International tourism, receipts (current US$)

Source: worldbank.org, 03.09.2025

Year: 2020

Flag Country Value Value change, % Rank
Aruba Aruba 1,077,000,000 -48.9% 57
Afghanistan Afghanistan 75,000,000 -11.8% 111
Angola Angola 19,000,000 -95.2% 120
Albania Albania 1,243,000,000 -49.4% 53
United Arab Emirates United Arab Emirates 24,615,400,391 -35.9% 4
Argentina Argentina 1,702,000,000 -69.9% 46
Armenia Armenia 303,000,000 -80.5% 84
Antigua & Barbuda Antigua & Barbuda 416,000,000 -58.1% 79
Australia Australia 26,234,000,000 -45.3% 3
Austria Austria 15,362,000,000 -40.7% 6
Azerbaijan Azerbaijan 340,000,000 -83% 82
Belgium Belgium 7,447,000,000 -29.6% 19
Bangladesh Bangladesh 217,899,994 -44.3% 93
Bulgaria Bulgaria 1,792,000,000 -62.9% 43
Bahrain Bahrain 724,000,000 -81.2% 66
Bahamas Bahamas 1,007,000,000 -75.7% 60
Bosnia & Herzegovina Bosnia & Herzegovina 438,000,000 -64.2% 77
Belarus Belarus 542,000,000 -58% 74
Bermuda Bermuda 94,000,000 -84.4% 108
Bolivia Bolivia 243,000,000 -75.1% 88
Brazil Brazil 3,099,000,000 -49.4% 33
Bhutan Bhutan 84,000,000 -30% 110
Botswana Botswana 217,000,000 -69.5% 94
Switzerland Switzerland 9,994,000,000 -53% 16
Chile Chile 1,034,000,000 -68.5% 59
Côte d’Ivoire Côte d’Ivoire 199,300,003 -63.8% 97
Cameroon Cameroon 437,000,000 -35.8% 78
Colombia Colombia 1,959,000,000 -71.1% 41
Comoros Comoros 18,500,000 -74.6% 121
Cape Verde Cape Verde 169,000,000 -70.2% 102
Costa Rica Costa Rica 1,479,000,000 -65.5% 48
Cuba Cuba 1,152,000,000 -56.4% 54
Curaçao Curaçao 282,000,000 -60.1% 86
Cyprus Cyprus 663,000,000 -79.6% 71
Czechia Czechia 3,890,000,000 -51.2% 29
Algeria Algeria 50,000,000 -64.3% 115
Ecuador Ecuador 705,000,000 -69.2% 67
Egypt Egypt 4,874,000,000 -65.8% 24
Estonia Estonia 865,000,000 -62.6% 63
Ethiopia Ethiopia 2,282,000,000 -35.3% 39
Finland Finland 1,757,000,000 -70.4% 44
Fiji Fiji 236,000,000 -82.5% 90
France France 35,958,000,000 -49.2% 2
Georgia Georgia 586,000,000 -83.5% 73
Ghana Ghana 191,000,000 -87.2% 99
Guinea Guinea 1,290,000 -87.6% 125
Gambia Gambia 53,000,000 -66.2% 114
Greece Greece 6,193,000,000 -73.1% 20
Guatemala Guatemala 298,500,000 -75.7% 85
Honduras Honduras 189,000,000 -66% 100
Croatia Croatia 5,631,853,027 -53% 22
Hungary Hungary 4,224,000,000 -58.7% 27
Indonesia Indonesia 3,533,000,000 -80.8% 30
India India 13,413,000,000 -57.6% 10
Ireland Ireland 4,160,000,000 -71.9% 28
Iraq Iraq 955,000,000 -73.4% 62
Israel Israel 2,661,000,000 -68.5% 37
Italy Italy 20,459,000,000 -60.6% 5
Jordan Jordan 1,745,000,000 -74.2% 45
Japan Japan 11,395,000,000 -76.8% 13
Kazakhstan Kazakhstan 589,000,000 -79.8% 72
Kyrgyzstan Kyrgyzstan 195,000,000 -72.5% 98
Cambodia Cambodia 1,119,000,000 -78.9% 55
South Korea South Korea 11,776,000,000 -53.7% 11
