Disability Type and Prior Unemployment Exits as Predictors of Labour Market Reintegration among People with Disabilities: Evidence from Romania

Disability Type and Prior Unemployment Exits as Predictors of Labour Market Reintegration among People with Disabilities: Evidence from Romania | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Disability Type and Prior Unemployment Exits as Predictors of Labour Market Reintegration among People with Disabilities: Evidence from Romania Daniela-Emanuela Dănăcică This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8302059/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The aim of this study is to analyse how disability type and other individual characteristics shape unemployment spells and labour market reintegration among people with disabilities in Romania. Using administrative micro-data from the National Employment Agency for 2017–2020, we examine 1,952 unemployment spells and apply a multi-model duration strategy that accounts for unobserved heterogeneity, competing exits and recurrent unemployment. The results show that disability type, age and unemployment benefit receipt are key correlates of reintegration. Sensory disabilities are associated with higher reintegration hazards than physical disabilities, with the advantage clearer in medium-to longer-duration spells. Neuro-psychiatric disabilities tend to display lower hazards, especially early in unemployment. Benefit receipt shows a strong and stable negative association with reintegration across specifications, while individuals aged 25–34 and 35–44 reintegrate faster than the youngest group. These findings underscore the importance of early, disability-sensitive activation and counselling, as well as regionally tailored policy responses aimed at improving sustainable employment outcomes for disabled jobseekers. Econometrics Other Economics s J64 Figures Figure 1 1. Introduction Labour market integration of people with disabilities remains a major challenge in both advanced and emerging economies and is a recurrent priority on public policy agendas. Despite visible progress over the past two decades, people with disabilities still face substantial disadvantages in employment, unemployment and job stability (Birău et al. 2019 ; Boman et al. 2015 ; Colella and Bruyère 2011 năcică and Cîrnu 2014; Sciulli et al. 2007 ). According to the Council of the European Union (2024), drawing on Eurostat data, around 107 million people live with some form of disability in the European Union. Demographic change (Linz and Stula 2010 ), population ageing and the retirement of the baby-boom generation (Vornholt et al. 2018 ) further increase the urgency of sustainable inclusion strategies. Beyond the macroeconomic relevance of a larger inclusive workforce, labour market participation is consistently associated with better well-being among people with disabilities. Employment provides daily structure, reduces the risk of social isolation and contributes to individual autonomy (Saunders and Nedelec 2014 ; Schur 2002 ; Vornholt et al. 2018 ). Previous research identifies several mechanisms behind persistent labour market disadvantages among people with disabilities. Lower levels of formal education, poorer health and reduced work capacity, employer prejudice and low accessibility of workplaces are frequently cited. At the same time, people with disabilities form a highly heterogeneous group, and integration chances depend on specific combinations of individual characteristics, disability type, institutional context, and macroeconomic shocks. Recent studies therefore document differentiated effects on labour market integration by disability type (Boman et al. 2015 ; Clausen et al. 2004 ; Sciulli et al. 2007 ). Individuals with mental or psychiatric disabilities often face particularly severe difficulties in accessing and sustaining employment (Birău et al. 2019 ; Boman et al. 2015 ; Harkko et al., 2018 ; Helgesson et al. 2024 ), while musculoskeletal and other sensory-related impairments have also been associated with long unemployment spells in several settings (Sciulli et al. 2007 ). By contrast, labour market integration tends to be smoother for individuals with hearing or speech impairments in some contexts (Boman et al. 2015 ; Sciulli et al. 2007 ). Sensory impairments can more easily be compensated by assistive technologies and workplace accommodations, whereas intellectual and mental health disabilities often pose more complex integration challenges. Research also points to a bidirectional relationship between unemployment and disability. Health status shapes the probability of becoming unemployed, while long-term unemployment can further deteriorate health and work capacity, reinforcing a vicious circle of marginalisation. Unfortunately, as Boman et al. ( 2015 ) point out, people with disabilities are often treated as a homogeneous group in policy design, although their needs and vulnerabilities in the labour market differ substantially by type and severity of disability and by employment history. A related body of research examines unemployment duration (Addison and Portugal 2003 ; Castro et al. 2025; Dănăcică 2013 ; Kavkler et al. 2009 ; Kupets 2006 ; Lalive 2007 ; Trang et al. 2024 ), the hazard of exiting unemployment and the “scarring” effects of repeated non-employment episodes (Arulampalam 2001 ; Gregg 2001 ; Gregg and Tominey 2005 ). This evidence shows that re-employment probabilities are strongly time-dependent and shaped by previous unemployment histories, and that exits into inactivity or benefit exhaustion may have persistent consequences for later labour market trajectories. However, most contributions focus on the general population or specific groups such as youth or older workers. Disability status and type are rarely integrated systematically into duration analyses, and prior exit routes from unemployment are seldom examined as determinants of subsequent reintegration among people with disabilities. As a result, we still know relatively little about how past labour market trajectories, like re-employment, transitions into inactivity, or benefit exhaustion, shape later unemployment spells for this vulnerable group. The Romanian context illustrates particularly well the tension between the normative framework and actual outcomes. The legal framework (Law no. 448/2006 on the protection and promotion of the rights of people with disabilities, with subsequent amendments and additions, including Government Emergency Ordinance no. 60/2017 and Law no. 193/2020) combines a system of mandatory employment quotas with financial incentives aimed at increasing the employment of people with disabilities. Nonetheless, the literature and implementation reports (Baciu and Lazăr 2017 ; National Authority for the Protection of the Rights of Persons with Disabilities, 2021; Sandvin and Alexiu, 2019 ; World Bank 2021 ) point to limited enforcement. Sanctions appear to have weak effects, procurement mechanisms involving sheltered units are used relatively infrequently, and wage subsidies are claimed by only a small number of employers. Recent assessments suggest that even in the public sector compliance with legal provisions falls short of the targets, indicating persistent institutional and attitudinal barriers (Baciu and Lazăr, 2017 ). In this setting, a fine-grained understanding of labour market reintegration dynamics becomes essential for calibrating active labour market policies, counselling and placement services, and benefit conditionalities. Studies that focus explicitly on unemployment spells and transitions out of unemployment among people with disabilities have typically relied on logistic regression or standard proportional hazards Cox models, paying less attention to recurrent unemployment episodes, competing exits, and unobserved heterogeneity. Moreover, Central and Eastern European labour markets remain underrepresented in administrative micro-data studies of disability-related unemployment dynamics, despite important differences in labour market institutions and social protection systems compared with North-Western Europe. To address these gaps, we use individual-level administrative data for Romania covering the 2017–2020 period and implement a multi-model duration strategy. We estimate parametric and semi-parametric frailty models to capture unobserved heterogeneity, a flexible Cox specification with a penalised spline baseline, a Fine-Gray model to account for competing exits, a Prentice-Williams-Peterson (PWP) framework to model recurrent spells by event order, and a piecewise Cox approach to explore time-varying hazard patterns. The paper addresses three core research questions, which structure the empirical analysis: (O1) To what extent does disability type predict labour market reintegration among people with disabilities, once we control for socio-demographic, educational, occupational, and regional characteristics? (O2) How do unemployment benefit receipt and the history of previous exits from unemployment (re-employment, inactivity, benefit exhaustion) affect the probability and timing of subsequent labour market reintegration? (O3) How robust are these effects across alternative baseline hazard specifications and different treatments of competing risks and recurrent events? These questions motivate our contribution to the literature in three ways. First, we provide a rigorous differentiation of the effects of disability types on the hazard of labour market reintegration in an empirically underexplored Eastern European context. Second, we introduce individuals’ histories of prior exits from unemployment as a predictor of subsequent labour market reintegration, thereby capturing scarring and path dependence among people with disabilities through a PWP recurrent-events framework. Third, we strengthen confidence in our findings through a multi-model robustness strategy that explicitly addresses intra-individual dependence, competing exits, and flexible baseline hazard dynamics. From a policy perspective, our results highlight critical timing for reintegration, most notably a pronounced hazard peak in the first weeks after registration, and substantial heterogeneity by disability profile, age, exit history, and region. This granularity helps identify windows of opportunity and profiles for which early activation, benefit conditionalities, and matching interventions can be targeted more effectively, with attention to regional differences. Taken together, our analysis provides an integrated assessment of labour market reintegration among people with disabilities, combining detailed administrative micro-data with a multi-model robustness strategy. The remainder of the paper is organised as follows. Section 2 describes the data and variables used in the analysis. Section 3 presents the methodological approach, model specifications and empirical results for each class of models and provides a multi-model synthesis. Section 4 concludes and discusses policy implications and the main limitations of the study. 2. Data and Variables The empirical analysis is based on a dataset comprising 2,859 unemployment spells of people with disabilities who were registered as unemployed at the National Employment Agency between 1 January 2017 and 31 December 2020, and who exited unemployment during this period. For each individual, information is available on the start and end dates of the unemployment spell, as well as on age, an individual identifier (ID_person), type of disability, sex, educational attainment, occupation code (COR), occupation, county of residence, area of residence (urban/rural), benefit status (benefit recipient/non-recipient) and the reason for exit from unemployment. The data cleaning process involved excluding the following categories of records: individuals aged over 65 years, fully duplicated observations, records with negative unemployment durations or with zero duration and records for which, although the start and end dates of the unemployment spell were available, the reason for exit from unemployment was missing, this being essential information for our analysis. After data processing, the final dataset used in the analysis comprised 1,952 unemployment spells. The duration of unemployment, the dependent variable in this study, is calculated as the difference between the end date and the start date of the unemployment spell and is expressed in days. Because the data include individual identifiers (ID_person), it was possible to identify individuals with multiple unemployment spells and to reconstruct the cumulative unemployment experience for each individual. Nineteen distinct reasons for exiting unemployment were identified. Owing to the small number of observations for some of these reasons, they were aggregated into four categories for the econometric analysis: 1- Exit from unemployment through labour market reintegration, which includes the following reasons: taking up employment; taking up fixed-term employment of at most 12 months; and earning monthly income above the social reference indicator (ISR); 2-Exit from unemployment due to the expiry of the legal entitlement to unemployment benefit; 3- Exit from unemployment through transition into inactivity, which comprises the following reasons: admission to an educational programme; receipt of an invalidity pension for more than 12 months; leaving the country at the individual’s request for less than three months; becoming eligible for an old-age pension; leaving the country for more than three months; the start date of the invalidity pension; the period of child-rearing; and periods of temporary incapacity for work; 4-Exit from unemployment due to other reasons, namely: cancellation of the record by the case worker; failure to request the maintenance of jobseeker status; expiry of the deadlines for the resumption of benefit after suspension; failure to report to the employment agency to receive support; unjustified refusal of a job offer; unjustified refusal to participate in activation services; and closure of the record due to transfer. Table 1 presents the coding of the explanatory variables used in the econometric analysis. These aggregations balance substantive relevance with sample size considerations. Table 1 Explanatory variables and their coding Explanatory variables Description Disability type Qualitative variable with the following categories: physical/locomotor, hearing, deafblindness, visual, psychiatric, somatic, intellectual, HIV/AIDS, rare diseases, associated, other. For the econometric analysis, these were grouped as follows: 1-physical/locomotor and somatic (physical disabilities); 2-intellectual and psychiatric (neuro-psychiatric disabilities); 3-hearing, visual and deafblindness (sensory disabilities); 4-other disabilities (other, associated, rare diseases, HIV/AIDS). Sex Dummy variable: 0 - female; 1 - male. Age Continuous variable (16–64), recoded into the following categories: 16–24; 25–34; 35–44; 45–54; and 55–64. Education Qualitative variable coded as follows: 0-Unknown/no schooling (including missing values and records coded as no schooling or unknown primary education); 1-Primary or lower secondary education; 2-Vocational/professional education (apprenticeship, special education, vocational school); 3-Upper secondary education (general or special upper secondary); 4-Post-secondary non-tertiary education (colleges, foremen schools, post-secondary schools); 5-Tertiary education (short-cycle higher education, bachelor’s degree or master’s degree). County of residence Grouped by NUTS 2 regions: North-East; South-East; South-Muntenia; Bucharest-Ilfov; West; North-West; Centre; South-West Oltenia. Area (urban/rural) Dummy variable: 0 - rural; 1-urban. Benefit status Dummy variable: 0-non-benefit file (non-benefit recipients); 1-benefit file (unemployment benefit recipient). Occupation and COR code Initially 354 distinct categories, grouped as follows: 1-Technical/industrial; 2 - Economic/administrative; 3-Medical/health; 4-Social/human services/education; 5-Commercial/services; 6-Textiles/manual/vocational; 7-IT/information technology; 8-Other/unknown. 3. Results and discussion To analyse how disability type and other individual characteristics shape unemployment spells and the hazard of labour market reintegration, we estimate a Weibull proportional hazards model with individual-level shared frailty. The model is fitted in R 4.4.3 using the frailtyPenal() function from the frailtypack package with a gamma frailty term specified at the ID_person level. This choice is motivated by the structure of the data, which includes multiple unemployment spells per individual, implying intra-individual dependence in unemployment durations and a potential role for unobserved heterogeneity (e.g., latent health severity, motivation, family support or access to informal job-search networks). By incorporating an individual-level frailty term, we account for unobserved time-invariant differences across individuals and obtain more robust estimates of the covariate effects on the reintegration hazard. The Weibull baseline provides a parsimonious parametric representation of the reintegration process, allowing the hazard to increase or decrease over time. Results are reported as hazard ratios (HR), where HR > 1 indicates a higher hazard of labour market reintegration, and HR < 1 indicates a lower reintegration hazard relative to the reference category. Table 2 Effects of explanatory variables on the labour market reintegration hazard Variable β S.E. HR 95% CI p -value Physical disability Reference category Neuro-psychiatric disability -0.2175 0.1407 0.8045 (0.6106–1.0601) 0.1222 Sensory disability + 0.4126 0.1386 1.5108 (1.1515–1.9822) 0.0029 Other disabilities -0.2706 0.1582 0.7629 (0.5596–1.0402) 0.0871 Female Reference category Male + 0.0324 0.1021 1.0329 (0.8455–1.2618) 0.7514 16–24 years Reference category 25–34 years + 0.6470 0.1291 1.9097 (1.4828–2.4596) < 0.001 35–44 years + 0.4079 0.1401 1.5037 (1.1426–1.9789) 0.0036 45–54 years + 0.1175 0.1701 1.1246 (0.8057–1.5698) 0.4899 55–64 years -0.3323 0.3829 0.7173 (0.3386–1.5194) 0.3856 Unknown/no schooling + 0.3952 0.2367 1.4847 (0.9336–2.3611) 0.0950 Primary + lower secondary + 0.5008 0.2282 1.6500 (1.0549–2.5806) 0.0282 Vocational/professional + 0.2521 0.2041 1.2867 (0.8625–1.9196) 0.2168 Upper secondary (high school) + 0.0051 0.1930 1.0051 (0.6885–1.4672) 0.9790 Post-secondary non-tertiary + 0.0227 0.3036 1.0229 (0.5642–1.8548) 0.9404 Tertiary education Reference category Bucharest-Ilfov -1.7850 0.3692 0.1678 (0.0814–0.3460) < 0.001 Centre -0.0860 0.2524 0.9176 (0.5595–1.5049) 0.7334 North-East -0.1157 0.2168 0.8907 (0.5823–1.3624) 0.5936 North-West -0.0968 0.2119 0.9078 (0.5992–1.3752) 0.6479 South-East + 0.2073 0.2443 1.2303 (0.7622–1.9859) 0.3962 South-Muntenia + 0.2019 0.2222 1.2237 (0.7917–1.8915) 0.3634 West -0.2007 0.2750 0.8181 (0.4772–1.4025) 0.4654 South-West Oltenia Reference category Rural Reference category Urban -0.1720 0.1055 0.8420 (0.6847–1.0355) 0.1031 Non-benefit recipient Reference category Benefit recipient -3.4355 0.1534 0.0322 (0.0238–0.0435) < 0.001 Technical/Industrial Reference category Economic/Administrative + 0.2773 0.3285 1.3196 (0.6931–2.5121) 0.3986 Medical/Health + 0.2119 0.2714 1.2360 (0.7261–2.1041) 0.4350 Social/Human Services/Education + 0.6596 0.3222 1.9340 (1.0284–3.6370) 0.0407 Commercial/Services + 0.0912 0.2008 1.0955 (0.7391–1.6238) 0.6496 Textiles/Manual/Vocational + 0.5577 0.1921 1.7466 (1.1987–2.5451) 0.0037 IT/Information Technology + 0.0520 0.1684 1.0534 (0.7573–1.4652) 0.7576 Other/Unknown + 0.3825 0.1771 1.4659 (1.0359–2.0744) 0.0308 Source: Author’s calculations using R 4.4.3 and the frailtypack package As shown in Table 2 , several factors are significantly associated with the hazard of labour market reintegration among unemployed people with disabilities. Regarding disability type, individuals with sensory disabilities have a significantly higher reintegration hazard relative to the physical-disability reference category (HR = 1.5108, p = 0.0029), suggesting faster transitions out of unemployment once socio-demographic, educational, regional and occupational characteristics are controlled for. By contrast, neuro-psychiatric disabilities (HR = 0.8045) and the “other disabilities” group (HR = 0.7629) are associated with lower reintegration hazards compared with physical disabilities, although these effects are not statistically significant at the 5% level. The marginal p -value for the “other disabilities” group ( p = 0.0871) motivates an additional specification that isolates the effect of disability type. Table 2 shows no statistically significant differences in the reintegration hazard between men and women (HR = 1.0329, p = 0.7514). Age, by contrast, plays an important role. Individuals aged 25–34 and 35–44 exhibit significantly higher reintegration hazards relative to the 16–24 age group (HR = 