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qqtdata.R
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rm(list=ls())
# mosaic datasets
# Hungary 1869
setwd('~/downloads/data/qqt')
source('~/dropbox/viennapaper/scripts/qqtheader.r')
library(bit64)
library(data.table)
library(foreign)
library(stringdist)
library(lmtest)
library(texreg)
########################
### general datasets ###
########################
edv <- read.csv('~/dropbox/viennapaper/data/comped.csv', stringsAsFactors=FALSE)
ec <- read.csv('~/dropbox/viennapaper/data/europeancities.csv', stringsAsFactors=FALSE)
vrbs <- c('smpl', 'year', 'iso3', 'age', 'child', 'schoolage', 'occupation', 'enrol',
'literate', 'male', 'female', 'married', 'relate', 'son', 'daughter',
'lateral', 'upward', 'ext', 'servant', 'head', 'spouse', 'religion',
'ethnic', 'race', 'hhid', 'persid', 'urban', 'region', 'occhisco')
#######################
### mosaic datasets ###
#######################
# bulgaria 1900 has no occupations
# french villages have too few student occupations
# st emilion has very few educ obs.
# st jean de luz has very few useful occs or lit
# germany 1846 ids only 10 students
# jasenica 1863 ids only 6 students
# zurich 1870 gives few useful obs
# kujavia=poland has no occ, no lit
# rostock 1819 has too few students
# schleswig-holstein has a handful of students
# hirzel too many missing obs in literacy
# colomera does not work
# german villages: all have very few students; one hohscheid has a few, but are all apprentices
# Canada 1911- messy data
#-------------
# hungary 1869
#-------------
hu <- fread('mosaic/hungary_1869/Datafile_152 Version 1.0_labels.csv',
sep=';', data.table=F, colClasses='character')
hu$smpl <- 'mos-hungary-1869'
hu$iso3 <- 'HUN'
hu$age[hu$age=='less than 1 year old'] <- 0
hu$age <- as.numeric(hu$age)
hu$child <- hu$age < 16
hu$schoolage <- hu$age > 5 & hu$age < edv$edageendlowerbound[edv$iso3 %in% hu$iso3 & edv$year %in% hu$year]
hu$occupat <- iconv(hu$occupat, 'utf8', 'utf8', sub='_') # no correct encoding available, _ removes inv. multibyate stings
hu$occupation <- tolower(hu$occupat)
hu$occhisco <- NA
# group_strings(hu$occupation[hu$child])
schlrgx <- 'tan|kol|\\Winas|^inas|növ|gya'
hu$enrol <- grepl(schlrgx, hu$occupation) & hu$child
hu$literate <- grepl('read|^liter', hu$lit)
hu$literate[hu$lit=='unknown'] <- NA
hu$male <- hu$sex=='male'
hu$female <- hu$sex=='female'
hu$married <- grepl('^married|widow|separate|divorced', hu$marst)
hu$relate <- tolower(hu$relate)
hu$son <- hu$relate %in% 'child' & hu$male
hu$daughter <- hu$relate %in% 'child' & hu$female
hu$lateral <- grepl('sibling|nephew|niece|aunt|uncle|cousin', hu$relate)
hu$upward <- grepl('parent', hu$relate)
hu$ext <- hu$lateral | hu$upward
hu$servant <- grepl('servant|maid|cook', hu$relate)
hu$head <- hu$relate %in% 'head/householder'
hu$spouse <- hu$relate %in% c('spouse', 'unmarried partner')
hu$religion[grepl('Catholic', hu$relig)] <- 'catholic'
hu$religion[grepl('Luther|Unit|Reform', hu$relig)] <- 'protestant'
hu$religion[grepl('Orthodox', hu$relig)] <- 'orthodox'
hu$religion[grepl('Jewish', hu$relig)] <- 'jewish'
hu$ethnic <- NA
hu$race <- NA
hu$urban <- hu$urban=='urban'
hu$region <- hu$region
hu$hhid <- hu$id_hhold
hu$persid <- hu$id_pers
hu <- hu[hu$gq %in% c('Household with 0-4 persons unrelated to the head',
'Household with 5-9 persons unrelated to the head'), ]
hu <- rm_dualhh(hu)
write.csv(hu[vrbs], paste0('cleandata/', unique(hu$smpl), '.csv'), row.names=F)
x <- hhaggregate(data.table(hu), extravrbs='literate')
x <- x[x$age > 5 & x$age < 16 & !x$schoolage & !is.na(x$age), ]
ss <- calcsumstats(hu, x, 'hhid')
write.table(ss, '~/dropbox/viennapaper/tabfigs/sumstats_all_harm.csv',
sep=',', row.names=F)
rm(hu)
rm(x)
#-------------
# rostock 1867
#-------------
ro <- read.spss('mosaic/_Rostock_1867_v0.1/Rostock 1867.sav')
ro <- data.frame(ro)
ro$smpl <- 'mos-rostock-1867'
ro$iso3 <- 'DEU'
ro$firstname <- gsub('\\s+$', '', ro$firstname)
ro$lastname <- gsub('\\s+$', '', ro$lastname)
ro$occupation <- gsub('\\s+$', '', ro$occupation)
ro$relationship <- gsub('\\s+$', '', ro$relationship)
ro$age[ro$age > 800] <- NA
ro$child <- ro$age < 16
ro$schoolage <- ro$age > 5 & ro$age < edv$edageendlowerbound[edv$iso3 %in% ro$iso3 & edv$year %in% ro$year]
ro$occupation <- tolower(ro$occupation)
ro$occhisco <- NA
# group_strings(ro$occupation[ro$child])
schlrgx <- 'sch.+l|leh?r|g[yÿiuü]m|burs|element'
