Country

Indicator

Value type

Values present

Net bilateral aid flows from DAC donors, Poland (current US$)

Source: worldbank.org, 03.09.2025

Year: 2022

Country	Value	Value change, %	Rank
Afghanistan	310,000	-69.9%	37
Angola	-2,270,000	-6.2%	63
Albania	200,000	+25%	42
Argentina	50,000	+25%	55
Armenia	640,000	-64.6%	28
Azerbaijan	2,680,000	+2.29%	12
Burundi	480,000	+300%	32
Benin	10,000	0%	58
Bangladesh	550,000	-96.9%	31
Bosnia & Herzegovina	-10,000	-96.2%	60
Belarus	48,439,999	-13.9%	2
Belize	10,000	0%	58
Bolivia	20,000	-50%	57
Brazil	340,000	-34.6%	35
Botswana	30,000	+50%	56
China	-10,760,000	-2.54%	65
Côte d’Ivoire	10,000	-50%	58
Cameroon	230,000	-54.9%	40
Congo - Kinshasa	1,140,000	+1,040%	19
Congo - Brazzaville	50,000	-37.5%	55
Colombia	300,000	-28.6%	38
Comoros	50,000	0%	55
Cape Verde	0		59
Costa Rica	20,000	-33.3%	57
Cuba	10,000	-75%	58
Dominica	20,000		57
Dominican Republic	20,000	0%	57
Algeria	740,000	+164%	27
Ecuador	150,000	-25%	46
Egypt	920,000	-73.8%	21
Eritrea	10,000	-75%	58
Ethiopia	-4,150,000	-289%	64
Georgia	2,490,000	-46.3%	14
Ghana	330,000	-38.9%	36
Guinea	10,000	0%	58
Gambia	30,000	+50%	56
Grenada	0	-100%	59
Guatemala	10,000	-50%	58
Honduras	10,000		58
Haiti	10,000	0%	58
Indonesia	750,000	-39%	26
India	5,690,000	-17.7%	6
Iran	7,550,000	+5.3%	5
Iraq	1,550,000	-25.8%	17
Jamaica	30,000	0%	56
Jordan	1,640,000	+105%	16
Kazakhstan	1,730,000	-9.42%	15
Kenya	1,730,000	-56.3%	15
Kyrgyzstan	20,000	-95.7%	57
Cambodia	3,000,000	+14,900%	10
Laos	0	-100%	59
Lebanon	2,550,000	-1.16%	13
Liberia	10,000	-75%	58
Libya	30,000	-70%	56
St. Lucia	170,000		44
Sri Lanka	90,000	-47.1%	52
Lesotho	0		59
Morocco	380,000	-7.32%	34
Moldova	26,299,999	+987%	3
Madagascar	60,000	0%	54
Mexico	340,000	-29.2%	35
North Macedonia	210,000	-81.7%	41
Mali	10,000	-50%	58
Myanmar (Burma)	340,000	+113%	35
Montenegro	-640,000	+129%	62
Mongolia	4,350,000	+1,454%	7
Mozambique	10,000	-75%	58
Mauritania	0	-100%	59
Mauritius	0	-100%	59
Malawi	20,000	-33.3%	57
Malaysia	280,000	-15.2%	39
Namibia	30,000	-25%	56
Niger	200,000	-78.5%	42
Nigeria	2,700,000	-45.8%	11
Nicaragua	10,000		58
Nepal	120,000	-25%	49
Pakistan	1,080,000	-21.2%	20
Panama	10,000	-50%	58
Peru	120,000	-40%	49
Philippines	130,000	-95.7%	48
Papua New Guinea	30,000	0%	56
North Korea	10,000	-75%	58
Palestinian Territories	3,930,000	+159%	8
Rwanda	580,000	-72.9%	29
Sudan	180,000	+125%	43
Senegal	400,000	+17.6%	33
Sierra Leone	100,000	+150%	51
El Salvador	20,000	-50%	57
Somalia	20,000	-98.9%	57
Serbia	-600,000	-600%	61
South Sudan	160,000	-15.8%	45
Eswatini	10,000	0%	58
Syria	1,240,000	-25.3%	18
Chad	0		59
Togo	0	-100%	59
Thailand	560,000	-30%	30
Tajikistan	140,000	-44%	47
Turkmenistan	170,000	+13.3%	44
Tunisia	330,000	-90.6%	36
Turkey	8,700,000	-7.25%	4
Tanzania	3,810,000	+25.7%	9
Uganda	110,000	-21.4%	50
Ukraine	317,619,995	+235%	1
Uzbekistan	230,000	-87.7%	40
Venezuela	820,000	+583%	24
Vietnam	150,000	-98.7%	46
Kosovo	910,000	0%	22
Yemen	780,000	0%	25
South Africa	130,000	-18.8%	48
Zambia	70,000	+133%	53
Zimbabwe	840,000	-3.45%	23

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 = 'DC.DAC.POLL.CD'

# 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 <- 'DC.DAC.POLL.CD'

# 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

Net bilateral aid flows from DAC donors, Poland (current US$)

Year: 2022

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