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Understanding the Social Determinants of Health: Applying the Grossman Health Capital Model

November 11, 2015
The Health, Education, and Welfare branch of Wonk Tank explores two social determinants of health by applying Michael Grossman’s health capital model to explain why disparities exist and solutions to combat them.
Grossman’s Health Capital Model

Notable health economist, Michael Grossman, constructed a model that demonstrates the demand for the commodity of “good health.” The central premise to this model is that health can be viewed as a durable capital stock that produces an output of healthy time. Using Grossman’s model, we know that people are both consumers and producers of their own health stock by combining both time and health inputs.

The model is as follows:

365 days - TH (time invested to improve health) - TL (time lost to illness) = TB (leisure time) + TW (time spent working). 

Sick time by Health Capital 

Figure 1         

Here is a scenario that can help better explain this model:                                                     We have a working father who suffers from heart disease. After year of spending large amounts of money on specific medical treatments for his illness, he decides that he would have better luck of maintaining his health if he were to spend more time investing in his health, TH, whereby he trades watching TV (TB) or even some of his working hours (TW) to exercise. Applying this to the model, the end result is fewer sick days, (TL), and more healthy days.  Thus, the overall effect would be an overall increase in health stock due to the increase in his health investment (pushing him farther along the x-axis in Figure 1) and, in turn, reducing the time lost to illness (pushing him down the y-axis in Figure 1), thereby increasing his ability to work (TW). The overall increased TW  would potentially increase his wages, which would move him to a higher indifference curve, making him and his family better off (Figure 2). 


Good Y vs Good X

Figure 2                     

This in turn would lead to a better quality of life, a potential increase in leisure time to re-invest in health, and an increase consumption of other (non-health related) goods and services, leading to increased economic growth. An increase in health stock also affects his family’s ability to invest in their health.  For example, this father can now place more time and money on his children’s health investment, causing them to have greater supply of health stock in the future.  This wealth of health stock these children accrue overtime then means that they can have a greater chance of having less sick days and better school attendance, thereby increasing the children’s education, which is known to produce higher incomes and healthier lifestyles in the future.

Thus, this model shows us that individuals are willing to make investments in health because the potential future benefits exceed the costs (time, lost wages, and leisure).  However, what this model fails to address or demonstrate is what social barriers are in place that make it difficult for people to invest in their health stock.

We examine two specific social determinants that affect health investment: 1) socioeconomic status (primarily income) and 2) race. We take these two separate determinants and apply them to the Grossman model to demonstrate why certain health disparities exist based on individual’s ability to invest in their health.


TH & Socioeconomic Status

Socioeconomic status (SES), as defined by an individual’s income, education, and occupation, plays a significant role in determining a person’s future health stock and ability to invest in their health [1]


Low-income individuals are at risk of having higher health problems and face higher mortality rates. In fact, a 20-year old man in the bottom income quartile in the United States, on average, reports having the same health as a 60-year-old male in top income quartile [2]/a>. Other research shows that groups with incomes below $15,000 have 3.9 times the mortality of the highest income groups. These two extreme comparisons highlight the correlation between low-income and poor health and quality of life. We capture a more accurate picture by combining the three factors together since they are interrelated. However, even by isolating individuals’ low-income, we can reach some explanations regarding its effect on health through lack of exercise and low-quality diet.

Indeed, only 17.6% of individuals from households with income lower than $15,000 report achieving recommended levels of physical activity [3] . This derives from several social and environmental barriers to physical activity including poor access to recreational facilities, such as fitness centers, community pools and parks, as well as lack of security in less privileged areas where violence and delinquency discourage individual activities and family-friendly initiatives [4]. All these obstacles constitute barriers for investing  in TH.

Man Buying Apples

Image (Harvard School of Public Health) 

In addition to physical activity, low-income negatively affects dietary habits. Obesity, which nearly affects about one-third of the U.S. population, is 8.4% higher for low-income individuals than those of a higher-income [5]. This could mainly be explained by the inaccessibility and unaffordability of fresh food, the main constituents of a “Food Desert,” as defined by the United States Department of Agriculture (USDA)[6]. Low-income neighborhoods have limited access to grocery stores that offer good-quality fruits, vegetables, whole grains and low-fat products[7]. Based on a healthy food availability survey in Baltimore, 46% of lower-income neighborhoods have limited access to health food compared with 13% in higher-income neighborhoods[8].