ro$enrol <- grepl(schlrgx, ro$occupation) & ro$child
ro$literate <- NA
ro$male <- ro$sex=='male'
ro$female <- ro$sex=='female'
ro$married <- grepl('^married|divorced|widowed', ro$MarrStatus)
ro$relate <- ro$relationship_coded
ro$son <- ro$relate %in% 'child' & ro$male
ro$daughter <- ro$relate %in% 'child' & ro$female
ro$lateral <- ro$relate %in%'sibling and other lateral kin'
ro$upward <- ro$relate %in% 'parent'
ro$ext <- ro$lateral | ro$upward
ro$servant <- ro$relate %in% 'servant'
ro$head <- ro$relate %in% 'household head'
ro$spouse <- ro$relate %in% 'spouse'
ro$religion <- NA
ro$ethnic <- NA
ro$race <- NA
ro$hhid <-ro$householdno
ro$persid <- ro$person_id
ro$urban <- TRUE
ro$region <- 'rostock'
ro$gq <- makegq(ro)
ro <- ro[ro$gq < 5 & !is.na(ro$gq), ]
ro <- rm_dualhh(ro)
write.csv(ro[vrbs], paste0('cleandata/', unique(ro$smpl), '.csv'), row.names=F)
x <- hhaggregate(data.table(ro))
x <- x[x$age > 5 & x$age < 16 & !x$schoolage & !is.na(x$age), ]
ro$religion <- 'unknown'
ss <- calcsumstats(ro, x, 'hhid')
write.table(ss, '~/dropbox/viennapaper/tabfigs/sumstats_all_harm.csv',
sep=',', append=T, col.names=F, row.names=F)
rm(ro)
rm(x)
# x$fatheroccupation <- Uniformize(x$fatheroccupation)
# x$motheroccupation <- Uniformize(x$motheroccupation)
# x$birthorder[x$birthorder > 5] <- '> 5'
# f <- enrol ~ nsib + age + male + servant + urban + factor(birthorder) +
# motheragefirstchild + spousagegap + nupw + nlat - hhid +
# fatheroccupation + motheroccupation
#---------------------
### san marcello, 1827
#---------------------
sa <- read.spss('mosaic/_San_Marcello_1827_v0.1/San Marcello 1827.sav',
to.data.frame=TRUE)
names(sa) <- tolower(names(sa))
sa <- factor2char(sa)
sa$smpl <- 'mos-san_marcello-1827'
sa$year <- 1827
sa$iso3 <- 'ITA'
sa$age <- sa$alter
sa$child <- sa$age < 16
sa$schoolage <- sa$age > 5 & sa$age < edv$edageendlowerbound[edv$iso3 %in% sa$iso3 & edv$year %in% sa$year]
sa$occupation <- tolower(sa$berufs)
sa$occhisco <- NA
# group_strings(sa$occupation[sa$child])
sa$enrol <- (grepl('ausbildung', sa$occupation) | sa$berstel=='Lehrling/Lehrmaedche') | sa$beruf %in% c('IM COLLEGIO', 'IM SEMINAR', 'STUDENT')
sa$enrol <- sa$enrol & sa$child
# sa$enrol[sa$occupation=='unbekannt'] <- NA
sa$literate <- NA
sa$male <- grepl('^m', sa$sexfama)
sa$female <- grepl('^f', sa$sexfama)
sa$married <- grepl('verhei|getrennt|verwit', sa$sexfama)
sa$relate <- sa$stellung
sa$son <- sa$stellung %in% c('SOHN', 'KIND') & sa$male
sa$daughter <- sa$stellung %in% c('TOCHTER', 'KIND') & sa$female
sa$lateral <- NA
sa$upward <- NA
sa$ext <- sa$stellung %in% 'VERWANDT'
sa$servant <- sa$stellung %in% 'GESINDE'
sa$head <- sa$stellung %in% 'HVORSTAND'
sa$spouse <- sa$stellung %in% 'HAUSFRAU'
sa$religion <- ifelse(sa$hvkonfes=='evangelisch' & !is.na(sa$hvkonfes), 'protestant', 'catholic')
sa$ethnic <- tolower(sa$hvherkod)
sa$race <- NA
sa$hhid <- as.numeric(as.factor(paste(sa$strasse, sa$wohnung, sa$hausnr)))
sa$persid <- sa$fallnr
sa$urban <- TRUE # Roman parish
sa$region <- 'Rome'
sa$gq <- makegq(sa)
sa <- sa[sa$gq < 5 & !is.na(sa$gq), ]
sa <- rm_dualhh(sa)
write.csv(sa[c(vrbs, 'berstel')], paste0('cleandata/', unique(sa$smpl), '.csv'), row.names=F)
sa$lateral <- sa$upward <- sa$ext
x <- hhaggregate(data.table(sa), extravrbs='berstel')
x$nextd <- x$nlat
x$nlat <- x$nupw <- NA
x <- x[x$age > 5 & x$age < 16 & !x$schoolage & !is.na(x$age), ]
ss <- calcsumstats(sa, x, 'hhid')
write.table(ss, '~/dropbox/viennapaper/tabfigs/sumstats_all_harm.csv',
sep=',', append=T, col.names=F, row.names=F)
# f <- enrol ~ nsib + age + male + servant + urban + factor(birthorder) +
# motheragefirstchild + spousagegap + nextd - hhid +
# fatherberstel
rm(sa)
rm(x)
#-----------
# kruja 1918
#-----------
kr <- read.spss('mosaic/_Kruja_1918_v0.1/Kruja 1918.sav',
to.data.frame=TRUE)
kr <- factor2char(kr)
kr$smpl <- 'mos-kruja-1918'
kr$year <- 1918
kr$iso3 <- 'ALB'
kr$firstnam <- gsub('\\s+$', '', kr$firstnam)
kr$lastname <- gsub('\\s+$', '', kr$lastname)
kr$sex <- gsub('\\s+$', '', kr$sex)
kr$occupat <- gsub('\\s+$', '', kr$occupat)
kr$age[kr$age > 900] <- NA
kr$child <- kr$age < 16
kr$schoolage <- kr$age > 5 & kr$age < edv$edageendlowerbound[edv$iso3 %in% kr$iso3 & edv$year %in% kr$year]
kr$occupation <- tolower(kr$occupat)
kr$occhisco <- NA
# group_strings(kr$occupation[kr$child])
schlrgx <- 'lehr|shk|schü'
kr$enrol <- grepl(schlrgx, kr$occupation) & kr$child
