2. What are current social and labour market outcomes for persons with mental health conditions?

This chapter presents a series of indicators across 32 of 38 OECD countries on the mental health, skills and work outcomes for individuals experiencing mental health issues. These indicators aim to convey the differences in labour market and well-being outcomes between these groups. The choice of indicators and time span partly reflects data constraints. However, each section aims to present some of the most recent data and highlight topics that may be of practical use to policy makers.

Throughout this chapter, an indicator of mental distress serves as a proxy for those with a mental health condition. In theory, when measuring the presence of a mental health condition, an interviewer could simply ask if a person is experiencing a given condition. However, in practice, this determination is difficult to achieve using a standardised questionnaire. Respondents may be hesitant to share information that they consider stigmatising (Clement et al., 2015[1]; Corrigan, Druss and Perlick, 2014[2]), or may not be aware of their mental health condition. An alternative approach would focus on those diagnosed with a mental health condition. This approach is problematic for the same reasons but also because not all people experiencing mental health conditions seek help, or even a diagnosis. Many people with common mental health conditions attempt to address their issues themselves (Mojtabai et al., 2011[3]). Therefore, restricting the scope to those with a diagnosed mental health condition will not be adequate.

In lieu of a direct measure of the presence of a mental health condition, this section uses indirect measures of mental health status (known as “mental health instruments”) commonly utilised in population surveys. It thereby follows previous OECD analysis (OECD, 2012[4]) and assumes that in each country examined, irrespective of the year, a constant 20% of the working-age population has some form of mental health condition, whether they have been diagnosed with that condition or not. This assumption is in broad agreement with epidemiological evidence, which suggests that up to 30% of the adult population have had a mental health condition over any 12-month period and around 20%, or around one in five, at any point in time (Kessler et al., 2005[5]; Alonso et al., 2004[6]; Steel et al., 2014[7]). This methodology also reflects the notion of mental health being a continuum, with poor mental health at one end and flourishing mental health on the other, rather than the absence or presence of a mental health condition.

As discussed in Chapter 4, studies point to population-wide increases in mental distress during the COVID-19 crisis. Although the indicators in this present chapter look at the mental health, skills and work outcomes of individuals experiencing mental health issues prior to the onset of the pandemic, and thus do not need to take this into account, trends in prevalence will need to be watched carefully going forward, as it is unclear whether or not prevalence of mental health conditions will return to the previous ‘norm’.

The methodology used in this chapter has strengths and drawbacks. Core to the current analysis, it permits cross-country and cross-survey comparisons. By taking the respondents with the bottom quintile of scores over a battery of questions on their mental health status, this approach abstracts away from the specification of the mental health instrument included in any given survey. For example, many European OECD countries have contributed to the European Health Interview Survey (EHIS), which employs an 8-question version of the Patient Health Questionnaire (PHQ-8) and is specific to measuring risk for depression, while non-European countries (and some European ones) include a series of mental health questions that are survey specific. Results from these various surveys are better comparable using this relative mental health indicator.

A downside of this proxy approach is that there is no certainty that a person classed as having a mental health condition actually has one. To some extent, this is an intended feature, as the mental health indicator aims to capture also those who have a mental health condition, but have not sought professional help (including those who are unaware of their mental health condition). However, it is possible that there are some differences in mental health prevalence across countries, which the indicator assumes away. The same limitation applies for comparisons over time within countries. In considering this limitation, the set of indicators below does not focus on differences in prevalence across countries or over time, and instead presents outcomes that could be useful to policy makers. It thus also avoids discussing potentially large cross-country cultural differences in the levels of awareness, stigma and discrimination.

In every OECD country examined, people with some form of mental health issue (that is, those scoring in the lowest 20% in a battery of questions about respondents’ mental health status) had lower and sometimes much lower employment rates than those not reporting signs of a mental health issue. Figure 2.1 2.1 depicts the ratio of the employment rates for these two groups. Values below 100 indicate lower employment rates for people with mental health conditions than those without.

On average, the employment rate for persons with a mental health condition was 20% less than for those without. The mental health employment gap ranges from over 30% for Hungary, Norway and the United Kingdom, to 10% in Italy and only 3.6% in Japan. For those with more severe mental health issues – that is, those with a mental health score in the lowest 5% of respondents – the gap is even larger, averaging almost 38% and ranging from 55% in the United States to 9% in Japan (not shown in the figure).

This indicator provides a simple indication of the relative employment outcomes of these groups. As transitions to unemployment negatively affect mental health (Paul and Moser, 2009[8]; Murphy and Athanasou, 1999[9]), it is unlikely that any country will ever achieve a value of 100 (indicating equal employment outcomes). However, countries can close the employment gap through various measures, by engaging unemployed workers to address their mental health condition, improving the job prospects of people with a mental health condition, adapting the workplace to encourage workers to stay in employment where appropriate, and enhancing the early identification and treatment of mental health conditions (OECD, 2015[10]). Successful policy measures in each of these domains would improve the indicator shown in Figure 2.1 2.1, pushing it toward 100.

Presenting employment rates as a ratio controls for overall labour market context and the business cycle. Figure 2.2 presents the underlying raw employment rates by severity of the mental health condition, which shows that the ratio is only part of the story. For example, both Italy and Switzerland score highly in Figure 2.1 2.1, demonstrating that those with a mental health condition in those countries have more similar employment outcomes as those without. However, Figure 2.2 shows that these high ratios stem from two different realities. Switzerland has high overall employment rates, while employment rates in Italy are relatively low, regardless of the mental health status. More generally, across the OECD, there is a strong correlation (0.89) between the employment rates of those with and without a mental health condition. That these two different scenarios for Switzerland and Italy result in similar ratios suggest similar outcomes, but different potential policy solutions. Italy could likely yield greater gains by improving the overall labour market situation than focusing on those facing a mental health condition, as broad labour market measures will likely benefit both groups. Conversely, Switzerland may find greater improvements in the well-being of people with a mental health condition by implementing targeted interventions for people in need.

