Country

Indicator

Value type

Values present

Merchandise exports (current US$)

Source: worldbank.org, 01.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Aruba	212,000,000	+27.7%	176
Afghanistan	880,000,000	+3.9%	156
Angola	39,223,000,000	-0.716%	65
Albania	4,000,000,000	-8.55%	127
Andorra	258,000,000	+2.38%	173
United Arab Emirates	603,313,000,000	+5.8%	11
Argentina	79,721,000,000	+19.4%	45
Armenia	13,092,000,000	+53.1%	92
American Samoa	400,000,000	+1.27%	168
Antigua & Barbuda	23,000,000	-25.8%	196
Australia	341,390,000,000	-8.08%	22
Austria	214,328,000,000	-4.34%	31
Azerbaijan	26,559,000,000	-21.7%	72
Burundi	153,000,000	-27.1%	180
Belgium	535,745,000,000	-5.65%	13
Benin	3,772,000,000	-6.86%	131
Burkina Faso	5,479,000,000	+22.7%	121
Bangladesh	47,245,000,000	+6.8%	57
Bulgaria	46,425,000,000	-3.24%	58
Bahrain	23,443,000,000	-5.53%	74
Bahamas	601,000,000	-18.3%	161
Bosnia & Herzegovina	8,890,000,000	-3.7%	104
Belarus	40,327,000,000	+1.22%	63
Belize	460,000,000	-5.74%	167
Bermuda	68,000,000	+209%	184
Bolivia	9,379,000,000	-14%	103
Brazil	337,046,000,000	-0.78%	23
Barbados	461,000,000	0%	166
Brunei	11,308,000,000	+0.596%	96
Bhutan	755,000,000	+17.8%	158
Botswana	4,119,000,000	-28.3%	126
Central African Republic	166,000,000	+3.75%	179
Canada	567,982,000,000	-0.391%	12
Switzerland	446,648,000,000	+6.35%	16
Chile	100,162,000,000	+5.93%	41
China	3,576,765,000,000	+5.85%	1
Côte d’Ivoire	21,016,000,000	+14.5%	76
Cameroon	6,000,000,000	+21.8%	119
Congo - Kinshasa	30,000,000,000	+8.2%	69
Congo - Brazzaville	6,900,000,000	-6.86%	114
Colombia	49,564,000,000	+0.0383%	56
Comoros	33,000,000	+6.45%	193
Cape Verde	75,000,000	+36.4%	183
Costa Rica	21,688,000,000	+14%	75
Cuba	1,788,000,000	+13%	142
Curaçao	374,000,000	-33.2%	170
Cayman Islands	47,000,000	+4.44%	189
Cyprus	4,383,000,000	-12.7%	124
Czechia	262,714,000,000	+2.8%	28
Germany	1,682,888,000,000	-1.14%	3
Djibouti	3,830,000,000	-20%	128
Dominica	10,000,000	-37.5%	197
Denmark	133,262,000,000	+2.41%	35
Dominican Republic	13,888,000,000	+7.39%	87
Algeria	49,580,000,000	-10.8%	55
Ecuador	33,670,000,000	+8.17%	66
Egypt	42,137,000,000	+0.133%	61
Eritrea	482,000,000	+1.47%	165
Spain	424,234,000,000	+0.414%	18
Estonia	18,838,000,000	-4.05%	82
Ethiopia	4,501,000,000	+29.8%	123
Finland	78,063,000,000	-5.45%	47
Fiji	1,163,000,000	+8.9%	151
France	640,247,000,000	-1.67%	9
Micronesia (Federated States of)	186,000,000	+75.5%	177
Gabon	13,100,000,000	+3.19%	91
United Kingdom	512,840,000,000	-2.32%	14
Georgia	6,560,000,000	+7.79%	115
Ghana	19,965,000,000	+19.5%	78
Guinea	10,053,000,000	+9.51%	101
Gambia	267,000,000	-6.97%	172
Guinea-Bissau	246,000,000	+2.5%	174
Equatorial Guinea	4,700,000,000	-14.2%	122
Greece	53,988,000,000	-2.11%	53
Grenada	50,000,000	+8.7%	188
Greenland	827,000,000	-8.42%	157
Guatemala	14,601,000,000	+2.83%	86
Guam	44,000,000	0%	190
Guyana	19,434,000,000	+47.1%	81
Hong Kong SAR China	645,537,000,000	+12.5%	8
Honduras	11,057,000,000	-2.86%	98
Croatia	25,897,000,000	+3.75%	73
Haiti	710,000,000	-20.9%	159
Hungary	157,252,000,000	-2.51%	34
Indonesia	264,706,000,000	+2.26%	27
India	442,600,000,000	+2.56%	17
Ireland	240,597,000,000	+15.5%	30
Iran	105,900,000,000	+8.77%	38
Iraq	101,637,000,000	+2.51%	39
Iceland	6,932,000,000	+2.11%	113
Israel	62,270,000,000	-1.7%	52
Italy	674,447,000,000	-0.336%	7
Jamaica	1,928,000,000	-3.7%	141
Jordan	13,292,000,000	+5.9%	89
Japan	707,028,000,000	-1.43%	5
Kazakhstan	82,471,000,000	+5.01%	44
Kenya	8,248,000,000	+14.7%	108
Kyrgyzstan	3,828,000,000	+13.1%	129
Cambodia	26,661,000,000	+13.6%	71
Kiribati	24,000,000	+26.3%	195
St. Kitts & Nevis	34,000,000	+36%	192
South Korea	683,609,000,000	+8.13%	6
Kuwait	75,847,000,000	-9.69%	48
Laos	9,438,000,000	+12.8%	102
