The American Journal of Managed Care April 2007
A Review of Methods to Measure Health-related Productivity Loss
Perhaps more complicated than trying to account for the total time lost by quantifying presenteeism is trying to understand the many competing methods for monetizing (ie, estimating the cost of) lost productivity. These methods comprise the following 3 main types: (1) salary conversion methods, which use survey responses and salary information to estimate productivity loss; (2) introspective methods, which use survey responses as a basis for thought experiments to give businesses an idea of the magnitude of their lost productivity; and (3) firm-level methods, which attempt to monetize productivity losses based on the cost of countermeasures used to deal with absenteeism and presenteeism.
Salary Conversion Methods. Salary conversion methods attempt to estimate productivity losses based on self-reported lost time or decreased productivity. The simplest version is the human capital approach (HCA), which expresses the loss as the product of missed workdays multiplied by daily salaries.26 Originally developed for monetizing absenteeism, the method has been extended to presenteeism losses by using self-reported unproductive hours or self-reported percentage reduction of performance instead of missed days.24,27,28 The obvious attractions of this method are its computational ease, its intuitive plausibility, and its consistency with economic theory (assuming perfectly competitive labor markets) that wages should reflect a worker's marginal contribution to a firm's output. Although its validity has not yet been assessed (to our knowledge), there was consensus among the experts we interviewed that the HCA provides at least a lower-bound estimate for the true cost of lost productivity (telephone interviews with Sean Nicholson [February 22, 2005] and Thomas Parry, PhD [February 15, 2005]). To increase the accuracy of the estimate of productivity losses, one expert suggested also including the cost of fringe benefits (telephone interview with Ronald C. Kessler, PhD [February 8, 2005]). The HCA is the method typically used in studies reporting the economic effect of health-related productivity losses. Depending on the available data sources, authors have used actual salaries of the respondents,6,29 mean salaries for the corporation,30 or national median wages4 for the conversion.
An extension of the HCA is the team production model developed by Pauly and colleagues,31 who argue that simple salary-based conversion is appropriate for workers performing discrete tasks in isolation, but the model fails to take into account the interdependence of job functions in the modern economy. For example, if the only surgeon in a hospital stayed home sick, the entire operating room would remain idle, causing much greater losses than just the surgeon's salary. The authors proposed to operationalize this interdependence by the following 3 criteria: (1) the replaceability of an employee, (2) the extent to which an employee works as part of a team, and (3) the time sensitivity of an employee's work.31 Initial empirical work by Nicholson and colleagues32 derived a set of multipliers that can be applied to a worker's salary for 35 different job categories. Simple jobs, like that of a fast-food cook, have a multiplier of 1.00, suggesting that the productivity loss equals the actual salary, while more demanding occupations, such as construction engineering, have higher multipliers that reflect overall effect on the firm. Different multipliers are used for short-term (3 day) and long-term (2 week) absences. Ongoing work aims at a larger set of multipliers and at methods to capture the interaction between medical conditions and job characteristics (telephone interview with Sean Nicholson, PhD [February 22, 2005]). This approach has 2 practical challenges. First, a large library of multipliers would have to be created and maintained. Second, the method is based entirely on individual-level characteristics and does not take firm-level factors into account. For example, it is conceivable that the absence of an analyst would have different implications for a consulting firm than for a not-for-profit research organization. Other firm-level factors, such as unionization and competitive position, may also modify the effect of loss of productivity in a given job category.
A more fundamental challenge was posed by Koopmanschap and colleagues,33 who argued that the HCA overestimates the true absence-related productivity losses because short-term absences might be partially compensated with greater effort or unpaid overtime, whereas longer-term absences would lead to replacement of workers with new hires. Based on those considerations, the authors proposed the friction cost method that aims at estimating only the actual lost production, as opposed to the potential lost production estimated by the HCA. They tested their method on national data from the Netherlands and found the estimates of lost productivity to be consistently lower than those derived by the HCA.33 We identified no attempt to apply the method to US data or to data at the company level. Other authors have challenged the friction cost method as inconsistent with concepts of standard economic theory, such as opportunity cost and profit maximization.34 However, this discourse remains largely theoretical at this point because neither of the salary conversion methods has been evaluated empirically, to our knowledge.
