Country

Indicator

Value type

Values present

Manufacturing, value added (% of GDP)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	8.26	+3.33%	94
Albania	6.17	-10.3%	107
Argentina	15.1	-7.54%	35
Armenia	10.5	-0.959%	75
Australia	5.49	+2.41%	112
Austria	15.4	-3.21%	33
Azerbaijan	5.24	+2.04%	114
Belgium	10.7	-5.18%	73
Benin	10.2	+1.12%	77
Burkina Faso	9.99	-6.54%	81
Bangladesh	21.9	-2.01%	11
Bahamas	0.63	-26.4%	129
Bosnia & Herzegovina	11.6	-12.8%	64
Belarus	20.3	-8.34%	12
Brazil	12.4	-6.58%	60
Brunei	11	+6.28%	70
Botswana	5.55	-1.38%	111
Switzerland	17.7	-0.819%	24
Chile	9.01	-4.21%	89
China	24.9	-2.47%	5
Côte d’Ivoire	12.9	-2.46%	54
Cameroon	13.9	+3.1%	41
Congo - Kinshasa	18	-0.153%	21
Colombia	10.1	-7.06%	78
Cape Verde	4.86	+3.66%	116
Costa Rica	13	-4.81%	53
Cyprus	4.18	-3.95%	120
Czechia	20	+0.0973%	14
Germany	17.8	-2.99%	22
Dominica	4.73	+23.2%	117
Denmark	16.8	+5.37%	26
Dominican Republic	12.5	-0.501%	59
Ecuador	12.9	-3.18%	55
Egypt	13.9	-7.77%	42
Spain	10.7	-1.67%	72
Estonia	11.3	-5.13%	67
Ethiopia	4.41	-1.63%	118
Finland	13.9	-4.25%	43
Fiji	8.89	-5.06%	91
France	9.43	-3.07%	85
Gabon	19.6	+0.532%	16
United Kingdom	8.01	-3.33%	98
Georgia	8.12	-2.8%	95
Ghana	10.1	-9.59%	80
Guinea-Bissau	9.38	+5.93%	86
Equatorial Guinea	24.8	+0.683%	6
Greece	8.73	+0.0193%	92
Guatemala	13.6	-3.02%	46
Honduras	14.9	-2.24%	36
Croatia	10.8	-11.1%	71
Haiti	26.2	+7.16%	4
Hungary	15.8	-8.47%	29
Indonesia	19	+1.7%	19
India	12.5	-3.72%	58
Ireland	27.2	-7.56%	3
Iraq	4.09	+35.5%	122
Iceland	7.61	-10.7%	103
Israel	11.1	-5.11%	68
Italy	14.6	-4.66%	37
Jamaica	7.75	-2.53%	101
Jordan	17.7	+2.42%	23
Kazakhstan	12	-2.7%	63
Kyrgyzstan	12.6	+0.422%	57
Cambodia	27.8	+5.59%	2
St. Kitts & Nevis	4.04	-0.973%	123
Kuwait	8	+5.91%	100
Laos	9.05	-2.1%	88
St. Lucia	2.93	-11.5%	126
Sri Lanka	17.6	-2.54%	25
Lesotho	13.6	-2.42%	45
Lithuania	14	-3.68%	40
Luxembourg	3.43	-11.5%	125
Latvia	9.93	-5.78%	83
Morocco	14.3	-1.37%	38
Moldova	7.72	-7.61%	102
Maldives	1.58	-20.3%	128
Mexico	19.6	-3.1%	15
North Macedonia	13.5	-0.772%	49
Mali	7.38	+2.18%	104
Malta	6.05	-2.05%	108
Myanmar (Burma)	22.5	+3%	9
Mongolia	5.66	-10.1%	110
Mozambique	7.26	+1.99%	105
Mauritania	6.04	-6.89%	109
Mauritius	11.1	-3.15%	69
Malaysia	22.5	-2.26%	10
Namibia	10.6	-3.77%	74
Nigeria	13.5	-12.1%	47
Nicaragua	13.5	-6.43%	48
Netherlands	10.4	-3.66%	76
Norway	6.19	+2.33%	106
Nepal	4.37	-3.84%	119
Oman	10.1	+7.55%	79
Pakistan	13.1	-3.59%	51
Panama	4.87	-2.33%	115
Peru	12.3	-2.49%	61
Philippines	15.7	-3.41%	30
Papua New Guinea	1.63	-1.88%	127
Poland	15.4	-11.8%	32
Puerto Rico	44.2	+1.4%	1
Portugal	11.5	-2.65%	65
Paraguay	19	-1.98%	18
Qatar	8.12	-6.32%	96
Romania	11.4	-9.57%	66
Russia	13.3	+3.98%	50
Rwanda	8.94	-6.29%	90
Saudi Arabia	15.6	-2.03%	31
Senegal	14.1	-3.16%	39
Singapore	16.3	-3.79%	27
Sierra Leone	8.04	+4.87%	97
El Salvador	12	-3.56%	62
Slovakia	16.3	-3.14%	28
Slovenia	19.2	-1.86%	17
Sweden	13.8	-2.51%	44
Seychelles	4.17	-11.2%	121
Turks & Caicos Islands	0.447	-4.71%	130
Thailand	24.3	-2.63%	8
Tanzania	8	-4.73%	99
Uganda	15.2	-2.93%	34
Ukraine	8.41	+1.19%	93
Uruguay	9.69	-0.544%	84
United States	9.98	-2.59%	82
Uzbekistan	20.2	+4.29%	13
St. Vincent & Grenadines	3.76	+2.15%	124
Vietnam	24.4	+1.04%	7
Samoa	5.31	+4.07%	113
Kosovo	13.1	+1.19%	52
South Africa	12.8	-1.27%	56
Zambia	9.28	+9.19%	87
Zimbabwe	18.6	+13.2%	20

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.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 <- 'NV.IND.MANF.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

Manufacturing, value added (% of GDP)

Year: 2024

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