x = d1$edyrs
y = d1$log.norm.pay
plot(x, y)
# regression
x = d1$edyrs
y = d1$log.norm.pay
r = lm(y ~ x)
r.square = summary(r)$r.squared
x = seq(10, 30, length.out = 50)
x = data.frame(x)
regression = predict(r, x,  interval = "prediction", level = 0.95)
regression = data.frame(x = x, exp(regression))
intensity.r2 =  paste("R^2 == ", round(r.square,2))
edu.plot = ggplot() +
geom_ribbon(data = regression, aes( x = x, ymin = lwr, ymax = upr), fill = "black", alpha = 0.05) +
geom_line(data = regression, aes(x = x, y = fit), col = "grey50") +
geom_jitter(data = d1, aes(x = edyrs, y= exp(log.norm.pay)), size = 0.3, col = "dodgerblue4") +
scale_x_continuous("Years of Education", breaks = seq(0, 40,2)) +
scale_y_log10("Normalized Pay", breaks = c(0.2, 0.5, 1, 2, 5, 10)) +
ggtitle("A.  Education") +
theme_bw() +
theme(panel.border = element_rect(color = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "none",
plot.title = element_text(face="bold", size = rel(1), hjust = 0.5, vjust = -0.2),
axis.line = element_line(color = "black"),
axis.title.x=element_text(size=rel(0.9)),
axis.title.y=element_text(vjust= 1.1, size=rel(0.9)),
axis.text.x = element_text(margin=margin(5,5,0,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size = text.size, family="Times"))
edu.plot
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
library(ggplot2)
library(gridExtra)
library(here)
library(data.table)
library(grid)
text.size = 10
dir = here()
wd = gsub("Figures", "Data/Case Studies/BGH", dir)
setwd(wd)
d1 = fread("BGH_human_capital.csv")
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_rank.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_rank.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_rank.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_rank.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_rank.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_rank.R')
library(data.table)
library(magrittr)
library(ppcor)
d = fread("BGH.csv", stringsAsFactors = F)
d$Year = paste("19", d$year, sep = "")
d$Year = as.numeric(d$Year)
d$level = as.numeric(d$level)
# remove BGH outlier years
remove = c(1986, 1987, 1988)
d = subset(d, !Year %in% remove)
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_rank.R')
wd = gsub("Figures", "Data/Hunter", dir)
library(ggplot2)
library(gridExtra)
library(here)
library(data.table)
library(grid)
library(ineq)
text.size = 10
dir = here()
# BGH rank income inequality
wd = gsub("Figures", "Data/Case Studies", dir)
setwd(wd)
d = fread("Case Results.csv")
d = d[Source == "BGH"]
rank.gini = d[, ineq(Mean), by = .(Year, Source)]
# Hunter data
wd = gsub("Figures", "Data/Hunter", dir)
setwd(wd)
sigma = function(x){sqrt(log((x/100)^2 +1))}
g = function(x){ Re(erf(x/2))}
wd = gsub("Figures", "Data/Hunter", dir)
setwd(wd)
sigma = function(x){sqrt(log((x/100)^2 +1))}
g = function(x){ Re(erf(x/2))}
hunter =   read.csv("Hunter_Data.csv")
hunter$Gini = g(hunter$CV)
m.productivity = paste("Mean = ",   sprintf("%.2f", round(mean(hunter$Gini),2)) )
prod.dens = density(hunter$Gini)
prod.dens = data.frame(x = prod.dens$x, ymin = 0, ymax =  prod.dens$y)
hunter =   read.csv("Hunter_Data.csv")
hunter$Gini = g(hunter$CV)
library(extremevalues)
wd = gsub("Figures", "Data/Hunter", dir)
setwd(wd)
sigma = function(x){sqrt(log((x/100)^2 +1))}
g = function(x){ Re(erf(x/2))}
hunter =   read.csv("Hunter_Data.csv")
hunter$Gini = g(hunter$CV)