kr$literate <- ifelse(kr$turk=='no' & kr$latin=='no', FALSE, TRUE)
kr$male <- kr$sex=='male'
kr$female <- kr$sex=='female'
kr$married <- grepl('^married|widow', kr$mst)
kr$relate <- kr$rhh
kr$son <- kr$relate %in% 'son'
kr$daughter <- kr$relate %in% 'daughter'
kr$lateral <- grepl('brother|sister|cousin|nephew|niece|aunt|uncle', kr$rhh)
kr$upward <- grepl('mother|father', kr$rhh)
kr$ext <- kr$lateral | kr$upward
kr$servant <- grepl("servant$|maid", kr$relate)
kr$head <- grepl('male household head', kr$relate) # includes 42 female hhh
kr$spouse <- kr$relate %in% c('wife', '2nd wife')
kr$religion[grepl('Catholic', kr$confessi)] <- 'catholic'
kr$religion[grepl('Orthodox', kr$confessi)] <- 'orthodox'
kr$religion[grepl('Muslim', kr$confessi)] <- 'muslim'
kr$ethnic <- NA
kr$ethnic[grepl('Albanian', kr$ethnicit)] <- 'albanian'
kr$ethnic[grepl('ypsy', kr$ethnicit)] <- 'gypsy'
kr$ethnic[grepl('Arab', kr$ethnicit)] <- 'arab'
kr$ethnic[grepl('erbo', kr$ethnicit)] <- 'serbo-croat'
kr$ethnic[grepl('Vlach', kr$ethnicit)] <- 'vlach'
kr$race <- NA
kr$hhid <- kr$id_hh
kr$persid <- kr$id_person
kr$urban <- kr$place_re %in% 'O Kruja' # 3000+ inh, not rly enough
kr$region <- kr$place_re
kr$gq <- makegq(kr)
kr <- kr[kr$gq < 5 & !is.na(kr$gq), ]
kr <- rm_dualhh(kr)
write.csv(kr[vrbs], paste0('cleandata/', unique(kr$smpl), '.csv'), row.names=F)
x <- hhaggregate(data.table(kr), extravrbs=c('literate', 'ethnic'))
x <- x[x$age > 5 & x$age < 16 & !x$schoolage & !is.na(x$age), ]
ss <- calcsumstats(kr, x, 'hhid')
write.table(ss, '~/dropbox/viennapaper/tabfigs/sumstats_all_harm.csv',
sep=',', append=T, col.names=F, row.names=F)
# x$fatheroccupation[grepl('bulk|bauer|grund$', x$fatheroccupation)] <- 'Farmer'
# x$fatheroccupation[grepl('rbeiter', x$fatheroccupation)] <- 'Labourer'
# x$fatheroccupation[lowfreql(x$fatheroccupation, freq=80)] <- 'Other'
# x$fatheroccupation[x$fatheroccupation=='*'] <- NA
# x$birthorder[x$birthorder > 4] <- '> 4'
# ftable(x$enrol, x$fatherethnic)
# ftable(x$enrol, x$fatherreligion)
# ftable(x$enrol, x$fatheroccupation) # perfect predictors, not in case of lit
# f <- enrol ~ nsib + age + male + servant + factor(birthorder) +
# motheragefirstchild + spousagegap + nupw + nlat - hhid
rm(kr)
rm(x)
#-------------
# rostock 1900
#-------------
ro <- fread('mosaic/rostock 1900 germany/Datafile_06 Version 1.0_labels.csv',
sep=';', data.table=F, colClasses='character')
ro_numeric <- fread('mosaic/rostock 1900 germany/Datafile_06 Version 1.0_codes.csv',
sep=';', data.table=F, colClasses='character')
ro$occhisco <- ro_numeric$occhisco
rm(ro_numeric)
ro$smpl <- 'mos-rostock-1900'
ro$iso3 <- 'DEU'
ro$age[ro$age=='less than 1 year old'] <- 0
ro$age <- as.numeric(ro$age)
ro$child <- ro$age < 16
ro$schoolage <- ro$age > 5 & ro$age < edv$edageendlowerbound[edv$iso3 %in% ro$iso3 & edv$year %in% ro$year]
ro$occupation <- tolower(ro$occupat)
# group_strings(ro$occupation[ro$age])
schlrgx <- 'sch[u|ü]|lehr|gym'
ro$enrol <- grepl(schlrgx, ro$occupation) & ro$child
ro$literate <- NA
ro$male <- ro$sex %in% 'male'
ro$female <- ro$sex %in% 'female'
ro$married <- grepl('^married|divorced|widow|separated', ro$marst)
ro$son <- ro$relate %in% 'child' & ro$male
ro$daughter <- ro$relate %in% 'child' & ro$female
ro$lateral <- grepl('sibling|niece|nephew|cousin|aunt|uncle', ro$relate)
ro$upward <- grepl('parent', ro$relate)
ro$ext <- ro$lateral | ro$upward
ro$servant <- grepl('ser|cook|housek|nurse', ro$relate)
ro$head <- ro$relate %in% 'head/householder'
ro$spouse <- ro$relate %in% 'spouse'
ro$religion[grepl('Luther|Angli|other Chris|Pent|Prot|Ref', ro$relig)] <- 'protestant'
ro$religion[grepl('Catholic', ro$relig)] <- 'catholic'
ro$religion[grepl('Orthodox', ro$relig)] <- 'orthodox'
ro$religion[grepl('Jewish', ro$relig)] <- 'jewish'
ro$religion[grepl('Buddhist|no religion|unknown', ro$relig)] <- 'other'
ro$ethnic <- NA
ro$race <- NA
ro$hhid <- ro$id_hhold
ro$persid <- ro$id_pers
ro$urban <- ro$urban %in% 'urban'
ro$region <- 'rostock'
ro <- ro[ro$gq %in% c('household with 0-4 persons unrelated to the head',
'household with 5-9 persons unrelated to the head'), ]
ro <- rm_dualhh(ro)
write.csv(ro[vrbs], paste0('cleandata/', unique(ro$smpl), '.csv'), row.names=F)
x <- hhaggregate(data.table(ro), extravrbs='occhisco')
x <- x[x$age > 5 & x$age < 16 & !x$schoolage & !is.na(x$age), ]
ss <- calcsumstats(ro, x, 'hhid')
write.table(ss, '~/dropbox/viennapaper/tabfigs/sumstats_all_harm.csv',
