THE RELATIONSHIP BETWEEN MACROECONOMICS VARIABLES AND INCOME INEQUALITY – A LONGITUDINAL STUDY ACROSS DEVELOPING AND DEVELOPED COUNTRIES Key words: Gini coefficient, Inequality, macro-ec
Research introduction
Motivations
In recent years, income inequality has emerged as a critical issue for policymakers, highlighted by the United Nations' Sustainable Development Goals (SDGs) aimed at reducing such disparities by 2030 Addressing inequities in income, wealth, education, and access to services is essential for fostering a more inclusive society where opportunities are available to all, regardless of background Since 1980, global trends indicate a significant rise in income inequality, particularly in developing countries, attributed to factors like the capital-income ratio While some studies suggest a gradual increase in inequality during the 1990s followed by a slight decline, evidence indicates a persistent surge in income inequality across many nations This raises important questions about the potential convergence of inequality levels between developing and developed countries from 2000 to 2016.
This study aims to provide statistical insights into the issue of inequality, which has been extensively explored in developed nations However, there is a notable lack of empirical data to support these findings Additionally, the dynamics of inequality in developing nations, especially in relation to developed countries, remain under-researched Understanding the inequality gap between these two groups is crucial for a comprehensive analysis.
This study aims to estimate the trajectory of income inequality across various nations, marking a pioneering effort to directly assess inequality trends Unlike previous research that often relies on informal observations or comparisons of inequality distributions over time, this study utilizes available data to provide a more precise analysis Notable contributions to the field include works by Sala-i-Martin, Piketty and Zucman, Ravallion, and Alvaredo Understanding the slope of inequality is crucial, as it reveals whether trends are statistically significant and whether they are deterministic or stochastic A deterministic trend indicates that shocks to income inequality have lasting effects, while a stochastic trend suggests only temporary impacts Additionally, since the 2000s, income inequality has become less homogeneous, complicating its analysis and interpretation.
It is essential to investigate the potential disruption of inequality patterns during the 1980s, as highlighted by researchers like Piketty (2014) A thorough understanding of the underlying characteristics and temporal trends of fundamental series will improve our ability to forecast fluctuations, as noted by Arezki et al (2014b) and Harvey et al (2017).
Recent growth in Official Development Assistance (ODA) and financial flows to least developed countries may help reduce income inequality between nations, as suggested by the Sustainable Development Goals (SDGs) Sala-i-Martin (2006) highlights that the economic expansion of large, economically challenged countries like China and India has contributed to this reduction in inequality This study further explores the economic disparity between developed and developing countries through various methodological approaches, aiming to assess income inequality trajectories By employing statistical tests such as the F-test, Hausman test, and Breusch-Godfrey test, we identify countries on similar paths toward achieving economic stability without prior classification The Gini index serves as the primary measure of inequality, and our analysis includes a diverse sample of 20 countries with a robust 16-year data set, spanning from 2000 to 2016 These findings offer valuable insights into the evolving trends of inequality across different regions during this period.
Research Scope
This research provides a comprehensive analysis of income inequality trends from 2000 to 2016 across 20 selected countries, encompassing both developed and developing nations Utilizing the Gini index as the primary metric, the study examines the influence of factors such as Official Development Assistance (ODA), financial flows to least developed countries, and the economic growth of major disadvantaged nations like China and India Additionally, it investigates the disruption in inequality patterns during the 1980s and the subsequent decline in homogeneity post-2000.
Research Objectives
This study aims to provide statistical insights into the secular trends and shifts in income inequality across developing and developed countries from 2000 to 2016 It focuses on estimating the trajectory of income inequality, assessing levels of inequality within various nations, and examining disparities between these two groups The research intends to enhance the understanding of income inequality fluctuations and contribute to the literature on inequality dynamics, especially in developing nations compared to their developed counterparts Ultimately, the study seeks to illuminate the differing trends in inequality observed in various regions during this period.
Research questions
To achieve those research objectives, several questions that would be answered are listed as follow:
Question 1: What are the primary macroeconomic variables associated with income inequality?
