Country

Indicator

Value type

Values present

Agriculture, forestry, and fishing, value added (% of GDP)

Source: worldbank.org, 01.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Angola	16.4	+10.3%	36
Albania	15.5	-4.47%	39
Andorra	0.466	-2.63%	149
Argentina	5.98	+0.873%	77
Armenia	7.92	-6.57%	65
Australia	2.22	-13.8%	118
Austria	1.23	-5.25%	134
Azerbaijan	5.66	+2.66%	79
Belgium	0.801	+4.11%	142
Benin	24.2	-4.61%	15
Burkina Faso	18.6	+13.8%	27
Bangladesh	11.2	+1.46%	49
Bulgaria	2.07	-18%	119
Bahamas	0.515	+13.3%	147
Bosnia & Herzegovina	4.25	-8.57%	90
Belarus	6.87	-5.28%	71
Brazil	5.58	-7.36%	81
Brunei	1.18	+0.351%	136
Botswana	1.71	+2.86%	127
Central African Republic	32.5	+13.6%	7
Switzerland	0.629	+0.606%	145
Chile	3.91	+12.1%	94
China	6.78	-1.65%	72
Côte d’Ivoire	17.9	+17.9%	30
Cameroon	17.4	+0.851%	31
Congo - Kinshasa	17.1	-1.98%	33
Congo - Brazzaville	9.44	+5.45%	56
Colombia	9.27	+5.23%	57
Comoros	36.6	+1.9%	2
Cape Verde	4.7	+1.34%	86
Costa Rica	3.56	-6.26%	96
Cyprus	1.18	+0.087%	135
Czechia	1.5	-13.4%	129
Germany	0.826	-1.86%	141
Djibouti	2.58	+3.75%	110
Dominica	12.2	+0.946%	46
Denmark	0.742	-1.89%	143
Dominican Republic	4.45	+3.72%	87
Ecuador	9.48	+18.5%	55
Egypt	13.7	+29.4%	42
Spain	2.54	+1.57%	114
Estonia	1.91	-0.706%	124
Ethiopia	34.9	-2.56%	3
Finland	2.46	+6.85%	115
Fiji	8.36	-28.7%	61
France	1.43	-17.7%	131
Gabon	6.15	-2.47%	75
United Kingdom	0.558	-3.14%	146
Georgia	5.42	-9.98%	83
Ghana	20.7	-1.05%	24
Guinea	29.6	+0.576%	10
Gambia	24.1	+3.94%	16
Guinea-Bissau	36.8	+8.28%	1
Equatorial Guinea	3.15	+1.8%	102
Greece	3.32	-0.744%	100
Grenada	2.75	-13.7%	106
Guatemala	9.78	+0.962%	54
Guyana	8.02	-17.6%	64
Honduras	11.2	-6.35%	48
Croatia	3.41	+1.46%	98
Haiti	15.9	-9.15%	38
Hungary	2.37	-18.3%	117
Indonesia	12.6	+0.632%	45
India	16.4	+0.987%	37
Ireland	1.05	+20.2%	139
Iran	13	+1.25%	43
Iraq	3.39	+9.29%	99
Iceland	4.05	-5.54%	92
Israel	1.28	-0.316%	133
Italy	2.03	+8.35%	122
Jamaica	9.8	+8.65%	53
Jordan	5.07	+5.31%	85
Kazakhstan	3.94	+2.89%	93
Kenya	21.3	-2.12%	22
Kyrgyzstan	8.61	-9.32%	60
Cambodia	16.6	-2.94%	35
St. Kitts & Nevis	1.3	-13.7%	132
Kuwait	0.493	+5.17%	148
Laos	16.8	+3.92%	34
Liberia	33.6	-2.64%	5
Libya	1.74	-2.16%	126
St. Lucia	1.13	+0.398%	138
Sri Lanka	8.3	+2.16%	62
Lesotho	6.5	-2.11%	73
Lithuania	2.57	-5.23%	112
Luxembourg	0.173	-18.5%	153
Latvia	4.1	+4.76%	91
Morocco	10.1	-8.83%	52
Moldova	7.11	+0.31%	69
Madagascar	22.5	+0.237%	19
Maldives	3.05	-40.3%	104
Mexico	3.77	-1.88%	95
North Macedonia	5.97	-9.86%	78
Mali	33.4	+2.94%	6
Malta	0.208	+5,236%	152
Myanmar (Burma)	20.8	-8.31%	23
Montenegro	5.16	-5.78%	84
Mongolia	7.38	-25.6%	66
Mozambique	26.3	+1.34%	11
Mauritania	18.6	-0.629%	26
Mauritius	4.26	+6.37%	89
Malawi	32.4	+6.52%	8
Malaysia	8.16	+4.84%	63
Namibia	7.29	-5.03%	67
Niger	33.8	+4.02%	4
Nigeria	20.4	-10.4%	25
Nicaragua	14.4	-5.98%	41
Netherlands	1.68	-2.47%	128
Norway	2.04	-2.02%	120
Nepal	21.9	+2.69%	21
Oman	2.6	+6.32%	108
Pakistan	23.5	+0.848%	17
Panama	2.58	+5.15%	111
Peru	6.06	-16.4%	76
Philippines	9.08	-3.38%	58
Papua New Guinea	17.2	+1.14%	32
Poland	2.6	-5.88%	109
Puerto Rico	0.693	-0.507%	144
Portugal	1.97	-6.73%	123
Paraguay	10.7	-7.53%	51
Qatar	0.289	-1.34%	151
Romania	3.28	-15.5%	101
Russia	2.74	-7.69%	107
Rwanda	24.6	-7.63%	14
Saudi Arabia	2.54	+3.36%	113
Sudan	22.1	-27.1%	20
Senegal	15.5	-11%	40
Singapore	0.0274	-4.89%	154
Sierra Leone	25.4	-12.5%	12
El Salvador	4.38	-1.57%	88
Serbia	3.15	-16.9%	103
São Tomé & Príncipe	12.8	-6.32%	44
Slovakia	2.03	-4.53%	121
Slovenia	1.49	-1.78%	130
Sweden	1.15	+15.6%	137
Seychelles	2.45	-1.82%	116
Turks & Caicos Islands	0.352	-4.13%	150
Chad	32.2	-8.25%	9
Togo	18	-0.506%	29
Thailand	8.71	+1.78%	59
Turkey	5.59	-9.2%	80
Tanzania	23.4	-1.18%	18
Uganda	24.7	+2.41%	13
Ukraine	7.11	-5.84%	68
Uruguay	6.42	+5.43%	74
United States	0.851	-14%	140
Uzbekistan	18.3	-9.13%	28
St. Vincent & Grenadines	3.55	-15.6%	97
Vietnam	11.9	+0.0153%	47
Samoa	11	+0.213%	50
Kosovo	6.91	-4.24%	70
South Africa	2.92	+11.6%	105
Zambia	1.8	-19.3%	125
Zimbabwe	5.44	+32.5%	82

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.AGR.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.AGR.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

Agriculture, forestry, and fishing, value added (% of GDP)

Year: 2024

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