Econometric Analysis of GDP and Income Inequality in Greece: A Diachronic Study Across Pre-Crisis, Crisis, and Post-Crisis Periods
Description
This study examines the relationship between GDP and income inequality in Greece across three key periods: pre-crisis (2003-2008), crisis (2009-2014), and post-crisis (2015-2020). The data, sourced from the World Bank, includes annual GDP figures and Gini coefficient measurements. The hypotheses tested whether economic growth increased inequality before the crisis, whether the financial downturn exacerbated inequality, and whether post-crisis GDP recovery improved income distribution. Findings reveal that in the pre-crisis period, while GDP grew steadily, income inequality also increased moderately, suggesting uneven distribution of economic gains. During the crisis, GDP contracted sharply, and income inequality worsened significantly due to austerity measures, high unemployment, and reduced social protections. In the post-crisis period, despite some economic recovery, inequality remained high, indicating that structural issues, such as unemployment and weak social mobility, continued to prevent significant reductions in inequality. The data suggest that while economic growth is important, it is insufficient by itself to reduce income inequality without targeted policies. The findings highlight the need for comprehensive reforms, including progressive taxation, stronger social safety nets, and inclusive economic growth policies to address persistent inequality in Greece.
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Data Collection and Gathering Process 1. Data Sources The data for this study were sourced from World Bank Open Data, a publicly available and globally recognized database that provides comprehensive economic and social data for countries worldwide. The specific datasets used include: Gross Domestic Product (GDP): Measured in constant US dollars, this dataset reflects the total annual value of goods and services produced in Greece. Gini Coefficient: This measure of income inequality, ranging from 0 (perfect equality) to 1 (maximum inequality), was used as the primary indicator for assessing income distribution in Greece. These datasets were chosen for their reliability, consistency, and widespread use in academic and policy research. They provide annual data from 2003 to 2020, covering the pre-crisis, crisis, and post-crisis periods. 2. Methodology and Protocols Data Extraction: Data for GDP and the Gini coefficient were directly extracted from the World Bank's online platform. The datasets were filtered to include only Greece and the relevant time periods (2003-2020). Data Cleaning: The extracted data were cleaned to ensure accuracy. Missing values, if any, were identified and either interpolated or omitted based on the availability of adjacent data points. Outliers were checked, but the World Bank's data is already processed for accuracy, so minimal adjustments were necessary. Statistical Analysis: The cleaned data were analyzed using IBM SPSS Statistics software. The analysis included: Pearson Correlation to assess the linear relationship between GDP and the Gini coefficient. Linear Regression Models to quantify how changes in GDP affected income inequality over time. These models were applied separately for each of the three periods: pre-crisis, crisis, and post-crisis. 3. Reproducibility To reproduce this research, the following steps should be followed: Access Data: Retrieve the GDP and Gini coefficient datasets for Greece from the World Bank's online data portal. Ensure you filter the data for the years 2003 to 2020. Use Statistical Software: Perform correlation analysis and linear regression using software like IBM SPSS, Stata, or R. The analysis protocols include correlating the GDP with the Gini coefficient and running linear regression models for each of the three periods. Period Segmentation: Divide the data into three key periods: 2003-2008 (pre-crisis), 2009-2014 (crisis), and 2015-2020 (post-crisis) for a time-bound analysis. This segmentation allows for a clearer comparison of how different economic phases impact income inequality. By following these methods and using the same data sources and software tools, other researchers can replicate the study and test its findings.