Country

Indicator

Value type

Values present

New businesses registered (number)

Source: worldbank.org, 01.09.2025

Year: 2018

Country	Value	Value change, %	Rank
Afghanistan	4,274	+41.4%	100
Albania	2,990	+0.235%	113
United Arab Emirates	17,907	+9.96%	52
Argentina	5,667	-8.73%	90
Armenia	6,137	+38.1%	85
Antigua & Barbuda	280	-26.3%	146
Australia	235,684	-5.37%	5
Austria	5,876	-2.7%	88
Azerbaijan	11,611	+5%	66
Belgium	25,728	+1.96%	39
Benin	3,315	-1.84%	109
Burkina Faso	3,416	+16.2%	107
Bangladesh	4,473	-41.4%	99
Bulgaria	46,131	-1.32%	25
Bahrain	4,938	-1.12%	96
Bosnia & Herzegovina	2,493	+2.47%	118
Belarus	8,700	+10.7%	77
Belize	763	-1.8%	137
Bolivia	3,593		105
Brazil	414,854	+12%	2
Barbados	859	-9.1%	134
Brunei	731	-8.4%	138
Bhutan	43	-36.8%	158
Botswana	7,487	+4.83%	82
Central African Republic	340	+28.3%	145
Canada	47,101	+1,443%	24
Switzerland	25,637	-0.593%	40
Chile	101,997	+16.5%	12
China	228,559	-0.323%	6
Côte d’Ivoire	10,507	+26.1%	71
Congo - Kinshasa	2,102	+43.6%	123
Congo - Brazzaville	113	+18.9%	156
Colombia	67,560	+1.73%	21
Comoros	132	-17%	154
Cape Verde	5,974	+33.3%	87
Costa Rica	20,758	+18.1%	46
Cayman Islands	16,326	+25.1%	55
Cyprus	14,526	+6.21%	60
Czechia	30,336	-2.75%	35
Germany	72,844	+1.45%	19
Dominica	169	+28%	152
Denmark	37,196	+1.95%	32
Dominican Republic	10,204	+6.32%	74
Algeria	15,708	-5.84%	57
Egypt	10,411	+93.3%	72
Spain	93,560	+1.02%	14
Estonia	19,950	+8.07%	48
Ethiopia	10,935	-2.93%	68
Finland	15,829	+14.3%	56
France	222,555	+5.83%	7
Micronesia (Federated States of)	25		160
Gabon	1,218	+2.18%	128
United Kingdom	664,974	+5.13%	1
Georgia	25,562	-1.35%	41
Ghana	26,402	-3.94%	38
Guinea	2,929	+24.9%	114
Greece	9,921	+28.3%	76
Grenada	247	-9.52%	150
Guatemala	5,497	-3.14%	91
Guyana	589	+25.6%	139
Hong Kong SAR China	151,739	-5.3%	8
Honduras	2,321	-4.37%	121
Croatia	15,585	+4.47%	58
Hungary	24,467	+6.72%	42
Isle of Man	1,672	-8.73%	126
India	117,834	+9.62%	9
Ireland	22,217	+0.767%	45
Iraq	2,398	-15.1%	119
Iceland	2,312	-10.7%	122
Israel	17,456	+1.41%	53
Italy	114,939	+3.78%	10
Jamaica	2,673	+14.5%	116
Jordan	3,499	-16.2%	106
Japan	29,243	+6.56%	37
Kazakhstan	23,464	-16.2%	44
Kenya	44,381	+17.6%	28
Cambodia	7,007	+29%	84
Kiribati	2	-50%	162
St. Kitts & Nevis	262	+79.5%	148
Kuwait	18,157	+87.6%	51
Laos	427	+35.6%	142
Liberia	58	+81.3%	157
St. Lucia	350	+8.7%	144
Liechtenstein	163	+28.3%	153
Sri Lanka	10,510	+10.9%	70
Lesotho	3,725	+52.5%	104
Lithuania	6,072	-7.69%	86
Luxembourg	7,309	+10.1%	83
Latvia	9,939	+5.55%	75
Morocco	45,680	+13.5%	26
Moldova	4,849	-8.01%	97
Madagascar	1,737	-0.686%	125
Maldives	1,092	-13.5%	129
Mexico	83,903	+1.15%	17
North Macedonia	5,273	-2.94%	95
Mali	3,151	+137%	111
Malta	5,756	+9.1%	89
Myanmar (Burma)	14,051	+85.9%	61
Montenegro	4,694	+47.5%	98
Mongolia	11,544	-6.32%	67
Mozambique	3,300	-7.15%	110
Mauritania	959	-14.5%	130
Mauritius	8,285	-5.39%	81
Malaysia	51,732	-0.706%	23
Namibia	2,850	+150%	115
Niger	931	+22.7%	131
Nigeria	86,309	+9.89%	15
Netherlands	33,084	+3.45%	34
Norway	29,963	+0.238%	36
Nepal	24,088	+25%	43
New Zealand	56,417	-2.67%	22
Oman	5,425	-6.58%	92
Pakistan	12,560	+34.3%	65
Panama	13,204	-15.2%	63
Peru	80,029	+0.548%	18
Philippines	20,092	-17.6%	47
Papua New Guinea	3,881		103
Poland	37,281	-5.42%	31
Puerto Rico	8,477	+46.3%	79
Portugal	43,114	+14.8%	29
Paraguay	568	+37.9%	140
Qatar	14,824	+5.27%	59
Romania	94,244	-4.23%	13
Russia	317,475	-18.7%	4
Rwanda	10,547	+5.42%	69
Saudi Arabia	13,817	+11.5%	62
Senegal	8,465	+12.6%	80
Singapore	43,050	+15.8%	30
Solomon Islands	444	+33.3%	141
Sierra Leone	1,980	+60.6%	124
El Salvador	2,361	+9.87%	120
Somalia	800	-38.5%	136
Serbia	8,667	+0.324%	78
São Tomé & Príncipe	266	-10.7%	147
Suriname	890	+3.49%	132
Slovakia	19,723	-0.454%	49
Slovenia	4,118	-8.73%	101
Sweden	45,590	-6.08%	27
Eswatini	2,626	+18.7%	117
Seychelles	174	-10.8%	151
Chad	878	+12.6%	133
Togo	3,020	+48.6%	112
Thailand	72,109	-3.23%	20
Tajikistan	831	+4.79%	135
Timor-Leste	1,574	-20.4%	127
Tonga	32	+18.5%	159
Trinidad & Tobago	4,115	+6.14%	102
Tunisia	12,977	+10.2%	64
Turkey	86,210	+15%	16
Tuvalu	7	+75%	161
Tanzania	5,276	-18.9%	94
Uganda	18,862	+8.26%	50
Uruguay	3,326	-17.7%	108
Uzbekistan	35,801	+36.7%	33
St. Vincent & Grenadines	414	+137%	143
Vietnam	106,931	+4.38%	11
Vanuatu	258	+53.6%	149
Samoa	126	-8.7%	155
Kosovo	5,367	+15.3%	93
South Africa	401,054	+0.783%	3
Zambia	10,240	+8.94%	73
Zimbabwe	16,810	+76.6%	54

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 = 'IC.BUS.NREG'

# 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 <- 'IC.BUS.NREG'

# 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

New businesses registered (number)

Year: 2018

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