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---
title: "Why Are Some Countries Richer Than Others?"
subtitle: POSC 1020 -- Introduction to International Relations
author: Steven V. Miller
institute: Department of Political Science
titlegraphic: /Dropbox/teaching/clemson-academic.png
date:
fontsize: 10pt
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}
library(WDI)
library(tidyverse)
library(stevemisc)
# library(maddison)
library(fredr)
Colhist <- read_csv("~/Dropbox/data/icow/ICOW Colonial History 1.0/coldata100.csv")
```
# Introduction
### Puzzle(s) for Today
*Why are some countries rich and others poor?*
###
```{r gddpc-zambia-rok-1960-1975, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
WDI(country = c("ZM", "KR"), indicator = c("NY.GDP.PCAP.CD"),
start = 1960, end = 2019, extra = FALSE, cache = NULL) %>%
rename(gdppc = `NY.GDP.PCAP.CD`) %>%
mutate(Country = country) %>%
filter(year <= 1975) %>%
ggplot(.,aes(year, gdppc, group = Country, color=Country, linetype = Country)) + theme_steve_web() +
geom_line(size=1.1) +
scale_color_brewer(palette="Set1") +
labs(caption = "Data: World Bank",
title = "GDP per Capita for Zambia (Northern Rhodesia) and South Korea, 1960-1975",
subtitle = "Zambia (Rhodesia) was much better endowed and had more growth potential in the 1960s.") +
scale_x_continuous(breaks = seq(1960, 1975, by = 1)) +
xlab("Year") + ylab("GDP per Capita")
```
###
```{r gddpc-zambia-rok-1960-2015, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
WDI(country = c("ZM", "KR"), indicator = c("NY.GDP.PCAP.CD"),
start = 1960, end = 2016, extra = FALSE, cache = NULL) %>%
rename(gdppc = `NY.GDP.PCAP.CD`) %>%
mutate(Country = country) %>%
ggplot(.,aes(year, gdppc, group = Country, color=Country, linetype = Country)) + theme_steve_web() +
geom_line(size=1.1) +
scale_color_brewer(palette="Set1") +
labs(caption = "Data: World Bank",
title = "GDP per Capita for Zambia (Northern Rhodesia) and South Korea, 1960 to the Present",
subtitle = "South Korea's development far exceeded what we would've expected in the 1960s.") +
scale_x_continuous(breaks = seq(1960, 2020, by = 5)) +
scale_y_continuous(labels = scales::comma) +
ylab("GDP per Capita") + xlab("Year")
```
# What Explains Development?
### What Explains Development?
1. Geography
2. Domestic factors
3. Domestic institutions
### Geography
Geography is less an argument for development and more a correlation. Factors include:
- Southern Hemisphere
- Landlocked territory
- Tropical weather/excessive heat
### Geography
There are intuitive arguments here, but geographical explanations mask substantial variation within cases.
- e.g. Chile and Australia in the Southern Hemisphere.
- Austria, Kazakhstan, and Switzerland are thriving landlocked countries.
- Oil states in the gulf and Sinagpore are hot/tropical and developed.
### Domestic Factors
Development follows important investment in infrastructure.
- Physical infrastructure
- Social infrastructure
- Property rights
Long-term growth requires a meaningful social investment.
### Domestic Factors
Sometimes key actors have no interest in that social investment.
- i.e. wealthy elites see these public goods as redistribution.
- Infrastructure may siphon cheap labor from the countryside.
- Actors motivated by race/ethnicity unlikely to see interest in common good.
### Domestic Institutions
Countries better endowed in natural resources generally have less incentive to promote meaningful development.
- We call this a "resource curse."
Intuition behind the "resource curse":
- States rich in primary commodities tend to generate most income from foreign receipts.
- This decreases incentive to innovate to improve productive capacities.
Recall our previous example of Northern Rhodesia/Zambia and South Korea.
