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May 9, 2014 11:00
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| ### R code from vignette source 'article3.Rnw' | |
| ################################################### | |
| ### code chunk number 1: loadlibs | |
| ################################################### | |
| library(stargazer) | |
| library(ggplot2) | |
| library(tables) | |
| booktabs() | |
| library(causalsens) | |
| textAnd <- function(x)paste(paste(x[-length(x)],collapse=", ")," and ",x[length(x)],sep="") | |
| ################################################### | |
| ### code chunk number 2: bigchunk | |
| ################################################### | |
| dat <- read.csv("../data/union-with-ranks.csv", | |
| header=T) | |
| ### Create facts of listings | |
| dat$App1.SameYearListed <- dat$App2.SameYearListed <-dat$App3.SameYearListed <-dat$App4.SameYearListed <-dat$App5.SameYearListed <-NA | |
| dat$Resp1.SameYearListed <- dat$Resp2.SameYearListed <-dat$Resp3.SameYearListed <-dat$Resp4.SameYearListed <-dat$Resp5.SameYearListed <-NA | |
| dat$App1.FollowingYearListed <- dat$App2.FollowingYearListed <-dat$App3.FollowingYearListed <-dat$App4.FollowingYearListed <-dat$App5.FollowingYearListed <-NA | |
| dat$Resp1.FollowingYearListed <- dat$Resp2.FollowingYearListed <-dat$Resp3.FollowingYearListed <-dat$Resp4.FollowingYearListed <-dat$Resp5.FollowingYearListed <-NA | |
| dat$App1.PrecedingYearListed <- dat$App2.PrecedingYearListed <-dat$App3.PrecedingYearListed <-dat$App4.PrecedingYearListed <-dat$App5.PrecedingYearListed <-NA | |
| dat$Resp1.PrecedingYearListed <- dat$Resp2.PrecedingYearListed <-dat$Resp3.PrecedingYearListed <-dat$Resp4.PrecedingYearListed <-dat$Resp5.PrecedingYearListed <-NA | |
| dat$App1.AllTimeListed <- dat$App2.AllTimeListed <-dat$App3.AllTimeListed <-dat$App4.AllTimeListed <-dat$App5.AllTimeListed <-NA | |
| dat$Resp1.AllTimeListed <- dat$Resp2.AllTimeListed <-dat$Resp3.AllTimeListed <-dat$Resp4.AllTimeListed <-dat$Resp5.AllTimeListed <-NA | |
| for (i in 1:5) { | |
| target.var <- paste("App",i,".SameYearListed",sep="") | |
| source.var <- paste("App",i,".SameYearRank",sep="") | |
| dat[,target.var] <- as.numeric(!is.element(dat[,source.var],c(1,8))) | |
| target.var <- paste("Resp",i,".SameYearListed",sep="") | |
| source.var <- paste("Resp",i,".SameYearRank",sep="") | |
| dat[,target.var] <- as.numeric(!is.element(dat[,source.var],c(1,8))) | |
| target.var <- paste("App",i,".FollowingYearListed",sep="") | |
| source.var <- paste("App",i,".FollowingYearRank",sep="") | |
| dat[,target.var] <- as.numeric(!is.element(dat[,source.var],c(1,8))) | |
| target.var <- paste("Resp",i,".FollowingYearListed",sep="") | |
| source.var <- paste("Resp",i,".FollowingYearRank",sep="") | |
| dat[,target.var] <- as.numeric(!is.element(dat[,source.var],c(1,8))) | |
| target.var <- paste("App",i,".PrecedingYearListed",sep="") | |
| source.var <- paste("App",i,".PrecedingYearRank",sep="") | |
| dat[,target.var] <- as.numeric(!is.element(dat[,source.var],c(1,8))) | |
| target.var <- paste("Resp",i,".PrecedingYearListed",sep="") | |
| source.var <- paste("Resp",i,".PrecedingYearRank",sep="") | |
| dat[,target.var] <- as.numeric(!is.element(dat[,source.var],c(1,8))) | |
| target.var <- paste("App",i,".AllTimeListed",sep="") | |
| source.var <- paste("App",i,".AllTimeRank",sep="") | |
| dat[,target.var] <- as.numeric(!is.element(dat[,source.var],c(1,8))) | |
| target.var <- paste("Resp",i,".AllTimeListed",sep="") | |
| source.var <- paste("Resp",i,".AllTimeRank",sep="") | |
| dat[,target.var] <- as.numeric(!is.element(dat[,source.var],c(1,8))) | |
| } | |
| ### Appellants, average same-year rank | |
| dat$App.SameYearRank.avg <- rowMeans(dat[, | |
| c("App1.SameYearRank","App2.SameYearRank","App3.SameYearRank","App4.SameYearRank","App5.SameYearRank")], | |
| na.rm=TRUE) | |
| ### Respondents, average same year rank | |
| dat$Resp.SameYearRank.avg <- rowMeans(dat[, | |
| c("Resp1.SameYearRank","Resp2.SameYearRank","Resp3.SameYearRank","Resp4.SameYearRank","Resp5.SameYearRank")], | |
| na.rm=TRUE) | |
| ### Appellants, average Following-year rank | |
| dat$App.FollowingYearRank.avg <- rowMeans(dat[, | |
| c("App1.FollowingYearRank","App2.FollowingYearRank","App3.FollowingYearRank","App4.FollowingYearRank","App5.FollowingYearRank")], | |
| na.rm=TRUE) | |
| ### Respondents, average Following year rank | |
| dat$Resp.FollowingYearRank.avg <- rowMeans(dat[, | |
| c("Resp1.FollowingYearRank","Resp2.FollowingYearRank","Resp3.FollowingYearRank","Resp4.FollowingYearRank","Resp5.FollowingYearRank")], | |
