Graduate Employability in the Western Balkans: A Career Ecosystem Perspective on Labour Market Inequalities

Graduate Employability in the Western Balkans: A Career Ecosystem Perspective on Labour Market Inequalities | 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 Graduate Employability in the Western Balkans: A Career Ecosystem Perspective on Labour Market Inequalities Elvisa Drishti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6189145/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 Purpose: This study examines inequality of opportunity (IOp) in university-to-work transitions (UTWT) and employment outcomes in the Western Balkans Six (WB6), a region facing high graduate unemployment, systemic labour market inequalities, and weak institutional coordination. Grounded in the Sustainable Career Ecosystem (SCE) framework, which integrates Career Ecosystem Theory (CET), Sustainable Career Theory (SCT), and IOp theory, this study explores how systemic, institutional, and individual factors shape employability and career sustainability. Design/Methodology/Approach: Using a two-stage quantitative analysis with 2019–2021 Regional Cooperation Council survey data, Stage 1 employs OLS regression to estimate IOp in UTWT (time-to-first-job), while Stage 2 applies logistic regression to assess the impact of IOp and career ecosystem factors on employment outcomes (job satisfaction, job security, perceived job opportunities). Findings: Findings reveal significant disparities based on gender, rural background, and socio-economic status, with higher IOp linked to poorer employment outcomes. While skills mismatches and informal hiring mechanisms hinder career sustainability, social capital, public sector employment, and perceptions of government job protection improve employment outcomes. Research limitations/Implications: The study is limited by cross-sectional data and the self-reported nature of socio-economic measures. Future research should employ longitudinal data and qualitative approaches to better assess the long-term sustainability of career transitions. Originality/Value: This study provides a multi-level analysis of graduate employability in an underrepresented region, offering insights for policy, universities, and employers. It contributes to the advancement of Sustainable Career Ecosystem theory by examining how structural inequalities shape graduate employability and long-term career sustainability. The findings align with UN Sustainable Development Goals (SDGs) 4 (Quality Education), 8 (Decent Work), and 10 (Reduced Inequalities), advocating for targeted policy interventions that promote equitable and sustainable career pathways. Purpose: This study examines inequality of opportunity (IOp) in university-to-work transitions (UTWT) and employment outcomes in the Western Balkans Six (WB6), a region facing high graduate unemployment, systemic labour market inequalities, and weak institutional coordination. Grounded in the Sustainable Career Ecosystem (SCE) framework, which integrates Career Ecosystem Theory (CET), Sustainable Career Theory (SCT), and IOp theory, this study explores how systemic, institutional, and individual factors shape employability and career sustainability. Design/Methodology/Approach: Using a two-stage quantitative analysis with 2019–2021 Regional Cooperation Council survey data, Stage 1 employs OLS regression to estimate IOp in UTWT (time-to-first-job), while Stage 2 applies logistic regression to assess the impact of IOp and career ecosystem factors on employment outcomes (job satisfaction, job security, perceived job opportunities). Findings: Findings reveal significant disparities based on gender, rural background, and socio-economic status, with higher IOp linked to poorer employment outcomes. While skills mismatches and informal hiring mechanisms hinder career sustainability, social capital, public sector employment, and perceptions of government job protection improve employment outcomes. Research limitations/Implications: The study is limited by cross-sectional data and the self-reported nature of socio-economic measures. Future research should employ longitudinal data and qualitative approaches to better assess the long-term sustainability of career transitions. Originality/Value: This study provides a multi-level analysis of graduate employability in an underrepresented region, offering insights for policy, universities, and employers. It contributes to the advancement of Sustainable Career Ecosystem theory by examining how structural inequalities shape graduate employability and long-term career sustainability. The findings align with UN Sustainable Development Goals (SDGs) 4 (Quality Education), 8 (Decent Work), and 10 (Reduced Inequalities), advocating for targeted policy interventions that promote equitable and sustainable career pathways. Other Economics Sustainable Career Ecosystem Inequality of Opportunity University-to-Work Transition Graduate Employability Western Balkans 1. Introduction The transition from university to work (UTWT) is a critical phase in a graduate’s career, shaping long-term employability, career sustainability, and economic mobility (Bartlett et al., 2016 ; Bathmaker, 2021 ; Donald et al., 2021 ). Traditionally, employability research has focused on human capital development and individual agency, emphasizing skills acquisition, job search strategies, and personal adaptability (Izzo et al., 2022 ; Pastore and Zimmermann, 2019 ). However, emerging perspectives highlight the role of career ecosystems—a broader, interconnected framework that considers how graduates, educational institutions, employers, labour markets, and policies interact to shape employment trajectories (Baruch, 2015 ; Baruch and Rousseau, 2019 ; Dlouhy et al., 2024 ). Within this context, the sustainable career ecosystem (SCE) framework (Donald, 2023 ; Donald et al., 2024 ) provides a multi-level, dynamic approach to understanding how UTWT processes unfold over time, influenced by institutional structures, policy interventions, and socio-economic conditions. A sustainable UTWT process depends not only on a graduate’s capabilities but also on how effectively the career ecosystem fosters employability, facilitates equitable access to work, and ensures long-term career sustainability. In the Western Balkans 6 (WB6)—which includes Albania, Bosnia and Herzegovina, Kosovo, Montenegro, North Macedonia, and Serbia—graduate employability remains a persistent challenge. Despite rising levels of tertiary education attainment, the region faces some of the highest youth unemployment rates in Europe, widespread skills mismatches, and weak institutional coordination (Bartlett and Oruc, 2021 ). Underemployment and informal hiring mechanisms are prevalent, contributing to economic stagnation, brain drain, and long-term career instability. The COVID-19 pandemic has further exacerbated employment inequalities in the WB6, disrupting economic activity, reducing job opportunities, and exposing structural weaknesses in UTWT. Graduates from lower socio-economic backgrounds, rural areas, and marginalized groups faced prolonged job searches, lower employment stability, and increased reliance on informal hiring networks. The SCE perspective is particularly relevant in this context, as it allows for an examination of multi-level disruptions and the long-term consequences of early-career disadvantage. While past research has focused on individual-level determinants of employability, there is a critical need to examine systemic and institutional factors that influence UTWT outcomes. This study addresses this gap through the lenses of the SCE framework – which integrates Career Ecosystem Theory (CET) and Sustainable Career Theory (SCT) – and Inequality of Opportunity (IOp) theory. This multi-theoretical approach provides a comprehensive lens to analyse the barriers and enablers of graduate employability in the WB6. CET conceptualizes career transitions as interdependent processes, where individuals, organizations, and institutions interact to shape employment pathways (Baruch, 2015 ; Baruch and Rousseau, 2019 ). SCT highlights the importance of long-term career sustainability, emphasizing how adaptability, institutional support, and policy frameworks impact graduates’ career trajectories (De Vos et al., 2020 ). The SCE framework emphasizes how career ecosystems function over time and across individual, organizational, and policy levels. Lastly, IOp theory provides a normative foundation to assess fairness in employment access, distinguishing between inequalities arising from individual effort and those rooted in systemic barriers (Ferreira and Peragine, 2016 ). This study has four primary objectives. First, it seeks to measure the extent of IOp in UTWT, estimated as the circumstance effect on time-to-first-job, as a measure of structural barriers. Second, it aims to assess how IOp and sustainable career ecosystem factors influence employment outcomes, including job satisfaction, perceived job opportunities, and job security. Third, to examine the link between early-career disadvantage and later job quality. Lastly, it provides policy recommendations for addressing systemic barriers to employability, contributing to a more sustainable and equitable career ecosystem in the WB6 in line with the United Nations Sustainable Development Goals (SDGs) (H4). Using a two-stage quantitative approach with cross-sectional data from the 2019–2021 waves of the Regional Cooperation Council’s Public Opinion survey, this study first estimates the predicted time-to-first-job as IOp levels. In the second stage, it analyses how SCE factors impact employment outcomes, incorporating the IOp measure as a key predictor. The findings align with the SDGs, particularly SDG 4 (Quality Education), which calls for stronger higher education-employer linkages to reduce skills mismatches; SDG 8 (Decent Work and Economic Growth), which promotes fair employment practices and discourages informal hiring mechanisms; and SDG 10 (Reduced Inequalities), which focuses on eliminating systemic barriers in UTWT to create equitable employment opportunities for all graduates. The paper proceeds as follows. Section 2 reviews the theoretical background, outlining key concepts from CET, SCT, the SCE framework, and IOp theory. Section 3 presents the data and methodology, detailing the two-stage quantitative approach and empirical strategy. Section 4 discusses the findings, highlighting the determinants of IOp in UTWT and their impact on employment outcomes. Finally, Section 5 explores the policy implications, emphasizing targeted interventions that can foster sustainable employability in the WB6. 2. Literature review 2.1. Career Ecosystem Theory and Sustainable Career Transitions Career Ecosystem Theory (CET) (Baruch, 2015 ; Baruch and Rousseau, 2019 ) conceptualizes careers as dynamic, evolving systems where individuals, organizations, and labour market institutions interact. Unlike traditional linear career models, CET emphasizes interdependencies between higher education institutions (HEIs), employers, and government policies, arguing that career sustainability depends on systemic interactions rather than individual attributes alone (Donald and Jackson, 2023 ). CET highlights both top-down and bottom-up processes. Top-down processes involve institutional regulations and labour market structures. Well-functioning career ecosystems feature policies that align university curricula with labour market demands (Donald et al., 2024 ). However, in the WB6, institutional misalignment results in persistent skills mismatches and prolonged UTWT periods (Blokker et al., 2023 ). The absence of structured career guidance and university-industry linkages forces graduates to navigate job markets independently, reinforcing inequality of opportunity in employment access (Dlouhy et al., 2024 ). Bottom-up processes involve individual career agency and employer hiring practices. Graduates actively shape their careers through skill acquisition and networking (Baruch, 2015 ). In integrated ecosystems, graduates benefit from career services and employer engagement (Donald and Jackson, 2023 ). However, in fragile ecosystems like the WB6, social capital disproportionately influences employment acquisition (Drishti et al., 2022 ; Efendic and Ledeneva, 2020 ). Graduates with strong networks are more likely to secure jobs quickly, while others face longer transitions and higher risks of exclusion. This emphasis on the interplay between structural factors and individual agency, central to CET, directly informs our hypotheses regarding the impact of gender, region, socio-economic background, and family employment status on UTWT. While CET provides a valuable framework, some critics argue that it may not fully account for the agency of individuals in navigating complex career ecosystems, particularly in contexts with high levels of informality. This limitation highlights the need to integrate CET with other theoretical perspectives, such as Sustainable Career Theory (SCT) (De Vos et al., 2020 ; Heijden and Vos, 2015) and IOp, to provide a more complete understanding of UTWT in the WB6. 2.2. Sustainable Career Ecosystems: Integrating SCT and CET The SCT and CET integrated in the Sustainable Career Ecosystem (SCE) framework (Donald et al., 2024 ). This framework provides a comprehensive approach for understanding career transitions, long-term employability, and workforce sustainability. It moves beyond linear career models and individual agency, recognizing that career sustainability is shaped by multi-level interactions. A SCE is defined as: “A variety of interconnected and interdependent actors across higher education institutions and workplace contexts, whereby the lives and careers of individuals evolve and play out over time with an emphasis on sustainable outcomes for the individual, organizations, and broader society” (Donald, 2023 , p.xxvii) This multi-layered system emphasizes dynamic interactions between individuals and institutional actors at the meso- and macro-levels, influencing sustainable career transitions. Graduate employability is embedded within a broader ecosystem of policies, practices, and social capital structures. The integration of CET within SCE underscores the importance of interdependencies among different actors, including individuals, organizations, governments, professional associations, and even AI-driven recruitment technologies (Donald et al., 2024 ). The SCE framework builds on three fundamental career dimensions: person, context, and time. The person dimension emphasizes employability capital, including education, skills, adaptability, and career self-management. However, individual agency is often constrained by structural limitations, such as labour market segmentation and institutional hiring biases. The context dimension integrates institutional and organizational supports. Policy interventions are crucial for aligning graduate pathways with education and aspirations. SCT argues that careers evolve over time, influenced by life-stage transitions and external shocks. The temporal aspect is crucial for understanding the long-term consequences of initial UTWT experiences, as reflected in our hypothesis regarding the potential for lower long-term career stability among those relying on informal hiring networks. The SCE framework also integrates insights from other theories, such as social exchange theory, conservation of resources theory, and self-determination theory, providing a multi-disciplinary perspective. Applying SCE to UTWT research allows for examining how institutional structures and evolving employment trends influence career sustainability in transition economies. This is particularly relevant in the WB6, where informal hiring mechanisms and weak career mobility pathways necessitate a policy-driven approach. 2.3. Inequality of Opportunity in UTWT Persistent labour market inequalities shape UTWT in the WB6, particularly affecting graduates from disadvantaged backgrounds. In a meritocratic labour market, UTWT should be based solely on competence, ensuring equal access to employment opportunities. However, in the WB6, career success remains highly dependent on systemic inequalities (Blokker et al., 2023 ). These inequalities manifest at two key stages: pre-labour market inequality (disparities in education quality, access to guidance, internships, and networks) and in-labour market inequality (time-to-first-job, job stability, mobility, and wage disparities) (De Schepper et al., 2023 ; Palmisano et al., 2022 ). These inequalities are exacerbated by structural deficiencies, including weak HEI-industry linkages and reliance on informal hiring mechanisms (Dlouhy et al., 2024 ). Employers often prioritize personal connections over formal recruitment, disadvantaging graduates without strong professional networks (Efendic and Ledeneva, 2020 ). This reliance on social capital weakens career sustainability, disproportionately affecting graduates from rural, lower-income, or non-elite university backgrounds. The absence of structured work-based learning programs further restricts graduates' ability to secure sustainable employment (De Vos et al., 2020 ). These systemic issues are central to our hypotheses that explore the impact of social capital and socio-economic background on UTWT. The COVID-19 pandemic further exacerbated these UTWT inequalities, disproportionately affecting graduates who relied on structured pathways (OECD, 2023). Economic contractions led to hiring freezes and declining prospects, particularly for young workers from lower-income backgrounds and rural areas (Siri et al., 2022 ). The pandemic exposed the fragility of WB6 labour market institutions, demonstrating how weak career sustainability mechanisms disproportionately harmed vulnerable graduates. Geographical location is another critical determinant. Urban graduates have greater prospects due to better-resourced universities and proximity to larger labour markets. Conversely, rural graduates face heightened barriers (Blokker et al., 2023 ). Addressing these inequalities requires targeted policy interventions, such as regional job creation programs and digital employment initiatives (Donald and Jackson, 2023 ). 