Country

Indicator

Value type

Values present

Industrial design applications, nonresident, by count

Source: worldbank.org, 03.09.2025

Year: 2021

Country	Value	Value change, %	Rank
Albania	433	-15.1%	44
United Arab Emirates	869	+37.1%	28
Argentina	893	+3.72%	27
Armenia	282	-25.2%	57
Australia	5,536	+17.9%	10
Austria	168	+205%	63
Azerbaijan	597	+8.35%	36
Bangladesh	86	+8.86%	71
Bulgaria	86	-65.3%	71
Bahrain	114	+90%	67
Bosnia & Herzegovina	678	-2.31%	32
Belarus	511	+99.6%	40
Belize	294	+10.9%	54
Bolivia	38	+81%	85
Brazil	2,191	+9.28%	19
Brunei	72	-58.1%	75
Botswana	52	-43.5%	80
Canada	8,776	+29.2%	5
Switzerland	8,405	+16%	6
Chile	346	-9.42%	49
China	19,853	+10.2%	3
Colombia	466	+9.13%	42
Costa Rica	66	-4.35%	77
Cuba	1	-75%	97
Czechia	86	+34.4%	71
Germany	3,549	-27.2%	15
Djibouti	8	-77.8%	94
Denmark	185	-36%	62
Dominican Republic	17	+41.7%	91
Algeria	282	+6.42%	57
Ecuador	83	+31.7%	72
Egypt	674	-20.1%	33
Spain	284	-6.89%	56
Estonia	90	-10%	70
Finland	74	-24.5%	74
France	1,224	-24.8%	24
United Kingdom	46,481	+309%	1
Georgia	379	-17.1%	46
Gambia	10	+900%	93
Greece	162	-40.9%	64
Guatemala	57	-56.5%	79
Hong Kong SAR China	2,912	+4.37%	16
Croatia	428	-38.7%	45
Hungary	39	-56.2%	84
Indonesia	1,409	+15.5%	20
India	3,949	+3.08%	13
Ireland	19	+35.7%	90
Iran	113	+28.4%	68
Iceland	457	+12.6%	43
Israel	1,329	+30.8%	22
Italy	308	+0.654%	52
Jamaica	6	0%	95
Jordan	15	0%	92
Japan	10,696	+15.5%	4
Kazakhstan	196	+15.3%	61
Kenya	27	+42.1%	88
Kyrgyzstan	314	-3.09%	51
South Korea	8,015	+17.6%	7
Kuwait	345	+105%	50
Liechtenstein	667	-17.6%	34
Sri Lanka	27	-15.6%	88
Lithuania	212	-57.9%	60
Latvia	43	-39.4%	82
Macao SAR China	235	+35.8%	59
Morocco	1,178	+12.7%	25
Monaco	615	-14.9%	35
Moldova	478	-19.4%	41
Mexico	4,128	+46.5%	11
North Macedonia	555	-11.2%	37
Mongolia	358	-10.5%	47
Mozambique	30	-25%	86
Mauritius	3	+50%	96
Malaysia	1,271	+12.9%	23
Norway	3,952	+39.4%	12
New Zealand	1,111	+9.46%	26
Oman	545	+15.5%	39
Pakistan	109	+11.2%	69
Panama	80	+90.5%	73
Peru	292	+59.6%	55
Philippines	690	+9.87%	31
Poland	266	+88.7%	58
Portugal	121	+175%	66
Paraguay	71	-45.4%	76
Romania	303	-17.9%	53
Russia	6,414	+11.1%	9
Rwanda	46	-43.2%	81
Saudi Arabia	547	+33.1%	38
Singapore	3,911	+23.3%	14
El Salvador	24	-4%	89
San Marino	86	-36.8%	71
Serbia	758	-16%	30
Slovakia	146	+65.9%	65
Slovenia	350	-40%	48
Sweden	29	+190%	87
Seychelles	1	-87.5%	97
Syria	62	-22.5%	78
Thailand	1,364	-13.3%	21
Trinidad & Tobago	1	-75%	97
Turkey	6,571	+17.8%	8
Ukraine	2,596	+8.57%	17
United States	37,564	+29.3%	2
Uzbekistan	42	+35.5%	83
Vietnam	2,203	+5%	18
Yemen	3	-50%	96
South Africa	833	+12.9%	29

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.IDS.NRCT'

# 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.IDS.NRCT'

# 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))

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Industrial design applications, nonresident, by count

Year: 2021

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