The Impact of Health Status on Human Capital

Mary O’Mahony

Mary O’Mahony is Professor of Applied Economics at King’s Business School, King’s College London.

Health and work

It is uncontroversial to say that being in poor health affects people’s ability to work effectively. Quantifying the impact of health status on work is, however, much more difficult. Poor health can permanently remove individuals from the labour force, through early retirement or long-term illness. For those in employment, poor health affects the amount of time they spend in their jobs, absenteeism, and their productivity when in work, known as presenteeism. How might we bring together these various aspects to obtain an overall measure of the impact on work and, ultimately, on growth and productivity?

Measuring human capital stocks

A starting point is in recognising that health status is embodied in people and so should focus on the notion of human capital. A common approach used by statistical offices, including the UK Office for National Statistics, to measure the stock of human capital in a country is to calculate the potential lifetime earnings of each person in the active population, differentiated by gender, age and education and taking account of the probabilities that those in younger age groups remain in education. These earnings are then summed across people in each age, gender and education qualification group to yield an overall figure for Human Capital Stocks (HCS). This is known as the incomes approach and originated in the work of Dale Jorgenson and Barbara Fraumeni in the US. Measures of the productive human capital stock also take into account employment rates for the various groups.

Incorporating health status into this framework is relatively straightforward, through dividing the population according to health status and estimating the impact of health on probabilities of employment, hourly wages and hours worked. This allows an estimate of the relative values of the human capital of those in poor health relative to good health, how much is due to working versus not working, and how it impacts those in work.

Health status, work and earnings

Implementation of the framework requires defining health status and a careful analysis of the impact of this on employment, earnings and hours, and by implication, good data on all these variables. Research by the authors attempted such a calculation for the UK, using data from the Understanding Society Surveys (USS) and the Labour Force Surveys (LFS). A health index is measured by regressing a self-assessed health measure – if the individual surveyed considered themselves in excellent, very good, good, fair or poor health – on a range of medical conditions. This health index was then used to estimate the impact of being in poor or fair health on probabilities of retirement and hourly earnings, combined with data on actual hours worked by health status.

The research suggests that the probability of retiring, on average for men aged over 50, is about three times higher for those in poor than in good health. For women the ratio is about double. Men in poor health on average earn about 75% of the hourly wages of those in good health, whereas for women the figure is just over 80%. In terms of hours worked per year, both men and women in poor health work about 10% less than those in good health. Not surprisingly, these differences vary by age and qualification. For example, males aged 65 with a university degree are far less likely to retire due to ill health than those of the same age with GCSEs as their highest qualification. Similarly, the impact of poor health on wages and hours worked also vary depending on qualification level.  

Including health status in HCS

The health impacts outlined in the previous paragraph can be combined with estimates of lifetime earnings to calculate the reductions in potential and productive HCS arising from ill health, defined as those in the bottom 10% of the health index. Starting from the potentially active labour force, those aged 16 to 69 not in education, in 2014, 36% of potential HCS was not used in production due to people being unemployed, retired, long-term ill or inactive in other ways. This rises to about 60% for those aged 50 or older, the population most affected by health issues. Absence from work due to long-term illness reduced the total HCS by 8% and the HCS for the age 50 plus group by about 18%. Reductions due to retirement for those in poor health is only 2% in the overall HCS, but this increased to 16% for those aged 50 plus, comparable to the long-term illness reduction for that age group.

Taking account of both absenteeism and presenteeism suggests a further reduction in the productive HCS by 1% in aggregate and about 2% for the 50 plus age group. These smaller reductions reflect the fact that there are relatively few people in poor health in the workforce and they tend to be concentrated among those with low qualifications. Therefore, overall the UK economy would have had about 11% more human capital in 2014 if those in ill health were in good health, and a greater 36% for the 50 plus age group. The contribution of males to this reduction is greater than for females, reflecting both higher retirement rates and lower relative wages due to poor health and the fact that males account for a greater share of the aggregate HCS due to their higher earnings. Likewise, the HCS reduction for those with low qualifications is relatively greater than for those with university degrees.

Future work

Although the research to date only shows a snapshot of health effects for one year there are likely to be significant changes over time. Therefore, the next step is to construct a time series which involves some difficult mapping of data from different sources across time. The implications for aggregate economy growth and productivity depend on how human capital stocks can be incorporated within the framework of national accounts, which also involve complex conceptual and data issues and so require further research. Finally, these calculations only account for the impact of poor health on individuals themselves but it is likely that workers in poor health also affect other workers by producing bottlenecks when absent or working less effectively in teams. Quantifying these ‘spillover’ effects is also an important avenue for future research.