Informal payments to public officials (% of firms)

Source: worldbank.org, 03.09.2025

Year: 2024

Flag Country Value Value change, % Rank
Angola Angola 19.1 -60.9% 8
Armenia Armenia 1.65 +45.9% 39
Azerbaijan Azerbaijan 5.98 +3.09% 24
Belgium Belgium 5.23 +473% 28
Benin Benin 4.71 -85.9% 31
Burkina Faso Burkina Faso 17.8 +108% 9
Bahrain Bahrain 5.64 26
Bhutan Bhutan 1.77 +1,841% 38
Canada Canada 5.35 27
China China 0.288 -97.3% 49
Cameroon Cameroon 21.6 -52.6% 7
Congo - Kinshasa Congo - Kinshasa 25.9 -48.6% 5
Congo - Brazzaville Congo - Brazzaville 26.4 -67.7% 4
Cape Verde Cape Verde 0.586 -90.3% 46
Cyprus Cyprus 3.24 -70.3% 32
Czechia Czechia 10.9 -45.3% 14
Ecuador Ecuador 12.8 +189% 11
Spain Spain 0.907 +17.3% 42
United Kingdom United Kingdom 0.618 45
Equatorial Guinea Equatorial Guinea 32.8 2
Ireland Ireland 0.0859 50
Iceland Iceland 0.863 44
Israel Israel 10.3 +4,276% 15
Italy Italy 1.6 -81.1% 40
Jamaica Jamaica 2.27 -87.3% 37
Jordan Jordan 8.21 +132% 19
Kazakhstan Kazakhstan 12.4 +56.8% 12
South Korea South Korea 1.44 -89.7% 41
Laos Laos 26.4 -28% 3
Latvia Latvia 2.98 -31.6% 33
Moldova Moldova 6.81 -45.4% 21
Mali Mali 8.48 -80.7% 18
Malta Malta 2.69 34
Malaysia Malaysia 4.86 +131% 30
Namibia Namibia 5.9 +28.4% 25
Papua New Guinea Papua New Guinea 43.4 -51.4% 1
Senegal Senegal 7.04 +12.6% 20
Serbia Serbia 6.4 -18% 23
South Sudan South Sudan 22.8 -36.8% 6
Slovenia Slovenia 2.42 -44.8% 36
Sweden Sweden 0.483 48
Eswatini Eswatini 9.74 -42.7% 16
Tajikistan Tajikistan 4.98 -57.4% 29
Turkmenistan Turkmenistan 17.2 10
Tonga Tonga 11 -12.9% 13
Tunisia Tunisia 8.62 +211% 17
Turkey Turkey 2.52 +268% 35
Uruguay Uruguay 0.876 -56.8% 43
United States United States 0.56 47
Uzbekistan Uzbekistan 6.71 -8.71% 22 

                    
# 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 = 'IC.FRM.CORR.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 <- 'IC.FRM.CORR.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))