Country

Indicator

Value type

Values present

Manufacturing, value added (current US$)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	6,638,529,307	-2.12%	75
Albania	1,676,748,929	+3.53%	101
Argentina	95,540,071,949	-9.37%	22
Armenia	2,696,374,099	+6.04%	90
Australia	96,227,550,642	+3.84%	21
Austria	80,140,458,685	-1.32%	26
Azerbaijan	3,892,941,176	+4.7%	85
Belgium	70,932,072,211	-2.25%	29
Benin	2,185,718,278	+10.4%	97
Burkina Faso	2,323,150,342	+6.92%	94
Bangladesh	98,544,469,168	+0.836%	20
Bahamas	99,700,000	-23.7%	122
Bosnia & Herzegovina	3,280,346,920	-10.5%	86
Belarus	15,429,185,951	-3.93%	56
Brazil	269,825,744,066	-7.08%	10
Brunei	1,705,665,653	+8.87%	100
Botswana	1,076,156,780	-1.43%	113
Switzerland	165,689,268,917	+3.85%	14
Chile	29,758,618,474	-5.71%	44
China	4,661,441,535,071	+0.0569%	1
Côte d’Ivoire	11,186,901,088	+6.02%	65
Cameroon	7,137,110,681	+7.38%	74
Congo - Kinshasa	12,717,945,885	+5.42%	61
Colombia	42,279,490,339	+6.2%	37
Cape Verde	134,634,005	+13%	120
Costa Rica	12,361,206,548	+4.93%	62
Cyprus	1,517,226,077	+2.99%	105
Czechia	69,130,874,868	+0.631%	30
Germany	829,955,272,491	-0.114%	3
Dominica	32,611,111	+28.7%	128
Denmark	72,218,060,926	+11.2%	28
Dominican Republic	15,490,441,994	+2.66%	55
Ecuador	16,053,574,200	-0.362%	53
Egypt	54,053,889,062	-9.37%	33
Spain	184,494,998,040	+4.56%	13
Estonia	4,833,558,317	-1.75%	79
Finland	41,543,927,152	-2.67%	38
Fiji	519,093,343	+1.89%	117
France	298,284,249,625	+0.433%	7
Gabon	4,095,117,046	+4.6%	84
United Kingdom	291,795,431,154	+4.53%	8
Georgia	2,743,112,689	+6.67%	89
Ghana	8,342,980,591	-7.03%	70
Guinea-Bissau	198,823,074	+8.07%	119
Equatorial Guinea	3,167,561,464	+4.18%	88
Greece	22,454,556,486	+5.62%	48
Guatemala	15,381,059,924	+5.18%	57
Honduras	5,514,574,978	+5.56%	78
Croatia	9,967,205,125	-2.54%	67
Haiti	6,616,692,062	+36.2%	76
Hungary	35,238,816,031	-4.67%	41
Indonesia	265,073,987,435	+3.56%	11
India	490,403,941,493	+3.53%	4
Ireland	157,117,331,406	-3.21%	15
Iraq	11,429,111,077	+40.9%	64
Iceland	2,545,197,356	-4.97%	92
Israel	60,139,111,897	+0.111%	31
Italy	345,289,219,194	-1.84%	6
Jamaica	1,545,306,326	+0.0153%	104
Jordan	9,446,229,155	+6.96%	68
Kazakhstan	34,482,687,848	+7.17%	42
Kyrgyzstan	2,204,806,769	+15.6%	96
Cambodia	12,888,711,532	+15.6%	59
St. Kitts & Nevis	43,051,852	-0.043%	127
Kuwait	12,815,326,474	+2.61%	60
Laos	1,494,121,132	+1.98%	106
St. Lucia	74,670,370	-7.14%	124
Sri Lanka	17,409,393,565	+15.2%	51
Lesotho	309,959,395	+4.66%	118
Lithuania	11,916,091,102	+2.45%	63
Luxembourg	3,197,481,790	-5.8%	87
Latvia	4,320,171,190	-3.68%	81
Morocco	22,073,269,417	+5.47%	49
Moldova	1,404,530,571	+0.613%	110
Maldives	110,078,753	-15.6%	121
Mexico	363,787,714,933	+0.0837%	5
North Macedonia	2,246,367,147	+5.03%	95
Mali	1,961,312,310	+10.3%	98
Malta	1,471,279,048	+7.26%	107
Myanmar (Burma)	16,683,989,844	+14.3%	52
Mongolia	1,335,235,981	+4.31%	111
Mozambique	1,626,712,490	+9.1%	103
Mauritania	650,293,431	-5.88%	114
Mauritius	1,662,039,000	+2.7%	102
Malaysia	94,928,152,465	+3.18%	23
Namibia	1,422,822,721	+3.71%	109
Nigeria	25,364,113,924	-54.6%	45
Nicaragua	2,659,803,641	+3.5%	91
Netherlands	128,213,378,426	+2.45%	17
Norway	29,936,719,659	+2.5%	43
Nepal	1,873,584,152	+0.53%	99
Oman	10,790,637,191	+8.62%	66
Pakistan	48,912,180,603	+6.45%	35
Panama	4,197,585,700	+1.12%	83
Peru	35,587,382,459	+5.64%	40
Philippines	72,368,483,741	+2.02%	27
Papua New Guinea	530,099,994	+3.6%	116
Poland	140,903,937,743	-0.658%	16
Puerto Rico	55,632,200,000	+7.79%	32
Portugal	35,597,314,785	+3.73%	39
Paraguay	8,457,072,456	+1.06%	69
Qatar	17,693,681,319	-4.13%	50
Romania	43,687,956,681	-1.33%	36
Russia	288,112,754,153	+9.12%	9
Rwanda	1,274,221,869	-6.81%	112
Saudi Arabia	192,669,600,000	-0.508%	12
Senegal	4,544,422,868	+1.79%	80
Singapore	89,403,045,726	+4.2%	24
Sierra Leone	607,109,442	+23.4%	115
El Salvador	4,259,880,000	+0.74%	82
Slovakia	23,093,579,844	+2.56%	47
Slovenia	13,938,445,406	+2.87%	58
Sweden	84,145,305,860	+1.59%	25
Seychelles	90,278,514	-12%	123
Turks & Caicos Islands	7,801,000	+1.5%	129
Thailand	128,038,197,814	-0.645%	18
Tanzania	6,303,524,456	-5.07%	77
Uganda	8,150,076,433	+6.79%	72
Ukraine	16,043,825,284	+6.5%	54
Uruguay	7,846,191,113	+3.24%	73
United States	2,913,114,000,000	+2.56%	2
Uzbekistan	23,215,945,912	+16.8%	46
St. Vincent & Grenadines	43,544,444	+10.2%	126
Vietnam	116,383,680,082	+10.9%	19
Samoa	56,678,443	+18.5%	125
Kosovo	1,457,468,624	+7.77%	108
South Africa	51,224,111,835	+3.8%	34
Zambia	2,444,104,588	+4.23%	93
Zimbabwe	8,221,526,036	+42%	71

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 = 'NV.IND.MANF.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 <- 'NV.IND.MANF.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))

Compare

Manufacturing, value added (current US$)

Year: 2024

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