1.9097 and HR = 1.5037, respectively), indicating faster exits from unemployment for these age groups. For older categories (45–54 and 55–64), the estimated hazards do not differ significantly from those of the youngest group. Turning to educational attainment, the “primary + lower secondary” category shows a significantly higher reintegration hazard relative to tertiary education (HR = 1.6500, p = 0.0282), while the remaining education categories do not display robust differences from the tertiary reference group. This pattern may reflect faster entry into lower-skill segments with higher turnover, rather than superior job prospects per se. The regional estimates point to notable geographical heterogeneity. Individuals residing in the Bucharest-Ilfov region show a substantially lower reintegration hazard relative to the reference region (South-West Oltenia) (HR = 0.1678, p < 0.001). This counterintuitive pattern may reflect compositional differences in the registered unemployed population, differential access to disability-adapted jobs, or region-specific institutional practices. For the remaining NUTS 2 regions, the hazard ratios are not statistically different, indicating broadly similar reintegration dynamics to those observed in South-West Oltenia. We have a particularly strong result regarding unemployment benefit receipt. Benefit recipients display a dramatically lower reintegration hazard than non-benefit recipients (HR = 0.0322, p < 0.001), indicating substantially slower exits from unemployment. Any causal interpretation should be treated with caution, as benefit eligibility reflects prior employment histories and may capture selection and institutional mechanisms rather than the pure behavioural effect of benefits. The occupational profile of unemployed persons with disabilities also matters. Relative to the Technical/Industrial reference category, reintegration hazards are significantly higher in Social/Human Services/Education (HR = 1.9340, p = 0.0407), Textiles/Manual/Vocational (HR = 1.7466, p = 0.0037), and the “Other/Unknown” category (HR = 1.4659, p = 0.0308). The remaining occupational groups do not differ significantly from the reference. These patterns may capture differences in the structure of labour demand, the availability of suitable positions, and the match between functional limitations and job requirements across occupational fields. Table 3 reports the results of a univariate Weibull proportional hazards model with individual-level shared frailty, estimated using the same frailtyPenal() function and the same gamma frailty specification at the ID_person level as in the baseline model. Disability type is the only predictor in this model. The results indicate that individuals with neuro-psychiatric disabilities have a significantly lower hazard of labour market reintegration than those with physical disabilities (HR = 0.632, p < 0.001). For sensory disabilities and other disabilities, the estimated hazard ratios are not statistically significant in this univariate specification. The analysis of the isolated effect of disability type shows a clear disadvantage for individuals with neuro-psychiatric disabilities when disability is entered as the sole predictor. However, once additional covariates such as age, sex, educational attainment, region of residence, benefit status and occupation are controlled for in the multivariate frailty model (Table 2 ), this effect becomes weaker and statistically insignificant. This pattern suggests that part of the observed univariate disadvantage for neuro-psychiatric disabilities may reflect differences in correlated demographic, educational and occupational profiles. By contrast, the positive association for sensory disabilities becomes apparent only after adjustment, indicating that compositional differences may mask their conditional reintegration advantage in the univariate model. Table 3 Univariate effect of disability type on the hazard of labour market reintegration Variable \(\:\varvec{\beta\:}\) S.E. HR 95% CI p -value Physical disability Reference category Neuro-psychiatric disability -0.459 0.1342 0.632 (0.4859–0.8222) < 0.001 Sensory disability + 0.066 0.1386 1.068 (0.8141–1.4018) 0.634 Other disabilities -0.250 0.1562 0.778 (0.5734–1.0578) 0.110 Source: Author’s calculations using R 4.4.3 and the frailtypack package. In addition to the baseline Weibull proportional hazards frailty model, we analysed potential effect heterogeneity of disability type across key individual characteristics. We estimated a set of Weibull proportional hazards frailty models that included interaction terms between disability type and sex, age group, educational attainment, and unemployment benefit status. These models rely on the same individual-level gamma frailty structure (ID_person). For interpretability, we also report time ratios (TR) derived from the Weibull parameterization, which provide a complementary perspective on the interaction effects. TR 1 indicates a longer time to reintegration, relative to the reference category. Table 4 Summary of interaction effects between disability type and individual characteristics Tested interaction Interaction effects (TR [95% CI], p ) Summary of results Disability × Sex Neuro-psychiatric × male: TR = 1.30 [0.79–2.13], p = 0.310; Sensory × male: TR = 1.13 [0.68–1.89], p = 0.639; Other × male: TR = 0.84 [0.46–1.53], p = 0.568. No statistically significant interaction. The effect of disability type is similar for women and men. Disability × Age group Neuro-psychiatric × 25–34: TR = 0.57 [0.30–1.06], p = 0.074; Sensory × 25–34: TR = 0.59 [0.31–1.14], p = 0.120; Other × 25–34: TR = 0.58 [0.26–1.26], p = 0.168; Neuro-psychiatric × 35–44: TR = 0.37 [0.18–0.74], p = 0.005; Sensory × 35–44: TR = 0.63 [0.31–1.28], p = 0.201; Other × 35–44: TR = 0.49 [0.22–1.10], p = 0.085; Neuro-psychiatric × 45–54: TR = 0.28 [0.10–0.81], p = 0.019; Sensory × 45–54: TR = 0.81 [0.36–1.78], p = 0.593; Other × 45–54: TR = 0.71 [0.27–1.85], p = 0.477; Neuro-psychiatric × 55–64: TR = 0.13 [0.01–1.58], p = 0.108; Sensory × 55–64: TR = 1.23 [0.18–8.32], p = 0.831; Other × 55–64: TR = 0.32 [0.04–2.35], p = 0.265. Statistically significant interactions: Neuro-psychiatric × 35–44; Neuro-psychiatric × 45–54. Disability × Education Neuro-psychiatric × unknown/no schooling: TR = 1.52 [0.41–5.72], p = 0.532; Sensory × unknown/no schooling: TR = 1.08 [0.36–3.26], p = 0.885; Other × unknown/no schooling: TR = 1.06 [0.33–3.44], p = 0.923; Neuro-psychiatric × primary + lower secondary: TR = 2.88 [0.81–10.18], p = 0.101; Sensory × primary + lower secondary: TR = 1.19 [0.40–3.54], p = 0.761; Other × primary + lower secondary: TR = 0.77 [0.27–2.24], p = 0.634; Neuro-psychiatric × vocational/professional: TR = 4.29 [1.25–14.71], p = 0.021; Sensory × vocational/professional: TR = 1.58 [0.65–3.83], p = 0.310; Other × vocational/professional: TR = 2.62 [1.02–6.72], p = 0.046; Neuro-psychiatric × upper secondary: TR = 3.43 [1.00-11.76], p = 0.050; Sensory × upper secondary: TR = 1.57 [0.67–3.66], p = 0.299; Other × upper secondary: TR = 1.83 [0.77–4.37], p = 0.172; Neuro-psychiatric × post-secondary non-tertiary: TR = 0.69 [0.04–10.91], p = 0.789; Sensory × post-secondary non-tertiary: TR = 1.38 [0.36–5.30], p = 0.640; Other × post-secondary non-tertiary: TR = 1.97 [0.48–8.08], p = 0.348. Statistically significant interactions: Neuro-psychiatric × vocational/professional; Other × vocational/professional; Neuro-psychiatric × upper secondary. Disability × Unemployment benefit Neuro-psychiatric × benefit recipient: TR = 1.31 [0.78–2.20], p = 0.305; Sensory × benefit recipient: TR = 1.00 [0.59–1.69], p = 0.987; Other × benefit recipient: TR = 1.29 [0.66–2.51], p = 0.460. The effect of unemployment benefit receipt remains broadly constant across disability types (no significant interaction). Source: Author’s calculations using R 4.4.3. Table 4 suggests limited evidence of systematic effect heterogeneity by sex or unemployment benefit status, as none of the disability-by-sex or disability-by-benefit interaction terms reach conventional levels of statistical significance. By contrast, the disability-by-age models indicate significant interaction terms for neuro-psychiatric disabilities in the 35–44 and 45–54 age groups (TR = 0.37 and TR = 0.28, respectively), pointing to meaningful age-related heterogeneity in the association between neuro-psychiatric disability and the time to reintegration. The disability-by-education specification also reveals notable heterogeneity. Significant interaction terms for neuro-psychiatric disabilities among individuals with vocational/professional education (TR = 4.29) and upper secondary education (TR = 3.43), as well as for other disabilities among those with vocational/professional education (TR = 2.62), indicate that the disability-related time to reintegration varies across educational strata. These results suggest that the influence of disability type on reintegration may be more strongly conditioned by age and education than by gender or benefit status. To assess the robustness of the baseline Weibull frailty results, we additionally estimate a semi-parametric Cox frailty model with individual-level random effects. By leaving the baseline hazard unspecified, this specification provides an independent check of the estimated covariate effects on labour market reintegration. We estimate the model using both a standard Cox frailty implementation and a penalised-spline frailty approach. While coefficient magnitudes and p-values differ slightly across estimators, the direction of effects for the main covariates is stable. In particular, the conditional advantage associated with sensory disabilities and the strong negative association of unemployment benefit receipt remain robust. Table 5 reports results from the penalised-spline specification. Table 5 Robustness check. Cox frailty model with individual-level random effects (hazard ratios) Variable β S.E. p -value HR 95% CI Physical disability Reference category Neuro-psychiatric disability -0.2169 0.1165 0.063 0.805 (0.64–1.01) Sensory disability 0.3427 0.1152 0.003 1.409 (1.12–1.77) Other disabilities -0.2735 0.1310 0.037 0.761 (0.59–0.98) Female Reference category Male 0.0340 0.0852 0.690 1.035 (0.88–1.22) Rural Reference category Urban -0.1619 0.0891 0.069 0.851 (0.71–1.01) Technical/Industrial Reference category Economic/Administrative 0.1774 0.2624 0.500 1.194 (0.71-2.00) Medical/Health 0.1099 0.2209 0.620 1.116 (0.72–1.72) Social/Human Services/Education 0.5350 0.2675 0.045 1.708 (1.01–2.88) Commercial/Services 0.1099 0.1665 0.510 1.116 (0.81–1.55) Textiles/Manual/Vocational 0.5109 0.1608 0.0015 1.667 (1.22–2.28) IT/Information Technology -0.0057 0.1405 0.970 0.994 (0.76–1.31) Other/Unknown 0.3435 0.1465 0.019 1.410 (1.06–1.88) North-East -0.2287 0.1826 0.210 0.796 (0.56–1.14) South-East 0.0852 0.2056 0.680 1.089 (0.73–1.63) South-Muntenia 0.0594 0.1869 0.750 1.061 (0.74–1.53) Bucharest-Ilfov -1.6926 0.2985 < 0.001 0.184 (0.10–0.33) West -0.2354 0.2298 0.310 0.790 (0.50–1.24) North-West -0.1631 0.1786 0.360 0.850 (0.60–1.21) Centre -0.1569 0.2135 0.460 0.855 (0.56–1.30) South-West Oltenia Reference category Unknown/no schooling 0.2349 0.1974 0.230 1.265 (0.86–1.86) Primary + lower secondary 0.3332 0.1919 0.082 1.395 (0.96–2.03) Vocational/professional 0.1825 0.1690 0.280 1.200 (0.86–1.67) Upper secondary 0.0101 0.1610 0.950 1.010 (0.74–1.38) Post-secondary non-tertiary -0.0370 0.2486 0.880 0.964 (0.59–1.57) Tertiary education Reference category Non-benefit recipient Reference category Benefit recipient -2.6396 0.1000 < 0.001 0.071 (0.059–0.087) 16–24 years Reference category 25–34 years 0.5274 0.1071 < 0.001 1.694 (1.37–2.09) 35–44 years 0.3291 0.1168 0.0048 1.390 (1.11–1.75) 45–54 years 0.1116 0.1424 0.430 1.118 (0.85–1.48) 55–64 years -0.4546 0.3339 0.170 0.635 (0.33–1.22) Source: Author’s calculations using R 4.4.3. As shown in Table 5 , individuals with sensory disabilities have significantly higher reintegration hazard than those with physical disabilities, while individuals with other types of disabilities exhibit significantly slower reintegration. For neuro-psychiatric disabilities, the estimated effect is below one and only marginally significant. The 25–34 and 35–44 age groups are associated with a higher reintegration hazard relative to the 16–24 reference group, and these differences are statistically significant. For individuals aged 45–54 and 55–64, the differences relative to the reference category are not statistically significant. Unemployment benefit receipt has a strong negative effect on labour market reintegration (HR = 0.071; p < 0.001). This finding is fully consistent with the baseline Weibull frailty results and strengthens the conclusion that benefit receipt is strongly associated with longer unemployment spells among persons with disabilities. No significant effects are identified for sex, area of residence (urban/rural) or educational attainment at the 5% level. Thus, after controlling for other covariates, these characteristics do not appear to play a decisive role in the pace of labour market reintegration for disabled unemployed individuals. From a regional perspective, Bucharest-Ilfov shows a significantly lower reintegration hazard than South-West Oltenia (HR = 0.18; p < 0.001), while the remaining regions do not differ significantly from the reference category. Regarding occupation, reintegration is faster for individuals in Social/Human Services/Education (HR = 1.71; p = 0.045), Textiles/Manual/Vocational (HR = 1.67; p = 0.0015) and Other/Unknown occupations (HR = 1.41; p = 0.019), compared with the Technical/Industrial reference group. The other occupational fields do not display statistically significant differences relative to the reference category. The baseline hazard plot shows how the instantaneous hazard of labour market reintegration changes as unemployment duration increases, after adjusting for covariates and individual frailty. A pronounced peak is observed during the first 30–60 days of unemployment. In this interval, the probability of reintegration among persons with disabilities appears to be highest, suggesting the role of rapid placements and interventions concentrated at the onset of unemployment. Thereafter, the hazard declines markedly and remains low, with moderate oscillations around approximately 200, 600 and 1,000 days, which may correspond to institutional milestones or other dynamics of the job-search process. In the long run, the overall pattern is downward. After 1,200 days of unemployment, the hazard approaches zero, indicating that as unemployment duration increases, the likelihood of reintegration in the immediate subsequent period decreases substantially. Overall, the baseline hazard estimated with penalised splines is clearly non-monotonic, displaying variation throughout the unemployment spell and suggesting the existence of potential time windows for better calibrated interventions aimed at supporting the reintegration of persons with disabilities. To account explicitly for competing risks, we estimated a Fine-Gray subdistribution hazard model. We restricted the analysis to the first recorded unemployment spell for each individual, selected chronologically (1,609 spells). This restriction ensures a single-event structure per person and avoids dependence arising from recurrent spells. The Fine-Gray approach allows us to model the cumulative incidence of reintegration while accounting for competing exits from unemployment, such as transitions into inactivity or exits associated with the expiry of unemployment benefit entitlement. The results complement the Weibull proportional hazards frailty and Cox frailty estimates and further support the robustness of the main conclusions (Table 6 ). Table 6 Results of the Fine-Gray model Variable β S.E. p -value sHR 95% CI Physical disability Reference category Neuro-psychiatric disability -0.2197 0.0887 0.013 0.803 (0.675–0.955) Sensory disability 0.3055 0.0858 < 0.001 1.357 (1.147–1.606) Other disabilities -0.2285 0.0995 0.022 0.796 (0.655–0.967) Female Reference category Male 0.0271 0.0661 0.682 1.027 (0.903–1.17) 16–24 years Reference category 25–34 years 0.5163 0.0813 < 0.001 1.676 (1.429–1.965) 35–44 years 0.3769 0.0941 < 0.001 1.458 (1.212–1.753) 45–54 years 0.2377 0.1112 0.033 1.268 (1.02–1.577) 55–64 years -0.1732 0.2641 0.512 0.841 (0.501–1.411) Unknown/no schooling 0.0878 0.1415 0.535 1.092 (0.827–1.441) Primary + lower secondary 0.0597 0.1467 0.684 1.062 (0.796–1.415) Vocational/Professional -0.0150 0.1223 0.902 0.985 (0.775–1.252) Upper secondary -0.0628 0.1181 0.595 0.939 (0.745–1.184) Post-secondary non-tertiary 0.0186 0.1722 0.914 1.019 (0.727–1.428) Tertiary education Reference category Bucharest-Ilfov -1.1141 0.2378 < 0.001 0.328 (0.206–0.523) Centre -0.1430 0.1658 0.388 0.867 (0.626-1.200) North-East -0.3294 0.1453 0.023 0.719 (0.541–0.956) North-West -0.1075 0.1373 0.434 0.898 (0.686–1.175) South-East -0.0461 0.1606 0.774 0.955 (0.697–1.308) South-Muntenia -0.0623 0.1484 0.675 0.940 (0.702–1.257) West -0.1742 0.1696 0.304 0.840 (0.603–1.171) South-West Oltenia Reference category Rural Reference category Urban -0.0667 0.0715 0.351 0.935 (0.813–1.076) Technical/Industrial Reference category Economic/Administrative 0.3002 0.1896 0.113 1.350 (0.931–1.958) Medical/Health -0.1033 0.1711 0.546 0.902 (0.645–1.261) Social/Human Services/Education 0.3518 0.1845 0.057 1.422 (0.99–2.041) Commercial/Services 0.0924 0.1257 0.462 1.097 (0.857–1.403) Textiles/Manual/Vocational 0.4632 0.1259 < 0.001 1.589 (1.242–2.034) IT/Information Technology -0.0186 0.1098 0.865 0.982 (0.791–1.217) Other/Unknown 0.3069 0.1113 0.006 1.359 (1.093–1.691) Non-benefit recipient Reference category Benefit recipient -1.7860 0.0822 < 0.001 0.168 (0.143–0.197) Source: Author’s calculations using R 4.4.3. The Fine-Gray subdistribution hazard estimates largely corroborate the main findings from the baseline Weibull frailty and Cox frailty models, confirming that the key patterns remain robust when competing risks are explicitly considered. Relative to physical disabilities, sensory disabilities are associated with a significantly higher subdistribution hazard of labour market reintegration (sHR = 1.357, p < 0.001), whereas neuro-psychiatric disabilities (sHR = 0.803, p = 0.013) and other disabilities (sHR = 0.796, p = 0.022) display significantly lower subdistribution hazards. The age gradient is consistent with earlier results: individuals aged 25–34 (sHR = 1.676, p < 0.001) and 35–44 (sHR = 1.458, p < 0.001), and also those aged 45–54 (sHR = 1.268, p = 0.033), exhibit higher cumulative incidence of reintegration than the 16–24 reference group, while the effect for the 55–64 group is not statistically significant (sHR = 0.841, p = 0.512). Educational attainment does not emerge as a robust predictor of reintegration in the presence of competing risks. Regionally, Bucharest-Ilfov again stands out with a markedly lower subdistribution hazard compared with South-West Oltenia (sHR = 0.328, p < 0.001). In this competing-risks specification, North-East also shows a significantly lower reintegration hazard (sHR = 0.719, p = 0.023), while the remaining regional differences are not statistically significant. Unemployment benefit receipt continues to show a strong negative association with reintegration (sHR = 0.168, p < 0.001), indicating substantially lower cumulative incidence of reintegration once the risks of alternative exits from unemployment are considered. Occupational patterns remain broadly consistent, with faster reintegration in Textiles/Manual/Vocational (sHR = 1.589, p < 0.001) and the Other/Unknown category (sHR = 1.359, p = 0.006), while most other occupational differences are not statistically significant. Overall, the competing-risks analysis reinforces the central conclusion that disability type, age and benefit status are the most consistent correlates of reintegration among unemployed persons with disabilities in Romania. A Prentice-Williams-Peterson (PWP) model for recurrent events is used to evaluate whether the type of previous exit from unemployment predicts reintegration hazards in later spells. Table 7 Results of the PWP model estimation Variable β S.E. HR 95% CI p -value Type of previous exit from unemployment Expiry of legal entitlement to unemployment benefits Reference category Labour market reintegration 0.5451 0.1844 1.725 (1.176–2.529) 0.0052 Inactivity -2.4705 0.7877 0.085 (0.010–0.702) 0.0222 Other reasons -0.2869 0.2892 0.751 (0.403–1.398) 0.3661 Type of disability Physical disability Reference category Neuro-psychiatric disability -0.3502 0.2085 0.705 (0.474–1.047) 0.0828 Sensory disability -0.2778 0.2132 0.757 (0.512–1.121) 0.1650 Other disabilities -0.3588 0.2350 0.698 (0.429–1.137) 0.1487 Female Reference category Male -0.0930 0.1382 0.911 (0.697–1.192) 0.4973 16–24 years Reference category 25–34 years 0.0598 0.1959 1.062 (0.706–1.596) 0.7735 