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007_entry_modes.qmd
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---
title: "Selecting and managing entry modes"
author: "Luis Francisco Gomez Lopez"
date: 2023-07-12
format:
beamer:
colortheme: dolphin
fonttheme: structurebold
theme: AnnArbor
link-citations: true
linkcolor: blue
include-in-header:
- text: |
\usepackage{booktabs}
\usepackage{longtable}
\usepackage{array}
\usepackage{multirow}
\usepackage{wrapfig}
\usepackage{float}
\usepackage{colortbl}
\usepackage{pdflscape}
\usepackage{tabu}
\usepackage{threeparttable}
\usepackage{threeparttablex}
\usepackage[normalem]{ulem}
\usepackage{makecell}
\usepackage{xcolor}
\usepackage{fontawesome5}
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(DiagrammeR)
# This package has been archived
# The UN Comtrade API has been rewritten
# and the package need to be rewritten
# library(comtradr)
library(tidyverse)
# This package was used to plot the treemap
# using comtradr package
# library(countrycode)
# This package was used to plot the treemap
# using comtradr package
# library(treemapify)
library(tidyquant)
library(fs)
library(knitr)
library(kableExtra)
```
# Contents
- Please Read Me
- Purpose
- Entry modes for international markets
- Exporting goods in Colombia
- Importing goods in Colombia
- Franchising in Colombia
- Turnkey project and Joint venture: cement plant Río Claro (Sonsón, Colombia) case study
- 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 13]
# Purpose
Introduce the students to the different entry modes companies use to do international business and participate directly into the international markets
# Entry modes for international markets
```{r, out.width="100%", dev='png'}
grViz("
digraph boxes_and_circles {
graph [rankdir = LR]
node [shape = circle, color = '#2C3E50']
'Entry\nmodes';
node [shape = oval, color = '#E31A1C']
'Trade';
node [shape = oval, color = '#18BC9C']
'Exporting';
'Importing';
'Countertrade';
node [shape = oval, color = '#CCBE93']
'Direct';
'Indirect';
'Barter';
'Counterpurchase';
'Offset';
'Switching trading';
'Buyback';
node [shape = oval, color = '#A6CEE3']
'Sales\nrepresentative';
'Distributor';
'Agent';
'Export management\ncompany';
'Export trading\ncompany';
node [shape = oval, color = '#CCBE93']
'Direct';
'Entry\nmodes'-> 'Trade' ;
'Trade' -> 'Exporting';
'Trade' -> 'Importing';
'Trade' -> 'Countertrade';
'Exporting' -> 'Direct';
'Exporting' -> 'Indirect';
'Countertrade'-> 'Barter';
'Countertrade'-> 'Counterpurchase';
'Countertrade'-> 'Offset';
'Countertrade'-> 'Switching trading';
'Countertrade'-> 'Buyback';
'Direct' -> 'Sales\nrepresentative';
'Direct' -> 'Distributor';
'Indirect'-> 'Agent';
'Indirect'-> 'Export management\ncompany';
'Indirect'-> 'Export trading\ncompany';
}
")
```
# Entry modes for international markets
```{r, out.width="100%", dev='png'}
grViz("
digraph boxes_and_circles {
graph [rankdir = LR]
node [shape = circle, color = '#2C3E50']
'Entry\nmodes';
node [shape = oval, color = '#E31A1C']
'Contractual';
'Investment';
node [shape = oval, color = '#18BC9C']
'Licensing';
'Franchising';
'Management contracts';
'Turnkey project';
'Wholly owned subsidiary';
'Joint venture';
'Strategic alliance';
node [shape = oval, color = '#CCBE93']
'Forward Integration';
'Backward Integration';
'Buyback';
'Multistage';
'Entry\nmodes'-> 'Contractual';
'Entry\nmodes'-> 'Investment';
'Contractual' -> 'Licensing';
'Contractual' -> 'Franchising';
'Contractual' -> 'Management contracts';
'Contractual' -> 'Turnkey project';
'Investment' -> 'Wholly owned subsidiary';
'Investment' -> 'Joint venture';
'Investment' -> 'Strategic alliance';
'Joint venture' -> 'Forward Integration';
'Joint venture' -> 'Backward Integration';
'Joint venture' -> 'Buyback';
'Joint venture' -> 'Multistage';
}
")
```
