Appendix A: Regions and their Countries

Table of content

List regions of the WIR2022 and their countries

The World Inequality Report 2022 (WIR2022) describes global trends in inequality. It mostly condense data in eight different regions. See the following graph as an example:

The image shows the income shares for the bottom 50%, middle 40% and top 10% in 2021 dividing the world into eight different regions. With the exception of Europe the top 10% earn the biggest share of the income with the following order from lowest to the highest 10%: Europe, East Asia, North America, Russia & Central Asia, South & South-East Asia, Latin America, Sub-Saharan Africa, MENA (Middle East & North Africa). Example: In Latin America the top 10% captures 55% of national income compared to 36% of Europe.
Graph A.1: A typical example for a graph in the World Inequality Report 2022 (WIR2022), showing the division of the world into eight different world regions (“MENA” stands for Middle East & North Arfica).

In this appendix I want to know the countries that form each of these eight regions.

A.1 Data

My first task was to look for data where I could extract the information I am interested in. I scanned the files of the free accessible GitHub repository of the WID. I found country-codes-core.xlsx, an Excel file with the data I am looking for. It is sorted by a two letter code in the first column named appropriately code. “Core” means – in contrast to other country-codes files – that it includes a column corecountry where the number 1 functions as a marker for a core country. The file itself has many hidden rows which feature either regions smaller than a country (like Alabama for US or Bavaria for Germany) or bigger than a country (like Asia or Western Europe).

Important

country-codes-core.xlsx contains many hidden rows. It is therefore necessary to filter by corecountry == 1.

A.1.1 Download data

The following code chunk is only applied once. It downloads the dataset, selects only the core countries and stores the file as country-code.RDS

R Code A.1 : Download the country-codes-core file, manipulate and save it

Code
## run this code chunk only once (manually)

## define variables
url <- "https://github.com/WIDworld/wid-world/raw/master/data-input/country-codes/country-codes-core.xlsx"
chapter_folder = "chap80"

## create folder for chapter if not already done
chap_folder <-
        base::paste0(
            here::here(),
            paste0("/data/", chapter_folder, "/")
        )
    if (!base::file.exists(chap_folder))
    {base::dir.create(chap_folder)}

## get country-codes-core.xlsx
destfile <- base::paste0(chap_folder, "country-codes-core.xlsx")
utils::download.file(url, destfile)
tmp <- readxl::read_xlsx(destfile)


country_codes <- tmp |> 
    ## filter for core countries
    dplyr::filter(corecountry == 1) |> 
    ## convert all region columns to factor variables
    dplyr::mutate(dplyr::across(
        tidyselect::starts_with("region"), forcats::as_factor)
        )

## save cleaned data
pb_save_data_file("chap80", country_codes, "country_codes.rds")

(For this R code chunk is no output available)

A.1.2 Eplore Data

An inspection of the data file shows that column region5 contains the regions used in WIR2022.

R Code A.2 : Explore Data

Code
country_codes <- base::readRDS("data/chap80/country_codes.rds")

skimr::skim(country_codes)

glue::glue(" ")
glue::glue("############################################################")
glue::glue("Display number of countries for each region")
glue::glue("############################################################")
glue::glue(" ")
country_codes |> 
    dplyr::pull(region5) |> 
    forcats::fct_count()
Data summary
Name country_codes
Number of rows 216
Number of columns 10
_______________________
Column type frequency:
character 3
factor 5
numeric 2
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
code 0 1 2 2 0 216 0
titlename 0 1 4 32 0 216 0
shortname 0 1 3 32 0 216 0

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
region1 0 1.00 FALSE 5 Afr: 54, Asi: 53, Ame: 47, Eur: 46
region2 0 1.00 FALSE 18 Wes: 27, Car: 23, Wes: 20, Eas: 19
region3 184 0.15 FALSE 1 Eur: 32
region4 24 0.89 FALSE 9 Oth: 48, Oth: 38, Oth: 22, Oth: 18
region5 0 1.00 FALSE 8 Sub: 49, Eur: 46, Lat: 43, MEN: 20

