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---
title: "The Future of International Politics"
subtitle: POSC 1020 -- Introduction to International Relations
author: Steven V. Miller
institute: Department of Political Science
titlegraphic: /Dropbox/teaching/clemson-academic.png
date:
output:
beamer_presentation:
template: ~/Dropbox/miscelanea/svm-r-markdown-templates/svm-latex-beamer.tex
latex_engine: xelatex
dev: cairo_pdf
fig_caption: false
slide_level: 3
make149: true
mainfont: "Open Sans"
titlefont: "Titillium Web"
---
```{r setup, include=FALSE, cache=F, message=F, warning=F, results="hide"}
knitr::opts_chunk$set(cache=TRUE)
knitr::opts_chunk$set(fig.path='figs/')
knitr::opts_chunk$set(cache.path='cache/')
knitr::opts_chunk$set(
fig.process = function(x) {
x2 = sub('-\\d+([.][a-z]+)$', '\\1', x)
if (file.rename(x, x2)) x2 else x
}
)
```
```{r loadstuff, include=FALSE}
knitr::opts_chunk$set(cache=TRUE)
library(tidyverse)
library(lubridate)
library(stringr)
library(stevemisc)
library(scales)
library(WDI)
library(rvest)
library(ggrepel)
library(OECD)
library(forecast)
FAS <- read_csv("~/Dropbox/data/fas-nukes/number-of-nuclear-warheads-in-the-inventory-of-the-nuclear-powers-1945-2014.csv") %>%
rename(nukes = `Nuclear weapons inventory by country`)
tribble(
~Country, ~Year, ~nukes,
"China", 2014, 255, # simple interpolation from 2013 to 2015, using Bulletin data
) %>%
bind_rows(FAS, .) -> FAS
MB <- read_csv("~/Dropbox/data/military-balance/2015/mb2015.csv")
SWIID <- read_csv("~/Dropbox/data/swiid/swiid6_2/swiid6_2_summary.csv")
MB$nukes <- 0
MB$nukes[MB$country == "United States"] <- 4760
MB$nukes[MB$country == "United Kingdom"] <- 225
MB$nukes[MB$country == "Russia"] <- 4300
MB$nukes[MB$country == "Pakistan"] <- 120
MB$nukes[MB$country == "Israel"] <- 80
MB$nukes[MB$country == "India"] <- 110
MB$nukes[MB$country == "France"] <- 300
MB$nukes[MB$country == "China"] <- 250
Trade <- read_csv("~/Dropbox/data/cow/trade/Dyadic_COW_4.0.csv")
library(fredr)
library(pwt9)
data(pwt9.0)
Immig <- read_csv("~/Dropbox/teaching/posc1020/future-1/immig-policy.csv", skip=2) %>%
mutate(dummy = c(0,0,0,1)) %>%
select(Category, dummy, everything()) %>%
gather(Country, Value, `Italy`:`Germany`) %>%
group_by(dummy, Country) %>%
summarize(sum = sum(Value)) %>% arrange(Country)
```
# Introduction
### Puzzle(s) for Today
*We've learned a lot of international politics' present and past, but what does the future hold?*
