The Question of Inequality

Abstract: I look at the question of income inequality in different societies and how excessive inequality is significantly correlated with negative societal factors. I look at the causes of income inequality and the ways that different governments address it as seen in other research, and look at why some societies deal with it more aggressively than others. I ran tests comparing the GINI coefficient to homicide rates, incarcerations rates, economic mobility, literacy scores, life expectancy, Gross Domestic Product, gun ownership rates, and average hours worked per year. I found significant correlations for six out of the eight variables, and explain why each test might yield the results it does. I conclude with the purpose of the paper and remark on what these results might mean in the larger context of policy making.

Introduction

For this paper I was interested in finding the relationship between income inequality and societal variables both negative and positive including homicide rates, incarcerations rates, economic mobility, literacy scores, life expectancy, Gross Domestic Product, gun ownership rates, and average hours worked per year between a comprehensive list of nations both rich and developing, if such a relationship were to be found. Income distribution is the age old question between economists, theorists, and governments which has spawned revolutions and dictatorships. The ideological clash between parties from left to right over how much inequality is tolerable could be aided with data, which could guide future discourse on tax and spending policies in a more empirical way. Income inequality is often a touchy subject in American politics. Talk of income inequality is often thrown back with the assumption of a desire to steal from the wealthy and give to the poor and take individualism and freedom out of the economy in pursuit of cold war visions of utopia. But the academic and political analysis of income distribution should be accepted in modern times as a proactive and non-partisan activity aimed at fixing obvious problems where there are appropriate solutions. At the very least there needs to be an understanding of the effects, consequences, and rewards for different levels of inequality if measurable. Through this paper I seek to add a piece to the conversation.

Literature Review

There is already a substantial amount of papers, journals, and books going into great detail about inequality in society and how economies work with technology factored into them. Yasuhiro Sato, Ken Tabata and Kazuhiro Yamamoto published a study in the Journal of Population Economics in 2008 titled “Technological progress, Income inequality, and Fertility”.  The article goes into a model that has been developed to explain transitioning economies natural tendencies for population growth and income inequality. They use numerous examples specifically during the industrial revolution of both the United States and the United Kingdom.

The tendency for nations before rapid technological improvement was to have a large number of children per family because A). The life expectancy of adults was low especially for lower income families, B). Child and infant mortality was also very high because of the lack of sanitation and medicine predating modern medicine, meaning that more children were needed to assure an heir and continuation of the family even for wealthier households and C.) Large families meant more ways to earn income to be pooled and contributed to food and other necessities as well as perform household tasks. This trend held firmly until the industrial revolution, when technology, medicine, and labor were rapidly evolving to create what persists in developed nations as a middle class.

As technological advancements arise in an economy, a more educated and skilled set of labor is needed to operate and maintain machinery. This machinery in turn reduced the amount of labor that was needed to do a task, and converted the excess labor into mass industry able to produce goods on a global scale. This increased wages for the skilled labor running new technology. The rising middle class had higher wages which improved life conditions, making life expectancy rise. This meant there was more of an opportunity cost for having more children since that time could instead be spent earning even more of a decent salary. No longer are families dependent on large numbers to survive or support a household. This also means that the children that a family does decide to have are fewer, not only because of the opportunity cost but also because families prefer to provide more for their children in education and lifestyle so that they too may have a similar living style, rather than divide the modest living among many children.  This is how nearly all developed countries have made the transition and how developing countries are expected to do the same. As a middle class rises from a better industrial labor force, life expectancy goes up, population growth slows unless followed up with considerable immigration, and income inequality also rises as the middle class and wealthy progress and the economy grows, and then inequality dips as governments redistribute the wealth into infrastructure and aid to the poor.

This cycle has remained true except in nations dominated by colonization or imperialism in the past, many examples still being in Africa. As resources are gathered and sent abroad, there is minimal return to the community in which these resources originate. These nations receive compensation, but not enough to support a middle class. India is also a different example, because Hindu culture encourages large families even as living standards increase. Less shackled today by an exterior benefactor, India has some of the world’s fastest population and economic growth. The economic cycles that nations naturally go through as they advance helps explain why income inequality rises in these developed industrial market economies, and why the final distribution of wealth depends on government policy.

Koichi Kawamoto produced an article in the journal Economic Theory in 2009 titled “Status-Seeking Behavior, the Evolution of Income Inequality, and Growth”. This article looks more at the society and the individual’s desires for increased standards assuming there is relative difference between them and others. The phrase “Keeping up with the Joneses” is an idiom in parts of the English speaking world referring to the comparison to one’s neighbor as a benchmark for social status. This is the prime factor driving consumerism and competition for social mobility.