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
corecountry 0 1 1.00 0.00 1 1 1 1 1 ▁▁▇▁▁
TH 0 1 0.19 0.39 0 0 0 0 1 ▇▁▁▁▂ 
#>  
#> ############################################################
#> Display number of countries for each region
#> ############################################################
#>  
#> # A tibble: 8 × 2
#>   f                           n
#>   <fct>                   <int>
#> 1 Europe                     46
#> 2 MENA                       20
#> 3 South & South-East Asia    20
#> 4 Latin America              43
#> 5 Russia & Central Asia      11
#> 6 Sub-Saharan Africa         49
#> 7 North America & Oceania    19
#> 8 East Asia                   8

A.2 List countries

Code Collection A.1 : List countries for each regions used in WIR2022

R Code A.3 : Countries of the European region used in the WIR2022

Code
wir2022_country_codes <- country_codes |> 
    dplyr::select(code, shortname, region5)

wir2022_country_codes |> 
    dplyr::filter(region5 == "Europe") |> 
    print(n = 50)
#> # A tibble: 46 × 3
#>    code  shortname              region5
#>    <chr> <chr>                  <fct>  
#>  1 AD    Andorra                Europe 
#>  2 AL    Albania                Europe 
#>  3 AT    Austria                Europe 
#>  4 BA    Bosnia and Herzegovina Europe 
#>  5 BE    Belgium                Europe 
#>  6 BG    Bulgaria               Europe 
#>  7 CH    Switzerland            Europe 
#>  8 CY    Cyprus                 Europe 
#>  9 CZ    Czech Republic         Europe 
#> 10 DE    Germany                Europe 
#> 11 DK    Denmark                Europe 
#> 12 EE    Estonia                Europe 
#> 13 ES    Spain                  Europe 
#> 14 FI    Finland                Europe 
#> 15 FR    France                 Europe 
#> 16 GB    United Kingdom         Europe 
#> 17 GG    Guernsey               Europe 
#> 18 GI    Gibraltar              Europe 
#> 19 GR    Greece                 Europe 
#> 20 HR    Croatia                Europe 
#> 21 HU    Hungary                Europe 
#> 22 IE    Ireland                Europe 
#> 23 IM    Isle of Man            Europe 
#> 24 IS    Iceland                Europe 
#> 25 IT    Italy                  Europe 
#> 26 JE    Jersey                 Europe 
#> 27 KS    Kosovo                 Europe 
#> 28 LI    Liechtenstein          Europe 
#> 29 LT    Lithuania              Europe 
#> 30 LU    Luxembourg             Europe 
#> 31 LV    Latvia                 Europe 
#> 32 MC    Monaco                 Europe 
#> 33 MD    Moldova                Europe 
#> 34 ME    Montenegro             Europe 
#> 35 MK    North Macedonia        Europe 
#> 36 MT    Malta                  Europe 
#> 37 NL    Netherlands            Europe 
#> 38 NO    Norway                 Europe 
#> 39 PL    Poland                 Europe 
#> 40 PT    Portugal               Europe 
#> 41 RO    Romania                Europe 
#> 42 RS    Serbia                 Europe 
#> 43 SE    Sweden                 Europe 
#> 44 SI    Slovenia               Europe 
#> 45 SK    Slovakia               Europe 
#> 46 SM    San Marino             Europe

R Code A.4 : Countries of the MENA region (Middle East & North Afrika)

