Lost mind, lost job? Unequal effects of corporate downsizings on employees

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Lost mind, lost job? Unequal effects of corporate downsizings on employees © 2024 the Authors Published version Böckerman, Petri; Haapanen, Mika; Johansson, Edvard Böckerman, P., Haapanen, M., & Johansson, E. (2024). Lost mind, lost job? Unequal effects of corporate downsizings on employees. German Journal of Human Resource Management, OnlineFirst. https://doi.org/10.1177/23970022241244988 2024 https://doi.org/10.1177/23970022241244988 German Journal of Human Resource Management 1 –17 © The Author(s) 2024 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/23970022241244988 journals.sagepub.com/home/gjh Lost mind, lost job? Unequal effects of corporate downsizings on employees Petri Böckerman Labour Institute for Economic Research LABORE, University of Jyväskylä, Finland IZA-Institute of Labor Economics, Germany Mika Haapanen University of Jyväskylä, Finland Edvard Johansson Åbo Akademi University, Finland Abstract We investigate whether employees with mental health disorders are likelier to be laid off during corporate downsizings. Our study uses nationwide administrative data from all private sector firms and their employees in Finland from 2001 to 2017 and focuses on firms with at least 20 employees that reduced their workforce by at least 20% over two consecutive years. We analyse whether the employees who were laid off had more diagnosed mental health disorders prior to downsizing compared than those who were not laid off. Controlling for employee characteristics, our baseline results show that a mental health disorder diagnosis in the 3 years before downsizing increases the likelihood of being laid off by about 6 percentage points. This highlights the increased vulnerability of employees with mental health disorders in mass layoff situations. Keywords corporate downsizing, health, job displacement, mass layoff, mental health, unemployment Introduction Modern labour markets are characterised by turbulence. Extensive literature has examined the effects of firm closures/downsizing on an individual’s health (Black et al., 2015; Corresponding author: Edvard Johansson, Åbo Akademi University, Vänrikinkatu 3, Abo 20500, Finland. Email: [email protected] 1244988GJH0010.1177/23970022241244988German Journal of Human Resource ManagementBöckerman et al. research-article2024 Article 2 German Journal of Human Resource Management 00(0) Böckerman and Ilmakunnas, 2009; Browning et al., 2006; Browning and Heinesen, 2012; Eliason and Storrie, 2009). There are also studies within this body of literature that explicitly focus on mental health effects (Bach et al., 2021; Eliason and Storrie, 2010; Farré et al., 2018). These findings point to unemployment having a causal effect on health problems experienced by individual employees. This research is of broader societal interest because although firm closures and corporate downsizings are a necessary part of creative destruction and subsequent productivity gains, they can lead to painful economic and non-economic consequences for employees who are laid off. Methodologically, these studies rely on a valid research design. Significant corporate downsizing acts as an exogenous shock to the individual employee. Consequently, it is plausible that the individual-level effects observed post-downsizing are caused by downsizing itself, rather than the reverse. This empirical strategy also helps to ensure that unaccounted confounding factors are not deemed the underlying cause of health problems and unemployment incidence. Moreover, research has found that those who are not laid off but are left in a shrinking firm after downsizing tend to suffer from pronounced mental health disorders due to work-related stress and increased perceived job insecurity (Kivimäki et al., 2000; Vahtera et al., 1997, 2004). Notably, psychological well-being is becoming an increasingly important aspect of overall employee well-being in high-income countries. Considerably less attention has been given to the precursors of downsizing, particularly on whether employees with diagnosed mental health disorders are likelier to be laid off during mass downsizing events. This is an important issue because in most countries, it is illegal to prioritise healthy workers at the onset of a mass layoff.1 For example, in Finland, according to binding collective labour agreements that cover practically all workforces, this prioritisation is not allowed apart from very severe cases. Finnish legal practice has established that these severe cases involve situations in which the employee has been absent from work for more than 40%–50% of working days, which is extremely rare in practical settings.2 Individual employees only seldom sue Finnish firms for discriminatory behaviour in the context of layoffs; such cases are almost always brought by trade unions on behalf of an individual employee.3 The primary reason is that the process is time-consuming (decisions from the Finnish labour court can take several years) and requires specialised knowledge of labour law. As a result, only a small fraction of disputed cases reach the labour court. Annually, there are typically only a few cases related to discriminatory behaviour. Regarding research on this issue, Andreeva et al. (2015) provided