The effects of depressive symptoms on earnings.

• Author: Cseh, Attila
• Position: Table

1. Introduction

   A widespread view of depression is that it puts an enormous social and economic burden on both the individual and society. Depression may reduce the individual's productivity, increase absenteeism and cut-back days, and cause loss of employment (Kessler and Frank 1997; Berndt et al. 1998; Lim, Sanderson, and Andrews 2000; Alexandre and French 2001; Marcotte and Wilcox-Gok 2001; Stewart et al. 2003; Alexandre, Fede, and Mullings 2004). If depression is treated, there are further costs in terms of payments or co-payments related to office visits, costs of medication, and time cost of the treatment. In the United States depression alone had a total cost of more than $83 billion in the year 2000, of which $51.5 billion was the estimated cost at the workplace (Greenberg et al. 2003). Much of this cost is borne by the affected individual.

   In this study, I focus on the wage impacts of depression, which is one of the most common diagnoses in the health care sector (World Health Organization 2001), by using the National Longitudinal Survey of Youth 1979 (NLSY79). It has been estimated that the lifetime prevalence of major depressive disorder is 16.2%, and the annual incidence rate is about 6.6% in the United States (Kessler et al. 2003). Antidepressants ranked fourth in annual sales among medications, with $10.2 billion in sales, and they were the fifth most frequently prescribed medications in the United States in 2005 (Intercontinental Marketing Services Health 2006). More than 122 million antidepressant prescriptions were written in 2005, yet depression and other mental disorders are generally underdiagnosed and undertreated (U.S. Department of Health and Human Services 1999; World Health Organization 2001).

   The causal effect of depression on workplace performance is difficult to measure, since the relationship between depression and labor market outcomes is rather complex. Depressed people could have lower earnings potential for several reasons. First, and most importantly, depression could hinder efficiency at the workplace. This can take various forms. For instance, during times of depression workers may find it harder to concentrate on a particular task, and everything may seem to take extra effort, or depression could increase the number of missing workdays (absenteeism) or the amount of lost productivity time (Kessler et al. 1999; Stewart et al. 2003). On the other hand, it is possible that poor labor market outcomes or stressful work environments create mental problems (Marcotte, Wilcox-Gok, and Redmon 1999; Clark, Georgellis, and Sanfey 2001; Paterniti et al. 2002). At the same time, having a job can be beneficial to the extent that employment facilitates the creation of social networks; colleagues can help those who are in need of emotional support. Furthermore, people who are prone to periods of mental stress may prefer jobs that provide generous health insurance, or alternatively, these people may select into jobs that can be done even while the individual is in a fragile mental state and that will allow them to be as productive as people without the mental illness.

   Finally, it is also possible that depression itself is not the cause or consequence of poor performance, but that it is instead an observable event that occurs in certain people with certain characteristics and that these people tend to perform below average because of the same characteristics that make them prone to psychological imbalance. In other words, depression could merely reflect certain personality traits that are also correlated with productivity.

   In the psychology literature, it is well documented that people with certain types of underlying character or personality traits are more prone to depression than are people who lack these traits (Clark, Watson, and Mineka 1994; Hettema 1995; Robins 1995; Watson and Clark 1995; Clark et al. 2003). Personality, on the other hand, has been documented to play an important role in hiring decisions and in later performance in the workplace (Barrick and Mount 1991; Tett, Jackson, and Rothstein 1991; Hogan, Hogan, and Roberts 1996; Goodstein and Lanyon 1999; Bono and Judge 2003). Usual types of personal characteristics that are important in the workplace and for the psychological wellness of the individual include commitment, responsibility, enthusiasm, activity, and optimistic life orientation positive characteristics; and sensitivity, indecisiveness, dependency, passivity, and pessimistic life orientation--negative personal characteristics.