35–44 years -0.1309 0.1990 0.877 (0.572–1.345) 0.5479 45–54 years 0.2767 0.2362 1.319 (0.878–1.982) 0.1829 55–64 years -1.2336 0.7937 0.291 (0.029–2.880) 0.2914 Unknown/no schooling 0.6183 0.3679 1.856 (1.028–3.349) 0.0401 Primary + lower secondary 0.7839 0.3786 2.190 (1.004–4.775) 0.0487 Vocational/professional 0.5540 0.2928 1.740 (1.014–2.986) 0.0443 Upper secondary 0.3865 0.2889 1.472 (0.884–2.450) 0.1371 Post-secondary non-tertiary 0.2135 0.4144 1.238 (0.622–2.463) 0.5431 Tertiary education Reference category Bucharest-Ilfov -2.8771 1.0580 0.056 (0.007–0.486) 0.0089 Centre -0.2708 0.3872 0.763 (0.368–1.581) 0.4667 North-East 0.1475 0.3201 1.159 (0.633–2.122) 0.6327 North-West -0.1212 0.3152 0.886 (0.478–1.643) 0.7006 South-East 0.0962 0.3759 1.101 (0.551–2.198) 0.7851 South-Muntenia -0.1146 0.3210 0.892 (0.458–1.736) 0.7359 West 0.0155 0.3700 1.016 (0.498–2.073) 0.9660 South-West Oltenia Reference category Rural Reference category Urban -0.2339 0.1595 0.791 (0.567–1.105) 0.1701 Non-benefit recipient Reference category Benefit recipient -1.6471 0.2025 0.193 (0.133–0.279) < 0.0001 Technical/Industrial Reference category Economic/Administrative -0.5508 0.4234 0.576 (0.281–1.183) 0.1331 Medical/Health -0.0392 0.3313 0.962 (0.546–1.694) 0.8920 Social/Human Services/Education 0.0242 0.4711 1.024 (0.257–4.090) 0.9727 Commercial/Services -0.1153 0.2630 0.891 (0.557–1.425) 0.6304 Textiles/Manual/Vocational 0.0361 0.2527 1.037 (0.639–1.683) 0.8839 IT/Information Technology -0.2469 0.2410 0.781 (0.496–1.231) 0.2869 Other/Unknown 0.0465 0.2237 1.048 (0.676–1.625) 0.8354 Source: Author’s calculations using R 4.4.3 As shown in Table 7 , the type of previous exit from unemployment is a strong predictor of labour market reintegration in subsequent spells. Relative to exits due to the expiry of legal unemployment benefit entitlement, individuals whose previous spell ended through labour market reintegration display a significantly higher reintegration hazard in the next spell (HR = 1.725). By contrast, a prior transition into inactivity is associated with a markedly lower hazard of subsequent reintegration (HR = 0.085). This pattern is consistent with strong state dependence. “Successful” previous exits appear to facilitate later returns to work, whereas inactivity may signal persistent or cumulative barriers to employment. Unemployment benefit receipt remains a robust negative correlate of reintegration in recurrent spells (HR = 0.193), in line with the baseline frailty and competing-risks results. Regional effects are generally muted, although Bucharest-Ilfov again stands out with a substantially lower hazard relative to the reference region. Disability-type coefficients are below one but do not reach conventional significance thresholds, suggesting that in the recurrent-event framework disability-related differences are partly absorbed by prior-exit history and benefit status. Finally, lower and intermediate educational levels show higher hazards than tertiary education, plausibly reflecting faster re-entry into lower-skill segments with higher turnover rather than superior employment prospects. As a next step, we relaxed the proportional hazards assumption for disability type and examined whether the effect of disability type on labour market reintegration varies across the unemployment spell. Given the wide range of the duration variable in days ( \(\:{x}_{min}=1,\:{x}_{max}=1278\) ), we split the unemployment spell into three intervals: 1–60 days, 61–200 days, and 201–600 days. We focus on intervals up to 600 days, as the number of spells exceeding this threshold is small, leading to imprecise estimates if modelled separately. This choice reflects a trade-off between capturing time heterogeneity and maintaining sufficient statistical precision. Using a start-stop specification, we interacted disability type with the interval indicators, while keeping the remaining covariate effects time-invariant. The resulting time-varying hazard ratios for disability type are reported in Table 8 . All estimates are adjusted for the full set of covariates and were obtained in R 4.4.3 using the coxph() function from the survival package, applied to the episode-split dataset. Table 8 Time-varying effects of disability type on labour market reintegration: piecewise Cox model results Type of disability Time interval β S.E. HR 95% CI p -value Neuro-psychiatric 1–60 -0.2208 0.0872 0.802 (0.68–0.95) 0.011 Neuro-psychiatric 61–200 -0.1693 0.1798 0.844 (0.59–1.2) 0.346 Neuro-psychiatric 201–600 0.4548 0.3925 1.576 (0.73–3.4) 0.247 Sensory 1–60 0.2351 0.0863 1.265 (1.07–1.5) 0.006 Sensory 61–200 0.1324 0.1985 1.142 (0.77–1.68) 0.505 Sensory 201–600 0.7529 0.3398 2.123 (1.09–4.13) 0.027 Other 1–60 -0.2745 0.0991 0.760 (0.63–0.92) 0.006 Other 61–200 -0.0249 0.2069 0.975 (0.65–1.46) 0.904 Other 201–600 -0.3538 0.4895 0.702 (0.27–1.83) 0.470 Source: Author’s calculations using R 4.4.3. The results in Table 8 show that the effect of disability type on labour market reintegration is not constant over the unemployment spell. For individuals with neuro-psychiatric disabilities, the hazard of exiting unemployment is significantly lower than for those with physical disabilities during the first 60 days (HR = 0.802; p = 0.011), indicating a clear early disadvantage. In the 61–200 interval, the hazard ratio remains below unity (HR = 0.844), but the effect is no longer statistically significant ( p = 0.346). In the 201–600 interval, the estimated hazard ratio rises above one (HR = 1.576), yet the wide confidence interval (0.73–3.40) and non-significant p -value ( p = 0.247) prevent firm conclusions about a late-stage reversal of this disadvantage. By contrast, individuals with sensory disabilities display a persistent and eventually very pronounced advantage in terms of reintegration. In the first 60 days, their hazard ratio is 1.265 (p = 0.006), pointing to a significantly higher probability of exiting unemployment compared to those with physical disabilities. The effect remains positive but statistically imprecise in the 61–200 interval (HR = 1.142, p = 0.505). Over longer spells (201–600 days), the hazard ratio increases to 2.123 and becomes statistically significant ( p = 0.027), suggesting that sensory disabilities are associated with substantially higher exit rates from medium-to long-term unemployment. The heterogeneous “other” disabilities category exhibits a different pattern. During the first 60 days, the hazard ratio is significantly below one (HR = 0.760, p = 0.006), indicating an early disadvantage relative to physical disabilities. In the 61–200 interval, the hazard becomes almost indistinguishable from that of the reference group (HR = 0.975, p = 0.904), and in the 201–600 interval it remains below one (HR = 0.702), but with a wide confidence interval (0.27–1.83) and a non-significant p -value ( p = 0.470). Overall, these findings confirm that the impact of disability type on reintegration is strongest and most clearly identified in the early phase of unemployment, while the differences become more heterogeneous and less precisely estimated at later stages. While Table 8 highlights time heterogeneity in the disability effect, Table 9 summarises the key predictors across model families. The broad consistency of signs and magnitudes supports the robustness of our main results. Table 9 Comparative multi-model summary for the most important predictors Variable Weibull PH frailty full Weibull PH frailty univariate Cox frailty (penalised spline) Fine-Gray PWP recurrent Neuro-psychiatric disability -19.55% (ns) -36.79% (***) -19.50% (*) -19.70% (**) -29.50% (*) Sensory disability + 51.08% (**) + 6.83% (ns) + 40.90% (**) + 35.70% (***) -24.30% (ns) Other disabilities -23.71% (*) -22.12% (ns) -23.90% (**) -20.40% (**) -30.20% (ns) 25–34 years + 90.97% (***) - + 69.40% (***) + 67.60% (***) + 6.20% (ns) 35–44 years + 50.37% (**) - + 39.00% (**) + 45.80% (***) -12.30% (ns) 45–54 years + 12.46% (ns) - + 11.80% (ns) + 26.80% (**) + 31.90% (ns) Unemployment benefit receipt -96.78% (***) - -92.90% (***) -83.20% (***) -80.70% (***) Bucharest-Ilfov -83.22% (***) - -81.60% (***) -67.20% (***) -94.40% (**) Source: Author’s calculations using R 4.4.3. Negative percentages indicate a lower reintegration hazard relative to the reference category (ratio 1). Percentages are computed as (ratio-1)×100. Fine-Gray percentages are derived from subdistribution hazard ratios (sHR). The other models report hazard ratios (HR). (***) highly significant, p < 0.001 (**) significant at the 5% level (*) significant at the 10% level (ns) = not statistically significant Table 9 shows substantial convergence across model specifications regarding the most important predictors of labour market reintegration. The reported percentages summarise the approximate relative change in the relevant hazard ratio compared with the reference category. In the Fine-Gray model, they refer to changes in the subdistribution hazard. Because the Fine-Gray and PWP frameworks target different risk structures, magnitudes are not directly comparable, but the direction and ranking of effects remain informative. With respect to disability type, sensory disability is associated with faster reintegration in most multivariate hazard-based models, with effect sizes of about + 51% in the Weibull PH frailty full model, + 41% in the Cox frailty (penalised spline) model, and + 35.7% in the Fine-Gray specification. The main exceptions are the Weibull PH frailty univariate model, where the point estimate is small and insignificant, and the PWP recurrent model, which yields a negative but non-significant effect (-24.3%). This pattern suggests that the advantage associated with sensory disabilities becomes clearer once additional covariates are controlled for and when the analysis is not conditioned on event order. Other disabilities are more systematically linked to slower reintegration. The estimated disadvantage is relatively stable across the Cox frailty and Fine-Gray specifications (around − 20% to -24%) and remains statistically significant in most multivariate models, indicating a robust negative association compared with physical disabilities. For neuro-psychiatric disabilities, the results indicate a moderate disadvantage of approximately − 19% in the multivariate hazard-based models. However, the strongest and clearest evidence emerges in the univariate Weibull PH frailty model (-36.79%), while adjusted specifications yield weaker or only marginally significant estimates. This is consistent with the idea that part of the raw disadvantage for neuro-psychiatric disabilities may be mediated by correlated demographic, educational, and occupational factors. Age effects reveal a clear advantage for the 25–34 age group, with estimates of about + 91% in the Weibull PH frailty full model and around + 68% to + 69% in both the Cox frailty and Fine-Gray models. A consistent advantage is also observed for the 35–44 group, ranging from roughly + 39% in the Cox frailty model to about + 50% in the Weibull full model and + 45.8% in the Fine-Gray specification. For the 45–54 group, effects are smaller and do not indicate a robust advantage across specifications. The absence of age estimates in the univariate column reflects the single-predictor design of that model, while the muted age effects in the PWP framework are expected given event-order conditioning and the role of prior-exit history. The effect of unemployment benefit receipt is large, negative, and remarkably stable across specifications (approximately − 97% in the Weibull PH frailty full model, -93% in the Cox frailty model, − 83% in the Fine-Gray model, and − 81% in the PWP specification), indicating a strong and persistent association between benefit receipt and slower reintegration. Finally, Bucharest-Ilfov shows a pronounced disadvantage across all models, with estimated reductions in the reintegration hazard ranging from about − 67.2% in the Fine-Gray model to -94.4% in the PWP recurrent specification relative to the reference region. Overall, the consistency in effect directions and the relative stability of the main estimates strengthen confidence in the robustness of our results on the determinants of reintegration among unemployed persons with disabilities in Romania. 4. Conclusions The aim of this study is to analyse how disability type and other individual characteristics shape unemployment spells and labour market reintegration among people with disabilities in Romania. Using administrative data, we examined reintegration dynamics with several complementary duration frameworks (Weibull and Cox frailty, Fine-Gray competing risks, PWP recurrent events, and a piecewise Cox model). This multi-model approach yields a consistent picture of reintegration among unemployed persons with disabilities in Romania. The empirical results point to several consistent patterns. Disability type clearly structures reintegration prospects. Across parametric, semi-parametric and competing-risks specifications, individuals with sensory disabilities generally exhibit higher hazards (or subdistribution hazards) of labour market reintegration than those with physical disabilities, with the advantage most visible in medium-to longer-duration spells. By contrast, neuro-psychiatric disabilities and the heterogeneous “other disabilities” group tend to be associated with lower reintegration hazards, though statistical significance varies across models. The univariate frailty model indicates a pronounced raw disadvantage for neuro-psychiatric disabilities, which attenuates after adjustment for age, education, region, benefit status and occupation, suggesting that part of this gap reflects correlated socio-demographic and occupational differences. Age is a robust predictor of reintegration. Across the Weibull frailty, Cox frailty and Fine-Gray models, individuals aged 25–34 and 35–44 show significantly higher hazards of re-employment than those aged 16–24. In the competing-risks specification, a similar advantage is also observed for the 45–54 group. The 55–64 group does not differ significantly from the youngest cohort. Interaction results indicate that the effect of neuro-psychiatric disability varies by age, with particularly long reintegration times in several prime-age categories. Unemployment benefit receipt is a remarkably stable and strong predictor of slower reintegration. In all hazard-based models, benefit recipients display dramatically lower hazards or subdistribution hazards of labour market reintegration than non-recipients, with estimated reductions ranging from about 80% to more than 95%. While caution is needed in drawing causal conclusions, benefit eligibility and duration reflect prior employment histories, institutional rules and selection processes. This consistent association suggests that benefit recipients face more persistent and severe barriers to re-employment, which standard activation tools may not fully offset. Regional and occupational patterns matter. An important result is that registered unemployed persons with disabilities in Bucharest-Ilfov show significantly lower reintegration hazards than those in the South-West Oltenia reference region, a result that is robust across Weibull, Cox and Fine-Gray models and also appears, in amplified form, in the PWP recurrent-event framework. This may reflect a combination of compositional factors, mismatches between disability profiles and the structure of labour demand, or institutional practices specific to the capital region. Occupation-wise, individuals with disabilities in Textiles/Manual/Vocational and in Social/Human Services/Education tend to exhibit higher reintegration hazards than those in Technical/Industrial occupations, indicating that lower-and mid-skill segments with higher turnover may offer more frequent re-entry opportunities, albeit potentially at the cost of job quality and stability. The baseline hazard estimates highlight important temporal patterns. The Cox frailty model with penalised splines reveals a strongly non-monotonic baseline hazard of reintegration, with a pronounced peak in the first 30–60 days of unemployment, followed by a decline and moderate oscillations around longer durations. This pattern is consistent with rapid early placements, diminishing prospects as spells lengthen, and sporadic reintegration opportunities linked to institutional milestones (e.g. completion of training programmes). The piecewise Cox model with time-varying coefficients for disability type confirms that disability-related gaps are most clearly identified in the early phase of the unemployment spell: neuro-psychiatric and “other” disabilities are associated with significantly lower hazards than physical disabilities in the first 60 days, while sensory disabilities already enjoy an advantage that becomes particularly pronounced for spells lasting between 201 and 600 days. These findings have several policy implications. The early peak in the baseline hazard and the strong early disadvantages for neuro-psychiatric and “other” disabilities suggest that the first months of unemployment constitute a critical window for intervention. Tailored support, such as specialised counselling, intensive job-search assistance, mental health support, workplace accommodation advice and closer coordination between employment services and social/health services, may be especially effective if deployed shortly after registration. The robust and large negative association between benefit receipt and reintegration points to the need for carefully designed activation and conditionality mechanisms that encourage job search and facilitate placement, while avoiding undue pressure or penalisation of individuals facing genuine health-related constraints. The results also highlight the importance of taking heterogeneity by age, education and occupation seriously when designing interventions. For example, the interaction patterns suggest that neuro-psychiatric disabilities combined with certain educational levels (vocational/professional and upper secondary) are associated with particularly long reintegration times, indicating that standard programmes may not adequately address the combined barriers of skill profiles and mental health conditions. Regionally, the persistent disadvantage observed in Bucharest-Ilfov and, in some specifications, in the North-East region suggests that one-size-fits-all policies are unlikely to be sufficient: region-specific strategies that address local labour demand, employer attitudes, accessibility of workplaces and coordination among institutions are needed. This study has several limitations that should be acknowledged. The analysis focuses on registered unemployed individuals with disabilities who exit unemployment and thus cannot capture the situation of those who remain long-term registered, who never register, or who are in informal or precarious employment; the administrative data do not include detailed information on disability severity, functional limitations, workplace accommodations, discrimination, or job quality after reintegration, all of which may mediate the estimated relationships; the observational nature of the data and the absence of strong instruments mean that the estimated associations cannot be straightforwardly interpreted as causal effects; and finally, some subgroup analyses, especially those based on interactions or long-duration intervals, are based on relatively small numbers of spells, leading to wide confidence intervals and limited statistical power. This is a limitation of the study, but it also points to a clear direction for future research on disability severity, workplace accommodations and post-reintegration job quality. Despite these limitations, the convergence of results across multiple modelling strategies, parametric, semi-parametric, competing-risks and recurrent-event frameworks, provides strong evidence that disability type, age and benefit status are the most consistent predictors of re-employment among unemployed persons with disabilities in Romania. Future research could build on this work by incorporating richer information on disability severity and workplace adaptations, exploring job quality and sustainability after reintegration, and evaluating specific programmes or policy reforms using quasi-experimental designs. From a policy perspective, the evidence presented here underscores the need for early, targeted and disability-sensitive labour market interventions that recognise the diversity of trajectories and barriers faced by people with disabilities. Declarations Funding This research received no funding. Acknowledgements During manuscript preparation, the author used two generative AI assistants, ChatGPT 5.1 (OpenAI) and Gemini 3 Pro (Google), for R coding assistance, for troubleshooting scripts during the model estimation stage, and for assistance with language clarity and manuscript refinement. Data analysis, results validation and interpretation were performed by the author. All code and results were verified and are the sole responsibility of the author. Data Availability Statement The dataset used in this study is available from the corresponding author upon reasonable request. The dataset is not publicly archived because it contains pseudonymised unique identifiers (ID_person), and its disclosure would contravene the EU General Data Protection Regulation (GDPR) and institutional privacy policies. References Addison JT, Portugal P (2003) Unemployment Duration: Competing and Defective Risks. J Hum Resour 38:156–191. https://doi.org/10.2307/1558760 Arulampalam W (2001) Is Unemployment Really Scarring? Effects of Unemployment Experiences on Wages. Econ J 111(475):F585–F606 Baciu EL, Lazăr TA (2017) Between Equality and Discrimination: Disabled Persons in Romania. Transylv Rev Administrative Sci 13(51):5–19. 