National Economic and Demographic Development Indicators: A Relational Dataset for 50 Countries (2000–2024)
Description
This dataset provides a normalized relational database of economic, demographic, and investment indicators for 50 countries spanning the years 2000 to 2024. Countries were selected to represent a broad cross-section of the global economy, including nations from six continents, and both developed and developing economies. The dataset is organized into four relational tables: Country, EconomicIndicators, Demographics, and InvestmentIndicators. The Country table contains static reference information for each of the 50 countries, including continent, geographic region, development status, capital city, and official language. The EconomicIndicators table includes annual time-series data on GDP (in current US dollars), GDP per capita, consumer price inflation rate, unemployment rate, and trade balance as a percentage of GDP. The Demographics table captures population dynamics including total population, urban population share, age dependency ratio, population growth rate, and life expectancy at birth. The InvestmentIndicators table records government expenditure on education, current health expenditure, gross fixed capital formation (used as a proxy for infrastructure investment), net foreign direct investment inflows, and gross capital formation, all expressed as a percentage of GDP. The dataset is designed to support relational database modeling, normalization analysis, and economic research. All tables satisfy Third Normal Form (3NF). Primary keys and foreign key relationships are clearly defined. This dataset is suitable for use in database design coursework, comparative economic analysis, and longitudinal demographic studies.
Files
Steps to reproduce
Step 1 The database was designed around the topic of National Economic and Demographic Development. Four entity groups were identified: Country (reference data), EconomicIndicators (annual GDP and market data), Demographics (population and health data), and InvestmentIndicators (government and capital spending data). Each entity was designed to contain at least five attributes and to satisfy Third Normal Form (3NF), meaning no partial or transitive dependencies exist among non-key attributes. Step 2 Fifty countries were manually selected to ensure geographic diversity across North America, South America, Europe, Asia, Africa, and Oceania, and to include both developed and developing economies as classified by the World Bank and IMF. Countries were assigned integer IDs from 1 to 50. Step 3 The primary data source was the World Bank World Development Indicators (WDI), accessed via the DataBank portal (databank.worldbank.org). For all 50 countries and years 2000–2024, I downloaded GDP and GDP per capita (current US$), inflation (consumer prices), unemployment (total, ILO estimate), external balance on goods and services, total population, urban population (%), life expectancy, population growth, age dependency ratio, government expenditure on education, current health expenditure, gross capital formation, foreign direct investment net inflows, and gross fixed capital formation (all as % of GDP). The data was exported in Excel format directly from the DataBank interface. Step 4 Some indicators had missing values for specific countries and years. These gaps were filled using data from the IMF World Economic Outlook (WEO) database (imf.org/en/publications/weo) for macroeconomic indicators including inflation for Argentina and the UAE, and from UNESCO Institute for Statistics (uis.unesco.org) for education expenditure. South Korea (listed as "Korea, Rep." in World Bank) and Israel were not included in the original download and were added manually using verified IMF WEO figures. Step 5 All raw data was loaded into Python using the pandas and openpyxl libraries. Country names were standardized (e.g., "Viet Nam" → "Vietnam", "Turkiye" → "Turkey"). GDP values were converted from current US dollars to billions of USD for readability. Missing values were filled using linear interpolation between known data points where applicable. The final dataset was structured into four normalized tables and exported to Excel (.xlsx) format. Step 6 The Entity-Relationship diagram was created in MySQL Workbench. The Country table serves as the parent table with CountryID as the primary key. The three time-series tables (EconomicIndicators, Demographics, InvestmentIndicators) each use a composite primary key of (CountryID, Year) and include CountryID as a foreign key referencing Country. All relationships are one-to-many from Country to each indicator table. Step 7- SQL Implementation SQL scripts were written to create the database schema and insert all data values.
Institutions
- Wentworth Institute of TechnologyMassachusetts, Boston