# The Future of International Politics
### The Future of International Politics
1. The proliferation of WMDs
2. The future of American global leadership (i.e. "The Rise of China?")
3. Globalization and its discontents
## The Proliferation of WMDs
###
```{r nuke-inventories-1945-2014, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
FAS %>%
group_by(Year) %>%
summarize(sum = sum(nukes)) %>%
ggplot(.,aes(Year, sum)) + theme_steve_web() +
geom_bar(stat="identity", color="black", alpha=I(0.5),fill = "yellowgreen") +
scale_x_continuous(breaks = seq(1945, 2015, by =5)) +
scale_y_continuous(labels = scales::comma) +
xlab("Year") + ylab("Number of Nuclear Warheads in Inventory") +
labs(title = "Number of Nuclear Warheads in Inventory of Nuclear Countries, 1945-2014",
subtitle = "Nuclear treaties, prominently between the U.S. and USSR/Russia, have seen an important decline in global nuclear inventory",
caption = "Data: Federation of American Scientists")
```
###
```{r nuke-inventories-1945-2014-no-usa-rus, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
FAS %>%
filter(Country != "United States" & Country != "Russia") %>%
group_by(Year) %>%
summarize(sum = sum(nukes)) %>%
ggplot(.,aes(Year, sum)) + theme_steve_web() +
geom_bar(stat="identity", color="black", alpha=I(0.5),fill = "yellowgreen") +
scale_x_continuous(breaks = seq(1945, 2015, by =5)) +
scale_y_continuous(labels = scales::comma) +
xlab("Year") + ylab("Number of Nuclear Warheads in Inventory") +
labs(title = "Number of Nuclear Warheads in Inventory of Nuclear Countries (Excluding the U.S. and Russia), 1945-2014",
subtitle = "Notice that declines in global nuclear inventory are effectively functions of de-nuclearization in the U.S. and Russia.",
caption = "Data: Federation of American Scientists")
```
###
```{r nuclear-proliferation-1945-2014, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
FAS %>%
filter(Country != "United States" & Country != "Russia" & Country != "United Kingdom" & Country != "France") %>%
group_by(Year, Country) %>%
summarize(sum = sum(nukes)) %>%
ggplot(.,aes(Year, sum, fill=Country, group = Country)) + theme_steve_web() +
geom_bar(aes(fill=Country), stat="identity", color="black", alpha=I(0.5)) +
scale_x_continuous(breaks = seq(1945, 2015, by =5)) +
xlab("Year") + ylab("Number of Nuclear Warheads in Inventory") +
theme(legend.position = "bottom") + scale_fill_brewer(palette="Set1") +
labs(title = "Number of Nuclear Warheads in Inventory of Select ''Problem'' Countries, 1945-2014",
subtitle = "Non-proliferation measures haven't stopped some countries from pushing for and even expanding nuclear arsenals.",
caption = "Data: Federation of American Scientists.
Qualifier: Most onlookers believe DPRK has around 20-40 warheads as of 2016.
Source for that estimation: https://www.wsj.com/articles/china-warns-north-korean-nuclear-threat-is-rising-1429745706")
```
### So is North Korea a Nuclear Country?
![Kim celebrates](kim-jong-un.JPG)
Yes, and we are *way* past that part of the puzzle.
### So What Is At Stake With North Korea?
There are a few things still on the table:
1. Delivery/guidance
2. Payload
3. Second-strike
4. Acceptance/legitimacy
These are more about scope and repercussions. North Korea is already a nuclear-armed country.
### Is Non-Proliferation Even a ''Bad Thing?''
There is a strand of scholarship that argues for proliferation.
- i.e. "mutually assured destruction"
- "The Long Peace"
### How Would Mutual Deterrence Work?
1. Guaranteed second-strike
2. Leaders must be rational/strategic (i.e. value survival)
3. Identification of first-strike initiator
### Why Should We Be Skeptical of Nuclear Deterrence?
- "Small *n*" and conspicuous cases
- Rivals like India and Pakistan may be only a bit more cautious, and still as conflict-prone.
- Nuclear weapons still alter distribution of power.
- Nuclear countries may not meet some of the previous assumptions (see: Pakistan)
- Proliferation into non-state actors
### How Can We Prevent Nuclear Proliferation?
Same way you discourage anyone from doing anything:
1. "Carrots and sticks"
2. Prevention of access to raw materials
### Providing Assurances
So many nuclear weapons programs follow direct fears from rivals.
- The U.S. developed theirs in response to Nazi Germany's efforts.
- The Soviets developed theirs in response to the Americans.
- Likewise: UK and France vis-a-vis the Soviets.
- Sino-Soviet split = nuclear weapons in China
- India in response to China
- Pakistan in response to India
- DPRK in response to the U.S.
### Providing Assurances
Guaranteed security interests can dissuade states from developing their own arsenals.
- The Soviets dissuaded Syria from a nuclear program.
- The U.S. has blocked nuclear programs in Germany, Japan, Taiwan, and South Korea.
However, *these assurances must be credible and indefinite*.
- So much of the current problem in North Korea is a function of broken assurances to Libya.
### Nuclear Non-Proliferation Treaty
The Nuclear Non-Proliferation Treaty (NPT) is an important milestone in non-proliferation.
- Signals strong interest from all nuclear powers, with some credible punishments.
However, the empirical record is mixed.
- NPT can't fundamentally alter state interests (e.g. India, DPRK).