###
```{r top-ten-oil-states, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
WDI(country = "all", indicator = c("NY.GDP.PCAP.CD",
"NY.GDP.PETR.RT.ZS",
"NY.GDP.MKTP.CD"),
start = 2016, end = 2016, extra = FALSE, cache = NULL) %>%
rename(gdppc = `NY.GDP.PCAP.CD`,
oilrent = `NY.GDP.PETR.RT.ZS`,
gdp = `NY.GDP.MKTP.CD`) %>%
arrange(-oilrent) %>%
filter(iso2c != "1A" & iso2c != "ZQ") %>%
head(10) %>%
mutate(oilround = mround2(oilrent/100),
mediangdppc = median(gdppc, na.rm=T),
mediangdpb = median(gdp, na.rm=T)/1000000000) -> top10_oilstates
# mediangdppc <- prettyNum(round(median(top10_oilstates$gdppc), 2),",")
mediangdppc <- median(top10_oilstates$gdppc, na.rm=T)
mediangdpb = round(median(top10_oilstates$gdp, na.rm=T)/1000000000,2)
maxgdppc = max(top10_oilstates$gdppc, na.rm=T)
maxgdp = round(max(top10_oilstates$gdp, na.rm=T)/1000000000, 2)
scgdpb = 183873000000/1000000000 # https://www.deptofnumbers.com/gdp/south-carolina/
scgdppc = 37063
my_subtitle = paste0("There are some conspicuous states in here, but: the median GDP per capita is ", prettyNum(round(mediangdppc, 2),",")," USD and the median GDP (", mediangdpb," billion USD) is less than half the GDP of South Carolina.")
top10_oilstates %>%
ggplot(.,aes(reorder(country, -oilrent), oilrent)) + theme_steve_web() +
geom_bar(stat="identity", alpha=0.8, color="black") +
xlab("Country") + ylab("Oil Rents as Percentage of GDP") +
labs(title = "These Are the Ten Countries Whose Economies Are Most Indebted to Selling Oil Abroad (2016)",
subtitle = my_subtitle,
caption = "Data: World Bank") +
geom_text(aes(label=oilround), vjust=-.5, colour="black",
position=position_dodge(.9), size=4, family = "Open Sans")
```
###
```{r oil-states-vs-south-carolina, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
tribble(
~Country, ~Metric, ~Value,
"South Carolina", "GDP per Capita\n(in thousands USD)", scgdppc/1000,
"Top Ten Oil Rent States (Median)", "GDP per Capita\n(in thousands USD)", round(mediangdppc/1000, 2),
# "Top Ten Oil Rent States (Maximum)", "GDP per Capita\n(in thousands USD)", maxgdppc/1000, # Qatar. UAE is higher too. That's it.
"South Carolina", "GDP\n(in billions USD)", round(scgdpb, 2),
"Top Ten Oil Rent States (Median)", "GDP\n(in billions USD)", mediangdpb,
# "Top Ten Oil Rent States (Maximum)", "GDP\n(in billions USD)", maxgdp, # Saudi Arabia. UAE is higher too. That's it.
) %>%
ggplot(.,aes(Metric, Value, color=Metric)) + theme_steve_web() +
geom_bar(aes(fill = Country), position = "dodge", stat="identity", alpha = I(0.8),color = I("black")) +
theme(legend.title=element_blank()) +
scale_fill_brewer(palette="Set1") +
geom_text(aes(label=Value, group=Country), color="black",
position=position_dodge(width=.9), size=4, vjust = -.5, family="Open Sans") +
labs(title = "The Typical Economy and Quality of Life in These Countries Are Well Below Even South Carolina (the 5th Poorest State)",
subtitle = "Saudi Arabia and UAE have bigger economies and Qatar is wealthier per head, but these oil-exporting countries are much poorer than a poor U.S. state.")
```
###
```{r resource-curse-illustration, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
WDI(country = "all", indicator = c("NY.GDP.MKTP.KD", "NY.GDP.PETR.RT.ZS"),
start = 1960, end = 2016, extra = FALSE, cache = NULL) %>%
rename(gdppc = `NY.GDP.MKTP.KD`,
oilrent = `NY.GDP.PETR.RT.ZS`) %>%
mutate(Country = country,
loggdppc = log(gdppc + 1),
logoil = log(oilrent + 1)) %>%
ggplot(.,aes(logoil, loggdppc)) + theme_steve_web() +
geom_point(size=2) + geom_smooth(method="loess", size=2) +
labs(caption = "Data: World Bank",
title = "Less Developed Countries Tend to Be Better Endowed in Natural Resources",
subtitle = "We call this the ''resource curse'' in the development literature.") +
# scale_x_continuous(breaks = seq(1960, 2015, by = 5)) +
ylab("GDP (Logged)") + xlab("Oil Rents (Logged)")
```
# Are Rich Countries Responsible for LDCs?
## The Legacy of Colonialism
### Are Rich Countries Responsible for Problems in the Developing World?
Short answer: yes, that's an important part of it.
