Capital in the 21st Century: Chapter 9

Data provenance

The data were downloaded as Excel files from: http://piketty.pse.ens.fr/en/capital21c2.

Loading relevant libraries and data

This document depends on the xlsx, reshape2, and ggplot2 packages.

library(ggplot2)
library(xlsx)
library(reshape2)

Figures 9.1 and Supplementary Figures

These figures are based on Table TS9.1. Here we read
data from the excel file and name the data series

## Table TS9.1
ts91 = read.xlsx("../_data/Chapter9TablesFigures.xlsx",sheetName="TS9.1",rowIndex=7:70,colIndex=1:8,header=FALSE)
names(ts91) = c("year","france_francs_cur","france_euros_cur", "france_euros_2013", "france_cpi", "us_dollars_cur", "us_dollars_2013", "us_cpi")

The caption of the table lists the sources for this table
as

Sources. France: file “IPP-prelevements-sociaux-avril2012.xls”
available on www.ipp.eu (we selected the values corresponding to January the 1st of each year; the
complete revaluation series are given in the IPP table)
USA: official series of Bureau of Labor Statistics (we
selected the values corresponding to January the 1st of
each year; the complete revaluation series are given in
the BLS file)
(consumer price index for France et US from Piketty-Zucman 2013,
files France.xls et USA.xls; links frozen on 2-20-13)

Now we make Figure F9.1

f91dat <- ts91[,c("year","france_euros_2013", "us_dollars_2013")]
names(f91dat) <- c("year","France","USA")
 
# ggplot2 doesn't support plots with 2 y axes 
# so it only shows wages in Euros
 
# scale to trick the two axes plot
f91dat$USA <- f91dat$USA / 1.2
f91dat <- melt(f91dat, id.var="year")
names(f91dat) <- c("year","country","wage")
 
plt <- ggplot(data=f91dat,aes(x=year, y=wage,group=country)) 
plt <- plt + geom_line() + geom_point(aes(shape=country))
plt <- plt + scale_y_continuous(breaks=seq(0,10), limits=c(0,10), name="Hourly minimum wage") 
plt <- plt + scale_x_continuous(breaks=seq(1950,2015,by=5), name="")
plt <- plt + ggtitle("Figure 9.1. Minimum wage in France and the U.S., 1950-2013")
plt

plot of chunk figF9.1

And Figures S9.1 and S9.2

fs91dat <- ts91[,c("year","france_euros_2013", "france_euros_cur")]
names(fs91dat) <- c("year", "2013 euros", "current euros")
fs91dat <- melt(fs91dat, id.var="year")
names(fs91dat) <- c("year", "currency", "wage")
 
plt <- ggplot(data=fs91dat, aes(x=year,y=wage,group=currency))
plt <- plt + geom_line() + geom_point(aes(shape=currency))
plt <- plt + scale_y_continuous(name="Hourly minimum wage", limits=c(0,10), breaks=seq(0,10,by=1))
plt <- plt + scale_x_continuous(name="", limits=c(1950,2013), breaks=seq(1950,2015,by=5))
plt <- plt + ggtitle("Figure S9.1. Minimum wage in France, 1950-2013")
 
plt

plot of chunk figs9.1

fs92dat <- ts91[,c("year","us_dollars_2013", "us_dollars_cur")]
names(fs92dat) <- c("year", "2013 dollars", "current dollars")
fs92dat <- melt(fs92dat, id.var="year")
names(fs92dat) <- c("year", "currency", "wage")
 
plt <- ggplot(data=fs92dat, aes(x=year,y=wage,group=currency))
plt <- plt + geom_line() + geom_point(aes(shape=currency))
plt <- plt + scale_y_continuous(name="Hourly minimum wage", limits=c(0,10), breaks=seq(0,10,by=1))
plt <- plt + scale_x_continuous(name="", limits=c(1950,2013), breaks=seq(1950,2015,by=5))
plt <- plt + ggtitle("Figure S9.2. Minimum wage in USA, 1950-2013")
 
plt

## Warning: Removed 1 rows containing missing values (geom_point).

plot of chunk figs9.2

Table S8.1 (copied)

ts81 <- read.xlsx("../_data//Chapter9TablesFigures.xlsx", sheetName="CopyTS8.1", colIndex=1:6, rowIndex=6:116, header=FALSE)
names(ts81) <- c("year", "fr_10.0", "fr_1.0", "fr_0.1",
                 "fr_wage_10.0", "fr_wage_1.0")