Many people reporting a mental health condition want jobs, but cannot find them. This is evident in Figure 2.3, which shows that unemployment is much more prevalent amongst those who have a mental health condition. Across OECD countries, the unemployment rate was, on average, 85% (7.7 percentage points) higher for people reporting a mental health condition than for those not reporting such condition. While the mere act of being unemployed can be very distressing, this difference in unemployment rates also suggests either that people with poorer mental health are looking for jobs without success, or that they are transitioning more frequently into and out of work, or both.

The consistency of this pattern across countries highlights the link between a higher incidence of mental health conditions and reduced well-being within the unemployed found in many studies (Clark, 2003[11]; Strandh et al., 2014[12]; Brand, 2015[13]). Moving from employment to unemployment can be a stressful experience for many individuals, and can lead to lower life satisfaction, doubt, and loss of self-esteem, and longer durations of unemployment are associated with a higher burden of disease and mental distress (Herbig, Dragano and Angerer, 2013[14]). Indeed, unemployment can leave lasting negative mental health effects, many of which may even outlast the unemployment spell itself (Knabe and Ratzel, 2011[15]).

Despite the scarring effects that periods of unemployment have on mental health, the variation apparent in Figure 2.3 suggests that policy can play a role in lessening the impact. For example, Germany and Switzerland have similar levels of unemployment among persons without mental health conditions, while the unemployment rate for those with a mental health condition is higher in Germany than in Switzerland. Similar comparisons are possible with Israel and Denmark, New Zealand and Sweden, the Czech Republic and the United Kingdom, to name a few. Further in-depth comparisons of the demographic and policy landscapes between these pairs of countries could uncover insights into effective policies to limit the mental distress that stems from unemployment.

In some cases, working age individuals opt to take early retirement rather than remain in work or search for a job in unemployment. The share of those taking early retirement varies considerably across OECD countries but, for most countries, does not differ greatly empirically by the presence of a mental health condition (Figure 2.4). On average, 5.1% of those not reporting a mental health condition have taken early retirement, compared with 6.6% of those reporting such conditions. However, some countries show much greater differences between those with and without mental health conditions, particularly those from central Europe and the Baltics. Within these countries, people with mental health conditions are more than 80% more likely to take early retirement than those not reporting mental health conditions. Research confirms that premature exits from the labour market via early retirement can often be driven by poor (mental) health (Biffl and Leoni, 2009[16]; Olesen, Butterworth and Rodgers, 2012[17]; OECD, 2015[10]). Other evidence suggests that voluntary early retirement can be associated with improved mental health (Melzer, Buxton and Villamil, 2004[18]), implying that much of the incidence of early retirement in those with mental health conditions was likely motivated by involuntary exits from work.

Individuals with mental health conditions are more likely to live in lower-income households than those without such conditions. On average across OECD countries, those with moderate mental health conditions were 31% more likely to live in households in the lowest income quintile than expected if evenly distributed amongst the income distribution (Figure 2.5). Comparatively, those with no mental health conditions were 12% less likely to live in low-income households. People reporting severe mental health conditions fare far worst: on average, they are 83% more likely than expected to live in low-income households. They are almost 2.5 times more likely than expected to be in low-income households in Israel and more than twice as likely in Chile, Denmark, Finland, Hungary, Latvia and the United Kingdom.

Examining individual wages directly, the same pattern is evident: people with mental health conditions receive lower wages than those without. Figure 2.6 presents a comparison of full-time wages between those with and without mental health conditions for a subset of European OECD countries, for which such data is available. Values below 100 indicate that workers with mental health conditions earn less than those without, which was the case in all countries examined. On average across all countries, workers with mental health conditions were earning 83% of workers without mental health conditions. For example, in Portugal, workers reporting a mental health condition earned about 70% of the wage of their peers without mental health conditions, while the difference between the two groups was smaller in the Netherlands, Poland and the Slovak Republic (with a wage gap around 90%). A more detailed analysis of the worker characteristics could shed more light on the nature of these differences, and explore how outcomes differ within further refined groups, such as by age, gender, education and occupation.

While it is likely that simply having a low income, or being a member of a low-income household, is mentally distressing, these variations in the income and earnings distribution of individuals by mental health status suggest that countries have policy tools at their disposal that can help alleviate the distress associated with low incomes. One policy tool is the encouragement of employment amongst those with mental health conditions. There is a moderate correlation (0.5) between the employment gap between people with and without mental health conditions and their concentration within the lowest income quintile. This suggests that efforts to improve employment can also help lift those people out of poverty.

There are some, but rather minor, differences in the work arrangements between people with and without mental health conditions. Figure 2.7 presents the share of dependent employment (that is, employees) that works part-time. In each country, those with mental health conditions were more likely to work part-time, though this difference was minimal in some countries, notably in Ireland, Latvia, the Czech Republic, Poland, Hungary and Luxembourg. On average 19.5% of workers with mental health conditions worked part-time hours, compared with 13.6% of those without such conditions. Within workers with mental health conditions, the share working part-time ranged from 6.7% in the Slovak Republic to 45.1% in Switzerland.

A high share of workers with mental health conditions working part-time is not necessarily an undesirable outcome. While many of these workers could possibly be involuntary part-time workers, who cannot find full-time jobs, another subset of part-time workers may be using reduced hours to balance the demands of a mental health condition. For those for whom this is the case, access to flexible hours or work schemes could be an essential means of managing their condition while remaining attached to the labour market. Some research suggests that part-time sick leave schemes in Sweden and Norway lead to positive outcomes for workers with mental health conditions (Andrén, 2011[19]; Markussen, Mykletun and Roed, 2010[20]), though other work examining a similar scheme in Denmark suggests these positive effects disappear after controlling for unobserved factors (Høgelund and Holm, 2011[21]). More research could clarify the value of part-time work as a management strategy for those with mental health conditions.

Separately, and maybe counterintuitively, there is a less clear relationship between temporary contracts and mental health status. Figure 2.8 presents the share of workers that report mental health issues who are on temporary contracts compared with those without mental health issues. For most countries, there is a negligible difference between these two groups. While on average, those with mental health issues are slightly more likely to work on temporary contracts – 13.9%, versus 11.5% for those without mental health issues – much of this difference stems from differences in a few countries, notably Greece, Hungary and Iceland, and to a lesser extent Germany, Norway and Sweden.