### The Legacy of Colonialism
Colonialism has an important legacy on development.
- Colonial powers were not interested in promoting meaningful development.
- Colonial powers were also not benign and "enlightening."
Colonialism is not a total explanation for LDC problems, but it's a real legacy.
###
```{r postcolonial-development-by-colony, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
Colhist %>%
filter(State != 2 & State != 20) %>%
filter(State < 900) %>%
filter(!(State >= 200 & State < 400)) %>%
filter(ColRuler != -9 & ColRuler != 710 & ColRuler != 740) %>%
select(State, Name, ColRuler,IndFrom) %>%
rename(ccode = State) %>%
mutate(ColRulerN = factor(countrycode::countrycode(ColRuler,"cown","country.name")),
ColRulerN = forcats::fct_recode(ColRulerN,
"Former British Colony" = "United Kingdom",
"Former French Colony" = "France",
"Former Spanish Colony" = "Spain",
"Former Belgian Colony" = "Belgium",
"Former Italian Colony" = "Italy",
"Former Dutch Colony" = "Netherlands",
"Former Portuguese Colony" = "Portugal")) %>%
filter(ColRulerN != "Russian Federation" & ColRulerN != "Turkey" & ColRulerN != "Russia") %>%
filter(ColRulerN != "Former Italian Colony" & ColRulerN != "Former Dutch Colony") %>%
mutate(ColRulerN = as.character(ColRulerN)) -> colhists
WDI(country = "all", indicator = c("NY.GDP.PCAP.KD"),
start = 1960, end = 2016, extra = FALSE, cache = NULL) %>%
rename(gdppc = `NY.GDP.PCAP.KD`) %>% tbl_df() %>%
mutate(ccode = countrycode::countrycode(iso2c, "iso2c", "cown")) %>%
filter(!is.na(ccode)) %>%
left_join(.,colhists) %>%
mutate(Category = ifelse(ccode == 200 | ccode == 230 | ccode == 235 | ccode == 220 |
ccode == 211 , # ccode == 210 | ccode == 365 | ccode == 325 | ccode == 2 | ccode == 640
"Former Colonial Master", ColRulerN)) %>%
filter(!is.na(Category)) %>%
group_by(Category, year) %>%
summarize(meangdppc = mean(gdppc, na.rm=T)) %>%
ggplot(.,aes(year, meangdppc, group = Category, color=Category, linetype = Category)) + theme_steve_web() +
geom_line(size=1.1) +
scale_x_continuous(breaks = seq(1960, 2015, by = 5)) +
xlab("Year") + ylab("Average GDP per Capita") +
scale_y_continuous(labels = scales::comma) +
scale_color_brewer(palette="Dark2") +
labs(caption = "Data: World Bank (GDP per Capita in Constant USD) and ICOW (Colonial History Data).
Note: Data clearly omit states formerly held by Japan (e.g. Koreas), China (e.g. Mongolia), Russia (e.g. Central Asia), Turkey (i.e. Middle East), Italy (Somalia, Eritrea), and Netherlands (Indonesia).
Data here also omit the U.S., Canada, and all of Oceania.",
title = "Colonialism is a Partial Explanation for Stunted Development but it's a Real Legacy",
subtitle = "GDP per capita of former colonial states drags well behind those that were previously colonial masters.")
```
###
Obligatory trigger-warning for next slide...
### The Consequence for Not Meeting a Daily Rubber Quota in Belgian Congo
![The consequences of failing to meet a daily rubber quota in Belgian Congo, 1904](cong_hands_1904.jpg)
### Is the International Economy Biased Against LDCs?
Short answer: yes, that's part of it too.
## The Terms of Trade
### The Trade Bias Against LDCs
LDCs specialize in primary products like raw materials and agricultural goods.
- These markets are usually very competitive (e.g. coffee), which depresses prices.
