Country

Indicator

Value type

Values present

Other manufacturing (% of value added in manufacturing)

Source: worldbank.org, 03.09.2025

Year: 2021

Country	Value	Value change, %	Rank
Albania	-95.9	+12.5%	43
United Arab Emirates	74.4	-4.64%	4
Argentina	39.8	+4.17%	15
Armenia	-67.9	+42.2%	36
Australia	21.5	+3.6%	21
Austria	-322	+12.6%	75
Azerbaijan	-241	+20%	68
Belgium	-172	-14.9%	56
Bulgaria	-114	+13.8%	48
Bosnia & Herzegovina	-129	+24.3%	51
Belarus	-57.4	-68.6%	31
Brazil	50.3	-14.1%	12
Botswana	7.69	+88.4%	23
Canada	50.5	-5.72%	11
Switzerland	-181	+32.1%	58
China	-673	+28.9%	89
Côte d’Ivoire	-61.4	+2,867%	33
Colombia	-84.7	+22.1%	40
Cyprus	-76	+19.1%	37
Czechia	-298	-14.6%	74
Germany	-452	+2.54%	80
Denmark	-158	-8.03%	55
Ecuador	-59.1	-221%	32
Spain	-210	+11.8%	62
Estonia	-234	+19.6%	66
Finland	-204	+19.5%	61
Fiji	-88.7	-33.9%	41
France	-218	+12.6%	64
United Kingdom	-192	+5.85%	59
Georgia	-65.1	-2.63%	35
Greece	-56.3	-2.61%	30
Hong Kong SAR China	-47.6	+4.87%	27
Croatia	-99	+4.52%	44
Hungary	-542	+3.11%	85
Indonesia	58.7	-2.33%	7
India	-62.6	+98.5%	34
Ireland	-53.5	+78.7%	28
Iraq	56.5	+15%	8
Iceland	-103	+103%	45
Italy	-256	+23.3%	71
Jordan	-247	+41.5%	69
Japan	-115	-17.1%	49
Kazakhstan	-521	+8.46%	83
Kyrgyzstan	-529	+53.4%	84
South Korea	64.2	+0.0319%	5
Kuwait	61.8	0.00000%	6
Lithuania	-174	+23%	57
Luxembourg	-7.85	-63.1%	25
Latvia	-200	+22.8%	60
Macao SAR China	45.6	+7.22%	14
Morocco	11.3	-60.8%	22
Moldova	-109	-26.5%	47
Madagascar	30.2	+0.885%	20
Marshall Islands	1.89	+0.00010%	24
North Macedonia	-457	+85%	81
Malta	-262	-29.4%	72
Myanmar (Burma)	96.7	-1.7%	1
Mongolia	34.6	-18.1%	17
Mauritius	-77.4	+102%	38
Namibia	-35.6	-49.3%	26
Nicaragua	36	-12.1%	16
Netherlands	-211	+23.6%	63
Norway	-53.6	-35.3%	29
New Zealand	-398	+6.06%	79
Panama	34.2	-1.45%	18
Peru	-235	+23.5%	67
Philippines	-155	-1.58%	53
Poland	-219	+21.9%	65
Puerto Rico	-2,005	-14.7%	90
Portugal	-250	+55.9%	70
Paraguay	31.1	-26.1%	19
Qatar	-89.7	+41.8%	42
Romania	-393	+26%	78
Russia	-286	+0.403%	73
Rwanda	-581	+8.07%	86
Senegal	84.9	-32.9%	2
Singapore	79.8	+1.28%	3
Serbia	-126	+20.6%	50
Slovakia	-587	+90.3%	87
Slovenia	-370	+41.7%	77
Sweden	-468	+43.1%	82
Thailand	53.1	-10.4%	10
Turkey	-359	+55.9%	76
Tanzania	-134	+0.000102%	52
Ukraine	-82.1	-8.27%	39
Uruguay	-156	+16.8%	54
United States	47.7	-4.05%	13
Uzbekistan	-618	+167%	88
Vietnam	-108	+23%	46
South Africa	54.4	0.00000%	9

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.MNF.OTHR.ZS.UN'

# 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.MNF.OTHR.ZS.UN'

# 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

Other manufacturing (% of value added in manufacturing)

Year: 2021

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