Working hours mismatch, job strain and mental health among mature age workers in Australia

The Journal of the Economics of Ageing 15 (2020) 100227

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Working hours mismatch, job strain and mental health among mature age workers in Australia

T

Riyana Miranti , Jinjing Li ⁎

The National Centre for Social and Economic Modelling (NATSEM), Institute for Governance and Policy Analysis (IGPA), Faculty of Business, Government and Law, University of Canberra, ACT 2601, Australia

ARTICLE INFO

ABSTRACT

Keywords: Hours mismatch Mature age workers Job strain Mental Health

Previous literature has discussed the relationships between non-participation and underemployment in the labour market with health and wellbeing, including the impact of these circumstances on mental health. While it is sometimes argued that a mismatch between the actual number of hours worked and people’s preferences about the amount of work they want has an association with mental health, findings are mixed. Moreover, job characteristics and age-specific factors are often not included in studies exploring these relationships. This paper examines the link between working hours mismatch, job strain and mental health in Australia, focusing on mature age workers (aged 45–64 years), a group of workers for whom these issues have seldom been explored. Results from our panel data estimations, using the Household, Income and Labour Dynamics in Australia (HILDA) Survey, show significant associations between a mismatch of working hours – that is, either working more or less than one’s desired hours – and poorer mental health. Our analysis also suggests that jobs characterised by low levels of control and high demands have a significant negative impact on reported mental health scores. Designing flexible working environments and giving workers more autonomy should be priorities in order to improve the general mental health of mature age workers.

Introduction A 2017 survey found that 40 per cent of the Australian workforce is over 45 years old (ABS, 2017) and this number will likely increase in the upcoming years. Over the past decade, there has been a notable increase in the labour market participation of people aged between 45 and 64 years. However, working at a mature age may not be easy; with some employers favouring younger workers, many mature age workers, particularly those in part-time jobs, work fewer than their desired hours. Such a phenomenon is often described as underemployment and mature age workers in Australia are prone to longer periods of underemployment than younger people (Li et al., 2015). Growing part-time and “flexible” work arrangements are likely to push these numbers even higher. Moreover, it is not only sufficiency in terms of working hours that matters; job quality and a fit with employees’ needs and preferences contribute to the quality of life of mature age workers, as discussed in Brown et al. (2015). Unfortunately, the number of working hours does not depend only on an employee’s choice, but is also influenced by employers’ decision making, and this is a reality that is relevant for older as well as younger workers (Gielen, 2009). This paper focuses on examining the

⁎

association between hours mismatch and mental health among mature age workers in Australia, an area which is still understudied. Previously, a stress process model (Pearlin, 1989; Pearlin et al., 1981) has been used, providing a theoretical framework to study the effects of underemployment by identifying potential mechanisms linking stressors to adverse health outcomes. More specifically, previous literature has noted that work hour constraints may have an effect on (i) selfassessed health in general, (ii) subjective wellbeing such as life satisfaction or job satisfaction, (iii) psychological wellbeing or (iv) mental health (see, for example, Bell et al., 2012; Burns et al., 2016). The separation between subjective wellbeing and psychological wellbeing in the literature follows the approach used in Burns et al. (2016, p. 726), in which psychological wellbeing refers to psychological functioning such as a sense of competence, autonomy, resilience, relatedness, purpose in life, personal growth and self-acceptance. Although not focusing specifically on mature age workers, previous research has produced mixed findings on the association between actual and preferred hours worked and health. For example, Bassanini and Caroli (2015) provided an extensive literature review regarding the relationship between work and health and concluded that there is a negative association between working hour gap (the difference between

Corresponding author. E-mail addresses: [email protected] (R. Miranti), [email protected] (J. Li).

https://doi.org/10.1016/j.jeoa.2019.100227

Available online 02 November 2019 2212-828X/ © 2019 Elsevier B.V. All rights reserved.

The Journal of the Economics of Ageing 15 (2020) 100227

R. Miranti and J. Li

the actual and desired hours of work) and health. Further, Dollard and Winefield (2002) discussed various work states and drew upon a range of work stress and unemployment theories to explain the associations between work states and mental health in Australia. In the Australian context, there are mixed findings, with some studies identifying an association between an hours mismatch and either mental health or subjective wellbeing, while others suggest no association between the two (see, for example, Adam and Flatau, 2006; Dockery, 2006; Kugler et al., 2014; Milner et al., 2015; Otterbach et al., 2016; Wilkins, 2007; Wooden et al., 2009). Interestingly, Dockery (2006), Otterbach et al. (2016) and Wooden et al. (2009) all argued that, compared with underemployment, overemployment has a larger impact on mental health, while Adam and Flatau (2006) and Wunder and Heineck (2013) have argued the opposite. De Moortel et al. (2017) found that the impact varies depending on gender. They found that involuntary overwork was positively associated with poor mental wellbeing for men, but for women overwork (regardless of voluntariness) and involuntary underwork were positively associated with poor mental wellbeing. The literature has also discussed other factors that can influence mental health, including prior employment status, initial mental health status, marital status, types of job contract and the types of work that people do. For the latter, particularly, the issue of job strain is strongly relevant. The job strain literature aims to discuss the negative consequences of strain at work represented by job demand and job control. However, we were unable to find a study in Australia that has investigated this issue. In practice, beyondblue (2016) has also identified common risk factors for mental wellbeing in the workplace, which include heavy workloads; strict deadlines and targets; unclear roles and responsibilities; low control over how the job is carried out; challenging working hours; high emotional, mental or physical demands; lack of recognition or feedback; bullying and/or poorly managed behaviours; and discrimination. Mental health issues at work are an important component of wellbeing, as occupational stress and work-related mental health issues are the cause of many disruptions, such as loss of productivity and absenteeism (Harvey et al., 2014). This study will fill a gap in the literature, and its findings will be highly relevant for policy and program development. First, the nature of work and the expectation of work among mature age workers may change as this group enters the pre-retirement period. Second, none of the previous literature has focused on mature age workers, despite evidence that this group of workers in Australia may be particularly prone to long period of underemployment. As highlighted in Li et al. (2015), which used data from the Household, Income and Labour Dynamics in Australia (HILDA) survey and the Australian Bureau of Statistics (ABS) (ABS (2012), mature age workers in Australia tend to be trapped in underemployment and have a longer average duration of underemployment than younger people. Although the rates of underemployment were highest among the younger age group of 15–24 years at 17.4 per cent compared to 7.5 per cent for the mature age workers 45–54 years and 6.5 per cent for the age group of 55 years or over in 2016, the rates of underemployment for people aged 45–54 years and those aged 55 years and over have been increasing in recent years (ABS, 2016a). Further, mature age workers in Australia generally have a longer duration of underemployment than younger people. The median durations of part-time underemployment in 2019 were 26 weeks for people aged 15–24, 32 weeks for people aged 25–34 and 48 weeks for people aged 35–44. These underemployment durations were lower than the 52 weeks median duration of underemployment among mature age workers aged 45–54 and 65.4 weeks among mature age workers aged 55–64 years (ABS, 2019). Further, while only less than one third of people aged 15–19 years who were underemployed (part-time) experienced insufficient work for one year or more, this duration was experienced by over half of those aged 55 years and over and 46 per cent of those aged 45–54 years (ABS, 2014). Milner et al. (2015) examined the association between working less

