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Gendered adulthood trajectories and extended working lives across liberal regime countries

Published online by Cambridge University Press:  17 October 2025

Ignacio Cabib
Affiliation:
Instituto de Sociología, Pontificia Universidad Católica de Chile, Santiago, Chile Centro UC Estudios de Vejez y Envejecimiento, Pontificia Universidad Católica de Chile, Santiago, Chile Escuela de Salud Pública, Pontificia Universidad Catolica de Chile, Santiago, Chile
Ariel Azar*
Affiliation:
Department of Sociology, Purdue University, West Lafayette, IN, USA
Andrés Biehl
Affiliation:
Instituto de Sociología, Pontificia Universidad Católica de Chile, Santiago, Chile
Eric Mautz
Affiliation:
Department of Economics, University of Chile, Santiago, Chile
Juan Pablo Ormeño
Affiliation:
Instituto de Sociología, Pontificia Universidad Católica de Chile, Santiago, Chile
Martina Yopo-Díaz
Affiliation:
Instituto de Sociología, Pontificia Universidad Católica de Chile, Santiago, Chile
*
Corresponding author: Ariel Azar; Email: arazar@purdue.edu
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Abstract

While there is consensus about the combined relevance of liberal regime policies and gendered employment and family trajectories in adulthood, for shaping employment in later life, so far there is no single cross-national study simultaneously addressing these dimensions. Drawing on exceptionally rich, harmonised life history data, we explore the association between employment and family patterns in adulthood, and the prevalence, duration and diversity of extended working lives beyond full pension age, among men and women in four predominantly liberal countries: two from Europe (England and Switzerland) and two from the Americas (the United States and Chile). Our findings indicate that employment trajectories – unlike partnership and fertility trajectories – play a significant role in shaping the prevalence, duration and diversity of later-life employment across the four countries examined. Furthermore, gender differences in later-life employment patterns are particularly notable in England and Chile. Our comparative perspective reveals that while liberal regimes share certain characteristics, they also exhibit significant diversity in how extended working lives manifest and are influenced by lifecourse trajectories.

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Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press.
Figure 0

Figure 1. Labour force participation rate among individuals aged 55–64 (A) and 65+ (B).

Note: Elaboration from authors based on OECD (2023) data.
Figure 1

Figure 2. Mean values of dependent variables by country and sample (from left to right: values for the whole sample in black, values for the female sample in grey, values for the male sample in white).

Figure 2

Table 1. Dependent, independent and control variables (SD = standard deviation)

Figure 3

Figure 3. Adjusted regression models with interaction terms between employment trajectories and gender, over extended working lives variables (male estimation indicated with the dotted line, female estimation indicated with the dashed line; ‘1. FT Wo’ = persistent full-time work; ‘2. PT Wo’ = mostly part-time work; ‘3. FT In’ = incomplete full-time work; ‘4. EA Ex’ = early-adulthood exit).

Notes: Predicted probabilities estimated for logistic regressions. Average marginal effects estimated for linear regressions.
Figure 4

Figure 4. Adjusted regression models with interaction terms between partnership trajectories and gender, over extended working lives variables (male estimation indicated with the dotted line, female estimation indicated with the dashed line; ‘1. Er Mar’ = persistent marriage; ‘2. Ms Sin’ = mostly single; ‘3. Sep’ = separated; ‘4. Wid’ = widowhood; ‘5. Re Mar’ = re-marriage).

Note: Predicted probabilities estimated for logistic regressions. Average marginal effects estimated for linear regressions.
Figure 5

Figure 5. Adjusted regression models with interaction terms between fertility trajectories and gender, over extended working lives variables (male estimation indicated with the dotted line, female estimation indicated with the dashed line; ‘CH 0’ = childless; ‘CH 1’ = one child; ‘CH 2’ = two children; ‘CH 3+’ = three or more children).

Note: Predicted probabilities estimated for logistic regressions. Average marginal effects estimated for linear regressions.
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