Country

Indicator

Value type

Values present

Children out of school, male (% of male primary school age)

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Albania	5.91	+32.3%	47
Andorra	6.11	-14%	46
Armenia	8.5	+21.6%	32
Australia	0.398	-18.9%	96
Austria	1.58	+93.5%	84
Azerbaijan	3.84	-49.7%	60
Belgium	1.2	-18.2%	87
Benin	2.48	-48.9%	71
Burkina Faso	32.5	+11.8%	4
Bulgaria	5.34	+6%	50
Bahrain	8.38	+203%	35
Bosnia & Herzegovina	14.5	+2.73%	18
Belarus	3.55	-23.7%	65
Belize	10	+53.1%	27
Bolivia	3.61	-17.3%	64
Brazil	4.25	-11.8%	55
Barbados	6.4	+62.1%	45
Brunei	4.35	-28.9%	54
Bhutan	8.79	+1.65%	30
Botswana	11.1	-30.8%	25
Canada	3.76	+12.4%	62
Switzerland	0.296	-0.394%	97
Chile	0.62	-61.5%	92
Côte d’Ivoire	6.43	-16.8%	44
Colombia	7.84	+6.25%	38
Costa Rica	4.74	-0.507%	53
Cuba	4.79	+106%	52
Cayman Islands	2.18	-53.5%	74
Germany	2.29	+31.1%	73
Dominica	11.8	-2.11%	23
Denmark	0.627	+21.5%	91
Dominican Republic	8.08	-27%	37
Eritrea	42	+0.0839%	1
Spain	1.88	-3.23%	78
Estonia	1.86	-14.7%	79
Ethiopia	21.2	-3.02%	9
Finland	1.93	+44.2%	77
Micronesia (Federated States of)	17	-0.601%	16
Georgia	1.63	-86.4%	82
Guatemala	7.62	-1.15%	39
Hong Kong SAR China	0.0428	-87.4%	98
Honduras	21.7	-8.04%	8
Hungary	3.75	-1.53%	63
Iceland	1.45	-2.58%	85
Israel	3.83	+1.6%	61
Italy	2.43	+19.3%	72
Jamaica	8.74	-46.2%	31
Jordan	1.59	-28.5%	83
Kazakhstan	9.38	+1.07%	28
Kyrgyzstan	5.48	+12.8%	49
Cambodia	14.3	+60.2%	19
Kiribati	8.49	+94.2%	33
South Korea	0.972	-28.2%	89
Laos	7.54	+44.4%	40
Liberia	38.6	+10.8%	2
Lesotho	25.1	-4.81%	7
Luxembourg	0.51	-66.2%	95
Latvia	1.8	+11%	80
Macao SAR China	18.3	+33.3%	12
Maldives	7.04	+80.1%	42
Marshall Islands	4.16	-4.93%	58
North Macedonia	3.25	-57.9%	67
Malta	6.95	+41.2%	43
Mongolia	5.89	-1.75%	48
Malaysia	5.34	+14.9%	51
Niger	36.8	+1.27%	3
Netherlands	0.53	+71.8%	94
Norway	0.94	-6.95%	90
Nauru	12.7	-22.7%	21
New Zealand	2.68	+87.2%	69
Oman	11.3	-0.433%	24
Pakistan	20.3	-7.75%	10
Panama	4.17	-2.17%	57
Philippines	10.5	-8.49%	26
Palau	17.2	+408%	14
Paraguay	12	-2.77%	22
Palestinian Territories	8.95	+24.2%	29
Qatar	8.41	-33%	34
Romania	15.5	+64.2%	17
Russia	3.27	+25.5%	66
Senegal	32.3	+3.58%	5
Singapore	1.06	+82.9%	88
El Salvador	17.1	-0.211%	15
San Marino	7.24	+5.15%	41
Serbia	1.76	-55.8%	81
Slovakia	2.93	-32.7%	68
Slovenia	1.22	-21.5%	86
Sweden	0.56	+13.1%	93
Seychelles	2.12	+141%	76
Syria	29.2	-12.7%	6
Chad	18.9	+0.651%	11
Tunisia	2.13	+30.3%	75
Tuvalu	2.59	-77.2%	70
Tanzania	17.5	+4.27%	13
United States	4.17	+3.5%	56
Uzbekistan	4.03	+118%	59
Vanuatu	8.29	+67.3%	36
South Africa	13	+11.6%	20

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

Children out of school, male (% of male primary school age)

Year: 2022

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