Country

Indicator

Value type

Values present

Industrial design applications, resident, by count

Source: worldbank.org, 03.09.2025

Year: 2021

Country	Value	Value change, %	Rank
Albania	57	+50%	75
United Arab Emirates	103	+98.1%	64
Argentina	1,479	+19.5%	24
Armenia	78	+160%	70
Antigua & Barbuda	1	0%	100
Australia	2,584	-3%	20
Austria	232	-27%	54
Azerbaijan	63	+96.9%	73
Bangladesh	1,338	+15.1%	26
Bulgaria	388	-27.3%	47
Bahrain	10	-23.1%	94
Bosnia & Herzegovina	52	-65.1%	76
Belarus	273	+78.4%	52
Belize	5	0%	97
Bolivia	23	+35.3%	87
Brazil	4,520	+6.15%	14
Bhutan	3	-66.7%	99
Botswana	18	+50%	89
Canada	715	-3.12%	35
Switzerland	3,403	+16.7%	17
Chile	42	-45.5%	79
China	785,857	+4.46%	1
Colombia	486	+33.2%	41
Costa Rica	9	0%	95
Cuba	14	-26.3%	92
Cyprus	20	-75.6%	88
Czechia	522	-14.7%	40
Germany	33,448	-6.48%	4
Denmark	135	-4.93%	58
Dominican Republic	4	0%	98
Algeria	879	-27.2%	30
Ecuador	78	+47.2%	70
Egypt	2,252	+28%	22
Spain	12,010	-0.505%	12
Estonia	36	-10%	80
Finland	150	+6.38%	56
France	30,120	+1.85%	5
United Kingdom	28,299	+32.5%	6
Georgia	103	-29%	64
Greece	721	-9.99%	34
Guatemala	35	+483%	81
Guyana	1		100
Hong Kong SAR China	946	-13.1%	29
Croatia	285	+14%	51
Hungary	868	+68.5%	31
Indonesia	2,959	+28.7%	19
India	17,497	+95.2%	10
Ireland	95	-59.4%	66
Iran	13,903	-6.67%	11
Iceland	7	-12.5%	96
Israel	626	-16.1%	39
Italy	26,386	+5.3%	7
Jamaica	94	-55.5%	67
Jordan	114	+54.1%	62
Japan	22,051	-1.52%	8
Kazakhstan	134	+61.4%	59
Kenya	126	-42.7%	61
Kyrgyzstan	7	-61.1%	96
South Korea	61,233	-4.33%	2
Kuwait	47	-26.6%	77
Liechtenstein	25	-59.7%	85
Sri Lanka	112	-55%	63
Lithuania	132	+24.5%	60
Latvia	84	-40.4%	69
Macao SAR China	29	-14.7%	82
Morocco	3,208	+12.1%	18
Monaco	28	+300%	83
Moldova	654	+140%	38
Madagascar	328	+58.5%	49
Mexico	1,225	+16.7%	27
North Macedonia	67	+67.5%	72
Malta	17	-51.4%	90
Mongolia	1,391	+125%	25
Mozambique	35	-12.5%	81
Mauritius	24	-71.4%	86
Malaysia	468	-18.6%	42
Norway	452	-5.83%	44
New Zealand	320	-9.09%	50
Oman	17	-15%	90
Pakistan	463	+26.5%	43
Panama	5	+66.7%	97
Peru	97	+4.3%	65
Philippines	682	+2.56%	37
Poland	2,489	+28.4%	21
Portugal	1,008	-41.4%	28
Paraguay	7	-30%	96
Romania	378	-30.4%	48
Russia	6,128	+27.2%	13
Rwanda	11	+83.3%	93
Saudi Arabia	853	+58.8%	32
Singapore	683	+73.4%	36
Sierra Leone	4	-20%	98
El Salvador	16	+77.8%	91
Serbia	137	-16%	57
Slovakia	262	-6.43%	53
Slovenia	26	-21.2%	84
Sweden	390	+35.9%	46
Syria	406	+23.8%	45
Thailand	4,323	+1.84%	15
Trinidad & Tobago	58	+287%	74
Turkey	59,353	+41.1%	3
Uganda	90	+105%	68
Ukraine	3,526	+9.23%	16
United States	21,913	+1.05%	9
Uzbekistan	231	+55%	55
Vietnam	2,107	-7.55%	23
Vanuatu	4		98
Yemen	77	+32.8%	71
South Africa	728	-24.9%	33
Zambia	46	-62.9%	78
Zimbabwe	14		92

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.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.IDS.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))

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Industrial design applications, resident, by count

Year: 2021

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