This is first due to the unavailability of these commodities in less privileged areas and second to lack of transportation to farther stores. In Mississippi, which counts as one of the highest obesity-rate states in the country,  over 70% of food stamp eligible households travel more than 30 miles to reach a supermarket[9].  Furthermore, according to the USDA, vehicle access is perhaps the best measure of families’ accessibility to affordable and nutritious food[10]. Low-income families are less likely to own a vehicle and are therefore less likely to access farther stores. This makes health investment  (TH) very burdensome, time-consuming and inconvenient. On the other hand, convenience stores, which offer lower-quality food, are more common in low-income neighborhoods, offering convenience and time saving for households in these areas. Indeed, low-income zip codes have 30% more convenience stores, than middle-income zip codes at the national level[11].

Besides accessibility, healthier food is more expensive with healthier items costing $1.50 per day or about $550 per year than less healthy items, according to a research based on a meta-review of 27 studies in 10 countries from Harvard School of Public Health[12].

This justifies why low-income shoppers in richer neighborhoods with more accessible healthy food, do not change their food shopping habits.  Low-income limits both possibilities of physical activity and good dietary habits, leading to lower possibility of investment in TH, and therefore, to poorer health. Continued poorer health results in more sick days which leans less days invested in work. Consequently, wages continue to decrease, reflecting a lower indifference curve and a lower purchase power. This results in a closed cycle where low investment in health leads to poorer health and therefore to  lower purchase power of fresh food, which means low investment in health, and so on. 


To begin with, education is highly correlated with income. According to the Bureau of Labor Statistics, the higher the educational attainment, the higher the earning and the lower the unemployment[13]. This means that more education prevents low-income negative effects in health and less education reinforces these negative effects.


Earnings and Unemployment Rates by Educational Attainment 

Figure 3

The State of Obesity

Additionally, more educated individuals usually have higher awareness and better well-being and make better health-orientation choices. For instance, school dropouts are six times more likely to abuse alcohol or drugs than college graduates. They are also more likely to be smokers as adults. In 2009, about 25% of drop-outs were current smokers, compared to 11% of adults with undergraduate degree, and 5.6% of those with graduate degree[14]. This demonstrates a negative correlation between education and addiction to harmful substances. More generally, education is a positive input in health not only because it leads to higher income but also because it encourages a more positive behavior regarding investment in health.


In context of low-income occupations, most low-wage jobs are paid hourly, so one more working hour means a higher income and vice versa. Therefore, low-income workers are less likely to trade a paid hour for an hour of physical activity, even in case social and environmental barriers previously mentioned are not existent. In other words, they are unwilling to invest less Tw  in favor of TH since T is closely tied to income. Further, the risk of losing the job makes the investment in health even less likely. In fact, health disparities are found in the U.S. where the protection from loss of work is low and Netherlands where the welfare system is stronger[15].  Moreover, low-income individuals more often perform physically demanding manual labor than high-income individuals, adding another factor to a faster health deterioration[16].  However, detrimental health conditions are not only related to low-income occupations, but also to high-income jobs. In fact, The Wall Street Journal published the “Hazard of the Trade” which shares the University of Southern California research that observed roughly two dozen investment bankers over a decade and found that some developed “insomnia, alcoholism, heart palpitations and eating disorders”[17]. Working conditions, whether related to low-income or high-income jobs, highly affect health and investment in TH.

Better amenities in low-income neighborhoods, higher education associated with higher awareness and better working conditions can increase time and effort invested in health and therefore lead to a better health, as Grossman’s Health Capital Model suggests.

TH & Race

Though it is difficult to disentangle race from other socioeconomic factors when studying its impact on TH, it certainly has a large impact on health outcomes. The differences in quality of care, access to care, and health care vary widely by race. Examples of these disparities are shown in Figures 3 and 4. White Americans received better care than Hispanics for over 80% of measures, Blacks for over 30%, and Asians and Native Americans for 20%. Disparities in access to care are also frequent; Hispanics had worse access to care than White Americans for 5 out of 6 core measures, that ratio shifting to 2 in 6 for Black Americans.[18]


Figure 4

Figure 5

It is tricky to ascertain the causes of these disparities, and the various contributing factors. The above example could be related to other factors mentioned previously, such as the class differences that affect access to health-enable products health insurance. And this certainly is important; 2013, non-elderly White Americans had an insured rate of 12%, compared with 26% for their Hispanic counterparts, 17% for Blacks, and 15% for all others, compared with 15% across all races.[19] But not all race and class factors are as easy to measure; more complex metrics include tracking the lower quality or overextended health resources in impoverished neighborhoods, where minorities are more likely to live. Evidently, it is quite difficult to separate these influences.