sep=',', append=T, col.names=F, row.names=F)
# ftable(x$fatherreligion, x$enrol)
# ftable(x$motherreligion, x$enrol)
# x$fatheroccupation <- substr(x$fatherocchisco, 1, 1)
# x$motheroccupation <- substr(x$motherocchisco, 1, 1)
# x$fatherreligion[grepl('orthodox|other|jewish', x$fatherreligion)] <- 'other'
# x$motherreligion[grepl('orthodox|other|jewish', x$motherreligion)] <- 'other'
# x$birthorder[x$birthorder > 6] <- '> 6'
# f <- enrol ~ nsib + age + male + servant + factor(birthorder) +
# motheragefirstchild + spousagegap + nupw + nlat - hhid +
# fatherreligion + motherreligion +
# factor(fatheroccupation)
rm(ro)
rm(x)
#--------------
# Jasenica 1884
#--------------
ja <- fread('mosaic/Jasenica 1884 serbia/Datafile_148 Version 1.1_labels.csv',
sep=';', colClasses='character', header=T, data.table=F)
# colClasses=char because id_pers > 2e9, exceeds max integer value
ja_num <- fread('mosaic/Jasenica 1884 serbia/Datafile_148 Version 1.1_codes.csv',
sep=';', colClasses='character', header=T, data.table=F)
ja$occhisco <- ja_num$occhisco
rm(ja_num)
ja$smpl <- 'mos-jasenica-1884'
ja$iso3 <- 'SRB'
ja$age[ja$age=='less than 1 year old'] <- 0
ja$age <- as.numeric(ja$age)
ja$child <- ja$age < 16
ja$schoolage <- ja$age > 5 & ja$age < edv$edageendlowerbound[edv$iso3 %in% ja$iso3 & edv$year==1880]
ja$occupation <- tolower(ja$occupat)
# group_strings(ja$occupation[ja$child])
ja$enrol <- ja$occupation %in% 'djak' & ja$child
ja$literate <- ja$lit %in% 'literate'
ja$male <- ja$sex=='male'
ja$female <- ja$sex=='female'
ja$married <- grepl('^married|widow|separated', ja$marst)
ja$son <- grepl('^child$', ja$relate) & ja$male
ja$daughter <- grepl('child$', ja$relate) & ja$female
ja$lateral <- grepl('sibling|niece|nephew|cousin|aunt|uncle', ja$relate)
ja$upward <- grepl('parent', ja$relate)
ja$ext <- ja$lateral | ja$upward
ja$servant <- grepl('ser|cook|housek|nurse', ja$relate)
ja$head <- ja$relate %in% 'head/householder'
ja$spouse <- ja$relate %in% 'spouse'
ja$religion <- 'unknown'
ja$ethnic <- NA
ja$race <- NA
ja$hhid <- ja$id_hhold
ja$persid <- ja$id_pers
ja$urban <- ja$urban %in% 'urban'
ja$region <- 'jasenica'
ja <- ja[ja$gq %in% c("household with 0-4 persons unrelated to the head",
"household with 5-9 persons unrelated to the head"), ]
ja <- rm_dualhh(ja)
write.csv(ja[vrbs], paste0('cleandata/', unique(ja$smpl), '.csv'), row.names=F)
x <- hhaggregate(data.table(ja), extravrbs=c('literate', 'occhisco'))
x <- x[x$age > 5 & x$age < 16 & !x$schoolage & !is.na(x$age), ]
ss <- calcsumstats(ja, x, 'hhid')
write.table(ss, '~/dropbox/viennapaper/tabfigs/sumstats_all_harm.csv',
sep=',', append=T, col.names=F, row.names=F)
# ftable(x$enrol, x$birthorder)
# ftable(x$enrol, x$motherliterate)
# # x$fatheroccupation <- substring(x$fatherocchisco, 1, 1)
# # x$motheroccupation <- substring(x$motherocchisco, 1, 1)
# x$fatheroccupation <- Uniformize(x$fatheroccupation)
# x$motheroccupation <- Uniformize(x$motheroccupation)
# f <- enrol ~ nsib + age + male + servant + factor(birthorder) +
# motheragefirstchild + spousagegap + nupw + nlat - hhid +
# fatherliterate + motherliterate +
# fatheroccupation
rm(ja)
rm(x)
###################
### zurich 1870 ###
###################
zu <- read.spss('mosaic/_Zuerich_1870_v0.1/ZÅrich 1870.sav',
to.data.frame=TRUE)
zu <- factor2char(zu)
names(zu) <- tolower(names(zu))
zu$smpl <- 'mos-zurich-1870'
zu$year <- 1870
zu$iso3 <- 'CHE'
zu$age <- zu$alter - 1000
zu$child <- zu$age < 16
zu$schoolage <- zu$age > 5 & zu$age < edv$edageendlowerbound[edv$iso3 %in% zu$iso3 & edv$year %in% zu$year]
zu$occupation <- tolower(zu$beruf)
zu$occhisco <- NA
enrolled <- grepl('stud|schu|gym|lehr', zu$occupation)
zu$occupation <- tolower(zu$berufs)
zu$enrol <- (grepl('ausbildung', zu$occupation) | enrolled) & zu$child
# zu$enrol[is.na(zu$beruf)] <- NA
# few enrolled; NAs setting NA or TRUE changes little
zu$literate <- NA
zu$male <- grepl('^m', zu$sexfama)
zu$female <- grepl('^f', zu$sexfama)
zu$married <- grepl('verheiratet|verwitwet', zu$sexfama)
zu$relate <- tolower(zu$stellung)
zu$son <- zu$relate %in% 'sohn'
zu$daughter <- zu$relate %in% 'tochter'
zu$ext <- zu$relate %in% 'verwandt'
zu$lateral <- zu$upward <- NA
zu$servant <- zu$relate %in% 'gesinde'
zu$head <- zu$relate %in% 'hvorstand'
zu$spouse <- zu$relate %in% 'hausfrau'
zu$religion[zu$klauselc==11] <- 'catholic'
zu$religion[zu$klauselc==12] <- 'protestant'
