The Correlation Between Education and Income Distribution; with Focus on Income Inequality Mathew Chan
Nov 16, London, Ontario Professor Wokia Kumase
The Correlation Between Education and Income Distribution; with Focus on Income Inequality In this paper the link between education and income and the subsequent effects on income distribution and income inequality are covered. This is done using a case study of American males with varying levels of education and examining their incomes, as well as the income distribution among them. Lifetime earnings, median incomes, and percent change in income will all be used as proxies to study income. Furthermore the changing value of education will be examined. From these measurements a correlation between education and income and the mechanisms behind it will be demonstrated and explained. Drawing upon these findings, combined with size distribution models, will demonstrate how income distribution has been effected; highlighting the relationship between education and income inequality. From this, recommended policies will be proposed on what should be done to change the income distribution in order to address poverty. The Connection Between Education and Income To understand how education and income distribution are related, first the link between education and income must be established. One can then continue by establishing a relationship between income and income distribution; thus using income to as the medium to connect education and income distribution. The link between education and income is based upon the human capital model, a longstanding model that can be traced back to Adam Smith (Wolff, 2009). Perna (2005) states that the human capital model “assumes that individuals decide to enroll in higher education and persist to degree completion based on a comparison of the expected benefits and costs of all alternatives.” Furthermore the model states that there is a basic tradeoff between schooling and working (Wolff 2009). Schooling leads to increased productivity and consequently higher future wages, however the opportunity cost of schooling is the money one could have earned working instead of attending school. Working leads to wages earned during the time that one would otherwise be at school, though this decision means the worker does not achieve increased productivity and therefore doesn’t enjoy higher future wages (Sweetland 1996). The rational decision is then based upon which option leads to higher income. The problem then arises of how to measure income. Using synthetic work-life earnings (see figure 1 notes for calculation) as a proxy for income and level of degree attained as a proxy for education we can examine which path, going to school or working, leads to greater total earnings over ones working career. These proxies capture a long-term measure of income as well as accounting for the time one spends in school and the ‘head start’ in income one enjoys by forgoing school and joining the workforce. For American men synthetic work-life earnings increase along with the level of education (Figure 1). The worklife earnings progressively increase along with level of education from a low of 1.1 million dollars, the work-life earnings of a non-high-school to, to 1.4 million, that of a high school graduate. This is followed by 1.7 million earned by a man with some college experience, closely succeeded by a man with an associate’s degree whose lifetime earnings is 1.8 million. With a master’s degree the lifetime earnings is 2.8
million. A doctoral degree brings lifetime earnings up to 3.8 million and the peak lifetime earnings is set at 4.8 million by men who hold a professional degree (436% more then a non-high-school graduates lifetime earnings). Using synthetic work-life earning and level of degree attained as proxies for income and education, respectively, shows there is a positive correlation between education and income. To determine if the correlation is consistent using other methods of measurement we look at the relationship between education and income using a cross sectional study of median annual earnings (proxy for income) of American males ages 25–34 by education level (figure 2). The strength of this measure lies in its ability to provide a more relatable measure of income as well as provide a historical view of income. Where it fails is that it doesn’t capture the time spent and income forgone by individuals in school. Figure 2 shows that from 1972 to 2003 American men with an education level of grade 9 to 11 (essentially high school drop-outs) have consistently had the lowest median annual earnings. Over the same period the median annual earnings of high school graduates have remained above those with education levels of grade 9 to 11 and those with a college or associates degree’s median annual earnings have topped both of the groups previously mentioned. From 1972 to 2003 American men with a bachelor degree or higher enjoyed the greatest median annual earnings. Using the cross sectional study from 1972 to 2003 of median annual earnings of American males by education level we see that education is positively correlated with income, which is consistent with our previous research. Using the cross sectional study also shows how the median income of American males with varying levels of education has changed. From figure 2 we can see that from 1972 to 2003, while median income decreased for all American men, the severity of the decrease in median income (measured in constant 2003 dollars) varied between those with different levels of education. Those with the least education (grades 9-11 level) had the steepest decline in median income, steadily