evidence using a relatively small sample of Swedish workers and reported that women with major depression are at a higher risk of employment exclusion during organisational downsizing. In contrast, for men, job loss does not appear to be significantly influenced by their health.4 Our study is also connected to a current debate in management literature, particularly where it intersects with the domains of organisational behaviour and employee wellbeing. The relationship between exposure to various organisational changes, such as mergers and acquisitions and hostile takeovers, and its subsequent impact on employee health and well-being has been a focal point in recent management literature (for a comprehensive survey of this literature, see Rafferty, 2022). The management literature has also recognised that employee well-being is comprised of both physical and mental Böckerman et al. 3 components (Guest, 2017). This holistic view suggests that physical and mental wellbeing are interdependent and that both aspects are crucial for the overall health and productivity of employees (Inceoglu et al., 2018). Such an understanding is increasingly important in modern workspaces, where the impact of workplace culture and support systems on an individual’s well-being is more pronounced than ever before. Our results are potentially helpful for human resource management towards fostering an inclusive and supportive workplace culture. This could include mental health services and programmes aimed for improving work–life balance. Another strand of literature that is partly related to our work involves research on purchases of psychotropic drugs (Blomqvist et al., 2023; Kaspersen et al., 2016; Magnusson Hanson et al., 2016). In this research, large cohorts of individuals are followed over time, and (importantly for our purposes) investigated to determine whether those who encounter layoffs in the future are likelier to have purchased psychotropic drugs before being laid off. The main conclusion from these studies is that purchases of drugs increase before downsizing for those who are later laid off. These findings are most likely explained by anticipation effects. Although studies on purchases of psychotropic drugs shed some light on potential selection in terms of mental health in the situation of mass layoffs, these studies provide only partial evidence of the relationship between poor mental health and the probability of being laid off in a mass layoff. In this paper, we contribute to the empirical research by analysing information about employees’ actual mental health disorder diagnoses. Our analysis is based on data on all private sector establishments and their employees in Finland between 2001 and 2017.5 We investigate firms with at least 20 employees (or 50 employees in robustness checks) who lay off at least 20% (or 30% in robustness checks) of their total workforce over the span of 2 years. We then compare employees from firms that were downsizing and analyse whether those employees who had mental health disorder diagnoses before downsizing are more likely to be laid off. Our empirical approach is based on the assumption that workplace downsizings can be viewed as natural experiments since they are independent of employee characteristics, such as educational attainment and prior health status (Black et al., 2015; Browning et al., 2006; Browning and Heinesen, 2012). Consequently, the larger the downsizing in a workplace, the less likely that individual characteristics will influence the probability of losing a job in the event of workplace downsizing. Our results show that poor mental health significantly increases the likelihood of job loss during a mass layoff. According to our baseline specification, any mental health disorder diagnosis in the 3 years that precede a layoff increases the probability that an employee will be laid off by 6 percentage points. We also investigate various types of mental health disorder diagnoses and find that the two most important types of mental health disorder diagnoses are depression and substance use disorder. In the models, we control for a comprehensive set of potential confounders, such as employee demographic characteristics, the average earnings of the employee during the 3 years prior to the layoff, and the employee’s general health condition, which is measured by the number of sick days taken during the pre-displacement period. The models also include the full set of firm’s fixed effects that account for time-invariant employer characteristics. 4 German Journal of Human Resource Management 00(0) Data The data used in this paper are the result of a combination of information from administrative registers in Finland.6 We used nationwide linked employee–employer data, constructed from several different registers on individuals, firms and establishments that are maintained by Statistics Finland. Employee characteristics such as educational attainment are based on Employment Statistics and firm/establishment characteristics are from Business Register. The data are linked using unique identifiers for employees and firms/ establishments. Matching is exact, with (essentially) no missing observations. The linked employee–employer data contain detailed information on all private sector establishments and their employees in Finland for the period 2001–2017. The