library(NORMT3)
sigma = function(x){sqrt(log((x/100)^2 +1))}
g = function(x){ Re(erf(x/2))}
hunter =   read.csv("Hunter_Data.csv")
hunter$Gini = g(hunter$CV)
m.productivity = paste("Mean = ",   sprintf("%.2f", round(mean(hunter$Gini),2)) )
prod.dens = density(hunter$Gini)
prod.dens = data.frame(x = prod.dens$x, ymin = 0, ymax =  prod.dens$y)
View(prod.dens)
m.rank = paste("Mean = ",   sprintf("%.2f", round(mean(rank.gini$V1),2)) )
prod.dens = density(rank.gini$V1)
m.rank = paste("Mean = ",   sprintf("%.2f", round(mean(rank.gini$V1),2)) )
rank.dens = density(rank.gini$V1)
rank.dens = data.frame(x = prod.dens$x, ymin = 0, ymax =  prod.dens$y)
library(ggplot2)
library(gridExtra)
library(here)
library(data.table)
library(grid)
library(ineq)
library(NORMT3)
text.size = 10
dir = here()
# BGH rank income inequality
wd = gsub("Figures", "Data/Case Studies", dir)
setwd(wd)
d = fread("Case Results.csv")
d = d[Source == "BGH"]
rank.gini = d[, ineq(Mean), by = .(Year, Source)]
m.rank = paste("Mean = ",   sprintf("%.2f", round(mean(rank.gini$V1),2)) )
rank.dens = density(rank.gini$V1)
rank.dens = data.frame(x = prod.dens$x, ymin = 0, ymax =  prod.dens$y)
# Hunter data
##########################################
wd = gsub("Figures", "Data/Hunter", dir)
setwd(wd)
sigma = function(x){sqrt(log((x/100)^2 +1))}
g = function(x){ Re(erf(x/2))}
hunter =   read.csv("Hunter_Data.csv")
hunter$Gini = g(hunter$CV)
m.productivity = paste("Mean = ",   sprintf("%.2f", round(mean(hunter$Gini),2)) )
prod.dens = density(hunter$Gini)
prod.dens = data.frame(x = prod.dens$x, ymin = 0, ymax =  prod.dens$y)
#plot
####################################################
productivity = ggplot() +
geom_ribbon(data = prod.dens, aes(x = x, ymin = ymin, ymax = ymax), alpha = 0.5, fill = "red", col = "black" ) +
geom_ribbon(data = rank.dens, aes(x = x, ymin = ymin, ymax = ymax), alpha = 0.5, fill = "red", col = "black" ) +
geom_segment(aes( x = mean(hunter$Gini), y = 0, xend = mean(hunter$Gini), yend = m.prod.y ),
col = "red", linetype = "dashed") +
geom_segment(aes( x = mean(rank.gini$V1), y = 0, xend = mean(rank.gini$V1), yend = m.nation.y ),
col = "dodgerblue3", linetype = "dashed") +
scale_x_continuous(breaks = seq(0, 1, 0.1)) +
scale_y_continuous(breaks = seq(0, 20, 2)) +
labs(x = "Gini Index", y = "Density") +
theme_bw() +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.title = element_text(face="bold", hjust = 0.5),
legend.box.just = "left",
legend.position = c(0.6, 0.75),
legend.key.size = unit(0.35, "cm"),
legend.key.height = unit(0.5, "cm"),
legend.key.width = unit(0.5, "cm"),
legend.text = element_text(size = rel(0.7)),
legend.title = element_blank(),
axis.line = element_line(color = "black"),
axis.title.x=element_text(vjust=-0.3, size=rel(0.9)),
axis.title.y=element_text(vjust= 1.1, size=rel(0.9)),
axis.text.x = element_text(margin=margin(5,5,0,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size=9, family="Times")) +
annotate("text", x = 0.47, y = 1, label = m.nation,  family = "Times", size = 2 ) +
annotate("text", x = 0.5, y = 3.8,
label = "Income Inequality \nWithin Nation-States", family = "Times", size = 2.5 ) +
annotate("text", x = mean(hunter$Gini) , y = 13, label = m.productivity, family = "Times", size = 2 ) +
annotate("text", x = 0.26, y = 10,
label = "Productivity Inequality Among \nWorkers Doing the Same Task", family = "Times", size = 2.5 )
