Final consumption expenditure (annual % growth)

The indicator 'Final consumption expenditure (annual % growth)' is a crucial metric in understanding the economic health of nations. It represents the yearly growth rate of the total value of all final goods and services consumed within a country's borders. This consumption is a significant component of gross domestic product (GDP) and is essential for measuring how much the economy is growing or contracting. Growth in final consumption spending typically reflects an increase in consumer confidence, employment rates, and wages. This indicator serves to illustrate how effectively households and individuals spend their income, thus providing insight into both microeconomic trends and broader economic conditions.

In the context of 2023, the median value of final consumption expenditure growth stands at 3.07%. This figure provides a benchmark for evaluating various economies worldwide, indicating a moderate but stable level of consumption growth. The highest growth rates are observed in Turkey (11.73%), the United Arab Emirates (11.68%), Armenia (9.0%), Kyrgyzstan (8.9%), and the Maldives (8.84%). Such elevated growth rates in these nations suggest robust consumer demand and a possibly expanding middle class. Rapid expansion in these regions often correlates with significant infrastructure projects and shifts in economic policy geared towards improving living standards and increasing consumer access to goods and services.

Conversely, the bottom performers reveal a disheartening narrative. Nations like Sudan (-20.05%), Madagascar (-10.06%), and the Marshall Islands (-9.41%) exhibit significant declines in consumption expenditure. These negative values often signal severe economic distress, potentially due to political instability, high inflation rates, or natural disasters that undermine economic activities. For instance, a country facing internal conflict may see a decline in consumer confidence, resulting in decreased consumption levels as households prioritize basic needs over discretionary spending.

The relationship between final consumption expenditure and other economic indicators is intricate. For instance, consumer expenditure impacts GDP directly; when consumption increases, it generally implies higher GDP growth. Additionally, unemployment rates are connected; a decrease in unemployment typically leads to higher disposable income, bolstering consumer spending. Inflation also plays a vital role; if consumer prices rise too quickly, real purchasing power declines, leading to reduced spending. This interplay highlights the delicate balance policymakers need to maintain to foster sustainable economic growth.

Several factors influence final consumption expenditure growth. Consumer confidence, which can be swayed by economic conditions, job security, and political stability, heavily dictates spending behavior. Additionally, interest rates set by central banks affect borrowing costs; lower interest rates usually encourage spending through cheaper loans for major purchases such as homes and cars. Inflation rates also factor significantly; if wages do not keep pace with rising living costs, consumers may cut back on spending, leading to negative growth in this indicator.

In terms of strategies to enhance final consumption expenditure, governments can adopt various approaches. Fiscal policies, such as tax incentives for households or increased government spending on social programs, can stimulate consumption. In times of economic downturn, direct transfers or subsidies can empower consumers to maintain levels of spending. On a broader scale, improving infrastructure and reducing barriers to trade can help lower prices and increase the availability of goods. Moreover, promotional measures that foster consumer confidence, especially in emerging markets, can lead to sustained consumption growth over time.

However, this indicator is not without its flaws. For instance, high consumption growth rates do not always equate to economic prosperity, as they might reflect unsustainable borrowing or spending patterns. Additionally, in economies heavily reliant on foreign goods, a rise in consumption expenditure could inflate trade deficits, ultimately affecting currency valuations and economic stability. Moreover, narrow focus on consumption may overshadow essential areas such as savings or investments, which are equally vital for long-term economic health.

Examining historical data reveals patterns that can inform current economic strategy. In the early 2000s, global consumption expenditure exhibited generally healthy rates around 4%, with notable fluctuations due to economic shocks such as the 2008 financial crisis, which caused a sharp decline to 0.82% in 2009. The COVID-19 pandemic in 2020 led to a significant drop to -3.03%, highlighting the vulnerability of consumer spending to global events. The rebound in 2021 to 6.37% followed by stabilization around 3.5% to 3.18% in subsequent years signifies recovery but also underlines the need for ongoing supportive measures by governments and economic planners to sustain growth.

In summary, final consumption expenditure (annual % growth) serves as a multifunctional gauge of economic health, complexly interlaced with various economic indicators and influenced by a multitude of factors. The experiences and data from 2023 illustrate the diverse landscape of global economies, revealing both encouraging growth stories and troubling declines. Understanding and addressing the weaknesses in consumption patterns while promoting strategies conducive to sustainable economic growth will be vital for countries navigating the future economic terrain.

                    
# 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.TOTL.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.TOTL.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))