Country

Indicator

Value type

Values present

Real effective exchange rate index (2010 = 100)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Armenia	122	-3.68%	12
Antigua & Barbuda	116	+1.68%	20
Australia	92	+1.84%	69
Austria	108	+0.891%	36
Burundi	141	+4.18%	4
Belgium	104	+1.03%	47
Bulgaria	118	-0.191%	18
Bahrain	104	-0.503%	46
Bahamas	96.2	-2.45%	61
Belize	106	+1.17%	40
Bolivia	157	+5.2%	3
Brazil	60.7	-4.2%	91
Central African Republic	128	-0.617%	10
Canada	80.5	-0.929%	83
Switzerland	111	+1.37%	31
Chile	84.1	-8.5%	80
China	113	-2.57%	25
Côte d’Ivoire	99.4	+2.49%	54
Cameroon	109	+3.67%	33
Congo - Kinshasa	128	+0.456%	11
Colombia	74.6	+11.8%	85
Costa Rica	119	+3.54%	17
Cyprus	88.1	-0.166%	74
Czechia	120	-4.47%	15
Germany	97.4	+0.291%	58
Dominica	94.5	-1.55%	64
Denmark	94.2	-0.892%	65
Dominican Republic	85.8	-5.07%	77
Algeria	106	+3.62%	43
Spain	97.2	+0.584%	59
Finland	98.6	-0.492%	55
Fiji	108	+1.64%	35
France	91.9	-0.0899%	70
Gabon	101	-0.105%	52
United Kingdom	108	+4.25%	34
Georgia	112	-6.49%	28
Ghana	67.8	-4.87%	89
Gambia	104	-0.998%	45
Equatorial Guinea	102	+1.27%	49
Greece	87.3	-0.183%	75
Grenada	88.4	-2.14%	73
Guyana	113	+0.148%	27
Croatia	98.1	+0.475%	57
Hungary	93.2	-2.1%	67
Ireland	88.8	-0.0709%	72
Iran	625	+30.9%	1
Iceland	138	+3.2%	6
Israel	106	+0.25%	39
Italy	95	-1.11%	63
Japan	55	-5.36%	92
St. Kitts & Nevis	90.1	-2.37%	71
St. Lucia	97.2	-3.96%	60
Lesotho	77.8	+4.04%	84
Luxembourg	98.5	-0.168%	56
Latvia	116	-0.947%	21
Morocco	101	+0.288%	50
Moldova	180	+4%	2
Mexico	100	+0.172%	53
North Macedonia	111	+1.16%	30
Malta	92.4	+0.0612%	68
Malawi	84.2	-15.5%	79
Malaysia	80.9	+1.09%	82
Nigeria	63.9	-44.7%	90
Nicaragua	101	+2.24%	51
Netherlands	105	+1.18%	44
Norway	73.6	-0.603%	86
New Zealand	106	+0.44%	41
Pakistan	102	+12.1%	48
Philippines	114	+0.506%	23
Papua New Guinea	121	-6.86%	13
Poland	113	+7.45%	26
Portugal	95.6	-0.047%	62
Paraguay	107	+0.394%	38
Romania	112	+2.5%	29
Russia	83	+1.34%	81
Saudi Arabia	120	+0.578%	16
Singapore	121	+2.86%	14
Solomon Islands	139	+2.24%	5
Sierra Leone	113	+19.3%	24
Slovakia	114	+0.421%	22
Sweden	84.5	+1.27%	78
Togo	107	+3.45%	37
Trinidad & Tobago	128	-1%	9
Tunisia	93.2	+5.21%	66
Uganda	110	+2.27%	32
Ukraine	87.1	-5%	76
Uruguay	129	+0.928%	8
United States	131	+2.36%	7
St. Vincent & Grenadines	106	+0.127%	42
Samoa	117	-0.244%	19
South Africa	72.6	+3.94%	88
Zambia	73.2	-13.3%	87

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 = 'PX.REX.REER'

# 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 <- 'PX.REX.REER'

# 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

Real effective exchange rate index (2010 = 100)

Year: 2024

Acquire full dataset with Python or R from source