There are three main, non-mutually exclusive causes of the racial disparities in health outcomes: biological differences between different racial groups that cause varied health outcomes; racial disparities that are confounded by class; and differences in race that are independent of income and wealth.[20]

The biological argument does not appear to be a significant factor in health outcomes disparities. Further genetics research may support or reject this theory more completely in the future, but today’s research largely disproves it. This line of reasoning was originally used in racist arguments attempting to prove the inherent inferiority of certain races.[21] However, researchers have demonstrated some racial variation in a few cases. For example, sickle cell disease is more prevalent among people of Middle Eastern, Indian, Mediterranean and African ancestry due to the gene variations people developed in those areas to combat malaria. Other attempts to show this—such as an explanation for the fact that the prevalence of hypertension and diabetes is two to three times higher for Black than White American—have been disproven when looking at people with similar genetic backgrounds, such as West African and African-origin Caribbean populations that have similar rates to White Americans. Though there are a few cases like sickle cell disease, ultimately biological differences to not contribute significantly to racial differences in health outcomes.

However, class distribution across races does account for some of the disparity in health outcomes.  In fact, minorities are more likely to be low-income than White Americans, thus less likely to be able to invest in T as mentioned above. For example, Black Americans are 2.5 times more likely to be impoverished than White Americans. Taking this into account can explain much—and in some cases, all—of the difference. As shown in Figure 5 through mortality rates from heart disease, low-income people have similar rates in mortality regardless of race.[22] Despite higher death rates from black men, the prevalence of reported heart disease is virtually identical between black and white men. This points to a variety of factors that are difficult to trace and measure; part of this is under diagnosis due to lower insurance rates, and Black Americans are less likely to be insured.[23]

Heart Disease Death Rates Among Men and Women

Figure 6 

However, it is important to note that there are still factors unexplained by income—for both sexes across each income level, White Americans are always less likely to die from heart disease. Just as race influences class position, it may also be influencing other factors, independent of class, that researchers still cannot fully explain. As such, we must next look to the impact of race on health outcomes independent of income. Some cases of the black-white disparity are actually exacerbated by income and educational attainment; for instance, low birth weight (a main cause of infant mortality) actually increases with higher levels of education. College-educated black women are more likely than their white counterparts to have low birth weight babies.[24]

One of the factors that likely contributes to racial disparities of health that is difficult to measure and fully understand is the impact of racism. A popular framework describes racism on three levels. The first is institutionalized racism, which is the differential access to health resources, including health-relevant goods and services like health insurance, nutritious diets, and means of exercising. The next is personally-mediated racism, which is the personal bias not necessarily involved in institutionalized racism that includes prejudice (assumptions about others based on their race) and discrimination (actions based on those assumptions) that could include poor service or failure to communicate with patients. The third level is internalized racism, which can lead to feelings of helplessness and self-devaluation that ultimately limit one’s self-expression and could result in a failure to fully interact with the health care system or communicate all of their needs to their clinicians. The three often intersect; for example, a minority patient may live in an low-income community and visit underfunded health clinics, where his doctor treats him differentially and the patient does not advocate for himself.[25]

Studies have shown that minority patients receive less preventive care, including cancer screening and influenza vaccinations, fewer prescriptions, fewer medical tests, and less treatment than their white counterparts.[26] Minority patients also report engaging in less shared decision making, which can significantly bolster patient satisfaction and health outcomes. Looking at diabetes specifically, minority diabetes patients self-reported “lower quality physician interactions, worse care, and worse outcomes. A common case researchers found is doctors tend to assume that minorities will be less likely to adhere to medication instructions and thus will not prescribe the medication.[27]

As seen through analysis of Grossman’s Health Capital Model, it is necessary to study the impact of the race and other socioeconomic factors on health outcomes. These social determinants and the ways they interact with each other provide a more holistic view of individuals lives and environments and thus offer important insights that can inform public policy.

  [1]“Socioeconomic Status.” American Psychological Association. Accessed November 8, 2015.


  [2] Galama, Titus, and Hans Van Kippersluis. “Health Inequalities through the Lens of Health Capital Theory Issues, Solutions, and Future Directions.” June 1, 2013. Accessed November 8, 2015.