- Still recognizes rights to a civilian nuclear program, which compounds commitment problem (e.g. Iran)
### Coercive Disarmament
One final option: threat and use of military force. However, this is fraught with problems:
1. Commitment problems (see: Libya)
2. Not a good option when nuclear weapons aren't the focal point of the problem (see: Iran, DPRK)
3. Most nuclear development is *sub rosa* after the Osirak reactor bombing.
## The ''Rise of China'' and the Future of American Leadership
###
```{r gdp-forecasts-usa-chn, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
# You could also do E095_LTB instead of EO103_LTB
# get_dataset("EO103_LTB",
# filter = list(c("USA","CHN"),
# c("GDP")),
# start_time = 2018, end_time = 2060) %>%
# rename(year = obsTime) %>%
# mutate(Country = ifelse(LOCATION == "CHN", "China", "United States"),
# gdp = obsValue/1000000,
# year = as.integer(year))
usa_chn_gdp_forecasts %>%
mutate(gdp = ifelse(is.na(gdp), f_gdp, gdp)) %>%
mutate(gdp = gdp/1000000000000,
f_lo80 = f_lo80/1000000000000,
f_hi80 = f_hi80/1000000000000) %>%
# filter(year == 2037)
ggplot(.,aes(year, gdp, linetype=Country, color = Country, fill=Country)) +
theme_steve_web() +
geom_ribbon(aes(ymin = f_lo80, ymax = f_hi80), alpha = 0.4) +
geom_line(size=1.5) +
scale_color_brewer(palette = "Set1") +
scale_x_continuous(breaks = seq(1960, 2050, by = 10)) +
xlab("") + ylab("Gross Domestic Product (Observed and Forecasted) in Trillions Constant 2010 US$") +
geom_vline(xintercept = 2037,linetype = "dashed") +
scale_color_brewer(palette = "Set1") +
annotate("text", x=2036, y = 35,
label = "China surpasses the U.S. in GDP\n(2037)",
hjust = 1, family = "Open Sans") +
labs(title = "China Should Surpass the U.S. in GDP by 2037",
subtitle = "China's yearly growth in economic activity to date is greater than the growth we observe in U.S. economic output even as (reasonable) worries about China's economic trajectory persist.",
caption = "Data: World Bank national accounts data, and OECD National Accounts data files. Forecast based on last year in World Bank data (2017).")
# oecd_forecasts %>%
# rename(Country = country) %>%
# filter(Country == "CHN" | Country == "USA") %>%
# mutate(Country = ifelse(Country == "CHN", "China", "United States"),
# gdp = value/1000000) %>%
# ggplot(.,aes(year, gdp, linetype=Country, color = Country)) +
# theme_steve_web() +
# geom_line(size=1.5) +
# theme(legend.position = "bottom") +
# scale_x_continuous(breaks = seq(1990, 2060, by = 5)) +
# xlab("Year") + ylab("Gross Domestic Product (Observed and Forecasted, in Millions and in PPPs)") +
# geom_vline(xintercept = 2021,linetype = "dashed") +
# scale_color_brewer(palette = "Set1") +
# annotate("text", x=2021.5, y = 35,
# label = "China surpasses the U.S. in GDP", hjust = 0, family = "Open Sans") +
# labs(title = "In 2014, the OECD Forecasted China Will Surpass the U.S. in GDP in 2021",
# subtitle = "China's yearly growth in economic activity to date is greater than the growth we observe in U.S. economic output even as (reasonable) worries about China persist.",
# caption = "Data: OECD Outlook No 95 - May 2014 - Long-term baseline projections provided by Organisation for Economic Co-operation and Development (OECD)")
```
###
```{r military-balance-usa-chn-2015, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
MB %>%
mutate(`Defense\nBudget` = mround2(defbudg/sum(defbudg, na.rm=T)),
`Population` = round((pop/sum(as.numeric(pop), na.rm = T))*100, 2),
`Active\nPersonnel` = round((active/sum(active, na.rm = T))*100, 2),
`Size of\nArmy\n(Personnel)` = round((army/sum(army, na.rm = T))*100, 2),
`Size of\nNavy\n(Personnel)` = round((navy/sum(navy, na.rm = T))*100, 2),
`Size of\nAir Force\n(Personnel)` = round((af/sum(af, na.rm = T))*100, 2),
`Total Equipment\n(Army)` = round((armyequip/sum(armyequip, na.rm = T))*100, 2),
`Total Equipment\n(Navy)` = round((navyequip/sum(navyequip, na.rm = T))*100, 2),
`Total Equipment\n(Air Force)` = round((afequip/sum(afequip,na.rm=T))*100, 2),
`Military\nSatellites` = round((otherequip/sum(otherequip, na.rm = T))*100, 2),
`Nuclear\nWarheads` = round((nukes/sum(nukes, na.rm = T))*100, 2)) %>%
select(country, `Defense\nBudget`:`Nuclear\nWarheads`) %>%
filter(country == "United States" | country == "China") %>%
rename(Country = country) %>%
gather(variable, value, -Country) %>%
mutate(lab = paste0(value,"%")) %>%
ggplot(.,aes(variable, value, color = Country)) + theme_steve_web() +
geom_bar(aes(fill=Country), stat="identity", position = "dodge", alpha=0.8, color = "black") +
xlab("Military Indicator") + ylab("Percentage of World Total") +
theme(legend.position = "bottom") +
scale_fill_brewer(palette="Set1") +
geom_text(aes(label=lab, group=Country), color="black",
position=position_dodge(width=.9),
size=3.75, vjust = -.5, family = "Open Sans") +
labs(title = "China's Eventual ''Rise'' Still Comes Amid Major Military Disparity with the United States",
subtitle = "Generally: China beats U.S. on manpower and never quality or equipment. We expects these trends to persist even as China surpasses the U.S. in economic output.",
caption = "Data: Military Balance (2015) and Federation of American Scientists (for Nukes)
Army Equipment: Tanks, recon equipment, armored fighting vehicles, artillery
Navy Equipment: submarines, principal surface combatants, amphibious equipment,
Air Force Equipment: Combat-capable aircraft")