Kawamoto theorizes and presents a case that the presence of wealthier households in society mixed with lower income households positively incentivizes children of lower households to work hard in their education to earn a better lifestyle than their parents. This is the model of the American Dream; through hard work, children will have a better life than their parents. Kawamoto develops a model comparing two types of economies based on the English idiom of “Keeping up with the Joneses”. There is the KUJ micro economy, or Keeping Up with the Joneses, and the RAJ micro economy, or Running Away from the Joneses. Both are exhibited in segments of modern economies. When citizens perceive their neighbors as wealthier, they feel inferior and work to improve their standing. This KUJ economy displays a wealth gap that is decreasing, but not necessarily reaching equilibrium. Then there is the RAJ, in which people are not incentivized or concerned with improving their standing. Kawamoto illustrates that these two economic circumstances can produce either increasing or decreasing income inequality despite the inclination to assume that a KUJ system would always be decreasing inequality. The factor that drives income inequality the greatest is economic growth, which provides more incentive to become educated and increase social standing. Poverty traps can occur when there is a low rate of growth and a high level of income inequality. The author’s goal is to indicate that when determining policy to deal with income inequality, examining individual behavior with status seeking motives is just as important for the larger economic scale.

While Kawamoto’s insight is helpful with understanding the phycology of how status seeking behavior affects economic activity and income inequality, it is rather assumptive on the geographic intermixing of wealthy and poor. There are segments of the population in many cities that are segregated by a quality of living because of market prices for land, exclusive communities, and availability of work. In economies with aggressive economic growth, there will be more of the microeconomic activity he talks about with the consumer drive to improve lifestyle. But he also assumes that the desire to educate based on wealth status also equates to the same quality of education, or sufficient conditions that have been shown to affect educational attainment like poverty. This study therefore may be more indicative of the evolution of inequality between higher economic segments in the middle class and upper class during more average economic growth, and less so of society as a whole.

Cecilia García-Peñalosa and Stephen J. Turnovsky released a study in the Journal of Money, Credit, and Banking in 2007 titled “Growth, Income Inequality, and Fiscal Policy: What Are the Relevant Trade-offs?”. This article examines the relationship between economic growth and income inequality in relation to government policy. They conclude that fiscal policy aimed at growth, like lower taxes and reduced distribution of welfare, are effective yet contribute to the widening of income inequality, which has a negative effect on economic efficiency. Economic growth is directly associated with increased inequality because growth stems from investment, which requires capital from wealthy private sources because capital is more unevenly distributed than labor. Therefore the return on investments is greater to the capital sources, increasing inequality as economic growth increases.

What the authors found is that particular policies to raise revenue for public investment have a more neutral effect on income inequality while simultaneously increasing growth. These policies included capital taxes, which are taxes on the movement of wealth on investments, and a tax on consumption. These policies are shown by their model to increase growth and reduce inequality. On the flipside, policies raising revenue that seem to expand inequality are those focused on income and wage.  This is an important relationship to determine because many governments have radically different methods of achieving the same optimal distribution. Rather than proposing nations limit their growth or increase marginal tax rates on top income tax brackets as many nations do, they imply that it is possible to achieve growth and a more even income distribution.

Markus Hadler published an article in the journal Acta Sociologica in 2005 titled “Why Do People Accept Different Income Ratios? A Multi-Level Comparison of Thirty Countries”. The study encompasses a large international survey of more than 30 countries and 3,500 respondents.  The results are an exercise more on the psychological and societal variances which create different levels of preferred income inequality. Ideological differences matter less so than cultural and historical aspects. Post-communist and post-socialist states in Eastern Europe for example are much less comfortable with large degrees of inequality than the United States is. Soviet satellite states were conditioned to live in a state planned economy for many decades, and even today there is an egalitarian view in the more market driven economies of Eastern Europe today about how much inequality is right for society. Also found is an acceptance for greater inequality from respondents in the highest income brackets, and the opposite for people in the lowest income brackets. Those surveyed who also have lower acceptance of inequality are those who feel unrewarded in countries where the difference between perceived and actual income inequality is greatest. Nations in Europe like Germany, Norway, Sweden, and Denmark maintain a high level of the welfare state, which reflects their policies of fairly high marginal tax rates to pay for universal health coverage and generous sick and maternity leave. Some sociologists think this is due to the feudal culture that prevailed over many of these regions during the medieval ages in which peasants would give a certain amount of crop of resource to a lord who in return would protect them from ever present raids. These more liberal societal structures have created a scale in which even conservative politicians in these countries feel less comfortable with high levels of inequality. People in the United States however were much more comfortable with higher levels of inequality than even their neighbor Canada. The United States is a society which has focused more on individual effort more than group effort. This reflects the very high level of inequality that the U.S. has in relation to the rest of the world’s developed countries.