or more than standard full-time hours and mental health, while Otterbach et al. (2016) investigated the association between hours mismatch and mental health, but they did not focus on mature age workers. Thus, our study fills this gap and, to strengthen its contribution, it also includes, as a comparison, an investigation of the effects of work hour constraints on mental health among younger workers. Second, with the exception of Otterbach et al. (2016), previous studies examining hours of work and mental health have mostly depended on short panel data and have not focused on the gap between the hours a person would like to work and the hours they usually work (in other words, the hours mismatch). Our study also takes into account the type of work that mature age workers do, including the demands of the work and the extent of employees’ decision making, particularly whether a person has control over their work. Third, in terms of measurement, our study complements Otterbach et al. (2016), who used the reduced SF-12 form of the SF-36 questionnaire (Medical Outcomes Study Short-Form General Health Survey) as the basis for their mental health measure while our study uses the SF36, which is more comprehensive. We use four variants of the mental health components of the SF-36: mental health (symptoms related to anxiety and depression and measure of positive affect); role-emotional (interference with work or other daily activities due to emotional problems); social functioning (interference with normal social activities) as discussed in Butterworth and Crosier (2004); and vitality (energy and fatigue measures) as discussed in Brown et al. (2011). This paper poses three research questions: Is working hours mismatch associated with mental health among mature age workers in Australia? What are the impacts of job strain on mental health? Are there any gender differences? We also examine how the findings about hours mismatch and job strain for older workers compare with the younger generation. We focus on the implication of hours mismatch and job strain on mental health. Exploration of factors that determine hours mismatch and job strain is beyond the scope of this paper. The remaining sections are as follows. In Section 2, we present the literature review. Section 3 discusses data and methodology and patterns of mental health by gender. In Section 4 we discuss the results and draw out some policy implications. The final section concludes. Literature review Hours mismatch, health and wellbeing In this paper, we consider that an individual has experienced working hours mismatch when there is a discrepancy between the desired and the actual number of working hours. Unlike underemployment, which is a concept sometimes applied only to those with a part-time job (ABS, 2016b), we use the term “underwork” rather than underemployment when the desired hours exceed the actual working hours per week (with coverage not limited to those who work parttime). We identify “hours underwork” as the difference between the desired and the actual hours when the desired hours exceed the actual hours. Likewise, the term “overwork” is used to describe these circumstances in the other direction. Bell and Freeman (2001) indicated that the incentive to work more hours is motivated by the (perceived) likelihood of promotions or a better employment position and the likelihood of a larger wage or salary or a better earnings position. The latter is perhaps more relevant for mature age workers, as this group is transitioning into retirement age. Previous literature has also argued that a mismatch may happen at the same time as (and is commonly attributed to) changes in life events, particularly motherhood, loss of partners and job loss (Drago et al., 2009). As explained earlier, literature that has examined the effects of work hour constraints has used a variety of measures of health or wellbeing (self-assessed health, subjective wellbeing measures, psychological wellbeing and mental health). Bell et al. (2012) examined the impact of the gap between actual and desired working hours on self-assessed 2

UK

3

Australia

Europe

Australia

Mental health Adam and Flatau (2006)

De Moortel et al. (2017)

Dollard and Winefield (2002)

Psychological wellbeing Robone et al. (2011) UK

Robone et al. (2011)

Germany and United Kingdom

Various

Health in general Bassanini and Caroli (2014)

Self-assessed health Bell et al. (2012)

Country

Authors

Literature review

Applied research

Applied research

Applied research

Applied research

Applied research

Literature review

Type

N/A

European Social Survey Round 2 (2004–2005) and Round 5 (2010)

HILDA Waves 1–2

British Household Panel Survey (BHPS) (1991/1992–2002/2003), 12 waves

British Household Panel Survey (BHPS) (1991/1992–2002/2003), 12 waves

German Socio-economic Panel Survey (GSOEP), 1992–2008, 17 waves and British Household Panel Survey (BHPS), 1991 to 2007, 17 waves

N/A

Data

Mental health

Mental wellbeing, which is measured by three out of five items from the World Health Organization Wellbeing Index

Calculating MCS (Mental Component Summary Scale) based on factor analysis of SF-36

Self-assessed health and psychological wellbeing

Self-assessed health and psychological wellbeing

Self-assessed health or health satisfaction

General health

Dep variable(s)

Table 1 Summary of selected studies on the association between hours mismatch and health or wellbeing status.

• •

• • • • •

Demographic variables: age, household composition, number of children, education, long-term health condition. Industry, occupation. Underemployed 1–9 hours, 10–19 hours, 20–29 hours, 30 hours or more. Working hours: Standard hours (35–40 hours per week) Voluntary short hours (work and prefer to work < 35 hours per week) Voluntary long hours (work and prefer to work > = 41 hours per week) Involuntary short hours (work < 35 hours per week but prefer more hours) Involuntary long hours (work > =41 hours per week but prefer less hours). Includes economic context (GDP growth rates and unemployment rates) and job characteristic indicators. Overemployment, underemployment, unemployment and healthy jobs.

• •

Preferences for number of hours worked Less hours More hours Also includes other demographic and socioeconomic variables, contractual arrangements and working conditions.

Hours preferences (overemployed, unconstrained, underemployed) by categories of hours.For example: 20–34 hours: underemployed, 20–34 hours: unconstrained, 20–34 hours: overemployed. Also includes other demographic and socioeconomic variables. Preferences for number of hours worked: Less hours More hours Also includes other demographic and socioeconomic variables, contractual arrangements and working conditions.

independent of working hours. Included in this category are health consequences of retirement or being unemployed/ experiencing job loss

margin which measures how many • Intensive hours an individual works. margin which measures whether • Extensive an individual is in employment or not,

Explanatory variables

(continued on next page)

Various work states,and draws upon a range of work stress and unemployment theories – empirical evidence to describe the relationships between work and mental health.

Working voluntary long hours is associated with poor mental health for men. Working voluntary long, involuntary long and involuntary short hours are associated with poor mental health for women.The study also concludes that women are more vulnerable to the effects of long working hours and hours mismatch on mental wellbeing.

High working hours gap in underemployment matters, it is associated negatively with SF-36 scores.

Preference for working less hours among parttime British women is associated with lower selfassessed health, but interestingly the finding is not significant for men.The same result was found for psychological wellbeing.

Preference for working less hours among parttime British women is associated with lower selfassessed health, but interestingly the finding is not significant for men.The same result was found for psychological wellbeing.

Working more than desired working hours has a negative impact on workers’ self-assessed health and health satisfaction

At the intensive margin, the negative effect of work on health is found when workers do not have control over the hours of work they do.At the extensive margin, involuntarily not working also has a negative impact on health.

Results

R. Miranti and J. Li

The Journal of the Economics of Ageing 15 (2020) 100227

Australia

Australia and Germany

Milner, Smith and LaMontagne (2015)

Otterbach (2016)

4

Germany and United Kingdom

Australia and Germany

Australia

Bell et al. (2012)

Kugler et al. (2014)

Wilkins (2007)

UK

Australia

Dockery (2006)

Subjective wellbeing Angrave and Charlwood (2015)

Country

Authors

Table 1 (continued)

Applied research

Applied research

Applied research

Applied research

Applied research

Applied research

Applied research

Type

HILDA Wave 1

HILDA Waves 1–2 and the German Socioeconomic Panel (SOEP) 2001–2012

German Socio-economic Panel Survey (GSOEP), 1992–2008, 17 waves and British Household Panel Survey (BHPS), 1991 to 2007, 17 waves

British Household Panel Survey (BHPS) waves 1–18

HILDA 2001–2013, 13 wavesGerman Socio-Economic Panel (SOEP), 2002, 2004, 2006, 2008, 2010 and 2012 (six waves)

HILDA Waves 1–12

HILDA Waves 1–4

Data

Job and life satisfaction

Subjective wellbeing (life satisfaction)

Self-assessed health or health satisfaction

Job satisfaction, psychological wellbeing and life satisfaction

Mental Component Summary (MCS) score from the 12 items version of the Medical Outcomes Study Short Form Health Survey (SF-12)

Mental Component Summary (MCS) mental health based on mental health, emotional role, vitality and social functioning

Mental Health Components of SF-36 (summary of mental health, roleemotional, vitality and social functioning)

Dep variable(s)

Dummy for underemployment, cross-sectional analysis

Categories of working time and working time match (e.g. < 35 hours underemployed, < 35 hours matched, < 35 hours overemployed). Other controlled variables such as labour force status: self-employed, unemployed, not in the labour force; disability status. Hours preferences (overemployed, unconstrained, underemployed) by categories of hours. For example: 20–34 hours: underemployed, 20–34 hours: unconstrained, 20–34 hours: overemployed. Also includes other demographic and socioeconomic variables. Underemployment and overemployment. Also includes other demographic, socioeconomic variables, industry and occupation fixed effects.