zu$religion[zu$klauselc==13] <- 'jewish'
zu$religion[zu$klauselc==15] <- 'orthodox'
zu$religion[zu$klauselc==14] <- 'catholic' # assuming griech uniert is greek catholic
zu$religion[zu$klauselc==0] <- 'other'
# zu$herkod <- gsub('\\d', '', zu$herkod)
# regs <- unique(tolower(zu$herkod))
# regsg <- lapply(regs, geocode, reg='de')
# regsd <- do.call(rbind, regsg)
# regsd$ctr <- sapply(regsd$loc_frmtd, function(x) regexprr('[A-z ]+$', x))
# regsd$ctr <- gsub('^\\s', '', regsd$ctr)
# regsd$ctr[is.na(regsd$ctr)] <- 'Germany'
# regsd$ctr[regsd$ctr=='Namibia'] <- 'Germany' # schl.-holtst in 1870
# regsd$ctr[regsd$ctr=='The Bahamas'] <- 'Germany' # nassau
# zu$ethnic <- regsd$ctr[match(tolower(zu$herkod), regsd$loc)]
zu$ethnic <- tolower(zu$herkod)
zu$race <- NA
zu$hhid <- as.numeric(as.factor(paste(zu$strasse, zu$wohnung, zu$hausnr)))
zu$persid <- zu$fallnr
zu$urban <- TRUE
zu$region <- 'zurich'
zu$gq <- makegq(zu)
zu <- zu[zu$gq < 5 & !is.na(zu$gq), ]
zu <- rm_dualhh(zu)
write.csv(zu[vrbs], paste0('cleandata/', unique(zu$smpl), '.csv'), row.names=F)
zu$lateral <- zu$upward <- zu$ext
x <- hhaggregate(data.table(zu), extravrbs='ethnic')
zu$lateral <- zu$upward <- NA
x$nlat <- x$nupw <- NA
x <- x[x$age > 5 & x$age < 16 & !x$schoolage & !is.na(x$age), ]
ss <- calcsumstats(zu, x, 'hhid')
write.table(ss, '~/dropbox/viennapaper/tabfigs/sumstats_all_harm.csv',
sep=',', append=T, col.names=F, row.names=F)
# ftable(x$fatherreligion, x$enrol)
# ftable(x$motherreligion, x$enrol)
# ftable(x$birthorder, x$enrol)
# x$fatherreligion[grepl('catho|jew|other', x$fatherreligion)] <- 'other'
# x$motherreligion[grepl('catho|jew|other', x$motherreligion)] <- 'other'
# f <- enrol ~ nsib + age + servant + factor(birthorder) +
# motheragefirstchild + spousagegap + nextd - hhid +
# fatherreligion +
# fatheroccupation + motheroccupation
# didn't get it to estimate in earlier file, estimates unreliable
rm(zu)
rm(x)
#------------------
# Austria 1632-1947
#------------------
au <- read.spss('mosaic/_Austrian_Numeric_Datafile_v0.1/Austrian numeric data.sav',
to.data.frame=TRUE)
au <- factor2char(au)
au$smpl <- 'mos-austria-1632_1947'
au$year <- au$erjahr
au$iso3 <- 'AUT'
au$age <- au$alter # gives awful lot of people aged 99...
au$age[au$age > 900] <- NA
au$child <- au$age < 16
au$schoolage <- NA
au$schoolage[au$year < 1774] <- 5
au$schoolage[au$year >= 1774] <- 12
au$schoolage[au$year >= 1869] <- 13
au$schoolage <- au$age > 5 & au$age < au$schoolage
au$occupation <- tolower(au$berufs) # careful: berufs/occ says nothing about pupil status
au$occhisco <- NA
# group_strings(au$berufs[au$child])
# group_strings(au$berstel[au$child])
au$enrol <- grepl('LEHRLING|GESELLE', au$berstel) & au$child
au$literate <- NA
au$male <- grepl('MAENN', au$sex)
au$female <- grepl('WEIB', au$sex)
au$married <- au$fam %in% c(2, 3, 4) # maybe set 9 & 5 to NA
au$relate <- au$alstel
au$son <- au$alstel %in% 3
au$daughter <- au$alstel %in% 4
au$ext <- au$alstel %in% 5
au$lateral <- NA
au$upward <- NA
au$servant <- au$alstel %in% 7
au$head <- au$alstel %in% 1
au$spouse <- au$alstel %in% 2
au$religion[au$konfess %in% 1] <- 'catholic'
au$religion[au$konfess %in% 2] <- 'protestant'
au$religion[au$konfess %in% 3] <- 'jewish'
au$religion[au$konfess %in% 4] <- 'orthodox'
au$ethnic <- tolower(au$herkss)
# lapply(unique(au$ethnic), geocode)
au$race <- NA
au$hhid <- as.numeric(as.factor(paste(au$gmde, au$erjahr, au$hausnr, au$wohnnr)))
au$persid <- 1:nrow(au)
austria_hungary <- ec[ec$Code %in% c(40, 276), ]
urbs <- c(austria_hungary$City[ain(austria_hungary$City, unique(au$gmd))],
"Herrengasse", "Gaudenzdorf", "Ringturm", # these are neighbourhoods in Vienna
"Schottenfeld", "Leopoldsdorf","Gumpendorf",
"Kaiserebersdorf", "Josefstadt", "Neubau")
au$urban <- au$gmd %in% urbs
au$region <- au$gmd
au$gq <- makegq(au)
au <- au[au$gq < 5 & !is.na(au$gq), ]
au <- rm_dualhh(au)
write.csv(au[vrbs], paste0('cleandata/', unique(au$smpl), '.csv'), row.names=F)
au$lateral <- au$upward <- au$ext
x <- hhaggregate(data.table(au))
au$lateral <- au$upward <- au$lateral <- NA
x$nlat <- x$nupw <- NA
x <- x[x$age > 5 & x$age < 16 & !x$schoolage & !is.na(x$age), ]
ss <- calcsumstats(au, x, 'hhid')
write.table(ss, '~/dropbox/viennapaper/tabfigs/sumstats_all_harm.csv',
sep=',', append=T, col.names=F, row.names=F)