- Bigger economies specialize in manufactured goods, which cluster on oligopolies. This raises prices.
Generally, the terms of trade works against LDCs.
- They get less for what they sell and pay more for what they buy.
###
```{r coffee-consumption-since-1990, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
# Check directory for PDF.
tribble(
~Year, ~Value,
1990, 74131,
1991, 71282,
1992, 79216,
1993, 77120,
1994, 75024,
1995, 72371,
1996, 77854,
1997, 81063,
1998, 82767,
1999, 85432,
2000, 87642,
2001, 88950,
2002, 91266,
2003, 93394,
2004, 95487,
2005, 96376,
2006, 101567,
2007, 104655,
2008, 106798,
2009, 104513,
2010, 109145,
2011, 111812,
2012, 113171,
2013, 115931,
2014, 120028,
2015, 121384,
2016, 127178,
2017, 126110
) %>%
mutate(Value = Value/100) %>%
ggplot(.,aes(factor(Year), Value)) + theme_steve_web() +
geom_bar(stat="identity", color="black", alpha = 0.8, fill="#6f4e37") +
xlab("") + ylab("Coffee Consumption in Hundreds of Thousands 60kg Bags") +
geom_text(aes(label=round(Value, 1)), vjust=-.5, colour="black",
position=position_dodge(.9), size=3, family = "Open Sans") +
labs(title = "The World is Drinking More and More Coffee",
subtitle = "Coffee consumption is up over 70% since 1990 in these select countries (i.e. Europe + USA and Japan), suggesting demand for coffee is increasing in rich countries.",
caption = "Data: International Coffee Organization")
```
###
```{r price-of-coffee-arabica-robustas, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
# CPIAUCSL: urban consumers
fredr(series_id = "CPALCY01USM661N",
observation_start = as.Date("1980-01-01")) %>%
rename(CPI = value) -> Price
fredr(series_id = "PCOFFOTMUSDM",
observation_start = as.Date("1980-01-01")) %>%
rename(Arabica = value) -> Arabica
# robustas PCOFFROBUSDM
fredr(series_id = "PCOFFROBUSDM",
observation_start = as.Date("1980-01-01")) %>%
rename(Robustas = value) -> Robustas
imf_coffee_data %>%
right_join(.,Price) %>%
mutate(last = last(CPI),
index = (CPI/last)*100,
Arabica = (arabica/index)*100,
Robustas = (robustas/index)*100) %>%
gather(`Type of Bean`, Value, Arabica:Robustas) %>%
filter(!is.na(Value)) %>%
ggplot(.,aes(date, Value, linetype=`Type of Bean`, color=`Type of Bean`)) +
geom_line(size=1.1) + theme_steve_web() +
scale_color_brewer(palette="Set1") +
scale_x_date(date_breaks = "2 years",
date_labels = "%Y") +
ylab("Inflation Adjusted Price in Jan. 2019 USD Dollars per Metric Ton") + xlab("") +
labs(title = "The World is Drinking More Coffee, but Coffee is Fetching Lower Prices in the Global Market",
subtitle = "Coffee markets are far more competitive than the markets for the machinery necessary to mass produce coffee beans, which forms a kind of trade bias against LDCs.",
caption = "Data: International Monetary Fund via Federal Reserve Bank of St. Louis.
Note: prices are manually converted from nominal to real prices with a Jan. 2019 index.",
color = "Type of Bean",
linetype = "Type of Bean")
# left_join(Price, Arabica, by="date") %>%
# left_join(., Robustas, by="date") %>%
# select(date, CPI, Arabica, Robustas) %>%
# mutate(last = last(CPI),
# index = (CPI/last)*100,
# Arabica = (Arabica/index)*100,
# Robustas = (Robustas/index)*100) %>%
# gather(`Type of Bean`, Value, Arabica:Robustas) %>%
# filter(!is.na(Value)) %>%
# ggplot(.,aes(date, Value, linetype=`Type of Bean`, color=`Type of Bean`)) +
# geom_line(size=1.1) + theme_steve_web() +
# scale_color_brewer(palette="Set1") +
# scale_x_date(date_breaks = "2 years",
# date_labels = "%Y") +
# ylab("Inflation Adjusted Price in Aug. 2018 USD Dollars per Metric Ton") + xlab("") +
# labs(title = "The World is Drinking More Coffee, but Coffee is Fetching Lower Prices in the Global Market",
# subtitle = "Coffee markets are far more competitive than the markets for the machinery necessary to mass produce coffee beans, which forms a kind of trade bias against LDCs.",