The primary aim of this research is to explore the factors contributing to income inequality across different countries globally To achieve this, the study identifies key elements by reviewing existing literature on the subject and focusing on macroeconomic variables Data is collected from 10 developing and 10 developed nations, spanning the years 2000 to the present.
In 2016, the authors selected countries for investigation based on the availability of comprehensive public data They focused on nations with the most complete datasets as secondary data sources Subsequently, the Fixed Effects Model (FEM) and Random Effects Model (REM) were employed to analyze the relationship between various factors and income equality using this dataset.
Question 2: What are the directions and magnitudes of the association between income inequality and macroeconomic variables
The authors examine the factors associated with income inequality and assess their positive or negative impacts By analyzing the correlation between changes in these variables and shifts in income inequality, they investigate causal relationships using the random effects model (REM) The findings serve as a basis for the authors to offer targeted recommendations for policymakers aimed at reducing income inequality.
Question 3: What are specific recommendations for policymakers to reduce income inequality?
The findings from the research highlight the impact of macroeconomic variables on income inequality, revealing both positive and negative relationships In light of these insights, the authors offer targeted recommendations for policymakers aimed at promoting equitable development and improving resource distribution These strategies are essential for addressing income inequality in both developing and developed nations.
Research structure
The research is divided into five main parts
Chapter 1: Research introduction This chapter provides an overview of the study's justification, research inquiries, extent and magnitude, and briefly presents the research framework
Chapter 2: Literature review This chapter provides an overview of prior studies, highlighting how past research has investigated this particular subject and develop hypotheses for the research
Chapter 3: Methodology and Data In this chapter, the research methodology is delineated, and the panel model that examines the correlation between various financial factors and the Gini index of ten developed and ten developing countries is introduced
Chapter 4: Research results and discussions This chapter introduces the panel model that examines the relationship between various financial factors and the Gini index for ten developed and ten developing nations A logical inference can be made regarding this chapter
Chapter 5: Conclusions and suggestions This concluding chapter offers policymakers potential ramifications and perspectives on how to mitigate income inequality in developed and developing nations.
Literature review
Theoretical background
Income inequality remains a significant issue in both developing and developed countries, prompting extensive research on the topic Key theories addressing income inequality include Economic Theory, which highlights the influence of labor productivity, capital, and education, and underscores the role of economic policy in mitigating disparities Social Theory posits that income inequality arises from competition among social groups and is necessary for social stability and growth, emphasizing human agency in perpetuating these inequalities The Kuznets Theory suggests that income inequality initially increases during the early stages of economic growth before gradually declining Lastly, Life Cycle Theory explains income inequality through variations in individual income across different life stages.
Income inequality has significantly risen in both emerging and developed nations in recent decades, with developing countries experiencing a wider gap Various factors contribute to these income disparities, including globalization, technological advancements, economic policies, education, and labor market dynamics in industrialized nations While emerging countries share similar causes for income inequality, they also confront additional challenges such as discrimination, climate change, and conflict.
Empirical research
Research has highlighted the wealth disparities between developed and developing countries, with Milanovic (2012) conducting a comprehensive analysis of global wealth inequality from both historical and contemporary viewpoints By utilizing diverse income data sources, he compares income disparities between industrialized and developing nations, revealing not only the economic gap between countries but also the income differences among demographic groups within those countries.
The "elephant curve," introduced by Alvaredo et al in 2018, visually represents global income disparities, emphasizing the growing gap between the middle class and the wealthy and its effects on economic growth Luebker (2014) compares behavioral and rational choice perspectives on income inequality, exploring income redistribution systems and their influence on poverty levels Shatkin's 2007 research examines how socioeconomic disparities affect economic expansion worldwide, while Sehrawat and Giri (2017) provide evidence linking economic growth, income inequality, and poverty in developing nations Milanovic (2012) analyzes global income inequality from historical and contemporary viewpoints, highlighting disparities between developed and developing countries, as well as within demographic segments Collectively, these studies underscore the complexity of income inequality and its implications for economic development and policy recommendations.
Luebker (2014) compared the rational decision approach and the behavioral perspective on income disparities, analyzing their effects on poverty rates and evaluating income redistribution systems aimed at reducing inequality Similarly, Shatkin's 2007 study explored the relationship between income disparity and global economic development, offering insights from various countries on how income inequality influences economic growth and sustainable development.