In Hungary, more than a fifth (22.8%) of people with mental health issues are temporary contract workers, versus only 10.7% of those without such issues. Likewise, in Greece, one-quarter (24.1%) of workers with mental health conditions hold temporary contracts, while 14.6% of workers without conditions do. The differences in these countries, in light of the lack of difference in most other OECD countries, suggests that cultural work practices or policy regimes in these countries may favour temporary contracts for those with mental health conditions over other workers. For instance, in employment protection legislation regulating the eligible use of temporary contracts, the maximum successive duration and allowable number of successive renewals varies across OECD countries. In Greece and Hungary, there are no restrictions on the usage of fixed-term contracts, while in countries such as Italy, Lithuania and Luxembourg fixed-term contracts are primarily only allowed when replacing temporarily absent workers or when there is a clear time-limited need (OECD, 2020[22]).

Previous work examining a subset of OECD countries has shown that females have been overrepresented amongst those with mental health conditions (OECD, 2012[4]).This remains true when examining a larger set of OECD countries (Figure 2.9). On average, working-age females were 45% more likely to report mental health conditions than males of that age. This observation was common across all examined countries, although with some variation in magnitude. For instance, females were only 19% more likely to report mental health conditions than their male counterparts in Canada and Japan, but more than twice as likely (113%) in Portugal.

Though many studies have observed that females have a higher lifetime prevalence of many common mental health conditions, including depression and mood disorders, the cause of this difference is not well understood (Riecher-Rössler, 2017[23]; Kuehner, 2017[24]). Certainly, some of this is due to differences in self-reporting, as males have more negative stigmas against mental health care (Ojeda and Bergstresser, 2008[25]; Schnyder et al., 2017[26]; Corrigan, 2004[27]) and perceive themselves as having less of a need for care (Villatoro et al., 2018[28]). External factors also influence mental health status. For example, females are more likely to experience violence, gender discrimination, and gender inequality (Riecher-Rössler, 2017[23]). Rectifying these societal-level gender imbalances is already a priority for many OECD countries, and a by-product of these efforts could be improved mental health outcomes for females.

Despite a higher prevalence of mental health conditions for females, this does not necessarily translate into diminished employment outcomes relative to men with mental health conditions. Figure 2.10 presents an indicator comparing the employment gaps by mental health status for males and females. Values above one indicate that females have a larger gap in employment rates than males. On average, males were slightly more likely to have a larger gap in employment rates between those with and without mental health conditions. As an example, in Portugal, the employment rate was 52.5% and 59% for females with and without mental health conditions, respectively. For men in Portugal, comparable values were 50.1% and 66.2%. These values implied an employment rate ratio for Portuguese females of 0.89, and 0.76 for Portuguese males, indicating a larger gap for men with mental health conditions (value of one=no gap). Of the 29 OECD countries examined, 21 exhibited similar patterns, though Denmark, Spain, Ireland, Norway, Iceland, Israel and Sweden exhibited the opposite.

A number of factors could explain the gender difference in employment gaps. Some evidence suggests that unemployment has a larger effect on the mental health of males than females (Artazcoz et al., 2004[29]) and that having a job is linked with lower anxiety disorder in males, but not in females (Plaisier et al., 2008[30]; Barnay, 2016[31]). Alternatively, the gender differences in perceptions toward mental health can discourage males from reporting a mental health condition unless it severely limits their quality of life (Ojeda and Bergstresser, 2008[25]; Villatoro et al., 2018[28]). Both of these explanations touch on subjective cultural norms and gender perspectives, such as males’ self-perceived role as the main income provider, rather than objective differences in the prevalence of mental health conditions.

Accessible health care for those who need it is essential to providing adequate care. Reflecting an increased need and use for mental health care services, across the OECD, people with mental health conditions are more likely to have visited a mental health professional over the past year (Figure 2.11). In the mid-2010s, on average, more than 13% of those with moderate mental health conditions had visited a mental health care professional, compared with approximately 4% of those with no condition. Among those with more severe mental health conditions, 30% sought the help of a mental health professional. The share of people seeking help varied across OECD countries, ranging from just under 5% in the Czech Republic to almost 30% in Switzerland for those with moderate mental health conditions. For people with severe conditions, the share seeking help ranged from almost 12% in Slovenia to just under 50% in Iceland.

While the share seeking help for those with severe mental health conditions is larger than either of the other two sub-groups, as expected, it also implies that the majority of people with mental health conditions did not visit a specialist in the past year, irrespective of the severity of their condition. There are many reasons why a person with mental health conditions may not seek help. Some people may not perceive a need for care, or would prefer to treat their issues themselves (Codony et al., 2009[32]; Thornicroft et al., 2017[33]; Van Beljouw et al., 2010[34]). Others may see treatment options as ineffective or have had negative experiences in the past with health care providers (Andrade et al., 2014[35]).

However, while many persons with mental health issues do not seek help or do not want help, there are many who want help but may have difficulty accessing it. As shown in Figure 2.12 many people with mental health conditions across Europe needed mental health care, but either could not afford it or experienced a delay in accessing it. On average across Europe, two in three of those with mental health conditions who expressed a need for care, had difficult accessing it. This was the case whether looking at those with only severe conditions (among them, 68.5% had difficulty accessing care) or when restricting the analysis to those with mild-to-moderate mental health conditions (66.3%). Importantly, Figure 2.12 does not indicate a complete lack of access to health care, but rather that, at least once within the past 12 months, mental health care was difficult to access. Those with mental health conditions are more likely to require medical help, and so difficulties accessing medical care can fall more heavily on them than those without such conditions. A lack of access to medical care can lead to worse outcomes, as common mental health problems can evolve into more serious and debilitating problems if left untreated.

Many health care authorities have highlighted the role of primary care providers in mental health care (Reiter, Dobmeyer and Hunter, 2018[36]). As expanded on throughout this report, primary care providers serve as gatekeepers to specialised mental health services, and improved training for these gatekeepers can facilitate greater access to mental health professionals for those who need it, and can also facilitate a move away from a binary view of mental health in order to improve prevention efforts (Williams, 2020[37]; OECD, 2015[10]).