This study helps interpret the levels of inequality through cultural and historical lenses in countries compared to what the population in each country is comfortable with. What citizens are okay with or what their culture encourages may be the greatest determining factor of the distribution of income because it is the citizens in democratic nations which influence policy makers and elect them. If inequality becomes too high or low, the citizenry will influence the change necessary to adjust distribution by how they see fit.

John Hassler, José V. Rodríguez Mora and Joseph Zeira published an article in the Journal of Economic Growth in 2007 titled “Inequality and Mobility”. In it they establish the strong link between wealth inequality and social mobility. Inequality first and foremost affects the ability of parents to invest in their children’s educational attainment. Educational access is the greatest way in which social mobility in a society is increased. Societies with high levels of income inequality are less likely or unable to invest adequately in public education, which creates large disparities in skill sets between wealthy and poorer families. This reduces the ability of the next generation to earn more money or advance a social class, and has macroeconomic consequences for the labor force, tax revenue, and welfare resources. As a share of G.D.P., societies with less income inequality spend more on their educational system particularly past secondary school for specialized sectors of the economy. This creates either positive or negative circles in economic cycles. The connection between inequality, mobility and educational spending and educational standards is important at the very foundational level for analyzing income inequality. The attitudes towards education in a given country shape the nature and skill of the work force for decades to come, whether income distribution is a conscious factor being shaped or not.

John R. Carter published an article in Public Choice in 2007 titled “An Empirical Note on Economic Freedom and Income Inequality”. His question is one measuring the tradeoff that occurs at different levels of economic freedom in comparison to income inequality. He notes the difficulty of measuring income inequality using the GINI coefficient in the early stages of national development as a standard because of the number of different ways in which it has been calculated. His research measures economic freedom through the Fraser Institute’s Economic Freedom of the World index, which measures tax rates, regulations, government attitudes towards private business, and government distribution of resources. His research lays out the empirical evidence indicating that the more economic freedom there is in a country, the greater the inequality but also the potential for considerable social mobility. He also finds circumstances in which more economic freedom can also lower income inequality by providing more income opportunities for lower classes. This is achieved through economic growth.

Carter’s paper addresses the other long debated ideological question in political circles with regards to inequality. The effect of excessive inequality is usually accepted as bad for macro-economic activity. Yet the same economics experts would also agree that not enough income inequality is just as potentially harmful to competition. The question becomes a search for the sweet spot for the distribution of wealth in society, and how much government intervention in the economy is going to be tolerated by one population over another? Different levels of economic freedom certainly mean different standards of living for poor and wealthy alike. Less economic freedom curtails wealth excess in favor of a more communal economic system, while those with more economic freedom focus on the entrepreneurial spirit that many yearn for to expand their wealth and create employment. The effects of that scaling economic environment are central to the implications of inequality.

The Spirit Level: Why More Equal Societies Almost Always Do Better is a book by Richard G. Wilkinson and Kate Pickett published in 2009. It was a key factor in determining my research. The book takes 30 years of data from a variety of sources on factors including homicide, obesity, international aid,  surveys on happiness and quality of life, educational attainment and spending, anxiety, civil rights, and mental wellbeing among many other factors in the world’s wealthiest countries, and compares those numbers to the levels of inequality in those countries. The results, as suggested by the title, overwhelmingly support the thesis that greater amounts of inequality harms societies negatively in many ways more than just macroeconomic efficiency. They even tie this negative effect into worse off standards of living for all economic classes rather than just the poor.

The most important element in The Spirit Level is the strong foundational credibility their research has. The data they collected are from multiple non-partisan international groups and institutions rather than relying on their own surveys or data. Instead they took all of the data available and applied a narrative to explain the results through a lot of psychological and sociological examples. The book also had significant peer review from several scholars in the field. This paper is a further continuation of the findings of The Spirit Level with more recent data aimed at finding some different factors.

Hypothesis

With the findings and evidence in the above literature, income inequality is a predictable indicator based off of government policies and economic growth. It can be exasperated by weak educational systems through which lower income families have the best opportunity to advance social class. Economic systems go through a pattern as they go from undeveloped to developing and finally developed with population growth, economic growth, and income distribution. Different societies are more comfortable with certain levels of inequality in the distribution of wealth based on their cultural and historical past, which means the acceptance or rejection of degrees of economic freedom vs. communal security. And despite these different preferences, it is still possible to achieve considerable economic growth and decrease inequality through revenue streams that focus on consumption and capital.