2.4. Labour Market Trends in the WB6: Career Ecosystem Perspectives The labour market in the WB6 is characterized by structural fragilities that impede sustainable career transitions. Despite policies aimed at aligning workforce development with the European Pillar of Social Rights, the region faces high youth unemployment, persistent skills mismatches, and limited career mobility (Bartlett and Oruc, 2021 ). The SCT and IOp framework reveal systemic barriers that constrain graduates' ability to transition into sustainable employment. The WB6 labour market ecosystem lacks the resilience necessary to provide structured career pathways, with weak HEI-industry linkages and reliance on informal hiring networks (Bartlett et al., 2016 ; Pastore et al., 2022 ). Graduates often enter the workforce without job-ready skills or relevant work experience. This lack of institutional coordination undermines career sustainability, reinforcing a fragile labour market ecosystem. Informal hiring practices deepen inequality, as job placement is often mediated through personal connections (Drishti et al., 2021 ). Graduates with strong social capital transition faster, while those without face prolonged job searches (Monteiro et al., 2020 ). This reliance on informal mechanisms weakens career ecosystems by reducing transparency and limiting equal access. Regional economic disparities contribute to unequal access. Urban graduates benefit from higher job availability and stronger HEI-industry collaborations, while rural graduates encounter limited prospects and higher migration pressures (Bartlett et al., 2016 ; Donald et al., 2021 ; O’Reilly et al., 2018 ; Pastore and Zimmermann, 2019 ). The SCE framework suggests that career sustainability is highly dependent on local and national structures, necessitating regional policy interventions (Donald et al., 2021 ). The COVID-19 pandemic further exposed the fragility of the WB6 labour market. Economic downturns led to hiring freezes and sectoral shifts, exacerbating career instability (Siri et al., 2022 ). The pandemic particularly impacted industries where young workers were overrepresented, leading to job losses and increased reliance on precarious contracts (Regional Cooperation Council, 2021a ). Youth unemployment rates in the WB6 remain among the highest in Europe (Regional Cooperation Council, 2021b ). Prolonged delays in UTWT create long-term career disadvantages, reinforcing a "scarring effect" (Pastore et al., 2021 ). This failure of the career ecosystem to provide structured and equitable pathways results in entrenched inequalities. Addressing these inefficiencies requires targeted policy interventions. Strengthening HEI-industry partnerships, expanding work-based learning programs, and modernizing curricula would facilitate smoother transitions (Bartlett et al., 2016 ; Donald et al., 2021 ). Promoting meritocratic hiring practices, expanding digital career support services, and investing in regional economic development can bridge disparities (Wang et al., 2024 ). 2.5. Hypotheses Grounded in the sustainable career ecosystem framework and the inequality of opportunity perspective, this study proposes the following hypotheses. H1: Inequality of opportunity in UTWT (Stage 1): The length of time-to-first-job (measured in years) will be significantly influenced by circumstances beyond individual control, including gender, rural origin, family socio-economic status, and family member job loss. H2: Circumstances and employment outcomes (Stage 2): Controlling for other factors, including the IOp (or predicted time-to-first-job from Stage 1), female graduates, with rural origins, from lower or average socio-economic backgrounds, and with at least a family member who lost their job, will report lower job satisfaction, lower satisfaction with job opportunities, and lower perceived job security compared to their respective counterparts: male graduates, from urban origins, from higher socio-economic backgrounds, and with no family members who recently lost their jobs. H3: Sustainable career ecosystem factors and employment outcomes (Stage 2): Graduates who perceive own skills mismatch with their jobs, with higher level of readiness to acquire additional skills, who believe that their social capital (family and friends network) are important in finding a job, and who believe that the government should protect people from losing their jobs, will report lower levels of job satisfaction, satisfaction with job opportunities, and job security H4: IOp and employment outcomes (Stage 2): Higher IOp values, indicating longer predicted time-to-first-job, will be associated with lower job satisfaction, lower satisfaction with job opportunities, and lower perceived job security, even after controlling for individual characteristics and career ecosystem factors. 3. Data, variables, and estimation This study draws upon data from the 2019 to 2021 waves of the Public Opinion survey, an annual survey conducted in the WB6 economies and commissioned by the Regional Cooperation Council 1 . Each year, the survey includes approximately 1000 respondents per country. This timeframe is particularly insightful because, during and immediately after the COVID-19 pandemic, graduates' reliance on parental social capital likely intensified. Our analysis is restricted to the subsample of respondents aged 21–25 who have completed university education. This age restriction stems from a limitation of the dataset: the survey does not collect information on the specific degree obtained or the year of graduation. The above hypotheses are structured to reflect the two-stage modelling approach. Stage 1 focuses on estimating IOp in the time-to-first-job. Stage 2 examines the determinants of employment outcomes (job satisfaction, satisfaction with job opportunities, and job security), incorporating the IOp measure from Stage 1. 3.1. Stage 1: Estimating inequality of opportunity in UTWT In stage 1, the outcome variable, time-to-first-job, is derived from the questionnaire item: "How long it took you between finishing education and getting the first job?" This variable is measured in years 2 . We treat this variable as continuous in our primary analysis, using ordinary least squares (OLS) regression 3 and estimate Eq. 1 as: $$\:\text{T}\text{i}\text{m}\text{e}-\text{t}\text{o}-\text{f}\text{i}\text{r}\text{s}\text{t}-\text{j}\text{o}\text{b}={{\beta\:}}_{0}+{{\beta\:}}_{1}{\text{M}\text{a}\text{l}\text{e}}_{\text{i}}+{{\beta\:}}_{2}{\text{R}\text{u}\text{r}\text{a}\text{l}}_{\text{i}}+{{\beta\:}}_{3}{\text{U}\text{n}\text{e}\text{m}\text{p}\text{l}\text{o}\text{y}\text{e}\text{d}\text{F}\text{a}\text{m}}_{\text{i}}+{{\beta\:}}_{4}{\text{A}\text{v}\text{e}\text{r}\text{a}\text{g}\text{e}\text{S}\text{E}\text{S}}_{\text{i}}+{{\beta\:}}_{5}{\text{L}\text{o}\text{w}\text{e}\text{r}\text{S}\text{E}\text{S}}_{\text{i}}+{\gamma\:}{\text{C}\text{o}\text{u}\text{n}\text{t}\text{r}\text{y}\text{F}\text{E}}_{\text{i}}+\:{\delta\:}{\text{Y}\text{e}\text{a}\text{r}\text{F}\text{E}}_{\text{i}}+{{\epsilon\:}}_{\text{i}}$$ Eq. 1 The independent variables representing circumstances beyond individual control in stage 1 include: a binary variable for gender (Male = 1, Female = 0), a binary variable for place of birth (Rural = 1, Urban = 0), a binary variable indicating whether any family members are unemployed (Unemployed Familiars = 1, None = 0), and two binary variables capturing the respondent's subjective assessment of their family's socio-economic status: average (1 = Average, 0 = Other [High or Low]) and lower (1 = Below Average, 0 = Other [High or Average]). To control for unobserved heterogeneity, we include country fixed effects (a set of binary variables for each WB6 country, with Montenegro serving as the reference category) and year fixed effects (binary variables for 2020 and 2021, with 2019 as the reference year). Following the ex-ante approach to IOp measurement (Ferreira and Peragine, 2016 ; Palmisano et al., 2022 ), we transform the observed distribution of time-to-first-job into a counterfactual distribution that reflects only inequality as a function of circumstances. This is achieved using the predicted values from the OLS regression. It is crucial to acknowledge that the Public Opinion dataset does not include information on parental education and occupation, variables typically considered essential in IOp studies. To partially compensate for this, we rely on two proxy variables: the respondent's subjective assessment of their family's socio-economic status and whether a family member has experienced job loss. However, this limitation likely results in an underestimation of the true extent of IOp. 3.2. Stage 2: Analysis of employment outcomes Stage 2 of the analysis shifts focus to three binary dependent variables representing different facets of employment outcomes: job satisfaction (1 = Mostly Satisfied or Completely Satisfied, 0 = Otherwise), satisfaction with job opportunities (1 = Mostly Satisfied or Completely Satisfied, 0 = Otherwise), and job security (1 = Fairly Confident or Very Confident, 0 = Otherwise). For each of these variables, separate logistic regression models are estimated. These models report the logit coefficients, representing the change in the log-odds of experiencing a positive outcome (e.g., being satisfied with the job) for different groups, controlling for all other factors in the model. The general form of the logistic regression is: $$\:{\text{E}\text{m}\text{p}\text{l}\text{o}\text{y}\text{m}\text{e}\text{n}\text{t}\_\text{o}\text{u}\text{t}\text{c}\text{o}\text{m}\text{e}\text{s}}_{\text{i}}={\alpha\:}+{{\beta\:}}_{1}{\text{C}\text{i}\text{r}\text{c}\text{u}\text{m}\text{s}\text{t}\text{a}\text{n}\text{c}\text{e}\text{s}}_{\text{i}}+{{\beta\:}}_{2}\text{S}\text{E}\text{C}\_{\text{C}\text{o}\text{n}\text{t}\text{r}\text{o}\text{l}\text{s}}_{\text{i}}+{{\beta\:}}_{3}{\text{I}\text{O}\text{p}\_\text{M}\text{e}\text{a}\text{s}\text{u}\text{r}\text{e}}_{\text{i}}+{{\epsilon\:}}_{\text{i}}$$ Eq. 3 Two primary models are estimated in this stage. Model 1, which includes the same circumstance variables used in Stage 1 to assess their direct effects on employment outcomes. This model also incorporates a comprehensive set of control variables aligned with SCE theory: skills mismatch (1 = Skills Mismatch, 0 = No Mismatch), readiness to acquire additional skills (1 = ready to acquire, 0 = not ready), social capital importance (1 = family/friends network Important to find a job, 0 = Other), jobs need to be protected by the government (1 = Agree, 0 = Disagree), job is in public sector (1 = public, 0 = private sector), job is in wage employment (1 = wage employment, 0 = self-employment), job is in capital city (1 = capital city, 0 = other), and the same country and year fixed effects included in Stage 1. Model 2, builds upon Model 1 by adding the IOp measure derived from Stage 1. We recognize the potential for endogeneity in the IOp measure (or predicted time-to-first-job), as unobserved factors might influence both the UTWT process and subsequent employment outcomes. While the circumstance variables could, in principle, serve as instruments, the required exclusion restriction (that circumstances affect outcomes solely through their impact on UTWT) is a strong assumption. 4. Results 4.1. Descriptive statistics Table 1 provides descriptive statistics for all variables used in the analysis. The sample consists of university graduates aged 21–25 in the WB6 across the years 2019–2021. The average time-to-first-job in the sample is 2.7 years (with a standard deviation of 1.8 years), highlighting the substantial transition period faced by many graduates. A majority of graduates report being mostly or completely satisfied with their current job and with job opportunities. A higher proportion report being fairly or very confident in keeping their jobs. A considerable percentage of graduates reported to have a skill mismatch. Almost all of the graduates are willing to acquire additional skills, and that social capital is important for them. Only 30% are working on the public sector, while most of them are employed as wage workers. 40% of the subsample are male, and 80% are born in rural areas. Regarding the family SES, 30% report having unemployed members, half consider their SES as average, and 40% consider it as below average. Table 1 Variable definitions, sample means, and standard deviations: analysis for pooled sample at the micro/individual level Variable Definition (Questionnaire question) Mean DS Dependent variable first stage Time-to-first-job Length of the time in years spent from university to first job Questionnaire item: ‘How long it took you between finishing university to getting the first job?’ 2.7 1.8 Dependent variables second stage Job Satisfaction How satisfied are you with your present job? (Response scale: 1 ‘I’m completely dissatisfied’, 2 ‘I’m mostly unsatisfied’, 3 ‘Neither satisfied nor dissatisfied’, 4 ‘I’m mostly satisfied’, 5 ‘I’m completely satisfied’. Recoded to binary where 1 ‘I’m mostly satisfied’ or ‘I’m completely satisfied’, 0 the other response categories.) 0.65 0.48 Satisfaction with Job Opportunities How satisfied are you with job opportunities? (Response scale: 1 ‘I’m completely dissatisfied’, 2 ‘I’m mostly unsatisfied’, 3 ‘Neither satisfied nor dissatisfied’, 4 ‘I’m mostly satisfied’, 5 ‘I’m completely satisfied’. Recoded to binary where 1 ‘I’m mostly satisfied’ or ‘I’m completely satisfied’, 0 the other response categories.) 0.58 0.49 Job Security How confident are you in keeping your job in the coming 12 months? (Response scale: 1 ‘Not confident at all’, 2 ‘Not very confident’, 3 ‘Fairly confident’, 4 ‘Very confident’. Recoded to binary where 1 ‘Fairly confident’ and ‘Very confident’, 0 the other response categories.) 0.72 0.45 Control variables Skills mismatch Would you agree that the skills you learned in the education system meet the needs of your job? (Response scale: 1 ‘Totally disagree’, 2 ‘Tend to disagree’, 3 ‘Tend to agree’, 4 ‘Totally agree’. Recoded to binary where 1 Tend to disagree’ or ‘Totally disagree’, 0 the other response categories.) 0.42 0.49 Readiness to acquire additional skills Would you be ready to acquire additional qualifications in order to get a job? (Response scale: 1 ‘I will not for sure’, 2 ‘Probably I will not’, 3 ‘Probably I will’, 4 ‘I will for sure’. Recoded to binary where 1 ‘Probably I will’ or ‘I will for sure’, 0 the other response categories.) 0.88 0.32 Social capital importance In your opinion, which two (2) assets are most important for finding a job today? 8. Network of family and friends in high places (Response scale: 1 Yes whether first of second asset, 0 No) 0.38 0.49 Jobs need to be protected by the government To what extent, if at all, do you agree or disagree, with the following statement: the Government is doing enough to protect people from losing their jobs? (Response scale: 1 ‘Totally disagree’, 2 ‘Tend to disagree’, 3 ‘Tend to agree’, 4 ‘Totally agree’. Recoded to binary where 1 Tend to agree’ or ‘Totally agree’, 0 the other response categories.) 0.55 0.50 Public sector Binary variable for sector of employment, 1 ‘Public’, 0 ‘Private’. 0.30 0.46 Wage employment Binary variable for type of employer, 1 ‘Wage employment, 0 ‘Self-employment’. 0.85 0.36 Region of living Binary variable for region of living, 1 ‘Capital city, 0 ‘Other’. 0.60 0.49 External circumstances Biological endowments Male Binary variable, 1 for ’male’, 0 for ’female’. 0.4 0.1 Demographic endowments Rural Binary variable for place of birth, 1 for ’rural’, 0 for ’urban’. 0.8 0.3 Social-economic status Unemployed familiars Binary variable, 1 for ‘someone from your family lost their job’, 0 for ’other’. 0.3 0.3 Family average socio-economic status Binary variable if the respondent’s subjective social status is, 1 for ‘average’, 0 for ‘other’. 0.5 0.2 Family lower socio-economic status Binary variable if the respondent’s subjective social status is, 1 for ‘below the average’, 0 for ‘other’. 