A key component of the societal burden of mental health is reduced productivity in the form of lost working hours (absenteeism) and reduced capacity while working (presenteeism). Mental health conditions drain workers of their motivation and capacity to work effectively. Consequently, workers with mental health conditions are more likely to report having missed work over the past 12 months than those without (Figure 2.13). On average, half (47.6%) of those with mental health conditions had been absent from work during the past year, compared with just under a third (30.4%) of those without such conditions. This is a common problem throughout the OECD, reflecting that many people with mental health conditions need more time away from work, as a means to manage or to address their underlying mental health issues.

When workers are absent from work, those with mental health conditions require more time off than those without (Figure 2.14). Given that a worker has been absent from work, those with mental health conditions take on average 33.6 days of leave per year, compared with 21.4 days for those with no mental health conditions. Longer absence durations for those with mental health conditions are observable within every country with the exception of Greece. While these differences are quite small for some countries such as Finland, Lithuania and Poland, the difference is notable for other countries, including Switzerland, Ireland and Belgium. Longer absence durations due to mental health conditions can be costly to workers, as they are associated with more severe depressive symptoms (Shin et al., 2018[38]), which in turn are associated with larger limitations in work functioning when returning to work (Lagerveld et al., 2010[39]).

Working time lost to mental health conditions can be costly to employers as well. US research estimated that absenteeism due to major depressive disorders cost USD 23.3 billion in 2010 (Greenberg et al., 2015[40]). However, the same study estimates that this represented only 12% of the total incremental cost, echoing previous research that more than three-quarters of the workplace-related costs were due to presenteeism or reduced productivity while at work (Stewart et al., 2003[41]; OECD, 2012[4]).

The prevalence of presenteeism highlights that many workers are capable of working with their mental health condition, but that they may require support to maintain their productivity. Research suggests that workers with depression had trouble with interpersonal, time management, and physical tasks, and that these issues can remain even after the symptoms of depression are treated (Adler et al., 2006[42]). This suggests that targeted interventions can help workers with mental health conditions to cope with the demands of work. Other research (Bubonya, Cobb-Clark and Wooden, 2017[43]; D’Souza et al., 2006[44]) has found that increased job security is associated with lower presenteeism, though it has an ambiguous effect on work absences. This research suggests that reducing and managing job stress can be an effective means of improving productivity. Mental health training for managers and developing effective back-to-work management processes can be key routes to achieving this goal (OECD, 2015[10]).

Many people with mental health conditions also report a limiting physical disability. Figure 2.15 presents the co-morbidity of mental and limiting chronic general health problems. On average, 39.5% of individuals with mental health conditions face activity-limiting chronic health problems, compared with 12.2% for those without mental health conditions. While this gap is notable, still a majority of people with mental health conditions do not report such co-morbidity (approximately 60%), suggesting that mental health must be considered both individually and within the context of comorbid physical disabilities and not solely as a subset of a country’s disability policy.

Further, interactions between physical health problems and mental health issues can be diverse and complex. For example, some physical and mental health problems are often found together, such as depression and chronic back pain (Patten, 2001[45]; Lépine and Briley, 2004[46]) or asthma and panic disorders (Carr, 1998[47]; Yellowlees et al., 1987[48]; Vermeulen et al., 2017[49]). In addition, treatment of patients with mental health problems can be complicated by poor adherence to medical treatment plans, possible cognitive impairment, and increased alcohol and substance abuse (Hirschfield, 2001[50]; Sato and Yeh, 2013[51]; Gallo et al., 2013[52]). Likewise, the existence of physical health problems can exacerbate or even generate mental health issues, such as anxiety and depression.

Oftentimes, comorbidity takes individuals out of the labour market. Figure 2.16 presents the share of individuals who report being unable to work due to either a permanent disability or early retirement, by mental health status. As noted in Figure 2.4, the policy environment in some countries encourages early retirement over disability benefits and so, to facilitate cross-country comparisons, Figure 2.16 presents a combined measure of the two reasons for not working. There are notable differences by mental health status. While, on average, 6.3% of people without mental health conditions report being unable to work due to either a permanent disability or taking early retirement, 14.6% of those with mental health conditions report so. This represents a gap of 8.3 percentage points. This difference can be even greater in some countries, with a gap of almost 20 percentage points in Hungary and at least 14 percentages points in Estonia, the Czech Republic, Poland, Lithuania and the Slovak Republic. However, some countries have smaller gaps, which is often the case when relatively few individuals leave work due to disability. Examples include Chile, Finland, Israel, Ireland, Japan, Luxembourg and Slovenia, which all have gaps of less than 3.5 percentage points. The variety in these gaps suggests that the policy environment could play a role in encouraging workers with interacting health problems to either remain in work or search for employment.

Mental health issues often manifest themselves at a young age, and can remain an ongoing issue throughout a person’s life (Burke et al., 1990[53]; Patton et al., 2014[54]; Rohde et al., 2013[55]; Naicker et al., 2013[56]). The onset of mental health conditions such as depression and anxiety have been linked with decreased school performance (Owens et al., 2012[57]; Fröjd et al., 2008[58]). Childhood is also a critical time for the promotion of well-being and the formation of skills that prepare students for their work life. Thus, it is important to quickly identify and address mental health issues in youth.

Figure 2.17 presents a ratio of the share of students that repeated a grade during their schooling, comparing those indicating mental distress to those not. It shows that, on average across the OECD, students indicating mental distress are 35% more likely to have repeated a grade. This is not the case for all countries. In Slovenia, Portugal, Poland and Colombia, this group of students is slightly less likely to have repeated a grade, while in the United Kingdom there is no difference between the two groups. On the other side of the spectrum, students indicating mental distress in Greece, Estonia, Denmark and Iceland are all at least 75% more likely to have repeated a grade.