• •

Demographic variables, labour force status (FT, PT, unemployed). Employed with low, medium, high and very high job satisfaction. Employment FT and prefer more/less hours; PT and prefer more/less hours. Job security. Covers all ages. Average number of hours worked in a person’s main job per week (34 or under, 35–40, 41–48, 49–59, 60 and over). Demographic variables, household structure, type of contract, occupation. Mismatch status: Hours underemployed Hours overemployed Both with cut-off, if the gap between the actual and desired hours (hours underemployed or hours overemployed) is at least four hour per week.Also includes other demographic and socioeconomic variables.

Explanatory variables

(continued on next page)

Wellbeing is highest when there is no hours mismatch. In general, the perception of overemployment is statistically significantly different from the perception of underemployment in both countries. Negative effects of hours mismatch are found for both part-time and full-time workers who would prefer to work more hours, but impacts are greater for underemployed part-time workers, and are particularly large for part-time workers who would like to work full-time.

Working more than desired working hours has a negative impact on workers’ self-assessed health and health satisfaction.

Overemployment and underemployment are associated with lower levels of subjective wellbeing.

Overemployment (actual working hours is greater than the desired working hours) has a negative association with mental health in both Australia and Germany, while underemployment is negatively associated with mental health only in Australia.

Usual hours worked 41–48; 49–59 and 60 and over are negatively associated with SF-36 mental health measures, that is poorer mental health (particularly working more than 49 hours).

Unemployment contributed to poorer mental health for those participating in the labour force.Larger differences in mental health are identified among those in employment, conditional upon characteristics of their work or attitudes towards their jobs.(Overemployment seems to matter more.)

Results

R. Miranti and J. Li

The Journal of the Economics of Ageing 15 (2020) 100227

The Journal of the Economics of Ageing 15 (2020) 100227

health and health satisfaction in Germany and the United Kingdom and found that working more than desired hours had a negative impact on workers in terms of both self-assessed health and health satisfaction. This result is in line with Robone et al. (2011) who found that a preference for working less hours among part-time British women was associated with lower self-assessed health, but interestingly the finding was not significant for men. The same result was found for measures of psychological wellbeing (Robone et al., 2011). Table 1 shows a summary of selected international and Australian studies focusing on the association between hours mismatch and health, covering self-assessed health, mental health status, subjective wellbeing and psychological wellbeing. The results show mixed findings about the correlation between these two variables.

Regardless of gender, both underemployment and overemployment are associated with losses in wellbeing. In general, losses from underemployment are larger than losses from overemployment. Germany Wunder and Heineck (2013)

Applied research

German Socio-Economic PanelStudy (SOEP), covering the period 1985–2011

Life satisfaction

• • •

Job strain, health and wellbeing Source: Authors’ summary. This is a selected list of papers focusing on working hours mismatch and mental health status and may not cover all existing literature.

Underemployment and overemployment are negatively associated with job satisfaction and life satisfaction for men and women.

Underemployment and overemployment by categories according to number of hours. For example: < 35 hours: underemployed < 35 hours: matched < 35 hours: overemployed. Also includes other demographic and socioeconomic variables. Hours underemployment and hours overemployment. Job and life satisfaction HILDA Waves 1–5 Australia Wooden et al. (2009)

Applied research

Country Authors

Table 1 (continued)

Type

Data

Dep variable(s)

Explanatory variables

Results

R. Miranti and J. Li

Previous literature has discussed the job strain or demand-control model (JDC model discussed in De Lange et al., 2003; Ek et al., 2014; Karasek, 1979, 1990; Karasek et al., 1981). The job strain model argues that psychological strain is the result of a combination of job demand and whether there are options available for the employee to respond to this demand. As a result of the combinations between job demand and control, the model proposed four combinations of job strain category as shown in Fig. 1. The bottom right quadrant reflects the high strain condition (the most disadvantaged position) where a person works in a highly demanding job but does not have control over his or her work. The top left represents the low strain condition (the most advantaged combination) where a person has low demand and also high control over his or her work. In the development of this field of research, Johnson and Hall (1988) expanded the model by incorporating the role of social support – their JDCS model argues that jobs that are characterised by high demand, low control and low social support are the most detrimental for workers. Häusser et al. (2010) argued that the effects of job demand and control can be additive or interactive/multiplicative. These concepts are reflected in two hypotheses: (i) the strain hypothesis, which focuses on an increase in the likelihood of worsening mental health in the case

Fig. 1. Job strain model. Source: Authors’ summary based on Karasek (1979, 1990) and Karasek et al. (1981). 5

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R. Miranti and J. Li

of high strain jobs, and can capture both additive or interactive effects of high demand and low job control; and (ii) the buffer hypothesis, which focuses only on the interactive effect of job demand and control, where having job control will counter the influence of job demand on mental health. In the JDCS model, social support will moderate the negative impact of job strain. Empirically, the association between job strain and mental health has been found to be mixed; some literature has found that job strain has a significant impact on mental health while other studies have found no statistically significant correlation (see, for example, a metaanalytic review in Stansfeld and Candy, 2006). Burns et al. (2016) examined the association between job strain with three dependent variables, not only mental health (depression), but also subjective wellbeing (life satisfaction) and psychological wellbeing. They found that high job strain jobs are associated positively with all proximal (crosssectional) depression outcomes, while high job strain is associated negatively with proximal and distal (contemporaneous and lagged) elements of life satisfaction and psychological wellbeing. The difference across gender is also highlighted. Ek et al. (2014) found the strongest association between high job strain and depression among women. Further, the correlations between job strain categories and mental health issues such as depression may be unequally distributed – LaMontagne et al. (2008) found this correlation was, in general, particularly strong among male workers in lower skill level jobs, despite high job strain itself being more prevalent among women. McDonough and Walters (2001) also found that women reported more distress and chronic conditions than men. Nevertheless, the finding of the correlation across genders is also mixed. Häusser et al. (2010) found no significant difference in the association between job strain and mental health by gender. A similar pattern was found by Vermeulen and Mustard (2000); that is, that high strain and active work were correlated with higher distress levels, regardless of gender. Moreover, the literature also suggests that additive effects were empirically consistent in cross-sectional studies but this was not the case for studies that used longitudinal data, while the findings on the interactive effect in the longitudinal data were weak (Häusser et al., 2010). The inconsistent findings of the relationship between job strain and mental health (proxied by depression) were also discussed in Stansfeld and Candy (2006).

0 and 100. Two summary variables are derived – those which are more physically related (physical functioning, role-physical, bodily pain and general health) and those which are mentally related (mental health, role-emotional, vitality and social functioning). Some studies may have also used a reduced set of the SF-36, called the SF-12, which contains 12 of the original 36 questions in the SF-36 questionnaire (see for example, Otterbach et al., 2016), but the general principle remains. Components of the SF-36 can be presented as separate measures, or can be multidimensional, or used as a summary variable. For example, Dockery (2006) utilised the SF-36 transformed mental health component of the scale, taking into account dimensions that correlated with mental health: that is, the four indicators explained earlier. Similarly, De Moortel et al. (2017) used a combination of three of the five items from the World Health Organization Wellbeing Index (WHO-5), reflecting positive affect. Data and methodology Data All data used in this paper are derived from the Household, Income and Labour Dynamics in Australia (HILDA) Survey, a longitudinal survey conducted annually since 2001, which contains a representative sample of the Australian population. HILDA included 19,914 individuals and 7682 households in the first wave and was topped up to 23,415 individuals and 9543 households in 2011. It records a wide range of socioeconomic characteristics, including detailed individual employment patterns, job characteristics and mental health measures that are used in this study (Wooden et al., 2002; Summerfield et al., 2017). The individuals covered in our study are the mature age population, defined as the population aged 45–64 years within the sample. This age cut-off is also used by the ABS (2005). We use several scales extracted from the SF-36, available in the HILDA data, as measures of mental health. As noted earlier, the SF-36 is a well-recognised health and wellbeing assessment tool (Fukuhara, et al., 1998; Loge and Kaasa, 1998; Ware et al., 1995; Ware et al. 2000; Ware and Sherbourne, 1992). It consists of 36 items, measuring eight distinct health concepts. The scoring rules in HILDA follow Ware et al. (2000), with a scale ranging from 0 to 100. We use the following scales of the SF-36 related to mental health: (i) mental health; (ii) role-emotional; (iii) vitality and (iv) social functioning. As explained earlier, mental health refers to symptoms related to anxiety and depression, role-emotional refers to interference with work or other daily activities due to emotional problems, social functioning covers interference with normal social activities and vitality denotes energy and fatigue measures. These indicators describe four related but different dimensions of mental health status. In this paper we define hours mismatch as occurring when the desired number of working hours differs from the actual number of working hours. We include both full-time and part-time workers in our sample, but we also control for variation in job characteristics, including part-time status as dummy variables, in the regression. Fulltime workers are defined as those who work at least 35 hours per week, while part-time workers work less than 35 hours per week in all jobs (ABS, 2013). The estimation focuses on employees with regular working hours. We exclude self-employed workers for the following reasons. First, the group of individuals we focus on are those who have a formal job attachment and are more likely to experience underemployment relative to those who are self-employed, as the latter group presumably have more flexibility in arranging their hours of work. We would also expect self-employed people to have very different working behaviour and working patterns, and thus they should not be mixed with employees in the estimations. Second, there are also concerns regarding the quality of wage and salary information for self-employed people in general (Buddelmeyer et al., 2006; Cassells et al., 2009). The additional