Based on all of this, I would expect to find significant correlations between the amount of inequality, measured by the GINI coefficient, and a variety of well documented social factors spanning nearly two dozen countries. While The Spirit Level and other papers have focused primarily on the differences in wealthy countries, I am also using several emerging and developing economies, which will hopefully yield similar correlations despite the different economic stages.

Method

I started by gathering what is known as the GINI coefficient, which is the statistical measurement of wealth distribution, from a list of 22 countries, which included the most developed and advanced countries in the world mostly by GDP (U.S., U.K., Japan, China, France, Germany, Italy, Canada, Australia, Denmark, New Zealand, Norway, Sweden, Finland, Switzerland, Spain), and then I chose other less developed countries with emerging economies from different regions of the world that had all or most of the data that I would be collecting (Argentina, Brazil, Chile, Pakistan, Peru, Singapore,). These less developed countries were from regions of the world that aren’t generally thought of as world economic forces, but certainly emerging ones. I steered away from countries that lacked a stable government, or were in a civil war. Originally I was going to include only about a dozen of the most developed countries in the world, but to reinforce my findings I decided to expand that list of developed countries as well as to include the several emerging economies which will tend to have more differences in income distribution. Hopefully this rules out a lot of cultural, regional, or governmental anomalies that would be put forward as counter evidence. Though there are certainly other nations I could have included, I believe this list is comprehensive enough to provide a good snapshot of data around the world. My goal was to get the best possible data set while also making it manageable. I then moved forward to finding societal variables that were well documented and spanned decades, though I would only be using the most recent data going from 2007 to 2012. I wanted factors that would reach beyond economic data so that the relationships and findings might have greater significance. I chose homicide rates, incarcerations rates, social mobility, literacy scores, life expectancy, Gross Domestic Product, gun ownership rates, and average hours worked per year. These were among the most documented and interesting variables that I found data to compare. In each section I will explain how each of these is sourced. The sources used are respected and reputable places, often international institutions like the World Health Organization, the World Bank, and the U.N. In the eight variables I ran a correlation analysis using Pearson’s R, since the entire data set is consisted of interval and ratio level data. Six of the test results showed a strong relationship and statistical significance. After displaying the results for each variable, I will explain why the relationships might or might not exist and use different examples and resources to help.

Data

Country	GINI	Homici.	Incar.	Mobil	Litera.	Life exp.	GDP	Guns	Hours
Argentina	0.46	3.4	147	0.49	398	76	10942	10.2
Australia	0.30	1	130	0.26	515	82	61789	15	1692
Brazil	0.52	21	276	0.58	412	74	12594	8
Canada	0.32	1.6	114	0.19	524	82	50344	30.8	1701
Chile	0.52	3.7	337.8	0.52	449	79	14394	10.7	2047
China	0.47	1	121	0.6	533	76	5445	4.9
Denmark	0.25	0.9	68	0.15	495	79	59889	12	1522
Finland	0.27	2.2	60	0.18	536	81	48812	45.3	1684
France	0.33	1.1	101	0.41	496	82	42379	31.2	1476
Germany	0.27	0.8	80	0.32	497	81	44021	30.3	1413
Italy	0.32	0.9	108	0.5	486	82	36130	11.9	1774
Japan	0.38	0.4	54	0.34	520	83	45903	0.06	1728
New Zealand	0.36	0.9	194	0.29	521	81	36254	22.6	1762
Norway	0.25	0.6	71	0.17	503	81	98081	31.3	1426
Pakistan	0.31	7.8	41	0.46		67	1189	11.6
Peru	0.46	10.3	199	0.67	370	77	6018	18.8
Singapore	0.48	0.3	230	0.44	526	82	46241	0.05
Spain	0.32	0.8	149	0.4	481	82	31985	10.4	1690
Sweden	0.23	1	70	0.27	497	82	57114	31.6	1644
Switzerland	0.30	0.7	76	0.46	501	83	83326	45.7	1632
United Kingdom	0.40	1.2	149	0.5	494	80	38974		1625
United States	0.45	4.8	716	0.47	500	79	48112	88.8	1787

1                            2              3                4              5                6               7                  8                 9               10

1: Country     2: GINI coefficent    3: Homicides per 100k     4: Incarcerations per 100k   5: Social mobility  6: Average literacy scores     7:Average  life expectancy    8:GDP per capita    9:Guns owned per  100 people    10: Average hours worked per year

Above is the table consisting all of the data I collected, with a key to what each column stands for at the bottom. My results were correlation tests between the Gini coefficients (2), against the other numerical values (3-10) individually. For each section I will apply the columns being used for explaining the results. The GINI coefficient is the calculated distribution of income in a given country scaling from 0-1, 0 being total equality, and 1 being a single person having all of the wealth. The numbers for the GINI coefficient are from the 2011 CIA world Fact book. Occasionally the GINI is calculated differently based on data available, yet there was no significant difference between the several sources that calculate the GINI coefficient. All of the GINI coefficients were collected between 2005 and 2011.