Introspective Methods. Introspective methods reflect an attempt to overcome the theoretical and practical challenges of converting self-reported productivity reduction into monetary units. Some researchers have argued that conversion should be abandoned in favor of providing guidance to firms on deriving their own estimates: for example, managers would be provided with an analysis of the productivity survey and asked to consider questions such as "How much would you be willing to pay a contractor who can raise everyone's productivity by 20%?" or "How many full-time employees could you cut if the productivity of your chronically ill workers increased by 20%?" (telephone interview with Ronald C. Kessler, PhD [February 8, 2005]). Another approach is to encourage managers to estimate the revenue that various staff members contribute and to use this number for conversion (telephone interview with Ronald C. Kessler, PhD [February 8, 2005]). The aim of such thought experiments is to illustrate the magnitude of the problem rather than to derive precise estimates. Although helpful, their validity remains untested (to our knowledge), and their results have not been compared with those of the HCA approach as far as we know.
Firm-level Methods. Firm-level methods represent a logical extension of the introspective methods and use a topdown approach that assesses firm-level information to derive cost estimates for lost productivity. These methods are based on the premise that managers have a good sense of how their company's productivity is affected by health-related problems and use countermeasures to deal with them. For example, they may have redundant staff to compensate for absences, or they may hire temporary workers or offer overtime payment to maintain output. Alternatively, they could forgo revenues. Economic theory suggests that a competitive firm combines these different strategies to maximize profits. Therefore, information about a firm's cost for those countermeasures can be used to approximate its lost productivity. The attraction of this approach is that it does not require detailed individual-level data and that the cost of many of the countermeasures (such as the fees paid to temporary employment agencies) is easy to quantify. The downsides are that some of the cost may be intangible and that forgone revenue estimation must rely on a manager's perceptions. It may also prove difficult to elicit countermeasures to presenteeism as opposed to absenteeism because presenteeism is not immediately visible to a firm and may not provoke a conscious response. Furthermore, the correct attribution of the cost items to health-related productivity losses needs to be assured, as (for example) part of the temporary staff could also be part of a firm's usual staffing mix.
As for other firm-level methods, empirical evidence remains sparse. One study35 used staffing cost to cover short-term disability absences to estimate productivity losses, but no published evidence was found of attempts to generalize this approach into a broader framework for measurement.
The interest in measuring and monetizing the effect of health on corporate productivity has resulted in the development of numerous instruments to capture this important concept. Most have undergone validity testing and have gained acceptance as reliable tools for research and benefits decisions. Among those instruments, the biggest gap remains the lack of an established and validated method to derive monetary estimates of the cost of lost productivity. Although many users are comfortable in applying a salary conversion method and believe that it provides at least a lower-bound estimate, a more rigorous evaluation of this method and its alternatives is warranted before it becomes the basis for potentially far-reaching policy and managerial decisions. A first step could be to benchmark the different methods used to determine whether and to what degree estimates based on different methods differ.
Conducting such research is by no means a straightforward task because direct measurement of job productivity is difficult, particularly in knowledge-based occupations. This mandates that a research agenda should involve multiple disciplines or job descriptions. It should also reflect the interest of various stakeholders, as (for example) the weight of evidence that businesses will require for operational decisions will differ from the standards of the research community. Any method will also have to be tailored to the precise question it aspires to answer. For example, the social welfare perspective on measuring cost is different from a corporate perspective, and various sectors of the economy may require different approaches. The review herein should stimulate interest in this field and contribute to endeavors that push it forward.
We thank Ronald C. Kessler, PhD, Sean Nicholson, PhD, and Thomas Parry, PhD, for contributing their expertise to this review. We are indebted to Ron Loeppke, MD, MPH, for his input and guidance and Emily Taylor for her help with preparing the manuscript.
Author Affiliation: From RAND Corporation, Arlington, Va.
Funding Source: This study was supported by CorSolutions, Inc through a contract with RAND Corporation.
Correspondence Author: Soeren Mattke, MD, DSc, RAND Corporation, 1200 S Hayes St, Arlington, VA 22202. E-mail: firstname.lastname@example.org.
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