dropping from 1972 to 1996 and then essentially stagnating there, barring miniscule deviance around the 1996 level. The median income of those with the highest levels of education (bachelor degree or higher) suffered the least; their median income declined from 1972 to 1982 then rebounded to just over $50000 by 1986. It then proceeded to decline below $50000 till 2000 where it stayed until 2003, just dipping below the $50000 mark. During the same time span the median income of high school graduates and those with some college or associates degree followed similar trajectories; both declined till 1995 followed by a modest increase up to 2000, whereupon they decreased till 2003. In 1972 the difference between the median incomes of those with grade 9-11 educations and those with bachelor degrees or higher was less then $15000. In 2003 the gap had expanded to over $25000, the majority of change attributed to a decrease in median income of those with low levels of education (grades 9-11). Such divergence in median incomes among American men with varying levels of education demonstrates increasing income inequality, while also suggesting the value of education has changed over time. To determine what has happened to the value of education we examined the percent change of constant-dollar median usual weekly earnings for American men
by educational attainment from 1979 to 2010 (figure 3). This measure uses the percent change of constant-dollar median usual weekly earnings as a way of measuring the change in value of education. For if one is making greater earnings, educational attainment being held as a constant over time, then it can be said that the return on the education is greater and therefore the value of the education is higher. Conjointly the opposite claim is true; if one is making lower earnings, education attainment being held as a constant over time, then the return on education is worse and thus the value of education is decreased. Constant-dollars were used to account for inflation and again level of attainment was used as a proxy for education. From figure 3 we can see that the constant-dollar median usual weekly earnings for American men with less then a high school diploma decreased by a substantial 31.2% from 1979 to 2010. During the same time span the median usual weekly earnings of American men with a high school diploma and those with some college or associates degree decreased by 17.7% and 8.4% respectively. The only group that showed an increase in usual weekly earnings from 1979 to 2010 was men who had a bachelor degree or higher; their median usual weekly earnings increased by 19.9%. As the level of education attained increases the decrease in median usual weekly earnings lessens until we reach the highest level of education (bachelor degrees or higher) whereupon the median usual weekly earnings actually increases. This tells us that, from 1979 to 2010, three things have happened: (1) The value of a high level of education (bachelors degree or higher) has appreciated significantly, (2) the value of midlevel educations (high school diploma and some college or associates degree) has decreased modestly and (3) the value of a low level (less then a high school diploma) of education has declined significantly. This is explained by the paradigm shift in the US economy; from an industrial economy to an information based economy (William, 1999). The new information based economy places a higher demand on highly skilled and educated workers (The Economist). This growing demand, combined with a lack of supply of said workers, places a greater wage premium on highly educated workers while also depressing the wages of less knowledgeable workers as reflected by figure 3 (Laurent 2008). Looking through three lenses, we established the link between education and income. Using synthetic life-time earnings of American men and degree attained as proxies for income and education, respectively, we accounted for the time spent in school and captured an overall measure of income. From these measures we demonstrated that education and income are positively correlated. To capture a more brief measurement of income as well as reinforce consistency a cross sectional study of median annual earnings of American males by education level was examined. This study also indicated that education and income are positively correlated, reinforcing the previous conclusion. Furthermore the cross sectional study showed that, from 1972 to 2003 median income levels had changed for all levels of education. This indicated that there had been some type of change in the value of education. To determine empirically what had happened to the value of education we looked at the percent change of constant-dollar median usual weekly earnings for American men by educational attainment from 1979 to 2010. From this we discerned that, while the value of a high education, as represented by a