Caption:

Top income shares series based upon WTID series;
missing values interpolated using moving averages and
top 5% and top 1% series (see formulas and “Details”
sheet)

Top wage series: Piketty 2001 (figure 3.2) (missing
values for 1910-1918, 1939-1946, and 1999-2010
interpolated using income series and composition
series, and series from Landais 2007 and Godechot 2012
)

copied from TS8.1 (links frozen on 2-25-2013)

Table S8.2 (copied)

ts82 <- read.xlsx("../_data/Chapter9TablesFigures.xlsx", sheetName="CopyTS8.2", colIndex=1:11, rowIndex=6:116, header=FALSE)
names(ts82) <- c("year", "us_10.0", "us_10.0-5.0","us_5.0",
                 "us_1.0", "us_0.1", "us_10.0_noCapGains", "us_1.0_noCapGains", "us_0.1_noCapGains", "us_wageShare_10.0", "us_wageShare_1.0")

The caption of the table lists the sources for this table as

Top income shares series based upon WTID series;
missing values interpolated using moving averages and
top 5% and top 1% series (see formulas and “Details”
sheet)

Top wage series: Piketty-Saez 2003 (Table B2, updated
2012) (missing values for 1913-1926 interpolated using
income series and composition series (see also series
on officers compensation 1917-1926 referred to by
Piketty-Saez 2003 pp.29-30

copied from TS8.2 (links frozen on 2-25-2013)

Table S9.2

ts92 <- read.xlsx("../_data/Chapter9TablesFigures.xlsx",sheetName="TS9.2",rowIndex=6:116,colIndex=1:12,header=FALSE)
names(ts92) = c("year","royaume_10.0","royaume_1.0", "royaume_0.1", "germany_10.0", "germany_1.0", "germany_0.1", "sweden_10.0", "sweden_1.0", "sweden_0.1", "japan_1.0", "japan_0.1")

The caption of the table lists the sources for this table as

Top income shares series based upon WTID series;
missing values interpolated using moving averages and
top 5% and top 1% series (see formulas and “Details”
sheet)
copied from DetailsTS9.2 (links frozen on 2-25-2013)

The DetailsTS9.2 table is in Chapter 8.

Table S9.3

ts93 <- read.xlsx("../_data//Chapter9TablesFigures.xlsx", sheetName="TS9.3", rowIndex=6:116,
                  colIndex=1:15,header=FALSE)
names(ts93) <- c("year", "canada_1.0", "canada_0.1",
                 "aus_1.0","aus_0.1", "nzl_1.0", "nzl_0.1",
                 "den_1.0","den_0.1","ita_1.0","ita_0.1",
                 "hol_1.0","hol_0.1","spa_1.0","spa_0.1")

Caption from spreadsheet:

Top income shares series based upon WTID series;
missing values interpolated using moving averages (see
formulas and “Details” sheet)

copied from DetailsTS9.3 (links frozen on 2-25-2013)

Figure 9.2

f92dat <- data.frame(year=ts92$year,
  `U.S.`=ts82$`us_1.0_noCapGains`,
  `U.K.`=ts92$`royaume_1.0`,
  `Canada`=ts93$`canada_1.0`,
  `Australia`=ts93$`aus_1.0`
  )
f92dat <- melt(f92dat, id.var="year")
names(f92dat) <- c("year", "country", "share")
 
plt <- ggplot(data=f92dat, aes(x=year,y=share,group=country))
plt <- plt + geom_line() + geom_point(aes(shape=country))
plt <- plt + scale_y_continuous(name="Share of top percentile in total income", breaks=seq(0,24,by=2)/100,limits=c(0,24)/100)
plt <- plt + scale_x_continuous(name="", breaks=seq(1910,2010,by=10),limits=c(1910,2010))
plt <- plt + ggtitle("Figure 9.2. Income inequality in Anglo-saxon countries, 1910-2010")
plt