# ftable(x$enrol, x$motherreligion)
# ftable(x$enrol, x$motheroccupation)
# x$fatheroccupation <- Uniformize(x$fatheroccupation)
# x$motheroccupation <- Uniformize(x$motheroccupation)
# x$motheroccupation[lowfreql(x$motheroccupation, 13)] <- 'Other'
# x$fatheroccupation[lowfreql(x$fatheroccupation, 50)] <- 'Other'
# x$birthorder[x$birthorder > 5] <- '> 5'
# x$region[x$region %in% c("Herrengasse", "Gaudenzdorf", "Ringturm",
# "Schottenfeld", "Leopoldsdorf","Gumpendorf",
# "Kaiserebersdorf", "Josefstadt", "Neubau")] <- 'Vienna'
# f <- enrol ~ nsib + age + male + servant + urban + factor(birthorder) +
# motheragefirstchild + spousagegap + nextd - hhid + factor(year) +
# fatheroccupation + motheroccupation
# negative but insignificant, interesting to get year/occup dummies right
rm(au)
rm(x)
#-------------
# austria 1910
#-------------
au <- fread('mosaic/austria_1910/Datafile_219 Version 1.0_labels.csv',
colClasses='character', sep=';', data.table=F)
au$smpl <- 'mos-austria-1910'
au$iso3 <- 'AUT'
au$age[au$age=='less than 1 year old'] <- 0
au$age <- as.numeric(au$age)
au$child <- au$age < 16
au$schoolage <- au$age > 5 & au$age < edv$edageendlowerbound[edv$iso3 %in% au$iso3 & edv$year %in% au$year]
au$occupation <- tolower(au$occupat)
au$occhisco <- NA # not coded yet
# group_strings(au$occupation[au$child])
au$enrol <- grepl('lerl|chul|chül|stud|gym|lehr|lerj', au$occupation) & au$child
au$enrol[grepl('schulunfähig', au$occupation)] <- FALSE
au$literate <- !grepl('illiterate|unknown', au$lit)
au$male <- au$sex %in% 'male'
au$female <- au$sex %in% 'female'
au$married <- grepl('^married|div|separ|widow', au$marst)
au$son <- au$relate %in% 'child' & au$male
au$daughter <- au$relate %in% 'child' & au$female
au$lateral <- grepl('nephew|niece|sibling|aunt|uncle|cousin', au$relate)
au$upward <- grepl('parent', au$relate)
au$ext <- au$lateral | au$upward
au$servant <- grepl('ser|cook|housek|nurse', au$relate)
au$head <- au$relate %in% 'head/householder'
au$spouse <- au$relate %in% 'spouse'
au$religion[grepl('Protest|Reform|Luther', au$relig)] <- 'protestant'
au$religion[grepl('Catholic', au$relig)] <- 'catholic'
au$religion[grepl('Orthodox', au$relig)] <- 'orthodox'
au$religion[grepl('Jewish', au$relig)] <- 'jewish'
au$religion[grepl('no relig|unknown', au$relig)] <- 'other'
au$ethnic <- NA
au$race <- NA
au$hhid <- au$id_hhold
au$persid <- au$id_pers
au$urban <- au$urban %in% 'urban'
au <- au[au$gq %in% c("Household with 0-4 persons unrelated to the head",
"Household with 5-9 persons unrelated to the head") ,]
au <- rm_dualhh(au)
write.csv(au[vrbs], paste0('cleandata/', unique(au$smpl), '.csv'), row.names=F)
x <- hhaggregate(data.table(au), extravrbs='literate')
x <- x[x$age > 5 & x$age < 16 & !x$schoolage & !is.na(x$age), ]
ss <- calcsumstats(au, x, 'hhid', sample=nam)
write.table(ss, '~/dropbox/viennapaper/tabfigs/sumstats_all_harm.csv',
sep=',', append=T, col.names=F, row.names=F)
# ftable(x$enrol, x$birthorder)
# ftable(x$enrol, x$motherreligion)
# ftable(x$enrol, x$fatherreligion)
# x$fatheroccupation <- Uniformize(x$fatheroccupation)
# x$fatheroccupation[lowfreql(x$fatheroccupation, 3)] <- 'Other'
# x$motheroccupation <- Uniformize(x$motheroccupation)
# sort(table(x$motheroccupation))
# x$birthorder[x$birthorder > 5] <- '> 5'
# x$fatherreligion[grepl('jew|prot', x$fatherreligion)] <- 'other'
# f <- enrol ~ nsib + age + male + servant + urban + factor(birthorder) +
# motheragefirstchild + spousagegap + nupw + nlat - hhid +
# fatherliterate + motherliterate +
# fatherreligion + # motherreligion +
# fatheroccupation + motheroccupation + region
rm(au)
rm(x)
####################
### irish census ###
####################
#-------------
# ireland 1911
#-------------
ir <- fread('ireland19115pc.csv',
sep=',', colClasses='character', data.table=F)
setnames(ir, gsub(' ', '.', names(ir)))
ir <- ir[!duplicated(ir), ] # 99% of observations are duplicates
ir$smpl <- 'archie-ireland-1911'
ir$year <- 1911
ir$iso3 <- 'IRL'
ir$Age[ir$Age==''] <- 0
ir$age <- as.numeric(ir$Age)
ir$child <- ir$age < 16
ir$schoolage <- ir$age > 5 & ir$age < edv$edageendlowerbound[edv$iso3 %in% ir$iso3 & edv$year %in% ir$year]
# NB edv's compulsory ages differ from Fernihough's
ir$occupation <- tolower(ir$Occupation)
ir$occhisco <- NA
# group_strings(ir$occupation[ir$child])
ir$enrol <- grepl('cho|sco|shol|schl|pupil|appr|instruc|stud', ir$occupation) & ir$child
ir$enrol[grepl('no|moni|l assis', ir$occupation) & ir$child] <- FALSE
litrgx <- '^r|can [^n]|c r|can w|^rd|both|yes|^w|r read|^educ|engl|only r|instructed'
