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Risk and the School-to-Work Transition in East Germany and the United States.Barabasch, Antje 12 September 2006 (has links)
This study investigates how young adults in vocational education perceive risk in regard to their professional futures in East Germany and the United States. It analyzes students’ career aspirations and life plans in both countries and how they cope with uncertainties at the labor market. It further outlines underlying values, beliefs, and attitudes that guide young Americans and East Germans in their planning. Several theoretical frameworks ground this study and comprise the majority of the relevant literature. This cross-cultural comparative case study takes a mixed method approach using a concurrent triangulation design. The inquiry is framed by theories in the field of risk and cultural risk perception, school-to-work transition, vocational education and training, and welfare studies. In both countries, 129 students filled out a questionnaire. Additionally, narratives from nine focus groups and 29 biographical interviews were conducted. A three level analysis of the data was compiled that outlines the themes and categorizes them according to an individual, institutional, and macro-structural level of influence on risk perception in each country. Emerging premises on an individual level were choice, family and career planning concerns, geographical and occupational flexibility, further education and training, and agency. On the institutional level the influence of public career advisement institutions, teachers, parents, peers and friends was outlined. On the macro-structural level unemployment, political welfare reforms, the vocational education and training system were themes that have been of concern among the East German population. The dissertation also offers a comparative analysis of the data. This study reveals that young adults in East Germany are highly concerned about their occupational futures and tend to be pessimistic about current welfare reforms. They hold on to the idea of a standard biography and try to make strategic career plans. Their counterparts in the United States are highly optimistic about their futures, expressed little concern about labor market policies, but also appeared to be short term oriented in their life planning in order to remain flexible and mobile.
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Onward Migration : The Transnational Trajectories of Iranians Leaving SwedenKelly, Melissa January 2013 (has links)
Onward migration is an understudied process whereby people leave their country of origin, settle in a second country for a period of time, and then migrate on to a third country. This dissertation explores the transnational trajectories of one specific group of onward migrants. These are highly educated people who moved from Iran to Sweden as refugees following the Iranian Revolution in 1979. Then, after settling in Sweden for a period of time they subsequently moved on to London, England. Melissa Kelly explores how people live their lives across places. Using life history interviews conducted with individual onward migrants, Kelly draws out and contex-tualizes the individual and shared experiences of these migrants in specific space-time contexts, and highlights the meaning of both settlement and mobility in their lives. In doing so, she explores the circumstances that underlie the onward migration phenomenon, drawing attention to different geographical levels of scale, and linking social, economic and cultural perspectives. The main argument of the dissertation is that while place continues to be of sig-nificance, a broader understanding of migrant integration processes is required. Onward migration disrupts the categories usually used to comprehend the integration of migrants in narrowly defined nation state contexts, and encourages a more nuanced understanding of how we conceptualize both migration and settlement.
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Classification Tree Based Algorithms in Studying Predictors for Long-Term Unemployment in Early Adulthood : An Exploratory Analysis Combining Supervised Machine Learning and Administrative Register DataKuikka, Sanni January 2020 (has links)
Unemployment at young age is a negative life event that has been found to have scarring effects for future life outcomes, especially when continuing long-term. Understanding precursors for long-term unemployment in early adulthood is important to be able to target policy interventions in critical junctures in the life course. Paths to unemployment are complex and a comprehensive outlook on the most important factors and mechanisms is difficult to obtain. This study proposes a data-driven, exploratory approach for studying individual and family level factors during ages 0-24, that predict long-term unemployment at the age of 25-30. A supervised machine learning approach was applied to understand associations deriving from longitudinal, individual-level administrative data from a full birth cohort in Finland. The data comprise information about physical and social wellbeing, life course events, as well as demographics, including the parents of the cohort members. Potential predictors were chosen from the data based on theories and previous research, and used to train a model aiming to correctly classify unemployed individuals. A CART algorithm was used to build a classification tree that reveals important variables, ranges of them as well as combinations of factors that together are predictive of long-term unemployment. A random forest algorithm was used to build several trees producing smoothed predictions that reduce overfitting of one tree. CARTs and random forest models were compared to each other to understand how they perform in a research task predicting life outcomes. Both individual and family level factors were found to be predictive of the outcome. Combinations of variables such as GPA lower than ~7.5, ego’s low education level, late work history start, depressive disorders and low parental education and income levels were found to be particularly predictive of unemployment. CART models correctly classified up to 87% of the unemployed, while misclassifying 70% of the employed and having 45% overall accuracy. Testing for CART model stability, finding consistency across several tree models improved robustness. Random forest correctly predicted up to 59% of the unemployed, while also correctly classifying 65% of the employed and producing robust results. The two algorithms together provided valuable insight for better understanding factors contributing to unemployment. The study shows promise for classification tree based methods in studying life course and life outcomes.
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