employee–employer data are linked to comprehensive information recording mental health disorders, using identifiers for employees. Our main source for health information is the Finnish Hospital Discharge Register (HDR), which was compiled by the National Institute for Health and Welfare from 1969 to 2017. The data include information on the dates of admission to the hospital, dates of discharge and primary reasons for hospitalisation. Hospitalisation captures only severe mental health problems, which may lead to the underestimation of overall mental health-related problems.7 Mental health disorders correspond to diagnostic codes beginning with the letter F in the International Statistical Classification of Diseases and Related Health Problems (ICD)-10 classification (and 290–319 in ICD-8 or 9). Validation studies have confirmed that the HDR is of high quality from 1972 onwards (Sund, 2012). Supplemental Appendix A contains information regarding the Finnish healthcare system and highlights the importance of occupational healthcare for those who are employed. To measure medical absenteeism from work, we analysed the total data on medical leaves and sick days over the period 1998–2017. The comprehensive register-based data originate from the Social Insurance Institution of Finland (Kela), specifically the database used to pay out medical benefits to affected individuals. Before receiving any medical benefits from Kela, an employee must undergo a 9-day waiting period. The applicant’s inability to work must be certified by a physician (i.e. general physicians, occupational physicians, and psychiatrists), and the employer is obliged to notify Kela of the medical leave. Employees are entitled to normal full salaries during the 9-day waiting period (for a description of the Finnish medical insurance system, see Böckerman et al., 2018a, 2018b). Thus, due to the characteristics of the benefits system, the data recorded by Kela contain medical leave periods lasting longer than 9 days. Empirical approach Sample construction We selected establishments from the private sector of the Finnish economy and examined the displacements that took place from 2005 to 2017. Following previous research (Black et al., 2015), we first define a base year ( b) that constitutes all the years between 2004 and 2016. The observation unit is a person-year. Böckerman et al. 5 The sample consisted of employees for whom three conditions were met. First, the establishment at which the employee was working at time b decreased its number of employees by at least 20% between b and b+1.8 Second, the establishment had at least 20 persons employed at time b. Third, the establishment had a positive number of persons employed at time b+1. We divided the employees into a treatment group and a comparison group. The treatment group consisted of workers who were no longer employed in the same establishment at time b+1 compared to at time b. The comparison group consisted of all workers who remained employed in the same establishment at times b and b+1. This setup represents a variation of research designs similar to those used in studies examining the effects of company shutdowns on various outcomes (Huttunen and Kellokumpu, 2016). Thus, the sample included only individuals who worked at establishments that underwent substantial downsizing between b and b+1. We then compared those who were displaced with those who were not displaced in these organisations. Consequently, organisations that shut down altogether or did not downsize were excluded from the study sample. We applied further restrictions to the sample. First, we excluded public sector workers because their establishment codes are not well defined by Statistics Finland. Public sector units also typically do not resort to mass layoffs to reorganise their operations. Therefore, we focussed on the private business sector. Second, early retirement is a potential option for laid-off workers who are relatively close to the official retirement age.9 Thus, we excluded wage and salary earners over 59, as the analysis may otherwise be affected by retirement decisions (Hakola and Uusitalo, 2005).10 Notably, there has been a significant tightening of early retirement options in Finland over the past few decades (Kyyrä, 2015). Opting for early retirement results in a substantial reduction in disposable income compared to remaining in full-time employment. Finland also lacks nationwide, publicly subsidised hive-off or transfer companies specifically designed to mitigate the effects of mass layoffs. Empirical approach Using linked data, we estimated linear probability models with fixed effects of the following type: EHXZ ij biib jb bjijb,, +=+ ++++ 1 αβ µγ δε (1) where Eij b,+1 is a dummy variable taking the value of 1 if individual i is not employed in the same establishment j at b and b+1 and 0 otherwise. Hi is an indicator variable that takes the value of 1 if the individual had any mental health disorders diagnosed between b and bX ib −3. is a vector of the pre-displacement characteristics of the individual, including age dummies, gender, education, sick days between b−3 and b, and the log of average earnings between b−3 and b. Earnings were deflated using the consumer price index with 2015 as the base year. Finally, Zjb refers to the log of the size of the establishment j at the base year b, γ b is