Country

Indicator

Value type

Values present

Insurance and financial services (% of commercial service imports)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	7.58	+0.58%	50
Albania	3.6	-16.1%	88
Argentina	3.65	-1.98%	86
Armenia	9.29	+9.69%	39
Antigua & Barbuda	10.6	-0.887%	28
Australia	2.67	+1.12%	100
Austria	3.92	-3%	84
Azerbaijan	3.11	-2.29%	93
Belgium	8.34	+0.62%	47
Bangladesh	7.77	-7.04%	49
Bulgaria	8.46	+9.2%	46
Bahrain	35	-1.22%	2
Bahamas	12.7	-7.31%	20
Bosnia & Herzegovina	7.05	+10.7%	55
Belize	13.2	+20.9%	18
Brazil	4.66	+3.9%	73
Brunei	2.68	-22.1%	99
Bhutan	11.4	+42.2%	25
Canada	8.91	+2.18%	43
Switzerland	3.43	-4.49%	90
Chile	11.5	-8.19%	24
China	2.97	-20.2%	95
Colombia	10.5	+1.49%	30
Cape Verde	4.16	+20.1%	81
Costa Rica	11.2	-6.62%	26
Cyprus	19.8	+2.87%	3
Czechia	6.78	-6.66%	59
Germany	7.52	-0.865%	51
Djibouti	2.69	-23.5%	98
Dominica	16.6	-2.86%	11
Denmark	1.67	-11.3%	109
Dominican Republic	12.2	-8.07%	21
Ecuador	13.1	+11.7%	19
Spain	4.56	-6.87%	74
Estonia	2.53	+2.63%	103
Finland	4.5	+20.4%	77
France	9.79	+9.19%	36
United Kingdom	9.97	-0.426%	35
Georgia	7.51	+8.18%	52
Gambia	19.8	+9.2%	4
Greece	8.93	-4.32%	42
Grenada	8.94	-5.45%	41
Guatemala	10.9	-8.91%	27
Hong Kong SAR China	11.9	-4.62%	23
Honduras	6.92	+10.8%	56
Croatia	5.3	+36.5%	71
Hungary	4.18	-9.56%	80
Indonesia	10.3	+0.289%	34
India	3.82	-7.42%	85
Iceland	5.31	+5.26%	70
Israel	1.62	+20.4%	110
Italy	9.61	-0.0665%	38
Jamaica	6.29	+19.4%	62
Japan	13.5	+10.4%	17
Kazakhstan	5.68	+12.2%	68
Cambodia	9.18	+11.7%	40
St. Kitts & Nevis	17.7	+26.2%	6
South Korea	3.59	-11.5%	89
Kuwait	10.3	-7.21%	33
St. Lucia	7.87	-2.25%	48
Lesotho	2.6	+0.026%	101
Lithuania	4.26	-0.322%	78
Luxembourg	44.8	+2.56%	1
Latvia	2.28	-31.3%	105
Moldova	2.53	-2.65%	102
Maldives	7.32	+27.7%	53
Mexico	17.5	+5.19%	7
North Macedonia	1.27	-17.1%	111
Malta	2.5	-11.9%	104
Montenegro	1.81	+49.4%	106
Mozambique	6.78	-15.3%	58
Malaysia	5.91	-4.21%	66
Namibia	1.79	+4.63%	107
Nigeria	8.59	+41.3%	45
Nicaragua	16.3	-1.1%	12
Netherlands	5.97	-0.3%	65
Norway	3.95	-1.21%	83
Nepal	4.5	+1.46%	76
New Zealand	13.8	+7.91%	16
Pakistan	9.71	+2.38%	37
Panama	19.3	+9.8%	5
Peru	10.4	+1.53%	31
Philippines	12.1	-0.582%	22
Poland	5.26	-0.212%	72
Portugal	5.47	-5.73%	69
Paraguay	6.39	-12.9%	60
Palestinian Territories	5.75	+38.8%	67
Qatar	4.02	+19.7%	82
Romania	3.4	-7.42%	91
Russia	1.76	+4.6%	108
Saudi Arabia	6.3	-10.8%	61
Singapore	7.22	+5.62%	54
Solomon Islands	4.19	-19.5%	79
El Salvador	16.7	+2.62%	10
Suriname	8.69	+33.8%	44
Slovakia	4.54	+3.17%	75
Slovenia	3.62	+7.57%	87
Sweden	2.71	-3.38%	97
Thailand	6.79	-7.93%	57
Tajikistan	13.8	+79.9%	15
Timor-Leste	10.3	+30.4%	32
Tonga	2.84	+21.6%	96
Trinidad & Tobago	17.1	+1.66%	9
Turkey	10.6	+17.2%	29
Ukraine	3.01	+6.1%	94
Uruguay	6.06	+4.17%	64
United States	17.2	-2.19%	8
Uzbekistan	3.33	-22.1%	92
St. Vincent & Grenadines	14	+3.77%	14
Samoa	14.9	+56.1%	13
South Africa	6.08	+37.8%	63

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 = 'TM.VAL.INSF.ZS.WT'

# 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 <- 'TM.VAL.INSF.ZS.WT'

# 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

Insurance and financial services (% of commercial service imports)

Year: 2024

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