Kuwait Kuwait 524,000,000 -56.3% 75
Laos Laos 227,000,000 -76.7% 92
Lebanon Lebanon 2,369,000,000 -72.8% 38
Sri Lanka Sri Lanka 1,076,000,000 -76.9% 58
Luxembourg Luxembourg 4,454,000,000 -21.1% 26
Macao SAR China Macao SAR China 9,442,000,000 -77.1% 17
Morocco Morocco 4,514,000,000 -54.6% 25
Moldova Moldova 354,000,000 -32.8% 81
Madagascar Madagascar 202,000,000 -78.8% 96
Maldives Maldives 1,409,000,000 -55.6% 51
Mexico Mexico 11,449,000,000 -55.7% 12
North Macedonia North Macedonia 253,000,000 -36.9% 87
Montenegro Montenegro 180,000,000 -85.9% 101
Mongolia Mongolia 49,000,000 -91.9% 116
Mozambique Mozambique 113,000,000 -65.1% 105
Mauritania Mauritania 6,400,000 -53.3% 124
Mauritius Mauritius 518,000,000 -74.4% 76
Malawi Malawi 35,000,000 -43.5% 118
Malaysia Malaysia 3,386,000,000 -84.7% 31
Namibia Namibia 155,000,000 -65.6% 103
Nigeria Nigeria 321,000,000 -78.2% 83
Netherlands Netherlands 10,926,200,195 -53.9% 14
Norway Norway 2,196,000,000 -68.8% 40
Nepal Nepal 238,000,000 -70.3% 89
Oman Oman 669,000,000 -78.3% 70
Pakistan Pakistan 765,000,000 -22.9% 64
Panama Panama 1,841,000,000 -73.9% 42
Peru Peru 1,002,000,000 -78.7% 61
Philippines Philippines 2,769,000,000 -75.8% 35
Poland Poland 8,379,000,000 -46.7% 18
Puerto Rico Puerto Rico 2,921,000,000 -19.1% 34
Portugal Portugal 10,522,000,000 -57.2% 15
Paraguay Paraguay 104,000,000 -74.6% 106
Qatar Qatar 14,318,000,000 -8.49% 8
Romania Romania 1,611,000,000 -62% 47
Russia Russia 4,961,000,000 -71.2% 23
Rwanda Rwanda 212,000,000 -66.7% 95
Saudi Arabia Saudi Arabia 5,960,000,000 -70% 21
Solomon Islands Solomon Islands 7,000,000 -91.5% 123
El Salvador El Salvador 755,000,000 -54.6% 65
Serbia Serbia 1,422,000,000 -28.9% 49
Suriname Suriname 19,000,000 -70.3% 120
Slovakia Slovakia 1,303,000,000 -61.1% 52
Slovenia Slovenia 1,418,000,000 -57.7% 50
Eswatini Eswatini 7,300,000 -49% 122
Sint Maarten Sint Maarten 243,000,000 -65.7% 88
Seychelles Seychelles 228,000,000 -63.1% 91
Thailand Thailand 15,360,000,000 -76.1% 7
Tajikistan Tajikistan 102,400,002 -42.8% 107
Tonga Tonga 47,700,001 -16.5% 117
Trinidad & Tobago Trinidad & Tobago 151,000,000 -68.6% 104
Tunisia Tunisia 1,007,000,000 -62.5% 60
Turkey Turkey 13,771,000,000 -66.7% 9
Uganda Uganda 518,000,000 -63% 76
Ukraine Ukraine 687,000,000 -73.5% 68
Uruguay Uruguay 1,085,000,000 -53.5% 56
United States United States 84,205,000,000 -64.8% 1
Uzbekistan Uzbekistan 395,000,000 -76.5% 80
St. Vincent & Grenadines St. Vincent & Grenadines 85,000,000 -66.7% 109
U.S. Virgin Islands U.S. Virgin Islands 686,000,000 -33.5% 69
Vietnam Vietnam 3,232,000,000 -72.7% 32
Vanuatu Vanuatu 67,000,000 -77.3% 112
Samoa Samoa 23,059,999 -88.8% 119
South Africa South Africa 2,716,000,000 -70% 36
Zimbabwe Zimbabwe 66,000,000 -76.8% 113

