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Dictionary to look up region geographies based on countries used in UN DESA International Migrant Stock.

Source: R/dict_ims.R
dict_ims.Rd

Intended for use as a custom dictionary with the countrycode package, where the existing UN region and area codes do not match those used by UN DESA in the WPP, see https://github.com/vincentarelbundock/countrycode/issues/253

Usage

dict_ims

Format

Data frame with 243 rows and 18 columns. One of first three columns intended as input for origin in countrycode.

name

Country name

iso3c

ISO numeric code

iso3n

ISO 3 letter code

Remaining columns intended as input for destination in countrycode.

name_short

Short country name

ims

Country in UN DESA International Migration Stock data. Some codes added for older political geographies to match World Bank data and older country units in IMS

region

Geographic region of country (6)

region_sub

Geographic sub region of country (22). Filled using region if none given in original data

region_sdg

SDG region of country (8)

region_sdg_sub

Sub SDG region of country (9). Filled using region_sdg if none given in original data

region_wb

World Bank region

un_develop

UN development group of country (3)

wb_income

World Bank income group of country (3)

wb_income_detail

Detailled World Bank income group of country (4)

lldc

Indicator variable for Land-Locked Developing Countries (32)

sids

Indicator variable for Small Island Developing States (58)

region_as2014

Region grouping used for global chord diagram plots by Abel and Sander (2014)

region_sab2014

Region grouping used for global chord diagram plots by Sander, Abel and Bauer (2014)

region_a2018

Region grouping used for global chord diagram plots by Abel (2018)

region_ac2022

Region grouping used for global chord diagram plots by Abel and Cohen (2022)

Source

The aggregates_correspondence_table_2020_1.xlsx file of United Nations Department of Economic and Social Affairs, Population Division (2020). International Migrant Stock 2020.

Examples

dict_ims
#> # A tibble: 243 × 18
#>    name      iso3c iso3n name_short ims   region region_sub region_sdg region_wb
#>    <chr>     <chr> <dbl> <chr>      <lgl> <chr>  <chr>      <chr>      <chr>    
#>  1 Burundi   BDI     108 Burundi    TRUE  Africa Eastern A… Sub-Sahar… Sub-Saha…
#>  2 Comoros   COM     174 Comoros    TRUE  Africa Eastern A… Sub-Sahar… Sub-Saha…
#>  3 Djibouti  DJI     262 Djibouti   TRUE  Africa Eastern A… Sub-Sahar… Middle E…
#>  4 Eritrea   ERI     232 Eritrea    TRUE  Africa Eastern A… Sub-Sahar… Sub-Saha…
#>  5 Ethiopia  ETH     231 Ethiopia   TRUE  Africa Eastern A… Sub-Sahar… Sub-Saha…
#>  6 Ethiopia… ETI     231 Ethiopia … FALSE Africa Eastern A… Sub-Sahar… Sub-Saha…
#>  7 Kenya     KEN     404 Kenya      TRUE  Africa Eastern A… Sub-Sahar… Sub-Saha…
#>  8 Madagasc… MDG     450 Madagascar TRUE  Africa Eastern A… Sub-Sahar… Sub-Saha…
#>  9 Malawi    MWI     454 Malawi     TRUE  Africa Eastern A… Sub-Sahar… Sub-Saha…
#> 10 Mauritius MUS     480 Mauritius  TRUE  Africa Eastern A… Sub-Sahar… Sub-Saha…
#> # ℹ 233 more rows
#> # ℹ 9 more variables: un_develop <chr>, wb_income <chr>,
#> #   wb_income_detail <chr>, lldc <lgl>, sids <lgl>, region_as2014 <chr>,
#> #   region_sab2014 <chr>, region_a2018 <chr>, region_ac2022 <chr>
if (FALSE) {
library(tidyverse)
library(countrycode)
# download Abel and Cohen (2019) estimates
f <- read_csv("https://ndownloader.figshare.com/files/38016762", show_col_types = FALSE)
f

# use dictionary to get region to region flows
d <- f %>%
  mutate(
    orig = countrycode(
      sourcevar = orig, custom_dict = dict_ims,
      origin = "iso3c", destination = "region"),
    dest = countrycode(
      sourcevar = dest, custom_dict = dict_ims,
      origin = "iso3c", destination = "region")
  ) %>%
  group_by(year0, orig, dest) %>%
  summarise_all(sum)
d
}