a set of base year dummies, δ j represents fixed effects for each establishment, and ε ijb is an error term. To prevent employee from being included in the data twice (i.e. the 6 German Journal of Human Resource Management 00(0) individual was laid off more than once), we used only the first instance in which an employee was displaced. The reason for this is that previous displacement may affect later mental health, which in turn may affect later employment and the probability of being laid off again. This restriction is the same as that adopted in the work of Huttunen and Kellokumpu (2016). By construction, this implies that there is no additional need to control for being subject to past downsizings. Our interest lies in the indicator variable Hi, of which the corresponding parameter α gives the magnitude of the effect of having a mental health disorder diagnosis during b−3 and b on the probability of being displaced between b and b+1. In an extension, we also analyse the main categories of mental health disorder diagnoses, such depression, anxiety and substance use disorder (Böckerman et al., 2021; Santavirta et al., 2015; Suvisaari et al., 2009). In the main models, we included only individuals who were employed in the establishment under consideration during all years between b−3 and b. The reason for this is that individuals with previously diagnosed mental health disorders may have more unstable work histories than individuals without mental health disorder diagnoses (Bartel and Taubman, 1986). In a robustness check, we also included in the treated group those who left the establishment between b−1 and b, the so-called ‘early leavers’ (Schwerdt, 2011). Before discussing the results, we provide additional explanations for the control variables used in equation (1). We included a control for the number of sick days other than for mental health disorders per year that an employee has had. There are two reasons for this. First, as explained in the introduction, collective labour agreements that govern labour relations in Finland state that very long medical leaves can be a valid reason for job dismissal; therefore, it is necessary to account for this factor. Second, sick days are a useful measure of the overall health of an employee directly related to work capacity, capturing different aspects of health that may correlate with (current) mental health status. This was also found in earlier research (Sareen et al., 2006). Thus, if previous medical leaves were not included, the results regarding the effect of mental health on being laid off may be overstated, as they may hide the effects of other health problems. We also included the log of average (annual) earnings among the control variables. This is because earnings are a proxy for employee productivity, which may negatively affect the probability that an employee will be dismissed in a mass layoff.11 Moreover, we included controls for employees’ education levels. Education (as a key measure of human capital) correlates with earnings or, otherwise, with job tasks or positions in the firm and may affect one’s probability of being laid off in a mass layoff (Beuermann et al., 2021). The log of the size of the establishment is included to control for potential nonlinear effects in the growth and reductions in the workforce. It is possible that smaller firms grow relatively more quickly but also shrink more quickly than larger firms, which would affect an employee’s probability of being laid off. The regressions also include a full set of (base) year dummies, age dummies and an indicator for being female. In all regressions, we also use a full set of firm (establishment) fixed effects, thereby accounting for all permanent differences between establishments. Böckerman et al. 7 Results Descriptive patterns In Finland, as in other high-income countries, there is a considerable prevalence of mental health disorders, with approximately 10% of Finnish establishments employing at least one worker with a mental health-related diagnosis during the study period. Employed individuals with mental health disorder diagnoses have significantly lower earnings and go on more medical leave days than those without a diagnosis (Table B1). Mental health diagnoses are also slightly more common among women (see also Lehtinen et al., 1990). Additionally, 42% of those with a diagnosis experienced job separation during this period, which is higher than the 31% figure for those without. Table 1 compares workers who were displaced with those who were not displaced. Mental health disorders, such as depression, are more common among employees who faced layoffs than among those who did not. This finding was also reflected in the number of sick days taken, which was much higher for those facing layoffs. Notably, average earnings were lower among those facing layoffs (€40,400 per year) compared to those who were not (€43,200). Table 1. Characteristics of displaced and not displaced individuals. Not displaced Displaced Age (years) 42.18 (10.03) 40.60 (10.65) Number of sick days per year between b and b−32.326 (16.03) 4.730 (26.42) Any mental health diagnosis between b and b−3 (yes/no) 0.017 0.026 Anxiety disorder between b and b − 3 (yes/no) 0.003 0.005 Depression between b and b−3 (yes/no) 0.007 0.011 Bipolar disorder between b and b − 3 (yes/no) 0.001 0.002 Other nonaffective