002_inter_trade.qmd

---
title: "International trade"
author: Luis Francisco Gomez Lopez
date: 2023-07-10
format:
  beamer:
    colortheme: dolphin
    fonttheme: structurebold
    theme: AnnArbor
    link-citations: true
    linkcolor: blue
bibliography: econ_glob_faedis.bib
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(echo      = FALSE, 
                      warning   = FALSE, 
                      message   = FALSE,
                      fig.align = "center")
```

```{r libraries}

library(tidyverse)
library(wbstats)
library(tidyquant)
library(knitr)
library(DiagrammeR)
```

# Contents

- Please Read Me
- Purpose
- Volume of international trade
- Patterns of international trade
- Benefits of international trade
- Theories of international trade
- Acknowledgments
- References

# Please Read Me

- Check the message __Welcome greeting__ published in the News Bulletin Board.

- Dear student please edit your profile uploading a photo where your face is clearly visible.

- The purpose of the virtual meetings is to answer questions and not to make a summary of the study material.

- This presentation is based on [@wild_international_2020, Chapter 5]

# Purpose

Describe the theories about international trade and examine data about its benefits, volume and  patterns in the context of the global economy

# Volume of international trade 

```{r exports_imports_col, out.width="90%"}

# Data
data <- wbstats::wb_data(country   = "COL",
                         indicator = c("NE.IMP.GNFS.KD", "NE.EXP.GNFS.KD"), 
                         return_wide = FALSE) %>%
  filter(!is.na(value))
  
data %>%   
  select(country, date, value, indicator_id) %>% 
  mutate(indicator_id = case_when(
    indicator_id == "NE.IMP.GNFS.KD" ~ "Imports",
    indicator_id == "NE.EXP.GNFS.KD" ~ "Exports",
    TRUE                             ~ indicator_id)) %>%
  
  #Plot
  ggplot() +
  geom_point(aes(x = date, y = value, fill = indicator_id),
             shape = 21,
             color = "black",
             show.legend = FALSE) +
  geom_line(aes(x     = date,
                y     = value,
                group = indicator_id,
                color = indicator_id)) +
  scale_y_continuous(labels = scales::number_format(scale    = 1e-9,
                                                    suffix   = "B",
                                                    accuracy = 1)) +
  scale_color_tq() +
  scale_fill_tq() +
  labs(x        = "Year",
       y        = "Billions (Short scale)",
       color    = "",
       title    = str_glue("Colombia imports and exports: {min(data$date)}-{max(data$date)}"),
       subtitle = str_glue("Variables Units: constant USD Base Year 2010
                              Imports WDI code : NE.IMP.GNFS.KD
                              Exports WDI: NE.EXP.GNFS.KD"),
       caption  = str_glue("Source: World Development Indicators - World Bank,
                              Last update: {unique(data$last_updated)}")) +
  theme(panel.border      = element_rect(fill = NA, color = "black"),
        plot.background   = element_rect(fill = "#f3fcfc"),
        panel.background  = element_rect(fill = "#f3f7fc"),
        legend.background = element_rect(fill = "#f3fcfc"),
        plot.title        = element_text(face = "bold"),
        axis.title        = element_text(face = "bold"),
        legend.title      = element_text(face = "bold"),
        axis.text         = element_text(face = "bold"))
```

# Volume of international trade

```{r}

# Clean data
tbl_exports_imports <- wbstats::wb_data(indicator   = c("BX.GSR.GNFS.CD", 
                                                        "BM.GSR.GNFS.CD",
                                                        "SP.POP.TOTL"), 
                                        return_wide = TRUE,
                                        start_date  = 2022, 
                                        end_date    = 2022) %>%
  filter(if_all(BM.GSR.GNFS.CD:SP.POP.TOTL, .fns = ~ !is.na(.))) %>% 
  # Transform data
  select(country:SP.POP.TOTL) %>%
  set_names(c("country", "date", "exports", "imports", "pop")) %>%
  mutate(exports_pop = exports / pop,
         imports_pop = imports / pop) %>%
  mutate(text_exports = scales::dollar(exports, scale = 1e-9, suffix = "B", accuracy = 1),
         text_imports = scales::dollar(imports, scale = 1e-9, suffix = "B", accuracy = 1),
         text_exports_pop = scales::dollar(exports_pop),
         text_imports_pop = scales::dollar(imports_pop))
```