| na.rm=TRUE) | |
| ### Appellants, average Preceding-year rank | |
| dat$App.PrecedingYearRank.avg <- rowMeans(dat[, | |
| c("App1.PrecedingYearRank","App2.PrecedingYearRank","App3.PrecedingYearRank","App4.PrecedingYearRank","App5.PrecedingYearRank")], | |
| na.rm=TRUE) | |
| ### Respondents, average Preceding year rank | |
| dat$Resp.PrecedingYearRank.avg <- rowMeans(dat[, | |
| c("Resp1.PrecedingYearRank","Resp2.PrecedingYearRank","Resp3.PrecedingYearRank","Resp4.PrecedingYearRank","Resp5.PrecedingYearRank")], | |
| na.rm=TRUE) | |
| ### Appellants, average all-time rank | |
| dat$App.AllTimeRank.avg <- rowMeans(dat[, | |
| c("App1.AllTimeRank","App2.AllTimeRank","App3.AllTimeRank","App4.AllTimeRank","App5.AllTimeRank")], | |
| na.rm=TRUE) | |
| ### Respondents, average all-time rank | |
| dat$Resp.AllTimeRank.avg <- rowMeans(dat[, | |
| c("Resp1.AllTimeRank","Resp2.AllTimeRank","Resp3.AllTimeRank","Resp4.AllTimeRank","Resp5.AllTimeRank")], | |
| na.rm=TRUE) | |
| ### Appellants, max same-year rank | |
| dat$App.SameYearRank.max <- apply(dat[, | |
| c("App1.SameYearRank","App2.SameYearRank","App3.SameYearRank","App4.SameYearRank","App5.SameYearRank")],1,max, | |
| na.rm=TRUE) | |
| ### Respondents, max same year rank | |
| dat$Resp.SameYearRank.max <- apply(dat[, | |
| c("Resp1.SameYearRank","Resp2.SameYearRank","Resp3.SameYearRank","Resp4.SameYearRank","Resp5.SameYearRank")],1,max, | |
| na.rm=TRUE) | |
| ### Appellants, max Following-year rank | |
| dat$App.FollowingYearRank.max <- apply(dat[, | |
| c("App1.FollowingYearRank","App2.FollowingYearRank","App3.FollowingYearRank","App4.FollowingYearRank","App5.FollowingYearRank")],1,max, | |
| na.rm=TRUE) | |
| ### Respondents, max Following year rank | |
| dat$Resp.FollowingYearRank.max <- apply(dat[, | |
| c("Resp1.FollowingYearRank","Resp2.FollowingYearRank","Resp3.FollowingYearRank","Resp4.FollowingYearRank","Resp5.FollowingYearRank")],1,max, | |
| na.rm=TRUE) | |
| ### Appellants, max Preceding-year rank | |
| dat$App.PrecedingYearRank.max <- apply(dat[, | |
| c("App1.PrecedingYearRank","App2.PrecedingYearRank","App3.PrecedingYearRank","App4.PrecedingYearRank","App5.PrecedingYearRank")],1,max, | |
| na.rm=TRUE) | |
| ### Respondents, max Preceding year rank | |
| dat$Resp.PrecedingYearRank.max <- apply(dat[, | |
| c("Resp1.PrecedingYearRank","Resp2.PrecedingYearRank","Resp3.PrecedingYearRank","Resp4.PrecedingYearRank","Resp5.PrecedingYearRank")],1,max, | |
| na.rm=TRUE) | |
| ### Appellants, max all-time rank | |
| dat$App.AllTimeRank.max <- apply(dat[, | |
| c("App1.AllTimeRank","App2.AllTimeRank","App3.AllTimeRank","App4.AllTimeRank","App5.AllTimeRank")],1,max, | |
| na.rm=TRUE) | |
| ### Respondents, max all-time rank | |
| dat$Resp.AllTimeRank.max <- apply(dat[, | |
| c("Resp1.AllTimeRank","Resp2.AllTimeRank","Resp3.AllTimeRank","Resp4.AllTimeRank","Resp5.AllTimeRank")],1,max, | |
| na.rm=TRUE) | |
| ### Appellants, average same-year Listed | |
| dat$App.SameYearListed.avg <- rowMeans(dat[, | |
| c("App1.SameYearListed","App2.SameYearListed","App3.SameYearListed","App4.SameYearListed","App5.SameYearListed")], | |
| na.rm=TRUE) | |
| ### Respondents, average same year Listed | |
| dat$Resp.SameYearListed.avg <- rowMeans(dat[, | |
| c("Resp1.SameYearListed","Resp2.SameYearListed","Resp3.SameYearListed","Resp4.SameYearListed","Resp5.SameYearListed")], | |
| na.rm=TRUE) | |
| ### Appellants, average Following-year Listed | |
| dat$App.FollowingYearListed.avg <- rowMeans(dat[, | |
| c("App1.FollowingYearListed","App2.FollowingYearListed","App3.FollowingYearListed","App4.FollowingYearListed","App5.FollowingYearListed")], | |
| na.rm=TRUE) | |
| ### Respondents, average Following year Listed | |
| dat$Resp.FollowingYearListed.avg <- rowMeans(dat[, | |
| c("Resp1.FollowingYearListed","Resp2.FollowingYearListed","Resp3.FollowingYearListed","Resp4.FollowingYearListed","Resp5.FollowingYearListed")], | |
| na.rm=TRUE) | |
| ### Appellants, average Preceding-year Listed | |
| dat$App.PrecedingYearListed.avg <- rowMeans(dat[, | |
| c("App1.PrecedingYearListed","App2.PrecedingYearListed","App3.PrecedingYearListed","App4.PrecedingYearListed","App5.PrecedingYearListed")], | |
| na.rm=TRUE) | |
| ### Respondents, average Preceding year Listed | |
| dat$Resp.PrecedingYearListed.avg <- rowMeans(dat[, | |
| c("Resp1.PrecedingYearListed","Resp2.PrecedingYearListed","Resp3.PrecedingYearListed","Resp4.PrecedingYearListed","Resp5.PrecedingYearListed")], | |
| na.rm=TRUE) | |
| ### Appellants, average all-time Listed | |
| dat$App.AllTimeListed.avg <- rowMeans(dat[, | |