Correlations
		Gini	Homicide
Gini	Pearson Correlation	1	.492*
	Sig. (2-tailed)		.020
	N	22	22
Homicide	Pearson Correlation	.492*	1
	Sig. (2-tailed)	.020
	N	22	22
*. Correlation is significant at the 0.05 level (2-tailed).

Country	GINI	Homicide
Argentina	0.46	3.4
Australia	0.3	1
Brazil	0.52	21
Canada	0.32	1.6
Chile	0.52	3.7
China	0.47	1
Denmark	0.25	0.9
Finland	0.27	2.2
France	0.33	1.1
Germany	0.27	0.8
Italy	0.32	0.9
Japan	0.38	0.4
New Zealand	0.36	0.9
Norway	0.25	0.6
Pakistan	0.31	7.8
Peru	0.46	10.3
Singapore	0.48	0.3
Spain	0.32	0.8
Sweden	0.23	1
Switzerland	0.3	0.7
United Kingdom	0.4	1.2

United States

0.45

4.8

Per 100k

The above table and correlation is comparing the GINI coefficient to the number of total homicides in the given country per hundred thousand people. The data for homicides is from the United Nations office on Homicide and Drugs 2012 statistics. The results are fairly strong with a Pearson value of almost .5, showing a positive correlation between higher inequality by the GINI coefficient and homicides, and a low chance of statistical randomness.

Correlations
		Gini	Guns
Gini	Pearson Correlation	1	-.228
	Sig. (2-tailed)		.320
	N	22	21
Guns	Pearson Correlation	-.228	1
	Sig. (2-tailed)	.320
	N	21	21

Country	GINI	Guns
Argentina	0.46	10.2
Australia	0.3	15
Brazil	0.52	8
Canada	0.32	30.8
Chile	0.52	10.7
China	0.47	4.9
Denmark	0.25	12
Finland	0.27	45.3
France	0.33	31.2
Germany	0.27	30.3
Italy	0.32	11.9
Japan	0.38	0.06
New Zealand	0.36	22.6
Norway	0.25	31.3
Pakistan	0.31	11.6
Peru	0.46	18.8
Singapore	0.48	0.05
Spain	0.32	10.4
Sweden	0.23	31.6
Switzerland	0.3	45.7
United States	0.45	88.8

In several of my sources including the book The Spirit Level (2009), social scientists try and explain this as a case of the haves and the have not. This doesn’t imply that rich or poor people are killing each other constantly, but the strain on a society that comes from increased levels of inequality leads to a lack of trust between individuals because of more intense competition, which leads to more instances of violence.

Per 100 people

The table above shows the GINI coefficient matched against the number of guns per hundred people. The data comes from the 2007 Small Arms Survey based off graduate research in Geneva, Switzerland, which included 178 countries. It surveyed the number of guns per 100 people. The U.S. for example, has 88.8 guns per 100 citizens. Oddly enough, the United Kingdom was not included in the study, so it has been left out of this table.  While there is a moderate negative correlation meaning lower GINI coefficients means fewer guns, the statistical significance is reduced due to a 32% chance the data is caused by random chance.

This was the data set out of all that had the lowest statistical significance. I assumed there would be a clear correlation similar to the relationship found between the GINI coefficient and homicide rates. In most study’s on firearms, nations with more firearms, especially unregulated firearms, experience more homicides overall. It was recently reported by multiple sources including NBC and The Washington Post that the U.S. has had over 900,000 deaths from gun violence since 1970, and that it has nearly twice the amount of guns per capita as any other country. Meanwhile a country like Japan, which has virtually no civilian firearms since WWII, has one of the lowest rates in homicides in the developed world. I believe that if this data were to be extended to more countries in the developed world, the statistical significance and data strength would increase.

Country	GINI	Incarceration
Argentina	0.46	147
Australia	0.3	130
Brazil	0.52	276
Canada	0.32	114
Chile	0.52	337.8
China	0.47	121
Denmark	0.25	68
Finland	0.27	60
France	0.33	101
Germany	0.27	80
Italy	0.32	108
Japan	0.38	54
New Zealand	0.36	194
Norway	0.25	71
Pakistan	0.31	41
Peru	0.46	199
Singapore	0.48	230
Spain	0.32	149
Sweden	0.23	70
Switzerland	0.3	76
United Kingdom	0.4	149
United States	0.45	716

Correlations
		Gini	Incarceration
Gini	Pearson Correlation	1	.595**
	Sig. (2-tailed)		.003
	N	22	22
Incarceration	Pearson Correlation	.595**	1
	Sig. (2-tailed)	.003
	N	22	22
**. Correlation is significant at the 0.01 level (2-tailed).