The studies on income disparity reveal both commonalities and distinctions, focusing on the critical issue of income inequality in developed and developing nations Utilizing rigorous research methods and diverse data sources, the authors highlight the necessity of understanding and addressing income disparities for economic and social advancement While all research papers tackle the theme of income inequality, they employ various analytical techniques, such as curves, models, regression analysis, and statistical methods, to explore the topic from unique perspectives These perspectives may include the effects of income inequality on economic development, poverty, or income distribution policies, with each study emphasizing different aspects, whether at a global, regional, or national level Ultimately, the varying approaches underscore the complex nature of income inequality and its multifaceted impact on society.
The research literature on income inequality offers comprehensive insights and diverse analyses of the issue, with each study employing unique methodologies Branko Milanovic's "Global Income Inequality in Numbers: History and Present" focuses on comparing income disparities across countries over time, utilizing data from various sources to reveal the structure of global income inequality and its connection to historical and current economic factors In contrast, Alvaredo et al.'s "The Elephant Curve of Inequality and Global Growth" visually illustrates income inequality through the "elephant curve," demonstrating how economic growth affects the distribution of wealth among different social classes.
Hypothesis development
In the table below, we will present selected variables that have a negative or positive impact on income inequality
Variables Abbreviations Expected coefficient sign
Unemployment rate Unemployment rate - Andrés, 2006; Mocan,
Gross Domestic GDP + Bouincha and Karim,
Export Export + McCulloch and Baulch,
Source(s): Table by the authors
Hypothesis 1: The relationship between the Unemployment rate and income inequality
The unemployment rate significantly contributes to income inequality within a country, as rising unemployment often leads to decreased family incomes, particularly affecting low-income households and skilled workers facing job cuts (Andrés, 2006) This trend exacerbates disparities between social classes, further widening the gap in income inequality (Andrés, 2006) Additionally, increasing unemployment levels create societal anxiety and uncertainty, especially among those who have lost their jobs, leading to psychological issues such as mental stress, diminished self-esteem, and heightened financial burdens on families (Mocan, 1999).
High unemployment rates can significantly increase income inequality by putting additional financial pressure on low-income families, thereby widening the income gap between them and higher-income individuals As unemployment rises, it exacerbates economic disparities and contributes to social instability and unrest (Autor & Dorn, 2009).
Hypothesis 2: The relationship between the GDP and income inequality
There is a notable relationship between GDP growth and income inequality, with economic growth often leading to increased inequality (Bouincha and Karim, 2018) To harness the positive effects of GDP on income distribution, it is essential to create accessible employment and income opportunities for all social classes As GDP rises, labor demand typically grows, resulting in higher incomes and improved job prospects (Bouincha and Karim, 2018) This dynamic can help bridge the income gap between various socioeconomic groups and mitigate overall income inequality (Bouincha and Karim, 2018).
Dollar and Kraay (2002) highlight that economic development, as indicated by GDP growth, plays a significant role in diminishing income inequality by generating job opportunities and enhancing income for low-income earners Additionally, a rise in GDP empowers governments to adopt more robust social and welfare policies, including those focused on education, health, and social security These initiatives can substantially mitigate inequality and enhance the overall social conditions for individuals Consequently, it is evident that increasing GDP not only promotes employment prospects and raises income levels but also facilitates the implementation of essential social welfare programs, thereby creating a more equitable society.
Hypothesis 3: The relationship between the INF and income inequality
The inflation rate (INF) significantly influences income inequality, with effects varying based on a country's unique economic and political context While economic inequality often leads to negative outcomes, it can also present certain advantages Research by Bulir (2001) indicates that inflation can positively affect low-income individuals by increasing their ability to acquire valuable financial assets, such as housing and land However, inflation also raises the cost of essential goods and services, disproportionately impacting the wealthy, who are generally better equipped to cope with these price hikes (Stiglitz, 2012) This dynamic can exacerbate economic inequality, as the poor may struggle to afford daily necessities The relationship between INF and income inequality is complex, influenced by factors like asset access and living expenses (Glawe and Wagner, 2024) Therefore, understanding the specific effects of inflation on income inequality requires a thorough examination of each country's distinct circumstances.
