General government final consumption expenditure (annual % growth)

The indicator of General Government Final Consumption Expenditure (annual % growth) represents the annual percentage change in the collective spending of all levels of government on goods and services. Understanding this indicator is crucial for evaluating the fiscal health of a nation and analyzing government spending trends over time. Consumption expenditure refers to expenditures by government authorities on items such as salaries for public servants, purchasing of goods and infrastructure, and providing public services that enhance the welfare of citizens.

This indicator is vital for several reasons. Firstly, it serves as a gauge for government policy responses to economic conditions. In times of economic downturns, governments may increase spending to stimulate growth, whereas in booming economies, they may cut back. Furthermore, it reflects the priorities of a government, signaling where resources are being allocated to address public needs or creative recovery strategies.

General government final consumption expenditure also correlates with other economic indicators. For instance, it is closely related to GDP growth rates. An increase in government consumption can lead to higher GDP figures, as spending on public services and infrastructure can enhance productivity. Conversely, stagnant or declining government expenditure may indicate a slowdown in growth, leading to concerns about economic sustainability. Moreover, this indicator intersects with other macroeconomic measures such as inflation, employment rates, and investment levels. Increased government spending can lead to inflationary pressures if the economy is already operating near full capacity, while also providing jobs and supporting industries.

Several factors influence the fluctuations in this indicator. Economic conditions, such as growth rates and employment levels, play a significant role. Governments may adjust their expenditure based on budget surpluses or deficits, economic forecasts, and external financial pressures like international loans or agreements. Politics also significantly shapes consumption expenditures, with changes in administration often resulting in shifts in priority areas for funding. Additionally, your policy direction—whether it focuses on austerity measures or stimulus spending—can fundamentally influence government consumption.

Strategies to optimize general government final consumption expenditure often hinge on balanced fiscal approaches. Effective budget planning ensures that funds are allocated to sectors that offer the best return on investment, such as education, healthcare, or public infrastructure projects that can spur future growth. Policymakers should emphasize transparency and accountability in spending decisions, thereby ensuring that resources are efficiently distributed. Additionally, diversifying funding sources and leveraging partnerships with the private sector can enhance the effectiveness of government expenditure while mitigating some financial risks.

However, flaws exist within the measurement and interpretation of this indicator. One significant issue is that high growth rates in government consumption do not always correlate directly with positive economic outcomes; for instance, they may reflect wasteful spending or misallocation of resources rather than genuine investments in public welfare. Furthermore, external pressures such as economic sanctions or global market shifts can distort government expenditures, skewing the growth figures. Analysts must exercise caution, as the context of the spending growth—what is being funded and how effectively—plays a crucial role in determining its ultimate impact on the economy.

As of 2023, the global median for general government final consumption expenditure growth stands at 2.11%. This reflects a moderate increase compared to historical values, which varied significantly over the decades. Observing the top five areas in consumption growth, Armenia leads with a striking 28.3%, showcasing an aggressive fiscal stance likely driven by rebuilding efforts or social programs. Following this, Seychelles at 13.47%, Tanzania at 10.65%, Mauritania at 10.6%, and Kazakhstan at 10.3% exhibit similarly high growth rates, hinting at substantial government involvement in stimulating their economies.

In stark contrast, the bottom five areas present a concerning picture. For instance, Angola's -36.73% represents a dramatic contraction in government spending, possibly indicative of significant economic crises, perhaps due to fundamental fiscal mismanagement or external factors like commodity price drops. Sudan, with a -36.5%, and Nepal at -21.21%, show similar patterns, highlighting challenges such as political instability or humanitarian crises that hinder government spending capabilities. The Congo - Kinshasa and the Marshall Islands follow, both showcasing adverse percentage changes that reflect serious economic disarray.

Historical data illustrates fluctuations over the years, showing how global events shape government consumption. From a high of 5.55% in 1975, the figure has ebbed and flowed, dropping sharply into the 2000s before again gaining momentum in recent years, with a notable resurgence at 3.68% in 2021, which may have been influenced by post-pandemic recovery initiatives.

As nations move forward, the state of general government final consumption expenditure will remain a critical area for analysis, providing insights into economic strategies, priorities, and the capacity of governments to foster resilient economies amidst the changing global landscape.

                    
# 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 = 'NE.CON.GOVT.KD.ZG'

# 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 <- 'NE.CON.GOVT.KD.ZG'

# 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))