Per 100k people

This table above shows the GINI coefficient compared to the incarceration of citizens in each country per 100k people. This data comes from the International Centre for Prison Studies which compiled the most recent criminal data from countries with public access to criminal justice statistics. The relationship between the two variables is very strong with a Pearson value of almost .6, and a low probability that they are the result of random error.

This data goes in hand with the results between the GINI and homicides. A conclusion that might be made is that societies with a greater strain on wealth distribution have harsher justice systems and more crime. There is a tendency for prison populations to be poor and part of a minority ethnic group. This is true in the United States for example where 15% of the population is black, but close to 40% of the federal prison population is black (Bureau of Prisons, 2013).

Country	Gini	Mobility
Argentina	0.46	0.49
Australia	0.3	0.26
Brazil	0.52	0.58
Canada	0.32	0.19
Chile	0.52	0.52
China	0.47	0.6
Denmark	0.25	0.15
Finland	0.27	0.18
France	0.33	0.41
Germany	0.27	0.32
Italy	0.32	0.5
Japan	0.38	0.34
New Zealand	0.36	0.29
Norway	0.25	0.17
Pakistan	0.31	0.46
Peru	0.46	0.67
Singapore	0.48	0.44
Spain	0.32	0.4
Sweden	0.23	0.27
Switzerland	0.3	0.46
United Kingdom	0.4	0.5
United States	0.45	0.47

Correlations
		Gini	Mobility
Gini	Pearson Correlation	1	.765**
	Sig. (2-tailed)		.000
	N	22	22
Mobility	Pearson Correlation	.765**	1
	Sig. (2-tailed)	.000
	N	22	22
**. Correlation is significant at the 0.01 level (2-tailed).

The above chart compares the GINI coefficient to intergenerational earnings, also known as social mobility. This data comes from The State of Workings America, 12^th edition, which comes from the non-partisan, non-profit Economic Policy Institute. Social mobility is measured by calculating the elasticity of earnings from one generation to the next. A higher elasticity number implies that it is more difficult for a person to move outside the income class he or she was born into. The results are some of the strongest in the paper, with a Pearson score of .765 and significance score of .000, showing that the higher the GINI is in a given country, the lower the mobility.

These results are probably some of the least disputed in the political science community. Emerging economies generally have higher income inequality, and developed countries with strong economies differ in the policies that redistribute income like taxes and social programs. Nordic countries like Norway, Sweden, and Denmark have much higher marginal tax rates on their wealthiest populations, meaning income is much more flexible in these societies. In the United States, the notion of economic freedom means that Americans are more comfortable with higher levels of inequality, with the rags-to-riches story being a common idea counter intuitive to the data.

Table 10

Country	Gini	Literacy
Argentina	0.46	398
Australia	0.3	515
Brazil	0.52	412
Canada	0.32	524
Chile	0.52	449
China	0.47	533
Denmark	0.25	495
Finland	0.27	536
France	0.33	496
Germany	0.27	497
Italy	0.32	486
Japan	0.38	520
New Zealand	0.36	521
Norway	0.25	503
Pakistan	0.31
Peru	0.46	370
Singapore	0.48	526
Spain	0.32	481
Sweden	0.23	497
Switzerland	0.3	501
United Kingdom	0.4	494
United States	0.45	500

Correlations
		Gini	Literacy
Gini	Pearson Correlation	1	-.473*
	Sig. (2-tailed)		.030
	N	22	21
Literacy	Pearson Correlation	-.473*	1
	Sig. (2-tailed)	.030
	N	21	21
*. Correlation is significant at the 0.05 level (2-tailed).

This data compares the GINI variable to literacy scores compiled from the PISA (Program for International Student assessment) highlights in 2009, reported in 2011. It measures the performance of 15 year old students in participating countries in reading, mathematics, and science literacy. The score for each country is the average score of all three categories per student. The results show a strong negative correlation based off the Pearson value of -.473, with good statistical significance as well, meaning higher GINI coefficients yield lower literacy scores overall.

There are many factors that could explain this correlation. The countries that happen to have some of the lowest income inequality like the Nordic countries spend more on education  per pupil than nations like the U.S. do. Of course it is easier to deal with smaller populations to educate as well. There could be different educational standards for teaching kids, maybe more family involvement or stricter requirements for schooling.  Countries with more defined income differences could also exhibit wild variations in educating wealthier populations of kids compared to poorer urban neighborhoods, which would bring down the average.

