license: apache-2.0
This dataset is a one-stop, meticulously curated resource designed for macroeconomists, data scientists, policy analysts, and business strategists. It bridges the gap between official macroeconomic statistics and real-world consumer expenses by combining real-time inflation data from the World Bank with crowd-sourced, granular cost-of-living indicators across all 194 recognized countries.
What does this dataset include? The dataset is structured to provide a 360-degree view of economic affordability. It is divided into three core layers:
Official Macroeconomic Data: Annual inflation rates (consumer prices, annual %) sourced directly from the World Bank API (indicator FP.CPI.TOTL.ZG), covering the latest available years (2020–2024), alongside country metadata including geographic region, income level (e.g., High-income, Lower-middle-income), and capital city coordinates.
Comparative Living Cost Indices: Standardized benchmark indices (relative to New York City = 100) that allow for easy cross-country comparison. These include the overall Cost of Living Index, Rent Index, Groceries Index, Restaurants Index, and the Purchasing Power Index.
Everyday Essential Goods & Services (in USD): To give a tangible sense of daily financial burden, the dataset includes average market prices for:
Food: Price of a 500g white loaf of bread.
Housing: Average monthly rent for a 1-bedroom apartment in the city center.
Transportation: Cost of a one-way local transport ticket and a standard monthly transport pass.
Utilities: Average monthly cost of basic utilities (electricity, heating, cooling, water, garbage) for a standard 85m² apartment.
Data Sources & Methodology:
Inflation & Country List: World Bank Open Data API (retrieved in real-time).
Cost Indices & Specific Prices: Aggregated from reputable international cost-of-living databases including Numbeo, Compare the Market AU analyses, Sputnik global surveys, and various national statistical offices.
Standardization: All monetary values are normalized and presented in US Dollars (USD) to ensure direct comparability across borders.
Potential Use Cases:
Machine learning models predicting inflation trends or housing affordability.
Exploratory data analysis (EDA) for academic papers on global economic disparity.
Dashboard creation for tracking "cost of living crises" or "purchasing power parity (PPP)" anomalies.
International relocation and expatriate compensation benchmarking.
Important Caveats (Read Me):
Time Variance: While the indices (Numbeo) represent a recent "point-in-time" snapshot, the World Bank inflation data is lagged by a few months (official annual releases). Users should treat the price data as indicative of the latest available period (2025–2026).
Data Availability: Not every country has complete reporting for every metric (especially small island nations or conflict zones). Missing values are left as NaN and clearly indicated in the data quality summary.
Crowd-sourced Nature: The price data (bread, rent, etc.) are averages derived from consumer contributions and may not perfectly represent rural versus urban extremes.
This dataset serves as a robust foundation for analyzing the global economic landscape, answering questions like "Where does my dollar go the furthest?" and "How does inflation impact everyday purchasing power?"