Effect of Education Attainment on Life Expectancy

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23/09/19 Education Reference this

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The Effect of Education Attainment on Life Expectancy

Introduction

The United States’ healthcare system is notorious for its inefficiency. The US spends more than 17% of its GDP on healthcare—much more than its peers in the Organization for Economic Cooperation and Development (OECD), a club of mostly rich countries—and yet the life expectancy of the US is still lower than that of other developed nations (Kaplan et al. 2014). At the current trajectory, the US will continue to have one of the lowest gains of life expectancy among its peers by 2030 (Kontis et al. 2017). Interestingly, researchers have found that the variance in health outcomes is mostly explained by behavioral and social factors unrelated to healthcare, as opposed to medical care, which only explains about 10% (Kaplan et al. 2014). Thus, to raise America’s life expectancy, scientists and policy makers have begun to investigate social and behavioral issues that impact life expectancy; among them, education attainment is a popular point of scrutiny.

Certain theories have been proposed about the link between education and life expectancy. Positing an indirect relationship between education and life expectancy, Friedman et al. (1995) discovered that personality traits that induce one to pursue higher levels of education are related to enhanced longevity. One such personality trait they examined was conscientiousness, or social dependability. They concluded that not only are conscientious individuals more likely to pursue higher levels of education, but they also tended to engage in beneficial health practices, such as exercising and maintaining a healthy diet.

While education may confer a personal benefit, it brings positive externalities to society as well. Individuals with access to quality primary and secondary education are “less likely to commit crimes, less likely to place high demands on the public health care system, and less likely to be enrolled in welfare assistance programs” (Mitra 2011). Such findings thus provide an incentive for state officials to improve the quality of and access to education.

Literature Review

 Existing empirical literature on the topic of education attainment and life expectancy is one-sided with nearly all research papers concluding that there is a positive relationship between the two variables. In their study, Montez et al. (2012) used standard educational categories, such as high school and beyond high school, to predict the probability of US adult mortality over a 20-year period. They found a significant reduction in the probability of adult mortality for those who obtained a high school degree and an even steeper decline for those who were educated beyond high school.

 Yet such a phenomenon is not just observed in the US. A study conducted on developed countries around the world showed that there are significant differences in life expectancy between the highly educated and the less educated segments of many different peoples (Jasilionis and Shnkolnikov 2016). For one specific example, the researchers saw that, in the 2000’s, the life expectancy differential between highly educated and less educated Russian men was 13 years.

 This study contributes to previous literature by examining the effect of education attainment on life expectancy on a state-wide level using a standard education category: high school.

Empirical Model

A linear regression is used to estimate the relationship between education attainment and life expectancy. In this paper, we estimate the following model:

Life Expectancy = α + β1Education+ β2Obesity + β3Income + β4Uninsured + β5Region + u

Life expectancy is the dependent variable, which is measured in years. Education is the independent variable of interest, measured in high school graduation rates (%) per state.

Although not the focus of this study, obesity, income, uninsured, and region are other independent variables that can potentially explain life expectancy. “Obesity” measures the percentage of the population that is either overweight or obese; the “income” variable represents the percentage of the population that falls below the federal poverty line; the “uninsured” variable measures the proportion of state populations without health insurance; lastly, the “region” variable indicates the geographical region of each state. For control purposes, northeast, a region variable, is excluded.

Data

Life expectancy data are obtained from the Kaiser Family Foundation and are based on calculations of mortality counts by the CDC and National Center for Health Statistics for 2009. Independent variables aside from education, which is based on 2015-2016 data, are obtained from publicly available state-level data for 2016-2017. The education variable represents the percentage of public high school freshmen who graduate with a regular diploma within 4 years of starting 9th grade. The final percentage is adjusted by adding any students who transfer into the cohort of high school students and subtracting those that “transfer out, emigrate to another country, or die” (National Center for Education Statistics 2017). Graduation rates were collected by the U.S Department of Education, Office of Elementary and Secondary Education.[1]

Data for state obesity rates stem from the Behavioral Risk Factor Surveillance System, a telephone survey of non-institutionalized civilian adults aged 18 years and older. All state characteristics, such as annual median income and proportion of uninsured populations, are obtained from the Kaiser Family Foundation for 2016.