Hypothesis 4: The relationship between the Export and income inequality
An increase in exports is generally viewed as a positive factor influencing income inequality, reflecting the disparity in income levels within a community Exporting can significantly enhance employment opportunities and boost revenue for businesses and individuals, particularly in developed nations As companies expand their export operations, the demand for labor tends to rise, resulting in higher wages and more job opportunities for workers.
A reduction in income inequality is anticipated as the average income of various social groups rises Expanding the export sector can play a significant role in this process by generating employment opportunities and boosting income for low-income earners (McCulloch and Baulch, 2000) Additionally, export activities can drive comprehensive economic growth, fostering advancements in education, healthcare, and infrastructure (Nguyen and Su, 2022) These developments not only help mitigate inequality but also create a more favorable business environment for all demographics Ultimately, increasing exports yields numerous economic and social benefits, including improved job prospects and enhanced revenue for diverse income groups (Nguyen and Su, 2022).
Methodology and Data
Sample and data selection methods used
This study aims to assess the relationship between income inequality and marginal indicators across 20 countries, utilizing data from 2000 to 2016 By incorporating variables such as GDP growth and unemployment rates from both developing and developed nations, the research seeks to provide a comprehensive overview of income disparity The analysis is grounded in the "elephant curve" concept introduced by Alvaredo et al in 2018, which highlights varying income growth rates among different social groups and emphasizes the widening gap between the middle class and the affluent To deepen the understanding of these dynamics, the study employs GDP growth lagged data and utilizes Fixed Effect Model (FEM) and Random Effect Model (REM) methodologies, with the Hausman test applied to determine the most suitable model for accurate results The findings will be presented in a comparative table of the two groups of countries under investigation.
Table 2: List of Developed & Developing Countries
China, United States, Germany, Russia,
Canada, Israel, Italy, France, Denmark,
Vietnamese, Indonesia, Peru, Ecuador, Belarus, Uzbekistan, Bolivia, Netherland, Costa Rica, Belgium
Source: Results obtained by the authors
Estimation method
The pooled regression model is essential for understanding our intended models, as it features constant coefficients for both the intercept and slope (Stukel et al., 2001) This model allows researchers to aggregate all data and perform an ordinary least squares regression analysis effectively.
Pooled OLS offers both advantages and disadvantages for researchers to consider On the positive side, utilizing insect models enhances the accuracy of toxicological estimates and enables robust hypothesis testing in larger settings This model effectively manages unsupervised non-simultaneous efficiency by presuming that the activities of various tools may be disregarded after data aggregation However, there are notable drawbacks, such as the reliance on the assumption of independence between killings, which can introduce bias if dependencies exist within groups Additionally, pooled models may oversimplify relationships by assuming uniformity across groups, potentially overlooking critical differences and leading to erroneous conclusions Furthermore, the occurrence of coefficients in the model is a minor factor that should be addressed By considering these challenges and leveraging opportunities, researchers can enhance the accuracy and reliability of findings related to toxic materials in statistical analysis.
In meta-analysis, two widely used models are the fixed application model and the random application model Although both models utilize similar formulas, they often operate under different assumptions about the data, leading to varied results Therefore, it is crucial to understand and select the appropriate model to ensure accurate conclusions and enhance the effectiveness of subsequent statistical analyses.
The Fixed Effects Model (FEM) is widely utilized across various fields to estimate causal effects from non-experimental data by addressing unit heterogeneity and temporal variations (Venot et al., n.d.) For the FEM to be effective, two key conditions must be satisfied: first, it is essential that all studies exhibit functional similarity; second, the model must focus on a general effect description that is restricted to the narrowly defined population within the analysis (Borenstein et al., 2010).
A random effects model is a statistical tool designed to analyze the influence of various types of variables on a continuous outcome, while recognizing that categorical variables represent a random sample from a broader population This stochastic model permits variability in how these variable types affect the outcome level (Borenstein, Hedges, and Rothstein, 2007).