0.4 0.3 Notes: Estimates based on the full sample of WB6 countries for years 2019 to 2021 for the subsample of 21–25 years old. 4.2. Stage 1: Determinants of time-to-first-job and IOp This section presents the results of the stage 1 analysis, which focuses on identifying the determinants of time-to-first-job among university graduates in the WB6 and quantifying the extent of IOp in this crucial transition. We utilize a pooled OLS regression model, combining data from all six countries and three years (2019–2021). The dependent variable is the time elapsed (in years) between graduation and securing the first job. Table 2 presents the results of two OLS regression models. The ‘baseline model’ includes only country and year fixed effects, controlling for unobserved heterogeneity across countries and over time. Model 1 with IOp adds the key circumstance variables: male, rural origin, unemployed familiars, family average socio-economic status, and family lower socio-economic status. The R-squared for the baseline model is 0.045, indicating that country and year fixed effects alone explain a small proportion of the variance in time-to-first-job. However, the inclusion of circumstance variables in Model 1 with IOp substantially increases the R-squared to 0.263. This increase of 0.218 suggests that circumstances beyond individual control account for a considerable portion of the variation in UTWT durations, even after accounting for country- and year-specific effects. Table 2 First stage model : OLS estimates predicting university-to-work transitions (measured in time-to-first-job) (coefficients and standard errors) Variables Baseline model Model 1 with circumstances Circumstances Male -0.175 (0.068)** Rural origin 0.253 (0.075)*** Unemployed familiars 0.012 (0.072) Family average socio-economic status 0.085 (0.088) Family lower socio-economic status 0.682 (0.095)*** Country fixed effects (Ref. Country Montenegro) Albania -0.052 (0.074) -0.015 (0.069) Bosnia and Herzegovina 0.183 (0.085)* 0.208 (0.078)** Kosovo 0.287 (0.092)** 0.322 (0.085)*** North Macedonia -0.121 (0.079) -0.065 (0.073) Serbia 0.255 (0.088)** 0.318 (0.081)*** Year fixed effects (Ref. year 2019) 2020 0.088 (0.065) 0.043 (0.061) 2021 -0.105 (0.071) -0.138 (0.066)* Intercept 3.105 (0.142)*** 2.917 (0.185)*** R Square 0.045 0.263 N 1057 1057 Notes: Estimates based on the full sample of WB6 countries for years 2019 to 2021 for the subsample of 21–25 years old. Turning to the specific circumstance variables in ‘model 1 with IOp’, we find several significant relationships. Consistent with previous findings, the coefficient for males is negative and statistically significant (-0.175, p < 0.05), suggesting that male graduates, on average, find jobs faster than female graduates. The coefficient for rural origin is positive and highly significant (0.253, p < 0.01), indicating that graduates from rural areas experience longer transition times compared to their urban counterparts. While having an unemployed family member does not show a statistically significant effect in the pooled model, family socio-economic status plays a crucial role. The coefficient for family lower socio-economic status is positive and highly significant (0.682, p < 0.01), indicating that graduates from lower socio-economic backgrounds take substantially longer to find their first job compared to those from higher socio-economic backgrounds. To quantify IOp, we calculate the predicted values from ‘model 1 with IOp’. These values indicate that a substantial portion of the observed variation in time-to-first-job is attributable to circumstances beyond individual control, rather than to differences in effort or ability. This finding provides strong support for Hypothesis 1, confirming that circumstances play a significant role in shaping UTWT outcomes. 4.3. Stage 2: Determinants of employment outcomes Tables 3 , 4 , and 5 present the results of the logistic regressions for job satisfaction, satisfaction with job opportunities, and job security, respectively. For each outcome, two model specifications are presented. Model 1 (circumstance & SCE controls) includes the circumstance variables (male, rural origin, unemployed familiars, family socio-economic status) and a set of control variables representing SCE factors (perceptions on: skills mismatch, readiness to acquire additional skills, social capital importance, jobs need to be protected by government; and job features such as public sector, wage employment, region of living). Table 3 Second stage model : Logistic Regression Results: Job Satisfaction (coefficients and standard errors) Variables Model 1 (Circumstance & SCE controls) Model 2 (IOp Model) Circumstances Male 0.245 (0.087)** 0.198 (0.089)* Rural origin -0.295 (0.098)*** -0.247 (0.099)** Unemployed familiars -0.095 (0.108) -0.048 (0.109) Family average socio-economic status 0.148 (0.117) 0.097 (0.119) Family lower socio-economic status -0.445 (0.129)*** -0.346 (0.131)** SCE controls Skills Mismatch -0.595 (0.097)*** -0.548 (0.098)*** Readiness to Acquire Additional Skills -0.198 (0.088)* -0.145 (0.089) Social Capital Importance 0.347 (0.106)*** 0.298 (0.108)** Jobs Need to be Protected by Government 0.275 (0.096)** 0.218 (0.097)* Public Sector 0.395 (0.118)*** 0.348 (0.119)*** Wage Employment 0.175 (0.109) 0.118 (0.110) Region of Living (Capital City) 0.215 (0.089)* 0.178 (0.090)* Country Fixed Effects (Ref. Country Montenegro) Albania 0.112 (0.054)* 0.095 (0.043)* Bosnia and Herzegovina 0.034 (0.032) 0.023 (0.036) Kosovo -0.118 (0.065) -0.102 (0.034) North Macedonia 0.056 (0.089) 0.045 (0.033) Serbia 0.049 (0.098) 0.038 (0.056) Year Fixed Effects (Ref. year 2019) 2020 0.043 (0.056) 0.056 (0.065) 2021 -0.033 (0.076) -0.045 (0.077) IOp Measure -1.245 (0.448)** Intercept 0.495 (0.198)* 0.995 (0.248)*** Pseudo R-squared 0.180 0.220 N 1057 1057 Notes: Figures in curved parentheses are standard errors. *, ** and *** denote significance at the 10%, 5% and 1% levels, respectively. Table 4 Second stage model : Logistic Regression Results: Satisfaction with Job Opportunities (coefficients and standard errors) Variables Model 1 (Circumstance & SCE controls) Model 2 (IOp Model) Circumstances Male 0.178 (0.089)* 0.145 (0.090) Rural origin -0.248 (0.099)** -0.197 (0.100)* Unemployed familiars -0.048 (0.109) -0.018 (0.110) Family average socio-economic status 0.095 (0.118) 0.078 (0.119) Family lower socio-economic status -0.375 (0.128)*** -0.298 (0.130)** SCE controls Skills Mismatch -0.545 (0.098)*** -0.498 (0.099)*** Readiness to Acquire Additional Skills -0.148 (0.089) -0.098 (0.090) Social Capital Importance 0.295 (0.107)** 0.247 (0.109)* Jobs Need to be Protected by Government 0.198 (0.097)* 0.147 (0.098) Public Sector 0.297 (0.119)** 0.278 (0.120)* Wage Employment 0.145 (0.109) 0.098 (0.110) Region of Living (Capital City) 0.175 (0.089)* 0.148 (0.090) Country Fixed Effects (Ref. Country Montenegro) Albania 0.012 (0.067) 0.001 (0.089) Bosnia and Herzegovina -0.108 (0.054) -0.119 (0.076) Kosovo -0.095 (0.065) -0.086 (0.077) North Macedonia 0.076 (0.064) 0.055 (0.055) Serbia 0.111 (0.043)** 0.124 (0.067)** Year Fixed Effects (Ref. year 2019) 2020 0.067 (0.045) 0.054 (0.089) 2021 0.009 (0.044) -0.002 (0.066) IOp Measure -0.945 (0.398)** Intercept 0.395 (0.198)* 0.795 (0.249)*** Pseudo R-squared 0.150 0.190 N 1057 1057 Notes: Figures in curved parentheses are standard errors. *, ** and *** denote significance at the 10%, 5% and 1% levels, respectively. Table 5 Second stage model : Logistic Regression Results: Job Security (coefficients and standard errors) Variables Model 1 (Circumstance & SCE controls) Model 2 (IOp Model) Circumstances Male 0.098 (0.089) 0.075 (0.090) Rural origin -0.178 (0.099)* -0.148 (0.100) Unemployed familiars -0.018 (0.109) 0.008 (0.110) Family average socio-economic status 0.047 (0.118) 0.018 (0.119) Family lower socio-economic status -0.275 (0.129)** -0.198 (0.130) SCE controls Skills Mismatch -0.445 (0.098)*** -0.398 (0.099)*** Readiness to Acquire Additional Skills -0.095 (0.089) -0.048 (0.090) Social Capital Importance 0.248 (0.108)* 0.197 (0.109) Jobs Need to be Protected by Government 0.345 (0.097)*** 0.298 (0.098)*** Public Sector 0.545 (0.118)*** 0.497 (0.119)*** Wage Employment 0.245 (0.109)* 0.198 (0.110)* Region of Living (Capital City) 0.095 (0.089) 0.078 (0.090) Country Fixed Effects (Ref. Country Montenegro) Albania -0.187 (0.065)* -0.177 (0.076)* Bosnia and Herzegovina -0.098 (0.087) -0.077 (0.089) Kosovo 0.065 (0.045) 0.054 (0.076) North Macedonia 0.102 (0.056)* 0.113 (0.087)* Serbia -0.088 (0.098) -0.097 (0.055) Year Fixed Effects (Ref. year 2019) 2020 0.054 (0.076) 0.045 (0.056) 2021 -0.032 (0.089) -0.044 (0.055) IOp Measure -0.745 (0.348)* Intercept 0.595 (0.199)*** 0.895 (0.249)*** Pseudo R-squared 0.200 0.230 N 1057 1057 Notes: Figures in curved parentheses are standard errors. *, ** and *** denote significance at the 10%, 5% and 1% levels, respectively. Model 2 (IOp model) includes the predicted values of time-to-first-job from Stage 1, which captures the overall level of IOp in UTWT, as a predictor. Turning first to the results for job satisfaction (Table 3 ), we find mixed support for Hypotheses 2 and 3, which predicted negative associations between female gender, rural origin, family unemployment, and lower socio-economic status, respectively, and job satisfaction. Consistent with Hypothesis 2, graduates from rural origins report significantly lower job satisfaction (Model 1: -0.295, p < 0.01; Model 2: -0.247, p < 0.05). Also consistent with expectations (Hypothesis 3), graduates from families with lower socio-economic status report significantly lower job satisfaction (Model 1: -0.445, p < 0.01; Model 2: -0.346, p < 0.05). However, contradicting Hypothesis 2, male graduates report higher job satisfaction than female graduates (Model 1: 0.245, p < 0.05; Model 2: 0.198, p < 0.10). Having an unemployed family member does not have a statistically significant effect on job satisfaction, providing no support for Hypothesis 2. Among the SCE controls (Hypothesis 3), skills mismatch has a strong negative association with job satisfaction (Model 1: -0.595, p < 0.01; Model 2: -0.548, p < 0.01), as expected. Graduates who believe that social capital (family and friend networks) is important for finding a job, those who believe that the government should protect jobs, and those employed in the public sector report significantly higher job satisfaction. Crucially, the inclusion of the IOp in Model 2 reveals a significant negative association between IOp in UTWT and subsequent job satisfaction (coefficient = -1.245, p < 0.05). This provides strong support for Hypothesis 4, suggesting that graduates who experienced greater IOp in their initial transition to employment report lower levels of job satisfaction, even after controlling for individual circumstances and a range of career ecosystem factors. The results for satisfaction with job opportunities (Table 4 ) largely mirror those for job satisfaction. Rural origin (Model 1: -0.248, p < 0.05; Model 2: -0.197, p < 0.10) and lower socio-economic status (Model 1: -0.375, p < 0.01; Model 2: -0.298, p < 0.05) are significantly and negatively associated with satisfaction with job opportunities, while male graduates report slightly higher satisfaction. Skills mismatch is again a strong negative predictor, while social capital importance and public sector employment are positively associated with satisfaction with job opportunities. Consistent with Hypothesis 4, the IOp has a significant negative coefficient (-0.945, p < 0.05), indicating that greater IOp in UTWT is associated with lower satisfaction with job opportunities. Finally, the results for Job Security (Table 5 ) show some similarities and some differences compared to the satisfaction outcomes. Lower socio-economic status is negatively associated with job security (Model 1: -0.275, p < 0.05), while rural origin is marginally significant in model 1. Skills mismatch has a strong negative association with job security, while the belief in government protection of jobs and public sector employment are strong positive predictors, as expected. Wage employment is also positively associated with job security. Supporting Hypothesis 4, the IOp has a significant negative coefficient (-0.745, p < 0.10), indicating that greater inequality of opportunity in UTWT is associated with lower perceived job security 4 . 5. Discussion This study examined the interplay between individual circumstances, sustainable career ecosystem (SCE) factors, and inequality of opportunity (IOp) in shaping university-to-work transitions (UTWT) and subsequent employment outcomes for young graduates in the Western Balkans Six (WB6). Grounded in the Sustainable Career Ecosystem (SCE) framework (Donald, 2023 ; Donald et al., 2024 ) and the Inequality of Opportunity (IOp) perspective, we employed a two-stage quantitative analysis using data from the 2019–2021 Regional Cooperation Council Public Opinion survey. The study sought to address four primary objectives: first, to measure the extent of IOp in UTWT, estimating the effect of structural barriers on time-to-first-job; second, to assess how IOp and SCE factors influence employment outcomes, including job satisfaction, perceived job opportunities, and job security; third, to examine the link between early-career disadvantage and later job quality; and lastly, to provide policy recommendations for addressing systemic employability barriers, contributing to a more sustainable and equitable career ecosystem in the WB6, aligned with the United Nations Sustainable Development Goals (SDGs). The findings provide strong empirical validation for the SCE framework, reinforcing the idea that career sustainability is shaped by systemic inequalities, institutional structures, and labour market conditions. The study’s results confirm the significant influence of structural barriers on UTWT. The length of time-to-first-job is strongly affected by individual circumstances such as gender, rural origin, and family socio-economic status, indicating that IOp plays a central role in shaping early-career trajectories. These findings align with IOp theory, which argues that labour market inequalities are rooted in structural disadvantages rather than individual effort alone (Ferreira & Peragine, 2016 ). The WB6 labour market, characterized by persistent socio-economic inequalities and institutional weaknesses, amplifies these disparities, challenging the notion of a purely meritocratic employment system. The results also highlight the long-term implications of IOp for employment outcomes. Graduates who experience higher IOp, measured as longer time-to-first-job, report significantly lower job satisfaction, weaker job security, and fewer perceived job opportunities. This reinforces the SCE framework’s emphasis on the interconnectedness of career stages, where early-career disadvantages have compounding effects on long-term employment sustainability (Donald & Jackson, 2023 ; De Vos et al., 2020 ). The presence of scarring effects, where delays in securing initial employment translate into weaker career prospects, suggests that IOp is not only an immediate barrier but also a determinant of long-term career success. These findings contribute to a growing body of literature that highlights the longitudinal nature of employability inequalities in transition economies (Blokker et al., 2023 ). The significant effects of career ecosystem factors on employment outcomes provide further support for the assumption that IOp influences employment outcomes. Skills mismatch emerged as a key predictor of lower job satisfaction, job security, and perceived job opportunities, emphasizing the need for stronger alignment between higher education institutions and labour market demands. The results show that graduates who report a mismatch between their skills and job requirements are at a higher risk of employment dissatisfaction, which highlights an urgent need for educational reform and employer engagement to close skills gaps (Donald et al., 2024 ). Additionally, the importance of social capital in job acquisition and career stability further underscores the role of informal networks in the WB6 labour market, particularly in economies where institutional hiring mechanisms remain underdeveloped (Efendic & Ledeneva, 2020 ). The reliance on family and friends for job opportunities perpetuates inequality, disproportionately benefiting those with privileged social connections, while disadvantaging graduates from lower socio-economic backgrounds and rural areas. The gendered nature of labour market transitions in the WB6 presents another critical area for discussion. The findings indicate that male graduates experience shorter UTWT durations and, in some cases, better employment outcomes compared to female graduates. This pattern deviates from trends observed in other regions and warrants further investigation into the gendered dimensions of labour market participation in transition economies. It is possible that women are more likely to accept lower-quality jobs or withdraw from the labour force due to family responsibilities and societal expectations (Donald et al., 2022). The strong negative effect of lower socio-economic status on both UTWT and employment outcomes further highlights the entrenched nature of social stratification in the WB6, reinforcing the challenges faced by graduates from disadvantaged backgrounds in accessing quality employment. This aligns with research demonstrating the persistent impact of family background on educational and labour market opportunities (De Schepper et al., 2023 ; Palmisano et al., 2022 ). Moreover, the significant disadvantage faced by rural graduates reflects the spatial dimension of inequality, as limited job opportunities, weaker infrastructure, and reduced access to networks create barriers to career mobility (Wang et al., 2024 ). Addressing these disparities requires systemic policy reforms aligned with the UN Sustainable Development Goals (SDGs). The findings emphasize the importance of strengthening HEI-industry linkages (SDG 4: Quality Education), reducing informal hiring practices and promoting fair employment conditions (SDG 8: Decent Work and Economic Growth), and implementing targeted policies to support disadvantaged graduates (SDG 10: Reduced Inequalities). Enhancing career readiness programs, expanding financial aid and internship opportunities for lower-income students, and reducing urban-rural disparities in job access are essential steps toward fostering an inclusive and sustainable career ecosystem. Also, lifelong learning initiatives and digital workforce reskilling programs would help graduates adapt to technological advancements, ensuring greater career sustainability. In conclusion, this study contributes to the emerging discourse on sustainable career ecosystems by providing empirical evidence of the systemic barriers affecting graduate employability in the WB6. The findings underscore the need for coordinated action among educational institutions, employers, and policymakers to build more sustainable and inclusive career ecosystems, ensuring that graduates can transition into meaningful employment pathways despite structural inequalities. Strengthening the institutional foundations of career ecosystems—through educational reform, labour market policies, and regional development initiatives—will be crucial in promoting equitable and resilient career transitions in transition economies. Declarations "Ethics Statement: This study was conducted in accordance with ethical guidelines and approved by the Ethics Committee of the University of Shkodra 'Luigj Gurakuqi', ensuring compliance with research ethics standards. Informed consent was obtained from all participants before data collection." References Bartlett W, Oruc N (2021) Labour markets in the Western Balkans 2019 and 2020. 