Econometrics report factors attributing to income inequality in the united states 2000 – 2020

TABLE OF FIGURES Table 1. Variables used in the model 14 Table 2 Statistical description of variables 16 Table 3 Correlation description of data 18 Table 4 Initial regression of the model 19 Table 5 Correlation between variables 21 Table 6 Regression after fixing Multicollinearity 22 Table 7 Test for Normality of u 24 Table 8 95% confidence interval 26   ABSTRACT This research study examines income inequality in the United States from 2000 to 2020. Income inequality has been a persistent issue in the United States, with significant implications for economic, social, and political dynamics. The objective of this study is to analyze data of income inequality over the selected period and explore the factors that contribute to its dynamics. The research utilizes a comprehensive dataset obtained from reliable sources, such as government agencies and academic research, to analyze key indicators of income inequality, including measures of income disparities and wealth gaps. Econometric models and statistical analyses are employed to examine the relationships between income inequality and various factors, such as GDP per capita, population growth, unemployment rate, poverty headcount ratio, and age dependency. The findings of this study reveal the extent and evolution of income inequality in the United States over the analyzed period. The research highlights the increasing concentration of income among the top earners, as well as the challenges faced by lowerincome households in attaining economic prosperity. Moreover, the study investigates the potential impact of income inequality on economic growth, social mobility, and overall societal wellbeing.

FOREIGN TRADE UNIVERSITYFACULTY OF INTERNATIONAL ECONOMICINTERNATIONAL ECONOMIC HIGH-QUALITY PROGRAM

TABLE OF FIGURES

Table 1 Variables used in the model 14

Table 2 Statistical description of variables 16

Table 3 Correlation description of data 18

Table 4 Initial regression of the model 19

Table 5 Correlation between variables 21

Table 6 Regression after fixing Multi-collinearity 22

Table 7 Test for Normality of u 24

Table 8 95% confidence interval 26

This research study examines income inequality in the United States from 2000 to

2020 Income inequality has been a persistent issue in the United States, withsignificant implications for economic, social, and political dynamics The objective

of this study is to analyze data of income inequality over the selected period andexplore the factors that contribute to its dynamics

The research utilizes a comprehensive dataset obtained from reliable sources, such

as government agencies and academic research, to analyze key indicators ofincome inequality, including measures of income disparities and wealth gaps.Econometric models and statistical analyses are employed to examine therelationships between income inequality and various factors, such as GDP percapita, population growth, unemployment rate, poverty headcount ratio, and agedependency

The findings of this study reveal the extent and evolution of income inequality inthe United States over the analyzed period The research highlights the increasingconcentration of income among the top earners, as well as the challenges faced bylower-income households in attaining economic prosperity Moreover, the studyinvestigates the potential impact of income inequality on economic growth, socialmobility, and overall societal well-being

I Introduction:

1   Research objective:

The objective of this research is to comprehensively examine the trends anddeterminants of income inequality in the United States from 2000 to 2020 Thestudy aims to shed light on the complex dynamics of income distribution over thisperiod and provide a deeper understanding of the factors contributing to theobserved patterns Specifically, the research aims to achieve the followingobjectives:

1.1 Analyze the long-term trends in income inequality in the United States overthe specified time period and identify any significant changes or patterns: Thisobjective entails conducting a detailed analysis of income inequality measures,such as the Gini coefficient The research will investigate whether incomeinequality has been increasing, decreasing, or exhibiting fluctuations during thestudy period

1.2 Assess the impact of economic factors, such as GDP growth, unemploymentrates, and poverty headcount ratio, on income inequality during the study period:This objective involves examining the relationship between macroeconomicindicators and income inequality The research will investigate whether periods ofeconomic growth or recession are associated with changes in income inequality Itwill also analyze how changes in labor market conditions, such as shifts inunemployment rates or wage levels, influence income disparities among differentincome groups By doing so, the research seeks to gain a deeper understanding ofthe extent of poverty and inform policies and interventions aimed at reducingpoverty and improving the well-being of individuals and communities

1.3 Provide policy recommendations and interventions aimed at mitigating incomeinequality in the United States, considering the findings and insights derived fromthe analysis: This final objective involves translating the research findings intoactionable policy recommendations The research will identify evidence-basedstrategies and interventions that can effectively address income inequality in theUnited States It will consider the multi-faceted nature of income disparities andpropose comprehensive policy approaches, encompassing areas such as taxation,education, labor market regulations, social welfare programs, and wealthredistribution

By addressing these research objectives, this study aims to make significantcontributions to the existing literature on income inequality in the United States Itseeks to enhance our understanding of the causes and consequences of incomedisparities, provide valuable insights for policymakers and stakeholders, andcontribute to the ongoing discourse on creating a more equitable society.