```{r top_exporters_2019, out.width="90%"}

tbl_exports_top_10 <- tbl_exports_imports %>%
  slice_max(order_by = exports, n = 10) %>%
  select(country, date, exports, text_exports)

tbl_exports_top_10 %>% 
  ggplot(aes(x = exports, y = fct_reorder(country, exports))) +
  geom_point(size = 4,
             color = palette_light()[[1]]) +
  geom_label(aes(label = text_exports),
             fill = palette_light()[[2]],
             hjust = -0.5) +
  geom_segment(aes(xend = 0, yend = country)) +
  xlim(0, tbl_exports_top_10$exports[1] + 0.5e12) +
  labs(x        = "",
       y        = "",
       title    = str_glue("Top 10 exporters in year {unique(tbl_exports_imports$date)}"),
       subtitle = str_glue("Variables Units: current USD Billions (Long scale)
                           Exports WDI code : BX.GSR.GNFS.CD"),
       # Use the data to extract the update date
       # for the Source: World Development Indicators - World Bank
       caption  = str_glue("Source: World Development Indicators - World Bank,
                           Last update: {unique(data$last_updated)}")) +
  theme(panel.border      = element_rect(fill = NA, color = "black"),
        plot.background   = element_rect(fill = "#f3fcfc"),
        panel.background  = element_rect(fill = "#f3f7fc"),
        legend.background = element_rect(fill = "#f3fcfc"),
        legend.position   = "bottom",
        plot.title        = element_text(face = "bold"),
        axis.title        = element_text(face = "bold"),
        legend.title      = element_text(face = "bold"),
        axis.text         = element_text(face = "bold"),
        axis.text.x       = element_blank(),
        axis.ticks.x      = element_blank())
```

# Volume of international trade

```{r top_importers_2019, out.width="90%"}

tbl_imports_top_10 <-tbl_exports_imports %>%
    slice_max(order_by = imports, n = 10) %>%
    select(country, date, imports, text_imports)

tbl_imports_top_10 %>% 
    ggplot(aes(x = imports, y = fct_reorder(country, imports))) +
    geom_point(size = 4,
               color = palette_light()[[1]]) +
    geom_label(aes(label = text_imports),
               fill = palette_light()[[2]],
               hjust = -0.5) +
    geom_segment(aes(xend = 0, yend = country)) +
    xlim(0, tbl_imports_top_10$imports[1] + 0.5e12) +
    labs(x        = "",
         y        = "",
         title    = str_glue("Top 10 importers in year {unique(tbl_exports_imports$date)}"),
         subtitle = str_glue("Variables Units: current USD Billions (Long scale)
                              Imports WDI code : BM.GSR.GNFS.CD"),
         # Use the data to extract the update date
         # for the Source: World Development Indicators - World Bank
         caption  = str_glue("Source: World Development Indicators - World Bank,
                              Last update: {unique(data$last_updated)}")) +
    theme(panel.border      = element_rect(fill = NA, color = "black"),
          plot.background   = element_rect(fill = "#f3fcfc"),
          panel.background  = element_rect(fill = "#f3f7fc"),
          legend.background = element_rect(fill = "#f3fcfc"),
          legend.position   = "bottom",
          plot.title        = element_text(face = "bold"),
          axis.title        = element_text(face = "bold"),
          legend.title      = element_text(face = "bold"),
          axis.text         = element_text(face = "bold"),
          axis.text.x       = element_blank(),
          axis.ticks.x      = element_blank())
```

# Volume of international trade