The fixed effect model (FEM) and random effects model (REM) are key approaches in panel data analysis, differing primarily in how they handle panel-specific effects FEM treats these effects as fixed and unique to each panel member, estimating separate intercepts and focusing on within-group variation, while assuming a correlation with observed explanatory variables Conversely, REM views panel-specific effects as random, drawn from a population distribution, and incorporates both within-group and between-group variation, assuming independence from explanatory variables The choice between FEM and REM hinges on the assumptions regarding panel-specific effects and their relationship with observed variables, impacting the interpretation and application of these models in panel data analysis.
Variables
We present a comprehensive table of variables designed to enhance understanding and facilitate their calculation This table will aid readers in grasping the intricate relationships between the variables, ultimately leading to clearer analytical conclusions.
GDP growth indicates the percentage increase in the real value of all goods and services produced within a country's borders over a specific period, usually assessed annually or quarterly, and adjusted for inflation.
(Carrion-i-Silvestre, del Barrio- Castro and López-Bazo, 2005)
Real GDP growth rate = (most recent year's real GDP - the last year's real GDP) / the previous year's real GDP
Inflation, defined as the annual percentage increase in prices for goods and services, results in a decline in purchasing power within an economy It is measured using the Consumer Price Index (CPI) or the Producer Price Index (PPI) (Huang and Liu, 2005).
EXP_G Export growth rate is the percentage change in the value of a
The export growth rate measures the value of a nation's exports over a specific period, typically calculated on an annual or quarterly basis This key metric is a crucial indicator of a country's international trade performance and overall economic health, highlighting the significance of export activities in driving economic growth.
1 - Export Value Year 2)/Export Value Year 1)) × 100%
The unemployment rate is a key indicator that effectively measures labor productivity in a specific country or region, reflecting both employed and unemployed individuals This statistic is crucial for evaluating the overall health of an economy's labor market.
GDP_G (-1) Past observations to assess the implications of last year's data on the current GINI index INF (-1)
The Gini coefficient, also referred to as the Gini index or Gini ratio, measures income and wealth inequality within a country's population This important metric is used to evaluate and illustrate the distribution of economic resources across a nation.
A is the area between the Lorenz curve and the line of perfect equality
B is the area under the line of perfect equality
Source: Results obtained by the authors
Descriptive statistics and initial data visualization
After collecting and describing the definition of all the indexes, we take some outstanding countries with specific indexes below here
Figure 1: Time series plot of some countries as an example
Source: Results obtained by the authors
Figure 2: Relationship between each variable with GINI Index
Source: Results obtained by the authors
Research model
To understand their relationship, we propose a model of variables affecting Gini shown below
Source: Results obtained by the authors
This article discusses the use of FEM and REM models for comprehensive model evaluation, alongside the Hausman test to strengthen the proposed arguments Furthermore, analyzing two distinct groups of countries enhances the structured analysis of comparative economic performance by highlighting relevant factors This approach not only reinforces readers' confidence in assessing global economic phenomena but also deepens their understanding of worldwide trends.
Research results and discussions
Model
4.1.1 Pooled Ordinary Least Squares (POLS) Model
First, we will apply the Pooled model to test the input data The results are described in the following table:
Variables Estimate Std.Err t-value P>|t|
Source: Results obtained by the authors
The evaluation of the model's overall fit reveals an R-squared value of 0.5307, indicating that approximately 53.07% of the variability in the GINI coefficient is explained by the independent variables The adjusted R-squared is slightly lower at 0.5251, suggesting a good model fit while accounting for additional factors Importantly, the intercept term exhibits a significant p-value, indicating a substantial departure from zero, which suggests that even with all independent variables at zero, a considerable level of inequality persists.
To understand the findings obtained, we shall delve further into evaluating each independent variable in the pooling model
The analysis reveals a GDP growth rate coefficient of 0.7155, indicating that a one-unit increase in GDP growth correlates with a 0.7155 unit rise in the GINI coefficient With a t-value of 6.6035, this relationship is statistically significant (p < 0.0001), highlighting a strong positive link between GDP growth and income inequality The findings suggest that periods of robust economic growth may exacerbate income disparity, supporting the notion that economic advancement does not equally benefit all societal groups and can widen existing income gaps.
