Income Inequality and its Associated Negative Outcomes in the USA

BY: Andrew Rupp

Income Inequality and its Associated Negative Outcomes in the USA

PEJOURNAL – Since the 1970’s, most member countries of the Organisation for Economic Co-operation and Development (OECD) have shown rising levels of income inequality (d’Agostino et al., 2020). In the USA the existence of income inequality is widely known, yet many remain unaware of the current, colossal levels of inequality (Norton and Ariely, 2011). Indeed, as recent as 2016, the combined global wealth of 99% of the world’s population was still less than that owned by the richest 1% (Oxfam International, 2017).

In America in 2016, the wealthiest 10% owned nearly 80% of the nation’s wealth while the top 1% owned 38.5% of the wealth (considerably growing since 1989 when their share was nearly 30%) (Bruenig, 2017). Another alarming trend is that skewed income distributions have been shown to correlate with high levels of debt of which American citizens have also seen rise in for the past three decades (Azzimonti et al., 2014).

Other negative outcomes have been rising alongside levels of income inequality, though potential causalities have not been universally agreed upon. Consumer debt, for example, has also been on the rise; this coinciding along with a distressing lack of increase in overall wages (Pressman and Scott, 2009). Indeed, the rising debt levels have become normalised despite the difficulties Americans face in attempting to save money; this leading to higher rates of bankruptcy (Pressman and Scott, 2009). Due to the high caseload, bankruptcy laws were changed, essentially raising the thresholds required for one wishing to declare it (White, 2007).

Trends of Health Problems and Their Correlations with Income Inequality

Recent Events and Current Trends

With current low levels of governmental trust (Pew Research Center for the People and the Press, 2015), extremely high levels of income inequality (Bruenig, 2017), and the increasing number of correlations found with health disparities (Heger, 2018), policy makers should be considering a more egalitarian approach. The USA’s budget for health care is higher than all other developed nations (OECD, 2020) yet is the only country which does not offer its citizens universal health care; this despite the public opinion that health care expenditure is the most important issue when it comes to governmental jurisdiction (Aitalieva and Park, 2018). Furthermore, politicians and policy makers remain hesitant to look beyond their border to learn how other nations approach this issue (Diamond, 2019).

Recent statistics in the health sector have shown, along with increased income inequality, higher disparities emerging in measures of cost of health care, measures of longevity (Bor et al., 2017), and outcomes in heart failure (Dewan et al., 2019). For decades there have been various proposed solutions, concomitant with increasing health coverage prices, with the result of confusion for Americans when choosing health care options and understanding their eligibility (Hisnanick and Coddington, 1994).

Though those Americans who were surveyed wish for more decisive and effective governance (Aitalieva and Park, 2018), there is the stark reality of the powerlessness of the average citizen in comparison to elites, corporations, and interest groups (Gilens and Page, 2014).

American health coverage can be a tricky subject as there exists an interplay of public and private insurance as well as those relying on employer-based contracts. Furthermore, the increased level of inequality (Bor et al., 2017) and raising costs in health care, paired with an abundance of coverage options and unclear customer eligibility have led to further dissatisfaction for Americans (Hisnanick and Coddington, 1994).

The American government did once attempt to provide citizens with universal health care when it passed the Patient Protection and Affordable Care Act (ACA) in 2010. Passing the ACA, former-President Barrack Obama hoped to: make health care costs more reasonable, provide citizens a wider selection of coverage options, place standards to prevent discrimination based on pre-existing medical conditions and/or gender, and improve plan benefits (Anderson, 2019). Though this plan has been controversial for some and unpopular with Republicans, it continues to operate in America.

Throughout the world, citizens seem to experience an increasingly higher number of health disparities the lower one finds themselves on the social gradient scale, even in countries where universal health care is available (Sapolsky, 2018). Throughout Trump’s presidency there have been consistent yet unsuccessful attempts to overhaul the ACA (United States Congress, 2017), further complicating matters, as no better alternatives have been proposed.

