Country

Indicator

Value type

Values present

Industry (including construction), value added (% of GDP)

Source: worldbank.org, 01.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	44.2	-2.4%	13
Albania	22.4	-2.66%	96
Andorra	12.8	+2.61%	140
Argentina	24	-4.25%	81
Armenia	23.2	-0.678%	86
Australia	26	-6.09%	63
Austria	23.1	-8.67%	88
Azerbaijan	42.6	-7.04%	14
Belgium	17.6	-5.17%	122
Benin	17.4	+0.472%	124
Burkina Faso	29.7	+1.4%	45
Bangladesh	34.1	-1.44%	27
Bulgaria	22.5	-0.765%	94
Bahamas	9.63	-1.55%	149
Bosnia & Herzegovina	22	-7.1%	99
Belarus	30.7	-3.11%	40
Brazil	21.3	-3.68%	103
Brunei	61.7	-0.0377%	3
Botswana	29.4	-13.8%	47
Central African Republic	17.8	-14%	121
Switzerland	24.7	+0.218%	75
Chile	30.1	+4.45%	43
China	36.5	-0.807%	25
Côte d’Ivoire	22.1	-3.51%	98
Cameroon	25.6	+0.369%	66
Congo - Kinshasa	46.6	+0.362%	10
Congo - Brazzaville	40.1	-11.3%	16
Colombia	23.1	-6.56%	87
Comoros	9.56	+0.308%	150
Cape Verde	10.5	-3.14%	146
Costa Rica	19.7	-3.68%	111
Cyprus	10.3	-1.65%	147
Czechia	30.2	-0.616%	42
Germany	25.8	-5.32%	65
Djibouti	15.4	+7.47%	133
Dominica	13.9	+8.83%	139
Denmark	24	+2.15%	80
Dominican Republic	28.7	-1.99%	52
Ecuador	26.5	-3.85%	58
Egypt	32.6	-0.525%	29
Spain	19.5	-3.05%	112
Estonia	20.5	-5.78%	106
Ethiopia	25.4	+3.84%	69
Finland	22.1	-5.76%	97
Fiji	14.1	-0.369%	138
France	17.5	-4.99%	123
Gabon	50.9	-1.69%	8
United Kingdom	16.7	-4.29%	129
Georgia	19.1	+1.03%	114
Ghana	28.8	-2.24%	50
Guinea	25.3	-1.73%	70
Gambia	14.7	-11.8%	137
Guinea-Bissau	16.6	+5.32%	130
Equatorial Guinea	45.8	-1.62%	11
Greece	15.4	+0.499%	135
Grenada	14.8	-1.01%	136
Guatemala	21.7	-2.56%	100
Guyana	74.3	+7.21%	1
Honduras	26.1	+1.17%	62
Croatia	19.8	-5.57%	110
Haiti	33.4	+4.3%	28
Hungary	23.9	-6.46%	82
Indonesia	39.3	-2.23%	17
India	24.5	-3.31%	77
Ireland	30.8	-7.07%	38
Iran	36.4	+0.443%	26
Iraq	51.6	-4.43%	7
Iceland	19.4	-3.58%	113
Israel	17.3	-6.25%	125
Italy	21.7	-5.89%	101
Jamaica	18.3	-1.95%	117
Jordan	25.1	+1.37%	71
Kazakhstan	31.4	-3.14%	36
Kenya	16.1	-4.63%	131
Kyrgyzstan	24.7	+4.91%	74
Cambodia	41.8	+3.04%	15
St. Kitts & Nevis	21.1	-1.42%	104
Kuwait	57.1	-5.25%	5
Laos	29	-4.87%	48
Liberia	23.3	-0.275%	84
Libya	68.3	-11.7%	2
St. Lucia	9.75	+0.708%	148
Sri Lanka	25.5	-0.985%	67
Lesotho	31	+7.99%	37
Lithuania	23.4	-3.48%	83
Luxembourg	9.01	-11.4%	153
Latvia	19.9	-8.41%	109
Morocco	24.1	-1.7%	79
Moldova	16.8	-5.13%	127
Madagascar	22.8	-0.564%	90
Maldives	9.02	-6.91%	152
Mexico	31.6	-2.61%	35
North Macedonia	22.7	-2.55%	92
Mali	22.7	-7.1%	91
Malta	11.4	-3.22%	143
Myanmar (Burma)	37.8	+0.584%	19
Montenegro	11.6	-6.63%	142
Mongolia	38.1	-3.71%	18
Mozambique	24.6	+15.5%	76
Mauritania	30.6	-0.113%	41
Mauritius	17.8	+0.0726%	120
Malawi	16	+4.21%	132
Malaysia	37.1	-1.58%	23
Namibia	28.9	-4.39%	49
Niger	17.8	-0.99%	119
Nigeria	29.6	-9.01%	46
Nicaragua	27.6	+1.31%	56
Netherlands	17.9	-4.71%	118
Norway	37	-5.17%	24
Nepal	11.4	-4.57%	144
Oman	54.2	-1.98%	6
Pakistan	20	-3.05%	107
Panama	26.3	-5.59%	60
Peru	32.2	-5.09%	31
Philippines	27.7	-1.79%	55
Papua New Guinea	37.2	+2.44%	22
Poland	26.4	-10.3%	59
Puerto Rico	48	+1.43%	9
Portugal	18.4	+1.08%	116
Paraguay	32.5	-1.26%	30
Qatar	58.5	-3.11%	4
Romania	25	-6.22%	72
Russia	30.7	+1.03%	39
Rwanda	21	+1.27%	105
Saudi Arabia	44.8	-5.82%	12
Sudan	23	-18.7%	89
Senegal	25.4	+11.2%	68
Singapore	21.4	-3.47%	102
Sierra Leone	27.3	+4.87%	57
El Salvador	22.4	-3.22%	95
Serbia	23.3	-2.99%	85
São Tomé & Príncipe	2.91	+3.26%	154
Slovakia	28.5	-2.12%	54
Slovenia	28.8	-3.16%	51
Sweden	22.6	-2.88%	93
Seychelles	12.3	-6.58%	141
Turks & Caicos Islands	9.34	-1.53%	151
Chad	29.7	-1.91%	44
Togo	20	-0.699%	108
Thailand	32.1	-2.58%	32
Turkey	25.9	-8.58%	64
Tanzania	28.7	+2.37%	53
Uganda	24.9	-3.47%	73
Ukraine	19	-0.323%	115
Uruguay	16.8	+0.0312%	128
United States	17.3	-2.37%	126
Uzbekistan	31.8	+2.53%	33
St. Vincent & Grenadines	15.4	+4.57%	134
Vietnam	37.6	+0.159%	20
Samoa	10.9	+0.343%	145
Kosovo	26.2	-0.561%	61
South Africa	24.4	-0.84%	78
Zambia	37.5	+6.74%	21
Zimbabwe	31.8	+20.9%	34

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.TOTL.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.TOTL.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

Industry (including construction), value added (% of GDP)

Year: 2024

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