Grade repetition risks disrupting social connections with a student’s peer group, removing a potential source of protection against mental health problems (La Greca and Harrison, 2005[59]). Furthermore, grade repetition can reduce the chance of graduating high school, especially if the retention occurs later in their school career (Roderick, 1994[60]; Jacob and Lefgren, 2009[61]). If retained students do manage to graduate high school, they still face a risk of lower overall educational attainment (Manacorda, 2012[62]). Similar outcomes are evident for people with mental health conditions more generally in Figure 2.18, which shows that those with mental health conditions are less likely to reach a high level of education: Only 28% of those with mental health conditions had achieved a tertiary education, compared with 35% for those not experiencing mental health conditions.

The negative outcomes for students with mental health conditions or experiencing mental distress highlight the need for quickly identifying struggling students and for providing both targeted support and universal prevention measures (OECD, 2015[10]). These can include school-based measures to improve resilience, life skills, and emotional intelligence that can be both effective and cost effective (Weare and Nind, 2011[63]). More targeted interventions, such as cognitive behavioural therapy and interpersonal therapy have a strong evidence base and can reduce the risk of students with depressive symptoms from relapse (Merry et al., 2012[64]). Evidence suggests that the most effective measures to reduce drop-out rates target students on three levels: within school, outside of school, and at a systematic level (Lyche, 2010[65]). Examples include targeted mentoring for at-risk students, and encouraging the involvement of parents in their children’s education, and supporting strong positive relationships between students and teachers.

Beyond health services, social benefits are key mechanisms with which governments provide support to people in need. Figure 2.19 presents the share of workers who receive any type of income support by their mental health status. On average within the countries examined, 20.6% of those without mental health conditions received some form of income support compared with 31.7% of those with mild-to-moderate mental health conditions and 42.8% for people with more severe mental health conditions. While many people with mental health conditions receive social protection benefits, it is notable that many do not. For instance, about three in four individuals with mental health conditions in Switzerland, Greece, Germany and New Zealand do not receive any income support.

Those people who are receiving social protection benefits may likely be long-term benefit recipients. Oftentimes, mental health issues are not identified or addressed early within a spell of unemployment or inactivity (OECD, 2012[4]). This represents a missed opportunity as ignoring (mental) health concerns often leads to poor labour market reintegration (OECD, 2015[10]). Early action, when a person is newly out of work, can help to retain their connection to the labour market. Once that connection is lost, either through the passage of time, inadequate work incentives, or excessive barriers to work, it is difficult to re-establish.

Figure 2.20 presents the distribution of the type of main benefits received by individuals with mental health conditions. The distribution of benefit types varies, both within and between countries. On average, unemployment benefits (37%), disability benefits (33%), and other types of income support (30%) are equally important for the population with mental health conditions. This distribution, however, varies across countries. Persons with mental health conditions in Denmark, Germany, Spain and Austria are more likely to receive unemployment benefits than any other type of benefit, provided they receive a benefit at all. In Estonia, Switzerland and Norway, on the other hand, they are most likely to receive disability benefits as their primary benefit. In yet other countries such as Slovenia, Portugal, Poland and Greece, persons with mental health conditions are more likely to receive benefits other than unemployment or disability.

People with severe mental health conditions are on average slightly more likely to be receiving disability benefits (39.3% of those receiving benefits) than are those with mild-to-moderate conditions (30%). Previous evidence suggests that people with mental health issues make the bulk of new disability claims, and is more often cited as the reason for work problems when people have multiple health issues (OECD, 2015[10]; OECD, 2012[4]). This highlights the need for an awareness of mental health issues and the work capacity of those with poor mental health within the disability insurance system, and that policy attempts facilitate, where possible, a return to work. Early interventions to curb disability claims, financial incentives for workers and employers to hire and retain workers with mental health issues, and restricting full disability benefits to those who truly cannot work, can all help towards this goal (OECD, 2015[10]).

This section presents selected five-year trends for a subset of indicators and a subset of countries for which data is available for two points in time, based on two special modules from the EU-SILC in 2013 and 2018 – thus representing changes pre-COVID-19 (for more on the impact of the COVID-19 crisis, see Chapter 4). Changes are indicative of the labour market trends for persons with mental health conditions but cannot be connected directly with the policy changes over the past five years discussed in more depth in this report, even if the observed period is broadly in line with the period of implementation of the Recommendation. Any policy change will take time to translate into measurable changes in, for instance, employment or unemployment rates, and linking outcomes to reforms requires more in-depth analysis and controlling for factors other than a reform in question.

This section looks at four critical labour market dimensions: employment participation (measured by employment rates), job quality (measured by hourly wages), job security (measured by unemployment rates) and job exits (measured by the share receiving out-of-work income-replacement benefits). The results suggest that, across all countries, persons with mental health conditions generally were able to benefit from the strong labour market conditions in the observed period although less so than persons without mental health conditions. Country-specific findings sometimes tell a slightly different story.

Figure 2.21 presents five-year trends in employment rates and employment ratios. Employment rates have increased in the period 2013-18 in most countries among persons with and without mental health conditions. For those with mental health conditions, rates increased by 3 percentage points on average (Panel A) while the employment gap remained largely unchanged (Panel B). Three countries, Ireland, Hungary and the Slovak Republic, saw a larger increase in employment rates for persons with mental health conditions and a reduction in the employment gap while three other countries, Norway, Luxembourg and Poland, saw the opposite development.

Figure 2.22 presents five-year trends in average hourly wages and wage gaps. Over all countries, the wage gap (Panel B) has changed little: on average across the 25 countries for which comparable data is available, employees with mental health conditions faced a wage gap of about 17% in both 2013 and 2018. This comes on top of the 20% employment gap shown in Figure 2.21. Real wages for employees with mental health conditions have changed little in most countries, with a few exceptions, Norway seeing the largest drop and Lithuania, Latvia and Iceland seeing the largest increase in real wages (Panel A). The impact on the wage gap was small in most cases.

