Country

Indicator

Value type

Values present

External health expenditure per capita (current US$)

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Afghanistan	17.2	+8.75%	43
Angola	4.74	+52.6%	104
Albania	14.3	+464%	53
United Arab Emirates	0		145
Argentina	17.2	+30.6%	42
Armenia	3.46	-34.7%	109
Antigua & Barbuda	1.65	+64%	119
Australia	0		145
Azerbaijan	8.84	+861%	79
Burundi	13.1	+19.6%	57
Belgium	0.657		132
Benin	10.8	-0.69%	68
Burkina Faso	11.8	+14.4%	60
Bangladesh	11.3	+153%	65
Bahrain	0		145
Bahamas	10.6	+9.21%	70
Bosnia & Herzegovina	19.4	+319%	39
Belarus	0.854	-14.8%	129
Belize	13.6	+50.8%	55
Bolivia	15.1	+276%	50
Brazil	0.698	-31.3%	131
Barbados	8.75	-5.79%	82
Brunei	0		145
Bhutan	23.1	+130%	34
Botswana	30.3	+14.7%	25
Central African Republic	17	+3%	44
Canada	0		145
Switzerland	0		145
Chile	0		145
China	0.327	+21,232%	138
Côte d’Ivoire	15.5	+25.8%	49
Cameroon	10.3	-5.24%	71
Congo - Kinshasa	9.42	+16.3%	75
Congo - Brazzaville	7.67	+41.7%	85
Comoros	52.6	+106%	13
Cape Verde	23.7	+163%	33
Costa Rica	0.294	-35.2%	139
Cyprus	33.5	+19%	21
Djibouti	21.1	-45.6%	35
Dominica	2.55	-95.7%	115
Denmark	0		145
Dominican Republic	24.6	+287%	31
Algeria	0.834	-34.6%	130
Ecuador	1.58	-26.3%	120
Egypt	2.6	+133%	113
Eritrea	6.08	-24.2%	94
Estonia	0.213	-24.5%	141
Ethiopia	6.85	-7.08%	90
Finland	0		145
Fiji	15	-32.9%	51
Micronesia (Federated States of)	306	-4%	3
Gabon	8.78	-2.83%	81
United Kingdom	0.5	-2.57%	136
Georgia	2.06	-24%	116
Ghana	13	-6.83%	58
Guinea	11.4	+14.1%	64
Gambia	6.99	+65.1%	89
Guinea-Bissau	12	-24.8%	59
Equatorial Guinea	1.57	+52.5%	121
Greece	3.77	+54.1%	106
Grenada	9.62	-46.7%	74
Guatemala	8.1	+1.8%	83
Guyana	17.3	+214%	41
Honduras	8.83	-30.1%	80
Croatia	0.119	-17.7%	142
Haiti	16.2	-20.6%	47
Hungary	0		145
Indonesia	1.26	-61.4%	123
India	0.93	+21.6%	126
Iran	1.02	+79.4%	124
Iraq	3.24	+72.1%	110
Iceland	0		145
Israel	37	+68.8%	17
Jamaica	5.96	+16.8%	96
Jordan	20.4	-18.8%	37
Japan	0		145
Kazakhstan	1.01	+178%	125
Kenya	16.6	-4.46%	46
Kyrgyzstan	6.74	+65%	91
Cambodia	10.2	-40.6%	73
Kiribati	34.4	-40.6%	20
St. Kitts & Nevis	0		145
South Korea	0		145
Kuwait	0		145
Laos	15	-42.5%	52
Lebanon	28.6	-12%	26
Liberia	19.8	+2.19%	38
Libya	5.08	-2.62%	101
St. Lucia	86.7	-0.56%	8
Sri Lanka	20.9	+203%	36
Lesotho	48.6	-5.38%	15
Lithuania	11.7	+28.2%	61
Luxembourg	77.9	-10.1%	10
Latvia	2.98	+15.7%	112
Morocco	6.22	-22.6%	92
Moldova	10.2	-28.2%	72
Madagascar	5.36	-22.9%	99
Maldives	32.8	-75.7%	23
Mexico	0	-100%	145
Marshall Islands	456	+3.82%	1
Mali	2.58	+3.19%	114
Malta	0		145
Myanmar (Burma)	7.6	+6.06%	86
Mongolia	110	+243%	7
Mozambique	26.8	+3.5%	27
Mauritania	15.8	+131%	48
Mauritius	3.65	-53.8%	107
Malawi	25.6	-10.1%	30
Malaysia	0.0196	-2.8%	144
Namibia	36.2	+8.17%	18
Niger	4.81	-4.86%	103
Nigeria	6.15	-4.51%	93
Nicaragua	10.8	+21.9%	69
Netherlands	0.599	+10.6%	133
Norway	0		145
Nepal	9.13	+7.75%	76
Nauru	254	-26.5%	5
Oman	0		145
Pakistan	5.1	+18.7%	100
Panama	6.06	-60.5%	95
Peru	0.917	-9.77%	127
Philippines	0.568	-94.6%	135
Palau	422	+11.6%	2
Papua New Guinea	26.1	+8.06%	29
Poland	0.858	+30%	128
Portugal	2.03	-24.1%	117
Paraguay	0.0607	-95.4%	143
Palestinian Territories	48.6	-6.65%	14
Qatar	0		145
Romania	0	-100%	145
Rwanda	33	+51.6%	22
Saudi Arabia	0		145
Sudan	5.59	+162%	98
Senegal	13.5	+2.63%	56
Singapore	0		145
Solomon Islands	26.5	+0.9%	28
Sierra Leone	11	-4.46%	66
El Salvador	5.71	+35.6%	97
Somalia	7.74	+14.6%	84
South Sudan	24.3	+50.7%	32
São Tomé & Príncipe	55.7	-31.8%	12
Suriname	5.06	-30.2%	102
Slovenia	4.61	-24.9%	105
Eswatini	79.5	+18.9%	9
Seychelles	0		145
Syria	7.01	+80.9%	88
Chad	7.52	+78.7%	87
Togo	11.5	+35%	63
Thailand	0.268	-16.6%	140
Tajikistan	8.85	+1.68%	78
Turkmenistan	3.62	+313%	108
Timor-Leste	39.5	-2.56%	16
Tonga	173	+9.77%	6
Trinidad & Tobago	0.572	-7.68%	134
Tunisia	11.7	+117%	62
Tuvalu	294	-46.5%	4
Tanzania	13.8	-20.8%	54
Uganda	18.4	-28.1%	40
Uruguay	0	-100%	145
United States	0		145
Uzbekistan	0.359	+808%	137
St. Vincent & Grenadines	2	-86.9%	118
Venezuela	1.47	+90.9%	122
Vietnam	2.99	+49.9%	111
Vanuatu	66.8	-25%	11
Samoa	16.9	-51.6%	45
Yemen	9.12	+29%	77
South Africa	10.8	+35.3%	67
Zambia	31.5	-14.9%	24
Zimbabwe	35.3	+23.4%	19

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.PC.CD'

# 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.PC.CD'

# 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 per capita (current US$)

Year: 2022

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