Socioeconomic Status (SES), Health Coverage and Health Outcomes

One metric worth examining is where one is on the SES spectrum regarding the assessment of health outcomes. To avoid confusion of terms, this essay will utilise the American Psychology Association (2020) definition of SES: “… the social standing or class of an individual or group. It is often measured as a combination of education, income and occupation.”

SES levels have been examined and researched for decades, but not until recently has there been links found with a wide array of negative outcomes; including mental health, drug abuse, educational performance, violence, risky behaviour, crime, and heart disease (Wilkinson and Pickett, 2009).

Low SES has recently been shown to correlate with poorer health outcomes, even spanning across a lifetime (Yang et al., 2018). Moreover, poor SES corelates with negative outcomes for maternal health, potentially becoming a multigenerational effect as maternal health can be a risk factor for children (Hardie and Landale, 2013).

One with low SES may, for example, have a low level of income, be homeless, illiterate, impoverished, and/or health illiterate (which means unable to comprehend medical information and/or follow medical advice given) (Komaromy et al., 2018). Compared to those of higher income brackets, low-income earners seem to show a lower degree of engagement between them and their healthcare teams, which can be further harmful for those with complex health issues (Komaromy et al., 2018).

Income Inequality with Regard to Specific Outcomes

Obesity and Heart Disease

The USA has had an ongoing problem with obesity since the 1970’s, which does not seem to be slowing (Mitchell et al., 2011). Most distressing is that obesity has affected not just adults and the elderly, but also children (Wilkinson and Pickett, 2009). This problem is, of course, complex; yet the USA has been, and continues to be, world leaders in obesity for those in all age groups (OECD, 2017).

Many plausible explanations have been asserted as to why this is the case, such as the higher likelihood of cheaper foods being low in nutritional quality and/or the prevalence of sugar in many types of food; yet it is not only the USA experiencing this issue with a potential link to that of income inequality (Sapolsky, 2018). Applying the Gini coefficient has given further depth to these phenomena. The Gini coefficient is a measure of inequality commonly utilised in examining income inequality which ranges from a score of 0 to 1, where 0 would mean a every person in the population earns exactly the same and a score of 1 would mean one person in the population holds all of the earnings (Giovanni-Bellù and Liberati, 2006).

Countries with mid to high Gini coefficients, such as the USA, the United Kingdom (UK), Mexico, Spain, and China (World Population Review, 2020) have shown higher amounts of gross domestic product (GDP) spent on health care, higher number of cases of heart failure, as well as childhood and overall mortality (Dewan et al., 2019). Furthermore, those who fall into the lower levels of SES within a country with higher levels of inequality may face even greater hurdles when it comes to health outcomes. For example, in the USA, Mexican and African Americans have higher rates of obesity than Caucasian Americans (Mitchell et al., 2011).

Recent studies in epigenetics have found that high levels of stress and pain can decrease lengths of telomeres, which are complexes within DNA that are vital in healthy cell replication (Sibille et al., 2012). Continuous, negative experiences over time that shorten telomere lengths may be central with regards to increasing one’s biological age (i.e., pre-mature aging) (Mitnitski et al., 2002). Low SES in childhood, for example, might present one with additional stressors which can follow into adulthood (Sapolsky, 2018). What is most alarming is that growing up in a low SES can bring with it affiliated states and experiences; such as: poverty, poor mental health, racial discrimination, and residing in polluted neighbourhoods which may all contribute to shortening telomere lengths (Sapolsky, 2018).

Mental Health and Addiction

Recent research has not always agreed that disparate income levels correlate to poorer health outcomes, as one study in 2002 suggests (Sturm and Gresenz, 2002). This could be due to response bias of participants and/or the study being one of the first to test for specific outcomes, as it was performed in 2002; this also being mentioned as a limitation to the study by the authors (Sturm and Gresenz, 2002). Since then, there has been an abundance of research indicating significant correlations between income inequality and negative physical, behavioural, and mental health outcomes; such as: obesity, heart disease, diabetes, and depression (Matthew and Brodersen, 2018).

Some surveys have sought further clarification and assessed individuals based upon their placement throughout the Gini coefficient spectrum; noticing a strong correlation between high income inequality and levels of stress (Lopez, 2004). Although having high stress levels may not be a diagnosis in and of itself, it has been shown that stress can play a central part in determining one’s mental health (Sapolsky, 2018).