Figure 2.23 presents five-year trends in unemployment rates and unemployment ratios. In the period 2013-18, a period of stable economic growth in most countries, unemployment rates have fallen for persons with mental health conditions in most cases (Panel A). Countries in the south and east of Europe, i.e. countries with relatively high unemployment rates in 2013, saw the fastest decline in unemployment. However, the unemployment gap is very large in most countries and relative to the population without mental health conditions, the unemployment rate has increased in most countries. On average across all 25 countries included, people with mental health conditions are now almost three times more likely to be unemployed which is a notable upward shift from five years earlier (Panel B). The unemployment gap has increased in 21 of the 25 countries, sometimes considerably, and only two countries (Switzerland and the Netherlands) saw a relevant decline in this gap though from a rather high initial level.

Figure 2.24 presents five-year trends in rates and ratios of benefit receipt. These estimates include all main income-replacement benefits, irrespective of the reason of benefit receipt: sickness benefit, disability benefit, unemployment benefit, social assistance or welfare benefit, and (early) retirement benefit. While unemployment has fallen in most countries in the period 2013-18, with a 6-percentage-points decline on average as shown in Figure 2.23, inactivity has increased in many countries and the share of people receiving any main benefit has remained largely unchanged over this period on average (Panel A). In nine of the 25 countries, the share of people receiving a social benefit has increased, by up to 10 percentage points, while in the other 16 countries the share has declined by up to 6 percentage points (only Ireland saw a larger decline). Already in 2013, on average across all countries, persons with mental health conditions were about 40% more likely to receive social benefits. By 2018, this gap had increased to about 45% due to significant increases in about one-third of the countries. This suggests that the good economic conditions during the observation period broadly speaking have not helped in bringing inactive persons with mental health conditions into the labour market and/or have not stopped a significant number of those people from exiting the labour market.

The OECD will update the indicators presented in this chapter regularly, for as many countries as possible. Longer time trends in some of the outcome indicators will, sooner or later, allow a deeper examination of the links between policy trends (including trends towards more integrated policies) and trends in social, skills and labour market outcomes for persons with mental health conditions. Poor outcomes remain too costly for the economy and for the people concerned.


[42] Adler, D. et al. (2006), “Job performance deficits due to depression”, American Journal of Psychiatry, Vol. 163/9, pp. 1569-1576, https://doi.org/10.1176/ajp.2006.163.9.1569.

[6] Alonso, J. et al. (2004), “Prevalence of mental disorders in Europe: results from the European Study of the Epidemiology of Mental Disorders (ESEMeD) project”, Acta Psychiatrica Scandinavica, Vol. 109/s420, pp. 21-27, https://doi.org/10.1111/j.1600-0047.2004.00327.x.

[35] Andrade, L. et al. (2014), “Barriers to mental health treatment: Results from the WHO World Mental Health surveys”, Psychological Medicine, Vol. 44/6, pp. 1303-1317, https://doi.org/10.1017/S0033291713001943.

[19] Andrén, D. (2011), ““Half empty or half full”: The importance of the definition of part-time sick leave when estimating its effects”, Working Paper, No. 4/2011, Örebro University, Örebro, http://www.oru.se/Akademier/Handelshogskolan/Forskning/Working-papers/ (accessed on 2 June 2020).

[29] Artazcoz, L. et al. (2004), “Unemployment and Mental Health: Understanding the Interactions Among Gender, Family Roles, and Social Class”, American Journal of Public Health, Vol. 94/1, pp. 82-88, https://doi.org/10.2105/AJPH.94.1.82.

[31] Barnay, T. (2016), “Health, work and working conditions: a review of the European economic literature”, European Journal of Health Economics, Vol. 17/6, pp. 693-709, https://doi.org/10.1007/s10198-015-0715-8.

[16] Biffl, G. and T. Leoni (2009), Arbeitsplatzbelastungen, arbeitsbedingte Krankheiten und Invalidität [Workplace pressures, work-related illness and disability], Institut für Wirtschaftsforschung (WIFO), Wein, http://www.donau-uni.ac.at/mis (accessed on 26 May 2020).

[13] Brand, J. (2015), “The Far-Reaching Impact of Job Loss and Unemployment”, Annual Review of Sociology, Vol. 41/1, pp. 359-375, https://doi.org/10.1146/annurev-soc-071913-043237.

[43] Bubonya, M., D. Cobb-Clark and M. Wooden (2017), “Mental health and productivity at work: Does what you do matter?”, Labour Economics, Vol. 46, pp. 150-165, https://doi.org/10.1016/j.labeco.2017.05.001.

[53] Burke, K. et al. (1990), “Age at Onset of Selected Mental Disorders in Five Community Populations”, Archives of General Psychiatry, Vol. 47/6, pp. 511-518, https://doi.org/10.1001/archpsyc.1990.01810180011002.

[47] Carr, R. (1998), “Panic disorder and asthma: Causes, effects and research implications”, Journal of Psychosomatic Research, Vol. 44/1, pp. 43-52, https://doi.org/10.1016/S0022-3999(97)00137-2.

[11] Clark, A. (2003), “Unemployment as a Social Norm: Psychological Evidence from Panel Data”, Journal of Labor Economics, Vol. 21/2, pp. 323-351, https://doi.org/10.1086/345560.

[1] Clement, S. et al. (2015), “What is the impact of mental health-related stigma on help-seeking? A systematic review of quantitative and qualitative studies”, Psychological Medicine, Vol. 45/1, pp. 11-27, https://doi.org/10.1017/S0033291714000129.

[32] Codony, M. et al. (2009), “Perceived need for mental health care and service use among adults in Western Europe: Results of the ESEMeD Project”, Psychiatric Services, Vol. 60/8, pp. 1051-1058, https://doi.org/10.1176/ps.2009.60.8.1051.

[27] Corrigan, P. (2004), How stigma interferes with mental health care, https://doi.org/10.1037/0003-066X.59.7.614.

[2] Corrigan, P., B. Druss and D. Perlick (2014), “The impact of mental illness stigma on seeking and participating in mental health care”, Psychological Science in the Public Interest, Supplement, Vol. 15/2, pp. 37-70, https://doi.org/10.1177/1529100614531398.

[44] D’Souza, R. et al. (2006), “Work demands, job insecurity and sickness absence from work. How productive is the new, flexible labour force?”, Australian and New Zealand Journal of Public Health, Vol. 30/3, pp. 205-212, https://doi.org/10.1111/j.1467-842X.2006.tb00859.x.

