Age dependency ratio (% of working-age population)

The Age Dependency Ratio (ADR) is a critical demographic indicator that measures the ratio of dependents, defined as individuals aged 0-14 and those aged 65 and above, to the working-age population, generally considered to be those aged 15-64 years. Expressed as a percentage of the working-age cohort, this ratio offers insights into the economic pressures faced by the productive section of a population. A higher ADR implies a greater burden on the workforce, which must support more dependents through taxation, social security, and other social services, whereas a lower ADR suggests that a greater proportion of the population is economically productive.

In 2023, the world exhibited a median age dependency ratio of 54.57%. This figure reflects an important transitional phase for many societies worldwide, indicating that over half of the working-age population is supporting dependents. Analyzing this median against historical data reveals notable trends. For instance, in 1960, the global ADR was markedly higher at 74.48%, suggesting that over the decades, many regions have transitioned towards lower ratios due in part to declining birth rates and increasing life expectancy. As we evaluate this decrease in age dependency, it is essential to link this ratio with other demographic and economic indicators such as birth rates, life expectancy, and the employment rate.

The highest age dependency ratios in 2023 were observed in the Central African Republic (105.18%), Niger (98.16%), Monaco (98.16%), Somalia (97.02%), and Congo - Kinshasa (96.68%). Such high values are indicative of significant demographic challenges. Countries like the Central African Republic face severe economic strains, exacerbated by political instability, which impedes growth and limits the productive capacity of the workforce. High dependency ratios correlate strongly with low economic growth rates because when the working-age population must support a large number of dependents, resources become stretched. This is particularly critical in regions of high poverty where families rely heavily on government services.

In contrast, the lowest age dependency ratios noted in Qatar (20.1%), the United Arab Emirates (22.0%), Kuwait (27.48%), Bahrain (29.25%), and the British Virgin Islands (32.17%) showcase an entirely different demographic landscape. These nations benefit from a relatively smaller proportion of dependents compared to their working-age population. The low age dependency ratios in these areas often reflect robust economic conditions characterized by high employment rates and lucrative job markets. Furthermore, these high-income countries have the resources to invest in technology and infrastructure that further alleviate pressures on their workforce.

Considering the relationships between age dependency ratios and other indicators, it becomes evident that this ratio can offer crucial insights into overall economic health and societal structure. For instance, a country’s educational attainment and health care system often directly influence its age dependency ratio. Higher educational attainment generally leads to delayed childbirth and reduced fertility rates, thus contributing to a lower ADR. Improved healthcare also means higher life expectancy, which, when combined with lower birth rates, shifts the population demographic towards older age brackets, thereby influencing the dependency ratio. Conversely, a high birth rate—common in developing countries—has historically led to elevated dependency ratios, creating economic burdens for societies that may not have the infrastructure or resources to support growth.

Factors affecting the age dependency ratio are multifaceted, encompassing economic, social, and political dimensions. Changes in governmental policies regarding health care or retirement can influence the ratio by either extending the working age or altering the support structures for the elderly. Economic changes, such as shifts from agrarian to industrial economies, can lead to varying family dynamics, influencing both fertility rates and lifespan. Additionally, migration patterns work as a double-edged sword; as young individuals seek opportunities abroad, the countries they leave may experience rising dependency ratios while the destination countries experience a workforce bolstered by younger workers.

To address challenges posed by high dependency ratios, various strategies can be implemented. Improving access to education, particularly for women, is paramount as it often leads to delayed family formation and reduced fertility. Policies that promote family planning and reproductive health can empower citizens to make informed decisions about procreation, leading to lower birth rates. Furthermore, investing in the health care system ensures longevity, support for the elderly, and the overall well-being of the dependents. Gradually increasing the retirement age can also capitalize on the talent pool of older adults, contributing positively to the workforce.

Nonetheless, solutions are not without their flaws. Strategies that focus on increasing employment opportunities must be grounded in an understanding of local job markets and economic realities. Encouraging higher birth rates, for instance, through incentives, could backfire if not accompanied by adequate support systems for families, including childcare services and parental leave policies.

In conclusion, the Age Dependency Ratio serves as a mirror reflecting the demographic pressures faced by societies worldwide. Its implications are profound, affecting economic stability, social structures, and policy decisions. As we move forward into an increasingly complex global landscape, understanding the nuances of the age dependency ratio will be vital in crafting policies that aim for sustainable economic growth and social equity.

                    
# 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 = 'SP.POP.DPND'

# 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 <- 'SP.POP.DPND'

# 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))