Country

Indicator

Value type

Values present

Insurance and financial services (% of commercial service exports)

Source: worldbank.org, 03.09.2025

Year: 2023

Country	Value	Value change, %	Rank
Aruba	1.35	+10.2%	113
Albania	0.667	-15.3%	137
Andorra	3.73	+54.4%	68
Argentina	1.43	-15.9%	111
Armenia	5.26	-22.7%	53
Antigua & Barbuda	5.82	-3.75%	46
Australia	5.82	-23.8%	47
Austria	3.68	-18.6%	69
Azerbaijan	0.815	+12.5%	131
Burundi	16.1	-7.24%	13
Belgium	8.76	+7.05%	33
Burkina Faso	17.2	-19.3%	12
Bangladesh	4.87	+38.8%	59
Bulgaria	5.3	-15.2%	52
Bahrain	33.8	-6.75%	3
Bosnia & Herzegovina	0.241	+7.48%	144
Belize	1.27	-4.46%	115
Bolivia	1.34	+619%	114
Brazil	4.98	-15%	57
Brunei	0.213	-68.5%	145
Bhutan	12.5	-82.9%	19
Botswana	1.04	+159%	125
Canada	9.38	-9.13%	32
Switzerland	22.8	+1.35%	7
Chile	8.07	-6.63%	35
China	2.32	-5.61%	94
Cameroon	3.33	-21.7%	74
Congo - Kinshasa	3.07	+30.8%	83
Colombia	1.22	+43.4%	118
Comoros	1.44	-6.43%	110
Cape Verde	0.451	-28.8%	141
Costa Rica	1.1	+18.5%	121
Curaçao	1.47	+14.4%	109
Cyprus	20.3	-29.1%	8
Czechia	3.04	+17.3%	84
Germany	13.1	+8.3%	18
Dominica	4.83	-6.02%	61
Denmark	1.79	+62.4%	100
Dominican Republic	2.32	-50.5%	95
Algeria	10.5	-1.43%	24
Ecuador	1.79	+18.7%	101
Egypt	2.56	+13.9%	90
Spain	4.4	+27.8%	64
Estonia	1.89	+30.6%	98
Ethiopia	0.0244	-86.2%	151
Finland	1.58	+2.66%	107
France	11.9	+36.3%	21
United Kingdom	26.4	+4.39%	6
Georgia	3.32	+80.9%	75
Ghana	17.7	+1.3%	11
Guinea	6.51	+177%	42
Gambia	0.0821	-11.5%	148
Greece	3.34	+24.2%	73
Grenada	1.7	-8.96%	102
Guatemala	4.72	-5.48%	62
Guyana	10.5	-76%	26
Hong Kong SAR China	27.3	-16.4%	5
Honduras	0.713	+47.1%	136
Croatia	1.16	-3.81%	119
Haiti	11.2	-24.7%	22
Hungary	2.36	+17.5%	93
Indonesia	6.61	+33.9%	41
India	3.49	+0.802%	70
Ireland	9.53	-4.52%	28
Iraq	0.316	+83.5%	143
Iceland	3.25	+16.4%	76
Israel	0.0535	-1.38%	149
Italy	7.55	+8.94%	38
Jamaica	0.36	-14.1%	142
Jordan	1.09	-30%	122
Japan	7.76	-7.95%	37
Kazakhstan	10.3	+46.3%	27
Kenya	19.3	+25.5%	10
Cambodia	0.782	-65.6%	133
Kiribati	6.48	-17.3%	43
St. Kitts & Nevis	4.95	-3.13%	58
South Korea	5.48	+78.4%	51
Kuwait	15.5	-11.5%	15
Laos	0.654	-77.1%	138
Lebanon	9.46	+28.9%	30
St. Lucia	0.559	-13.7%	139
Sri Lanka	1.82	-49.7%	99
Lesotho	0.157	-10.1%	147
Lithuania	7.04	+50.2%	39
Luxembourg	56	+1.18%	1
Latvia	2.41	-19%	92
Macao SAR China	8.68	-63.4%	34
Morocco	1.05	+16.8%	124
Moldova	0.459	+36.1%	140
Mexico	9.43	+7.44%	31
North Macedonia	0.205	+49%	146
Mali	4.3	+72.2%	65
Malta	4.14	+13.8%	66
Montenegro	1.64	+71.5%	103
Mongolia	1.39	-9.33%	112
Mozambique	1.62	-24.7%	104
Mauritania	0.0134	-97.5%	152
Mauritius	5.63	+5.05%	49
Malawi	1.6	0%	105
Malaysia	2.62	-29.3%	88
Namibia	5.18	+104%	55
Niger	13.4	+92.4%	17
Nigeria	28.1	+35.6%	4
Nicaragua	0.778	+48.7%	134
Netherlands	7.04	+23.3%	40
Norway	7.97	-9.89%	36
Nepal	0.952	-54.1%	128
New Zealand	2.79	-36.1%	86
Oman	1.6	-36.3%	106
Pakistan	3.04	-36.1%	85
Panama	10.9	+27.6%	23
Peru	4.86	-5.22%	60
Philippines	0.806	-3.53%	132
Papua New Guinea	1.13	-28.4%	120
Poland	2.42	+10.8%	91
Portugal	1.53	+22.1%	108
Paraguay	0.762	-8.53%	135
Qatar	6.21	+0.769%	44
Romania	1.27	+5.27%	116
Russia	3.46	+0.0209%	71
Rwanda	3.25	+29%	77
Saudi Arabia	4.41	+14.4%	63
Senegal	5.16	+49%	56
Singapore	15.9	+10.1%	14
Solomon Islands	0.973	-76.5%	126
Sierra Leone	39.3	-266%	2
El Salvador	2.6	-10.6%	89
Serbia	0.863	-19.5%	130
South Sudan	12.3	+4.05%	20
Suriname	5.23	+23.9%	54
Slovakia	3.13	+37%	82
Slovenia	3.39	+10.7%	72
Sweden	6.06	+12.7%	45
Eswatini	10.5	-37.6%	25
Sint Maarten	0.0522	+134%	150
Thailand	2.08	-20.8%	96
Tajikistan	3.89	+127%	67
Timor-Leste	5.66	-29.6%	48
Trinidad & Tobago	13.7	-11.6%	16
Tunisia	1.26	-1.41%	117
Turkey	3.18	+87.4%	80
Tanzania	0.902	+165%	129
Uganda	3.24	-9.51%	78
Ukraine	1.96	+56.1%	97
Uruguay	5.6	+22.3%	50
United States	20.2	-3.09%	9
Uzbekistan	2.68	-9.42%	87
St. Vincent & Grenadines	3.2	-24.5%	79
Samoa	3.15	-32.9%	81
Kosovo	0.965	+28.2%	127
South Africa	9.49	-7.28%	29
Zambia	1.07	+42.1%	123

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 = 'TX.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 <- 'TX.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 exports)

Year: 2023

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