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The goal of gdho is to provide a detailed list of global humanitarian organizations complied by Humanitarian Outcomes which contains basic information such as organization website and headquarter location, as well as, operational information such as annual expenditure. The original database is updated annually and this package version uses data retrieved on September 20, 2023.

Humanitarian Organization Headquarters Concentration


You can install the development version of gdho from GitHub with:

# install.packages("devtools")

Alternatively, you can download the individual datasets as CSV or XLSX file from the table below.

dataset CSV XLSX
gdho_full Download CSV Download XLSX
gdho Download CSV Download XLSX


The package provides access to 2 datasets gdho and gdho_full. They are essentially the same data where the former is a concise version that removes detailed country columns (200+ columns) about whether this country has the humanitarian organization. Therefore here we only describe the dataset gdho.

All the organisations included the database have responded to humanitarian needs in at least one emergency context, individually or in partnership with other organisations, even if their stated mission is not strictly humanitarian. Not included are NGOs devoted to development, human rights, or political causes, or that do not work in emergency settings.


The gdho data set has 33 variables and 4556 observations.

gdho |>
  head() |>
  gt::gt() |>
id year name abbreviated_name type international_or_national website hq_location year_founded year_closed countries_of_operation_count sector religious_or_secular religion red_cross_code_of_conduct_signatory chs_member interaction_member icva_member staff staff_imputed natl natl_imputed intl intl_imputed percent_intl ope_approx_usd ope_imputed ope/staff ope_inflation_adjusted ope_original_currency humexp_approx_usd humexp_imputed humexp_inflation_adjusted
5420 2021 Al Ta’alouf Charity Al Ta’alouf NNGO NA Syrian Arab Republic NA NA 1 NA NA NA NA NA NA NA 890 A 890 A NA NA NA 13386794 A 15041.34 14437416 NA 12850182 A 13858689
22 2020 Action Africa Help-International AAH-I INGO International Kenya 1987 NA 4 NA Secular NA NA NA NA NA NA NA NA NA NA NA NA 14658708 A 17682.40 16578200 NA 12833628 A 14514134
23 2022 Action Contre la Faim International (ACF/ACH/AAH) ACF INGO International France 1979 NA 58 NA Secular NA 1 1 1 1 7912 A NA NA NA NA NA 526 A 0.07 526 Euro 463128000 NA NA NA
32 2022 ActionAid International NA INGO International South Africa 1972 NA 45 NA Secular NA 1 NA NA NA 3249 A NA NA NA NA NA 188 A 0.06 188 Euro 165240000 NA NA NA
40 2021 Adventist Development and Relief Agency ADRA INGO International United States 1956 NA 74 NA Religious Christian 1 NA NA NA NA NA NA NA NA NA NA 254598415 A 89647.33 274579794 NA 77966928 A 84085924
46 2021 Afghanaid A-Aid INGO International United Kingdom 1983 NA 2 NA Secular NA 1 NA NA NA NA NA NA NA NA NA NA 14357419 A 12627.46 15484217 GBP 10480916 NA NA NA

For an overview of the variable names, see the following table.

#> Rows: 67 Columns: 5
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (5): directory, file_name, variable_name, variable_type, description
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
variable_name variable_type description
id integer A unique Id for each organisation
year integer Latest year information is available for
name character Full organisation name
abbreviated_name character Organisation abbreviation or acronym
type integer National NGO (NNGO), International NGO (INGO), UN, Red Cross/Crescent classification
international_or_national integer Organisation’s reach
website character Organisation page url
hq_location character Country where HQ office is located
year_founded integer Year of establishment
year_closed integer Year closed or last year of visible activity
countries_of_operation_count integer Number of countries operational in
sector integer Children/youth, coordination, disabilities, education, environment, food security/agriculture, health, landmines, livelihoods, logistics/communications, nutrition, refugees, shelter, veterinary, water/sanitation, women
religious_or_secular integer Organisations religious affiliation
religion character Specific religious affiliation (ex. Catholic, Islamic, Jewish)
red_cross_code_of_conduct_signatory integer Signatory status of the organisation
chs_member integer Signatory status to the Core Humanitarian Standard on Quality and Accountability 
interaction_member integer Membership status
icva_member integer Membership status
staff integer Actual number of total staff
staff_imputed character Imputed number of total staff
natl integer Actual number of national staff
natl_imputed character Imputed number of national staff
intl integer Actual number of international staff
intl_imputed character Imputed number of international staff
percent_intl double Percent of total staff that are international
ope_approx_usd double Actual approximate annual operational program expenditure in USD
ope_imputed character Imputed approximate annual operational program expenditure in USD
ope/staff double Percent of operational program expenditure per staff member
ope_inflation_adjusted double Operational program expenditure adjusted for inflation
ope_original_currency character Actual approximate operational program expenditure in original currency used by organisation
humexp_approx_usd double Approximate humanitarian expenditure in USD
humexp_imputed character Imputed approximate humanitarian expenditure in USD
humexp_inflation_adjusted double Approximate humanitarian expenditure adjusted for inflation


The humanitarian organizations are categorized into 5 types: INGO (International NGO), NNGO (National NGO), UN (United Nation), Red Cross/Crescent, and NA (Not Available). Most of the organizations fall into the NNGO type. For different types of organizations, how is their organization reach distribute?

ggplot(data = gdho) +
  geom_bar(aes(x = type, fill=`international_or_national`)) +
  labs(title = "Organization type distribution", fill = "organization reach")

Throughout the years, how do different types of humanitarian organizations increase?

count_by_year <- gdho |>
  filter(! |>
  group_by(year_founded, type) |>
  summarise(count = n())
#> `summarise()` has grouped output by 'year_founded'. You can override using the
#> `.groups` argument.
ggplot(data = count_by_year) +
  geom_line(aes(x = year_founded, y = count, color = type)) +
  labs(title = "Temporal trend of founding humanitarian organizations", color = "organization type")


Data are available as CC-BY.


#> To cite package 'gdho' in publications use:
#>   Zhong M, Stoddard A, Mangono T (2024). "gdho: Global Database of
#>   Humanitarian Organizations." doi:10.5281/zenodo.10727977
#>   <>.
#> A BibTeX entry for LaTeX users is
#>   @Misc{zhongstoddard,
#>     title = {gdho: Global Database of Humanitarian Organizations},
#>     author = {Mian Zhong and Abby Stoddard and Tichakunda Mangono},
#>     year = {2024},
#>     doi = {10.5281/zenodo.10727977},
#>     abstract = {A dataset of global humanitarian organizations collected by Humanitarian Outcomes.},
#>     version = {0.0.1},
#>   }

[1] GDHO project description