10.24193/tras.51E.1 Birău FR, Dănăcică D-E, Spulbar CM (2019) Social Exclusion and Labor Market Integration of People with Disabilities: A Case Study for Romania. Sustainability 11(18):5014. https://doi.org/10.3390/su11185014 Boman T, Kjellberg A, Danermark B, Boman E (2015) Employment Opportunities for Persons with Different Types of Disability. Alter 9(2):116–129. https://doi.org/10.1016/j.alter.2014.11.003 Castro Landsman R, Tapia Gertosio J, Mejías González D (2025) Educated Workers Do Not Experience Shorter Unemployment Spells: Evidence from a Literature Review. J Labour Market Res 59(1):25. https://doi.org/10.1186/s12651-025-00405-1 Clausen T, Greve Pedersen J, Olsen BE, Bengtsson S (2004) Handicap og beskæftigelse-et forhindringsløb? [Disability and Employment–An Obstacle Course?]. Socialforskningsinstituttet, Copenhagen Colella AJ, Bruyère SM (2011) Disability and Employment: New Directions for Industrial and Organizational Psychology. In: Zedeck S (ed) APA handbook of industrial and organizational psychology, vol. 1: Building and developing the organization. American Psychological Association, Washington, DC, pp 473–504. https://doi.org/10.1037/12169-015 . Council of the European Union: Disability in the EU: Facts and Figures (infographic) (2024) [online] Available at: https://www.consilium.europa.eu/en/infographics/disability-eu-facts-figures/ Accessed 24 Nov 2025 Dănăcică DE (2013) Cercetări Privind Impactul Factorilor ce Influenţează Durata Şomajului şi Probabilitatea (Re)angajării în România. Editura Expert, București Dănăcică DE, Cîrnu D (2014) Unemployment Duration and Exit States of Disabled People in Romania. Romanian J Economic Forecast 17(1):35–52 Gregg P (2001) The Impact of Youth Unemployment on Adult Employment in the NCDS. Econ J 111(475). https://doi.org/10.1111/1468-0297.00666 . F623-F653 Gregg P, Tominey E (2005) The Wage Scar from Male Youth Unemployment. Labour Econ 12(4):487–509. https://doi.org/10.1016/j.labeco.2005.05.004 Harkko J, Virtanen M, Kouvonen A (2018) Unemployment and Work Disability Due to Common Mental Disorders Among Young Adults: Selection or Causation? Eur J Pub Health 28(5):791–797. https://doi.org/10.1093/eurpub/cky024 Helgesson M, Pettersson E, Lindsäter E, Taipale H, Tanskanen A, Mittendorfer-Rutz E, Cullen AE (2024) Trajectories of Work Disability Among Individuals with Anxiety-, Mood/Affective-, or Stress-Related Disorders in a Primary Healthcare Setting. BMC Psychiatry 24(1):623. https://doi.org/10.1186/s12888-024-06068-5 Kavkler A, Dănăcică D, Babucea AG, Bicanic I, Bohm B, Tevdovski D, Tosevska K, Borsic D (2009) Cox Regression Models for Unemployment Duration in Romania, Austria, Slovenia, Croatia and Macedonia. Romanian J Economic Forecast 6(2):81–104 Kupets O (2006) Determinants of Unemployment Duration in Ukraine. J Comp Econ 34(2):228–247. https://doi.org/10.1016/j.jce.2006.02.006 Lalive R (2007) Unemployment Benefits, Unemployment Duration, and Post-Unemployment Jobs: A Regression Discontinuity Approach. Am Econ Rev 97(2):108–112. 10.1257/aer.97.2.108 Linz K, Stula S (2010) Demographic Change in Europe-An Overview. Observatory Sociopolitical Developments Europe 4(1):2–10 National Authority for the Protection of the Rights of Persons with Disabilities: Diagnosis of the situation of people with disabilities in Romania (in Romanian). Bucharest (2021) https://anpd.gov.ro/web/wp-content/uploads/2023/01/Diagnoza-situatiei-persoanelorcu-dizabilitati-in-Romania.pdf , Accessed 24 Nov 2025 Sandvin JT, Alexiu TM (2019) How Can We Understand the Poor Implementation of Disability Inclusion Policy in Romania? EU Social Inclusion Policies in Post-Socialist Countries. Routledge, London, pp 99–115 Saunders SL, Nedelec B (2014) What Work Means to People with Work Disability: A Scoping Review. J Occup Rehabil 24:100–110. https://doi.org/10.1007/s10926-013-9436-y Schur LA (2002) The Difference a Job Makes: The Effects of Employment Among People with Disabilities. J Econ Issues 36(2):339–347. https://doi.org/10.1080/00213624.2002.11506476 Sciulli D, de Menezes AG, Vieira JC (2007) Unemployment Duration and Disability: Evidence from Portugal. IZA Discussion Paper No. 3028 Trang LM, Ha DT, Son DT, Lan NTH, Anh TK (2024) Survival Analysis of Unemployment Duration: A Case Study of Vietnam. J Educ Work 37(1–4):216–233. https://doi.org/10.1080/13639080.2024.2383562 Vornholt K, Villotti P, Muschalla B, Bauer J, Colella A, Zijlstra F, Van Ruitenbeek G, Uitdewilligen S, Corbière M (2018) Disability and Employment-Overview and Highlights. Eur J Work Organizational Psychol 27(1):40–55. https://doi.org/10.1080/1359432X.2017.1387536 World, Bank (2021) National Authority for the Rights of Persons with Disabilities, Children and Adoptions.: Diagnosis of the Situation of Persons with Disabilities in Romania. World Bank, Bucharest Additional Declarations The authors declare no competing interests. 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12:57:18","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":190189,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8302059/v1/e9d407873f215c01f87e15c3.html"},{"id":97797168,"identity":"54224519-c23e-479f-a820-acd8fb49f2f6","added_by":"auto","created_at":"2025-12-09 12:57:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":102146,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated baseline hazard in the Cox frailty model with penalised spline baseline (event is labour market reintegration)\u003c/p\u003e\n\u003cp\u003eSource: Author’s calculations using R 4.4.3.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8302059/v1/babbcced3d742b78f26f9f63.png"},{"id":97897653,"identity":"9cec3907-b001-452e-b42c-0929b5aab21c","added_by":"auto","created_at":"2025-12-10 15:38:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1419891,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8302059/v1/a0641b9f-dfb7-4a9b-85f0-5faf079bd170.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDisability Type and Prior Unemployment Exits as Predictors of Labour Market Reintegration among People with Disabilities: Evidence from Romania\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLabour market integration of people with disabilities remains a major challenge in both advanced and emerging economies and is a recurrent priority on public policy agendas. Despite visible progress over the past two decades, people with disabilities still face substantial disadvantages in employment, unemployment and job stability (Birău et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Boman et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Colella and Bruy\u0026egrave;re \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003enăcică and C\u0026icirc;rnu 2014; Sciulli et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). According to the Council of the European Union (2024), drawing on Eurostat data, around 107\u0026nbsp;million people live with some form of disability in the European Union. Demographic change (Linz and Stula \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), population ageing and the retirement of the baby-boom generation (Vornholt et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) further increase the urgency of sustainable inclusion strategies. Beyond the macroeconomic relevance of a larger inclusive workforce, labour market participation is consistently associated with better well-being among people with disabilities. Employment provides daily structure, reduces the risk of social isolation and contributes to individual autonomy (Saunders and Nedelec \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Schur \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Vornholt et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrevious research identifies several mechanisms behind persistent labour market disadvantages among people with disabilities. Lower levels of formal education, poorer health and reduced work capacity, employer prejudice and low accessibility of workplaces are frequently cited. At the same time, people with disabilities form a highly heterogeneous group, and integration chances depend on specific combinations of individual characteristics, disability type, institutional context, and macroeconomic shocks. Recent studies therefore document differentiated effects on labour market integration by disability type (Boman et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Clausen et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Sciulli et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Individuals with mental or psychiatric disabilities often face particularly severe difficulties in accessing and sustaining employment (Birău et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Boman et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Harkko et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Helgesson et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while musculoskeletal and other sensory-related impairments have also been associated with long unemployment spells in several settings (Sciulli et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). By contrast, labour market integration tends to be smoother for individuals with hearing or speech impairments in some contexts (Boman et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sciulli et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Sensory impairments can more easily be compensated by assistive technologies and workplace accommodations, whereas intellectual and mental health disabilities often pose more complex integration challenges.\u003c/p\u003e\u003cp\u003eResearch also points to a bidirectional relationship between unemployment and disability. Health status shapes the probability of becoming unemployed, while long-term unemployment can further deteriorate health and work capacity, reinforcing a vicious circle of marginalisation. Unfortunately, as Boman et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) point out, people with disabilities are often treated as a homogeneous group in policy design, although their needs and vulnerabilities in the labour market differ substantially by type and severity of disability and by employment history.\u003c/p\u003e\u003cp\u003eA related body of research examines unemployment duration (Addison and Portugal \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Castro et al. 2025; Dănăcică \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kavkler et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Kupets \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Lalive \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Trang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the hazard of exiting unemployment and the \u0026ldquo;scarring\u0026rdquo; effects of repeated non-employment episodes (Arulampalam \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Gregg \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Gregg and Tominey \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This evidence shows that re-employment probabilities are strongly time-dependent and shaped by previous unemployment histories, and that exits into inactivity or benefit exhaustion may have persistent consequences for later labour market trajectories. However, most contributions focus on the general population or specific groups such as youth or older workers. Disability status and type are rarely integrated systematically into duration analyses, and prior exit routes from unemployment are seldom examined as determinants of subsequent reintegration among people with disabilities. As a result, we still know relatively little about how past labour market trajectories, like re-employment, transitions into inactivity, or benefit exhaustion, shape later unemployment spells for this vulnerable group.\u003c/p\u003e\u003cp\u003eThe Romanian context illustrates particularly well the tension between the normative framework and actual outcomes. The legal framework (Law no. 448/2006 on the protection and promotion of the rights of people with disabilities, with subsequent amendments and additions, including Government Emergency Ordinance no. 60/2017 and Law no. 193/2020) combines a system of mandatory employment quotas with financial incentives aimed at increasing the employment of people with disabilities. Nonetheless, the literature and implementation reports (Baciu and Lazăr \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; National Authority for the Protection of the\u003c/p\u003e\u003cp\u003eRights of Persons with Disabilities, 2021; Sandvin and Alexiu, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; World Bank \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) point to limited enforcement. Sanctions appear to have weak effects, procurement mechanisms involving sheltered units are used relatively infrequently, and wage subsidies are claimed by only a small number of employers. Recent assessments suggest that even in the public sector compliance with legal provisions falls short of the targets, indicating persistent institutional and attitudinal barriers (Baciu and Lazăr, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this setting, a fine-grained understanding of labour market reintegration dynamics becomes essential for calibrating active labour market policies, counselling and placement services, and benefit conditionalities.\u003c/p\u003e\u003cp\u003eStudies that focus explicitly on unemployment spells and transitions out of unemployment among people with disabilities have typically relied on logistic regression or standard proportional hazards Cox models, paying less attention to recurrent unemployment episodes, competing exits, and unobserved heterogeneity. Moreover, Central and Eastern European labour markets remain underrepresented in administrative micro-data studies of disability-related unemployment dynamics, despite important differences in labour market institutions and social protection systems compared with North-Western Europe. To address these gaps, we use individual-level administrative data for Romania covering the 2017\u0026ndash;2020 period and implement a multi-model duration strategy. We estimate parametric and semi-parametric frailty models to capture unobserved heterogeneity, a flexible Cox specification with a penalised spline baseline, a Fine-Gray model to account for competing exits, a Prentice-Williams-Peterson (PWP) framework to model recurrent spells by event order, and a piecewise Cox approach to explore time-varying hazard patterns.\u003c/p\u003e\u003cp\u003eThe paper addresses three core research questions, which structure the empirical analysis:\u003c/p\u003e\u003cp\u003e(O1) To what extent does disability type predict labour market reintegration among people with disabilities, once we control for socio-demographic, educational, occupational, and regional characteristics?\u003c/p\u003e\u003cp\u003e(O2) How do unemployment benefit receipt and the history of previous exits from unemployment (re-employment, inactivity, benefit exhaustion) affect the probability and timing of subsequent labour market reintegration?\u003c/p\u003e\u003cp\u003e(O3) How robust are these effects across alternative baseline hazard specifications and different treatments of competing risks and recurrent events?\u003c/p\u003e\u003cp\u003eThese questions motivate our contribution to the literature in three ways. First, we provide a rigorous differentiation of the effects of disability types on the hazard of labour market reintegration in an empirically underexplored Eastern European context. Second, we introduce individuals\u0026rsquo; histories of prior exits from unemployment as a predictor of subsequent labour market reintegration, thereby capturing scarring and path dependence among people with disabilities through a PWP recurrent-events framework. Third, we strengthen confidence in our findings through a multi-model robustness strategy that explicitly addresses intra-individual dependence, competing exits, and flexible baseline hazard dynamics.\u003c/p\u003e\u003cp\u003eFrom a policy perspective, our results highlight critical timing for reintegration, most notably a pronounced hazard peak in the first weeks after registration, and substantial heterogeneity by disability profile, age, exit history, and region. This granularity helps identify windows of opportunity and profiles for which early activation, benefit conditionalities, and matching interventions can be targeted more effectively, with attention to regional differences.\u003c/p\u003e\u003cp\u003eTaken together, our analysis provides an integrated assessment of labour market reintegration among people with disabilities, combining detailed administrative micro-data with a multi-model robustness strategy.\u003c/p\u003e\u003cp\u003eThe remainder of the paper is organised as follows. Section 2 describes the data and variables used in the analysis. Section 3 presents the methodological approach, model specifications and empirical results for each class of models and provides a multi-model synthesis. Section 4 concludes and discusses policy implications and the main limitations of the study.\u003c/p\u003e"},{"header":"2. Data and Variables","content":"\u003cp\u003eThe empirical analysis is based on a dataset comprising 2,859 unemployment spells of people with disabilities who were registered as unemployed at the National Employment Agency between 1 January 2017 and 31 December 2020, and who exited unemployment during this period. For each individual, information is available on the start and end dates of the unemployment spell, as well as on age, an individual identifier (ID_person), type of disability, sex, educational attainment, occupation code (COR), occupation, county of residence, area of residence (urban/rural), benefit status (benefit recipient/non-recipient) and the reason for exit from unemployment.\u003c/p\u003e\u003cp\u003eThe data cleaning process involved excluding the following categories of records: individuals aged over 65 years, fully duplicated observations, records with negative unemployment durations or with zero duration and records for which, although the start and end dates of the unemployment spell were available, the reason for exit from unemployment was missing, this being essential information for our analysis. After data processing, the final dataset used in the analysis comprised 1,952 unemployment spells.\u003c/p\u003e\u003cp\u003eThe duration of unemployment, the dependent variable in this study, is calculated as the difference between the end date and the start date of the unemployment spell and is expressed in days. Because the data include individual identifiers (ID_person), it was possible to identify individuals with multiple unemployment spells and to reconstruct the cumulative unemployment experience for each individual.\u003c/p\u003e\u003cp\u003eNineteen distinct reasons for exiting unemployment were identified. Owing to the small number of observations for some of these reasons, they were aggregated into four categories for the econometric analysis: 1- Exit from unemployment through labour market reintegration, which includes the following reasons: taking up employment; taking up fixed-term employment of at most 12 months; and earning monthly income above the social reference indicator (ISR); 2-Exit from unemployment due to the expiry of the legal entitlement to unemployment benefit; 3- Exit from unemployment through transition into inactivity, which comprises the following reasons: admission to an educational programme; receipt of an invalidity pension for more than 12 months; leaving the country at the individual\u0026rsquo;s request for less than three months; becoming eligible for an old-age pension; leaving the country for more than three months; the start date of the invalidity pension; the period of child-rearing; and periods of temporary incapacity for work; 4-Exit from unemployment due to other reasons, namely: cancellation of the record by the case worker; failure to request the maintenance of jobseeker status; expiry of the deadlines for the resumption of benefit after suspension; failure to report to the employment agency to receive support; unjustified refusal of a job offer; unjustified refusal to participate in activation services; and closure of the record due to transfer.