Estimating the economic burden using the value-of-a-statistical-life method

Monetizing the days missed and multiplying by the number of employees in each weight category yields the following findings:

• The average employee with a normal BMI reported missed 1.78 days per year due to illness or injury. • Overweight/obesity increased reported annual absenteeism by 0.13 and 1.07 days, respectively. However, only the estimate for obesity was statistically significant. • Based on these estimates and the prevalence of overweight/obesity, total annual absenteeism costs due to excess weight are estimated to be $26.8 billion (2018 international $), which represented 1.42 percent of GDP in 2018.

Comparable estimates from Saudi Arabia are not available. However, our estimates are broadly consistent with previous published estimates in the United States. Finkelstein et al. (2010), for example, found that overweight employees in the United States miss up to 1.1 days more per year and obese individuals miss between 0.5 and 9.4 days more, depending on the degree of excess weight. The findings of this chapter are also consistent with two studies that found that obesity, but not overweight, is significantly associated with greater absenteeism among full-time male employees in the United States (Cawley, Rizzo, and Haas 2007; Finkelstein, Fiebelkorn, and Wang 2005).

Note that these estimates do not take into account presenteeism (that is, reduced productivity while working) or other productivity-related expenses that may result from excess weight, such as those due to disability or inability to work. For obese full-time employees in the United States, presenteeism costs are estimated to be US$30 billion (2010 dollars) or 41 percent of total costs attributable to obesity annually (Finkelstein et al. 2010). Thus, these estimates understate the total indirect costs attributable to excess weight.

ESTIMATING THE ECONOMIC BURDEN USING THE VALUE-OF-A-STATISTICAL-LIFE METHOD

Burden-of-illness studies such as those described earlier tend to use market rates for forgone earnings. An alternative paradigm is to apply a value of a statistical life (vSL). A vSL is quantified based on the marginal rate of substitution between income (or wealth) and mortality risk. Using the vSL method, the value of premature death is inferred from real or hypothetical trade-offs that people willingly make (how much individuals are willing to pay to reduce the risk of death). These trade-offs typically entail taking on greater health risks in exchange for something of value, such as trading off the risk of working in a smoke-filled bar or on a deep sea fishing vessel, both risky occupations, in exchange for a higher salary. This higher salary can be interpreted as a risk premium and can be used to estimate the vSL.

The main advantage of this approach is that it is most consistent with economic theory (that is, utility maximization). The cost-of-illness approach accurately quantifies the burden of disease from an accounting perspective but does not take into account the changes in utility (value) that individuals may accrue from, say, not having to diet and exercise or the intrinsic value that people place on being alive. An additional advantage of the vSL approach is that, unlike the cost-of-illness approach, it can be used to generate unique estimates that each

individual or set of individuals places on a particular risky scenario. These estimates, if aggregated across individuals, can be interpreted as the total statistical value of the loss due to a condition (for example, diabetes) and may include all direct, indirect, and intangible costs not easily measured, such as pain and suffering and premature mortality.

The logic of the vSL approach can be illustrated through the following thought experiment (US EPA 2020).Suppose each person in a sample of 100,000 people were asked how much they would be willing to pay for a reduction in their individual risk of dying of 1 in 100,000, or 0.001 percent, over the next year. Since this reduction means we would expect one fewer death among the sample over the next year, this is sometimes described as “one statistical life saved.” Now suppose that the average response to this hypothetical question was $100. Then the total dollar amount that the group would be willing to pay to save one statistical life in a year would be $100 per person times 100,000 people, or $10 million. This is an estimate of the vSL.

Although this approach is intuitively appealing and has been used in policy analyses in a range of fields, from environment to transportation to health, it has several limitations. Weaknesses include problems with stated preference questions, such as the one posed above, where responses are skewed by people’s inability to differentiate between changes in small risks, framing issues, and hypothetical bias, as well as oversimplification.

An alternative to eliciting information through survey questions is to observe behavior in real-world situations, such as the wage premia required by workers who engage in risky occupations, as compared to similar workers who engage in lower-risk occupations. This approach, known as revealed preference, avoids many of the problems of the survey-based approach, but it suffers from other methodological issues such as possible selection bias and the variation in risk perceptions across individuals. For these reasons, results of the vSL vary greatly across studies, with estimates ranging between $45,000 and $18.3 million (viscusi and Masterman 2017).

A recent OECD report indicates that the societal cost of overweight/obesity using the vSL method is $3,000 (US$ purchasing power parity, or PPP) per capita per year on average between 2020 and 2050 (Cecchini and vuik 2019). Based on population estimates, obesity prevalence, and 2018 GDP (US$ PPP) (World Bank 2018a, 2018b) (current international $), this suggests an annual societal cost of obesity of 5.42 percent of GDP worldwide.

Although no direct measures of vSL attributable to obesity in Saudi Arabia were identified, a published estimate of the US vSL converted to Saudi Arabia, after adjusting for PPP, indicates a Saudi Arabia vSL of $4.05 million (2015 US$) per statistical life saved (viscusi and Masterman 2017). Published estimates indicate that, globally, obesity contributes to 7.1 percent of deaths from all causes (GBD 2015 Obesity Collaborators 2017). Given this figure, the Saudi population count of 31.72 million for 2015 and an overall death rate of 0.35 percent (IHME 2019), taken together, suggest that, in 2015, 7,684 premature deaths (= 0.071 × 0.003412 × 31,717,667) were attributable to obesity. Multiplying the obesity-attributable deaths in Saudi Arabia times the Saudi Arabia vSL ($4.05 million in 2015 US$) suggests that the value of premature mortality resulting from obesity is $31.1 billion (2015 US$) or 4.76 percent of GDP in 2015 (World Bank 2018a), which is in line with the OECD estimate.