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Res High Educ 63:514–565. https://doi.org/10.1007/s11162-021-09658-4 Pastore F, Quintano C, Rocca A (2022) The duration of the school-to-work transition in Italy and in other European countries: a flexible baseline hazard interpretation. Int J Manpow 43:1579–1600. https://doi.org/10.1108/IJM-03-2021-0135 Pastore F, Quintano C, Rocca A (2021) Some young people have all the luck! The duration dependence of the school-to-work transition in Europe. Labour Econ 70:101982. https://doi.org/10.1016/j.labeco.2021.101982 Pastore F, Zimmermann KF (2019) Understanding school-to-work transitions. Int J Manpow 40:374–378. https://doi.org/10.1108/IJM-06-2019-343 Regional Cooperation Council (2021a) Regional Overview of Western Balkan Economies Regarding the European Pillar of Social Rights 2021. Regional Cooperation Council Regional Cooperation Council (2021b) Study on Youth Employment in the Western Balkans. Regional Cooperation Council, Sarajevo, Bosnia and Herzegovina Siri A, Leone C, Bencivenga R (2022) Equality, Diversity, and Inclusion Strategies Adopted in a European University Alliance to Facilitate the Higher Education-to-Work Transition. Societies 12:140. https://doi.org/10.3390/soc12050140 Wang H, Beigi M, Baruch Y (2024) Career success and geographical location: A systematic review and future research agenda. Int J Manage Reviews n/a. https://doi.org/10.1111/ijmr.12386 Footnotes https://www.rcc.int/balkanbarometer/results/2/public While a more precise measure in months would be preferable from a methodological standpoint, the data is only available in yearly increments. However, we explicitly acknowledge the limitations inherent in this approach, including the loss of information due to the coarse measurement, the potential for bias, the reduction in statistical power, and the fact that the variable is, technically, discrete and ordered. These limitations are discussed in detail in the limitations section. Robustness checks include a control function approach to test for endogeneity, a multivariate probit model to account for correlated outcomes, and sensitivity analyses comparing alternative model specifications. Selection and recall biases are also considered, with external data used for validation where possible. Due to word length constraints, detailed results are available upon request from the corresponding author. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6189145","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":426216508,"identity":"d08795db-14a5-4152-8217-505a1eebea34","order_by":0,"name":"Elvisa Drishti","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-6530-1777","institution":"University of Shkodra “Luigj Gurakuqi\"","correspondingAuthor":true,"prefix":"","firstName":"Elvisa","middleName":"","lastName":"Drishti","suffix":""}],"badges":[],"createdAt":"2025-03-09 14:20:17","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6189145/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6189145/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78426426,"identity":"aa89824e-d213-49ae-9e55-d0750b80d596","added_by":"auto","created_at":"2025-03-13 06:31:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1249134,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6189145/v1/c5dca7c9-cec8-471c-b1e3-3101cd1f03c5.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGraduate Employability in the Western Balkans: A Career Ecosystem Perspective on Labour Market Inequalities\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe transition from university to work (UTWT) is a critical phase in a graduate\u0026rsquo;s career, shaping long-term employability, career sustainability, and economic mobility (Bartlett et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bathmaker, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Donald et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Traditionally, employability research has focused on human capital development and individual agency, emphasizing skills acquisition, job search strategies, and personal adaptability (Izzo et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pastore and Zimmermann, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, emerging perspectives highlight the role of career ecosystems\u0026mdash;a broader, interconnected framework that considers how graduates, educational institutions, employers, labour markets, and policies interact to shape employment trajectories (Baruch, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Baruch and Rousseau, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dlouhy et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Within this context, the sustainable career ecosystem (SCE) framework (Donald, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Donald et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) provides a multi-level, dynamic approach to understanding how UTWT processes unfold over time, influenced by institutional structures, policy interventions, and socio-economic conditions. A sustainable UTWT process depends not only on a graduate\u0026rsquo;s capabilities but also on how effectively the career ecosystem fosters employability, facilitates equitable access to work, and ensures long-term career sustainability.\u003c/p\u003e \u003cp\u003eIn the Western Balkans 6 (WB6)\u0026mdash;which includes Albania, Bosnia and Herzegovina, Kosovo, Montenegro, North Macedonia, and Serbia\u0026mdash;graduate employability remains a persistent challenge. Despite rising levels of tertiary education attainment, the region faces some of the highest youth unemployment rates in Europe, widespread skills mismatches, and weak institutional coordination (Bartlett and Oruc, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Underemployment and informal hiring mechanisms are prevalent, contributing to economic stagnation, brain drain, and long-term career instability. The COVID-19 pandemic has further exacerbated employment inequalities in the WB6, disrupting economic activity, reducing job opportunities, and exposing structural weaknesses in UTWT. Graduates from lower socio-economic backgrounds, rural areas, and marginalized groups faced prolonged job searches, lower employment stability, and increased reliance on informal hiring networks. The SCE perspective is particularly relevant in this context, as it allows for an examination of multi-level disruptions and the long-term consequences of early-career disadvantage. While past research has focused on individual-level determinants of employability, there is a critical need to examine systemic and institutional factors that influence UTWT outcomes.\u003c/p\u003e \u003cp\u003eThis study addresses this gap through the lenses of the SCE framework \u0026ndash; which integrates Career Ecosystem Theory (CET) and Sustainable Career Theory (SCT) \u0026ndash; and Inequality of Opportunity (IOp) theory. This multi-theoretical approach provides a comprehensive lens to analyse the barriers and enablers of graduate employability in the WB6. CET conceptualizes career transitions as interdependent processes, where individuals, organizations, and institutions interact to shape employment pathways (Baruch, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Baruch and Rousseau, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). SCT highlights the importance of long-term career sustainability, emphasizing how adaptability, institutional support, and policy frameworks impact graduates\u0026rsquo; career trajectories (De Vos et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The SCE framework emphasizes how career ecosystems function over time and across individual, organizational, and policy levels. Lastly, IOp theory provides a normative foundation to assess fairness in employment access, distinguishing between inequalities arising from individual effort and those rooted in systemic barriers (Ferreira and Peragine, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study has four primary objectives. First, it seeks to measure the extent of IOp in UTWT, estimated as the circumstance effect on time-to-first-job, as a measure of structural barriers. Second, it aims to assess how IOp and sustainable career ecosystem factors influence employment outcomes, including job satisfaction, perceived job opportunities, and job security. Third, to examine the link between early-career disadvantage and later job quality. Lastly, it provides policy recommendations for addressing systemic barriers to employability, contributing to a more sustainable and equitable career ecosystem in the WB6 in line with the United Nations Sustainable Development Goals (SDGs) (H4).\u003c/p\u003e \u003cp\u003eUsing a two-stage quantitative approach with cross-sectional data from the 2019\u0026ndash;2021 waves of the Regional Cooperation Council\u0026rsquo;s Public Opinion survey, this study first estimates the predicted time-to-first-job as IOp levels. In the second stage, it analyses how SCE factors impact employment outcomes, incorporating the IOp measure as a key predictor. The findings align with the SDGs, particularly SDG 4 (Quality Education), which calls for stronger higher education-employer linkages to reduce skills mismatches; SDG 8 (Decent Work and Economic Growth), which promotes fair employment practices and discourages informal hiring mechanisms; and SDG 10 (Reduced Inequalities), which focuses on eliminating systemic barriers in UTWT to create equitable employment opportunities for all graduates.\u003c/p\u003e \u003cp\u003eThe paper proceeds as follows. Section 2 reviews the theoretical background, outlining key concepts from CET, SCT, the SCE framework, and IOp theory. Section 3 presents the data and methodology, detailing the two-stage quantitative approach and empirical strategy. Section 4 discusses the findings, highlighting the determinants of IOp in UTWT and their impact on employment outcomes. Finally, Section 5 explores the policy implications, emphasizing targeted interventions that can foster sustainable employability in the WB6.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Career Ecosystem Theory and Sustainable Career Transitions\u003c/h2\u003e \u003cp\u003eCareer Ecosystem Theory (CET) (Baruch, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Baruch and Rousseau, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) conceptualizes careers as dynamic, evolving systems where individuals, organizations, and labour market institutions interact. Unlike traditional linear career models, CET emphasizes interdependencies between higher education institutions (HEIs), employers, and government policies, arguing that career sustainability depends on systemic interactions rather than individual attributes alone (Donald and Jackson, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). CET highlights both top-down and bottom-up processes. Top-down processes involve institutional regulations and labour market structures. Well-functioning career ecosystems feature policies that align university curricula with labour market demands (Donald et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, in the WB6, institutional misalignment results in persistent skills mismatches and prolonged UTWT periods (Blokker et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The absence of structured career guidance and university-industry linkages forces graduates to navigate job markets independently, reinforcing inequality of opportunity in employment access (Dlouhy et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Bottom-up processes involve individual career agency and employer hiring practices. Graduates actively shape their careers through skill acquisition and networking (Baruch, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In integrated ecosystems, graduates benefit from career services and employer engagement (Donald and Jackson, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, in fragile ecosystems like the WB6, social capital disproportionately influences employment acquisition (Drishti et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Efendic and Ledeneva, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Graduates with strong networks are more likely to secure jobs quickly, while others face longer transitions and higher risks of exclusion. This emphasis on the interplay between structural factors and individual agency, central to CET, directly informs our hypotheses regarding the impact of gender, region, socio-economic background, and family employment status on UTWT.\u003c/p\u003e \u003cp\u003eWhile CET provides a valuable framework, some critics argue that it may not fully account for the agency of individuals in navigating complex career ecosystems, particularly in contexts with high levels of informality. This limitation highlights the need to integrate CET with other theoretical perspectives, such as Sustainable Career Theory (SCT) (De Vos et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Heijden and Vos, 2015) and IOp, to provide a more complete understanding of UTWT in the WB6.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sustainable Career Ecosystems: Integrating SCT and CET\u003c/h2\u003e \u003cp\u003eThe SCT and CET integrated in the Sustainable Career Ecosystem (SCE) framework (Donald et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This framework provides a comprehensive approach for understanding career transitions, long-term employability, and workforce sustainability. It moves beyond linear career models and individual agency, recognizing that career sustainability is shaped by multi-level interactions. A SCE is defined as:\u003c/p\u003e \u003cp\u003e\u0026ldquo;A variety of interconnected and interdependent actors across higher education institutions and workplace contexts, whereby the lives and careers of individuals evolve and play out over time with an emphasis on sustainable outcomes for the individual, organizations, and broader society\u0026rdquo; (Donald, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, p.xxvii)\u003c/p\u003e \u003cp\u003eThis multi-layered system emphasizes dynamic interactions between individuals and institutional actors at the meso- and macro-levels, influencing sustainable career transitions. Graduate employability is embedded within a broader ecosystem of policies, practices, and social capital structures. The integration of CET within SCE underscores the importance of interdependencies among different actors, including individuals, organizations, governments, professional associations, and even AI-driven recruitment technologies (Donald et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The SCE framework builds on three fundamental career dimensions: person, context, and time. The person dimension emphasizes employability capital, including education, skills, adaptability, and career self-management. However, individual agency is often constrained by structural limitations, such as labour market segmentation and institutional hiring biases. The context dimension integrates institutional and organizational supports. Policy interventions are crucial for aligning graduate pathways with education and aspirations. SCT argues that careers evolve over time, influenced by life-stage transitions and external shocks. The temporal aspect is crucial for understanding the long-term consequences of initial UTWT experiences, as reflected in our hypothesis regarding the potential for lower long-term career stability among those relying on informal hiring networks. The SCE framework also integrates insights from other theories, such as social exchange theory, conservation of resources theory, and self-determination theory, providing a multi-disciplinary perspective. Applying SCE to UTWT research allows for examining how institutional structures and evolving employment trends influence career sustainability in transition economies. This is particularly relevant in the WB6, where informal hiring mechanisms and weak career mobility pathways necessitate a policy-driven approach.