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
productivity = ggplot() +
geom_ribbon(data = prod.dens, aes(x = x, ymin = ymin, ymax = ymax), alpha = 0.5, fill = "red", col = "black" ) +
geom_ribbon(data = rank.dens, aes(x = x, ymin = ymin, ymax = ymax), alpha = 0.5, fill = "red", col = "black" ) +
geom_segment(aes( x = mean(hunter$Gini), y = 0, xend = mean(hunter$Gini), yend = m.prod.y ),
col = "red", linetype = "dashed") +
geom_segment(aes( x = mean(rank.gini$V1), y = 0, xend = mean(rank.gini$V1), yend = m.nation.y ),
col = "dodgerblue3", linetype = "dashed")
library(ggplot2)
library(gridExtra)
library(here)
library(data.table)
library(grid)
library(ineq)
library(NORMT3)
text.size = 10
dir = here()
# BGH rank income inequality
wd = gsub("Figures", "Data/Case Studies", dir)
setwd(wd)
d = fread("Case Results.csv")
d = d[Source == "BGH"]
rank.gini = d[, ineq(Mean), by = .(Year, Source)]
m.rank = paste("Mean = ",   sprintf("%.2f", round(mean(rank.gini$V1),2)) )
rank.dens = density(rank.gini$V1)
rank.dens = data.frame(x = prod.dens$x, ymin = 0, ymax =  prod.dens$y)
# Hunter data
##########################################
wd = gsub("Figures", "Data/Hunter", dir)
setwd(wd)
sigma = function(x){sqrt(log((x/100)^2 +1))}
g = function(x){ Re(erf(x/2))}
hunter =   read.csv("Hunter_Data.csv")
hunter$Gini = g(hunter$CV)
m.productivity = paste("Mean = ",   sprintf("%.2f", round(mean(hunter$Gini),2)) )
prod.dens = density(hunter$Gini)
prod.dens = data.frame(x = prod.dens$x, ymin = 0, ymax =  prod.dens$y)
library(ggplot2)
library(gridExtra)
library(here)
library(data.table)
library(grid)
library(ineq)
library(NORMT3)
text.size = 10
dir = here()
# BGH rank income inequality
wd = gsub("Figures", "Data/Case Studies", dir)
setwd(wd)
d = fread("Case Results.csv")
d = d[Source == "BGH"]
rank.gini = d[, ineq(Mean), by = .(Year, Source)]
m.rank = paste("Mean = ",   sprintf("%.2f", round(mean(rank.gini$V1),2)) )
rank.dens = density(rank.gini$V1)
rank.dens = data.frame(x = rank.dens$x, ymin = 0, ymax =  rank.dens$y)
# Hunter data
##########################################
wd = gsub("Figures", "Data/Hunter", dir)
setwd(wd)
sigma = function(x){sqrt(log((x/100)^2 +1))}
g = function(x){ Re(erf(x/2))}
hunter =   read.csv("Hunter_Data.csv")
hunter$Gini = g(hunter$CV)
m.productivity = paste("Mean = ",   sprintf("%.2f", round(mean(hunter$Gini),2)) )
prod.dens = density(hunter$Gini)
prod.dens = data.frame(x = prod.dens$x, ymin = 0, ymax =  prod.dens$y)
productivity = ggplot() +
geom_ribbon(data = prod.dens, aes(x = x, ymin = ymin, ymax = ymax), alpha = 0.5, fill = "red", col = "black" ) +
geom_ribbon(data = rank.dens, aes(x = x, ymin = ymin, ymax = ymax), alpha = 0.5, fill = "red", col = "black" ) +
geom_segment(aes( x = mean(hunter$Gini), y = 0, xend = mean(hunter$Gini), yend = m.prod.y ),
col = "red", linetype = "dashed") +
geom_segment(aes( x = mean(rank.gini$V1), y = 0, xend = mean(rank.gini$V1), yend = m.nation.y ),
col = "dodgerblue3", linetype = "dashed")
productivity
m.prod.y =  hunter$Gini %>% mean %>% (hunter$Gini %>% density %>% approxfun)
library(magrittr)
m.rank.y =  gini.nation$Gini %>% mean %>% (gini.nation$Gini %>% density %>% approxfun)
m.rank = paste("Mean = ",   sprintf("%.2f", round(mean(rank.gini$V1),2)) )
rank.dens = density(rank.gini$V1)
rank.dens = data.frame(x = rank.dens$x, ymin = 0, ymax =  rank.dens$y)
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