Measures of mental health Most measures of mental health in the studies discussed above are subjective rather than objective measures. For example, Ek et al. (2014) used a combination of subjective psychological resources and psychological distress. Subjective psychological resources included three items from the Work Ability Index (WAI) (Tuomi et al., 1994) covering enjoyment of daily tasks, feeling active and alert, and feeling full of hope for the future, while psychological distress was assessed by the HSCL25, which is a 25-item instrument assessing global psychological distress derived from the SCL-90 (Derogatis et al., 1976). The HSCL-25 is composed of 15 depression items (e.g. feeling low in energy/slowed down, blaming yourself for things, feeling hopeless about the future, feeling blue, feeling worthless) and 10 anxiety items including somatisation items (e.g. “Have you been distressed by feeling weak in your body?”) and phobic anxiety items (e.g. “Have you been distressed by feeling tense or keyed-up?”) from the SCL-90. On the other hand, Dooley et al. (2000) and Friedland and Price (2003) used a combination of indicators of both general wellbeing (e.g. life satisfaction, depression symptoms, and positive self-concept) and context-specific wellbeing (e.g. job satisfaction). The SF-36 is another measure that is commonly used for health and wellbeing assessment (Fukuhara, et al., 1998; Loge and Kaasa, 1998; Ware, 2000; Ware et al., 1995, 2000; Ware and Sherbourne, 1992). It is a self-reported 36-item survey measuring health-related quality of life. Thirty-six items are used to construct eight scales, normalised between 6

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measurement errors of working hours among the self-employed may also create extra difficulties in estimations. Among those with suitable jobs, we include all individuals from waves 1 to 16 of HILDA who answered with valid responses on all explanatory variables used in this paper. Consequently, this is an unbalanced panel. The hours mismatch (gap) is defined as the gap between current hours an individual usually works per week and the hours he or she would like to work per week with the following further definitions:

and mental health, and differences in model specification, affect our conclusions. Table 3 provides descriptive statistics of hours mismatch, job strain and other selected socioeconomic and job characteristics among our sample of mature age workers. Although around 17 per cent of women are concentrated in the low job strain category, and this is 22 per cent for men, around 21 per cent of women and 14 per cent of men are in the high job strain category. Thus, a greater percentage of women are in the high job strain category while more men are in the low job strain category.

• Hours overwork is defined as the gap or number of hours where •

Methodology

current regular working hours are more than the desired working hours. Hours underwork is defined as the gap or number of hours where current regular working hours are less than the desired working hours.

This study uses panel data with fixed effect estimations, taking into account the unobserved heterogeneity across mature age workers in the sample, as described above. As illustrated in Equation 1, the mental health score of an individual (i) in year (t) is assumed to be a function of the following drivers or factors: socioeconomic or demographic factors such as age, partner status, number of children and the status of chronic health conditions; labour market/job characteristics such as current employment status, previous labour force status, industry, occupation, contract type, job changes, hourly wage and our variables of interest – hours mismatch and job strain status. To allow for a non-linear relationship between mental health score and age, a quadratic function of age is used. The model also includes a remoteness classification.

We selected variables from HILDA to measure both job demand and job control as follows: Job control (eight indicators):

• My working times can be flexible. • My job requires me to take initiative. • My job provides me with a variety of interesting things to do. • I can decide when to take a break. • I use many of my skills and abilities in my current job. • I have a lot of say about what happens in my job. • I have a lot of freedom to decide when I do my work. • I have a lot of choice in deciding what I do at work.

MHScorei, t =

0

+ Xi, t

1

+ Zi, t

2

+ MHScorei, t

1

+ ci +

i, t

where the dependent variable MHScorei, t is the transformed score of SF36 mental health status (either mental health, vitality, role-emotional or social functioning, as reported in HILDA) of individual i at time t, 0 , 1 and 2 are the parameters to be estimated. ci is the individual fixed effect and i, t is the error term. We also include a one-year lag of mental health scores to capture potential state dependency. To assess the robustness of the model specification, we estimate four models with different combinations of controlled variables:

Job demand (five indicators):

• The job is stressful. • The job is complex and difficult. • The job requires new skills. • I have to work fast in my job. • I have to work very intensely in my job.

1. Basic: only includes age, working hours mismatch, current employment status and previous labour force status as the controlled variables. 2. Intermediate: basic model, plus socioeconomic/demographic characteristics and basic job employment characteristics (actual working hours, job change, remoteness classification and regional unemployment rate based on major statistical region). We use the regional unemployment rates available in HILDA. HILDA compiled this information using data from the ABS. 3a. Full without lag: intermediate model with other job characteristics, including job strain. 3b. Full: intermediate model with other job characteristics, including job strain and lagged mental health status.

Participants rated their response to each of these indicators on a 7point scale from 1 = strongly disagree to 7 = strongly agree. We sum the scores of the eight indicators of job control and those who are at or above the mean value are considered “high control” while the rest are considered “low control”, in line with the approach used by Ek et al. (2014), although they used median rather than mean. In this paper, we wanted to test the strain hypothesis. Thus, we also categorise individuals into “low demand” jobs and “high demand” jobs based on each individual’s relative position in relation to the mean value of the sum of the five job demand indicators. The mean values are calculated based on the sample of the population aged 25–64 years – those who are considered as being in their prime working age. The job strain indicator is derived based on the four combinations between job control and job demand as shown in Fig. 1. We also exclude missing values from all variables we used. In the end, our total number of observations was 12,250 for females and 11,420 for males, covering 2616 female individuals and 2450 male individuals. The data filtering process is shown in in Table 2. We include examinations of other age groups for comparison with mature age workers, particularly focusing on a younger cohort of workers, aged 25–44 years, and a reduced population of mature age workers (aged 45–59), which excludes those whose ages are closer to the retirement age (aged 60–64), as Gielen (2009) argued that older workers who experience high mismatches retire earlier. Thus, it is important to examine the robustness of the specifications by comparing the regression results with and without the inclusion of those aged between 60 and 64 years. Further, we undertake several other robustness tests to examine whether changes in the construction of job stress

It is worth noting that there is potential reverse causality from mental health to working hours. For example, Sachiko and Isamu (2016) argued that workers with mental health issues may be less productive at work and may require longer hours to complete their work. Contrary to this, workers with mental health issues can also be more likely to work less or be absent from work (Almond and Healey, 2003) rather than work more. An endogeneity issue driven by omitted variable bias may also exist, but we minimise this by including wave dummy variables. Thus omitted variables which may change over time will be captured by these wave dummies. One thing to note that the inclusion of lagged mental health status in model 3b may create the Nickell bias (Nickell, 1981), as the demeaning process at the fixed effect create bias in the estimate of the coefficient of the lagged dependent variable, which is not solved by increasing number of observations (Baum, 2013). Some econometricians have solved this issue by undertaking dynamic panel data estimations including the Arellano and Bond estimator (Arellano and Bond, 1991; Phillips and Sul, 2007). This, 7

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Table 2 Sample construction.

