This paper, “Health as Human Capital: Synthesis and Extensions” by Gary S. Becker, provides a profound economic analysis of health, moving beyond the typical focus on health care delivery to explore health as a form of human capital. The author emphasizes that while human capital literature extensively covers education and job training, health has received comparatively less attention, partly because it involves somewhat different concepts. The article integrates and extends previous contributions to present an emerging theory of health as human capital.
The field builds on three interconnected developments:
Optimal investments in health: This includes investments made by individuals, drug companies, and to a lesser extent by governments, stemming from Grossman’s work and discussions in the insurance literature of self-protection, as well as investments by pharmaceuticals.
The value of life literature: This analyzes how much people are willing to pay for improvements in their probabilities of surviving different ages.
The importance of complementarities: This highlights the crucial links between health and other human capital investments like education, as well as connections between health investments, discount rates, and progress in fighting various diseases and other sources of overall changes in survivorship rates.
Dramatic Rise in Life Expectancy: A strong motivation for this economic interest in health is the remarkable increase in life expectancy. While life expectancy at birth in OECD countries hovered around 40 years in the 19th century, it “took off dramatically” in the 20th century, reaching the mid-sixties by 1950 and nearly 80 years by the early 21st century. This decline in mortality across all ages is considered one of the most significant economic and social developments of the 20th century.
The Statistical Value of Life (SVL): The SVL measures the “compensating variation” in wealth that makes a person equally well off with improved survivorship. It quantifies how much people are willing to pay for improvements in their probabilities of surviving. Crucially, the SVL is not constant for the same person; it varies with initial wealth (generally rising), age (generally falling, though not always monotonically), interest rates (falling as they increase), and the level and magnitude of changes in survivorship. Empirical estimates for a young person in the United States typically range from $2 million to $9 million, with a central tendency of $3-5 million. It is vital to recognize that the vast majority of this statistical value of life comes not from foregone earnings, but from the loss of leisure time and the differences between average and marginal utilities. For example, if lost earnings account for about $1 million out of a $4.4 million estimate, the remaining $3.4 million is attributed to these other factors. This also helps to explain why large health expenditures are often made at older ages to delay death.
Pervasive Complementarities in Health:
Between Diseases: An increase in the probability of surviving one disease tends to raise the amount spent on fighting other diseases. For instance, advances in reducing cardiovascular disease deaths have led to greater attention and spending on other diseases of old age, such as cancer, diabetes, and Alzheimer’s. Conversely, high mortality from diseases like AIDS in some African countries can reduce concern for preventing other diseases.
Between Ages: An increased probability of surviving in future periods creates more incentive to try to survive current periods. This means an exogenous increase in future survival probabilities induces greater spending on present survival. Similarly, if the probability of surviving childhood is low, there’s less incentive to invest in health for older ages.
Health & Addictions: The probability of surviving into the future affects the expected utility from consuming addictive goods in the present. A greater probability of surviving incentivizes the consumption of beneficial addictive goods (e.g., regular exercise, religion, stable relationships) and disincentivizes harmful ones (e.g., heavy drinking, hard drugs, smoking, fatty foods). This implies that good health is complementary with good habits and beneficial addictions, while poor health is complementary with bad habits and harmful addictions. The paper suggests that causation also runs from better health to better habits, not just the other way around.
Education & Health: Increased survivorship at later ages raises the returns from investments in education because educational costs are incurred earlier, and returns are reaped later. More educated individuals tend to take better care of their health, for example, by visiting better doctors, adhering to prescriptions, and eating nutritious diets, even with a given level of medical spending. Education directly raises survivorship by improving productivity in health investments.
Health & Discount Rates: There is a positive relationship between health and lower discount rates on future utilities. This is not merely selection; healthier persons have a greater incentive to invest in “imagination capital” which helps to reduce how much future utilities are discounted. Lower discount rates, in turn, lead to more investment in human capital (education and health), savings, and the development of beneficial habits. The paper challenges the traditional economic view of discount rates as exogenous, arguing that individuals can affect them.
Population’s Impact on Medical Innovation and Value of Mortality Declines: Market Size Drives R&D: Medical innovations exhibit increasing returns to scale because the cost of developing innovations is largely independent of the demand scale. A larger number of people in vulnerable age groups (“market size”) significantly increases the profitability of medical innovations. The Orphan Drug Act of 1982, which provides market exclusivity for drugs treating rare diseases, illustrates how market size affects R&D incentives, though the most profitable drugs still target large markets. Empirical evidence shows a direct correlation between the number of people in age groups (e.g., 45-64, 65+) and the number of new drugs introduced to treat diseases affecting those ages.
The Immense Value of Mortality Declines: The economic value of gains from declining death rates is enormous. Between 1970 and 2000, the estimated net gain for Americans was approximately $60 trillion (after subtracting increased medical expenditures). For all OECD countries, this figure rises to about $190 trillion for the same period. These massive aggregate gains are primarily driven by the sheer size of the population; even small individual gains become monumental when multiplied by millions of people.
Behavioral Responses to Expected Medical Progress: People are forward-looking and anticipate the development of new drugs. This expectation can influence current behaviors that might appear to reduce health, such as increased consumption of fatty foods or sedentary activities, contributing to rising obesity. The underlying assumption is that future medical advances will mitigate negative health consequences.
Limits to Life Expectancy: The paper argues that while biological factors are crucial, economic incentives also play a significant role in extending human life. As the number of people reaching older ages increases due to progress against diseases at earlier ages, the aggregate willingness to pay for treatments at old age rises, justifying more research into diseases of old age that was not worthwhile in the past.
Rethinking Inequality and Pandemic Costs: Traditional national income accounts like GDP are incomplete measures of welfare because they do not incorporate the value of increased life expectancy or other non-market determinants of well-being. When national income accounts are adjusted to include improvements in life expectancy (using willingness-to-pay estimates for health), there has been substantial convergence in inequality between rich and poor nations since 1960. This convergence is much more rapid than what changes in per capita incomes alone would suggest. However, this convergence varies by disease, being significant for infectious and respiratory diseases but negative for cardiovascular diseases and cancers, reflecting differing health priorities in poorer countries.
The Potential Cost of a Pandemic: Drawing on the 1918-19 flu pandemic (which killed an estimated 50 million people worldwide, 2.8% of the global population at the time), the paper estimates that a comparable avian flu pandemic today could result in 168 million deaths worldwide (2.8% of 6 billion people). Valuing each life conservatively at $3 million, such a pandemic could lead to an aggregate loss of around $25 trillion for the US and approximately $110 trillion worldwide. The loss of life would be the principal loss, dwarfing any declines in GDP. The justification for precautionary actions depends critically on the probability of such a pandemic.
Reference: Becker, G. S. (2007). Health as human capital: Synthesis and extensions. Oxford Economic Papers, 59(3), 379–410. https://www.jstor.org/stable/4500116