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the coding of the explanatory variables used in the econometric analysis. These aggregations balance substantive relevance with sample size considerations.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eExplanatory variables and their coding\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExplanatory variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisability type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative variable with the following categories: physical/locomotor, hearing, deafblindness, visual, psychiatric, somatic, intellectual, HIV/AIDS, rare diseases, associated, other. For the econometric analysis, these were grouped as follows:\u003c/p\u003e\u003cp\u003e1-physical/locomotor and somatic (physical disabilities);\u003c/p\u003e\u003cp\u003e2-intellectual and psychiatric (neuro-psychiatric disabilities);\u003c/p\u003e\u003cp\u003e3-hearing, visual and deafblindness (sensory disabilities);\u003c/p\u003e\u003cp\u003e4-other disabilities (other, associated, rare diseases, HIV/AIDS).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDummy variable: 0 - female; 1 - male.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContinuous variable (16\u0026ndash;64), recoded into the following categories: 16\u0026ndash;24; 25\u0026ndash;34; 35\u0026ndash;44; 45\u0026ndash;54; and 55\u0026ndash;64.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative variable coded as follows:\u003c/p\u003e\u003cp\u003e0-Unknown/no schooling (including missing values and records coded as no schooling or unknown primary education);\u003c/p\u003e\u003cp\u003e1-Primary or lower secondary education;\u003c/p\u003e\u003cp\u003e2-Vocational/professional education (apprenticeship, special education, vocational school);\u003c/p\u003e\u003cp\u003e3-Upper secondary education (general or special upper secondary); 4-Post-secondary non-tertiary education (colleges, foremen schools, post-secondary schools);\u003c/p\u003e\u003cp\u003e5-Tertiary education (short-cycle higher education, bachelor\u0026rsquo;s degree or master\u0026rsquo;s degree).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCounty of residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrouped by NUTS 2 regions: North-East; South-East; South-Muntenia; Bucharest-Ilfov; West; North-West; Centre; South-West Oltenia.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArea (urban/rural)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDummy variable: 0 - rural; 1-urban.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBenefit status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDummy variable: 0-non-benefit file (non-benefit recipients); 1-benefit file (unemployment benefit recipient).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupation and COR code\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInitially 354 distinct categories, grouped as follows:\u003c/p\u003e\u003cp\u003e1-Technical/industrial; 2 - Economic/administrative; 3-Medical/health;\u003c/p\u003e\u003cp\u003e4-Social/human services/education; 5-Commercial/services;\u003c/p\u003e\u003cp\u003e6-Textiles/manual/vocational; 7-IT/information technology;\u003c/p\u003e\u003cp\u003e8-Other/unknown.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003eTo analyse how disability type and other individual characteristics shape unemployment spells and the hazard of labour market reintegration, we estimate a Weibull proportional hazards model with individual-level shared frailty. The model is fitted in R 4.4.3 using the frailtyPenal() function from the frailtypack package with a gamma frailty term specified at the ID_person level. This choice is motivated by the structure of the data, which includes multiple unemployment spells per individual, implying intra-individual dependence in unemployment durations and a potential role for unobserved heterogeneity (e.g., latent health severity, motivation, family support or access to informal job-search networks). By incorporating an individual-level frailty term, we account for unobserved time-invariant differences across individuals and obtain more robust estimates of the covariate effects on the reintegration hazard. The Weibull baseline provides a parsimonious parametric representation of the reintegration process, allowing the hazard to increase or decrease over time. Results are reported as hazard ratios (HR), where HR\u0026thinsp;\u0026gt;\u0026thinsp;1 indicates a higher hazard of labour market reintegration, and HR\u0026thinsp;\u0026lt;\u0026thinsp;1 indicates a lower reintegration hazard relative to the reference category.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEffects of explanatory variables on the labour market reintegration hazard\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeuro-psychiatric disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.6106\u0026ndash;1.0601)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1222\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensory disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.4126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.5108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.1515\u0026ndash;1.9822)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther disabilities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.5596\u0026ndash;1.0402)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0871\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.0324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.8455\u0026ndash;1.2618)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7514\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u0026ndash;24 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;34 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.6470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.9097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.4828\u0026ndash;2.4596)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;44 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.4079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.5037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.1426\u0026ndash;1.9789)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;54 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.1175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.8057\u0026ndash;1.5698)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4899\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e55\u0026ndash;64 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.3323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.3386\u0026ndash;1.5194)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3856\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown/no schooling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.3952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.9336\u0026ndash;2.3611)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0950\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u0026thinsp;+\u0026thinsp;lower secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.5008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.6500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.0549\u0026ndash;2.5806)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0282\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVocational/professional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.2521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.8625\u0026ndash;1.9196)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2168\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper secondary (high school)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.0051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.6885\u0026ndash;1.4672)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9790\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-secondary non-tertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.0227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.5642\u0026ndash;1.8548)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9404\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary education\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBucharest-Ilfov\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.7850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0814\u0026ndash;0.3460)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentre\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2524\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.5595\u0026ndash;1.5049)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7334\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth-East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.5823\u0026ndash;1.3624)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5936\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth-West\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.5992\u0026ndash;1.3752)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6479\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth-East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.2073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.7622\u0026ndash;1.9859)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3962\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth-Muntenia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.7917\u0026ndash;1.8915)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3634\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.4772\u0026ndash;1.4025)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4654\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth-West Oltenia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.6847\u0026ndash;1.0355)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-benefit recipient\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBenefit recipient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.4355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.0238\u0026ndash;0.0435)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnical/Industrial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEconomic/Administrative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.2773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.3196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.6931\u0026ndash;2.5121)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3986\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical/Health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.2119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.7261\u0026ndash;2.1041)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4350\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial/Human Services/Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.6596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.9340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.0284\u0026ndash;3.6370)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0407\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommercial/Services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.0912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.7391\u0026ndash;1.6238)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6496\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTextiles/Manual/Vocational\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.5577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.7466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.1987\u0026ndash;2.5451)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIT/Information Technology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.0520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.7573\u0026ndash;1.4652)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7576\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther/Unknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.3825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.0359\u0026ndash;2.0744)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0308\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSource: Author\u0026rsquo;s calculations using R 4.4.3 and the frailtypack package\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, several factors are significantly associated with the hazard of labour market reintegration among unemployed people with disabilities. Regarding disability type, individuals with sensory disabilities have a significantly higher reintegration hazard relative to the physical-disability reference category (HR\u0026thinsp;=\u0026thinsp;1.5108, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0029), suggesting faster transitions out of unemployment once socio-demographic, educational, regional and occupational characteristics are controlled for. By contrast, neuro-psychiatric disabilities (HR\u0026thinsp;=\u0026thinsp;0.8045) and the \u0026ldquo;other disabilities\u0026rdquo; group (HR\u0026thinsp;=\u0026thinsp;0.7629) are associated with lower reintegration hazards compared with physical disabilities, although these effects are not statistically significant at the 5% level. The marginal \u003cem\u003ep\u003c/em\u003e-value for the \u0026ldquo;other disabilities\u0026rdquo; group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0871) motivates an additional specification that isolates the effect of disability type.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows no statistically significant differences in the reintegration hazard between men and women (HR\u0026thinsp;=\u0026thinsp;1.0329, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.7514). Age, by contrast, plays an important role. Individuals aged 25\u0026ndash;34 and 35\u0026ndash;44 exhibit significantly higher reintegration hazards relative to the 16\u0026ndash;24 age group (HR\u0026thinsp;=\u0026thinsp;1.9097 and HR\u0026thinsp;=\u0026thinsp;1.5037, respectively), indicating faster exits from unemployment for these age groups. For older categories (45\u0026ndash;54 and 55\u0026ndash;64), the estimated hazards do not differ significantly from those of the youngest group.\u003c/p\u003e\u003cp\u003eTurning to educational attainment, the \u0026ldquo;primary\u0026thinsp;+\u0026thinsp;lower secondary\u0026rdquo; category shows a significantly higher reintegration hazard relative to tertiary education (HR\u0026thinsp;=\u0026thinsp;1.6500, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0282), while the remaining education categories do not display robust differences from the tertiary reference group. This pattern may reflect faster entry into lower-skill segments with higher turnover, rather than superior job prospects per se.\u003c/p\u003e\u003cp\u003eThe regional estimates point to notable geographical heterogeneity. Individuals residing in the Bucharest-Ilfov region show a substantially lower reintegration hazard relative to the reference region (South-West Oltenia) (HR\u0026thinsp;=\u0026thinsp;0.1678, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This counterintuitive pattern may reflect compositional differences in the registered unemployed population, differential access to disability-adapted jobs, or region-specific institutional practices. For the remaining NUTS 2 regions, the hazard ratios are not statistically different, indicating broadly similar reintegration dynamics to those observed in South-West Oltenia.\u003c/p\u003e\u003cp\u003eWe have a particularly strong result regarding unemployment benefit receipt. Benefit recipients display a dramatically lower reintegration hazard than non-benefit recipients (HR\u0026thinsp;=\u0026thinsp;0.0322, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating substantially slower exits from unemployment. Any causal interpretation should be treated with caution, as benefit eligibility reflects prior employment histories and may capture selection and institutional mechanisms rather than the pure behavioural effect of benefits.\u003c/p\u003e\u003cp\u003eThe occupational profile of unemployed persons with disabilities also matters. Relative to the Technical/Industrial reference category, reintegration hazards are significantly higher in Social/Human Services/Education (HR\u0026thinsp;=\u0026thinsp;1.9340, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0407), Textiles/Manual/Vocational (HR\u0026thinsp;=\u0026thinsp;1.7466, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0037), and the \u0026ldquo;Other/Unknown\u0026rdquo; category (HR\u0026thinsp;=\u0026thinsp;1.4659, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0308). The remaining occupational groups do not differ significantly from the reference. These patterns may capture differences in the structure of labour demand, the availability of suitable positions, and the match between functional limitations and job requirements across occupational fields.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the results of a univariate Weibull proportional hazards model with individual-level shared frailty, estimated using the same frailtyPenal() function and the same gamma frailty specification at the ID_person level as in the baseline model. Disability type is the only predictor in this model. The results indicate that individuals with neuro-psychiatric disabilities have a significantly lower hazard of labour market reintegration than those with physical disabilities (HR\u0026thinsp;=\u0026thinsp;0.632, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For sensory disabilities and other disabilities, the estimated hazard ratios are not statistically significant in this univariate specification.\u003c/p\u003e\u003cp\u003eThe analysis of the isolated effect of disability type shows a clear disadvantage for individuals with neuro-psychiatric disabilities when disability is entered as the sole predictor. However, once additional covariates such as age, sex, educational attainment, region of residence, benefit status and occupation are controlled for in the multivariate frailty model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), this effect becomes weaker and statistically insignificant. This pattern suggests that part of the observed univariate disadvantage for neuro-psychiatric disabilities may reflect differences in correlated demographic, educational and occupational profiles. By contrast, the positive association for sensory disabilities becomes apparent only after adjustment, indicating that compositional differences may mask their conditional reintegration advantage in the univariate model.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate effect of disability type on the hazard of labour market reintegration\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeuro-psychiatric disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.4859\u0026ndash;0.8222)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensory disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.8141\u0026ndash;1.4018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.634\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther disabilities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.5734\u0026ndash;1.0578)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.110\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSource: Author\u0026rsquo;s calculations using R 4.4.3 and the frailtypack package.\u003c/p\u003e\u003cp\u003eIn addition to the baseline Weibull proportional hazards frailty model, we analysed potential effect heterogeneity of disability type across key individual characteristics. We estimated a set of Weibull proportional hazards frailty models that included interaction terms between disability type and sex, age group, educational attainment, and unemployment benefit status. These models rely on the same individual-level gamma frailty structure (ID_person). For interpretability, we also report time ratios (TR) derived from the Weibull parameterization, which provide a complementary perspective on the interaction effects. TR\u0026thinsp;\u0026lt;\u0026thinsp;1 indicates a shorter time to reintegration, whereas TR\u0026thinsp;\u0026gt;\u0026thinsp;1 indicates a longer time to reintegration, relative to the reference category.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of interaction effects between disability type and individual characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTested interaction\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInteraction effects (TR [95% CI], \u003cem\u003ep\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSummary of results\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisability \u0026times; Sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeuro-psychiatric \u0026times; male: TR\u0026thinsp;=\u0026thinsp;1.30 [0.79\u0026ndash;2.13], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.310; Sensory \u0026times; male: TR\u0026thinsp;=\u0026thinsp;1.13 [0.68\u0026ndash;1.89], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.639; Other \u0026times; male: TR\u0026thinsp;=\u0026thinsp;0.84 [0.46\u0026ndash;1.53], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.568.