| c("App1.AllTimeListed","App2.AllTimeListed","App3.AllTimeListed","App4.AllTimeListed","App5.AllTimeListed")], | |
| na.rm=TRUE) | |
| ### Respondents, average all-time Listed | |
| dat$Resp.AllTimeListed.avg <- rowMeans(dat[, | |
| c("Resp1.AllTimeListed","Resp2.AllTimeListed","Resp3.AllTimeListed","Resp4.AllTimeListed","Resp5.AllTimeListed")], | |
| na.rm=TRUE) | |
| ### Appellants, max same-year Listed | |
| dat$App.SameYearListed.max <- apply(dat[, | |
| c("App1.SameYearListed","App2.SameYearListed","App3.SameYearListed","App4.SameYearListed","App5.SameYearListed")],1,max, | |
| na.rm=TRUE) | |
| ### Respondents, max same year Listed | |
| dat$Resp.SameYearListed.max <- apply(dat[, | |
| c("Resp1.SameYearListed","Resp2.SameYearListed","Resp3.SameYearListed","Resp4.SameYearListed","Resp5.SameYearListed")],1,max, | |
| na.rm=TRUE) | |
| ### Appellants, max Following-year Listed | |
| dat$App.FollowingYearListed.max <- apply(dat[, | |
| c("App1.FollowingYearListed","App2.FollowingYearListed","App3.FollowingYearListed","App4.FollowingYearListed","App5.FollowingYearListed")],1,max, | |
| na.rm=TRUE) | |
| ### Respondents, max Following year Listed | |
| dat$Resp.FollowingYearListed.max <- apply(dat[, | |
| c("Resp1.FollowingYearListed","Resp2.FollowingYearListed","Resp3.FollowingYearListed","Resp4.FollowingYearListed","Resp5.FollowingYearListed")],1,max, | |
| na.rm=TRUE) | |
| ### Appellants, max Preceding-year Listed | |
| dat$App.PrecedingYearListed.max <- apply(dat[, | |
| c("App1.PrecedingYearListed","App2.PrecedingYearListed","App3.PrecedingYearListed","App4.PrecedingYearListed","App5.PrecedingYearListed")],1,max, | |
| na.rm=TRUE) | |
| ### Respondents, max Preceding year Listed | |
| dat$Resp.PrecedingYearListed.max <- apply(dat[, | |
| c("Resp1.PrecedingYearListed","Resp2.PrecedingYearListed","Resp3.PrecedingYearListed","Resp4.PrecedingYearListed","Resp5.PrecedingYearListed")],1,max, | |
| na.rm=TRUE) | |
| ### Appellants, max all-time Listed | |
| dat$App.AllTimeListed.max <- apply(dat[, | |
| c("App1.AllTimeListed","App2.AllTimeListed","App3.AllTimeListed","App4.AllTimeListed","App5.AllTimeListed")],1,max, | |
| na.rm=TRUE) | |
| ### Respondents, max all-time Listed | |
| dat$Resp.AllTimeListed.max <- apply(dat[, | |
| c("Resp1.AllTimeListed","Resp2.AllTimeListed","Resp3.AllTimeListed","Resp4.AllTimeListed","Resp5.AllTimeListed")],1,max, | |
| na.rm=TRUE) | |
| ### Now create treatments | |
| dat$SameYearTreatment.AvgRank <- dat$App.SameYearRank.avg > dat$Resp.SameYearRank.avg | |
| dat$FollowingYearTreatment.AvgRank <- dat$App.FollowingYearRank.avg > dat$Resp.FollowingYearRank.avg | |
| dat$PrecedingYearTreatment.AvgRank <- dat$App.PrecedingYearRank.avg > dat$Resp.PrecedingYearRank.avg | |
| dat$AllTimeTreatment.AvgRank <- dat$App.AllTimeRank.avg > dat$Resp.AllTimeRank.avg | |
| dat$SameYearTreatment.MaxRank <- dat$App.SameYearRank.max > dat$Resp.SameYearRank.max | |
| dat$FollowingYearTreatment.MaxRank <- dat$App.FollowingYearRank.max > dat$Resp.FollowingYearRank.max | |
| dat$PrecedingYearTreatment.MaxRank <- dat$App.PrecedingYearRank.max > dat$Resp.PrecedingYearRank.max | |
| dat$AllTimeTreatment.MaxRank <- dat$App.AllTimeRank.max > dat$Resp.AllTimeRank.max | |
| dat$SameYearTreatment.AvgListed <- dat$App.SameYearListed.avg > dat$Resp.SameYearListed.avg | |
| dat$FollowingYearTreatment.AvgListed <- dat$App.FollowingYearListed.avg > dat$Resp.FollowingYearListed.avg | |
| dat$PrecedingYearTreatment.AvgListed <- dat$App.PrecedingYearListed.avg > dat$Resp.PrecedingYearListed.avg | |
| dat$AllTimeTreatment.AvgListed <- dat$App.AllTimeListed.avg > dat$Resp.AllTimeListed.avg | |
| dat$SameYearTreatment.MaxListed <- dat$App.SameYearListed.max > dat$Resp.SameYearListed.max | |
| dat$FollowingYearTreatment.MaxListed <- dat$App.FollowingYearListed.max > dat$Resp.FollowingYearListed.max | |
| dat$PrecedingYearTreatment.MaxListed <- dat$App.PrecedingYearListed.max > dat$Resp.PrecedingYearListed.max | |
| dat$AllTimeTreatment.MaxListed <- dat$App.AllTimeListed.max > dat$Resp.AllTimeListed.max | |
| treatments <- c("SameYearTreatment.AvgRank", | |
| "FollowingYearTreatment.AvgRank", | |
| "PrecedingYearTreatment.AvgRank", | |
| "AllTimeTreatment.AvgRank", | |
| "SameYearTreatment.MaxRank", | |
| "FollowingYearTreatment.MaxRank", | |
| "PrecedingYearTreatment.MaxRank", | |
| "AllTimeTreatment.MaxRank", | |
| "SameYearTreatment.AvgListed", | |
| "FollowingYearTreatment.AvgListed", | |
| "PrecedingYearTreatment.AvgListed", | |
| "AllTimeTreatment.AvgListed", | |
| "SameYearTreatment.MaxListed", | |
| "FollowingYearTreatment.MaxListed", | |