The "International tourism, receipts (current US$)" indicator serves as a crucial measure within the tourism sector, representing the income generated from international visitors who travel abroad for leisure, business, or other purposes but do not reside in the destination country. This metric accounts for the money spent within the host country by these visitors on services such as accommodation, food, transportation, entertainment, and attractions. It is an essential economic indicator as it reflects the financial health and appeal of countries in attracting tourists, significantly impacting local economies and employment rates.

The importance of international tourism receipts extends beyond mere financial metrics. These earnings contribute substantially to the gross domestic product (GDP) of nations, especially those heavily reliant on tourism as a primary source of income. Countries that are tourist hot spots often find that these receipts help subsidize public services, infrastructure improvements, and cultural preservation. As such, fluctuations in tourism receipts can lead to broader economic implications, including changes in national policies and investment in tourism infrastructure.

The relationship between international tourism receipts and other economic indicators is complex yet direct. For instance, there is a correlation between international tourism revenues and employment rates in the hospitality and service sectors. Higher tourism income typically translates to more job opportunities in these areas. Additionally, tourism receipts can influence foreign exchange reserves, national investment levels, and overall economic stability. The interplay of international tourism with other sectors like transport and retail also highlights how interdependent economic systems function.

Several factors can affect international tourism receipts significantly. Economic conditions, such as global recessions or local financial crises, can diminish travel spending capacity. Political stability plays a crucial role; destinations that experience unrest or instability often see sharp declines in tourist numbers and, consequently, receipts. Health crises, such as the COVID-19 pandemic, have a profound impact on global travel patterns, with international restrictions causing an unprecedented drop in tourism receipts in recent years. Additionally, the competitive landscape of tourism can shift; emerging markets may attract travelers away from traditional destinations, altering the balance of tourism income across regions.

Strategies for mitigating declines in international tourism receipts involve enhancing marketing efforts to attract tourists, diversifying offerings to appeal to varied demographics, and implementing sustainable tourism practices that ensure long-term benefits to both communities and the environment. Countries can foster cultural exchanges and expand visa access to make traveling easier. Building partnerships with local businesses and engaging with international travel organizations can also enhance a destination's profile, drawing more visitors and boosting sales locally.

Innovative solutions include harnessing technology to reach potential tourists digitally, leveraging online platforms for marketing and booking, or embracing sustainable tourism practices that resonate with contemporary travelers concerned about their environmental footprint. Countries that invest in enhancing the tourist experience—be it through improving infrastructure, maintaining safety, or curating unique experiences—are likely to see more substantial receipts over time.

Despite the economic promise indicated by tourism receipts, the indicator is not without flaws. For one, it may skew perceptions of economic growth if used in isolation from other indicators. For example, significant tourism receipts could be offset by high unemployment and underemployment rates in other sectors, indicating an economy that may not be as healthy as it appears. Additionally, heavy reliance on tourism revenue can lead to economic vulnerability—destinations that do not diversify their economies risk significant downturns in the face of global crises that affect travel. Moreover, indicators can be influenced by transient trends or seasonal fluctuations, which means that they must be analyzed within broader economic contexts.

Examining the latest available data for 2020, the average international tourism receipts globally experienced a dramatic decline, reaching only $765 billion compared to previous years. The top five areas that reported the highest international tourism receipts were the United States with approximately $84 billion, France at about $36 billion, Australia at $26.2 billion, the United Arab Emirates at roughly $24.6 billion, and Italy with around $20.5 billion. This aligns with the expected trend where established destinations continue to attract significant numbers of travelers, despite economic fluctuations.

In contrast, the bottom five areas reflect the challenge faced by less prominent tourism destinations, with Guinea generating just about $1.3 million, Mauritania at $6.4 million, and the Solomon Islands at $7 million. These figures underscore the stark disparity in tourism revenue potential between developed and emerging economies, revealing how limited infrastructure, marketing, and opportunities can restrict growth in these areas.

Historically, the trend of international tourism receipts shows considerable growth, with receipts increasing from about $522 billion in 1995 to highs above $1.8 trillion just before the COVID-19 pandemic struck. Data showed steady increases year-on-year until 2019, exemplifying a robust global travel market where international tourism flourished. Each spike in revenue often correlated with rising middle-class populations in various countries, driving demand for international experiences.

In summary, international tourism receipts are a pivotal economic indicator that reveals much about a country’s accessibility, appeal, and overall economic health. While it is a powerful measure of how well a destination attracts international visitors, analyzing it within the broader context of other economic indicators and global trends is essential for accurate assessments. To build resilient tourism economies, countries must adopt diverse strategies that address the challenges posed by evolving global landscapes, ensuring that tourism remains a vibrant component of their economic future.

                    
# 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 = 'ST.INT.RCPT.CD'

# 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 <- 'ST.INT.RCPT.CD'

# 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))