Households and NPISHs Final consumption expenditure (annual % growth)

Households and Non-Profit Institutions Serving Households (NPISHs) Final Consumption Expenditure is a critical economic indicator that measures the annual percentage growth in total spending by households and non-profit organizations on goods and services. This expenditure directly reflects consumer confidence and purchasing power and serves as a vital gauge of overall economic health. In essence, it provides insights into the relative prosperity of households and highlights trends in consumer behavior that can affect macroeconomic policies.

The importance of monitoring this indicator cannot be overstated. An increase in final consumption expenditure typically indicates a growing economy, as consumers feel secure enough to spend rather than save. Conversely, a decline can signal economic distress, where households are cutting back on spending due to dwindling incomes or rising unemployment rates. Understanding these trends helps governments and businesses tailor their strategies to better meet the needs of their communities and consumers. For instance, policymakers might decide to stimulate the economy by promoting job growth or providing financial incentives to encourage spending.

This indicator is closely related to other significant economic metrics like Gross Domestic Product (GDP), the unemployment rate, and consumer confidence indices. A rise in consumer spending often correlates with a robust GDP growth rate, as consumption forms a significant portion of GDP. Additionally, the unemployment rate can influence spending patterns; lower unemployment generally leads to higher household disposable incomes and spending. Conversely, stagnant or rising unemployment can depress household expenditure, illustrating the interconnectedness of various economic indicators.

Several factors can influence households and NPISHs final consumption expenditure. Economic conditions, such as inflation, availability of credit, and interest rates, play crucial roles. For instance, elevated inflation can reduce purchasing power, causing households to restrain their spending. Similarly, interest rates significantly impact consumer credit; lower rates typically promote borrowing, leading to increased expenditure, whereas higher rates may deter consumers from making large purchases like homes or vehicles.

Moreover, cultural and demographic factors can shape consumer behavior and spending patterns. In regions where tradition and family ties are strong, spending might prioritize family-oriented purchases, such as education or healthcare. Similarly, in younger populations or urban areas, trends toward technology might drive consumption in digital products and services.

Strategically, stakeholders can utilize insights from this indicator to enhance economic stability and growth. Governments can initiate stimulus packages to encourage spending, providing financial aid or tax cuts to households. Businesses may choose to innovate or market towards specific consumer demographics that show significant expenditure growth patterns. Moreover, in addressing regional disparities in consumption expenditure, local governments can tailor policies to uplift economically distressed areas, enhancing overall national economic performance.

Despite its utility, this indicator is not without its flaws. The statistics can be skewed by extreme outliers, such as the drastic reductions noted in the bottom-performing regions in 2023, which included Sudan (-18.0%), Madagascar (-12.32%), and Marshall Islands (-9.12%). These figures highlight economic turmoil due to factors such as political instability, natural disasters, or poor governance, which can disproportionately affect household spending. Furthermore, reliance solely on this indicator without considering quality of life or household welfare may overlook essential aspects of economic health, such as income distribution and poverty levels.

The world values spanning from 1971 to 2023 reveal a historical trajectory of consumption expenditure growth, showcasing fluctuations influenced by global events. From 1971, where the value stood at 4.58%, through economic crises such as in 2008, where it dropped to 1.83%, and turning negative in 2009, these figures reflect the economic responses to external shocks and domestic challenges. The rebound in 2021 at 7.31% illustrates a significant recovery phase following the global downturn induced by the COVID-19 pandemic, culminating in a 2023 rate of 2.68%, grounding into a more moderate but stable growth trajectory.

This year, the global median value reported is 3.12%, which serves as a threshold that can guide expectations for national economies. The top five areas demonstrate robust consumer expenditure growth, with the United Arab Emirates leading at an impressive 14.11%, indicating a vibrant economic climate. Similarly, Turkey at 13.63% and Macao SAR China at 13.31% reflect strong consumer confidence and effective economic policies, demonstrating how strategic investments can yield positive results in household expenditures. In stark contrast, the bottom five areas underscore the perilous economic conditions affecting household spending, where drastic declines such as -18.0% in Sudan mark significant challenges that require immediate intervention.

In conclusion, understanding Households and NPISHs Final Consumption Expenditure not only reflects current economic conditions but also shapes future policies and business strategies. By analyzing this indicator in conjunction with related economic metrics and understanding the factors influencing its variations, stakeholders can better navigate the complexities of modern economies, making informed decisions that promote growth and stability.

                    
# 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.PRVT.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.PRVT.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))