 The following are descriptive statistics for the data used in this study:

Variables

Mean

(std. dev)

Min Value

Max Value

 

Life Expectancy

78.62

(1.67)

75

81.3

High School Graduation Rate (%)

83.84

(5.01)

69

91

Obesity Rate (%)

29.79

(3.74)

22.3

37.7

Uninsured Rate (%)

8.21

(2.58)

5

15

Median Income

59,338.86

(9,086.83)

41,099

76,260

Poverty Rate (%)

12.61

(3.05)

7

21

West

0.25

(0.44)

0

1

South

0.33

(0.48)

0

1

Midwest

0.24

(0.43)

0

1

Northeast (excluded)

0.18

(0.39)

0

1

The above table shows a range of life expectancies across states from 75 years in Mississippi to 81.3 in Hawaii with the average of 78.62 years. High school graduation rates in the United States similarly experience a wide differential with the lowest rate in DC at 69% and the highest in Iowa at 91%. The mean graduation rate sits at 83.84%.

Empirical Results

The table below reveals the results from the regression. Evidently, the six (South, Midwest, and West are part of the region variable) variables combine to explain over 80% of the variance in life expectancy, as indicated by the R-squared metric. The education variable (represented in the table below as yearhighschoolgraduationrate2015) seems to be an important determinant of life expectancy (p-value<0.05). Its regression coefficient is close to 0.08, which indicates that a 1% percent increase in the high school graduation rate increases life expectancy by 0.08 years.

Obesity and region seem to explain life expectancy as well. The regression coefficient of the obesity variable indicates that a 1% increase in obesity rates would induce a decrease of 0.21 years in life expectancy (p-value<0.01). With regards to region, the South has a lower life expectancy than the Northeast (p-value<0.01).

Conclusion and Policy Recommendation

Empirical results show that education, more specifically high school education, is an important determinant of life expectancy—an increase in education leads to an increase in life expectancy. Such a conclusion is in agreement with Montez et al. (2012) and Jasilionis and Shnkolnikov (2016). Considering the positive relationship between life expectancy and education, state officials and policy makers should do their part in ensuring a quality education for state residents as an attempt to boost overall life expectancy in America. Yet to implement the appropriate policies toward that goal, they should first understand the obstructions to getting an education, specifically at the high school level.

In a study that aggregated many survey results of reasons for not finishing high school, Doll et al. (2013) found that prominent reasons include the necessity to work to support family and teenage pregnancy. Policy makers can counter these forces by granting income subsidies for certain disadvantaged areas to alleviate the financial burdens of low-income families, mandating more discussions on sex education in schools, and implementing mentorship programs that provide academic and social guidance to students who may be at risk of dropping out.

This study also demonstrates that obesity plays a major role in determining life expectancy. Accordingly, state officials should encourage a healthier lifestyle by implementing policies such as creating nutrition education programs in schools and communities and improving street conditions for walking and biking.

Importantly, this study contains a limitation in that high school is only one education category. The results of one category therefore should not constitute the entire effect education can have on life expectancy. According to Montez et al. (2012), mortality rates drop even more significantly for people who obtain beyond a high school degree. Clearly, the topic of education is much more complex than a single policy variable and deserves further exploration.

References

  • Doll JJ, et al. 2013. Understanding Why Students Drop Out of High School, According to Their Own Reports: Are They Pushed or Pulled, or Do They Fall Out? A Comparative Analysis of Seven Nationally Representative Studies. SAGE Journals: 1-15.
  • Friedman HS, et al. 1995. Childhood conscientiousness and longevity: Health behaviors and cause of death. Journal of Personality and Social Psychology 68(4): 696-703.
  • Jasilionis D, Shkolnikov MV. 2016. Longevity and education: a demographic perspective. Gerontology 62(3): 253-262.
  • Kaplan RM, et al. 2014. Education Attainment and Life Expectancy. SAGE Journals 1(1): 189-194.
  • Kontis V, et al. 2017. Future Life Expectancy in 35 Industrialized Countries: Projections with a Bayesian Model Ensemble. The Lancet 389(10076):1323-1335.
  • Mitra D. 2011. Pennsylvania’s best investment: The social and economic benefits of public education. State College, PA: Pennsylvania State University.
  • Montez JK, et al. 2012. Education Attatinment and Adult Mortality in the United States: A Systematic Analysis with Functional Form. Demography 49(1): 315-336.
  • National Center for Education Statistics. 2017. Public High School 4-year Adjusted Cohort Graduation Rate (ACGR), by Selected Student Characteristics and State: 2010-11 Through 2015-16. U.S Department of Education.

[1] For more information, see: https://nces.ed.gov/programs/digest/d17/tables/dt17_219.46.asp

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