Military expenditure (% of GDP)

The indicator 'Military expenditure (% of GDP)' measures the proportion of a country’s gross domestic product (GDP) that is allocated to military spending. This percentage gives insight into a nation’s prioritization of defense and military expenditures relative to its overall economic output. The significance of this indicator lies not only in its representation of a country’s military capabilities but also in how it reflects public policy decisions, geopolitical stability, and potential threats faced by a nation.

Military expenditure, as a percentage of GDP, serves several important roles. First, it provides a comparative framework to assess military spending across different countries. By examining this data, analysts can identify trends in military investment that indicate a country’s strategic intentions and readiness. For example, countries experiencing conflict or heightened tensions often increase their military budgets, which can be observed in the case of Ukraine, with military expenditure reaching an exceptional rate of 33.55% of GDP in 2022. This stark contrast to the median value of 1.46% highlights the extreme circumstances that necessitate such financial commitment towards military funding.

The relation of military expenditure to other indicators is multifaceted. A high military expenditure percentage can correlate to geopolitical tensions, defense contracts, and a nation's economic health. For instance, strong economies with stable political environments might allocate a smaller percentage of their GDP to military investments, instead focusing on social programs or infrastructure. In contrast, nations like Saudi Arabia (7.42%) and Qatar (6.96%), while having relatively strong economies due to oil revenue, showcase a significant focus on military spending, likely influenced by regional conflicts and security challenges. This connects directly to the notion of the “security-dilemma,” where one nation's military build-up may prompt neighboring nations to increase their military spending in response.

Various factors can impact the military expenditure as a percentage of GDP. These include political scenarios, historical contexts, economic conditions, and the global security environment. Economic downturns may compel governments to adjust defense budgets, often leading to decreased military spending. Additionally, international treaties and obligations, such as NATO commitments, can dictate the levels of spending a country must maintain. On the other hand, geopolitical threats or ongoing military engagements can lead to increased spending. The data indicates a general trend where military expenditure relative to GDP tended to decline from the 1960s (averaging over 6%) steadily down to around 2.28% in 2022, possibly reflecting the end of the Cold War era and shifting global priorities.

Strategies for managing military expenditure often focus on balancing national security with economic constraints. Countries may seek to optimize defense spending by adopting technology that enhances efficiency or reduces costs. Joint military exercises and shared resources among allied nations can also alleviate budget burdens. Additionally, transparency in military spending can help reduce corruption and promote efficient use of resources. Nevertheless, states need to ensure that their military capacities are sufficient to protect their sovereignty and interests, illustrating the need for an intricate balance.

In addressing flaws in military expenditure assessments, it is crucial to consider the potential inadequacies of the GDP metric itself. GDP does not accurately capture the informal economy or living standards, which can skew the military expenditure percentage. Additionally, the focus on expenditure alone can misrepresent the actual military effectiveness if that spending does not translate into operational success or military readiness.

The latest data from 2022 gives us a clearer picture of these dynamics. The median military expenditure across countries stands at 1.46%, a reflection of a general trend towards reduced military spending among many nations. However, specific countries exhibit extreme deviations from this median. While Ukraine's military expenditure is largely a response to the ongoing conflict and geopolitical instability, nations such as Saudi Arabia and Qatar maintain high spending partly due to oil wealth and regional security dynamics. Conversely, on the lower end of the spectrum, countries like Haiti (0.07%) and Mauritius (0.17%) allocate minimal resources to military expenditures, often reflecting different priorities in governance and security challenges.

Overall, military expenditure (% of GDP) serves as a critical indicator in assessing a country’s military priorities and potential. Understanding its context requires analyzing geopolitical dynamics, economic factors, and the interplay of global security trends. The disparities in military spending among countries point to varying security needs and economic strategies, making this indicator a vital tool for policymakers, analysts, and scholars alike in assessing national defense strategies and international relations.

                    
# 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 = 'MS.MIL.XPND.GD.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 <- 'MS.MIL.XPND.GD.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))