| "PrecedingYearTreatment.MaxListed", | |
| "AllTimeTreatment.MaxListed") | |
| dat$app.nlawyers <- apply(dat[,c("AppLawyer1","AppLawyer2","AppLawyer3","AppLawyer4","AppLawyer5")],1,function(x)length(na.omit(x))) | |
| dat$resp.nlawyers <- apply(dat[,c("RespLawyer1","RespLawyer2","RespLawyer3","RespLawyer4","RespLawyer5")],1,function(x)length(na.omit(x))) | |
| dat$deltaLawyers <- dat$app.nlawyers - dat$resp.nlawyers | |
| dat$appellant.wins <- as.numeric(dat$Outcome == 3) | |
| dat$Govt <- ifelse(dat$AppType %in% c("govt","othergovt"),1, | |
| ifelse(dat$RespType %in% c("govt","othergovt"),-1,0)) | |
| dat$Govt[which(dat$AppType %in% c("govt","othergovt") & dat$RespType %in% c("govt","othergovt"))] <- 0 | |
| dat$Individual <- ifelse(dat$AppType=="individual",1, | |
| ifelse(dat$RespType=="individual",-1,0)) | |
| dat$Individual[which(dat$AppType %in% c("individual") & dat$RespType %in% c("individual"))] <- 0 | |
| dat$Ltd <- ifelse(dat$AppType=="ltd",1, | |
| ifelse(dat$RespType=="ltd",-1,0)) | |
| dat$Ltd[which(dat$AppType %in% c("ltd") & dat$RespType %in% c("ltd"))] <- 0 | |
| dat$PLC <- ifelse(dat$AppType=="plc",1, | |
| ifelse(dat$RespType=="plc",-1,0)) | |
| dat$PLC[which(dat$AppType %in% c("plc") & dat$RespType %in% c("plc"))] <- 0 | |
| dat$Trust <- ifelse(dat$AppType=="trust",1, | |
| ifelse(dat$RespType=="trust",-1,0)) | |
| dat$Trust[which(dat$AppType %in% c("trust") & dat$RespType %in% c("trust"))] <- 0 | |
| dat$SoleTrader <- ifelse(dat$AppType=="soletrader",1, | |
| ifelse(dat$RespType=="soletrader",-1,0)) | |
| dat$SoleTrader[which(dat$AppType %in% c("soletrader") & dat$RespType %in% c("soletrader"))] <- 0 | |
| dat$Individual <- ifelse(dat$AppType %in% c("individual","soletrader"),1, | |
| ifelse(dat$RespType %in% c("individual","soletrader"),-1,0)) | |
| dat$Individual[which(dat$AppType %in% c("individual","soletrader") & dat$RespType %in% c("individual","soletrader"))] <- 0 | |
| dat$Govt.2 <- dat$AppType %in% "govt" | |
| dat$Individual.2 <- dat$AppType %in% c("individual","soletrader") | |
| dat$Trust.2 <- dat$AppType %in% "trust" | |
| dat$Ltd.2 <- dat$AppType %in% "ltd" | |
| dat$PLC.2 <- dat$AppType %in% "plc" | |
| dat$Complexity <- scale(log(dat$words)) | |
| dat$Level <- factor(dat$Level) | |
| dat$HighCt <- dat$Level == "-1" | |
| dat$ApexCt <- dat$Level == "1" | |
| dat$post2008 <- dat$Year > 2008 | |
| ################################################### | |
| ### code chunk number 3: origrankings | |
| ################################################### | |
| ### Load the rank information | |
| rankings <- read.csv("../Tax rankings.csv",header=T) | |
| ### convert ranks to order | |
| rankings$rank.num <- as.numeric(as.character(car:::recode(rankings$Rank, | |
| "c('Juniors 1','Juniors - 1')=6; | |
| c('Juniors 2','Juniors - 2')=5; | |
| c('Juniors 3','Juniors - 3')=4; | |
| c('Juniors - 4') = 3; | |
| c('Juniors - star','Juniors - Star')=7; | |
| c('Juniors - Up-and-coming')=2; | |
| c('New Silks') = 9; | |
| c('Silks 1','Silks - 1') = 13; | |
| c('Silks 2','Silks - 2') = 12; | |
| c('Silks 3','Silks - 3') = 11; | |
| c('Silks 4','Silks - 4') = 10; | |
| c('Silks - star','Silks - Star') = 14"))) | |
| ################################################### | |
| ### code chunk number 4: rankdistr | |
| ################################################### | |
| rank.mat <- as.matrix(dat[,c("app.rank1","app.rank2","app.rank3","app.rank4","app.rank5", | |
| "resp.rank1","resp.rank2","resp.rank3","resp.rank4","resp.rank5")]) | |
| rank.distrib <- na.omit(as.vector(rank.mat)) | |
| ################################################### | |
| ### code chunk number 5: splitjoins | |
| ################################################### | |
| njoins <- length(names(which(table(dat$refno)>2))) | |
| splitjoins <- names(which(by(dat$appellant.wins,dat$refno,var)>0)) | |
| ################################################### | |
| ### code chunk number 6: smrytab | |
| ################################################### | |
| stargazer(dat[,c("Govt.2","Ltd.2","Trust.2","PLC.2","Individual.2","deltaLawyers","words","HighCt","ApexCt","post2008")], | |
| summary = TRUE, | |
| title = "Summary statistics", | |
| label ="tab:smry", | |
| covariate.labels = c("Govt. is appellant", | |
| "Limited company is appellant", | |
| "Trust is appellant", | |
| "Publicly-traded company is appellant", | |
| "Individual or sole trader is appellant", | |
| "App. lawyers - resp. lawyers", | |
| "Complexity (wordcount)", | |
| "High Court or equiv.", | |
| "Final court (HoL/UKSC/PC)", | |
| "Post 2008")) | |