The predicted coefficient for the Inflation Rate is 0.0318, with a standard error of 0.0234, indicating a positive association between inflation and income inequality However, the t-value of 1.3594 suggests that this relationship lacks statistical significance at conventional levels (p = 0.1749) Consequently, the data does not support the conclusion that fluctuations in the inflation rate significantly impact income inequality within this model.
The coefficient for Export Growth rate is estimated at -0.2929, with a standard error of 0.0174, and a highly significant t-value of -16.8235 (p < 0.0001) This negative coefficient indicates that a one-unit increase in export growth rate correlates with a decrease of approximately 0.2929 units in the GINI coefficient, suggesting that stronger export growth is associated with reduced income inequality This relationship may be attributed to positive effects on employment, income distribution, and overall economic development.
The predicted coefficient for the Unemployment Rate is 0.0703, accompanied by a standard error of 0.0667 With a t-value of 1.0542 and a p-value of 0.2925, this coefficient is not statistically significant at conventional levels Consequently, the data does not support the conclusion that fluctuations in the unemployment rate have a meaningful impact on income disparity within this model.
While the data offers valuable insights into the causes of income disparity, caution is essential before drawing definitive conclusions The pooling model suggests a continuous relationship between independent and dependent variables across all countries and time periods, but this assumption may not accurately reflect real-world socio-economic conditions.
The pooling approach overlooks critical variability across entities and time periods, as differences in institutional frameworks, policy actions, and economic structures can lead to diverse relationships between independent and dependent variables across nations This oversight may result in inaccurate estimations and flawed conclusions Moreover, the pooling model assumes no correlation between individual effects and independent variables, while unobserved factors unique to each country can significantly influence both To address these limitations and enhance the reliability of our estimates, we will utilize advanced panel data techniques such as fixed effects models (FEM) or random effects models (REM), which account for unobserved variations across entities and time, thereby providing more robust insights into the relationships being studied.
We will employ the Fixed Effect model to re-evaluate the data and compare it with the outcomes of the Pooled model to identify any significant changes The findings from this model are displayed in the table below.
Table 5: Fixed effect Model Result Variables Estimate Std.Err t-value P>|t|
Source: Results obtained by the authors
This study examines the relationship between the GINI coefficient and various independent variables, such as GDP growth, inflation rate, export growth, and unemployment rate, along with their lagged effects across different countries, utilizing a regression model for analysis.
The analysis reveals that with all independent variables held constant, the estimated GINI coefficient stands at 41.10, indicating a significant baseline of income inequality The coefficient estimates for GDP growth, inflation, export growth, and unemployment rate suggest a complex interplay of factors influencing income inequality However, none of these variables demonstrate statistical significance at conventional levels (p > 0.05), implying that their individual impacts on the GINI coefficient may lack reliability.
The coefficients for the lagged variables (GDP_G (-1), INF (-1), EXP_G (-1), UER (-1)) exhibit more convincing findings
The coefficient estimate for GDP_Growth (-1) is 0.0788, with a standard error of 0.0532 and a p-value of 0.1401, indicating a positive relationship between GDP growth and the variable GDP_G (-1) However, the p-value suggests that this relationship is not statistically significant, as it does not meet the traditional threshold of p < 0.05 Despite this, the relatively low p-value hints at a potential association between past GDP growth and current income disparity Our findings suggest that historical GDP growth may influence present levels of income disparity, although the statistical strength of this relationship in our model is limited.
The lagged inflation rate (INF (-1)) shows a significant coefficient estimate of 0.095 with a standard error of 0.0243 and a p-value of 0.0001, highlighting a strong correlation between past inflation rates and income disparity This indicates that higher inflation rates in the past are associated with increasing income inequality, suggesting that the historical pattern of inflation significantly impacts income distribution dynamics.
The estimated coefficient for Export Growth (-1) is - 0.1984 with a standard error of
A significant negative correlation exists between delayed export growth and income inequality, as indicated by a coefficient of 0.0351 and a p-value of less than 0.0001 This suggests that higher export growth in the past is associated with reduced income inequality Consequently, the historical success of export-oriented industries may play a crucial role in shaping current income distribution.