ir$literate <- grepl(litrgx, ir$Literacy, ignore.case=T)
ir$literate[grepl('Read None|nor', ir$Literacy)] <- FALSE
ir$male <- ir$Sex %in% 'Male'
ir$female <- ir$Sex %in% 'Female'
ir$male[ir$Sex=='-'] <- NA
ir$female[ir$Sex=='-'] <- NA
ir$married <- grepl('^Married|Wido|Wife|Yes', ir$Marital.Status)
ir$married[ir$Marital.Status=='-'] <- NA
ir$fid <- gsub('/pages/1911/', '', ir$fid)
ir$region <- vstrsplit(ir$fid, '/')[, 1]
ir$ded <- vstrsplit(ir$fid, '/')[, 2]
ir$street <- vstrsplit(ir$fid, '/')[, 3]
ir$hhid <- vstrsplit(ir$fid, '/')[, 4]
ir$persid <- 1:nrow(ir)
ir$nhhid <- as.numeric(ir$hhid)
ids <- which(!diff(ir$nhhid)==0)
ir$newhhid <- 1:nrow(ir) %in% (ids + 1) # to check hhh
ir$relate <- tolower(ir$Relation.to.head)
sonrgx <- "^son$|^sons$|^son[^'].*(wife|head|illeg|husb)|step.*son|ad[oa]pt.*(son|child)|^child"
ir$son <- grepl(sonrgx, ir$relate)
dgtrgx <- "^daughter$|daughter[^'].*(wife|head|illeg|husb)|step.*daught|ad.*p.*(daug|child)|his d|^child"
ir$daughter <- grepl(dgtrgx, ir$relate)
ir$daughter[ir$relate=="Daughter and Wife to no 3"] <- FALSE
ir[ir$relate=='Childrens Governess', c('son', 'daughter')] <- FALSE
ir$lateral <- grepl('niece|nephew|brother|sister|uncle|aunt', ir$relate)
ir$upward <- grepl('father|mother|parent', ir$relate)
ir$ext <- ir$lateral | ir$upward
srvrgx <- "coach|cook|serv.*nt|keep|help|maid|nurse|porter|stew|wait|butl|domestic|govern"
ir$servant <- grepl(srvrgx, ir$relate)
ir$head <- grepl('^head|(widow|husband) head|householder|son head', ir$relate)
ir$head[grepl('matron|head wife|head of family mother in law|head of son', ir$relate)] <- FALSE
ir$spouse <- grepl('^wife$|wife [^ot]|second wife|(house|mother|family|farmer) wife', ir$relate)
catrgx <- 'r.*(ca[tch]|chat)|rom|cat|roam|calith|chat|cac|^r c|^st'
prtrgx <- "ir.*|ang.*|eng.*|c.*of (i|e)|scot|pro|germ|pr(e|i)|pyres|me|un|ep|esp|ba|con|chris|wes|br.*t|i c|u f|friend|cov|sal|mora|c (i|e)|^l|clare|pil|apo|swe|adv|qua|wel|zwing"
jewrgx <- "jew|heb|isr|jen|jsea"
ir$religion[grepl(prtrgx, ir$Religion, ignore.case=TRUE)] <- 'protestant'
ir$religion[grepl(catrgx, ir$Religion, ignore.case=TRUE)] <- 'catholic'
ir$religion[grepl(jewrgx, ir$Religion, ignore.case=TRUE)] <- 'jewish'
ir$religion[is.na(ir$religion)] <- 'other'
ir$ethnic <- 'willfully ignored'
ir$race <- NA
deds <- table(paste0(ir$ded, ir$region))
urbandeds <- deds[deds > 200] # workaround: ded not straightfwd to match to list of urb locs
ir$urban <- paste0(ir$ded, ir$region) %in% names(urbandeds)
# geocode('Aghadown_SouthCork', reg='ir')
ir$region <- tolower(ir$region)
ir$gq <- makegq(ir)
ir <- ir[ir$gq < 5 & !is.na(ir$gq), ]
ir <- rm_dualhh(ir)
write.csv(ir[vrbs], paste0('cleandata/', unique(ir$smpl), '.csv'), row.names=F)
x <- hhaggregate(data.table(ir), extravrbs='literate')
x <- x[x$age > 5 & x$age < 16 & !x$schoolage & !is.na(x$age), ]
ss <- calcsumstats(ir, x, 'hhid')
x <- x[x$age > 13 & !x$schoolage & x$age < 16 & !is.na(x$age), ]
write.table(ss, '~/dropbox/viennapaper/tabfigs/sumstats_all_harm.csv',
sep=',', append=T, col.names=F, row.names=F)
# x$fatheroccupation <- Uniformize(x$fatheroccupation)
# x$fatheroccupation[lowfreql(x$fatheroccupation, freq=5)] <- 'other'
# x$motheroccupation <- Uniformize(x$motheroccupation)
# x$motheroccupation[lowfreql(x$motheroccupation, freq=5)] <- 'other'
# x$motherreligion[x$motherreligion=='jewish'] <- 'other'
# x$fatherreligion[x$fatherreligion=='jewish'] <- 'other'
# ftable(x$enrol, x$motherreligion)
# x$birthorder[x$birthorder > 7] <- '> 7'
# f <- enrol ~ nsib + age + male + servant + urban + factor(birthorder) +
# motheragefirstchild + spousagegap + nupw + nlat - hhid +
# fatherliterate + motherliterate +
# fatherreligion + motherreligion +
# fatheroccupation + motheroccupation + region
# mlin <- lm(f, data=x)
# mlgt <- glm(f, data=x, family=binomial(link='logit'))
# modlist <- RegressionList(list(mlin, mlgt))
# screenreg(modlist$models, digits=3)
# screenreg(modlist$models, omit.coef='occupation', digits=3)
# screenreg(modlist$models, override.se=modlist$se, override.pval=modlist$pv,
# omit.coef='occupation', digits=3)
rm(ir)
rm(x)
#-------------
# ireland 1901
#-------------
ir <- fread('ireland19015pc.csv', sep=',',
data.table=FALSE, colClasses='character')
setnames(ir, gsub(' ', '.', names(ir)))
ir <- ir[!duplicated(ir), ] # for some reason 99% of families are double
ir$smpl <- 'archie-ireland-1901'
