migest (version 2.0.4)

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

Description

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)

Examples

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
}