bachelor’s degree or higher, has risen, the value of other levels of education have dropped by increasing amounts as the level of education decreases. The Effects on Income Distribution and Income Inequality Now that income has been established as a medium from which education and income distribution can be linked together we turn our focus towards the question of how education and income distribution are related; in particular how education effects income inequality. It is clear that in the US income inequality is a major socioeconomic issue; Saez (2008) finds that that income inequality in the US is at all-time high. Gordon and Dew-Baker (2008) have proposed many explanations for this, however this paper will only focus upon education as an explanation of income inequality. By utilizing size distribution models of income distribution in the US, tracking the US gini coefficient, and applying the previous research the relationships between education, income distribution and income inequality will be examined. To see how income inequality in the US has changed the gini coefficient was employed to measure US income inequality over time. The gini coefficient was chosen as it is called “the most commonly used measure of inequality” by the World Bank14 and it fulfills all four principles of an inequality index (Wolff, 2009). Figure 4 plots the path of the gini coefficient of the US from 1967 to 1993. During that time span the US gini coefficient showed an upward trend, climbing steadily from a low of ~0.39 in 1968 to ~0.45 in 1993. To get more recent valuations of the US gini coefficient and to derive consistency from the parts overlapping the US gini coefficient for household income from 1967 to 2007 was examined (figure 5). Figure 5 displayed a similar trajectory of the US gini coefficient from 1967 to 1992 (see note on 1993, figure 5), from a low of ~0.39 in 1968 to ~0.43 in 1992. From 1993 to 2007 the US gini coefficient then sporadically climbed from ~0.45 to ~0.47, which echoes Saez’s (2008) claim that income inequality was at an all-time high (as of 2007; the year his data was up to). So clearly income inequality in the US has risen. The question then becomes how does education explain this. Looking at the distribution of household income (in constant 2003 dollars) in the United States of America from 1967 to 2003 (figure 6) we are given another view of the income inequality within the US, in the context of a size distribution model. The household incomes of the 95th, 90th, 80th, 50th, 20th, and 10th percentile were displayed to capture the incomes of the upper and lower class households as well as that of the median household. Furthermore it enables us to explain the income inequality with relationship to education. Figure 6 shows that from 1967 to 2003 the upper class, as represented by the 95th, 90th, and 80th percentile, all showed significant increases in household income: the 95th percentile increased by 74%, from $88678 to $154120; the 90th percentile by 68%, from $70443 to $118200; the 80th percentile by 57%, from $55265 to $86867. From figure 1 and 2 we have already established American men with the highest level of education (a bachelors degree or higher) have the highest incomes, this trait makes them valid proxies for the top quintile of US households in terms of income. Furthermore from figure 3 we saw that, during a similar time span as that used by figure 6, American men with the highest levels of education (bachelor
degree or higher) have had their median usual weekly earnings increase by 19.9%. Assuming American men with bachelors degrees or higher are be representative of the upper class households; the rising household income of the 95th, 90th, and 80th percentile can be explained by the rising income premium placed on a high level of education. For as the income of American men with high levels of education has increased so too has the household income of the upper class percentiles. Clearly the rich have gotten richer, however seeing as inequality is being investigated it is necessary to address what has happened to everyone else. From figure 6 it is also visible that the household incomes of the median households, the 50th percentile, and lower class households, as represented by the 20th and 10th percentiles, haven’t experienced the same kind of income escalation as the top quintile. Figure 3 shows that the household income of the 50th percentile increased by 30%, from $33338 to $43318; the 20th percentile by 28%, from $14002 to $17984; the 10th percentile by 35%, from $7790 to $10536. So while the median and poor households have in fact benefitted, it has been to a much more insignificant degree. It has previously been established that American men without a bachelors degree or higher have significantly lower incomes those with them, and the lower the education the lower the income becomes (figure 1 and 2). Their income levels are consistent with households of the bottom quintile. As such they are a good proxy for the households of the bottom quintile in this manner: the lower the education; then the lower the percentile of household income. It has also been established that the income of American men without a bachelors degree has decreased over time, and the lower the education the more the income decreased (figure 2 and 3). This decrease in income by lower education level then explains why the bottom quintile of US households has had such poor income growth; however by that reasoning there should be a decline in household income not an increase, however minor. The explanation for this can be traced back to WWII, where as Wolff (2009) and the US bureau of Labor Statistics16 report female work-force participation began increasing. By using households as a proxy we also captured the income of the women in the household. The consequent income the women generated, due to their increasing work-force participation, offset the decrease in household income experienced by men of lower education levels and in fact is the cause of the modest increases in household incomes for the bottom quintile. By utilizing our findings on the relationship between education and income, tracking the gini coefficient of the US, and looking at the US’ income distribution in the context of a size distribution model the relationships between education, income distribution and income inequality were established. By tracking the US gini coefficient we saw that US income inequality had reached an all-time high. We preceded by explained this inequality in the context of education. Using figure 1 and 2 we saw how the most educated and the less educated American men could be used as proxies for the top and bottom quintiles of household income distribution. From figures 3 we determined the increase the top quintiles household income could be attributed to the rising value of a high level of education. Furthermore it gave a partial explanation to the weak growth of the bottom quintiles income growth: the depreciating value of a mid- and low-level education, however the depreciating value of a mid- and low-level education should have caused a decrease in the bottom
