Country

Indicator

Value type

Values present

Trademark applications, resident, by count

Source: worldbank.org, 03.09.2025

Year: 2021

Country	Value	Value change, %	Rank
Albania	1,823	+41.6%	87
Andorra	919	+27.6%	98
United Arab Emirates	8,281	+29.1%	55
Argentina	70,131	+8.88%	19
Armenia	4,233	+22%	69
Antigua & Barbuda	371	+24.1%	104
Australia	97,211	+8.03%	15
Austria	17,472	+4.79%	41
Azerbaijan	5,657	+42.6%	60
Bangladesh	10,831	+10.7%	50
Bulgaria	10,906	+1.07%	49
Bahrain	405	+11.6%	102
Bosnia & Herzegovina	1,013	+17.2%	97
Belarus	4,610	-7.39%	68
Bolivia	4,838	+25.1%	64
Brazil	346,752	+33%	6
Barbados	194	+32%	107
Brunei	177	-24.4%	108
Bhutan	155	-39.2%	110
Canada	66,458	+6.23%	20
Switzerland	46,420	+3.03%	22
Chile	53,520	+14.6%	21
China	9,192,753	+0.836%	1
Colombia	33,617	+7.16%	28
Cape Verde	65	-16.7%	112
Costa Rica	8,878	+4.1%	54
Cuba	1,840	-35.6%	86
Curaçao	157		109
Cyprus	1,603	+21.4%	88
Czechia	22,855	+6.51%	36
Germany	246,296	+3.34%	11
Djibouti	36	+16.1%	114
Dominica	8	+60%	120
Denmark	5,520	+4.64%	61
Dominican Republic	9,806	+21.5%	53
Algeria	11,147	-9.74%	48
Ecuador	13,951	+12.8%	45
Egypt	43,212	+48.3%	25
Spain	72,620	+0.996%	18
Estonia	3,392	+18.4%	73
Ethiopia	1,428	-11.6%	92
Finland	5,746	-7.87%	59
France	298,103	+8.77%	9
United Kingdom	223,508	+20.3%	12
Georgia	2,876	+10.7%	78
Gambia	121	-22.4%	111
Grenada	13	-51.9%	117
Hong Kong SAR China	31,154	+5.05%	31
Honduras	2,293	+33.2%	83
Croatia	3,351	-30.3%	75
Hungary	7,979	+8%	56
Indonesia	91,362	+13.4%	16
India	435,581	+13.9%	4
Iran	506,935	-3.7%	3
Iraq	1,068	+226%	96
Iceland	1,419	+4.65%	93
Israel	5,080	+8.15%	62
Italy	108,275	+20.1%	14
Jamaica	2,576	-15.3%	79
Jordan	3,242	+17.8%	76
Japan	269,517	-21.4%	10
Kazakhstan	13,043	+24.8%	46
Kyrgyzstan	495	+7.14%	101
South Korea	299,622	+11.3%	8
Liechtenstein	396	+8.49%	103
Sri Lanka	6,321	-5.32%	57
Lithuania	3,377	-0.618%	74
Latvia	2,321	+27.5%	82
Macao SAR China	2,504	+7.98%	81
Morocco	20,432	+10.6%	38
Monaco	1,525	+9.01%	90
Moldova	3,973	+1.61%	70
Madagascar	2,966	+8.92%	77
Mexico	142,012	+20%	13
North Macedonia	2,137	+53.9%	85
Malta	1,245	+13.2%	94
Mongolia	19,104	+42.5%	40
Mozambique	1,499	-12.2%	91
Mauritius	2,540	+3%	80
Malaysia	20,128	+9.3%	39
Norway	11,792	+6.63%	47
New Zealand	24,096	+6.5%	35
Oman	6,250	+16.4%	58
Pakistan	43,233	+18.6%	24
Panama	4,782	-11.6%	66
Peru	29,557	+12.5%	32
Philippines	34,976	+13.1%	27
Papua New Guinea	300	+40.2%	106
Poland	35,855	+13.6%	26
Portugal	32,344	+7.64%	30
Paraguay	16,680	+12.5%	42
Qatar	1,545	+20.5%	89
Romania	20,988	-0.733%	37
Russia	327,409	-4.1%	7
Rwanda	682	+72.2%	99
Saudi Arabia	24,411	+20.3%	34
Singapore	15,088	+17.8%	44
El Salvador	4,883	+55.5%	63
Somalia	12	+100%	118
Serbia	3,833	-6.69%	71
São Tomé & Príncipe	4	-82.6%	121
Suriname	340	+7.94%	105
Slovakia	10,112	+15.2%	52
Slovenia	3,568	-6.99%	72
Sweden	15,190	-3.6%	43
Seychelles	33	-60.7%	115
Thailand	33,401	+0.712%	29
Tonga	3	-57.1%	122
Trinidad & Tobago	662	-26.1%	100
Turkey	395,159	+19.2%	5
Uganda	2,158	+38%	84
Ukraine	44,202	+14.8%	23
Uruguay	4,835	+18%	65
United States	551,764	+0.427%	2
Uzbekistan	10,476	+27.9%	51
St. Vincent & Grenadines	17	-50%	116
Vietnam	77,404	+0.739%	17
Vanuatu	11	-8.33%	119
Samoa	47	-44.7%	113
Yemen	4,662	+2.15%	67
South Africa	25,786	+16.7%	33
Zambia	1,171	-38.5%	95

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 = 'IP.TMK.RSCT'

# 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 <- 'IP.TMK.RSCT'

# 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

Trademark applications, resident, by count

Year: 2021

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