Country

Indicator

Value type

Values present

Taxes less subsidies on products (current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	921,543,850	-11.1%	116
Albania	3,590,492,030	+19.1%	80
Andorra	371,353,915	+11.7%	125
Argentina	105,575,592,027	+3%	15
Armenia	2,580,602,704	-0.531%	86
Australia	108,553,472,644	+1.3%	14
Austria	54,208,962,760	+3.91%	21
Azerbaijan	7,272,352,941	+8.74%	63
Belgium	63,247,170,883	+2.18%	19
Benin	2,041,905,833	+8.41%	94
Burkina Faso	1,995,015,948	+13.5%	98
Bangladesh	14,950,122,421	+3.88%	46
Bulgaria	14,437,640,015	+14.5%	47
Bahamas	1,952,500,000	+0.159%	99
Bosnia & Herzegovina	4,453,817,403	+2.72%	72
Belarus	9,681,805,636	+9.04%	51
Brazil	300,265,484,924	+8.3%	6
Brunei	-247,417,638	-6.5%	143
Botswana	1,054,326,213	+7.12%	112
Central African Republic	331,538,813	+15.8%	127
Canada	150,062,828,700	+1.79%	11
Switzerland	24,507,680,906	+7.83%	39
Chile	32,776,784,175	-3.32%	32
Côte d’Ivoire	7,939,951,401	+8.69%	60
Cameroon	3,605,525,513	+4.72%	79
Congo - Kinshasa	2,341,775,245	-2.22%	92
Congo - Brazzaville	854,709,775	+3.35%	117
Colombia	39,618,261,919	+10.8%	27
Comoros	59,068,075	+4.11%	141
Cape Verde	427,684,548	+12.9%	124
Costa Rica	7,577,789,043	+13.1%	61
Cyprus	4,209,505,461	+6.35%	75
Czechia	30,257,141,011	+7.81%	36
Germany	439,249,477,067	+11.4%	2
Djibouti	261,502,917	+4.32%	131
Dominica	116,825,926	+5.34%	139
Denmark	48,207,481,305	+4.52%	22
Dominican Republic	8,711,482,624	+6.26%	54
Ecuador	8,546,045,200	+21.5%	56
Egypt	18,647,457,250	-6.11%	41
Spain	153,412,278,119	+8.58%	10
Estonia	5,356,015,793	+10%	69
Finland	37,407,069,000	+2.1%	29
Fiji	1,243,208,809	+20.5%	109
France	335,820,338,377	+8.65%	5
Gabon	1,134,149,922	+12.9%	111
United Kingdom	361,299,503,958	+13.6%	4
Georgia	4,294,345,316	+6.93%	74
Ghana	5,478,868,100	+5.01%	67
Guinea	1,921,085,366	+13.1%	100
Gambia	181,452,419	+16.9%	134
Guinea-Bissau	95,412,154	+4.52%	140
Equatorial Guinea	132,133,144	+11%	138
Greece	34,260,666,909	+10.5%	31
Grenada	251,015,059	+4.96%	132
Guatemala	7,522,652,077	+9.15%	62
Guyana	732,438,976	+47.3%	121
Honduras	3,652,162,188	+6.6%	78
Croatia	15,739,184,931	+12.6%	44
Haiti	622,520,057	+4.18%	122
Hungary	31,250,304,788	+8.87%	34
Indonesia	60,053,912,243	+0.0963%	20
India	365,028,988,917	+11.5%	3
Ireland	32,247,564,793	+7.71%	33
Iran	11,599,251,437	+8%	50
Iceland	3,685,057,795	+12.2%	77
Israel	47,744,353,012	+15.6%	23
Italy	252,976,127,917	+7.41%	7
Jamaica	3,434,048,860	+2.69%	82
Jordan	6,716,389,014	+2.6%	64
Kazakhstan	18,581,424,528	-4.13%	42
Kenya	8,256,790,583	-5.17%	59
Kyrgyzstan	2,536,074,655	+6.92%	87
Cambodia	2,817,197,528	+6.43%	85
St. Kitts & Nevis	140,362,963	+3.56%	137
Kuwait	-11,530,048,461	-0.889%	146