  [3] “Low Income Populations and Physical Activity An Overview of Issues Related to Active Living.” Accessed November 8, 2015.


  [4] “Low Income Populations and Physical Activity An Overview of Issues Related to Active Living.” Accessed November 8, 2015.


  [5] “Obesity Rates & Trends.” The State of Obesity. September 1, 2015. Accessed November 8, 2015.


  [6] Agricultural Marketing Service - Creating Access to Healthy, Affordable Food. Accessed November 8, 2015


  [7] Why Low-Income and Food Insecure People Are Vulnerable to Obesity « Food Research & Action Center.” Food Research Action Center. Accessed November 8, 2015.


  [8] Treuhaft, Sarah, and Allison Karpyn. “The Grocery Gap: Who Has Access to Health Food and Why It Matters.” The Food Trust. Accessed November 8, 2015.


  [9] Treuhaft, Sarah, and Allison Karpyn. “The Grocery Gap: Who Has Access to Health Food and Why It Matters.” The Food Trust. Accessed November 8, 2015.


  [10] Ver Ploeg, Michele. “Access to Affordable, Nutritious Food Is Limited in “Food Deserts”” USDA ERS. March 1, 2010. Accessed November 8, 2015.


  [11] Treuhaft, Sarah, and Allison Karpyn. “The Grocery Gap: Who Has Access to Health Food and Why It Matters.” The Food Trust. Accessed November 8, 2015


  [12] Dwyer, Marge. “Eating Healthy vs. Unhealthy Diet Costs about $1.50 More per Day.” Harvard School of Public Health. December 5, 2013. Accessed November 8, 2015.


  [13] “Earnings and Unemployment Rates by Educational Attainment.” U.S. Bureau of Labor Statistics. April 2, 2015. Accessed November 8, 2015.


  [14] “Understanding the Links between Education and Smoking.” Understanding the Links between Education and Smoking. Accessed November 8, 2015.


  [15] Galama, Titus, and Hans Van Kippersluis. “Health Inequalities through the Lens of Health Capital Theory Issues, Solutions, and Future Directions.” June 1, 2013. Accessed November 8, 2015.


  [16] Galama, Titus, and Hans Van Kippersluis. “Health Inequalities through the Lens of Health Capital Theory Issues, Solutions, and Future Directions.” June 1, 2013. Accessed November 8, 2015.


  [17] Kwoh, Leslie. “Hazard of the Trade: Bankers’ Health.” WSJ. February 15, 2012. Accessed November 8, 2015.


  [18] “Highlights from the National Healthcare Quality and Disparities Report,” Agency for Healthcare Research and Quality, accessed November 7, 2015, http://archive.ahrq.gov/research/findings/nhqrdr/nhdr10/Key.html.


  [19] “Uninsured Rates for the Nonelderly By Race/Ethnicity,” Kaiser Family Foundation, accessed November 7, 2015, http://kff.org/uninsured/state-indicator/rate-by-raceethnicity/.


  [20] Ichiro Kawachi, Ichiro, Norman Daniels, and Dean E. Robinson C. “Health Disparities By Race And Class: Why Both Matter.” Health Affairs 24 (2005): 343-352.


  [21] Ichiro Kawachi, Ichiro, Norman Daniels, and Dean E. Robinson C. “Health Disparities By Race And Class: Why Both Matter.” Health Affairs 24 (2005): 343-352.


  [22] Ichiro Kawachi, Ichiro, Norman Daniels, and Dean E. Robinson C. “Health Disparities By Race And Class: Why Both Matter.” Health Affairs 24 (2005): 343-352.


  [23] Hayward, Mark D., Toni P. Miles, Eileen M. Crimmins, and Yu Yang. “The Significance of Socioeconomic Status in Explaining the Racial Gap in Chronic Health Conditions.” American Sociological Review 65.6 (2000): 910.


  [24] Schoendorf, Kenneth C., Carol Hogue, Joel C. Kleinman Jr., and Diane Rowley. “Mortality among Infants of Black as Compared with White College-Educated Parents.” New England Journal of Medicine 326.23 (1992): 1522-526.



  [25] Jones, Camara. “Levels of Racism: A Theoretic Framework and a Gardener’s Tale.” American Journal of Public Health 90.8 (2000): 1212-215.