| ################################################### | |
| ### code chunk number 7: out1 | |
| ################################################### | |
| dat$Representation <- ifelse(dat$SameYearTreatment.AvgRank,"Appellant counsel better","Appellant counsel not better") | |
| dat$Outcome <- ifelse(dat$appellant.wins==1,"Appellant wins","Appellant loses") | |
| outtab1 <- table( | |
| dat$Representation, | |
| dat$Outcome) | |
| latex(tabular(Heading() * factor(Representation) ~ Heading() * factor(Outcome)*((n=1)+Percent("row")), | |
| data=dat)) | |
| dat$Representation <- NULL | |
| dat$Outcome <- NULL | |
| ################################################### | |
| ### code chunk number 8: out2 | |
| ################################################### | |
| dat$Representation <- ifelse(dat$SameYearTreatment.AvgListed,"Appellant counsel better","Appellant counsel not better") | |
| dat$Outcome <- ifelse(dat$appellant.wins==1,"Appellant wins","Appellant loses") | |
| outtab2 <- table( | |
| dat$Representation, | |
| dat$Outcome) | |
| latex(tabular(Heading() * factor(Representation) ~ Heading() * factor(Outcome)*((n=1)+Percent("row")), | |
| data=dat)) | |
| dat$Representation <- NULL | |
| dat$Outcome <- NULL | |
| ################################################### | |
| ### code chunk number 9: rankmods | |
| ################################################### | |
| sel.formula <- as.formula("SameYearTreatment.AvgRank ~ | |
| Govt.2 + Trust.2 + PLC.2 + Individual.2 + scale(log(words)) + HighCt + ApexCt + post2008") | |
| outcome.formula <- as.formula("appellant.wins ~ | |
| SameYearTreatment.AvgRank + Govt.2 + Trust.2 + PLC.2 + Individual.2 + scale(deltaLawyers) + scale(log(words)) + HighCt + ApexCt + post2008") | |
| sel.mod <- glm(sel.formula,data = dat, family=binomial) | |
| outcome.mod <- lm(outcome.formula,data = dat) | |
| outcome.glm <- glm(outcome.formula,data = dat,family=binomial) | |
| stargazer(outcome.mod, | |
| # outcome.glm, | |
| sel.mod, | |
| dep.var.labels = c("Appellant wins","Treated"), | |
| title = "Regression models, rank-based measure", | |
| label = "tab:rankreg", | |
| font.size = "footnotesize", | |
| no.space = TRUE, | |
| covariate.labels = c("Better-ranked lawyers","Appellant: Government","Appellant: Trust","Appellant: Public limited company","Appellant: Individual or sole trader","No. appellant lawyers - no. respondent lawyers","log(Complexity)","High Court v. Court of Appeal","HoL/UKSC/PC v. Court of Appeal","Post 2008","Constant"), | |
| # notes = "Coefficient values for continuous variables are standardized", | |
| intercept.bottom = TRUE) | |
| ################################################### | |
| ### code chunk number 10: listedmods | |
| ################################################### | |
| sel.formula <- as.formula("SameYearTreatment.AvgListed ~ | |
| Govt.2 + Trust.2 + PLC.2 + Individual.2 + scale(log(words)) + HighCt + ApexCt + post2008") | |
| outcome.formula <- as.formula("appellant.wins ~ | |
| SameYearTreatment.AvgListed + Govt.2 + Trust.2 + PLC.2 + Individual.2 + scale(deltaLawyers) + scale(log(words)) + HighCt + ApexCt + post2008") | |
| sel.mod <- glm(sel.formula,data = dat, family=binomial) | |
| outcome.mod <- lm(outcome.formula,data = dat) | |
| outcome.glm <- glm(outcome.formula,data = dat,family=binomial) | |
| stargazer(outcome.mod, | |
| # outcome.glm, | |
| sel.mod, | |
| dep.var.labels = c("Appellant wins","Treated"), | |
| title = "Regression models, listing-based measure", | |
| label = "tab:listreg", | |
| font.size = "footnotesize", | |
| no.space = TRUE, | |
| covariate.labels = c("Better-ranked lawyers","Appellant: Government","Appellant: Trust","Appellant: Public limited company","Appellant: Individual or sole trader","No. appellant lawyers - no. respondent lawyers","log(Complexity)","High Court v. Court of Appeal","HoL/UKSC/PC v. Court of Appeal","Post 2008","Constant"), | |
| # notes = "Coefficient values for continuous variables are standardized", | |
| intercept.bottom = TRUE) | |
| ################################################### | |
| ### code chunk number 11: sensitivity | |
| ################################################### | |
| dat$Complexity <- scale(log(dat$words)) | |
| dat$deltaLawyers.scaled <- scale(dat$deltaLawyers) | |
| tr <- "SameYearTreatment.AvgRank" | |
| dat[,tr] <- as.numeric(dat[,tr]) | |
| sel.formula <- as.formula( | |
| paste(tr,"~Govt.2 + Trust.2 + PLC.2 + Individual.2 + Complexity + HighCt + ApexCt + post2008", | |
| sep=" ")) | |
| outcome.formula <- as.formula( | |