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Inequality of Opportunity in UTWT\u003c/h2\u003e \u003cp\u003ePersistent labour market inequalities shape UTWT in the WB6, particularly affecting graduates from disadvantaged backgrounds. In a meritocratic labour market, UTWT should be based solely on competence, ensuring equal access to employment opportunities. However, in the WB6, career success remains highly dependent on systemic inequalities (Blokker et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These inequalities manifest at two key stages: pre-labour market inequality (disparities in education quality, access to guidance, internships, and networks) and in-labour market inequality (time-to-first-job, job stability, mobility, and wage disparities) (De Schepper et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Palmisano et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These inequalities are exacerbated by structural deficiencies, including weak HEI-industry linkages and reliance on informal hiring mechanisms (Dlouhy et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Employers often prioritize personal connections over formal recruitment, disadvantaging graduates without strong professional networks (Efendic and Ledeneva, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This reliance on social capital weakens career sustainability, disproportionately affecting graduates from rural, lower-income, or non-elite university backgrounds. The absence of structured work-based learning programs further restricts graduates' ability to secure sustainable employment (De Vos et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese systemic issues are central to our hypotheses that explore the impact of social capital and socio-economic background on UTWT. The COVID-19 pandemic further exacerbated these UTWT inequalities, disproportionately affecting graduates who relied on structured pathways (OECD, 2023). Economic contractions led to hiring freezes and declining prospects, particularly for young workers from lower-income backgrounds and rural areas (Siri et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The pandemic exposed the fragility of WB6 labour market institutions, demonstrating how weak career sustainability mechanisms disproportionately harmed vulnerable graduates. Geographical location is another critical determinant. Urban graduates have greater prospects due to better-resourced universities and proximity to larger labour markets. Conversely, rural graduates face heightened barriers (Blokker et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Addressing these inequalities requires targeted policy interventions, such as regional job creation programs and digital employment initiatives (Donald and Jackson, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Labour Market Trends in the WB6: Career Ecosystem Perspectives\u003c/h2\u003e \u003cp\u003eThe labour market in the WB6 is characterized by structural fragilities that impede sustainable career transitions. Despite policies aimed at aligning workforce development with the European Pillar of Social Rights, the region faces high youth unemployment, persistent skills mismatches, and limited career mobility (Bartlett and Oruc, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The SCT and IOp framework reveal systemic barriers that constrain graduates' ability to transition into sustainable employment.\u003c/p\u003e \u003cp\u003eThe WB6 labour market ecosystem lacks the resilience necessary to provide structured career pathways, with weak HEI-industry linkages and reliance on informal hiring networks (Bartlett et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pastore et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Graduates often enter the workforce without job-ready skills or relevant work experience. This lack of institutional coordination undermines career sustainability, reinforcing a fragile labour market ecosystem.\u003c/p\u003e \u003cp\u003eInformal hiring practices deepen inequality, as job placement is often mediated through personal connections (Drishti et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Graduates with strong social capital transition faster, while those without face prolonged job searches (Monteiro et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This reliance on informal mechanisms weakens career ecosystems by reducing transparency and limiting equal access. Regional economic disparities contribute to unequal access. Urban graduates benefit from higher job availability and stronger HEI-industry collaborations, while rural graduates encounter limited prospects and higher migration pressures (Bartlett et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Donald et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; O\u0026rsquo;Reilly et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pastore and Zimmermann, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The SCE framework suggests that career sustainability is highly dependent on local and national structures, necessitating regional policy interventions (Donald et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic further exposed the fragility of the WB6 labour market. Economic downturns led to hiring freezes and sectoral shifts, exacerbating career instability (Siri et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The pandemic particularly impacted industries where young workers were overrepresented, leading to job losses and increased reliance on precarious contracts (Regional Cooperation Council, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). Youth unemployment rates in the WB6 remain among the highest in Europe (Regional Cooperation Council, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). Prolonged delays in UTWT create long-term career disadvantages, reinforcing a \"scarring effect\" (Pastore et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This failure of the career ecosystem to provide structured and equitable pathways results in entrenched inequalities.\u003c/p\u003e \u003cp\u003eAddressing these inefficiencies requires targeted policy interventions. Strengthening HEI-industry partnerships, expanding work-based learning programs, and modernizing curricula would facilitate smoother transitions (Bartlett et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Donald et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Promoting meritocratic hiring practices, expanding digital career support services, and investing in regional economic development can bridge disparities (Wang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Hypotheses\u003c/h2\u003e \u003cp\u003eGrounded in the sustainable career ecosystem framework and the inequality of opportunity perspective, this study proposes the following hypotheses.\u003c/p\u003e \u003cp\u003eH1: Inequality of opportunity in UTWT (Stage 1): The length of time-to-first-job (measured in years) will be significantly influenced by circumstances beyond individual control, including gender, rural origin, family socio-economic status, and family member job loss.\u003c/p\u003e \u003cp\u003eH2: Circumstances and employment outcomes (Stage 2): Controlling for other factors, including the IOp (or predicted time-to-first-job from Stage 1), female graduates, with rural origins, from lower or average socio-economic backgrounds, and with at least a family member who lost their job, will report lower job satisfaction, lower satisfaction with job opportunities, and lower perceived job security compared to their respective counterparts: male graduates, from urban origins, from higher socio-economic backgrounds, and with no family members who recently lost their jobs.\u003c/p\u003e \u003cp\u003eH3: Sustainable career ecosystem factors and employment outcomes (Stage 2): Graduates who perceive own skills mismatch with their jobs, with higher level of readiness to acquire additional skills, who believe that their social capital (family and friends network) are important in finding a job, and who believe that the government should protect people from losing their jobs, will report lower levels of job satisfaction, satisfaction with job opportunities, and job security\u003c/p\u003e \u003cp\u003eH4: IOp and employment outcomes (Stage 2): Higher IOp values, indicating longer predicted time-to-first-job, will be associated with lower job satisfaction, lower satisfaction with job opportunities, and lower perceived job security, even after controlling for individual characteristics and career ecosystem factors.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Data, variables, and estimation","content":"\u003cp\u003eThis study draws upon data from the 2019 to 2021 waves of the Public Opinion survey, an annual survey conducted in the WB6 economies and commissioned by the Regional Cooperation Council\u003csup\u003e1\u003c/sup\u003e. Each year, the survey includes approximately 1000 respondents per country. This timeframe is particularly insightful because, during and immediately after the COVID-19 pandemic, graduates' reliance on parental social capital likely intensified.\u003c/p\u003e \u003cp\u003eOur analysis is restricted to the subsample of respondents aged 21\u0026ndash;25 who have completed university education. This age restriction stems from a limitation of the dataset: the survey does not collect information on the specific degree obtained or the year of graduation. The above hypotheses are structured to reflect the two-stage modelling approach. Stage 1 focuses on estimating IOp in the time-to-first-job. Stage 2 examines the determinants of employment outcomes (job satisfaction, satisfaction with job opportunities, and job security), incorporating the IOp measure from Stage 1.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Stage 1: Estimating inequality of opportunity in UTWT\u003c/h2\u003e \u003cp\u003eIn stage 1, the outcome variable, time-to-first-job, is derived from the questionnaire item: \u003cem\u003e\"How long it took you between finishing education and getting the first job?\"\u003c/em\u003e This variable is measured in years\u003csup\u003e2\u003c/sup\u003e. We treat this variable as continuous in our primary analysis, using ordinary least squares (OLS) regression\u003csup\u003e3\u003c/sup\u003e and estimate Eq.\u0026nbsp;1 as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{T}\\text{i}\\text{m}\\text{e}-\\text{t}\\text{o}-\\text{f}\\text{i}\\text{r}\\text{s}\\text{t}-\\text{j}\\text{o}\\text{b}={{\\beta\\:}}_{0}+{{\\beta\\:}}_{1}{\\text{M}\\text{a}\\text{l}\\text{e}}_{\\text{i}}+{{\\beta\\:}}_{2}{\\text{R}\\text{u}\\text{r}\\text{a}\\text{l}}_{\\text{i}}+{{\\beta\\:}}_{3}{\\text{U}\\text{n}\\text{e}\\text{m}\\text{p}\\text{l}\\text{o}\\text{y}\\text{e}\\text{d}\\text{F}\\text{a}\\text{m}}_{\\text{i}}+{{\\beta\\:}}_{4}{\\text{A}\\text{v}\\text{e}\\text{r}\\text{a}\\text{g}\\text{e}\\text{S}\\text{E}\\text{S}}_{\\text{i}}+{{\\beta\\:}}_{5}{\\text{L}\\text{o}\\text{w}\\text{e}\\text{r}\\text{S}\\text{E}\\text{S}}_{\\text{i}}+{\\gamma\\:}{\\text{C}\\text{o}\\text{u}\\text{n}\\text{t}\\text{r}\\text{y}\\text{F}\\text{E}}_{\\text{i}}+\\:{\\delta\\:}{\\text{Y}\\text{e}\\text{a}\\text{r}\\text{F}\\text{E}}_{\\text{i}}+{{\\epsilon\\:}}_{\\text{i}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eEq.\u0026nbsp;1\u003c/p\u003e \u003cp\u003eThe independent variables representing circumstances beyond individual control in stage 1 include: a binary variable for gender (Male\u0026thinsp;=\u0026thinsp;1, Female\u0026thinsp;=\u0026thinsp;0), a binary variable for place of birth (Rural\u0026thinsp;=\u0026thinsp;1, Urban\u0026thinsp;=\u0026thinsp;0), a binary variable indicating whether any family members are unemployed (Unemployed Familiars\u0026thinsp;=\u0026thinsp;1, None\u0026thinsp;=\u0026thinsp;0), and two binary variables capturing the respondent's subjective assessment of their family's socio-economic status: average (1\u0026thinsp;=\u0026thinsp;Average, 0\u0026thinsp;=\u0026thinsp;Other [High or Low]) and lower (1\u0026thinsp;=\u0026thinsp;Below Average, 0\u0026thinsp;=\u0026thinsp;Other [High or Average]). To control for unobserved heterogeneity, we include country fixed effects (a set of binary variables for each WB6 country, with Montenegro serving as the reference category) and year fixed effects (binary variables for 2020 and 2021, with 2019 as the reference year).\u003c/p\u003e \u003cp\u003eFollowing the ex-ante approach to IOp measurement (Ferreira and Peragine, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Palmisano et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), we transform the observed distribution of time-to-first-job into a counterfactual distribution that reflects only inequality as a function of circumstances. This is achieved using the predicted values from the OLS regression. It is crucial to acknowledge that the Public Opinion dataset does not include information on parental education and occupation, variables typically considered essential in IOp studies. To partially compensate for this, we rely on two proxy variables: the respondent's subjective assessment of their family's socio-economic status and whether a family member has experienced job loss. However, this limitation likely results in an underestimation of the true extent of IOp.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Stage 2: Analysis of employment outcomes\u003c/h2\u003e \u003cp\u003eStage 2 of the analysis shifts focus to three binary dependent variables representing different facets of employment outcomes: job satisfaction (1\u0026thinsp;=\u0026thinsp;Mostly Satisfied or Completely Satisfied, 0\u0026thinsp;=\u0026thinsp;Otherwise), satisfaction with job opportunities (1\u0026thinsp;=\u0026thinsp;Mostly Satisfied or Completely Satisfied, 0\u0026thinsp;=\u0026thinsp;Otherwise), and job security (1\u0026thinsp;=\u0026thinsp;Fairly Confident or Very Confident, 0\u0026thinsp;=\u0026thinsp;Otherwise). For each of these variables, separate logistic regression models are estimated. These models report the logit coefficients, representing the change in the log-odds of experiencing a positive outcome (e.g., being satisfied with the job) for different groups, controlling for all other factors in the model. The general form of the logistic regression is:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{\\text{E}\\text{m}\\text{p}\\text{l}\\text{o}\\text{y}\\text{m}\\text{e}\\text{n}\\text{t}\\_\\text{o}\\text{u}\\text{t}\\text{c}\\text{o}\\text{m}\\text{e}\\text{s}}_{\\text{i}}={\\alpha\\:}+{{\\beta\\:}}_{1}{\\text{C}\\text{i}\\text{r}\\text{c}\\text{u}\\text{m}\\text{s}\\text{t}\\text{a}\\text{n}\\text{c}\\text{e}\\text{s}}_{\\text{i}}+{{\\beta\\:}}_{2}\\text{S}\\text{E}\\text{C}\\_{\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{o}\\text{l}\\text{s}}_{\\text{i}}+{{\\beta\\:}}_{3}{\\text{I}\\text{O}\\text{p}\\_\\text{M}\\text{e}\\text{a}\\text{s}\\text{u}\\text{r}\\text{e}}_{\\text{i}}+{{\\epsilon\\:}}_{\\text{i}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eEq.\u0026nbsp;3\u003c/p\u003e \u003cp\u003eTwo primary models are estimated in this stage. Model 1, which includes the same circumstance variables used in Stage 1 to assess their direct effects on employment outcomes. This model also incorporates a comprehensive set of control variables aligned with SCE theory: skills mismatch (1\u0026thinsp;=\u0026thinsp;Skills Mismatch, 0\u0026thinsp;=\u0026thinsp;No Mismatch), readiness to acquire additional skills (1\u0026thinsp;=\u0026thinsp;ready to acquire, 0\u0026thinsp;=\u0026thinsp;not ready), social capital importance (1\u0026thinsp;=\u0026thinsp;family/friends network Important to find a job, 0\u0026thinsp;=\u0026thinsp;Other), jobs need to be protected by the government (1\u0026thinsp;=\u0026thinsp;Agree, 0\u0026thinsp;=\u0026thinsp;Disagree), job is in public sector (1\u0026thinsp;=\u0026thinsp;public, 0\u0026thinsp;=\u0026thinsp;private sector), job is in wage employment (1\u0026thinsp;=\u0026thinsp;wage employment, 0\u0026thinsp;=\u0026thinsp;self-employment), job is in capital city (1\u0026thinsp;=\u0026thinsp;capital city, 0\u0026thinsp;=\u0026thinsp;other), and the same country and year fixed effects included in Stage 1. Model 2, builds upon Model 1 by adding the IOp measure derived from Stage 1.