Results 1-2 of 2.
((uid:50043010))

Bookmark and Share    
Full Text
See detailDoes (Re-)Entering the Labor Market at Advanced Ages Protect Against Cognitive Decline? A Panel-Matching Difference-in-differences Approach
Kim, Jung Hyun UL; Muniz-Terrera, Graciela; Leist, Anja UL

E-print/Working paper (2022)

While prolonged labor market participation becomes increasingly important in ageing societies, evidence of the impacts of entering or exiting work beyond age 65 on cognitive functioning is scarce. We ... [more ▼]

While prolonged labor market participation becomes increasingly important in ageing societies, evidence of the impacts of entering or exiting work beyond age 65 on cognitive functioning is scarce. We estimate these effects using panel-matching difference-in-differences with populationrepresentative panel datasets from South Korea and the United States. We compare countries and across socioeconomic characteristics. We find general positive effects of entering the labor market in South Korea, while only individuals with high assets in the US benefit from entering the labor market. Exiting the labor market does not result in changes in cognitive functioning in Korea but is followed by a cognitive decline in individuals with low assets in the US. Findings suggest that the benefits and disincentives from late-life labor status transitions on cognitive functioning vary between South Korea and the US and across socioeconomic groups. [less ▲]

Detailed reference viewed: 24 (3 UL)
Full Text
See detailMachine learning in the social and health sciences
Leist, Anja UL; Klee, Matthias UL; Kim, Jung Hyun UL et al

E-print/Working paper (2021)

The uptake of machine learning (ML) approaches in the social and health sciences has been rather slow, and research using ML for social and health research questions remains fragmented. This may be due to ... [more ▼]

The uptake of machine learning (ML) approaches in the social and health sciences has been rather slow, and research using ML for social and health research questions remains fragmented. This may be due to the separate development of research in the computational/data versus social and health sciences as well as a lack of accessible overviews and adequate training in ML techniques for non data science researchers. This paper provides a meta-mapping of research questions in the social and health sciences to appropriate ML approaches, by incorporating the necessary requirements to statistical analysis in these disciplines. We map the established classification into description, prediction, and causal inference to common research goals, such as estimating prevalence of adverse health or social outcomes, predicting the risk of an event, and identifying risk factors or causes of adverse outcomes. This meta-mapping aims at overcoming disciplinary barriers and starting a fluid dialogue between researchers from the social and health sciences and methodologically trained researchers. Such mapping may also help to fully exploit the benefits of ML while considering domain-specific aspects relevant to the social and health sciences, and hopefully contribute to the acceleration of the uptake of ML applications to advance both basic and applied social and health sciences research. [less ▲]

Detailed reference viewed: 96 (15 UL)