3.1. Descriptive analysis
Our register-based longitudinal dataset consists of individual-level observations of employment rehabilitation matched with the background data and employment data, from 2016 to 2020. People are eligible for employment rehabilitation services if they are meeting all the following criteria: (a) age 16 until retirement age; (b) diagnosis of impairment/disability, indicating reduced working capacity; (c) employed or unemployed as registered job seekers.
The number of unique individuals is 9,244 and nearly 80% of them used rehabilitation only once (see Table 1). Employment rehabilitation appears to be a female-dominated service (see Figure 1) and female entrants tend to be employed more often (see Table 2). In general, around 42% of clients are employed and the more middle-aged or higher educated they are the more they enter the service as workers who need support. Workers with mental impairments such as learning or adaptation problems are less often employed compared to the average (see Table 2).
Table 1: Clients and multiple entries to employment rehabilitation services
Figure 1: First entries to the employment rehabilitation services by years and gender Table 2: Descriptive statistics of the first entries by employment status during entry
Figure 2 reveals the age profile has not considerably changed over time and the mean age for entry is 46 years. Panel (b) indicates a right skew of the age distribution, hence, the median age is 50. Also, there are some gender differences – males are less heterogeneous (respectively 50% of observations from men are between 39-57 and women between 32-55 and t-tests reveal significant differences in mean age by gender). During the Covid-19 period in 2020, we witness a decrease in clients above the median age. We see from Figure 3 that more than 50% of clients enter the service with only physical impairments, followed by both mental and physical ones. The category of only mental impairments is the rarest, however, mental impairments are more pronounced in the case of young males.
(a) Mean age of clients (b) Distribution of the age of clients
Figure 2: Age profile of the clients: rehabilitation is an old-age service
Figure 3: Impairments and disabilities of the clients by gender
We can also assess the severity of the impediments. For this, we constructed a standardized score based on the International Classification of Functioning, Disability and Health[1] (ICF). These are standardized compound scores, indicating the severity of the impairment. In general, there are few clients with communication and self-care issues, while mobility and manual activity is often a severe issue. Also, employed clients have fewer mental and cognitive impairments (see Table 2).
Correlation analysis (see Figure 4) reveals that mental and physical impairments are relatively strongly associated, while mental impairments are negatively and physical impairments are positively correlated with age. Furthermore, Figure 5 indicates that the duration of the service is employment specific – on average the employed stay a few days longer than the unemployed (the difference is statistically significant, p<0.01), the average duration of the service is 25 days and in rare cases, it is longer than 2 months. At the same time, there are significant differences in days depending on the completion of rehabilitation service. In the case when the service was not completed, the average number of days was only 11.6 days, while service completion was attributed to a mean of 29.5 days.
Figure 4: The Pearson correlation coefficients between age, physical and mental impairments (standardized scores)
Figure 5: Number of days spent on employment rehabilitation service by employment status at entry (left panel) and service completion status (right panel)
3.2. Estimation strategy
For subsequent analysis, a matched sample is generated to investigate the effect of completing rehabilitation services on post-rehabilitation employment. Comparable pairs of people, who completed and who discontinued the service, are created by applying propensity score matching (PSM).
To minimize heterogeneity between pairs, PSM was conducted with a caliper of 0.03. Deciding the caliper involves a trade-off between the similarity of the pairs and the number of matches [31]. Smaller calipers yield a greater similarity of pairs, while the number of matches will decrease. A common practice is to obtain its optimal value via a trial and error process [32], which we apply here. Another analytical decision involved removing missing cases[2], resulting in 5,141 remaining individuals. Thereafter, PSM is applied with the following model:
Prob(Completedi = 1) = logit(β0 + β1Genderi + β2Agei + (1)
β3Educationi + β4WorkingCapacityi + β5Residencei + β6Disabilityi + ϵi)
where the subscript i denotes an individual, β0 is the constant and ϵ is the error term. The dependent variable Completed is 1 in case rehabilitation was completed, otherwise 0 (service discontinued). Other covariates include Gender (male or female), Age (birth year), Education (no education - 0, primary - 1, secondary - 2 or higher education - 3), WorkingCapacity (reduced or permanent incapacity) and Residence (regional dummy). Lastly, Disability referred to the severity of impairment (0 = absent, 1 = slight, 2 = medium, 3 = severe, 4 = complete) in impairment such as mobility, manual activity, adaptation, learning and applying knowledge, interpersonal interactions, self-care, as well as communication. The matching result of PSM, based on the defined covariates is shown in Table 3.
Table 3: Comparison of two groups: service completed (Treatment = 1) and discontinued (Treatment = 0), n = 833 in both groups
Thus, PSM paired 883 individuals in both treatment and control groups, which could be considered identical in terms of the specified characteristics, according to independent t-tests (see Table 3). Such a matched sample is used in the following regression model:
EmploymentDurationi,t = β1Completedi,t + β2Completedi,t ×Di + αi + σt + ϵi,t (2)
where EmploymentDuration denotes the length of employment in months after receiving rehabilitation services for individual i at time t, while it takes the value of 0 in periods during and before the rehabilitation. Furthermore, Completed refers to the treatment variable (operationalized in Equation 1). Di = [EmployedEntry, Gender, OnlyMental, OnlyPhysical] denotes a vector of four dummy variables: EmployedEntry (1 = employed while entering to service), Gender (1 = male), OnlyMental (1 = has only mental impairments), OnlyPhysical (1 = has only physical impairments). These dummies are used for estimating interactive effects with the treatment variable Completed.
Furthermore, for controlling unobserved heterogeneity, the model is specified with αi and σt, which denote fixed effects of individuals and months, respectively. The latter enabled to control for the potential distortions in the labor market due to the Covid-19 pandemic in 2020. However, we were unable to include any directly observable control variables since most of our data consisted of time-invariant factors, which fixed effects omit. For the same reason, the dummy variables could be included only as interactions in the models.
3.3. Results
Table 4 revealed a treatment effect on post-rehabilitation employment by 2.6 months. This effect is positively moderated by employment status, i.e., initial employment upon the first entry to the service resulted in about 4 months longer post-rehabilitation employment (when compared to individuals that discontinued the service). Moreover, the first entry into the treatment as unemployed is associated with greater post-rehabilitation employment only by 1.1 months (when compared to the individuals that discontinued the service). In contrast, other dummy variables produced smaller moderation effects.
Table 4: The monthly effect sizes of employment rehabilitation service completion on post-rehabilitation employment duration based on panel regressions
The findings in Table 4 indicate that treated males experienced nearly 1.2 months longer post-rehabilitation employment than females. However, individuals, who had only mental disabilities, experienced about 1.3 months shorter employment, while physical disabilities resulted in insignificant findings.
[1] The International Classification of Functioning, Disability and Health is a classification of the health components of functioning and disability
[2] Robustness of this decision was tested with multiple imputations using predictive mean matching. The findings are not significantly different from the ones reported in the current study.