\u003c/p\u003e \u003cp\u003eWe recognize the potential for endogeneity in the IOp measure (or predicted time-to-first-job), as unobserved factors might influence both the UTWT process and subsequent employment outcomes. While the circumstance variables could, in principle, serve as instruments, the required exclusion restriction (that circumstances affect outcomes solely through their impact on UTWT) is a strong assumption.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Descriptive statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides descriptive statistics for all variables used in the analysis. The sample consists of university graduates aged 21\u0026ndash;25 in the WB6 across the years 2019\u0026ndash;2021. The average time-to-first-job in the sample is 2.7 years (with a standard deviation of 1.8 years), highlighting the substantial transition period faced by many graduates. A majority of graduates report being mostly or completely satisfied with their current job and with job opportunities. A higher proportion report being fairly or very confident in keeping their jobs. A considerable percentage of graduates reported to have a skill mismatch. Almost all of the graduates are willing to acquire additional skills, and that social capital is important for them. Only 30% are working on the public sector, while most of them are employed as wage workers. 40% of the subsample are male, and 80% are born in rural areas. Regarding the family SES, 30% report having unemployed members, half consider their SES as average, and 40% consider it as below average.\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\u003eVariable definitions, sample means, and standard deviations: analysis for pooled sample at the micro/individual level\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \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\u003eDefinition (Questionnaire question)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent variable first stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime-to-first-job\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLength of the time in years spent from university to first job\u003c/p\u003e \u003cp\u003eQuestionnaire item:\u003c/p\u003e \u003cp\u003e\u0026lsquo;How long it took you between finishing university to getting the first job?\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent variables second stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHow satisfied are you with your present job?\u003c/p\u003e \u003cp\u003e(Response scale: 1 \u0026lsquo;I\u0026rsquo;m completely dissatisfied\u0026rsquo;, 2 \u0026lsquo;I\u0026rsquo;m mostly unsatisfied\u0026rsquo;, 3 \u0026lsquo;Neither satisfied nor dissatisfied\u0026rsquo;, 4 \u0026lsquo;I\u0026rsquo;m mostly satisfied\u0026rsquo;, 5 \u0026lsquo;I\u0026rsquo;m completely satisfied\u0026rsquo;.\u003c/p\u003e \u003cp\u003eRecoded to binary where 1 \u0026lsquo;I\u0026rsquo;m mostly satisfied\u0026rsquo; or \u0026lsquo;I\u0026rsquo;m completely satisfied\u0026rsquo;, 0 the other response categories.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatisfaction with Job Opportunities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHow satisfied are you with job opportunities?\u003c/p\u003e \u003cp\u003e(Response scale: 1 \u0026lsquo;I\u0026rsquo;m completely dissatisfied\u0026rsquo;, 2 \u0026lsquo;I\u0026rsquo;m mostly unsatisfied\u0026rsquo;, 3 \u0026lsquo;Neither satisfied nor dissatisfied\u0026rsquo;, 4 \u0026lsquo;I\u0026rsquo;m mostly satisfied\u0026rsquo;, 5 \u0026lsquo;I\u0026rsquo;m completely satisfied\u0026rsquo;.\u003c/p\u003e \u003cp\u003eRecoded to binary where 1 \u0026lsquo;I\u0026rsquo;m mostly satisfied\u0026rsquo; or \u0026lsquo;I\u0026rsquo;m completely satisfied\u0026rsquo;, 0 the other response categories.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob Security\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHow confident are you in keeping your job in the coming 12 months?\u003c/p\u003e \u003cp\u003e(Response scale: 1 \u0026lsquo;Not confident at all\u0026rsquo;, 2 \u0026lsquo;Not very confident\u0026rsquo;, 3 \u0026lsquo;Fairly confident\u0026rsquo;, 4 \u0026lsquo;Very confident\u0026rsquo;.\u003c/p\u003e \u003cp\u003eRecoded to binary where 1 \u0026lsquo;Fairly confident\u0026rsquo; and \u0026lsquo;Very confident\u0026rsquo;, 0 the other response categories.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkills mismatch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWould you agree that the skills you learned in the education system meet the needs of your job?\u003c/p\u003e \u003cp\u003e(Response scale: 1 \u0026lsquo;Totally disagree\u0026rsquo;, 2 \u0026lsquo;Tend to disagree\u0026rsquo;, 3 \u0026lsquo;Tend to agree\u0026rsquo;, 4 \u0026lsquo;Totally agree\u0026rsquo;.\u003c/p\u003e \u003cp\u003eRecoded to binary where 1 Tend to disagree\u0026rsquo; or \u0026lsquo;Totally disagree\u0026rsquo;, 0 the other response categories.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReadiness to acquire additional skills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWould you be ready to acquire additional qualifications in order to get a job?\u003c/p\u003e \u003cp\u003e(Response scale: 1 \u0026lsquo;I will not for sure\u0026rsquo;, 2 \u0026lsquo;Probably I will not\u0026rsquo;, 3 \u0026lsquo;Probably I will\u0026rsquo;, 4 \u0026lsquo;I will for sure\u0026rsquo;.\u003c/p\u003e \u003cp\u003eRecoded to binary where 1 \u0026lsquo;Probably I will\u0026rsquo; or \u0026lsquo;I will for sure\u0026rsquo;, 0 the other response categories.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial capital importance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIn your opinion, which two (2) assets are most important for finding a job today?\u003c/p\u003e \u003cp\u003e8. Network of family and friends in high places (Response scale: 1 Yes whether first of second asset, 0 No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJobs need to be protected by the government\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo what extent, if at all, do you agree or disagree, with the following statement: the Government is doing enough to protect people from losing their jobs?\u003c/p\u003e \u003cp\u003e(Response scale: 1 \u0026lsquo;Totally disagree\u0026rsquo;, 2 \u0026lsquo;Tend to disagree\u0026rsquo;, 3 \u0026lsquo;Tend to agree\u0026rsquo;, 4 \u0026lsquo;Totally agree\u0026rsquo;.\u003c/p\u003e \u003cp\u003eRecoded to binary where 1 Tend to agree\u0026rsquo; or \u0026lsquo;Totally agree\u0026rsquo;, 0 the other response categories.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic sector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBinary variable for sector of employment, 1 \u0026lsquo;Public\u0026rsquo;, 0 \u0026lsquo;Private\u0026rsquo;.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWage employment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBinary variable for type of employer, 1 \u0026lsquo;Wage employment, 0 \u0026lsquo;Self-employment\u0026rsquo;.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion of living\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBinary variable for region of living, 1 \u0026lsquo;Capital city, 0 \u0026lsquo;Other\u0026rsquo;.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExternal circumstances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiological endowments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003eBinary variable, 1 for \u0026rsquo;male\u0026rsquo;, 0 for \u0026rsquo;female\u0026rsquo;.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic endowments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\" colname=\"c2\"\u003e \u003cp\u003eBinary variable for place of birth, 1 for \u0026rsquo;rural\u0026rsquo;, 0 for \u0026rsquo;urban\u0026rsquo;.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial-economic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed familiars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBinary variable, 1 for \u0026lsquo;someone from your family lost their job\u0026rsquo;, 0 for \u0026rsquo;other\u0026rsquo;.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily average socio-economic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBinary variable if the respondent\u0026rsquo;s subjective social status is, 1 for \u0026lsquo;average\u0026rsquo;, 0 for \u0026lsquo;other\u0026rsquo;.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily lower socio-economic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBinary variable if the respondent\u0026rsquo;s subjective social status is, 1 for \u0026lsquo;below the average\u0026rsquo;, 0 for \u0026lsquo;other\u0026rsquo;.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eEstimates based on the full sample of WB6 countries for years 2019 to 2021 for the subsample of 21\u0026ndash;25 years old.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Stage 1: Determinants of time-to-first-job and IOp\u003c/h2\u003e \u003cp\u003eThis section presents the results of the stage 1 analysis, which focuses on identifying the determinants of time-to-first-job among university graduates in the WB6 and quantifying the extent of IOp in this crucial transition. We utilize a pooled OLS regression model, combining data from all six countries and three years (2019\u0026ndash;2021). The dependent variable is the time elapsed (in years) between graduation and securing the first job. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of two OLS regression models. The \u0026lsquo;baseline model\u0026rsquo; includes only country and year fixed effects, controlling for unobserved heterogeneity across countries and over time. Model 1 with IOp adds the key circumstance variables: male, rural origin, unemployed familiars, family average socio-economic status, and family lower socio-economic status. The R-squared for the baseline model is 0.045, indicating that country and year fixed effects alone explain a small proportion of the variance in time-to-first-job. However, the inclusion of circumstance variables in Model 1 with IOp substantially increases the R-squared to 0.263. This increase of 0.218 suggests that circumstances beyond individual control account for a considerable portion of the variation in UTWT durations, even after accounting for country- and year-specific effects.\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\u003e\u003cb\u003eFirst stage model\u003c/b\u003e: OLS estimates predicting university-to-work transitions (measured in time-to-first-job) (coefficients and standard errors)\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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003cp\u003emodel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003cp\u003ewith circumstances\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCircumstances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.175\u003c/p\u003e \u003cp\u003e(0.068)**\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural origin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003cp\u003e(0.075)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed familiars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003cp\u003e(0.072)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily average socio-economic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003cp\u003e(0.088)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily lower socio-economic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003cp\u003e(0.095)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry fixed effects (Ref. Country Montenegro)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003cp\u003e(0.074)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003cp\u003e(0.069)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBosnia and Herzegovina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003cp\u003e(0.085)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003cp\u003e(0.078)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKosovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003cp\u003e(0.092)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003cp\u003e(0.085)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Macedonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.121\u003c/p\u003e \u003cp\u003e(0.079)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003cp\u003e(0.073)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerbia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003cp\u003e(0.088)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003cp\u003e(0.081)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear fixed effects (Ref. year 2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003cp\u003e(0.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003cp\u003e(0.061)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003cp\u003e(0.071)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.138\u003c/p\u003e \u003cp\u003e(0.066)*\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.105\u003c/p\u003e \u003cp\u003e(0.142)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.917\u003c/p\u003e \u003cp\u003e(0.185)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eEstimates based on the full sample of WB6 countries for years 2019 to 2021 for the subsample of 21\u0026ndash;25 years old.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTurning to the specific circumstance variables in \u0026lsquo;model 1 with IOp\u0026rsquo;, we find several significant relationships. Consistent with previous findings, the coefficient for males is negative and statistically significant (-0.175, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that male graduates, on average, find jobs faster than female graduates. The coefficient for rural origin is positive and highly significant (0.253, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that graduates from rural areas experience longer transition times compared to their urban counterparts. While having an unemployed family member does not show a statistically significant effect in the pooled model, family socio-economic status plays a crucial role. The coefficient for family lower socio-economic status is positive and highly significant (0.682, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that graduates from lower socio-economic backgrounds take substantially longer to find their first job compared to those from higher socio-economic backgrounds.\u003c/p\u003e \u003cp\u003eTo quantify IOp, we calculate the predicted values from \u0026lsquo;model 1 with IOp\u0026rsquo;. These values indicate that a substantial portion of the observed variation in time-to-first-job is attributable to circumstances beyond individual control, rather than to differences in effort or ability. This finding provides strong support for Hypothesis 1, confirming that circumstances play a significant role in shaping UTWT outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Stage 2: Determinants of employment outcomes\u003c/h2\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e present the results of the logistic regressions for job satisfaction, satisfaction with job opportunities, and job security, respectively. For each outcome, two model specifications are presented. Model 1 (circumstance \u0026amp; SCE controls) includes the circumstance variables (male, rural origin, unemployed familiars, family socio-economic status) and a set of control variables representing SCE factors (perceptions on: skills mismatch, readiness to acquire additional skills, social capital importance, jobs need to be protected by government; and job features such as public sector, wage employment, region of living).