The dynamics of economic growth and income inequality in Romania: a statistical analysis of economic transformation in post-eu accession (2006-2021)
Description
Research Hypothesis The central hypothesis of this study is that economic growth, as represented by Romania's Gross Domestic Product (GDP), significantly impacts income inequality, measured using the GINI index. Specifically: Null Hypothesis (H₀): There is no statistically significant relationship between GDP and the GINI index in Romania from 2006 to 2021. This implies that changes in GDP do not influence income inequality. Alternative Hypothesis (H₁): There is a statistically significant negative relationship between GDP and the GINI index in Romania from 2006 to 2021. This suggests that as GDP increases, income inequality decreases. Data Description and Collection The study relies on secondary data sourced from the World Bank Open Database. Two primary variables were used: Gross Domestic Product (GDP): Representing Romania's economic output, GDP was measured in constant USD to account for inflation. It reflects the total value of goods and services produced within the country each year. This variable serves as the independent variable, influencing income inequality. GINI Index: The GINI index quantifies income inequality on a scale of 0 to 100, where 0 represents perfect equality and 100 represents maximum inequality. This variable acts as the dependent variable, influenced by changes in GDP. The dataset spans 2006 to 2021, providing a comprehensive view of Romania’s economic and social landscape during its post-European Union (EU) accession period. Methodology Linear Regression Analysis To test the relationship between GDP and the GINI index, a simple linear regression model was constructed. Diagnostic Checks Several diagnostic tests were conducted to validate the regression model: Residual Analysis: Checked for normality using the Shapiro-Wilk test. Homoscedasticity: Assessed using the Breusch-Pagan test to verify constant variance in residuals. Autocorrelation: Evaluated using the Durbin-Watson test to detect correlations in residuals over time. Findings Model Results Correlation Coefficient (R): 0.739 F-Statistic: 16.850 (p = 0.001) Indicates that the overall model is statistically significant at a 1% level, reinforcing the relationship between GDP and the GINI index. GDP Coefficient (Unstandardized): -2.472E-11 P-Value for GDP Coefficient: 0.001 Demonstrates that the relationship between GDP and the GINI index is statistically significant. Diagnostic Test Results Homoscedasticity: The Breusch-Pagan test identified evidence of heteroscedasticity (p = 0.033), indicating non-constant variance in residuals. Autocorrelation: The Durbin-Watson statistic (1.126) revealed some positive autocorrelation in residuals, suggesting temporal patterns in unexplained factors.
Files
Steps to reproduce
ata Source All data were sourced from publicly available datasets, ensuring accessibility for reproduction: World Bank Open Database: GDP data were obtained in constant USD to adjust for inflation. GINI index data were extracted as annual measures of income inequality. The World Bank's repository (https://data.worldbank.org) provided reliable, globally recognized datasets with consistent methodologies. Key Characteristics: Timeframe: 2006–2021. Country: Romania. Variables: Independent Variable: GDP (constant USD). Dependent Variable: GINI index (scale of 0 to 100). 2. Methodology 2.1 Research Design The study employs a quantitative research design to explore the relationship between economic growth and income inequality using statistical methods. Core Question: Does GDP growth impact income inequality in Romania, and if so, to what extent? Hypotheses: H₀ (Null): GDP has no significant effect on the GINI index. H₁ (Alternative): GDP has a significant negative effect on the GINI index. 2.2 Data Collection Workflow The following steps outline the collection and organization of data: Accessing Data: Visit the World Bank Open Database platform. Query GDP and GINI index data for Romania within the specified period. Export the datasets in .CSV format for compatibility with analytical tools. Preprocessing Data: Verify the completeness of records (e.g., no missing values for the selected variables). Normalize GDP values to constant USD to control for inflation effects. Ensure annual alignment between GDP and GINI index data points. Validation: Cross-check dataset consistency with other open sources (e.g., Eurostat, International Monetary Fund) to ensure reliability. Reproducibility Reproducing the study requires adherence to the steps outlined above, particularly in data acquisition, processing, and analysis. Below are key considerations for replication: Dataset: Source the same GDP and GINI index data for Romania from the World Bank. Verify consistency with alternative sources (e.g., Eurostat) to ensure data integrity. Software: Use SPSS or R for statistical analysis. While SPSS is user-friendly for beginners, R provides more advanced diagnostic capabilities for experienced users. Preprocessing: Ensure GDP values are in constant USD and aligned annually with the GINI index. Address any missing data using imputation techniques if discrepancies exist. Model Re-specification: Follow the regression model specification provided in the methodology. Perform diagnostic checks to validate assumptions. Document Steps: Maintain a clear record of all procedures, including decisions during data cleaning, transformations, and analysis.