm(literate ~ nsib - hhid + age + male + nserv + urban
+ factor(birthorder)
+ motheragefirstchild + spousagegap - hhid, data=dat, family=binomial(link='logit'))
mbas <- logitmfx2(m, clustervar1='hhid')
# male <- mbas$mfx['male', 1:2]
nsib <- mbas$mfx['nsib', 1:2]
lits <- rbind(lits, nsib)
}
rownames(lits) <- sapply(litdats, function(x) unique(x$smpl))
lits <- as.data.frame(lits)
names(lits) <- c('nsib_est', 'nsib_stder') #, 'nbro_est', 'nbro_stder', 'nsis_est', 'nsis_stder', 'male_est', 'male_stder')
lits$iso3 <- sapply(litdats, function(x) unique(x$iso3))
lits$year <- sapply(litdats, function(x) max(x$year))
lits$ccode <- countrycode(lits$iso3, 'iso3c', 'iso3n')
lits <- cbind(lits, clio[match(paste0(lits$ccode, lits$year), paste0(clio$ccode, clio$year)), ])
lits <- cbind(lits, ss[match(rownames(lits), ss$sample), ])
pdf('tabfigs/nsibcoefs_literate.pdf')
par(mfrow=c(1,1))
dotchart(lits$nsib_est, labels=rownames(lits))
segments(lits$nsib_est + 2*lits$nsib_stder, 1:24, lits$nsib_est - 2*lits$nsib_stder, 1:24)
# dotplot(x=lits$nsib_est, ses=lits$nsib_stder*2, labels=rownames(lits),
# mar=c(4, 9, 3.5, 0.5))
abline(v=0)
dev.off()
###################
### appendix #####
### regressions ###
###################
note <- '%stars. Standard errors clustered at household-level. Occup. and region controls included'
modnames <- c('OLS', 'OLS', 'IV', 'Logit', 'Mfx')
modnames_lit <- c('OLS', 'OLS', 'IV', 'OLS-lit', 'Logit-enrol', 'Mfx')
# Austria 1910
# ------------
au10$fatheroccupation <- uniformise_string(au10$fatheroccupation)
au10$fatheroccupation[lowfreqind(au10$fatheroccupation, 3)] <- 'other'
au10$motheroccupation <- uniformise_string(au10$motheroccupation)
au10$motheroccupation[lowfreqind(au10$motheroccupation, 3)] <- 'other'
au10$motherreligion [grepl('prot|jew', au10$motherreligion)] <- 'other'
au10$region[au10$region=='Habsburg Empire/Archduchy of Lower Austria'] <-
"Habsburg Empire/Archduchy of Upper Austria"
mb_au10 <- lm(enrol ~ nsib + age + male + nserv + urban
+ nupw + nlat
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
# + fatherreligion + motherreligion
+ fatheroccupation + motheroccupation
+ region
- hhid, data=au10)
mli_au10 <- lm(literate ~ nsib + age + male + nserv + urban
+ nupw + nlat
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
# + fatherreligion + motherreligion
+ fatheroccupation + motheroccupation
+ region
- hhid, data=au10)
mi_au10 <- ivreg(enrol ~ nsib + age + male + nserv + urban
+ nupw + nlat
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
# + fatherreligion + motherreligion
+ fatheroccupation + motheroccupation
+ region
- hhid | . - nsib + hhtwin, data=au10)
mg_au10 <- lm(enrol ~ nbro + nsis + age + male + nserv + urban
+ nupw + nlat
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
# + fatherreligion + motherreligion
+ fatheroccupation + motheroccupation
+ region
- hhid, data=au10)
ml_au10 <- glm(enrol ~ nsib + age + male + nserv + urban
+ nupw + nlat
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
# + fatherreligion + motherreligion
+ fatheroccupation + motheroccupation
+ region
- hhid, data=au10, family=binomial(link='logit'))
models <- reglist(list(mb_au10, mg_au10, mi_au10, mli_au10, ml_au10))
texreg(models$models, override.se=models$se, override.pval=models$pv,
omit.coef='occup|region|race', stars=c(0.1, 0.05, 0.01), digits=3,
caption=paste0(gsub('[-_]', ' ', unique(au10$smpl)), ', enrolment'),
custom.note=note,
file='tabfigs/au10_enrol.tex',
custom.model.names=modnames_lit)
# austria 1632-
#--------------
# ml model fails on region
ftable(au32$motherreligion, au32$enrol) # leave out
ftable(au32$motheroccupation, au32$enrol)
au32$fatheroccupation <- uniformise_string(au32$fatheroccupation)
au32$motheroccupation <- uniformise_string(au32$motheroccupation)
au32$motheroccupation[lowfreqind(au32$motheroccupation, 13)] <- 'Other'