```{r top_per_capita_exporters_2019, out.width="90%"}

tbl_exports_per_capita_top_10 <- tbl_exports_imports %>%
    slice_max(order_by = exports_pop, n = 10) %>%
    select(country, date, exports_pop, text_exports_pop)

tbl_exports_per_capita_top_10 %>% 
    ggplot(aes(x = exports_pop, y = fct_reorder(country, exports_pop))) +
    geom_point(size = 4,
               color = palette_light()[[1]]) +
    geom_label(aes(label = text_exports_pop),
               fill = palette_light()[[2]],
               hjust = -0.5) +
    geom_segment(aes(xend = 0, yend = country)) +
    xlim(0, tbl_exports_per_capita_top_10$exports_pop[1] + 5e4) +
    labs(x        = "",
         y        = "",
         title    = str_glue("Top 10 per-capita exporters in year {unique(tbl_exports_imports$date)}"),
         subtitle = str_glue("Variables Units: current USD per inhabitant
                              Exports WDI code : BX.GSR.GNFS.CD,
                              Populations WDI code : SP.POP.TOTL"),
         # Use the data to extract the update date
         # for the Source: World Development Indicators - World Bank
         caption  = str_glue("Source: World Development Indicators - World Bank,
                              Last update: {unique(data$last_updated)}")) +
    theme(panel.border      = element_rect(fill = NA, color = "black"),
          plot.background   = element_rect(fill = "#f3fcfc"),
          panel.background  = element_rect(fill = "#f3f7fc"),
          legend.background = element_rect(fill = "#f3fcfc"),
          legend.position   = "bottom",
          plot.title        = element_text(face = "bold"),
          axis.title        = element_text(face = "bold"),
          legend.title      = element_text(face = "bold"),
          axis.text         = element_text(face = "bold"),
          axis.text.x       = element_blank(),
          axis.ticks.x      = element_blank())
```

# Volume of international trade

```{r top_per_capita_importers_2019, out.width="90%"}

tbl_imports_per_capita_top_10 <- tbl_exports_imports %>%
    slice_max(order_by = imports_pop, n = 10) %>%
    select(country, date, imports_pop, text_imports_pop)

tbl_imports_per_capita_top_10 %>% 
    ggplot(aes(x = imports_pop, y = fct_reorder(country, imports_pop))) +
    geom_point(size = 4,
               color = palette_light()[[1]]) +
    geom_label(aes(label = text_imports_pop),
               fill = palette_light()[[2]],
               hjust = -0.5) +
    geom_segment(aes(xend = 0, yend = country)) +
    xlim(0, tbl_imports_per_capita_top_10$imports_pop[1] + 5e4) +
    labs(x        = "",
         y        = "",
         title    = str_glue("Top 10 per-capita importers in year {unique(tbl_exports_imports$date)}"),
         subtitle = str_glue("Variables Units: current USD per inhabitant
                              Imports WDI code : BM.GSR.GNFS.CD,
                              Populations WDI code : SP.POP.TOTL"),
         # Use the data to extract the update date
         # for the Source: World Development Indicators - World Bank
         caption  = str_glue("Source: World Development Indicators - World Bank,
                              Last update: {unique(data$last_updated)}")) +
    theme(panel.border      = element_rect(fill = NA, color = "black"),
          plot.background   = element_rect(fill = "#f3fcfc"),
          panel.background  = element_rect(fill = "#f3f7fc"),
          legend.background = element_rect(fill = "#f3fcfc"),
          legend.position   = "bottom",
          plot.title        = element_text(face = "bold"),
          axis.title        = element_text(face = "bold"),
          legend.title      = element_text(face = "bold"),
          axis.text         = element_text(face = "bold"),
          axis.text.x       = element_blank(),
          axis.ticks.x      = element_blank())
```

# Patterns of international trade

- The __Harmonized System (HS)__ is a standardized numerical method of classifying traded products that is internationally accepted and maintained by the __World Customs Organization (WCO)__

- The HS code consists of 6-digits that are the same independent of the country:

    + First 2 digits designate the __HS__ chapter
    + Second 2 digits designate the __HS__ heading
    + Third 2 digits designate the __HS__ subheading
    
- Example of __HS__ code: 090111 (Coffee, not roasted, not decaffeinated)

    + Chapter __09__: Cofee, tea, mate and spices
    + Heading __01__: Coffee, whether or not roasted or decaffeinated; coffee husks and skins; coffee substitutes containing coffee in any proportion
    + Subheading __11__: Coffee, not roasted, not decaffeinated

# Patterns of international trade 

![Colombian exports __HS 4 digits (HS92) edition__ year 1995 [@oec_observatory_2022]](002_col_exports_1995_hs4_92.png){#fig-colombian_exports_1995 fig-alt="Colombian exports 1995" fig-align="center" width="450px" height="450px"}
    
# Patterns of international trade 

![Colombian exports __HS 4 digits (HS92) edition__ year 2021 [@oec_observatory_2022]](002_col_exports_2021_hs4_92.png){#fig-colombian_exports_2021 fig-alt="Colombian exports 2021" fig-align="center" width="450px" height="450px"}

# Patterns of international trade

![Coffee exporters __HS 4 digits (0901) (HS92) edition__ year 1995 [@oec_observatory_2022]](002_coffee_exports_1995_hs4_92.png){#fig-coffee_exporters_1995 fig-alt="Coffee exporters 1995" fig-align="center" width="450px" height="450px"}

# Patterns of international trade 

![Coffee exporters __HS 4 digits (0901) (HS92) edition__ year 2021 [@oec_observatory_2022]](002_coffee_exports_2021_hs4_92.png){#fig-coffee_exporters_2021 fig-alt="Coffee exporters 2021" fig-align="center" width="450px" height="450px"}

# Benefits of international trade

- International trade, economic growth and social progress [@vallejo_tlc_2006]

