Arbeitsbereich

Bevölkerungsdynamik und Nachhaltiges Wohlbefinden

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Projekt

Predicting Work-Family Life-Course Sequences

Linda Vecgaile, Emilio Zagheni, Luca Badolato (The Ohio State University, Columbus, Vereinigte Staaten); in Zusammenarbeit mit Luiz Felipe Vecchietti (Institute for Basic Science, Daejeon, Korea, Süd)

Ausführliche Beschreibung

The demographic structure of societies is changing on a global scale due to a combination of factors including decreasing fertility rates and increasing life expectancy. This transformation is commonly referred to as population aging. According to UN projections, the number of people aged 60 or older will reach two billion by 2050, then comprising 22% of the global population. In Europe, roughly 25% of the population has already reached age 60 or above, and the percentage is expected to increase to 35% by 2050. This has numerous implications for future social structures and economies, including changes in labor markets and shifts in family composition. 

With increasing life expectancy, combined with recurrent financial and political turbulences and tight public finance constraints, individuals are now expected to work longer and adapt to changing job markets; and this leads to the destandardization of career paths. Moreover, as technology continues to advance and automation increases, some jobs may become obsolete or require new skills. This puts pressure on individuals to requalify and update their skills over the life of their career, particularly in midcareer, i.e., at a time when birth choices of higher parity are often made, or in late career, i.e., when only few years are left until retirement. The former can impact fertility decisions and the overall ability to balance work and family responsibilities; the latter can lead to long-term unemployment, and this can be particularly challenging for older workers who may face age discrimination or other barriers to reentering the workforce. Further, not all individuals are aging equally, the reason being unequal employment opportunities over the life course and disparities in income and access to health care. These dynamics can create greater diversity in life-course outcomes, especially at older ages, with some adults experiencing greater challenges and vulnerabilities than others. Within the context of an increasing share of older people, this may lead to more individuals experiencing hardship.

Against this background, emerging machine-learning techniques provide useful tools for modeling individual career outcomes and life-course trajectories. In this project, we use these tools to predict life-course sequences and to provide novel approaches in social science research. Machine-learning algorithms can process large amounts of data and provide insights into patterns and trends that may be difficult to detect through traditional statistical methods and theories. We use these predictions to identify individuals in different economic sectors, and across different social groups, who are at risk of experiencing long-term unemployment or financial hardship. The results are intended to provide the basis for targeted interventions to address existing inequalities early on and to favor the transition to a more sustainable employment for those at high risk. By supporting individuals in transitioning to new industries or acquiring new skills, societies would reduce the burden on social welfare systems and improve the overall quality of life for society. This can also lead to more diverse and resilient economies that are better equipped to adapt to changes in the job market and technological advancements.