Country	Gini	Life
Argentina	0.46	76
Australia	0.3	82
Brazil	0.52	74
Canada	0.32	82
Chile	0.52	79
China	0.47	76
Denmark	0.25	79
Finland	0.27	81
France	0.33	82
Germany	0.27	81
Italy	0.32	82
Japan	0.38	83
New Zealand	0.36	81
Norway	0.25	81
Pakistan	0.31	67
Peru	0.46	77
Singapore	0.48	82
Spain	0.32	82
Sweden	0.23	82
Switzerland	0.3	83
United Kingdom	0.4	80
United States	0.45	79

Correlations
		Gini	Life
Gini	Pearson Correlation	1	-.328
	Sig. (2-tailed)		.136
	N	22	22
Life	Pearson Correlation	-.328	1
	Sig. (2-tailed)	.136
	N	22	22

In years

This table compares the GINI coefficient to the average life expectancy of a male individual in years in each country. While the data shows a strong negative correlation meaning higher GINI values yield a lower life expectancy, the statistical significance score is a little too high to be considered significant for this data set.

There is already data doing this comparison between the world’s wealthiest countries in books like The Spirit Level, showing the connection between higher income inequality and lower life expectancy. While there is indication that this correlation crosses income classes—meaning wealthier individuals in countries with high inequality live shorter lives, the real factor driving the correlation is that the poorer citizens die at younger ages from lack of access to medical care or quality of living. This is more evident in the U.S. which does not have single payer system, while nations with universal coverage and more generous welfare systems no doubt bring up their populations life expectancy since their poor live longer. I’m extremely confident that this relationship would increase in significance if more countries in the developed world were

Country	Gini	GDP
Argentina	0.46	10942
Australia	0.3	61789
Brazil	0.52	12,594
Canada	0.32	50,344
Chile	0.52	14,394
China	0.47	5,445
Denmark	0.25	59,889
Finland	0.27	48,812
France	0.33	42,379
Germany	0.27	44,021
Italy	0.32	36,130
Japan	0.38	45,903
New Zealand	0.36	36,254
Norway	0.25	9,8081
Pakistan	0.31	1,189
Peru	0.46	6,018
Singapore	0.48	46,241
Spain	0.32	31,985
Sweden	0.23	57,114
Switzerland	0.3	83,326
United Kingdom	0.4	38,974
United States	0.45	48,112

included or even set apart from less developed countries.

Correlations
		Gini	GDP
Gini	Pearson Correlation	1	-.640**
	Sig. (2-tailed)		.001
	N	22	22
GDP	Pearson Correlation	-.640**	1
	Sig. (2-tailed)	.001
	N	22	22
**. Correlation is significant at the 0.01 level (2-tailed).

The above data shows the GINI coefficient compared to each nation’s GDP per capita in dollars, calculated by the World Bank in 2011. There are slight variations between World Bank data and other organizations like the IMF (International Monetary Fund), and the UN, but nothing significant enough to draw wildly different results between them. This data shows a very strong and statistically significant negative correlation between the GINI coefficient and GDP per capita, meaning higher inequality yields lower GDP per capita in this data set.

This is a data set that I was surprised to find such a strong correlation too. I had not seen any studies conducted on this previously and thought it would be interesting to compare. Of course countries like China and the United States are going to have lower per capita GDP simply because there is not enough wealth in them to compensate for the large populations they have. This test doesn’t mean any individual country has a weak economy. Wealth also depends on natural resources, a strong work force, and good trade connections. But with a correlation this strong, it is likely that this trend goes even beyond these countries. Generally higher economic growth means more inequality. Economists might explain this by the more relaxed flow of money in economies of more equal wealth distributions simply because large amounts of accumulated wealth are not spent as quickly in the broader economy. A million dollars for one man to spend in one day as necessity is unlikely, while that same wealth split between 10,000 different middle class and poor families would be spent on bills, groceries, loan payments, etc. This immediate need spending generates a lot more demand and economic activity. Economic downturns like the Great Depression are often explained as being exasperated or triggered by the tightened flow of money in the economy because of concentrated wealth in certain market sectors. This can’t possibly explain in a broad context why each economy does well or doesn’t, but economic principles can certainly help understand why there is a correlation at all.