au32$fatheroccupation[lowfreqind(au32$fatheroccupation, 50)] <- 'Other'
au32$birthorder[au32$birthorder > 5] <- '> 5'
au32$region[au32$region %in% c("Herrengasse", "Gaudenzdorf", "Ringturm",
"Schottenfeld", "Leopoldsdorf","Gumpendorf",
"Kaiserebersdorf", "Josefstadt", "Neubau")] <- 'Vienna'
au32$century <- substr(au32$year, 1, 2)
mb_au32 <- lm(enrol ~ nsib + age + male + nserv + urban
+ nextd
+ factor(birthorder) +
motheragefirstchild + spousagegap - hhid
# + fatherliterate + motherliterate
# + fatherreligion + motherreligion
+ fatheroccupation # + motheroccupation
# + region
+ factor(century)
, data=au32)
mg_au32 <- lm(enrol ~ nbro + nsis + age + male + nserv + urban
+ nextd
+ factor(birthorder) +
motheragefirstchild + spousagegap - hhid
# + fatherliterate + motherliterate
# + fatherreligion + motherreligion
+ fatheroccupation # + motheroccupation
# + region
+ factor(century)
, data=au32)
mi_au32 <- ivreg(enrol ~ nsib + age + male + nserv + urban
+ nextd
+ factor(birthorder) +
motheragefirstchild + spousagegap - hhid
# + fatherliterate + motherliterate
# + fatherreligion + motherreligion
+ fatheroccupation # + motheroccupation
# + region
+ factor(century) | . - nsib + hhtwin
, data=au32)
ml_au32 <- glm(enrol ~ nsib + age + male + nserv + urban
+ nextd
+ factor(birthorder) +
motheragefirstchild + spousagegap - hhid
# + fatherliterate + motherliterate
# + fatherreligion + motherreligion
+ fatheroccupation # + motheroccupation
# + region
+ factor(century)
, data=au32, family=binomial(link='logit'))
models <- reglist(list(mb_au32, mg_au32, mi_au32, ml_au32))
texreg(models$models, override.se=models$se, override.pval=models$pv,
omit.coef='occup|region|race', stars=c(0.1, 0.05, 0.01), digits=3,
caption='Austria 1632-1947, enrolment',
custom.note='%stars. Standard errors clustered at household-level. Occup. controls included.',
file='tabfigs/au32_enrol.tex',
custom.model.names=modnames)
# canada 1891
#------------
# canada 1891 is very difficult to estimate with age > 13
# ftable(ca91$fatheroccupation, ca91$enrol)
# ftable(ca91$motheroccupation, ca91$enrol)
ca91$fatheroccupation <- substring(ca91$fatherocchisco, 1, 1)
ca91$motheroccupation <- substring(ca91$motherocchisco, 1, 1)
ca91$motheroccupation[ca91$motheroccupation <= 6] <- 1
ca91$motheroccupation[ca91$motheroccupation >= 7] <- 7
# ftable(ca91$region, ca91$enrol)
# ftable(ca91$fatherreligion, ca91$enrol)
# ftable(ca91$motherreligion, ca91$enrol)
ca91$fatherreligion[grepl('catholic', ca91$fatherreligion)] <- 'other'
ca91$motherreligion[grepl('catholic', ca91$motherreligion)] <- 'other'
# ftable(ca91$fatherliterate, ca91$enrol)
# ftable(ca91$motherliterate, ca91$enrol)
# ftable(ca91$fatheroccupation, ca91$enrol)
# ftable(ca91$birthorder, ca91$enrol)
ca91$birthorder[ca91$birthorder > 4] <- '> 4'
mb_ca91 <- lm(enrol ~ nsib + age + male + nserv + # urban +
nlat + nupw + # factor(birthorder) +
motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
+ fatherreligion + motherreligion
+ factor(fatheroccupation) + factor(motheroccupation)
# + region
- hhid
, data=ca91)
mli_ca91 <- lm(literate ~ nsib + age + male + nserv + # urban +
nlat + nupw + # factor(birthorder) +
motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
+ fatherreligion + motherreligion
+ factor(fatheroccupation) + factor(motheroccupation)
# + region
- hhid
, data=ca91)
mi_ca91 <- ivreg(enrol ~ nsib + age + male + nserv + # urban +
nlat + nupw + # factor(birthorder) +
motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
+ fatherreligion + motherreligion
+ factor(fatheroccupation) + factor(motheroccupation)
# + region
- hhid | . - nsib + hhtwin
, data=ca91)
mg_ca91 <- lm(enrol ~ nbro + nsis + age + male + nserv + # urban +
nlat + nupw + # factor(birthorder) +
motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
+ fatherreligion + motherreligion
+ factor(fatheroccupation) + factor(motheroccupation)