Lebanon	3,781,000,000	-8.65%	130
Liberia	1,304,000,000	+16.1%	146
Libya	29,603,000,000	-10.5%	70
St. Lucia	78,000,000	-2.5%	182
Sri Lanka	12,772,000,000	+7.23%	93
Lesotho	967,000,000	+17.2%	153
Lithuania	39,715,000,000	-7.6%	64
Luxembourg	16,213,000,000	-5.68%	83
Latvia	20,473,000,000	-9.61%	77
Macao SAR China	1,678,000,000	+1.45%	144
Morocco	45,039,000,000	+8.16%	59
Moldova	3,555,000,000	-12.2%	134
Madagascar	2,582,000,000	-22.7%	137
Maldives	383,000,000	-9.03%	169
Mexico	617,100,000,000	+4.06%	10
Marshall Islands	58,000,000	-3.33%	186
North Macedonia	8,422,000,000	-6.41%	107
Mali	5,486,000,000	-2.99%	120
Malta	3,771,000,000	+8.77%	132
Myanmar (Burma)	13,329,000,000	-9.65%	88
Montenegro	633,000,000	-13.2%	160
Mongolia	15,783,000,000	+3.92%	84
Northern Mariana Islands	4,000,000	0%	199
Mozambique	8,211,000,000	-0.785%	109
Mauritania	4,228,000,000	+4.01%	125
Mauritius	2,391,000,000	+4.18%	139
Malawi	947,000,000	-1.97%	154
Malaysia	330,426,000,000	+5.58%	24
Namibia	6,490,000,000	+18.3%	116
New Caledonia	1,245,000,000	-41.2%	149
Niger	1,749,000,000	+76.3%	143
Nigeria	52,933,000,000	-4.08%	54
Nicaragua	7,522,000,000	+1.95%	111
Netherlands	921,356,000,000	-1.58%	4
Norway	168,286,000,000	-3.33%	33
Nepal	1,244,000,000	+8.65%	150
Nauru	34,000,000	-10.5%	192
New Zealand	42,410,000,000	+2.44%	60
Oman	63,016,000,000	+6.79%	51
Pakistan	32,321,000,000	+13.5%	67
Panama	12,666,000,000	-24.3%	94
Peru	70,250,000,000	+8.55%	50
Philippines	72,969,000,000	-0.759%	49
Palau	3,000,000	-57.1%	200
Papua New Guinea	13,268,000,000	+3.41%	90
Poland	380,064,000,000	-0.381%	21
North Korea	501,000,000	+51.8%	163
Portugal	85,794,000,000	+2.6%	43
Paraguay	10,880,000,000	-8.5%	99
French Polynesia	172,000,000	-29.2%	178
Qatar	94,286,000,000	-3.54%	42
Romania	100,309,000,000	-0.296%	40
Russia	417,170,000,000	-1.72%	19
Rwanda	3,200,000,000	+29.8%	136
Saudi Arabia	305,350,000,000	-4.58%	25
Sudan	3,770,000,000	-20.6%	133
Senegal	6,408,000,000	+20.5%	118
Singapore	505,658,000,000	+6.17%	15
Solomon Islands	488,000,000	+13.2%	164
Sierra Leone	1,263,000,000	-4.89%	148
El Salvador	6,448,000,000	-0.769%	117
Somalia	1,100,000,000	+19.6%	152
Serbia	31,586,000,000	+2.1%	68
South Sudan	890,000,000	+2.3%	155
São Tomé & Príncipe	26,000,000	+8.33%	194
Suriname	1,296,000,000	-43.9%	147
Slovakia	115,875,000,000	-1.38%	36
Slovenia	78,961,000,000	+8.1%	46
Sweden	194,598,000,000	-1.45%	32
Eswatini	2,420,000,000	+15.5%	138
Sint Maarten	273,000,000	+18.2%	171
Seychelles	570,000,000	+5.36%	162
Syria	3,417,000,000	+5.4%	135
Turks & Caicos Islands	4,000,000	0%	199
Chad	4,700,000,000	+2.09%	122
Togo	1,510,000,000	+3.71%	145
Thailand	300,529,000,000	+5.42%	26
Tajikistan	1,977,000,000	-19.3%	140
Turkmenistan	12,602,000,000	-12.4%	95
Timor-Leste	87,000,000	-70.4%	181
Tonga	8,000,000	-20%	198
Trinidad & Tobago	8,702,000,000	+10.7%	106
Tunisia	19,943,000,000	-0.21%	79
Turkey	261,855,000,000	+2.44%	29
Tanzania	8,767,000,000	+20.5%	105
Uganda	7,700,000,000	+22.1%	110
Ukraine	40,593,000,000	+12.6%	62
Uruguay	10,215,000,000	+10.8%	100
United States	2,065,180,000,000	+2.33%	2
Uzbekistan	19,744,000,000	+2.68%	80
St. Vincent & Grenadines	52,000,000	+26.8%	187
Venezuela	15,290,000,000	+96%	85
Vietnam	403,241,000,000	+14%	20
Vanuatu	61,000,000	+1.67%	185
Samoa	42,000,000	-2.33%	191
Yemen	218,000,000	-12.4%	175
South Africa	110,076,000,000	-0.703%	37
Zambia	11,198,000,000	+7.35%	97
Zimbabwe	7,432,000,000	+2.87%	112

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.MRCH.CD.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.MRCH.CD.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

Merchandise exports (current US$)

Year: 2024

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