2   Research Questions:

Just over a decade since the conclusion of the Great Recession in 2009, the UnitedStates is experiencing positive economic developments across various aspects Thejob market has been consistently generating employment opportunities, resulting inover 110 consecutive months of job growth, a milestone unprecedented since thepost-World War II era As of November 2019, the unemployment rate stood at3.5%, a level last witnessed in the 1960s These positive employment trends havealso contributed to an improvement in household incomes, which have shownsigns of recovery in recent years

However, not all economic indicators paint an optimistic picture Householdincomes have experienced only modest growth in the 21st century, and householdwealth has yet to reach pre-recession levels Economic inequality, whethermeasured through income disparities or wealth gaps between affluent and lower-income households, continues to widen Despite the overall positive economicclimate, these persistent disparities raise concerns about the unequal distribution ofeconomic resources and opportunities within society

2.1 What factors contribute mainly to income inequality in the United States?2.2 What measures are proposed to reduce income inequality in the US?

By addressing these research questions, the study aims to provide insights into thecomplex relationship between income inequality and various socioeconomicfactors, including GDP per capita, population growth, unemployment, povertyrates, and age dependency This research will contribute to a better understanding

of the drivers of income inequality in the United States and inform policies andstrategies for promoting more equitable income distribution

3   Scope of study:

3.1 Data Collection: The study will utilize reliable and comprehensive statisticaldata from various sources, including but not limited to: The Investopedia team,National Library of Medicine of the US,

3.2 Timeframe: The study will focus on the period from 2000 to 2020 to capturethe long-term trends and dynamics of income inequality in the United States Thistime frame allows for a substantial analysis of income disparities over nearly twodecades.

3.3 Measurement of Income Inequality: The study will employ various commonlyused indicators to measure income inequality, including:

 Gini coefficient: A widely used measure that quantifies income inequality on

a scale of 0 to 1, where 0 represents perfect equality and 1 representsmaximum inequality. 

3.4 Statistical Analysis: The study will utilize rigorous quantitative methods, such

as descriptive statistics, regression analysis, and trend analysis, to analyze andinterpret the data It will also assess the significance of relationships and identifyany notable patterns or shifts in income inequality over the study period

3.5 Limitations: The study acknowledges potential limitations in data accuracy,sampling biases, and other constraints inherent in secondary data sources It willprovide a comprehensive analysis within the available data's scope and time frame

4   Methodology overview: The Ordinary Least Squares (OLS) method is mainlyapplied in this research

5   Structure overview:

5.1 Introduction: Provides background information on income inequality in the

US, states the research problem or objective, and explains the significance andrelevance of studying this topic

5.2 Literature Review: Summarizes existing research and studies related to incomeinequality in the US, identifies gaps or limitations in previous literature, andestablishes the theoretical framework or conceptual basis for the current research.5.3 Methodology: Describes the research design, data collection methods(secondary data analysis), sample selection, and data analysis techniques specific

to studying income inequality in the US

5.4 Data Analysis and Findings: Presents the results of the data analysis, includingstatistical measures and visual representations, to examine the levels and trends of

income inequality in the US over the selected period (2002 to 2020): statisticaldescription and Correlation between variables, estimation and hypothesis testing5.5 Conclusion and recommendation: Summarizes the main findings andconclusions of the research on income inequality in the US, emphasizes thesignificance of the study, and suggests possible avenues for future research.

