Country

Indicator

Value type

Values present

Persistence to last grade of primary, male (% of cohort)

Source: worldbank.org, 01.09.2025

Year: 2009

Country	Value	Value change, %	Rank
Angola	36.6	+25.1%	93
Albania	95.1	+2.01%	36
Argentina	96.7	+2.25%	28
Armenia	96.3	-1.75%	29
Azerbaijan	95.4	-4.61%	35
Burundi	51.8	-7.34%	91
Benin	56.1	-13.4%	87
Burkina Faso	61.2	-13.6%	83
Bangladesh	61.9	-7.73%	81
Bulgaria	97.6	+3.85%	23
Bahamas	91.4	+17.4%	48
Bosnia & Herzegovina	99.1		10
Belize	89.1	-4.5%	53
Bolivia	85.2		58
Brunei	96	-0.365%	32
Bhutan	81.5	-3.49%	61
Central African Republic	53.1	+4.32%	89
Cameroon	66.8	-2.69%	76
Congo - Kinshasa	58	-26%	86
Colombia	84	+2.99%	59
Costa Rica	87.6	-5.68%	54
Cuba	96.2	+0.673%	30
Czechia	98.5	-0.883%	14
Germany	95.7	+0.577%	33
Dominica	85.8	-3.94%	57
Denmark	98.6	-0.812%	11
Algeria	93.2	+1.98%	46
Ecuador	91.2	-6.23%	49
Egypt	95.6	+3.69%	34
Eritrea	70.9	-4.29%	73
Estonia	97.4	-0.807%	25
Ethiopia	35.9	+2.33%	94
Finland	99.5	-0.192%	2
Georgia	94.1	-0.836%	40
Guinea	74.3	+9.43%	71
Equatorial Guinea	59.7	+13%	84
Guatemala	68.1	-13.6%	75
Hong Kong SAR China	99.2	-0.284%	7
Honduras	76.1	+10.6%	69
Croatia	98.5	-0.118%	13
Hungary	98.1	+0.608%	20
Iran	92.9	-3.87%	47
Iceland	97.4	+0.836%	24
Italy	99.5	+0.122%	3
Kazakhstan	98.4	-0.249%	17
Kyrgyzstan	98	+2.33%	22
St. Kitts & Nevis	77.6	+16.1%	67
South Korea	99.2	+0.331%	6
Kuwait	96	-1.92%	31
Laos	70.6	+4.42%	74
St. Lucia	93.5	+3.07%	44
Liechtenstein	81.3	+1.99%	63
Lesotho	61.8	+30.2%	82
Lithuania	98.2	-0.0215%	19
Latvia	94.7	-0.768%	37
Morocco	90.6	+16.4%	50
Moldova	93.6	-1.21%	42
Madagascar	33.8	-30.2%	96
Mexico	93.2	+0.237%	45
Mali	78.7	-3.09%	66
Myanmar (Burma)	72.2	+3.1%	72
Mozambique	37.1	+0.392%	92
Mauritius	98.5	+4.51%	15
Malawi	52.1	+23.6%	90
Namibia	81.8	+2.5%	60
Niger	62.7	-9.56%	79
Nigeria	62.5	-22.3%	80
Norway	99.3	-0.558%	4
Pakistan	63.7	+3.57%	77
Panama	93.7	+9.21%	41
Peru	89.8	+2.52%	52
Poland	99.2	+1.86%	8
Paraguay	81.4	+7.02%	62
Romania	97.1	+4.73%	27
Rwanda	35	-5.51%	95
Sudan	78.8		65
Senegal	58.1	+2.96%	85
El Salvador	86	+15.9%	56
Serbia	98.4	-0.53%	18
Suriname	75.6	-7.68%	70
Slovakia	98.1	+0.501%	21
Slovenia	99.1	-0.599%	9
Sweden	99.3	+0.527%	5
Eswatini	81.2	+16.7%	64
Syria	94.3	+1.63%	39
Chad	28.3	-16.2%	97
Togo	54.6	-28.1%	88
Tajikistan	98.6	+0.359%	12
Timor-Leste	63.3	-10.6%	78
Trinidad & Tobago	86.9	-6.87%	55
Tunisia	94.6	+0.679%	38
Turkey	98.5	+8.6%	16
Tanzania	76.4	+7.41%	68
Ukraine	97.4	-0.148%	26
Uruguay	93.5	-0.00281%	43
Uzbekistan	99.7	+1.87%	1
Venezuela	90.1	+1.13%	51

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.PRSL.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.PRSL.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

Persistence to last grade of primary, male (% of cohort)

Year: 2009

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