Pills and Productivity: What Economic Theory Tells Us about Employees’ Work Behaviors

Laura T. Pizzi PharmD, MPH, and Joshua J. Gagne PharmD, Department of Health Policy, Jefferson Medical College, Philadelphia, PA, USA; and Kenneth D. Smith PhD, Department of Health, City of Philadelphia, Philadelphia, PA, USA

At a time when health care expenditures are the fastest growing cost component within Fortune 500 companies, outgrowing both employee earnings and profits [1, 2]; improving worker productivity has never been so critical. Based on the current rate of escalation, health care costs to employers are expected to exceed profits by 2008 [2]. To improve worker productivity, employers have started getting involved in health promotion and disease prevention to keep employees healthy and at work and also to help the workers maintain effectiveness while working [3-6]. Several chronic conditions such as asthma, depression, diabetes, and migraine are associated with excessive losses in worker productivity, but these losses may be abated by effective pharmaceutical interventions [7-12].

Recognizing that effective pharmaceutical interventions have the potential to reduce employers’ overall health-related expenses, researchers have been studying the effects of drugs on lost workplace productivity. Randomized clinical trials, considered the gold standard for measuring clinical efficacy, are increasingly being used as a vehicle for measuring work productivity-usually as a secondary endpoint. In this article, we present a model that raises several important considerations for researchers interested in measuring productivity within a clinical trial.

Individuals, with or without health conditions, have a multitude of labor supply decisions to make during the course of their lifetime, which can be categorized into three types - life cycle decisions, intermediate-run decisions, and shortrun decisions. Life cycle decisions, or decisions that have effects throughout the duration of one’s work life, include career choices, education, and training. Decisions made over the intermediaterun may involve job selection within a career or vacation planning. Individuals must also make short- run decisions on nearly a daily basis.

Short-run decisions, such as choosing to take time off (absenteeism) or being less effective at work (presenteeism) on any given day may be affected by a pharmaceutical intervention. For example, an individual suffering from irritable bowel disease (IBD) may be prone to miss work during an exacerbation (absenteeism). However, an effective medication could decrease the severity, or all together prevent a flare up, thereby allowing the individual to attend work. Hence, the medication had an impact on the worker’s productivity. However, longer-run decisions whether life cycle or intermediate-run, are more fixed and therefore are usually less likely to be influenced by pharmaceutical interventions. This is so because individuals with functional limitations adapt to and may select themselves into occupations that mitigate an impairment or functional limitation [13]. For example, an individual with a physical disability, which makes physical labor difficult, would be naturally more inclined to seek a job with minimal physical tasks, such as computer programming. It is important to keep in mind, that short-run decisions are more likely to be affected by medications but estimation of short-run effects may be subject to bias resulting from individuals’ pre-selection into careers or job types that fit their function, i.e., a longer-term decision that they have made.

Intuitively, one would expect a pharmaceutical intervention, which improves health, to decrease both absenteeism and presenteeism. That is, an effective intervention would prevent employees from missing work due to a disease or condition, as was the case with the patient with IBD in the example above, and it would also help them maintain their effectiveness while on the job. Indeed, a number of existing productivity studies support this hypothesis [12, 14, 15], but we constructed a simple model of employee behavior based on economic theory to demonstrate that effective pharmaceuticals may not always improve a standard measure of productivity

All of the variables included in the model are defined in the table. The model predicts the effect of the use of a drug on time worked and consists of a basic static equation of labor supply, where individuals choose hours of work (T) and consumption (C) to maximize total utility (U). We also incorporated job characteristics (J), health (H), and other basic relationships in the model to simulate important occupational and clinical factors. We assume that the price of the pharmaceutical intervention (x) is zero within our model, to simulate productivity measurement within a clinical trial where drug is provided free. The price of C is expressed as pc and can be proxied using cost of living data.

Next, we need to define relationships within the model then solve the model to yield a comparative static:

If the model predicts that use of a pharmaceutical intervention (x) leads to increasing T, then, in theory, use of x reduces absenteeism. However, drug efficacy, h(x), is non-decreasing in x, that is, the pharmaceutical intervention can have a net beneficial impact or no impact at all (note that a net negative effect due, for example, to a serious adverse event that outweighs any clinical benefit, is not considered in this model since the model assumes participants would likely withdraw from the trial in such a case). Quality of leisure time, v(h(x)) is non-decreasing in h, that is, if the drug has any impact on leisure time, it will be a positive impact. Job satisfaction, V(h(x),J) is non decreasing in h, and its magnitude depends on J, an expression of job characteristics.

What does the model tell us about whether a drug (x) will decrease absenteeism? Since the denominator of the comparative static is negative, according to the law of declining marginal utility, T will increase as a result of x only if [vh(h)-Vh(h,J)] <0, where vh(h) and Vh(h,J) represent the impact of an increase in health or functioning, due to drug x, on the value of work and non-work time, respectively. That is, the use of the drug improves job satisfaction more than it increases the quality of leisure time. We call this expression “the impact of health on the quality of net time allocation.” For example, a worker may be more willing to trade leisure for work if a pharmaceutical intervention makes work less burdensome, but has little or no impact on non-work time.

The model also reveals several important considerations for researchers who seek to measure productivity within the context of a clinical trial. First, work hours (T ) are determined by several variables which include but are not limited to the pharmaceutical intervention. Wages, the cost of other goods, job characteristics, and health status are other important predictors of time worked that should be considered when designing a productivity study. Second, since self-selection into jobs may be inherent within the study population, randomization by job type is appropriate (or alternatively, stratifying the analysis by job type after data are collected). Finally, the model suggests that pharmaceuticals could improve the quality and quantity of non-work time, the latter of which is therefore important to capture.

Estimates of absenteeism are subject to influence from a number of variables. As this model shows, even in a randomized clinical trial, the impact of a drug on hours worked may be indeterminate. Even when effective in improving health status, a drug could demonstrate either a positive or negative effect on hours worked by increasing the value of one’s leisure time. Researchers can benefit from understanding that work productivity is influenced by multiple social and economic factors, and if these factors are not considered, results may not be interpretable.

References

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