| paste("appellant.wins~",tr,"+Govt.2 + Trust.2 + PLC.2 + Individual.2 + deltaLawyers.scaled + Complexity + HighCt + ApexCt + post2008", | |
| sep=" ")) | |
| sel.mod <- glm(sel.formula,data = dat, family=binomial) | |
| out.mod <- lm(outcome.formula,data = dat) | |
| alpha <- seq(max(abs(coef(out.mod)[-1])) * -1.5, max(abs(coef(out.mod)[-1])) * 1.5, length.out = 101) | |
| rank.one.side.sens <- causalsens(out.mod, sel.mod, ~ Govt.2 + Trust.2 + PLC.2 + Individual.2 + deltaLawyers.scaled + Complexity + HighCt + ApexCt + post2008, data = dat, | |
| alpha = alpha, confound = one.sided) | |
| tr <- "SameYearTreatment.AvgListed" | |
| dat[,tr] <- as.numeric(dat[,tr]) | |
| sel.formula <- as.formula( | |
| paste(tr,"~Govt.2 + Trust.2 + PLC.2 + Individual.2 + Complexity + HighCt + ApexCt + post2008", | |
| sep=" ")) | |
| outcome.formula <- as.formula( | |
| paste("appellant.wins~",tr,"+Govt.2 + Trust.2 + PLC.2 + Individual.2 + deltaLawyers.scaled + Complexity + HighCt + ApexCt + post2008", | |
| sep=" ")) | |
| sel.mod2 <- glm(sel.formula,data = dat, family=binomial) | |
| out.mod2 <- lm(outcome.formula,data = dat) | |
| alpha <- seq(max(abs(coef(out.mod2)[-1])) * -1.5, max(abs(coef(out.mod2)[-1])) * 1.5, length.out = 101) | |
| list.one.side.sens <- causalsens(out.mod2, sel.mod2, | |
| ~ Govt.2 + Trust.2 + PLC.2 + Individual.2 + deltaLawyers.scaled + Complexity + HighCt + ApexCt + post2008, | |
| data = dat, | |
| alpha = alpha, confound = one.sided) | |
| lowest.rank <- alpha[max(which(rank.one.side.sens$sens$lower>0))] | |
| lowest.list <- alpha[max(which(list.one.side.sens$sens$lower>0))] | |
| par(mfrow=c(2,2),mar=c(3,3,2,1), mgp=c(2,.7,0), tck=-.01) | |
| plot(rank.one.side.sens, type = "raw",main = "Rank-based measure", xlab = expression(paste("Amount of confounding ", alpha))) | |
| arrows(x0=lowest.rank,x1=lowest.rank,y0=-0.15,y1=0,length=0.05) | |
| text(x=lowest.rank,y=-0.2,labels="Rank-based\n measure\nbecomes\nsignificant",cex=.75) | |
| points(x= abs(coef(out.mod)[-1]), | |
| y = rep(0,length(coef(out.mod))-1), | |
| pch = 4) | |
| points(x= abs(coef(out.mod)[-1]) * -1, | |
| y = rep(0,length(coef(out.mod))-1), | |
| pch = 4) | |
| plot(list.one.side.sens, type = "raw",main = "Listing-based measure", xlab = expression(paste("Amount of confounding ", alpha))) | |
| points(x= abs(coef(out.mod2)[-1]), | |
| y = rep(0,length(coef(out.mod))-1), | |
| pch = 4) | |
| points(x= abs(coef(out.mod2)[-1]) * -1, | |
| y = rep(0,length(coef(out.mod))-1), | |
| pch = 4) | |
| plot(rank.one.side.sens, type = "r.squared",main = "Rank-based measure") | |
| plot(list.one.side.sens, type = "r.squared",main = "Listing-based measure") | |
| ################################################### | |
| ### code chunk number 12: othercoefs | |
| ################################################### | |
| othercoefs <- read.csv("../coefs.csv",header=T) | |
| ### just get dummies that are not intercepts and are significant | |
| othercoefs$tval <- abs(othercoefs$beta / othercoefs$se) | |
| othercoefs <- subset(othercoefs,isIntercept == 0 & grepl("dummy",Type,ignore.case=TRUE)) | |
| othercoefs$predprob <- abs(othercoefs$beta) / 4 | |
| rank.comparison <- round(mean(abs(lowest.rank) > othercoefs$predprob)*100,1) | |
| list.comparison <- round(mean(abs(lowest.list) > othercoefs$predprob)*100,1) | |
| ################################################### | |
| ### code chunk number 13: robustnesscheck | |
| ################################################### | |
| tr <- NULL | |
| treats <- grep("Treat",names(dat),value=TRUE) | |
| coef.mat <- matrix(NA,nrow=length(treats),ncol=3) | |
| for (tr in treats) { | |
| out.formula <- as.formula( | |
| paste("appellant.wins~",tr,"+Govt.2 + Trust.2 + PLC.2 + Individual.2 + deltaLawyers.scaled + Complexity + HighCt + ApexCt + post2008", | |
| sep=" ")) | |
| out.mod <- lm(out.formula,data = dat) | |
| coef.mat[which(tr==treats),1] <- coef(out.mod)[charmatch(tr,names(coef(out.mod)))] | |
| coef.mat[which(tr==treats),2:3] <- confint(out.mod)[charmatch(tr,names(coef(out.mod))),] | |
| } | |
| coef.df <- as.data.frame(coef.mat) | |
| coef.df$config <- treats | |
| coef.df$Basis <- "Basis: Rank-based" | |
| coef.df$Basis[grepl("List",coef.df$config)] <- "Basis: Listing-based" | |
| coef.df$Aggregation <- ifelse(grepl("Max",coef.df$config),"Aggregation: Maximum","Aggregation: Average") | |
| coef.df$Temporal <- "Temporal: Same year" | |
| coef.df$Temporal[grepl("AllTime",coef.df$config)] <- "Temporal: All-time" | |
| coef.df$Temporal[grepl("Following",coef.df$config)] <- "Temporal: Following year" | |
| coef.df$Temporal[grepl("Preceding",coef.df$config)] <- "Temporal: Preceding year" | |