[58] Fröjd, S. et al. (2008), “Depression and school performance in middle adolescent boys and girls”, Journal of Adolescence, Vol. 31/4, pp. 485-498, https://doi.org/10.1016/j.adolescence.2007.08.006.

[52] Gallo, J. et al. (2013), “Long term effect of depression care management on mortality in older adults: Follow-up of cluster randomized clinical trial in primary care”, BMJ (Online), Vol. 346/7911, https://doi.org/10.1136/bmj.f2570.

[40] Greenberg, P. et al. (2015), “The Economic Burden of Adults With Major Depressive Disorder in the United States (2005 and 2010)”, Journal of Clinical Psychiatry, Vol. 76/2, pp. 155-162, https://doi.org/10.4088/JCP.14m09298.

[14] Herbig, B., N. Dragano and P. Angerer (2013), “Health in the Long-term Unemployed”, Deutsches Arzteblatt International, Vol. 110/23-24, pp. 413-419, https://doi.org/10.3238/arztebl.2013.0413.

[50] Hirschfield, R. (2001), “The Comorbidity of Major Depression and Anxiety Disorders”, The Primary Care Companion to The Journal of Clinical Psychiatry, Vol. 03/06, pp. 244-254, https://doi.org/10.4088/pcc.v03n0609.

[21] Høgelund, J. and A. Holm (2011), “The effects of part-time sick leave for employees with mental disorders”, Working Paper, No. 01:2011, Danish National Centre for Social Research.

[61] Jacob, B. and L. Lefgren (2009), “The Effect of Grade Retention on High School Completion”, American Economic Journal: Applied Economics, Vol. 1/3, pp. 33-58, https://doi.org/10.1257/app.1.3.33.

[5] Kessler, R. et al. (2005), Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication, American Medical Association, https://doi.org/10.1001/archpsyc.62.6.617.

[15] Knabe, A. and S. Ratzel (2011), “Scarring or Scaring? The Psychological Impact of Past Unemployment and Future Unemployment Risk”, Economica, Vol. 78/310, pp. 283-293, https://doi.org/10.1111/j.1468-0335.2009.00816.x.

[24] Kuehner, C. (2017), “Why is depression more common among women than among men?”, The Lancet Psychiatry, Vol. 4/2, pp. 146-158, https://doi.org/10.1016/S2215-0366(16)30263-2.

[59] La Greca, A. and H. Harrison (2005), “Adolescent peer relations, friendships, and romantic relationships: Do they predict social anxiety and depression?”, Journal of Clinical Child and Adolescent Psychology, Vol. 34/1, pp. 49-61, https://doi.org/10.1207/s15374424jccp3401_5.

[39] Lagerveld, S. et al. (2010), “Factors Associated with Work Participation and Work Functioning in Depressed Workers: A Systematic Review”, Journal of Occupational Rehabilitation, Vol. 20, pp. 275-292, https://doi.org/10.1007/s10926-009-9224-x.

[46] Lépine, J. and M. Briley (2004), “The epidemiology of pain in depression”, Human Psychopharmacology, Vol. 19/SUPPL. 1, pp. S3-S7, https://doi.org/10.1002/hup.618.

[65] Lyche, C. (2010), “Taking on the Completion Challenge: A Literature Review on Policies to Prevent Dropout and Early School Leaving”, OECD Education Working Papers, No. 53, OECD Publishing, Paris, https://dx.doi.org/10.1787/5km4m2t59cmr-en.

[62] Manacorda, M. (2012), “The cost of grade retention”, Review of Economics and Statistics, Vol. 94/2, pp. 596-606, https://doi.org/10.1162/REST_a_00165.

[20] Markussen, S., A. Mykletun and K. Roed (2010), “The Case for Presenteeism”, IZA Discussion Paper, No. 5243, IZA, Bonn, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1720325 (accessed on 2 June 2020).

[18] Melzer, D., J. Buxton and E. Villamil (2004), “Decline in common mental disorder prevalence in men during the sixth decade of life”, Social Psychiatry and Psychiatric Epidemiology, Vol. 39/1, pp. 33-38, https://doi.org/10.1007/s00127-004-0704-1.

[64] Merry, S. et al. (2012), Cochrane Review: Psychological and educational interventions for preventing depression in children and adolescents, John Wiley & Sons, Ltd, https://doi.org/10.1002/ebch.1867.

[3] Mojtabai, R. et al. (2011), “Barriers to mental health treatment: Results from the National Comorbidity Survey Replication”, Psychological Medicine, Vol. 41/8, pp. 1751-1761, https://doi.org/10.1017/S0033291710002291.

[9] Murphy, G. and J. Athanasou (1999), “The effect of unemployment on mental health”, Journal of Occupational and Organizational Psychology, Vol. 72/1, pp. 83-99, https://doi.org/10.1348/096317999166518.

[56] Naicker, K. et al. (2013), “Social, demographic, and health outcomes in the 10 years following adolescent Depression”, Journal of Adolescent Health, Vol. 52/5, pp. 533-538, https://doi.org/10.1016/j.jadohealth.2012.12.016.

[22] OECD (2020), “Recent trends in employment protection legislation”, in OECD Employment Outlook 2020: Worker Security and the COVID-19 Crisis, OECD Publishing, Paris, https://dx.doi.org/10.1787/af9c7d85-en.

[10] OECD (2015), Fit Mind, Fit Job: From Evidence to Practice in Mental Health and Work, Mental Health and Work, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264228283-en.

[4] OECD (2012), Sick on the Job?: Myths and Realities about Mental Health and Work, Mental Health and Work, OECD Publishing, Paris, https://dx.doi.org/10.1787/9789264124523-en.

[25] Ojeda, V. and S. Bergstresser (2008), “Gender, Race-Ethnicity, and Psychosocial Barriers to Mental Health Care: An Examination of Perceptions and Attitudes among Adults Reporting Unmet Need”, Journal of Health and Social Behavior, Vol. 49/3, pp. 317-334, https://doi.org/10.1177/002214650804900306.