Minorities in larger, metropolitan cities generally encounter a higher ratio of income inequality than Caucasian Americans, potentially making them more vulnerable and be at an increased risk of negative mental health outcomes (Lopez, 2004). It could be, however, that the more inequality one faces, whatever ethnicity they might be, they may be at a higher risk of poor mental health when experiencing poverty, discrimination, etc. Indeed, when examining this issue internationally, other countries such as France, the Netherlands, and Belgium, seem to share this relationship between income inequality and prevalence of mental illness (Pickett et al., 2006).

Recent research has yielded mixed results regarding income inequality and it link(s) to addiction. One analysis found a negative relationship between income inequality and alcohol abuse, showing that the more economically advantaged one is may put one at a higher risk of alcoholism (Matthew and Brodersen, 2018). However, these trends were different for those of minority ethnicities, where African and Native Americans faced higher risk of diagnoses of particular negative outcomes compared with that of Caucasian Americans (Matthew and Brodersen, 2018). Despite somewhat incongruent conclusions, meta-analysis has shown that providing disadvantaged citizens a fairer income would generally improve the mental health of a nation (Lynch et al., 2004).

Trends of Social Problems and Their Correlations with Income Inequality

Unemployment

Over the past 10 years, unemployment rates have fallen back to pre-crisis levels (Bureau of Labor Statistics Data, 2020) yet in the same time span citizens are experiencing unseen levels of wealth inequality (Oxfam International, 2017). One theory put forth decades ago is that the USA is once again facing a compromise between efficiency and equality (Heise, 2008). The trade-off is whether an economy can be egalitarian (which causes higher unemployment) with the alternative being that of low unemployment yet yielding increased levels of income inequality (Okun, 1975).

Although this theory was conceived during the time of “communism vs. capitalism”, the USA is indeed facing this dilemma once more with its low unemployment rates (this was before the COVID-19 pandemic) (Bureau of Labor Statistics Data, 2020) yet coincides with unprecedented levels of inequalities (Dwyer, 2018).

Another factor which may heavily influence unemployment rates is that of job automation, which has the potential of further displacing those in the low-income bracket and increasing income inequality (Hong and Shell, 2018). Job automation notwithstanding, the COVID-19 pandemic has caused the number of filing for unemployment to soar to nearly 15% (Civilian unemployment rate, 2020), When combined with the pre-existing levels of income inequality, the current situation is indeed dire. Can this economic and social “trade-off” be ignored and both of these measures improve?

Debt

As beforementioned, the USA is currently experiencing unprecedented levels of income inequality coinciding with high levels of debt, an increase in bankruptcy, and stagnant overall wages (Pressman and Scott, 2009). These conditions overwhelmingly effect the bottom 90% of Americans, minorities especially, whom also hold over 75% of the nation’s debt (Wealth Inequality – Inequality.org, 2020).

Student loan debt is yet another type of debt which has been steadily rising. This has increased for decades and shows no signs of slowing any time soon, with recent estimates at 1.5 trillion dollars (Iuliano, 2019). With higher consumer and/or student loan debt levels, citizens may face more difficulties concerning oppressive social relations, social inclusion/exclusion, and decreasing one’s potential life chances (Dwyer, 2018). Currently (pre-COVID-19) it is estimated that only 60 percent are able to make payments on their student loans while policy makers have not yet proposed any plans so as to ameliorate student this issue (Iuliano, 2019).

 It is largely believed that household debt levels (which include consumer debt and mortgage loans) share a positive correlation with income inequality (Berisha and Meszaros, 2018). However, there has not been any proof found that shows lowering inequality would result in a decrease of household debt (Fasianos et al., 2017). Though much research has been devoted to these correlations, there may yet be one or more confounding variables linking these. Other seemingly contradictory findings have been shown, such that an increase in in household debt may actually decrease income inequality (Berisha and Meszaros, 2018). Berisha and Meszaros (2018), however, warned readers to use caution in these interpretations, stating that that more work should be undertaken to find other potential factors and relationships.

