Secondary education, teachers

Source: worldbank.org, 03.09.2025

Year: 2023

Flag Country Value Value change, % Rank
Albania Albania 23,161 -0.979% 43
Andorra Andorra 630 +4.48% 66
United Arab Emirates United Arab Emirates 96,991 +37.6% 19
Armenia Armenia 23,682 +0.535% 42
Azerbaijan Azerbaijan 116,261 -0.627% 17
Burkina Faso Burkina Faso 67,573 +2.09% 30
Bangladesh Bangladesh 546,754 -1.47% 6
Bahrain Bahrain 8,882 +1.65% 50
Bahamas Bahamas 2,637 +17.4% 59
Bosnia & Herzegovina Bosnia & Herzegovina 27,426 -2.36% 40
Belarus Belarus 72,617 -1.11% 28
Belize Belize 2,319 -7.17% 60
Bolivia Bolivia 71,093 +1.69% 29
Barbados Barbados 1,256 +1.21% 62
Brunei Brunei 5,040 -0.885% 54
China China 7,251,617 +2.72% 1
Côte d’Ivoire Côte d’Ivoire 96,802 +1.12% 20
Cameroon Cameroon 118,092 +2.53% 16
Congo - Kinshasa Congo - Kinshasa 621,578 +11.4% 5
Congo - Brazzaville Congo - Brazzaville 38,064 +47.3% 36
Cuba Cuba 84,190 -4.89% 24
Cayman Islands Cayman Islands 369 +5.43% 69
Djibouti Djibouti 3,554 +2.63% 57
Dominica Dominica 511 +1.79% 67
Dominican Republic Dominican Republic 80,736 +10.5% 25
Ecuador Ecuador 92,693 +1.67% 22
Egypt Egypt 462,917 -3.84% 7
Gibraltar Gibraltar 204 -8.52% 72
Guatemala Guatemala 92,934 -19.4% 21
Guyana Guyana 4,930 -4.86% 55
Hong Kong SAR China Hong Kong SAR China 31,510 -0.524% 39
Indonesia Indonesia 1,005,473 -23.2% 4
India India 6,730,253 +0.769% 2
Jamaica Jamaica 12,546 -0.767% 47
Jordan Jordan 72,901 +11.4% 27
Kazakhstan Kazakhstan 251,366 -6.96% 11
St. Lucia St. Lucia 1,082 +0.839% 63
Lesotho Lesotho 5,672 +2.24% 53
Macao SAR China Macao SAR China 2,848 +2.85% 58
Morocco Morocco 168,713 +7.29% 15
Monaco Monaco 445 -6.12% 68
Moldova Moldova 19,811 -1.94% 44
Mali Mali 80,395 +35.1% 26
Montenegro Montenegro 7,463 51
Mauritius Mauritius 9,279 -2.89% 49
Malaysia Malaysia 239,115 +2.89% 12
Niger Niger 24,942 -14.7% 41
Nicaragua Nicaragua 12,739 -14% 46
Nepal Nepal 99,283 -16.8% 18
Nauru Nauru 19 -24% 75
Oman Oman 43,020 +8.15% 35
Peru Peru 220,058 +2.03% 14
Palau Palau 152 +4.11% 73
Palestinian Territories Palestinian Territories 50,739 +3.13% 34
Qatar Qatar 11,503 +7.9% 48
Russia Russia 1,335,531 +0.17% 3
Rwanda Rwanda 33,641 +16.1% 37
Senegal Senegal 55,703 +1.76% 31
El Salvador El Salvador 18,676 -1.33% 45
San Marino San Marino 280 -2.44% 70
Suriname Suriname 4,520 +4.75% 56
Seychelles Seychelles 701 +3.7% 65
Syria Syria 53,486 -18% 32
Turks & Caicos Islands Turks & Caicos Islands 269 +8.03% 71
Chad Chad 33,149 +4.44% 38
Togo Togo 51,488 +56.4% 33
Thailand Thailand 226,399 -1.17% 13
Trinidad & Tobago Trinidad & Tobago 7,350 -2.42% 52
Tunisia Tunisia 89,644 +0.932% 23
Tuvalu Tuvalu 123 -44.8% 74
Ukraine Ukraine 282,556 -10.1% 9
Uzbekistan Uzbekistan 312,964 +3.04% 8
St. Vincent & Grenadines St. Vincent & Grenadines 822 +0.244% 64
Venezuela Venezuela 272,326 +342% 10
Vanuatu Vanuatu 1,348 +11.6% 61 

                    
# 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.SEC.TCHR'

# 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.SEC.TCHR'

# 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))