# + region
- hhid
, data=ca91)
ml_ca91 <- glm(enrol ~ nsib + age + male + nserv + # urban +
nlat + nupw + # factor(birthorder) +
motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
+ fatherreligion + motherreligion
+ factor(fatheroccupation) + factor(motheroccupation)
# + region
- hhid
, data=ca91, family=binomial(link='logit'))
models <- reglist(list(mb_ca91, mg_ca91, mi_ca91, mli_ca91, ml_ca91))
texreg(models$m, override.se=models$se, override.pval=models$pv,
stars=c(0.1, 0.05, 0.01), digits=3,
caption=paste0(gsub('[-_]', ' ', unique(ca91$smpl)), ', enrolment'),
custom.note='%stars. Standard errors clustered at household-level. Occup. controls included.',
omit.coef='occup|region|race',
file='tabfigs/ca91_enrol.tex',
custom.model.names=modnames_lit)
# canada 1901
# -----------
# ca01$fatherethnic[ca01$fatherethnic < 10000] <- 'USA'
# ca01$fatherethnic[ca01$fatherethnic < 16000 & ca01$fatherethnic > 10000] <- 'Canadian'
# ca01$motherethnic[ca01$motherethnic < 10000] <- 'USA'
# ca01$motherethnic[ca01$motherethnic < 16000 & ca01$motherethnic > 10000] <- 'Canadian'
ca01$fatheroccupation <- substring(ca01$fatheroccode, 1, 1)
ca01$fatheroccupation[ca01$fatheroccupation <= 3] <- 3
ca01$region[grepl('AT|KE', ca01$region)] <- 'ATKE'
ca01$region[lowfreqind(ca01$region, 132)] <- 'other'
ca01$motheroccupation <- substring(ca01$fatheroccode, 1, 1)
ca01$birthorder[ca01$birthorder > 6] <- '> 6'
ca01$fatherreligion[lowfreqind(ca01$fatherreligion, 100)] <- 'other'
ca01$motherreligion[lowfreqind(ca01$motherreligion, 100)] <- 'other'
mb_ca01 <- lm(enrol ~ nsib + age + male + nserv + urban
+ nlat + nupw
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate # lose sign here
+ fatherreligion + motherreligion
+ factor(fatherethnic) + factor(motherethnic)
+ factor(fatheroccupation) # + factor(motheroccupation)
+ region
- hhid
, data=ca01)
mli_ca01 <- lm(literate ~ nsib + age + male + nserv + urban
+ nlat + nupw
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate # lose sign here
+ fatherreligion + motherreligion
+ factor(fatherethnic) + factor(motherethnic)
+ factor(fatheroccupation) # + factor(motheroccupation)
+ region
- hhid
, data=ca01)
mg_ca01 <- lm(enrol ~ nbro + nsis + age + male + nserv + urban
+ nlat + nupw
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
+ fatherreligion + motherreligion
+ factor(fatherethnic) + factor(motherethnic)
+ factor(fatheroccupation) # + factor(motheroccupation)
+ region
- hhid
, data=ca01)
mi_ca01 <- ivreg(enrol ~ nsib + age + male + nserv + urban
+ nlat + nupw
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
+ fatherreligion + motherreligion
+ factor(fatherethnic) + factor(motherethnic)
+ factor(fatheroccupation) # + factor(motheroccupation)
+ region
- hhid | . - nsib + hhtwin
, data=ca01)
ml_ca01 <- glm(enrol ~ nsib + age + male + nserv + urban
+ nlat + nupw
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
+ fatherreligion + motherreligion
+ factor(fatherethnic) + factor(motherethnic)
+ factor(fatheroccupation) # + factor(motheroccupation)
+ region
- hhid
, data=ca01, family=binomial(link='logit'))
models <- reglist(list(mb_ca01, mg_ca01, mi_ca01, mli_ca01, ml_ca01))
texreg(models$m, override.se=models$se, override.pval=models$pv,
omit.coef='occup|region|race', stars=c(0.1, 0.05, 0.01), digits=3,
caption=paste0(gsub('[-_]', ' ', unique(ca01$smpl)), ', enrolment'),
custom.note=note,
file='tabfigs/ca01_enrol.tex',
custom.model.names=modnames_lit)
# hungary 1869
#-------------
ftable(hu69$birthorder, hu69$enrol)
hu69$birthorder[hu69$birthorder > 4] <- '> 4'
ftable(hu69$fatherreligion, hu69$enrol) # keep motherreligion out
ftable(hu69$fatheroccupation, hu69$enrol)
hu69$fatheroccupation <- uniformise_string(hu69$fatheroccupation)
hu69$fatheroccupation[lowfreqind(hu69$fatheroccupation, 2)] <- 'other'
hu69$motheroccupation <- uniformise_string(hu69$motheroccupation)