\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\u003e\u003cb\u003eSecond stage model\u003c/b\u003e: Logistic Regression Results: Job Satisfaction (coefficients and standard errors)\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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003cp\u003e(Circumstance \u0026amp; SCE controls)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003cp\u003e(IOp Model)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCircumstances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003e0.245\u003c/p\u003e \u003cp\u003e(0.087)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003cp\u003e(0.089)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural origin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.295\u003c/p\u003e \u003cp\u003e(0.098)***\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.247\u003c/p\u003e \u003cp\u003e(0.099)**\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed familiars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.095\u003c/p\u003e \u003cp\u003e(0.108)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.048\u003c/p\u003e \u003cp\u003e(0.109)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily average socio-economic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003cp\u003e(0.117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003cp\u003e(0.119)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily lower socio-economic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.445\u003c/p\u003e \u003cp\u003e(0.129)***\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.346\u003c/p\u003e \u003cp\u003e(0.131)**\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCE controls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkills Mismatch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.595\u003c/p\u003e \u003cp\u003e(0.097)***\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.548\u003c/p\u003e \u003cp\u003e(0.098)***\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReadiness to Acquire Additional Skills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.198\u003c/p\u003e \u003cp\u003e(0.088)*\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.145\u003c/p\u003e \u003cp\u003e(0.089)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Capital Importance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003cp\u003e(0.106)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003cp\u003e(0.108)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJobs Need to be Protected by Government\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003cp\u003e(0.096)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003cp\u003e(0.097)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic Sector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003cp\u003e(0.118)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003cp\u003e(0.119)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWage Employment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003cp\u003e(0.109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003cp\u003e(0.110)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion of Living (Capital City)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003cp\u003e(0.089)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003cp\u003e(0.090)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCountry Fixed Effects (Ref. Country Montenegro)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003cp\u003e(0.054)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003cp\u003e(0.043)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBosnia and Herzegovina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003cp\u003e(0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003cp\u003e(0.036)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKosovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.118\u003c/p\u003e \u003cp\u003e(0.065)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003cp\u003e(0.034)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Macedonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003cp\u003e(0.089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003cp\u003e(0.033)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerbia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003cp\u003e(0.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003cp\u003e(0.056)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear Fixed Effects (Ref. year 2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003cp\u003e(0.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003cp\u003e(0.065)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003cp\u003e(0.076)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003cp\u003e(0.077)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIOp Measure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.245\u003c/p\u003e \u003cp\u003e(0.448)**\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003cp\u003e(0.198)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003cp\u003e(0.248)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eFigures in curved parentheses are standard errors.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*, ** and *** denote significance at the 10%, 5% and 1% levels, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003e\u003cb\u003eSecond stage model\u003c/b\u003e: Logistic Regression Results: Satisfaction with Job Opportunities (coefficients and standard errors)\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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003cp\u003e(Circumstance \u0026amp; SCE controls)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003cp\u003e(IOp Model)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCircumstances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003e0.178\u003c/p\u003e \u003cp\u003e(0.089)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003cp\u003e(0.090)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural origin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.248\u003c/p\u003e \u003cp\u003e(0.099)**\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.197\u003c/p\u003e \u003cp\u003e(0.100)*\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed familiars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.048\u003c/p\u003e \u003cp\u003e(0.109)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003cp\u003e(0.110)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily average socio-economic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003cp\u003e(0.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003cp\u003e(0.119)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily lower socio-economic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.375\u003c/p\u003e \u003cp\u003e(0.128)***\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.298\u003c/p\u003e \u003cp\u003e(0.130)**\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCE controls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkills Mismatch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.545\u003c/p\u003e \u003cp\u003e(0.098)***\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.498\u003c/p\u003e \u003cp\u003e(0.099)***\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReadiness to Acquire Additional Skills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.148\u003c/p\u003e \u003cp\u003e(0.089)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.098\u003c/p\u003e \u003cp\u003e(0.090)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Capital Importance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003cp\u003e(0.107)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003cp\u003e(0.109)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJobs Need to be Protected by Government\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003cp\u003e(0.097)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003cp\u003e(0.098)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic Sector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003cp\u003e(0.119)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003cp\u003e(0.120)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWage Employment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003cp\u003e(0.109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003cp\u003e(0.110)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion of Living (Capital City)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003cp\u003e(0.089)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003cp\u003e(0.090)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCountry Fixed Effects (Ref. Country Montenegro)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003cp\u003e(0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003cp\u003e(0.089)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBosnia and Herzegovina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003cp\u003e(0.054)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003cp\u003e(0.076)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKosovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.095\u003c/p\u003e \u003cp\u003e(0.065)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.086\u003c/p\u003e \u003cp\u003e(0.077)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Macedonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003cp\u003e(0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003cp\u003e(0.055)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerbia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003cp\u003e(0.043)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003cp\u003e(0.067)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear Fixed Effects (Ref. year 2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003cp\u003e(0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003cp\u003e(0.089)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003cp\u003e(0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003cp\u003e(0.066)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIOp Measure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.945\u003c/p\u003e \u003cp\u003e(0.398)**\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003cp\u003e(0.198)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003cp\u003e(0.249)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eFigures in curved parentheses are standard errors.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*, ** and *** denote significance at the 10%, 5% and 1% levels, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003e\u003cb\u003eSecond stage model\u003c/b\u003e: Logistic Regression Results: Job Security (coefficients and standard errors)\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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003cp\u003e(Circumstance \u0026amp; SCE controls)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003cp\u003e(IOp Model)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCircumstances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\u003e0.098\u003c/p\u003e \u003cp\u003e(0.089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003cp\u003e(0.090)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural origin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.178\u003c/p\u003e \u003cp\u003e(0.099)*\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.148\u003c/p\u003e \u003cp\u003e(0.100)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed familiars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003cp\u003e(0.109)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003cp\u003e(0.110)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily average socio-economic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003cp\u003e(0.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003cp\u003e(0.119)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily lower socio-economic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.275\u003c/p\u003e \u003cp\u003e(0.129)**\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.198\u003c/p\u003e \u003cp\u003e(0.130)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCE controls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkills Mismatch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.445\u003c/p\u003e \u003cp\u003e(0.098)***\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.398\u003c/p\u003e \u003cp\u003e(0.099)***\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReadiness to Acquire Additional Skills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.095\u003c/p\u003e \u003cp\u003e(0.089)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.048\u003c/p\u003e \u003cp\u003e(0.090)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Capital Importance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003cp\u003e(0.108)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003cp\u003e(0.109)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJobs Need to be Protected by Government\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003cp\u003e(0.097)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003cp\u003e(0.098)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic Sector\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003cp\u003e(0.118)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003cp\u003e(0.119)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWage Employment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003cp\u003e(0.109)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003cp\u003e(0.110)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion of Living (Capital City)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003cp\u003e(0.089)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003cp\u003e(0.090)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCountry Fixed Effects (Ref. Country Montenegro)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.187\u003c/p\u003e \u003cp\u003e(0.065)*\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.177\u003c/p\u003e \u003cp\u003e(0.076)*\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBosnia and Herzegovina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.098\u003c/p\u003e \u003cp\u003e(0.087)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.077\u003c/p\u003e \u003cp\u003e(0.089)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKosovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003cp\u003e(0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003cp\u003e(0.076)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth Macedonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003cp\u003e(0.056)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003cp\u003e(0.087)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerbia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.088\u003c/p\u003e \u003cp\u003e(0.098)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \u003cp\u003e(0.055)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear Fixed Effects (Ref. year 2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003cp\u003e(0.076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003cp\u003e(0.056)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.032\u003c/p\u003e \u003cp\u003e(0.089)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.044\u003c/p\u003e \u003cp\u003e(0.055)\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIOp Measure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.745\u003c/p\u003e \u003cp\u003e(0.348)*\u003c/p\u003e\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003cp\u003e(0.199)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003cp\u003e(0.249)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eFigures in curved parentheses are standard errors.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*, ** and *** denote significance at the 10%, 5% and 1% levels, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel 2 (IOp model) includes the predicted values of time-to-first-job from Stage 1, which captures the overall level of IOp in UTWT, as a predictor. Turning first to the results for job satisfaction (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), we find mixed support for Hypotheses 2 and 3, which predicted negative associations between female gender, rural origin, family unemployment, and lower socio-economic status, respectively, and job satisfaction. Consistent with Hypothesis 2, graduates from rural origins report significantly lower job satisfaction (Model 1: -0.295, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Model 2: -0.247, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Also consistent with expectations (Hypothesis 3), graduates from families with lower socio-economic status report significantly lower job satisfaction (Model 1: -0.445, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Model 2: -0.346, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, contradicting Hypothesis 2, male graduates report higher job satisfaction than female graduates (Model 1: 0.245, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Model 2: 0.198, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10). Having an unemployed family member does not have a statistically significant effect on job satisfaction, providing no support for Hypothesis 2. Among the SCE controls (Hypothesis 3), skills mismatch has a strong negative association with job satisfaction (Model 1: -0.595, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Model 2: -0.548, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), as expected. Graduates who believe that social capital (family and friend networks) is important for finding a job, those who believe that the government should protect jobs, and those employed in the public sector report significantly higher job satisfaction.\u003c/p\u003e \u003cp\u003eCrucially, the inclusion of the IOp in Model 2 reveals a significant negative association between IOp in UTWT and subsequent job satisfaction (coefficient = -1.245, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This provides strong support for Hypothesis 4, suggesting that graduates who experienced greater IOp in their initial transition to employment report lower levels of job satisfaction, even after controlling for individual circumstances and a range of career ecosystem factors.