5.6 Reference: Lists all the sources cited in the research paper following a specificcitation style (e.g., APA, MLA)

 II Literature review:

1 Theoretical framework:

Definition of each variable:

- GDP per capita: Gross domestic product (GDP) per capita is an economic metricthat breaks down a country's economic output per person Economists use GDP percapita to determine how prosperous countries are based on their economic growthGDP per capita is calculated by dividing the GDP of a nation by its population.Countries with the higher GDP per capita tend to be those that are industrial,developed countries

- Population growth: population growth, in population ecology, a change in thenumber of members of a certain plant animal species in a particular location during

a particular time period Factors affecting population growth include fertility,mortality, and in animals, migration—i.e., immigration to or emigration from aparticular location The average change in a population over time is referred to asthe population growth rate A positive growth rate indicates a population increase,and a negative growth rate indicates a population decrease The upper limit of apopulation in a given environment, referred to as the environment’s carryingcapacity, is determined by the amount and availability of resources that are life-sustaining for that population

- Unemployment rate: The unemployment rate is the percentage of the labor forcewithout a job It is a lagging indicator, meaning that it generally rises or falls in thewake of changing economic conditions, rather than anticipating them When theeconomy is in poor shape and jobs are scarce, the unemployment rate can be

expected to rise When the economy grows at a healthy rate and jobs are relativelyplentiful, it can be expected to fall.

- Poverty headcount ratio: National poverty headcount ratio is the percentage of thepopulation living below the national poverty line(s) National estimates are based

on population-weighted subgroup estimates from household surveys Foreconomies for which the data are from EU-SILC, the reported year is the incomereference year, which is the year before the survey year

- Age dependency: The dependency ratio is a measure of the number of dependentsaged zero to 14 and over the age of 65, compared with the total population aged 15

to 64 This demographic indicator gives insight into the number of people of working age, compared with the number of those of working age

non-It is also used to understand the relative economic burden of the workforce and hasramifications for taxation The dependency ratio is also referred to as the total oryouth dependency ratio

2 Overview of income inequality in the United States

Income inequality is consistently a major topic in U.S presidential races Incomeinequality refers to how unevenly income is distributed throughout a population.The less equal the distribution, the greater the income inequality Incomeinequality is often accompanied by wealth inequality, which is the unevendistribution of wealth Gini coefficient figures have ranked the U.S as one of theworst places for income equality among developed economies for many years nowand this issue is only getting worse

The reasons for growing inequality, according to economists, are multifaceted andinclude long-standing racial and gender discrimination, a failure to adjust toglobalization and technological advancement, changing tax laws, and decreasingworker bargaining strength Equally diverse are the repercussions of inequality,which have widened societal gaps and intensified crises like the epidemic.Additionally, inequality can undermine democracy and fuel moves towardauthoritarianism. 

With a host of social ills—such as slavery, immigration problems, and Japaneseinternment camps—correlated with high levels of income inequality, it is crucialfor the U.S to figure out how to reduce its income inequality Fortunately, history

gives us a useful guide to policies that can be implemented to aid in that goal Abrief history of income inequality in the U.S from the beginning of the 20thcentury until the present day shows that the nation's level of income inequality hasbeen substantially affected by government policies concerning taxation and labor.Barely 10 years past the end of the Great Recession in 2009, the U.S economy isdoing well on several fronts The labor market is on a job-creating streak that hasrung up more than 110 months straight of employment growth, a record for thepost-World War II era The unemployment rate in November 2019 was 3.5%, alevel not seen since the 1960s Gains on the jobs front are also reflected inhousehold incomes, which have rebounded in recent years.

But not all economic indicators appear promising Household incomes have grownonly modestly in this century, and household wealth has not returned to its pre-recession level Economic inequality, whether measured through the gaps inincome or wealth between richer and poorer households, continues to widen. 

From 2000 to 2018, the growth in household income slowed to an annual averagerate of only 0.3% The shortfall in household income is attributable in part to tworecessions since 2000 The first recession, lasting from March 2001 to November

2001, was relatively short-lived.7 Yet household incomes were slow to recoverfrom the 2001 recession and it was not until 2007 that the median income wasrestored to about its level in 2000

The shortfall in household income is attributable in part to two recessions since

2000 The first recession, lasting from March 2001 to November 2001, wasrelatively short-lived Yet household incomes were slow to recover from the 2001recession and it was not until 2007 that the median income was restored to aboutits level in 2000

But 2007 also marked the onset of the Great Recession, and that delivered anotherblow to household incomes This time it took until 2015 for incomes to approachtheir pre-recession level Indeed, the median household income in 2015 – $70,200– was no higher than its level in 2000, marking a 15-year period of stagnation, anepisode of unprecedented duration in the past five decades.8

More recent trends in household income suggest that the effects of the GreatRecession may finally be in the past From 2015 to 2018, the median U.S.household income increased from $70,200 to $74,600, at an annual average rate of2.1% This is substantially greater than the average rate of growth from 1970 to

2000 and more in line with the economic expansion in the 1980s and the dot-combubble era of the late 1990s.

