Inequality and Human Capital Investment

Over two-hundred years after its inception, and despite its tumultuous history with race relations, the United States is still widely held as a bastion of equality and opportunity. However, in the past decade there has been growing attention to the topic of the distribution of wealth  in the United States. The highly skewed distribution of wealth has historically been a cause for concern, but recent analysis of the recovery of income growth since the 2008 financial crisis has revealed the gap between the rich and the poor is getting wider. According to a recent analysis of IRS data, the recession saw a decline in 39.1% for the bottom four quartiles, while the top quartile only suffered a 14%  loss (Saez 2016). The recovery widened the gap, as the bottom 93% of U.S households suffered a 4% loss in net worth from 2009-2011. The top 7% of households saw a 24% increase in net worth during the same years (Landy 2013).

It is unquestionable that the income gap between the rich an the poor continues to grow in the United States. The question is, can government policy assist in slowing the separation? In this paper I will look at how inequality is measured, the effects of the redistribution of wealth through government policy, countries with heavy government spending on human capital, and how that has helped them maintain a fairly equal distribution of wealth. Finally, I will look income distribution within the United States,  and I will present the idea that by increasing spending in educational programs, specifically in the developmental years,  could even out income disbursement regionally, and have an impact on inequality as a whole.


Though there are many accepted ways to measure economic inequality, my focus will be on the Lorenz curve and the corresponding Gini coefficient, and the Kuznets curve. The Lorenz curve is a method of presenting personal income in a graphical representation, and contrasting it to a 45-degree line, which measures perfect equality. The Gini coefficient is the numerical representation of this curve, where 0 is perfect equality and 1 is perfect inequality. The Gini coefficient is the measure of the area between a Lorenz curve and the line of equality. This is a common method to measure a country’s level of income inequality.

According to a U.S. Census Bureau, the current money income Gini index for the United states stands at .479; income inequality has risen 5.5% since the earliest comparable measure in 1993 (Procter, Semega, and Koller.  2016. p. 8). In looking at the top decile of personal income in the United States, as it relates to Gini index, the percentage of income held by the top 10% has not been at our current level since shortly before the Great Depression. After a period of significantly less income inequality prompted by the onset of the Great Depression, and solidified through The New Deal and the onset of WWII, it has been a slow climb to our current Gini index levels.

In his book Capital in the 21st Century, French economist Thomas Piketty presents the theory that a higher return on capital than the rate of economic growth will cause a concentration of wealth over time (Piketty 2014). In brief, the concentration of wealth will reach a point where the bulk of the top economies will come from patrimonial capitalism, as opposed to income earned from labor.  Though it is thought by many critics that the execution of Piketty’s theory is flawed, it presents an idea that this concentration is an inevitable byproduct of capitalism, and the best method for lessening income inequality are by measures of taxation.

The country of Sweden is listed as one of the most equal under the Gini measure, but this doesn’t present a full picture of income inequality.  According to an Organization for Economic Cooperation and Development (OECD) report, the taxation policies play an important role in the low Gini coefficient in Sweden. Through income tax and tax benefits, income inequality has been reduced by 28% in 2015. This percentage has declined from 35-40% in the mid 2000s (OECD 2015).  The reduced efforts of taxation have coincided with the rise in the ratio between the top and the bottom 10% of earners. The report also notes that capital played a part in increasing inequality as it became more concentrated over time.

The question then becomes as the proportion of wealthy to non-wealthy households rises, will taxes on the wealthy become more lenient? Professors of political science, Noam Lupu and Jonas Pontussen, support the theory that government distribution is more prevalent as the upper half of the distribution is more dispersed (Lupu & Pontussen 2011). As wealth in Sweden is pushed toward the topmost percent, as it is in the United States, it is possible that the tax burden on the wealthy might be diminished. As reported by the OECD, lowered taxation on the wealthy is already happening. As stated in the report, “Sweden’s richest 1% of earners saw their share of total pre-tax income nearly double, from 4% in 1980 to 7% in 2012. Including capital gains, income shares of the top percentile reached 9% in 2012. During the same time, the top marginal income tax rate dropped from 87% in 1979 to 57% in 2013” (OECD 2015)

Whether income inequality is akin to capitalism is widely debated. However, it is not necessarily the mechanism of capitalism that breeds inequality, but rather, the concentration of wealth over time, as proposed by Piketty. By looking at inequality as expressed by another method of measure, the Kuznets curve, it can be considered whether the inequality is cyclical and corresponds to shifts in industry.