Homelessness and Poverty

Progressing from the previous section, recent research in other fields may shed light on the complexities with the coexisting issues of income inequality, debt, and poverty. Some believe that it is the accumulated interest payments that is a major cause for not only increased poverty levels, but also for the flawed data in measuring poverty; as:

  • Many Americans have high levels of debt and are measured to be above the poverty line, yet fall below it when interest on loans is accounted for (of which this number is increasing).
  • Recent research has used different measurements from that of 35 years ago, which may cause previous findings to be put into question (Pressman and Scott, 2009).

In 2017, the United Nations estimated that around 40 million Americans were living in poverty (Alston, 2017). This is disconcerting not only due to the immense number of afflicted but also because of the continuously stagnant wages (Wealth Inequality – Inequality.org, 2020) and continuous increase in income inequality despite the overall increase in levels of economy-wide productivity (Bivens and Mishel, 2013). Many Americans showed concern for their future during the 2016 presidential primaries, when policies to improve income inequality were seemingly glossed over (Sommeiller et al., 2016).

In 2017, estimates stated that there were more than 553,700 homeless Americans (Henry et al., 2017). Unfortunately, plans to further cut social expenditure is underway (Alston, 2017), effecting:

“… health care, food assistance through the Supplemental Nutrition Assistance Program (SNAP, formerly known as food stamps), housing and home energy assistance, income assistance for people with disabilities, funding to states for other supports for low-income families, grants and loans to make college more affordable, and non-defense discretionary programs as a whole.”

(Parrott et al., 2018).

Those who are homeless and/or extremely poor potentially face more hardships in the occurrence of government cuts to social welfare (Vickery, 2019). Those who are homeless face the additional stressors of poor financial stability and social mobility and experience higher eviction rates; which all may lead to negative effects to their mental and physical health (Alston, 2017). With less money going to social welfare programs, would this necessarily lead to heightened levels of income inequality? A recent meta-analysis (containing 84 studies and over 900 estimations) that examined potential correlations of these yielded important insights, such as:

  • Depending on the measurement, such as the Gini coefficient, the strength of the relationship can alter somewhat when examining the poorest 20% vs. the richest 20%. However, whatever the sample used, there exists a negative correlation; meaning that the lower the social expenditure, the higher the income inequality.
    • As a side note to the above statement, it is important to mention that when it comes to social welfare spending, the strength of the correlation is even higher.
  • No matter period of time the that was examined, these relationships persisted.
    • The authors did address the possibility of publication bias where publishers may tend to publish those studies which show statistically significant findings.
    • The authors also mentioned the importance of additional research on these matters so as to refine these phenomena, as there existed some outliers in their findings (Anderson et al., 2016).

Discrimination, Racism, and Crime Rates

The USA, despite a decrease in overall rates of crime, has the highest number of people incarcerated than that of any other country (Pettit and Gutierrez, 2018). Furthermore, there has been an ongoing proclivity for unequal rates of incarcerations for African American and Hispanic citizens (Pewewardy and Severson, 2003).

Many agree that this is due mainly because of the innate structural and institutional racism since the nascence of the nation (Bailey at al., 2017), although the ensuing prejudice and bias can still be seen in the news. With notable events such as the 1992 Los Angeles riots and the ongoing Black Lives Matter activist movement, the USA holds a longstanding problem of racial inequality that has yet to be resolved.

Not only do African American and Hispanics face disproportionately higher rates of being incarcerated (Pettit and Gutierrez, 2018) but also experience higher levels of income inequality than Caucasian citizens (Hero and Levy, 2016). These two populations have been in a continuous struggle to move up in the occupational hierarchy despite gaining more presence in the American workforce (Alba and Yrizar Barbosa, 2015). This difficulty to progress along the socioeconomic spectrum has been linked to the presence of income inequality as well (Corak, 2013).

The association between populations experiencing income inequality and high crime/incarceration rates has been seen in other countries (Rufrancos et al., 2013). Although rates of violent crime are more varied, the relationship is especially prominent with regard to property crime (Rufrancos et al., 2013); which includes burglary, theft, vandalism, arson, shoplifting and larceny (USLegal, 2019).