The period from 2001 to 2010 is unique in the post-WWII era Families in all strataexperienced a loss in income in this decade, with those in the poorer strataexperiencing more pronounced losses The pattern in income growth from 2011 to

2018 is more balanced than the previous three decades, with gains more broadlyshared across poorer and better-off families Nonetheless, income growth remainstilted to the top, with families in the top 5% experiencing greater gains than otherfamilies since 2011

3 The econometric model and variables selection:

Income inequality can be measured using a variety of methods, such as the Lorenzcurve (Lorenz 1905), the Gini coefficient (Gini 1913, 1921) and the Theil index(Akita el al, 1999) In this study, the team uses the Gini coefficient to representincome inequality

  GDP:Dr Barro(1991) is from the Department of Economics in Harvard

University In his research, he finds that evidence shows little overall relationbetween income inequality and rates of growth and investment He thinks thateconomic growth will fall with greater inequality when GDP per capita is belowaround $2000 (1985 US dollars) and to rise with inequality when GDP per capita isabove $2000 The data he uses dated through 1995 is from the World Bank

Jauch and Watzka (2016) believe it is a good proxy for financial development,because the correlation between private credit over GDP and access to finance ishigh Since gross income excludes all income from non-private sources and netincome includes all types of public transfers and deductions, they use both grossincome and net income to measure income inequality so that the number reflectsboth the actual amount of an individual to spend on and also the individuals’earning entitlements on pensions and other social benefits Their results suggestthat economic theories predicting an income inequality reducing effect of financialdevelopment should be rejected

Population: The level of income inequality varies greatly across localities,

including between rural and urban areas Since 1970, non-metropolitan countieshave tended to have much higher average levels of income inequality thanmetropolitan counties (Moller et al 2009; Thiede et al 2019) Although growth inurban inequality has led to significant rural-urban convergence in county-level

income inequality in recent years, high levels of local income inequality have been

a disproportionally rural issue over the past half-century (Thiede et al 2019) Suchincome disparities represent an important challenge for rural populations andcommunities given that high levels of local income challenges to communitydevelopment (Chetty and Hendren 2018; Dillard 1941; Duncan 2014; McLaughlinand Stokes 2002; Piketty 2014; Smith 2012). 

Unemployment: inequality is associated with a range of adverse social and health outcomes, the concentration of economic and political power, and corresponding:

Historically, unemployment and income inequality have been studied mainly intwo separate stands of literature On the unemployment side, the literature paysmore attention to its relationship with inflation and monetary policy, led by theclassic work of Phillips (1958), Samuelson and Solow (1960), Friedman (1968)and Lucas (1976).1 On the income inequality side, the literature is more focusing

on its relationship with economic growth and efficiency, following the classicwork of Kuznets (1955), Okun (1975) and others

Poverty: Econimic growth positively affects absolute poverty by raising the levels

on income However, it inversely affects relative poverty as the benefits fromeconomic growth in the country may not be shared equally poor socio-economicstatus (e.g., high-income inequality) could impede the fight against energy povertyalleviation because access to energy costs money Theoretically, Galvin andSunikka-Blank (2018) argue that energy poverty increases with increasing incomeinequality since income inequality increases income poverty, which is a driver ofenergy poverty Further, Galvin and Sunikka-Blank (2018) opine that incomeinequality does not only increase energy poverty by increasing the number of poorhouseholds, but income inequality can distort energy markets, which makes itharder for the poor to access energy services. 