The coefficient estimate for the unemployment rate (UER) is -0.0176, with a standard error of 0.0275 and a p-value of 0.5224, indicating a lack of statistical significance This suggests that historical unemployment rates do not have a meaningful direct impact on current income disparity levels.
Developed nations such as Canada, France, Germany, Italy, the Netherlands, Russia, the USA, and Israel exhibit lower GINI coefficients, indicating less income inequality In contrast, developing countries like Bolivia, Colombia, Costa Rica, Ecuador, Peru, Uzbekistan, and Vietnam show higher levels of economic disparity Notably, emerging economies such as China and Vietnam have significant positive GINI coefficients, reflecting substantial income inequality Conversely, Indonesia's near-zero coefficient suggests minimal income disparity This research highlights the stark differences in income distribution between developed and developing nations, underscoring the necessity for targeted policy initiatives aimed at mitigating income inequality across diverse socio-economic contexts.
Model selection test
4.2.1 F test for cross-sectional effects
Based on our initial observations, the Fixed Effect and Random Effect models appear to be the most appropriate for this research To ensure the accuracy of our initial assessment, we have opted to conduct a further evaluation using the F-test.
Hypothesis 0 There is no cross-sectional effect
Hypothesis 1 There is cross-sectional effect
Source: Results obtained by the authors
The F-test for individual effects is a crucial statistical method used to assess the overall significance of a model by evaluating whether any independent variables have coefficients that differ from zero The null hypothesis suggests that there are no cross-sectional effects, meaning that none of the independent variables significantly influence the dependent variable Conversely, the alternative hypothesis indicates that at least one independent factor has a substantial effect, evidenced by a non-zero coefficient.
The F-statistic of 75.852 and a p-value close to zero (p < 0.0001) allow us to confidently reject the null hypothesis in favor of the alternative, indicating that the Pool Model is not suitable for this study Therefore, we will explore alternative tests to identify the most appropriate model.
Next is the Hausman test that we will apply to select the most suitable model The results are shown in the following table:
Degree of Freedom 8 p-value 1 chisq |t|
Source: Results obtained by the authors
We utilized the Heteroskedasticity-consistent covariance matrix estimator (HC0) to update our standard errors, addressing the potential for heteroskedasticity in our data This approach ensures that our standard errors remain robust and unbiased, accommodating variations in variances across observations The HC0 estimator delivers reliable standard errors, enhancing the accuracy of significance testing for coefficients in the presence of heteroskedasticity By mitigating the risk of underestimating standard errors, it helps prevent incorrect conclusions regarding the importance of coefficients.
Conclusion and suggestion
Conclusion
Table 14 presents a summary of the hypotheses derived from earlier sections, detailing the relationships examined and indicating whether these hypotheses were supported or rejected The final column of the table outlines the concluding results of the analysis.
Table 14: The results for hypothesis Hypothesis Relationships Results Conclusion
Unemployment rate does not impact the income inequality
Supported Gross Domestic Product positively impacts the income inequality This effect is statistically significant
Supported Inflation rate positively impacts the income inequality This effect is statistically significant
Supported Export positively impact income inequality This effect is statistically significant
Over the past fifty years, the link between income inequality and economic growth has been widely studied, revealing inconsistent results due to methodological flaws and incomplete data on income inequality This study addresses these gaps by examining both developing and developed countries, providing new empirical insights into the inequality-growth relationship By conducting a detailed empirical analysis with a robust panel dataset and considering various factors as suggested by economic theory, we aim to deepen the understanding of the determinants of income inequality in both contexts.
The analysis reveals a significant correlation between GDP growth and income inequality, indicating that economic expansion often exacerbates income disparities, as higher-income individuals tend to reap more benefits Conversely, a strong relationship between export growth and income inequality suggests that nations with higher export rates typically experience lower income inequality This highlights the role of trade policy and global economic integration in mitigating income disparities by fostering job creation and economic development Furthermore, the study of various countries underscores the pronounced differences in income inequality between developed and developing nations, emphasizing the necessity for tailored policy interventions to effectively address income inequality in diverse socio-economic contexts.