quintiles income instead of just stifling it. Scratching a layer deeper, we found the increasing participation of woman in the labor force counteracted the decrease and accounted for the weak growth. The changes in both the top and bottom quintile of household income have now thusly been explained through the lens of education. Furthermore the discrepancy in increases in household income between the top and bottom quintile, as attributed to the varying values of different levels of education, explain the rising income inequality reflected by the rising gini coefficient (figure 4 and 5). Playing President: Recommended Policies on Income Inequality Now that the connections between education, income distribution, and income inequality have been established and the causes behind them identified, a framework of information has been established. Drawing upon this framework recommended policies to address income inequality are proposed. (1) Increase accessibility to universities that offer bachelor degrees or higher. The recommendation of this policy is based upon the findings that American men with a bachelors degree or higher consistently have the highest amount of income (figure 1 and 2) and are the only group to have experienced an increase in income since 1979 (figure 3). To go about this policy it is suggested that governments heavily subsidize student tuition fees with the ultimate goal of a free tertiary education, mimicking the Finnish education system, which is tied for 1st on the UN’s education index17. Furthermore incentives should be put in place to encourage start up universities and reward successful programs. (2) Efficient reform of the taxation system. This policy is recommended based upon the prevalent income inequality within the US (figure 4, 5, and 6). While acknowledging that there must be a balance between equity and inequality, it is clear that inequality has deviated from that delicate balance, evident in Warren Buffett’s recent tax disclosure, which displayed Warren buffet paid a tax rate of 17.4%18 and the global Occupy Wallstreet movement. In order to sway the balance back there must be greater taxation of the upper percentiles, though not to so a radical degree as to destroy incentives. The income from these taxes can then be used to fund policies, such as the one previously recommended, that address income inequality through education. (3) Promote social awareness on the importance between education in relationship to income. If people are more aware of the positive correlation between education and income then the frequency of people with higher education should increase, as people desire a higher income. In conjunction with policy 1 this will reduce income inequality as it should reduce the number of low educated/low income people and move them into the highly educated/high income population. By implementing these policies the US should be able to reduce its income inequality while also keeping the incentives necessary for economic growth intact.
Figure 1: Synthetic Work-Life Estimates for Full-time US Male Workers
Notes: “Synthetic estimates of work-life earnings are created by using the working population’s 1-year annual earnings and summing their age-specific average earnings for people ages 25 to 64 years. The resulting totals represent what individuals with the same educational level could expect to earn, on average, in today’s dollars, during a hypothetical 40-year working life. A typical work- life is defined as the period from age 25 through age 64. While many people stop working at an age other than 65, or start before age 25, this range of 40 years provides a practical benchmark for many people.”4 Plotted by: Mathew Chan Data Source: The Big Payoff: Educational Attainment and Synthetic Estimates of Work-Life Earnings4
Figure 2: Median Annual Earnings of American Males Ages 25–34 by Education Level, 1972–2003 (Constant 2003 Dollars)
Notes: includes full-time, year-round workers Source: Baum and Payea (2004)
Figure 3: Percent change of constant-dollar median usual weekly earnings for American men by educational attainment, 1979-2010
% Change 30 20
Axis Title
10 0 -10 -20 -30 -40
% Change
Less then a Highschool Diploma
Highschool Graduate
Some College or Associates Degree
Bachelor Degree or Higher
-31.2
-17.7
-8.4
19.9
Notes: Data relate to earnings of full-time wage and salary workers age 25 years and older Plotted by: Mathew Chan Data Source: U.S Bureau of Labor Statistics8
Figure 4: Gini Index of US household income distribution, 1967-1993
Source: Ryscavage (1995) Figure 5: The US Gini Coefficient for Household Income, 1967-2007
Notes: In 1993, the Census Bureau begun using a new method of collecting data, so pre-1993 and post-1992 estimates are not comparable
Source: The US Census Bureau and Wikipedia.com Figure 6: Distribution of household income in the United States of America, 19672003
Source: Wikipedia15
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