The presence of consistent income inequality can also be associated with ongoing prejudicial attitudes (Wilkinson and Pickett, 2017). These views continue to have serious effects in communities as it prolongs the problem of racial stratification and non-acceptance (Edwards at al., 2019). As beforementioned, minorities face a higher likelihood of being incarcerated than Caucasians, which can be seen in their treatment from law enforcement. Viewing the news, police brutality continues to afflict African Americans, with loud protests continuing. These were once again ignited after the brutal death of George Floyd. A recent study has found that police violence is the main killer for young men of colour, with class inequality inherent in the rationale (Edwards et al., 2019).

Conclusion

This article has touched on recent developments and current trends of income inequality while examining its relationships (whether positive, negative, weak and/or strong) with categories such as:

  • Physical and mental health as well as ethnic disparities
  • Socioeconomic Status (SES) and social mobility
  • Health Coverage and Health Outcomes
  • Debt and unemployment
  • Poverty and homelessness
  • Discrimination, racism, and comparison of crime rates.

These issues may be intensified with the ongoing COVID-19 pandemic as unemployment continues to rise (Civilian unemployment rate, 2020) with African American, Hispanic American, Native American and immigrant populations facing disproportionate racial and health inequalities and mortality rates; all the while these minority groups make up the majority of those who are considered essential workers (Dorn et al., 2020).

Correlations continue to be found between income inequality not only to the above categories but also to that of levels of happiness (Oishi et al., 2018), educational disparity and performance (Wilkinson and Pickett, 2009), influence of foreign banks (Delis et al., 2020) and foreign investment (Bogliaccini and Egan, 2017), trade union decline (Kollmeyer, 2018), job outsourcing (Choi and Yu, 2019), weaker governance and weaker morality (Buttrick and Oishi, 2017).

Researching the problem of inequality is obvious if a society hopes to promote wellbeing, yet its importance today is imperative, with unprecedented levels occurring in the USA (Inequality.org, 2020) as well as worldwide (Inequality.org, 2020). Even before the outbreak of COVID-19 and the Black Lives Matter protests, many Americans were worried of their country’s future (Pew Research Center for the People and the Press, 2015). With these additional societal stressors, it is critical that leaders take these findings on inequalities seriously if the USA is to one day be truly great again.

References

Aitalieva, N. and Park, S. (2018). Political Trust, Ideology, and Public Support in the United States for Government Spending on Health Care. International Journal of Public Administration, 42(9), pp.776-785.

Alba, R. and Yrizar Barbosa, G., 2015. Room at the top? Minority mobility and the transition to demographic diversity in the USA. Ethnic and Racial Studies, 39(6), pp.917-938.

Alston, P., 2017. OHCHR | Statement On Visit To The USA, By Professor Philip Alston, United Nations Special Rapporteur On Extreme Poverty And Human Rights*. [online] Ohchr.org. Available at: <https://www.ohchr.org/EN/NewsEvents/Pages/DisplayNews.aspx?NewsID=22533&LangID=E> [Accessed 26 May 2020].

Anderson, E., Jalles D’Orey, M., Duvendack, M. and Esposito, L., 2016. DOES GOVERNMENT SPENDING AFFECT INCOME INEQUALITY? A META-REGRESSION ANALYSIS. Journal of Economic Surveys, 31(4), pp.961-987.

Anderson, S. (2019). Obamacare: the Affordable Care Act. [online] Available at: https://www.healthinsurance.org/obamacare/ [Accessed 5 Feb. 2020].

Apa.org. 2020. [online] <https://www.apa.org/topics/socioeconomic-status/> [Accessed 26 April 2020].

Azzimonti, M., de Francisco, E. and Quadrini, V. (2014). Financial Globalization, Inequality, and the Rising Public Debt. American Economic Review, 104(8), pp.2267-2302.

Bailey, Z., Krieger, N., Agénor, M., Graves, J., Linos, N. and Bassett, M., 2017. Structural racism and health inequities in the USA: evidence and interventions. The Lancet, 389(10077), pp.1453-1463.