```
### What's the Problem With China's Rise?
"Rising" states expect to do better in war.
- As a result, it's more likely to threaten force to revise the status quo.
- A "risen" China is less likely to honor the terms of a pre-power transition agreement.
Indeed, commitment problems magnify the war-proneness of power transitions.
- Further, the U.S. and China have more disagreements than previous cases like Germany and Japan after WWII.
### Is There Cause for Optimism?
Optmistic analysts see promise in China's globalization/leadership efforts.
- e.g. participation in WTO, Asian Infrastructure and Investment Bank
Indeed, China seems to have a vested stake in the current economic/political order, disputes with the U.S. notwithstanding.
- However, Chinese behavior still permits major skepticism.
## Globalization and Its Discontents
###
```{r cell-subscriptions-1980-2016, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
WDI(country=c("US", "GB", "DE", "CA", "FR"), indicator = "IT.CEL.SETS.P2", start=1980, end=2016) %>%
rename(cell = "IT.CEL.SETS.P2", Country = country) %>%
ggplot(.,aes(year, cell,linetype = Country, group=Country, color=Country)) +
theme_steve_web() +
geom_line(size=1.5) +
scale_color_brewer(palette="Set1") +
geom_hline(yintercept = 0, linetype="dashed") +
scale_x_continuous(breaks = seq(1980, 2016, by =4)) +
xlab("Year") + ylab("Mobile Cellular Subscriptions (per 100 people)") +
labs(caption = "Data: International Telecommunication Union, via the World Bank",
title = "More and More People Are Owning Cell Phones",
subtitle = "Globalization has made cell phone ownership readily accessible, which has been a major boon to quality of life in Western countries.") +
theme(legend.position = "bottom")
```
###
```{r usa-household-computers-1984-2015, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
tibble(year = c(1984, 1989, 1993, 1997, 2000, 2001, 2003, 2007, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016),
perc = c(8.2, 15, 22.9, 36.6, 51, 56.3, 61.8, 69.7, 74.1, 76.7, 75.6, 78.9, 83.8, 85.1, 86.8, 89.3)) %>%
mutate(lab = paste0(perc,"%")) %>%
ggplot(.,aes(as.factor(year),perc)) + theme_steve_web() +
geom_bar(stat="identity", position = "dodge", alpha=0.8, color = "black", fill="#00BFC4") +
geom_text(aes(label=lab), vjust = -.5, colour = "black",
position = position_dodge(.9), family="Open Sans") +
ylim(0, 100) +
xlab("Year") + ylab("Percentage of Households") +
labs(caption = "Data: Current Population Survey Estimates",
title = "Almost Every Household Has a Computer These Days, Thanks to Globalization",
subtitle = "Household computers used to be a luxury item before parts could be cheaply made/imported from Asia. In fact, the old Apple Macintosh from 1984 cost more than $6,000 in 2017 USD.")