Total observations with valid SF-36 variables With valid status as an employee and part-time or full-time status in current employment With valid reported actual working hours (usually worked in all jobs) and desired working hours With valid job strain and hours mismatch variables With lag of previous year labour force status With valid socioeconomic/demographic variables With valid other employment characteristics, regional unemployment rates and excluding all missing values(Total number of observations) Total number of individuals

Females aged 45–64

Males aged 45–64

34,482 19,145 19,114 19,114 17,368 17,355 12,250

30,937 17,365 17,338 17,338 15,671 15,658 11,420

2,616

2,450

Note: Data are pooled from HILDA Waves 1–16. Table 3 Descriptive statistics of selected variables (people aged 45 to 64 years). Variables

SF-36 transformed

Hours mismatch Regular working hours per week Employment status Job strain

Employment contract Age Chronic health issue Having partner Children Composition Number of individuals Number of observations

Unit

Mental health Role-emotional Vitality Social functioning Hours overwork Hours underwork Full-time Part-time Low Passive Active High Permanent/ongoing Fixed term Casual

Children age 0–4 Children age 5–14 Children age 15–24

Females

Hours/week Hours/week Hours/week % % % % % % % % % Years % % Number of children Number of children Number of children

Males

Mean

Standard deviation

Mean

Standard deviation

76.01 88.87 60.46 85.64 4.26 1.02 34.23 59.59 40.41 17.03 26.48 35.18 21.31 77.38 9.20 13.4 52.42 16.56 71.88 0.01 0.24 0.34 2,616 12,250

15.72 26.62 19.44 20.76 7.40 3.56 12.25

77.46 90.80 64.47 88.05 4.34 0.77 42.95 91.15 8.85 21.67 22.99 41.14 14.20 31.03 8.54 7.78 52.65 16.02 82.68 0.04 0.38 0.36 2,450 11,420

15.05 24.38 17.53 18.88 7.52 3.27 10.71

5.14 0.10 0.58 0.65

5.26 0.23 0.75 0.66

Note: Only individuals who reported their working hours and desired working hours and answered all questions that are relevant for this paper are included. Data are pooled from HILDA Waves 1 to 16.

however, has shown to be difficult to implement in our case due to the lack of reliable instrument. We therefore test the likely impact of the bias by using an alternative specification without lags.

lower than the rate for younger women, while the rate of hours overwork for mature age men was higher than the rate of hours overwork for younger men. However, rates of hours underwork among mature age women (for all and part time) but among full time mature age men in 2016 were higher than they were in 2006. These observations indicate that underwork may be a an increasingly significant issue for mature age workers. Data published by the ABS suggests that mature age workers tend to experience a much longer spell of underemployment compared with younger workers (ABS, 2019).1 Fig. 2 plots the relationships between four different mental health measures and the mismatch between the actual and the desired hours of work among mature age workers. The SF-36 mental health measures have scores ranging from 0 to 100, with 0 representing the most disadvantaged category (worst mental health score) while 100 refers to the least disadvantaged category (the best mental health score). The negative gap on the left side of the chart reflects hours underwork while the positive gap on the right side reflects hours overwork. The shaded areas are the 95 per cent confidence intervals of the local polynomial

Patterns of hours mismatch and SF-36 mental health Table 4 shows the rates of women and men who experience hours overwork and underwork (as a percentage of employed women and men) calculated from our sample of mature age workers. For a comparison, we also provide the patterns of hours mismatch among the younger cohort aged 25–44 years. HILDA data shows that during the overall periods investigated the rates of hours overwork among mature age workers are higher than for their younger counterparts, while the rates of underwork are higher among the younger (25–44) age group. In line with the underemployment literature, part-time women are also more prone to experiencing hours underwork than men. Comparing the situation in 2006 and 2016, there were increasing rates of hours underwork among mature age women, younger women and men, while the rates of overwork for both mature age and younger groups of women and men declined during this decade. Further, in 2006, the rate of hours underwork among mature age women was

1 As HILDA is an annual survey, the duration of underemployment, which is often measured in weeks, cannot be reliably inferred based on the employment status at the time of interview.

8

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Table 4 Patterns of hours mismatch by employment status. 45–64

25–44

Hours overwork

Hours underwork

Hours overwork

Females

Hours underwork

Hours overwork

Males

Hours underwork

Hours overwork

Females

Hours underwork Males

All periods % employed Full-time Part-time All 2006 % employed Full-time Part-time All 2016 % employed Full-time Part-time All

28.38 4.78 33.15

1.28 8.97 10.25

32.97 0.69 33.66

4.47 2.82 7.29

25.24 4.12 29.36

1.86 9.25 11.11

28.25 0.28 28.53

7.31 2.70 10.01

30.86 5 35.86

1.14 7.57 8.71

35.22 0.61 35.83

3.83 4.13 7.96

26.26 4.83 31.09

1.01 7.97 8.98

31.3 0.43 31.73

5.33 2.17 7.50

27.57 5 32.57

0.94 9.92 10.86

30.09 0.71 30.80

3.95 3.44 7.39

22.27 3.73 26.00

1.99 9.45 11.44

24.41 0.08 24.49

8.22 3.25 11.47

Note: Authors’ calculation from HILDA Waves 1–16. Only individuals who reported their working hours and desired working hours and answered all questions that are relevant for this paper are included.

Fig. 2. Relationship between mental health and working hours mismatch (aged 45 to 64 years). Note: The line plots the estimates from the local polynomial smoothing, and the shaded areas indicate the 95% confidence interval. Negative values in mismatched hours indicate the current regular working hours in all jobs are less than desired (underwork), while positive values indicate the opposite situation (overwork). Data are pooled from the HILDA survey wave 1 to wave 16. The figure only includes observations with data for reported working hours and the desired number of hours, and with a mismatch of no more than 20 hours. 9

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R. Miranti and J. Li

variants of the model. There is the basic model (Model 1), intermediate model (Model 2) and the full models (Models 3a and 3b). We estimate the model separately for males and females.2 The results show the significant gender differences across the coefficients, particularly for the intermediate and full models. Thus, although the results of the basic model are also presented, our discussion of the results focus on the intermediate and full models (Model 2, 3a and 3b). There are also significant differences across the coefficients between young and mature age workers in the full model we used. The results show that working hours mismatch is correlated negatively with mental health. These results hold for most positive (hours overwork) and negative (hours underwork) gaps except for males and hours underwork in the intermediate model. The results, although they are not directly comparable, provide similar key messages to Otterbach et al. (2016), in that working fewer and more hours than desired is correlated negatively with mental health. For women, hours underwork seems to consistently have a bigger impact on mental health than hours overwork. Female coefficients of hours underwork are also higher than those of their male counterparts. This is in contrast with research on the impact of unemployment on mental health (e.g. Artazcoz et al., 2004) where males tend to suffer more in the case of losing a job. This difference between our results for underwork and the relationships that have been found in relation to unemployment may have to do with the likely job trajectory once an individual falls into underwork. Li et al. (2015) found that women are more prone to underemployment on average and their likelihood of closing this gap in working hours after an episode of underemployment is lower than for men (although this is true only for part-time workers). Thus an expectation that males may be much more likely to be able to reverse an underwork situation than females could contribute to a lessened shock to mental health from underwork for men. The coefficients of the key variables are stable in all intermediate and full model specifications, with varying controls on socioeconomic characteristics and job characteristics, including for the version with or without the lagged variable of mental health. This stability suggests that the findings are robust. The results for job strain indicate a stronger impact of this experience on men than women. In comparison to the low strain category, all other job strain categories have higher and significant negative impacts on mental health for men, while for women only the high strain job category has a robust and higher negative impact on mental health. It is possible that women may have coped better than men with other challenges in their life that may have impacted their mental health, affording them a degree of protection in regard to job strain. The results for other job characteristics are limited, with many of these not being statistically significant. For women, being in a professional occupation has a stronger positive impact on mental health than being in a managerial occupation, but this is not so for men. The previous year’s labour force status does not have robust findings for either gender. For men, there are several industries (as indicated in Table 6) that are associated positively with current mental health. Experiencing a job change has a negative and significant association with mental health for women but not for men, which may perhaps indicate that men have coped better in this situation. Socioeconomic characteristics also matter. Age has a negative

Table 5 Mean of mental health scores by job strain and gender (people aged 45 to 64 years). Job strain Low Passive Active High Low Passive Active High