# Exporting goods in Colombia
![Colombian exports by destination __HS (HS92) edition__ year 2021 [@oec_observatory_2022]](007_col_exports_by_destination_2021.png){#fig-colombian_exports_by_destination_2021 fig-alt="Colombian exports by destination 2021" fig-align="center" width="450px" height="450px"}
```{r, out.width="90%"}
# Useless code taking into account that
# the package comtradr need to be rewritten
# Data
# col_2021_exports <- comtradr::ct_search(reporters = "Colombia",
# partners = "All",
# trade_direction = "exports",
# freq = "annual",
# start_date = 2021,
# end_date = 2021,
# commod_codes = "TOTAL",
# type = "goods")
#
# # Data clean
# col_2021_exports_clean <- col_2021_exports %>%
# as_tibble() %>%
# select(year, trade_flow,
# reporter_iso, reporter,
# partner_iso, partner,
# commodity_code, commodity,
# trade_value_usd) %>%
# arrange(desc(trade_value_usd)) %>%
# # Areas excluded: "World", "Other Asia, nes", "Free Zones", "Areas, nes"
# filter(!(partner %in% c("World", "Other Asia, nes", "Free Zones", "Areas, nes", "Br. Indian Ocean Terr."))) %>%
# # Adding continent
# left_join(countrycode::codelist %>%
# select(continent, country.name.en, iso3c), by = c("partner_iso" = "iso3c")) %>%
# select(year, trade_flow, reporter, partner_iso, partner, trade_value_usd, continent) %>%
# mutate(label_text = str_glue("{partner}
# {(trade_value_usd / sum(trade_value_usd)) %>% scales::percent(accuracy = 0.01)}"))
#
# # Plot
# col_2021_exports_clean %>%
# ggplot(aes(area = trade_value_usd,
# label = label_text,
# subgroup = continent)) +
# geom_treemap(aes(fill = continent),
# color = "black",
# alpha = 0.8) +
# geom_treemap_text() +
# scale_fill_tq() +
# labs(x = "",
# y = "",
# fill = "",
# title = "Colombia goods exports by destination as percent of total exports: year 2021",
# subtitle = str_glue("Territories excluded: Free zones, British Indian Ocean Territory, Areas not elsewhere specified
# and other Asia areas not elsewhere specified,
# Products excluded: exports of services are not included"),
# caption = str_glue("Source: United Nations International Trade Statistics Database (UN Comtrade),
# Last update: 2023-01-07")) +
# 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"),
# legend.position = "bottom")
```
# Exporting goods in Colombia[^1]
```{r}
# Files
csv_files <- dir_ls(path = "007_export_col_monthly/2022/", regexp = r'([.]csv$)')
# Data
export_col_comp_2022 <- csv_files %>%
map_dfr(.f = read_csv, col_types = cols(NIT = col_character())) %>%
select(FECH, FOBDOL, NIT, RAZ_SIAL) %>%
# Delete leading zeros in the NIT
mutate(NIT = str_replace(string = NIT, pattern = r'(^0+)', replacement = ""))
# Unique names export
unique_name_export <- export_col_comp_2022 %>%
select(NIT, RAZ_SIAL) %>%
distinct(NIT, .keep_all = TRUE)
export_col_comp_2022 %>%
group_by(NIT) %>%
summarise(export_fob_us = sum(FOBDOL)) %>%
ungroup() %>%
arrange(desc(export_fob_us)) %>%
left_join(unique_name_export, by = "NIT") %>%
select(NIT, RAZ_SIAL, export_fob_us) %>%
set_names(nm = c("NIT", "Name", "Exports (US dollars)")) %>%
slice(1:10) %>%
mutate(`Exports (US dollars)` = scales::dollar(`Exports (US dollars)`),
NIT = str_remove(string = NIT, pattern = "^0+")) %>%
# Update the year manually in the caption
kable(format = "latex", booktabs = TRUE, caption = "Top 10 companies according to exports (FOB) in 2022") %>%
kable_styling(font_size = 6, latex_options = "striped") %>%
row_spec(row = 0, bold = TRUE)
```
[^1]: Check out the full data set in https://www.dane.gov.co/ > Datos abiertos Microdatos y Metadatos > Comercio internacional > Estadísticas de Exportaciones - EXPO - 2011 A 2023 > Obtener Microdatos > Expo_2022
# Importing goods in Colombia
![Colombian imports by origin __HS (HS92) edition__ year 2021 [@oec_observatory_2022]](007_col_imports_by_origin_2021.png){#fig-colombian_imports_by_origin_2021 fig-alt="Colombian imports by origin 2021" fig-align="center" width="450px" height="450px"}