# https://www.census.gov/content/dam/Census/library/publications/2018/acs/ACS-39.pdf
```
###
```{r usa-price-index-tech-goods-1997-2015, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
read_html("https://www.bls.gov/opub/ted/2015/long-term-price-trends-for-computers-tvs-and-related-items.htm") %>%
html_node("table") %>%
html_table() %>% tbl_df() %>%
mutate(Date = seq(as.Date("1997-12-01"), as.Date("2015-08-01"), by = "1 month")) -> LTPT
LTPT %>%
select(Date, everything(), -Month,
-`Internet services and electronic information providers`, -`Photographic equipment and supplies`,
-`Computer software and accessories`) %>%
gather(Category, Value, 2:ncol(.)) %>%
ggplot(.,aes(Date, Value, color=Category, group=Category, linetype=Category)) + theme_steve_web() +
geom_line(size=1.1) +
scale_color_brewer(palette="Set1") +
# scale_color_manual(values=c("#A6CEE3", "#1F78B4", "#B2DF8A", "#33A02C", "#FB9A99", "#E31A1C", "#FDBF6F")) +
scale_x_date(date_breaks = "2 years",
breaks = seq(1996, 2016, by = 2),
date_labels = "%Y") +
xlab("") + ylab("") +
labs(title = "The Price Indices For Computers and TVs Have Declined More Than 95% Since December 1997",
subtitle = "Most of the decline happened between 1998 and 2003 following a boom in cheaper parts and products coming from trading partners in Asia.",
caption = "Data: Bureau of Labor and Statistics. Base: December 1997.\nSource: https://www.bls.gov/opub/ted/2015/long-term-price-trends-for-computers-tvs-and-related-items.htm")
```
###
```{r let-them-watch-tv, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
ggplot() +
geom_line(data = cpi_various_items, aes(date, cpi, color = Category), size=1.1) +
theme_steve_web() +
guides(color = FALSE) +
geom_hline(yintercept = 100, linetype = "dashed") +
scale_x_date(limits = as.Date(c("2000-01-01","2020-01-01")),
date_labels = "%Y",
date_breaks = "2 year") +
scale_colour_brewer(palette = "RdBu") +
# scale_x_date(#date_breaks = "1 year",
# breaks = seq(as.Date("2000-01-01"),
# as.Date("2022-12-31"), by="1 year"),
# #date_minor_breaks = "3 months",
# date_labels = "%Y") +
# geom_line(size=1.1) +
geom_text(data = cpi_various_items %>% filter(date == "2018-12-01"), aes(label = Category,
x = as.Date("2019-01-01"),
y = cpi,
hjust = 0,
color = Category,
fontface = "bold",
family = "Open Sans"),
size = 3) +
xlab("Date") + ylab("Consumer Price Index for Urban Consumers") +
labs(title = "''Let Them Watch TV''",
caption = "Data: Bureau of Labor Statistics. Base period = Jan. 1, 2000.",
subtitle = "Liberalized trade has made consumer electronics (like TVs) fractions of their past prices. Yet, young adults face mounting costs for college, child-raising,\nand health care that government policy has failed to address.")
# geom_label_repel(aes(label = Category),
# nudge_x = 1,
# na.rm = TRUE)
```
###
```{r usa-rok-trade-balance-1949-2018, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
Trade %>% filter(ccode1 == 2 & ccode2 == 732) %>%
mutate(deficit = flow2 - flow1) %>%
select(year, deficit) %>%
bind_rows(., tibble(year = c(2015, 2016, 2017, 2018),
deficit = c(-28273.2, -27571.8, -22887.4,-17946.4 ))) %>%
ggplot(.,aes(year, deficit)) + theme_steve_web() +
geom_bar(stat="identity", position = "dodge", alpha=0.8, color = "black", fill="#F8766D") +
scale_x_continuous(breaks=seq(1950, 2015, by = 5)) +
scale_y_continuous(labels = scales::comma) +
xlab("Year") + ylab("Balance of Trade, in Millions (Negative Values = ''Deficits'')") +
labs(title = "The Boom in Cheap Household Computers Followed a Surge of Imports from South Korea (Among Others)",
subtitle = "South Korea specializes in cheaply producing high-quality consumer electronics for consumption in the U.S.",
caption = "Data: Correlates of War Dyadic Trade Data (v. 4.0, 1949-2014). U.S. Census Bureau: 2015-2018.")
