This article proposes a novel measurement model of labour market segmentation in Europe for cross-national comparisons, tackling three drawbacks of current approaches: First, as segmentation is a multi-dimensional concept, it necessitates a complex measurement approach combining several indicators. Second, to date, we lack methodological evidence that earlier used measures are comparable across countries. Third, as any measure of social phenomena contains measurement error, segmentation research may be confounded by misclassification error. To overcome these drawbacks, we argue for modelling segmentation as a latent categorical concept by means of characteristics of the employment relationship. Our analysis shows that accounting for measurement non-equivalence in cross-national labour market segmentation research is crucial to arrive at reliable and unbiased comparative conclusions. The results demonstrate the importance of increased complexity in measuring labour market segmentation. Overall, this article serves as a methodological cross-national comparative framework for future quantitative analysis of labour market segmentation.

Conjoint analysis is an experimental technique that has become quite popular to understand people's decisions in multi-dimensional decision-making processes. Despite the importance of power analysis for experimental techniques, current literature has largely disregarded statistical power considerations when designing conjoint experiments. The main goal of this article is to provide researchers and practitioners with a practical tool to calculate the statistical power of conjoint experiments. To this end, we first conducted an extensive literature review to understand how conjoint experiments are designed and gauge the plausible effect sizes discovered in the literature. Second, we formulate a data generating model that is sufficiently flexible to accommodate a wide range of conjoint designs and hypothesized effects. Third, we present the results of an extensive series of simulation experiments based on the previously formulated data generation process. Our results show that---even with relatively large sample size and the number of trials---conjoint experiments are not suited to draw inferences for experiments with large numbers of experimental conditions and relatively small effect sizes. Specifically, Type S and Type M errors are especially pronounced for experimental designs with relatively small effective sample sizes (< 3000) or a high number of levels (> 15) that find small but statistically significant effects (< 0.03). The proposed online tool based on the simulation results can be used by researchers to perform power analysis of their designs and hence achieve adequate design for future conjoint experiments.

Online labor markets-freelance marketplaces, where digital labor is distributed via a web-based platform-commonly use reputation systems to overcome uncertainties in the hiring process, that can arise from a lack of objective information about employees' abilities. Research shows, however, that reputation systems tend to create winner-takes-all dynamics, in which differences in candidates' reputations become disconnected from differences in their objective abilities. In this paper, we use an empirically validated agent-based computational model to investigate the extent to which reputation systems can create segmented hiring patterns that are biased toward freelancers with good reputation. We explore how jobs and earnings become distributed on a stylized platform, under different contextual conditions of information asymmetry. Our results suggest that information asymmetry influences the extent to which reputation systems may lead to inequality between freelancers, but contrary to our expectations, lower levels of information asymmetry can facilitate higher inequality in outcomes.

A large body of research suggests that generous welfare provisions for jobseekers create a disincentive to work. Other scholars argue that generous benefits can reduce unemployment by serving as a job-search subsidy. One caveat in this literature is that, when testing the two hypotheses, many scholars conceive of labour markets as homogeneous entities or they theoretically assume a certain insider/outsider divide. In this article, we claim that the employment effect of generous benefits varies between labour market segments. Analysing EU-SILC panel data of 27 European countries, we find that more-generous unemployment cash benefits enhance the transition from unemployment into more-secure work while discouraging transition into less-secure work in terms of temporal, economic and organisational security. Contrary to existing research, welfare generosity is measured by aggregated information on individual benefit receipt. Labour market segments are identified by latent class analysis and transitions between segments are estimated by Multilevel Latent Markov Models.

Technological innovations have enabled the emergence of online labour market platforms, empowering individuals to penetrate the world of traditional offshoring and challenging localised labour market dynamics. A great number of workers thrive at online platforms and embrace these tools to find customers for their businesses, to counterbalance market fluctuations, and earn wages above the local average. However, online labour market workers are also known to suffer numerous drawbacks, such as precarious working conditions, unpaid work, and severe fragmentation of jobs into tasks that limit skill use and development. Yet, our understanding of what causes this divergence in experiences is limited. Adopting propositions from the labour market segmentation literature, I show that, similarly to offline markets, online labour markets are composed of structurally delimited segments with different social processes governing the allocation of work. Using unsupervised clustering techniques from network science, I show that the clustered skill topology constrains mobility between segments in online platforms. I also show that this segmentation explains large differences in the earnings potential of individual workers. Together, these results provide a new explanation for the persistence of diversified experiences in online labour markets and inform strategies for future research of online platforms as highly segmented labour markets.