\u003c/p\u003e \u003cp\u003eThe results for satisfaction with job opportunities (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) largely mirror those for job satisfaction. Rural origin (Model 1: -0.248, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Model 2: -0.197, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10) and lower socio-economic status (Model 1: -0.375, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; Model 2: -0.298, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are significantly and negatively associated with satisfaction with job opportunities, while male graduates report slightly higher satisfaction. Skills mismatch is again a strong negative predictor, while social capital importance and public sector employment are positively associated with satisfaction with job opportunities. Consistent with Hypothesis 4, the IOp has a significant negative coefficient (-0.945, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that greater IOp in UTWT is associated with lower satisfaction with job opportunities.\u003c/p\u003e \u003cp\u003eFinally, the results for Job Security (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) show some similarities and some differences compared to the satisfaction outcomes. Lower socio-economic status is negatively associated with job security (Model 1: -0.275, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while rural origin is marginally significant in model 1. Skills mismatch has a strong negative association with job security, while the belief in government protection of jobs and public sector employment are strong positive predictors, as expected. Wage employment is also positively associated with job security. Supporting Hypothesis 4, the IOp has a significant negative coefficient (-0.745, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10), indicating that greater inequality of opportunity in UTWT is associated with lower perceived job security\u003csup\u003e4\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study examined the interplay between individual circumstances, sustainable career ecosystem (SCE) factors, and inequality of opportunity (IOp) in shaping university-to-work transitions (UTWT) and subsequent employment outcomes for young graduates in the Western Balkans Six (WB6). Grounded in the Sustainable Career Ecosystem (SCE) framework (Donald, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Donald et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and the Inequality of Opportunity (IOp) perspective, we employed a two-stage quantitative analysis using data from the 2019\u0026ndash;2021 Regional Cooperation Council Public Opinion survey. The study sought to address four primary objectives: first, to measure the extent of IOp in UTWT, estimating the effect of structural barriers on time-to-first-job; second, to assess how IOp and SCE factors influence employment outcomes, including job satisfaction, perceived job opportunities, and job security; third, to examine the link between early-career disadvantage and later job quality; and lastly, to provide policy recommendations for addressing systemic employability barriers, contributing to a more sustainable and equitable career ecosystem in the WB6, aligned with the United Nations Sustainable Development Goals (SDGs).\u003c/p\u003e \u003cp\u003eThe findings provide strong empirical validation for the SCE framework, reinforcing the idea that career sustainability is shaped by systemic inequalities, institutional structures, and labour market conditions. The study\u0026rsquo;s results confirm the significant influence of structural barriers on UTWT. The length of time-to-first-job is strongly affected by individual circumstances such as gender, rural origin, and family socio-economic status, indicating that IOp plays a central role in shaping early-career trajectories. These findings align with IOp theory, which argues that labour market inequalities are rooted in structural disadvantages rather than individual effort alone (Ferreira \u0026amp; Peragine, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The WB6 labour market, characterized by persistent socio-economic inequalities and institutional weaknesses, amplifies these disparities, challenging the notion of a purely meritocratic employment system. The results also highlight the long-term implications of IOp for employment outcomes. Graduates who experience higher IOp, measured as longer time-to-first-job, report significantly lower job satisfaction, weaker job security, and fewer perceived job opportunities. This reinforces the SCE framework\u0026rsquo;s emphasis on the interconnectedness of career stages, where early-career disadvantages have compounding effects on long-term employment sustainability (Donald \u0026amp; Jackson, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; De Vos et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The presence of scarring effects, where delays in securing initial employment translate into weaker career prospects, suggests that IOp is not only an immediate barrier but also a determinant of long-term career success. These findings contribute to a growing body of literature that highlights the longitudinal nature of employability inequalities in transition economies (Blokker et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe significant effects of career ecosystem factors on employment outcomes provide further support for the assumption that IOp influences employment outcomes. Skills mismatch emerged as a key predictor of lower job satisfaction, job security, and perceived job opportunities, emphasizing the need for stronger alignment between higher education institutions and labour market demands. The results show that graduates who report a mismatch between their skills and job requirements are at a higher risk of employment dissatisfaction, which highlights an urgent need for educational reform and employer engagement to close skills gaps (Donald et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, the importance of social capital in job acquisition and career stability further underscores the role of informal networks in the WB6 labour market, particularly in economies where institutional hiring mechanisms remain underdeveloped (Efendic \u0026amp; Ledeneva, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The reliance on family and friends for job opportunities perpetuates inequality, disproportionately benefiting those with privileged social connections, while disadvantaging graduates from lower socio-economic backgrounds and rural areas.\u003c/p\u003e \u003cp\u003eThe gendered nature of labour market transitions in the WB6 presents another critical area for discussion. The findings indicate that male graduates experience shorter UTWT durations and, in some cases, better employment outcomes compared to female graduates. This pattern deviates from trends observed in other regions and warrants further investigation into the gendered dimensions of labour market participation in transition economies. It is possible that women are more likely to accept lower-quality jobs or withdraw from the labour force due to family responsibilities and societal expectations (Donald et al., 2022). The strong negative effect of lower socio-economic status on both UTWT and employment outcomes further highlights the entrenched nature of social stratification in the WB6, reinforcing the challenges faced by graduates from disadvantaged backgrounds in accessing quality employment. This aligns with research demonstrating the persistent impact of family background on educational and labour market opportunities (De Schepper et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Palmisano et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, the significant disadvantage faced by rural graduates reflects the spatial dimension of inequality, as limited job opportunities, weaker infrastructure, and reduced access to networks create barriers to career mobility (Wang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAddressing these disparities requires systemic policy reforms aligned with the UN Sustainable Development Goals (SDGs). The findings emphasize the importance of strengthening HEI-industry linkages (SDG 4: Quality Education), reducing informal hiring practices and promoting fair employment conditions (SDG 8: Decent Work and Economic Growth), and implementing targeted policies to support disadvantaged graduates (SDG 10: Reduced Inequalities). Enhancing career readiness programs, expanding financial aid and internship opportunities for lower-income students, and reducing urban-rural disparities in job access are essential steps toward fostering an inclusive and sustainable career ecosystem. Also, lifelong learning initiatives and digital workforce reskilling programs would help graduates adapt to technological advancements, ensuring greater career sustainability.\u003c/p\u003e \u003cp\u003eIn conclusion, this study contributes to the emerging discourse on sustainable career ecosystems by providing empirical evidence of the systemic barriers affecting graduate employability in the WB6. The findings underscore the need for coordinated action among educational institutions, employers, and policymakers to build more sustainable and inclusive career ecosystems, ensuring that graduates can transition into meaningful employment pathways despite structural inequalities. Strengthening the institutional foundations of career ecosystems\u0026mdash;through educational reform, labour market policies, and regional development initiatives\u0026mdash;will be crucial in promoting equitable and resilient career transitions in transition economies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\"Ethics Statement: This study was conducted in accordance with ethical guidelines and approved by the Ethics Committee of the University of Shkodra 'Luigj Gurakuqi', ensuring compliance with research ethics standards. Informed consent was obtained from all participants before data collection.\"\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBartlett W, Oruc N (2021) Labour markets in the Western Balkans 2019 and 2020. 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Int J Manage Reviews n/a. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ijmr.12386\u003c/span\u003e\u003cspan address=\"10.1111/ijmr.12386\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcc.int/balkanbarometer/results/2/public\u003c/span\u003e\u003cspan address=\"https://www.rcc.int/balkanbarometer/results/2/public\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e While a more precise measure in months would be preferable from a methodological standpoint, the data is only available in yearly increments.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e However, we explicitly acknowledge the limitations inherent in this approach, including the loss of information due to the coarse measurement, the potential for bias, the reduction in statistical power, and the fact that the variable is, technically, discrete and ordered. These limitations are discussed in detail in the limitations section.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Robustness checks include a control function approach to test for endogeneity, a multivariate probit model to account for correlated outcomes, and sensitivity analyses comparing alternative model specifications. Selection and recall biases are also considered, with external data used for validation where possible. Due to word length constraints, detailed results are available upon request from the corresponding author.\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":"University of Shkodra \"Luigj Gurakuqi\"","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":"Sustainable Career Ecosystem, Inequality of Opportunity, University-to-Work Transition, Graduate Employability, Western Balkans","lastPublishedDoi":"10.21203/rs.3.rs-6189145/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6189145/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e This study examines inequality of opportunity (IOp) in university-to-work transitions (UTWT) and employment outcomes in the Western Balkans Six (WB6), a region facing high graduate unemployment, systemic labour market inequalities, and weak institutional coordination. Grounded in the Sustainable Career Ecosystem (SCE) framework, which integrates Career Ecosystem Theory (CET), Sustainable Career Theory (SCT), and IOp theory, this study explores how systemic, institutional, and individual factors shape employability and career sustainability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign/Methodology/Approach:\u003c/strong\u003e Using a two-stage quantitative analysis with 2019–2021 Regional Cooperation Council survey data, Stage 1 employs OLS regression to estimate IOp in UTWT (time-to-first-job), while Stage 2 applies logistic regression to assess the impact of IOp and career ecosystem factors on employment outcomes (job satisfaction, job security, perceived job opportunities).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings: \u003c/strong\u003eFindings reveal significant disparities based on gender, rural background, and socio-economic status, with higher IOp linked to poorer employment outcomes. While skills mismatches and informal hiring mechanisms hinder career sustainability, social capital, public sector employment, and perceptions of government job protection improve employment outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch limitations/Implications:\u003c/strong\u003e The study is limited by cross-sectional data and the self-reported nature of socio-economic measures. Future research should employ longitudinal data and qualitative approaches to better assess the long-term sustainability of career transitions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOriginality/Value:\u003c/strong\u003e This study provides a multi-level analysis of graduate employability in an underrepresented region, offering insights for policy, universities, and employers. It contributes to the advancement of Sustainable Career Ecosystem theory by examining how structural inequalities shape graduate employability and long-term career sustainability. The findings align with UN Sustainable Development Goals (SDGs) 4 (Quality Education), 8 (Decent Work), and 10 (Reduced Inequalities), advocating for targeted policy interventions that promote equitable and sustainable career pathways.\u003cstrong\u003ePurpose:\u003c/strong\u003e This study examines inequality of opportunity (IOp) in university-to-work transitions (UTWT) and employment outcomes in the Western Balkans Six (WB6), a region facing high graduate unemployment, systemic labour market inequalities, and weak institutional coordination. Grounded in the Sustainable Career Ecosystem (SCE) framework, which integrates Career Ecosystem Theory (CET), Sustainable Career Theory (SCT), and IOp theory, this study explores how systemic, institutional, and individual factors shape employability and career sustainability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign/Methodology/Approach:\u003c/strong\u003e Using a two-stage quantitative analysis with 2019–2021 Regional Cooperation Council survey data, Stage 1 employs OLS regression to estimate IOp in UTWT (time-to-first-job), while Stage 2 applies logistic regression to assess the impact of IOp and career ecosystem factors on employment outcomes (job satisfaction, job security, perceived job opportunities).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings: \u003c/strong\u003eFindings reveal significant disparities based on gender, rural background, and socio-economic status, with higher IOp linked to poorer employment outcomes. While skills mismatches and informal hiring mechanisms hinder career sustainability, social capital, public sector employment, and perceptions of government job protection improve employment outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch limitations/Implications:\u003c/strong\u003e The study is limited by cross-sectional data and the self-reported nature of socio-economic measures. Future research should employ longitudinal data and qualitative approaches to better assess the long-term sustainability of career transitions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOriginality/Value:\u003c/strong\u003e This study provides a multi-level analysis of graduate employability in an underrepresented region, offering insights for policy, universities, and employers. It contributes to the advancement of Sustainable Career Ecosystem theory by examining how structural inequalities shape graduate employability and long-term career sustainability. The findings align with UN Sustainable Development Goals (SDGs) 4 (Quality Education), 8 (Decent Work), and 10 (Reduced Inequalities), advocating for targeted policy interventions that promote equitable and sustainable career pathways.\u003c/p\u003e","manuscriptTitle":"Graduate Employability in the Western Balkans: A Career Ecosystem Perspective on Labour Market Inequalities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-13 06:23:17","doi":"10.21203/rs.3.rs-6189145/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":"ffbc809c-8327-4cac-a6a2-90a88b35a88f","owner":[],"postedDate":"March 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45418269,"name":"Other Economics"}],"tags":[],"updatedAt":"2025-03-13T06:23:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-13 06:23:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6189145","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6189145","identity":"rs-6189145","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}