Berisha, E. and Meszaros, J., 2018. Household debt, expected economic conditions, and income inequality. International Journal of Finance & Economics, 23(3), pp.283-295.

Bivens, J. and Mishel, L., 2013. The Pay of Corporate Executives and Financial Professionals as Evidence of Rents in Top 1 Percent Incomes. Journal of Economic Perspectives, 27(3), pp.57-78.

Bls.gov. 2020. Civilian Unemployment Rate. [online] Available at: <https://www.bls.gov/charts/employment-situation/civilian-unemployment-rate.htm> [Accessed 2 June 2020].

Bogliaccini, J. and Egan, P., 2017. Foreign direct investment and inequality in developing countries: Does sector matter?. Economics & Politics, 29(3), pp.209-236.

Bor, J., Cohen, G. and Galea, S. (2017). Population health in an era of rising income inequality: USA, 1980–2015. The Lancet, 389(10077), pp.1475-1490.

Bruenig, M. (2017). Wealth Inequality Is Higher Than Ever. [online] Jacobinmag.com. Available at: https://jacobinmag.com/2017/10/wealth-inequality-united-states-federal-reserve [Accessed 11 Nov. 2019].

Buttrick, N. and Oishi, S., 2017. The psychological consequences of income inequality. Social and Personality Psychology Compass, 11(3), p.e12304.

Choi, J. and Yu, E., 2019. International Outsourcing, Unemployment and Welfare: A Re-Examination. East Asian Economic Review, 23(3), pp.261-284.

Corak, M., 2013. Income Inequality, Equality of Opportunity, and Intergenerational Mobility. Journal of Economic Perspectives, 27(3), pp.79-102.

d’Agostino, G., Pieroni, L. and Scarlato, M. (2020). Social transfers and income inequality in OECD countries. Structural Change and Economic Dynamics, 52, pp.313-327.

Data.bls.gov. 2020. Bureau Of Labor Statistics Data. [online] Available at: <https://data.bls.gov/timeseries/LNS14000000> [Accessed 11 May 2020].

Definitions.uslegal.com. 2019. Property Crime Law And Legal Definition | Uslegal, Inc.. [online] Available at: <https://definitions.uslegal.com/p/property-crime/> [Accessed 31 May 2020].

Delis, M., Hasan, I. and Mylonidis, N., 2020. Foreign bank ownership and income inequality: empirical evidence. Applied Economics, 52(11), pp.1240-1258.

Dewan, P., Rørth, R., Jhund, P., Ferreira, J., Zannad, F., Shen, L., Køber, L., Abraham, W., Desai, A., Dickstein, K., Packer, M., Rouleau, J., Solomon, S., Swedberg, K., Zile, M. and McMurray, J., 2019. Income Inequality and Outcomes in Heart Failure. JACC: Heart Failure, 7(4), pp.336-346.

Diamond, J. (2019). Upheaval: Turning Points for Nations in Crisis. [S.I.]: Little, Brown and Company.

Dorn, A., Cooney, R. and Sabin, M., 2020. COVID-19 exacerbating inequalities in the US. The Lancet, 395(10232), pp.1243-1244.

Dwyer, R. (2018). Credit, Debt, and Inequality. Annual Review of Sociology, 44(1), pp.237-261.

Edwards, F., Lee, H. and Esposito, M., 2019. Risk of being killed by police use of force in the United States by age, race–ethnicity, and sex. Proceedings of the National Academy of Sciences, 116(34), pp.16793-16798.

Fasianos, A., Raza, H. and Kinsella, S., 2017. Exploring the link between household debt and income inequality: an asymmetric approach. Applied Economics Letters, 24(6), pp.404-409.

Gilens, M. and Page, B. (2014). Testing Theories of American Politics: Elites, Interest Groups, and Average Citizens. Perspectives on Politics, 12(3), pp.564-581.

Giovanni-Bellù, L. and Liberati, P., 2006. Inequality Analysis. [online] Fao.org. <http://www.fao.org/docs/up/easypol/329/gini_index_040en.pdf> [Accessed 7 May 2020].