The study reveals no statistically significant relationship between inflation and income inequality, suggesting that these variables may not have a meaningful linear impact on income distribution in this context While the theoretical possibility exists, the lack of significance highlights the need for further research to investigate potential nonlinear dynamics.
Our analysis reveals significant disparities in income distribution between developed and developing countries, with developed nations exhibiting lower GINI coefficients that indicate reduced income inequality Policymakers must prioritize interventions to tackle income inequality, such as fostering inclusive growth, enhancing labor market access, and implementing fair trade policies Additionally, further research is needed to explore the intricate relationship between economic variables and income distribution, including potential nonlinearities and cross-country comparisons These efforts will deepen our understanding of these critical issues and inform evidence-based policy decisions.
Limitations
This study sheds light on the factors influencing income inequality in middle-income countries, but it has notable limitations The reliance on secondary data may introduce measurement errors and biases that could weaken the findings Additionally, the use of regression analysis might oversimplify the complex relationships between economic variables and income distribution Concerns regarding heterogeneity suggest that economic factors may bias coefficient estimates Furthermore, the focus on middle-income countries limits the generalizability of the results, underscoring the necessity for further research across different income levels, geographical regions, and institutional contexts To enhance the validity and applicability of income inequality research, it is essential to address these limitations through improved data quality, alternative methodologies, and broader contextual analyses.
Recommendations for further research
Future research on income inequality should focus on nonlinear dynamics and interaction effects to uncover complex patterns and underlying factors Utilizing advanced methodologies like structural equation modeling and longitudinal analyses will enhance the understanding of income distribution dynamics Additionally, evaluating the effectiveness of policy interventions aimed at wealth redistribution and social safety nets remains crucial By studying diverse socio-economic contexts, researchers can gain insights into the varying impacts of these policies This knowledge will inform evidence-based strategies to promote economic growth and social equity Overall, further exploration in these areas will deepen our understanding of income inequality and contribute to the development of more effective reduction strategies.
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To read data from Excel files in R, you can use the `readxl` library First, load the library with `library(readxl)` Then, import your data by using the `read_excel` function, specifying the file path For example, to read a dataset from "nckh.xlsx," use `dat = read_excel("C:/Users/DELL/OneDrive/Máy tính/Year 3/Business Intelligence/nckh.xlsx")` Similarly, to read another dataset from "Nam.xlsx," use `nam = read_excel("C:/Users/DELL/OneDrive/Máy tính/Year 3/Business Intelligence/Nam.xlsx")`.
This article presents a series of visual analyses using ggplot2 to explore key economic indicators in developing and developed countries The Gini index, representing income inequality, is plotted over the years for various countries, highlighting trends in economic disparity Additionally, the Export Growth Rate, Unemployment Rate, Inflation Rate, and GDP Growth Rate are examined through similar line and point graphs, providing insights into their respective impacts on economic performance The relationships between these indicators and the Gini index are further analyzed through scatter plots, illustrating how GDP growth, export growth, inflation, and unemployment correlate with income inequality across different nations Each visualization is designed to enhance understanding of the economic dynamics at play, making the data accessible and informative.
############ II THREE TYPES OF MODELS #############
# 1 OLS, pooled library(plm) pool = plm(data, GINI ~ GDP_G + INF + EXP_G + UER, index =c("country", "year"), model = "pooling") summary(pool)
# 2a Estimate using dummy variables dummy = lm(data, GINI ~ country + GDP_G + INF + EXP_G + UER + GDP_G1 + INF1 + EXP_G1 + UER1) summary(dummy)
# 2b Estimate using demeaned data fixed = plm(data, GINI ~ country + GDP_G + INF + EXP_G + UER + GDP_G1 + INF1 + EXP_G1 + UER1, index=c("country","year")) summary(fixed)
# 3 Random effect random = plm(data, GINI ~ country + GDP_G + INF + EXP_G + UER + GDP_G1 + INF1 + EXP_G1 + UER1, index=c("country","year"), model="random", random.method
# 1 F test whether fixed or Pooled pFtest(fixed, pool)
# 2 Hausman test whether fixed or random effect (*) phtest(random, fixed)
# 3 Random or Pooling: Breusch-Pagan LM test plmtest(pool, type=c("bp"))