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo statistically significant interaction. The effect of disability type is similar for women and men.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisability \u0026times; Age group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeuro-psychiatric \u0026times; 25\u0026ndash;34: TR\u0026thinsp;=\u0026thinsp;0.57 [0.30\u0026ndash;1.06], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.074; Sensory \u0026times; 25\u0026ndash;34: TR\u0026thinsp;=\u0026thinsp;0.59 [0.31\u0026ndash;1.14], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.120; Other \u0026times; 25\u0026ndash;34: TR\u0026thinsp;=\u0026thinsp;0.58 [0.26\u0026ndash;1.26], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.168; Neuro-psychiatric \u0026times; 35\u0026ndash;44: TR\u0026thinsp;=\u0026thinsp;0.37 [0.18\u0026ndash;0.74], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005; Sensory \u0026times; 35\u0026ndash;44: TR\u0026thinsp;=\u0026thinsp;0.63 [0.31\u0026ndash;1.28], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.201; Other \u0026times; 35\u0026ndash;44: TR\u0026thinsp;=\u0026thinsp;0.49 [0.22\u0026ndash;1.10], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.085; Neuro-psychiatric \u0026times; 45\u0026ndash;54: TR\u0026thinsp;=\u0026thinsp;0.28 [0.10\u0026ndash;0.81], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019; Sensory \u0026times; 45\u0026ndash;54: TR\u0026thinsp;=\u0026thinsp;0.81 [0.36\u0026ndash;1.78], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.593; Other \u0026times; 45\u0026ndash;54: TR\u0026thinsp;=\u0026thinsp;0.71 [0.27\u0026ndash;1.85], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.477; Neuro-psychiatric \u0026times; 55\u0026ndash;64: TR\u0026thinsp;=\u0026thinsp;0.13 [0.01\u0026ndash;1.58], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.108; Sensory \u0026times; 55\u0026ndash;64: TR\u0026thinsp;=\u0026thinsp;1.23 [0.18\u0026ndash;8.32], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.831; Other \u0026times; 55\u0026ndash;64: TR\u0026thinsp;=\u0026thinsp;0.32 [0.04\u0026ndash;2.35], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.265.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStatistically significant interactions: Neuro-psychiatric \u0026times; 35\u0026ndash;44; Neuro-psychiatric \u0026times; 45\u0026ndash;54.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisability \u0026times; Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeuro-psychiatric \u0026times; unknown/no schooling: TR\u0026thinsp;=\u0026thinsp;1.52 [0.41\u0026ndash;5.72], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.532; Sensory \u0026times; unknown/no schooling: TR\u0026thinsp;=\u0026thinsp;1.08 [0.36\u0026ndash;3.26], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.885; Other \u0026times; unknown/no schooling: TR\u0026thinsp;=\u0026thinsp;1.06 [0.33\u0026ndash;3.44], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.923; Neuro-psychiatric \u0026times; primary\u0026thinsp;+\u0026thinsp;lower secondary: TR\u0026thinsp;=\u0026thinsp;2.88 [0.81\u0026ndash;10.18], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.101; Sensory \u0026times; primary\u0026thinsp;+\u0026thinsp;lower secondary: TR\u0026thinsp;=\u0026thinsp;1.19 [0.40\u0026ndash;3.54], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.761; Other \u0026times; primary\u0026thinsp;+\u0026thinsp;lower secondary: TR\u0026thinsp;=\u0026thinsp;0.77 [0.27\u0026ndash;2.24], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.634; Neuro-psychiatric \u0026times; vocational/professional: TR\u0026thinsp;=\u0026thinsp;4.29 [1.25\u0026ndash;14.71], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021; Sensory \u0026times; vocational/professional: TR\u0026thinsp;=\u0026thinsp;1.58 [0.65\u0026ndash;3.83], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.310; Other \u0026times; vocational/professional: TR\u0026thinsp;=\u0026thinsp;2.62 [1.02\u0026ndash;6.72], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046; Neuro-psychiatric \u0026times; upper secondary: TR\u0026thinsp;=\u0026thinsp;3.43 [1.00-11.76], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.050; Sensory \u0026times; upper secondary: TR\u0026thinsp;=\u0026thinsp;1.57 [0.67\u0026ndash;3.66], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.299; Other \u0026times; upper secondary: TR\u0026thinsp;=\u0026thinsp;1.83 [0.77\u0026ndash;4.37], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.172; Neuro-psychiatric \u0026times; post-secondary non-tertiary: TR\u0026thinsp;=\u0026thinsp;0.69 [0.04\u0026ndash;10.91], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.789; Sensory \u0026times; post-secondary non-tertiary: TR\u0026thinsp;=\u0026thinsp;1.38 [0.36\u0026ndash;5.30], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.640; Other \u0026times; post-secondary non-tertiary: TR\u0026thinsp;=\u0026thinsp;1.97 [0.48\u0026ndash;8.08], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.348.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStatistically significant interactions: Neuro-psychiatric \u0026times; vocational/professional; Other \u0026times; vocational/professional; Neuro-psychiatric \u0026times; upper secondary.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisability \u0026times; Unemployment benefit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeuro-psychiatric \u0026times; benefit recipient: TR\u0026thinsp;=\u0026thinsp;1.31 [0.78\u0026ndash;2.20], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.305; Sensory \u0026times; benefit recipient: TR\u0026thinsp;=\u0026thinsp;1.00 [0.59\u0026ndash;1.69], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.987; Other \u0026times; benefit recipient: TR\u0026thinsp;=\u0026thinsp;1.29 [0.66\u0026ndash;2.51], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.460.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe effect of unemployment benefit receipt remains broadly constant across disability types (no significant interaction).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSource: Author\u0026rsquo;s calculations using R 4.4.3.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e suggests limited evidence of systematic effect heterogeneity by sex or unemployment benefit status, as none of the disability-by-sex or disability-by-benefit interaction terms reach conventional levels of statistical significance. By contrast, the disability-by-age models indicate significant interaction terms for neuro-psychiatric disabilities in the 35\u0026ndash;44 and 45\u0026ndash;54 age groups (TR\u0026thinsp;=\u0026thinsp;0.37 and TR\u0026thinsp;=\u0026thinsp;0.28, respectively), pointing to meaningful age-related heterogeneity in the association between neuro-psychiatric disability and the time to reintegration. The disability-by-education specification also reveals notable heterogeneity. Significant interaction terms for neuro-psychiatric disabilities among individuals with vocational/professional education (TR\u0026thinsp;=\u0026thinsp;4.29) and upper secondary education (TR\u0026thinsp;=\u0026thinsp;3.43), as well as for other disabilities among those with vocational/professional education (TR\u0026thinsp;=\u0026thinsp;2.62), indicate that the disability-related time to reintegration varies across educational strata. These results suggest that the influence of disability type on reintegration may be more strongly conditioned by age and education than by gender or benefit status.\u003c/p\u003e\u003cp\u003eTo assess the robustness of the baseline Weibull frailty results, we additionally estimate a semi-parametric Cox frailty model with individual-level random effects. By leaving the baseline hazard unspecified, this specification provides an independent check of the estimated covariate effects on labour market reintegration. We estimate the model using both a standard Cox frailty implementation and a penalised-spline frailty approach. While coefficient magnitudes and p-values differ slightly across estimators, the direction of effects for the main covariates is stable. In particular, the conditional advantage associated with sensory disabilities and the strong negative association of unemployment benefit receipt remain robust. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reports results from the penalised-spline specification.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobustness check. Cox frailty model with individual-level random effects (hazard ratios)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeuro-psychiatric disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.64\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensory disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.12\u0026ndash;1.77)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther disabilities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.59\u0026ndash;0.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.88\u0026ndash;1.22)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.71\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnical/Industrial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEconomic/Administrative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.71-2.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical/Health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.72\u0026ndash;1.72)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial/Human Services/Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.01\u0026ndash;2.88)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommercial/Services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.81\u0026ndash;1.55)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTextiles/Manual/Vocational\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.22\u0026ndash;2.28)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIT/Information Technology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.76\u0026ndash;1.31)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther/Unknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.06\u0026ndash;1.88)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth-East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1826\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.56\u0026ndash;1.14)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth-East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.73\u0026ndash;1.63)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth-Muntenia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.74\u0026ndash;1.53)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBucharest-Ilfov\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.6926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.10\u0026ndash;0.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.790\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.50\u0026ndash;1.24)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth-West\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.60\u0026ndash;1.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentre\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.56\u0026ndash;1.30)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth-West Oltenia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown/no schooling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1974\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.86\u0026ndash;1.86)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u0026thinsp;+\u0026thinsp;lower secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.96\u0026ndash;2.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVocational/professional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.86\u0026ndash;1.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.74\u0026ndash;1.38)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-secondary non-tertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.59\u0026ndash;1.57)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-benefit recipient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBenefit recipient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.6396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.059\u0026ndash;0.087)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u0026ndash;24 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;34 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.694\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.37\u0026ndash;2.09)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;44 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.390\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.11\u0026ndash;1.75)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;54 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.85\u0026ndash;1.48)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e55\u0026ndash;64 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.4546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.33\u0026ndash;1.22)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSource: Author\u0026rsquo;s calculations using R 4.4.3.\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, individuals with sensory disabilities have significantly higher reintegration hazard than those with physical disabilities, while individuals with other types of disabilities exhibit significantly slower reintegration. For neuro-psychiatric disabilities, the estimated effect is below one and only marginally significant. The 25\u0026ndash;34 and 35\u0026ndash;44 age groups are associated with a higher reintegration hazard relative to the 16\u0026ndash;24 reference group, and these differences are statistically significant. For individuals aged 45\u0026ndash;54 and 55\u0026ndash;64, the differences relative to the reference category are not statistically significant.\u003c/p\u003e\u003cp\u003eUnemployment benefit receipt has a strong negative effect on labour market reintegration (HR\u0026thinsp;=\u0026thinsp;0.071; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding is fully consistent with the baseline Weibull frailty results and strengthens the conclusion that benefit receipt is strongly associated with longer unemployment spells among persons with disabilities. No significant effects are identified for sex, area of residence (urban/rural) or educational attainment at the 5% level. Thus, after controlling for other covariates, these characteristics do not appear to play a decisive role in the pace of labour market reintegration for disabled unemployed individuals.\u003c/p\u003e\u003cp\u003eFrom a regional perspective, Bucharest-Ilfov shows a significantly lower reintegration hazard than South-West Oltenia (HR\u0026thinsp;=\u0026thinsp;0.18; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the remaining regions do not differ significantly from the reference category. Regarding occupation, reintegration is faster for individuals in Social/Human Services/Education (HR\u0026thinsp;=\u0026thinsp;1.71; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045), Textiles/Manual/Vocational (HR\u0026thinsp;=\u0026thinsp;1.67; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0015) and Other/Unknown occupations (HR\u0026thinsp;=\u0026thinsp;1.41; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019), compared with the Technical/Industrial reference group. The other occupational fields do not display statistically significant differences relative to the reference category.\u003c/p\u003e\u003cp\u003eThe baseline hazard plot shows how the instantaneous hazard of labour market reintegration changes as unemployment duration increases, after adjusting for covariates and individual frailty. A pronounced peak is observed during the first 30\u0026ndash;60 days of unemployment. In this interval, the probability of reintegration among persons with disabilities appears to be highest, suggesting the role of rapid placements and interventions concentrated at the onset of unemployment. Thereafter, the hazard declines markedly and remains low, with moderate oscillations around approximately 200, 600 and 1,000 days, which may correspond to institutional milestones or other dynamics of the job-search process.\u003c/p\u003e\u003cp\u003eIn the long run, the overall pattern is downward. After 1,200 days of unemployment, the hazard approaches zero, indicating that as unemployment duration increases, the likelihood of reintegration in the immediate subsequent period decreases substantially. Overall, the baseline hazard estimated with penalised splines is clearly non-monotonic, displaying variation throughout the unemployment spell and suggesting the existence of potential time windows for better calibrated interventions aimed at supporting the reintegration of persons with disabilities.\u003c/p\u003e\u003cp\u003eTo account explicitly for competing risks, we estimated a Fine-Gray subdistribution hazard model. We restricted the analysis to the first recorded unemployment spell for each individual, selected chronologically (1,609 spells). This restriction ensures a single-event structure per person and avoids dependence arising from recurrent spells. The Fine-Gray approach allows us to model the cumulative incidence of reintegration while accounting for competing exits from unemployment, such as transitions into inactivity or exits associated with the expiry of unemployment benefit entitlement. The results complement the Weibull proportional hazards frailty and Cox frailty estimates and further support the robustness of the main conclusions (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of the Fine-Gray model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003esHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeuro-psychiatric disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.675\u0026ndash;0.955)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensory disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026nbsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.147\u0026ndash;1.606)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther disabilities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.655\u0026ndash;0.967)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0661\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.903\u0026ndash;1.17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u0026ndash;24 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;34 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026nbsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.429\u0026ndash;1.965)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;44 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0941\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026nbsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.212\u0026ndash;1.753)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;54 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.02\u0026ndash;1.577)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e55\u0026ndash;64 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.501\u0026ndash;1.411)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown/no schooling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.827\u0026ndash;1.441)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u0026thinsp;+\u0026thinsp;lower secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.796\u0026ndash;1.415)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVocational/Professional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.775\u0026ndash;1.252)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.745\u0026ndash;1.184)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-secondary non-tertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.727\u0026ndash;1.428)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBucharest-Ilfov\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.1141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.206\u0026ndash;0.523)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentre\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.626-1.200)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth-East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.3294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.541\u0026ndash;0.956)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth-West\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.686\u0026ndash;1.175)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth-East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.697\u0026ndash;1.308)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth-Muntenia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1484\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.702\u0026ndash;1.257)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1742\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.603\u0026ndash;1.171)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth-West Oltenia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0715\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.813\u0026ndash;1.076)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnical/Industrial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEconomic/Administrative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.931\u0026ndash;1.958)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical/Health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.645\u0026ndash;1.261)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial/Human Services/Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.99\u0026ndash;2.041)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommercial/Services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.857\u0026ndash;1.403)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTextiles/Manual/Vocational\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026nbsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.242\u0026ndash;2.034)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIT/Information Technology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.791\u0026ndash;1.217)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther/Unknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(1.093\u0026ndash;1.691)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-benefit recipient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBenefit recipient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.7860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026nbsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.143\u0026ndash;0.197)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSource: Author\u0026rsquo;s calculations using R 4.4.3.