The Kuznets curve is a measure of inequality developed by economist Sam Kuznets in the 1950s. It is a U shaped curve which shows the relationship between income per capita and inequality. Kuznets hypothesizes that inequality increases as a country undergoes industrialization and then descends when a level of per capita income is reached. The result is a convex curve. Many economist feel that in the development of the United States, we have followed more than one curve; the first curve followed us through the industrial revolution, and the second curve we now follow as we adjust to the digital revolution.

Many economists have followed the upswing of inequality from the low level of stability that began in the 1940 and the consequent upswing starting in the late sixties. Neilson and Alderson (1990 p.p. 12-33) note that the level of stability, followed by an upswing in inequality followed the parallel declines and consequent increases in other countries such as Canada, Sweden, West Germany. Though Neilson and Alderson (1990) attributed much of the upswing in inequality to a myriad of different factors, such at the changing place of women in the workforce and racial dualism, it is theorized by others that the upswing in inequality can be attributed to the development of new technology, and our entrance into the digital era.

The possibility of a modern day interpretation of the Kuznets curve that pertains to the impact  of skill-based technological change has been explored.  Though the models purposed by Grimalda and Vivarelli (2004) are applied primarily to globalized middle-income developing countries, it is theorized that the introduction of skill-based technology will increase the demand for skilled labor, and a skill premium, inducing inequality until the unskilled population can invest more in education and training.

Much of this theory is based on the initial model developed by Galor and Moav (2000 p.p. 469-97). The model argues that technological progress transforms the nature of available occupations. This creates, in a sense, a form of creative destruction in existing human capital. According to Galor and Moav, “… the time required for learning the new technology diminishes with the level of ability and increases with the rate of technological change” (Galor and Moav 2000). This introduction to, and eventual absorption of, new technology is in line with the initial Kuznets model and appears to be following the same curve as when the technologies of the industrial revolution were adopted.


There are exceptions to the Kuznets curve, most notably the rise of several east Asian countries as they ascended to technological progress while keeping poverty and inequality at a steady level. This phenomenon has been dubbed The East Asian Miracle. Between the period of 1965-1990 high levels of growth were experienced in 23 east Asian countries. The countries often linked to this phenomenon are: Japan, Hong Kong, Taiwan, The Republic of Korea, and Southeast Asian countries, Indonesia, Thailand, and Malaysia. These countries all experienced rapid growth, while retaining a consistent Gini coefficient. Concurrently, the levels of poverty were reduced experiencing more equal income distribution. (Kolluru & Rao 1997).  In many ways the countries included in the East Asian Miracle seem to  have skipped the Kuznets curve.

Though there are many causes cited for this growth, for the purposes of this paper, the focus will be on equal income distribution, productivity growth, and  human capital investment. As reinterpreted by Robertson, using accounting methods and Mankiw’s linearized growth model, much of their growth was due to capital accumulation(Robertson 2002).  This is also concurred by Page, who states, “Depending on the estimates used, between 60% and 120% of their output growth derives from accumulation of physical and human capital and labor force growth” (Page 1994). The boom in capital growth and productivity was driven forward by the labor force. However, it must be explored how during this period, the return on that capital kept up with their economic growth.

One key to the ability of this group of countries to avoid any significant lag in the ability of the labor force, to adjust to the increase in technology, may lie in government investment in education. Page notes that in most of the East Asian countries that experienced rapid growth, there was a significantly higher investment in education, and better response to education market failures. It is noted that most of the positive effects associated with this increased investment can be seen in the level of literacy at the primary level. Page feels the focus on a strong primary education, and post-secondary education based primarily on vocational skills, while showing restraint of public subsidies in higher education, helped workers upgrade their skills and adapt new technology (Page 1994).

The stress on the importance of  early education in the East Asian Miracle is not only expressed by Page. Hanushek and Woessman note that it is not merely the percentage of GDP that is focused on education that is important, but the quality of basic education provided. They state, “ Gains from providing both universal access and basic skills for all are projected to be six times those of just providing access. If there is going to be inclusive economic development across the world, attention must focus on school quality and having all students achieve basic skills”(Hanushek & Woessman 2005 p.p. 345). Page affirms this on inspection of policy where he notes that education spending in these countries was very focused. The share of spending on basic education has been significantly higher in comparison to other countries, and public spending on post-secondary education has been lower (Page 1994).