Hardie, J. and Landale, N., 2013. Profiles of Risk: Maternal Health, Socioeconomic Status, and Child Health. Journal of Marriage and Family, 75(3), pp.651-666.

Heger, D. (2018). Decomposing differences in health and inequality using quasi-objective health indices. Applied Economics, 50(26), pp.2844-2859.

Heise, A., 2008. The Political Economy of Meritocracy: A Post-Kaleckian, Post-Olsonian Approach to Unemployment and Income Inequality in Modern Varieties of Capitalism. Review of Radical Political Economics, 40(1), pp.67-88.

Henry, M., Watt, R., Rosenthal, L. and Shivji, A., 2017. The 2017 Annual Homeless Assessment Report (AHAR) To Congress. [online] Available at: <https://files.hudexchange.info/resources/documents/2017-AHAR-Part-1.pdf> [Accessed 26 May 2020].

Hero, R. and Levy, M., 2016. The Racial Structure of Economic Inequality in the United States: Understanding Change and Continuity in an Era of “Great Divergence”*. Social Science Quarterly, 97(3), pp.491-505.

Hisnanick, J. and Coddington, D. (1994). Measuring human betterment through avoidable mortality: a case for universal health care in the USA. Health Policy, 34(1), pp.9-19.

Hong, S. and Shell, H., 2018. The Impact of Automation on Inequality. Economic Synopses, 2018(29).

Inequality.org. 2020. Global Inequality – Inequality.Org. [online] Available at: <https://inequality.org/facts/global-inequality/> [Accessed 1 June 2020].

Inequality.org. 2020. Income Inequality – Inequality.Org. [online] Available at: <https://inequality.org/facts/income-inequality/#income-inequality> [Accessed 1 June 2020].

Inequality.org. 2020. Wealth Inequality – Inequality.Org. [online] Available at: <https://inequality.org/facts/wealth-inequality/> [Accessed 18 May 2020].

Iuliano, J. (2019). Student Loan Bankruptcy and the Meaning of Educational Benefit. American Bankruptcy Law Journal, 93(2), pp.277-315.

Kollmeyer, C., 2018. Trade union decline, deindustrialization, and rising income inequality in the United States, 1947 to 2015. Research in Social Stratification and Mobility, 57, pp.1-10.

Komaromy, M., Madden, E., Zurawski, A., Kalishman, S., Barker, K., O’Sullivan, P., Jurado, M. and Arora, S., 2018. Contingent engagement: What we learn from patients with complex health problems and low socioeconomic status. Patient Education and Counseling, 101(3), pp.524-531.

Lopez, R., 2004. Income inequality and self-rated health in US metropolitan areas: A multi-level analysis. Social Science & Medicine, 59, pp.2409–2419.

Lynch, J., Smith, G., Harper, S., Hillemeir, M., Ross, N., Kaplan, G. and Wolfson, M., 2004. Is Income Inequality a Determinant of Population Health? Part 1. A Systematic Review. The Milbank Quarterly, 82(1), pp.5-99.

Matthew, P. and Brodersen, D., 2018. Income inequality and health outcomes in the United States: An empirical analysis. The Social Science Journal, 55(4), pp.432-442.

Mitchell, N., Catenacci, V., Wyatt, H. and Hill, J., 2011. Obesity: Overview of an Epidemic. Psychiatric Clinics of North America, 34(4), pp.717-732.

Mitnitski, A., Graham, J., Mogilner, A. and Rockwood, K., 2002. Frailty, fitness and late-life mortality in relation to chronological and biological age. BMC Geriatrics, 2(1).

Norton, M. and Ariely, D. (2011). Building a Better America—One Wealth Quintile at a Time. Perspectives on Psychological Science, 6(1), pp.9-12.

OECD, 2017. Obesity Update 2017. [online] OECD. Available at: <http://www.oecd.org/els/health-systems/Obesity-Update-2017.pdf> [Accessed 6 May 2020].

Oishi, S., Kushlev, K. and Schimmack, U., 2018. Progressive taxation, income inequality, and happiness. American Psychologist, 73(2), pp.157-168.