Females

Males

Mental health 79.24 81.68 75.92 76.67 75.89 77.70 73.76 71.58 Role-emotional 91.43 93.80 89.78 92.36 88.55 90.14 86.22 85.58

Difference −2.44*** −0.75* −1.82*** 2.18*** −2.38*** −2.59*** −1.59*** 0.64

Females

Males

Vitality 64.18 68.76 60.27 63.39 60.91 64.99 56.97 58.18 Social functioning 87.78 90.94 85.59 87.65 86.08 88.26 83.28 83.66

Difference −4.58*** −3.12*** −4.08*** −1.21* −3.16*** −2.06*** −2.19*** −0.38

Note: Authors’ calculations from HILDA Waves 1–16. *** means statistically significant at 0.01 level, ** means statistically significant at 0.05 level and *means statistically significant at 0.1 level. Mental health, vitality, role-emotional and social functioning are mental health components of the SF-36 measure. High scores indicate better mental health while low scores indicate poorer mental health.

estimates, while the lines plot the smoothed local average. The figure shows an inverse U-shaped pattern between hours mismatch and all four selected mental health measures (SF-36 mental health, SF-36 role-emotional, SF-36 social functioning, and SF-36 vitality). Such a pattern suggests a negative correlation between hours mismatch and mental health status. The confidence intervals in Fig. 2 are narrow when the hours mismatch value is relatively small and become larger when the absolute value of the mismatch grows; this is due to the decreasing number of observations. Except for the vitality measure, all outcome measures seem to indicate that mental health status declines faster when the desired hours of work are less than the actual hours of work, compared with the opposite situation of actual working hours being higher than desired. For the vitality measure, the gradients of the declines seem to be about the same for both overwork and underwork. The average size of the decline at 10 hours mismatch tends to be between 2 and 10 points and statistically significant in all measures when the gap is negative. When we examine the distribution by gender (results not shown), we find a difference in terms of the shape of the curves, the gradient declines due to mismatch are slightly different and the absolute value of the outcome measures reported by females tend to be one to three points lower than for males. Patterns of job strain in the Australian labour force Table 5 shows the mean of the mental health scores for each job strain category. Further, we can also observe the following patterns. First, people working in a job with less strain generally have better mental health scores. Second, women tend to have a lower (poorer) mental health score than men in the same job category. Third, although the magnitude of the gaps between the men and women are small, the scores are significantly different across gender. These patterns hold across different components of mental health except for the role-emotional category for individuals experiencing high job strain. Regression results and discussion

2

In addition to the reported model specifications, we also attempted to use the Arellano-Bond estimator to address the possible Nickell bias in the dynamic regression. However, we were not able to find proper instruments and thus our alternative estimate suffered from a weak instrument issue. This may relate to the issue that Phillips and Sul (2007) identified, arguing that weak instrumentation problems may not solve the Nickell bias in dynamic panel regressions. Thus, our fixed effects model (3b) remains our preferred model. Although we acknowledge that we still have the Nickell bias limitation, the limited differences between the specifications with and without lag variables indicates the bias is unlikely to cause any major change to the conclusions.

The data we have presented so far suggests a negative correlation between working hours mismatch and reported mental health status (that is, generally the higher the degree of mismatch, the poorer the mental health score). It also shows that workers with high job strain tend to have lower scores (so poorer mental health) in all four components of SF-36 mental health. In this section, we further our analysis by controlling for demographic and other socioeconomic variables. Table 6 shows the regression results. As indicated earlier, there are four 10

The Journal of the Economics of Ageing 15 (2020) 100227

R. Miranti and J. Li

Table 6 Estimation results for SF-36 – mental health (aged 45 to 64 years). Basic model (1)

Intermediate model (2)

Full model (3a)

Females Males

Females Males

Females

Full model (3b) Males

Previous mental health score (t-1) Hours mismatch Hours overwork Hours underwork Job characteristics: Current employment status (base) Full-time Part-time Job strain (base) Low Passive Active High Contract type (base) Permanent/ongoing Fixed termCasual basis Job change(base) Does not experience job changeExperiences job change Actual working hours Occupation (base) Manager Professional

0.011

−0.048** −0.160****

−0.076*** −0.083**

−0.045** −0.156***

−0.069*** −0.074*

−0.045** −0.156***

−0.069*** −0.074*

0.005

−0.749

−0.104

−0.786

−0.088

−0.730

−0.095

−0.717

−0.607 −0.625 −1.796***

−1.283*** −1.079*** −4.152***

−0.614 −0.647 −1.830***

−1.273*** −1.076*** −4.141***

−0.902**

−0.248

−0.136−0.662 −1.035**

−0.078−0.573 −0.230

−0.152−0.630 −1.077**

−0.084−0.571 −0.247

−0.006

−0.003

−0.011

0.007

−0.012

0.007

1.485**

0.037

1.453***

0.029

Professional, Scientific and Technical Services, Administrative and Support Service (9) Public Administration and Safety (10) Education and Training, Health Care and Social Assistance (11) Art and Recreation Services and Other (12) Previous LFS (t-1) (base) Previously full-time Previously part-time Previously unemployed, looking for FT Previously unemployed, looking for PT Previously NILF, marginally attached Previously NILF, not marginally attached Employed, but usual hours worked unknown

R-sq within R-sq between R-sq overall Prob > F

0.029*** −0.075*** −0.082**

Electricity, Gas, Water and Waste Service (4) Construction (5) Wholesale Trade, Retail Trade, Accommodation and Food Services (6)Transport, Postal and Warehousing, Information Media and Telecommunication (7)Financial and Insurance Services, Rental, Hiring and Real Estate Services (8)

N

Males

−0.047** −0.164***

Industry (base) Agriculture, Forestry and Fishing (1) Mining (2)Manufacturing (3)

Hourly wage (in log format) Socioeconomic characteristics:Age Age Age_sq Chronic health condition Having partner Number of children children aged 0–4 children aged 5–14 children aged 15–24 Remoteness area (base) = Major city Inner and outer regional Australia Remote and very remote Australia Regional unemployment rateConstant

Females

0.227 −0.001

67.911*** 12,250 0.0041 0.0110 0.0096 0.0000

−1.007** 0.010**

101.804*** 11,420 0.0048 0.0260 0.0198 0.0000

0.466 1.679 7.2796 −0.898** −0.441 −12.137*

0.527 1.735 3.854 5.426 −1.810 −0.052

−0 0.465 1.874 7.095 −0.791 −0.306 −12.530*

0.500 1.650 2.270 5.230 −1.425 0.955

4.323 0.556 −0.635 −0.892

1.056 1.075 3.26* 1.500

4.246 0.536 −0.672 −0.954

1.040 1.072 3.249** 1.483

−1.427

2.230

1.386

2.226

−0.037 −0.387 −1.028 0.728 0.158 1.558

2.036 2.013 1.600 2.161 1.863 1.916

−0.083 −0.439

2.028 1.994

−1.056 0.665 0.138 1.570

1.589 2.143 1.852 1.920

0.525 1.99 7.443 −0.301 −0.054 −12.323*

−545 1.801 4.382 5.910 −1.580 2.248

0.517 2.309 7.330 −0.142 0.111 −12.226*

0.545 1.809 4.346 5.966 −1.538 2.442

−0.180

0.658

−0.163

0.652

0.094 −0.000 −1.874*** 1.174*

−0.817* 0.009** −1.585*** 1.552**

0.264 −0.001 −1.901*** 1.221*

−0.749 0.008* −1.556*** 1.678***

0.255 −0.001 −1.887 1.108*

−0.739 0.007* −1.550*** 1.643***

−0.454 −0.208−0.452*

0.322 0.113 −0.432*

−0.536 −0.231 -0.0438*

0.415 0.177 −0.395*

−0.593 −0.228 −0.418*

0.407 0.176 −0.394*

0.812 0.658

−1.565* −1.327

0.731 0.933

1.593* −2.077

0.780 0.939

1.587* −2.102

−0.040 72.156*** 12,250 0.0083 0.0406 0.0269 0.000

−0.379*** 96.953*** 11,420 0.0102 0.0.513 0.0405 0.0000

0.171 64.085*** 12,250 0.0147 0.0410 0.0322 0.0000

−0.480*** 94.346*** 11,420 0.0243 0.0671 0.0615 0.0000

0.171 64.086*** 12,250 0.0155 0.1332 0.0955 0.0000

−0.477*** 93.316*** 11,420 0.0244 0.1034 0.0865 0.0000

Note: Unbalanced panel. *** means statistically significant at 0.01 level, ** means statistically significant at 0.05 level and *means statistically significant at 0.1 level. LFS means labour force status. NILF means not in the labour force. Results from other classifications of occupation and industry are not statistically significant so they are not shown in the table. Includes the wave dummies. The coefficients of female and male equations are statistically significant at 0.1 level for the intermediate and full models. 11

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Table 7 Regression estimates, robustness across age groups. SF-36 Mental health (45 to 64)

Hours mismatch Hours overwork Hours underwork Job strain (base) 1 = Low 2 = Passive 3 = Active 4 = High N

Table 8 Regression estimates, comparison to younger age group.