Laos	1,757,081,754	+18.9%	103
Libya	-2,050,780,927	+0.256%	145
St. Lucia	336,951,852	+7.15%	126
Sri Lanka	8,611,774,118	+64.5%	55
Lesotho	328,211,833	+10.2%	129
Lithuania	8,863,308,427	+11.6%	53
Luxembourg	8,336,464,064	+10.1%	58
Latvia	5,628,390,344	+8.77%	65
Morocco	17,951,910,194	+23.1%	43
Moldova	2,520,206,619	+9.98%	88
Madagascar	1,448,009,415	+19.1%	106
Maldives	983,205,412	+2.32%	115
Mexico	118,868,329,107	+10.1%	13
North Macedonia	2,033,868,443	-0.031%	95
Mali	1,906,216,365	+11.8%	101
Malta	1,843,573,028	+17.2%	102
Montenegro	1,700,134,795	+13.9%	104
Mongolia	2,447,219,447	+26.7%	89
Mozambique	2,413,885,247	-3.83%	90
Mauritania	822,893,923	+13%	118
Mauritius	2,027,591,489	+8.47%	97
Malawi	745,643,319	-13.4%	120
Malaysia	4,973,237,637	+10.7%	70
Namibia	1,255,223,491	+14.4%	108
Niger	584,581,798	+17.4%	123
Nigeria	5,550,657,565	-20.8%	66
Nicaragua	2,206,374,137	+12.9%	93
Netherlands	124,106,826,927	+8.78%	12
Norway	44,276,975,572	+3.28%	24
Nepal	4,928,714,863	+5.71%	71
Oman	-794,538,362	+31.1%	144
Pakistan	21,971,681,514	+24.1%	40
Panama	2,032,461,200	-0.0549%	96
Peru	25,966,287,052	+28.3%	37
Papua New Guinea	1,372,134,384	-5.77%	107
Poland	101,518,218,903	+29.6%	16
Portugal	40,805,608,374	+8.53%	25
Paraguay	3,582,513,861	+12.6%	81
Qatar	1,137,637,363	+10.2%	110
Romania	35,322,307,759	+15.7%	30
Russia	195,877,452,894	-4.33%	8
Rwanda	983,822,992	-1.52%	114
Saudi Arabia	67,635,200,000	+7.11%	17
Senegal	3,215,472,008	+3.58%	83
Singapore	30,468,638,329	+17.4%	35
Sierra Leone	185,449,002	+6.82%	133
El Salvador	4,294,940,000	+9.34%	73
Serbia	13,412,399,943	+12.5%	49
São Tomé & Príncipe	58,280,662	+12.9%	142
Slovakia	13,455,794,637	+16.3%	48
Slovenia	8,349,992,737	+9.48%	57
Sweden	63,391,090,728	+2.68%	18
Seychelles	329,603,867	-0.487%	128
Turks & Caicos Islands	308,510,000	+13.7%	130
Chad	809,979,657	+18.9%	119
Togo	989,144,308	+8.16%	113
Turkey	154,042,350,550	+21.5%	9
Tanzania	15,329,195,432	-0.984%	45
Uganda	3,975,398,665	+6.63%	76
Ukraine	25,269,149,972	+14.8%	38
Uruguay	9,357,619,008	+4.29%	52
United States	626,659,000,000	+6.32%	1
Uzbekistan	5,371,669,727	+7.79%	68
St. Vincent & Grenadines	169,307,407	+7.65%	136
Vietnam	38,756,317,701	+8.11%	28
Samoa	171,269,059	+3.85%	135
Kosovo	2,358,892,410	+13.4%	91
South Africa	40,006,748,884	+3.5%	26
Zambia	1,470,255,766	-8.79%	105
Zimbabwe	3,089,654,546	+22.1%	84

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 = 'NY.TAX.NIND.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 <- 'NY.TAX.NIND.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

Taxes less subsidies on products (current US$)

Year: 2024

Acquire full dataset with Python or R from source