ir$year <- 1901
ir$iso3 <- 'IRL'
ir$Age[ir$Age==''] <- 0
ir$age <- as.numeric(ir$Age)
ir$child <- ir$age < 16
ir$schoolage <- ir$age > 5 & ir$age < edv$edageendlowerbound[edv$iso3 %in% ir$iso3 & edv$year %in% ir$year]
ir$occupation <- tolower(ir$Occupation)
ir$occhisco <- NA
schlrgx <- "sch|scol|cho+l|sho+l|sco|sca|pupil|appr|apr|^ap|cool|appern|sclol|sllol"
ir$enrol <- grepl(schlrgx, ir$occupation) & ir$child
ir$enrol[grepl('monit|(not|never).*scho', ir$occupation)] <- FALSE
litrgx <- "^r|^c r|^w r|wre|^can [^n]|both|ye|can r|^w|^fre|only|educ|^able|all|engl|good|grad|litt|can$"
ir$literate <- grepl(litrgx, ir$Literacy, ignore.case=T)
# unique(grepr(, ir$Literacy[ir$literate], ignore.case=T))
ir$literate[grepl('not$|not read|nor', ir$Literacy, ignore.case=T)] <- FALSE
ir$male <- ir$Sex=='Male'
ir$male[ir$Sex=='-'] <- NA
ir$female <- ir$Sex=='Female'
ir$female[ir$Sex=='-'] <- NA
ir$married <- grepl('^Married|Wido|Wife|Yes', ir$Marital.Status)
ir$married[ir$Marital.Status %in% c('-', '?')] <- NA
ir$relate <- tolower(ir$Relation.to.head)
sonrgx <- "^sons?$|son.*head|[0-9]nd son|step.?son|eldest son|^child( |r)|child$|adopt|farm.*son|son wife"
ir$son <- grepl(sonrgx, ir$relate) & ir$male
# look again, starting fro d.*t.*r|da.*r
dgtrgx <- "^daughter?$|daughter.*head|[0-9]nd daughter|step.?daughter|eldest daughter|^child( |r)|adopt|farm.*daughter|daughter wife|^child$"
ir$daughter <- grepl(dgtrgx, ir$relate) & ir$female
ir$lateral <- grepl('niece|nephew|brother|sister|uncle|aunt', ir$relate)
ir$upward <- grepl('father|mother|parent', ir$relate)
ir$ext <- ir$lateral | ir$upward
srvrgx <- "coach|cook|serv.*nt|keep|help|maid|nurse|porter|stew|wait|butl|domestic|govern"
ir$servant <- grepl(srvrgx, ir$relate)
ir$head <- grepl('^head ?[^n]', ir$relate)
ir$spouse <- grepl('^wife$|^wife.*(head|family|mohter)|house wife', ir$relate)
catrgx <- "r.*(ca[tch]|chat)|rom|cat|roam|calith|chat|cac|^r c|^st|cotho|caatl"
prtrgx <- "ir.*|ang.*|eng.*|c.*of (i|e)|scot|prot|germ|pr[sey]|pyres|pers|pes|pris|un|ep|pisc|esp|ba|con|chris|wes|br.*t?h?|ih? c|u f|friend|cov|sal|mora|^c (i|e)|^l|clare|pil|swe|adv|qua|wel|zwing|m[ie]a?[td]|inde?p|plym|esta"
jewrgx <- "jew|[ij]sr|heb|istr?a"
ir$religion[grepl(catrgx, ir$Religion, ignore.case=T)] <- 'catholic'
ir$religion[grepl(prtrgx, ir$Religion, ignore.case=T)] <- 'protestant'
ir$religion[grepl(jewrgx, ir$Religion, ignore.case=T)] <- 'jewish'
ir$religion[is.na(ir$religion)] <- 'other'
ir$ethnic <- 'willfully ignored'
ir$race <- NA
ir$fid <- gsub('/pages/1901/', '', ir$fid)
ir$region <- vstrsplit(ir$fid, '/')[, 1]
ir$ded <- vstrsplit(ir$fid, '/')[, 2]
ir$street <- vstrsplit(ir$fid, '/')[, 3]
ir$hhid <- vstrsplit(ir$fid, '/')[, 4]
ir$persid <- 1:nrow(ir)
deds <- table(paste0(ir$ded, ir$region))
urbandeds <- deds[deds > 200]
ir$urban <- paste0(ir$ded, ir$region) %in% names(urbandeds)
ir$region <- tolower(ir$region)
ir$gq <- makegq(ir)
ir <- ir[ir$gq < 5 & !is.na(ir$gq), ]
ir <- rm_dualhh(ir)
write.csv(ir[vrbs], paste0('cleandata/', unique(ir$smpl), '.csv'), row.names=F)
x <- hhaggregate(data.table(ir), extravrbs='literate')
x <- x[x$age > 5 & x$age < 16 & !x$schoolage & !is.na(x$age), ]
ss <- calcsumstats(ir, x, 'hhid')
write.table(ss, '~/dropbox/viennapaper/tabfigs/sumstats_all_harm.csv',
sep=',', append=T, col.names=F, row.names=F)
# x <- x[x$age > 13 & !x$schoolage & x$age < 16 & !is.na(x$age), ]
# x$fatheroccupation <- Uniformize(x$fatheroccupation)
# x$motheroccupation <- Uniformize(x$motheroccupation)
# x$fatheroccupation[lowfreql(x$fatheroccupation, freq=10)] <- 'other'
# x$motheroccupation[lowfreql(x$motheroccupation, freq=10)] <- 'other'
# # x$birthorder[x$birthorder > 5] <- '> 5'
# f <- enrol ~ nsib + age + male + servant + urban + factor(birthorder) +
# motheragefirstchild + spousagegap + nupw + nlat - hhid +
# fatherliterate + motherliterate +
# fatherreligion + motherreligion +
# fatheroccupation + motheroccupation + region
rm(ir)
rm(x)
############
### NAPP ###
############
# iceland1901.csv # no scholars
# mecklschwer1819.csv # no relate
# canada1881.csv # no relate
# norwaynapp.csv # very few student strings
# napp relate codes
mothercodes <- c(0201, 0202, 0203)
childcodes <- c(301, 302, 306)
latcodes <- c(701:704, 801:802, 1021:1044)
upwcodes <- c(501:503, 601:602, 1011:1013)
servantcodes <- c(1210:1217)
#--------------
# Scotland 1881
#--------------
sc81 <- fread('scotland1881.csv', header=T,