Institutional and socio-economic drivers of work-to-retirement trajectories in the Netherlands

Abstract

Institutional exit pathways shape individual trajectories from work to retirement. In the Netherlands, early retirement schemes as well as disability and unemployment benefits structure the timing and complexity of transitions within such trajectories. Simultaneously, access to these exit pathways depends on the individuals’ entitlements to various social security programmes as well as their freedom to decide on the timing and path of exit. In this study, sequence analysis was applied to register data of primary sources of income with the aim of identifying the main trajectories from work to retirement between the ages of 56 and 66 for a sample of Dutch men and women born between 1943 and 1945 (N = 2,277). Seven distinct trajectories were found: ‘early retirement’, ‘premature retirement’, ‘late retirement’, ‘disability’, ‘unemployment’, ‘inactivity’ and ‘drop-out’. Multinomial logistic regression analysis was applied to investigate the relations of these trajectories with a set of individual and socio-economic characteristics, as well as factors at a firm level. Especially women, non-natives, the lower educated and the self-employed were found to have a greater risk of ending up in the ‘involuntary’ trajectories of late retirement, disability, unemployment and inactivity. Public-sector employees, farmers and craftsmen, and skilled blue-collar workers were less likely to differentiate from the norm of entering into premature retirement between the ages of 60 and 64.

Gender Differences in Retirement in a Welfare State with High Female Labour Market Participation and Competing Exit Pathways

“…We found eight distinct trajectories that differed in terms of timing of retirement (early, at the statutory retirement age and late) and that were shaped by exit pathways (old-age, parttime, unemployment and disability pensions). Comparable studies in the United States, Germany, the United Kingdom and the Netherlands discovered gender differences in the use of retirement trajectories (Calvo, Madero-Cabib and Staudinger, 2017;Fasang, 2010;Riekhoff, 2016). However, when controlling for other factors, there were no substantial gender differences in the Finnish context.…”

Retirement timing in Italy: rising age and the advantages of a stable working career

“…There is no clear, unambiguous definition of retirement timing (Fisher et al, 2016). Some studies refer to the age at which a worker exits the labour market and therefore ceases to be part of the workforce; others refer to the source of income (specifically being paid a pension) (Riekhoff, 2016(Riekhoff, , 2018. This article will refer to the former, considering an individual as retired according to the self-reported current job situation.…”

Healthcare utilization after a first hospitalization for COPD: a new approach of State Sequence Analysis based on the '6W' multidimensional model of care trajectories

“…This dataset used linked medicoadministrative data from multiple sources which provides a comprehensive picture of healthcare services utilization at both inpatient and outpatient settings, including community-based healthcare services provided in ambulatory care (CLSCs). Moreover, the multidimensional approach of care trajectories allowed the possibility to include as much as 19 states, since these states are partitioned into three distinct sequence-dimensions, thus reducing the complexity of each sequence and avoiding the "overplotting" issue [28,30,31,33]. To our knowledge, this is the first study which proposes a comprehensive perspective of care trajectories, allowing a more intuitive and straightforward examination of the most common shared patterns of care use as a whole.…”

“…A powerful method for the analysis of longitudinal sequential data has recently risen in healthcare research, although to our knowledge only a few studies have used this approach to describe CTs [23][24][25][26]. The State Sequence Analysis (SSA) is largely used in social sciences to describe and visualize longitudinal patterns such as life course or employment status trajectories, where each individual's trajectory consists of a succession of states and transitions [14,24,25,[27][28][29][30][31][32].…”

“…As long as a limited number of states are considered, SSA is a powerful method to analyze and visualize CTs. However, given the high number of healthcare use events, which are not mutually exclusive for a large part, the complexity of sequences may lead to noisy plots, or overplotting, a common issue in data visualization [28,30,31,33].…”