[17] Olesen, S., P. Butterworth and B. Rodgers (2012), “Is poor mental health a risk factor for retirement? Findings from a longitudinal population survey”, Social Psychiatry and Psychiatric Epidemiology, Vol. 47/5, pp. 735-744, https://doi.org/10.1007/s00127-011-0375-7.

[57] Owens, M. et al. (2012), “Anxiety and depression in academic performance: An exploration of the mediating factors of worry and working memory”, School Psychology International, Vol. 33/4, pp. 433-449, https://doi.org/10.1177/0143034311427433.

[45] Patten, S. (2001), “Long-term medical conditions and major depression in a Canadian population study at waves 1 and 2”, Journal of Affective Disorders, Vol. 63/1-3, pp. 35-41, https://doi.org/10.1016/S0165-0327(00)00186-5.

[54] Patton, G. et al. (2014), “The prognosis of common mental disorders in adolescents: A 14-year prospective cohort study”, The Lancet, Vol. 383/9926, pp. 1404-1411, https://doi.org/10.1016/S0140-6736(13)62116-9.

[8] Paul, K. and K. Moser (2009), “Unemployment impairs mental health: Meta-analyses”, Journal of Vocational Behavior, Vol. 74/3, pp. 264-282, https://doi.org/10.1016/j.jvb.2009.01.001.

[30] Plaisier, I. et al. (2008), “Work and family roles and the association with depressive and anxiety disorders: Differences between men and women”, Journal of Affective Disorders, Vol. 105/1-3, pp. 63-72, https://doi.org/10.1016/j.jad.2007.04.010.

[36] Reiter, J., A. Dobmeyer and C. Hunter (2018), “The Primary Care Behavioral Health (PCBH) Model: An Overview and Operational Definition”, Journal of Clinical Psychology in Medical Settings, Vol. 25/2, pp. 109-126, https://doi.org/10.1007/s10880-017-9531-x.

[23] Riecher-Rössler, A. (2017), Sex and gender differences in mental disorders, Elsevier Ltd, https://doi.org/10.1016/S2215-0366(16)30348-0.

[60] Roderick, M. (1994), “Grade Retention and School Dropout: Investigating the Association”, American Educational Research Journal, Vol. 31/4, pp. 729-759, https://doi.org/10.3102/00028312031004729.

[55] Rohde, P. et al. (2013), “Key Characteristics of Major Depressive Disorder Occurring in Childhood, Adolescence, Emerging Adulthood, and Adulthood”, Clinical Psychological Science, Vol. 1/1, pp. 41-53, https://doi.org/10.1177/2167702612457599.

[51] Sato, S. and T. Yeh (2013), Challenges in treating patients with Major depressive disorder: The impact of biological and social factors, Springer, https://doi.org/10.1007/s40263-012-0028-8.

[26] Schnyder, N. et al. (2017), Association between mental health-related stigma and active help-seeking: Systematic review and meta-analysis, Royal College of Psychiatrists, https://doi.org/10.1192/bjp.bp.116.189464.

[38] Shin, C. et al. (2018), “Sickness absence indicating depressive symptoms of working population in South Korea”, Journal of Affective Disorders, Vol. 227, pp. 443-449, https://doi.org/10.1016/j.jad.2017.11.030.

[7] Steel, Z. et al. (2014), “The global prevalence of common mental disorders: a systematic review and meta-analysis 1980–2013”, International Journal of Epidemiology, Vol. 43/2, pp. 476-493, https://doi.org/10.1093/ije/dyu038.

[41] Stewart, W. et al. (2003), “Cost of Lost Productive Work Time among US Workers with Depression”, Journal of the American Medical Association, Vol. 289/23, pp. 3135-3144, https://doi.org/10.1001/jama.289.23.3135.

[12] Strandh, M. et al. (2014), “Unemployment and mental health scarring during the life course”, European Journal of Public Health, Vol. 24/3, pp. 440-445, https://doi.org/10.1093/eurpub/cku005.

[33] Thornicroft, G. et al. (2017), Undertreatment of people with major depressive disorder in 21 countries, Royal College of Psychiatrists, https://doi.org/10.1192/bjp.bp.116.188078.

[34] Van Beljouw, I. et al. (2010), “Reasons and determinants for not receiving treatment for common mental disorders”, Psychiatric Services, Vol. 61/3, pp. 250-257, https://doi.org/10.1176/ps.2010.61.3.250.

[49] Vermeulen, F. et al. (2017), “Relationship between the sensation of activity limitation and the results of functional assessment in asthma patients”, Journal of Asthma, Vol. 54/6, pp. 570-577, https://doi.org/10.1080/02770903.2016.1242138.

[28] Villatoro, A. et al. (2018), “Perceived Need for Mental Health Care: The Intersection of Race, Ethnicity, Gender, and Socioeconomic Status”, Society and Mental Health, Vol. 8/1, pp. 1-24, https://doi.org/10.1177/2156869317718889.

[63] Weare, K. and M. Nind (2011), “Mental health promotion and problem prevention in schools: what does the evidence say? | Health Promotion International | Oxford Academic”, Health Promotion International, Vol. 26/1, pp. 29-69, https://academic.oup.com/heapro/article/26/suppl_1/i29/687644 (accessed on 29 July 2020).

[37] Williams, A. (2020), “The Next Step in Integrated Care: Universal Primary Mental Health Providers”, Journal of Clinical Psychology in Medical Settings, Vol. 27/1, pp. 115-126, https://doi.org/10.1007/s10880-019-09626-2.

[48] Yellowlees, P. et al. (1987), “Psychiatric morbidity in patients with chronic airflow obstruction”, Medical Journal of Australia, Vol. 146/6, pp. 305-307, https://doi.org/10.5694/j.1326-5377.1987.tb120267.x.

Metadata, Legal and Rights

This document, as well as any data and map included herein, are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. Extracts from publications may be subject to additional disclaimers, which are set out in the complete version of the publication, available at the link provided.

© OECD 2021

The use of this work, whether digital or print, is governed by the Terms and Conditions to be found at http://www.oecd.org/termsandconditions.