Country

Indicator

Value type

Values present

External health expenditure (% of current health expenditure)

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Afghanistan	21.3	+9.92%	38
Angola	4.68	+1.03%	79
Albania	3.45	+534%	84
United Arab Emirates	0		146
Argentina	1.25	+2.95%	100
Armenia	0.512	-42.3%	113
Antigua & Barbuda	0.152	+27.5%	130
Australia	0		146
Azerbaijan	2.91	+693%	88
Burundi	53.2	+12.4%	5
Belgium	0.0122		140
Benin	32	-0.232%	22
Burkina Faso	20.9	+15.3%	40
Bangladesh	18.5	+143%	45
Bahrain	0		146
Bahamas	0.453	-4.96%	116
Bosnia & Herzegovina	2.91	+338%	87
Belarus	0.161	-22%	129
Belize	4.57	+57.3%	80
Bolivia	4.92	+238%	78
Brazil	0.0822	-37.8%	131
Barbados	0.672	+0.684%	109
Brunei	0		146
Bhutan	15	+54.5%	55
Botswana	6.34	+18.1%	75
Central African Republic	35.6	-0.588%	19
Canada	0		146
Switzerland	0		146
Chile	0		146
China	0.0486	+21,163%	135
Côte d’Ivoire	18	+34.9%	49
Cameroon	14.4	-4.46%	56
Congo - Kinshasa	38.6	+2.97%	16
Congo - Brazzaville	15.1	+122%	54
Comoros	42.8	+68%	12
Cape Verde	8.28	+161%	68
Costa Rica	0.0301	-35.3%	138
Cuba	0.0345	+176%	137
Cyprus	1.17	+29.6%	101
Djibouti	25.8	-42.1%	29
Dominica	0.444	-96.1%	117
Denmark	0		146
Dominican Republic	5.33	+249%	76
Algeria	0.464	-24.1%	114
Ecuador	0.321	-25.5%	121
Egypt	1.52	+141%	96
Eritrea	22.5	-22.8%	35
Estonia	0.0107	-21%	141
Ethiopia	25.3	-10.5%	30
Finland	0		146
Fiji	6.91	+2.49%	72
Micronesia (Federated States of)	77.2	-3.52%	1
Gabon	3.55	-10%	82
United Kingdom	0.00993	+8.58%	143
Georgia	0.431	-34.2%	118
Ghana	15.8	+6.85%	53
Guinea	20.7	-8.58%	41
Gambia	24.2	+45%	32
Guinea-Bissau	18.2	-22.1%	47
Equatorial Guinea	0.827	+90%	107
Greece	0.213	+60.9%	124
Grenada	1.85	-47.4%	93
Guatemala	2.04	-12.3%	91
Guyana	3.25	+173%	85
Honduras	3.52	-29.3%	83
Croatia	0.00888	-15.3%	144
Haiti	31	-12.5%	23
Hungary	0		146
Indonesia	0.985	-51.8%	105
India	1.17	+15.5%	102
Iran	0.427	+76.3%	119
Iraq	1.27	+70%	98
Iceland	0		146
Israel	0.876	+67.4%	106
Jamaica	1.27	-7.46%	99
Jordan	6.91	-17.1%	73
Japan	0		146
Kazakhstan	0.239	+159%	123
Kenya	18.4	-0.587%	46
Kyrgyzstan	7.85	+39.4%	69
Cambodia	9.34	-35.1%	65
Kiribati	15.8	-28.3%	52
St. Kitts & Nevis	0		146
South Korea	0		146
Kuwait	0		146
Laos	36.3	-4.53%	18
Lebanon	7.3	-30.1%	70
Liberia	19.9	-5.32%	43
Libya	1.82	+7.28%	94
St. Lucia	13.4	-9.26%	59
Sri Lanka	14.4	+227%	57
Lesotho	36.3	-17.5%	17
Lithuania	0.637	+29.9%	110
Luxembourg	1.11	-2.26%	104
Latvia	0.181	+33.8%	127
Morocco	3.12	-17.7%	86
Moldova	2.57	-26.1%	90
Madagascar	32.8	-18.9%	20
Maldives	2.85	-77.7%	89
Mexico	0	-100%	146
Marshall Islands	60.1	+7.07%	3
Mali	8.68	+6.23%	67
Malta	0		146
Myanmar (Burma)	13.1	+30.2%	61
Mongolia	24.5	+152%	31
Mozambique	54.2	-5.38%	4
Mauritania	17.5	+87.9%	51
Mauritius	0.618	-53.6%	112
Malawi	64.7	+7.88%	2
Malaysia	0.00429	+1.17%	145
Namibia	8.92	+10.3%	66
Niger	18.2	+27%	48
Nigeria	6.76	-14%	74
Nicaragua	5.16	+19.4%	77
Netherlands	0.0103	+25%	142
Norway	0		146
Nepal	10.3	-18.8%	64
Nauru	11.2	-41.3%	63
Oman	0		146
Pakistan	13.2	+31.1%	60
Panama	0.412	-62%	120
Peru	0.206	-7.78%	126
Philippines	0.311	-93.9%	122
Palau	21.3	+8.12%	36
Papua New Guinea	32.2	-18.7%	21
Poland	0.0719	+29%	134
Portugal	0.0786	-18.8%	132
Paraguay	0.0126	-95.4%	139
Palestinian Territories	13.9	-2.98%	58
Qatar	0		146
Romania	0	-100%	146
Rwanda	42.9	+19.3%	11
Saudi Arabia	0		146
Sudan	17.7	+70%	50
Senegal	21.2	+14.5%	39
Singapore	0		146
Solomon Islands	27.4	+3.05%	27
Sierra Leone	28	+9.11%	25
El Salvador	1.14	+27.1%	103
Somalia	50.4	+9.26%	6
South Sudan	49.1	+5.37%	9
São Tomé & Príncipe	30.9	-29.3%	24
Suriname	1.47	-40.3%	97
Slovenia	0.168	-23.7%	128
Eswatini	28	+24.4%	26
Seychelles	0		146
Syria	20.4	+35.8%	42
Chad	18.7	+67.3%	44
Togo	21.3	+31.8%	37
Thailand	0.0725	-18%	133
Tajikistan	11.3	-2.86%	62
Turkmenistan	0.625	+237%	111
Timor-Leste	22.6	-26.4%	34
Tonga	45.7	-0.906%	10
Trinidad & Tobago	0.0442	-16.2%	136
Tunisia	4.38	+120%	81
Tuvalu	27.1	-43.2%	28
Tanzania	38.7	-16.5%	15
Uganda	41.6	-16.9%	13
Uruguay	0	-100%	146
United States	0		146
Uzbekistan	0.212	+738%	125
St. Vincent & Grenadines	0.461	-86.3%	115
Venezuela	0.705	+48.7%	108
Vietnam	1.59	+33.4%	95
Vanuatu	49.3	-21.8%	8
Samoa	7.14	-44.6%	71
Yemen	23.9	+8.39%	33
South Africa	1.9	+40.7%	92
Zambia	41.4	-16.3%	14
Zimbabwe	50	+10.9%	7

Python
R

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'SH.XPD.EHEX.CH.ZS'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)

                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'SH.XPD.EHEX.CH.ZS'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))

Compare

External health expenditure (% of current health expenditure)

Year: 2022

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