A Tale of Two Economies: Diachronic Comparative Analysis of Diverging Paths of Growth and Inequality in the United States and the United Kingdom

Published: 10 September 2024| Version 1 | DOI: 10.17632/fjdcr7twyg.1
Contributor:
PANAGIOTIS KAROUNTZOS

Description

Research Hypothesis: This study hypothesizes a positive correlation between GDP and income inequality, measured by the Gini index, in both the United States and the United Kingdom. Specifically, we test whether GDP growth is associated with rising income inequality in these two economies. For the US: H1: GDP growth correlates positively with the Gini index. H0: No significant correlation exists. For the UK: H1: GDP growth correlates positively with the Gini index. H0: No significant correlation exists. Data Description: The dataset consists of annual observations from 1968 to 2021, measuring GDP and the Gini index for both countries. GDP reflects total economic output, while the Gini index measures income inequality (0 = perfect equality, 100 = extreme inequality). The data were sourced from the World Bank, ensuring reliability and comparability across time. Notable Findings: United States: A significant positive correlation was found between GDP and the Gini index, meaning that economic growth is associated with increasing income inequality. The R-squared value (0.38) indicates that 38% of the variation in income inequality can be explained by GDP changes. United Kingdom: No significant correlation between GDP and the Gini index was detected, suggesting that redistributive policies might mitigate the impact of economic growth on inequality. Interpretation and Use of Data: The data shows that in the United States, economic growth tends to exacerbate income inequality, implying the need for policies that address wealth distribution. In contrast, the UK's weaker correlation suggests that existing social welfare systems may effectively moderate income inequality despite GDP growth. Researchers and policymakers can use these findings to design tailored strategies. In the US, reforms like progressive taxation and inclusive growth policies could reduce inequality. In the UK, maintaining or enhancing redistributive policies could be key to sustaining equity amid growth. The results highlight that the relationship between growth and inequality is complex and varies between countries. This study contributes to understanding these dynamics and can guide further policy development and research on inequality.

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Data Collection and Sources: The data used in this study were obtained from the World Bank database, which provides reliable and globally standardized economic statistics. Specifically, the dataset includes annual observations of GDP and the Gini index for the United States and the United Kingdom over the period 1968-2021. The World Bank compiles these statistics from national statistical offices and other international sources, ensuring consistency and comparability across countries and time periods. Methodology and Protocols: Stationarity Tests: We first applied stationarity tests to the time series data. These tests ensure that the statistical properties of the variables (mean, variance) remain constant over time, which is essential for valid time series analysis. Specifically, we used the Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests to detect non-stationarity. If the data were found to be non-stationary, we applied first differencing to transform the series into their stationary forms. Descriptive Statistics: After confirming stationarity, we calculated descriptive statistics for the differenced GDP and Gini index data. This step provides essential information about the range, mean, variance, and distribution of the variables, which is necessary for understanding the characteristics of the data. Linear Regression Analysis: To investigate the relationship between GDP and income inequality, we employed linear regression. In this model, the Gini index (income inequality) was the dependent variable, and GDP was the independent variable. The regression model was tested separately for the United States and the United Kingdom to account for differences in their economic systems and policies. Software and Tools: The data processing and statistical analyses were conducted using SPSS and R software, which are widely used for econometric and statistical analysis. These tools were employed for data cleaning, stationarity testing, and regression analysis. Reproducibility: To reproduce this research, researchers can follow these steps: Data Sourcing: Collect GDP and Gini index data from the World Bank for the desired period (1968–2021 in this study). Stationarity Tests: Apply ADF and KPSS tests to check for non-stationarity. If necessary, use differencing to achieve stationarity. Statistical Analysis: Perform descriptive statistics to understand data characteristics. Follow this with linear regression analysis to test the relationship between GDP and income inequality. Software: Use SPSS or R for analysis. R packages such as "tseries" or "urca" are commonly used for stationarity testing, and the "lm" function can be used for linear regression.

Institutions

Geoponoko Panepistemio Athenon Schole Epharmosmenon Oikonomikon kai Koinonikon Epistemon

Categories

Economics, Development Policy

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