m.rank.y =  rank.gini$V1 %>% mean %>% (gini.nation$Gini %>% density %>% approxfun)
m.rank.y =  rank.gini$V1 %>% mean %>% (rank.gini$V1%>% density %>% approxfun)
m.rank = paste("Mean = ",   sprintf("%.2f", round(mean(rank.gini$V1),2)) )
rank.dens = density(rank.gini$V1)
rank.dens = data.frame(x = rank.dens$x, ymin = 0, ymax =  rank.dens$y)
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
View(rank.dens)
productivity = ggplot() +
geom_ribbon(data = prod.dens, aes(x = x, ymin = ymin, ymax = ymax), alpha = 0.5, fill = "red", col = "black" ) +
geom_ribbon(data = rank.dens, aes(x = x, ymin = ymin, ymax = ymax), alpha = 0.5, fill = "red", col = "black" ) +
geom_segment(aes( x = mean(hunter$Gini), y = 0, xend = mean(hunter$Gini), yend = m.prod.y ),
col = "red", linetype = "dashed") +
geom_segment(aes( x = mean(rank.gini$V1), y = 0, xend = mean(rank.gini$V1), yend = m.nation.y ),
col = "dodgerblue3", linetype = "dashed")
productivity
productivity = ggplot() +
geom_ribbon(data = prod.dens, aes(x = x, ymin = ymin, ymax = ymax), alpha = 0.5, fill = "red", col = "black" ) +
geom_ribbon(data = rank.dens, aes(x = x, ymin = ymin, ymax = ymax), alpha = 0.5, fill = "red", col = "black" ) +
geom_segment(aes( x = mean(hunter$Gini), y = 0, xend = mean(hunter$Gini), yend = m.prod.y ),
col = "red", linetype = "dashed") +
geom_segment(aes( x = mean(rank.gini$V1), y = 0, xend = mean(rank.gini$V1), yend = m.rank.y ),
col = "dodgerblue3", linetype = "dashed")
productivity
productivity = ggplot() +
geom_ribbon(data = prod.dens, aes(x = x, ymin = ymin, ymax = ymax), alpha = 0.5, fill = "red", col = "black" ) +
geom_ribbon(data = rank.dens, aes(x = x, ymin = ymin, ymax = ymax), alpha = 0.5, fill = "blue", col = "black" ) +
geom_segment(aes( x = mean(hunter$Gini), y = 0, xend = mean(hunter$Gini), yend = m.prod.y ),
col = "red", linetype = "dashed") +
geom_segment(aes( x = mean(rank.gini$V1), y = 0, xend = mean(rank.gini$V1), yend = m.rank.y ),
col = "dodgerblue3", linetype = "dashed")
scale_x_continuous(breaks = seq(0, 1, 0.1)) +
scale_y_continuous(breaks = seq(0, 20, 2)) +
labs(x = "Gini Index", y = "Density") +
theme_bw() +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.title = element_text(face="bold", hjust = 0.5),
legend.box.just = "left",
legend.position = c(0.6, 0.75),
legend.key.size = unit(0.35, "cm"),
legend.key.height = unit(0.5, "cm"),
legend.key.width = unit(0.5, "cm"),
legend.text = element_text(size = rel(0.7)),
legend.title = element_blank(),
axis.line = element_line(color = "black"),
axis.title.x=element_text(vjust=-0.3, size=rel(0.9)),
axis.title.y=element_text(vjust= 1.1, size=rel(0.9)),
axis.text.x = element_text(margin=margin(5,5,0,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size=9, family="Times")) +
annotate("text", x = 0.47, y = 1, label = m.rank,  family = "Times", size = 2 ) +
annotate("text", x = 0.5, y = 3.8,
label = "Income Inequality \nWithin Nation-States", family = "Times", size = 2.5 ) +
annotate("text", x = mean(hunter$Gini) , y = 13, label = m.productivity, family = "Times", size = 2 ) +
annotate("text", x = 0.26, y = 10,
label = "Productivity Inequality Among \nWorkers Doing the Same Task", family = "Times", size = 2.5 )
productivity
#plot
####################################################
productivity = ggplot() +
geom_ribbon(data = prod.dens, aes(x = x, ymin = ymin, ymax = ymax), alpha = 0.5, fill = "red", col = "black" ) +