psychosis disorder between b and b−3 (yes/no) 0.001 0.002 Schizophrenia between b and b−3 (yes/no) 0.000 0.000 Substance use disorder between b and b − 3 (yes/no) 0.002 0.003 Education: ISCED levels 1–2 (yes/no) 0.145 0.150 Education: ISCED levels 3–4 (yes/no) 0.471 0.480 Education: ISCED levels 5–8 (yes/no) 0.384 0.370 Average earnings between b and b − 3 (€/year) 43,215 (31,848) 40,409 (28,423) Working in the manufacturing sector (yes/no) 0.470 0.430 Number of observations 378,669 170,520 Mean values are reported. Standard deviations are in parentheses. The total number of observations is 549,189. The included individuals were aged 18–59 working in the private sector who had worked at least 3 years in the same establishment before layoffs. Establishments included had >20 employees and a workforce that decreased by at least 20%. Earnings in euros are deflated to 2015 prices using the consumer price index. 8 German Journal of Human Resource Management 00(0) Figure 1 illustrates the difference in the distribution of sick days by mental healthrelated diagnosis during the last 3 years. The figure shows that individuals with mental health-related diagnoses use up a higher number of sick days. Similarly, Figure 2 illustrates the difference in the distribution of sick days between individuals who were laid off and those who were not during downsizing. It is evident that the two groups differ significantly in terms of their prior health status, with those being laid off having notably poorer health before downsizing. When considered jointly, these figures provide compelling evidence for the positive correlation between the diagnosis of mental health disorders and the risk of layoff from work. Baseline results Table 2 shows the results of equation (1) for establishments that had a minimum of 20 employees at time b and experienced a workforce reduction of at least 20%. Column 1 of Table 2 presents the unconditional correlation (without any covariates) between the variables of interest, showing that having a mental health disorder diagnosis is associated with a roughly 8 percentage point increase in the likelihood of being laid off compared to not having a diagnosis. When covariates are included in the model, as shown in column 2, this effect is reduced to approximately 6 percentage points. In column 3, we divide the mental health disorder diagnosis variable into separate dummy variables for different diagnoses. The table shows that substance abuse Figure 1. Distribution of the number of sick days by mental health diagnosis status. The included individuals were aged 18–59 working in the private sector who had worked at least 3 years in the same establishment before layoffs. The included establishments had >20 employees and a workforce that decreased by at least 20%. Workers with zero sick days were not included. Böckerman et al. 15 5. An establishment/plant is defined by Statistics Finland as a local unit. It is a specific physical location that specialises in the production of certain types of products or services. Because most firms have only one establishment/plant, this paper uses the terms ‘establishment’ and ‘firm’ interchangeably. For clarity and strictly within the context of our data, we use the term ‘establishment’. 6. Register data allow us to avoid common method variance concerns. 7. There is no nationally representative information covering the use of mental health-related services in the Finnish primary care system. Employees are entitled to occupational healthcare that is not included in the official statistics gathered by Finnish Institute for Health and Welfare (THL) (see also Supplemental Appendix A). The likelihood of hospitalisation for physical health reasons is relatively low among working-age population who are employed and have at least 3 years of tenure, which is the focus of our analysis. There are no nationwide programmes or subsidies specifically designed to assist laid-off individuals with mental health problems in re-entering the workforce. However, in the context of some large-scale mass layoffs, such as in the paper and pulp industry, Finnish municipalities have occasionally provided additional primary health care services tailored to the needs of the affected workers. 8. Employment status and employer code were determined at the last week of each year. 9. According to the Finnish Centre for Pensions (ETK), mental health disorders are nowadays the most common reason for transitioning to disability pension in Finland (Finnish Centre for Pensions, 2020). In 2023, the average age of individuals transitioning to a disability pension due to mental health reasons was approximately 45 years, with a total of 5550 persons affected. For comparison, in 2023, according to Statistics Finland, the size of the labour force in Finland was approximately 2,800,000 persons. 10. Since the 2005 pension reform, the most common retirement age in Finland has been 63. 11. Annual earnings are strongly correlated with annual working hours. The data do not contain detailed descriptions of weekly working hours for all workers because a substantial fraction of Finnish employees (e.g. almost all white-collar workers) are paid on a monthly basis; for these workers, there is no information weekly working hours nor hourly wages in the register data. 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