\u003c/p\u003e\u003cp\u003eThe Fine-Gray subdistribution hazard estimates largely corroborate the main findings from the baseline Weibull frailty and Cox frailty models, confirming that the key patterns remain robust when competing risks are explicitly considered. Relative to physical disabilities, sensory disabilities are associated with a significantly higher subdistribution hazard of labour market reintegration (sHR\u0026thinsp;=\u0026thinsp;1.357, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas neuro-psychiatric disabilities (sHR\u0026thinsp;=\u0026thinsp;0.803, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) and other disabilities (sHR\u0026thinsp;=\u0026thinsp;0.796, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022) display significantly lower subdistribution hazards.\u003c/p\u003e\u003cp\u003eThe age gradient is consistent with earlier results: individuals aged 25\u0026ndash;34 (sHR\u0026thinsp;=\u0026thinsp;1.676, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 35\u0026ndash;44 (sHR\u0026thinsp;=\u0026thinsp;1.458, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and also those aged 45\u0026ndash;54 (sHR\u0026thinsp;=\u0026thinsp;1.268, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033), exhibit higher cumulative incidence of reintegration than the 16\u0026ndash;24 reference group, while the effect for the 55\u0026ndash;64 group is not statistically significant (sHR\u0026thinsp;=\u0026thinsp;0.841, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.512). Educational attainment does not emerge as a robust predictor of reintegration in the presence of competing risks.\u003c/p\u003e\u003cp\u003eRegionally, Bucharest-Ilfov again stands out with a markedly lower subdistribution hazard compared with South-West Oltenia (sHR\u0026thinsp;=\u0026thinsp;0.328, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In this competing-risks specification, North-East also shows a significantly lower reintegration hazard (sHR\u0026thinsp;=\u0026thinsp;0.719, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023), while the remaining regional differences are not statistically significant.\u003c/p\u003e\u003cp\u003eUnemployment benefit receipt continues to show a strong negative association with reintegration (sHR\u0026thinsp;=\u0026thinsp;0.168, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating substantially lower cumulative incidence of reintegration once the risks of alternative exits from unemployment are considered. Occupational patterns remain broadly consistent, with faster reintegration in Textiles/Manual/Vocational (sHR\u0026thinsp;=\u0026thinsp;1.589, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the Other/Unknown category (sHR\u0026thinsp;=\u0026thinsp;1.359, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), while most other occupational differences are not statistically significant. Overall, the competing-risks analysis reinforces the central conclusion that disability type, age and benefit status are the most consistent correlates of reintegration among unemployed persons with disabilities in Romania.\u003c/p\u003e\u003cp\u003eA Prentice-Williams-Peterson (PWP) model for recurrent events is used to evaluate whether the type of previous exit from unemployment predicts reintegration hazards in later spells.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of the PWP model estimation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eType of previous exit from unemployment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExpiry of legal entitlement to unemployment benefits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLabour market reintegration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.176\u0026ndash;2.529)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInactivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.4705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.010\u0026ndash;0.702)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0222\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther reasons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.403\u0026ndash;1.398)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3661\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eType of disability\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeuro-psychiatric disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.3502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.474\u0026ndash;1.047)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensory disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.512\u0026ndash;1.121)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1650\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther disabilities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.3588\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.429\u0026ndash;1.137)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1487\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.697\u0026ndash;1.192)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4973\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u0026ndash;24 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;34 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1959\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.706\u0026ndash;1.596)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7735\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;44 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.572\u0026ndash;1.345)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5479\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;54 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.878\u0026ndash;1.982)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1829\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e55\u0026ndash;64 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.2336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.029\u0026ndash;2.880)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2914\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown/no schooling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.6183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.028\u0026ndash;3.349)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0401\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary\u0026thinsp;+\u0026thinsp;lower secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.7839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.004\u0026ndash;4.775)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0487\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVocational/professional\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5540\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.014\u0026ndash;2.986)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0443\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.472\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.884\u0026ndash;2.450)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1371\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-secondary non-tertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.622\u0026ndash;2.463)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5431\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBucharest-Ilfov\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.8771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.007\u0026ndash;0.486)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0089\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentre\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.368\u0026ndash;1.581)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4667\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth-East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.633\u0026ndash;2.122)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6327\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth-West\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.478\u0026ndash;1.643)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth-East\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.551\u0026ndash;2.198)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7851\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth-Muntenia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.458\u0026ndash;1.736)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7359\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.498\u0026ndash;2.073)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9660\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth-West Oltenia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1595\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.567\u0026ndash;1.105)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1701\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-benefit recipient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBenefit recipient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.6471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.133\u0026ndash;0.279)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnical/Industrial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eReference category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEconomic/Administrative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.5508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.281\u0026ndash;1.183)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1331\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical/Health\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.546\u0026ndash;1.694)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8920\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial/Human Services/Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.257\u0026ndash;4.090)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9727\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommercial/Services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.557\u0026ndash;1.425)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6304\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTextiles/Manual/Vocational\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.639\u0026ndash;1.683)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8839\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIT/Information Technology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.781\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.496\u0026ndash;1.231)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2869\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther/Unknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.676\u0026ndash;1.625)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8354\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eSource: Author\u0026rsquo;s calculations using R 4.4.3\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the type of previous exit from unemployment is a strong predictor of labour market reintegration in subsequent spells. Relative to exits due to the expiry of legal unemployment benefit entitlement, individuals whose previous spell ended through labour market reintegration display a significantly higher reintegration hazard in the next spell (HR\u0026thinsp;=\u0026thinsp;1.725). By contrast, a prior transition into inactivity is associated with a markedly lower hazard of subsequent reintegration (HR\u0026thinsp;=\u0026thinsp;0.085). This pattern is consistent with strong state dependence. \u0026ldquo;Successful\u0026rdquo; previous exits appear to facilitate later returns to work, whereas inactivity may signal persistent or cumulative barriers to employment.\u003c/p\u003e\u003cp\u003eUnemployment benefit receipt remains a robust negative correlate of reintegration in recurrent spells (HR\u0026thinsp;=\u0026thinsp;0.193), in line with the baseline frailty and competing-risks results. Regional effects are generally muted, although Bucharest-Ilfov again stands out with a substantially lower hazard relative to the reference region. Disability-type coefficients are below one but do not reach conventional significance thresholds, suggesting that in the recurrent-event framework disability-related differences are partly absorbed by prior-exit history and benefit status. Finally, lower and intermediate educational levels show higher hazards than tertiary education, plausibly reflecting faster re-entry into lower-skill segments with higher turnover rather than superior employment prospects.\u003c/p\u003e\u003cp\u003eAs a next step, we relaxed the proportional hazards assumption for disability type and examined whether the effect of disability type on labour market reintegration varies across the unemployment spell. Given the wide range of the duration variable in days (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{min}=1,\\:{x}_{max}=1278\\)\u003c/span\u003e\u003c/span\u003e), we split the unemployment spell into three intervals: 1\u0026ndash;60 days, 61\u0026ndash;200 days, and 201\u0026ndash;600 days. We focus on intervals up to 600 days, as the number of spells exceeding this threshold is small, leading to imprecise estimates if modelled separately. This choice reflects a trade-off between capturing time heterogeneity and maintaining sufficient statistical precision.\u003c/p\u003e\u003cp\u003eUsing a start-stop specification, we interacted disability type with the interval indicators, while keeping the remaining covariate effects time-invariant. The resulting time-varying hazard ratios for disability type are reported in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. All estimates are adjusted for the full set of covariates and were obtained in R 4.4.3 using the coxph() function from the survival package, applied to the episode-split dataset.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTime-varying effects of disability type on labour market reintegration: piecewise Cox model results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of disability\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTime interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS.E.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeuro-psychiatric\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.2208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e(0.68\u0026ndash;0.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeuro-psychiatric\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61\u0026ndash;200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.1693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e(0.59\u0026ndash;1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeuro-psychiatric\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e201\u0026ndash;600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e(0.73\u0026ndash;3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e(1.07\u0026ndash;1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61\u0026ndash;200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e(0.77\u0026ndash;1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.505\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e201\u0026ndash;600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e(1.09\u0026ndash;4.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.2745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e(0.63\u0026ndash;0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61\u0026ndash;200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.0249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.2069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e(0.65\u0026ndash;1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.904\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e201\u0026ndash;600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.3538\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.4895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.702\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e(0.27\u0026ndash;1.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.470\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSource: Author\u0026rsquo;s calculations using R 4.4.3.\u003c/p\u003e\u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e show that the effect of disability type on labour market reintegration is not constant over the unemployment spell. For individuals with neuro-psychiatric disabilities, the hazard of exiting unemployment is significantly lower than for those with physical disabilities during the first 60 days (HR\u0026thinsp;=\u0026thinsp;0.802; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), indicating a clear early disadvantage. In the 61\u0026ndash;200 interval, the hazard ratio remains below unity (HR\u0026thinsp;=\u0026thinsp;0.844), but the effect is no longer statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.346). In the 201\u0026ndash;600 interval, the estimated hazard ratio rises above one (HR\u0026thinsp;=\u0026thinsp;1.576), yet the wide confidence interval (0.73\u0026ndash;3.40) and non-significant \u003cem\u003ep\u003c/em\u003e-value (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.247) prevent firm conclusions about a late-stage reversal of this disadvantage.\u003c/p\u003e\u003cp\u003eBy contrast, individuals with sensory disabilities display a persistent and eventually very pronounced advantage in terms of reintegration. In the first 60 days, their hazard ratio is 1.265 (p\u0026thinsp;=\u0026thinsp;0.006), pointing to a significantly higher probability of exiting unemployment compared to those with physical disabilities. The effect remains positive but statistically imprecise in the 61\u0026ndash;200 interval (HR\u0026thinsp;=\u0026thinsp;1.142, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.505). Over longer spells (201\u0026ndash;600 days), the hazard ratio increases to 2.123 and becomes statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027), suggesting that sensory disabilities are associated with substantially higher exit rates from medium-to long-term unemployment.\u003c/p\u003e\u003cp\u003eThe heterogeneous \u0026ldquo;other\u0026rdquo; disabilities category exhibits a different pattern. During the first 60 days, the hazard ratio is significantly below one (HR\u0026thinsp;=\u0026thinsp;0.760, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), indicating an early disadvantage relative to physical disabilities. In the 61\u0026ndash;200 interval, the hazard becomes almost indistinguishable from that of the reference group (HR\u0026thinsp;=\u0026thinsp;0.975, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.904), and in the 201\u0026ndash;600 interval it remains below one (HR\u0026thinsp;=\u0026thinsp;0.702), but with a wide confidence interval (0.27\u0026ndash;1.83) and a non-significant \u003cem\u003ep\u003c/em\u003e-value (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.470). Overall, these findings confirm that the impact of disability type on reintegration is strongest and most clearly identified in the early phase of unemployment, while the differences become more heterogeneous and less precisely estimated at later stages.