geom_ribbon(data = rank.dens, aes(x = x, ymin = ymin, ymax = ymax), alpha = 0.5, fill = "dodgerblue4", col = "black" ) +
geom_segment(aes( x = mean(hunter$Gini), y = 0, xend = mean(hunter$Gini), yend = m.prod.y ),
col = "red", linetype = "dashed") +
geom_segment(aes( x = mean(rank.gini$V1), y = 0, xend = mean(rank.gini$V1), yend = m.rank.y ),
col = "dodgerblue3", linetype = "dashed")
scale_x_continuous(breaks = seq(0, 1, 0.1)) +
scale_y_continuous(breaks = seq(0, 20, 2)) +
labs(x = "Gini Index", y = "Density") +
theme_bw() +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.title = element_text(face="bold", hjust = 0.5),
legend.box.just = "left",
legend.position = c(0.6, 0.75),
legend.key.size = unit(0.35, "cm"),
legend.key.height = unit(0.5, "cm"),
legend.key.width = unit(0.5, "cm"),
legend.text = element_text(size = rel(0.7)),
legend.title = element_blank(),
axis.line = element_line(color = "black"),
axis.title.x=element_text(vjust=-0.3, size=rel(0.9)),
axis.title.y=element_text(vjust= 1.1, size=rel(0.9)),
axis.text.x = element_text(margin=margin(5,5,0,0,"pt")),
axis.text.y = element_text(margin=margin(3,5,0,3,"pt")),
axis.ticks.length = unit(-0.7, "mm"),
text=element_text(size=9, family="Times")) +
annotate("text", x = 0.47, y = 1, label = m.rank,  family = "Times", size = 2 ) +
annotate("text", x = 0.5, y = 3.8,
label = "Income Inequality \nWithin Nation-States", family = "Times", size = 2.5 ) +
annotate("text", x = mean(hunter$Gini) , y = 13, label = m.productivity, family = "Times", size = 2 ) +
annotate("text", x = 0.26, y = 10,
label = "Productivity Inequality Among \nWorkers Doing the Same Task", family = "Times", size = 2.5 )
productivity
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
productivity
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
library(ineq)
library(NORMT3)
library(ggplot2)
library(grid)
library(readr)
library(dplyr)
library(data.table)
library(here)
text.size = 10
# Hunter data
##########################################
wd = gsub("Figures", "Data/Hunter", dir)
setwd(wd)
sigma = function(x){sqrt(log((x/100)^2 +1))}
g = function(x){ Re(erf(x/2))}
hunter =   read.csv("Hunter_Data.csv")
hunter$Gini = g(hunter$CV)
m.prod.y =  hunter$Gini %>% mean %>% (hunter$Gini %>% density %>% approxfun)
prod.dens = density(hunter$Gini)
prod.dens = data.frame(x = prod.dens$x, ymin = 0, ymax =  prod.dens$y)
m.productivity = paste("Mean = ",   sprintf("%.2f", round(mean(hunter$Gini),2)) )
wd = gsub("Figures", "Data/world bank", dir)
wd = gsub("Figures", "Data/world bank", dir)
library(ineq)
library(NORMT3)
library(ggplot2)
library(grid)
library(readr)
library(dplyr)
library(data.table)
library(here)
text.size = 10
# Hunter data
##########################################
wd = gsub("Figures", "Data/Hunter", dir)
setwd(wd)
sigma = function(x){sqrt(log((x/100)^2 +1))}
g = function(x){ Re(erf(x/2))}
hunter =   read.csv("Hunter_Data.csv")
hunter$Gini = g(hunter$CV)
m.prod.y =  hunter$Gini %>% mean %>% (hunter$Gini %>% density %>% approxfun)
prod.dens = density(hunter$Gini)
prod.dens = data.frame(x = prod.dens$x, ymin = 0, ymax =  prod.dens$y)
m.productivity = paste("Mean = ",   sprintf("%.2f", round(mean(hunter$Gini),2)) )
wd = gsub("Figures", "Data/Hunter", dir)
dir = here()
wd = gsub("Figures", "Data/Hunter", dir)
setwd(wd)
sigma = function(x){sqrt(log((x/100)^2 +1))}
g = function(x){ Re(erf(x/2))}
