Country

Indicator

Value type

Values present

Children out of school, primary, male

Source: worldbank.org, 01.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Albania	4,758	+28.8%	61
Andorra	140	-14.6%	90
Armenia	7,498	+22.8%	54
Australia	4,640	-19%	62
Austria	2,817	+95.5%	68
Azerbaijan	13,068	-51.4%	45
Belgium	4,898	-18.8%	59
Benin	26,653	-47.4%	35
Burkina Faso	635,559	+14.2%	6
Bulgaria	6,688	+3.82%	56
Bahrain	5,319	+219%	58
Bosnia & Herzegovina	12,179	+0.512%	46
Belarus	8,671	-22.5%	52
Belize	2,336	+55.1%	70
Bolivia	26,027	-17.3%	36
Brazil	319,994	-11.1%	11
Barbados	606	+59.9%	87
Brunei	932	-28.4%	82
Bhutan	3,402	-1.85%	66
Botswana	22,079	-21%	38
Canada	47,805	+12.8%	26
Switzerland	808	+0.748%	86
Chile	4,869	-61.6%	60
Côte d’Ivoire	144,985	-15.5%	15
Colombia	158,138	+6.63%	14
Costa Rica	10,882	-0.43%	48
Cuba	18,547	+106%	41
Cayman Islands	49	-54.2%	98
Germany	36,131	+34.3%	31
Dominica	393	-3.68%	88
Denmark	1,398	+19.2%	77
Dominican Republic	47,271	-27.2%	27
Eritrea	101,796	-0.39%	19
Spain	27,232	-4.71%	34
Estonia	841	-16.3%	83
Ethiopia	1,954,473	-2.05%	2
Finland	3,664	+43.5%	64
Micronesia (Federated States of)	1,211	-2.18%	79
Georgia	2,792	-86.3%	69
Guatemala	89,725	-0.467%	20
Hong Kong SAR China	77	-88.1%	96
Honduras	138,222	-8.31%	17
Hungary	7,054	-0.592%	55
Iceland	246	-3.53%	89
Israel	20,769	+3.49%	40
Italy	32,310	+16.9%	32
Jamaica	10,459	-47.2%	49
Jordan	9,144	-28.8%	51
Kazakhstan	74,558	+2.89%	23
Kyrgyzstan	17,339	+16.1%	42
Cambodia	144,927	+60.7%	16
Kiribati	816	+97.6%	85
South Korea	13,453	-28.7%	44
Laos	29,190	+45.7%	33
Liberia	165,518	+11.6%	13
Lesotho	44,681	-3.64%	29
Luxembourg	109	-65.9%	94
Latvia	1,104	+11.1%	80
Macao SAR China	4,222	+47.3%	63
Maldives	1,721	+59.4%	76
Marshall Islands	127	-11.2%	93
North Macedonia	1,831	-60.6%	75
Malta	1,047	+45.6%	81
Mongolia	11,786	+1.99%	47
Malaysia	85,302	+16.5%	21
Niger	796,959	+4.98%	4
Netherlands	2,975	+70.2%	67
Norway	2,143	-7.87%	74
Nauru	131	-20.1%	92
New Zealand	5,341	+85.2%	57
Oman	21,136	+4.91%	39
Pakistan	2,988,654	-7.57%	1
Panama	9,941	-1.34%	50
Philippines	752,911	-8.32%	5
Palau	136	+404%	91
Paraguay	47,069	-1.55%	28
Palestinian Territories	25,046	+27.1%	37
Qatar	7,582	-34.4%	53
Romania	81,626	+73.9%	22
Russia	127,688	+28.6%	18
Saudi Arabia	40,075	-33.8%	30
Senegal	456,317	+5.48%	9
Singapore	1,281	+84.8%	78
El Salvador	59,341	-0.585%	24
San Marino	59	0%	97
Serbia	2,333	-56.8%	71
Slovakia	3,503	-32.4%	65
Slovenia	833	-22.2%	84
Sweden	2,172	+13.5%	73
Seychelles	105	+156%	95
Syria	436,379	-18.4%	10
Chad	300,130	+3.74%	12
Tunisia	13,923	+33.3%	43
Tuvalu	19	-77.4%	99
Tanzania	1,056,194	+7.04%	3
United States	517,634	+3.36%	8
Uzbekistan	54,815	+124%	25
Vanuatu	2,182	+70.5%	72
South Africa	524,454	+12.9%	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 = 'SE.PRM.UNER.MA'

# 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 <- 'SE.PRM.UNER.MA'

# 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

Children out of school, primary, male

Year: 2022

Acquire full dataset with Python or R from source