Navigating the Maze of Global Debt: An Analysis of International Debt Statistics with PostgreSQL

Abstract

Abstract Countries often take on debt as a means of financing their economic development, such as funding infrastructure projects, investing in education or healthcare, or stimulating economic growth. This paper aims to analyze international debt statistics using the PostgreSQL database management system, specifically focusing on the external debt of countries as presented in the World Bank's International Debt Statistics (IDS) database. The analysis examines the most prevalent debt indicators and investigates the amount of debt owed by countries to identify potential economic issues and findings show that long-term external debt is the most common form of external debt for low- and middle-income countries. The IDS dataset displays that the most prevalent debt indicators are DT.INT.OFFT.CD, DT.INT.MLAT.CD, DT.INT.DLXF.CD, DT.AMT.OFFT.CD, DT.AMT.MLAT.CD, and DT.AMT.DLXF.CD. The study also finds that among the top five countries with the highest maximum debt, four are from emerging market economies, suggesting that they may be facing economic challenges. However, the study emphasizes that the findings should be interpreted with caution and that other economic indicators, such as the debt-to-GDP ratio and debt sustainability, should be considered when analyzing a country's overall economic health.