hunter =   read.csv("Hunter_Data.csv")
hunter$Gini = g(hunter$CV)
m.prod.y =  hunter$Gini %>% mean %>% (hunter$Gini %>% density %>% approxfun)
prod.dens = density(hunter$Gini)
prod.dens = data.frame(x = prod.dens$x, ymin = 0, ymax =  prod.dens$y)
m.productivity = paste("Mean = ",   sprintf("%.2f", round(mean(hunter$Gini),2)) )
wd = gsub("Figures", "Data/world bank", dir)
setwd(wd)
gini.nation = fread("data_format.csv")
gini.nation = gini.nation$SI.POV.GINI %>% na.omit()
gini.nation= gini.nation/100
m.nation.y =  gini.nation %>% mean %>% (gini.nation %>% density %>% approxfun)
m.nation = paste("Mean = ",  sprintf("%.2f", round(mean(gini.nation),2)))
gini.nation = fread("data_format.csv")
gini.nation = gini.nation$SI.POV.GINI %>% na.omit()
gini.nation = fread("data_format.csv")
gini.nation = gini.nation$SI.POV.GINI %>% na.omit()
gini.nation = data.table(Gini = gini.nation)
m.nation.y =  gini.nation$Gini %>% mean %>% (gini.nation$Gini %>% density %>% approxfun)
m.nation = paste("Mean = ",  sprintf("%.2f", round(mean(gini.nation$Gini),2)))
source('~/Desktop/human_capital/Supplementary Material/Figures/Productivity.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Productivity.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Productivity.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Productivity.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Productivity.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Productivity.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Productivity.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Productivity.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Productivity.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Productivity.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Productivity.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Productivity.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
m.rank.y =  rank.gini$V1 %>% mean %>% (rank.gini$V1%>% density(., adjust = 2) %>% approxfun)
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Rank_gini.R')
dir = here()
wd = gsub("Figures", "Data/Case Studies/Number of Subordinates", dir)
dir = here()
wd = gsub("Figures", "Data/Case Studies/Number of Subordinates", dir)
setwd(wd)
result = read_csv("Subordinates Results.csv")
result = fread("Subordinates Results.csv")
source('~/Desktop/human_capital/Supplementary Material/Figures/Income v Subordinates.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Income v Subordinates.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Income v Subordinates.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Income v Subordinates.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Income v Subordinates.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Income v Subordinates.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Income v Subordinates.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Income v Subordinates.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Income v Subordinates.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Income v Subordinates.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Income v Subordinates.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Income v Subordinates.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/Income v Subordinates.R')
source('~/Desktop/human_capital/Supplementary Material/Figures/BGH_human_capital.R')
