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| license: apache-2.0 |
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| 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. |
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| 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: |
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| 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. |
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| 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. |
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| Everyday Essential Goods & Services (in USD): To give a tangible sense of daily financial burden, the dataset includes average market prices for: |
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| Food: Price of a 500g white loaf of bread. |
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| Housing: Average monthly rent for a 1-bedroom apartment in the city center. |
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| Transportation: Cost of a one-way local transport ticket and a standard monthly transport pass. |
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| Utilities: Average monthly cost of basic utilities (electricity, heating, cooling, water, garbage) for a standard 85m² apartment. |
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| Data Sources & Methodology: |
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| Inflation & Country List: World Bank Open Data API (retrieved in real-time). |
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| 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. |
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| Standardization: All monetary values are normalized and presented in US Dollars (USD) to ensure direct comparability across borders. |
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| Potential Use Cases: |
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| Machine learning models predicting inflation trends or housing affordability. |
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| Exploratory data analysis (EDA) for academic papers on global economic disparity. |
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| Dashboard creation for tracking "cost of living crises" or "purchasing power parity (PPP)" anomalies. |
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| International relocation and expatriate compensation benchmarking. |
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| Important Caveats (Read Me): |
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| 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). |
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| 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. |
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| Crowd-sourced Nature: The price data (bread, rent, etc.) are averages derived from consumer contributions and may not perfectly represent rural versus urban extremes. |
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| 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?" |
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