| ggplot(coef.df,aes(x=Temporal,y=V1,ymin=V2,ymax=V3)) + geom_pointrange() + | |
| scale_y_continuous("Coefficient value") + | |
| facet_grid(Aggregation~Basis) + | |
| geom_hline(yintercept=0,col='gray',lty=2) + | |
| coord_flip() + | |
| theme_classic() | |
| ################################################### | |
| ### code chunk number 14: rankmods2 | |
| ################################################### | |
| sel.formula2 <- as.formula("SameYearTreatment.AvgRank ~ | |
| Govt.2 + Trust.2 + PLC.2 + Individual.2 + scale(log1p(CasesReferredInJudgment)) + HighCt + ApexCt + post2008") | |
| outcome.formula2 <- as.formula("appellant.wins ~ | |
| SameYearTreatment.AvgRank + Govt.2 + Trust.2 + PLC.2 + Individual.2 + scale(deltaLawyers) + scale(log1p(CasesReferredInJudgment)) + HighCt + ApexCt + post2008") | |
| sel.mod2 <- glm(sel.formula2,data = dat, family=binomial) | |
| outcome.mod2 <- lm(outcome.formula2,data = dat) | |
| outcome.glm2 <- glm(outcome.formula2,data = dat,family=binomial) | |
| stargazer(outcome.mod2, | |
| # outcome.glm, | |
| sel.mod2, | |
| dep.var.labels = c("Appellant wins","Treated"), | |
| title = "Regression models, ranking-based measure", | |
| label = "tab:rankregaux", | |
| font.size = "footnotesize", | |
| no.space = TRUE, | |
| covariate.labels = c("Better-ranked lawyers","Appellant: Government","Appellant: Trust","Appellant: Public limited company","Appellant: Individual or sole trader","No. appellant lawyers - no. respondent lawyers","log(Complexity (alt.))","High Court v. Court of Appeal","HoL/UKSC/PC v. Court of Appeal","Post 2008","Constant"), | |
| # notes = "Coefficient values for continuous variables are standardized", | |
| intercept.bottom = TRUE) | |
| ################################################### | |
| ### code chunk number 15: listedmods2 | |
| ################################################### | |
| sel.formula2 <- as.formula("SameYearTreatment.AvgListed ~ | |
| Govt.2 + Trust.2 + PLC.2 + Individual.2 + scale(log1p(CasesReferredInJudgment)) + HighCt + ApexCt + post2008") | |
| outcome.formula2 <- as.formula("appellant.wins ~ | |
| SameYearTreatment.AvgListed + Govt.2 + Trust.2 + PLC.2 + Individual.2 + scale(deltaLawyers) + scale(log1p(CasesReferredInJudgment)) + HighCt + ApexCt + post2008") | |
| sel.mod2 <- glm(sel.formula2,data = dat, family=binomial) | |
| outcome.mod2 <- lm(outcome.formula2,data = dat) | |
| outcome.glm2 <- glm(outcome.formula2,data = dat,family=binomial) | |
| stargazer(outcome.mod2, | |
| # outcome.glm, | |
| sel.mod2, | |
| dep.var.labels = c("Appellant wins","Treated"), | |
| title = "Regression models, listing-based measure", | |
| label = "tab:listregaux", | |
| font.size = "footnotesize", | |
| no.space = TRUE, | |
| covariate.labels = c("Better-ranked lawyers","Appellant: Government","Appellant: Trust","Appellant: Public limited company","Appellant: Individual or sole trader","No. appellant lawyers - no. respondent lawyers","log(Complexity (alt.))","High Court v. Court of Appeal","HoL/UKSC/PC v. Court of Appeal","Post 2008","Constant"), | |
| # notes = "Coefficient values for continuous variables are standardized", | |
| intercept.bottom = TRUE) | |
| ################################################### | |
| ### code chunk number 16: altsensitivity | |
| ################################################### | |
| dat$Complexity <- scale(log1p(dat$CasesReferredInJudgment)) | |
| dat$deltaLawyers.scaled <- scale(dat$deltaLawyers) | |
| tr <- "SameYearTreatment.AvgRank" | |
| dat[,tr] <- as.numeric(dat[,tr]) | |
| sel.formula <- as.formula( | |
| paste(tr,"~Govt.2 + Trust.2 + PLC.2 + Individual.2 + Complexity + HighCt + ApexCt + post2008", | |
| sep=" ")) | |
| outcome.formula <- as.formula( | |
| paste("appellant.wins~",tr,"+Govt.2 + Trust.2 + PLC.2 + Individual.2 + deltaLawyers.scaled + Complexity + HighCt + ApexCt + post2008", | |
| sep=" ")) | |
| sel.mod <- glm(sel.formula,data = dat, family=binomial) | |
| out.mod <- lm(outcome.formula,data = dat) | |
| alpha <- seq(max(abs(coef(out.mod)[-1])) * -1.5, max(abs(coef(out.mod)[-1])) * 1.5, length.out = 101) | |
| rank.one.side.sens <- causalsens(out.mod, sel.mod, ~ Govt.2 + Trust.2 + PLC.2 + Individual.2 + deltaLawyers.scaled + Complexity + HighCt + ApexCt + post2008, data = dat, | |
| alpha = alpha, confound = one.sided) | |
| tr <- "SameYearTreatment.AvgListed" | |
| dat[,tr] <- as.numeric(dat[,tr]) | |
| sel.formula <- as.formula( | |
| paste(tr,"~Govt.2 + Trust.2 + PLC.2 + Individual.2 + Complexity + HighCt + ApexCt + post2008", | |
| sep=" ")) | |