Code
wir2022_country_codes |> 
    dplyr::filter(region5 == "MENA") |> 
    print(n = 50)
#> # A tibble: 20 × 3
#>    code  shortname            region5
#>    <chr> <chr>                <fct>  
#>  1 AE    United Arab Emirates MENA   
#>  2 BH    Bahrain              MENA   
#>  3 DZ    Algeria              MENA   
#>  4 EG    Egypt                MENA   
#>  5 IL    Israel               MENA   
#>  6 IQ    Iraq                 MENA   
#>  7 IR    Iran                 MENA   
#>  8 JO    Jordan               MENA   
#>  9 KW    Kuwait               MENA   
#> 10 LB    Lebanon              MENA   
#> 11 LY    Libya                MENA   
#> 12 MA    Morocco              MENA   
#> 13 OM    Oman                 MENA   
#> 14 PS    Palestine            MENA   
#> 15 QA    Qatar                MENA   
#> 16 SA    Saudi Arabia         MENA   
#> 17 SY    Syrian Arab Republic MENA   
#> 18 TN    Tunisia              MENA   
#> 19 TR    Turkey               MENA   
#> 20 YE    Yemen                MENA

R Code A.5 : Countries of the South & South-East Asia region used in the WIR2022

Code
wir2022_country_codes |> 
    dplyr::filter(region5 == "South & South-East Asia") |> 
    print(n = 50)
#> # A tibble: 20 × 3
#>    code  shortname         region5                
#>    <chr> <chr>             <fct>                  
#>  1 AF    Afghanistan       South & South-East Asia
#>  2 BD    Bangladesh        South & South-East Asia
#>  3 BN    Brunei Darussalam South & South-East Asia
#>  4 BT    Bhutan            South & South-East Asia
#>  5 ID    Indonesia         South & South-East Asia
#>  6 IN    India             South & South-East Asia
#>  7 KH    Cambodia          South & South-East Asia
#>  8 LA    Lao PDR           South & South-East Asia
#>  9 LK    Sri Lanka         South & South-East Asia
#> 10 MM    Myanmar           South & South-East Asia
#> 11 MV    Maldives          South & South-East Asia
#> 12 MY    Malaysia          South & South-East Asia
#> 13 NP    Nepal             South & South-East Asia
#> 14 PG    Papua New Guinea  South & South-East Asia
#> 15 PH    Philippines       South & South-East Asia
#> 16 PK    Pakistan          South & South-East Asia
#> 17 SG    Singapore         South & South-East Asia
#> 18 TH    Thailand          South & South-East Asia
#> 19 TL    Timor-Leste       South & South-East Asia
#> 20 VN    Viet Nam          South & South-East Asia

R Code A.6 : Countries of the Latin America region used in the WIR2022

Code
wir2022_country_codes |> 
    dplyr::filter(region5 == "Latin America") |> 
    print(n = 50)
#> # A tibble: 43 × 3
#>    code  shortname                        region5      
#>    <chr> <chr>                            <fct>        
#>  1 AG    Antigua and Barbuda              Latin America
#>  2 AI    Anguilla                         Latin America
#>  3 AR    Argentina                        Latin America
#>  4 AW    Aruba                            Latin America
#>  5 BB    Barbados                         Latin America
#>  6 BO    Bolivia                          Latin America
#>  7 BQ    Bonaire, Sint Eustatius and Saba Latin America
#>  8 BR    Brazil                           Latin America
#>  9 BS    Bahamas                          Latin America
#> 10 BZ    Belize                           Latin America
#> 11 CL    Chile                            Latin America
#> 12 CO    Colombia                         Latin America
#> 13 CR    Costa Rica                       Latin America
#> 14 CU    Cuba                             Latin America
#> 15 CW    Curacao                          Latin America
#> 16 DM    Dominica                         Latin America
#> 17 DO    Dominican Republic               Latin America
#> 18 EC    Ecuador                          Latin America
#> 19 GD    Grenada                          Latin America
#> 20 GT    Guatemala                        Latin America
#> 21 GY    Guyana                           Latin America
#> 22 HN    Honduras                         Latin America
#> 23 HT    Haiti                            Latin America
#> 24 JM    Jamaica                          Latin America
#> 25 KN    Saint Kitts and Nevis            Latin America
#> 26 KY    Cayman Islands                   Latin America
#> 27 LC    Saint Lucia                      Latin America
#> 28 MS    Montserrat                       Latin America
#> 29 MX    Mexico                           Latin America
#> 30 NI    Nicaragua                        Latin America
#> 31 PA    Panama                           Latin America
#> 32 PE    Peru                             Latin America
#> 33 PR    Puerto Rico                      Latin America
#> 34 PY    Paraguay                         Latin America
#> 35 SR    Suriname                         Latin America
#> 36 SV    El Salvador                      Latin America
#> 37 SX    Sint Maarten (Dutch part)        Latin America
#> 38 TC    Turks and Caicos Islands         Latin America
#> 39 TT    Trinidad and Tobago              Latin America
#> 40 UY    Uruguay                          Latin America
#> 41 VC    Saint Vincent and the Grenadines Latin America
#> 42 VE    Venezuela                        Latin America
#> 43 VG    Virgin Islands, British          Latin America