Females

Males

SF-36 Mental health (45 to 59) Females Males

−0.045** −0.156***

−0.069*** −0.074*

−0.046** −0.074*** −0.164*** −0.079*

−0.614 −0.647 −1.830*** 12,250

−1.273*** −1.076*** −4.141*** 11,420

−0.835** −1.361*** −0.936** −1.054*** −2.171*** −4.546*** 10,848 9,933

SF-36 Mental health (25 to 44)

Hours mismatch Hours overwork Hours underwork Job strain (base) 1 = Low 2 = Passive 3 = Active 4 = High N

Females

Males

−0.059*** −0.070 **

−0.087*** −0.010

−1.195*** −0.849** −2.160*** 14,110

−1.030*** −1.338*** −3.336*** 15,161

Note: *** means statistically significant at 0.01 level, ** means statistically significant at 0.05 level and *means statistically significant at 0.1 level. Coefficients refer to the results of the full model (Model 3b).

Note: *** means statistically significant at 0.01 level, ** means statistically significant at 0.05 level and *means statistically significant at 0.1 level. Coefficients refer to the results of the full model (Model 3b).

impact on mental health but the strength of this relationship weakens at older ages, but this variable is only significant for men. A chronic health condition in the previous year has a negative impact on mental health regardless of gender, which is expected. The previous year’s mental health score is statistically significant only for females, indicating possible path dependency for this group. The findings also show that the lag coefficients are small in magnitude, suggesting they are probably not important once a fixed effects model is used and that the implication of Nickell bias is limited, as noted earlier. Having a partner appears to be a protective factor for mental wellbeing, as its impact on mental health is positive, but men seem to benefit more from a domestic partnership in terms of mental health. This is consistent with the broader literature on the impact of marriage on health, such as the Ploubidis et al. (2015) study. The local unemployment rate is negatively associated with mental health while living in inner and outer regional Australia is positively related to mental health, but these results are only significant for men. Having older children aged 15–24 years is associated with lower (poorer) mental health. Thapa et al. (2018) have suggested that out-migration of older children from the family home is associated with a lower level of mental health of the left-behind parents, although whether this observation is relevant to our findings would need to be investigated further in future research. One possible concern about the sample we have used for our estimation is that some individuals included may be approaching retirement age, and this may affect the way hours mismatch affects mental health. To assess the robustness of the results from this perspective, we compare the results with estimations derived from different age groups and contrast these findings with those for a younger age group. Table 7 reports the estimation results for the age groups 45–64 years and 45–59 years, and gender differences are statistically significant across the coefficients. As shown, limiting the estimation to individuals who are less likely to be affected by retirement decisions does not alter our conclusions. In fact, the coefficients for both hours overwork and hours underwork are only slightly changed and the point estimates for the job strain variables have decreased (i.e. they show an increase in negative values), thus suggesting job strain may have a higher impact if we exclude the closeto-retirement population. This makes sense, as the expectation of moving towards retirement may make people less critical of their current work conditions and mature age workers may have achieved the stage of career plateauing (Bown-Wilson and Parry, 2009; Ference et al., 1977). We also compared the results with the younger (25–44 years) age group, as shown in Table 8. Although the findings show similar patterns to those of the older population, younger men (25–44 years) seem to be more affected in terms of mental health by hours overwork and younger

women are more likely to be negatively affected when they experience underwork, but job strain has the same direction of influence compared with mature adult workers. The impact of overwork on mental health among men seems to be larger than among women. This pattern is similar to the findings of the mature age group. In contrast, the coefficient of hours underwork is only significant for young women. In addition to the robustness assessment of the findings with various model specifications and sample inclusions, we further examine whether our inclusion of even small gaps between desired and actual working hours may have affected the results. In Table 9, the first column of data replicates the results for males and females from the full model in Table 6, while the second column shows the results where minor reporting discrepancies between the reported actual hours and the desired working hours are ignored. In this case, individuals with a working hours discrepancy of fewer than four hours are coded as zero – the implicit assumption behind this is that such small deviations may contain substantial measurement or recall error. We created the working hours discrepancy as dummy variables, i.e. hours overwork greater than four hours is coded as one, or otherwise zero. A similar treatment was used for hours underwork. Our findings suggest that increased tolerance of the reporting discrepancy only has a minimal effect on the estimation results, given that changes in the coefficients for the key variables are only observed at the third decimal place. In the same table, we also show results that test whether the inclusion of job stress alters the estimation. In the third results column in Table 9, where the job stress variable is excluded from the estimation (to test the robustness of the results), there is only limited change to the coefficients of the hours mismatch variables. Among the job strain variables, the results are similar to the full model, with the only statistically significant change being observed in the active job strain category. The results show that the coefficients of active and high job strain become smaller (although this is not the case for the passive category). This result is unsurprising, as the removal of the job stress variable means we may have excluded a potential mechanical relation between job strain and mental health, as it can be argued that stress contributes to poorer mental health. We also compared the main mental health results with other SF-36 mental health measurements. While the SF-36 role-emotional, SF-36 vitality and SF-36 social functioning components are different measures of mental wellbeing, they are all considered to be strongly correlated with the mental health domain (Dockery, 2006). These tests can also serve as robustness checks. If our findings are robust, we would expect to find similar, but not identical, findings across all SF-36 measures associated with mental health. Table 10 reports the associations between hours mismatch, job strain and the mental health-related components of the SF-36. Interestingly, for men only, hours overwork has negative and significant correlations with the other components of SF12

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Table 9 Regression estimates, robustness across different specifications of hours overwork, underwork or job strain. SF-36 Mental health (45 to 64) (1)

Hours mismatch Hours overwork Hours underwork Job strain (base) 1 = Low 2 = Passive 3 = Active 4 = High N

SF-36 Mental health Hours overwork or underwork > =4 or otherwise (45 to 64)(2)

SF-36 Mental health Job stress is excluded from job strain measures (3)

Females

Males

Females

Males

Females

Males

−0.045*** −0.156***

−0.069*** −0.074*

−0.825*** −1.674***

−1.245*** −0.384

−0.046** −0.157***

−0.071*** −0.079**

−0.614 −0.647 −1.830*** 12,250

−1.273*** −1.076*** −4.141*** 11,420

−0.804* −0.435 −1.393*** 12,250

−1.596*** −0.527 −3.052*** 11,420

−0.791* −0.440 −1.404*** 12,250

−1.586*** −0.544 −3.067*** 11,420

Note: *** means statistically significant at 0.01 level, ** means statistically significant at 0.05 level and *means statistically significant at 0.1 level. Coefficients refer to the results of the full model (Model 3b). Hours overwork and underwork are dummy variables in (2).