Okun, A., 1975. Equality And Efficiency: The Big Tradeoff. Washington, DC: Brookings Institution Press.

Oxfam International. (2017). An economy for the 99% | Oxfam International. [online] Available at:< https://www.oxfam.org/en/research/economy-99 >[Accessed 11 Nov. 2019].

Parrott, S., Aron-Dine, A., Rosenbaum, D., Rice, D., Floyd, I. and Romig, K., 2018. Trump Budget Deeply Cuts Health, Housing, Other Assistance For Low- And Moderate-Income Families. [online] Center on Budget and Policy Priorities. Available at: <https://www.cbpp.org/research/federal-budget/trump-budget-deeply-cuts-health-housing-other-assistance-for-low-and> [Accessed 27 May 2020].

Pettit, B. and Gutierrez, C. (2018). Mass Incarceration and Racial Inequality. American Journal of Economics and Sociology, 77(3-4), pp.1153-1182.

Pew Research Center for the People and the Press. (2015). Trust in government: 1958-2015. [online] Available at: <https://www.people-press.org/2015/11/23/1-trust-in-government-1958-2015/ >[Accessed 10 Nov. 2019].

Pewewardy, N. and Severson, M., 2003. A Threat to Liberty. Journal of Progressive Human Services, 14(2), pp.53-74.

Pickett, K., James, O. and Wilkinson, R., 2006. Income inequality and the prevalence of mental illness: a preliminary international analysis. Journal of Epidemiology & Community Health, 60(7), pp.646-647.

Pressman, S. and Scott, R. (2009). Consumer Debt and the Measurement of Poverty and Inequality in the US∗. Review of Social Economy, 67(2), pp.127-148.

Rufrancos, H., Power, M., Pickett, K. and Wilkinson, R., 2013. Income Inequality and Crime: A Review and Explanation of the Time?series Evidence. Sociology and Criminology-Open Access, 1(1).

Sapolsky, R., 2018. The Health-Wealth Gap. Scientific American, 319(5), pp.62-67.

Sibille, K., Witek-Janusek, L., Mathews, H. and Fillingim, R., 2012. Telomeres and epigenetics: Potential relevance to chronic pain. Pain, 153(9), pp.1789-1793.

Sommeiller, E., Price, M. and Wazeter, E., 2016. Income inequality in the U.S. by state, metropolitan area, and county. Economic Policy Institute, pp.1-47.

Stats.oecd.org. (2020). Health expenditure and financing. [online] Available at: <https://stats.oecd.org/Index.aspx?DataSetCode=SHA >[Accessed 22 Jan. 2020].

Sturm, R. and Gresenz, C., 2002. Relations of income inequality and family income to chronic medical conditions and mental health disorders: national survey. BMJ, 324(7328), pp.1-5.

United States Congress (2017). H.R.1628 – American Health Care Act of 2017. US Congress, https://www.congress.gov/bill/115th-congress/house-bill/1628.

Vickery, J., 2019. Homelessness And Inequality In The US: Challenges For Community Disaster Resilience. In Emerging Voices In Natural Hazards Research. Butterworth-Heinemann, pp.145-177.

White, M. (2007). Bankruptcy Reform and Credit Cards. Journal of Economic Perspectives, 21(4), pp.175-199.

Wilkinson, R. and Pickett, K., 2009. The Spirit Level. London: Allen Lane.

Wilkinson, R. and Pickett, K., 2017. The enemy between us: The psychological and social costs of inequality. European Journal of Social Psychology, 47(1), pp.11-24.

Worldpopulationreview.com. 2020. Gini Coefficient By Country 2020. [online] Available at: <https://worldpopulationreview.com/countries/gini-coefficient-by-country/> [Accessed 7 May 2020].

Yang, Y., Schorpp, K., Boen, C., Johnson, M. and Harris, K., 2018. Socioeconomic Status and Biological Risks for Health and Illness Across the Life Course. The Journals of Gerontology: Series B,.

Leave a Reply

Your email address will not be published. Required fields are marked *