  [26] Peek, Monica E., Angela Odoms-Young, Michael T. Quinn, Rita Gorawara-Bhat, Shannon C. Wilson, and Marshall H. Chin. “Racism in Healthcare: Its Relationship to Shared Decision-making and Health Disparities: A Response to Bradby.” Social Science & Medicine 71.1 (2010): 13-17.


  [27] Peek, Monica E., Angela Odoms-Young, Michael T. Quinn, Rita Gorawara-Bhat, Shannon C. Wilson, and Marshall H. Chin. “Racism in Healthcare: Its Relationship to Shared Decision-making and Health Disparities: A Response to Bradby.” Social Science & Medicine 71.1 (2010): 13-17.
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  • <h3>National Center for Education Statistics</h3><p><strong><img width="400" height="80" alt="" src="/live/image/gid/4/width/400/height/80/479_nces.rev.1407787656.jpg" class="lw_image lw_image479 lw_align_right" data-max-w="400" data-max-h="80"/>The National Center for Education Statistics (NCES) is the primary federal entity for collecting and analyzing data related to education in the U.S. and other nations.</strong> NCES is located within the U.S. Department of Education and the Institute of Education Sciences. NCES has an extensive Statistical Standards Program that consults and advises on methodological and statistical aspects involved in the design, collection, and analysis of data collections in the Center. To learn more about the NCES, <a href="http://nces.ed.gov/about/" target="_blank">click here</a>.</p><p> Quick link to NCES Data Tools: <a href="http://nces.ed.gov/datatools/index.asp?DataToolSectionID=4" target="_blank">http://nces.ed.gov/datatools/index.asp?DataToolSectionID=4</a></p><p> Quick link to Quick Tables and Figures: <a href="http://nces.ed.gov/quicktables/" target="_blank">http://nces.ed.gov/quicktables/</a></p><p> Quick link to NCES Fast Facts (Note: The primary purpose of the Fast Facts website is to provide users with concise information on a range of educational issues, from early childhood to adult learning.): <a href="http://nces.ed.gov/fastfacts/" target="_blank">http://nces.ed.gov/fastfacts/#</a></p><p>See all <a href="/data-resources/">data and resources</a> »</p>
  • <h3>The Penn World Table</h3><p> The Penn World Table provides purchasing power parity and national income accounts converted to international prices for 189 countries/territories for some or all of the years 1950-2010.</p><p><a href="https://pwt.sas.upenn.edu/php_site/pwt71/pwt71_form.php" target="_blank">Quick link.</a> </p><p>See all <a href="/data-resources/">data and resources</a> »</p>
  • <h3>USDA Nutrition Assistance Data</h3><p><img width="180" height="124" alt="" src="/live/image/gid/4/width/180/height/124/485_usda_logo.rev.1407789238.jpg" class="lw_image lw_image485 lw_align_right" srcset="/live/image/scale/2x/gid/4/width/180/height/124/485_usda_logo.rev.1407789238.jpg 2x, /live/image/scale/3x/gid/4/width/180/height/124/485_usda_logo.rev.1407789238.jpg 3x" data-max-w="1233" data-max-h="850"/>Data and research regarding the following <strong>USDA Nutrition Assistance</strong> programs are available through this site:</p><ul><li>Supplemental Nutrition Assistance Program (SNAP) </li><li>Food Distribution Programs </li><li>School Meals </li><li>Women, Infants and Children </li></ul><p> Quick link: <a href="http://www.fns.usda.gov/data-and-statistics" target="_blank">http://www.fns.usda.gov/data-and-statistics</a></p><p>See all <a href="/data-resources/">data and resources</a> »</p>
  • <h3>The World Bank Data (U.S.)</h3><p><img width="130" height="118" alt="" src="/live/image/gid/4/width/130/height/118/484_world-bank-logo.rev.1407788945.jpg" class="lw_image lw_image484 lw_align_left" srcset="/live/image/scale/2x/gid/4/width/130/height/118/484_world-bank-logo.rev.1407788945.jpg 2x, /live/image/scale/3x/gid/4/width/130/height/118/484_world-bank-logo.rev.1407788945.jpg 3x" data-max-w="1406" data-max-h="1275"/>The <strong>World Bank</strong> provides World Development Indicators, Surveys, and data on Finances and Climate Change.</p><p> Quick link: <a href="http://data.worldbank.org/country/united-states" target="_blank">http://data.worldbank.org/country/united-states</a></p><p>See all <a href="/data-resources/">data and resources</a> »</p>