```
###
```{r usa-manufacturing-employment-1987-2018, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
fredr(series_id = "PRS30006013",
observation_start = as.Date("1947-01-01")) %>%
ggplot(.,aes(date, value)) + geom_line(size=1.5, color="#00BFC4") +
theme_steve_web() +
scale_x_date(date_breaks = "2 years",
breaks = seq(1988, 2018, by = 2),
date_labels = "%Y",
limits = as.Date(c('1986-01-01','2019-01-01'))) +
xlab("") + ylab("Manufacturing Employment (Seasonally Adjusted, Indexed [2012])") +
labs(caption = "Data: U.S. Bureau of Labor Statistics. Data are seasonally adjusted with 2012 as the index year. Shaded areas are recessions.",
title = "U.S. Manufacturing Employment Has Declined as Globalization Has Increased",
subtitle = "You'll almost always hear of this as ''jobs leaving the country'' because of globalization.") +
annotate("rect", xmin=as.Date("1990-07-01"), xmax=as.Date("1991-04-01"), ymin=-Inf, ymax=Inf, alpha = .6, fill = "gray66") +
annotate("rect", xmin=as.Date("2001-04-01"), xmax=as.Date("2001-10-01"), ymin=-Inf, ymax=Inf, alpha = .6, fill = "gray66") +
annotate("rect", xmin=as.Date("2008-01-01"), xmax=as.Date("2009-07-01"), ymin=-Inf, ymax=Inf, alpha = .6, fill = "gray66") +
ylim(90, 155)
```
###
```{r usa-manufacturing-output-1987-2018, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
fredr(series_id = "OUTMS",
observation_start = as.Date("1987-01-01")) %>%
ggplot(.,aes(date, value)) + geom_line(size=1.5, color="#00BFC4") + theme_steve_web() +
scale_x_date(date_breaks = "2 years",
breaks = seq(1986, 2018, by = 2),
date_labels = "%Y",
limits = as.Date(c('1986-01-01','2019-01-01'))) +
xlab("") + ylab("Manufacturing Output (Seasonally Adjusted, Indexed [2012])") +
labs(caption = "Data: U.S. Bureau of Labor Statistics. Data are seasonally adjusted with 2012 as the index year. Shaded areas are recessions.",
title = "U.S. Manufacturing Output Has Generally Risen Despite the Drop in Manufacturing Employment",
subtitle = "Automation explains more of this phenomenon than globalization, but globalization gets the blame.") +
annotate("rect", xmin=as.Date("1990-07-01"), xmax=as.Date("1991-04-01"), ymin=-Inf, ymax=Inf, alpha = .6, fill = "gray66") +
annotate("rect", xmin=as.Date("2001-04-01"), xmax=as.Date("2001-10-01"), ymin=-Inf, ymax=Inf, alpha = .6, fill = "gray66") +
annotate("rect", xmin=as.Date("2008-01-01"), xmax=as.Date("2009-07-01"), ymin=-Inf, ymax=Inf, alpha = .6, fill = "gray66") +
ylim(60, 140)
```
###
```{r usa-manufacturing-compensation-1987-2018, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
fredr(series_id = "COMPRMS",
observation_start = as.Date("1987-01-01")) %>%
ggplot(.,aes(date, value)) + geom_line(size=1.5, color="#00BFC4") + theme_steve_web() +
scale_x_date(date_breaks = "2 years",
breaks = seq(1986, 2018, by = 2),
date_labels = "%Y",
limits = as.Date(c('1986-01-01','2019-01-01'))) +
xlab("") + ylab("Manufacturing Compensation per Hour (Seasonally Adjusted, Indexed [2012])") +
labs(caption = "Data: U.S. Bureau of Labor Statistics. Data are seasonally adjusted with 2012 as the index year. Shaded areas are recessions.",
title = "Manufacturing Pays More Now Than It Did When There Was More Manufacturing Employment",
subtitle = "Real compensation per hour is on the rise because automation/globalization has decreased demand for low-skilled labor and increased demand for high-skilled labor. This raises wages.") +
annotate("rect", xmin=as.Date("1990-07-01"), xmax=as.Date("1991-04-01"), ymin=-Inf, ymax=Inf, alpha = .6, fill = "gray66") +
annotate("rect", xmin=as.Date("2001-04-01"), xmax=as.Date("2001-10-01"), ymin=-Inf, ymax=Inf, alpha = .6, fill = "gray66") +
annotate("rect", xmin=as.Date("2008-01-01"), xmax=as.Date("2009-07-01"), ymin=-Inf, ymax=Inf, alpha = .6, fill = "gray66")
