Country

Indicator

Value type

Values present

Agricultural raw materials imports (% of merchandise imports)

Source: worldbank.org, 01.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Albania	0.768	-8.71%	60
Argentina	0.803	-7.18%	56
Armenia	0.7	-20.1%	64
Antigua & Barbuda	2.09	-15.2%	6
Australia	0.535	-0.667%	71
Azerbaijan	1.37	-9.24%	25
Belgium	0.971	+10.1%	43
Burkina Faso	0.421	-1%	77
Bulgaria	0.761	-22.4%	61
Bosnia & Herzegovina	0.981	-10.2%	42
Belize	1.69	-5.91%	16
Bolivia	0.869	+3.97%	50
Brazil	0.783	-1.71%	59
Barbados	0.872	-12.1%	49
Canada	0.741	+3.68%	62
Switzerland	0.484	+6.83%	75
Chile	0.663	+1.39%	66
China	2.66	+1.25%	3
Cyprus	0.558	+30.1%	68
Czechia	1.03	+11.5%	38
Germany	1.07	+5.85%	37
Denmark	1.95	+1.31%	8
Dominican Republic	0.875	-7.28%	48
Ecuador	0.67	-2.47%	65
Egypt	2.1	-1.2%	5
Spain	1.01	+5.1%	40
Estonia	2.68	+11.4%	2
Finland	1.69	+11%	15
Fiji	0.356	+16.9%	80
United Kingdom	1.14	+6.66%	35
Georgia	0.816	+3.32%	53
Greece	0.811	+1.21%	54
Grenada	1.88	+5.22%	11
Guatemala	1.8	+0.197%	12
Guyana	0.0765	-17.8%	86
Hong Kong SAR China	0.0913	-14.6%	85
Croatia	0.912	-5%	46
Hungary	1.08	+6.93%	36
India	1.54	+4.17%	20
Ireland	0.53	+7.69%	72
Iceland	1.25	+9.54%	32
Israel	0.786	+1.95%	58
Italy	1.64	+0.724%	17
Japan	1.53	-0.423%	21
Kyrgyzstan	0.472	+45%	76
South Korea	0.896	-1.31%	47
Sri Lanka	1.89	+26.6%	10
Lithuania	1.8	-9.36%	13
Luxembourg	1.59	-6.06%	18
Latvia	2.2	+4.77%	4
Macao SAR China	0.143	+20.9%	83
Moldova	1.37	+12%	24
Maldives	1.35	+13.4%	28
Mexico	0.808	-2.31%	55
North Macedonia	0.589	-6.58%	67
Malta	0.218	-17.8%	82
Myanmar (Burma)	0.544	+12.2%	70
Montenegro	0.519	-5.5%	73
Mauritius	1.35	-7.72%	27
Malaysia	1.55	+9.21%	19
Namibia	0.491	-12.3%	74
Netherlands	1.41	+2.02%	23
Norway	0.985	-0.877%	41
New Zealand	0.74	+18.6%	63
Pakistan	4.34	-13.1%	1
Panama	0.375	+17.1%	79
Philippines	0.554	-0.89%	69
Poland	1.24	+3.32%	33
Portugal	1.53	-9.54%	22
Paraguay	0.787	-19.4%	57
French Polynesia	0.918	+10.4%	45
Romania	1.35	+2.25%	29
El Salvador	1.97	+5.02%	7
Suriname	0.106	+5.95%	84
Slovakia	0.962	+3.74%	44
Slovenia	1.23	-16.1%	34
Sweden	1.31	+16.6%	31
Togo	1.36	+7.4%	26
Thailand	1.31	-2.81%	30
Trinidad & Tobago	0.4	-3.45%	78
Turkey	1.94	+7.07%	9
Ukraine	0.841	-1.67%	51
United States	0.823	-1.44%	52
Uzbekistan	1.74	+1.77%	14
South Africa	1.03	+19.7%	39
Zimbabwe	0.313	-3.17%	81

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 = 'TM.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 <- 'TM.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 imports (% of merchandise imports)

Year: 2024

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