Country

Indicator

Value type

Values present

Insurance and financial services (% of service exports, BoP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	0.0124	-108%	106
Albania	0.444	-32.9%	94
Argentina	1.4	-1.05%	82
Armenia	9.91	+89.5%	19
Antigua & Barbuda	6.03	+3.79%	29
Australia	4.67	-19.1%	39
Austria	3.32	-8.93%	53
Azerbaijan	1.97	+143%	70
Belgium	9.08	+5.14%	20
Bangladesh	3.1	-10.9%	56
Bulgaria	5.95	+13.1%	30
Bahrain	31.3	-7.49%	2
Bosnia & Herzegovina	0.217	-9.99%	101
Belize	1.66	+38.8%	77
Brazil	5.77	+18.3%	31
Brunei	0.233	+16%	99
Bhutan	8.32	-31.9%	22
Canada	10.1	+8.21%	18
Switzerland	21.4	-4.77%	6
Chile	6.07	-18.6%	28
China	1.7	-27.4%	74
Colombia	1.3	+10.7%	84
Cape Verde	0.391	-10.4%	95
Costa Rica	1.08	-1.84%	86
Cyprus	21	+3.91%	8
Czechia	3.11	+2.02%	55
Germany	13.2	+4.5%	13
Dominica	4.13	-12.9%	44
Denmark	2.25	+25.7%	67
Dominican Republic	2.91	+27.8%	59
Ecuador	1.83	+5.64%	72
Spain	4.74	+7.75%	37
Estonia	1.6	-14.9%	78
Finland	1.49	-5.28%	80
France	13.4	+12.9%	12
United Kingdom	25.7	+2.99%	5
Georgia	3.53	+8.02%	51
Gambia	0.0688	-7.98%	104
Greece	3.65	+9.62%	49
Grenada	1.69	-0.327%	76
Guatemala	4.68	+1.05%	38
Hong Kong SAR China	26.8	-1.75%	4
Honduras	0.729	+8.62%	91
Croatia	1.14	-1.86%	85
Hungary	2.42	+6.64%	63
Indonesia	4.61	-29.4%	41
India	3.12	-10.5%	54
Iceland	2.95	-8.99%	58
Israel	0.0579	+8.23%	105
Italy	8.15	+7.8%	23
Jamaica	0.331	-7.37%	97
Japan	7.04	-6.85%	26
Kazakhstan	10.1	+0.677%	17
Cambodia	0.308	-59.9%	98
St. Kitts & Nevis	4.29	+16.6%	43
South Korea	4.65	-14.3%	40
Kuwait	13	-11.9%	14
St. Lucia	0.47	-15.8%	93
Lesotho	0.156	+12.1%	102
Lithuania	7.73	+10.1%	25
Luxembourg	51.6	+2.46%	1
Latvia	2.39	-0.419%	65
Moldova	0.357	-20.5%	96
Mexico	10.2	+8.3%	16
North Macedonia	0.144	-29.5%	103
Malta	4.87	+17.7%	36
Montenegro	1.93	+17.5%	71
Mozambique	1.47	-9%	81
Malaysia	2.46	-6.07%	62
Namibia	3.93	-19%	47
Nigeria	28.4	+13.1%	3
Nicaragua	0.847	+16.9%	90
Netherlands	6.85	-2.31%	27
Norway	7.88	-1.01%	24
Nepal	0.983	+19.8%	88
New Zealand	0.219	-92.1%	100
Pakistan	1.8	-31.2%	73
Panama	10.5	-1.32%	15
Peru	4.37	-6.18%	42
Philippines	1.08	+33.9%	87
Poland	2.4	-0.608%	64
Portugal	1.69	+10.8%	75
Paraguay	0.663	-5.21%	92
Qatar	5.63	-7.22%	34
Romania	1.39	+9.83%	83
Russia	4.05	+17.4%	46
Saudi Arabia	4.1	-2.53%	45
Singapore	15.9	+0.26%	10
Solomon Islands	0.858	-4.91%	89
El Salvador	2.29	-8.99%	66
Suriname	4.97	-1.74%	35
Slovakia	3.32	+6.65%	52
Slovenia	3.56	+4.93%	50
Sweden	5.71	-3.67%	33
Thailand	1.58	-23.1%	79
Tajikistan	21.2	+456%	7
Timor-Leste	2.84	-38.6%	60
Trinidad & Tobago	14.6	+8.07%	11
Turkey	3.07	-2.96%	57
Ukraine	2.06	+9.31%	69
Uruguay	5.76	+3.69%	32
United States	19.7	+0.962%	9
Uzbekistan	2.06	-22.8%	68
St. Vincent & Grenadines	2.67	-16.1%	61
Samoa	3.82	+21.4%	48
South Africa	8.87	-4.67%	21

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 = 'BX.GSR.INSF.ZS'

# 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 <- 'BX.GSR.INSF.ZS'

# 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 service exports, BoP)

Year: 2024

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