# caption = "Data: International Monetary Fund via Federal Reserve Bank of St. Louis.
# Note: prices are manually converted from nominal to real prices with a Aug. 2018 index.",
# color = "Type of Bean",
# linetype = "Type of Bean")
```
## Institutional Biases
### Are Institutions Biased Against LDCs?
Short answer: yes, that an important part of the problem as well.
### The Institutional Bias Against LDCs
Bigger IGOs are dominated by richer countries.
- IMF, WB, and WTO all weight votes by economic size, and not population.
- The U.S. with just a little support from Europe, can veto any proposal in these IGOs it doesn't like.
###
```{r imf-voting-power, echo=F, eval=T, fig.width = 14, fig.height = 8.5, warning = F, message = F}
IMF <- readxl::read_xlsx("~/Dropbox/teaching/posc1020/development-1/Quota_SDRs.xlsx", sheet=2) %>% slice(1:5)
IMF %>%
gather(Year, Value, `1950`:`2016`) %>%
group_by(Year) %>%
mutate(perc = Value/max(Value)) %>%
ungroup() %>%
rename(Category = 1) %>%
mutate( Year = as.numeric(Year)) %>%
filter(Category != "World") %>%
ggplot(.,aes(Year, perc, color=Category, group=Category,linetype=Category)) + theme_steve_web() +
geom_line(size=1.1) +
scale_x_continuous(breaks=seq(1950, 2015, by =5)) +
scale_y_continuous(labels=scales::percent) +
scale_color_brewer(palette="Set1") +
xlab("Year") + ylab("Percentage of Voting Power") +
labs(title = "IMF Voting Rules Privilege the Wealthy States Over the Developing States",
subtitle = "Voting power is weighted by economic size/openness/reserves in the IMF, which favor countries like the U.S. despite the majority of the world's population residing in poorer countries.",
caption = "Data: International Monetary Fund")
```
### The Institutional Bias Against LDCs
International trade agreements have the same problem.
- Rich countries structure these to their advantage and also protect their industries (esp. agriculture) at the expense of poorer countries.
# Conclusion
### Conclusion
Why are some countries rich and others poor?
- Correlates: poorer countries are landlocked, have tropical climates, and are in the Southern Hemisphere.
- Problem: these are noisy indicators at best, and don't explain much.
- Development hinges on domestic factors/institutions.
- Problem: these are also noisy and elites may not see an incentive in infrastructure.
Rich countries structured the world in their image.
- Colonial legacies, and fundamental economic/institutional biases all loom large.
<!--
# What Can LDCs Do About This Bias?
1. Import-substituting industrialization (ISI)
2. Export-oriented industrialization (EOI)
3. Commodity cartels
###
![Some countries, like Brazil, chose to cut off imports and protect/foster nascent industries](fnm-fabrica.jpg)
### The ISI Framework
- Enact trade barriers to shield industries from competition
- Subsidize modern industrial sector
- Invest in infrastructure to make industrialization possible.
Problems:
- Industries were inefficient
- Lack of competition usually meant products were low quality
- Uncompetitive exports compounded economic downturns
**Washington Consensus** to address ISI problems largely ended those policies.
###
![ISI products like the Yugo usually became punch-lines](yugo-00.jpg)
### The EOI Framework
Latin America generally turned inward. The "Asian Tigers" looked outward.
- Market products for quality for richer countries (esp. America)
- Depress currency to make exports cheap
- Extend low-interest loans and tax breaks to exporters toward that end.
### Commodity Cartels
Poorer countries sharing common resource have found cartels useful
- OPEC is clearly best example of this.
However, commodity cartels are no quick fix.
- Cartels are hard to govern!
- Oil is a unique commodity too.
# Conclusion
### Conclusion
- While everyone prefers development, powerful groups can block it.
- Domestic institutions play an important role—they may promote or hinder development.
- Rich countries adopt policies that hurt the poor.
- Successful development requires a country to overcome both internal and external obstacles. -->
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