We have looked at two measures of inequality, where the United States presents in those models, and explored some of the factors that led several high performing countries in East and South Asia to traverse a technological boom, while keeping inequality and income distribution steady. Looking more closely at the education policy, on the whole, in the high performing Asian countries, we can now explore the contribution and distribution of education in the United States and explore whether restructuring our policy to be more reminiscent of the Asian model can have an effect on our highly skewed income distribution.


In 2009, the United States took the first leap into a finalized version of common core education.  It has been the intent of the government to set core standards of knowledge that every child must know at certain checkpoints in their education. While the idea of standardized primary education is much closer to the East Asian model, there is still wild variances in education spending, not only per states, but per contribution from the federal government. The differences in funding to education and access to technology is still causing huge divides in quality of education along socioeconomic lines. Though the requirements are uniform, the quality of education distributed is still lacking.

In theorizing the factors that effect government spending, Lindert hypothesizes that as the percentage of school-aged children rise, there would be no effect on education spending as a share of GDP, but education spending per child would be cut (Lindert 1996). Examining the data pertaining to per state spending per child, and the percentage of the population under age 18 seems to generally support these findings, though spending tends to be higher in the North East region.  Utah currently holds the position of  both the lowest spent per student at $6500 and percentage of the population under 18 at 30.1%.  The highest amount spent per student is in New York, at $20, 610 (U.S Census Bureau 2014).  As the states with the lowest school-aged population, on average, exhibit higher levels of per-student spending, this brings into question whether every child is receiving the same core education. The effects of the discrepancy in education spending is seen strongly in rural areas, as well as areas where the majority of children are English language learners (ELL), and areas with high levels of poverty.

When the common core curriculum was unveiled in 2009, there was no strong stance on how assistance was given to students who begin school not speaking English fluently, or at all. It was an issue that was initially deferred to the states to figure out how to bring ELL students to the level that they would need to be to benefit from common core (Wiley 2014). It wasn’t until 2012 that there was a framework available to accommodate ELL students.  According to the U.S. Department of Education, approximately 4.4 million students speak English as a second language; the state with the highest population is California, where 22% of children are ELL students.

The fact that ESL students have to attain a mastery of academic English, often sacrificing the gain of sufficient knowledge in other academic materials has  made this population at higher risk for non-completion of high school, though it is acknowledged that other risk factors may also contribute (Callahan 2013). Though the average graduation rate for ELL students is around 89%, this number varies widely from state-to-state. In Arizona, for example, the on-time graduation rate of ELL students was only 18%

There are many challenges facing schools in rural areas. Among them are underfunding, failure to attract employees and limited opportunities for professional development. Another issues is that many rural schools lack the same access to technology that is available within schools in larger areas. Since many grant awards are based on the number of students, grant awards are disproportionally small, not enough to get rural schools caught up with their urban counterparts in respect to staff, technology, and facilities (NEA 2015). In many small rural communities where the economic base of the community is manufacturing, mining and farming, economic shifts have led the areas to lower paying jobs, and increasing service jobs. This shift has contributed to rural poverty, and decreased the amount of money contributed to school by budget amount, or community contribution (Khattari, et al., 1997).

Though there have been more recent efforts made to revitalize rural schools through the use of school improvement grants, research conducted on a sample of 35 rural schools receiving federal funding over a three year period revealed that many factors continued to impede improvement efforts. These factors included transportation difficulties, parental involvement, opportunities for staff professional development, and staffing issues (NCEE 2014). These roadblocks have made it difficult for rural schools to comply with the goals of student achievement set as conditions to the grant award.

The schools in urban poor communities are facing roadblocks as well. Though the causation is different, urban schools are facing a lot of the same issues as rural schools, such as adequate facilities, staff retention, and parental involvement. In urban schools, this is compounded with other mitigating factors, such as high crime rates, poor health care, and drug and alcohol addiction. In cities where there exists a poor population, and a population of more affluence, the divide is visible in the drop-out and performance rates between urban and suburban areas in major cities.  According to the National Center for Policy Analysis, citing a study conducted by America’s Promise Alliance, the average  dropout rate in suburban areas is 71%, in contrast, the average dropout rate in urban schools is 53%. The largest discrepancy was found in Cleveland, where the suburban graduation rate was 80%, compared with 30% in the inner-city (NCPA 2009).

