Country

Indicator

Value type

Values present

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

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	7.35	+1.13%	52
Albania	3.54	-16.5%	88
Argentina	3.61	-1.79%	85
Armenia	9.19	+9.56%	38
Antigua & Barbuda	10.6	-0.887%	28
Australia	2.64	+1.18%	98
Austria	3.91	-3%	81
Azerbaijan	3.07	-2.14%	93
Belgium	8.34	+0.609%	46
Bangladesh	7.5	-7.74%	49
Bulgaria	8.45	+9.2%	45
Bahrain	35	-1.22%	2
Bahamas	11.3	-8.15%	23
Bosnia & Herzegovina	7.02	+10.9%	55
Belize	12.1	+20.7%	19
Brazil	4.58	+4.87%	73
Brunei	2.63	-22%	99
Bhutan	11	+41.4%	26
Canada	8.84	+2.37%	42
Switzerland	3.43	-4.48%	90
Chile	11.2	-8.16%	24
China	2.95	-20.2%	94
Colombia	10.4	+1.41%	29
Cape Verde	3.9	+19.6%	82
Costa Rica	11.1	-6.8%	25
Cyprus	19.8	+2.91%	4
Czechia	6.77	-6.65%	57
Germany	7.49	-0.859%	50
Djibouti	2.56	-24%	101
Dominica	16.6	-2.86%	10
Denmark	1.66	-11.3%	109
Dominican Republic	11.8	-8.44%	22
Ecuador	12.9	+11.3%	18
Spain	4.56	-6.87%	74
Estonia	2.52	+2.68%	102
Finland	4.5	+20.4%	76
France	9.79	+9.19%	35
United Kingdom	9.9	+0.0786%	33
Georgia	7.39	+8.26%	51
Gambia	19.8	+9.2%	3
Greece	8.81	-4.83%	43
Grenada	8.85	-5.87%	41
Guatemala	10.7	-8.75%	27
Hong Kong SAR China	11.9	-4.44%	21
Honduras	6.83	+10.9%	56
Croatia	5.27	+36.6%	70
Hungary	4.15	-9.42%	79
Indonesia	10.2	+0.272%	32
India	3.8	-7.45%	83
Iceland	5.29	+5.27%	69
Israel	1.61	+20.4%	110
Italy	9.53	-0.112%	37
Jamaica	6.17	+19.4%	61
Japan	13.3	+10.2%	17
Kazakhstan	5.6	+12.1%	67
Cambodia	9.05	+11.9%	40
St. Kitts & Nevis	17.5	+26%	6
South Korea	3.56	-11.5%	87
Kuwait	9.89	-2.56%	34
St. Lucia	7.87	-2.25%	48
Lesotho	2.56	+0.0196%	100
Lithuania	4.23	-0.344%	78
Luxembourg	44.7	+2.52%	1
Latvia	2.27	-31.3%	105
Moldova	2.47	-2.77%	104
Maldives	7.26	+27.1%	53
Mexico	17.3	+5.07%	7
North Macedonia	1.25	-17.2%	111
Malta	2.49	-11.9%	103
Montenegro	1.78	+49.2%	106
Mozambique	6.56	-16.1%	59
Malaysia	5.88	-4.17%	65
Namibia	1.77	+5.14%	107
Nigeria	8.28	+39.6%	47
Nicaragua	15.9	-0.11%	12
Netherlands	5.96	-0.303%	64
Norway	3.94	-1.2%	80
Nepal	4.45	+1.18%	77
New Zealand	13.7	+7.96%	16
Pakistan	9.17	+0.479%	39
Panama	19	+9.72%	5
Peru	10.3	+1.6%	31
Philippines	12	-0.399%	20
Poland	5.25	-0.144%	71
Portugal	5.46	-5.69%	68
Paraguay	6.34	-12.9%	60
Palestinian Territories	5.09	+33.2%	72
Qatar	3.48	+17.3%	89
Romania	3.38	-7.51%	91
Russia	1.74	+4.43%	108
Saudi Arabia	5.81	-9%	66
Singapore	7.21	+5.62%	54
Solomon Islands	3.7	-20.4%	84
El Salvador	16.4	+2.74%	11
Suriname	8.68	+33.9%	44
Slovakia	4.53	+3.18%	75
Slovenia	3.58	+8.04%	86
Sweden	2.7	-3.37%	96
Thailand	6.76	-7.9%	58
Tajikistan	13.7	+80.3%	15
Timor-Leste	9.57	+23.7%	36
Tonga	2.69	+21.9%	97
Trinidad & Tobago	16.6	+0.669%	9
Turkey	10.3	+17.3%	30
Ukraine	2.85	+9.96%	95
Uruguay	6.02	+4.16%	62
United States	16.7	-1.93%	8
Uzbekistan	3.32	-22%	92
St. Vincent & Grenadines	13.8	+3.74%	14
Samoa	14	+55.3%	13
South Africa	6	+38.2%	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 = 'BM.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 <- 'BM.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 imports, BoP)

Year: 2024

Acquire full dataset with Python or R from source