R Code A.7 : Countries of the Russia & Central Asia region used in the WIR2022

Code
wir2022_country_codes |> 
    dplyr::filter(region5 == "Russia & Central Asia") |> 
    print(n = 50)
#> # A tibble: 11 × 3
#>    code  shortname          region5              
#>    <chr> <chr>              <fct>                
#>  1 AM    Armenia            Russia & Central Asia
#>  2 AZ    Azerbaijan         Russia & Central Asia
#>  3 BY    Belarus            Russia & Central Asia
#>  4 GE    Georgia            Russia & Central Asia
#>  5 KG    Kyrgyzstan         Russia & Central Asia
#>  6 KZ    Kazakhstan         Russia & Central Asia
#>  7 RU    Russian Federation Russia & Central Asia
#>  8 TJ    Tajikistan         Russia & Central Asia
#>  9 TM    Turkmenistan       Russia & Central Asia
#> 10 UA    Ukraine            Russia & Central Asia
#> 11 UZ    Uzbekistan         Russia & Central Asia

R Code A.8 : Countries of the Sub-Saharan African region used in the WIR2022

Code
wir2022_country_codes |> 
    dplyr::filter(region5 == "Sub-Saharan Africa") |> 
    print(n = 50)
#> # A tibble: 49 × 3
#>    code  shortname                region5           
#>    <chr> <chr>                    <fct>             
#>  1 AO    Angola                   Sub-Saharan Africa
#>  2 BF    Burkina Faso             Sub-Saharan Africa
#>  3 BI    Burundi                  Sub-Saharan Africa
#>  4 BJ    Benin                    Sub-Saharan Africa
#>  5 BW    Botswana                 Sub-Saharan Africa
#>  6 CD    DR Congo                 Sub-Saharan Africa
#>  7 CF    Central African Republic Sub-Saharan Africa
#>  8 CG    Congo                    Sub-Saharan Africa
#>  9 CI    Cote d'Ivoire            Sub-Saharan Africa
#> 10 CM    Cameroon                 Sub-Saharan Africa
#> 11 CV    Cabo Verde               Sub-Saharan Africa
#> 12 DJ    Djibouti                 Sub-Saharan Africa
#> 13 ER    Eritrea                  Sub-Saharan Africa
#> 14 ET    Ethiopia                 Sub-Saharan Africa
#> 15 GA    Gabon                    Sub-Saharan Africa
#> 16 GH    Ghana                    Sub-Saharan Africa
#> 17 GM    Gambia                   Sub-Saharan Africa
#> 18 GN    Guinea                   Sub-Saharan Africa
#> 19 GQ    Equatorial Guinea        Sub-Saharan Africa
#> 20 GW    Guinea-Bissau            Sub-Saharan Africa
#> 21 KE    Kenya                    Sub-Saharan Africa
#> 22 KM    Comoros                  Sub-Saharan Africa
#> 23 LR    Liberia                  Sub-Saharan Africa
#> 24 LS    Lesotho                  Sub-Saharan Africa
#> 25 MG    Madagascar               Sub-Saharan Africa
#> 26 ML    Mali                     Sub-Saharan Africa
#> 27 MR    Mauritania               Sub-Saharan Africa
#> 28 MU    Mauritius                Sub-Saharan Africa
#> 29 MW    Malawi                   Sub-Saharan Africa
#> 30 MZ    Mozambique               Sub-Saharan Africa
