‘Dual’ labour market? Patterns of segmentation in European labour markets and the varieties of precariousness

Abstract

This article aims to empirically explore how European labour markets are segmented and who the outsiders are. The article moves beyond the dichotomous approach to understanding labour market division, often based solely on examining employment relationships. Taking a multi-dimensional approach to defining labour market precariousness, this study incorporates aspects such as income, job prospects and subjective insecurity. Latent Class Analysis is used on data taken from the 2015 European Working Conditions Survey to extend the traditional definition of outsider-ness. Four labour market segments are found: insiders and three different types of outsiders: typical outsiders, dead-end insiders and subjective outsiders. Looking at the cross-national aspect, variations are found in the segmentation patterns, especially in terms of who the outsiders are. The findings show the need to examine various aspects of labour precariousness in order to capture the complexity of post-industrialised labour markets and identify different types of outsiders across Europe that need to be protected for building a more cohesive society.

Hybrid Work Patterns: A Latent Class Analysis of Platform Workers in Denmark

“…Methodologically, recent studies have increasingly applied LCA to explore segmentation as a multidimensional phenomenon (Lukac et al 2019;Seo 2021;Yoon & Chung 2016). Inspired by this work, we apply LCA to explore the often-dichotomized view of the labor market into core and periphery labor markets as well as uncover patterns of segmentation at the nexus of the online and traditional labor markets.…”

“…More recent research utilizing SLM theory emphasize increasingly supply-led factors, illustrating that individual worker characteristics such as gender, age, skills, financial situation, and other jobs also influence labor market segmentation, where individuals tend to join different segments based on their bargaining power (Palier & Thelen 2010;Rubery & Piasna 2017). In this context, much segmentation literature use employment stability as an indicator of core and periphery, which tend to be portrayed as standard vis-a-vis non-standard employment (e.g., temporary and part-time work) (Lukac et al 2019;Seo 2021;Yoon & Chung 2016). Likewise, educational level is an important indicator in much segmentation literature, as high-skilled and low-skilled workers are generally understood to be in different segments of the labor market (Kalleberg 2011;Leontaridi 1998).…”

Gendered labour market patterns across Europe: Does family policy mitigate feminization of outsiders?

“…They are limited in terms of career progressions, not only due to their higher likelihood of having jobs without job ladders, but also due to glass ceilings. Based on the four labour market segments discussed above, Seo (2021) finds that roughly 70% of men are Insiders in Europe, compared to roughly 40% of women. Women are more likely than men to be both Typical Outsiders and Dead-end Insiders while men are more likely to be Subjective Outsiders (taking up roughly 6% of the European labour market).…”

“…It is based on the different characteristics of the different types of Outsiders especially in terms of employment contracts and income level, which can be influenced by policies through a variety of mechanisms. For instance, the significant associations between motherhood and Typical Outsiders and Dead-end Insiders (Seo, 2021) allow us to infer that the two may portray different forms of motherhood penalty. The difference between the two can be distinguished through the relatively lower age of workers among Typical Outsiders compared to Dead-end Insiders.…”

“…I then explore whether the impact of these policies varies depending on the types of Outsiders. Three Outsiders are examined based on the findings from Seo (2021) that are derived from a Latent Class Analysis on the European Working Conditions Survey (EWCS) data 2015 (pooled data covering 30 countries). The individual-level data is matched with national-level data to apply multi-level random slopes modelling.…”