We have established that though there is a common core education, there are significant gaps in the attainment of that education, based on mobility and economic factors. As the East Asian contribution to education spending focus was primarily on early education and literacy, it is pertinent to explore illiteracy as a causation to many societal issues, a barrier to equal opportunity, increased cost in public assistance, and a continued roadblock to productivity in the labor market. A shocking statistic by the National Assessment of Adult literacy is that two-thirds of the children who cannot read proficiently by the fourth grade will end up in jail or on welfare.

The poverty divide in our country, on the basis of income distribution, starts at the base level of equal education attainment for all children. As wealth concentrates, so does the inequality of early education; as the majority of school funds are derived at the state level and by community donations. Though attainment of college level education would be beneficial to those in rural and urban communities, increased focus on vocational skills would help fill shortages in industries, such as education, medical, and technology, and give opportunity to migrate to areas with better economic opportunities.

All three levels of government – federal, state, and local – are responsible for financing public education. Currently, the largest share comes from local funding. The federal government contributes around 14%. The distribution of federal funds is accomplished primarily through federal grant programs. The percentage of federal funding per state varies widely. The reliance of state and local funding, as the majority of revenue for public schools, causes major disparities in school funding. This is due to the affluence of some areas over others, even inequity among districts in the same state.

Increasing social spending in the area of education can help with an increase in the growth of income at the bottom quartiles of income distribution, and thereby reduce poverty and inequality. A World Bank study looked into the steady recent decrease in the Gini coefficient of some Latin American countries, such as Brazil, Argentina, and Mexico. They found that the reduction can be attributed primarily to changes in public expenditure, such as higher spending in education, and making sure children in poverty are receiving an education. In looking at the decline in poverty, 60% can be attributed to the decline in inequality (World Bank 2015).

In increasing the spending and funding distribution, it can be ensured that common core education is supplemented, confirming that schools have similar resources and facilities. Moreover, the quality of education will not decrease with an increase in school-aged population.  By adjusting expenditure per student in the early stages of education, focusing on the quality of basic education and literacy skills, it can translate to positive effects on income distribution  (Keller 2010).


By looking at the levels and reasons for growing inequality in the United States, comparing them to previous patterns, and considering them in terms of a Kuznets curve model, it can be surmised that our growing levels of inequality are due to advancing technology as we enter into the digital age. If this theory is correct, inequality will peak and eventually decline. In countries, such as Sweden, with traditionally lower Gini Coefficients, income distribution is high, but equality is attained through redistribution of income through taxation and social programs.  Evidence has shown that as the millionaire population of Sweden increases, the taxation of the wealthy is decreasing and their Gini coefficient is rising, supporting income distribution and electoral factors purposed by Lindert (1996).

A group of East and South Asian countries to traverse a technological boom from 1960-1989, while holding steady levels of inequality and decreasing poverty, and seemingly avoiding a Kuznets curve. A primary factor in keeping their levels of capital growth attuned to income levels is the increase in government spending on education, specifically primary education, and a focus on literacy. This has been attributed to the ease in which new technology and skills are learned by the labor force.

There is significant inequality in the United States in terms of primary and secondary education. Because the majority of funding exists primarily in the state and local levels, a divide in per-student spending exists on socioeconomic lines. ESL students, students in rural areas, and urban poor students are the most at risk. As the schools contend with poor facilities, staff retention and training issues and unequal access to technology, the incidences of school dropout and illiteracy are much higher in these populations. This translates to the inability to migrate to a stronger economic area, a higher probability of entering the prison and welfare systems and they inability to ascend from poverty.

Though the United States has taken steps toward a core education system, the insurance that all schools have access to the same material, technology, staff development, and parental involvement is left up to the school districts. This presents the undeniable fact that all students are not receive the same education, and are not subject to the same opportunities. By increasing the share of the GDP spent on education and revamping the policies to ensure equal tools to receive an equal education, we could not only reduce inequality, but increase the employability of those areas of the population that are suffering with the effects of poor literacy skills.



Works Cited

Callahan, Rebecca M. “The English Learner’s Dropout Dilemma: Multiple Risks, Multiple Resources. February 2013. (accessed November 18, 2016).