```{r out.width='95%', dev='png'}
grViz("
digraph boxes_and_circles {

  node [shape = circle]
  'Economic\ngrowth\nwith\nSocial\nprogress' [color = '#E31A1C'];

  node [shape = oval]

  'Macroeconomic\nstability';
  'Social\nsafety net';
  'Institutions';
  'Efficiency' [color = '#E31A1C'];

  node [shape = square]

  'Regulatory\nand\ncompetition\npolicy';
  'International\ntrade\npolicy' [color = '#E31A1C'];

  'Economic\ngrowth\nwith\nSocial\nprogress' -> 'Macroeconomic\nstability';
  'Economic\ngrowth\nwith\nSocial\nprogress' -> 'Social\nsafety net';
  'Economic\ngrowth\nwith\nSocial\nprogress' -> 'Institutions';
  'Economic\ngrowth\nwith\nSocial\nprogress' -> 'Efficiency' [color = '#E31A1C'];


  'Macroeconomic\nstability'-> 'Fiscal\npolicy';
  'Macroeconomic\nstability'-> 'Monetary\nand\nexchange\nrate\npolicies';

  'Social\nsafety net' -> 'Health,\neducation\nand\nlabor\npolicies';
  'Social\nsafety net' -> 'Pension\nand\nsocial\nassistance\npolicies';

  'Institutions' -> 'Political\nsystem';
  'Institutions' -> 'Legal\nsystem';

  'Efficiency' -> 'Regulatory\nand\ncompetition\npolicy';
  'Efficiency' -> 'International\ntrade\npolicy' [color = '#E31A1C'];