hu69$motheroccupation[lowfreqind(hu69$motheroccupation, 2)] <- 'other'
hu69$region[grepl('Partium|Transylvania', hu69$region)] <- 'Partium/Transylvania'
mb_hu69 <- lm(enrol ~ nsib + age + male + nserv # + urban
+ nlat
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
+ fatherreligion # + motherreligion
+ fatheroccupation # motheroccupation
+ region # does better w/o the regional controls
- hhid
, data=hu69)
mli_hu69 <- lm(literate ~ nsib + age + male + nserv # + urban
+ nlat
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
+ fatherreligion # + motherreligion
+ fatheroccupation # motheroccupation
+ region # does better w/o the regional controls
- hhid
, data=hu69)
mi_hu69 <- ivreg(enrol ~ nsib + age + male + nserv # + urban
+ nlat
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
+ fatherreligion # + motherreligion
+ fatheroccupation # motheroccupation
+ region
- hhid | . - nsib + hhtwin
, data=hu69)
mg_hu69 <- lm(enrol ~ nbro + nsis + age + male + nserv # + urban
+ nlat
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
+ fatherreligion # + motherreligion
+ fatheroccupation # motheroccupation
+ region
- hhid
, data=hu69)
ml_hu69 <- glm(enrol ~ nsib + age + male + nserv # + urban
+ nlat
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
+ fatherreligion # + motherreligion
# + fatheroccupation # motheroccupation # fatherocc causes clustered SE failure
+ region
- hhid
, data=hu69, family=binomial(link='logit'))
models <- reglist(list(mb_hu69, mg_hu69, mi_hu69, mli_hu69, ml_hu69))
texreg(models$m, override.se=models$se, override.pval=models$pv,
omit.coef='occup|region|race', stars=c(0.1, 0.05, 0.01), digits=3,
caption=paste0(gsub('[-_]', ' ', unique(hu69$smpl)), ', enrolment'),
custom.note=paste0(note, ', except in logit model.'),
file='tabfigs/hu69_enrol.tex',
custom.model.names=modnames_lit)
# ireland 1901
#-------------
ie01$fatheroccupation <- uniformise_string(ie01$fatheroccupation)
ie01$motheroccupation <- uniformise_string(ie01$motheroccupation)
ie01$fatheroccupation[lowfreqind(ie01$fatheroccupation, freq=10)] <- 'other'
ie01$motheroccupation[lowfreqind(ie01$motheroccupation, freq=10)] <- 'other'
ie01$motherreligion[grepl('jewish', ie01$motherreligion)] <- 'other'
# # ie01$birthorder[ie01$birthorder > 5] <- '> 5'
mb_ie01 <- lm(enrol ~ nsib + age + male + nserv + urban
+ nlat + nupw
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
+ fatherreligion + motherreligion
+ fatheroccupation + motheroccupation
+ region
- hhid
, data=ie01)
mli_ie01 <- lm(literate ~ nsib + age + male + nserv + urban
+ nlat + nupw
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
+ fatherreligion + motherreligion
+ fatheroccupation + motheroccupation
+ region
- hhid
, data=ie01)
mg_ie01 <- lm(enrol ~ nbro + nsis + age + male + nserv + urban
+ nlat + nupw
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
+ fatherreligion + motherreligion
+ fatheroccupation + motheroccupation
+ region
- hhid
, data=ie01)
mi_ie01 <- ivreg(enrol ~ nsib + age + male + nserv + urban
+ nlat + nupw
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
+ fatherreligion + motherreligion
# + fatheroccupation + motheroccupation
+ region
- hhid | . - nsib + hhtwin
, data=ie01)
ml_ie01 <- glm(enrol ~ nsib + age + male + nserv + urban
+ nlat + nupw
+ factor(birthorder)
+ motheragefirstchild + spousagegap
+ fatherliterate + motherliterate
+ fatherreligion + motherreligion
+ fatheroccupation + motheroccupation
+ region
- hhid
, data=ie01, family=binomial(link='logit'))
models <- reglist(list(mb_ie01, mg_ie01, mi_ie01, mli_ie01, ml_ie01))
texreg(models$models, override.se=models$se, override.pval=models$pv,
caption=paste0(gsub('[-_]', ' ', unique(ie01$smpl)), ', enrolment'),
custom.note=paste0(note),
custom.model.names=modnames_lit,
file='tabfigs/ie01_enrol.tex',
omit.coef='occup|region|race', stars=c(0.1, 0.05, 0.01), digits=3)