## Warning: Removed 62 rows containing missing values (geom_path).

## Warning: Removed 65 rows containing missing values (geom_point).

plot of chunk fig9.2

Figure 9.3

f93dat <- data.frame(year=ts92$year,
                     `France`=ts81$`fr_1.0`,
                     `Germany`=ts92$`germany_1.0`,
                     `Sweden`=ts92$`sweden_1.0`,
                     `Japan`=ts92$`japan_1.0`)
f93dat <- melt(f93dat, id.var="year")
names(f93dat) <- c("year","country","share")
 
plt <- ggplot(data=f93dat, aes(x=year, y=share, group=country))
plt <- plt + geom_line() + geom_point(aes(shape=country))
plt <- plt + scale_y_continuous(name="Share of top percentile in total income", breaks=seq(0,24,by=2)/100,
                                limits=c(0,.24))
plt <- plt + scale_x_continuous(name="", breaks=seq(1910,2010,by=10), limits=c(1910,2010))
plt <- plt + ggtitle("Figure 9.3. Income inequality: Continental Europe and Japan, 1910-2010")
plt

## Warning: Removed 41 rows containing missing values (geom_path).

## Warning: Removed 119 rows containing missing values (geom_point).

plot of chunk fig9.3

Figure 9.4

f94dat <- data.frame(year=ts92$year,
                     `France`=ts81$`fr_1.0`,
                     `Denmark`=ts93$`den_1.0`,
                     `Italy`=ts93$`ita_1.0`,
                     `Spain`=ts93$`spa_1.0`)
f94dat <- melt(f94dat, id.var="year")
names(f94dat) <- c("year", "country", "share")
 
plt <- ggplot(data=f94dat, aes(x=year, y=share, group=country))
plt <- plt + geom_line() + geom_point(aes(shape=country))
plt <- plt + scale_y_continuous(name="Share of top percentile in total income", breaks=seq(0,24,by=2)/100, limits=c(0,.24))
plt <- plt + scale_x_continuous(name="", breaks=seq(1900,2010,by=10), limits=c(1910,2010))
plt <- plt + ggtitle("Figure 9.4. Income inequality: Northern and Southern Europe, 1910-2010")
plt

## Warning: Removed 129 rows containing missing values (geom_path).

## Warning: Removed 162 rows containing missing values (geom_point).

plot of chunk fig9.4

Figure 9.5

f92dat <- data.frame(year=ts92$year,
  `U.S.`=ts82$`us_0.1_noCapGains`,
  `U.K.`=ts92$`royaume_0.1`,
  `Canada`=ts93$`canada_0.1`,
  `Australia`=ts93$`aus_0.1`
  )
f92dat <- melt(f92dat, id.var="year")
names(f92dat) <- c("year", "country", "share")
 
plt <- ggplot(data=f92dat, aes(x=year,y=share,group=country))
plt <- plt + geom_line() + geom_point(aes(shape=country))
plt <- plt + scale_y_continuous(name="Share of top 0.1% in total income", breaks=seq(0,12,by=1)/100,limits=c(0,12)/100)
plt <- plt + scale_x_continuous(name="", breaks=seq(1910,2010,by=10),limits=c(1910,2010))
plt <- plt + ggtitle("Figure 9.5. The top 0.1% income share in Anglo-saxon countries, 1910-2010")
plt

## Warning: Removed 62 rows containing missing values (geom_path).

## Warning: Removed 65 rows containing missing values (geom_point).

plot of chunk fig9.5

Figure 9.6

f93dat <- data.frame(year=ts92$year,
                     `France`=ts81$`fr_0.1`,
                     `Germany`=ts92$`germany_0.1`,
                     `Sweden`=ts92$`sweden_0.1`,
                     `Japan`=ts92$`japan_0.1`)
f93dat <- melt(f93dat, id.var="year")
names(f93dat) <- c("year","country","share")
 
plt <- ggplot(data=f93dat, aes(x=year, y=share, group=country))
plt <- plt + geom_line() + geom_point(aes(shape=country))
plt <- plt + scale_y_continuous(name="Share of top 0.1% in total income", breaks=seq(0,12,by=1)/100,
                                limits=c(0,.12))
plt <- plt + scale_x_continuous(name="", breaks=seq(1910,2010,by=10), limits=c(1910,2010))
plt <- plt + ggtitle("Figure 9.6. The top 0.1% income share: Continental Europe and Japan, 1910-2010")
plt

## Warning: Removed 41 rows containing missing values (geom_path).

## Warning: Removed 119 rows containing missing values (geom_point).

plot of chunk fig9.6