```
###
```{r usa-labor-share-income-1987-2018, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
fredr(series_id = "PRS30006173",
observation_start = as.Date("1987-01-01")) %>%
ggplot(.,aes(date, value)) + geom_line(size=1.5, color="#00BFC4") + theme_steve_web() +
scale_x_date(date_breaks = "2 years",
breaks = seq(1986, 2016, by = 2),
date_labels = "%Y",
limits = as.Date(c('1986-01-01','2016-01-01'))) +
xlab("") + ylab("Manufacturing Compensation per Hours (Seasonally Adjusted, Indexed [2012])") +
labs(caption = "Data: U.S. Bureau of Labor Statistics. Data are seasonally adjusted with 2012 as the index year. Shaded areas are recessions.",
title = "Labor Share of Income in Manufacturing Has Gone Down",
subtitle = "Gains in income/productivity result in wages for skilled labor/management or reinvestment in capital.") +
annotate("rect", xmin=as.Date("1990-07-01"), xmax=as.Date("1991-04-01"), ymin=-Inf, ymax=Inf, alpha = .6, fill = "gray66") +
annotate("rect", xmin=as.Date("2001-04-01"), xmax=as.Date("2001-10-01"), ymin=-Inf, ymax=Inf, alpha = .6, fill = "gray66") +
annotate("rect", xmin=as.Date("2008-01-01"), xmax=as.Date("2009-07-01"), ymin=-Inf, ymax=Inf, alpha = .6, fill = "gray66")
```
###
```{r labor-share-income-1950-2014, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
pwt9.0 %>% tbl_df() %>%
mutate(region = countrycode::countrycode(country, "country.name", "region")) %>%
mutate(Category = ifelse(region %in% c("Northern America",
"Western Europe",
"Australia and New Zealand",
"Northern Europe"),
"Advanced Western Economy", "Other Economy")) %>%
group_by(year, Category) %>%
summarize(labsh = mean(labsh, na.rm=T)) %>%
ggplot(.,aes(year, labsh, color=Category, linetype=Category)) + theme_steve_web() +
geom_line(size=1.1) + scale_color_brewer(palette="Set1") +
scale_y_continuous(labels = scales::percent) +
scale_x_continuous(breaks = seq(1950, 2015, by = 5)) +
xlab("") + ylab("Labor Share of Income") +
labs(caption = "Data: Penn World Table 9.0\n''Advanced Western Economy'' determined by World Bank region for convenience. These observations are the U.S., Canada, Australia, New Zealand, and countries in Western/Northern Europe.",
title = "It's Not Just the United States: Labor Share of Income is Falling Everywhere",
subtitle = "The trend has been observable as long we have data with more pronounced slides happening in the 1980s.")
```
###
```{r what-explains-declining-share-of-income-imf, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
tibble::tribble(
~Group, ~`Actual change`, ~Technology, ~`GVC Participation`, ~`Financial Integration`, ~`Policy/institutions`, ~Unexplained,
"AEs", -3.55, -1.64, -0.65, -0.48, -0.17, -0.61,
"EMs", -5.28, -0.31, -4.37, 0.96, -0.13, -1.43,
"EMs exluding China", -2.58, -1.24, -3, 1.12, -0.16, 0.69
) %>%
gather(Category, Value, `Actual change`:Unexplained) %>%
filter(Category != "Actual change" & Group == "AEs") %>%
mutate(perc = Value/sum(Value),
label = paste0(mround2(perc),"%")) %>%
ggplot(.,aes(Category, perc)) + theme_steve_web() +
geom_bar(stat="identity", color="black", alpha=0.8) +
xlab("") + ylab("Percent of Explained Decrease in Labor Share of Income") +
scale_y_continuous(labels = scales::percent) +
geom_text(aes(label=label), vjust = -.5, colour = "black",
position = position_dodge(.9), family="Open Sans") +
labs(title = "What Explains Declining Share of Income in Advanced Economies? Technology.",
subtitle = "An IMF report from April 2017 found most of the movement is explained by declining relative price of investment and initial exposure to routinization across sectors.",
caption = "Data: IMF. Reconfiguration of Figure 3.11 in ''World Economic Outlook, April 2017: Gaining Momentum?''")