Country	Gini	Working
Argentina	0.46
Australia	0.3	1692
Brazil	0.52
Canada	0.32	1701
Chile	0.52	2047
China	0.47
Denmark	0.25	1522
Finland	0.27	1684
France	0.33	1476
Germany	0.27	1413
Italy	0.32	1774
Japan	0.38	1728
New Zealand	0.36	1762
Norway	0.25	1426
Pakistan	0.31
Peru	0.46
Singapore	0.48
Spain	0.32	1690
Sweden	0.23	1644
Switzerland	0.3	1632
United Kingdom	0.4	1625

Correlations
		Gini	Working
Gini	Pearson Correlation	1	.744**
	Sig. (2-tailed)		.001
	N	22	16
Working	Pearson Correlation	.744**	1
	Sig. (2-tailed)	.001
	N	16	16
**. Correlation is significant at the 0.01 level (2-tailed).

United States

0.45

1787

Average Hours Worked per year

The above data compares the GINI coefficient to the average number of hours worked per worker per year, taken from the OECD (Organization for Economic Development and Cooperation). This is the data set which has the most information missing, mostly from developing economies or countries not in OECD.  Many of the nations’ not included do not have strong labor laws for maximum hours, overtime, or child labor protection, and probably don’t collect this data to avoid international spotlight. Instead what we have is a list of the most developed and democratic countries, which might be best for this test because it only includes countries out of the developing economic development period in which labor laws are loose and citizens work for more strenuous periods of time. The correlation for the remaining 16 countries is very strong and statistically significant, with a Pearson score of .744, showing a relationship in which higher income inequality means a higher average of hours per year for workers.

It’s important to note that since all of these countries are relatively prosperous, developed, and mostly considered “rich” first world countries, that the difference in the average hours worked does not directly mean increased benefit for work. What the test shows instead is that greater wealth inequality means on average more hours are being worked by the population as a whole. This could result from an increasingly competitive society where resources are getting more concentrated and it is harder to maintain a desired quality of life. This could also result from different flexibility in the labor force and in employment. European countries tend to have very generous and lengthy vacation periods, maternity/paternity leave, and sick leave, while keeping up impressive economic strength. More service and financial based economies like the U.S. however have much less time off for employees; and these countries also have good economic strength overall. But happiness and a quality of life for workers come into question as the average climbs higher.

Conclusion

In conclusion, the results of this paper indicate that there are indeed many significant correlations between the inequality of different nations and social problems. Six of the eight tests yielded the results I had expected to find, even in tests that I was unsure would do so. The tests that did not yield significant results were those linking guns per capita to the GINI coefficient, and average life expectancy compared to the GINI coefficient. Though there was a clear indication of a relationship in these tests, I believe that the life expectancy would have yielded more significant results if there were even more countries included since life expectancy between many of the countries had enough to develop a strong correlation. Developing nations like Brazil, Pakistan, Peru, Chile, and Argentina also consistently differ in life expectancy from the more developed countries, which may have made the correlation more prone to random error. The guns vs. GINI test may be the biggest outlier test of this paper, because although more guns per capita in a country tends to increase the number of violent crimes, the survey in which the numbers were drawn made no differentiation in the type of weapons owned, which could matter if there was a higher number of weapons for hunting compared to self -defense or recreational use, which is what the bulk of U.S. guns are. The different cultural attitude toward firearms, and the access to firearms is separate from the income distribution, as one unlikely causes the other. The purpose of the test was to see if the more defined class structure of unequal countries meant more citizens were compelled to own weapons. Future research could certainly expand both of these tests to include more nations, or possibly be more exclusive and compare only the wealthiest countries since they will have the most similarities.

If I had more time to compile data, I would do an expansive study comparing most of the world’s nations that collect enough data and include many more social factors. After that, I would divide the study between the wealthy and poorer nations which would exclude many undeveloped countries which do not have stable societal structures still. Through this I would see how the different cultures, ideologies, and governments match up according to their wealth and distribution of wealth. There is already extensive research and tests mentioned in this paper, mostly identifying these relationships between the wealthiest nations, but outside of the list of a few dozen countries there is little study of inequality between poorer countries for understandable reasons like extensive corruption or unstable governments which would yield unreliable data. With the age of technology now in full swing, the data collection will only extend in depth for inequality studies, and without a doubt many researchers will be doing precisely these kinds of studies in the near future.

The goal of this paper was not to argue or advocate for states to start dividing wealth more evenly since this and other research indicates extensive inequality leads to macroeconomic efficiency and undesirable social consequences. It is the intent of studies to report why the world works the way it does, and what the results of those functions are. If a common consensus emerges from the discussion over any topic, the task on each nation then becomes a choice of what to do. Different nations and policymakers will respond differently to the question of inequality, but if the population at large is more educated on the cause and effect of an issue like this, they will be more inclined to influence the change in policy to produce a more desirable state. As long as the issue of inequality is not in the dark, reducing inequality will be cited more frequently as a possible solution to fix long term social friction, and instead of the question being whether excessive inequality is a problem, it will be a conversation of what approach is best in dealing with it.