Table 10 Regression estimates, summary of hours mismatch and job strain association with SF-36 – mental health components (age 45–64). SF-36 Mental health

SF-36 Role-emotional

SF-36 Vitality

SF-36 Social functioning

Females

Males

Females

Males

Females

Males

Females

Males

−0.045*** −0.156***

−0.069*** −0.074*

−0.155*** −0.270***

−0.092** 0.0093

−0.060** 0.047

−0.105*** −0.036

−0.023 −0.215***

−0.108*** 0.007

−0.614 −0.647 −1.830*** 12,250

−1.273*** −1.076*** −4.141*** 11,420

−1.238 −1.765 ** −3.341*** 12,250

−0.149 0.111 −3.186*** 11,420

−0.990** −0.097 −1.854*** 12,250

−1.177*** −0.541 −3.374*** 11,420

−0.260 −0.468 −1.122* 12,250

−0.929* −1.541*** −3.079*** 11,420

Hours mismatch Hours overwork Hours underwork Job strain (base) 1 = Low 2 = Passive 3 = Active 4 = High N

Note: *** means statistically significant at 0.01 level, ** means statistically significant at 0.05 level and *means statistically significant at 0.1 level. Coefficients refer to the results of the full model (Model 3b) with different dependent variables.

36 mental health – role-emotional, vitality and social functioning. These results showing that overwork (but not underwork) is significant may reflect the traditional expectation for men to act as the main breadwinner for families. For women, the association between hours mismatch and the role-emotional component shows a similar pattern to the results for the overall SF-36 mental health measure. On the other hand, the results show that overwork among women hampers their vitality while it is underwork that has a negative relationship with social functioning. Overall the findings across the four SF-36 scores show a generally consistent pattern, suggesting a negative impact of hours mismatch on mental wellbeing and suggesting a stronger impact for men than women. These findings of the links between mental health, working hours mismatch and job strain have important practical policy implications for mature age workers. Working fewer hours than preferred has been associated with job insecurity and increased casualisation and signals an inefficiency in the utilisation of skilled labour. Any policy interventions should include efforts to increase employer awareness around the many benefits of employing mature age workers and offer employer incentives to encourage the employment of this group of workers. As highlighted in Li et al. (2015), targeted interventions have been required to solve these underemployment barriers, with the aim of highlighting the role of improved labour market attachment in promoting the wellbeing and economic contribution of mature age workers. Policies that encourage continuing education and general human capital accumulation would both improve the productivity of the workers and reduce the likelihood of underemployment. From the perspective of employers, an understanding of working time preferences will be useful to improve the general mental health of

workers, increase employees’ motivation and productivity and reduce labour mobility costs due to job changes (Constant and Otterbach, 2011; Otterbach, 2010). This is particularly the case for underwork, as the findings show the stronger impact of underwork on mental health among mature workers than among younger workers. Appannah and Biggs (2015) have proposed the concept of “age-friendly organisations”, a term which includes a combination of a flexible workplace and the promotion of health through job design or a culture of inclusion. In addition, both employers and employees need to increase their awareness that mental health in the workplace is as important as physical health, and that this applies not only for younger workers but also for mature age workers. This increased awareness has been identified by Harvey et al. (2014) as one of six factors that contribute to a mentally healthy workplace. Four of these factors are categorised as preventive. In addition to raising awareness, these are: smarter work design (so that employees have more control over their job and can work flexibly); building better work cultures (organisational resilience) through a safe and positive environment; and building individual resilience. The two other factors (categorised as “curative”) are promoting and facilitating early help-seeking and supporting recovery. Despite older workers being more exposed to underwork, in terms of job strain, as highlighted earlier, the patterns and direction of the impacts do not differ much for younger and older workers. While the provision of flexible work arrangements which enhance employees’ perceived autonomy and control might apply to both groups, for mature age workers this may be more necessary, as the nature and pattern of their work may change during this life stage due to ageing. This in turn creates job stability and positive working environments of benefit to both employers and employees and helps to keep mature age workers 13

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participating in the labour market. It should be noted, however, that flexible work arrangements do not necessarily translate to an increase in employees’ perceived autonomy nor have they always created positive impacts, as the details of the implementation matter. For example, Baltes et al. (1999) found that highly flexible programs could have unintended consequences such as difficulties in communicating with other employees who did not work during the same work period or increasing costs associated with monitoring employees’ different working hours. Baltes et al. (1999) also argued that an increase in autonomy through flexibility may be more related to positive attitudes towards the work itself, as Ronen (1981) suggested that autonomy can be reached if flexibility can assist workers to fulfil not only their work requirements but also their self-actualisation needs. Further research is required to understand the optimal work environment for enhancing perceived autonomy. From a demand side perspective, such flexibility could even be a helpful tool for employers to be able to readily make use of underemployed workers whenever the economic situation requires this. Another finding from our research is the negative correlation between mental health and living in regional areas, conditional on all other variables. The negative link suggests that the local job market and living environment may also have a major impact on maintaining workers’ mental health. On average, regional areas tend to have fewer job opportunities which means individuals may take a longer time to find a suitable job, potentially increasing the stress when in a mismatched job position. Therefore, policies that assist mature age workers in regional Australia to overcome job mismatch could bring positive change to the mental health of the workforce. A recent governmentlaunched “Career Transition Assistance Program” has the similar aim to enhance the employability of the mature age workers to apply for jobs in the local area and to better adapt to the changing needs of the economy Australian Department of Employment, Skills, Small and Family Business, 2019). The policy change, besides its expected benefit, also offers an opportunity to evaluate the impact of the additional job training and supports on the wellbeing of the mature age workers in the future. Moreover, for mature age workers good mental health has been associated with healthy ageing (Peel et al., 2004; Beard et al., 2016), so promoting healthy ageing at work will be important for keeping mature age workers participating in the labour market. To address these factors, workplaces need to have policies in place that focus on positive statements about the value the organisation places on promoting healthy ageing and wellbeing. At the national level, the importance of mental health in the workplace has also been acknowledged and promoted by the Australian Government in its Fifth National Mental Health and Suicide Prevention Plan, 2017 (Australian Department of health, 2017).

results also suggest that a low-control–high-demand job has a significant negative impact on reported mental health scores. Job strain tends to have a larger (negative) impact on men than women, while women are more severely affected by the experience of underwork. In the case of overwork, however, the mental health impacts are about the same for both men and women. The gender differences observed for underwork may be associated with different job market prospects in the case of underemployment, differences in societal expectations, and perhaps varying abilities in stress management. The results highlight the importance of creating mentally healthy workplaces. Interestingly, in contrast to mature age workers, the younger cohort (aged 25–44) seem to be more affected by overwork and less likely to be negatively affected when experiencing underwork. Some drawbacks of the research should also be acknowledged. The identification of working hours mismatch, job strain and mental health relies on the cross-sectional and longitudinal variations that are reported in HILDA. The endogeneity between job characteristics and mental health may result in biased estimates, although we do control for extended job characteristics and previous mental health scores. Future work will also include improvements in terms of the methodology used to create job control and job demand measures by using a variable reduction technique. The paper nonetheless suggests the mature age workforce will be an important area for further research, given the significant decline in mental health related to working hours mismatch and job strain for this group. As suggested earlier, policy recommendations include the use of flexible work arrangements in age friendly organisations and promotion of healthy ageing at work, supporting mature age workers to keep contributing to the labour market. Future research could use natural events, such as a financial crisis or a major regional industry shutdown, to refine the estimates and potentially compare the differences between subpopulations in the labour force. Acknowledgement This paper uses unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The HILDA Project was initiated and funded by the Australian Government Department of Social Services (DSS) and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute). The findings and views reported in this paper, however, are those of the authors and should not be attributed to either DSS or the Melbourne Institute. The authors would like to acknowledge the assistance received from Australian Research Council Linkage grant (LP120100624) – ‘Understanding and preventing workforce vulnerabilities in mid-life and beyond’ which funded the initial version of this paper.

Concluding remarks

References

Previous literature has explored the relationships between health and wellbeing and various ways in which labour may be underutilised in the general labour force. Different measures have been used in earlier studies as proxies of health and wellbeing, including self-assessed health, subjective wellbeing and mental health. Empirical results (e.g. Adam and Flatau, 2006; Dockery, 2006) tend to support the hypothesis that working hours mismatch may negatively affect a worker’s mental health. However, empirical results for the mature age population – the group which is most likely to experience workforce underemployment – are scarce. Using the latest HILDA survey data, this paper estimates the impact of job strain and working hours mismatch on mental health among the mature age population, controlling for detailed job characteristics. Results from four different mental health components of SF-36 scores suggest a robust pattern showing that working hours mismatch, in the form of both overwork and underwork, has a significant negative effect on mental health. These findings apply to both men and women. Our

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