```
###
```{r income-inequality-labor-share-income, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
SWIID %>%
mutate(country = ifelse(country == "United States", "United States of America", country)) %>%
select(country, year, gini_disp) %>%
left_join(., pwt9.0) %>%
select(country, year, gini_disp, labsh) %>%
mutate(region = countrycode::countrycode(country, "country.name", "region")) %>%
mutate(Category = ifelse(region %in% c("Northern America",
"Western Europe",
"Australia and New Zealand",
"Northern Europe"),
"Advanced Western Economy", "Other Economy")) %>%
filter(Category == "Advanced Western Economy" & year > 1970) %>%
ggplot(.,aes(gini_disp, labsh)) + theme_steve_web() +
geom_point() + geom_smooth(method="loess") + xlim(20, 36) +
xlab("GINI") + ylab("Labor Share of Income") +
labs(title = "Income Inequality Generally Rises in the West as Labor Share of Income Falls",
subtitle = "Labor share of income decline happens for a variety of reasons but can have important political/economic effects.",
caption = "Data: Penn World Table (v. 9.0). SWIID (v. 6.2)")
```
###
```{r income-inequality-labor-share-income-usa, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
SWIID %>%
select(country, year, gini_disp) %>%
filter(country == "United States") %>%
mutate(country = "United States of America") %>%
left_join(., pwt9.0) %>%
select(country, year, gini_disp, labsh) %>%
filter(country == "United States of America") %>%
mutate(z_gini = scale(gini_disp),
z_labsh = scale(labsh)) %>%
gather(Category, value, z_gini:z_labsh) %>%
mutate(Category = ifelse(Category == "z_gini", "GINI", "Labor Share of Income")) %>%
ggplot(.,aes(year, value, color=Category, linetype=Category)) + theme_steve_web() +
geom_line(size=1.1) + scale_color_brewer(palette="Set1") +
scale_x_continuous(breaks = seq(1960, 2016, by = 4)) +
xlab("") + ylab("GINI or Labor Share of Income (Standardized)") +
labs(title = "The Correlation Between Income Inequality and Labor Share of Income Is Particularly Strong in the U.S. (r = -.702)",
subtitle = "Labor share of income's decline is global and has multiple reasons but U.S. policy may be making its effects even worse.",
caption = "Data: Penn World Table (v. 9.0). SWIID (v. 6.2)")
```
###
```{r income-inequality-usa-ukg-fra-gmy-can, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
SWIID %>%
filter(country == "United States" | country == "United Kingdom" | country == "France" | country == "Germany" | country == "Canada") %>%
rename(Country = country) %>%
ggplot(.,aes(year, gini_disp, color = Country, linetype=Country)) + theme_steve_web() +
xlab("Year") + ylab("GINI") +
scale_x_continuous(breaks = seq(1960, 2015, by = 5)) +
geom_line(size=1.5) +
scale_color_brewer(palette="Set1") +
theme(legend.position="bottom") +
labs(title = "Globalization Skepticism Appears Most Concentrated in Western Countries With Severe Income Inequality",
subtitle = "Globalization and trade become easy scapegoats to justify austerity measures and cuts to social spending that only compound the problem.",
caption = "Data: SWIID (v. 6.2)")
```
###
```{r wvs-swiid-income-inequality-scapegoating-immigrants, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
# Andorra: 28.22657
# South Korea: 31
SWIID %>%
filter(year >= 2000 & year <= 2008) %>%
group_by(country) %>%
summarize(meangini = mean(gini_disp, na.rm=T)) %>%
rename(Country = country) %>%
left_join(Immig, .) %>%
mutate(meangini = ifelse(Country == "South Korea", 31, meangini),
meangini = ifelse(Country == "Andorra", 28.22657, meangini)) %>%
filter(dummy == 1) %>%
mutate(region = countrycode::countrycode(Country, "country.name", "region")) %>%
mutate(Category = ifelse(region %in% c("Northern America",
"Western Europe",
"Australia and New Zealand",
"Northern Europe", "Southern Europe", "Eastern Europe"),
"Advanced Economy", "Other Economy"),
Category = ifelse(Country == "Serbia" | Country == "Moldova" | Country == "Ukraine", "Other Economy", Category),
Category = ifelse(Country == "Japan" | Country == "Chile" | Country == "Cyprus", "Advanced Economy", Category)) -> giniimmig
giniimmig %>%
group_by(Category) %>%
summarize(cor = cor(sum, meangini)) -> cors
giniimmig %>%
ggplot(.,aes(meangini, sum)) + theme_steve_web() +
geom_point(aes(color=Category)) + geom_smooth(method="loess") +
geom_text_repel(aes(meangini, sum, label = Country)) +
xlab("Average GINI (2000-2008)") + ylab("Percent Saying Would-Be Immigrants Should Be Prohibited From Entering Country") +
labs(title = "Higher Income Inequality Coincides With Societies Scapegoating Immigrants As Part of the Problem",
subtitle = "Less-developed countries are generally more anti-immigrant but the correlation is near identical in more developed economies.",
caption = "Data: World Values Survey (Wave 5). SWIID (v. 6.2)")
```
# Conclusion
### Conclusion
- WMDs have become easier and cheaper to produce, and more countries are producing them.
- Solutions require altering the incentives of would-be proliferators, through both carrots and sticks.
- China's "rise" may lead to war with the U.S.
- Preventing this requires integrating China into U.S.-created institutions.
- Globalization is a force for peace, but it's a policy that creates winners and losers.
- How you compensate the losers will condition the future of globalization.
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