| outcome.formula <- as.formula( | |
| paste("appellant.wins~",tr,"+Govt.2 + Trust.2 + PLC.2 + Individual.2 + deltaLawyers.scaled + Complexity + HighCt + ApexCt + post2008", | |
| sep=" ")) | |
| sel.mod2 <- glm(sel.formula,data = dat, family=binomial) | |
| out.mod2 <- lm(outcome.formula,data = dat) | |
| alpha <- seq(max(abs(coef(out.mod2)[-1])) * -1.5, max(abs(coef(out.mod2)[-1])) * 1.5, length.out = 101) | |
| list.one.side.sens <- causalsens(out.mod2, sel.mod2, | |
| ~ Govt.2 + Trust.2 + PLC.2 + Individual.2 + deltaLawyers.scaled + Complexity + HighCt + ApexCt + post2008, | |
| data = dat, | |
| alpha = alpha, confound = one.sided) | |
| lowest.rank <- alpha[max(which(rank.one.side.sens$sens$lower>0))] | |
| lowest.list <- alpha[max(which(list.one.side.sens$sens$lower>0))] | |
| par(mfrow=c(2,2),mar=c(3,3,2,1), mgp=c(2,.7,0), tck=-.01) | |
| plot(rank.one.side.sens, type = "raw",main = "Rank-based measure", xlab = expression(paste("Amount of confounding ", alpha))) | |
| arrows(x0=lowest.rank,x1=lowest.rank,y0=-0.15,y1=0,length=0.05) | |
| text(x=lowest.rank,y=-0.2,labels="Rank-based\n measure\nbecomes\nsignificant",cex=.75) | |
| points(x= abs(coef(out.mod)[-1]), | |
| y = rep(0,length(coef(out.mod))-1), | |
| pch = 4) | |
| points(x= abs(coef(out.mod)[-1]) * -1, | |
| y = rep(0,length(coef(out.mod))-1), | |
| pch = 4) | |
| plot(list.one.side.sens, type = "raw",main = "Listing-based measure", xlab = expression(paste("Amount of confounding ", alpha))) | |
| points(x= abs(coef(out.mod2)[-1]), | |
| y = rep(0,length(coef(out.mod))-1), | |
| pch = 4) | |
| points(x= abs(coef(out.mod2)[-1]) * -1, | |
| y = rep(0,length(coef(out.mod))-1), | |
| pch = 4) | |
| plot(rank.one.side.sens, type = "r.squared",main = "Rank-based measure") | |
| plot(list.one.side.sens, type = "r.squared",main = "Listing-based measure") | |
| ################################################### | |
| ### code chunk number 17: rankmodshighct | |
| ################################################### | |
| sel.formula <- as.formula("SameYearTreatment.AvgRank ~ | |
| Govt.2 + Trust.2 + PLC.2 + Individual.2 + scale(log(words)) + ApexCt + post2008") | |
| outcome.formula <- as.formula("appellant.wins ~ | |
| SameYearTreatment.AvgRank + Govt.2 + Trust.2 + PLC.2 + Individual.2 + scale(deltaLawyers) + scale(log(words)) + ApexCt + post2008") | |
| sel.mod <- glm(sel.formula,data = subset(dat,HighCt==FALSE), family=binomial) | |
| outcome.mod <- lm(outcome.formula,data = subset(dat,HighCt==FALSE)) | |
| outcome.glm <- glm(outcome.formula,data = subset(dat,HighCt==FALSE),family=binomial) | |
| stargazer(outcome.mod, | |
| sel.mod, | |
| dep.var.labels = c("Appellant wins","Treated"), | |
| title = "Regression models, rank-based measure, Court of Appeal and Lords only", | |
| label = "tab:highrankreg", | |
| font.size = "footnotesize", | |
| no.space = TRUE, | |
| covariate.labels = c("Better-ranked lawyers","Appellant: Government","Appellant: Trust","Appellant: Public limited company","Appellant: Individual or sole trader","No. appellant lawyers - no. respondent lawyers","log(Complexity)","HoL/UKSC/PC v. Court of Appeal","Post 2008","Constant"), | |
| intercept.bottom = TRUE) | |
| ################################################### | |
| ### code chunk number 18: listedmodshighct | |
| ################################################### | |
| sel.formula <- as.formula("SameYearTreatment.AvgListed ~ | |
| Govt.2 + Trust.2 + PLC.2 + Individual.2 + scale(log(words)) + ApexCt + post2008") | |
| outcome.formula <- as.formula("appellant.wins ~ | |
| SameYearTreatment.AvgListed + Govt.2 + Trust.2 + PLC.2 + Individual.2 + scale(deltaLawyers) + scale(log(words)) + ApexCt + post2008") | |
| sel.mod <- glm(sel.formula,data = subset(dat,HighCt==FALSE), family=binomial) | |
| outcome.mod <- lm(outcome.formula,data = subset(dat,HighCt==FALSE)) | |
| outcome.glm <- glm(outcome.formula,data = subset(dat,HighCt==FALSE),family=binomial) | |
| stargazer(outcome.mod, | |
| sel.mod, | |
| dep.var.labels = c("Appellant wins","Treated"), | |
| title = "Regression models, listing-based measure, Court of Appeal and Lords only", | |
| label = "tab:highlistreg", | |
| font.size = "footnotesize", | |
| no.space = TRUE, | |
| covariate.labels = c("Better-ranked lawyers","Appellant: Government","Appellant: Trust","Appellant: Public limited company","Appellant: Individual or sole trader","No. appellant lawyers - no. respondent lawyers","log(Complexity)","HoL/UKSC/PC v. Court of Appeal","Post 2008","Constant"), | |
| intercept.bottom = TRUE) |
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