#> 31 NA    Namibia                  Sub-Saharan Africa
#> 32 NE    Niger                    Sub-Saharan Africa
#> 33 NG    Nigeria                  Sub-Saharan Africa
#> 34 RW    Rwanda                   Sub-Saharan Africa
#> 35 SC    Seychelles               Sub-Saharan Africa
#> 36 SD    Sudan                    Sub-Saharan Africa
#> 37 SL    Sierra Leone             Sub-Saharan Africa
#> 38 SN    Senegal                  Sub-Saharan Africa
#> 39 SO    Somalia                  Sub-Saharan Africa
#> 40 SS    South Sudan              Sub-Saharan Africa
#> 41 ST    Sao Tome and Principe    Sub-Saharan Africa
#> 42 SZ    Swaziland                Sub-Saharan Africa
#> 43 TD    Chad                     Sub-Saharan Africa
#> 44 TG    Togo                     Sub-Saharan Africa
#> 45 TZ    Tanzania                 Sub-Saharan Africa
#> 46 UG    Uganda                   Sub-Saharan Africa
#> 47 ZA    South Africa             Sub-Saharan Africa
#> 48 ZM    Zambia                   Sub-Saharan Africa
#> 49 ZW    Zimbabwe                 Sub-Saharan Africa

R Code A.9 : Countries of the North America & Oceania region used in the WIR2022

Code
wir2022_country_codes |> 
    dplyr::filter(region5 == "North America & Oceania") |> 
    print(n = 50)
#> # A tibble: 19 × 3
#>    code  shortname        region5                
#>    <chr> <chr>            <fct>                  
#>  1 AU    Australia        North America & Oceania
#>  2 BM    Bermuda          North America & Oceania
#>  3 CA    Canada           North America & Oceania
#>  4 FJ    Fiji             North America & Oceania
#>  5 FM    Micronesia       North America & Oceania
#>  6 GL    Greenland        North America & Oceania
#>  7 KI    Kiribati         North America & Oceania
#>  8 MH    Marshall Islands North America & Oceania
#>  9 NC    New Caledonia    North America & Oceania
#> 10 NR    Nauru            North America & Oceania
#> 11 NZ    New Zealand      North America & Oceania
#> 12 PF    French Polynesia North America & Oceania
#> 13 PW    Palau            North America & Oceania
#> 14 SB    Solomon Islands  North America & Oceania
#> 15 TO    Tonga            North America & Oceania
#> 16 TV    Tuvalu           North America & Oceania
#> 17 US    USA              North America & Oceania
#> 18 VU    Vanuatu          North America & Oceania
#> 19 WS    Samoa            North America & Oceania

R Code A.10 : Countries of the East Asia region used in the WIR2022

Code
wir2022_country_codes |> 
    dplyr::filter(region5 == "East Asia") |> 
    print(n = 50)
#> # A tibble: 8 × 3
#>   code  shortname   region5  
#>   <chr> <chr>       <fct>    
#> 1 CN    China       East Asia
#> 2 HK    Hong Kong   East Asia
#> 3 JP    Japan       East Asia
#> 4 KP    North Korea East Asia
#> 5 KR    Korea       East Asia
#> 6 MN    Mongolia    East Asia
#> 7 MO    Macao       East Asia
#> 8 TW    Taiwan      East Asia