}
")
```

# Benefits of international trade

- International trade

    + Increases the total availability of products in an economy [@vallejo_propuesta_2004] [@vallejo_tlc_2006]

        + The number of varieties of products for consumers increases
    
        + The number of varieties of inputs and capital goods for producers increases 
    
    + Makes the markets more contestable [@vallejo_propuesta_2004] [@vallejo_tlc_2006]

        + Monopoly and ologopoly markets tend to be more competitive reducing prices and increasing the consumption of products in those market structures
        
# Theories of international trade

- __Mercantilism__ [@magnusson_political_2018]
- __Absolute and comparative advantage__ [@smith_wealth_2012] & [@ricardo_principles_2004]
- __The Heckscher-Ohlin__ (factor proportions) model [@heckscher_heckscher-ohlin_1991]
- __New trade theory__ [@helpman_chapter_1984], [@krugman_chapter_1995] & [@brander_chapter_1995]
- __National competitive advantage__ [@porter_competitive_1998]

# Theories of international trade

- An outline of theories of international trade

    + An alternative view of __Mercantilism__
    
        + Mercantilism was  never a well- structured doctrine built on a number of well- settled  principles [@magnusson_political_2018]
        
        + Nowadays it is erroneously related with a policy that recommends a favourable  balance  of  trade (exports > imports) and a regime characterized by direct state intervention in order to protect domestic merchants and manufacturers [@magnusson_political_2018]
        
# Theories of international trade

- __Absolute advantage__: the ability of a party (individual, company, country) to produce a product more efficiently than any other party
    
    + A party should concentrate in producing a good in which it has an absolute advantage
        
    + ¿What happend when you don't have an absolute advantage?
    
- __Comparative advantage__: the ability of a party (individual, company, country) to produce a product at a lower relative opportunity cost
    
    + Under certain conditions, even if a party doesn't have an __absolute advantage__, trade with other agent can be benefical for both of them
        
# Theories of international trade

- Example of gains of trade using the concept of __comparative advantage__ [@ridley_when_2010]
    
    + __Agent 1__ produce 1 spear in 4 hours and 1 axe in 3 hours. 
    
        + To produce both products __agent 1__ will need to allocate 7 hours 
        
    + __Agent 2__ produce 1 spear in 1 hour and 1 axe in 2 hours. 
    
        + __Agent 2__ has the absolute advantage in the prodution of spears and axes. 
        + To produce both products __agent 2__ will need to allocate 3 hours 
            
    + ¿It will be benefical for agent 1 and 2 to trade? Yes!!! __Agent 1__ can produce 2 axes in 6 hours and __agent 2__ 2 spears in 2 hours. Then they can trade 1 spear for 1 axe.
        
        + __Agent 1__ has now 1 spear and 1 axe only working 6 hours. 
        + Furthermore __agent 2__ has now 1 spear and 1 axe only working 2 hours. 
        + Each of them saves 1 hour of work in contrast with a situation in which they don't trade and produce themselves both products.
        
# Theories of international trade

- __The Heckscher-Ohlin__ (factor proportions) model
    
    + An economy exports products that are abundant and therefore cheaper in the economy and imports products that are short in supply in the economy and therefore expensive
        
- __New trade theory__
    
    + Includes the posibility of increasing returns to scale where an increase in a same amount of all inputs generates more than a proportional increase in production
        
    + Includes the possibility of strategic interactions between companies in the context of international trade and olygopoly markets
        
    + Points out the conditions in which a government can use subsides to benefit a domestic company using strategic trade policies
        
# Theories of international trade
    
- __National competitive advantage__: 
    
    + The objective is to explain why some countries are more competitive in some industries using the framework known as _Porter Diamond_ [@porter_competitive_1998]:
        
        + Factor conditions: skilled labor and infraestructure
        + Demand conditions: characteristics of the domestic market
        + Related and supporting industries: other companies that supply or distribute products related to the specific industry being examined
        + Firms strategy, structure and rivalry: market strcuture of the industry being examined
        
    + These determinants create the environment in which companies emerge and become more or less competitive in relation to other companies in other countries
    
# Acknowledgments

- To my family that supports me

- To the taxpayers of Colombia and the __[UMNG students](https://www.umng.edu.co/estudiante)__ who pay my salary

- To the __[Business Science](https://www.business-science.io/)__ and __[R4DS Online Learning](https://www.rfordatasci.com/)__ communities where I learn __[R](https://www.r-project.org/about.html)__ 

- To the __[R Core Team](https://www.r-project.org/contributors.html)__, the creators of __[RStudio IDE](https://rstudio.com/products/rstudio/)__ and the authors and maintainers of the packages __[tidyverse](https://CRAN.R-project.org/package=tidyverse)__, __[knitr](https://CRAN.R-project.org/package=knitr)__, __[tidyquant](https://CRAN.R-project.org/package=tidyquant)__, __[wbstats](https://CRAN.R-project.org/package=wbstats)__, __[DiagrammeR](https://CRAN.R-project.org/package=DiagrammeR)__ and __[tinytex](https://CRAN.R-project.org/package=tinytex)__ for allowing me to access these tools without paying for a license

- To the __[Linux kernel community](https://www.kernel.org/category/about.html)__ for allowing me the possibility to use some __[Linux distributions](https://static.lwn.net/Distributions/)__ as my main __[OS](https://en.wikipedia.org/wiki/Operating_system)__ without paying for a license
    
# References {.allowframebreaks}