   My paper is an innovation in that it takes advantage of a longitudinal survey design that features the same mental health information in multiple years. I use fixed-effects (FE) estimators to eliminate the unobserved heterogeneity that might have biased the results in previous studies. Therefore, by using multiple years of information from a panel survey, I am able to control for personal characteristics that seem to be important when estimating the effects of mental health on productivity. I find that ordinary least-squares (OLS) estimates are negative and strongly significant; however, FE estimates are smaller in magnitude, and they are mostly insignificant. My results indicate that taking personal characteristics into account reduces the magnitude of coefficient estimates of depressive symptoms.

   Figure 1 foretells the main story of the paper. The four lines in the figure represent the wage profile of four groups of people in the NLSY79. The thin dashed line represents the wage evolution of those who do not show depressive symptoms in any of the years when NLSY79 administered the Center of Epidemiologic Studies Depression Scale (CES-D) questionnaire. (1) The thin solid line represents the wage evolution of those who show depressive symptoms in both the early period (either 1992 or 1994) and the late period. (2) The thick dashed line represents the wage evolvement of those who show depressive symptoms in the early years but no symptoms in the late period, and the thick solid line represents those who show depressive symptoms in the late period but do not have symptoms in the early years.

   [FIGURE 1 OMITTED]

   The graph depicts simple means of logarithm of hourly wages throughout time without controlling for other potential covariates that affect wages. However, this graph is rather indicative of the results of this study. By looking at the dynamics of the four lines, we would expect OLS models to produce negative mental health coefficients on the depression dummy in a cross-sectional analysis. Figure 1 also indicates that a cross-sectional analysis would not give insights to the full story. The graph reveals the importance of an analysis that takes time into consideration and pays attention to the dynamics of the evolution of wages. From Figure 1, it is easily noticeable that there are three distinctly different groups of people in terms of depressive symptoms. (3) It seems that people who never show depressive signs are inherently different from those who have symptoms more than once during the analyzed period. Additionally, there are the symptom changers, who seem to be remarkably similar to each other in terms of their wage development and inherently different from both the no-symptom group and those who show depressive symptoms in both periods. If mental distortions disrupt productivity, a separate drop would be expected to occur around the 1992-1994 period for those who show symptoms in the early years but not in the later period. Another drop would be expected to occur around the late years for those who did not show symptoms in the early period but did in the late years. Contrary to expectations, the two lines representing the symptom changers line up very nicely. It is possible that these two groups in the middle are not much different overall; they could be prone to depression, and the only difference between them is the timing of the depressive spell. The timing of their depressive shocks could be random; they come out of and go into depression spells at certain times, but their productivity seems to be unaffected by the timing of these shocks. This could occur because these two groups may have very similar characteristics that signal their levels of productivity to their employers.

   The remainder of the paper is arranged as follows: In section 2, I review the findings of the existing literature, and in section 3, I introduce the econometric tools that I apply. Section 4 describes the data, section 5 lays out the main results of the paper, and concluding remarks can be found in section 6.

2. Literature Review

   Previous studies in economics have mainly focused on the wage effects of psychological problems in general. In the early literature, mental health is mostly assumed to be exogenous. Bartel and Taubman (1979) use the National Academy of Sciences--National Research Council twin sample and estimate that people with psychoses/neuroses have 16-27% lower earnings. In another paper, they revisit the problem of mental illness and find that psychoses are more impeding: They reduce wages by 32-47%, and neuroses cause a 12-14% reduction (Bartel and Taubman 1986). Frank and Gertler (1991) criticize some of the early studies because of their reliance on health care utilization without information of current mental health status, which could lead to substantially biased estimates. Their analysis, which uses mental health measures generated from survey responses, shows that men with mental disorder have about 21% lower earnings. Recognizing the problem of potential simultaneity, Ettner, Frank, and Kessler (1997) use Instrumental Variables (IV) methods to obtain estimates of the genuine effect of mental illness using the National Comorbidity Survey (NCS). The instruments used in the analysis are the number of psychiatric disorders exhibited by the respondent's parents and the number of psychiatric disorders experienced before age...