Galor, Oded and Moav, Omer. “Ability-Biased Technological Transition, Wage Inequality, and Economic Growth.” Quarterly Journal of Economics. 115:2 (2000), pp. 469-497.

Grimalda, Gianluca and Viverelli, Marco. “One or Many Kuznets Curves: Short and Long Run Effects of Skill Based Technological Change on Income Inequality.” IZA Discussion Paper no. 1223, July 2004.

Hanushek, Eric and Woessman, Ludger. “Knowledge, Capital, Growth, and the East Asian Miracle.” Science. 351:6271 (January 2016), pp. 344-345.

Keller, Katarina. “How can Education Policy Improve Income Distribution?: An Empirical of Education Stages and Measures on Income Inequality.” The Journal of Developing Areas. 43:2 (Spring 2010), pp. 51-77.

Kolluru, Srinivas and Rao, Bhanoji. “East Asian Experience on Growth and Equity: Lessons and Implications.” Global Business Review, 12:2 (June 2011), pp. 237-246

Landy, Benjamin. “A Tale of Two Recoveries: Wealth Inequality After the Great Recession.” . August, 2013. (accessed December 2, 2016).

Lindert, Peter. “What Limits Social Spending.” Explorations in Economic History, 33:1 (January 1996), pp. 1-34.

Lupu, Noam and Pontusson, Jonas. “The Structure of Inequality and the Politics of Redistribution.” American Political Science Review. 105:2 (May 2011), pp. 316-336

National Education Association. Rural Schools, Washington D.C., 2015

National Center for Education Evaluation. A Focused Look at Rural Schools Receiving School Improvement Grants. Washington, D.C., 2014

National Center for Policy Analysis. Large Suburban Gaps seen in Graduation Rates. Dallas, TX. 2009.

Nidhi, Khattri; Riley, Kevin and Kane, Michael. “Students at Risk in Poor Rural Areas.” Journal of Research in Rural Areas. 13:2 (Fall 1997), pp. 79-100.

Nielsen, Francois and Alderson, Arthur. “The Kuznets Curve and the Great U-Turn: Income Inequality in U.S. Countries, 1970 to 1990.” American Sociological Review. 62:1 (February 1997), pp. 12-33.

Page, John. “The East Asian Miracle: Four Lessons for Policy Development.” NBER Macroeconomics Annual: Volume 9. Cambridge, MA: MIT Press, 1994. P. 219-284.

Procter, Bernadette D., Smega, Jessica and Koller, Melissa. Income and Poverty in the United States: 2015. (Current population reports P60-256RV) Washington, U.S. Census Bureau, 2016.

Piketty, Thomas. Capital in the 21st Century. Cambridge, MA: Belknap Press, 2014.

Organization of Economic Cooperation and Development. Inequality Update: Sweden. Paris, France. 2015.

Robertson, Peter S. “Why the Tigers Roared: Capital Accumulation and the East Asian Miracle.” Pacific Economic Review. 7:2 (June 2002), pp. 259-274.

Saez, Emmanuel. “U.S. Top 1% of Income Earners Hit New High is 2015 Amid Strong Economic Growth.” . July 2016. (accessed December 2, 2016)

U.S. Census Bureau. Per Pupil Spending Varies Heavily Across the United States. Washington, D.C. 2005.

Wiley, Terrence. “The Common Core Standards and the Great Divide.” International Multilingual Research Journal. 8:1 (January 2014), pp. 38-55.
















Author: Mimi

My name is Mimi Hanson. I’m happy to have you here! I currently reside in the state of Utah. I have (or will have…in my last semester) a B.S. in economics from the University of Utah, where my primary interest is income inequality and labor economics. Much of my undergraduate work is in business, crisis, and strategic communications. Aside from my family, there are three things in my life that I am passionate about: The first of which is embracing the change that comes your way. By being flexible enough to see how you can accept criticism, grow from any situation tossed your way. I am passionate about solving problems. I believe that there is always common ground, always solution, and I strive to find them. Lastly I am passionate about communication. As we plunge neck-deep into the information age, we have lost the finite aspects of our skills in communicating and relating with each other. In my spare time, I love to spend time with my six-year-old, I love music, wine, cooking, and NPR. I am also working on a book, chronicling the history of poverty in America . This blog is a random place to stick my writing. If you disagree with anything that I have written, I would love to hear from you! I am always open to new lines of thinking and how to improve my writing.

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