```{r, out.width="90%"}
# Useless code taking into account that
# the package comtradr need to be rewritten
# Data
# col_2021_imports <- ct_search(reporters = "Colombia",
# partners = "All",
# trade_direction = "imports",
# freq = "annual",
# start_date = 2021,
# end_date = 2021,
# commod_codes = "TOTAL",
# type = "goods")
#
# # Data clean
# col_2021_imports_clean <- col_2021_imports %>%
# as_tibble() %>%
# select(year, trade_flow,
# reporter_iso, reporter,
# partner_iso, partner,
# commodity_code, commodity,
# trade_value_usd) %>%
# arrange(desc(trade_value_usd)) %>%
# # Areas excluded: "World", "Other Asia, nes", "Free Zones", "Areas, nes"
# filter(!(partner %in% c("World", "Other Asia, nes", "Free Zones", "Colombia",
# "Br. Indian Ocean Terr.", "Fr. South Antarctic Terr."))) %>%
# # Adding continent
# left_join(codelist %>%
# select(continent, country.name.en, iso3c), by = c("partner_iso" = "iso3c")) %>%
# select(year, trade_flow, reporter, partner_iso, partner, trade_value_usd, continent) %>%
# mutate(label_text = str_glue("{partner}
# {(trade_value_usd / sum(trade_value_usd)) %>% scales::percent(accuracy = 0.01)}"))
#
# # Plot
# col_2021_imports_clean %>%
# # Drop NA values. See continent variable.
# drop_na() %>%
# ggplot(aes(area = trade_value_usd,
# label = label_text,
# subgroup = continent)) +
# geom_treemap(aes(fill = continent),
# color = "black",
# alpha = 0.8) +
# geom_treemap_text() +
# scale_fill_tq() +
# labs(x = "",
# y = "",
# fill = "",
# title = "Colombia goods imports by origin as percent of total exports: year 2021",
# subtitle = str_glue("Territories excluded: Free zones, British Indian Ocean Territory, Colombia
# other Asia areas not elsewhere specified and French Southern and Antarctic Lands
# Products excluded: exports of services are not included"),
# caption = str_glue("Source: United Nations International Trade Statistics Database (UN Comtrade),
# Last update: 2022-10-06")) +
# 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"),
# legend.position = "bottom")
```
# Importing goods in Colombia[^2]
```{r}
# Files
csv_files <- dir_ls(path = "007_import_col_monthly/2022/", regexp = r'([.]csv$)')
# Data
import_col_comp_2021 <- csv_files %>%
map_dfr(.f = read_csv) %>%
select(FECH, VACID, NIT, RZIMPO) %>%
# Delete leading zeros in the NIT
mutate(NIT = str_replace(string = NIT, pattern = r'(^0+)', replacement = ""))
# Unique names
unique_name_import <- import_col_comp_2021 %>%
select(NIT, RZIMPO) %>%
distinct(NIT, .keep_all = TRUE)
# Table
import_col_comp_2021 %>%
group_by(NIT) %>%
summarise(import_cif_us = sum(VACID)) %>%
ungroup() %>%
arrange(desc(import_cif_us)) %>%
left_join(unique_name_import, by = "NIT") %>%
select(NIT, RZIMPO, import_cif_us) %>%
set_names(nm = c("NIT", "Name", "Imports (US dollars)")) %>%
# There are a lot of NAs in the NIT and RZIMPO
slice(1:11) %>%
drop_na() %>%
mutate(`Imports (US dollars)` = scales::dollar(`Imports (US dollars)`),
NIT = str_remove(string = NIT, pattern = "^0+")) %>%
# Update the year manually in the caption
kable(format = "latex", booktabs = TRUE, caption = "Top 10 companies according to imports (CIF) in 2022") %>%
kable_styling(font_size = 6, latex_options = "striped") %>%
row_spec(row = 0, bold = TRUE)
```
[^2]: Check out the full data set in https://www.dane.gov.co/ > Datos abiertos Microdatos y Metadatos > Comercio internacional > Estadísticas de Importaciones - IMPO - 2012 A 2023 > Obtener Microdatos > Impo_2022_1 e Impo_2022_2
# Franchising in Colombia
- Not explicit regulated by any local law or Andean regulation [@reyes_primer_2016]
+ Colombian Civil Code, Article 1602: franchisor and franchisee are free to perform a franchising relationship
+ Franchisor should register her trademarks, logos and slogans in agreement with the Andean Decision 486 of 2000[^3]
- Colfranquicias - Camara Colombiana de Franquicias: https://www.colfranquicias.com/
+ Franchises
+ Consultants
+ Suppliers
[^3]: It regulates the Common Regime on Industrial Property in Colombia
# Turnkey project and Joint venture: cement plant Río Claro (Sonsón, Colombia) case study
- Construction of a cement plant in Sonsón, Colombia[^4]
+ Production capacity:
+ 3150 tons per day of clinker
+ 1.35 million tons of cement per year
+ Cost
+ 238 million USD (224.6 million EUR)
+ Creation of Jobs
+ 800 jobs in the course of its construction
+ 220 direct and 250 indirect jobs when it becomes fully operational
[^4]: [@rowland_ecocementos_2017]
# Turnkey project and Joint venture: cement plant Río Claro (Sonsón, Colombia) case study
- Commissioned by Ecocementos S.A.S
+ Joint venture between Corona (Colombia) and Cementos Molins (Spain)
- Construction company
+ OHL Industrial (Spain)
- Equipment supplier company
+ FLSmidth (Denmark)
# 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)__, __[kableExtra](https://CRAN.R-project.org/package=kableExtra)__, __[tidyquant](https://CRAN.R-project.org/package=tidyquant)__, __[comtradr](https://CRAN.R-project.org/package=comtradr)__, __[countrycode](https://CRAN.R-project.org/package=countrycode)__,
__[treemapify](https://CRAN.R-project.org/package=treemapify)__,
__[fs](https://CRAN.R-project.org/package=fs)__,
__[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