Country

Indicator

Value type

Values present

Agricultural raw materials exports (% of merchandise exports)

Source: worldbank.org, 01.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Albania	0.479	+17.9%	70
Argentina	0.564	-17.7%	65
Armenia	0.319	-10.4%	75
Antigua & Barbuda	0		86
Australia	1.75	-2.05%	28
Azerbaijan	0.7	+76.1%	55
Belgium	1.18	+4.16%	36
Burkina Faso	6.03	+5.4%	6
Bulgaria	0.712	-1.73%	52
Bosnia & Herzegovina	3.98	-3.68%	14
Belize	0.942	-0.774%	44
Bolivia	1.03	+30.7%	40
Brazil	5.65	+35.5%	8
Barbados	0.0515	+6.84%	82
Canada	2.93	+2.38%	19
Switzerland	0.102	-9.89%	80
Chile	5.34	+10.3%	10
China	0.345	-4.64%	74
Cyprus	0.448	-19.2%	71
Czechia	0.906	-6.79%	46
Germany	0.706	-2.43%	54
Denmark	1.66	+1.87%	30
Dominican Republic	0.488	+2.68%	68
Ecuador	3.88	-1.4%	15
Egypt	1.09	-11%	37
Spain	0.945	-0.293%	43
Estonia	6.55	-0.624%	4
Finland	7.11	+4.79%	3
Fiji	5.28	-7.6%	11
United Kingdom	0.508	+12%	67
Georgia	0.693	-16.9%	57
Greece	1.33	+6.29%	33
Grenada	0.258	+19.6%	77
Guatemala	2.9	+5.57%	20
Guyana	0.0747	-56.8%	81
Hong Kong SAR China	0.0307	-12.6%	83
Croatia	3.54	-2.4%	17
Hungary	0.61	+5.94%	63
India	0.9	+9.44%	47
Ireland	0.261	-10.2%	76
Iceland	0.528	+6.2%	66
Israel	0.614	+6.26%	62
Italy	0.648	-0.0479%	58
Japan	0.583	+7.09%	64
Kyrgyzstan	0.987	-32.5%	41
South Korea	0.798	+3.2%	49
Sri Lanka	2.71	-15.4%	22
Lithuania	2.45	-3.78%	23
Luxembourg	0.635	-15.4%	60
Latvia	10.9	+11.5%	1
Macao SAR China	0.409	+118%	72
Moldova	0.39	-0.136%	73
Maldives	0.0184	+19.5%	85
Mexico	0.136	-7.95%	79
North Macedonia	0.485	+14%	69
Malta	0.146	+7.64%	78
Myanmar (Burma)	3.29	+55%	18
Montenegro	6.38	-7.05%	5
Mauritius	0.634	-26.4%	61
Malaysia	1.25	+6.41%	34
Namibia	1.88	+16.3%	26
Netherlands	2.75	+4.49%	21
Norway	0.707	+23.9%	53
New Zealand	9.71	-0.605%	2
Pakistan	1.6	-20.8%	32
Panama	4.69	+262%	12
Philippines	1.09	-9.63%	38
Poland	0.977	+6.05%	42
Portugal	2.12	+2.22%	24
Paraguay	1.8	+27%	27
French Polynesia	0.771	+19%	50
Romania	0.885	+7.19%	48
El Salvador	0.723	+7.97%	51
Suriname	5.95	+71.7%	7
Slovakia	0.696	+5.29%	56
Slovenia	1.06	-8.01%	39
Sweden	3.62	-0.811%	16
Togo	5.45	+75.8%	9
Thailand	4	+9.73%	13
Trinidad & Tobago	0.0284	+11.9%	84
Turkey	0.639	-8.84%	59
Ukraine	1.74	-21%	29
United States	1.63	-5.53%	31
Uzbekistan	1.22	-12.7%	35
South Africa	2.09	+4.16%	25
Zimbabwe	0.924	-26%	45

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 = 'TX.VAL.AGRI.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 <- 'TX.VAL.AGRI.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

Agricultural raw materials exports (% of merchandise exports)

Year: 2024

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