Age: The relationship between population aging and income inequality has been

well documented Early research has pointed out that the larger the share of elderlypeople, the more unequally income is distributed, indicating a positive correlationbetween aging and inequality (Lindert 1978; Repetto 1978) Later studies mostlyfocus on within-country evidence, including developed economies such as theUnited States, the United Kingdom, Taipei,China (Lam and Levison 1992; Deatonand Paxson 1994), Japan (Ohtake and Saito 1998), and OECD countries (Van Vlietand Wang 2015), as well as developing economies such as Java (Cameron 2000)and the PRC (Zhong 2011)

II Methodology:

The previous study on income inequality could be measured using variousmethods, such as the Lorenz curve (Lorenz, 1905), the Gini coefficient (Gini,

1913, 1921), and the Theil index (Akita et al., 1999) In this study, the researchgroup utilized the Gini coefficient to represent income inequality

Previous research on income inequality has focused on factors related to economicdevelopment, such as the wealth of a nation (often measured by per capita GDP),economic growth, technological development, and economic structure Moststudies examining the relationship between income inequality and a country'sgrowth are based on Kuznets' (1955) hypothesis of an inverted U-shapedrelationship: as GDP increases, inequality initially rises and then begins to decline.Higgins and Williamson (1999) analyzed panel data from 1960 to 1990, Clark and

Xu and Zou (2003) studied the period from 1960 to 1995, Barro (1999), Nielsenand Alderson (1997) analyzed cross-sectional data for the United States in the1970s, 1980s, and 1990s, and all demonstrated the relationship between per capitaGDP and income inequality However, Nielsen (1994) reached the conclusion thatthe impact of per capita GDP on income inequality could not be conclusivelydetermined Gustafsson and Johansson's (1997) study on OECD countries from

1966 to 1994 also yielded similar results, further complicating the understanding ofthe influence of a nation's wealth on income inequality

Edwards (1997) analyzed cross-sectional data from 1970 and 1980 and found thatfaster economic growth increased income inequality Xu and Zou's (2000) study ondata from China also yielded similar results Brülhart and Sbergami (2009) arguedthat poor countries have to choose between reducing income inequality andachieving higher economic growth However, empirical analysis by Chang andRam (2000) using panel data from 1980 showed that faster economic growthactually reduced income inequality Ahluwalia's (1974) study with cross-sectionaldata from 1960, on the other hand, found that economic growth had no significantimpact on income inequality Therefore, no singular assumption can be maderegarding the impact of economic growth on income inequality

Some studies suggest that rapid population growth can exacerbate incomeinequality For instance, a study conducted by Dollar and Kraay (2002) found thatcountries with higher population growth tend to experience greater incomeinequality They argue that population growth can put pressure on limitedresources, leading to increased competition, unequal access to resources, and ahigher concentration of wealth in the hands of a few individuals or groups On theother hand, there are also arguments that population growth can have a positiveimpact on income inequality For example, some researchers suggest that

population growth can lead to a larger labor force, which, if properly utilized, cancontribute to economic growth and reduce income inequality.

Research by Ravallion and Chen (2007) examined the relationship betweenpoverty and inequality using a global dataset They found that higher levels ofpoverty headcount ratios were indeed associated with higher income inequality,suggesting a positive relationship between the two variables However, it isimportant to note that the impact of poverty headcount ratio on income inequalitycan be influenced by various other factors, such as social policies, economicconditions, and government interventions

Some empirical analyses have shown that an increase in the unemployment rateleads to higher income inequality Jäntti (1994) analyzed data from the UnitedStates, Sharpe and Zyblock (1997) examined Canadian data, and Blejer andGuerrero (1990) studied the case of the Philippines

Based on the aforementioned research findings, this article proposes a researchmodel with the following variables:

GINI =^ β0+ ^β1GDP+^ β2UNEM +^ β3POPU +^ β4POV +^u i

Variables and scales

The variables used in the model are described in Table 1 below:

Table 1 Variables used in the model

(Source: Collected from STATA)

GINI dependent variable

Measure of income or wealth inequalitywithin a population

0-100closer to 100,more incomeinequality

GDP Gross Domestic Product per capita USD +

UNEM The unemployment rate measures the

percentage of the labor force that is without

a job

POPU The population growth rate represents the

rate at which a population increases ordecreases

percentage per

POV The poverty headcount ratio measures the

proportion of the population living belowthe poverty line 2.15$ (using PPP 2017)

AGE The age dependency ratio compares the

number of dependent individuals (typicallythe young and elderly) to the working-agepopulation (under 15 and from 15 to 64)

-1 Data source:

The authors of this study collected data from the World Bank for the periodfrom 2000 to 2020 for variables GINI, GDP, UNEM, and POV, and from MacroTrends for the variable POPU

methodology begins by formulating a linear relationship between a dependentvariable and one or more independent variables The objective is to find the best-fitting line that minimizes the sum of the squared differences between the observedvalues and the predicted values by the linear model To accomplish this, the OLSmethod employs a set of mathematical equations that iteratively calculates theestimates of the regression coefficients These estimates are obtained byminimizing the residual sum of squares, which represents the sum of the squareddifferences between the observed and predicted values By solving the normalequations derived from this minimization process, the OLS method provides theestimated coefficients that define the linear relationship Furthermore, the OLSmethod also enables the assessment of the statistical significance of the estimatedcoefficients, the calculation of the goodness-of-fit measures, and the identification

of potential violations of the underlying assumptions of the linear regression model

IV Results analysis:

1 Statistical description and Correlation between variables

1.1 Statistical description 

Table 2 Statistical description of variables

(Source: Collected from STATA)

Table 2 shows some results as follows

The Gini index of the United States fluctuated between 39.7 and 41.5 from 2000 to

2020, suggesting a moderate level of income inequality throughout the period.While the exact figures may vary depending on data sources and methodologies,these values highlight the persistent income gap within the country Addressingthis issue requires comprehensive policies focused on promoting equitableeconomic opportunities, reducing wealth disparities, and fostering inclusive growthfor all segments of society

The GDP of the United States experienced growth from 2000 to 2020, with aminimum value of $36,329.96 billion in 2000 and a maximum value of $65,120.39billion in 2019 The data also indicates a relatively low standard deviation of

$8835.117 billion, suggesting a degree of stability in the annual GDP figuresduring this period This sustained economic expansion highlights the resilience andstrength of the US economy over the years

The United States unemployment rate (UNEM) fluctuated between 2000 and 2020.3.7% was the lowest rate in 2019 and 9.6% was the highest in 2010 Theunemployment rate (UNEM) during this time had a moderate level ofunpredictability, as indicated by the standard deviation of 1.841053 Thesestatistics demonstrate the effects of economic crises, such as the Great Recession

of 2008, on the job market and emphasize the significance of employment policyand labor market stability

The United States population growth rate (POPU) varied between 2000 and 2020.

In 2020, the minimum rate was 0.49%, while in 2000, the highest rate was 1.15%.The data indicate a moderate level of fluctuation in population growth throughoutthis time period, with a standard deviation of 0.1492186 These statistics indicatevariables that affect the nation's overall population dynamics, such as birth rates,immigration trends, and sociocultural changes

The poverty headcount ratio (POV) in the United States varied from 2000 to 2020

In 2020, the minimum ratio was 0.2%, and in 2014, 2015, and 2017, the maximumratio was 1.2% The standard deviation of 0.2204973 indicates that there has beensome moderate variation in poverty rates during this time These statisticsdemonstrate the wide range of economic well-being and emphasize thesignificance of social welfare programs and policies meant to combat poverty andadvance equality

The age dependency ratio (AGE) in the United States varied somewhat between

2000 and 2020 In 2020, the ratio was at its lowest point, and in 2000, it was at itshighest point (32.33683) The data shows an age dependency ratio that has beencomparatively steady over this time period, with a standard deviation of 1.178907.The proportion of the dependent population (young and old) compared to theworking-age population is represented by the age dependency ratio, which shedslight on the potential effects on social and economic systems

1.2 Correlation description

The correlation of variables shown in Table 3 is established by the author's team asfollows: 

Table 3 Correlation description of data

(Source: Collected from STATA)

 The Gini index and the US unemployment rate have a -0.4612 correlationcoefficient, which suggests a negative link This indicates that there is atendency for income inequality, as measured by the Gini index, to drop asthe unemployment rate rises.

 The Gini index and the United States population growth rate have acorrelation coefficient of -0.1349, which points to a marginally negativerelationship This implies that there is a minor tendency for incomeinequality, as measured by the Gini index, to decrease as the populationgrowth rate rises

 The United States' poverty headcount ratio and the Gini index have a highpositive link, as indicated by their correlation coefficient of 0.7137 Thisshows that there is a strong propensity for income inequality, as measured

by the Gini index, to increase along with an increase in the povertyheadcount ratio (which denotes higher levels of poverty)

These expectations will be tested in part 2 below:

 The age dependence ratio in the United States and the Gini index have acorrelation coefficient of -0.3509, which indicates a moderately negativelink This implies that there is a tendency for income inequality, as measured