\u003c/p\u003e\u003cp\u003eWhile Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e highlights time heterogeneity in the disability effect, Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e summarises the key predictors across model families. The broad consistency of signs and magnitudes supports the robustness of our main results.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative multi-model summary for the most important predictors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeibull PH frailty full\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWeibull PH frailty univariate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCox frailty (penalised spline)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFine-Gray\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePWP recurrent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeuro-psychiatric disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-19.55% (ns)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-36.79% (***)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-19.50% (*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-19.70% (**)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-29.50% (*)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensory disability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;51.08% (**)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;6.83% (ns)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;40.90% (**)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;35.70% (***)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-24.30% (ns)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther disabilities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-23.71% (*)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-22.12% (ns)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-23.90% (**)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-20.40% (**)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-30.20% (ns)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;34 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;90.97% (***)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;69.40% (***)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;67.60% (***)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u0026thinsp;6.20% (ns)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35\u0026ndash;44 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;50.37% (**)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;39.00% (**)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;45.80% (***)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-12.30% (ns)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;54 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;12.46% (ns)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;11.80% (ns)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;26.80% (**)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u0026thinsp;31.90% (ns)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployment benefit receipt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-96.78% (***)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-92.90% (***)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-83.20% (***)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-80.70% (***)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBucharest-Ilfov\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-83.22% (***)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-81.60% (***)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-67.20% (***)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-94.40% (**)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSource: Author\u0026rsquo;s calculations using R 4.4.3.\u003c/p\u003e\u003cp\u003eNegative percentages indicate a lower reintegration hazard relative to the reference category (ratio\u0026thinsp;\u0026lt;\u0026thinsp;1), while positive percentages indicate a higher reintegration hazard (ratio\u0026thinsp;\u0026gt;\u0026thinsp;1). Percentages are computed as (ratio-1)\u0026times;100. Fine-Gray percentages are derived from subdistribution hazard ratios (sHR). The other models report hazard ratios (HR).\u003c/p\u003e\u003cp\u003e(***) highly significant, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003cp\u003e(**) significant at the 5% level\u003c/p\u003e\u003cp\u003e(*) significant at the 10% level\u003c/p\u003e\u003cp\u003e(ns)\u0026thinsp;=\u0026thinsp;not statistically significant\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows substantial convergence across model specifications regarding the most important predictors of labour market reintegration. The reported percentages summarise the approximate relative change in the relevant hazard ratio compared with the reference category. In the Fine-Gray model, they refer to changes in the subdistribution hazard. Because the Fine-Gray and PWP frameworks target different risk structures, magnitudes are not directly comparable, but the direction and ranking of effects remain informative.\u003c/p\u003e\u003cp\u003eWith respect to disability type, sensory disability is associated with faster reintegration in most multivariate hazard-based models, with effect sizes of about\u0026thinsp;+\u0026thinsp;51% in the Weibull PH frailty full model, +\u0026thinsp;41% in the Cox frailty (penalised spline) model, and +\u0026thinsp;35.7% in the Fine-Gray specification. The main exceptions are the Weibull PH frailty univariate model, where the point estimate is small and insignificant, and the PWP recurrent model, which yields a negative but non-significant effect (-24.3%). This pattern suggests that the advantage associated with sensory disabilities becomes clearer once additional covariates are controlled for and when the analysis is not conditioned on event order.\u003c/p\u003e\u003cp\u003eOther disabilities are more systematically linked to slower reintegration. The estimated disadvantage is relatively stable across the Cox frailty and Fine-Gray specifications (around \u0026minus;\u0026thinsp;20% to -24%) and remains statistically significant in most multivariate models, indicating a robust negative association compared with physical disabilities. For neuro-psychiatric disabilities, the results indicate a moderate disadvantage of approximately \u0026minus;\u0026thinsp;19% in the multivariate hazard-based models. However, the strongest and clearest evidence emerges in the univariate Weibull PH frailty model (-36.79%), while adjusted specifications yield weaker or only marginally significant estimates. This is consistent with the idea that part of the raw disadvantage for neuro-psychiatric disabilities may be mediated by correlated demographic, educational, and occupational factors.\u003c/p\u003e\u003cp\u003eAge effects reveal a clear advantage for the 25\u0026ndash;34 age group, with estimates of about\u0026thinsp;+\u0026thinsp;91% in the Weibull PH frailty full model and around +\u0026thinsp;68% to +\u0026thinsp;69% in both the Cox frailty and Fine-Gray models. A consistent advantage is also observed for the 35\u0026ndash;44 group, ranging from roughly\u0026thinsp;+\u0026thinsp;39% in the Cox frailty model to about\u0026thinsp;+\u0026thinsp;50% in the Weibull full model and +\u0026thinsp;45.8% in the Fine-Gray specification. For the 45\u0026ndash;54 group, effects are smaller and do not indicate a robust advantage across specifications. The absence of age estimates in the univariate column reflects the single-predictor design of that model, while the muted age effects in the PWP framework are expected given event-order conditioning and the role of prior-exit history.\u003c/p\u003e\u003cp\u003eThe effect of unemployment benefit receipt is large, negative, and remarkably stable across specifications (approximately \u0026minus;\u0026thinsp;97% in the Weibull PH frailty full model, -93% in the Cox frailty model, \u0026minus;\u0026thinsp;83% in the Fine-Gray model, and \u0026minus;\u0026thinsp;81% in the PWP specification), indicating a strong and persistent association between benefit receipt and slower reintegration.\u003c/p\u003e\u003cp\u003eFinally, Bucharest-Ilfov shows a pronounced disadvantage across all models, with estimated reductions in the reintegration hazard ranging from about \u0026minus;\u0026thinsp;67.2% in the Fine-Gray model to\u003c/p\u003e\u003cp\u003e-94.4% in the PWP recurrent specification relative to the reference region. Overall, the consistency in effect directions and the relative stability of the main estimates strengthen confidence in the robustness of our results on the determinants of reintegration among unemployed persons with disabilities in Romania.\u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe aim of this study is to analyse how disability type and other individual characteristics shape unemployment spells and labour market reintegration among people with disabilities in Romania. Using administrative data, we examined reintegration dynamics with several complementary duration frameworks (Weibull and Cox frailty, Fine-Gray competing risks, PWP recurrent events, and a piecewise Cox model). This multi-model approach yields a consistent picture of reintegration among unemployed persons with disabilities in Romania.\u003c/p\u003e\u003cp\u003eThe empirical results point to several consistent patterns. Disability type clearly structures reintegration prospects. Across parametric, semi-parametric and competing-risks specifications, individuals with sensory disabilities generally exhibit higher hazards (or subdistribution hazards) of labour market reintegration than those with physical disabilities, with the advantage most visible in medium-to longer-duration spells. By contrast, neuro-psychiatric disabilities and the heterogeneous \u0026ldquo;other disabilities\u0026rdquo; group tend to be associated with lower reintegration hazards, though statistical significance varies across models. The univariate frailty model indicates a pronounced raw disadvantage for neuro-psychiatric disabilities, which attenuates after adjustment for age, education, region, benefit status and occupation, suggesting that part of this gap reflects correlated socio-demographic and occupational differences.\u003c/p\u003e\u003cp\u003eAge is a robust predictor of reintegration. Across the Weibull frailty, Cox frailty and Fine-Gray models, individuals aged 25\u0026ndash;34 and 35\u0026ndash;44 show significantly higher hazards of re-employment than those aged 16\u0026ndash;24. In the competing-risks specification, a similar advantage is also observed for the 45\u0026ndash;54 group. The 55\u0026ndash;64 group does not differ significantly from the youngest cohort. Interaction results indicate that the effect of neuro-psychiatric disability varies by age, with particularly long reintegration times in several prime-age categories.\u003c/p\u003e\u003cp\u003eUnemployment benefit receipt is a remarkably stable and strong predictor of slower reintegration. In all hazard-based models, benefit recipients display dramatically lower hazards or subdistribution hazards of labour market reintegration than non-recipients, with estimated reductions ranging from about 80% to more than 95%. While caution is needed in drawing causal conclusions, benefit eligibility and duration reflect prior employment histories, institutional rules and selection processes. This consistent association suggests that benefit recipients face more persistent and severe barriers to re-employment, which standard activation tools may not fully offset.\u003c/p\u003e\u003cp\u003eRegional and occupational patterns matter. An important result is that registered unemployed persons with disabilities in Bucharest-Ilfov show significantly lower reintegration hazards than those in the South-West Oltenia reference region, a result that is robust across Weibull, Cox and Fine-Gray models and also appears, in amplified form, in the PWP recurrent-event framework. This may reflect a combination of compositional factors, mismatches between disability profiles and the structure of labour demand, or institutional practices specific to the capital region. Occupation-wise, individuals with disabilities in Textiles/Manual/Vocational and in Social/Human Services/Education tend to exhibit higher reintegration hazards than those in Technical/Industrial occupations, indicating that lower-and mid-skill segments with higher turnover may offer more frequent re-entry opportunities, albeit potentially at the cost of job quality and stability.\u003c/p\u003e\u003cp\u003eThe baseline hazard estimates highlight important temporal patterns. The Cox frailty model with penalised splines reveals a strongly non-monotonic baseline hazard of reintegration, with a pronounced peak in the first 30\u0026ndash;60 days of unemployment, followed by a decline and moderate oscillations around longer durations. This pattern is consistent with rapid early placements, diminishing prospects as spells lengthen, and sporadic reintegration opportunities linked to institutional milestones (e.g. completion of training programmes). The piecewise Cox model with time-varying coefficients for disability type confirms that disability-related gaps are most clearly identified in the early phase of the unemployment spell: neuro-psychiatric and \u0026ldquo;other\u0026rdquo; disabilities are associated with significantly lower hazards than physical disabilities in the first 60 days, while sensory disabilities already enjoy an advantage that becomes particularly pronounced for spells lasting between 201 and 600 days.\u003c/p\u003e\u003cp\u003eThese findings have several policy implications. The early peak in the baseline hazard and the strong early disadvantages for neuro-psychiatric and \u0026ldquo;other\u0026rdquo; disabilities suggest that the first months of unemployment constitute a critical window for intervention. Tailored support, such as specialised counselling, intensive job-search assistance, mental health support, workplace accommodation advice and closer coordination between employment services and social/health services, may be especially effective if deployed shortly after registration. The robust and large negative association between benefit receipt and reintegration points to the need for carefully designed activation and conditionality mechanisms that encourage job search and facilitate placement, while avoiding undue pressure or penalisation of individuals facing genuine health-related constraints.\u003c/p\u003e\u003cp\u003eThe results also highlight the importance of taking heterogeneity by age, education and occupation seriously when designing interventions. For example, the interaction patterns suggest that neuro-psychiatric disabilities combined with certain educational levels (vocational/professional and upper secondary) are associated with particularly long reintegration times, indicating that standard programmes may not adequately address the combined barriers of skill profiles and mental health conditions. Regionally, the persistent disadvantage observed in Bucharest-Ilfov and, in some specifications, in the North-East region suggests that one-size-fits-all policies are unlikely to be sufficient: region-specific strategies that address local labour demand, employer attitudes, accessibility of workplaces and coordination among institutions are needed.\u003c/p\u003e\u003cp\u003eThis study has several limitations that should be acknowledged. The analysis focuses on registered unemployed individuals with disabilities who exit unemployment and thus cannot capture the situation of those who remain long-term registered, who never register, or who are in informal or precarious employment; the administrative data do not include detailed information on disability severity, functional limitations, workplace accommodations, discrimination, or job quality after reintegration, all of which may mediate the estimated relationships; the observational nature of the data and the absence of strong instruments mean that the estimated associations cannot be straightforwardly interpreted as causal effects; and finally, some subgroup analyses, especially those based on interactions or long-duration intervals, are based on relatively small numbers of spells, leading to wide confidence intervals and limited statistical power. This is a limitation of the study, but it also points to a clear direction for future research on disability severity, workplace accommodations and post-reintegration job quality.\u003c/p\u003e\u003cp\u003eDespite these limitations, the convergence of results across multiple modelling strategies, parametric, semi-parametric, competing-risks and recurrent-event frameworks, provides strong evidence that disability type, age and benefit status are the most consistent predictors of re-employment among unemployed persons with disabilities in Romania. Future research could build on this work by incorporating richer information on disability severity and workplace adaptations, exploring job quality and sustainability after reintegration, and evaluating specific programmes or policy reforms using quasi-experimental designs. From a policy perspective, the evidence presented here underscores the need for early, targeted and disability-sensitive labour market interventions that recognise the diversity of trajectories and barriers faced by people with disabilities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research received no funding.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eDuring manuscript preparation, the author used two generative AI assistants, ChatGPT 5.1 (OpenAI) and Gemini 3 Pro (Google), for R coding assistance, for troubleshooting scripts during the model estimation stage, and for assistance with language clarity and manuscript refinement. Data analysis, results validation and interpretation were performed by the author. All code and results were verified and are the sole responsibility of the author.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e\u003cp\u003eThe dataset used in this study is available from the corresponding author upon reasonable request. The dataset is not publicly archived because it contains pseudonymised unique identifiers (ID_person), and its disclosure would contravene the EU General Data Protection Regulation (GDPR) and institutional privacy policies.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAddison JT, Portugal P (2003) Unemployment Duration: Competing and Defective Risks. 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World Bank, Bucharest\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"s, J64","lastPublishedDoi":"10.21203/rs.3.rs-8302059/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8302059/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe aim of this study is to analyse how disability type and other individual characteristics shape unemployment spells and labour market reintegration among people with disabilities in Romania. Using administrative micro-data from the National Employment Agency for 2017\u0026ndash;2020, we examine 1,952 unemployment spells and apply a multi-model duration strategy that accounts for unobserved heterogeneity, competing exits and recurrent unemployment. The results show that disability type, age and unemployment benefit receipt are key correlates of reintegration. Sensory disabilities are associated with higher reintegration hazards than physical disabilities, with the advantage clearer in medium-to longer-duration spells. Neuro-psychiatric disabilities tend to display lower hazards, especially early in unemployment. Benefit receipt shows a strong and stable negative association with reintegration across specifications, while individuals aged 25\u0026ndash;34 and 35\u0026ndash;44 reintegrate faster than the youngest group. These findings underscore the importance of early, disability-sensitive activation and counselling, as well as regionally tailored policy responses aimed at improving sustainable employment outcomes for disabled jobseekers.\u003c/p\u003e","manuscriptTitle":"Disability Type and Prior Unemployment Exits as Predictors of Labour Market Reintegration among People with Disabilities: Evidence from Romania","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-09 12:57:14","doi":"10.21203/rs.3.rs-8302059/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cdfdd5b5-6ac7-4a87-ac1f-c474b85bd7bd","owner":[],"postedDate":"December 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59233748,"name":"Econometrics"},{"id":59233749,"name":"Other Economics"}],"tags":[],"updatedAt":"2025-12-09T12:57:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-09 12:57:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8302059","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8302059","identity":"rs-8302059","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}